ScanRanker: Quality assessment of tandem mass spectra via sequence tagging

The astounding exhaustiveness and speed of the Astral mass analyzer for highly complex samples is a quantum leap in the functional analysis of microbiomes

BACKGROUND

By analyzing the proteins which are the workhorses of biological systems, metaproteomics allows us to list the taxa present in any microbiota, monitor their relative biomass, and characterize the functioning of complex biological systems.

RESULTS

Here, we present a new strategy for rapidly determining the microbial community structure of a given sample and designing a customized protein sequence database to optimally exploit extensive tandem mass spectrometry data. This approach leverages the capabilities of the first generation of Quadrupole Orbitrap mass spectrometer incorporating an asymmetric track lossless (Astral) analyzer, offering rapid MS/MS scan speed and sensitivity. We took advantage of data-dependent acquisition and data-independent acquisition strategies using a peptide extract from a human fecal sample spiked with precise amounts of peptides from two reference bacteria.

CONCLUSIONS

Our approach, which combines both acquisition methods, proves to be time-efficient while processing extensive generic databases and massive datasets, achieving a coverage of more than 122,000 unique peptides and 38,000 protein groups within a 30-min DIA run. This marks a significant departure from current state-of-the-art metaproteomics methodologies, resulting in broader coverage of the metabolic pathways governing the biological system. In combination, our strategy and the Astral mass analyzer represent a quantum leap in the functional analysis of microbiomes. Video Abstract.

Taxonomical and functional changes in COVID-19 faecal microbiome could be related to SARS-CoV-2 faecal load

Since the beginning of the pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the gastro-intestinal (GI) tract has emerged as an important organ influencing the propensity to and potentially the severity of the related COVID-19 disease. However, the contribution of the SARS-CoV-2 intestinal infection on COVID-19 pathogenesis remains to be clarified. In this exploratory study, we highlighted a possible link between alterations in the composition of the gut microbiota and the levels of SARS-CoV-2 RNA in the gastrointestinal tract, which could be more important than the presence of SARS-CoV-2 in the respiratory tract, COVID-19 severity and GI symptoms. As established by metaproteomics, altered molecular functions in the microbiota profiles of high SARS-CoV-2 RNA level faeces highlight mechanisms such as inflammation-induced enterocyte damage, increased intestinal permeability and activation of immune response that may contribute to vicious cycles. Uncovering the role of this gut microbiota dysbiosis could drive the investigation of alternative therapeutic strategies to favour the clearance of the virus and potentially mitigate the effect of the SARS-CoV-2 infection. This article is protected by copyright. All rights reserved.

SPEQ: quality assessment of peptide tandem mass spectra with deep learning

MOTIVATION

In proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may not be identified due to incompleteness of the database or failure of the software. Thus, spectrum quality (SPEQ) assessment tools are helpful programs that can eliminate poor-quality spectra before the database search and highlight the high-quality spectra that are not identified in the initial search. These spectra may be valuable candidates for further analyses.

RESULTS

We propose SPEQ: a spectrum quality assessment tool that uses a deep neural network to classify spectra into high-quality, which are worthy candidates for interpretation, and low-quality, which lack sufficient information for identification. SPEQ was compared with a few other prediction models and demonstrated improved prediction accuracy.

AVAILABILITY AND IMPLEMENTATION

Source code and scripts are freely available at github.com/sor8sh/SPEQ, implemented in Python.

In Silico Tools and Phosphoproteomic Software Exclusives

Proteomics and phosphoproteomics have been emerging as new dimensions of omics. Phosphorylation has a profound impact on the biological functions and applications of proteins. It influences everything from intrinsic activity and extrinsic executions to cellular localization. This post-translational modification has been subjected to detailed study and has been an object of analytical curiosity with the advent of faster instrumentation. The major strength of phosphoproteomic research lies in the fact that it gives an overall picture of the workforce of the cell. Phosphoproteomics gives deeper insights into understanding the mechanism behind development and progression of a disease. This review for the first time consolidates the list of existing bioinformatics tools developed for phosphoproteomics. The gap between development of bioinformatics tools and their implementation in clinical research is highlighted. The challenge facing progress is ideally believed to be the interdisciplinary arena this field of research is associated with. For meaningful solutions and deliverables, these tools need to be implemented in clinical studies for obtaining answers to pharmacodynamic questions, saving time, costs and energy. This review hopes to invoke some thought in this direction.

pClean: An Algorithm To Preprocess High-Resolution Tandem Mass Spectra for Database Searching

Database searches of MS/MS spectra are the main approach to peptide/protein identification in proteomics. Since most database search engines only utilize a small portion of the original MS/MS signals for peptide detection, how to improve the quality of MS/MS signals is a primary concern for enhancement of the peptide/protein identification rate. A fundamental issue is that some noise MS signals, informative or uninformative, have to be filtered out prior to database searching. Herein, an integrative preprocessing algorithm was designed, termed pClean, which incorporates three modules to preprocess MS/MS spectra, such as the removal of isobaric-labeling related ions, the reduction in isotopic peaks, the deconvolution of ions with higher charges, and the clearance of uninformative MS/MS signals. In contrast to the currently available approaches to MS/MS data preprocessing, pClean enables treatment of MS/MS spectra with high mass accuracy and favors filtering for the labeling or nonlabeling of peptides. Data sets at various scales gained from mass spectrometers with high resolution were used to assess the quality of peptides identified after pClean treatment and to compare the pClean improvement with those of other software programs. On the basis of the analysis of peptides identified and the Mascot ion score, pClean was proven to be effective in the removal of mass spectral noise and the reduction of random matching. Compared with other software programs, pClean appeared to be beneficial in terms of preprocessing performances for the enhancement of confidence scores and the increase in peptides identified. pClean is available at https://github.com/AimeeD90/pClean_release .

Characterization of the Molecular Mechanisms Underlying Glucose Stimulated Insulin Secretion from Isolated Pancreatic β-cells Using Post-translational Modification Specific Proteomics (PTMomics)

Normal pancreatic islet β-cells (PBCs) abundantly secrete insulin in response to elevated blood glucose levels, in order to maintain an adequate control of energy balance and glucose homeostasis. However, the molecular mechanisms underlying the insulin secretion are unclear. Improving our understanding of glucose-stimulated insulin secretion (GSIS) mechanisms under normal conditions is a prerequisite for developing better interventions against diabetes. Here, we aimed at identifying novel signaling pathways involved in the initial release of insulin from PBCs after glucose stimulation using quantitative strategies for the assessment of phosphorylated proteins and sialylated N-linked (SA) glycoproteins.Islets of Langerhans derived from newborn rats with a subsequent 9-10 days of maturation in vitro were stimulated with 20 mm glucose for 0 min (control), 5 min, 10 min, and 15 min. The isolated islets were subjected to time-resolved quantitative phosphoproteomics and sialiomics using iTRAQ-labeling combined with enrichment of phosphorylated peptides and formerly SA glycopeptides and high-accuracy LC-MS/MS. Using bioinformatics we analyzed the functional signaling pathways during GSIS, including well-known insulin secretion pathways. Furthermore, we identified six novel activated signaling pathways (e.g. agrin interactions and prolactin signaling) at 15 min GSIS, which may increase our understanding of the molecular mechanism underlying GSIS. Moreover, we validated some of the regulated phosphosites by parallel reaction monitoring, which resulted in the validation of eleven new phosphosites significantly regulated on GSIS. Besides protein phosphorylation, alteration in SA glycosylation was observed on several surface proteins on brief GSIS. Interestingly, proteins important for cell-cell interaction, cell movement, cell-ECM interaction and Focal Adhesion (e.g. integrins, semaphorins, and plexins) were found regulated at the level of sialylation, but not in protein expression. Collectively, we believe that this comprehensive Proteomics and PTMomics survey of signaling pathways taking place during brief GSIS of primary PBCs is contributing to understanding the complex signaling underlying GSIS.

Soil and leaf litter metaproteomics-a brief guideline from sampling to understanding

The increasing application of soil metaproteomics is providing unprecedented, in-depth characterization of the composition and functionality of in situ microbial communities. Despite recent advances in high-resolution mass spectrometry, soil metaproteomics still suffers from a lack of effective and reproducible protein extraction protocols and standardized data analyses. This review discusses the opportunities and limitations of selected techniques in soil-, and leaf litter metaproteomics, and presents a step-by-step guideline on their application, covering sampling, sample preparation, extraction and data evaluation strategies. In addition, we present recent applications of soil metaproteomics and discuss how such approaches, linking phylogenetics and functionality, can help gain deeper insights into terrestrial microbial ecology. Finally, we strongly recommend that to maximize the insights environmental metaproteomics may provide, such methods should be employed within a holistic experimental approach considering relevant aboveground and belowground ecosystem parameters.

JUMPg: An Integrative Proteogenomics Pipeline Identifying Unannotated Proteins in Human Brain and Cancer Cells

Proteogenomics is an emerging approach to improve gene annotation and interpretation of proteomics data. Here we present JUMPg, an integrative proteogenomics pipeline including customized database construction, tag-based database search, peptide-spectrum match filtering, and data visualization. JUMPg creates multiple databases of DNA polymorphisms, mutations, splice junctions, partially trypticity, as well as protein fragments translated from the whole transcriptome in all six frames upon RNA-seq de novo assembly. We use a multistage strategy to search these databases sequentially, in which the performance is optimized by re-searching only unmatched high-quality spectra and reusing amino acid tags generated by the JUMP search engine. The identified peptides/proteins are displayed with gene loci using the UCSC genome browser. Then, the JUMPg program is applied to process a label-free mass spectrometry data set of Alzheimer's disease postmortem brain, uncovering 496 new peptides of amino acid substitutions, alternative splicing, frame shift, and "non-coding gene" translation. The novel protein PNMA6BL specifically expressed in the brain is highlighted. We also tested JUMPg to analyze a stable-isotope labeled data set of multiple myeloma cells, revealing 991 sample-specific peptides that include protein sequences in the immunoglobulin light chain variable region. Thus, the JUMPg program is an effective proteogenomics tool for multiomics data integration.

Metaproteomics of complex microbial communities in biogas plants

Production of biogas from agricultural biomass or organic wastes is an important source of renewable energy. Although thousands of biogas plants (BGPs) are operating in Germany, there is still a significant potential to improve yields, e.g. from fibrous substrates. In addition, process stability should be optimized. Besides evaluating technical measures, improving our understanding of microbial communities involved into the biogas process is considered as key issue to achieve both goals. Microscopic and genetic approaches to analyse community composition provide valuable experimental data, but fail to detect presence of enzymes and overall metabolic activity of microbial communities. Therefore, metaproteomics can significantly contribute to elucidate critical steps in the conversion of biomass to methane as it delivers combined functional and phylogenetic data. Although metaproteomics analyses are challenged by sample impurities, sample complexity and redundant protein identification, and are still limited by the availability of genome sequences, recent studies have shown promising results. In the following, the workflow and potential pitfalls for metaproteomics of samples from full-scale BGP are discussed. In addition, the value of metaproteomics to contribute to the further advancement of microbial ecology is evaluated. Finally, synergistic effects expected when metaproteomics is combined with advanced imaging techniques, metagenomics, metatranscriptomics and metabolomics are addressed.

Development of an enhanced metaproteomic approach for deepening the microbiome characterization of the human infant gut

The establishment of early life microbiota in the human infant gut is highly variable and plays a crucial role in host nutrient availability/uptake and maturation of immunity. Although high-performance mass spectrometry (MS)-based metaproteomics is a powerful method for the functional characterization of complex microbial communities, the acquisition of comprehensive metaproteomic information in human fecal samples is inhibited by the presence of abundant human proteins. To alleviate this restriction, we have designed a novel metaproteomic strategy based on double filtering (DF) the raw samples, a method that fractionates microbial from human cells to enhance microbial protein identification and characterization in complex fecal samples from healthy premature infants. This method dramatically improved the overall depth of infant gut proteome measurement, with an increase in the number of identified low-abundance proteins and a greater than 2-fold improvement in microbial protein identification and quantification. This enhancement of proteome measurement depth enabled a more extensive microbiome comparison between infants by not only increasing the confidence of identified microbial functional categories but also revealing previously undetected categories.

Metaproteomics: extracting and mining proteome information to characterize metabolic activities in microbial communities

Contemporary microbial ecology studies usually employ one or more "omics" approaches to investigate the structure and function of microbial communities. Among these, metaproteomics aims to characterize the metabolic activities of the microbial membership, providing a direct link between the genetic potential and functional metabolism. The successful deployment of metaproteomics research depends on the integration of high-quality experimental and bioinformatic techniques for uncovering the metabolic activities of a microbial community in a way that is complementary to other "meta-omic" approaches. The essential, quality-defining informatics steps in metaproteomics investigations are: (1) construction of the metagenome, (2) functional annotation of predicted protein-coding genes, (3) protein database searching, (4) protein inference, and (5) extraction of metabolic information. In this article, we provide an overview of current bioinformatic approaches and software implementations in metaproteome studies in order to highlight the key considerations needed for successful implementation of this powerful community-biology tool.

Bromine is an essential trace element for assembly of collagen IV scaffolds in tissue development and architecture

Bromine is ubiquitously present in animals as ionic bromide (Br(-)) yet has no known essential function. Herein, we demonstrate that Br(-) is a required cofactor for peroxidasin-catalyzed formation of sulfilimine crosslinks, a posttranslational modification essential for tissue development and architecture found within the collagen IV scaffold of basement membranes (BMs). Bromide, converted to hypobromous acid, forms a bromosulfonium-ion intermediate that energetically selects for sulfilimine formation. Dietary Br deficiency is lethal in Drosophila, whereas Br replenishment restores viability, demonstrating its physiologic requirement. Importantly, Br-deficient flies phenocopy the developmental and BM defects observed in peroxidasin mutants and indicate a functional connection between Br(-), collagen IV, and peroxidasin. We establish that Br(-) is required for sulfilimine formation within collagen IV, an event critical for BM assembly and tissue development. Thus, bromine is an essential trace element for all animals, and its deficiency may be relevant to BM alterations observed in nutritional and smoking-related disease. PAPERFLICK:

A unique covalent bond in basement membrane is a primordial innovation for tissue evolution

Basement membrane, a specialized ECM that underlies polarized epithelium of eumetazoans, provides signaling cues that regulate cell behavior and function in tissue genesis and homeostasis. A collagen IV scaffold, a major component, is essential for tissues and dysfunctional in several diseases. Studies of bovine and Drosophila tissues reveal that the scaffold is stabilized by sulfilimine chemical bonds (S = N) that covalently cross-link methionine and hydroxylysine residues at the interface of adjoining triple helical protomers. Peroxidasin, a heme peroxidase embedded in the basement membrane, produces hypohalous acid intermediates that oxidize methionine, forming the sulfilimine cross-link. We explored whether the sulfilimine cross-link is a fundamental requirement in the genesis and evolution of epithelial tissues by determining its occurrence and evolutionary origin in Eumetazoa and its essentiality in zebrafish development; 31 species, spanning 11 major phyla, were investigated for the occurrence of the sulfilimine cross-link by electrophoresis, MS, and multiple sequence alignment of de novo transcriptome and available genomic data for collagen IV and peroxidasin. The results show that the cross-link is conserved throughout Eumetazoa and arose at the divergence of Porifera and Cnidaria over 500 Mya. Also, peroxidasin, the enzyme that forms the bond, is evolutionarily conserved throughout Metazoa. Morpholino knockdown of peroxidasin in zebrafish revealed that the cross-link is essential for organogenesis. Collectively, our findings establish that the triad-a collagen IV scaffold with sulfilimine cross-links, peroxidasin, and hypohalous acids-is a primordial innovation of the ECM essential for organogenesis and tissue evolution.

Investigating the role of respiration in plant salinity tolerance by analyzing mitochondrial proteomes from wheat and a salinity-tolerant Amphiploid (wheat × Lophopyrum elongatum)

The effect of salinity on mitochondrial properties was investigated by comparing the reference wheat variety Chinese Spring (CS) to a salt-tolerant amphiploid (AMP). The octoploid AMP genotype was previously generated by combining hexaploid bread wheat (CS) with the diploid wild wheatgrass adapted to salt marshes, Lophopyrum elongatum. Here we used a combination of physiological, biochemical, and proteomic analyses to explore the mitochondrial and respiratory response to salinity in these two genotypes. The AMP showed greater growth tolerance to salinity treatments and altered respiration rate in both roots and shoots. A proteomic workflow of 2D-DIGE and MALDI TOF/TOF mass spectrometry was used to compare the protein composition of isolated mitochondrial samples from roots and shoots of both genotypes, following control or salt treatment. A large set of mitochondrial proteins were identified as responsive to salinity in both genotypes, notably enzymes involved in detoxification of reactive oxygen species. Genotypic differences in mitochondrial composition were also identified, with AMP exhibiting a higher abundance of manganese superoxide dismutase, serine hydroxymethyltransferase, aconitase, malate dehydrogenase, and β-cyanoalanine synthase compared to CS. We present peptide fragmentation spectra derived from some of these AMP-specific protein spots, which could serve as biomarkers to track superior protein variants.

A tool to evaluate correspondence between extraction ion chromatographic peaks and peptide-spectrum matches in shotgun proteomics experiments

Chromatographed peptide signals form the basis of further data processing that eventually results in functional information derived from data-dependent bottom-up proteomics assays. We seek to rank LC/MS parent ions by the quality of their extracted ion chromatograms. Ranked extracted ion chromatograms act as an intuitive physical/chemical preselection filter to improve the quality of MS/MS fragment scans submitted for database search. We identify more than 4900 proteins when considering detector shifts of less than 7 ppm. High quality parent ions for which the database search yields no hits become candidates for subsequent unrestricted analysis for PTMs. Following this rational approach, we prioritize identification of more than 5000 spectrum matches from modified peptides and confirmed the presence of acetylaldehyde-modified His/Lys. We present a logical workflow that scores data-dependent selected ion chromatograms and leverage information about semianalytical LC/LC dimension prior to MS. Our method can be successfully used to identify unexpected modifications in peptides with excellent chromatography characteristics, independent of fragmentation pattern and activation methods. We illustrate analysis of ion chromatograms detected in two different modes by RF linear ion trap and electrostatic field orbitrap.

Quality assessment for clinical proteomics

Proteomics has emerged from the labs of technologists to enter widespread application in clinical contexts. This transition, however, has been hindered by overstated early claims of accuracy, concerns about reproducibility, and the challenges of handling batch effects properly. New efforts have produced sets of performance metrics and measurements of variability that establish sound expectations for experiments in clinical proteomics. As researchers begin incorporating these metrics in a quality by design paradigm, the variability of individual steps in experimental pipelines will be reduced, regularizing overall outcomes. This review discusses the evolution of quality assessment in 2D gel electrophoresis, mass spectrometry-based proteomic profiling, tandem mass spectrometry-based protein inventories, and proteomic quantitation. Taken together, the advances in each of these technologies are establishing databases that will be increasingly useful for decision-making in clinical experimentation.

QuaMeter: multivendor performance metrics for LC-MS/MS proteomics instrumentation

LC-MS/MS-based proteomics studies rely on stable analytical system performance that can be evaluated by objective criteria. The National Institute of Standards and Technology (NIST) introduced the MSQC software to compute diverse metrics from experimental LC-MS/MS data, enabling quality analysis and quality control (QA/QC) of proteomics instrumentation. In practice, however, several attributes of the MSQC software prevent its use for routine instrument monitoring. Here, we present QuaMeter, an open-source tool that improves MSQC in several aspects. QuaMeter can directly read raw data from instruments manufactured by different vendors. The software can work with a wide variety of peptide identification software for improved reliability and flexibility. Finally, QC metrics implemented in QuaMeter are rigorously defined and tested. The source code and binary versions of QuaMeter are available under Apache 2.0 License at http://fenchurch.mc.vanderbilt.edu.

Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment

Spectral libraries have emerged as a viable alternative to protein sequence databases for peptide identification. These libraries contain previously detected peptide sequences and their corresponding tandem mass spectra (MS/MS). Search engines can then identify peptides by comparing experimental MS/MS scans to those in the library. Many of these algorithms employ the dot product score for measuring the quality of a spectrum-spectrum match (SSM). This scoring system does not offer a clear statistical interpretation and ignores fragment ion m/z discrepancies in the scoring. We developed a new spectral library search engine, Pepitome, which employs statistical systems for scoring SSMs. Pepitome outperformed the leading library search tool, SpectraST, when analyzing data sets acquired on three different mass spectrometry platforms. We characterized the reliability of spectral library searches by confirming shotgun proteomics identifications through RNA-Seq data. Applying spectral library and database searches on the same sample revealed their complementary nature. Pepitome identifications enabled the automation of quality analysis and quality control (QA/QC) for shotgun proteomics data acquisition pipelines.