RESUMEN

This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on basic principles in biomarker discovery in an interactive format that uses appropriate cloud resources for data access and analyses. In collaboration with Google Cloud, Deloitte Consulting and NIGMS, the Rhode Island INBRE Molecular Informatics Core developed a cloud-based training module for biomarker discovery. The module consists of nine submodules covering various topics on biomarker discovery and assessment and is deployed on the Google Cloud Platform and available for public use through the NIGMS Sandbox. The submodules are written as a series of Jupyter Notebooks utilizing R and Bioconductor for biomarker and omics data analysis. The submodules cover the following topics: 1) introduction to biomarkers; 2) introduction to R data structures; 3) introduction to linear models; 4) introduction to exploratory analysis; 5) rat renal ischemia-reperfusion injury case study; (6) linear and logistic regression for comparison of quantitative biomarkers; 7) exploratory analysis of proteomics IRI data; 8) identification of IRI biomarkers from proteomic data; and 9) machine learning methods for biomarker discovery. Each notebook includes an in-line quiz for self-assessment on the submodule topic and an overview video is available on YouTube (https://www.youtube.com/watch?v=2-Q9Ax8EW84). This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Asunto(s)

Biomarcadores , Nube Computacional , Biomarcadores/metabolismo , Animales , Programas Informáticos , Humanos , Ratas , Aprendizaje Automático , Biología Computacional/métodos

RESUMEN

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) generates genome-wide chromatin accessibility profiles, providing valuable insights into epigenetic gene regulation at both pooled-cell and single-cell population levels. Comprehensive analysis of ATAC-seq data involves the use of various interdependent programs. Learning the correct sequence of steps needed to process the data can represent a major hurdle. Selecting appropriate parameters at each stage, including pre-analysis, core analysis, and advanced downstream analysis, is important to ensure accurate analysis and interpretation of ATAC-seq data. Additionally, obtaining and working within a limited computational environment presents a significant challenge to non-bioinformatic researchers. Therefore, we present Cloud ATAC, an open-source, cloud-based interactive framework with a scalable, flexible, and streamlined analysis framework based on the best practices approach for pooled-cell and single-cell ATAC-seq data. These frameworks use on-demand computational power and memory, scalability, and a secure and compliant environment provided by the Google Cloud. Additionally, we leverage Jupyter Notebook's interactive computing platform that combines live code, tutorials, narrative text, flashcards, quizzes, and custom visualizations to enhance learning and analysis. Further, leveraging GPU instances has significantly improved the run-time of the single-cell framework. The source codes and data are publicly available through NIH Cloud lab https://github.com/NIGMS/ATAC-Seq-and-Single-Cell-ATAC-Seq-Analysis. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Asunto(s)

Nube Computacional , Secuenciación de Nucleótidos de Alto Rendimiento , Programas Informáticos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Humanos , Biología Computacional/métodos , Secuenciación de Inmunoprecipitación de Cromatina/métodos , Análisis de la Célula Individual/métodos , Cromatina/genética , Cromatina/metabolismo

RESUMEN

This study describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module is designed to facilitate interactive learning of whole-genome bisulfite sequencing (WGBS) data analysis utilizing cloud-based tools in Google Cloud Platform, such as Cloud Storage, Vertex AI notebooks and Google Batch. WGBS is a powerful technique that can provide comprehensive insights into DNA methylation patterns at single cytosine resolution, essential for understanding epigenetic regulation across the genome. The designed learning module first provides step-by-step tutorials that guide learners through two main stages of WGBS data analysis, preprocessing and the identification of differentially methylated regions. And then, it provides a streamlined workflow and demonstrates how to effectively use it for large datasets given the power of cloud infrastructure. The integration of these interconnected submodules progressively deepens the user's understanding of the WGBS analysis process along with the use of cloud resources. Through this module, we can enhance the accessibility and adoption of cloud computing in epigenomic research, speeding up the advancements in the related field and beyond. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Asunto(s)

Nube Computacional , Metilación de ADN , Programas Informáticos , Secuenciación Completa del Genoma , Secuenciación Completa del Genoma/métodos , Sulfitos/química , Humanos , Epigénesis Genética , Biología Computacional/métodos

RESUMEN

Motivation: Understanding genetic variation at the single-cell level is crucial for insights into cellular heterogeneity, clonal evolution, and gene expression regulation, but there is a scarcity of tools for visualizing and analyzing cell-level genetic variants. Results: We introduce scSNViz, a comprehensive R-based toolset for visualization and analysis of cell-specific expressed Single Nucleotide Variants (sceSNVs) within cell-barcoded single-cell RNA-sequencing (scRNA-seq) data. ScSNViz offers 3D sceSNV visualization capabilities for dimensionally reduced scRNA-seq gene expression data, compatibility with popular scRNA-seq processing tools like Seurat, cell-type classification tools such as SingleR and scType, and trajectory inference computation using Slingshot. Furthermore, scSNViz conducts estimation, summary, and graphical representation of statistical metrics pertaining to sceSNVs distribution and expression across individual cells. It also provides support for the analysis of individual sceSNVs as well as sets comprising multiple expressed sceSNVs of interest. Availability: ScSNViz is implemented as user-friendly R-scripts, freely available on https://horvathlab.github.io/NGS/scSNViz , supported by help utilities, and requiring no specialized bioinformatics skills for use.

RESUMEN

This manuscript describes the development of a module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox . The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on machine learning and decision tree concepts in an interactive format that uses appropriate cloud resources for data access and analyses. Machine learning (ML) is an important tool in biomedical research and can lead to improvements in diagnosis, treatment, and prevention of diseases. During the COVID pandemic ML was used for predictions at the patient and community levels. Given its ubiquity, it is important that future doctors, researchers and teachers get acquainted with ML and its contributions to research. Our goal is to make it easier for everyone to learn about machine learning. The learning module we present here is based on a small COVID dataset, videos, annotated code and the use of Google Colab or the Google Cloud Platform (GCP). The benefit of these platforms is that students do not have to set up a programming environment on their computer which saves time and is also an important democratization factor. The module focuses on learning the basics of decision trees by applying them to COVID data. It introduces basic terminology used in supervised machine learning and its relevance to research. Our experience with biology students at San Francisco State University suggests that the material increases interest in ML.

RESUMEN

While rural-urban disparities in health and health outcomes have been demonstrated, because of their impact on (and intervenability to improve) health and health outcomes, we sought to examine cross-sectional and longitudinal inequities in health, clinical care, health behaviors, and social determinants of health (SDOH) between rural and non-rural counties in the pre-pandemic era (2015 to 2019), and to present a Health Equity Dashboard that can be used by policymakers and researchers to facilitate examining such disparities. Therefore, using data obtained from 2015-2022 County Health Rankings datasets, we used analysis of variance to examine differences in 33 county level attributes between rural and non-rural counties, calculated the change in values for each measure between 2015 and 2019, determined whether rural-urban disparities had widened, and used those data to create a Health Equity Dashboard that displays county-level individual measures or compilations of them. We followed STROBE guidelines in writing the manuscript. We found that rural counties overwhelmingly had worse measures of SDOH at the county level. With few exceptions, the measures we examined were getting worse between 2015 and 2019 in all counties, relatively more so in rural counties, resulting in the widening of rural-urban disparities in these measures. When rural-urban gaps narrowed, it tended to be in measures wherein rural counties were outperforming urban ones in the earlier period. In conclusion, our findings highlight the need for policymakers to prioritize rural settings for interventions designed to improve health outcomes, likely through improving health behaviors, clinical care, social and environmental factors, and physical environment attributes. Visualization tools can help guide policymakers and researchers with grounded information, communicate necessary data to engage relevant stakeholders, and track SDOH changes and health outcomes over time.

RESUMEN

Folded proteins are assumed to be built upon fixed scaffolds of secondary structure, α-helices and ß-sheets. Experimentally determined structures of >58,000 non-redundant proteins support this assumption, though it has recently been challenged by ~100 fold-switching proteins. Though ostensibly rare, these proteins raise the question of how many uncharacterized proteins have shapeshifting-rather than fixed-secondary structures. Here, we use a comparative sequence-based approach to predict fold switching in the universally conserved NusG transcription factor family, one member of which has a 50-residue regulatory subunit experimentally shown to switch between α-helical and ß-sheet folds. Our approach predicts that 24% of sequences in this family undergo similar α-helix ⇌ ß-sheet transitions. While these predictions cannot be reproduced by other state-of-the-art computational methods, they are confirmed by circular dichroism and nuclear magnetic resonance spectroscopy for 10 out of 10 sequence-diverse variants. This work suggests that fold switching may be a pervasive mechanism of transcriptional regulation in all kingdoms of life.

Asunto(s)

Factores de Transcripción , Secuencia de Aminoácidos , Conformación Proteica en Hélice alfa , Conformación Proteica en Lámina beta , Dominios Proteicos

RESUMEN

Molecular switches that respond to a biochemical stimulus in cells have proven utility as a foundation for developing molecular sensors and actuators that could be used to address important biological questions. Developing a molecular switch unfortunately remains difficult as it requires elaborate coordination of sensing and actuation mechanisms built into a single molecule. Here, we rationally designed a molecular switch that changes its subcellular localization in response to an intended stimulus such as an activator of protein kinase A (PKA). By arranging the sequence for Kemptide in tandem, we designed a farnesylated peptide whose localization can dramatically change upon phosphorylation by PKA. After testing a different valence number of Kemptide as well as modulating the linker sequence connecting them, we identified an efficient peptide switch that exhibited dynamic translocation between plasma membranes and internal endomembranes in a PKA activity dependent manner. Due to the modular design and small size, our PKA switch can have versatile utility in future studies as a platform for visualizing and perturbing signal transduction pathways, as well as for performing synthetic operations in cells.

Asunto(s)

Proteínas Quinasas Dependientes de AMP Cíclico/metabolismo , Proteínas de la Membrana/metabolismo , Péptidos/síntesis química , Electricidad Estática , Células HeLa , Humanos , Transporte de Proteínas , Transducción de Señal

RESUMEN

Although most experimentally characterized proteins with similar sequences assume the same folds and perform similar functions, an increasing number of exceptions is emerging. One class of exceptions comprises sequence-similar fold switchers, whose secondary structures shift from α-helix <-> ß-sheet through a small number of mutations, a sequence insertion, or a deletion. Predictive methods for identifying sequence-similar fold switchers are desirable because some are associated with disease and/or can perform different functions in cells. Here, we use homology-based secondary structure predictions to identify sequence-similar fold switchers from their amino acid sequences alone. To do this, we predicted the secondary structures of sequence-similar fold switchers using three different homology-based secondary structure predictors: PSIPRED, JPred4, and SPIDER3. We found that α-helix <-> ß-strand prediction discrepancies from JPred4 discriminated between the different conformations of sequence-similar fold switchers with high statistical significance (P < 1.8*10-19 ). Thus, we used these discrepancies as a classifier and found that they can often robustly discriminate between sequence-similar fold switchers and sequence-similar proteins that maintain the same folds (Matthews Correlation Coefficient of 0.82). We found that JPred4 is a more robust predictor of sequence-similar fold switchers because of (a) the curated sequence database it uses to produce multiple sequence alignments and (b) its use of sequence profiles based on Hidden Markov Models. Our results indicate that inconsistencies between JPred4 secondary structure predictions can be used to identify some sequence-similar fold switchers from their sequences alone. Thus, the negative information from inconsistent secondary structure predictions can potentially be leveraged to identify sequence-similar fold switchers from the broad base of genomic sequences.

Asunto(s)

Pliegue de Proteína , Proteínas , Secuencia de Aminoácidos , Estructura Secundaria de Proteína , Alineación de Secuencia

RESUMEN

Fold-switching proteins respond to cellular stimuli by remodeling their secondary structures and changing their functions. Whereas several previous reviews have focused on various structural, physical-chemical, and evolutionary aspects of this newly emerging class of proteins, this minireview focuses on how fold switching modulates protein function and regulates biological processes. It first compares and contrasts fold switchers with other known types of proteins. Second, it presents examples of how various proteins can change their functions through fold switching. Third, it demonstrates that fold switchers can regulate biological processes by discussing two proteins, RfaH and KaiB, whose dramatic secondary structure remodeling events directly affect gene expression and a circadian clock, respectively. Finally, this minireview discusses how the field of protein fold switching might advance.

Asunto(s)

Proteínas Bacterianas/química , Péptidos y Proteínas de Señalización del Ritmo Circadiano/química , Proteínas de Escherichia coli/química , Factores de Elongación de Péptidos/química , Transducción de Señal , Transactivadores/química , Proteínas Bacterianas/metabolismo , Péptidos y Proteínas de Señalización del Ritmo Circadiano/metabolismo , Proteínas de Escherichia coli/metabolismo , Factores de Elongación de Péptidos/metabolismo , Pliegue de Proteína , Transactivadores/metabolismo

RESUMEN

Protein Kinase A (PKA) exists as a tetrameric holoenzyme which activates with increase of cAMP and plays an important role in many physiological processes including cardiac physiology, neuronal development, and adipocyte function. Although this kinase has been the subject of numerous biosensor designs, a single-fluorophore reporter that performs comparably to Förster resonance energy transfer (FRET) has not yet been reported. Here, we have used basic observations of electrostatic interactions in PKA substrate recognition mechanism and nucleus localization sequence motif to design a phosphorylation switch that shuttles between the cytosol and the nucleus, a strategy that should be generalizable to all basophilic kinases. The resulting reporter yielded comparable kinetics and dynamic range to the PKA FRET reporter, AKAR3EV. We also performed basic characterization and demonstrated its potential use in monitoring multiple signaling molecules inside cells using basic fluorescence microscopy. Due to the single-fluorophore nature of this reporter, we envision that this could find broad applications in studies involving single cell analysis of PKA activity.

Asunto(s)

Núcleo Celular/metabolismo , Proteínas Quinasas Dependientes de AMP Cíclico/metabolismo , Transferencia Resonante de Energía de Fluorescencia , Transporte Activo de Núcleo Celular , Secuencias de Aminoácidos , Proteínas Quinasas Dependientes de AMP Cíclico/química , Células HeLa , Humanos , Cinética , Señales de Localización Nuclear/química , Fosforilación , Electricidad Estática

RESUMEN

Unraveling the spatiotemporal dynamics of 5'-AMP-activated protein kinase (AMPK) signaling is necessary to bridge the gap between nutrient signaling and downstream function. Three genetically encoded Förster Resonance Energy Transfer (FRET)-based AMPK biosensors are available yielding insight into how AMPK-derived signal propagates throughout a cell in response to particular inputs. These findings, together with accumulating evidence obtained from biochemical techniques, promise to give a holistic understanding of the AMPK signaling. In this protocol, we describe the procedures and materials required for imaging intracellular AMPK activity in an organelle-specific manner, with a focus on ABKAR, a FRET-based biosensor. In addition, we introduce a novel AMPK inhibitor peptide that allows us to inhibit AMPK activity at specific subcellular compartments.

Asunto(s)

Proteínas Quinasas Activadas por AMP/metabolismo , Técnicas Biosensibles/métodos , Transferencia Resonante de Energía de Fluorescencia/métodos , Microscopía Intravital/métodos , Imagen Molecular/métodos , Animales , Técnicas Biosensibles/instrumentación , Células COS , Chlorocebus aethiops , Fibroblastos , Transferencia Resonante de Energía de Fluorescencia/instrumentación , Células HeLa , Humanos , Microscopía Intravital/instrumentación , Proteínas Luminiscentes/química , Proteínas Luminiscentes/genética , Ratones , Microscopía Fluorescente/instrumentación , Microscopía Fluorescente/métodos , Imagen Molecular/instrumentación , Orgánulos/metabolismo , Transducción de Señal , Programas Informáticos

RESUMEN

PURPOSE: Our research goal was to complete a retrospective chart review to determine if there is a correlation between the level of diabetic retinopathy and diabetic neurosensory hearing loss. METHODS: A retrospective analysis of 175 Department of Veterans Affairs Computerized Patient Record System charts was completed at the VA Portland Health Care System. Subjects were classified by degree of diabetic retinopathy as follows: no diabetic retinopathy (n = 80), mild nonproliferative diabetic retinopathy (n = 51), moderate nonproliferative diabetic retinopathy (n = 25), and combined severe nonproliferative diabetic retinopathy and proliferative diabetic retinopathy (PDR) (n = 17). Degree of sensorineural hearing was collected for each ear. Additionally, measures of diabetic control, including hemoglobin A1C, and creatinine, were recorded. RESULTS: After controlling for diabetic control, as measured by HbA1C and creatinine, level of diabetic retinopathy was significantly associated with hearing loss severity in both ears (right ear, P = .018 and left ear, P = .007). CONCLUSIONS: When adjusted to include diabetes control, the severity of diabetic retinopathy showed a correlation with degree of hearing loss at most levels. Because of this association, recommendation for hearing evaluations may be considered for those with mild, moderate, or severe nonproliferative or proliferative diabetic retinopathy.

Asunto(s)

Retinopatía Diabética/fisiopatología , Pérdida Auditiva Sensorineural/fisiopatología , Creatinina/sangre , Diabetes Mellitus Tipo 2/complicaciones , Hemoglobina Glucada/metabolismo , Pruebas Auditivas , Humanos , Masculino , Persona de Mediana Edad , Estudios Retrospectivos , Estados Unidos , Salud de los Veteranos

RESUMEN

Biological phenomena, such as cellular differentiation and phagocytosis, are fundamental processes that enable cells to fulfill important physiological roles in multicellular organisms. In the field of synthetic biology, the study of these behaviors relies on the use of a broad range of molecular tools that enable the real-time manipulation and measurement of key components in the underlying signaling pathways. This Review will focus on a subset of synthetic biology tools known as bottom-up techniques, which use technologies such as optogenetics and chemically induced dimerization to reconstitute cellular behavior in cells. These techniques have been crucial not only in revealing causal relationships within signaling networks but also in identifying the minimal signaling components that are necessary for a given cellular function. We discuss studies that used these systems in a broad range of cellular and molecular phenomena, including the time-dependent modulation of protein activity in cellular proliferation and differentiation, the reconstitution of phagocytosis, the reconstitution of chemotaxis, and the regulation of actin reorganization. Finally, we discuss the potential contribution of synthetic biology to medicine.

Asunto(s)

Diferenciación Celular , Proliferación Celular , Fagocitosis , Transducción de Señal , Biología Sintética , Animales , Humanos , Optogenética

RESUMEN

Protein temporal dynamics play a critical role in time-dimensional pathophysiological processes, including the gradual cardiac remodeling that occurs in early-stage heart failure. Methods for quantitative assessments of protein kinetics are lacking, and despite knowledge gained from single-protein studies, integrative views of the coordinated behavior of multiple proteins in cardiac remodeling are scarce. Here, we developed a workflow that integrates deuterium oxide (2H2O) labeling, high-resolution mass spectrometry (MS), and custom computational methods to systematically interrogate in vivo protein turnover. Using this workflow, we characterized the in vivo turnover kinetics of 2,964 proteins in a mouse model of ß-adrenergic-induced cardiac remodeling. The data provided a quantitative and longitudinal view of cardiac remodeling at the molecular level, revealing widespread kinetic regulations in calcium signaling, metabolism, proteostasis, and mitochondrial dynamics. We translated the workflow to human studies, creating a reference dataset of 496 plasma protein turnover rates from 4 healthy adults. The approach is applicable to short, minimal label enrichment and can be performed on as little as a single biopsy, thereby overcoming critical obstacles to clinical investigations. The protein turnover quantitation experiments and computational workflow described here should be widely applicable to large-scale biomolecular investigations of human disease mechanisms with a temporal perspective.

Asunto(s)

Corazón/efectos de los fármacos , Isoproterenol/farmacología , Miocardio/metabolismo , Proteínas/metabolismo , Agonistas Adrenérgicos beta/farmacología , Adulto , Animales , Señalización del Calcio , Óxido de Deuterio , Insuficiencia Cardíaca/etiología , Insuficiencia Cardíaca/metabolismo , Humanos , Cinética , Masculino , Espectrometría de Masas , Ratones , Ratones Endogámicos ICR , Mitocondrias Cardíacas/metabolismo , Proteínas Musculares/metabolismo

RESUMEN

Proteasome complexes play essential roles in maintaining cellular protein homeostasis and serve fundamental roles in cardiac function under normal and pathological conditions. A functional detriment in proteasomal activities has been recognized as a major contributor to the progression of cardiovascular diseases. Therefore, approaches to restore proteolytic function within the setting of the diseased myocardium would be of great clinical significance. In this study, we discovered that the cardiac proteasomal activity could be regulated by acetylation. Histone deacetylase (HDAC) inhibitors (suberoylanilide hydroxamic acid and sodium valproate) enhanced the acetylation of 20S proteasome subunits in the myocardium and led to an elevation of proteolytic capacity. This regulatory paradigm was present in both healthy and acutely ischemia/reperfusion (I/R) injured murine hearts, and HDAC inhibition in vitro restored proteolytic capacities to baseline sham levels in injured hearts. This mechanism of regulation was also viable in failing human myocardium. With 20S proteasomal complexes purified from murine myocardium treated with HDAC inhibitors in vivo, we confirmed that acetylation of 20S subunits directly, at least in part, presents a molecular explanation for the improvement in function. Furthermore, using high-resolution LC-MS/MS, we unraveled the first cardiac 20S acetylome, which identified the acetylation of nine N-termini and seven internal lysine residues. Acetylation on four lysine residues and four N-termini on cardiac proteasomes were novel discoveries of this study. In addition, the acetylation of five lysine residues was inducible via HDAC inhibition, which correlated with the enhancement of 20S proteasomal activity. Taken as a whole, our investigation unveiled a novel mechanism of proteasomal function regulation in vivo and established a new strategy for the potential rescue of compromised proteolytic function in the failing heart using HDAC inhibitors.

Asunto(s)

Ventrículos Cardíacos/efectos de los fármacos , Inhibidores de Histona Desacetilasas/farmacología , Histona Desacetilasas/genética , Ácidos Hidroxámicos/farmacología , Daño por Reperfusión Miocárdica/enzimología , Miocardio/enzimología , Ácido Valproico/farmacología , Acetilación/efectos de los fármacos , Animales , Cromatografía Liquida , Regulación de la Expresión Génica , Insuficiencia Cardíaca/patología , Insuficiencia Cardíaca/cirugía , Trasplante de Corazón , Ventrículos Cardíacos/enzimología , Ventrículos Cardíacos/patología , Histona Desacetilasas/metabolismo , Humanos , Lisina/metabolismo , Masculino , Ratones , Daño por Reperfusión Miocárdica/tratamiento farmacológico , Daño por Reperfusión Miocárdica/genética , Daño por Reperfusión Miocárdica/patología , Miocardio/patología , Complejo de la Endopetidasa Proteasomal/efectos de los fármacos , Complejo de la Endopetidasa Proteasomal/genética , Complejo de la Endopetidasa Proteasomal/aislamiento & purificación , Complejo de la Endopetidasa Proteasomal/metabolismo , Proteolisis/efectos de los fármacos , Transducción de Señal , Espectrometría de Masas en Tándem , Vorinostat

RESUMEN

RATIONALE: Omics sciences enable a systems-level perspective in characterizing cardiovascular biology. Integration of diverse proteomics data via a computational strategy will catalyze the assembly of contextualized knowledge, foster discoveries through multidisciplinary investigations, and minimize unnecessary redundancy in research efforts. OBJECTIVE: The goal of this project is to develop a consolidated cardiac proteome knowledgebase with novel bioinformatics pipeline and Web portals, thereby serving as a new resource to advance cardiovascular biology and medicine. METHODS AND RESULTS: We created Cardiac Organellar Protein Atlas Knowledgebase (COPaKB; www.HeartProteome.org), a centralized platform of high-quality cardiac proteomic data, bioinformatics tools, and relevant cardiovascular phenotypes. Currently, COPaKB features 8 organellar modules, comprising 4203 LC-MS/MS experiments from human, mouse, drosophila, and Caenorhabditis elegans, as well as expression images of 10,924 proteins in human myocardium. In addition, the Java-coded bioinformatics tools provided by COPaKB enable cardiovascular investigators in all disciplines to retrieve and analyze pertinent organellar protein properties of interest. CONCLUSIONS: COPaKB provides an innovative and interactive resource that connects research interests with the new biological discoveries in protein sciences. With an array of intuitive tools in this unified Web server, nonproteomics investigators can conveniently collaborate with proteomics specialists to dissect the molecular signatures of cardiovascular phenotypes.

Asunto(s)

Bases de Datos de Proteínas , Bases del Conocimiento , Proteínas Musculares/metabolismo , Miocardio/metabolismo , Proteómica/métodos , Biología de Sistemas , Integración de Sistemas , Acceso a la Información , Animales , Caenorhabditis elegans , Difusión de Innovaciones , Drosophila , Humanos , Ratones , Diseño de Software , Flujo de Trabajo

RESUMEN

The innovations in mass spectrometry-based investigations in proteome biology enable systematic characterization of molecular details in pathophysiological phenotypes. However, the process of delineating large-scale raw proteomic datasets into a biological context requires high-throughput data acquisition and processing. A spectral library search engine makes use of previously annotated experimental spectra as references for subsequent spectral analyses. This workflow delivers many advantages, including elevated analytical efficiency and specificity as well as reduced demands in computational capacity. In this study, we created a spectral matching engine to address challenges commonly associated with a library search workflow. Particularly, an improved sliding dot product algorithm, that is robust to systematic drifts of mass measurement in spectra, is introduced. Furthermore, a noise management protocol distinguishes spectra correlation attributed from noise and peptide fragments. It enables elevated separation between target spectral matches and false matches, thereby suppressing the possibility of propagating inaccurate peptide annotations from library spectra to query spectra. Moreover, preservation of original spectra also accommodates user contributions to further enhance the quality of the library. Collectively, this search engine supports reproducible data analyses using curated references, thereby broadening the accessibility of proteomics resources to biomedical investigators. This article is part of a Special Issue entitled: From protein structures to clinical applications.

Asunto(s)

Algoritmos , Bases de Datos de Proteínas , Espectrometría de Masas/métodos , Biblioteca de Péptidos , Animales , Ratones

RESUMEN

Mitochondrial dysfunction is associated with many human diseases. Mitochondrial damage is exacerbated by inadequate protein quality control and often further contributes to pathogenesis. The maintenance of mitochondrial functions requires a delicate balance of continuous protein synthesis and degradation, i.e. protein turnover. To understand mitochondrial protein dynamics in vivo, we designed a metabolic heavy water ((2)H(2)O) labeling strategy customized to examine individual protein turnover in the mitochondria in a systematic fashion. Mice were fed with (2)H(2)O at a minimal level (<5% body water) without physiological impacts. Mitochondrial proteins were analyzed from 9 mice at each of the 13 time points between 0 and 90 days (d) of labeling. A novel multiparameter fitting approach computationally determined the normalized peak areas of peptide mass isotopomers at initial and steady-state time points and permitted the protein half-life to be determined without plateau-level (2)H incorporation. We characterized the turnover rates of 458 proteins in mouse cardiac and hepatic mitochondria and found median turnover rates of 0.0402 d(-1) and 0.163 d(-1), respectively, corresponding to median half-lives of 17.2 d and 4.26 d. Mitochondria in the heart and those in the liver exhibited distinct turnover kinetics, with limited synchronization within functional clusters. We observed considerable interprotein differences in turnover rates in both organs, with half-lives spanning from hours to months (≈ 60 d). Our proteomics platform demonstrates the first large-scale analysis of mitochondrial protein turnover rates in vivo, with potential applications in translational research.

Asunto(s)

Mitocondrias Cardíacas/metabolismo , Mitocondrias Hepáticas/metabolismo , Proteínas Mitocondriales/metabolismo , Biosíntesis de Proteínas , Proteolisis , Proteoma/metabolismo , Secuencia de Aminoácidos , Animales , Óxido de Deuterio , Semivida , Marcaje Isotópico , Ratones

RESUMEN

The research question examined in this study was: Does a promotional campaign impact the sales of heart-healthy menu items at community restaurants? The 8-week promotional campaign used professionally developed advertisements in daily and monthly print publications and posters and table tents in local restaurants. Nine restaurants tracked the sales of selected heart-healthy menu items and comparable menu items sold before and after a promotional campaign. The percentage of heart-healthy items sold after the campaign showed a trend toward a slight increase in heart-healthy menu item selections, although it was not statistically significant. This study and others indicate that dietetics professionals must continue to develop strategies to promote heart-healthy food choices in community restaurants.

Asunto(s)

Enfermedades Cardiovasculares/prevención & control , Comportamiento del Consumidor , Alimentos/economía , Promoción de la Salud , Planificación de Menú , Restaurantes/economía , Comercio , Humanos , Michigan