Applications of targeted proteomics in systems biology and translational medicine.
Ontology highlight
ABSTRACT: Biological systems are composed of numerous components of which proteins are of particularly high functional significance. Network models are useful abstractions for studying these components in context. Network representations display molecules as nodes and their interactions as edges. Because they are difficult to directly measure, functional edges are frequently inferred from suitably structured datasets consisting of the accurate and consistent quantification of network nodes under a multitude of perturbed conditions. For the precise quantification of a finite list of proteins across a wide range of samples, targeted proteomics exemplified by selected/multiple reaction monitoring (SRM, MRM) mass spectrometry has proven useful and has been applied to a variety of questions in systems biology and clinical studies. Here, we survey the literature of studies using SRM-MS in systems biology and clinical proteomics. Systems biology studies frequently examine fundamental questions in network biology, whereas clinical studies frequently focus on biomarker discovery and validation in a variety of diseases including cardiovascular disease and cancer. Targeted proteomics promises to advance our understanding of biological networks and the phenotypic significance of specific network states and to advance biomarkers into clinical use.
Project description:Red blood cells (RBCs) are the most abundant cell type in the blood, and play a critical role in oxygen transport. With the development of nanobiotechnology and synthetic biology, scientists have found multiple ways to take advantage of the characteristics of RBCs, such as their long circulation time, to construct universal RBCs, develop drug delivery systems, and transform cell therapies for cancer and other diseases. This article reviews the component and aging mystery of RBCs, the methods for the applied universal RBCs, and the application prospects of RBCs, such as the engineering modification of RBCs used in cytopharmaceuticals for drug delivery and immunotherapy. Finally, we summarize some perspectives on the biological features of RBCs and provide further insights into translational medicine.
Project description:The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.
Project description:The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).
Project description:BackgroundTo enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory.ResultsTo address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data.ConclusionsWe generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene--disease and gene--compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.
Project description:Various lipids and lipid metabolites are bound to and modify the proteins in eukaryotic cells, which are known as 'protein lipidation'. There are four major types of the protein lipidation, i.e. myristoylation, palmitoylation, prenylation, and glycosylphosphatidylinositol anchor. N-myristoylation refers to the attachment of 14-carbon fatty acid myristates to the N-terminal glycine of proteins by N-myristoyltransferases (NMT) and affects their physiology such as plasma targeting, subcellular tracking and localization, thereby influencing the function of proteins. With more novel pathogenic N-myristoylated proteins are identified, the N-myristoylation will attract great attentions in various human diseases including infectious diseases, parasitic diseases, and cancers. In this review, we summarize the current understanding of N-myristoylation in physiological processes and discuss the hitherto implication of crosstalk between N-myristoylation and other protein modification. Furthermore, we mention several well-studied NMT inhibitors mainly in infectious diseases and cancers and generalize the relation of NMT and cancer progression by browsing the clinic database. This review also aims to highlight the further investigation into the dynamic crosstalk of N-myristoylation in physiological processes as well as the potential application of protein N-myristoylation in translational medicine.
Project description:Advances in mass spectrometry-based proteomics now permit analysis of complex cellular structures. Application to epidermis and its appendages (nail plate, hair shaft) has revealed a wealth of information about their protein profiles. The results confirm known site-specific differences in levels of certain keratins and add great depth to our knowledge of site specificity of scores of other proteins, thereby connecting anatomy and pathology. An example is the evident overlap in protein profiles of hair shaft and nail plate, helping rationalize their sharing of certain dystrophic syndromes distinct from epidermis. In addition, interindividual differences in protein level are manifest as would be expected. This approach permits characterization of altered profiles as a result of disease, where the magnitude of perturbation can be quantified and monitored during treatment. Proteomic analysis has also clarified the nature of the isopeptide cross-linked residual insoluble material after vigorous extraction with protein denaturants, nearly intractable to analysis without fragmentation. These structures, including the cross-linked envelope of epidermal corneocytes, are comprised of hundreds of protein constituents, evidence for strengthening the terminal structure complementary to disulphide bonding. Along with other developing technologies, proteomic analysis is anticipated to find use in disease risk stratification, detection, diagnosis and prognosis after the discovery phase and clinical validation.
Project description:High-throughput experimental techniques for generating genomes, transcriptomes, proteomes, metabolomes, and interactomes have provided unprecedented opportunities to interrogate biological systems and human diseases on a global level. Systems biology integrates the mass of heterogeneous high-throughput data and predictive computational modeling to understand biological functions as system-level properties. Most human diseases are biological states caused by multiple components of perturbed pathways and regulatory networks rather than individual failing components. Systems biology not only facilitates basic biological research but also provides new avenues through which to understand human diseases, identify diagnostic biomarkers, and develop disease treatments. At the same time, systems biology seeks to assist in drug discovery, drug optimization, drug combinations, and drug repositioning by investigating the molecular mechanisms of action of drugs at a system's level. Indeed, systems biology is evolving to systems medicine as a new discipline that aims to offer new approaches for addressing the diagnosis and treatment of major human diseases uniquely, effectively, and with personalized precision.
Project description:Over past decades, nanotechnology has contributed to the biomedical field in areas including detection, diagnosis, and drug delivery via opto-electronic properties or enhancement of biological effects. Though generally considered inert delivery vehicles, a plethora of past and present evidence demonstrates that nanomaterials also exude unique intrinsic biological activity based on composition, shape, and surface functionalization. These intrinsic biological activities, termed self-therapeutic properties, take several forms, including mediation of cell-cell interactions, modulation of interactions between biomolecules, catalytic amplification of biochemical reactions, and alteration of biological signal transduction events. Moreover, study of biomolecule-nanomaterial interactions offers a promising avenue for uncovering the molecular mechanisms of biology and the evolution of disease. In this review, we observe the historical development, synthesis, and characterization of self-therapeutic nanomaterials. Next, we discuss nanomaterial interactions with biological systems, starting with administration and concluding with elimination. Finally, we apply this materials perspective to advances in intrinsic nanotherapies across the biomedical field, from cancer therapy to treatment of microbial infections and tissue regeneration. We conclude with a description of self-therapeutic nanomaterials in clinical trials and share our perspective on the direction of the field in upcoming years.
Project description:BackgroundExperimental datasets are becoming larger and increasingly complex, spanning different data domains, thereby expanding the requirements for respective tool support for their analysis. Networks provide a basis for the integration, analysis and visualization of multi-omics experimental datasets.ResultsHere we present VANTED (version 2), a framework for systems biology applications, which comprises a comprehensive set of seven main tasks. These range from network reconstruction, data visualization, integration of various data types, network simulation to data exploration combined with a manifold support of systems biology standards for visualization and data exchange. The offered set of functionalities is instantiated by combining several tasks in order to enable users to view and explore a comprehensive dataset from different perspectives. We describe the system as well as an exemplary workflow.ConclusionsVANTED is a stand-alone framework which supports scientists during the data analysis and interpretation phase. It is available as a Java open source tool from http://www.vanted.org.