ABDS: a bioinformatics tool suite for analyzing biologically diverse samples.

Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel. We developed the ABDS tool suite specifically for analyzing biologically diverse samples. Collectively, a mechanism-integrated group-wise pre-imputation scheme is proposed to retain informative missingness associated with signature genes, a cosine-based one-sample test is extended to detect group-silenced signature genes, and a unified heatmap is designed to display multiple sample groups. We describe the methodological principles and demonstrate the effectiveness of three analytics tools under targeted scenarios, supported by comparative evaluations and biomedical showcases. As an open-source R package, ABDS tool suite complements rather than replaces existing tools and will allow biologists to more accurately detect interpretable molecular signals among phenotypically diverse sample groups.

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.

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.

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.

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.

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.

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.

Proteomics of novel induced pluripotent stem cell-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 other diseases 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, which 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 nontissue-specific pan vascular ECs (iECs) with high purity. These iECs present with canonical endothelial cell markers and exhibit measures of endothelial cell functionality with the uptake of Dil fluorescent dye-labeled acetylated low-density lipoprotein (Dil-Ac-LDL) and tube formation. Using proteomic analysis, we revealed that the iECs are more proteomically similar to established human umbilical vein ECs (HUVECs) than to iPSCs. Posttranslational 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.NEW & NOTEWORTHY We have developed methods to differentiate induced pluripotent stem cells (iPSCs) across multiple iPSC lines into nontissue-specific pan vascular ECs (iECs) and demonstrated the proteomic similarity of these cells to a widely used endothelial cell line (HUVECs). We also identified posttranslational modifications and targets for increasing the proteomic similarity of iECs to HUVECs. In the future, iECs can be used to study 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.

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.

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.

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.

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.

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.

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

Objective: Very few clinical predictors of descending thoracic aorta dissection have been determined. Although aneurysms can dissect in a size-dependent process, most descending dissections will occur without prior enlargement. We compared the proteomic profiles of normal, dissected, aneurysm, and both aneurysm and dissected descending thoracic aortas to identify novel biomarkers and further understand the molecular pathways that lead to tissue at risk of dissection. Methods: We performed proteomic profiling of descending thoracic aortas with four phenotypes: normal (n = 46), aneurysm (n = 22), dissected (n = 12), and combined aneurysm and dissection (n = 8). Pairwise differential protein expression analyses using a Bayesian approach were then performed to identify common proteins that were dysregulated between each diseased tissue type and control aorta and to uncover unique proteins between aneurysmal and dissected aortas. Network and Markov cluster algorithms of differentially expressed proteins were used to find enriched ontology processes. A convex analysis of mixtures was also performed to identify the molecular subtypes within the different tissue types. Results: The diseased aortas had 71 common differentially expressed proteins compared with the control, including higher amounts of the protein thrombospondin 1. We found 42 differentially expressed proteins between the aneurysm and dissected tissue, with an abundance of apolipoproteins in the former and higher quantities of extracellular matrix proteins in the latter. The convex analysis of mixtures showed enhancement of a molecular subtype enriched in contractile proteins within the control tissue compared with the diseased tissue, in addition to increased proportions of molecular subtypes enriched in inflammation and red blood cell expression in the aneurysmal compared with the dissected tissue. Conclusions: We found some overlapping differentially expressed proteins in aneurysmal and nonaneurysmal descending thoracic aortas at risk of dissection compared with normal aortas. However, we also found uniquely altered molecular pathways that might uncover mechanisms for dissection.

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.

Comparative assessment and novel strategy on methods for imputing proteomics data.

Missing values are a major issue in quantitative proteomics analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, a comparative assessment of imputation accuracy remains inconclusive, mainly because mechanisms contributing to true missing values are complex and existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of future methodological development. We first re-evaluate the performance of eight representative methods targeting three typical missing mechanisms. These methods are compared on both simulated and masked missing values embedded within real proteomics datasets, and performance is evaluated using three quantitative measures. We then introduce fused regularization matrix factorization, a low-rank global matrix factorization framework, capable of integrating local similarity derived from additional data types. We also explore a biologically-inspired latent variable modeling strategy-convex analysis of mixtures-for missing value imputation and present preliminary experimental results. While some winners emerged from our comparative assessment, the evaluation is intrinsically imperfect because performance is evaluated indirectly on artificial missing or masked values not authentic missing values. Nevertheless, we show that our fused regularization matrix factorization provides a novel incorporation of external and local information, and the exploratory implementation of convex analysis of mixtures presents a biologically plausible new approach.

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, ß, and δ concentration, whereas CDC treatment led to a reversion of PKCß. Use of PKC isoform specific inhibition and overexpression of various PKC isoforms strongly suggests that PKCß 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 PKCß upregulation, PKCß merits being classified as a potential therapeutic target in HFpEF, a disease notoriously refractory to medical intervention.