TopDownApp: An open and modular platform for analysis and visualisation of top-down proteomics data.

Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise their adoption. In this context, there are numerous improvements that are possible in the area of open science practices, including a greater application of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. These include, for example, increased data sharing practices and readily available open data standards. Additionally, the field would benefit from the development of open data analysis workflows that can enable data reuse of public datasets, something that is increasingly common in other proteomics fields.

MetaNovo: An open-source pipeline for probabilistic peptide discovery in complex metaproteomic datasets.

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.

Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection.

Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.

Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work.

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

Comparative Proteomics Analysis between Maize and Sorghum Uncovers Important Proteins and Metabolic Pathways Mediating Drought Tolerance.

Drought severely affects crop yield and yield stability. Maize and sorghum are major crops in Africa and globally, and both are negatively impacted by drought. However, sorghum has a better ability to withstand drought than maize. Consequently, this study identifies differences between maize and sorghum grown in water deficit conditions, and identifies proteins associated with drought tolerance in these plant species. Leaf relative water content and proline content were measured, and label-free proteomics analysis was carried out to identify differences in protein expression in the two species in response to water deficit. Water deficit enhanced the proline accumulation in sorghum roots to a higher degree than in maize, and this higher accumulation was associated with enhanced water retention in sorghum. Proteomic analyses identified proteins with differing expression patterns between the two species, revealing key metabolic pathways that explain the better drought tolerance of sorghum than maize. These proteins include phenylalanine/tyrosine ammonia-lyases, indole-3-acetaldehyde oxidase, sucrose synthase and phenol/catechol oxidase. This study highlights the importance of phenylpropanoids, sucrose, melanin-related metabolites and indole acetic acid (auxin) as determinants of the differences in drought stress tolerance between maize and sorghum. The selection of maize and sorghum genotypes with enhanced expression of the genes encoding these differentially expressed proteins, or genetically engineering maize and sorghum to increase the expression of such genes, can be used as strategies for the production of maize and sorghum varieties with improved drought tolerance.

Expansion of nickel binding- and histidine-rich proteins during gastric adaptation of Helicobacter species.

Acquisition and homeostasis of essential metals during host colonization by bacterial pathogens rely on metal uptake, trafficking, and storage proteins. How these factors have evolved within bacterial pathogens is poorly defined. Urease, a nickel enzyme, is essential for Helicobacter pylori to colonize the acidic stomach. Our previous data suggest that acquisition of nickel transporters and a histidine-rich protein (HRP) involved in nickel storage in H. pylori and gastric Helicobacter spp. have been essential evolutionary events for gastric colonization. Using bioinformatics, proteomics, and phylogenetics, we extended this analysis to determine how evolution has framed the repertoire of HRPs among 39 Epsilonproteobacteria; 18 gastric and 11 non-gastric enterohepatic (EH) Helicobacter spp., as well as 10 other Epsilonproteobacteria. We identified a total of 213 HRPs distributed in 22 protein families named orthologous groups (OGs) with His-rich domains, including 15 newly described OGs. Gastric Helicobacter spp. are enriched in HRPs (7.7 ± 1.9 HRPs/strain) as compared to EH Helicobacter spp. (1.9 ± 1.0 HRPs/strain) with a particular prevalence of HRPs with C-terminal histidine-rich domains in gastric species. The expression and nickel-binding capacity of several HRPs was validated in five gastric Helicobacter spp. We established the evolutionary history of new HRP families, such as the periplasmic HP0721-like proteins and the HugZ-type heme oxygenases. The expansion of histidine-rich extensions in gastric Helicobacter spp. proteins is intriguing but can tentatively be associated with the presence of the urease nickel enzyme. We conclude that this HRP expansion is associated with unique properties of organisms that rely on large intracellular nickel amounts for their survival.

A Label-Free Proteomic and Complementary Metabolomic Analysis of Leaves of the Resurrection Plant Xerophytaschlechteri during Dehydration.

Vegetative desiccation tolerance, or the ability to survive the loss of ~95% relative water content (RWC), is rare in angiosperms, with these being commonly called resurrection plants. It is a complex multigenic and multi-factorial trait, with its understanding requiring a comprehensive systems biology approach. The aim of the current study was to conduct a label-free proteomic analysis of leaves of the resurrection plant Xerophyta schlechteri in response to desiccation. A targeted metabolomics approach was validated and correlated to the proteomics, contributing the missing link in studies on this species. Three physiological stages were identified: an early response to drying, during which the leaf tissues declined from full turgor to a RWC of ~80-70%, a mid-response in which the RWC declined to 40% and a late response where the tissues declined to 10% RWC. We identified 517 distinct proteins that were differentially expressed, of which 253 proteins were upregulated and 264 were downregulated in response to the three drying stages. Metabolomics analyses, which included monitoring the levels of a selection of phytohormones, amino acids, sugars, sugar alcohols, fatty acids and organic acids in response to dehydration, correlated with some of the proteomic differences, giving insight into the biological processes apparently involved in desiccation tolerance in this species.

Proteomic Identification and Meta-Analysis in Salvia hispanica RNA-Seq de novo Assemblies.

While proteomics has demonstrated its value for model organisms and for organisms with mature genome sequence annotations, proteomics has been of less value in nonmodel organisms that are unaccompanied by genome sequence annotations. This project sought to determine the value of RNA-Seq experiments as a basis for establishing a set of protein sequences to represent a nonmodel organism, in this case, the pseudocereal chia. Assembling four publicly available chia RNA-Seq datasets produced transcript sequence sets with a high BUSCO completeness, though the number of transcript sequences and Trinity "genes" varied considerably among them. After six-frame translation, ProteinOrtho detected substantial numbers of orthologs among other species within the taxonomic order Lamiales. These protein sequence databases demonstrated a good identification efficiency for three different LC-MS/MS proteomics experiments, though a seed proteome showed considerable variability in the identification of peptides based on seed protein sequence inclusion. If a proteomics experiment emphasizes a particular tissue, an RNA-Seq experiment incorporating that same tissue is more likely to support a database search identification of that proteome.

Dysregulated healing responses in diabetic wounds occur in the early stages postinjury.

Chronic wounds are a serious and debilitating complication of diabetes. A better understanding of the dysregulated healing responses following injury will provide insight into the optimal time frame for therapeutic intervention. In this study, a direct comparison was done between the healing dynamics and the proteome of acute and obese diabetic wounds on days 2 and 7 following injury. Full thickness excisional wounds were induced on obese diabetic (B6.Cg-lepob/J, ob/ob, n = 14) (blood glucose 423.25 ± 127.92 mg/dL) and WT control (C57BL/6J, n = 14) (blood glucose 186.67 ± 24.5 mg/dL) mice. Histological analysis showed no signs of healing in obese DM wounds whereas complete wound closure/re-epithelisation, the formation of granulation tissue and signs of re-vascularisation, was evident in acute wounds on day 7. In obese DM wounds, substance P deficiency and increased MMP-9 activity on day 2 coincided with increased cytokine/chemokine levels within wound fluid. LC-MS/MS identified 906 proteins, of which 23 (Actn3, Itga6, Epb41, Sncg, Nefm, Rsp18, Rsp19, Rpl22, Macroh2a1, Rpn1, Ppib, Snrnp70, Ddx5, Eif3g, Tpt1, FABP5, Cavin1, Stfa1, Stfa3, Cycs, Tkt, Mb, Chmp2a) were differentially expressed in wounded tissue on day 2 (P < 0.05; more than two-fold) and 6 (Cfd, Ptms, Hp, Hmga1, Cbx3, Syap1) (P < 0.05; more than two-fold) on day 7. A large number of dysregulated proteins on day 2 was associated with an inability to progress into the proliferative stage of healing and suggest that early intervention might be pivotal for effective healing outcomes. The proteomic approach highlighted the complexity of obese DM wounds in which the dysregulation involves multiple regulatory pathways and biological processes.

Microbial function and genital inflammation in young South African women at high risk of HIV infection.

BACKGROUND: Female genital tract (FGT) inflammation is an important risk factor for HIV acquisition. The FGT microbiome is closely associated with inflammatory profile; however, the relative importance of microbial activities has not been established. Since proteins are key elements representing actual microbial functions, this study utilized metaproteomics to evaluate the relationship between FGT microbial function and inflammation in 113 young and adolescent South African women at high risk of HIV infection. Women were grouped as having low, medium, or high FGT inflammation by K-means clustering according to pro-inflammatory cytokine concentrations. RESULTS: A total of 3186 microbial and human proteins were identified in lateral vaginal wall swabs using liquid chromatography-tandem mass spectrometry, while 94 microbial taxa were included in the taxonomic analysis. Both metaproteomics and 16S rRNA gene sequencing analyses showed increased non-optimal bacteria and decreased lactobacilli in women with FGT inflammatory profiles. However, differences in the predicted relative abundance of most bacteria were observed between 16S rRNA gene sequencing and metaproteomics analyses. Bacterial protein functional annotations (gene ontology) predicted inflammatory cytokine profiles more accurately than bacterial relative abundance determined by 16S rRNA gene sequence analysis, as well as functional predictions based on 16S rRNA gene sequence data (p < 0.0001). The majority of microbial biological processes were underrepresented in women with high inflammation compared to those with low inflammation, including a Lactobacillus-associated signature of reduced cell wall organization and peptidoglycan biosynthesis. This signature remained associated with high FGT inflammation in a subset of 74 women 9 weeks later, was upheld after adjusting for Lactobacillus relative abundance, and was associated with in vitro inflammatory cytokine responses to Lactobacillus isolates from the same women. Reduced cell wall organization and peptidoglycan biosynthesis were also associated with high FGT inflammation in an independent sample of ten women. CONCLUSIONS: Both the presence of specific microbial taxa in the FGT and their properties and activities are critical determinants of FGT inflammation. Our findings support those of previous studies suggesting that peptidoglycan is directly immunosuppressive, and identify a possible avenue for biotherapeutic development to reduce inflammation in the FGT. To facilitate further investigations of microbial activities, we have developed the FGT-DB application that is available at http://fgtdb.org/ . Video Abstract.

SWATH-MS based proteomic profiling of pancreatic ductal adenocarcinoma tumours reveals the interplay between the extracellular matrix and related intracellular pathways.

Pancreatic cancer accounts for 2.8% of new cancer cases worldwide and is projected to become the second leading cause of cancer-related deaths by 2030. Patients of African ancestry appear to be at an increased risk for pancreatic ductal adenocarcinoma (PDAC), with more severe disease and outcomes. The purpose of this study was to map the proteomic and genomic landscape of a cohort of PDAC patients of African ancestry. Thirty tissues (15 tumours and 15 normal adjacent tissues) were obtained from consenting South African PDAC patients. Optimisation of the sample preparation method allowed for the simultaneous extraction of high-purity protein and DNA for SWATH-MS and OncoArray SNV analyses. We quantified 3402 proteins with 49 upregulated and 35 downregulated proteins at a minimum 2.1 fold change and FDR adjusted p-value (q-value) ≤ 0.01 when comparing tumour to normal adjacent tissue. Many of the upregulated proteins in the tumour samples are involved in extracellular matrix formation (ECM) and related intracellular pathways. In addition, proteins such as EMIL1, KBTB2, and ZCCHV involved in the regulation of ECM proteins were observed to be dysregulated in pancreatic tumours. Downregulation of pathways involved in oxygen and carbon dioxide transport were observed. Genotype data showed missense mutations in some upregulated proteins, such as MYPN, ESTY2 and SERPINB8. Approximately 11% of the dysregulated proteins, including ISLR, BP1, PTK7 and OLFL3, were predicted to be secretory proteins. These findings help in further elucidating the biology of PDAC and may aid in identifying future plausible markers for the disease.

Interrogating Fractionation and Other Sources of Variability in Shotgun Proteomes Using Quality Metrics.

The increasing amount of publicly available proteomics data creates opportunities for data scientists to investigate quality metrics in novel ways. QuaMeter IDFree is used to generate quality metrics from 665 RAW files and 97 WIFF files representing publicly available "shotgun" mass spectrometry datasets. These experiments are selected to represent Mycobacterium tuberculosis lysates, mouse MDSCs, and exosomes derived from human cell lines. Machine learning techniques are demonstrated to detect outliers within experiments and it is shown that quality metrics may be used to distinguish sources of variability among these experiments. In particular, the findings demonstrate that according to nested ANOVA performed on an SDS-PAGE shotgun principal component analysis, runs of fractions from the same gel regions cluster together rather than technical replicates, close temporal proximity, or even biological samples. This indicates that the individual fraction may have had a higher impact on the quality metrics than other factors. In addition, sample type, instrument type, mass analyzer, fragmentation technique, and digestion enzyme are identified as sources of variability. From a quality control perspective, the importance of study design and in particular, the run order, is illustrated in seeking ways to limit the impact of technical variability.

Targeted next-generation sequencing identifies novel variants in candidate genes for Parkinson's disease in Black South African and Nigerian patients.

BACKGROUND: The prevalence of Parkinson's disease (PD) is increasing in sub-Saharan Africa, but little is known about the genetics of PD in these populations. Due to their unique ancestry and diversity, sub-Saharan African populations have the potential to reveal novel insights into the pathobiology of PD. In this study, we aimed to characterise the genetic variation in known and novel PD genes in a group of Black South African and Nigerian patients. METHODS: We recruited 33 Black South African and 14 Nigerian PD patients, and screened them for sequence variants in 751 genes using an Ion AmpliSeq™ Neurological Research panel. We used bcftools to filter variants and annovar software for the annotation. Rare variants were prioritised using MetaLR and MetaSVM prediction scores. The effect of a variant on ATP13A2's protein structure was investigated by molecular modelling. RESULTS: We identified 14,655 rare variants with a minor allele frequency ≤ 0.01, which included 2448 missense variants. Notably, no common pathogenic mutations were identified in these patients. Also, none of the known PD-associated mutations were found highlighting the need for more studies in African populations. Altogether, 54 rare variants in 42 genes were considered deleterious and were prioritized, based on MetaLR and MetaSVM scores, for follow-up studies. Protein modelling showed that the S1004R variant in ATP13A2 possibly alters the conformation of the protein. CONCLUSIONS: We identified several rare variants predicted to be deleterious in sub-Saharan Africa PD patients; however, further studies are required to determine the biological effects of these variants and their possible role in PD. Studies such as these are important to elucidate the genetic aetiology of this disorder in patients of African ancestry.

Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome.

There remains a pressing need for biomarkers that can predict who will progress to active tuberculosis (TB) after exposure to Mycobacterium tuberculosis (MTB) bacterium. By analyzing cohorts of household contacts of TB index cases (HHCs) and a stringent non-human primate (NHP) challenge model, we evaluated whether integration of blood transcriptional profiling with serum metabolomic profiling can provide new understanding of disease processes and enable improved prediction of TB progression. Compared to either alone, the combined application of pre-existing transcriptome- and metabolome-based signatures more accurately predicted TB progression in the HHC cohorts and more accurately predicted disease severity in the NHPs. Pathway and data-driven correlation analyses of the integrated transcriptional and metabolomic datasets further identified novel immunometabolomic signatures significantly associated with TB progression in HHCs and NHPs, implicating cortisol, tryptophan, glutathione, and tRNA acylation networks. These results demonstrate the power of multi-omics analysis to provide new insights into complex disease processes.

Characterization of Plant Glycoproteins: Analysis of Plant Glycopeptide Mass Spectrometry Data with plantGlycoMS, a Package in the R Statistical Computing Environment.

plantGlycoMS is a set of tools, implemented in R, which is used to assess and validate glycopeptide spectrum matches (gPSMs). Validity of gPSMs is based on characteristic fragmentation patterns of glycopeptides (gPSMvalidator), adherence of the glycan moiety to the known N-glycan biosynthesis pathway in plants (pGlycoFilter), and elution of the glycopeptide within the observed retention time window of other glycopeptides sharing the same peptide backbone (rt.Restrict). plantGlycoMS also contains a tool for relative quantitation of glycoforms based on selected ion chromatograms of glycopeptide ion precursors in the mass spectrometry level 1 data (glycoRQ). This protocol walks the user through this workflow with example mass spectrometry data obtained for a plant glycoprotein.

A Serum Circulating miRNA Signature for Short-Term Risk of Progression to Active Tuberculosis Among Household Contacts.

Biomarkers that predict who among recently Mycobacterium tuberculosis (MTB)-exposed individuals will progress to active tuberculosis are urgently needed. Intracellular microRNAs (miRNAs) regulate the host response to MTB and circulating miRNAs (c-miRNAs) have been developed as biomarkers for other diseases. We performed machine-learning analysis of c-miRNA measurements in the serum of adult household contacts (HHCs) of TB index cases from South Africa and Uganda and developed a c-miRNA-based signature of risk for progression to active TB. This c-miRNA-based signature significantly discriminated HHCs within 6 months of progression to active disease from HHCs that remained healthy in an independent test set [ROC area under the ROC curve (AUC) 0.74, progressors < 6 Mo to active TB and ROC AUC 0.66, up to 24 Mo to active TB], and complements the predictions of a previous cellular mRNA-based signature of TB risk.

Challenges and Opportunities for Biological Mass Spectrometry Core Facilities in the Developing World.

The developing world is seeing rapid growth in the availability of biological mass spectrometry (MS), particularly through core facilities. As proteomics and metabolomics becomes locally feasible for investigators in these nations, application areas associated with high burden in these nations, such as infectious disease, will see greatly increased research output. This article evaluates the rapid growth of MS in South Africa (currently approaching 20 laboratories) as a model for establishing MS core facilities in other nations of the developing world. Facilities should emphasize new services rather than new instruments. The reduction of the delays associated with reagent and other supply acquisition would benefit both facilities and the users who make use of their services. Instrument maintenance and repair, often mediated by an in-country business for an international vendor, is also likely to operate on a slower schedule than in the wealthiest nations. A key challenge to facilities in the developing world is educating potential facility users in how best to design experiments for proteomics and metabolomics, what reagents are most likely to introduce problematic artifacts, and how to interpret results from the facility. Here, we summarize the experience of 6 different institutions to raise the level of biological MS available to researchers in South Africa.

Quality control in mass spectrometry-based proteomics.

Mass spectrometry is a highly complex analytical technique and mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry experiment consists of multiple different phases including the sample preparation, liquid chromatography, mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry experiment occurring in all of these steps, and we discuss how to monitor and remedy the negative influences on the experimental results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the experimental workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.

Sipros Ensemble improves database searching and filtering for complex metaproteomics.

Motivation: Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of true identifications from false identifications by database searching and filtering algorithms in metaproteomics. Results: Sipros Ensemble was developed here for metaproteomics using an ensemble approach. Three diverse scoring functions from MyriMatch, Comet and the original Sipros were incorporated within a single database searching engine. Supervised classification with logistic regression was used to filter database searching results. Benchmarking with soil and marine microbial communities demonstrated a higher number of peptide and protein identifications by Sipros Ensemble than MyriMatch/Percolator, Comet/Percolator, MS-GF+/Percolator, Comet & MyriMatch/iProphet and Comet & MyriMatch & MS-GF+/iProphet. Sipros Ensemble was computationally efficient and scalable on supercomputers. Availability and implementation: Freely available under the GNU GPL license at http://sipros.omicsbio.org. Contact: cpan@utk.edu. Supplementary information: Supplementary data are available at Bioinformatics online.