A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development.

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.

Identification of Candidate Protein Biomarkers Associated with Domoic Acid Toxicosis in Cerebrospinal Fluid of California Sea Lions (Zalophus californianus).

Since 1998, California sea lion (Zalophus californianus) stranding events associated with domoic acid toxicosis (DAT) have consistently increased. Outside of direct measurement of domoic acid in bodily fluids at the time of stranding, there are no practical nonlethal clinical tests for the diagnosis of DAT that can be utilized in a rehabilitation facility. Proteomics analysis was conducted to discover candidate protein markers of DAT using cerebrospinal fluid from stranded California sea lions with acute DAT (n = 8), chronic DAT (n = 19), or without DAT (n = 13). A total of 2005 protein families were identified experiment-wide. A total of 83 proteins were significantly different in abundance across the three groups (adj. p < 0.05). MDH1, PLD3, ADAM22, YWHAG, VGF, and CLSTN1 could discriminate California sea lions with or without DAT (AuROC > 0.75). IGKV2D-28, PTRPF, KNG1, F2, and SNCB were able to discriminate acute DAT from chronic DAT (AuROC > 0.75). Proteins involved in alpha synuclein deposition were over-represented as classifiers of DAT, and many of these proteins have been implicated in a variety of neurodegenerative diseases. These proteins should be considered potential markers for DAT in California sea lions and should be prioritized for future validation studies as biomarkers.

compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets.

It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.

ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics.

Data set acquisition and curation are often the most difficult and time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based liquid chromatography (LC) coupled to mass spectrometry (MS) data sets, due to the high levels of data reduction that occur between raw data and machine learning-ready data. Since predictive proteomics is an emerging field, when predicting peptide behavior in LC-MS setups, each lab often uses unique and complex data processing pipelines in order to maximize performance, at the cost of accessibility and reproducibility. For this reason we introduce ProteomicsML, an online resource for proteomics-based data sets and tutorials across most of the currently explored physicochemical peptide properties. This community-driven resource makes it simple to access data in easy-to-process formats, and contains easy-to-follow tutorials that allow new users to interact with even the most advanced algorithms in the field. ProteomicsML provides data sets that are useful for comparing state-of-the-art machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available at https://www.proteomicsml.org/, and we welcome the entire proteomics community to contribute to the project at https://github.com/ProteomicsML/ProteomicsML.

Toward an Integrated Machine Learning Model of a Proteomics Experiment.

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.

Glycosylation and the global virome.

The sugars that coat the outsides of viruses and host cells are key to successful disease transmission, but they remain understudied compared to other molecular features. Understanding the comparative zoology of glycosylation - and harnessing it for predictive science - could help close the molecular gap in zoonotic risk assessment.

Proteogenomic Assessment of Intraspecific Venom Variability: Molecular Adaptations in the Venom Arsenal of Conus purpurascens.

Cone snails produce venom that contains diverse groups of peptides (conopeptides/conotoxins) and display a wide mass range, high rate of posttranslational modifications, and many potential pharmacological targets. Here we employ a proteogenomic approach to maximize conopeptide identification from the injected venom of Conus purpurascens. mRNA sequences from C. purpurascens venom ducts were assembled into a search database and complemented with known sequences and de novo approaches. We used a top-down peptidomic approach and tandem mass spectrometry identification to compare injected venom samples of 27 specimens. This intraspecific analysis yielded 543 unique conopeptide identifications, which included 33 base conopeptides and their toxiforms, 21 of which are novel. The results reveal two distinct venom profiles with different synergistic interactions to effectively target neural pathways aimed to immobilize prey. These venom expression patterns will aid target prediction, a significant step toward developing conotoxins into valuable drugs or neural probes.

Cloudy with a Chance of Peptides: Accessibility, Scalability, and Reproducibility with Cloud-Hosted Environments.

Cloud-hosted environments offer known benefits when computational needs outstrip affordable local workstations, enabling high-performance computation without a physical cluster. What has been less apparent, especially to novice users, is the transformative potential for cloud-hosted environments to bridge the digital divide that exists between poorly funded and well-resourced laboratories, and to empower modern research groups with remote personnel and trainees. Using cloud-based proteomic bioinformatic pipelines is not predicated on analyzing thousands of files, but instead can be used to improve accessibility during remote work, extreme weather, or working with under-resourced remote trainees. The general benefits of cloud-hosted environments also allow for scalability and encourage reproducibility. Since one possible hurdle to adoption is awareness, this paper is written with the nonexpert in mind. The benefits and possibilities of using a cloud-hosted environment are emphasized by describing how to setup an example workflow to analyze a previously published label-free data-dependent acquisition mass spectrometry data set of mammalian urine. Cost and time of analysis are compared using different computational tiers, and important practical considerations are described. Overall, cloud-hosted environments offer the potential to solve large computational problems, but more importantly can enable and accelerate research in smaller research groups with inadequate infrastructure and suboptimal local computational resources.

Rewinding the Molecular Clock: Looking at Pioneering Molecular Phylogenetics Experiments in the Light of Proteomics.

Science is full of overlooked and undervalued research waiting to be rediscovered. Proteomics is no exception. In this perspective, we follow the ripples from a 1960 study of Zuckerkandl, Jones, and Pauling comparing tryptic peptides across animal species. This pioneering work directly led to the molecular clock hypothesis and the ensuing explosion in molecular phylogenetics. In the decades following, proteins continued to provide essential clues on evolutionary history. While technology has continued to improve, contemporary proteomics has strayed from this larger biological context, rarely comparing species or asking how protein structure, function, and interactions have evolved. Here we recombine proteomics with molecular phylogenetics, highlighting the value of framing proteomic results in a larger biological context and how almost forgotten research, though technologically surpassed, can still generate new ideas and illuminate our work from a different perspective. Though it is infeasible to read all research published on a large topic, looking up older papers can be surprisingly rewarding when rediscovering a "gem" at the end of a long citation chain, aided by digital collections and perpetually helpful librarians. Proper literature study reduces unnecessary repetition and allows research to be more insightful and impactful by truly standing on the shoulders of giants. All data was uploaded to MassIVE (https://massive.ucsd.edu/) as dataset MSV000087993.

Surveying the Vampire Bat (Desmodus rotundus) Serum Proteome: A Resource for Identifying Immunological Proteins and Detecting Pathogens.

Bats are increasingly studied as model systems for longevity and as natural hosts for some virulent viruses. Yet the ability to characterize immune mechanisms of viral tolerance and to quantify infection dynamics in wild bats is often limited by small sample volumes and few species-specific reagents. Here, we demonstrate how proteomics can overcome these limitations by using data-independent acquisition-based shotgun proteomics to survey the serum proteome of 17 vampire bats (Desmodus rotundus) from Belize. Using just 2 µL of sample and relatively short separations of undepleted serum digests, we identified 361 proteins across 5 orders of magnitude. Levels of immunological proteins in vampire bat serum were then compared to human plasma via published databases. Of particular interest were antiviral and antibacterial components, circulating 20S proteasome complex and proteins involved in redox activity. Lastly, we used known virus proteomes to putatively identify Rh186 from Macacine herpesvirus 3 and ORF1a from Middle East respiratory syndrome-related coronavirus, indicating that mass spectrometry-based techniques show promise for pathogen detection. Overall, these results can be used to design targeted mass-spectrometry assays to quantify immunological markers and detect pathogens. More broadly, our findings also highlight the application of proteomics in advancing wildlife immunology and pathogen surveillance.

Liver proteome response to torpor in a basoendothermic mammal, Tenrec ecaudatus, provides insights into the evolution of homeothermy.

Many mammals use adaptive heterothermy (e.g., torpor, hibernation) to reduce metabolic demands of maintaining high body temperature (Tb). Torpor is typically characterized by coordinated declines in Tb and metabolic rate (MR) followed by active rewarming. Most hibernators experience periods of euthermy between bouts of torpor during which homeostatic processes are restored. In contrast, the common tenrec, a basoendothermic Afrotherian mammal, hibernates without interbout arousals and displays extreme flexibility in Tb and MR. We investigated the molecular basis of this plasticity in tenrecs by profiling the liver proteome of animals that were active or torpid with high and more stable Tb (∼32°C) or lower Tb (∼14°C). We identified 768 tenrec liver proteins, of which 50.9% were differentially abundant between torpid and active animals. Protein abundance was significantly more variable in active cold and torpid compared with active warm animals, suggesting poor control of proteostasis. Our data suggest that torpor in tenrecs may lead to mismatches in protein pools due to poor coordination of anabolic and catabolic processes. We propose that the evolution of endothermy leading to a more realized homeothermy of boreoeutherians likely led to greater coordination of homeostatic processes and reduced mismatches in thermal sensitivities of metabolic pathways.

Mixed-Methods Study to Predict Upstaging of DCIS to Invasive Disease on Mammography.

BACKGROUND. The incidence of ductal carcinoma in situ (DCIS) has steadily increased, as have concerns regarding overtreatment. Active surveillance is a novel treatment strategy that avoids surgical excision, but identifying patients with occult invasive disease who should be excluded from active surveillance is challenging. Radiologists are not typically expected to predict the upstaging of DCIS to invasive disease, though they might be trained to perform this task. OBJECTIVE. The purpose of this study was to determine whether a mixed-methods two-stage observer study can improve radiologists' ability to predict upstaging of DCIS to invasive disease on mammography. METHODS. All cases of DCIS calcifications that underwent stereotactic biopsy between 2010 and 2015 were identified. Two cohorts were randomly generated, each containing 150 cases (120 pure DCIS cases and 30 DCIS cases upstaged to invasive disease at surgery). Nine breast radiologists reviewed the mammograms in the first cohort in a blinded fashion and scored the probability of upstaging to invasive disease. The radiologists then reviewed the cases and results collectively in a focus group to develop consensus criteria that could improve their ability to predict upstaging. The radiologists reviewed the mammograms from the second cohort in a blinded fashion and again scored the probability of upstaging. Statistical analysis compared the performances between rounds 1 and 2. RESULTS. The mean AUC for reader performance in predicting upstaging in round 1 was 0.623 (range, 0.514-0.684). In the focus group, radiologists agreed that upstaging was better predicted when an associated mass, asymmetry, or architectural distortion was present; when densely packed calcifications extended over a larger area; and when the most suspicious features were focused on rather than the most common features. Additionally, radiologists agreed that BI-RADS descriptors do not adequately characterize risk of invasion, and that microinvasive disease and smaller areas of DCIS will have poor prediction estimates. Reader performance significantly improved in round 2 (mean AUC, 0.765; range, 0.617-0.852; p = .045). CONCLUSION. A mixed-methods two-stage observer study identified factors that helped radiologists significantly improve their ability to predict upstaging of DCIS to invasive disease. CLINICAL IMPACT. Breast radiologists can be trained to better predict upstaging of DCIS to invasive disease, which may facilitate discussions with patients and referring providers.

Low-touch, team-based care for co-morbidity management in cancer patients: the ONE TEAM randomized controlled trial.

BACKGROUND: As treatments for cancer have improved, more people are surviving cancer. However, compared to people without a history of cancer, cancer survivors are more likely to die of cardiovascular disease (CVD). Increased risk for CVD-related mortality among cancer survivors is partially due to lack of medication adherence and problems that exist in care coordination between cancer specialists, primary care physicians, and cardiologists. METHODS/DESIGN: The Onco-primary care networking to support TEAM-based care (ONE TEAM) study is an 18-month cluster-randomized controlled trial with clustering at the primary care clinic level. ONE TEAM compares the provision of the iGuide intervention to patients and primary care providers versus an education-only control. For phase 1, at the patient level, the intervention includes video vignettes and a live webinar; provider-level interventions include electronic health records-based communication and case-based webinars. Participants will be enrolled from across North Carolina one of their first visits with a cancer specialist (e.g., surgeon, radiation or medical oncologist). We use a sequential multiple assignment randomized trial (SMART) design. Outcomes (measured at the patient level) will include Healthcare Effectiveness Data and Information Set (HEDIS) quality measures of management of three CVD comorbidities using laboratory testing (glycated hemoglobin [A1c], lipid profile) and blood pressure measurements; (2) medication adherence assessed pharmacy refill data using Proportion of Days Covered (PDC); and (3) patient-provider communication (Patient-Centered Communication in Cancer Care, PCC-Ca-36). Primary care clinics in the intervention arm will be considered non-responders if 90% or more of their participating patients do not meet the modified HEDIS quality metrics at the 6-month measurement, assessed once the first enrollee from each practice reaches the 12-month mark. Non-responders will be re-randomized to either continue to receive the iGuide 1 intervention, or to receive the iGuide 2 intervention, which includes tailored videos for participants and specialist consults with primary care providers. DISCUSSION: As the population of cancer survivors grows, ONE TEAM will contribute to closing the CVD outcomes gap among cancer survivors by optimizing and integrating cancer care and primary care teams. ONE TEAM is designed so that it will be possible for others to emulate and implement at scale. TRIAL REGISTRATION: This study (NCT04258813) was registered in clinicaltrals.gov on February 6, 2020.

Proteomics in Non-model Organisms: A New Analytical Frontier.

For the last century we have relied on model organisms to help understand fundamental biological processes. Now, with advancements in genome sequencing, assembly, and annotation, non-model organisms may be studied with the same advanced bioanalytical toolkit as model organisms. Proteomics is one such technique, which classically relies on predicted protein sequences to catalog and measure complex proteomes across tissues and biofluids. Applying proteomics to non-model organisms can advance and accelerate biomimicry studies, biomedical advancements, veterinary medicine, agricultural research, behavioral ecology, and food safety. In this postmodel organism era, we can study almost any species, meaning that many non-model organisms are, in fact, important emerging model organisms. Herein we specifically focus on eukaryotic organisms and discuss the steps to generate sequence databases, analyze proteomic data with or without a database, and interpret results as well as future research opportunities. Proteomics is more accessible than ever before and will continue to rapidly advance in the coming years, enabling critical research and discoveries in non-model organisms that were hitherto impossible.

Progress and Challenges in Ocean Metaproteomics and Proposed Best Practices for Data Sharing.

Ocean metaproteomics is an emerging field enabling discoveries about marine microbial communities and their impact on global biogeochemical processes. Recent ocean metaproteomic studies have provided insight into microbial nutrient transport, colimitation of carbon fixation, the metabolism of microbial biofilms, and dynamics of carbon flux in marine ecosystems. Future methodological developments could provide new capabilities such as characterizing long-term ecosystem changes, biogeochemical reaction rates, and in situ stoichiometries. Yet challenges remain for ocean metaproteomics due to the great biological diversity that produces highly complex mass spectra, as well as the difficulty in obtaining and working with environmental samples. This review summarizes the progress and challenges facing ocean metaproteomic scientists and proposes best practices for data sharing of ocean metaproteomic data sets, including the data types and metadata needed to enable intercomparisons of protein distributions and annotations that could foster global ocean metaproteomic capabilities.

Proteomic Analysis of Urine from California Sea Lions ( Zalophus californianus): A Resource for Urinary Biomarker Discovery.

Urinary markers for the assessment of kidney diseases in wild animals are limited, in part, due to the lack of urinary proteome data, especially for marine mammals. One of the most prevalent kidney diseases in marine mammals is caused by Leptospira interrogans, which is the second most common etiology linked to stranding of California sea lions ( Zalophus californianus). Urine proteins from 11 sea lions with leptospirosis kidney disease and eight sea lions without leptospirosis or kidney disease were analyzed using shotgun proteomics. In total, 2694 protein groups were identified, and 316 were differentially abundant between groups. Major urine proteins in sea lions were similar to major urine proteins in dogs and humans except for the preponderance of resistin, lysozyme C, and PDZ domain containing 1, which appear to be over-represented. Previously reported urine protein markers of kidney injury in humans and animals were also identified. Notably, neutrophil gelatinase-associated lipocalin, osteopontin, and epidermal fatty acid binding protein were elevated over 20-fold in the leptospirosis-infected sea lions. Consistent with leptospirosis infection in rodents, urinary proteins associated with the renin-angiotensin system were depressed, including neprilysin. This study represents a foundation from which to explore the clinical use of urinary protein markers in California sea lions.

Changes in Protein Expression and Lysine Acetylation Induced by Decreased Glutathione Levels in Astrocytes.

Astrocytes and neurons form a highly specialized functional unit, and the loss or gain of astrocytic functions can influence the initiation and progression of different neurodegenerative diseases. Neurons depend on the antioxidant protection provided by neighboring astrocytes. Glutathione (γ-l-glutamyl-l-cysteinyl-glycine) is a major component of the antioxidant system that defends cells against the toxic effects of reactive oxygen/nitrogen species. A decline in glutathione levels has been observed in aging and neurodegenerative diseases, and it aggravates the pathology in an amyotrophic lateral sclerosis-mouse model. Using a SILAC-based quantitative proteomic approach, we analyzed changes in global protein expression and lysine acetylation in primary astrocyte cultures obtained from wild-type mice or those deficient in the glutamate-cysteine ligase modifier subunit (GCLM). GCLM knockout astrocytes display an ∼80% reduction in total glutathione levels. We identified potential molecular targets and novel sites of acetylation that are affected by the chronic decrease in glutathione levels and observed a response mediated by Nrf2 activation. In addition, sequence analysis of peptides displaying increased acetylation in GCLM knockout astrocytes revealed an enrichment of cysteine residues in the vicinity of the acetylation site, which suggests potential crosstalk between lysine-acetylation and cysteine modification. Regulation of several metabolic and antioxidant pathways was observed at the level of protein expression and lysine acetylation, revealing a coordinated response involving transcriptional and posttranslational regulation.

Application of new cholesterol guidelines to a population-based sample.

BACKGROUND: The 2013 guidelines of the American College of Cardiology and the American Heart Association (ACC-AHA) for the treatment of cholesterol expand the indications for statin therapy for the prevention of cardiovascular disease. METHODS: Using data from the National Health and Nutrition Examination Surveys of 2005 to 2010, we estimated the number, and summarized the risk-factor profile, of persons for whom statin therapy would be recommended (i.e., eligible persons) under the new ACC-AHA guidelines, as compared with the guidelines of the Third Adult Treatment Panel (ATP III) of the National Cholesterol Education Program, and extrapolated the results to a population of 115.4 million U.S. adults between the ages of 40 and 75 years. RESULTS: As compared with the ATP-III guidelines, the new guidelines would increase the number of U.S. adults receiving or eligible for statin therapy from 43.2 million (37.5%) to 56.0 million (48.6%). Most of this increase in numbers (10.4 million of 12.8 million) would occur among adults without cardiovascular disease. Among adults between the ages of 60 and 75 years without cardiovascular disease who are not receiving statin therapy, the percentage who would be eligible for such therapy would increase from 30.4% to 87.4% among men and from 21.2% to 53.6% among women. This effect would be driven largely by an increased number of adults who would be classified solely on the basis of their 10-year risk of a cardiovascular event. Those who would be newly eligible for statin therapy include more men than women and persons with a higher blood pressure but a markedly lower level of low-density lipoprotein cholesterol. As compared with the ATP-III guidelines, the new guidelines would recommend statin therapy for more adults who would be expected to have future cardiovascular events (higher sensitivity) but would also include many adults who would not have future events (lower specificity). CONCLUSIONS: The new ACC-AHA guidelines for the management of cholesterol would increase the number of adults who would be eligible for statin therapy by 12.8 million, with the increase seen mostly among older adults without cardiovascular disease. (Funded by the Duke Clinical Research Institute and others.).

Hormonally up-regulated neu-associated kinase: A novel target for breast cancer progression.

Hormonally up-regulated neu-associated Kinase (Hunk) is a protein kinase that was originally identified in the murine mammary gland and has been shown to be highly expressed in Human Epidermal Growth Factor Receptor 2 positive (HER2+/ErbB2+) breast cancer cell lines as well as MMTV-neu derived mammary tumor cell lines. However, the physiological role of Hunk has been largely elusive since its identification. Though Hunk is predicted to be a Serine/Threonine (Ser/Thr) protein kinase with homology to the SNF1/AMPK family of protein kinases, there are no known Hunk substrates that have been identified to date. Recent work demonstrates a role for Hunk in HER2+/ErbB2+ breast cancer progression, including drug resistance to HER2/ErbB2 inhibitors, with Hunk potentially acting downstream of HER2/ErbB2 and the PI3K/Akt pathway. These studies have collectively shown that Hunk plays a vital role in promoting mammary tumorigenesis, as Hunk knockdown via shRNA in xenograft tumor models or crossing MMTV-neu or Pten-deficient genetically engineered mouse models into a Hunk knockout (Hunk-/-) background impairs mammary tumor growth in vivo. Because the majority of HER2+/ErbB2+ breast cancer patients acquire drug resistance to HER2/ErbB2 inhibitors, the characterization of novel drug targets like Hunk that have the potential to simultaneously suppress tumorigenesis and potentially enhance efficacy of current therapeutics is an important facet of drug development. Therefore, work aimed at uncovering specific regulatory functions for Hunk that could contribute to this protein kinase's role in both tumorigenesis and drug resistance will be informative. This review focuses on what is currently known about this under-studied protein kinase, and how targeting Hunk may prove to be a potential therapeutic target for the treatment of breast cancer.