Two-dimensional target decoy strategy for shotgun proteomics

Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease

Population-level variation and mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized. We defined prototypical insulin secretion responses to three macronutrients in islets from 140 cadaveric donors, including those with type 2 diabetes. The majority of donors' islets exhibited the highest insulin response to glucose, moderate response to amino acid, and minimal response to fatty acid. However, 9% of donors' islets had amino acid responses, and 8% had fatty acid responses that were larger than their glucose-stimulated insulin responses. We leveraged this heterogeneity and used multi-omics to identify molecular correlates of nutrient responsiveness, as well as proteins and mRNAs altered in type 2 diabetes. We also examined nutrient-stimulated insulin release from stem cell-derived islets and observed responsiveness to fat but not carbohydrate or protein-potentially a hallmark of immaturity. Understanding the diversity of insulin responses to carbohydrate, protein, and fat lays the groundwork for personalized nutrition.

Poglut2/3 double knockout in mice results in neonatal lethality with reduced levels of fibrillin in lung tissues

Fibrillin microfibrils play a critical role in the formation of elastic fibers, tissue/organ development, and cardiopulmonary function. These microfibrils not only provide structural support and flexibility to tissues, but they also regulate growth factor signaling through a plethora of microfibril-binding proteins in the extracellular space. Mutations in fibrillins are associated with human diseases affecting cardiovascular, pulmonary, skeletal, and ocular systems. Fibrillins consist of up to 47 epidermal growth factor-like repeats, of which more than half are modified by protein O-glucosyltransferase 2 (POGLUT2) and/or POGLUT3. Loss of these modifications reduces secretion of N-terminal fibrillin constructs overexpressed in vitro. Here, we investigated the role of POGLUT2 and POGLUT3 in vivo using a Poglut2/3 double knockout (DKO) mouse model. Blocking O-glucosylation caused neonatal death with skeletal, pulmonary, and eye defects reminiscent of fibrillin/elastin mutations. Proteomic analyses of DKO dermal fibroblast medium and extracellular matrix provided evidence that fibrillins were more sensitive to loss of O-glucose compared to other POGLUT2/3 substrates. This conclusion was supported by immunofluorescent analyses of late gestation DKO lungs where FBN levels were reduced and microfibrils appeared fragmented in the pulmonary arteries and veins, bronchioles, and developing saccules. Defects in fibrillin microfibrils likely contributed to impaired elastic fiber formation and histological changes observed in DKO lung blood vessels, bronchioles, and saccules. Collectively, these results highlight the importance of POGLUT2/3-mediated O-glucosylation in vivo and open the possibility that O-glucose modifications on fibrillin influence microfibril assembly and or protein interactions in the ECM environment.

Beta-Hydroxybutyrate Promotes Basal Insulin Secretion While Decreasing Glucagon Secretion in Mouse and Human Islets

Dietary carbohydrates raise blood glucose levels, and limiting carbohydrate intake improves glycemia in patients with type 2 diabetes. Low carbohydrate intake (< 25 g) allows the body to utilize fat as its primary fuel. As a consequence of increased fatty acid oxidation, the liver produces ketones to serve as an alternative energy source. β-Hydroxybutyrate (βHB) is the most abundant ketone. While βHB has a wide range of functions outside of the pancreas, its direct effects on islet cell function remain understudied. We examined human islet secretory response to acute racemic βHB treatment and observed increased insulin secretion at a low glucose concentration of 3 mM. Because βHB is a chiral molecule, existing as both R and S forms, we further studied insulin and glucagon secretion following acute treatment with individual βHB enantiomers in human and C57BL/6J mouse islets. We found that acute treatment with R-βHB increased insulin secretion and decreased glucagon secretion at physiological glucose concentrations in both human and mouse islets. Proteomic analysis of human islets treated with R-βHB over 72 hours showed altered abundance of proteins that may promote islet cell health and survival. Collectively, our data show that physiological concentrations of βHB influence hormone secretion and signaling within pancreatic islets.

Integrated proteomic, phosphoproteomic, and N-glycoproteomic analyses of small extracellular vesicles from C2C12 myoblasts identify specific PTM patterns in ligand-receptor interactions

Small extracellular vesicles (sEVs) are important mediators of intercellular communication by transferring of functional components (proteins, RNAs, and lipids) to recipient cells. Some PTMs, including phosphorylation and N-glycosylation, have been reported to play important role in EV biology, such as biogenesis, protein sorting and uptake of sEVs. MS-based proteomic technology has been applied to identify proteins and PTM modifications in sEVs. Previous proteomic studies of sEVs from C2C12 myoblasts, an important skeletal muscle cell line, focused on identification of proteins, but no PTM information on sEVs proteins is available.In this study, we systematically analyzed the proteome, phosphoproteome, and N-glycoproteome of sEVs from C2C12 myoblasts with LC-MS/MS. In-depth analyses of the three proteomic datasets revealed that the three proteomes identified different catalogues of proteins, and PTMomic analysis could expand the identification of cargos in sEVs. At the proteomic level, a high percentage of membrane proteins, especially tetraspanins, was identified. The sEVs-derived phosphoproteome had a remarkably high level of tyrosine-phosphorylated sites. The tyrosine-phosphorylated proteins might be involved with EPH-Ephrin signaling pathway. At the level of N-glycoproteomics, several glycoforms, such as complex N-linked glycans and sialic acids on glycans, were enriched in sEVs. Retrieving of the ligand-receptor interaction in sEVs revealed that extracellular matrix (ECM) and cell adhesion molecule (CAM) represented the most abundant ligand-receptor pairs in sEVs. Mapping the PTM information on the ligands and receptors revealed that N-glycosylation mainly occurred on ECM and CAM proteins, while phosphorylation occurred on different categories of receptors and ligands. A comprehensive PTM map of ECM-receptor interaction and their components is also provided.In summary, we conducted a comprehensive proteomic and PTMomic analysis of sEVs of C2C12 myoblasts. Integrated proteomic, phosphoproteomic, and N-glycoproteomic analysis of sEVs might provide some insights about their specific uptake mechanism.

Protein disulfide isomerase cleaves allosteric disulfides in histidine-rich glycoprotein to regulate thrombosis

The essence of difference between hemostasis and thrombosis is that the clotting reaction is a highly fine-tuned process. Vascular protein disulfide isomerase (PDI) represents a critical mechanism regulating the functions of hemostatic proteins. Herein we show that histidine-rich glycoprotein (HRG) is a substrate of PDI. Reduction of HRG by PDI enhances the procoagulant and anticoagulant activities of HRG by neutralization of endothelial heparan sulfate (HS) and inhibition of factor XII (FXIIa) activity, respectively. Murine HRG deficiency (Hrg-/-) leads to delayed onset but enhanced formation of thrombus compared to WT. However, in the combined FXII deficiency (F12-/-) and HRG deficiency (by siRNA or Hrg-/-), there is further thrombosis reduction compared to F12-/- alone, confirming HRG's procoagulant activity independent of FXIIa. Mutation of target disulfides of PDI leads to a gain-of-function mutant of HRG that promotes its activities during coagulation. Thus, PDI-HRG pathway fine-tunes thrombosis by promoting its rapid initiation via neutralization of HS and preventing excessive propagation via inhibition of FXIIa.

Human brain glycoform coregulation network and glycan modification alterations in Alzheimer's disease

Despite the importance of protein glycosylation to brain health, current knowledge of glycosylated proteoforms or glycoforms in human brain and their alterations in Alzheimer's disease (AD) is limited. Here, we report a proteome-wide glycoform profiling study of human AD and control brains using intact glycopeptide-based quantitative glycoproteomics coupled with systems biology. Our study identified more than 10,000 human brain N-glycoforms from nearly 1200 glycoproteins and uncovered disease signatures of altered glycoforms and glycan modifications, including reduced sialylation and N-glycan branching and elongation as well as elevated mannosylation and N-glycan truncation in AD. Network analyses revealed a higher-order organization of brain glycoproteome into networks of coregulated glycoforms and glycans and discovered glycoform and glycan modules associated with AD clinical phenotype, amyloid-β accumulation, and tau pathology. Our findings provide valuable insights into disease pathogenesis and a rich resource of glycoform and glycan changes in AD and pave the way forward for developing glycosylation-based therapies and biomarkers for AD.

Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease

Population level variation and molecular mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized despite ramifications for personalized nutrition. Here, we define prototypical insulin secretion dynamics in response to the three macronutrients in islets from 140 cadaveric donors, including those diagnosed with type 2 diabetes. While islets from the majority of donors exhibited the expected relative response magnitudes, with glucose being highest, amino acid moderate, and fatty acid small, 9% of islets stimulated with amino acid and 8% of islets stimulated with fatty acids had larger responses compared with high glucose. We leveraged this insulin response heterogeneity and used transcriptomics and proteomics to identify molecular correlates of specific nutrient responsiveness, as well as those proteins and mRNAs altered in type 2 diabetes. We also examine nutrient-responsiveness in stem cell-derived islet clusters and observe that they have dysregulated fuel sensitivity, which is a hallmark of functionally immature cells. Our study now represents the first comparison of dynamic responses to nutrients and multi-omics analysis in human insulin secreting cells. Responses of different people's islets to carbohydrate, protein, and fat lay the groundwork for personalized nutrition.

ONE-SENTENCE SUMMARY

Deep phenotyping and multi-omics reveal individualized nutrient-specific insulin secretion propensity.

Characterization of N-glycosylation and its functional role in SIDT1-Mediated RNA uptake

The mammalian SID-1 transmembrane family members, SIDT1 and SIDT2, are multipass transmembrane proteins that mediate the cellular uptake and intracellular trafficking of nucleic acids, playing important roles in the immune response and tumorigenesis. Previous work has suggested that human SIDT1 and SIDT2 are N-glycosylated, but the precise site-specific N-glycosylation information and its functional contribution remain unclear. In this study, we use high-resolution liquid chromatography tandem mass spectrometry to comprehensively map the N-glycosites and quantify the N-glycosylation profiles of SIDT1 and SIDT2. Further molecular mechanistic probing elucidates the essential role of N-linked glycans in regulating cell surface expression, RNA binding, protein stability, and RNA uptake of SIDT1. Our results provide crucial information about the potential functional impact of N-glycosylation in the regulation of SIDT1-mediated RNA uptake and provide insights into the molecular mechanisms of this promising nucleic acid delivery system with potential implications for therapeutic applications.

Human brain glycoform co-regulation network and glycan modification alterations in Alzheimer's disease

Despite the importance of protein glycosylation to brain health, current knowledge of glycosylated proteoforms or glycoforms in human brain and their alterations in Alzheimer's disease (AD) is limited. Here, we present a new paradigm of proteome-wide glycoform profiling study of human AD and control brains using intact glycopeptide-based quantitative glycoproteomics coupled with systems biology. Our study identified over 10,000 human brain N-glycoforms from nearly 1200 glycoproteins and uncovered disease signatures of altered glycoforms and glycan modifications, including reduced sialylation and N-glycan branching as well as elevated mannosylation and N-glycan truncation in AD. Network analyses revealed a higher-order organization of brain glycoproteome into networks of co-regulated glycoforms and glycans and discovered glycoform and glycan modules associated with AD clinical phenotype, amyloid-β accumulation, and tau pathology. Our findings provide novel insights and a rich resource of glycoform and glycan changes in AD and pave the way forward for developing glycosylation-based therapies and biomarkers for AD.

Co-translational formation of disulfides guides folding of the SARS-CoV-2 receptor binding domain

Many secreted proteins, including viral proteins, contain multiple disulfide bonds. How disulfide formation is coupled to protein folding in the cell remains poorly understood at the molecular level. Here, we combine experiment and simulation to address this question as it pertains to the SARS-CoV-2 receptor binding domain (RBD). We show that the RBD can only refold reversibly if its native disulfides are present before folding. But in their absence, the RBD spontaneously misfolds into a nonnative, molten-globule-like state that is structurally incompatible with complete disulfide formation and that is highly prone to aggregation. Thus, the RBD native structure represents a metastable state on the protein's energy landscape with reduced disulfides, indicating that nonequilibrium mechanisms are needed to ensure native disulfides form before folding. Our atomistic simulations suggest that this may be achieved via co-translational folding during RBD secretion into the endoplasmic reticulum. Namely, at intermediate translation lengths, native disulfide pairs are predicted to come together with high probability, and thus, under suitable kinetic conditions, this process may lock the protein into its native state and circumvent highly aggregation-prone nonnative intermediates. This detailed molecular picture of the RBD folding landscape may shed light on SARS-CoV-2 pathology and molecular constraints governing SARS-CoV-2 evolution.

Integrative Proteogenomics Using ProteomeGenerator2

Recent advances in nucleic acid sequencing now permit rapid and genome-scale analysis of genetic variation and transcription, enabling population-scale studies of human biology, disease, and diverse organisms. Likewise, advances in mass spectrometry proteomics now permit highly sensitive and accurate studies of protein expression at the whole proteome-scale. However, most proteomic studies rely on consensus databases to match spectra to peptide and protein sequences, and thus remain limited to the analysis of canonical protein sequences. Here, we develop ProteomeGenerator2 (PG2), based on the scalable and modular ProteomeGenerator framework. PG2 integrates genome and transcriptome sequencing to incorporate protein variants containing amino acid substitutions, insertions, and deletions, as well as noncanonical reading frames, exons, and other variants caused by genomic and transcriptomic variation. We benchmarked PG2 using synthetic data and genomic, transcriptomic, and proteomic analysis of human leukemia cells. PG2 can be integrated with current and emerging sequencing technologies, assemblers, variant callers, and mass spectral analysis algorithms, and is available open-source from https://github.com/kentsisresearchgroup/ProteomeGenerator2.

Exploring the Proteomic Profile of Soybean Bran: Unlocking the Potential for Improving Protein Quality and Quantity

Soybean is a rich source of vegetal protein for both animal and human consumption. Despite the high levels of protein in soybean seeds, industrial processing to obtain soybean bran significantly decreases the final protein content of the byproducts. To overcome this problem, cultivars with higher protein contents must be developed. However, selecting the target proteins is difficult because of the lack of information on the proteome profile of soybean bran. Therefore, this study obtained the comparative proteomic profiles of both natural coatless seeds and defatted bran from an elite tropical-soybean cultivar. Thus, their extracts were characterized using LC-MS/MS and a total of 550 proteins were identified. Among these, 526 proteins were detected in coatless seeds and 319 proteins in defatted bran. Moreover, a total of 139 proteins were identified as presenting different levels of content in coatless seeds and defatted bran. Among them, only 46 were retained after the seed processing. These proteins were clustered in several important metabolic pathways, such as amino-acid biosynthesis, sugar biosynthesis, and antioxidant activity, meaning that they could act as targets for bioactive products or genome editing to improve protein quality and quantity in soybean grains. These findings can enhance our understanding regarding protein robustness for both soybean crops and the commercial bran improvement because target proteins must remain intact after processing and must be bioactive when overexpressed. Overall, the soybean bran proteomic profile was explored for the first time, providing a valuable catalogue of target proteins that can tolerate the industrial process.

Comprehensive glycoprofiling of oral tumours associates N-glycosylation with lymph node metastasis and patient survival

While altered protein glycosylation is regarded a trait of oral squamous cell carcinoma (OSCC), the heterogeneous and dynamic glycoproteome of tumour tissues from OSCC patients remain unmapped. To this end, we here employ an integrated multi-omics approach comprising unbiased and quantitative glycomics and glycoproteomics applied to a cohort of resected primary tumour tissues from OSCC patients with (n = 19) and without (n = 12) lymph node metastasis. While all tumour tissues displayed relatively uniform N-glycome profiles suggesting overall stable global N-glycosylation during disease progression, altered expression of six sialylated N-glycans was found to correlate with lymph node metastasis. Notably, glycoproteomics and advanced statistical analyses uncovered altered site-specific N-glycosylation revealing previously unknown associations with several clinicopathological features. Importantly, the glycomics and glycoproteomics data unveiled that comparatively high abundance of two core-fucosylated and sialylated N-glycans (Glycan 40a and Glycan 46a) and one N-glycopeptide from fibronectin were associated with low patient survival, while a relatively low abundance of N-glycopeptides from both afamin and CD59 were also associated with poor survival. This study provides novel insight into the complex OSCC tissue N-glycoproteome forming an important resource to further explore the underpinning disease mechanisms and uncover new prognostic glyco-markers for OSCC.

O-fucosylation of thrombospondin type I repeats is dispensable for trafficking thrombospondin 1 to platelet secretory granules

Thrombospondin 1 (THBS1) is a secreted extracellular matrix glycoprotein that regulates a variety of cellular and physiological processes. THBS1's diverse functions are attributed to interactions between the modular domains of THBS1 with an array of proteins found in the extracellular matrix. THBS1's three Thrombospondin type 1 repeats (TSRs) are modified with O-linked glucose-fucose disaccharide and C-mannose. It is unknown whether these modifications impact trafficking and/or function of THBS1 in vivo. The O-fucose is added by Protein O-fucosyltransferase 2 (POFUT2) and is sequentially extended to the disaccharide by β3glucosyltransferase (B3GLCT). The C-mannose is added by one or more of four C-mannosyltransferases. O-fucosylation by POFUT2/B3GLCT in the endoplasmic reticulum has been proposed to play a role in quality control by locking TSR domains into their three-dimensional fold, allowing for proper secretion of many O-fucosylated substrates. Prior studies showed the siRNA knockdown of POFUT2 in HEK293T cells blocked secretion of TSRs 1-3 from THBS1. Here we demonstrated that secretion of THBS1 TSRs 1-3 was not reduced by CRISPR-Cas9-mediated knockout of POFUT2 in HEK293T cells and demonstrated that knockout of Pofut2 or B3glct in mice did not reduce the trafficking of endogenous THBS1 to secretory granules of platelets, a major source of THBS1. Additionally, we demonstrated that all three TSRs from platelet THBS1 were highly C-mannosylated, which has been shown to stabilize TSRs in vitro. Combined, these results suggested that POFUT2 substrates with TSRs that are also modified by C-mannose may be less susceptible to trafficking defects resulting from the loss of the glucose-fucose disaccharide.

Glycoproteomic Analyses of Influenza A Viruses Using timsTOF Pro MS

N-Linked glycosylation in hemagglutinin and neuraminidase glycoproteins of influenza viruses affects antigenic and receptor binding properties, and precise analyses of site-specific glycoforms in these proteins are critical in understanding the antigenic and immunogenic properties of influenza viruses. In this study, we developed a glycoproteomic approach by using a timsTOF Pro mass spectrometer (MS) to determine the abundance and heterogeneity of site-specific glycosylation for influenza glycoproteins. Compared with a Q Exactive HF MS, the timsTOF Pro MS method without the hydrophilic interaction liquid chromatography column enrichment achieved similar glycopeptide coverage and quantities but was more effective in identifying low-abundance glycopeptides. We quantified the distributions of intact site-specific glycopeptides in hemagglutinin of A/chicken/Wuxi/0405005/2013 (H7N9) and A/mute swan/Rhode Island/A00325125/2008 (H7N3). Results showed that hemagglutinin for both viruses had complex N-glycans at N22, N38, N240, and N483 but only high-mannose glycans at N411 and, however, that the type and quantities of glycans were distinct between these viruses. Collisional cross section (CCS) provided by the ion mobility spectrometry from the timsTOF Pro MS data differentiated sialylation linkages of the glycopeptides. In summary, timsTOF Pro MS method can quantify intact site-specific glycans for influenza glycoproteins without enrichment and thus facilitate influenza vaccine development and production.

Integrative proteogenomics using ProteomeGenerator2

Recent advances in nucleic acid sequencing now permit rapid and genome-scale analysis of genetic variation and transcription, enabling population-scale studies of human biology, disease, and diverse organisms. Likewise, advances in mass spectrometry proteomics now permit highly sensitive and accurate studies of protein expression at the whole proteome-scale. However, most proteomic studies rely on consensus databases to match spectra to peptide and proteins sequences, and thus remain limited to the analysis of canonical protein sequences. Here, we develop ProteomeGenerator2 (PG2), based on the scalable and modular ProteomeGenerator framework. PG2 integrates genome and transcriptome sequencing to incorporate protein variants containing amino acid substitutions, insertions, and deletions, as well as non-canonical reading frames, exons, and other variants caused by genomic and transcriptomic variation. We benchmarked PG2 using synthetic data and genomic, transcriptomic, and proteomic analysis of human leukemia cells. PG2 can be integrated with current and emerging sequencing technologies, assemblers, variant callers, and mass spectral analysis algorithms, and is available open-source from https://github.com/kentsisresearchgroup/ProteomeGenerator2 .

dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts

The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.

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.

Analysis of pancreatic extracellular matrix protein post-translational modifications via electrostatic repulsion-hydrophilic interaction chromatography coupled with mass spectrometry

The pancreas is a vital organ with digestive and endocrine roles, and diseases of the pancreas affect millions of people yearly. A better understanding of the pancreas proteome and its dynamic post-translational modifications (PTMs) is necessary to engineer higher fidelity tissue analogues for use in transplantation. The extracellular matrix (ECM) has major roles in binding and signaling essential to the viability of insulin-producing islets of Langerhans. To characterize PTMs in the pancreas, native and decellularized tissues from four donors were analyzed. N-Glycosylated and phosphorylated peptides were simultaneously enriched via electrostatic repulsion-hydrophilic interaction chromatography and analyzed with mass spectrometry, maximizing PTM information from one workflow. A modified surfactant and chaotropic agent assisted sequential extraction/on-pellet digestion was used to maximize solubility of the ECM. The analysis resulted in the confident identification of 3650 proteins, including 517 N-glycoproteins and 148 phosphoproteins. We identified 214 ECM proteins, of which 99 were N-glycosylated, 18 were phosphorylated, and 9 were found to have both modifications. Collagens, a major component of the ECM, were the most highly glycosylated of the ECM proteins and several were also heavily phosphorylated, raising the possibility of structural and thus functional changes resulting from these modifications. To our knowledge, this work represents the first characterization of PTMs in pancreatic ECM proteins. This work provides a basal profile of PTMs in the healthy human pancreatic ECM, laying the foundation for future investigations to determine disease-specific changes such as in diabetes and pancreatic cancer, and potentially helping to guide the development of tissue replacement constructs. Data are available via ProteomeXchange with identifier PXD025048.

Modulation of the NOTCH1 Pathway by LUNATIC FRINGE Is Dominant over That of MANIC or RADICAL FRINGE

Fringes are glycosyltransferases that transfer a GlcNAc to O-fucose residues on Epidermal Growth Factor-like (EGF) repeats. Three Fringes exist in mammals: LUNATIC FRINGE (LFNG), MANIC FRINGE (MFNG), and RADICAL FRINGE (RFNG). Fringe modification of O-fucose on EGF repeats in the NOTCH1 (N1) extracellular domain modulates the activation of N1 signaling. Not all O-fucose residues of N1 are modified by all Fringes; some are modified by one or two Fringes and others not modified at all. The distinct effects on N1 activity depend on which Fringe is expressed in a cell. However, little data is available on the effect that more than one Fringe has on the modification of O-fucose residues and the resulting downstream consequence on Notch activation. Using mass spectral glycoproteomic site mapping and cell-based N1 signaling assays, we compared the effect of co-expression of N1 with one or more Fringes on modification of O-fucose and activation of N1 in three cell lines. Individual expression of each Fringe with N1 in the three cell lines revealed differences in modulation of the Notch pathway dependent on the presence of endogenous Fringes. Despite these cell-based differences, co-expression of several Fringes with N1 demonstrated a dominant effect of LFNG over MFNG or RFNG. MFNG and RFNG appeared to be co-dominant but strongly dependent on the ligands used to activate N1 and on the endogenous expression of Fringes. These results show a hierarchy of Fringe activity and indicate that the effect of MFNG and/or RFNG could be small in the presence of LFNG.

POGLUT2 and POGLUT3 O-glucosylate multiple EGF repeats in fibrillin-1, -2, and LTBP1 and promote secretion of fibrillin-1

Fibrillin-1 (FBN1) is the major component of extracellular matrix microfibrils, which are required for proper development of elastic tissues, including the heart and lungs. Through protein-protein interactions with latent transforming growth factor (TGF) β-binding protein 1 (LTBP1), microfibrils regulate TGF-β signaling. Mutations within the 47 epidermal growth factor-like (EGF) repeats of FBN1 cause autosomal dominant disorders including Marfan Syndrome, which is characterized by disrupted TGF-β signaling. We recently identified two novel protein O-glucosyltransferases, Protein O-glucosyltransferase 2 (POGLUT2) and 3 (POGLUT3), that modify a small fraction of EGF repeats on Notch. Here, using mass spectral analysis, we show that POGLUT2 and POGLUT3 also modify over half of the EGF repeats on FBN1, fibrillin-2 (FBN2), and LTBP1. While most sites are modified by both enzymes, some sites show a preference for either POGLUT2 or POGLUT3. POGLUT2 and POGLUT3 are homologs of POGLUT1, which stabilizes Notch proteins by addition of O-glucose to Notch EGF repeats. Like POGLUT1, POGLUT2 and 3 can discern a folded versus unfolded EGF repeat, suggesting POGLUT2 and 3 are involved in a protein folding pathway. In vitro secretion assays using the N-terminal portion of recombinant FBN1 revealed reduced FBN1 secretion in POGLUT2 knockout, POGLUT3 knockout, and POGLUT2 and 3 double-knockout HEK293T cells compared with wild type. These results illustrate that POGLUT2 and 3 function together to O-glucosylate protein substrates and that these modifications play a role in the secretion of substrate proteins. It will be interesting to see how disease variants in these proteins affect their O-glucosylation.

GlycoHybridSeq: Automated Identification of N-Linked Glycopeptides Using Electron Transfer/High-Energy Collision Dissociation (EThcD)

Glycosylation is one of the most common post-translational modifications (PTM) occurring in a large variety of proteins with important biological functions in human and other higher organisms. Liquid chromatography tandem mass spectrometry (LC-MS/MS) has been routinely used to characterize site-specific protein glycosylation at high throughput in complex glycoproteomic samples. Recently, electron transfer/high-energy collision dissociation (EThcD) was introduced for glycopeptide identification, which offers rich structural information on glycopepides with the fragment ions from the cleavages of both the glycan and the peptide backbone. Herein, we present the software GlycoHybridSeq for automated interpretation of EThcD-MS/MS spectra from glycoproteomic data using a customized scoring function, which enables the functionalities of identifying glycopeptides, characterizing glycosylation sites, and distinguishing some isomeric glycans. We evaluate GlycoHybridSeq on glycoproteomic data collected for cancer biomarker discovery. The results showed that it achieved comparable or better performance than that of Byonic and MSFragger. GlycoHybridSeq is released as an open source software and is ready to be used in large-scale glycoproteomic data analyses.

In-depth Site-specific Analysis of N-glycoproteome in Human Cerebrospinal Fluid and Glycosylation Landscape Changes in Alzheimer's Disease

As the body fluid that directly interchanges with the extracellular fluid of the central nervous system (CNS), cerebrospinal fluid (CSF) serves as a rich source for CNS-related disease biomarker discovery. Extensive proteome profiling has been conducted for CSF, but studies aimed at unraveling site-specific CSF N-glycoproteome are lacking. Initial efforts into site-specific N-glycoproteomics study in CSF yield limited coverage, hindering further experimental design of glycosylation-based disease biomarker discovery in CSF. In the present study, we have developed an N-glycoproteomic approach that combines enhanced N-glycopeptide sequential enrichment by hydrophilic interaction chromatography (HILIC) and boronic acid enrichment with electron transfer and higher-energy collision dissociation (EThcD) for large-scale intact N-glycopeptide analysis. The application of the developed approach to the analyses of human CSF samples enabled identifications of a total of 2893 intact N-glycopeptides from 511 N-glycosites and 285 N-glycoproteins. To our knowledge, this is the largest site-specific N-glycoproteome dataset reported for CSF to date. Such dataset provides molecular basis for a better understanding of the structure-function relationships of glycoproteins and their roles in CNS-related physiological and pathological processes. As accumulating evidence suggests that defects in glycosylation are involved in Alzheimer's disease (AD) pathogenesis, in the present study, a comparative in-depth N-glycoproteomic analysis was conducted for CSF samples from healthy control and AD patients, which yielded a comparable N-glycoproteome coverage but a distinct expression pattern for different categories of glycoforms, such as decreased fucosylation in AD CSF samples. Altered glycosylation patterns were detected for a number of N-glycoproteins including alpha-1-antichymotrypsin, ephrin-A3 and carnosinase CN1 etc., which serve as potentially interesting targets for further glycosylation-based AD study and may eventually lead to molecular elucidation of the role of glycosylation in AD progression.

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics

Recent studies have revealed diverse amino acid, post-translational, and noncanonical modifications of proteins in diverse organisms and tissues. However, their unbiased detection and analysis remain hindered by technical limitations. Here, we present a spectral alignment method for the identification of protein modifications using high-resolution mass spectrometry proteomics. Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. Using synthetic standards and controlled chemical labeling experiments, we demonstrate its high specificity and sensitivity for the discovery of substoichiometric protein modifications in complex cellular extracts. SAMPEI mapping of mouse macrophage differentiation revealed diverse post-translational protein modifications, including distinct forms of cysteine itaconatylation. SAMPEI's robust parametrization and versatility are expected to facilitate the discovery of biological modifications of diverse macromolecules. SAMPEI is implemented as a Python package and is available open-source from BioConda and GitHub (https://github.com/FenyoLab/SAMPEI).

The Complexity and Dynamics of the Tissue Glycoproteome Associated With Prostate Cancer Progression

The complexity and dynamics of the immensely heterogeneous glycoproteome of the prostate cancer (PCa) tumor microenvironment remain incompletely mapped, a knowledge gap that impedes our molecular-level understanding of the disease. To this end, we have used sensitive glycomics and glycoproteomics to map the protein-, cell-, and tumor grade-specific N- and O-glycosylation in surgically removed PCa tissues spanning five histological grades (n = 10/grade) and tissues from patients with benign prostatic hyperplasia (n = 5). Quantitative glycomics revealed PCa grade-specific alterations of the oligomannosidic-, paucimannosidic-, and branched sialylated complex-type N-glycans, and dynamic remodeling of the sialylated core 1- and core 2-type O-glycome. Deep quantitative glycoproteomics identified ∼7400 unique N-glycopeptides from 500 N-glycoproteins and ∼500 unique O-glycopeptides from nearly 200 O-glycoproteins. With reference to a recent Tissue and Blood Atlas, our data indicate that paucimannosidic glycans of the PCa tissues arise mainly from immune cell-derived glycoproteins. Furthermore, the grade-specific PCa glycosylation arises primarily from dynamics in the cellular makeup of the PCa tumor microenvironment across grades involving increased oligomannosylation of prostate-derived glycoproteins and decreased bisecting GlcNAcylation of N-glycans carried by the extracellular matrix proteins. Furthermore, elevated expression of several oligosaccharyltransferase subunits and enhanced N-glycoprotein site occupancy were observed associated with PCa progression. Finally, correlations between the protein-specific glycosylation and PCa progression were observed including increased site-specific core 2-type O-glycosylation of collagen VI. In conclusion, integrated glycomics and glycoproteomics have enabled new insight into the complexity and dynamics of the tissue glycoproteome associated with PCa progression generating an important resource to explore the underpinning disease mechanisms.

Peak Filtering, Peak Annotation, and Wildcard Search for Glycoproteomics

Glycopeptides in peptide or digested protein samples pose a number of analytical and bioinformatics challenges beyond those posed by unmodified peptides or peptides with smaller posttranslational modifications. Exact structural elucidation of glycans is generally beyond the capability of a single mass spectrometry experiment, so a reasonable level of identification for tandem mass spectrometry, taken by several glycopeptide software tools, is that of peptide sequence and glycan composition, meaning the number of monosaccharides of each distinct mass, e.g., HexNAc(2)Hex(5) rather than man5. Even at this level, however, glycopeptide analysis poses challenges: finding glycopeptide spectra when they are a tiny fraction of the total spectra; assigning spectra with unanticipated glycans, not in the initial glycan database; and finding, scoring, and labeling diagnostic peaks in tandem mass spectra. Here, we discuss recent improvements to Byonic, a glycoproteomics search program, that address these three issues. Byonic now supports filtering spectra by m/z peaks, so that the user can limit attention to spectra with diagnostic peaks, e.g., at least two out of three of 204.087 for HexNAc, 274.092 for NeuAc (with water loss), and 366.139 for HexNAc-Hex, all within a set mass tolerance, e.g., ± 0.01 Da. Also, new is glycan "wildcard" search, which allows an unspecified mass within a user-set mass range to be applied to N- or O-linked glycans and enables assignment of spectra with unanticipated glycans. Finally, the next release of Byonic supports user-specified peak annotations from user-defined posttranslational modifications. We demonstrate the utility of these new software features by finding previously unrecognized glycopeptides in publicly available data, including glycosylated neuropeptides from rat brain.

O-Fucosylation of ADAMTSL2 is required for secretion and is impacted by geleophysic dysplasia-causing mutations

ADAMTSL2 mutations cause an autosomal recessive connective tissue disorder, geleophysic dysplasia 1 (GPHYSD1), which is characterized by short stature, small hands and feet, and cardiac defects. ADAMTSL2 is a matricellular protein previously shown to interact with latent transforming growth factor-β binding protein 1 and influence assembly of fibrillin 1 microfibrils. ADAMTSL2 contains seven thrombospondin type-1 repeats (TSRs), six of which contain the consensus sequence for O-fucosylation by protein O-fucosyltransferase 2 (POFUT2). O-fucose-modified TSRs are subsequently elongated to a glucose β1-3-fucose (GlcFuc) disaccharide by β1,3-glucosyltransferase (B3GLCT). B3GLCT mutations cause Peters Plus Syndrome (PTRPLS), which is characterized by skeletal defects similar to GPHYSD1. Several ADAMTSL2 TSRs also have consensus sequences for C-mannosylation. Six reported GPHYSD1 mutations occur within the TSRs and two lie near O-fucosylation sites. To investigate the effects of TSR glycosylation on ADAMTSL2 function, we used MS to identify glycan modifications at predicted consensus sequences on mouse ADAMTSL2. We found that most TSRs were modified with the GlcFuc disaccharide at high stoichiometry at O-fucosylation sites and variable mannose stoichiometry at C-mannosylation sites. Loss of ADAMTSL2 secretion in POFUT2 ^-/- but not in B3GLCT ^-/- cells suggested that impaired ADAMTSL2 secretion is not responsible for skeletal defects in PTRPLS patients. In contrast, secretion was significantly reduced for ADAMTSL2 carrying GPHYSD1 mutations (S641L in TSR3 and G817R in TSR6), and S641L eliminated O-fucosylation of TSR3. These results provide evidence that abnormalities in GPHYSD1 patients with this mutation are caused by loss of O-fucosylation on TSR3 and impaired ADAMTSL2 secretion.

GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis

Mass spectrometry glycoproteomics is rapidly maturing, allowing unprecedented insights into the diversity and functions of protein glycosylation. However, quantitative glycoproteomics remains challenging. We developed GlypNirO, an automated software pipeline which integrates the complementary outputs of Byonic and Proteome Discoverer to allow high-throughput automated quantitative glycoproteomic data analysis. The output of GlypNirO is clearly structured, allowing manual interrogation, and is also appropriate for input into diverse statistical workflows. We used GlypNirO to analyse a published plasma glycoproteome dataset and identified changes in site-specific N- and O-glycosylation occupancy and structure associated with hepatocellular carcinoma as putative biomarkers of disease.

Exploring sample preparation and data evaluation strategies for enhanced identification of host cell proteins in drug products of therapeutic antibodies and Fc-fusion proteins

Manufacturing of biopharmaceuticals involves recombinant protein expression in host cells followed by extensive purification of the target protein. Yet, host cell proteins (HCPs) may persist in the final drug product, potentially reducing its quality with respect to safety and efficacy. Consequently, residual HCPs are closely monitored during downstream processing by techniques such as enzyme-linked immunosorbent assay (ELISA) or high-performance liquid chromatography combined with tandem mass spectrometry (HPLC-MS/MS). The latter is especially attractive as it provides information with respect to protein identities. Although the applied HPLC-MS/MS methodologies are frequently optimized with respect to HCP identification, acquired data is typically analyzed using standard settings. Here, we describe an improved strategy for evaluating HPLC-MS/MS data of HCP-derived peptides, involving probabilistic protein inference and peptide detection in the absence of fragment ion spectra. This data analysis workflow was applied to data obtained for drug products of various biotherapeutics upon protein A affinity depletion. The presented data evaluation strategy enabled in-depth comparative analysis of the HCP repertoires identified in drug products of the monoclonal antibodies rituximab and bevacizumab, as well as the fusion protein etanercept. In contrast to commonly applied ELISA strategies, the here presented workflow is process-independent and may be implemented into existing HPLC-MS/MS setups for drug product characterization and process development.

Optimization of skeletal protein preparation for LC-MS/MS sequencing yields additional coral skeletal proteins in Stylophora pistillata

Stony corals generate their calcium carbonate exoskeleton in a highly controlled biomineralization process mediated by a variety of macromolecules including proteins. Fully identifying and classifying these proteins is crucial to understanding their role in exoskeleton formation, yet no optimal method to purify and characterize the full suite of extracted coral skeletal proteins has been established and hence their complete composition remains obscure. Here, we tested four skeletal protein purification protocols using acetone precipitation and ultrafiltration dialysis filters to present a comprehensive scleractinian coral skeletal proteome. We identified a total of 60 proteins in the coral skeleton, 44 of which were not present in previously published stony coral skeletal proteomes. Extracted protein purification protocols carried out in this study revealed that no one method captures all proteins and each protocol revealed a unique set of method-exclusive proteins. To better understand the general mechanism of skeletal protein transportation, we further examined the proteins’ gene ontology, transmembrane domains, and signal peptides. We found that transmembrane domain proteins and signal peptide secretion pathways, by themselves, could not explain the transportation of proteins to the skeleton. We therefore propose that some proteins are transported to the skeleton via non-traditional secretion pathways.

Precision Mapping of O-Linked N-Acetylglucosamine Sites in Proteins Using Ultraviolet Photodissociation Mass Spectrometry

Despite its central importance as a regulator of cellular physiology, identification and precise mapping of O-linked N-acetylglucosamine (O-GlcNAc) post-translational modification (PTM) sites in proteins by mass spectrometry (MS) remains a considerable technical challenge. This is due in part to cleavage of the glycosidic bond occurring prior to the peptide backbone during collisionally activated dissociation (CAD), which leads to generation of characteristic oxocarbenium ions and impairs glycosite localization. Herein, we leverage CAD-induced oxocarbenium ion generation to trigger ultraviolet photodissociation (UVPD), an alternate high-energy deposition method that offers extensive fragmentation of peptides while leaving the glycosite intact. Upon activation using UV laser pulses, efficient photodissociation of glycopeptides is achieved with production of multiple sequence ions that enable robust and precise localization of O-GlcNAc sites. Application of this method to tryptic peptides originating from O-GlcNAcylated proteins TAB1 and Polyhomeotic confirmed previously reported O-GlcNAc sites in TAB1 (S395 and S396) and uncovered new sites within both proteins. We expect this strategy will complement existing MS/MS methods and be broadly useful for mapping O-GlcNAcylated residues of both proteins and proteomes.

Differential Quantitative Determination of Site-Specific Intact N-Glycopeptides in Serum Haptoglobin between Hepatocellular Carcinoma and Cirrhosis Using LC-EThcD-MS/MS

Intact N-glycopeptide analysis remains challenging due to the complexity of glycopeptide structures, low abundance of glycopeptides in protein digests, and difficulties in data interpretation/quantitation. Herein, we developed a workflow that involved advanced methodologies, the EThcD-MS/MS fragmentation method and data interpretation software, for differential analysis of the microheterogeneity of site-specific intact N-glycopeptides of serum haptoglobin between early hepatocellular carcinoma (HCC) and liver cirrhosis. Haptoglobin was immunopurified from 20 μL of serum in patients with early HCC, liver cirrhosis, and healthy controls, respectively, followed by trypsin/GluC digestion, glycopeptide enrichment, and LC-EThcD-MS/MS analysis. Identification and differential quantitation of site-specific N-glycopeptides were performed using a combination of Byonic and Byologic software. In total, 93, 87, and 68 site-specific N-glycopeptides were identified in early HCC, liver cirrhosis, and healthy controls, respectively, with high confidence. The increased variety of N-glycopeptides in liver diseases compared to healthy controls was due to increased branching with hyper-fucosylation and sialylation. Differential quantitation analysis showed that 5 site-specific N-glycopeptides on sites N184 and N241 were significantly elevated in early HCC compared to cirrhosis ( p < 0.05) and normal controls ( p ≤ 0.001). The result demonstrates that the workflow provides a strategy for detailed profiles of N-glycopeptides of patient samples as well as for relative quantitation to determine the level changes in site-specific N-glycopeptides between disease states.

ProteomeGenerator: A Framework for Comprehensive Proteomics Based on de Novo Transcriptome Assembly and High-Accuracy Peptide Mass Spectral Matching

Modern mass spectrometry now permits genome-scale and quantitative measurements of biological proteomes. However, analysis of specific specimens is currently hindered by the incomplete representation of biological variability of protein sequences in canonical reference proteomes and the technical demands for their construction. Here, we report ProteomeGenerator, a framework for de novo and reference-assisted proteogenomic database construction and analysis based on sample-specific transcriptome sequencing and high-accuracy mass spectrometry proteomics. This enables the assembly of proteomes encoded by actively transcribed genes, including sample-specific protein isoforms resulting from non-canonical mRNA transcription, splicing, or editing. To improve the accuracy of protein isoform identification in non-canonical proteomes, ProteomeGenerator relies on statistical target-decoy database matching calibrated using sample-specific controls. Its current implementation includes automatic integration with MaxQuant mass spectrometry proteomics algorithms. We applied this method for the proteogenomic analysis of splicing factor SRSF2 mutant leukemia cells, demonstrating high-confidence identification of non-canonical protein isoforms arising from alternative transcriptional start sites, intron retention, and cryptic exon splicing as well as improved accuracy of genome-scale proteome discovery. Additionally, we report proteogenomic performance metrics for current state-of-the-art implementations of SEQUEST HT, MaxQuant, Byonic, and PEAKS mass spectral analysis algorithms. Finally, ProteomeGenerator is implemented as a Snakemake workflow within a Singularity container for one-step installation in diverse computing environments, thereby enabling open, scalable, and facile discovery of sample-specific, non-canonical, and neomorphic biological proteomes.

Distinct urinary glycoprotein signatures in prostate cancer patients

Novel biomarkers are needed to complement prostate specific antigen (PSA) in prostate cancer (PCa) diagnostic screening programs. Glycoproteins represent a hitherto largely untapped resource with a great potential as specific and sensitive tumor biomarkers due to their abundance in bodily fluids and their dynamic and cancer-associated glycosylation. However, quantitative glycoproteomics strategies to detect potential glycoprotein cancer markers from complex biospecimen are only just emerging. Here, we describe a glycoproteomics strategy for deep quantitative mapping of N- and O-glycoproteins in urine with a view to investigate the diagnostic value of the glycoproteome to discriminate PCa from benign prostatic hyperplasia (BPH), two conditions that remain difficult to clinically stratify. Total protein extracts were obtained, concentrated and digested from urine of six PCa patients (Gleason score 7) and six BPH patients. The resulting peptide mixtures were TMT-labeled and mixed prior to a multi-faceted sample processing including hydrophilic interaction liquid chromatography (HILIC) and titanium dioxide SPE based enrichment, endo-/exoglycosidase treatment and HILIC-HPLC pre-fractionation. The isolated N- and O-glycopeptides were detected and quantified using high resolution mass spectrometry. We accurately quantified 729 N-glycoproteins spanning 1,310 unique N-glycosylation sites and observed 954 and 965 unique intact N- and O-glycopeptides, respectively, across the two disease conditions. Importantly, a panel of 56 intact N-glycopeptides perfectly discriminated PCa and BPH (ROC: AUC = 1). This study has generated a panel of intact glycopeptides that has a potential for PCa detection.

Status Report on the High-Throughput Characterization of Complex Intact O-Glycopeptide Mixtures

A very complex mixture of intact, human N- and O-glycopeptides, enriched from the tryptic digest of urinary proteins of three healthy donors using a two-step lectin affinity enrichment, was analyzed by LC-MS/MS, leading to approximately 45,000 glycopeptide EThcD spectra. Two search engines, Byonic and Protein Prospector, were used for the interpretation of the data, and N- and O-linked glycopeptides were assigned from separate searches. The identification rate was very low in all searches, even when results were combined. Thus, we investigated the reasons why was it so, to help to improve the identification success rate. Focusing on O-linked glycopeptides, we noticed that in EThcD, larger glycan oxonium ions better survive the activation than those in HCD. These fragments, combined with reducing terminal Y ions, provide important information about the glycan(s) present, so we investigated whether filtering the peaklists for glycan oxonium ions indicating the presence of a tetra- or hexasaccharide structure would help to reveal all molecules containing such glycans. Our study showed that intact glycans frequently do not survive even mild supplemental activation, meaning one cannot rely on these oxonium ions exclusively. We found that ETD efficiency is still a limiting factor, and for highly glycosylated peptides, the only information revealed in EThcD was related to the glycan structures. The limited overlap of results delivered by the two search engines draws attention to the fact that automated data interpretation of O-linked glycopeptides is not even close to being solved. Graphical abstract ᅟ.

An enrichment method based on synergistic and reversible covalent interactions for large-scale analysis of glycoproteins

Protein glycosylation is ubiquitous in biological systems and essential for cell survival. However, the heterogeneity of glycans and the low abundance of many glycoproteins complicate their global analysis. Chemical methods based on reversible covalent interactions between boronic acid and glycans have great potential to enrich glycopeptides, but the binding affinity is typically not strong enough to capture low-abundance species. Here, we develop a strategy using dendrimer-conjugated benzoboroxole to enhance the glycopeptide enrichment. We test the performance of several boronic acid derivatives, showing that benzoboroxole markedly increases glycopeptide coverage from human cell lysates. The enrichment is further improved by conjugating benzoboroxole to a dendrimer, which enables synergistic benzoboroxole–glycan interactions. This robust and simple method is highly effective for sensitive glycoproteomics analysis, especially capturing low-abundance glycopeptides. Importantly, the enriched glycopeptides remain intact, making the current method compatible with mass-spectrometry-based approaches to identify glycosylation sites and glycan structures.

Understanding the functions of protein glycosylation critically depends on methods to efficiently enrich glycoproteins from complex samples. Here, the authors develop a strategy using dendrimer-conjugated benzoboroxole to enhance glycopeptide enrichment, providing the basis for more comprehensive glycoprotein analyses.

N-linked Glycan Micro-heterogeneity in Glycoproteins of Arabidopsis

N-glycosylation is one of the most common protein post-translational modifications in eukaryotes and has a relatively conserved core structure between fungi, animals and plants. In plants, the biosynthesis of N-glycans has been extensively studied with all the major biosynthetic enzymes characterized. However, few studies have applied advanced mass spectrometry to profile intact plant N-glycopeptides. In this study, we use hydrophilic enrichment, high-resolution tandem mass spectrometry with complementary and triggered fragmentation to profile Arabidopsis N-glycopeptides from microsomal membranes of aerial tissues. A total of 492 N-glycosites were identified from 324 Arabidopsis proteins with extensive N-glycan structural heterogeneity revealed through 1110 N-glycopeptides. To demonstrate the precision of the approach, we also profiled N-glycopeptides from the mutant (xylt) of β-1,2-xylosyltransferase, an enzyme in the N-glycan biosynthetic pathway. This analysis represents the most comprehensive and unbiased collection of Arabidopsis N-glycopeptides revealing an unsurpassed level of detail on the micro-heterogeneity present in N-glycoproteins of Arabidopsis. Data are available via ProteomeXchange with identifier PXD006270.

Functional and proteomic analysis of Ceratonova shasta (Cnidaria: Myxozoa) polar capsules reveals adaptations to parasitism

Myxozoa is a diverse, speciose group of microscopic parasites, recently placed within the phylum Cnidaria. Myxozoans are highly reduced in size and complexity relative to free-living cnidarians, yet they have retained specialized organelles known as polar capsules, akin to the nematocyst stinging capsules of free-living species. Whereas in free-living cnidarians the stinging capsules are used for prey capture or defense, in myxozoans they have the essential function of initiating the host infection process. To explore the evolutionary adaptation of polar capsules to parasitism, we used as a model organism Ceratonova shasta, which causes lethal disease in salmonids. Here, we report the first isolation of C. shasta myxospore polar capsules using a tailored dielectrophoresis-based microfluidic chip. Using electron microscopy and functional analysis we demonstrated that C. shasta tubules have no openings and are likely used to anchor the spore to the host. Proteomic analysis of C. shasta polar capsules suggested that they have retained typical structural and housekeeping proteins found in nematocysts of jellyfish, sea anemones and Hydra, but have lost the most important functional group in nematocysts, namely toxins. Our findings support the hypothesis that polar capsules and nematocysts are homologous organelles, which have adapted to their distinct functions.

A peptide-retrieval strategy enables significant improvement of quantitative performance without compromising confidence of identification

Reliable quantification of low-abundance proteins in complex proteomes is challenging largely owing to the limited number of spectra/peptides identified. In this study we developed a straightforward method to improve the quantitative accuracy and precision of proteins by strategically retrieving the less confident peptides that were previously filtered out using the standard target-decoy search strategy. The filtered-out MS/MS spectra matched to confidently-identified proteins were recovered, and the peptide-spectrum-match FDR were re-calculated and controlled at a confident level of FDR≤1%, while protein FDR maintained at ~1%. We evaluated the performance of this strategy in both spectral count- and ion current-based methods. >60% increase of total quantified spectra/peptides was respectively achieved for analyzing a spike-in sample set and a public dataset from CPTAC. Incorporating the peptide retrieval strategy significantly improved the quantitative accuracy and precision, especially for low-abundance proteins (e.g. one-hit proteins). Moreover, the capacity of confidently discovering significantly-altered proteins was also enhanced substantially, as demonstrated with two spike-in datasets. In summary, improved quantitative performance was achieved by this peptide recovery strategy without compromising confidence of protein identification, which can be readily implemented in a broad range of quantitative proteomics techniques including label-free or labeling approaches.

SIGNIFICANCE

We hypothesize that more quantifiable spectra and peptides in a protein, even including less confident peptides, could help reduce variations and improve protein quantification. Hence the peptide retrieval strategy was developed and evaluated in two spike-in sample sets with different LC-MS/MS variations using both MS1- and MS2-based quantitative approach. The list of confidently identified proteins using the standard target-decoy search strategy was fixed and more spectra/peptides with less confidence matched to confident proteins were retrieved. However, the total peptide-spectrum-match false discovery rate (PSM FDR) after retrieval analysis was still controlled at a confident level of FDR≤1%. As expected, the penalty for occasionally incorporating incorrect peptide identifications is negligible by comparison with the improvements in quantitative performance. More quantifiable peptides, lower missing value rate, better quantitative accuracy and precision were significantly achieved for the same protein identifications by this simple strategy. This strategy is theoretically applicable for any quantitative approaches in proteomics and thereby provides more quantitative information, especially on low-abundance proteins.

Full-Featured Search Algorithm for Negative Electron-Transfer Dissociation

Negative electron-transfer dissociation (NETD) has emerged as a premier tool for peptide anion analysis, offering access to acidic post-translational modifications and regions of the proteome that are intractable with traditional positive-mode approaches. Whole-proteome scale characterization is now possible with NETD, but proper informatic tools are needed to capitalize on advances in instrumentation. Currently only one database search algorithm (OMSSA) can process NETD data. Here we implement NETD search capabilities into the Byonic platform to improve the sensitivity of negative-mode data analyses, and we benchmark these improvements using 90 min LC-MS/MS analyses of tryptic peptides from human embryonic stem cells. With this new algorithm for searching NETD data, we improved the number of successfully identified spectra by as much as 80% and identified 8665 unique peptides, 24 639 peptide spectral matches, and 1338 proteins in activated-ion NETD analyses, more than doubling identifications from previous negative-mode characterizations of the human proteome. Furthermore, we reanalyzed our recently published large-scale, multienzyme negative-mode yeast proteome data, improving peptide and peptide spectral match identifications and considerably increasing protein sequence coverage. In all, we show that new informatics tools, in combination with recent advances in data acquisition, can significantly improve proteome characterization in negative-mode approaches.

Site-specific characterization of N-linked glycosylation in human urinary glycoproteins and endogenous glycopeptides

Glycosylation is a very important post-translational modification involved in various cellular processes, such as cell adhesion, signal transduction and immune response. Urine is a rich source of glycoproteins and attractive biological fluid for biomarker discovery, owing to its availability, ease of collection, and correlation with pathophysiology of diseases. Although the urinary proteomics have been explored previously, the urinary glycoproteome characterization remains challenging requiring the development and optimization of analytical and bioinformatics methods for protein glycoprofiling. This study describes the high confident identification of 472 unique N-glycosylation sites covering 256 urinary glycoproteins. Besides, 202 unique N-glycosylation sites were identified in low molecular weight endogenous glycopeptides, which belong to 90 glycoproteins. Global site-specific characterization of the N-linked glycan heterogeneity was achieved by intact glycopeptide analysis, revealing 303 unique glycopeptides most of them displaying complex/hybrid glycans composed by sialic acid and fucose. These datasets consist in a valuable resource of glycoproteins and N-glycosylation sites found in healthy human urine that can be further explored in different disorders, in which the N-linked glycosylation may be aberrant.

The Metacaspase (Mca1p) Restricts O-glycosylation During Farnesol-induced Apoptosis in Candida albicans

Protein glycolysation is an essential posttranslational modification in eukaryotic cells. In pathogenic yeasts, it is involved in a large number of biological processes, such as protein folding quality control, cell viability and host/pathogen relationships. A link between protein glycosylation and apoptosis was established by the analysis of the phenotypes of oligosaccharyltransferase mutants in budding yeast. However, little is known about the contribution of glycosylation modifications to the adaptive response to apoptosis inducers. The cysteine protease metacaspase Mca1p plays a key role in the apoptotic response in Candida albicans triggered by the quorum sensing molecule farnesol. We subjected wild-type and mca1-deletion strains to farnesol stress and then studied the early phase of apoptosis release in quantitative glycoproteomics and glycomics experiments on cell-free extracts essentially devoid of cell walls. We identified and characterized 62 new glycosylated peptides with their glycan composition: 17 N-glycosylated, 45 O-glycosylated, and 81 additional sites of N-glycosylation. They were found to be involved in the control of protein folding, cell wall integrity and cell cycle regulation. We showed a general increase in the O-glycosylation of proteins in the mca1 deletion strain after farnesol challenge. We identified 44 new putative protein substrates of the metacaspase in the glycoprotein fraction enriched on concanavalin A. Most of these substrates are involved in protein folding or protein resolubilization and in mitochondrial functions. We show here that key Mca1p substrates, such as Cdc48p or Ssb1p, involved in degrading misfolded glycoproteins and in the protein quality control system, are themselves differentially glycosylated. We found putative substrates, such as Bgl2p (validated by immunoblot), Srb1p or Ugp1p, that are involved in the biogenesis of glycans. Our findings highlight a new role of the metacaspase in amplifying cell death processes by affecting several critical protein quality control systems through the alteration of the protein glycosylation machinery.Data are available via ProteomeXchange with identifier PXD003677.

ProteinInferencer: Confident protein identification and multiple experiment comparison for large scale proteomics projects

Shotgun proteomics generates valuable information from large-scale and target protein characterizations, including protein expression, protein quantification, protein post-translational modifications (PTMs), protein localization, and protein-protein interactions. Typically, peptides derived from proteolytic digestion, rather than intact proteins, are analyzed by mass spectrometers because peptides are more readily separated, ionized and fragmented. The amino acid sequences of peptides can be interpreted by matching the observed tandem mass spectra to theoretical spectra derived from a protein sequence database. Identified peptides serve as surrogates for their proteins and are often used to establish what proteins were present in the original mixture and to quantify protein abundance. Two major issues exist for assigning peptides to their originating protein. The first issue is maintaining a desired false discovery rate (FDR) when comparing or combining multiple large datasets generated by shotgun analysis and the second issue is properly assigning peptides to proteins when homologous proteins are present in the database. Herein we demonstrate a new computational tool, ProteinInferencer, which can be used for protein inference with both small- or large-scale data sets to produce a well-controlled protein FDR. In addition, ProteinInferencer introduces confidence scoring for individual proteins, which makes protein identifications evaluable. This article is part of a Special Issue entitled: Computational Proteomics.

Improving protein identification from tandem mass spectrometry data by one-step methods and integrating data from other platforms

MOTIVATION

Many approaches have been proposed for the protein identification problem based on tandem mass spectrometry (MS/MS) data. In these experiments, proteins are digested into peptides and the resulting peptide mixture is subjected to mass spectrometry. Some interesting putative peptide features (peaks) are selected from the mass spectra. Following that, the precursor ions undergo fragmentation and are analyzed by MS/MS. The process of identification of peptides from the mass spectra and the constituent proteins in the sample is called protein identification from MS/MS data. There are many two-step protein identification procedures, reviewed in the literature, which first attempt to identify the peptides in a separate process and then use these results to infer the proteins. However, in recent years, there have been attempts to provide a one-step solution to protein identification, which simultaneously identifies the proteins and the peptides in the sample.

RESULTS

In this review, we briefly introduce the most popular two-step protein identification procedure, PeptideProphet coupled with ProteinProphet. Following that, we describe the difficulties with two-step procedures and review some recently introduced one-step protein/peptide identification procedures that do not suffer from these issues. The focus of this review is on one-step procedures that are based on statistical likelihood-based models, but some discussion of other one-step procedures is also included. We report comparative performances of one-step and two-step methods, which support the overall superiorities of one-step procedures. We also cover some recent efforts to improve protein identification by incorporating other molecular data along with MS/MS data.

Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics

The analysis of a shotgun proteomics experiment results in a list of peptide-spectrum matches (PSMs) in which each fragmentation spectrum has been matched to a peptide in a database. Subsequently, most protein inference algorithms rank peptides according to the best-scoring PSM for each peptide. However, there is disagreement in the scientific literature on the best method to assess the statistical significance of the resulting peptide identifications. Here, we use a previously described calibration protocol to evaluate the accuracy of three different peptide-level statistical confidence estimation procedures: the classical Fisher's method, and two complementary procedures that estimate significance, respectively, before and after selecting the top-scoring PSM for each spectrum. Our experiments show that the latter method, which is employed by MaxQuant and Percolator, produces the most accurate, well-calibrated results.

Recognizing uncertainty increases robustness and reproducibility of mass spectrometry-based protein inferences

Parsimony and protein grouping are widely employed to enforce economy in the number of identified proteins, with the goal of increasing the quality and reliability of protein identifications; however, in a counterintuitive manner, parsimony and protein grouping may actually decrease the reproducibility and interpretability of protein identifications. We present a simple illustration demonstrating ways in which parsimony and protein grouping may lower the reproducibility or interpretability of results. We then provide an example of a data set where a probabilistic method increases the reproducibility and interpretability of identifications made on replicate analyses of Human Du145 prostate cancer cell lines.

ROCS: a reproducibility index and confidence score for interaction proteomics studies

Background

Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones.

Methods

To address the issues of reproducibility and accuracy of protein-protein interactions, we introduce a two-step method, called ROCS, which makes use of Indicator Prey Proteins to select reproducible AP-MS experiments, and of Confidence Scores to select specific protein-protein interactions. The Indicator Prey Proteins account for measures of protein identifiability as well as protein reproducibility, effectively allowing removal of outlier experiments that contribute noise and affect downstream inferences. The filtered set of experiments is then used in the Protein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing a Confidence Score, which accounts for the probability of occurrence of prey proteins in the bait experiments relative to the control experiment, where the significance cutoff parameter is estimated by simultaneously controlling false positives and false negatives against metrics of false discovery rate and biological coherence respectively. In summary, the ROCS method relies on automatic objective criterions for parameter estimation and error-controlled procedures.

Results

We illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions, allowing for systematic benchmarking of ROCS. We show that our method may be used on its own to make accurate identification of specific, biologically relevant protein-protein interactions, or in combination with other AP-MS scoring methods to significantly improve inferences.

Conclusions

Our method addresses important issues encountered in AP-MS datasets, making ROCS a very promising tool for this purpose, either on its own or in conjunction with other methods. We anticipate that our methodology may be used more generally in proteomics studies and databases, where experimental reproducibility issues arise. The method is implemented in the R language, and is available as an R package called “ROCS”, freely available from the CRAN repository http://cran.r-project.org/.