Secretome Prediction of Two M. tuberculosis Clinical Isolates Reveals Their High Antigenic Density and Potential Drug Targets

Development of multi-epitope based subunit vaccine against Mycobacterium Tuberculosis using immunoinformatics approach

The etiological agent of tuberculosis (TB), Mycobacterium tuberculosis, is a deadly pathogen that adapts to thrive within the host. Since 2020, the COVID-19 pandemic has had colossal health, societal, and economic consequences, which have affected the reporting of new incidences and mortality cases of TB. As per the WHO 2022 report, 10.6 million people were diagnosed with TB, and 1.6 million died worldwide. The increase in resistant strains of tuberculosis is making it more burdensome to reach the End TB strategy. A reliable and efficient TB vaccine that may avert both primary infection and recurrence of latent TB in adults and adolescents is of the utmost importance. In this study, we used computational techniques to predict the ability of HLA molecules to display epitopes for six TB proteins (PPE68, PE_PGRS17, EspC, LDT4, RpfD, and RpfC) to design the multi-epitope subunit vaccine. From the aimed proteins, the potential B-cell, helper T lymphocyte (HTL), and cytotoxic T lymphocyte (CTL) epitopes were predicted and linked together with LPA adjuvant, and the vaccine was designed. The vaccine's physicochemical analysis demonstrates that it is non-allergic, non-toxic, and antigenic. Then, the vaccine structure was predicted, improved, and verified to yield the optimal structure. The developed vaccine's binding mechanism with distinct immunogenic receptors (Tlr2 and MHC-II) was assessed utilizing molecular docking. The molecular dynamic simulation and MMPBSA analysis were performed to comprehend the complexes' dynamics and stability. The immune simulation was utilized to anticipate the vaccine's immunogenic attributes. In silico cloning was employed to demonstrate the efficient expression of the designed vaccine in E. coli as a host. Moreover, in vitro and in vivo animal testing is required to determine the efficacy of the in silico developed vaccine.Communicated by Ramaswamy H. Sarma.

Identification of virulence-associated factors in Vibrio parahaemolyticus with special reference to moonlighting protein: a secretomics study

Vibrio parahaemolyticus causes seafood-borne gastroenteritis infection in human which can even lead to death. The pathogenic strain of V. parahaemolyticus secretes different types of virulence factors that are directly injected into the host cell by a different type of secretion system which helps bacteria to establish its own ecological niche within the organism. Therefore, the aim of this study was to isolate the extracellular secreted proteins from the trh positive strain of V. parahaemolyticus and identify them using two-dimensional gel electrophoresis and MALDI-TOFMS/MS. Seventeen different cellular proteins viz, Carbamoyl-phosphate synthase, 5-methyltetrahydropteroyltriglutamate, tRNA-dihydrouridine synthase, Glycerol-3-phosphate dehydrogenase, Orotidine 5'-phosphate decarboxylase, Molybdenum import ATP-binding protein, DnaJ, DNA polymerase IV, Ribosomal RNA small subunit methyltransferase G, ATP synthase subunit delta and gamma, Ribosome-recycling factor, 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase, tRNA pseudouridine synthase B, Ditrans, polycis-undecaprenyl-diphosphate synthase, Oxygen-dependent coproporphyrinogen-III oxidase, and Peptide deformylase 2 were identified which are mainly involved in different metabolic and biosynthetic pathways. Furthermore, the molecular function of the identified proteins were associated with catalytic activity, ligase activity, transporter, metal binding, and ATP synthase when they are intercellular. However, to understand the importance of these secreted proteins in the infection and survival of bacteria inside the host cell, pathogen-host protein-protein interactions (PPIs) were carried out which identified the association of eight secreted proteins with 41 human proteins involved in different cellular pathways, including ubiquitination degradation, adhesion, inflammation, immunity, and programmed cell death. The present study provides unreported strategies on host-cell environment's survival and adaptation mechanisms for the successful establishment of infections and intracellular propagation.

A computational method for the prediction and functional analysis of potential Mycobacterium tuberculosis adhesin-related proteins

OBJECTIVES

Mycobacterial adherence plays a major role in the establishment of infection within the host. Adhesin-related proteins attach to host receptors and cell-surface components. The current study aimed to utilize in-silico strategies to determine the adhesin potential of conserved hypothetical (CH) proteins.

METHODS

Computational analysis was performed on the whole Mycobacterium tuberculosis H37Rv proteome using a software program for the prediction of adhesin and adhesin-like proteins using neural networks (SPAAN) to determine the adhesin potential of CH proteins. A robust pipeline of computational analysis tools: Phyre2 and pFam for homology prediction; Mycosub, PsortB, and Loctree3 for subcellular localization; SignalP-5.0 and SecretomeP-2.0 for secretory prediction, were utilized to identify adhesin candidates.

RESULTS

SPAAN revealed 776 potential adhesins within the whole MTB H37Rv proteome. Comprehensive analysis of the literature was cross-tabulated with SPAAN to verify the adhesin prediction potential of known adhesin (n = 34). However, approximately a third of known adhesins were below the probability of adhesin (Pad) threshold (Pad ≥0.51). Subsequently, 167 CH proteins of interest were categorized using essential in-silico tools.

CONCLUSION

The use of SPAAN with supporting in-silico tools should be fundamental when identifying novel adhesins. This study provides a pipeline to identify CH proteins as functional adhesin molecules.

Global genomic and proteomic analysis indicates co-evolution of Neisseria species and with their human host

Neisseria, a genus from the beta-proteobacteria class, is of potential clinical importance. This genus contains both pathogenic and commensal strains. Gonorrhea and meningitis are two major diseases caused by pathogens belonging to this genus. With the increased use of antimicrobial agents against these pathogens they have evolved the antimicrobial resistance capacity making these diseases nearly untreatable. The set of anti-bacterial resistance genes (resistome) and genes associated with signal processing (secretomes) are crucial for the host-microbial interaction. With the virtue of whole-genome sequences and computational biology, it is now possible to study the genomic and proteomic riddles of Neisseria along with their comprehensive evolutionary and metabolic profiling. We have studied relative synonymous codon usage, amino acid usage, reverse ecology, comparative genomics, evolutionary analysis and pathogen-host (Neisseria-human) interaction through bioinformatics analysis. Our analysis revealed the co-evolution of Neisseria genomes with the human host. Moreover, the co-occurrence of Neisseria and humans has been supported through reverse ecology analysis. A differential pattern of the evolutionary rate of resistomes and secretomes was evident among the pathogenic and commensal strains. Comparative genomics supported the presence of virulent genes in both pathogenic and commensal strains of the select genus. Our analysis also indicated a transition from commensal to pathogenic Neisseria strains through the long run of evolution.

Clinical and pathological characteristics associated with the presence of the IS6110 Mycobacterim tuberculosis transposon in neoplastic cells from non-small cell lung cancer patients

Lung cancer (LC) and pulmonary tuberculosis (TB) are the deadliest neoplastic and bacterial infectious diseases worldwide, respectively. Clinicians and pathologists have long discussed the co-existence of LC and TB, and several epidemiologic studies have presented evidence indicating that TB could be associated with the development of LC, particularly adenocarcinoma. Nonetheless, this data remains controversial, and the mechanism which could underlie the association remains largely unexplored. Some bioinformatic studies have shown that human cancer biopsies have a very high frequency of bacterial DNA integration; since Mycobacterium Tuberculosis (MTb) is an intracellular pathogen, it could play an active role in the cellular transformation. Our group performed an exploratory study in a cohort of 88 LC patients treated at the Instituto Nacional de Cancelorogía (INCan) of Mexico City to evaluate the presence of MTb DNA in LC tissue specimens. For the first time, our results show the presence of the MTb IS6110 transposon in 40.9% (n = 36/88) of patients with lung adenocarcinomas. Additionally, through in-situ PCR we identified the presence of IS6110 in the nuclei of tumor cells. Furthermore, shotgun sequencing from two samples identified traces of MTb genomes present in tumor tissue, suggesting that similar Mtb strains could be infecting both patients.

Targeted RNA-Seq Reveals the M. tuberculosis Transcriptome from an In Vivo Infection Model

Simple Summary

High-throughput sequencing techniques such as RNA-seq allow a more detailed characterization of the gene expression profile during in vivo infections. However, using this strategy for intracellular pathogens such as Mycobacterium tuberculosis (Mtb) entails technical limitations. Some authors have resorted to flow cytometers to separate infected cells or significantly increase sequencing depth to obtain pathogens’ gene expression. However, these options carry additional expenses in specialized equipment. We propose an experimental protocol based on differential cell lysis and a probe-based ribosomal depletion to determine the gene expression of Mtb and its host during in vivo infection. This method allowed us to increase the number of observed expressed genes from 13 using a traditional RNA-seq approach to 702. In addition, we observed the expression of genes essential for establishing the infection, codifying proteins such as PE-PGRS, lipoproteins lppN and LpqH, and three ncRNAs (small RNA MTS2823, transfer-messenger RNA RF00023, and ribozyme RF00010). We believe our method represents a valuable alternative to current RNA-seq approaches to study host–pathogen interactions and will help explore host–pathogen mechanisms in tuberculosis and other similar models of intracellular infections.

Abstract

The study of host-pathogen interactions using in vivo models with intracellular pathogens like Mycobacterium tuberculosis (Mtb) entails technical limitations, such as: (i) Selecting an efficient differential lysis system to enrich the pathogen cells; (ii) obtaining sufficient high-quality RNA; and (iii) achieving an efficient rRNA depletion. Thus, some authors had used flow cytometers to separate infected cells or significantly increase the sequencing depth of host–pathogen RNA libraries to observe the pathogens’ gene expression. However, these options carry additional expenses in specialized equipment typically not available for all laboratories. Here, we propose an experimental protocol involving differential cell lysis and a probe-based ribosomal depletion to determine the gene expression of Mtb and its host during in vivo infection. This method increased the number of observed pathogen-expressed genes from 13 using the traditional RNA-seq approach to 702. After eliminating rRNA reads, we observed that 61.59% of Mtb sequences represented 702 genes, while 38.41% represented intergenic regions. Some of the most expressed genes codified for IS1081 (Rv2512c) transposase and eight PE-PGRS members, such as PGRS49 and PGRS50. As expected, a critical percent of the expressed genes codified for secreted proteins essential for infection, such as PE68, lppN, and LpqH. Moreover, three Mtb ncRNAs were highly expressed (small RNA MTS2823, transfer-messenger RNA RF00023, and ribozyme RF00010). Many of the host-expressed genes were related to the inflammation process and the expression of surfactant proteins such as the Sftpa and Sftpc, known to bind Mtb to alveolar macrophages and mi638, a microRNA with no previous associations with pulmonary diseases. The main objective of this study is to present the method, and a general catalog of the Mtb expressed genes at one point of the in vivo infection. We believe our method represents a different approach to the existing ones to study host–pathogen interactions in tuberculosis and other similar intracellular infections, without the necessity of specialized equipment.

Computational identification and characterization of antigenic properties of Rv3899c of Mycobacterium tuberculosis and its interaction with human leukocyte antigen (HLA)

A rise in drug-resistant tuberculosis (TB) cases demands continued efforts towards the discovery and development of drugs and vaccines. Secretory proteins of Mycobacterium tuberculosis (H37Rv) are frequently studied for their antigenicity and their scope as protein subunit vaccines requires further analysis. In this study, Rv3899c of H37Rv emerges as a potential vaccine candidate on its evaluation by several bioinformatics tools. It is a non-toxic, secretory protein with an 'immunoglobulin-like' fold which does not show similarity with a human protein. Through BlastP and MEME suite analysis, we found Rv3899c homologs in several mycobacterial species and its antigenic score (0.54) to compare well with the known immunogens such as ESAT-6 (0.56) and Rv1860 (0.52). Structural examination of Rv3899c predicted ten antigenic peptides, an accessibility profile of the antigenic determinants constituting B cell epitope-rich regions and a low abundance of antigenic regions (AAR) value. Significantly, STRING analysis showed ESX-2 secretion system proteins and antigenic PE/PPE proteins of H37Rv as the interacting partners of Rv3899c. Further, molecular docking predicted Rv3899c to interact with human leukocyte antigen HLA-DRB1*04:01 through its antigenically conserved motif (RAAEQQRLQRIVDAVARQEPRISWAAGLRDDGTT). Interestingly, the binding affinity was observed to increase on citrullination of its Arg1 residue. Taken together, the computational characterization and predictive information suggest Rv3899c to be a promising TB vaccine candidate, which should be validated experimentally.

Secretome characterization of clinical isolates from the Mycobacterium abscessus complex provides insight into antigenic differences

BACKGROUND

Mycobacterium abscessus (MAB) is a widely disseminated pathogenic non-tuberculous mycobacterium (NTM). Like with the M. tuberculosis complex (MTBC), excreted / secreted (ES) proteins play an essential role for its virulence and survival inside the host. Here, we used a robust bioinformatics pipeline to predict the secretome of the M. abscessus ATCC 19977 reference strain and 15 clinical isolates belonging to all three MAB subspecies, M. abscessus subsp. abscessus, M. abscessus subsp. bolletii, and M. abscessus subsp. massiliense.

RESULTS

We found that ~ 18% of the proteins encoded in the MAB genomes were predicted as secreted and that the three MAB subspecies shared > 85% of the predicted secretomes. MAB isolates with a rough (R) colony morphotype showed larger predicted secretomes than isolates with a smooth (S) morphotype. Additionally, proteins exclusive to the secretomes of MAB R variants had higher antigenic densities than those exclusive to S variants, independent of the subspecies. For all investigated isolates, ES proteins had a significantly higher antigenic density than non-ES proteins. We identified 337 MAB ES proteins with homologues in previously investigated M. tuberculosis secretomes. Among these, 222 have previous experimental support of secretion, and some proteins showed homology with protein drug targets reported in the DrugBank database. The predicted MAB secretomes showed a higher abundance of proteins related to quorum-sensing and Mce domains as compared to MTBC indicating the importance of these pathways for MAB pathogenicity and virulence. Comparison of the predicted secretome of M. abscessus ATCC 19977 with the list of essential genes revealed that 99 secreted proteins corresponded to essential proteins required for in vitro growth.

CONCLUSIONS

This study represents the first systematic prediction and in silico characterization of the MAB secretome. Our study demonstrates that bioinformatics strategies can help to broadly explore mycobacterial secretomes including those of clinical isolates and to tailor subsequent, complex and time-consuming experimental approaches accordingly. This approach can support systematic investigation exploring candidate proteins for new vaccines and diagnostic markers to distinguish between colonization and infection. All predicted secretomes were deposited in the Secret-AAR web-server ( http://microbiomics.ibt.unam.mx/tools/aar/index.php ).

The genetic proteome: Using genetics to inform the proteome of mycobacterial pathogens

Mycobacterial pathogens pose a sustained threat to human health. There is a critical need for new diagnostics, therapeutics, and vaccines targeting both tuberculous and nontuberculous mycobacterial species. Understanding the basic mechanisms used by diverse mycobacterial species to cause disease will facilitate efforts to design new approaches toward detection, treatment, and prevention of mycobacterial disease. Molecular, genetic, and biochemical approaches have been widely employed to define fundamental aspects of mycobacterial physiology and virulence. The recent expansion of genetic tools in mycobacteria has further increased the accessibility of forward genetic approaches. Proteomics has also emerged as a powerful approach to further our understanding of diverse mycobacterial species. Detection of large numbers of proteins and their modifications from complex mixtures of mycobacterial proteins is now routine, with efforts of quantification of these datasets becoming more robust. In this review, we discuss the “genetic proteome,” how the power of genetics, molecular biology, and biochemistry informs and amplifies the quality of subsequent analytical approaches and maximizes the potential of hypothesis-driven mycobacterial research. Published proteomics datasets can be used for hypothesis generation and effective post hoc supplementation to experimental data. Overall, we highlight how the integration of proteomics, genetic, molecular, and biochemical approaches can be employed successfully to define fundamental aspects of mycobacterial pathobiology.

Evidence for the Effect of Vaccination on Host-Pathogen Interactions in a Murine Model of Pulmonary Tuberculosis by Mycobacterium tuberculosis

The global control of Tuberculosis remains elusive, and Bacillus Calmette-Guérin (BCG) -the most widely used vaccine in history-has proven insufficient for reversing this epidemic. Several authors have suggested that the mass presence of vaccinated hosts might have affected the Mycobacterium tuberculosis (MTB) population structure, and this could in turn be reflected in a prevalence of strains with higher ability to circumvent BCG-induced immunity, such as the recent Beijing genotype. The effect of vaccination on vaccine-escape variants has been well-documented in several bacterial pathogens; however the effect of the interaction between MTB strains and vaccinated hosts has never been previously described. In this study we show for the first time the interaction between MTB Beijing-genotype strains and BCG-vaccinated hosts. Using a well-controlled murine model of progressive pulmonary tuberculosis, we vaccinated BALB/c mice with two different sub-strains of BCG (BCG-Phipps and BCG-Vietnam). Following vaccination, the mice were infected with either one of three selected MTB strains. Strains were selected based on lineage, and included two Beijing-family clinical isolates (strains 46 and 48) and a well-characterized laboratory strain (H37Rv). Two months after infection, mice were euthanized and the bacteria extracted from their lungs. We characterized the genomic composite of the bacteria before and after exposure to vaccinated hosts, and also characterized the local response to the bacteria by sequencing the lung transcriptome in animals during the infection. Results from this study show that the interaction within the lungs of the vaccinated hosts results in the selection of higher-virulence bacteria, specifically for the Beijing genotype strains 46 and 48. After exposure to the BCG-induced immune response, strains 46 and 48 acquire genomic mutations associated with several virulence factors. As a result, the bacteria collected from these vaccinated hosts have an increased ability for immune evasion, as shown in both the host transcriptome and the histopathology studies, and replicates far more efficiently compared to bacteria collected from unvaccinated hosts or to the original-stock strain. Further research is warranted to ascertain the pathways associated with the genomic alterations. However, our results highlight novel host-pathogen interactions induced by exposure of MTB to BCG vaccinated hosts.

Metatranscriptomic analysis to define the Secrebiome, and 16S rRNA profiling of the gut microbiome in obesity and metabolic syndrome of Mexican children

Background

In the last decade, increasing evidence has shown that changes in human gut microbiota are associated with diseases, such as obesity. The excreted/secreted proteins (secretome) of the gut microbiota affect the microbial composition, altering its colonization and persistence. Furthermore, it influences microbiota-host interactions by triggering inflammatory reactions and modulating the host's immune response. The metatranscriptome is essential to elucidate which genes are expressed under diseases. In this regard, little is known about the expressed secretome in the microbiome. Here, we use a metatranscriptomic approach to delineate the secretome of the gut microbiome of Mexican children with normal weight (NW) obesity (O) and obesity with metabolic syndrome (OMS). Additionally, we performed the 16S rRNA profiling of the gut microbiota.

Results

Out of the 115,712 metatranscriptome genes that codified for proteins, 30,024 (26%) were predicted to be secreted, constituting the Secrebiome of the gut microbiome. The 16S profiling confirmed an increased abundance in Firmicutes and decreased in Bacteroidetes in the obesity groups, and a significantly higher richness and diversity than the normal weight group. We found novel biomarkers for obesity with metabolic syndrome such as increased Coriobacteraceae, Collinsela, and Collinsella aerofaciens; Erysipelotrichaceae, Catenibacterium and Catenibacterium sp., and decreased Parabacteroides distasonis, which correlated with clinical and anthropometric parameters associated to obesity and metabolic syndrome. Related to the Secrebiome, 16 genes, homologous to F. prausniitzi, were overexpressed for the obese and 15 genes homologous to Bacteroides, were overexpressed in the obesity with metabolic syndrome. Furthermore, a significant enrichment of CAZy enzymes was found in the Secrebiome. Additionally, significant differences in the antigenic density of the Secrebiome were found between normal weight and obesity groups.

Conclusions

These findings show, for the first time, the role of the Secrebiome in the functional human-microbiota interaction. Our results highlight the importance of metatranscriptomics to provide novel information about the gut microbiome’s functions that could help us understand the impact of the Secrebiome on the homeostasis of its human host. Furthermore, the metatranscriptome and 16S profiling confirmed the importance of treating obesity and obesity with metabolic syndrome as separate conditions to better understand the interplay between microbiome and disease.

Genome based screening of epitope ensemble vaccine candidates against dreadful visceral leishmaniasis using immunoinformatics approach

Visceral leishmaniasis (VL) is a dreadful protozoan disease caused by Leishmania donovani that severely affects huge populations in tropical and sub-tropical regions. The present study reports an unbiased genome based screening of 4 potent vaccine antigens against 8023 L. donovani proteins by following the criteria of presence of signal peptides, GPI-anchors and ≤1 transmembrane helix using advanced bioinformatics tools viz. SignalP4.0, PredGPI and TMHMM2.0, respectively. They are designated as genome based predicted signal peptide antigens (GBPSPA). The antigenicity/immunogenicity of chosen vaccine antigens (GBPSPA) with 4 randomly selected known leishmanial antigens (RSKLA) was compared by simulation study employing C-ImmSim software for human immune responses. This revealed better immunological responses. These antigens were further evaluated for the presence of B- and T-cell epitopes using immune epitope database (IEDB) based recommended consensus method of MHC class I and II tools. It was found to forecast CD4⁺ and CD8⁺ T-cell responses in genetically diverse human population worldwide as well as different endemic regions through IEDB based predicted population coverage (PPC) analysis tool. The worldwide percent PPC value of combined (HLA class I and II) epitope ensemble forecast was found to be 99.98, 99.96 and 50.04, respectively for GBPSPA, RSKLA and experimentally known epitopes (EKE) of L. donovani. Therefore, these potential antigens/epitope ensembles could favor the design of prospective and novel vaccine constructs like self-assembled epitopes as nano vaccine formulations against VL. Overall, the present study will serve as a model framework that might improve the effectiveness of designed vaccine against L. donovani and other related pathogens.

Elucidation of Akkermansia muciniphila Probiotic Traits Driven by Mucin Depletion

Akkermansia muciniphila is widely considered a next-generation beneficial microbe. This bacterium resides in the mucus layer of its host and regulates intestinal homeostasis and intestinal barrier integrity by affecting host signaling pathways. However, it remains unknown how the expression of genes encoding extracellular proteins is regulated in response to dynamic mucosal environments. In this study, we elucidated the effect of mucin on the gene expression and probiotic traits of A. muciniphila. Transcriptome analysis showed that the genes encoding most mucin-degrading enzymes were significantly upregulated in the presence of mucin. By contrast, most genes involved in glycolysis and energy metabolic pathways were upregulated under mucin-depleted conditions. Interestingly, the absence of mucin resulted in the upregulation of 79 genes encoding secreted protein candidates, including Amuc-1100 as well as members of major protein secretion systems. These transcript level changes were consistent with the fact that administration of A. muciniphila grown under mucin-depleted conditions to high-fat diet-induced diabetic mice reduced obesity and improved intestinal barrier integrity more efficiently than administration of A. muciniphila grown under mucin-containing conditions. In conclusion, mucin content in the growth medium plays a critical role in the improvement by A. muciniphila of high-fat diet-induced obesity, intestinal inflammation, and compromised intestinal barrier integrity related to a decrease in goblet cell density. Our findings suggest the depletion of animal-derived mucin in growth medium as a novel principle for the development of A. muciniphila for human therapeutics.

Secret-AAR: a web server to assess the antigenic density of proteins and homology search against bacterial and parasite secretome proteins

The secretome refers to all the Excreted/Secreted (ES) proteins of a cell, and these are involved in critical biological processes, such as cell-cell communication, and host immune responses. Recently, we introduced the Abundance of Antigenic Aegions (AAR) value to assess the protein antigenic density and to evaluate the antigenic potential of secretomes. Here, to facilitate the AAR calculation, we implemented it as a user-friendly webserver. We extended the webserver capabilities implementing a sequence-based tool for searching homologous proteins across secretomes, including experimental and predicted secretomes of Mycobacterium tuberculosis and Taenia solium. Additionally, twelve secretomes of helminths, five of Mycobacterium and two of Gram-negative bacteria are also available. Our webserver is a useful tool for researchers working on immunoinformatics and reverse vaccinology, aiming at discovering candidate proteins for new vaccines or diagnostic tests, and it can be used to prioritize the experimental analysis of proteins for druggability assays. The Secret-AAR web server is available at http://microbiomics.ibt.unam.mx/tools/aar/.

The exoproteome profiles of three Staphylococcus saprophyticus strains reveal diversity in protein secretion contents

Staphylococcus saprophyticus is a gram-positive microorganism responsible for urinary tract infections (UTIs). Although some virulence factors are characterized, such as urease, autolysins, adhesins and hemagglutinins, large-scale proteomic studies have not been performed within this species. We performed the characterization of the exoproteome from three S. saprophyticus strains: the reference strain ATCC 15,305, a non-capsular strain 7108 and the 9325 strain containing a thick capsule which were cultured in BHI medium and culture supernatants were analysed by using mass spectrometry approach. We observed a core of 72 secreted proteins. In addition, it was possible to detect diversity in the protein profiles of the exoproteomes. Interestingly, strain 7108 presented no secretion of three antigenic proteins, including the classical SsaA antigen. In addition, the level of antigenic proteins secreted by strain 9325 was higher than in ATCC 15,305. This result was confirmed by Western blot analysis using anti-SsaA polyclonal antibodies, and no production/ secretion of SsaA was detected in strain 7108. Transcriptional data shows that 7108 strain produces transcripts encoding SsaA, suggesting post-transcriptional regulation occurs in this strain. Moreover, when compared with the other strains that were analyzed, it was possible to detect higher levels of proteases secreted by strain 7108 and higher levels of antigenic proteins and transglycosylases secreted by 9325 strain. The results reveal diversity in protein secretion among strains. This research is an important first step towards understanding the variability in S. saprophyticus exoproteome profile and could be significant in explaining differences among strains.

Genome-wide analysis of Excretory/Secretory proteins in Trypanosoma brucei brucei: Insights into functional characteristics and identification of potential targets by immunoinformatics approach

Trypanosoma brucei brucei (T.b.brucei) is an extra-cellular parasite that causes Animal African Trypanosomiasis (AAT) disease in animals. Till day, this disease is more difficult to treat and control due to lack of efficient vaccines and early diagnosis of the parasite infection. T.b.brucei Excretory/Secretory (ES) proteins were involved in pathogenesis and key for understanding the host-parasite interactions. Functions of T.b.brucei's ES proteins were poorly investigated and experimental identification is expensive and time-consuming. Bioinformatics approaches are cost-effective by facilitating the experimental analysis of potential drug targets for parasitic diseases. Here we applied several bioinformatics tools to predict and functionalize the annotation of 1104 ES proteins and immunoinformatics approaches carried out to predict and evaluate the epitopes in T.b.brucei. Secretory information, functional annotations and potential epitopes of each ES proteins were available at http://tbb.insilico.in. This study provides functional information of T.b.brucei for experimental studies to identify potential targets for diagnosis and therapeutics development.

Comparative systems analysis of the secretome of the opportunistic pathogen Aspergillus fumigatus and other Aspergillus species

Aspergillus fumigatus and multiple other Aspergillus species cause a wide range of lung infections, collectively termed aspergillosis. Aspergilli are ubiquitous in environment with healthy immune systems routinely eliminating inhaled conidia, however, Aspergilli can become an opportunistic pathogen in immune-compromised patients. The aspergillosis mortality rate and emergence of drug-resistance reveals an urgent need to identify novel targets. Secreted and cell membrane proteins play a critical role in fungal-host interactions and pathogenesis. Using a computational pipeline integrating data from high-throughput experiments and bioinformatic predictions, we have identified secreted and cell membrane proteins in ten Aspergillus species known to cause aspergillosis. Small secreted and effector-like proteins similar to agents of fungal-plant pathogenesis were also identified within each secretome. A comparison with humans revealed that at least 70% of Aspergillus secretomes have no sequence similarity with the human proteome. An analysis of antigenic qualities of Aspergillus proteins revealed that the secretome is significantly more antigenic than cell membrane proteins or the complete proteome. Finally, overlaying an expression dataset, four A. fumigatus proteins upregulated during infection and with available structures, were found to be structurally similar to known drug target proteins in other organisms, and were able to dock in silico with the respective drug.

Proteogenomics in Aid of Host-Pathogen Interaction Studies: A Bacterial Perspective

By providing useful tools to study host-pathogen interactions, next-generation omics has recently enabled the study of gene expression changes in both pathogen and infected host simultaneously. However, since great discriminative power is required to study pathogen and host simultaneously throughout the infection process, the depth of quantitative gene expression profiling has proven to be unsatisfactory when focusing on bacterial pathogens, thus preferentially requiring specific strategies or the development of novel methodologies based on complementary omics approaches. In this review, we focus on the difficulties encountered when making use of proteogenomics approaches to study bacterial pathogenesis. In addition, we review different omics strategies (i.e., transcriptomics, proteomics and secretomics) and their applications for studying interactions of pathogens with their host.