TANDEM: matching proteins with tandem mass spectra

MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates

Immunopeptidomics aims to identify major histocompatibility complex (MHC)-presented peptides on almost all cells that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the nontryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS²PIP and retention time predictions by DeepLC have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS²PIP was tailored toward tryptic peptides, we have here retrained MS²PIP to include nontryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides but also yield further improvements for tryptic peptides. We show that the integration of new MS²PIP models, DeepLC, and Percolator in one software package, MS²Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS²Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Altogether, MS²Rescore thus allows substantially improved identification of novel epitopes from existing immunopeptidomics workflows.

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MS²Rescore significantly boosts immunopeptide identification rates

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Data-driven post-processing allows for a ten-fold increase in specificity

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MS²PIP and DeepLC predictors are integrated with Percolator post-processing

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MS²Rescore accepts identification results from MaxQuant, PEAKS, MS-GF+ and X!Tandem

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MS²Rescore shows great promise to extend current neo- and xeno-epitope landscapes

Proteomic repository data submission, dissemination, and reuse: key messages

INTRODUCTION

The creation of ProteomeXchange data workflows in 2012 transformed the field of proteomics, consisting of the standardization of data submission and dissemination and enabling the widespread reanalysis of public MS proteomics data worldwide. ProteomeXchange has triggered a growing trend toward public dissemination of proteomics data, facilitating the assessment, reuse, comparative analyses, and extraction of new findings from public datasets. By 2022, the consortium is integrated by PRIDE, PeptideAtlas, MassIVE, jPOST, iProX, and Panorama Public.

AREAS COVERED

Here, we review and discuss the current ecosystem of resources, guidelines, and file formats for proteomics data dissemination and reanalysis. Special attention is drawn to new exciting quantitative and post-translational modification-oriented resources. The challenges and future directions on data depositions including the lack of metadata and cloud-based and high-performance software solutions for fast and reproducible reanalysis of the available data are discussed.

EXPERT OPINION

The success of ProteomeXchange and the amount of proteomics data available in the public domain have triggered the creation and/or growth of other protein knowledgebase resources. Data reuse is a leading, active, and evolving field; supporting the creation of new formats, tools, and workflows to rediscover and reshape the public proteomics data.

Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples

Mass spectrometry-based metaproteomics is a relatively new field of research that enables the characterization of the functionality of microbiota. Recently, we demonstrated the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the previously used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the DDA-assisted DIA approach still required additional DDA data on the samples to assist the analysis. Here, we introduce, for the first time, an untargeted DIA metaproteomics tool that does not require any DDA data, but instead generates a pseudospectral library directly from the DIA data. This reduces the amount of required mass spectrometry data to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as a new open-source software package named glaDIAtor, including a modern web-based graphical user interface to facilitate wide use of the tool by the community.

The metabolic enzyme hexokinase 2 localizes to the nucleus in AML and normal haematopoietic stem and progenitor cells to maintain stemness

Mitochondrial metabolites regulate leukaemic and normal stem cells by affecting epigenetic marks. How mitochondrial enzymes localize to the nucleus to control stem cell function is less understood. We discovered that the mitochondrial metabolic enzyme hexokinase 2 (HK2) localizes to the nucleus in leukaemic and normal haematopoietic stem cells. Overexpression of nuclear HK2 increases leukaemic stem cell properties and decreases differentiation, whereas selective nuclear HK2 knockdown promotes differentiation and decreases stem cell function. Nuclear HK2 localization is phosphorylation-dependent, requires active import and export, and regulates differentiation independently of its enzymatic activity. HK2 interacts with nuclear proteins regulating chromatin openness, increasing chromatin accessibilities at leukaemic stem cell-positive signature and DNA-repair sites. Nuclear HK2 overexpression decreases double-strand breaks and confers chemoresistance, which may contribute to the mechanism by which leukaemic stem cells resist DNA-damaging agents. Thus, we describe a non-canonical mechanism by which mitochondrial enzymes influence stem cell function independently of their metabolic function.

Thomas, Egan et al. report that hexokinase 2 localizes to the nucleus of leukaemic and normal haematopoietic cells to maintain stemness by interacting with nuclear proteins and modulating chromatin accessibility independently of its kinase activity.

Bioactive Peptides: An Understanding from Current Screening Methodology

Bioactive peptides with high potency against numerous human disorders have been regarded as a promising therapy in disease control. These peptides could be released from various dietary protein sources through hydrolysis processing using physical conditions, chemical agents, microbial fermentation, or enzymatic digestions. Considering the diversity of the original proteins and the complexity of the multiple structural peptides that existed in the hydrolysis mixture, the screening of bioactive peptides will be a challenge task. Well-organized and well-designed methods are necessarily required to enhance the efficiency of studying the potential peptides. This article, hence, provides an overview of bioactive peptides with an emphasis on the current strategy used for screening and characterization methods. Moreover, the understanding of the biological activities of peptides, mechanism inhibitions, and the interaction of the complex of peptide–enzyme is commonly evaluated using specific in vitro assays and molecular docking analysis.

iMetaLab Suite: A one-stop toolset for metaproteomics

Metaproteomics is a recently thriving technique that studies the collection of proteins in complex microbiomes of the human, animal, plant, and environment. The bioinformatics workflow required for metaproteomics research, from the database search and protein quantification to downstream functional and taxonomic analysis has been challenging and thus limiting the accessibility of metaproteomics to microbiome researchers. To overcome these challenges, we have developed a set of tools named iMetaLab Suite. iMetaLab Suite includes the following components: (1) MetaLab Desktop, an automated database search software that facilities proteins identification and quantitation from microbiomes; (2) the automated iMetaReport that allows users to quickly access database search results and data set profiles; and (3) an interactive online toolset, iMetaShiny, covering most frequently used functional, taxonomic, and statistical analysis in metaproteomics. iMetaLab Suite is a free, easily accessible, and actively updated toolset available to assist researchers to explore metaproteomic data.

Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry

Reversed-phase liquid chromatography (RPLC) and capillary zone electrophoresis (CZE) are two primary proteoform separation methods in mass spectrometry (MS)-based top-down proteomics. Proteoform retention time (RT) prediction in RPLC and migration time (MT) prediction in CZE provide additional information for accurate proteoform identification and quantification. While existing methods are mainly focused on peptide RT and MT prediction in bottom-up MS, there is still a lack of methods for proteoform RT and MT prediction in top-down MS. We systematically evaluated eight machine learning models and a transfer learning method for proteoform RT prediction and five models and the transfer learning method for proteoform MT prediction. Experimental results showed that a gated recurrent unit (GRU)-based model with transfer learning achieved a high accuracy (R = 0.978) for proteoform RT prediction and that the GRU-based model and a fully connected neural network model obtained a high accuracy of R = 0.982 and 0.981 for proteoform MT prediction, respectively.

Multiaspect Examinations of Possible Alternative Mappings of Identified Variant Peptides: A Case Study on the HEK293 Cell Line

Adopting proteogenomics approach to validate single nucleotide variation events by identifying corresponding single amino acid variant peptides from mass spectrometry (MS)-based proteomics data facilitates translational and clinical research. Although variant peptides are usually identified from MS data with a stringent false discovery rate (FDR), FDR control could fail to eliminate dubious results caused by several issues; thus, postexamination to eliminate dubious results is required. However, comprehensive postexaminations of identification results are still lacking. Therefore, we propose a framework of three bottom-up levels, peptide-spectrum match, peptide, and variant event levels, that consists of rigorous 11-aspect examinations from the MS perspective to further confirm the reliability of variant events. As a proof of concept and showing feasibility, we demonstrate 11 examinations on the identified variant peptides from an HEK293 cell line data set, where various database search strategies were applied to maximize the number of identified variant PSMs with an FDR <1% for postexaminations. The results showed that only FDR criterion is insufficient to validate identified variant peptides and the 11 postexaminations can reveal low-confidence variant events detected by shotgun proteomics experiments. Therefore, we suggest that postexaminations of identified variant events based on the proposed framework are necessary for proteogenomics studies.

False discovery rate: the Achilles' heel of proteogenomics

Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

Isolation, profiling, and tracking of extracellular vesicle cargo in Caenorhabditis elegans

Extracellular vesicles (EVs) may mediate intercellular communication by carrying protein and RNA cargo. The composition, biology, and roles of EVs in physiology and pathology have been primarily studied in the context of biofluids and in cultured mammalian cells. The experimental tractability of C. elegans makes for a powerful in vivo animal system to identify and study EV cargo from its cellular source. We developed an innovative method to label, track, and profile EVs using genetically encoded, fluorescent-tagged EV cargo and conducted a large-scale isolation and proteomic profiling. Nucleic acid binding proteins (∼200) are overrepresented in our dataset. By integrating our EV proteomic dataset with single-cell transcriptomic data, we identified and validated ciliary EV cargo: CD9-like tetraspanin (TSP-6), ectonucleotide pyrophosphatase/phosphodiesterase (ENPP-1), minichromosome maintenance protein (MCM-3), and double-stranded RNA transporter SID-2. C. elegans EVs also harbor RNA, suggesting that EVs may play a role in extracellular RNA-based communication.

Human Monocyte-Derived Suppressor Cell Supernatant Induces Immunoregulatory Effects and Mitigates xenoGvHD

Recently developed cell-based therapies have shown potential for graft-versus-host disease (GvHD) mitigation. Our team previously developed a protocol to generate human monocyte-derived suppressor Cells (HuMoSC), a subpopulation of CD33+ suppressor cells of monocytic origin. CD33+HuMoSC successfully reduced xenoGvHD severity in NOD/SCID/IL-2Rγc^-/- (NSG) mice. While CD33+ HuMoSC culture supernatant inhibits T cell activation and proliferation, the recovery of CD33+ HuMoSC immunosuppressive cells and the subsequent production of their supernatant is limited. An attractive solution would be to use both the CD33+ and the large number of CD14+ cells derived from our protocol. Here, we assessed the immunoregulatory properties of the CD14+HuMoSC supernatant and demonstrated that it inhibited both CD4 and CD8 T cell proliferation and decreased CD8 cytotoxicity. In vivo, injection of CD14+HuMoSC supernatant reduced xenoGvHD in NSG mice. Furthermore, CD14+HuMoSC supernatant maintained its immunoregulatory properties in an inflammatory environment. Proteomic and multiplex analyses revealed the presence of immunosuppressive proteins such as GPNMB, galectin-3 and IL-1R(A) Finally, CD14+HuMoSC supernatant can be produced using good manufacturing practices and be used as complement to current immunosuppressive drugs. CD14+HuMoSC supernatant is thus a promising therapy for preventing GvHD. 

Metal stresses modify soluble proteomes and toxin profiles in two Mediterranean strains of the distributed dinoflagellate Alexandrium pacificum

HABs involving Alexandrium pacificum have been reported in metal-contaminated ecosystems, suggesting that this distributed species adapts to and/or can tolerate the effects of metals. Modifications in soluble proteomes and PST contents were characterized in two Mediterranean A. pacificum strains exposed to mono- or polymetallic stresses (zinc, lead, copper, cadmium). These strains were isolated from two anthropized locations: Santa Giusta Lagoon (Italy, SG C10-3) and the Tarragona seaport (Spain, TAR C5-4F). In both strains, metals primarily downregulated key photosynthesis proteins. Metals also upregulated other proteins involved in photosynthesis (PCP in both strains), the oxidative stress response (HSP 60, proteasome and SOD in SG C10-3; HSP 70 in TAR C5-4F), energy metabolism (AdK in TAR C5-4F), neoglucogenesis/glycolysis (GAPDH and PEP synthase in SG C10-3) and protein modification (PP in TAR C5-4F). These proteins, possibly involved in adaptive proteomic responses, may explain the development of these A. pacificum strains in metal-contaminated ecosystems. The two strains showed different proteomic responses to metals, with SG C10-3 upregulating more proteins, particularly PCP. Among the PSTs, regardless of the metal and the strain studied, C2 and GTX4 predominated, followed by GTX5. Under the polymetallic cocktail, (i) total PSTs, C2 and GTX4 reached the highest levels in SG C10-3 only, and (ii) total PSTs, C2, GTX5 and neoSTX were higher in SG C10-3 than in TAR C5-4F, whereas in SG C10-3 under copper stress, total PSTs, GTX5, GTX1 and C1 were higher than in the controls, revealing variability in PST biosynthesis between the two strains. Total PSTs, C2, GTX4 and GTX1 showed significant positive correlations with PCP, indicating that PST production may be positively related to photosynthesis. Our results showed that the A. pacificum strains adapt their proteomic and physiological responses to metals, which may contribute to their ecological success in highly anthropized areas.

Aggresome assembly at the centrosome is driven by CP110–CEP97–CEP290 and centriolar satellites

Protein degradation is critical to maintaining cellular homeostasis, and perturbation of the ubiquitin proteasome system leads to the accumulation of protein aggregates. These aggregates are either directed towards autophagy for destruction or sequestered into an inclusion, termed the aggresome, at the centrosome. Utilizing high-resolution quantitative analysis, here, we define aggresome assembly at the centrosome in human cells. Centriolar satellites are proteinaceous granules implicated in the trafficking of proteins to the centrosome. During aggresome assembly, satellites were required for the growth of the aggresomal structure from an initial ring of phosphorylated HSP27 deposited around the centrioles. The seeding of this phosphorylated HSP27 ring depended on the centrosomal proteins CP110, CEP97 and CEP290. Owing to limiting amounts of CP110, senescent cells, which are characterized by the accumulation of protein aggregates, were defective in aggresome formation. Furthermore, satellites and CP110–CEP97–CEP290 were required for the aggregation of mutant huntingtin. Together, these data reveal roles for CP110–CEP97–CEP290 and satellites in the control of cellular proteostasis and the aggregation of disease-relevant proteins.

Prosser et al. report that centriolar satellite and centrosomal proteins seed aggresomes, perinuclear inclusions of misfolded proteins, and may play a role in aggresome formation during senescence and huntingtin aggregation.

Auxiliary ATP binding sites support DNA unwinding by RecBCD

The RecBCD helicase initiates double-stranded break repair in bacteria by processively unwinding DNA with a rate approaching ∼1,600 bp·s⁻¹, but the mechanism enabling such a fast rate is unknown. Employing a wide range of methodologies — including equilibrium and time-resolved binding experiments, ensemble and single-molecule unwinding assays, and crosslinking followed by mass spectrometry — we reveal the existence of auxiliary binding sites in the RecC subunit, where ATP binds with lower affinity and distinct chemical interactions as compared to the known catalytic sites. The essentiality and functionality of these sites are demonstrated by their impact on the survival of E.coli after exposure to damage-inducing radiation. We propose a model by which RecBCD achieves its optimized unwinding rate, even when ATP is scarce, by using the auxiliary binding sites to increase the flux of ATP to its catalytic sites.

RecBCD is a remarkably fast DNA helicase. Using a battery of biophysical methods, Zananiri et. al reveal additional, non-catalytic ATP binding sites that increase the ATP flux to the catalytic sites that allows fast unwinding when ATP is scarce.

LRP1 loss in airway epithelium exacerbates smoke-induced oxidative damage and airway remodeling

The LDL receptor-related protein 1 (LRP1) partakes in metabolic and signaling events regulated in a tissue-specific manner. The function of LRP1 in airways has not been studied. We aimed to study the function of LRP1 in smoke-induced disease. We found that bronchial epithelium of patients with chronic obstructive pulmonary disease and airway epithelium of mice exposed to smoke had increased LRP1 expression. We then knocked out LRP1 in human bronchial epithelial cells in vitro and in airway epithelial club cells in mice. In vitro, LRP1 knockdown decreased cell migration and increased transforming growth factor β activation. Tamoxifen-inducible airway-specific LRP1 knockout mice (club Lrp1-/-) induced after complete lung development had increased inflammation in the bronchoalveolar space and lung parenchyma at baseline. After 6 months of smoke exposure, club Lrp1-/- mice showed a combined restrictive and obstructive phenotype, with lower compliance, inspiratory capacity, and forced expiratory volume0.05/forced vital capacity than WT smoke-exposed mice. This was associated with increased values of Ashcroft fibrotic index. Proteomic analysis of room air exposed-club Lrp1-/- mice showed significantly decreased levels of proteins involved in cytoskeleton signaling and xenobiotic detoxification as well as decreased levels of glutathione. The proteome fingerprint created by smoke eclipsed many of the original differences, but club Lrp1-/- mice continued to have decreased lung glutathione levels and increased protein oxidative damage and airway cell proliferation. Therefore, LRP1 deficiency leads to greater lung inflammation and damage and exacerbates smoke-induced lung disease.

Defining the interactomes of proteins involved in cytoskeletal dynamics using high-throughput proximity-dependent biotinylation in cellulo

Proximity-dependent biotinylation (BioID) screens are excellent tools to capture in cellulo interactomes for a large variety of baits, including transient and weak affinity interactions, as well as localization-specific proximity components, which are much harder to detect with conventional approaches. Here, we describe the major starting steps and a detailed protocol on how to perform BioID in mammalian cells. We also describe the mass spectrometry procedure and the bioinformatics pipeline for the data analysis.

For complete details on the use and execution of this profile, please refer to Bagci et al. (2020).

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Exploring the proximity interactome of proteins in cellulo in their native environment

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This BioID protocol is well-suited to determine the interactome of small GTPases

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Detailed steps of sample injection into mass spectrometer

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In-depth description of the bio-informatics analyses of BioID MS data

The Nitrate Transporter MtNPF6.8 Is a Master Sensor of Nitrate Signal in the Primary Root Tip of Medicago truncatula

Nitrate is not only an essential nutrient for plants, but also a signal involved in plant development. We have previously shown in the model legume Medicago truncatula, that the nitrate signal, which restricts primary root growth, is mediated by MtNPF6.8, a nitrate transporter. Nitrate signal also induces changes in reactive oxygen species accumulation in the root tip due to changes in cell wall peroxidase (PODs) activity. Thus, it was interesting to determine the importance of the role of MtNPF6.8 in the regulation of the root growth by nitrate and identify the POD isoforms responsible for the changes in POD activity. For this purpose, we compared in M. truncatula a npf6.8 mutant and nitrate insensitive line deficient in MtNPF6.8 and the corresponding wild and sensitive genotype for their transcriptomic and proteomic responses to nitrate. Interestingly, only 13 transcripts and no protein were differently accumulated in the primary root tip of the npf6.8-3 mutant line in response to nitrate. The sensitivity of the primary root tip to nitrate appeared therefore to be strongly linked to the integrity of MtNPF6.8 which acts as a master mediator of the nitrate signal involved in the control of the root system architecture. In parallel, 7,259 and 493 genes responded, respectively, at the level of transcripts or proteins in the wild type, 196 genes being identified by both their transcript and protein. By focusing on these 196 genes, a concordance of expression was observed for most of them with 143 genes being up-regulated and 51 being down-regulated at the two gene expression levels. Their ontology analysis uncovered a high enrichment in POD genes, allowing the identification of POD candidates involved in the changes in POD activity previously observed in response to nitrate.

The impact of noise and missing fragmentation cleavages on de novo peptide identification algorithms

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Most correct de novo peptides have ⩽1 missing fragmentation cleavages.

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DeepNovo outperforms Novor for peptide accuracy for both data types.

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Novor excels at amino acid recall when many fragmentation cleavages are missing.

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Deep learning allows DeepNovo to predict amino acids without adjacent peaks.

Proteomics aims to characterise system-wide protein expression and typically relies on mass-spectrometry and peptide fragmentation, followed by a database search for protein identification. It has wide ranging applications from clinical to environmental settings and virtually impacts on every area of biology. In that context, de novo peptide sequencing is becoming increasingly popular. Historically its performance lagged behind database search methods but with the integration of machine learning, this field of research is gaining momentum. To enable de novo peptide sequencing to realise its full potential, it is critical to explore the mass spectrometry data underpinning peptide identification. In this research we investigate the characteristics of tandem mass spectra using 8 published datasets. We then evaluate two state of the art de novo peptide sequencing algorithms, Novor and DeepNovo, with a particular focus on their performance with regard to missing fragmentation cleavage sites and noise. DeepNovo was found to perform better than Novor overall. However, Novor recalled more correct amino acids when 6 or more cleavage sites were missing. Furthermore, less than 11% of each algorithms’ correct peptide predictions emanate from data with more than one missing cleavage site, highlighting the issues missing cleavages pose. We further investigate how the algorithms manage to correctly identify peptides with many of these missing fragmentation cleavages. We show how noise negatively impacts the performance of both algorithms, when high intensity peaks are considered. Finally, we provide recommendations regarding further algorithms’ improvements and offer potential avenues to overcome current inherent data limitations.

Comparative proteomic analysis of Leishmania parasites isolated from visceral and cutaneous leishmaniasis patients

Leishmaniasis is an infectious disease in which different clinical manifestations are classified into three primary forms: visceral, cutaneous and mucocutaneous. These disease forms are associated with parasite species of the protozoan genus Leishmania. For instance, Leishmania infantum and Leishmania tropica are typically linked with visceral (VL) and cutaneous (CL) leishmaniasis, respectively; however, these two species can also cause other form to a lesser extent. What is more alarming is this characteristic, which threatens current medical diagnosis and treatment, is started to be acquired by other species. Our purpose was to address this issue; therefore, gel-based and gel-free proteomic analyses were carried out on the species L. infantum to determine the proteins differentiating between the parasites caused VL and CL. In addition, L. tropica parasites representing the typical cases for CL were included. According to our results, electrophoresis gels of parasites caused to VL were distinguishable regarding the repetitive down-regulation on some specific locations. In addition, a distinct spot of an antioxidant enzyme, superoxide dismutase, was shown up only on the gels of CL samples regardless of the species. In the gel-free approach, 37 proteins that were verified with a second database search using a different search engine, were recognized from the comparison between VL and CL samples. Among them, 31 proteins for the CL group and six proteins for the VL group were determined differentially abundant. Two proteins from the gel-based analysis, pyruvate kinase and succinyl-coA:3-ketoacid-coenzyme A transferase analysis were encountered in the protein list of the CL group.

Proteomic analysis of the umbilical cord in fetal growth restriction and preeclampsia

Fetal growth restriction (FGR) is associated with adverse perinatal outcomes. Pre-eclampsia (PreE) increases the associated perinatal morbidity and mortality. The structure of the umbilical cord in the setting of FGR and PreE is understudied. This study aimed to examine changes in the umbilical cord (UC) composition in pregnancies complicated by FGR and FGR with PreE. UC from gestational age-matched pregnancies with isolated FGR (n = 5), FGR+PreE (n = 5) and controls (n = 5) were collected, and a portion of the UC was processed for histologic and proteomic analysis. Manual segmentation analysis was performed to measure cross-section analysis of umbilical cord regions. Wharton’s Jelly samples were analyzed on a tims-TOF Pro. Spectral count and ion abundance data were analyzed, creating an intersection dataset from multiple mass spectrometry search and inference engines. UCs from FGR and FGR with PreE had lower cross-sectional area and Wharton’s Jelly area compared with control (p = 0.03). When comparing FGR to control, 28 proteins were significantly different in abundance analysis and 34 in spectral count analysis (p < 0.05). Differential expression analysis between PreE with FGR vs controls demonstrated that 48 proteins were significantly different in abundance and 5 in spectral count. The majority of changes occurred in proteins associated with extracellular matrix, cellular process, inflammatory, and angiogenesis pathways. The structure and composition of the UC is altered in pregnancies with FGR and FGR with PreE. Future work in validating these proteomic differences will enable identification of therapeutic targets for FGR and FGR with PreE.

LINE-1 expression in cancer correlates with p53 mutation, copy number alteration, and S phase checkpoint

Retrotransposons are genomic DNA sequences that copy themselves to new genomic locations via RNA intermediates; LINE-1 is the only active and autonomous retrotransposon in the human genome. The mobility of LINE-1 is largely repressed in somatic tissues but is derepressed in many cancers, where LINE-1 retrotransposition is correlated with p53 mutation and copy number alteration (CNA). In cell lines, inducing LINE-1 expression can cause double-strand breaks (DSBs) and replication stress. Reanalyzing multiomic data from breast, ovarian, endometrial, and colon cancers, we confirmed correlations between LINE-1 expression, p53 mutation status, and CNA. We observed a consistent correlation between LINE-1 expression and the abundance of DNA replication complex components, indicating that LINE-1 may also induce replication stress in human tumors. In endometrial cancer, high-quality phosphoproteomic data allowed us to identify the DSB-induced ATM-MRN-SMC S phase checkpoint pathway as the primary DNA damage response (DDR) pathway associated with LINE-1 expression. Induction of LINE-1 expression in an in vitro model led to increased phosphorylation of MRN complex member RAD50, suggesting that LINE-1 directly activates this pathway.

Xrn1 is a deNADding enzyme modulating mitochondrial NAD-capped RNA

The existence of non-canonical nicotinamide adenine diphosphate (NAD) 5′-end capped RNAs is now well established. Nevertheless, the biological function of this nucleotide metabolite cap remains elusive. Here, we show that the yeast Saccharomyces cerevisiae cytoplasmic 5′-end exoribonuclease Xrn1 is also a NAD cap decapping (deNADding) enzyme that releases intact NAD and subsequently degrades the RNA. The significance of Xrn1 deNADding is evident in a deNADding deficient Xrn1 mutant that predominantly still retains its 5′-monophosphate exonuclease activity. This mutant reveals Xrn1 deNADding is necessary for normal growth on non-fermenting sugar and is involved in modulating mitochondrial NAD-capped RNA levels and may influence intramitochondrial NAD levels. Our findings uncover a contribution of mitochondrial NAD-capped RNAs in overall NAD regulation with the deNADding activity of Xrn1 fulfilling a central role.

The cytoplasmic Xrn1 protein has long been established as the predominate 5′ to 3′ exoribonuclease that cleaves RNAs with an unprotected 5′ monophosphate end. Here the authors demonstrate Xrn1 can also degrade RNAs harboring the noncanonical nicotinamide adenine diphosphate (NAD) 5′ cap by removing the NAD cap and degrading the RNA.

Silencing of Opisthorchis viverrini Tetraspanin Gene Expression Results in Reduced Secretion of Extracellular Vesicles

Inter-phylum transfer of molecular information is exquisitely exemplified in the uptake of parasite extracellular vesicles (EVs) by their target mammalian host tissues. The oriental liver fluke, Opisthorchis viverrini is the major cause of bile duct cancer in people in Southeast Asia. A major mechanism by which O. viverrini promotes cancer is through the secretion of excretory/secretory products which contain extracellular vesicles (OvEVs). OvEVs contain microRNAs that are predicted to impact various mammalian cell proliferation pathways, and are internalized by cholangiocytes that line the bile ducts. Upon uptake, OvEVs drive relentless proliferation of cholangiocytes and promote a tumorigenic environment, but the underlying mechanisms of this process are unknown. Moreover, purification and characterization methods for helminth EVs in general are ill defined. We therefore compared different purification methods for OvEVs and characterized the sub-vesicular compartment proteomes. Two CD63-like tetraspanins (Ov-TSP-2 and TSP-3) are abundant on the surface of OvEVs, and could serve as biomarkers for these parasite vesicles. Anti-TSP-2 and -TSP-3 IgG, as well as different endocytosis pathway inhibitors significantly reduced OvEV uptake and subsequent proliferation of cholangiocytes in vitro. Silencing of Ov-tsp-2 and tsp-3 gene expression in adult flukes using RNA interference resulted in substantial reductions in OvEV secretion, and those vesicles that were secreted were deficient in their respective TSP proteins. Our findings shed light on the importance of tetraspanins in fluke EV biogenesis and/or stability, and provide a conceivable mechanism for the efficacy of anti-tetraspanin subunit vaccines against a range of parasitic helminth infections.

Pout2Prot: An Efficient Tool to Create Protein (Sub)groups from Percolator Output Files

In metaproteomics, the study of the collective proteome of microbial communities, the protein inference problem is more challenging than in single-species proteomics. Indeed, a peptide sequence can be present not only in multiple proteins or protein isoforms of the same species, but also in homologous proteins from closely related species. To assign the taxonomy and functions of the microbial species, specialized tools have been developed, such as Prophane. This tool, however, is not directly compatible with post-processing tools such as Percolator. In this manuscript we therefore present Pout2Prot, which takes Percolator Output (.pout) files from multiple experiments and creates protein group and protein subgroup output files (.tsv) that can be used directly with Prophane. We investigated different grouping strategies and compared existing protein grouping tools to develop an advanced protein grouping algorithm that offers a variety of different approaches, allows grouping for multiple files, and uses a weighted spectral count for protein (sub)groups to reflect abundance. Pout2Prot is available as a web application at https://pout2prot.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the Apache License 2.0 and is available at https://github.com/compomics/pout2prot.

Multi-Omics Profiling Specifies Involvement of Alternative Ribosomal Proteins in Response to Zinc Limitation in Mycobacterium smegmatis

Zinc ion (Zn²⁺) is an essential micronutrient and a potent antioxidant. However, Zn²⁺ is often limited in the environment. Upon Zn²⁺ limitation, Mycolicibacterium (basonym: Mycobacterium) smegmatis (Msm) undergoes a morphogenesis, which relies on alternative ribosomal proteins (AltRPs); i.e., Zn²⁺-independent paralogues of Zn²⁺-dependent ribosomal proteins. However, the underlying physiological changes triggered by Zn²⁺ limitation and how AltRPs contribute to these changes were not known. In this study, we expand the knowledge of mechanisms utilized by Msm to endure Zn²⁺ limitation, by comparing the transcriptomes and proteomes of Zn²⁺-limited and Zn²⁺-replete Msm. We further compare, corroborate and contrast our results to those reported for the pathogenic mycobacterium, M. tuberculosis, which highlighted conservation of the upregulated oxidative stress response when Zn²⁺ is limited in both mycobacteria. By comparing the multi-omics analysis of a knockout mutant lacking AltRPs (ΔaltRP) to the Msm wild type strain, we specify the involvement of AltRPs in the response to Zn²⁺ limitation. Our results show that AltRP expression in Msm does not affect the conserved oxidative stress response during Zn²⁺ limitation observed in mycobacteria, but AltRPs do significantly impact expression patterns of numerous genes that may be involved in morphogenesis or other adaptive responses. We conclude that AltRPs are not only important as functional replacements for their Zn²⁺-dependent paralogues; they are also involved in the transcriptomic response to the Zn²⁺-limited environment.

Common Decoy Distributions Simplify False Discovery Rate Estimation in Shotgun Proteomics

In shotgun proteomics, false discovery rate (FDR) estimation is a necessary step to ensure the quality of accepted peptide-spectrum matches (PSMs) from a database search. Popular statistical validation tools for FDR control tend to rely on target-decoy searching to build empirical, dataset-specific models, which often leads to inaccurate FDR estimates. In this paper, we propose a new approach named common decoy distribution (CDD) to FDR estimation using the idea of a fixed empirical null score distribution derived from millions of peptide tandem mass spectra. To demonstrate the viability of CDD, its stability with respect to noise and the presence of unexpected peptide modifications was evaluated. PeptideProphet-based implementation of CDD was benchmarked against decoy-based PeptideProphet, and both methods exhibited similar accuracy of FDR estimates and retrieval of correct PSMs. The finding of this study calls for a re-evaluation of the necessity of dataset-specific target-decoy searches and illustrates the potential of Big Data approaches for statistical analysis in proteomics.

Evidence that the transcriptional repressor ICER is regulated via the N-end rule for ubiquitination

ICER is a transcriptional repressor that is mono- or poly-ubiquitinated. This either causes ICER to be translocated from the nucleus, or degraded via the proteasome, respectively. In order to further studies the proteins involved in ICER regulation mass spectrometry analysis was performed to identify potential candidates. We identified twenty eight ICER-interacting proteins in human melanoma cells, Sk-Mel-24. In this study we focus on two proteins with potential roles in ICER proteasomal degradation in response to the N-end rule for ubiquitination: the N-alpha-acetyltransferase 15 (NAA15) and the E3 ubiquitin-protein ligase UBR4. Using an HA-tag on the N- or C-terminus of ICER (_NHAICER or ICER_CHA) it was found that the N-terminus of ICER is important for its interaction to UBR4, whereas NARG1 interaction is independent of HA-tag position. Silencing RNA experiments show that both NAA15 and UBR4 up-regulates ICER levels and that ICER's N-terminus is important for this regulation. The N-terminus of ICER was found to have dire consequences on its regulation by ubiquitination and cellular functions. The half-life of _NHAICER was found to be about twice as long as ICER_CHA. Polyubiquitination of ICER was found to be dependent on its N-terminus and mediated by UBR4. This data strongly suggests that ICER is ubiquitinated as a response to the N-end rule that governs protein degradation rate through recognition of the N-terminal residue of proteins. Furthermore, we found that _NHAICER inhibits transcription two times more efficiently than ICER_CHA, and causes apoptosis 5 times more efficiently than ICER_CHA. As forced expression of ICER has been shown before to block cells in mitosis, our data represent a potentially novel mechanism for apoptosis of cells in mitotic arrest.

Structural basis for safe and efficient energy conversion in a respiratory supercomplex

Proton-translocating respiratory complexes assemble into supercomplexes that are proposed to increase the efficiency of energy conversion and limit the production of harmful reactive oxygen species during aerobic cellular respiration. Cytochrome bc complexes and cytochrome aa₃ oxidases are major drivers of the proton motive force that fuels ATP generation via respiration, but how wasteful electron- and proton transfer is controlled to enhance safety and efficiency in the context of supercomplexes is not known. Here, we address this question with the 2.8 Å resolution cryo-EM structure of the cytochrome bcc-aa₃ (III₂-IV₂) supercomplex from the actinobacterium Corynebacterium glutamicum. Menaquinone, substrate mimics, lycopene, an unexpected Q_c site, dioxygen, proton transfer routes, and conformational states of key protonable residues are resolved. Our results show how safe and efficient energy conversion is achieved in a respiratory supercomplex through controlled electron and proton transfer. The structure may guide the rational design of drugs against actinobacteria that cause diphtheria and tuberculosis.

Aerobic energy metabolism is driven by proton-pumping respiratory supercomplexes. The study reports the structural basis for energy conversion in such supercomplex. It may aid metabolic engineering and drug design against diphtheria and tuberculosis.

Proteomic Shifts Reflecting Oxidative Stress and Reduced Capacity for Protein Synthesis, and Alterations to Mitochondrial Membranes in Neurospora crassa Lacking VDAC

Voltage-dependent anion-selective channels (VDAC) maintain the bidirectional flow of small metabolites across the mitochondrial outer membrane and participate in the regulation of multiple cellular processes. To understand the roles of VDAC in cellular homeostasis, preliminary proteomic analyses of S100 cytosolic and mitochondria-enriched fractions from a VDAC-less Neurospora crassa strain (ΔPor-1) were performed. In the variant cells, less abundant proteins include subunits of translation initiation factor eIF-2, enzymes in the shikimate pathway leading to precursors of aromatic amino acids, and enzymes involved in sulfate assimilation and in the synthesis of methionine, cysteine, alanine, serine, and threonine. In contrast, some of the more abundant proteins are involved in electron flow, such as the α subunit of the electron transfer flavoprotein and lactate dehydrogenase, which is involved in one pathway leading to pyruvate synthesis. Increased levels of catalase and catalase activity support predicted increased levels of oxidative stress in ΔPor-1 cells, and higher levels of protein disulfide isomerase suggest activation of the unfolded protein response in the endoplasmic reticulum. ΔPor-1 cells are cold-sensitive, which led us to investigate the impact of the absence of VDAC on several mitochondrial membrane characteristics. Mitochondrial membranes in ΔPor-1 are more fluid than those of wild-type cells, the ratio of C18:1 to C18:3n3 acyl chains is reduced, and ergosterol levels are lower. In summary, these initial results indicate that VDAC-less N. crassa cells are characterized by a lower abundance of proteins involved in amino acid and protein synthesis and by increases in some associated with pyruvate metabolism and stress responses. Membrane lipids and hyphal morphology are also impacted by the absence of VDAC.

The Cell Wall Proteome of Marchantia polymorpha Reveals Specificities Compared to Those of Flowering Plants

Primary plant cell walls are composite extracellular structures composed of three major classes of polysaccharides (pectins, hemicelluloses, and cellulose) and of proteins. The cell wall proteins (CWPs) play multiple roles during plant development and in response to environmental stresses by remodeling the polysaccharide and protein networks and acting in signaling processes. To date, the cell wall proteome has been mostly described in flowering plants and has revealed the diversity of the CWP families. In this article, we describe the cell wall proteome of an early divergent plant, Marchantia polymorpha, a Bryophyte which belong to one of the first plant species colonizing lands. It has been possible to identify 410 different CWPs from three development stages of the haploid gametophyte and they could be classified in the same functional classes as the CWPs of flowering plants. This result underlied the ability of M. polymorpha to sustain cell wall dynamics. However, some specificities of the M. polymorpha cell wall proteome could be highlighted, in particular the importance of oxido-reductases such as class III peroxidases and polyphenol oxidases, D-mannose binding lectins, and dirigent-like proteins. These proteins families could be related to the presence of specific compounds in the M. polymorpha cell walls, like mannans or phenolics. This work paves the way for functional studies to unravel the role of CWPs during M. polymorpha development and in response to environmental cues.

Insights into the Structure and Protein Composition of Moorella thermoacetica Spores Formed at Different Temperatures

The bacterium Moorella thermoacetica produces the most heat-resistant spores of any spoilage-causing microorganism known in the food industry. Previous work by our group revealed that the resistance of these spores to wet heat and biocides was lower when spores were produced at a lower temperature than the optimal temperature. Here, we used electron microcopy to characterize the ultrastructure of the coat of the spores formed at different sporulation temperatures; we found that spores produced at 55 °C mainly exhibited a lamellar inner coat tightly associated with a diffuse outer coat, while spores produced at 45 °C showed an inner and an outer coat separated by a less electron-dense zone. Moreover, misarranged coat structures were more frequently observed when spores were produced at the lower temperature. We then analyzed the proteome of the spores obtained at either 45 °C or 55 °C with respect to proteins putatively involved in the spore coat, exosporium, or in spore resistance. Some putative spore coat proteins, such as CotSA, were only identified in spores produced at 55 °C; other putative exosporium and coat proteins were significantly less abundant in spores produced at 45 °C. Altogether, our results suggest that sporulation temperature affects the structure and protein composition of M. thermoacetica spores.

Phosphoproteomic Approaches to Evaluate ABA Signaling

Abscisic acid (ABA) is a major phytohormone that regulates various processes in plants (e.g., seed dormancy/germination, abiotic/biotic stress responses). As protein phosphorylation is involved in the major pathways of ABA signaling, it is necessary to elucidate the phosphosignaling pathway involved in the ABA response. Phosphoproteomics enables determination of the proteins phosphorylated in vivo, and recent studies have applied a comparative phosphoproteomic approach to analyze ABA signaling in plants. For example, ABA-responsive phosphoproteins were identified in barley embryos. Furthermore, a phosphoproteomic approach is useful for screening protein kinase substrates by comparative analysis using kinase knockout mutants. Here, some technical points regarding phosphoproteomic analyses of ABA responses in plants are described.

Oncohistone interactome profiling uncovers contrasting oncogenic mechanisms and identifies potential therapeutic targets in high grade glioma

Histone H3 mutations at amino acids 27 (H3K27M) and 34 (H3G34R) are recurrent drivers of pediatric-type high-grade glioma (pHGG). H3K27M mutations lead to global disruption of H3K27me3 through dominant negative PRC2 inhibition, while H3G34R mutations lead to local losses of H3K36me3 through inhibition of SETD2. However, their broader oncogenic mechanisms remain unclear. We characterized the H3.1K27M, H3.3K27M and H3.3G34R interactomes, finding that H3K27M is associated with epigenetic and transcription factor changes; in contrast H3G34R removes a break on cryptic transcription, limits DNA methyltransferase access, and alters mitochondrial metabolism. All 3 mutants had altered interactions with DNA repair proteins and H3K9 methyltransferases. H3K9me3 was reduced in H3K27M-containing nucleosomes, and cis-H3K9 methylation was required for H3K27M to exert its effect on global H3K27me3. H3K9 methyltransferase inhibition was lethal to H3.1K27M, H3.3K27M and H3.3G34R pHGG cells, underscoring the importance of H3K9 methylation for oncohistone-mutant gliomas and suggesting it as an attractive therapeutic target.

pH/Acetonitrile-Gradient Reversed-Phase Fractionation of Enriched Hyper-Citrullinated Library in Combination with LC-MS/MS Analysis for Confident Identification of Citrullinated Peptides

Citrullination, the Ca2+-driven enzymatic conversion of arginine residues to citrulline, is a posttranslational modification, implicated in several physiological and pathological processes. Several methods to detect citrullinated proteins have been developed, including color development reagent, fluorescence, phenylglyoxal, and antibody-based methods. These methods yet suffer from limitations in sensitivity, specificity, or citrullinated site determination. Mass spectrometry (MS)-based proteomic analysis has emerged as a promising method to resolve these problems. However, due to low abundance of citrullinated proteins and similar MS features to deamidation of asparagine and glutamine, confident identification of citrullinated proteome is challenging. Here, we present a systematic approach to identify a compendium of steps to enhance the number of detected citrullinated residue and implement diagnostic MS feature that allow the confidence of MS-based identifications. Our method is based on the concept of generation of hyper-citrullinated library with high-pH reversed-phase peptide fractionation that allows to enrich in low abundance citrullinated peptides and amplify the effect of charge loss upon citrullination. Application of our approach to complex global citrullino-proteome datasets demonstrates the confident assessment of citrullinated peptides, thereby enhancing the size and functional interpretation of citrullinated proteomes.

Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Post-translational modifications (PTMs) regulate complex biological processes through the modulation of protein activity, stability, and localization. Insights into the specific modification type and localization within a protein sequence can help ascertain functional significance. Computational models are increasingly demonstrated to offer a low-cost, high-throughput method for comprehensive PTM predictions. Algorithms are optimized using existing experimental PTM data, thus accurate prediction performance relies on the creation of robust datasets. Herein, advancements in mass spectrometry-based proteomics technologies to maximize PTM coverage are reviewed. Further, requisite experimental validation approaches for PTM predictions are explored to ensure that follow-up mechanistic studies are focused on accurate modification sites.

Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies

Constant improvements in mass spectrometry technologies and laboratory workflows have enabled the proteomics investigation of biological samples of growing complexity. Microbiomes represent such complex samples for which metaproteomics analyses are becoming increasingly popular. Metaproteomics experimental procedures create large amounts of data from which biologically relevant signal must be efficiently extracted to draw meaningful conclusions. Such a data processing requires appropriate bioinformatics tools specifically developed for, or capable of handling metaproteomics data. In this chapter, we outline current and novel tools that can perform the most commonly used steps in the analysis of cutting-edge metaproteomics data, such as peptide and protein identification and quantification, as well as data normalization, imputation, mining, and visualization. We also provide details about the experimental setups in which these tools should be used.

Proteomic Sample Preparation and Data Analysis in Line with the Archaeal Proteome Project

Despite the ecological, evolutionary and economical significance of archaea, key aspects of their cell biology, metabolic pathways, and adaptations to a wide spectrum of environmental conditions, remain to be elucidated. Proteomics allows for the system-wide analysis of proteins, their changes in abundance between different conditions, as well as their post-translational modifications, providing detailed insights into the function of proteins and archaeal cell biology. In this chapter, we describe a sample preparation and mass spectrometric analysis workflow that has been designed for Haloferax volcanii but can be applied to a broad range of archaeal species. Furthermore, proteomics experiments provide a wealth of data that is invaluable to various disciplines. Therefore, we previously initiated the Archaeal Proteome Project (ArcPP), a community project that combines the analysis of multiple datasets with expert knowledge in various fields of archaeal research. The corresponding bioinformatic analysis, allowing for the integration of new proteomics data into the ArcPP, as well as the interactive exploration of ArcPP results is also presented here. In combination, these protocols facilitate an optimized, detailed and collaborative approach to archaeal proteomics.

Effect of polyethylene glycol 20 000 on protein extraction efficiency of formalin-fixed paraffin-embedded tissues in South Africa

BACKGROUND

Optimal protocols for efficient and reproducible protein extraction from formalin-fixed paraffin-embedded (FFPE) tissues are not yet standardised and new techniques are continually developed and improved. The effect of polyethylene glycol (PEG) 20 000 on protein extraction efficiency has not been evaluated using human FFPE colorectal cancer tissues and there is no consensus on the protein extraction solution required for efficient, reproducible extraction.

OBJECTIVE

The impact of PEG 20 000 on protein extraction efficiency, reproducibility and protein selection bias was evaluated using FFPE colonic tissue via liquid chromatography tandem mass spectrometry analysis.

METHODS

This study was conducted from August 2017 to July 2019 using human FFPE colorectal carcinoma tissues from the Anatomical Pathology department at Tygerberg Hospital in South Africa. Samples were analysed via label-free liquid chromatography tandem mass spectrometry to determine the impact of using PEG 20 000 in the protein extraction solution. Data were assessed regarding peptide and protein identifications, method efficiency, reproducibility, protein characteristics and organisation relating to gene ontology categories.

RESULTS

Polyethylene glycol 20 000 exclusion increased peptides and proteins identifications and the method was more reproducible compared to the samples processed with PEG 20 000. However, no differences were observed with regard to protein selection bias. We found that higher protein concentrations (> 10 µg) compromised the function of PEG.

CONCLUSION

This study indicates that protocols generating high protein yields from human FFPE tissues would benefit from the exclusion of PEG 20 000 in the protein extraction solution.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

ClusterSheep: A Graphics Processing Unit-Accelerated Software Tool for Large-Scale Clustering of Tandem Mass Spectra from Shotgun Proteomics

Modern shotgun proteomics experiments generate gigabytes of spectra every hour, only a fraction of which were utilized to form biological conclusions. Instead of being stored as flat files in public data repositories, this large amount of data can be better organized to facilitate data reuse. Clustering these spectra by similarity can be helpful in building high-quality spectral libraries, correcting identification errors, and highlighting frequently observed but unidentified spectra. However, large-scale clustering is time-consuming. Here, we present ClusterSheep, a method utilizing Graphics Processing Units (GPUs) to accelerate the process. Unlike previously proposed algorithms for this purpose, our method performs true pairwise comparison of all spectra within a precursor mass-to-charge ratio tolerance, thereby preserving the full cluster structures. ClusterSheep was benchmarked against previously reported clustering tools, MS-Cluster, MaRaCluster, and msCRUSH. The software tool also functions as an interactive visualization tool with a persistent state, enabling the user to explore the resulting clusters visually and retrieve the clustering results as desired.

Myofilament Phosphorylation in Stem Cell Treated Diastolic Heart Failure

RATIONALE

Phosphorylation of sarcomeric proteins has been implicated in heart failure with preserved ejection fraction (HFpEF); such changes may contribute to diastolic dysfunction by altering contractility, cardiac stiffness, Ca²⁺-sensitivity, and mechanosensing. Treatment with cardiosphere-derived cells (CDCs) restores normal diastolic function, attenuates fibrosis and inflammation, and improves survival in a rat HFpEF model.

OBJECTIVE

Phosphorylation changes that underlie HFpEF and those reversed by CDC therapy, with a focus on the sarcomeric subproteome were analyzed.

METHODS AND RESULTS

Dahl salt-sensitive rats fed a high-salt diet, with echocardiographically verified diastolic dysfunction, were randomly assigned to either intracoronary CDCs or placebo. Dahl salt-sensitive rats receiving low salt diet served as controls. Protein and phosphorylated Ser, Thr, and Tyr residues from left ventricular tissue were quantified by mass spectrometry. HFpEF hearts exhibited extensive hyperphosphorylation with 98% of the 529 significantly changed phospho-sites increased compared with control. Of those, 39% were located within the sarcomeric subproteome, with a large group of proteins located or associated with the Z-disk. CDC treatment partially reverted the hyperphosphorylation, with 85% of the significantly altered 76 residues hypophosphorylated. Bioinformatic upstream analysis of the differentially phosphorylated protein residues revealed PKC as the dominant putative regulatory kinase. PKC isoform analysis indicated increases in PKC α, β, and δ concentration, whereas CDC treatment led to a reversion of PKCβ. Use of PKC isoform specific inhibition and overexpression of various PKC isoforms strongly suggests that PKCβ is the dominant kinase involved in hyperphosphorylation in HFpEF and is altered with CDC treatment.

CONCLUSIONS

Increased protein phosphorylation at the Z-disk is associated with diastolic dysfunction, with PKC isoforms driving most quantified phosphorylation changes. Because CDCs reverse the key abnormalities in HFpEF and selectively reverse PKCβ upregulation, PKCβ merits being classified as a potential therapeutic target in HFpEF, a disease notoriously refractory to medical intervention.

Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library

Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact--increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.

Comparative Proteomic Analysis of HPV(+) Oropharyngeal Squamous Cell Carcinoma Recurrence

Deintensification therapy for human papillomavirus-related oropharyngeal squamous cell carcinoma (HPV(+) OPSCC) is under active investigation. An adaptive treatment approach based on molecular stratification could identify high-risk patients predisposed to recurrence and better select for appropriate treatment regimens. Collectively, 40 HPV(+) OPSCC FFPE samples (20 disease-free, 20 recurrent) were surveyed using mass spectrometry-based proteomic analysis via data-independent acquisition to obtain fold change and false discovery differences. Ten-year overall survival was 100.0 and 27.7% for HPV(+) disease-free and recurrent cohorts, respectively. Of 1414 quantified proteins, 77 demonstrated significant differential expression. Top enriched functional pathways included those involved in programmed cell death (73 proteins, p = 7.43 × 10^-30), apoptosis (73 proteins, p = 5.56 × 10^-9), β-catenin independent WNT signaling (47 proteins, p = 1.45 × 10^-15), and Rho GTPase signaling (69 proteins, p = 1.09 × 10^-5). PFN1 (p = 1.0 × 10^-3), RAD23B (p = 2.9 × 10^-4), LDHB (p = 1.0 × 10^-3), and HINT1 (p = 3.8 × 10^-3) pathways were significantly downregulated in the recurrent cohort. On functional validation via immunohistochemistry (IHC) staining, 46.9% (PFN1), 71.9% (RAD23B), 59.4% (LDHB), and 84.4% (HINT1) of cases were corroborated with mass spectrometry findings. Development of a multilateral molecular signature incorporating these targets may characterize high-risk disease, predict treatment response, and augment current management paradigms in head and neck cancer.

Characterization of urinary protein profile in regular kratom (Mitragyna speciosa korth.) users in Malaysia

Mitragyna speciosa (Korth.) also known as kratom or ketum has been traditionally used for its diverse medicinal value in Southeast Asia. Despite of its therapeutic value, kratom's safety profile remains deficiently elucidated. Our study aims to characterize the urinary protein profile of regular kratom users to determine its toxic effects on renal functioning. A total of 171 respondents (comprising of n = 88 regular kratom users, and n = 83 healthy controls) were recruited for this study. Urine specimens were collected and analyzed using SDS-PAGE, followed by LC/MS/MS analysis. Our results show albumin is the primary, and most abundant form of protein excreted in kratom user's urine specimens (n = 60/64), indicating that kratom users are predisposed to proteinuria. Kratom users had an elevated urinary protein (with an intensity of 66.7 kDa band), and protein: creatinine ratio (PCR) concentrations relative to healthy controls. However, kratom user's urinary creatinine concentration was found to be in the normal range as the healthy control group. While, kratom users who tested positive for illicit drug use had an elevated urinary albumin concentration. Our preliminary findings indicate that regular consumption of freshly brewed kratom solution over a protracted period (for an average of eleven years) seems to induce proteinuria, suggestive of an early stage of kidney injury. Hence, further studies are urgently needed to confirm our findings, and establish kratom's renal impairing effects.

Proteomics with Enhanced In-Source Fragmentation/Annotation: Applying XCMS-EISA Informatics and Q-MRM High-Sensitivity Quantification

Enhanced in-source fragmentation/annotation (EISA) has recently been shown to produce fragment ions that match tandem mass spectrometry data across a wide range of small molecules. EISA has been developed to facilitate data-dependent acquisition (DDA), data-independent acquisiton (DIA), and multiple-reaction monitoring (MRM), enabling molecular identifications in untargeted metabolomics and targeted quantitative single-quadrupole MRM (Q-MRM) analyses. Here, EISA has been applied to peptide-based proteomic analysis using optimized in-source fragmentation to generate fragmentation patterns for a mixture of 38 peptides, which were comparable to the b- and y-type fragment ions typically observed in tandem MS experiments. The optimal in-source fragmentation conditions at which high-abundance peptide fragments and precursor ions coexist were compared with automated data-dependent acquisition (DDA) in the same quadrupole time-of-flight (QTOF-MS) mass spectrometer, generating a significantly higher fragment percentage of peptides from both singly and doubly charged b- and y-type fragment (b⁺, y⁺, b²⁺, and y²⁺) ions. Higher fragment percentages were also observed for these fragment ion series over linear ion trap instrumentation. An XCMS-EISA annotation/deconvolution program was developed, making use of the retention time and peak shape continuity between precursor fragment ions, to perform automated proteomic data analysis on the enhanced in-source fragments. Post-translational modification (PTM) characterization on peptides was demonstrated with EISA, producing fragment ions corresponding to a neutral loss of phosphoric acid with greater intensity than observed with DDA on a QTOF-MS. Moreover, Q-MRM demonstrated the ability to use EISA for peptide quantification. The availability of more sophisticated in-source fragmentation informatics, beyond XCMS-EISA, will further enable EISA for sensitive autonomous identification and Q-MRM quantitative analyses in proteomics.