Neuroserpin gene therapy inhibits retinal ganglion cell apoptosis and promotes functional preservation in glaucoma.

Our research has proven that the inhibitory activity of the serine protease inhibitor neuroserpin (NS) is impaired because of its oxidation deactivation in glaucoma. Using genetic NS knockout (NS-/-) and NS overexpression (NS+/+ Tg) animal models and antibody-based neutralization approaches, we demonstrate that NS loss is detrimental to retinal structure and function. NS ablation was associated with perturbations in autophagy and microglial and synaptic markers, leading to significantly enhanced IBA1, PSD95, beclin-1, and LC3-II/LC3-I ratio and reduced phosphorylated neurofilament heavy chain (pNFH) levels. On the other hand, NS upregulation promoted retinal ganglion cell (RGC) survival in wild-type and NS-/- glaucomatous mice and increased pNFH expression. NS+/+Tg mice demonstrated decreased PSD95, beclin-1, LC3-II/LC3-I ratio, and IBA1 following glaucoma induction, highlighting its protective role. We generated a novel reactive site NS variant (M363R-NS) resistant to oxidative deactivation. Intravitreal administration of M363R-NS was observed to rescue the RGC degenerative phenotype in NS-/- mice. These findings demonstrate that NS dysfunction plays a key role in the glaucoma inner retinal degenerative phenotype and that modulating NS imparts significant protection to the retina. NS upregulation protected RGC function and restored biochemical networks associated with autophagy and microglial and synaptic function in glaucoma.

Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins.

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).

Role of Multiomics Data to Understand Host-Pathogen Interactions in COVID-19 Pathogenesis.

Human infectious diseases are contributed equally by the host immune system's efficiency and any pathogens' infectivity. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the coronavirus strain causing the respiratory pandemic coronavirus disease 2019 (COVID-19). To understand the pathobiology of SARS-CoV-2, one needs to unravel the intricacies of host immune response to the virus, the viral pathogen's mode of transmission, and alterations in specific biological pathways in the host allowing viral survival. This review critically analyzes recent research using high-throughput "omics" technologies (including proteomics and metabolomics) on various biospecimens that allow an increased understanding of the pathobiology of SARS-CoV-2 in humans. The altered biomolecule profile facilitates an understanding of altered biological pathways. Further, we have performed a meta-analysis of significantly altered biomolecular profiles in COVID-19 patients using bioinformatics tools. Our analysis deciphered alterations in the immune response, fatty acid, and amino acid metabolism and other pathways that cumulatively result in COVID-19 disease, including symptoms such as hyperglycemic and hypoxic sequelae.

Mass spectrometry-based protein identification in proteomics-a review.

Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification.

A high-stringency blueprint of the human proteome.

The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP's tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.

Research on the Human Proteome Reaches a Major Milestone: >90% of Predicted Human Proteins Now Credibly Detected, According to the HUPO Human Proteome Project.

According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020-01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019-01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020-01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data.

The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0.

The Human Proteome Organization's (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome.

Potential early clinical stage colorectal cancer diagnosis using a proteomics blood test panel.

BACKGROUND: One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current population-based screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin). METHODS: A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets. RESULTS: Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC. CONCLUSION: MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.

Progress on Identifying and Characterizing the Human Proteome: 2019 Metrics from the HUPO Human Proteome Project.

The Human Proteome Project (HPP) annually reports on progress made throughout the field in credibly identifying and characterizing the complete human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2019-01-11 contains 17 694 proteins with strong protein-level evidence (PE1), compliant with HPP Guidelines for Interpretation of MS Data v2.1; these represent 89% of all 19 823 neXtProt predicted coding genes (all PE1,2,3,4 proteins), up from 17 470 one year earlier. Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), has been reduced from 2949 to 2129 since 2016 through efforts throughout the community, including the chromosome-centric HPP. PeptideAtlas is the source of uniformly reanalyzed raw mass spectrometry data for neXtProt; PeptideAtlas added 495 canonical proteins between 2018 and 2019, especially from studies designed to detect hard-to-identify proteins. Meanwhile, the Human Protein Atlas has released version 18.1 with immunohistochemical evidence of expression of 17 000 proteins and survival plots as part of the Pathology Atlas. Many investigators apply multiplexed SRM-targeted proteomics for quantitation of organ-specific popular proteins in studies of various human diseases. The 19 teams of the Biology and Disease-driven B/D-HPP published a total of 160 publications in 2018, bringing proteomics to a broad array of biomedical research.

Proteomics Reveals Cell-Surface Urokinase Plasminogen Activator Receptor Expression Impacts Most Hallmarks of Cancer.

While metastasis is the primary cause of colorectal cancer (CRC) mortality, the molecular mechanisms underpinning it remains elusive. Metastasis is propagated through driver oncogene/suppressor gene mutations, accompanied by passenger mutations and underlying genomic instability. To understand cancer biology, a unifying framework called the "hallmarks of cancer" (HoCs) has been developed, which organizes cell biological alterations under ten key hallmarks. Underlying these HoCs, genome instability generates mutational diversity that is amplified by inflammation. Recognizing how critical cancer cell-surface proteins influence, these HoCs have been proposed to accelerate precision medicine therapeutic development. A moderate decrease (43%↓) in HCT116 cell surface urokinase plasminogen activator receptor (uPAR) expression mitigates against many HoCs driven by these cell's KRAS and PIK3CA mutational signature. Comprehensive proteomics (whole cell lysis with two membrane protein enrichments) coupled with ingenuity pathway analysis (IPA) demonstrates that uPAR negates essential pathways across the HoC spectrum, particularly those associated with metastasis, resisting cell death, and sustaining proliferation, and parallels Cancer Hallmarks Analytics Tool analysis. Decreasing uPAR predominantly alters metastasis-related and uPAR-interactome protein expression (e.g., EGFR, caveolin, vitronectin, integrin ß4). Collectively, it is demonstrated that uPAR is a lynchpin protein capable of regulating several HoC pathways in a classical CRC mutational background.

Oncoproteomics: Current status and future opportunities.

Oncoproteomics is the systematic study of cancer samples using omics technologies to detect changes implicated in tumorigenesis. Recent progress in oncoproteomics is already opening new avenues for the identification of novel biomarkers for early clinical stage cancer detection, targeted molecular therapies, disease monitoring, and drug development. Such information will lead to new understandings of cancer biology and impact dramatically on the future care of cancer patients. In this review, we will summarize the advantages and limitations of the key technologies used in (onco)proteogenomics, (the Omics Pipeline), explain how they can assist us in understanding the biology behind the overarching "Hallmarks of Cancer", discuss how they can advance the development of precision/personalised medicine and the future directions in the field.

In Silico Peptide Repertoire of Human Olfactory Receptor Proteomes on High-Stringency Mass Spectrometry.

Human olfactory receptors (ORs) are seven-pass transmembrane G-protein coupled receptors (GPCR) involved in smell perception and many other signaling pathways. They are primarily expressed in the olfactory epithelium and ectopically expressed in several other organs and tissues. neXtProt contains 4 PE1 (protein existence 1, evidenced at the protein level) ORs, determined on the basis of either protein interaction data (i.e., OR1D4 and OR2AG1) or convincing genetic, haplotype, or biochemical data (i.e., OR1D2 and OR2J3). Not a single OR currently qualifies for neXtProt PE1 status based on mass spectrometry (MS) evidence. Many reasons for this absence of MS-based identification have been proposed, including (i) confined or spatiotemporal or developmental expression, (ii) low copy number, (iii) OR repertoire gene silencing, and (iv) limited tissue availability. OR transmembrane domains (TMDs) inherently limit MS identification because the hydrophobic nature restricts the access of trypsin to potential cleavage sites. Equally, the extremely low frequency or lack of accessible arginine and lysine residues in TMDs renders trypsin cleavage ineffective. Here, we demonstrate an analytical approach specifically focused on the hydrophilic (trypsin-accessible) domains of ORs [i.e., with all transmembrane segments and anchored peptides excluded). We predicted the ability of OR soluble (hydrophilic) domains to yield 2 or more >9 amino acids (aa) length unique mapping (unique to a protein only), non-nested (peptides with varying length at the N or C terminal but containing the same core sequence), leucine/isoleucine (I/L) switch examined (I and L have same mass and cannot be distinguished by MS) tryptic peptides. Our analysis showed that ∼58% of the human OR proteome could potentially generate tryptic peptides that satisfy current the Human Proteome Project data interpretation guidelines (version 2.1) when no missed cleavages are allowed and increases to ∼78% when one missed cleavage is allowed. The utilization of current biological data (adjuvant genomics, expression profile, transcriptomics, epigenome silencing data, etc.) and the adoption of a non-conventional proteomics approach (e.g., Confetti multiprotease digestion, CNBr cleavage of TMDs, and more-extreme chromatographic and MS methods) could aid in the detection of the remaining ORs.

Proteomics and the microbiome: pitfalls and potential.

Introduction: Human symbiotic microbiota are now known to play important roles in human health and disease. Significant progress in our understanding of the human microbiome has been driven by recent technological advances in the fields of genomics, transcriptomics, and proteomics. As a complementary method to metagenomics, proteomics is enabling detailed protein profiling of the microbiome to decipher its structure and function and to analyze its relationship with the human body. Fecal proteomics is being increasingly applied to discover and validate potential health and disease biomarkers, and Therapeutic Goods Administration (TGA)-approved instrumentation and a range of clinical assays are being developed that will collectively play key roles in advancing personalized medicine. Areas covered: This review will introduce the complexity of the microbiome and its role in health and disease (in particular the gastrointestinal tract or gut microbiome), discuss current genomic and proteomic methods for studying this system, including the discovery of potential biomarkers, and outline the development of clinically accepted protocols leading to personalized medicine. Expert commentary: Recognition of the important role the microbiome plays in both health and disease is driving current research in this key area. A proteogenomics approach will be essential to unravel the biologies underlying this complex network.

iSwathX: an interactive web-based application for extension of DIA peptide reference libraries.

Summary: Large-scale peptide mass spectrometry (MS)/MS reference libraries are essential for the comprehensive analysis of data-independent acquisition (DIA) MS datasets, providing a comprehensive set of spectra for identification and quantification of proteins. We have developed a novel web-based R-package (iSwathX) for combining reference libraries that is compatible with different DIA analysis software. This open-source web GUI automates the process of normalization and combination of spectral libraries and provides a user-friendly method for performing library format conversions, analysis and visualizations, with no need for programing familiarity. Availability and implementation: iSwathX is freely accessible at https://biolinfo.shinyapps.io/iSwathX with the R-package and Shiny source code available from GitHub (https://github.com/znoor/iSwathX). Supplementary information: Supplementary data are available at Bioinformatics online.

Tissue and plasma proteomics for early stage cancer detection.

The pursuit of novel and effective biomarkers is essential in the struggle against cancer, which is a leading cause of mortality worldwide. Biomarkers can be used as specific diagnostic tools, prognostic predictors, markers of the development of therapeutic resistance or even as therapeutic targets themselves. Through the application of sensitive and specific proteomic techniques, oncoproteomics investigates the proteins associated with cancer processes, to better understand their biological function/s and their associated pathways during tumorigenesis. Such studies seek to identify both potential biomarkers and drug targets in order to improve patient survival and quality of life whilst reducing the global health budget. Tissue and plasma are the most commonly utilised biological samples for such studies as they are readily available, non-invasive and generally acceptable. Here, we outline the relative advantages and disadvantages of the most frequently used techniques for cancer diagnosis, prognosis, treatment and surveillance, concentrating on the latest advances and application of tissue and plasma proteomics for novel cancer biomarker discovery and disease surveillance.

Progress on Identifying and Characterizing the Human Proteome: 2018 Metrics from the HUPO Human Proteome Project.

The Human Proteome Project (HPP) annually reports on progress throughout the field in credibly identifying and characterizing the human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2018-01-17, the baseline for this sixth annual HPP special issue of the Journal of Proteome Research, contains 17 470 PE1 proteins, 89% of all neXtProt predicted PE1-4 proteins, up from 17 008 in release 2017-01-23 and 13 975 in release 2012-02-24. Conversely, the number of neXtProt PE2,3,4 missing proteins has been reduced from 2949 to 2579 to 2186 over the past two years. Of the PE1 proteins, 16 092 are based on mass spectrometry results, and 1378 on other kinds of protein studies, notably protein-protein interaction findings. PeptideAtlas has 15 798 canonical proteins, up 625 over the past year, including 269 from SUMOylation studies. The largest reason for missing proteins is low abundance. Meanwhile, the Human Protein Atlas has released its Cell Atlas, Pathology Atlas, and updated Tissue Atlas, and is applying recommendations from the International Working Group on Antibody Validation. Finally, there is progress using the quantitative multiplex organ-specific popular proteins targeted proteomics approach in various disease categories.

Systems-based approaches enable identification of gene targets which improve the flavour profile of low-ethanol wine yeast strains.

Metabolic engineering has been vital to the development of industrial microbes such as the yeast Saccharomyces cerevisiae. However, sequential rounds of modification are often needed to achieve particular industrial design targets. Systems biology approaches can aid in identifying genetic targets for modification through providing an integrated view of cellular physiology. Recently, research into the generation of commercial yeasts that can produce reduced-ethanol wines has resulted in metabolically-engineered strains of S. cerevisiae that are less efficient at producing ethanol from sugar. However, these modifications led to the concomitant production of off-flavour by-products. A combination of transcriptomics, proteomics and metabolomics was therefore used to investigate the physiological changes occurring in an engineered low-ethanol yeast strain during alcoholic fermentation. Integration of 'omics data identified several metabolic reactions, including those related to the pyruvate node and redox homeostasis, as being significantly affected by the low-ethanol engineering methodology, and highlighted acetaldehyde and 2,4,5-trimethyl-1,3-dioxolane as the main off-flavour compounds. Gene remediation strategies were then successfully applied to decrease the formation of these by-products, while maintaining the 'low-alcohol' phenotype. The data generated from this comprehensive systems-based study will inform wine yeast strain development programmes, which, in turn, could potentially play an important role in assisting winemakers in their endeavour to produce low-alcohol wines with desirable flavour profiles.

A Transferrin Triggered Pathway for Highly Targeted Delivery of Graphene-Based Nanodrugs to Treat Choroidal Melanoma.

The synthesis of transferrin (Tf)-modified pegylated graphene (PG) and its application as a highly efficient drug delivery carrier for therapy of Ocular Choroidal Melanoma-1 (OCM-1) cells is presented. For the first reported time, nanoscaled PG is prepared using an environmentally friendly ball-milling technique. The unique 2D nanostructure obtained using this PG synthesis approach offers considerable advantages in terms of drug loading and delivery, as well as the conjugation of Tf to PG providing a more targeted delivery vehicle. A highly efficient targeted pathway toward OCM-1 cells triggered by an affinity between Tf and Tf receptors expressed on the surface of OCM-1 cells is reported first here. PG-Tf is observed to easily anchor anticancer drugs such as doxorubicin via π-π stacking. This work performs a Transwell two cells coculture experiment, a 3D in vitro tumor model, and an in vivo mouse model with OCM-1 tumors to demonstrate the composite's therapeutic superiority over conventional systems for the targeted delivery and controlled release of antitumor drugs.

How many human proteoforms are there?

Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous questions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA- and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry-based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. We frame central issues regarding determination of protein-level variation and PTMs, including some paradoxes present in the field today. We use this framework to assess existing data and to ask the question, "How many distinct primary structures of proteins (proteoforms) are created from the 20,300 human genes?" We also explore prospects for improving measurements to better regularize protein-level biology and efficiently associate PTMs to function and phenotype.