Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.

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.

The Association of Biomolecular Resource Facilities Proteome Informatics Research Group Study on Metaproteomics (iPRG-2020)

Metaproteomics research using mass spectrometry data has emerged as a powerful strategy to understand the mechanisms underlying microbiome dynamics and the interaction of microbiomes with their immediate environment. Recent advances in sample preparation, data acquisition, and bioinformatics workflows have greatly contributed to progress in this field. In 2020, the Association of Biomolecular Research Facilities Proteome Informatics Research Group launched a collaborative study to assess the bioinformatics options available for metaproteomics research. The study was conducted in 2 phases. In the first phase, participants were provided with mass spectrometry data files and were asked to identify the taxonomic composition and relative taxa abundances in the samples without supplying any protein sequence databases. The most challenging question asked of the participants was to postulate the nature of any biological phenomena that may have taken place in the samples, such as interactions among taxonomic species. In the second phase, participants were provided a protein sequence database composed of the species present in the sample and were asked to answer the same set of questions as for phase 1. In this report, we summarize the data processing methods and tools used by participants, including database searching and software tools used for taxonomic and functional analysis. This study provides insights into the status of metaproteomics bioinformatics in participating laboratories and core facilities.

Proteomics as a Metrological Tool to Evaluate Genome Annotation Accuracy Following De Novo Genome Assembly: A Case Study Using the Atlantic Bottlenose Dolphin (Tursiops truncatus)

The last decade has witnessed dramatic improvements in whole-genome sequencing capabilities coupled to drastically decreased costs, leading to an inundation of high-quality de novo genomes. For this reason, the continued development of genome quality metrics is imperative. Using the 2016 Atlantic bottlenose dolphin NCBI RefSeq annotation and mass spectrometry-based proteomic analysis of six tissues, we confirmed 10,402 proteins from 4711 protein groups, constituting nearly one-third of the possible predicted proteins. Since the identification of larger proteins with more identified peptides implies reduced database fragmentation and improved gene annotation accuracy, we propose the metric NP10, which attempts to capture this quality improvement. The NP10 metric is calculated by first stratifying proteomic results by identifying the top decile (or 10th 10-quantile) of identified proteins based on the number of peptides per protein and then returns the median molecular weight of the resulting proteins. When using the 2016 versus 2012 Tursiops truncatus genome annotation to search this proteomic data set, there was a 21% improvement in NP10. This metric was further demonstrated by using a publicly available proteomic data set to compare human genome annotations from 2004, 2013 and 2016, which showed a 33% improvement in NP10. These results demonstrate that proteomics may be a useful metrological tool to benchmark genome accuracy, though there is a need for reference proteomic datasets across species to facilitate the evaluation of new de novo and existing genome.

2019 Association of Biomolecular Resource Facilities Multi-Laboratory Data-Independent Acquisition Proteomics Study

Despite the advantages of fewer missing values by collecting fragment ion data on all analytes in the sample as well as the potential for deeper coverage, the adoption of data-independent acquisition (DIA) in proteomics core facility settings has been slow. The Association of Biomolecular Resource Facilities conducted a large interlaboratory study to evaluate DIA performance in proteomics laboratories with various instrumentation. Participants were supplied with generic methods and a uniform set of test samples. The resulting 49 DIA datasets act as benchmarks and have utility in education and tool development. The sample set consisted of a tryptic HeLa digest spiked with high or low levels of 4 exogenous proteins. Data are available in MassIVE MSV000086479. Additionally, we demonstrate how the data can be analyzed by focusing on 2 datasets using different library approaches and show the utility of select summary statistics. These data can be used by DIA newcomers, software developers, or DIA experts evaluating performance with different platforms, acquisition settings, and skill levels.

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.

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.

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.

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.

The science of the host-virus network

Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.

Hi-C scaffolded short- and long-read genome assemblies of the California sea lion are broadly consistent for syntenic inference across 45 million years of evolution

With the advent of chromatin-interaction maps, chromosome-level genome assemblies have become a reality for a wide range of organisms. Scaffolding quality is, however, difficult to judge. To explore this gap, we generated multiple chromosome-scale genome assemblies of an emerging wild animal model for carcinogenesis, the California sea lion (Zalophus californianus). Short-read assemblies were scaffolded with two independent chromatin interaction mapping data sets (Hi-C and Chicago), and long-read assemblies with three data types (Hi-C, optical maps and 10X linked reads) following the "Vertebrate Genomes Project (VGP)" pipeline. In both approaches, 18 major scaffolds recovered the karyotype (2n = 36), with scaffold N50s of 138 and 147 Mb, respectively. Synteny relationships at the chromosome level with other pinniped genomes (2n = 32-36), ferret (2n = 34), red panda (2n = 36) and domestic dog (2n = 78) were consistent across approaches and recovered known fissions and fusions. Comparative chromosome painting and multicolour chromosome tiling with a panel of 264 genome-integrated single-locus canine bacterial artificial chromosome probes provided independent evaluation of genome organization. Broad-scale discrepancies between the approaches were observed within chromosomes, most commonly in translocations centred around centromeres and telomeres, which were better resolved in the VGP assembly. Genomic and cytological approaches agreed on near-perfect synteny of the X chromosome, and in combination allowed detailed investigation of autosomal rearrangements between dog and sea lion. This study presents high-quality genomes of an emerging cancer model and highlights that even highly fragmented short-read assemblies scaffolded with Hi-C can yield reliable chromosome-level scaffolds suitable for comparative genomic analyses.

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.

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 (T_b). Torpor is typically characterized by coordinated declines in T_b 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 T_b 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 T_b (∼32°C) or lower T_b (∼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.

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.

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.

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.

C3a receptor antagonism as a novel therapeutic target for chronic rhinosinusitis

Chronic rhinosinusitis with nasal polyps (CRSwNP) is an inflammatory disease with an unknown etiology. Recent studies have implicated the complement system as a potential modulator of disease immunopathology. We performed proteomic pathway enrichment analysis of differentially increased proteins, and found an enrichment of complement cascade pathways in the nasal mucus of individuals with CRSwNP as compared to control subjects. Sinonasal mucus levels of complement 3 (C3) correlated with worse subjective disease severity, whereas no significant difference in systemic C3 levels could be determined in plasma samples. Given that human sinonasal epithelial cells were the predominate sinonasal source of C3 and complement anaphylatoxin 3a (C3a) staining, we focused on their role in in vitro studies. Baseline intracellular C3 levels were higher in CRSwNP cells, and following exposure to Aspergillus fumigatus (Af) extract, they released significantly more C3 and C3a. Inhibition of complement 3a receptor (C3aR) signaling led to a decrease in Af-induced C3 and C3a release, both in vitro and in vivo. Finally, we found in vivo that C3aR deficiency or inhibition significantly reduced inflammation and CRS development in a mouse model of Af-induced CRS. These findings demonstrate that local sinonasal complement activation correlates with subjective disease severity, and that local C3aR antagonism significantly ameliorates Af-induced CRS in a rodent model.

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.

Proteomic Profiling of Serial Prediagnostic Serum Samples for Early Detection of Colon Cancer in the U.S. Military

Background: Serum proteomic biomarkers offer a promising approach for early detection of cancer. In this study, we aimed to identify proteomic profiles that could distinguish colon cancer cases from controls using serial prediagnostic serum samples.Methods: This was a nested case-control study of active duty military members. Cases consisted of 264 patients diagnosed with colon cancer between 2001 and 2009. Controls were matched to cases on age, gender, race, serum sample count, and collection date. We identified peaks that discriminated cases from controls using random forest data analysis with a 2/3 training and 1/3 validation dataset. We then included epidemiologic data to see whether further improvement of model performance was obtainable. Proteins that corresponded to discriminatory peaks were identified.Results: Peaks with m/z values of 3,119.32, 2,886.67, 2,939.23, and 5,078.81 were found to discriminate cases from controls with a sensitivity of 69% and a specificity of 67% in the year before diagnosis. When smoking status was included, sensitivity increased to 76% while histories of other cancer and tonsillectomy raised specificity to 76%. Peaks at 2,886.67 and 3,119.32 m/z were identified as histone acetyltransferases while 2,939.24 m/z was a transporting ATPase subunit.Conclusions: Proteomic profiles in the year before cancer diagnosis have the potential to discriminate colon cancer patients from controls, and the addition of epidemiologic information may increase the sensitivity and specificity of discrimination.Impact: Our findings indicate the potential value of using serum prediagnostic proteomic biomarkers in combination with epidemiologic data for early detection of colon cancer. Cancer Epidemiol Biomarkers Prev; 26(5); 711-8. ©2016 AACR.

Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma

Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of VHL that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC. Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant. These 344 differentially abundant proteins were enriched in metabolic pathways and further examination revealed metabolic dysfunction consistent with the Warburg effect. Additionally, the protein data indicated activation of ESRRA and ESRRG, and HIF1A, as well as inhibition of FOXA1, MAPK1 and WISP2. A subset analysis of complementary gene expression array data on 47 pairs of these same tissues indicated similar upstream changes, such as increased HIF1A activation with stage, though ESRRA and ESRRG activation and FOXA1 inhibition were not predicted from the transcriptomic data. The activation of ESRRA and ESRRG implied that HIF2A may also be activated during later stages of ccRCC, which was confirmed in the transcriptional analysis. This combined analysis highlights the importance of HIF1A and HIF2A in developing the ccRCC molecular phenotype as well as the potential involvement of ESRRA and ESRRG in driving these changes. In addition, cofilin-1, profilin-1, nicotinamide N-methyltransferase, and fructose-bisphosphate aldolase A were identified as candidate markers of late stage ccRCC. Utilization of data collected from heterogeneous biological domains strengthened the findings from each domain, demonstrating the complementary nature of such an analysis. Together these results highlight the importance of the VHL/HIF1A/HIF2A axis and provide a foundation and therapeutic targets for future studies. (Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.)

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.

Abstract 540: A MALDI imaging mass spectrometry approach using tissue microarrays to identify an N-glycan biomarker panel for pancreatic cancers

We have recently developed a MALDI imaging mass spectrometry (MALDI-IMS) method to spatially profile N-linked glycans in frozen and formalin-fixed paraffin-embedded (FFPE) tissue sections and tissue microarrays (TMAs). Tissues are incubated with peptide N-glycosidase, and released N-glycans are detected directly using MALDI-FTICR, linked directly with tissue histopathology. Other methods to detect the localization of glycans in tissues rely on detection of broader glycan structural motifs (i.e., lectins or carbohydrate antigen antibodies), whereas our method is able to simultaneously identify and distinguish 40 or more components of the N-glycome on a single slide. To demonstrate the ability of MALDI-IMS to generate biomarker panels, pancreatic cancer tissue blocks and six TMAs containing matched tumor and non-tumor regions from over 70 patients were profiled. Aberrant glycosylation, such as elevated CA-19-9, is well documented in pancreatic cancer, making this an ideal sample set. To best analyze this complex data set, we developed an in-house analysis script that can extract spectra from tissue cores, and return non-biased statistics for observed glycan ions. Panels were generated using a training set of data and tested on external validation data set. The most accurate panel of 12 glycans achieved an overall sensitivity of 92.9% and specificity of 86.7%. Structural identification of N-glycans has been confirmed by techniques such as on-tissue CID, ethylation analysis, sequential glycosidase digestions, and comparison to glycan structural databases. Furthermore, imaging of entire FFPE sections matched with histological analysis revealed a vast diversity of N-glycan localizations, which could distinguish not only tumor from normal tissue, but also regions of pancreatitis and pancreatic intraepithelial neoplasia. The generated glycan tissue maps will also be used to determine specific glycoprotein carriers of biomarker candidate glycans in tissue and biofluid samples.

Citation Format: Richard R. Drake, Thomas W. Powers, Benjamin A. Neely. A MALDI imaging mass spectrometry approach using tissue microarrays to identify an N-glycan biomarker panel for pancreatic cancers. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 540. doi:10.1158/1538-7445.AM2015-540

Proteomic Analysis of Plasma from California Sea Lions (Zalophus californianus) Reveals Apolipoprotein E as a Candidate Biomarker of Chronic Domoic Acid Toxicosis

Domoic acid toxicosis (DAT) in California sea lions (Zalophus californianus) is caused by exposure to the marine biotoxin domoic acid and has been linked to massive stranding events and mortality. Diagnosis is based on clinical signs in addition to the presence of domoic acid in body fluids. Chronic DAT further is characterized by reoccurring seizures progressing to status epilepticus. Diagnosis of chronic DAT is often slow and problematic, and minimally invasive tests for DAT have been the focus of numerous recent biomarker studies. The goal of this study was to retrospectively profile plasma proteins in a population of sea lions with chronic DAT and those without DAT using two dimensional gel electrophoresis to discover whether individual, multiple, or combinations of protein and clinical data could be utilized to identify sea lions with DAT. Using a training set of 32 sea lion sera, 20 proteins and their isoforms were identified that were significantly different between the two groups (p<0.05). Interestingly, 11 apolipoprotein E (ApoE) charge forms were decreased in DAT samples, indicating that ApoE charge form distributions may be important in the progression of DAT. In order to develop a classifier of chronic DAT, an independent blinded test set of 20 sea lions, seven with chronic DAT, was used to validate models utilizing ApoE charge forms and eosinophil counts. The resulting support vector machine had high sensitivity (85.7% with 92.3% negative predictive value) and high specificity (92.3% with 85.7% positive predictive value). These results suggest that ApoE and eosinophil counts along with machine learning can perform as a robust and accurate tool to diagnose chronic DAT. Although this analysis is specifically focused on blood biomarkers and routine clinical data, the results demonstrate promise for future studies combining additional variables in multidimensional space to create robust classifiers.

MALDI imaging mass spectrometry profiling of proteins and lipids in clear cell renal cell carcinoma

Reducing the incidence and mortality rates for clear cell renal cell carcinoma (ccRCC) remains a significant clinical challenge with poor 5-year survival rates. A unique tissue cohort was assembled of matched ccRCC and distal nontumor tissues (n = 20) associated with moderate risk of disease progression, half of these from individuals who progressed to metastatic disease and the other half who remained disease free. These tissues were used for MALDI imaging MS profiling of proteins in the 2-20 kDa range, resulting in a panel of 108 proteins that had potential disease-specific expression patterns. Protein lysates from the same tissues were analyzed by MS/MS, resulting in identification of 56 proteins of less than 20 kDa molecular weight. The same tissues were also used for global lipid profiling analysis by MALDI-FT-ICR MS. From the cumulative protein and lipid expression profile data, a refined panel of 26 proteins and 39 lipid species was identified that could either distinguish tumor from nontumor tissues, or tissues from recurrent disease progressors from nonrecurrent disease individuals. This approach has the potential to not only improve prognostic assessment and enhance postoperative surveillance, but also to inform on the underlying biology of ccRCC progression.

Targeted glycoprotein enrichment and identification in stromal cell secretomes using azido sugar metabolic labeling

PURPOSE

Effectively identifying the proteins present in the cellular secretome is complicated due to the presence of cellular protein leakage and serum protein supplements in culture media. A metabolic labeling and click chemistry capture method is described that facilitates the detection of lower abundance glycoproteins in the secretome, even in the presence of serum.

EXPERIMENTAL DESIGN

Two stromal cell lines were incubated with tetraacetylated sugar-azide analogs for 48 h in serum-free and low-serum conditions. Sugar-azide labeled glycoproteins were covalently linked to alkyne-beads, followed by on-bead trypsin digestion and MS/MS. The resulting glycoproteins were compared between media conditions, cell lines, and azide-sugar labels.

RESULTS

Alkyne-bead capture of sugar-azide modified glycoproteins in stromal cell culture media significantly improved the detection of lower abundance secreted glycoproteins compared to standard serum-free secretome preparations. Over 100 secreted glycoproteins were detected in each stromal cell line and significantly enriched relative to a standard secretome preparation.

CONCLUSION AND CLINICAL RELEVANCE

Sugar-azide metabolic labeling is an effective way to enrich for secreted glycoproteins present in cell line secretomes, even in culture media supplemented with serum. The method has utility for identifying secreted stromal proteins associated with cancer progression and the epithelial-to-mesenchymal transition.

Association of elevated urinary concentration of renin-angiotensin system components and severe AKI

BACKGROUND

Prognostic biomarkers that predict the severity of AKI at an early time point are needed. Urinary angiotensinogen was recently identified as a prognostic AKI biomarker. The study hypothesis is that urinary renin could also predict AKI severity and that in combination angiotensinogen and renin would be a strong predictor of prognosis at the time of AKI diagnosis.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

In this multicenter, retrospective cohort study, urine was obtained from 204 patients who developed AKI after cardiac surgery from August 2008 to June 1, 2012. All patients were classified as having Acute Kidney Injury Network (AKIN) stage 1 disease by serum creatinine criteria at the time of sample collection. Urine output was not used for staging. Urinary angiotensinogen and renin were measured, and the area under the receiver-operating characteristic curve (AUC) was used to test for prediction of progression to AKIN stage 3 or in-hospital 30-day mortality. These biomarkers were added stepwise to a clinical model, and improvement in prognostic predictive performance was evaluated by category free net reclassification improvement (cfNRI) and chi-squared automatic interaction detection (CHAID).

RESULTS

Both the urinary angiotensinogen-to-creatinine ratio (uAnCR; AUC, 0.75; 95% confidence interval [CI], 0.65 to 0.85) and the urinary renin-to-creatinine ratio (uRenCR; AUC, 0.70; 95% CI, 0.57 to 0.83) predicted AKIN stage 3 or death. Addition of uAnCR to a clinical model substantially improved prediction of the outcome (AUC, 0.85; cfNRI, 0.673), augmenting sensitivity and specificity. Further addition of uRenCR increased the sensitivity of the model (cfNRI(events), 0.44). CHAID produced a highly accurate model (AUC, 0.91) and identified the combination of uAnCR >337.89 ng/mg and uRenCR >893.41 pg/mg as the strongest predictors (positive predictive value, 80.4%; negative predictive value, 90.7%; accuracy, 90.2%).

CONCLUSION

The combination of urinary angiotensinogen and renin predicts progression to very severe disease in patients with early AKI after cardiac surgery.

Urinary angiotensinogen predicts adverse outcomes among acute kidney injury patients in the intensive care unit

INTRODUCTION

Acute kidney injury (AKI) is commonly observed in the intensive care unit (ICU), where it can be caused by a variety of factors. The objective of this study was to evaluate the prognostic value of urinary angiotensinogen, a candidate prognostic AKI biomarker identified in post-cardiac surgery patients, in this heterogeneous population.

METHODS

Urinary angiotensinogen was measured by ELISA and corrected for urine creatinine in 45 patients who developed AKI in the ICU. Patients were grouped by AKI etiology, and the angiotensinogen-to-creatinine ratio (uAnCR) was compared among the groups using the Kruskal-Wallis test. The ability of uAnCR to predict the following endpoints was tested using the area under the ROC curve (AUC): the need for renal replacement therapy (RRT) or death, increased length of stay (defined as hospital discharge>7 days or death≤7 days from sample collection), and worsening AKI (defined as an increase in serum creatinine>0.3 mg/dL after sample collection or RRT).

RESULTS

uAnCR was significantly elevated in patients who met the composite outcome RRT or death (89.4 vs 25.4 ng/mg; P=0.01), and it was a strong predictor of this outcome (AUC=0.73). Patients with uAnCR values above the median for the cohort (55.21 ng/mg) had increased length of stay compared to patients with uAnCR≤55.21 ng/mg (22 days vs 7 days after sample collection; P=0.01). uAnCR was predictive of the outcome increased length of stay (AUC=0.77). uAnCR was also a strong predictor of worsening of AKI (AUC=0.77). The uAnCR of patients with pre-renal AKI was lower compared to patients with AKI of other causes (median uAnCR 11.3 vs 80.2 ng/mg; P=0.02).

CONCLUSIONS

Elevated urinary angiotensinogen is associated with adverse events in AKI patients in the ICU. It could be used to identify high risk patients who would benefit from timely intervention that could improve their outcomes.

Blood-Based Indicators of Insulin Resistance and Metabolic Syndrome in Bottlenose Dolphins (Tursiops truncatus)

Similar to people with metabolic syndrome, bottlenose dolphins (Tursiops truncatus) can have a sustained postprandial hyperglycemia and hyperinsulinemia, dyslipidemia, and fatty liver disease. A panel of potential postprandial blood-based indicators of insulin resistance and metabolic syndrome were compared among 34 managed collection dolphins in San Diego Bay, CA, USA (Group A) and 16 wild, free-ranging dolphins in Sarasota Bay, FL, USA (Group B). Compared to Group B, Group A had higher insulin (2.1 ± 2.5 and 13 ± 13 μIU/ml), glucose (87 ± 19 and 108 ± 12 mg/dl), and triglycerides (75 ± 28 and 128 ± 45 mg/dl) as well as higher cholesterol (total, high-density lipoprotein cholesterol, and very low density lipoprotein cholesterol), iron, transferrin saturation, gamma-glutamyl transpeptidase (GGT), alanine transaminase, and uric acid. Group A had higher percent unmodified adiponectin. While Group A dolphins were older, the same blood-based differences remained when controlling for age. There were no differences in body mass index (BMI) between the groups, and comparisons between Group B and Group A dolphins have consistently demonstrated lower stress hormones levels in Group A. Group A dolphins with high insulin (greater than 14 μIU/ml) had higher glucose, iron, GGT, and BMI compared to Group A dolphins with lower insulin. These findings support that some dolphin groups may be more susceptible to insulin resistance compared to others, and primary risk factors are not likely age, BMI, or stress. Lower high-molecular weight adiponectin has been identified as an independent risk factor for type 2 diabetes in humans and may be a target for preventing insulin resistance in dolphins. Future investigations with these two dolphin populations, including dietary and feeding differences, may provide valuable insight for preventing and treating insulin resistance in humans.

Ratiometric Measurements of Adiponectin by Mass Spectrometry in Bottlenose Dolphins (Tursiops truncatus) with Iron Overload Reveal an Association with Insulin Resistance and Glucagon

High molecular weight (HMW) adiponectin levels are reduced in humans with type 2 diabetes and insulin resistance. Similar to humans with insulin resistance, managed bottlenose dolphins (Tursiops truncatus) diagnosed with hemochromatosis (iron overload) have higher levels of 2 h post-prandial plasma insulin than healthy controls. A parallel reaction monitoring assay for dolphin serum adiponectin was developed based on tryptic peptides identified by mass spectrometry. Using identified post-translational modifications, a differential measurement was constructed. Total and unmodified adiponectin levels were measured in sera from dolphins with (n = 4) and without (n = 5) iron overload. This measurement yielded total adiponectin levels as well as site specific percent unmodified adiponectin that may inversely correlate with HMW adiponectin. Differences in insulin levels between iron overload cases and controls were observed 2 h post-prandial, but not during the fasting state. Thus, post-prandial as well as fasting serum adiponectin levels were measured to determine whether adiponectin and insulin would follow similar patterns. There was no difference in total adiponectin or percent unmodified adiponectin from case or control fasting animals. There was no difference in post-prandial total adiponectin levels between case and control dolphins (mean ± SD) at 763 ± 298 and 727 ± 291 pmol/ml, respectively (p = 0.91); however, percent unmodified adiponectin was significantly higher in post-prandial cases compared to controls (30.0 ± 6.3 versus 17.0 ± 6.6%, respectively; p = 0.016). Interestingly, both total and percent unmodified adiponectin were correlated with glucagon levels in controls (r = 0.999, p  < 0.001), but not in cases, which is possibly a reflection of insulin resistance. Although total adiponectin levels were not significantly different, the elevated percent unmodified adiponectin follows a trend similar to HMW adiponectin reported for humans with metabolic disorders.

Urinary angiotensinogen and risk of severe AKI

BACKGROUND

Biomarkers of AKI that can predict which patients will develop severe renal disease at the time of diagnosis will facilitate timely intervention in populations at risk of adverse outcomes.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Liquid chromatography/tandem mass spectrometry was used to identify 30 potential prognostic urinary biomarkers of severe AKI in a group of patients that developed AKI after cardiac surgery. Angiotensinogen had the best discriminative characteristics. Urinary angiotensinogen was subsequently measured by ELISA and its prognostic predictive power was verified in 97 patients who underwent cardiac surgery between August 1, 2008 and October 6, 2011.

RESULTS

The urine angiotensinogen/creatinine ratio (uAnCR) predicted worsening of AKI, Acute Kidney Injury Network (AKIN) stage 3, need for renal replacement therapy, discharge >7 days from sample collection, and composite outcomes of AKIN stage 2 or 3, AKIN stage 3 or death, and renal replacement therapy or death. The prognostic predictive power of uAnCR was improved when only patients classified as AKIN stage 1 at the time of urine sample collection (n=79) were used in the analysis, among whom it predicted development of stage 3 AKI or death with an area under the curve of 0.81. Finally, category free net reclassification improvement showed that the addition of uAnCR to a clinical model to predict worsening of AKI improved the predictive power.

CONCLUSIONS

Elevated uAnCR is associated with adverse outcomes in patients with AKI. These data are the first to demonstrate the utility of angiotensinogen as a prognostic biomarker of AKI after cardiac surgery.

Diabetes-induced renal injury in rats is attenuated by suramin

Progression of hyperglycemia-induced renal injury is a contributing factor for diabetic nephropathy (DN)-induced end-stage renal disease (ESRD), and development of novel therapeutic strategies that act early to prevent progression of DN and ESRD are important. We examined the efficacy and mechanism(s) of suramin on hyperglycemia-induced renal injury before development of overt histological damage. Two groups of male Sprague-Dawley rats received streptozotocin (STZ) and one group received saline. Three weeks later, one STZ group received suramin (10 mg/kg). All animals were euthanized 1 week later (4 weeks). Although there was a decrease in creatinine clearance between control and STZ ± suramin rats, there was no difference in creatinine clearance between STZ rats ± suramin intervention. Liquid chromatography-tandem mass spectroscopy-based analysis revealed increases in urinary proteins that are early indicators of DN (e.g., cystatin C, clusterin, cathepsin B, retinol binding protein 4, and peroxiredoxin-1) in the STZ group, which were blocked by suramin. Endothelial intracellular adhesion molecule-1 (ICAM-1) activation, leukocyte infiltration, and inflammation; transforming growth factor-β1 (TGF-β1) signaling; TGF-β1/SMAD-3-activated fibrogenic markers fibronectin-1, α-smooth muscle actin, and collagen 1A2; activation of proinflammatory and profibrotic transcription factors nuclear factor-κB (NF-κB) and signal transducer and activator of transcription factor-3 (STAT-3), respectively, were all increased in STZ rats and suramin blocked these changes. In conclusion, delayed administration of suramin attenuated 1) urinary markers of DN, 2) inflammation by blocking NF-κB activation and ICAM-1-mediated leukocyte infiltration, and 3) fibrosis by blocking STAT-3 and TGF-β1/SMAD-3 signaling. These results support the potential use of suramin in DN.

Serum profiling by MALDI-TOF mass spectrometry as a diagnostic tool for domoic acid toxicosis in California sea lions

BACKGROUND

There are currently no reliable markers of acute domoic acid toxicosis (DAT) for California sea lions. We investigated whether patterns of serum peptides could diagnose acute DAT. Serum peptides were analyzed by MALDI-TOF mass spectrometry from 107 sea lions (acute DAT n = 34; non-DAT n = 73). Artificial neural networks (ANN) were trained using MALDI-TOF data. Individual peaks and neural networks were qualified using an independent test set (n = 20).

RESULTS

No single peak was a good classifier of acute DAT, and ANN models were the best predictors of acute DAT. Performance measures for a single median ANN were: sensitivity, 100%; specificity, 60%; positive predictive value, 71%; negative predictive value, 100%. When 101 ANNs were combined and allowed to vote for the outcome, the performance measures were: sensitivity, 30%; specificity, 100%; positive predictive value, 100%; negative predictive value, 59%.

CONCLUSIONS

These results suggest that MALDI-TOF peptide profiling and neural networks can perform either as a highly sensitive (100% negative predictive value) or a highly specific (100% positive predictive value) diagnostic tool for acute DAT. This also suggests that machine learning directed by populations of predictive models offer the ability to modulate the predictive effort into a specific type of error.

Changes in protein expression in Burkholderia vietnamiensis PR1 301 at pH 5 and 7 with and without nickel

Burkholderia vietnamiensis PR1(301) (PR1) exhibits pH-dependent nickel (Ni) tolerance, with lower Ni toxicity observed at pH 5 than at pH 7. The Ni tolerance mechanism in PR1 is currently unknown, and traditional mechanisms of Ni resistance do not appear to be present. Therefore, 2D gel electrophoresis was used to examine changes in protein expression in PR1 with and without Ni (3.4 mM) at pH 5 and 7. Proteins with both a statistically significant and at least a twofold difference in expression level between conditions (pH, Ni) were selected and identified using MALDI-TOF-MS or LC-MS. Results showed increased expression of proteins involved in cell shape and membrane composition at pH 5 compared with pH 7. Scanning electron microscopy indicated elongated cells at pH 5 and 6 compared with pH 7 in the absence of Ni. Fatty acid methyl ester analysis showed a statistically significant difference in the percentages of long- and short-chain fatty acids at pH 5 and 7. These findings suggest that changes in membrane structure and function may be involved in the ability of PR1 to grow at higher concentrations of Ni at pH 5 than at pH 7.

The 1.92-A structure of Streptomyces coelicolor A3(2) CYP154C1. A new monooxygenase that functionalizes macrolide ring systems

Evolutionary links between cytochrome P450 monooxygenases, a superfamily of extraordinarily divergent heme-thiolate proteins catalyzing a wide array of NADPH/NADH- and O(2)-dependent reactions, are becoming better understood because of availability of an increasing number of fully sequenced genomes. Among other reactions, P450s catalyze the site-specific oxidation of the precursors to macrolide antibiotics in the genus Streptomyces introducing regiochemical diversity into the macrolide ring system, thereby significantly increasing antibiotic activity. Developing effective uses for Streptomyces enzymes in biosynthetic processes and bioremediation requires identification and engineering of additional monooxygenases with activities toward a diverse array of small molecules. To elucidate the molecular basis for substrate specificity of oxidative enzymes toward macrolide antibiotics, the x-ray structure of CYP154C1 from Streptomyces coelicolor A3(2) was determined (Protein Data Bank code ). Relocation of certain common P450 secondary structure elements, along with a novel structural feature involving an additional beta-strand transforming the five-stranded beta-sheet into a six-stranded variant, creates an open cleft-shaped substrate-binding site between the two P450 domains. High sequence similarity to macrolide monooxygenases from other microbial species translates into catalytic activity of CYP154C1 toward both 12- and 14-membered ring macrolactones in vitro.