Integrative proteomic and metabolomic elucidation of cardiomyopathy with in vivo and in vitro models and clinical samples

Cardiomyopathy is a prevalent cardiovascular disease that affects individuals of all ages and can lead to life-threatening heart failure. Despite its variety in types, each with distinct characteristics and causes, our understanding of cardiomyopathy at a systematic biology level remains incomplete. Mass spectrometry-based techniques have emerged as powerful tools, providing a comprehensive view of the molecular landscape and aiding in the discovery of biomarkers and elucidation of mechanisms. This review highlights the significant potential of integrating proteomic and metabolomic approaches with specialized databases to identify biomarkers and therapeutic targets across different types of cardiomyopathies. In vivo and in vitro models, such as genetically modified mice, patient-derived or induced pluripotent stem cells, and organ chips, are invaluable in exploring the pathophysiological complexities of this disease. By integrating omics approaches with these sophisticated modeling systems, our comprehension of the molecular underpinnings of cardiomyopathy can be greatly enhanced, facilitating the development of diagnostic markers and therapeutic strategies. Among the promising therapeutic targets are those involved in extracellular matrix remodeling, sarcomere damage, and metabolic remodeling. These targets hold the potential to advance precision therapy in cardiomyopathy, offering hope for more effective treatments tailored to the specific molecular profiles of patients.

Personalized Medicine Approach to Proteomics and Metabolomics of Cytochrome P450 Enzymes: A Narrative Review

Cytochrome P450 enzymes (CYPs) represent a diverse family of heme-thiolate proteins involved in the metabolism of a wide range of endogenous compounds and xenobiotics. In recent years, proteomics and metabolomics have been used to obtain a comprehensive insight into the role of CYPs in health and disease aspects. The objective of the present work is to better understand the status of proteomics and metabolomics in CYP research in optimizing therapeutics and patient safety from a personalized medicine approach. The literature used in this narrative review was procured by electronic search of PubMed, Medline, Embase, and Google Scholar databases. The following keywords were used in combination to identify related literature: "proteomics," "metabolomics," "cytochrome P450," "drug metabolism," "disease conditions," "proteome," "liquid chromatography-mass spectrometry," "integration," "metabolites," "pathological conditions." We reviewed studies that utilized proteomics and metabolomics approaches to explore the multifaceted roles of CYPs in identifying disease markers and determining the contribution of CYP enzymes in developing treatment strategies. The applications of various cutting-edge analytical techniques, including liquid chromatography-mass spectrometry, nuclear magnetic resonance, and bioinformatics analyses in CYP proteomics and metabolomics studies, have been highlighted. The identification of CYP enzymes through metabolomics and/or proteomics in various disease conditions provides key information in the diagnostic and therapeutic landscape. Leveraging both proteomics and metabolomics presents a powerful approach for an exhaustive exploration of the multifaceted roles played by CYP enzymes in personalized medicine. Proteomics and metabolomics have enabled researchers to unravel the complex connection between CYP enzymes and metabolic markers associated with specific diseases. As technology and methodologies evolve, an integrated approach promises to further elucidate the role of CYPs in human health and disease, potentially ushering in a new era of personalized medicine.

Windows Scanning Multiomics: Integrated Metabolomics and Proteomics

Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.

Synovial biopsies for molecular definition of rheumatoid arthritis and treatment response phenotyping: where can we improve?

INTRODUCTION

The extensive knowledge gained in the cellular and molecular mechanisms underlying Rheumatoid Arthritis (RA) pathogenesis has led to therapeutic advances. However, up to 10-20% of patients fail to respond to multiple therapeutic agents being classified as multi-drugresistant. A key challenge moving forward will be the implementation of synovial biopsies in clinical practice to facilitate the shift from the current trial-and-error strategy toward new forms of clinical trials. Biomarker-driven trials have the potential to improve drug selection and patient stratification, reduce economic costs and unnecessary drug-related toxicity.

AREAS COVERED

This special report explores the clinical and research applications of synovial biopsy, the advancement in the molecular pathobiology of RA to better understand disease pathogenesis and treatment response, and the way forward for the paradigm shift needed.

EXPERT OPINION

In the current era of highly targeted biologic drugs which have dramatically transformed the outlook of RA patients, the use of synovial biopsy represents a valuable practical tool to dissect disease pathogenesis and, consequently, treatment response. In the near future, it is hoped that technological advances will allow for speeding up synovial molecular analysis and that the design of new biomarker-driven trials will enable the allocation of patients to more effective treatment.

Using mass spectrometry imaging to visualize age-related subcellular disruption

Metabolic homeostasis balances the production and consumption of energetic molecules to maintain active, healthy cells. Cellular stress, which disrupts metabolism and leads to the loss of cellular homeostasis, is important in age-related diseases. We focus here on the role of organelle dysfunction in age-related diseases, including the roles of energy deficiencies, mitochondrial dysfunction, endoplasmic reticulum (ER) stress, changes in metabolic flux in aging (e.g., Ca2+ and nicotinamide adenine dinucleotide), and alterations in the endoplasmic reticulum-mitochondria contact sites that regulate the trafficking of metabolites. Tools for single-cell resolution of metabolite pools and metabolic flux in animal models of aging and age-related diseases are urgently needed. High-resolution mass spectrometry imaging (MSI) provides a revolutionary approach for capturing the metabolic states of individual cells and cellular interactions without the dissociation of tissues. mass spectrometry imaging can be a powerful tool to elucidate the role of stress-induced cellular dysfunction in aging.

Development of a quantitative proteomics approach for cyclooxygenases and lipoxygenases in parallel to quantitative oxylipin analysis allowing the comprehensive investigation of the arachidonic acid cascade

Oxylipins derived from the cyclooxygenase (COX) and lipoxygenase (LOX) pathways of the arachidonic acid (ARA) cascade are essential for the regulation of the inflammatory response and many other physiological functions. Comprehensive analytical methods comprised of oxylipin and protein abundance analysis are required to fully understand mechanisms leading to changes within these pathways. Here, we describe the development of a quantitative multi-omics approach combining liquid chromatography tandem mass spectrometry-based targeted oxylipin metabolomics and proteomics. As the first targeted proteomics method to cover these pathways, it enables the quantitative analysis of all human COX (COX-1 and COX-2) and relevant LOX pathway enzymes (5-LOX, 12-LOX, 15-LOX, 15-LOX-2, and FLAP) in parallel to the analysis of 239 oxylipins with our targeted oxylipin metabolomics method from a single sample. The detailed comparison between MRM3 and classical MRM-based detection in proteomics showed increased selectivity for MRM3, while MRM performed better in terms of sensitivity (LLOQ, 16-122 pM vs. 75-840 pM for the same peptides), linear range (up to 1.5-7.4 μM vs. 4-368 nM), and multiplexing capacities. Thus, the MRM mode was more favorable for this pathway analysis. With this sensitive multi-omics approach, we comprehensively characterized oxylipin and protein patterns in the human monocytic cell line THP-1 and differently polarized primary macrophages. Finally, the quantification of changes in protein and oxylipin levels induced by lipopolysaccharide stimulation and pharmaceutical treatment demonstrates its usefulness to study molecular modes of action involved in the modulation of the ARA cascade.

Fate and PPARγ and STATs-driven effects of the mitochondrial complex I inhibitor tebufenpyrad in liver cells revealed with multi-omics

The biological effects of the pesticide and mitochondrial complex I inhibitor tebufenpyrad (TEBU) on liver cells were investigated by combining proteomics and metabolomics. Both cell culture media and cellular lysates were analyzed in dose-response and kinetic experiments on the HepaRG cell line. Responses were compared with those obtained on primary human and rat hepatocytes. A multitude of phase I and II metabolites (>80) mainly common to HepaRG cells and primary hepatocytes and an increase in metabolization enzymes were observed. Synthesis of mitochondrion and oxidative phosphorylation complex constituents, fatty acid oxidation, and cellular uptake of lipids were induced to compensate for complex I inhibition and the decrease in ATP intracellular contents caused by TEBU. Secretion of the 20 S circulating proteasome and overall inhibition of acute inflammation followed by IL-6 secretion in later stages were observed in HepaRG cells. These effects were associated with a decrease in STAT1 and STAT3 transcription factor abundances, but with different kinetics. Based on identified TEBU targets, docking experiments, and nuclear receptor reporter assays, we concluded that liver cell response to TEBU is mediated by its interaction with the PPARγ transcription factor.

Integration of metabolomics and proteomics analysis to explore the mechanism of neurotoxicity induced by receipt of isoniazid and rifampicin in mice

Isoniazid (INH) and rifampicin (RIF) are co-administered in tuberculosis treatment but can cause neurotoxicity, and the mechanism is not known. To explore this mechanism, we employed an integrated approach using metabolomics analysis (MA) and proteomics analysis (PA). Male mice were divided into three groups and administered vehicle (control group), or co-administered INH (120 mg/kg) and RIF (240 mg/kg), for 7 or 14 days. Mice brains were collected for mass spectrometry-based PA and MA plus lipidomics analysis. Measurement of brain levels of malondialdehyde and superoxide dismutase revealed time-dependent brain injury after exposure to INH+RIF for 7 and 14 days. Also, 422 proteins, 35 metabolites, and 21 lipids were dysregulated and identified. MA demonstrated "purine metabolism," "phenylalanine, tyrosine and tryptophan biosynthesis," "biosynthesis of unsaturated fatty acids," "phenylalanine metabolism," and "arginine biosynthesis" to be disturbed significantly. PA demonstrated pathways such as "lipids," "amino acids," and "energy metabolism" to be disrupted. Peroxisome proliferator-activated receptor (PPAR) pathways were changed in energy metabolism, which led to the neurotoxicity induced by INH+RIF. Immunohistochemical analyses of PPARs in mice brains verified that PPAR-α and -γ expression was downregulated. PPAR-α and -γ activation might be a key target for alleviating INH+RIF-induced neurotoxicity.

Starring Role of Biomarkers and Anticancer Agents as a Major Driver in Precision Medicine of Cancer Therapy

Precision medicine is the most modern contemporary medicine approach today, based on great amount of data on people's health, individual characteristics, and life circumstances, and employs the most effective ways to prevent and cure diseases. Precision medicine in cancer is the most precise and viable treatment for every cancer patient based on the disease's genetic profile. Precision medicine changes the standard one size fits all medication model, which focuses on average responses to care. Consolidating modern methodologies for streamlining and checking anticancer drugs can have long-term effects on understanding the results. Precision medicine can help explicit anticancer treatments using various drugs and even in discovery, thus becoming the paradigm of future cancer medicine. Cancer biomarkers are significant in precision medicine, and findings of different biomarkers make this field more promising and challenging. Naturally, genetic instability and the collection of extra changes in malignant growth cells are ways cancer cells adapt and survive in a hostile environment, for example, one made by these treatment modalities. Precision medicine centers on recognizing the best treatment for individual patients, dependent on their malignant growth and genetic characterization. This new era of genomics progressively referred to as precision medicine, has ignited a new episode in the relationship between genomics and anticancer drug development.

Integration of Metabolomic and Proteomic Data to Uncover Actionable Metabolic Pathways

Mass spectrometry (MS) is an important tool for biological studies because it is capable of interrogating a diversity of biomolecules (proteins, drugs, metabolites) not captured via alternate genomic platforms. Unfortunately, downstream data analysis becomes complicated when attempting to evaluate and integrate measurements of different molecular classes and requires the aggregation of expertise from different relevant disciplines. This complexity represents a significant bottleneck that limits the routine deployment of MS-based multi-omic methods, despite the unmatched biological and functional insight the data can provide. To address this unmet need, our group introduced Omics Notebook as an open-source framework for facilitating exploratory analysis, reporting and integrating MS-based multi-omic data in a way that is automated, reproducible and customizable. By deploying this pipeline, we have devised a framework for researchers to more rapidly identify functional patterns across complex data types and focus on statistically significant and biologically interesting aspects of their multi-omic profiling experiments. This chapter aims to describe a protocol which leverages our publicly accessible tools to analyze and integrate data from high-throughput proteomics and metabolomics experiments and produce reports that will facilitate more impactful research, cross-institutional collaborations, and wider data dissemination.

Exploration of the underlying mechanisms of isoniazid/rifampicin-induced liver injury in mice using an integrated proteomics and metabolomics approach

The hepatotoxic mechanism resulting from coadministration of isoniazid (INH) and rifampicin (RIF) are complex and studies remain inconclusive. To systematically explore the underlying mechanisms, an integrated mass-based untargeted metabolomics and label-free quantitative proteomics approach was used to clarify the mechanism of INH/RIF-induced liver injury. Thirty male mice were randomly divided into three groups: control (receiving orally administered vehicle solution), INH (150 mg/kg) + RIF (300 mg/kg) orally administered for either 7 or 14 days, respectively. Serum was collected for the analysis of biochemical parameters and liver samples were obtained for mass spectrum-based proteomics, metabolomics, and lipidomics analysis. Overall, 511 proteins, 31 metabolites, and 23 lipids were dysregulated and identified, and disordered biological pathways were identified. The network of integrated multiomics showed that glucose, lipid, and amino acid metabolism as well as energy metabolism were mainly dysregulated and led to oxidative stress, inflammation, liver steatosis, and cell death induced by INH and RIF. Coadministration of INH and RIF can induce liver injury by oxidative stress, inflammation, liver steatosis, and cell death, and the reduction in glutathione levels may play a critical role in these systematic changes and warrants further study.

Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics

Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.

Multiomic Analysis Reveals Disruption of Cholesterol Homeostasis by Cannabidiol in Human Cell Lines

The nonpsychoactive cannabinoid, cannabidiol (CBD), is Food and Dug Administration approved for treatment of two drug-resistant epileptic disorders and is seeing increased use among the general public, yet the mechanisms that underlie its therapeutic effects and side-effect profiles remain unclear. Here, we report a systems-level analysis of CBD action in human cell lines using temporal multiomic profiling. FRET-based biosensor screening revealed that CBD elicits a sharp rise in cytosolic calcium, and activation of AMP-activated protein kinase in human keratinocyte and neuroblastoma cell lines. CBD treatment leads to alterations in the abundance of metabolites, mRNA transcripts, and proteins associated with activation of cholesterol biosynthesis, transport, and storage. We found that CBD rapidly incorporates into cellular membranes, alters cholesterol accessibility, and disrupts cholesterol-dependent membrane properties. Sustained treatment with high concentrations of CBD induces apoptosis in a dose-dependent manner. CBD-induced apoptosis is rescued by inhibition of cholesterol synthesis and potentiated by compounds that disrupt cholesterol trafficking and storage. Our data point to a pharmacological interaction of CBD with cholesterol homeostasis pathways, with potential implications in its therapeutic use.

Pilot Study Showing Feasibility of Phosphoproteomic Profiling of Pathway-Level Molecular Alterations in Barrett’s Esophagus

(1) Background: Barrett’s esophagus is a major risk factor for esophageal adenocarcinoma. In this pilot study, we employed precision mass spectrometry to map global (phospho)protein perturbations in Barrett’s esophagus lesions and adjacent normal tissue to glean insights into disease progression. (2) Methods: Biopsies were collected from two small but independent cohorts. Comparative analyses were performed between Barrett’s esophagus samples and adjacent matched (normal) tissues from patients with known pathology, while specimens from healthy patients served as additional controls. (3) Results: We identified and quantified 6810 proteins and 6395 phosphosites in the discovery cohort, revealing hundreds of statistically significant differences in protein abundances and phosphorylation states. We identified a robust proteomic signature that accurately classified the disease status of samples from the independent patient cohorts. Pathway-level analysis of the phosphoproteomic profiles revealed the dysregulation of specific cellular processes, including DNA repair, in Barrett’s esophagus relative to paired controls. Comparative analysis with previously published transcriptomic profiles provided independent evidence in support of these preliminary findings. (4) Conclusions: This pilot study establishes the feasibility of using unbiased quantitative phosphoproteomics to identify molecular perturbations associated with disease progression in Barrett’s esophagus to define potentially clinically actionable targets warranting further assessment.

Metabolomics: a systems biology approach for enhancing heat stress tolerance in plants

Comprehensive metabolomic investigations provide a large set of stress-related metabolites and metabolic pathways, advancing crops under heat stress conditions. Metabolomics-assisted breeding, including mQTL and mGWAS boosted our understanding of improving numerous quantitative traits under heat stress. During the past decade, metabolomics has emerged as a fascinating scientific field that includes documentation, evaluation of metabolites, and chemical methods for cell monitoring programs in numerous plant species. A comprehensive metabolome profiling allowed the investigator to handle the comprehensive data groups of metabolites and the equivalent metabolic pathways in an extraordinary manner. Metabolomics, together with transcriptomics, plays an influential role in discovering connections between stress and genes/metabolite, phenotyping, and biomarkers documentation. Further, it helps to decode several metabolic systems connected with heat stress (HS) tolerance in plants. Heat stress is a critical environmental factor that is globally affecting the growth and productivity of plants. Thus, there is an urgent need to exploit modern breeding and biotechnological tools like metabolomics to develop cultivars with improved HS tolerance. Several studies have reported that amino acids, carbohydrates, nitrogen metabolisms, etc. and metabolites involved in the biosynthesis and catalyzing actions play a game-changing role in HS response and help plants to cope with the HS. The use of metabolomics-assisted breeding (MAB) allows a well-organized transmission of higher yield and HS tolerance at the metabolome level with specific properties. Progressive metabolomics systematic techniques have accelerated metabolic profiling. Nonetheless, continuous developments in bioinformatics, statistical tools, and databases are allowing us to produce ever-progressing, comprehensive insights into the biochemical configuration of plants and by what means this is inclined by genetic and environmental cues. Currently, assimilating metabolomics with post-genomic platforms has allowed a significant division of genetic-phenotypic connotation in several plant species. This review highlights the potential of a state-of-the-art plant metabolomics approach for the improvement of crops under HS. The development of plants with specific properties using integrated omics (metabolomics and transcriptomics) and MAB can provide new directions for future research to enhance HS tolerance in plants to achieve a goal of "zero hunger".

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.

Mass-Spectrometry-Based Functional Proteomic and Phosphoproteomic Technologies and Their Application for Analyzing Ex Vivo and In Vitro Models of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant disease thought to be principally caused by mutations in sarcomeric proteins. Despite extensive genetic analysis, there are no comprehensive molecular frameworks for how single mutations in contractile proteins result in the diverse assortment of cellular, phenotypic, and pathobiological cascades seen in HCM. Molecular profiling and system biology approaches are powerful tools for elucidating, quantifying, and interpreting dynamic signaling pathways and differential macromolecule expression profiles for a wide range of sample types, including cardiomyopathy. Cutting-edge approaches combine high-performance analytical instrumentation (e.g., mass spectrometry) with computational methods (e.g., bioinformatics) to study the comparative activity of biochemical pathways based on relative abundances of functionally linked proteins of interest. Cardiac research is poised to benefit enormously from the application of this toolkit to cardiac tissue models, which recapitulate key aspects of pathogenesis. In this review, we evaluate state-of-the-art mass-spectrometry-based proteomic and phosphoproteomic technologies and their application to in vitro and ex vivo models of HCM for global mapping of macromolecular alterations driving disease progression, emphasizing their potential for defining the components of basic biological systems, the fundamental mechanistic basis of HCM pathogenesis, and treating the ensuing varied clinical outcomes seen among affected patient cohorts.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

Mass Spectrometry-Based Phosphoproteomics and Systems Biology: Approaches to Study T Lymphocyte Activation and Exhaustion

T lymphocytes respond to extracellular cues and recognize and clear foreign bodies. These functions are tightly regulated by receptor-mediated intracellular signal transduction pathways and phosphorylation cascades resulting in rewiring of transcription, cell adhesion, and metabolic pathways, which leads to changes in downstream effector functions including cytokine secretion and target-cell killing. Given that these pathways become dysregulated in chronic diseases such as cancer, auto-immunity, diabetes, and persistent infections, mapping T cell signaling dynamics in normal and pathological states is central to understanding and modulating immune system behavior. Despite recent advances, there remains much to be learned from the study of T cell signaling at a systems level. The application of global phospho-proteomic profiling technology has the potential to provide unprecedented insights into the molecular networks that govern T cell function. These include capturing the spatiotemporal dynamics of the T cell responses as an ensemble of interacting components, rather than a static view at a single point in time. In this review, we describe innovative experimental approaches to study signaling mechanisms in the TCR, co-stimulatory receptors, synthetic signaling molecules such as chimeric antigen receptors, inhibitory receptors, and T cell exhaustion. Technical advances in mass spectrometry and systems biology frameworks are emphasized as these are poised to identify currently unknown functional relationships and dependencies to create causal predictive models that expand from the traditional narrow reductionist lens of singular components in isolation.

Proteomics of Cryptococcus neoformans: From the Lab to the Clinic

Fungal pathogens cause an array of diseases by targeting both immunocompromised and immunocompetent hosts. Fungi overcome our current arsenal of antifungals through the emergence and evolution of resistance. In particular, the human fungal pathogen, Cryptococcus neoformans is found ubiquitously within the environment and causes severe disease in immunocompromised individuals around the globe with limited treatment options available. To uncover fundamental knowledge about this fungal pathogen, as well as investigate new detection and treatment strategies, mass spectrometry-based proteomics provides a plethora of tools and applications, as well as bioinformatics platforms. In this review, we highlight proteomics approaches within the laboratory to investigate changes in the cellular proteome, secretome, and extracellular vesicles. We also explore regulation by post-translational modifications and the impact of protein-protein interactions. Further, we present the development and comprehensive assessment of murine models of cryptococcal infection, which provide valuable tools to define the dynamic relationship between the host and pathogen during disease. Finally, we explore recent quantitative proteomics studies that begin to extrapolate the findings from the bench to the clinic for improved methods of fungal detection and monitoring. Such studies support a framework for personalized medical approaches to eradicate diseases caused by C. neoformans.

Foodborne pathogens in the omics era

Outbreaks and deaths related to Foodborne Diseases (FBD) occur constantly in the world, as a result of the consumption of contaminated foodstuffs with pathogens such as Listeria monocytogenes, Escherichia coli, Staphylococcus aureus, Salmonella spp, Clostridium spp. and Campylobacter spp. The purpose of this review is to discuss the main omic techniques applied in foodborne pathogen and to demonstrate their functionalities through the food chain and to guarantee the food safety. The main techniques presented are genomic, transcriptomic, secretomic, proteomic, and metabolomic, which together, in the field of food and nutrition, are known as "Foodomics." This review had highlighted the potential of omics to integrate variables that contribute to food safety and to enable us to understand their application on foodborne diseases. The appropriate use of these techniques had driven the definition of critical parameters to achieve successful results in the improvement of consumers health, costs and to obtain safe and high-quality products.

Multi-Omic Single-Shot Technology for Integrated Proteome and Lipidome Analysis

Mass spectrometry (MS) serves as the centerpiece technology for proteome, lipidome, and metabolome analysis. To gain a better understanding of the multifaceted networks of myriad regulatory layers in complex organisms, integration of different multiomic layers is increasingly performed, including joint extraction methods of diverse biomolecular classes and comprehensive data analyses of different omics. Despite the versatility of MS systems, fractured methodology drives nearly all MS laboratories to specialize in analysis of a single ome at the exclusion of the others. Although liquid chromatography-mass spectrometry (LC-MS) analysis is similar for different biomolecular classes, the integration on the instrument level is lagging behind. The recent advancements in high flow proteomics enable us to take a first step towards integration of protein and lipid analysis. Here, we describe a technology to achieve broad and deep coverage of multiple molecular classes simultaneously through multi-omic single-shot technology (MOST), requiring only one column, one LC-MS instrument, and a simplified workflow. MOST achieved great robustness and reproducibility. Its application to a Saccharomyces cerevisiae study consisting of 20 conditions revealed 2842 protein groups and 325 lipids and potential molecular relationships.

Protein Interaction Network Biology in Neuroscience

Mapping the intricate networks of cellular proteins in the human brain has the potential to address unsolved questions in molecular neuroscience, including the molecular basis of cognition, synaptic plasticity, long-term potentiation, learning, and memory. Perturbations to the protein-protein interaction networks (PPIN) present in neurons, glia, and other cell-types have been linked to multifactorial neurological disorders. Yet while knowledge of brain PPINs is steadily improving, the complexity and dynamic nature of the heterogeneous central nervous system in normal and disease contexts poses a formidable experimental challenge. In this review, the recent applications of functional proteomics and systems biology approaches to study PPINs central to normal neuronal function, during neurodevelopment, and in neurodegenerative disorders are summarized. How systematic PPIN analysis offers a unique mechanistic framework to explore intra- and inter-cellular functional modules governing neuronal activity and brain function is also discussed. Finally, future technological advancements needed to address outstanding questions facing neuroscience are outlined.

Systems and synthetic biology to elucidate secondary metabolite biosynthetic gene clusters encoded in Streptomyces genomes

Covering: 2010 to 2020 Over the last few decades, Streptomyces have been extensively investigated for their ability to produce diverse bioactive secondary metabolites. Recent advances in Streptomyces research have been largely supported by improvements in high-throughput technology 'omics'. From genomics, numerous secondary metabolite biosynthetic gene clusters were predicted, increasing their genomic potential for novel bioactive compound discovery. Additional omics, including transcriptomics, translatomics, interactomics, proteomics and metabolomics, have been applied to obtain a system-level understanding spanning entire bioprocesses of Streptomyces, revealing highly interconnected and multi-layered regulatory networks for secondary metabolism. The comprehensive understanding derived from this systematic information accelerates the rational engineering of Streptomyces to enhance secondary metabolite production, integrated with the exploitation of the highly efficient 'Design-Build-Test-Learn' cycle in synthetic biology. In this review, we describe the current status of omics applications in Streptomyces research to better understand the organism and exploit its genetic potential for higher production of valuable secondary metabolites and novel secondary metabolite discovery.

Phosphoproteomic strategies in cancer research: a minireview

Understanding the cellular processes is central to comprehend disease conditions and is also true for cancer research. Proteomic studies provide significant insight into cancer mechanisms and aid in the diagnosis and prognosis of the disease. Phosphoproteome is one of the most studied complements of the whole proteome given its importance in the understanding of cellular processes such as signaling and regulations. Over the last decade, several new methods have been developed for phosphoproteome analysis. A significant amount of these efforts pertains to cancer research. The current use of powerful analytical instruments in phosphoproteomic approaches has paved the way for deeper and sensitive investigations. However, these methods and techniques need further improvements to deal with challenges posed by the complexity of samples and scarcity of phosphoproteins in the whole proteome, throughput and reproducibility. This review aims to provide a comprehensive summary of the variety of steps used in phosphoproteomic methods applied in cancer research including the enrichment and fractionation strategies. This will allow researchers to evaluate and choose a better combination of steps for their phosphoproteome studies.

Integration of Online Omics-Data Resources for Cancer Research

The manifestations of cancerous phenotypes necessitate alterations at different levels of information-flow from genome to proteome. The molecular alterations at different information processing levels serve as the basis for the cancer phenotype to emerge. To understand the underlying mechanisms that drive the acquisition of cancer hallmarks it is required to interrogate cancer cells using multiple levels of information flow represented by different omics - such as genomics, epigenomics, transcriptomics, and proteomics. The advantage of multi-omics data integration comes with a trade-off in the form of an added layer of complexity originating from inherently diverse types of omics-datasets that may pose a challenge to integrate the omics-data in a biologically meaningful manner. The plethora of cancer-specific online omics-data resources, if able to be integrated efficiently and systematically, may facilitate the generation of new biological insights for cancer research. In this review, we provide a comprehensive overview of the online single- and multi-omics resources that are dedicated to cancer. We catalog various online omics-data resources such as The Cancer Genome Atlas (TCGA) along with various TCGA-associated data portals and tools for multi-omics analysis and visualization, the International Cancer Genome Consortium (ICGC), Catalogue of Somatic Mutations in Cancer (COSMIC), The Pathology Atlas, Gene Expression Omnibus (GEO), and PRoteomics IDEntifications (PRIDE). By comparing the strengths and limitations of the respective online resources, we aim to highlight the current biological and technological challenges and possible strategies to overcome these challenges. We outline the available schemes for the integration of the multi-omics dimensions for stratifying cancer patients and biomarker prediction based on the integrated molecular-signatures of cancer. Finally, we propose the multi-omics driven systems-biology approaches to realize the potential of precision onco-medicine as the future of cancer research. We believe this systematic review will encourage scientists and clinicians worldwide to utilize the online resources to explore and integrate the available omics datasets that may provide a window of opportunity to generate new biological insights and contribute to the advancement of the field of cancer research.

Applying precision medicine to unmet clinical needs in psoriatic disease

Psoriatic disease (PsD) is a heterogeneous condition that can affect peripheral and axial joints (arthritis), entheses, skin (psoriasis) and other structures. Over the past decade, considerable advances have been made both in our understanding of the pathogenesis of PsD and in the treatment of its diverse manifestations. However, several major areas of continued unmet need in the care of patients with PsD have been identified. One of these areas is the prediction of poor outcome, notably radiographic outcome in patients with psoriatic arthritis, so that stratified medicine approaches can be taken; another is predicting response to the numerous current and emerging therapies for PsD, so that precision medicine can be applied to rapidly improve clinical outcome and reduce the risk of toxicity. In order to address these needs, novel approaches, including imaging, tissue analysis and the application of proteogenomic technologies, are proposed as methodological solutions that will assist the dissection of the critical immune-metabolic pathways in this complex disease. Learning from advances made in other inflammatory diseases, it is time to address these unmet needs in a multi-centre partnership aimed at improving short-term and long-term outcomes for patients with PsD.

Use of Integrative Interactomics for Improvement of Farm Animal Health and Welfare: An Example with Fescue Toxicosis

Rapid scientific advances are increasing our understanding of the way complex biological interactions integrate to maintain homeostatic balance and how seemingly small, localized perturbations can lead to systemic effects. The ‘omics movement, alongside increased throughput resulting from statistical and computational advances, has transformed our understanding of disease mechanisms and the multi-dimensional interaction between environmental stressors and host physiology through data integration into multi-dimensional analyses, i.e., integrative interactomics. This review focuses on the use of high-throughput technologies in farm animal research, including health- and toxicology-related papers. Although limited, we highlight recent animal agriculture-centered reports from the integrative multi-‘omics movement. We provide an example with fescue toxicosis, an economically costly disease affecting grazing livestock, and describe how integrative interactomics can be applied to a disease with a complex pathophysiology in the pursuit of novel treatment and management approaches. We outline how ‘omics techniques have been used thus far to understand fescue toxicosis pathophysiology, lay out a framework for the fescue toxicosis integrome, identify some challenges we foresee, and offer possible means for addressing these challenges. Finally, we briefly discuss how the example with fescue toxicosis could be used for other agriculturally important animal health and welfare problems.

Systems level profiling of arginine starvation reveals MYC and ERK adaptive metabolic reprogramming

Arginine auxotrophy due to the silencing of argininosuccinate synthetase 1 (ASS1) occurs in many carcinomas and in the majority of sarcomas. Arginine deiminase (ADI-PEG20) therapy exploits this metabolic vulnerability by depleting extracellular arginine, causing arginine starvation. ASS1-negative cells develop resistance to ADI-PEG20 through a metabolic adaptation that includes re-expressing ASS1. As arginine-based multiagent therapies are being developed, further characterization of the changes induced by arginine starvation is needed. In order to develop a systems-level understanding of these changes, activity-based proteomic profiling (ABPP) and phosphoproteomic profiling were performed before and after ADI-PEG20 treatment in ADI-PEG20-sensitive and resistant sarcoma cells. When integrated with metabolomic profiling, this multi-omic analysis reveals that cellular response to arginine starvation is mediated by adaptive ERK signaling and activation of the Myc–Max transcriptional network. Concomitantly, these data elucidate proteomic changes that facilitate oxaloacetate production by enhancing glutamine and pyruvate anaplerosis and altering lipid metabolism to recycle citrate for oxidative glutaminolysis. Based on the complexity of metabolic and cellular signaling interactions, these multi-omic approaches could provide valuable tools for evaluating response to metabolically targeted therapies.

Nonalcoholic fatty liver disease and portal hypertension

Nonalcoholic fatty liver disease (NAFLD) is a substantial and growing problem worldwide and has become the second most common indication for liver transplantation as it may progress to cirrhosis and develop complications from portal hypertension primarily caused by advanced fibrosis and erratic tissue remodeling. However, elevated portal venous pressure has also been detected in experimental models of fatty liver and in human NAFLD when fibrosis is far less advanced and cirrhosis is absent. Early increases in intrahepatic vascular resistance may contribute to the progression of liver disease. Specific pathophenotypes linked to the development of portal hypertension in NAFLD include hepatocellular lipid accumulation and ballooning injury, capillarization of liver sinusoidal endothelial cells, enhanced contractility of hepatic stellate cells, activation of Kupffer cells and pro-inflammatory pathways, adhesion and entrapment of recruited leukocytes, microthrombosis, angiogenesis and perisinusoidal fibrosis. These pathological events are amplified in NAFLD by concomitant visceral obesity, insulin resistance, type 2 diabetes and dysbiosis, promoting aberrant interactions with adipose tissue, skeletal muscle and gut microbiota. Measurement of the hepatic venous pressure gradient by retrograde insertion of a balloon-tipped central vein catheter is the current reference method for predicting outcomes of cirrhosis associated with clinically significant portal hypertension and guiding interventions. This invasive technique is rarely considered in the absence of cirrhosis where currently available clinical, imaging and laboratory correlates of portal hypertension may not reflect early changes in liver hemodynamics. Availability of less invasive but sufficiently sensitive methods for the assessment of portal venous pressure in NAFLD remains therefore an unmet need. Recent efforts to develop new biomarkers and endoscopy-based approaches such as endoscopic ultrasound-guided measurement of portal pressure gradient may help achieve this goal. In addition, cellular and molecular targets are being identified to guide emerging therapies in the prevention and management of portal hypertension.

Analytical techniques for multiplex analysis of protein biomarkers

INTRODUCTION

The importance of biomarkers for pharmaceutical drug development and clinical diagnostics is more significant than ever in the current shift toward personalized medicine. Biomarkers have taken a central position either as companion markers to support drug development and patient selection, or as indicators aiming to detect the earliest perturbations indicative of disease, minimizing therapeutic intervention or even enabling disease reversal. Protein biomarkers are of particular interest given their central role in biochemical pathways. Hence, capabilities to analyze multiple protein biomarkers in one assay are highly interesting for biomedical research.

AREAS COVERED

We here review multiple methods that are suitable for robust, high throughput, standardized, and affordable analysis of protein biomarkers in a multiplex format. We describe innovative developments in immunoassays, the vanguard of methods in clinical laboratories, and mass spectrometry, increasingly implemented for protein biomarker analysis. Moreover, emerging techniques are discussed with potentially improved protein capture, separation, and detection that will further boost multiplex analyses.

EXPERT COMMENTARY

The development of clinically applied multiplex protein biomarker assays is essential as multi-protein signatures provide more comprehensive information about biological systems than single biomarkers, leading to improved insights in mechanisms of disease, diagnostics, and the effect of personalized medicine.

Evaluation of MDA-MB-468 Cell Culture Media Analysis in Predicting Triple-Negative Breast Cancer Patient Sera Metabolic Profiles

Triple-negative breast cancer (TNBC) is characterized by limited survival, poor prognosis, and high recurrence. Understanding the metabolic adaptations of TNBC could help reveal improved treatment regiments. Here we performed a comprehensive ¹H NMR metabolic characterization of the MDA-MB-468 cell line, a commonly used model of TNBC, followed by an analysis of serum samples obtained from TNBC patients and healthy controls. MDA-MB-468 cells were cultured, and changes in the metabolic composition of the medium were monitored for 72 h. Based on time courses, metabolites were categorized as being consumed, being produced, or showing a mixed behavior. When comparing TNBC and control samples (HC), and by using multivariate and univariate analyses, we identified nine metabolites with differing profiles). The serum of TNBC patients was characterized by higher levels of glucose, glutamine, citrate, and acetoacetate and by lower levels of lactate, alanine, tyrosine, glutamate, and acetone. A comparative analysis between MDA-MB-468 cell culture media and TNBC patients' serum identified a potential systemic response to the carcinogenesis-associated processes, highlighting that MDA-MB-468 cells footprint does not reflect metabolic changes observed in studied TNBC serum fingerprint.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Rapid Solution-Phase Hydrogen/Deuterium Exchange for Metabolite Compound Identification

Rapid, solution-phase hydrogen/deuterium exchange (HDX) coupled with mass spectrometry (MS) is demonstrated as a means for distinguishing small-molecule metabolites. HDX is achieved using capillary vibrating sharp-edge spray ionization (cVSSI) to allow sufficient time for reagent mixing and exchange in micrometer-sized droplets. Different compounds are observed to incorporate deuterium with varying efficiencies resulting in unique isotopic patterns as revealed in the MS spectra. Using linear regression techniques, parameters representing contribution to exchange by different hydrogen types can be computed. In this proof-of-concept study, the exchange parameters are shown to be useful in the retrodiction of the amount of deuterium incorporated within different compounds. On average, the exchange parameters retrodict the exchange level with ~ 2.2-fold greater accuracy than treating all exchangeable hydrogens equally. The parameters can be used to produce hypothetical isotopic distributions that agree (± 16% RMSD) with experimental measurements. These initial studies are discussed in light of their potential value for identifying challenging metabolite species.