Search

Background: Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines. Results: We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database—but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation. Conclusions: By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself. Author summary: MetaNovo is an open-source software pipeline that integrates existing tools with a custom algorithm to produce targeted protein sequence databases for mass spectrometry metaproteomic analysis as an intermediate filtering step prior to standard sequence database search approaches. MetaNovo uses open-source tools to match peptide mass spectrometry spectra to sequence database entries in a parallelised and scalable manner and can be installed in a cluster or run standalone on a Linux machine. The software is scalable to the number of input files and search sequence database size. As inputs the software requires raw mass spectrometry data in MGF format, and a UniProt FASTA sequence database to search. The pipeline is relevant to users analysing protein data from multiple organisms, where the exact species composition is unknown, such as microbiome or environmental samples, and provides an avenue for analysis when matched metagenomics data or accurate taxonomic characterisation is not available as it infers the organisms and proteins present directly from the raw data and the parent sequence database. The targeted sequence database produced can be used with standard downstream peptide identification software that relies on a targeted input sequence database to search the raw data against and allows greater sensitivity in peptide spectral matching in metaproteomic datasets. [ABSTRACT FROM AUTHOR]

Most bacterial proteomic studies done to date utilise bacterial cells harvested from liquid culture media. However, it is widely accepted that many important determinants associated with virulence and host cell adhesion are exclusively expressed during growth on solid media, as a crude mimic of true biofilms. Here, we compare the observed proteome of Escherichia coli K12 from isolated single colonies on solid media with those observed at different growth phases in liquid culture; i.e. early-log, mid-log, early-, mid- and late-stationary growth phases. A total of 2044 protein groups covering approximately 47% of the total proteome were identified across all studied conditions, including 1650 proteins identified from single colonies and 1679 proteins from liquid cultured cells. Label-free quantitative analysis revealed that the E. coli proteome of single colonies on a solid agar differs from that observed in liquid culture. Notably, the presence of proteins in the Suf-operon that are involved in iron mobilisation and swarming motility was associated exclusively with single colony profiles, whereas proteins involved in motility such as motA, motB, fliH, flip, fliD and fliJ were associated exclusively with cells grown in liquid culture. The data presented here provide a valuable resource for understanding the role of key proteins within microenvironments surrounding E. coli single colonies. To date, most proteomics studies have used E. coli cells harvested from liquid culture media even though many important determinants associated with virulence and host cell adhesion are exclusively expressed during growth on solid media. In this study, we compare the observed proteome of E. coli K12 from isolated single colonies on solid media with those observed at different growth phases in liquid culture; i.e. early-log, mid-log, early-, mid- and late-stationary growth phases. By using label-free quantitative analysis we demonstrate that the E. coli proteome of single colonies on a solid agar differs from that observed in liquid culture with an overlap of 68% of proteins between the two culture conditions. Our analysis further reveal the presence of proteins in the Suf-operon that are involved in iron mobilisation and swarming motility was associated exclusively with single colony profiles. While those proteins involved in motility such as motA, motB, fliH, flip, fliD and fliJ were associated exclusively with cells grown in liquid culture. By comparison to E. coli proteomic data available on liquid culture and solid media, this research represents a first effort to describe the differential expression of key E. coli proteins within microenvironments surrounding single colonies. Unlabelled Image • Label-free quantitative proteomics analysis of E. coli k12 from isolated single colonies on solid media and different growth phases in liquid culture; i.e. early-log, mid-log, early-, mid- and late-stationary growth phases. • Proteins from the Suf-operon exclusively identified in single colony E. coli profiles are involved in iron mobilisation and swarming motility. • Proteins involved in motility such as motA, motB, fliH, flip, fliD and fliJ were associated exclusively with E. coli cells grown in liquid culture. • A valuable resource for understanding the role of key proteins within microenvironments surrounding E. coli single colonies. [ABSTRACT FROM AUTHOR]

Abstract Understanding the cell wall of mycobacteria is crucial for improving drug design or identifying new antigens suitable to vaccination. Yet this remains problematic due to the complexity of the cell wall composition. In this study, we successfully developed gel-free approaches to study cell wall proteins in Mycobacterium smegmatis. The cell wall was subjected to differential centrifugation, differential detergent solubilisation and phase separation to yield the genuine cell wall proteome. Next, protein extracts were digested by filter-assisted sample preparation for LC-MS/MS analysis on a Q Exactive mass spectrometer, and identified proteins filtered through a stringent bioinformatics pipeline. This yielded the unprecedented coverage of 96 lipoproteins, 475 membrane proteins and 73 secreted proteins. Employing this approach, we next quantified changes in the cell wall proteome during exposure of M. smegmatis to sub-lethal concentration of rifampicin. This facilitated detailed characterisation of the dysregulation of ABC transporters, virulence factors such as Mce proteins and PknG, and proteins involved in cell wall and lipid synthesis. Crucially, these cell wall proteins are under-represented in previous proteome analysis of M. smegmatis. This approach enables further quantitative proteomic studies of the role of the cell wall proteome of mycobacteria in virulence or during drug exposure. Significance We developed novel gel-free sample preparation workflows for the cell wall fraction of mycobacteria that significantly increase the coverage of the cell wall proteome compared to previous studies. We then provide a data analysis workflow that enables the removal of likely cytosolic contaminants in the cell wall fraction post-measurement. Combined, these approaches increase the coverage of the cell wall proteome while ensuring that the identified proteins are true cell wall proteins and not carry-over of high-abundance contaminants from the cytosol. We have applied these approaches to quantify the dysregulation of cell wall proteins during exposure of M. smegmatis to rifampicin, which has shed new light on the coordinated down-regulation of ABC transporters as well as virulence factors present in the cell wall proteome. Graphical abstract Unlabelled Image Highlights • Gel-free sample preparation increases coverage of cell wall proteome of M. smegmatis. • Bioinformatic analysis removes probable cytosolic contaminants post-measurement. • Facilitates study of dysregulation of cell wall proteome after rifampicin exposure. [ABSTRACT FROM AUTHOR]

Shiga-toxin-producing Escherichia coli (STEC) and enteropathogenic Escherichia coli (EPEC) are key diarrhoea-causing foodborne pathogens. We used proteomics to characterize the virulence and antimicrobial resistance protein profiles of three clinical pathogenic E. coli isolates (two EPEC [one resistant to ciprofloxacin] and one STEC) cultured on CHROMagar™STEC solid media after minimal laboratory passage. We identified 4767 unique peptides from 1630 protein group across all three clinical E. coli strains. Label-free proteomic analysis allowed the identification of virulence and drug resistance proteins that were unique to each of the clinical isolates compared in this study. The B subunit of Shiga toxin, ToxB, was uniquely detected in the STEC strain while several other virulence factors including SheA, OmpF, OmpC and OmpX were significantly more abundant in the STEC strain. The ciprofloxacin resistant EPEC isolate possessed reduced levels of key virulence proteins compared to the ciprofloxacin susceptible EPEC and STEC strains. Parallel reaction monitoring assays validated the presence of biologically relevant proteins across biologically-replicated cultures. Propagation of clinical isolates on a relevant solid medium followed by mass spectrometry analysis represents a convenient means to quantify virulence factors and drug resistance determinants that might otherwise be lost through extensive in vitro passage in enteropathogenic bacteria. Significance Through the use of quantitative proteomics, we have characterized the virulence and antimicrobial resistance attributes of three clinically isolated, pathogenic E. coli strains cultured on solid media. Our results provide new, quantitative data on the expressed proteomes of these tellurite-resistant, diarrhoeagenic E. coli strains and reveal a subset of antimicrobial resistance and virulence proteins that are differentially abundant between these clinical strains. Our quantitative proteomics-based approach should thus have applicability in microbiological diagnostic labs for the identification of pathogenic/drug resistant E. coli in the future. [ABSTRACT FROM AUTHOR]

Background: Schistosomiasis is a chronic neglected tropical disease that is characterized by continued inflammatory challenges to the exposed population and it has been established as a possible risk factor in the aetiology of bladder cancer. Improved diagnosis of schistosomiasis and its associated pathology is possible through mass spectrometry to identify biomarkers among the infected population, which will influence early detection of the disease and its subtle morbidity. Methodology: A high-throughput proteomic approach was used to analyse human urine samples for 49 volunteers from Eggua, a schistosomiasis endemic community in South-West, Nigeria. The individuals were previously screened for Schistosoma haematobium and structural bladder pathologies via microscopy and ultrasonography respectively. Samples were categorised into schistosomiasis, schistosomiasis with bladder pathology, bladder pathology, and a normal healthy control group. These samples were analysed to identify potential protein biomarkers. Results: A total of 1306 proteins and 9701 unique peptides were observed in this study (FDR = 0.01). Fifty-four human proteins were found to be potential biomarkers for schistosomiasis and bladder pathologies due to schistosomiasis by label-free quantitative comparison between groups. Thirty-six (36) parasite-derived potential biomarkers were also identified, which include some existing putative schistosomiasis biomarkers that have been previously reported. Some of these proteins include Elongation factor 1 alpha, phosphopyruvate hydratase, histone H4 and heat shock proteins (HSP 60, HSP 70). Conclusion: These findings provide an in-depth analysis of potential schistosoma and human host protein biomarkers for diagnosis of chronic schistosomiasis caused by Schistosoma haematobium and its pathogenesis. [ABSTRACT FROM AUTHOR]

Paradoxical tuberculosis-associated immune reconstitution inflammatory syndrome (TB-IRIS) occurs in 8–54% of South African patients undergoing treatment for tuberculosis/human immunodeficiency virus co-infection. Improved TB-IRIS molecular pathogenesis understanding would enhance risk stratification, diagnosis, prognostication, and treatment. We assessed how TB-IRIS status and dexamethasone influence leukocyte proteomic responses to Mycobacterium tuberculosis (Mtb). Patient blood was obtained three weeks post-anti-retroviral therapy initiation. Isolated mononuclear cells were stimulated ex vivo with heat-killed Mtb in the presence/absence of dexamethasone. Mass spectrometry-based proteomic comparison of TB-IRIS and non-IRIS patient-derived cells facilitated generation of hypotheses regarding pathogenesis. Few represented TB-IRIS-group immune-related pathways achieved significant activation, with relative under-utilisation of “inter-cellular interaction” and “Fcγ receptor-mediated phagocytosis” (but a tendency towards apoptosis-related) pathways. Dexamethasone facilitated significant activation of innate-related pathways. Differentially-expressed non-IRIS-group proteins suggest focused and co-ordinated immunological pathways, regardless of dexamethasone status. Findings suggest a relative deficit in TB-IRIS-group responses to and clearance of Mtb antigens, ameliorated by dexamethasone. [ABSTRACT FROM AUTHOR]