NPEPPS Is a Druggable Driver of Platinum Resistance

There is an unmet need to improve the efficacy of platinum-based cancer chemotherapy, which is used in primary and metastatic settings in many cancer types. In bladder cancer, platinum-based chemotherapy leads to better outcomes in a subset of patients when used in the neoadjuvant setting or in combination with immunotherapy for advanced disease. Despite such promising results, extending the benefits of platinum drugs to a greater number of patients is highly desirable. Using the multiomic assessment of cisplatin-responsive and -resistant human bladder cancer cell lines and whole-genome CRISPR screens, we identified puromycin-sensitive aminopeptidase (NPEPPS) as a driver of cisplatin resistance. NPEPPS depletion sensitized resistant bladder cancer cells to cisplatin in vitro and in vivo. Conversely, overexpression of NPEPPS in sensitive cells increased cisplatin resistance. NPEPPS affected treatment response by regulating intracellular cisplatin concentrations. Patient-derived organoids (PDO) generated from bladder cancer samples before and after cisplatin-based treatment, and from patients who did not receive cisplatin, were evaluated for sensitivity to cisplatin, which was concordant with clinical response. In the PDOs, depletion or pharmacologic inhibition of NPEPPS increased cisplatin sensitivity, while NPEPPS overexpression conferred resistance. Our data present NPEPPS as a druggable driver of cisplatin resistance by regulating intracellular cisplatin concentrations.

SIGNIFICANCE

Targeting NPEPPS, which induces cisplatin resistance by controlling intracellular drug concentrations, is a potential strategy to improve patient responses to platinum-based therapies and lower treatment-associated toxicities.

Refining a Framework to Enhance Communication in the Emergency Department During the Diagnostic Process: An eDelphi Approach

BACKGROUND

Emergency departments (EDs) are susceptible to diagnostic error. Suboptimal communication between the patient and the interdisciplinary care team increases risk to diagnostic safety. The role of communication remains underrepresented in existing diagnostic decision-making conceptual models.

METHODS

The authors used eDelphi methodology, whereby data are collected electronically, to achieve consensus among an expert panel of 18 clinicians, patients, family members, and other participants on a refined ED-based diagnostic decision-making framework that integrates several potential opportunities for communication to enhance diagnostic quality. This study examined the entire diagnostic process in the ED, from prehospital to discharge or transfer to inpatient care, and identified where communication breakdowns could occur. After four iterative rounds of the eDelphi process, including a final validation round by all participants, the project's a priori consensus threshold of 80% agreement was reached.

RESULTS

The authors developed a final framework that positions communication more prominently in the diagnostic process in the ED and enhances the original National Academies of Sciences, Engineering, and Medicine (NASEM) and ED-adapted NASEM frameworks. Specific points in the ED journey were identified where more attention to communication might be helpful. Two specific types of communication-information exchange and shared understanding-were identified as high priority for optimal outcomes. Ideas for communication-focused interventions to prevent diagnostic error in the ED fell into three categories: patient-facing, clinician-facing, and system-facing interventions.

CONCLUSION

This project's refinement of the NASEM framework adapted to the ED can be used to develop communications-focused interventions to reduce diagnostic error in this highly complex and error-prone setting.

Frontline Providers' and Patients' Perspectives on Improving Diagnostic Safety in the Emergency Department: A Qualitative Study

BACKGROUND

Few studies have described the insights of frontline health care providers and patients on how the diagnostic process can be improved in the emergency department (ED), a setting at high risk for diagnostic errors. The authors aimed to identify the perspectives of providers and patients on the diagnostic process and identify potential interventions to improve diagnostic safety.

METHODS

Semistructured interviews were conducted with 10 ED physicians, 15 ED nurses, and 9 patients/caregivers at two separate health systems. Interview questions were guided by the ED-Adapted National Academies of Sciences, Engineering, and Medicine Diagnostic Process Framework and explored participant perspectives on the ED diagnostic process, identified vulnerabilities, and solicited interventions to improve diagnostic safety. The authors performed qualitative thematic analysis on transcribed interviews.

RESULTS

The research team categorized vulnerabilities in the diagnostic process and intervention opportunities based on the ED-Adapted Framework into five domains: (1) team dynamics and communication (for example, suboptimal communication between referring physicians and the ED team); (2) information gathering related to patient presentation (for example, obtaining the history from the patients or their caregivers; (3) ED organization, system, and processes (for example, staff schedules and handoffs); (4) patient education and self-management (for example, patient education at discharge from the ED); and (5) electronic health record and patient portal use (for example, automatic release of test results into the patient portal). The authors identified 33 potential interventions, of which 17 were provider focused and 16 were patient focused.

CONCLUSION

Frontline providers and patients identified several vulnerabilities and potential interventions to improve ED diagnostic safety. Refining, implementing, and evaluating the efficacy of these interventions are required.

CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution

MOTIVATION

Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments.

RESULTS

We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable.

AVAILABILITY AND IMPLEMENTATION

The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

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.

ABDS: tool suite for analyzing biologically diverse samples

Motivation

Analytics tools are essential to identify informative molecular features about different phenotypic groups. Among the most fundamental tasks are missing value imputation, signature gene detection, and expression pattern visualization. However, most commonly used analytics tools may be problematic for characterizing biologically diverse samples when either signature genes possess uneven missing rates across different groups yet involving complex missing mechanisms, or multiple biological groups are simultaneously compared and visualized.

Results

We develop ABDS tool suite tailored specifically to analyzing biologically diverse samples. Mechanism-integrated group-wise imputation is developed to recruit signature genes involving informative missingness, cosine-based one-sample test is extended to detect enumerated signature genes, and unified heatmap is designed to comparably display complex expression patterns. We discuss the methodological principles and demonstrate the conceptual advantages of the three software tools. We also showcase the biomedical applications of these individual tools. Implemented in open-source R scripts, ABDS tool suite complements rather than replaces the existing tools and will allow biologists to more accurately detect interpretable molecular signals among diverse phenotypic samples.

Availability and implementation

The R Scripts of ABDS tool suite is freely available at https://github.com/niccolodpdu/ABDS.

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.

Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease

Systemic sclerosis is a rare connective tissue disease; and interstitial lung disease (SSc-ILD) is associated with significant morbidity and mortality. There are no clinical, radiologic features, nor biomarkers that identify the specific time when patients are at risk for progression at which the benefits from treatment outweigh the risks. Our study aimed to identify blood protein biomarkers associated with progression of interstitial lung disease in patients with SSc-ILD using an unbiased, high-throughput approach. We classified SSc-ILD as progressive or stable based on change in forced vital capacity over 12 months or less. We profiled serum proteins by quantitative mass spectrometry and analyzed the association between protein levels and progression of SSc-ILD via logistic regression. The proteins associated with at a p value of < 0.1 were queried in the ingenuity pathway analysis (IPA) software to identify interaction networks, signaling, and metabolic pathways. Through principal component analysis, the relationship between the top 10 principal components and progression was evaluated. Unsupervised hierarchical clustering with heatmapping was done to define unique groups. The cohort consisted of 72 patients, 32 with progressive SSc-ILD and 40 with stable disease with similar baseline characteristics. Of a total of 794 proteins, 29 were associated with disease progression. After adjusting for multiple testing, these associations did not remain significant. IPA identified five upstream regulators that targeted proteins associated with progression, as well as a canonical pathway with a higher signal in the progression group. Principal component analysis showed that the ten components with the highest Eigenvalues represented 41% of the variability of the sample. Unsupervised clustering analysis revealed no significant heterogeneity between the subjects. We identified 29 proteins associated with progressive SSc-ILD. While these associations did not remain significant after accounting for multiple testing, some of these proteins are part of pathways relevant to autoimmunity and fibrogenesis. Limitations included a small sample size and a proportion of immunosuppressant use in the cohort, which could have altered the expression of inflammatory and immunologic proteins. Future directions include a targeted evaluation of these proteins in another SSc-ILD cohort or application of this study design to a treatment naïve population.

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.

BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data

Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.

Impact of COVID-19 on the associated complications of high-risk conditions in a statewide pediatric emergency network

BACKGROUND

The COVID-19 pandemic affected the volume and epidemiology of pediatric emergency department (ED) visits. We aimed to determine the rate of associated complications for 16 high-risk conditions in a Michigan statewide network of academic and community EDs during the pandemic.

METHODS

We conducted a cross-sectional study of pediatric ED visits among a network of 5 Michigan health systems during the pre-pandemic (March 1, 2019-March 10, 2020) and pandemic (March 11, 2020-March 31, 2021) periods. Data were collected from the medical record and included patient demographics, ED visit characteristics, procedure codes, and final International Classification of Diseases, 10th Revision, Clinical Modification diagnosis codes. Selection of codes for 16 high-risk conditions and diagnostic complications were identified using previously described methods. Characteristics of ED visits were compared before versus during the pandemic using χ² and Fisher's exact tests. We used multilevel logistic regression to analyze covariates and potential confounders for being diagnosed with a high-risk condition or a complication of a high-risk condition.

RESULTS

A total of 417,038 pediatric ED visits were analyzed. The proportion of patients presenting with 10 of 16 high-risk conditions (including appendicitis, sepsis, and stroke) was higher in the pandemic period compared with pre-pandemic (P < 0.01). Despite this, there was no significant increase in the frequency of complications for any of the 16 high-risk conditions during the pandemic. The adjusted odds of being diagnosed with appendicitis (pre-pandemic 0.23% vs pandemic 0.52%; odds ratio [OR], 1.19 [95% confidence interval, CI, 1.00-1.41]), diabetic ketoacidosis (pre-pandemic 0.16% vs pandemic 0.52%; OR, 2.40 [95% CI, 2.07-2.78]), intussusception (pre-pandemic 0.05% vs pandemic 0.07%; OR, 1.64 [95% CI, 1.22-2.21)], and testicular torsion (pre-pandemic 0.10% vs pandemic 0.14%; OR, 1.64 [95% CI, 1.18-2.28]) was higher during the pandemic.

CONCLUSIONS

Despite a higher proportion of ED visits attributed to high-risk conditions, there was no increase in complications, suggesting minimal impact of the pandemic on outcomes of pediatric ED visits.

A myocardial infarct border-zone-on-a-chip demonstrates distinct regulation of cardiac tissue function by an oxygen gradient

After a myocardial infarction, the boundary between the injured, hypoxic tissue and the adjacent viable, normoxic tissue, known as the border zone, is characterized by an oxygen gradient. Yet, the impact of an oxygen gradient on cardiac tissue function is poorly understood, largely due to limitations of existing experimental models. Here, we engineered a microphysiological system to controllably expose engineered cardiac tissue to an oxygen gradient that mimics the border zone and measured the effects of the gradient on electromechanical function and the transcriptome. The gradient delayed calcium release, reuptake, and propagation; decreased diastolic and peak systolic stress; and increased expression of inflammatory cascades that are hallmarks of myocardial infarction. These changes were distinct from those observed in tissues exposed to uniform normoxia or hypoxia, demonstrating distinct regulation of cardiac tissue phenotypes by an oxygen gradient. Our border-zone-on-a-chip model advances functional and mechanistic insight into oxygen-dependent cardiac tissue pathophysiology.

Influenza vaccination coverage among an urban pediatric asthma population: Implications for population health

INTRODUCTION

Asthma is the most common chronic disease in children. Children with asthma are at high risk for complications from influenza; however annual influenza vaccination rates for this population are suboptimal. The overall aim of this study was to describe the characteristics of a high-risk population of children with asthma presenting to an urban pediatric emergency department according to influenza vaccination status.

METHODS

The study was a retrospective chart review of 4355 patients aged 2 to 18 years evaluated in a Michigan pediatric emergency department (PED) between November 1, 2017 and April 30, 2018 with an ICD-10-CM code for asthma (J45.x). Eligible patient PED records were matched with influenza vaccination records for the 2017-2018 influenza season from the Michigan Care Improvement Registry. Geospatial analysis was employed to examine the distribution of influenza vaccination status.

RESULTS

1049 patients (30.9%) with asthma seen in the PED had received an influenza vaccine. Influenza vaccination coverage varied by Census Tract, ranging from 10% to >99%. Most vaccines were administered in a primary care setting (84.3%) and were covered by public insurance (76.8%). The influenza vaccination rate was lowest for children aged 5-11 years (30.0%) and vaccination status was associated with race (p<0.001) and insurance type (p<0.001).

CONCLUSIONS

Identification of neighborhood Census Tract and demographic groups with suboptimal influenza vaccination could guide development of targeted public health interventions to improve vaccination rates in high-risk patients. Given the morbidity and mortality associated with pediatric asthma, a data-driven approach may improve outcomes and reduce healthcare-associated costs for this pediatric population.

Cosbin: cosine score-based iterative normalization of biologically diverse samples

Motivation

Data normalization is essential to ensure accurate inference and comparability of gene expression measures across samples or conditions. Ideally, gene expression data should be rescaled based on consistently expressed reference genes. However, to normalize biologically diverse samples, the most commonly used reference genes exhibit striking expression variability and size-factor or distribution-based normalization methods can be problematic when the amount of asymmetry in differential expression is significant.

Results

We report an efficient and accurate data-driven method—Cosine score-based iterative normalization (Cosbin)—to normalize biologically diverse samples. Based on the Cosine scores of cross-condition expression patterns, the Cosbin pipeline iteratively eliminates asymmetric differentially expressed genes, identifies consistently expressed genes, and calculates sample-wise normalization factors. We demonstrate the superior performance and enhanced utility of Cosbin compared with six representative peer methods using both simulation and real multi-omics expression datasets. Implemented in open-source R scripts and specifically designed to address normalization bias due to significant asymmetry in differential expression across multiple conditions, the Cosbin tool complements rather than replaces the existing methods and will allow biologists to more accurately detect true molecular signals among diverse phenotypic groups.

Availability and implementation

The R scripts of Cosbin pipeline are freely available at https://github.com/MinjieSh/Cosbin.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

COT: an efficient and accurate method for detecting marker genes among many subtypes

Motivation

Ideally, a molecularly distinct subtype would be composed of molecular features that are expressed uniquely in the subtype of interest but in no others-so-called marker genes (MGs). MG plays a critical role in the characterization, classification or deconvolution of tissue or cell subtypes. We and others have recognized that the test statistics used by most methods do not exactly satisfy the MG definition and often identify inaccurate MG.

Results

We report an efficient and accurate data-driven method, formulated as a Cosine-based One-sample Test (COT) in scatter space, to detect MG among many subtypes using subtype expression profiles. Fundamentally different from existing approaches, the test statistic in COT precisely matches the mathematical definition of an ideal MG. We demonstrate the performance and utility of COT on both simulated and real gene expression and proteomics data. The open source Python/R tool will allow biologists to efficiently detect MG and perform a more comprehensive and unbiased molecular characterization of tissue or cell subtypes in many biomedical contexts. Nevertheless, COT complements not replaces existing methods.

Availability and implementation

The Python COT software with a detailed user's manual and a vignette are freely available at https://github.com/MintaYLu/COT.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Sexual Dimorphism in Cardiovascular Biomarkers: Clinical and Research Implications

Sex-based differences in cardiovascular disease presentation, diagnosis, and response to therapies are well established, but mechanistic understanding and translation to clinical applications are limited. Blood-based biomarkers have become an important tool for interrogating biologic pathways. Understanding sexual dimorphism in the relationship between biomarkers and cardiovascular disease will enhance our insights into cardiovascular disease pathogenesis in women, with potential to translate to improved individualized care for men and women with or at risk for cardiovascular disease. In this review, we examine how biologic sex associates with differential levels of blood-based biomarkers and influences the effect of biomarkers on disease outcomes. We further summarize key differences in blood-based cardiovascular biomarkers along central biologic pathways, including myocardial stretch/injury, inflammation, adipose tissue metabolism, and fibrosis pathways in men versus women. Finally, we present recommendations for leveraging our current knowledge of sex differences in blood-based biomarkers for future research and clinical innovation.

Proteomic analysis of descending thoracic aorta identifies unique and universal signatures of aneurysm and dissection

Objective

Methods

Results

Conclusions

Diseases of the descending thoracic aorta such as aneurysms and dissections carry a high degree of morbidity and mortality. At present, a complete understanding is still lacking of the genetics that drive these diseases and why some aortic segments dissect in the presence or absence of an aneurysm. We compared and contrasted the whole proteome expression of descending aortas from patients with normal, dissected, aneurysmal, and aneurysmal with dissected pathology aortic tissue. We uncovered potential tissue markers that might serve as future targets for therapy or predictors of disease progression.

Myofilament Phosphorylation in Stem Cell Treated Diastolic Heart Failure

RATIONALE

Phosphorylation of sarcomeric proteins has been implicated in heart failure with preserved ejection fraction (HFpEF); such changes may contribute to diastolic dysfunction by altering contractility, cardiac stiffness, Ca²⁺-sensitivity, and mechanosensing. Treatment with cardiosphere-derived cells (CDCs) restores normal diastolic function, attenuates fibrosis and inflammation, and improves survival in a rat HFpEF model.

OBJECTIVE

Phosphorylation changes that underlie HFpEF and those reversed by CDC therapy, with a focus on the sarcomeric subproteome were analyzed.

METHODS AND RESULTS

Dahl salt-sensitive rats fed a high-salt diet, with echocardiographically verified diastolic dysfunction, were randomly assigned to either intracoronary CDCs or placebo. Dahl salt-sensitive rats receiving low salt diet served as controls. Protein and phosphorylated Ser, Thr, and Tyr residues from left ventricular tissue were quantified by mass spectrometry. HFpEF hearts exhibited extensive hyperphosphorylation with 98% of the 529 significantly changed phospho-sites increased compared with control. Of those, 39% were located within the sarcomeric subproteome, with a large group of proteins located or associated with the Z-disk. CDC treatment partially reverted the hyperphosphorylation, with 85% of the significantly altered 76 residues hypophosphorylated. Bioinformatic upstream analysis of the differentially phosphorylated protein residues revealed PKC as the dominant putative regulatory kinase. PKC isoform analysis indicated increases in PKC α, β, and δ concentration, whereas CDC treatment led to a reversion of PKCβ. Use of PKC isoform specific inhibition and overexpression of various PKC isoforms strongly suggests that PKCβ is the dominant kinase involved in hyperphosphorylation in HFpEF and is altered with CDC treatment.

CONCLUSIONS

Increased protein phosphorylation at the Z-disk is associated with diastolic dysfunction, with PKC isoforms driving most quantified phosphorylation changes. Because CDCs reverse the key abnormalities in HFpEF and selectively reverse PKCβ upregulation, PKCβ merits being classified as a potential therapeutic target in HFpEF, a disease notoriously refractory to medical intervention.

Alendronate-induced Perturbation of the Bone Proteome and Microenvironmental Pathophysiology

Objectives: Bisphosphonates (BPs) are powerful inhibitors of osteoclastogenesis and are used to prevent osteoporotic bone loss and reduce the risk of osteoporotic fracture in patients suffering from postmenopausal osteoporosis. Patients with breast cancer or gynecological malignancies being treated with BPs or those receiving bone-targeted therapy for metastatic prostate cancer are at increased risk of bisphosphonate-related osteonecrosis of the jaw (BRONJ). Although BPs markedly ameliorate osteoporosis, their adverse effects largely limit the clinical application of these drugs. This study focused on providing a deeper understanding of one of the most popular BPs, the alendronate (ALN)-induced perturbation of the bone proteome and microenvironmental pathophysiology. Methods: To understand the molecular mechanisms underlying ALN-induced side-effects, an unbiased and global proteomics approach combined with big data bioinformatics was applied. This was followed by biochemical and functional analyses to determine the clinicopathological mechanisms affected by ALN. Results: The findings from this proteomics study suggest that the RIPK3/Wnt/GSK3/β-catenin signaling pathway is significantly perturbed upon ALN treatment, resulting in abnormal angiogenesis, inflammation, anabolism, remodeling, and mineralization in bone cells in an in vitro cell culture system. Conclusion: Our investigation into potential key signaling mechanisms in response to ALN provides a rational basis for suppressing BP-induced adverse effect and presents various therapeutic strategies.

Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study

BACKGROUND

Diagnostic decision making, especially in emergency departments, is a highly complex cognitive process that involves uncertainty and susceptibility to errors. A combination of factors, including patient factors (eg, history, behaviors, complexity, and comorbidity), provider-care team factors (eg, cognitive load and information gathering and synthesis), and system factors (eg, health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Using electronic triggers to identify records of patients with certain patterns of care, such as escalation of care, has been useful to screen for diagnostic errors. Once errors are identified, sophisticated data analytics and machine learning techniques can be applied to existing electronic health record (EHR) data sets to shed light on potential risk factors influencing diagnostic decision making.

OBJECTIVE

This study aims to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques.

METHODS

This study plans to use trigger algorithms within EHR data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on whether they meet certain criteria. Samples from both data sets will be validated using medical record reviews, upon which we expect to find a higher number of diagnostic safety events in the trigger-positive subset. Machine learning will be used to evaluate relationships between certain patient factors, provider-care team factors, and system-level risk factors and diagnostic safety signals in the statistically matched groups of trigger-positive and trigger-negative charts.

RESULTS

This federally funded study was approved by the institutional review board of 2 academic medical centers with affiliated community hospitals. Trigger queries are being developed at both organizations, and sample cohorts will be labeled using the triggers. Machine learning techniques such as association rule mining, chi-square automated interaction detection, and classification and regression trees will be used to discover important variables that could be incorporated within future clinical decision support systems to help identify and reduce risks that contribute to diagnostic errors.

CONCLUSIONS

The use of large EHR data sets and machine learning to investigate risk factors (related to the patient, provider-care team, and system-level) in the diagnostic process may help create future mechanisms for monitoring diagnostic safety.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/24642.

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.

Identifying trigger concepts to screen emergency department visits for diagnostic errors

OBJECTIVES

The diagnostic process is a vital component of safe and effective emergency department (ED) care. There are no standardized methods for identifying or reliably monitoring diagnostic errors in the ED, impeding efforts to enhance diagnostic safety. We sought to identify trigger concepts to screen ED records for diagnostic errors and describe how they can be used as a measurement strategy to identify and reduce preventable diagnostic harm.

METHODS

We conducted a literature review and surveyed ED directors to compile a list of potential electronic health record (EHR) trigger (e-triggers) and non-EHR based concepts. We convened a multidisciplinary expert panel to build consensus on trigger concepts to identify and reduce preventable diagnostic harm in the ED.

RESULTS

Six e-trigger and five non-EHR based concepts were selected by the expert panel. E-trigger concepts included: unscheduled ED return to ED resulting in hospital admission, death following ED visit, care escalation, high-risk conditions based on symptom-disease dyads, return visits with new diagnostic/therapeutic interventions, and change of treating service after admission. Non-EHR based signals included: cases from mortality/morbidity conferences, risk management/safety office referrals, ED medical director case referrals, patient complaints, and radiology/laboratory misreads and callbacks. The panel suggested further refinements to aid future research in defining diagnostic error epidemiology in ED settings.

CONCLUSIONS

We identified a set of e-trigger concepts and non-EHR based signals that could be developed further to screen ED visits for diagnostic safety events. With additional evaluation, trigger-based methods can be used as tools to monitor and improve ED diagnostic performance.

Lysine and Arginine Protein Post-translational Modifications by Enhanced DIA Libraries: Quantification in Murine Liver Disease

Proteoforms containing post-translational modifications (PTMs) represent a degree of functional diversity only harnessed through analytically precise simultaneous quantification of multiple PTMs. Here we present a method to accurately differentiate an unmodified peptide from its PTM-containing counterpart through data-independent acquisition-mass spectrometry, leveraging small precursor mass windows to physically separate modified peptidoforms from each other during MS2 acquisition. We utilize a lysine and arginine PTM-enriched peptide assay library and site localization algorithm to simultaneously localize and quantify seven PTMs including mono-, di-, and trimethylation, acetylation, and succinylation in addition to total protein quantification in a single MS run without the need to enrich experimental samples. To evaluate biological relevance, this method was applied to liver lysate from differentially methylated nonalcoholic steatohepatitis (NASH) mouse models. We report that altered methylation and acetylation together with total protein changes drive the novel hypothesis of a regulatory function of PTMs in protein synthesis and mRNA stability in NASH.

Clinical factors associated with the use of dexamethasone for asthma in the pediatric emergency department

BACKGROUND

Dexamethasone is efficacious for the treatment of pediatric asthma exacerbations but is not specifically recommended by current national guidelines.

OBJECTIVES

To describe the factors associated with prescribed dexamethasone in a pediatric emergency department (PED) and upon patient discharge.

METHODS

Retrospective chart review of patients aged 2 to 18 years discharged home from a PED with a diagnostic code for asthma (J45x). Descriptive statistics are reported and binary logistic regression with generalized estimating equations was used to examine the demographic and clinical factors associated with dexamethasone use in the PED and upon discharge.

RESULTS

594 children contributed 690 visits for asthma. Two-thirds of patients received prednisone in the PED (n = 430; 62%). Among 260 children who received dexamethasone, 76% (n = 198) were prescribed a second dose for post-discharge administration. Multivariable models showed that patients triaged as most urgent had a 50% reduction in the odds of receiving dexamethasone in the PED (OR = 0.5; 95% CI = 0.28-0.87). Patients seen by a pediatrician (OR 4.2; 95%CI 2.1-8.3) and those triaged as urgent (OR 2.9; 95% CI = 1.8-7.8) were more likely to receive a single dose of dexamethasone.

CONCLUSIONS

Dexamethasone is less commonly used in the PED for asthmatic patients triaged as most urgent. Triage acuity and level of training were associated with single-dose treatment of asthma in those receiving dexamethasone. Further studies are needed to clarify the use of dexamethasone across the spectrum of asthma severity.

MitoPlex: A targeted multiple reaction monitoring assay for quantification of a curated set of mitochondrial proteins

Mitochondria are the major source of cellular energy (ATP), as well as critical mediators of widespread functions such as cellular redox balance, apoptosis, and metabolic flux. The organelles play an especially important role in the maintenance of cardiac homeostasis; their inability to generate ATP following impairment due to ischemic damage has been directly linked to organ failure. Methods to quantify mitochondrial content are limited to low throughput immunoassays, measurement of mitochondrial DNA, or relative quantification by untargeted mass spectrometry. Here, we present a high throughput, reproducible and quantitative mass spectrometry multiple reaction monitoring based assay of 37 proteins critical to central carbon chain metabolism and overall mitochondrial function termed 'MitoPlex'. We coupled this protein multiplex with a parallel analysis of the central carbon chain metabolites (219 metabolite assay) extracted in tandem from the same sample, be it cells or tissue. In tests of its biological applicability in cells and tissues, "MitoPlex plus metabolites" indicated profound effects of HMG-CoA Reductase inhibition (e.g., statin treatment) on mitochondria of i) differentiating C2C12 skeletal myoblasts, as well as a clear opposite trend of statins to promote mitochondrial protein expression and metabolism in heart and liver, while suppressing mitochondrial protein and ii) aspects of metabolism in the skeletal muscle obtained from C57Bl6 mice. Our results not only reveal new insights into the metabolic effect of statins in skeletal muscle, but present a new high throughput, reliable MS-based tool to study mitochondrial dynamics in both cell culture and in vivo models.

Identification of Putative Early Atherosclerosis Biomarkers by Unsupervised Deconvolution of Heterogeneous Vascular Proteomes

Coronary artery disease remains a leading cause of death in industrialized nations, and early detection of disease is a critical intervention target to effectively treat patients and manage risk. Proteomic analysis of mixed tissue homogenates may obscure subtle protein changes that occur uniquely in underlying tissue subtypes. The unsupervised 'convex analysis of mixtures' (CAM) tool has previously been shown to effectively segregate cellular subtypes from mixed expression data. In this study, we hypothesized that CAM would identify proteomic information specifically informative to early atherosclerosis lesion involvement that could lead to potential markers of early disease detection. We quantified the proteome of 99 paired abdominal aorta (AA) and left anterior descending coronary artery (LAD) specimens (N = 198 specimens total) acquired during autopsy of young adults free of diagnosed cardiac disease. The CAM tool was then used to segregate protein subsets uniquely associated with different underlying tissue types, yielding markers of normal and fibrous plaque (FP) tissues in LAD and AA (N = 62 lesions markers). CAM-derived FP marker expression was validated against pathologist estimated luminal surface involvement of FP, as well as in an orthogonal cohort of "pure" fibrous plaque, fatty streak, and normal vascular specimens. A targeted mass spectrometry (MS) assay quantified 39 of 62 CAM-FP markers in plasma from women with angiographically verified coronary artery disease (CAD, N = 46) or free from apparent CAD (control, N = 40). Elastic net variable selection with logistic regression reduced this list to 10 proteins capable of classifying CAD status in this cohort with <6% misclassification error, and a mean area under the receiver operating characteristic curve of 0.992 (confidence interval 0.968-0.998) after cross validation. The proteomics-CAM workflow identified lesion-specific molecular biomarker candidates by distilling the most representative molecules from heterogeneous tissue types.

Examining the clinical management of asthma exacerbations by nurse practitioners in a pediatric emergency department

BACKGROUND

Little is known about the clinical management or quality of asthma care provided by nurse practitioners (NP) in a pediatric emergency setting.

OBJECTIVE

To describe the clinical management of asthma by NPs in our institution's emergency department, and to compare the treatment strategies between NPs, pediatricians, and pediatric emergency physicians.

METHODS

We conducted a retrospective chart review at a level-one pediatric trauma center. Data were extracted from electronic medical records for all patients between 2 and 18 years of age presenting to the emergency department with an asthma exacerbation. Data were analyzed using binary logistic regression with generalized estimating equations.

RESULTS

NPs evaluated 18% of all children presenting for asthma care. When compared to pediatric emergency physicians, patients treated by NPs had approximately twice the odds of receiving a β2-agonist (OR = 2.02; 95% CI 1.02 - 3.99) or a systemic corticosteroid (OR = 2.31; 95% CI 1.35 - 3.95) within 60 minutes of clinical evaluation. Adherence rates were similar for the other asthma quality measures between these two clinician groups.

CONCLUSIONS

NPs were best able to meet time-sensitive asthma quality measures in the emergency department. The addition of NPs to emergency staffing models may improve access to timely care for children with asthma.

ACE overexpression in myeloid cells increases oxidative metabolism and cellular ATP

Angiotensin-converting enzyme (ACE) affects blood pressure. In addition, ACE overexpression in myeloid cells increases their immune function. Using MS and chemical analysis, we identified marked changes of intermediate metabolites in ACE-overexpressing macrophages and neutrophils, with increased cellular ATP (1.7-3.0-fold) and Krebs cycle intermediates, including citrate, isocitrate, succinate, and malate (1.4-3.9-fold). Increased ATP is due to ACE C-domain catalytic activity; it is reversed by an ACE inhibitor but not by an angiotensin II AT1 receptor antagonist. In contrast, macrophages from ACE knockout (null) mice averaged only 28% of the ATP levels found in WT mice. ACE overexpression does not change cell or mitochondrial size or number. However, expression levels of the electron transport chain proteins NDUFB8 (complex I), ATP5A, and ATP5β (complex V) are significantly increased in macrophages and neutrophils, and COX1 and COX2 (complex IV) are increased in macrophages overexpressing ACE. Macrophages overexpressing ACE have increased mitochondrial membrane potential (24% higher), ATP production rates (29% higher), and maximal respiratory rates (37% higher) compared with WT cells. Increased cellular ATP underpins increased myeloid cell superoxide production and phagocytosis associated with increased ACE expression. Myeloid cells overexpressing ACE indicate the existence of a novel pathway in which myeloid cell function can be enhanced, with a key feature being increased cellular ATP.

Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome: Where There Is a Will, There Is a Way

There is an exponential increase in biological complexity as initial gene transcripts are spliced, translated into amino acid sequence, and post-translationally modified. Each protein can exist as multiple chemical or sequence-specific proteoforms, and each has the potential to be a critical mediator of a physiological or pathophysiological signaling cascade. Here, we provide an overview of how different proteoforms come about in biological systems and how they are most commonly measured using mass spectrometry-based proteomics and bioinformatics. Our goal is to present this information at a level accessible to every scientist interested in mass spectrometry and its application to proteome profiling. We will specifically discuss recent data linking various protein post-translational modifications to cardiovascular disease and conclude with a discussion for enablement and democratization of proteomics across the cardiovascular and scientific community. The aim is to inform and inspire the readership to explore a larger breadth of proteoform, particularity post-translational modifications, related to their particular areas of expertise in cardiovascular physiology.

Multiomic Profiling of Tyrosine Kinase Inhibitor-Resistant K562 Cells Suggests Metabolic Reprogramming To Promote Cell Survival

Resistance to chemotherapy can occur through a wide variety of mechanisms. Resistance to tyrosine kinase inhibitors (TKIs) often arises from kinase mutations-however, "off-target" resistance occurs but is poorly understood. Previously, we established cell line resistance models for three TKIs used in chronic myeloid leukemia treatment, and found that resistance was not attributed entirely to failure of kinase inhibition. Here, we performed global, integrated proteomic and transcriptomic profiling of these cell lines to describe mechanisms of resistance at the protein and gene expression level. We used whole transcriptome sequencing and SWATH-based data-independent acquisition mass spectrometry (DIA-MS), which does not require isotopic labels and provides quantitative measurements of proteins in a comprehensive, unbiased fashion. The proteomic and transcriptional data were correlated to generate an integrated understanding of the gene expression and protein alterations associated with TKI resistance. We defined mechanisms of resistance and two novel markers, CA1 and alpha-synuclein, that were common to all TKIs tested. Resistance to all of the TKIs was associated with oxidative stress responses, hypoxia signatures, and apparent metabolic reprogramming of the cells. Metabolite profiling and glucose-dependence experiments showed that resistant cells had routed their metabolism through glycolysis (particularly through the pentose phosphate pathway) and exhibited disruptions in mitochondrial metabolism. These experiments are the first to report a global, integrated proteomic, transcriptomic, and metabolic analysis of TKI resistance. These data suggest that although the mechanisms are complex, targeting metabolic pathways along with TKI treatment may overcome pan-TKI resistance.

Lineage-specific events underlie aortic root aneurysm pathogenesis in Loeys-Dietz syndrome

The aortic root is the predominant site for development of aneurysm caused by heterozygous loss-of-function mutations in positive effectors of the transforming growth factor-β (TGF-β) pathway. Using a mouse model of Loeys-Dietz syndrome (LDS) that carries a heterozygous kinase-inactivating mutation in TGF-β receptor I, we found that the effects of this mutation depend on the lineage of origin of vascular smooth muscle cells (VSMCs). Secondary heart field-derived (SHF-derived), but not neighboring cardiac neural crest-derived (CNC-derived), VSMCs showed impaired Smad2/3 activation in response to TGF-β, increased expression of angiotensin II (AngII) type 1 receptor (Agtr1a), enhanced responsiveness to AngII, and higher expression of TGF-β ligands. The preserved TGF-β signaling potential in CNC-derived VSMCs associated, in vivo, with increased Smad2/3 phosphorylation. CNC-, but not SHF-specific, deletion of Smad2 preserved aortic wall architecture and reduced aortic dilation in this mouse model of LDS. Taken together, these data suggest that aortic root aneurysm predisposition in this LDS mouse model depends both on defective Smad signaling in SHF-derived VSMCs and excessive Smad signaling in CNC-derived VSMCs. This work highlights the importance of considering the regional microenvironment and specifically lineage-dependent variation in the vulnerability to mutations in the development and testing of pathogenic models for aortic aneurysm.

Proteomics reveals Rictor as a noncanonical TGF-β signaling target during aneurysm progression in Marfan mice

The objective of the present study was to 1) analyze the ascending aortic proteome within a mouse model of Marfan syndrome (MFS; Fbn1^C1041G/+) at early and late stages of aneurysm and 2) subsequently test a novel hypothesis formulated on the basis of this unbiased proteomic screen that links changes in integrin composition to transforming growth factor (TGF)-β-dependent activation of the rapamycin-independent component of mammalian target of rapamycin (Rictor) signaling pathway. Ingenuity Pathway Analysis of over 1,000 proteins quantified from the in vivo MFS mouse aorta by data-independent acquisition mass spectrometry revealed a predicted upstream regulator, Rictor, that was selectively activated in aged MFS mice. We validated this pattern of Rictor activation in vivo by Western blot analysis for phosphorylation on Thr¹¹³⁵ in a separate cohort of mice and showed in vitro that TGF-β activates Rictor in an integrin-linked kinase-dependent manner in cultured aortic vascular smooth muscle cells. Expression of β₃-integrin was upregulated in the aged MFS aorta relative to young MFS mice and wild-type mice. We showed that β₃-integrin expression and activation modulated TGF-β-induced Rictor phosphorylation in vitro, and this signaling effect was associated with an altered vascular smooth muscle cell proliferative-migratory and metabolic in vitro phenotype that parallels the in vivo aneurysm phenotype in MFS. These results reveal that Rictor is a novel, context-dependent, noncanonical TGF-β signaling effector with potential pathogenic implications in aortic aneurysm. NEW & NOTEWORTHY We present the most comprehensive quantitative analysis of the ascending aortic aneurysm proteome in Marfan syndrome to date resulting in novel and potentially wide-reaching findings that expression and signaling by β₃-integrin constitute a modulator of transforming growth factor-β-induced rapamycin-independent component of mammalian target of rapamycin (Rictor) signaling and physiology in aortic vascular smooth muscle cells.

Proteomic Architecture of Human Coronary and Aortic Atherosclerosis

BACKGOUND

The inability to detect premature atherosclerosis significantly hinders implementation of personalized therapy to prevent coronary heart disease. A comprehensive understanding of arterial protein networks and how they change in early atherosclerosis could identify new biomarkers for disease detection and improved therapeutic targets.

METHODS

Here we describe the human arterial proteome and proteomic features strongly associated with early atherosclerosis based on mass spectrometry analysis of coronary artery and aortic specimens from 100 autopsied young adults (200 arterial specimens). Convex analysis of mixtures, differential dependent network modeling, and bioinformatic analyses defined the composition, network rewiring, and likely regulatory features of the protein networks associated with early atherosclerosis and how they vary across 2 anatomic distributions.

RESULTS

The data document significant differences in mitochondrial protein abundance between coronary and aortic samples (coronary>>aortic), and between atherosclerotic and normal tissues (atherosclerotic<

CONCLUSIONS

The human arterial proteome can be viewed as a complex network whose architectural features vary considerably as a function of anatomic location and the presence or absence of atherosclerosis. The data suggest important reductions in mitochondrial protein abundance in early atherosclerosis and also identify a subset of plasma proteins that are highly predictive of angiographically defined coronary disease.

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Key Words

• atherosclerosis
• biomarkers
• mitochondria
• proteomics

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MESH Headings

• Adolescent
• Adult
• Aorta/chemistry
• Aorta/pathology
• Aortic Diseases/metabolism
• Aortic Diseases/pathology
• Atherosclerosis/metabolism
• Atherosclerosis/pathology
• Autopsy
• Biomarkers/analysis
• Coronary Artery Disease/metabolism
• Coronary Artery Disease/pathology
• Coronary Vessels/chemistry
• Coronary Vessels/pathology
• Female
• Humans
• Male
• Middle Aged
• Plaque, Atherosclerotic
• Protein Interaction Maps
• Proteins/analysis
• Proteomics/methods
• Tandem Mass Spectrometry
• Young Adult

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Grants

• R01 HL111362 NHLBI NIH HHS

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Abstract 261: Proteomic Analysis of Infrarenal and Suprarenal Aorta in a Kawasaki Disease Vasculitis Murine Model Associated with Infrarenal Abdominal Aorta Dilatation and Aneurysms

Background: Kawasaki Disease (KD) is a childhood vasculitis that leads to coronary artery aneurysms and is the leading cause of acquired heart diseases among children in the USA. We have reported that the L actobacillus casei - c ell w all e xtract (LCWE)-induced murine model of KD vasculitis and coronary arteritis also induces abdominal aorta dilatation and aneurysms (AAA) that involve exclusively the infrarenal (IR) region of the abdominal aorta (AA) without affecting the suprarenal (SR)-AA. AAA predisposes tissue to dissection and rupture, and is potentially life-threatening in humans. While differences in blood flow, tissue elasticity and different embryonic origin of vascular cells have been proposed among possible explanations for the preferential location of AAA disease in the IR aorta, the mechanism(s) for this is still not known.

Objective: To determine and compare the proteomes of the IR vs SR aorta regions in naïve mice and during KD vasculitis induced AAA development.

Methods and Results: By using data-independent mass-spectrometry, we compared the proteomes of the IR and SR aortas isolated from control naïve mice and LCWE-injected KD vasculitis mice. Proteomic analysis revealed significant differential expression of 340 proteins between the IR aorta from control vs KD mice, and 407 differentially expressed proteins between the IR and SR aorta regions of KD mice. The differentially expressed proteins include those involved in cytoskeletal reorganization, cell motility and adhesion as well as acute phase and oxidative stress responses. Ingenuity Pathway Analysis reveals the activation of key regulators of innate immune responses as well as the ER Stress response. TGF-β1, IL-6, and IL-1β are regulators of several protein that were activated in the IR aortic tissues.

Conclusion: Proteomic differences exist between normal AA vs AAA and in IR vs SR aorta regions isolated from mice developing AAA. We show that a broad inflammatory response is generated in the aneurysm which maybe critical for the development and progression of the AAA and may provide possible novel therapeutic targets. (Supported by AHA 17SDG33671141 to MNR and NIH R01AI07272607 to MA).

Abstract 41: Proteomic stratification of HPV(+) oropharyngeal squamous cell carcinoma identifies targets in insulin receptor signaling within nonresponders

Objective: The global rise of HPV(+) oropharyngeal squamous cell carcinoma (OPSCC) has generated considerable interest underlying its etiology and management. Relative to HPV(-) OPSCC, HPV(+) patients are younger and more responsive to chemoradiation, though non-responders within this sub-class of OPSCC indicate divergent molecular pathophysiology, and continue to challenge clinicians. Rather than an adaptive approach to what many consider distinct disease processes, treatment remain the same for all OPSCC. Improved molecular stratification would greatly enhance the clinician's ability to precisely tailor treatment without jeopardizing outcomes. Here, two HPV(+) OPSCC cohorts delineated based on treatment response are compared via a hybrid Data Dependent Acquisition/Data Independent Acquisition (DDA/DIA) approach via mass spectrometry, to detect low-abundance proteins and highlight pathways not detected through genomic platforms alone.

Methods: Fourteen HPV(+) OPSCC patients who underwent definitive chemoradiation were stratified as clinical responders (n=6) or nonresponders (n=8). Responders were defined as having no recurrence for at least three years after treatment. Formalin-fixed paraffin tissue (FFPE) slides of tissue at diagnosis were confirmed to be p16(+) OPSCC by an independent pathologist, microdissected to achieve &gt;80% tumor purity, and subjected to heat and pressure cycling via barocycler to enhance protein yield and identification. Equal mass of protein was digested and run on a Sciex TripleTOF mass spectrometer. Peptides identified with &lt;1% Protein FDR from DDA and DIA modes were combined into a high confidence assay library comprised of 1603 proteins, which were subsequently quantified using the openSWATH workflow. Identified proteins underwent normalization and statistical analysis using mapDIA and Ingenuity Pathway Analysis.

Results: Collectively, 1440 proteins were identified and quantified. Of those, 212 were found to be significantly different between responders and nonresponders (FDR&lt;0.01 and fold change greater than ± 1.25). Responders showed relative enrichment in targets of the insulin receptor (INSR) (p=2.86 x 10-14) whose expression pattern predicted inhibition of this pathway (z-score=-3.52). Targets linked to insulin-like growth factor 1 (IGF1R) indicated this related pathway was also inhibited (z-score=-2.60). IGF1R and INSR are both known to have oncogenic functions and increase cell growth, proliferation, and survival, with the biofunction “proliferation of cells” found to be inhibited in the responders (z-score=-1.54). Overall, tumor sub-types from non-responder patients demonstrated molecular profiles consistent with elevated growth, survival, and proliferation signaling.

Conclusions: We demonstrate the feasibility of a modified barocycler approach to obtain robust proteomic data from FFPE tissues at a comparable rate to fresh frozen tissues. We further identify via a hybrid mass spectrometry approach targets of insulin receptor signaling that may be a driver of pathogenesis at the proteomic level. Such targets may stratify HPV(+) OPSCC patients at diagnosis and help tailor the intensity of therapy to mitigate toxicities based on recurrence risk.

Citation Format: Aaron Robinson, Sarah J. Parker, Shiao L. Stephen, Beatrice S. Knudsen, Zachary S. Zumsteg, Jennifer E. Van Eyk, Allen S. Ho. Proteomic stratification of HPV(+) oropharyngeal squamous cell carcinoma identifies targets in insulin receptor signaling within nonresponders [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; April 23-25, 2017; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(23_Suppl):Abstract nr 41.

Highly Reproducible Automated Proteomics Sample Preparation Workflow for Quantitative Mass Spectrometry

Sample preparation for protein quantification by mass spectrometry requires multiple processing steps including denaturation, reduction, alkylation, protease digestion, and peptide cleanup. Scaling these procedures for the analysis of numerous complex biological samples can be tedious and time-consuming, as there are many liquid transfer steps and timed reactions where technical variations can be introduced and propagated. We established an automated sample preparation workflow with a total processing time for 96 samples of 5 h, including a 2 h incubation with trypsin. Peptide cleanup is accomplished by online diversion during the LC/MS/MS analysis. In a selected reaction monitoring (SRM) assay targeting 6 plasma biomarkers and spiked β-galactosidase, mean intraday and interday cyclic voltammograms (CVs) for 5 serum and 5 plasma samples over 5 days were <20%. In a highly multiplexed SRM assay targeting more than 70 proteins, 90% of the transitions from 6 plasma samples repeated on 3 separate days had total CVs below 20%. Similar results were obtained when the workflow was transferred to a second site: 93% of peptides had CVs below 20%. An automated trypsin digestion workflow yields uniformly processed samples in less than 5 h. Reproducible quantification of peptides was observed across replicates, days, instruments, and laboratory sites, demonstrating the broad applicability of this approach.

Bioinformatic Analysis of Coronary Disease Associated SNPs and Genes to Identify Proteins Potentially Involved in the Pathogenesis of Atherosclerosis

Factors that contribute to the onset of atherosclerosis may be elucidated by bioinformatic techniques applied to multiple sources of genomic and proteomic data. The results of genome wide association studies, such as the CardioGramPlusC4D study, expression data, such as that available from expression quantitative trait loci (eQTL) databases, along with protein interaction and pathway data available in Ingenuity Pathway Analysis (IPA), constitute a substantial set of data amenable to bioinformatics analysis. This study used bioinformatic analyses of recent genome wide association data to identify a seed set of genes likely associated with atherosclerosis. The set was expanded to include protein interaction candidates to create a network of proteins possibly influencing the onset and progression of atherosclerosis. Local average connectivity (LAC), eigenvector centrality, and betweenness metrics were calculated for the interaction network to identify top gene and protein candidates for a better understanding of the atherosclerotic disease process. The top ranking genes included some known to be involved with cardiovascular disease (APOA1, APOA5, APOB, APOC1, APOC2, APOE, CDKN1A, CXCL12, SCARB1, SMARCA4 and TERT), and others that are less obvious and require further investigation (TP53, MYC, PPARG, YWHAQ, RB1, AR, ESR1, EGFR, UBC and YWHAZ). Collectively these data help define a more focused set of genes that likely play a pivotal role in the pathogenesis of atherosclerosis and are therefore natural targets for novel therapeutic interventions.

Methods for SWATH™: Data Independent Acquisition on TripleTOF Mass Spectrometers

Data independent acquisition (DIA also termed SWATH) is an emerging technology in the field of mass spectrometry based proteomics. Although the concept of DIA has been around for over a decade, the recent advancements, in particular the speed of acquisition, of mass analyzers have pushed the technique into the spotlight and allowed for high-quality DIA data to be routinely acquired by proteomics labs. In this chapter we will discuss the protocols used for DIA acquisition using the Sciex TripleTOF mass spectrometers and data analysis using the Sciex processing software.

The Cohesive Metastasis Phenotype in Human Prostate Cancer

A critical barrier for the successful prevention and treatment of recurrent prostate cancer is detection and eradication of metastatic and therapy-resistant disease. Despite the fall in diagnoses and mortality, the reported incidence of metastatic disease has increased 72% since 2004. Prostate cancer arises in cohesive groups as intraepithelial neoplasia, migrates through muscle and leaves the gland via perineural invasion for hematogenous dissemination. Current technological advances have shown cohesive-clusters of tumor (also known as microemboli) within the circulation. Circulating tumor cell (CTC) profiles are indicative of disseminated prostate cancer, and disseminated tumor cells (DTC) are found in cohesive-clusters, a phenotypic characteristic of both radiation- and drug-resistant tumors. Recent reports in cell biology and informatics, coupled with mass spectrometry, indicate that the integrin adhesome network provides an explanation for the biophysical ability of cohesive-clusters of tumor cells to invade thorough muscle and nerve microenvironments while maintaining adhesion-dependent therapeutic resistance. Targeting cohesive-clusters takes advantage of the known ability of extracellular matrix (ECM) adhesion to promote tumor cell survival and represents an approach that has the potential to avoid the progression to drug- and radiotherapy-resistance. In the following review we will examine the evidence for development and dissemination of cohesive-clusters in metastatic prostate cancer.

Discordant signaling and autophagy response to fasting in hearts of obese mice: Implications for ischemia tolerance

Autophagy is regulated by nutrient and energy status and plays an adaptive role during nutrient deprivation and ischemic stress. Metabolic syndrome (MetS) is a hypernutritive state characterized by obesity, dyslipidemia, elevated fasting blood glucose levels, and insulin resistance. It has also been associated with impaired autophagic flux and larger-sized infarcts. We hypothesized that diet-induced obesity (DIO) affects nutrient sensing, explaining the observed cardiac impaired autophagy. We subjected male friend virus B NIH (FVBN) mice to a high-fat diet, which resulted in increased weight gain, fat deposition, hyperglycemia, insulin resistance, and larger infarcts after myocardial ischemia-reperfusion. Autophagic flux was impaired after 4 wk on a high-fat diet. To interrogate nutrient-sensing pathways, DIO mice were subjected to overnight fasting, and hearts were processed for biochemical and proteomic analysis. Obese mice failed to upregulate LC3-II or to clear p62/SQSTM1 after fasting, although mRNA for LC3B and p62/SQSTM1 were appropriately upregulated in both groups, demonstrating an intact transcriptional response to fasting. Energy- and nutrient-sensing signal transduction pathways [AMPK and mammalian target of rapamycin (mTOR)] also responded appropriately to fasting, although mTOR was more profoundly suppressed in obese mice. Proteomic quantitative analysis of the hearts under fed and fasted conditions revealed broad changes in protein networks involved in oxidative phosphorylation, autophagy, oxidative stress, protein homeostasis, and contractile machinery. In many instances, the fasting response was quite discordant between lean and DIO mice. Network analysis implicated the peroxisome proliferator-activated receptor and mTOR regulatory nodes. Hearts of obese mice exhibited impaired autophagy, altered proteome, and discordant response to nutrient deprivation.

Identification of a Set of Conserved Eukaryotic Internal Retention Time Standards for Data-independent Acquisition Mass Spectrometry

Accurate knowledge of retention time (RT) in liquid chromatography-based mass spectrometry data facilitates peptide identification, quantification, and multiplexing in targeted and discovery-based workflows. Retention time prediction is particularly important for peptide analysis in emerging data-independent acquisition (DIA) experiments such as SWATH-MS. The indexed RT approach, iRT, uses synthetic spiked-in peptide standards (SiRT) to set RT to a unit-less scale, allowing for normalization of peptide RT between different samples and chromatographic set-ups. The obligatory use of SiRTs can be costly and complicates comparisons and data integration if standards are not included in every sample. Reliance on SiRTs also prevents the inclusion of archived mass spectrometry data for generation of the peptide assay libraries central to targeted DIA-MS data analysis. We have identified a set of peptide sequences that are conserved across most eukaryotic species, termed Common internal Retention Time standards (CiRT). In a series of tests to support the appropriateness of the CiRT-based method, we show: (1) the CiRT peptides normalized RT in human, yeast, and mouse cell lysate derived peptide assay libraries and enabled merging of archived libraries for expanded DIA-MS quantitative applications; (2) CiRTs predicted RT in SWATH-MS data within a 2-min margin of error for the majority of peptides; and (3) normalization of RT using the CiRT peptides enabled the accurate SWATH-MS-based quantification of 340 synthetic isotopically labeled peptides that were spiked into either human or yeast cell lysate. To automate and facilitate the use of these CiRT peptide lists or other custom user-defined internal RT reference peptides in DIA workflows, an algorithm was designed to automatically select a high-quality subset of datapoints for robust linear alignment of RT for use. Implementations of this algorithm are available for the OpenSWATH and Skyline platforms. Thus, CiRT peptides can be used alone or as a complement to SiRTs for RT normalization across peptide spectral libraries and in quantitative DIA-MS studies.

Emerging proteomic technologies for elucidating context-dependent cellular signaling events: A big challenge of tiny proportions

Aberrant cell signaling events either drive or compensate for nearly all pathologies. A thorough description and quantification of maladaptive signaling flux in disease is a critical step in drug development, and complex proteomic approaches can provide valuable mechanistic insights. Traditional proteomics-based signaling analyses rely heavily on in vitro cellular monoculture. The characterization of these simplified systems generates a rich understanding of the basic components and complex interactions of many signaling networks, but they cannot capture the full complexity of the microenvironments in which pathologies are ultimately made manifest. Unfortunately, techniques that can directly interrogate signaling in situ often yield mass-limited starting materials that are incompatible with traditional proteomics workflows. This review provides an overview of established and emerging techniques that are applicable to context-dependent proteomics. Analytical approaches are illustrated through recent proteomics-based studies in which selective sample acquisition strategies preserve context-dependent information, and where the challenge of minimal starting material is met by optimized sensitivity and coverage. This review is organized into three major technological themes: (i) LC methods in line with MS; (ii) antibody-based approaches; (iii) MS imaging with a discussion of data integration and systems modeling. Finally, we conclude with future perspectives and implications of context-dependent proteomics.

Phosphorylation of hnRNP K by cyclin-dependent kinase 2 controls cytosolic accumulation of TDP-43

Cytosolic accumulation of TAR DNA binding protein 43 (TDP-43) is a major neuropathological feature of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). However, the mechanisms involved in TDP-43 accumulation remain largely unknown. Previously, we reported that inhibitors of cyclin-dependent kinases (CDKs) prevented cytosolic stress granule accumulation of TDP-43, correlating with depletion of heterogeneous ribonucleoprotein (hnRNP) K from stress granules. In the present study, we further investigated the relationship between TDP-43 and hnRNP K and their control by CDKs. Inhibition of CDK2 abrogated the accumulation of TDP-43 into stress granules. Phosphorylated CDK2 co-localized with accumulated TDP-43 and phosphorylated hnRNP K in stress granules. Inhibition of CDK2 phosphorylation blocked phosphorylation of hnRNP K, preventing its incorporation into stress granules. Due to interaction between hnRNP K with TDP-43, the loss of hnRNP K from stress granules prevented accumulation of TDP-43. Mutation of Ser216 and Ser284 phosphorylation sites on hnRNP K inhibited hnRNP K- and TDP-43-positive stress granule formation in transfected cells. The interaction between hnRNP K and TDP-43 was further confirmed by the loss of TDP-43 accumulation following siRNA-mediated inhibition of hnRNP K expression. A substantial decrease of CDK2 and hnRNP K expression in spinal cord motor neurons in ALS patients demonstrates a potential key role for these proteins in ALS and TDP-43 accumulation, indicating that further investigation of the association between hnRNP K and TDP-43 is warranted. Understanding how kinase activity modulates TDP-43 accumulation may provide new pharmacological targets for disease intervention.

Deregulation of subcellular biometal homeostasis through loss of the metal transporter, Zip7, in a childhood neurodegenerative disorder

Background

Aberrant biometal metabolism is a key feature of neurodegenerative disorders including Alzheimer’s and Parkinson’s diseases. Metal modulating compounds are promising therapeutics for neurodegeneration, but their mechanism of action remains poorly understood. Neuronal ceroid lipofuscinoses (NCLs), caused by mutations in CLN genes, are fatal childhood neurodegenerative lysosomal storage diseases without a cure. We previously showed biometal accumulation in ovine and murine models of the CLN6 variant NCL, but the mechanism is unknown. This study extended the concept that alteration of biometal functions is involved in pathology in these disorders, and investigated molecular mechanisms underlying impaired biometal trafficking in CLN6 disease.

Results

We observed significant region-specific biometal accumulation and deregulation of metal trafficking pathways prior to disease onset in CLN6 affected sheep. Substantial progressive loss of the ER/Golgi-resident Zn transporter, Zip7, which colocalized with the disease-associated protein, CLN6, may contribute to the subcellular deregulation of biometal homeostasis in NCLs. Importantly, the metal-complex, Zn^II(atsm), induced Zip7 upregulation, promoted Zn redistribution and restored Zn-dependent functions in primary mouse Cln6 deficient neurons and astrocytes.

Conclusions

This study demonstrates the central role of the metal transporter, Zip7, in the aberrant biometal metabolism of CLN6 variants of NCL and further highlights the key contribution of deregulated biometal trafficking to the pathology of neurodegenerative diseases. Importantly, our results suggest that Zn^II(atsm) may be a candidate for therapeutic trials for NCLs.

Angiotensin II-dependent TGF-β signaling contributes to Loeys-Dietz syndrome vascular pathogenesis

Loeys-Dietz syndrome (LDS) is a connective tissue disorder that is characterized by a high risk for aneurysm and dissection throughout the arterial tree and phenotypically resembles Marfan syndrome. LDS is caused by heterozygous missense mutations in either TGF-β receptor gene (TGFBR1 or TGFBR2), which are predicted to result in diminished TGF-β signaling; however, aortic surgical samples from patients show evidence of paradoxically increased TGF-β signaling. We generated 2 knockin mouse strains with LDS mutations in either Tgfbr1 or Tgfbr2 and a transgenic mouse overexpressing mutant Tgfbr2. Knockin and transgenic mice, but not haploinsufficient animals, recapitulated the LDS phenotype. While heterozygous mutant cells had diminished signaling in response to exogenous TGF-β in vitro, they maintained normal levels of Smad2 phosphorylation under steady-state culture conditions, suggesting a chronic compensation. Analysis of TGF-β signaling in the aortic wall in vivo revealed progressive upregulation of Smad2 phosphorylation and TGF-β target gene output, which paralleled worsening of aneurysm pathology and coincided with upregulation of TGF-β1 ligand expression. Importantly, suppression of Smad2 phosphorylation and TGF-β1 expression correlated with the therapeutic efficacy of the angiotensin II type 1 receptor antagonist losartan. Together, these data suggest that increased TGF-β signaling contributes to postnatal aneurysm progression in LDS.

Kinase Inhibitor Screening Identifies Cyclin-Dependent Kinases and Glycogen Synthase Kinase 3 as Potential Modulators of TDP-43 Cytosolic Accumulation during Cell Stress

Abnormal processing of TAR DNA binding protein 43 (TDP-43) has been identified as a major factor in neuronal degeneration during amyotrophic lateral sclerosis (ALS) or frontotemporal lobar degeneration (FTLD). It is unclear how changes to TDP-43, including nuclear to cytosolic translocation and subsequent accumulation, are controlled in these diseases. TDP-43 is a member of the heterogeneous ribonucleoprotein (hnRNP) RNA binding protein family and is known to associate with cytosolic RNA stress granule proteins in ALS and FTLD. hnRNP trafficking and accumulation is controlled by the action of specific kinases including members of the mitogen-activated protein kinase (MAPK) pathway. However, little is known about how kinase pathways control TDP-43 movement and accumulation. In this study, we used an in vitro model of TDP-43-positve stress granule formation to screen for the effect of kinase inhibitors on TDP-43 accumulation. We found that while a number of kinase inhibitors, particularly of the MAPK pathways modulated both TDP-43 and the global stress granule marker, human antigen R (HuR), multiple inhibitors were more specific to TDP-43 accumulation, including inhibitors of cyclin-dependent kinases (CDKs) and glycogen synthase kinase 3 (GSK3). Close correlation was observed between effects of these inhibitors on TDP-43, hnRNP K and TIAR, but often with different effects on HuR accumulation. This may indicate a potential interaction between TDP-43, hnRNP K and TIAR. CDK inhibitors were also found to reverse pre-formed TDP-43-positive stress granules and both CDK and GSK3 inhibitors abrogated the accumulation of C-terminal TDP-43 (219–414) in transfected cells. Further studies are required to confirm the specific kinases involved and whether their action is through phosphorylation of the TDP-43 binding partner hnRNP K. This knowledge provides a valuable insight into the mechanisms controlling abnormal cytoplasmic TDP-43 accumulation and may herald new opportunities for kinase modulation-based therapeutic intervention in ALS and FTLD.