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.

Differentiation between Descending Thoracic Aortic Diseases using Machine Learning and Plasma Proteomic Signatures

Background

Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection.

Methods

This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using 5-fold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis.

Results

Of the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p-value <0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important correlated groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation.

Conclusions

We conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

Decoding Angiotensin Receptors: TOMAHAQ-Based Detection and Quantification of Angiotensin Type-1 and Type-2 Receptors

Background The renin-angiotensin system plays a crucial role in human physiology, and its main hormone, angiotensin, activates 2 G-protein-coupled receptors, the angiotensin type-1 and type-2 receptors, in almost every organ. However, controversy exists about the location, distribution, and expression levels of these receptors. Concerns have been raised over the low sensitivity, low specificity, and large variability between lots of commercially available antibodies for angiotensin type-1 and type-2 receptors, which makes it difficult to reconciliate results of different studies. Here, we describe the first non-antibody-based sensitive and specific targeted quantitative mass spectrometry assay for angiotensin receptors. Methods and Results Using a technique that allows targeted analysis of multiple peptides across multiple samples in a single mass spectrometry analysis, known as TOMAHAQ (triggered by offset, multiplexed, accurate mass, high resolution, and absolute quantification), we have identified and validated specific human tryptic peptides that permit identification and quantification of angiotensin type-1 and type-2 receptors in biological samples. Several peptide sequences are conserved in rodents, making these mass spectrometry assays amenable to both preclinical and clinical studies. We have used this method to quantify angiotensin type-1 and type-2 receptors in postmortem frontal cortex samples of older adults (n=28) with Alzheimer dementia. We correlated levels of angiotensin receptors to biomarkers classically linked to renin-angiotensin system activation, including oxidative stress, inflammation, amyloid-β load, and paired helical filament-tau tangle burden. Conclusions These robust high-throughput assays will not only catalyze novel mechanistic studies in the angiotensin research field but may also help to identify patients with an unbalanced angiotensin receptor distribution who would benefit from angiotensin receptor blocker treatment.

A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious, requiring 2 h of mass spectrometry time per single muscle fiber; 50 fibers would take approximately 4 days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 min total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 h. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Ninety-four proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, oxidative phosphorylation, and muscle structure and contractile function. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

Protein Coronas on Functionalized Nanoparticles Enable Quantitative and Precise Large-Scale Deep Plasma Proteomics

Background

The wide dynamic range of circulating proteins coupled with the diversity of proteoforms present in plasma has historically impeded comprehensive and quantitative characterization of the plasma proteome at scale. Automated nanoparticle (NP) protein corona-based proteomics workflows can efficiently compress the dynamic range of protein abundances into a mass spectrometry (MS)-accessible detection range. This enhances the depth and scalability of quantitative MS-based methods, which can elucidate the molecular mechanisms of biological processes, discover new protein biomarkers, and improve comprehensiveness of MS-based diagnostics.

Methods

Investigating multi-species spike-in experiments and a cohort, we investigated fold-change accuracy, linearity, precision, and statistical power for the using the Proteograph™ Product Suite, a deep plasma proteomics workflow, in conjunction with multiple MS instruments.

Results

We show that NP-based workflows enable accurate identification (false discovery rate of 1%) of more than 6,000 proteins from plasma (Orbitrap Astral) and, compared to a gold standard neat plasma workflow that is limited to the detection of hundreds of plasma proteins, facilitate quantification of more proteins with accurate fold-changes, high linearity, and precision. Furthermore, we demonstrate high statistical power for the discovery of biomarkers in small- and large-scale cohorts.

Conclusions

The automated NP workflow enables high-throughput, deep, and quantitative plasma proteomics investigation with sufficient power to discover new biomarker signatures with a peptide level resolution.

Proteomics of novel iPSC-derived vascular endothelial cells reveal extensive similarity with an immortalized human endothelial cell line

The vascular endothelium constitutes the inner lining of the blood vessel, and malfunction and injuries of the endothelium can cause cardiovascular diseases as well as others including stroke, tumor growth, and chronic kidney failure. Generation of effective sources to replace injured endothelial cells (ECs) could have significant clinical impact and somatic cell sources like peripheral or cord blood cannot credibly supply enough endothelial cell progenitors for multitude of treatments. Pluripotent stem cells are a promising source for a reliable EC supply that have the potential to restore tissue function and treat vascular diseases. We have developed methods to differentiate induced pluripotent stem cells (iPSCs) efficiently and robustly across multiple iPSC lines into non-tissue-specific pan vascular ECs (iECs) with high purity. These iECs present with canonical endothelial cell markers and exhibit measures of endothelial cell functionality with uptake of acetylated low-density lipoprotein (Dil-Ac-LDL) and tube formation. Using proteomic analysis, we revealed the iECs are more proteomically similar to established umbilical vein ECs (HUVECs) than to iPSCs. Post-translational modifications (PTMs) were most shared between HUVECs and iECs, and potential targets for increasing the proteomic similarity of iECs to HUVECs were identified. Here we demonstrate an efficient robust method to differentiate iPSCs into functional ECs, and for the first time provide a comprehensive protein expression profile of iECs, which indicates their similarities with a widely used immortalized HUVECs, allowing for further mechanistic studies of EC development, signaling and metabolism for future regenerative applications.

High-Throughput Single-Cell Proteomic Analysis of Organ-Derived Heterogeneous Cell Populations by Nanoflow Dual-Trap Single-Column Liquid Chromatography

Identification and proteomic characterization of rare cell types within complex organ-derived cell mixtures is best accomplished by label-free quantitative mass spectrometry. High throughput is required to rapidly survey hundreds to thousands of individual cells to adequately represent rare populations. Here we present parallelized nanoflow dual-trap single-column liquid chromatography (nanoDTSC) operating at 15 min of total run time per cell with peptides quantified over 11.5 min using standard commercial components, thus offering an accessible and efficient LC solution to analyze 96 single cells per day. At this throughput, nanoDTSC quantified over 1000 proteins in individual cardiomyocytes and heterogeneous populations of single cells from the aorta.

High-Field Asymmetric Waveform Ion Mobility Spectrometry: Practical Alternative for Cardiac Proteome Sample Processing

Heart tissue sample preparation for mass spectrometry (MS) analysis that includes prefractionation reduces the cellular protein dynamic range and increases the relative abundance of nonsarcomeric proteins. We previously described "IN-Sequence" (IN-Seq) where heart tissue lysate is sequentially partitioned into three subcellular fractions to increase the proteome coverage more than a single direct tissue analysis by mass spectrometry. Here, we report an adaptation of the high-field asymmetric ion mobility spectrometry (FAIMS) coupled to mass spectrometry, and the establishment of a simple one step sample preparation coupled with gas-phase fractionation. The FAIMS approach substantially reduces manual sample handling, significantly shortens the MS instrument processing time, and produces unique protein identification and quantification approximating the commonly used IN-Seq method in less time.

Dupilumab inhibits vascular leakage of blood proteins into atopic dermatitis skin

BACKGROUND

Atopic dermatitis (AD) skin lesions are associated with oozing, bleeding, and erythema. This suggests AD is associated with vascular changes. Dupilumab is an antibody to the alpha-subunit of interleukin (IL) 4 receptor that demonstrates strong efficacy in the treatment of AD. IL-4 is known to reduce the permeability barrier function of vascular endothelium.

OBJECTIVE

To examine the effects of dupilumab on vascular barrier function in AD skin.

METHODS

Using proteomic analysis, we evaluated the plasma protein composition in skin tapes of lesional and non-lesional skin of adults and adolescents with moderate-to-severe AD over the course of a 16-week treatment with dupilumab and compared those to matched healthy subjects.

RESULTS

At baseline, 115 plasma proteins were detected in AD skin and globally increased (1.5-fold or greater) compared to healthy skin. Functionally, these proteins included immunoglobulins, proteins involved in the coagulation process, enzymes, protease inhibitors, transport proteins, acute-phase proteins, complement proteins, and other pleiotropic proteins. Noteworthy, fibrinogens, fibronectin, and heme-binding proteins haptoglobin and hemopexin were among the top proteins originating from plasma and were increased in AD lesional versus healthy skin at baseline (p<0.0001). Dupilumab treatment resulted in significantly reduced levels of plasma proteins in AD skin (p<0.0001), with the majority dropping to levels seen in healthy skin or no longer detectable at week 16.

CONCLUSIONS

Inhibition of IL-4/IL-13 action by dupilumab significantly reduces the efflux of plasma proteins into AD skin. Several of these proteins, such as fibrinogens and fibronectin, are known to enhance Staphylococcus aureuscolonization and are associated with AD skin severity.

Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious requiring two hours of mass spectrometry time per single muscle fiber; 50 fibers would take approximately four days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 minutes total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 hours. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Sixty-five proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, muscle structure and regulation. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

Hyperphosphorylation of hepatic proteome characterizes nonalcoholic fatty liver disease in S-adenosylmethionine deficiency

Methionine adenosyltransferase 1a (MAT1A) is responsible for hepatic S-adenosyl-L-methionine (SAMe) biosynthesis. Mat1a ^-/- mice have hepatic SAMe depletion, develop nonalcoholic steatohepatitis (NASH) which is reversed with SAMe administration. We examined temporal alterations in the proteome/phosphoproteome in pre-disease and NASH Mat1a ^-/- mice, effects of SAMe administration, and compared to human nonalcoholic fatty liver disease (NAFLD). Mitochondrial and peroxisomal lipid metabolism proteins were altered in pre-disease mice and persisted in NASH Mat1a ^-/- mice, which exhibited more progressive alterations in cytoplasmic ribosomes, ER, and nuclear proteins. A common mechanism found in both pre-disease and NASH livers was a hyperphosphorylation signature consistent with casein kinase 2α (CK2α) and AKT1 activation, which was normalized by SAMe administration. This was mimicked in human NAFLD with a metabolomic signature (M-subtype) resembling Mat1a ^-/- mice. In conclusion, we have identified a common proteome/phosphoproteome signature between Mat1a ^-/- mice and human NAFLD M-subtype that may have pathophysiological and therapeutic implications.

High Throughput Single Cell Proteomic Analysis of Organ Derived Heterogeneous Cell Populations by Nanoflow Dual Trap Single Column Liquid Chromatography

Identification and proteomic characterization of rare cell types within complex organ derived cell mixtures is best accomplished by label-free quantitative mass spectrometry. High throughput is required to rapidly survey hundreds to thousands of individual cells to adequately represent rare populations. Here we present parallelized nanoflow dual-trap single-column liquid chromatography (nanoDTSC) operating at 15 minutes of total run time per cell with peptides quantified over 11.5 minutes using standard commercial components, thus offering an accessible and efficient LC solution to analyze 96 single-cells per day. At this throughput, nanoDTSC quantified over 1,000 proteins in individual cardiomyocytes and heterogenous populations of single cells from aorta.

Parallelization with Dual-Trap Single-Column Configuration Maximizes Throughput of Proteomic Analysis

Proteomic analysis on the scale that captures population and biological heterogeneity over hundreds to thousands of samples requires rapid mass spectrometry methods, which maximize instrument utilization (IU) and proteome coverage while maintaining precise and reproducible quantification. To achieve this, a short liquid chromatography gradient paired to rapid mass spectrometry data acquisition can be used to reproducibly quantify a moderate set of analytes. High-throughput profiling at a limited depth is becoming an increasingly utilized strategy for tackling large sample sets but the time spent on loading the sample, flushing the column(s), and re-equilibrating the system reduces the ratio of meaningful data acquired to total operation time and IU. The dual-trap single-column configuration (DTSC) presented here maximizes IU in rapid analysis (15 min per sample) of blood and cell lysates by parallelizing trap column cleaning and sample loading and desalting with the analysis of the previous sample. We achieved 90% IU in low microflow (9.5 μL/min) analysis of blood while reproducibly quantifying 300-400 proteins and over 6000 precursor ions. The same IU was achieved for cell lysates and over 4000 proteins (3000 at CV below 20%) and 40,000 precursor ions were quantified at a rate of 15 min/sample. Thus, DTSC enables high-throughput epidemiological blood-based biomarker cohort studies and cell-based perturbation screening.

US Severe Acute Respiratory Syndrome Coronavirus 2 Epsilon Variant: Highly Transmissible but With an Adjusted Muted Host T-Cell Response

BACKGROUND

The multiple mutations comprising the epsilon variant demonstrate the independent convergent evolution of severe acute respiratory syndrome coronavirus (SARS-CoV-2), with its spike protein mutation L452R present in the delta (L452R), kappa (L452R), and lambda (L452Q) variants.

METHODS

Coronavirus disease 2019 (COVID-19) variants were detected in 1017 patients using whole-genome sequencing and were assessed for outcome and severity. The mechanistic effects of the epsilon versus non-epsilon variants were investigated using a multiomic approach including cellular response assays and paired cell and host transcriptomic and proteomic profiling.

RESULTS

We found that patients carrying the epsilon variant had increased mortality risk but not increased hospitalizations (P < .02). Cells infected with live epsilon compared with non-epsilon virus displayed increased sensitivity to neutralization antibodies in all patients but a slightly protective response in vaccinated individuals (P < .001). That the epsilon SARS-CoV-2 variant is more infectious but less virulent is supported mechanistically in the down-regulation of viral processing pathways seen by multiomic analyses. Importantly, this paired transcriptomics and proteomic profiling of host cellular response to live virus revealed an altered leukocyte response and metabolic messenger RNA processing with the epsilon variant. To ascertain host response to SARS-CoV-2 infection, primary COVID-19-positive nasopharyngeal samples were transcriptomically profiled and revealed a differential innate immune response (P < .001) and an adjusted T-cell response in patients carrying the epsilon variant (P < .002). In fact, patients infected with SARS-CoV-2 and those vaccinated with the BNT162b2 vaccine have comparable CD4+/CD8+ T-cell immune responses to the epsilon variant (P < .05).

CONCLUSIONS

While the epsilon variant is more infectious, by altering viral processing, we showed that patients with COVID-19 have adapted their innate immune response to this fitter variant. A protective T-cell response molecular signature is generated by this more transmissible variant in both vaccinated and unvaccinated patients.

A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Data Sets

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for clinical DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a strategy to assess imputation methods on clinical label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight imputation methods with multiple parameters at different levels of protein quantification: a dilution series data set, a small pilot data set, and a clinical proteomic data set comparing paired tumor and stroma tissue. We found that imputation methods based on local structures within the data, like local least-squares (LLS) and random forest (RF), worked well in our dilution series data set, whereas imputation methods based on global structures within the data, like BPCA, performed well in the other two data sets. We also found that imputation at the most basic protein quantification level-fragment level-improved accuracy and the number of proteins quantified. With this analytical framework, we quickly and cost-effectively evaluated different imputation methods using two smaller complementary data sets to narrow down to the larger proteomic data set's most accurate methods. This acquisition strategy allowed us to provide reproducible evidence of the accuracy of the imputation method, even in the absence of a ground truth. Overall, this study indicates that the most suitable imputation method relies on the overall structure of the data set and provides an example of an analytic framework that may assist in identifying the most appropriate imputation strategies for the differential analysis of proteins.

Quantitative Proteomics Reveals that the OGT Interactome Is Remodeled in Response to Oxidative Stress

The dynamic modification of specific serine and threonine residues of intracellular proteins by O-linked N-acetyl-β-D-glucosamine (O-GlcNAc) mitigates injury and promotes cytoprotection in a variety of stress models. The O-GlcNAc transferase (OGT) and the O-GlcNAcase are the sole enzymes that add and remove O-GlcNAc, respectively, from thousands of substrates. It remains unclear how just two enzymes can be specifically controlled to affect glycosylation of target proteins and signaling pathways both basally and in response to stress. Several lines of evidence suggest that protein interactors regulate these responses by affecting OGT and O-GlcNAcase activity, localization, and substrate specificity. To provide insight into the mechanisms by which OGT function is controlled, we have used quantitative proteomics to define OGT's basal and stress-induced interactomes. OGT and its interaction partners were immunoprecipitated from OGT WT, null, and hydrogen peroxide-treated cell lysates that had been isotopically labeled with light, medium, and heavy lysine and arginine (stable isotopic labeling of amino acids in cell culture). In total, more than 130 proteins were found to interact with OGT, many of which change their association upon hydrogen peroxide stress. These proteins include the major OGT cleavage and glycosylation substrate, host cell factor 1, which demonstrated a time-dependent dissociation after stress. To validate less well-characterized interactors, such as glyceraldehyde 3-phosphate dehydrogenase and histone deacetylase 1, we turned to parallel reaction monitoring, which recapitulated our discovery-based stable isotopic labeling of amino acids in cell culture approach. Although the majority of proteins identified are novel OGT interactors, 64% of them are previously characterized glycosylation targets that contain varied domain architecture and function. Together these data demonstrate that OGT interacts with unique and specific interactors in a stress-responsive manner.

Skin tape proteomics identifies pathways associated with transepidermal water loss and allergen polysensitization in atopic dermatitis

BACKGROUND

Atopic dermatitis (AD) and food allergy (FA) are associated with skin barrier dysfunction.

OBJECTIVE

Skin biomarkers are needed for skin barrier interventions studies.

METHODS

In this study, skin tape strip (STS) samples were collected from nonlesional skin of 62 children in AD FA+, AD FA-, and nonatopic groups for mass spectrometry proteomic analysis. transepidermal water loss and allergic sensitization were assessed. STS proteomic analysis results were validated in an independent cohort of 41 adults with AD with and without FA versus nonatopic controls.

RESULTS

A group of 45 proteins was identified as a principal component 1 (PC1) with the highest expression in AD FA+ STSs. This novel set of STS proteins was highly correlative to skin transepidermal water loss and allergic sensitization. PC1 proteins included keratin intermediate filaments; proteins associated with inflammatory responses (S100 proteins, alarmins, protease inhibitors); and glycolysis and antioxidant defense enzymes. Analysis of PC1 proteins expression in an independent adult AD cohort validated differential expression of STS PC1 proteins in the skin of adult patients with AD with the history of clinical reactions to peanut.

CONCLUSIONS

STS analysis of nonlesional skin of AD children identified a cluster of proteins with the highest expression in AD FA+ children. The differential expression of STS PC1 proteins was confirmed in a replicate cohort of adult AD patients with FA to peanut, suggesting a unique STS proteomic endotype for AD FA+ that persists into adulthood. Collectively, PC1 proteins are associated with abnormalities in skin barrier integrity and may increase the risk of epicutaneous sensitization to food allergens.

The nonlesional skin surface distinguishes atopic dermatitis with food allergy as a unique endotype

Skin barrier dysfunction has been reported in both atopic dermatitis (AD) and food allergy (FA). However, only one-third of patients with AD have FA. The purpose of this study was to use a minimally invasive skin tape strip sampling method and a multiomics approach to determine whether children with AD and FA (AD FA+) have stratum corneum (SC) abnormalities that distinguish them from AD without FA (AD FA-) and nonatopic (NA) controls. Transepidermal water loss was found to be increased in AD FA+. Filaggrin and the proportion of ω-hydroxy fatty acid sphingosine ceramide content in nonlesional skin of children with AD FA+ were substantially lower than in AD FA- and NA skin. These abnormalities correlated with morphologic changes in epidermal lamellar bilayer architecture responsible for barrier homeostasis. Shotgun metagenomic studies revealed that the nonlesional skin of AD FA+ had increased abundance of Staphylococcus aureus compared to NA. Increased expression of keratins 5, 14, and 16 indicative of hyperproliferative keratinocytes was observed in the SC of AD FA+. The skin transcriptome of AD FA+ had increased gene expression for dendritic cells and type 2 immune pathways. A network analysis revealed keratins 5, 14, and 16 were positively correlated with AD FA+, whereas filaggrin breakdown products were negatively correlated with AD FA+. These data suggest that the most superficial compartment of nonlesional skin in AD FA+ has unique properties associated with an immature skin barrier and type 2 immune activation.

Serum amyloid A1 (SAA1) protein in human colostrum

Proteins of the serum amyloid A (SAA) family have been remarkably conserved in evolution. Their biologic function(s) are not fully defined but they are likely to be a part of primordial host defense. We have detected a ∼ 12‐kDa protein reacting with antibodies against serum amyloid A (SAA) in human colostrum by western blotting. Mass spectrometry identified the reactive species as SAA1, previously identified as a prominent member of the acute‐phase response in serum. Our finding SAA1 in human colostrum contrasts with bovine, caprine and ovine colostrum where a species corresponding to putative SAA3 is uniformly present. SAA1 protein in human colostrum presumably contributes to neonatal protection.

Rapid Isolation of Extracellular Vesicles from Blood Plasma with Size-Exclusion Chromatography Followed by Mass Spectrometry-Based Proteomic Profiling

The presented procedure allows rapid isolation of extracellular vesicles (EVs) from plasma using size-exclusion chromatography (SEC). Additionally, an approach for reducing the lipid and salt content of the EV isolate in preparation for mass spectrometry (MS)-based proteomic analysis is presented. An example setup for proteomic profiling of the processed samples by nanoflow liquid chromatography coupled to tandem mass spectrometry (nLC-MS/MS) is also presented. Approximately 1000 protein groups in blood plasma-derived EVs can be identified and quantitated following this procedure and using the described instrumentation.

Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling

Conventional TopN data-dependent acquisition (DDA) LC-MS/MS analysis identifies only a limited fraction of all detectable precursors because the ion-sampling rate of contemporary mass spectrometers is insufficient to target each precursor in a complex sample. TopN DDA preferentially targets high-abundance precursors with limited sampling of low-abundance precursors and repeated analyses only marginally improve sample coverage due to redundant precursor sampling. In this work, advanced precursor ion selection algorithms were developed and applied in the bottom-up analysis of HeLa cell lysate to overcome the above deficiencies. Precursors fragmented in previous runs were efficiently excluded using an automatically aligned exclusion list, which reduced overlap of identified peptides to ∼10% between replicates. Exclusion of previously fragmented high-abundance peptides allowed deeper probing of the HeLa proteome over replicate LC-MS runs, resulting in the identification of 29% more peptides beyond the saturation level achievable using conventional TopN DDA. The gain in peptide identifications using the developed approach translated to the identification of several hundred low-abundance protein groups, which were not detected by conventional TopN DDA. Exclusion of only identified peptides compared with the exclusion of all previously fragmented precursors resulted in an increase of 1000 (∼10%) additional peptide identifications over four runs, suggesting the potential for further improvement in the depth of proteomic profiling using advanced precursor ion selection algorithms.

Granulin, a novel STAT3-interacting protein, enhances STAT3 transcriptional function and correlates with poorer prognosis in breast cancer

Since the neoplastic phenotype of a cell is largely driven by aberrant gene expression patterns, increasing attention has been focused on transcription factors that regulate critical mediators of tumorigenesis such as signal transducer and activator of transcription 3 (STAT3). As proteins that interact with STAT3 may be key in addressing how STAT3 contributes to cancer pathogenesis, we took a proteomics approach to identify novel STAT3-interacting proteins. We performed mass spectrometry-based profiling of STAT3-containing complexes from breast cancer cells that have constitutively active STAT3 and are dependent on STAT3 function for survival. We identified granulin (GRN) as a novel STAT3-interacting protein that was necessary for both constitutive and maximal leukemia inhibitory factor (LIF)induced STAT3 transcriptional activity. GRN enhanced STAT3 DNA binding and also increased the time-integrated amount of LIF-induced STAT3 activation in breast cancer cells. Furthermore, silencing GRN neutralized STAT3-mediated tumorigenic phenotypes including viability, clonogenesis, and migratory capacity. In primary breast cancer samples, GRN mRNA levels were positively correlated with STAT3 gene expression signatures and with reduced patient survival. These studies identify GRN as a functionally important STAT3-interacting protein that may serve as an important prognostic biomarker and potential therapeutic target in breast cancer.

Mass-spectrometry-based molecular characterization of extracellular vesicles: lipidomics and proteomics

This review discusses extracellular vesicles (EVs), which are submicron-scale, anuclear, phospholipid bilayer membrane enclosed vesicles that contain lipids, metabolites, proteins, and RNA (micro and messenger). They are shed from many, if not all, cell types and are present in biological fluids and conditioned cell culture media. The term EV, as coined by the International Society of Extracellular Vesicles (ISEV), encompasses exosomes (30-100 nm in diameter), microparticles (100-1000 nm), apoptotic blebs, and other EV subsets. EVs have been implicated in cell-cell communication, coagulation, inflammation, immune response modulation, and disease progression. Multiple studies report that EV secretion from disease-affected cells contributes to disease progression, e.g., tumor niche formation and cancer metastasis. EVs are attractive sources of biomarkers due to their biological relevance and relatively noninvasive accessibility from a range of physiological fluids. This review is focused on the molecular profiling of the protein and lipid constituents of EVs, with emphasis on mass-spectrometry-based "omic" analytical techniques. The challenges in the purification and molecular characterization of EVs, including contamination of isolates and limitations in sample quantities, are discussed along with possible solutions. Finally, the review discusses the limited but growing investigation of post-translational modifications of EV proteins and potential strategies for future in-depth molecular characterization of EVs.

Abstract P5-07-01: Granulin, a novel STAT3-interacting protein, promotes breast cancer tumorigenicity

Since the neoplastic phenotype of a cell is largely driven by its gene expression patterns, increasing attention is focused on transcription factors that regulate critical mediators of tumor formation and metastatic progression like the oncogenic transcription factor, signal transducer and activator of transcription 3 (STAT3). Whereas normal cells have transient activation of STAT3 due to tight control by negative regulators, cancer cells frequently have inappropriate constitutive activation of STAT3 which drives increased expression of genes involved in tumorigenesis. However, little is known about proteins that interact with STAT3 to modulate its function. To identify novel STAT3-interacting proteins, we performed liquid chromatography tandem mass spectrometry-based profiling of STAT3-containing complexes immunoprecipitated from the triple-negative breast cancer cell lines MDA-MB-468 and SUM159PT, which have constitutively active STAT3. We identified granulin (GRN) as a novel STAT3-interacting protein and validated the STAT3-GRN interaction in breast cancer cells by co-immunoprecipitation. To investigate the functional effect of GRN on STAT3 activity, we silenced GRN using small interfering RNA. We found that GRN was necessary for constitutive and maximal cytokine-induced STAT3 transcriptional activity in breast cancer cells. GRN modulated cytokine-induced STAT3 function by enhancing STAT3 DNA binding and increasing the time-integrated amount of STAT3 activation and nuclear translocation. Silencing GRN mirrored the effect of silencing STAT3 on reducing the viability, clonogenesis, and migratory capacity of triple-negative breast cancer cells. Furthermore, GRN mRNA levels were significantly and positively correlated with STAT3 gene expression signatures indicative of STAT3 activation as well as with reduced overall survival in breast cancer patients. These studies used a proteomics approach to identify GRN as a novel STAT3 interacting protein that may serve as an important prognostic biomarker and potential therapeutic target in breast cancer.

Citation Format: Jennifer E Yeh, Simion Kreimer, Sarah R Walker, Andrea Richardson, Alexander R Ivanov, David A Frank. Granulin, a novel STAT3-interacting protein, promotes breast cancer tumorigenicity [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P5-07-01.

Two closely linked genes encoding thioredoxin and thioredoxin reductase in Clostridium litorale

The genes encoding thioredoxin and thioredoxin reductase of Clostridium litorale were cloned and sequenced. The thioredoxin reductase gene (trxB) encoded a protein of 33.9 kDa, and the deduced amino acid sequence showed 44% identity to the corresponding protein from Escherichia coli. The gene encoding thioredoxin (trxA) was located immediately downstream of trxB. TrxA and TrxB were each encoded by two gene copies, both copies presumably located on the chromosome. Like other thioredoxins from anaerobic, amino-acid-degrading bacteria investigated to date by N-terminal amino acid sequencing, thioredoxin from C. litorale exhibited characteristic deviations from the consensus sequence, e.g., GCVPC instead of WCGPC at the redox-active center. Using heterologous enzyme assays, neither thioredoxin nor thioredoxin reductase were interchangeable with the corresponding proteins of the thioredoxin system from E. coli. To elucidate the molecular basis of that incompatibility, Gly-31 in C. litorale thioredoxin was substituted with Trp (the W in the consensus sequence) by site-directed mutagenesis. The mutant protein was expressed in E. coli and was purified to homogeneity. Enzyme assays using the G31W thioredoxin revealed that Gly-31 was not responsible for the observed incompatibility with the E. coli thioredoxin reductase, but it was essential for activity of the thioredoxin system in C. litorale.

Glycine reductase of Clostridium litorale. Cloning, sequencing, and molecular analysis of the grdAB operon that contains two in-frame TGA codons for selenium incorporation

A 2.8-kb HindIII fragment, containing three open reading frames, has been cloned and sequenced from Clostridium litorale. The first gene grdA encoded the selenocysteine-containing protein PA of the glycine reductase complex, a protein of 159 amino acids with a deduced molecular mass of 16.7 kDa. The second gene (grdB) encoded the 47-kDa subunit of the substrate-specific selenoprotein PB glycine that is composed of 437 amino acids. The third gene contained the 5'-region of the gene for thioredoxin reductase, trxB. All gene products shared high similarity with the corresponding proteins from Eubacterium acidaminophilum. In both genes grdA and grdB, the opal termination codon (TGA) was found inframe, indicating the presence of selenocysteine in both polypeptides. Northern-blot analysis showed that grdA and grdB are organized as one operon. Unlike Escherichia coli, no stable secondary structures of the corresponding mRNA were found immediately downstream of the UGA codons to direct an insertion of selenocysteine into the grdA and grdB transcripts of C. litorale. Instead, a secondary structure was identified in the 3'-untranslated region of grdB.