Proteomics and the analysis of proteomic data: 2013 overview of current protein-profiling technologies

Omics Approaches to Investigate the Pathogenesis of Suicide

Suicide is the second leading cause of death in U.S. adolescents and young adults and is generally associated with a psychiatric disorder. Suicidal behavior has a complex etiology and pathogenesis. Moderate heritability suggests genetic causes. Associations between childhood and recent life adversity indicate contributions from epigenetic factors. Genomic contributions to suicide pathogenesis remain largely unknown. This article is based on a workshop held to design strategies to identify molecular drivers of suicide neurobiology that would be putative new treatment targets. The panel determined that while bulk tissue studies provide comprehensive information, single-nucleus approaches that identify cell type-specific changes are needed. While single-nuclei techniques lack information on cytoplasm, processes, spines, and synapses, spatial multiomic technologies on intact tissue detect cell alterations specific to brain tissue layers and subregions. Because suicide has genetic and environmental drivers, multiomic approaches that combine cell type-specific epigenome, transcriptome, and proteome provide a more complete picture of pathogenesis. To determine the direction of effect of suicide risk gene variants on RNA and protein expression and how these interact with epigenetic marks, single-nuclei and spatial multiomics quantitative trait loci maps should be integrated with whole-genome sequencing and genome-wide association databases. The workshop concluded with a recommendation for the formation of an international suicide biology consortium that will bring together brain banks and investigators with expertise in cutting-edge omics technologies to delineate the biology of suicide and identify novel potential treatment targets to be tested in cellular and animal models for drug and biomarker discovery to guide suicide prevention.

Diabetes and Heart Failure: Multi-Omics Approaches

Diabetes and heart failure, as important global issues, cause substantial expenses to countries and medical systems because of the morbidity and mortality rates. Most people with diabetes suffer from type 2 diabetes, which has an amplifying effect on the prevalence and severity of many health problems such as stroke, neuropathy, retinopathy, kidney injuries, and cardiovascular disease. Type 2 diabetes is one of the cornerstones of heart failure, another health epidemic, with 44% prevalence. Therefore, finding and targeting specific molecular and cellular pathways involved in the pathophysiology of each disease, either in diagnosis or treatment, will be beneficial. For diabetic cardiomyopathy, there are several mechanisms through which clinical heart failure is developed; oxidative stress with mediation of reactive oxygen species (ROS), reduced myocardial perfusion due to endothelial dysfunction, autonomic dysfunction, and metabolic changes, such as impaired glucose levels caused by insulin resistance, are the four main mechanisms. In the field of oxidative stress, advanced glycation end products (AGEs), protein kinase C (PKC), and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) are the key mediators that new omics-driven methods can target. Besides, diabetes can affect myocardial function by impairing calcium (Ca) homeostasis, the mechanism in which reduced protein phosphatase 1 (PP1), sarcoplasmic/endoplasmic reticulum Ca2+ ATPase 2a (SERCA2a), and phosphorylated SERCA2a expressions are the main effectors. This article reviewed the recent omics-driven discoveries in the diagnosis and treatment of type 2 diabetes and heart failure with focus on the common molecular mechanisms.

Proteomic analysis of serum samples of paracoccidioidomycosis patients with severe pulmonary sequel

Background

Pulmonary sequelae (PS) in patients with chronic paracoccidioidomycosis (PCM) typically include pulmonary fibrosis and emphysema. Knowledge of the molecular pathways involved in PS of PCM is required for treatment and biomarker identification.

Methodology/Principal findings

This non-concurrent cohort study included 29 patients with pulmonary PCM that were followed before and after treatment. From this group, 17 patients evolved to mild/ moderate PS and 12 evolved severe PS. Sera from patients were evaluated before treatment and at clinical cure, serological cure, and apparent cure. A nanoACQUITY UPLC-Xevo QT MS system and PLGS software were used to identify serum differentially expressed proteins, data are available via ProteomeXchange with identifier PXD026906. Serum differentially expressed proteins were then categorized using Cytoscape software and the Reactome pathway database. Seventy-two differentially expressed serum proteins were identified in patients with severe PS compared with patients with mild/moderate PS. Most proteins altered in severe PS were involved in wound healing, inflammatory response, and oxygen transport pathways. Before treatment and at clinical cure, signaling proteins participating in wound healing, complement cascade, cholesterol transport and retinoid metabolism pathways were downregulated in patients with severe PS, whereas signaling proteins in gluconeogenesis and gas exchange pathways were upregulated. At serological cure, the pattern of protein expression reversed. At apparent cure pathways related with tissue repair (fibrosis) became downregulated, and pathway related oxygen transport became upregulated. Additionally, we identified 15 proteins as candidate biomarkers for severe PS.

Conclusions/Significance

Development of severe PS is related to increased expression of proteins involved in glycolytic pathway and oxygen exchange), indicative of the greater cellular activity and replication associated with early dysregulation of wound healing and aberrant tissue repair. Our findings provide new targets to study mechanisms of PS in PCM, as well as potential biomarkers.

Pulmonary fibrosis is the main sequel of paracoccidioidomycosis (PCM), a fungal disease that affects mainly men, rural workers. The development of pulmonary fibrosis is complex and involves several mechanisms that culminate in aberrant collagen production and deposition in the lungs making it became stiff and blocking the air passages. These changes lead to difficulty in breathing and in PCM patients dyspnea in response to high or low levels of exertion is common. Therefore, these patients show incapacity to work and the decreased quality of life. With the possibility of identifying some marker, for example, it could help the indication of respiratory physiotherapy, professional rehabilitation, or therapeutic intervention. This is the first study to examine the pulmonary sequelae (PS) in patients with paracoccidioidomycosis using an approach combining proteomics with bioinformatics. Here, we identify the specific proteome pattern found in PCM patients with severe sequelae that distinguishes these patients from that with mild/moderate sequelae. Our results showed that time points immediately before treatment and at clinical cure are key moments at which PS can progress to severe PS due a dysregulation in wound healing with consequent delayed in the healing processes resulting in an aberrant scar. As such, we suggest that the prognoses for severe PS should be considered as soon as possible and as early as diagnosis of PCM. Furthermore, we used proteomics to identify possible serum biomarkers with which to predict the likely development of severe PS, to be validated in future studies.

Crucial role of fatty acid oxidation in asthmatic bronchial smooth muscle remodelling

BACKGROUND

Bronchial smooth muscle (BSM) remodelling in asthma is related to an increased mitochondrial biogenesis and enhanced BSM cell proliferation in asthma. Since (i) mitochondria produce the highest levels of cellular energy and (ii) fatty acid beta-oxidation is the most powerful way to produce ATP, we hypothesized that, in asthmatic BSM cells, energetic metabolism is shifted towards the beta-oxidation of fatty acids.

OBJECTIVES

We aimed to characterize BSM cell metabolism in asthma both in vitro and ex vivo to identify a novel target for reducing BSM cell proliferation.

METHODS

Twenty-one asthmatic and 31 non-asthmatic patients were enrolled. We used metabolomic and proteomic approaches to study BSM cells. Oxidative stress, ATP synthesis, fatty acid endocytosis, metabolite production, metabolic capabilities, mitochondrial networks, cell proliferation and apoptosis were assessed on BSM cells. Fatty acid content was assessed in vivo using MALDI-spectrometry imaging.

RESULTS

Asthmatic BSM cells were characterized by an increased rate of mitochondrial respiration with a stimulated ATP production and mitochondrial β-oxidation. Fatty acid consumption was increased in asthmatic BSM both in vitro and ex vivo. Proteome remodelling of asthmatic BSM occurred via 2 canonical mitochondrial pathways. The levels of CPT2 and LDL-receptor, which internalize fatty acids through mitochondrial and cell membranes, respectively, were both increased in asthmatic BSM cells. Blocking CPT2 or LDL-receptor drastically and specifically reduced asthmatic BSM cell proliferation.

CONCLUSION

This study demonstrates a metabolic switch towards mitochondrial beta-oxidation in asthmatic BSM and identifies fatty acid metabolism as a new key target to reduce BSM remodelling in asthma.

Identification of Cytosolic Protein Targets of Catechol Estrogens in Breast Cancer Cells Using a Click Chemistry-Based Workflow

Catechol estrogens (CEs) are known to be toxic metabolites and the initiators of the oncogenesis of breast cancers via forming covalent adducts with DNAs. CEs shall also react with proteins, but their cellular protein targets remain unexplored. Here, we reported the identification of protein targets of CEs in the soluble cytosol of estrogen-sensitive breast cancer cells by multiple comparative proteomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) coupled with an improved click chemistry-based workflow. Multiple comparative proteomics composed of an experimental pair (probe versus solvent) and two control pairs (solvent versus solvent and probe versus solvent without enrichment) were studied using stable isotope dimethyl labeling. The use of 4-hydroxyethynylestradiol (4OHEE2) probe with an amide-free linker coupled with on-bead digestion and redigestion of the proteins cleaved from the beads was shown to greatly improve the recovery and identification of CE-adducted peptides. A total of 310 protein targets and 40 adduction sites were repeatedly (n ≥ 2) identified with D/H (probe/solvent) ratio >4 versus only one identified with D/H >4 from the two control pairs, suggesting that our workflow imposes only a very low background. Meanwhile, multiple comparative D/H ratios revealed that CEs may downregulate many target proteins involved in the metabolism or detoxification, suggesting a negative correlation between CE-induced adduction and expression of proteins acting on the alleviation of stress-induced cellular damages. The reported method and data will provide opportunities to study the progression of estrogen metabolism-derived diseases and biomarkers.

iSwathX 2.0 for Processing DDA Spectral Libraries for DIA Data Analysis

The iSwathX web application processes and normalizes mass spectrometry-based proteomics spectral libraries generated in the data-dependent acquisition (DDA) approach. These libraries are stored in various proteomics repositories such as PeptideAtlas and NIST, or are user-generated and provide reference data for data-independent acquisition (DIA) targeted data extraction and analysis. iSwathX 2.0 can efficiently normalize DDA data from different instruments, gathered at different instances, and make it compatible with specific DIA experiments. Novel functions for parallel processing of DDA libraries and DIA report files, along with various data visualizations, are available in iSwathX 2.0. The step-by-step protocols provided here describe how the libraries are uploaded, processed, visualized, and downloaded using various modules of the application. They also provide detailed guidelines on the use of DIA report files for data analysis and visualization. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Processing, combining, and visualizing two DDA libraries Basic Protocol 2: Parallel processing and combination of multiple DDA libraries Basic Protocol 3: Downstream processing, comparison, and visualization of DIA report files.

Detection and Characterization of Catechol Quinone-Derived Protein Adducts Using Biomolecular Mass Spectrometry

The catechol quinone (CQ) motif is present in many biologically relevant molecules throughout endogenous metabolic products, foods, drugs, and environmental pollutants. The CQ derivatives may undergo Michael addition, and has been shown to yield covalent bonds with nucleophilic sites of cysteine, lysine, or histidine residue of proteins. The CQ-adducted proteins may exhibit cytotoxicity or biological functions different from their un-adducted forms. Identification, characterization, and quantification of relevant protein targets are essential but challenging goals. Mass spectrometry (MS) is well-suited for the analysis of proteins and protein modifications. Technical development of bottom-up proteomics has greatly advanced the field of biomolecular MS, including protein adductomics. This mini-review focuses on the use of biomolecular MS in (1) structural and functional characterization of CQ adduction on standards of proteins, (2) identification of endogenous adduction targets, and (3) quantification of adducted blood proteins as exposure index. The reactivity and outcome of CQ adduction are discussed with emphases on endogenous species, such as dopamine and catechol estrogens. Limitations and advancements in sample preparation, MS instrumentation, and software to facilitate protein adductomics are also discussed.

Proteomic Biomarker Identification in Cerebrospinal Fluid for Leptomeningeal Metastases with Neurological Complications

Leptomeningeal metastases (LM) from solid tumours, lymphoma and leukaemia are characterized by multifocal neurological deficits with a high mortality rate. Early diagnosis and initiation of treatment are essential to kerb neurological deterioration. However, this is not always possible as 25% of cerebrospinal fluid samples produce false-negative results at first cytological examination. The identification of biomarkers that allow stratification of individuals according to risk for developing LM would be a major benefit. Proteomic-based approaches are now in increasing use for this purpose, and these are reviewed in this chapter with a focus on cerebrospinal fluid (CSF) analyses. The construction of a CSF proteome disease database would also facilitate analysis of other neurological disorders.

Chemical Glycoproteomics

Chemical tools have accelerated progress in glycoscience, reducing experimental barriers to studying protein glycosylation, the most widespread and complex form of posttranslational modification. For example, chemical glycoproteomics technologies have enabled the identification of specific glycosylation sites and glycan structures that modulate protein function in a number of biological processes. This field is now entering a stage of logarithmic growth, during which chemical innovations combined with mass spectrometry advances could make it possible to fully characterize the human glycoproteome. In this review, we describe the important role that chemical glycoproteomics methods are playing in such efforts. We summarize developments in four key areas: enrichment of glycoproteins and glycopeptides from complex mixtures, emphasizing methods that exploit unique chemical properties of glycans or introduce unnatural functional groups through metabolic labeling and chemoenzymatic tagging; identification of sites of protein glycosylation; targeted glycoproteomics; and functional glycoproteomics, with a focus on probing interactions between glycoproteins and glycan-binding proteins. Our goal with this survey is to provide a foundation on which continued technological advancements can be made to promote further explorations of protein glycosylation.

STRAP PTM: Software Tool for Rapid Annotation and Differential Comparison of Protein Post-Translational Modifications

The identification of protein post-translational modifications (PTMs) is an increasingly important component of proteomics and biomarker discovery, but very few tools exist for performing fast and easy characterization of global PTM changes and differential comparison of PTMs across groups of data obtained from liquid chromatography-tandem mass spectrometry experiments. STRAP PTM (Software Tool for Rapid Annotation of Proteins: Post-Translational Modification edition) is a program that was developed to facilitate the characterization of PTMs using spectral counting and a novel scoring algorithm to accelerate the identification of differential PTMs from complex data sets. The software facilitates multi-sample comparison by collating, scoring, and ranking PTMs and by summarizing data visually. The freely available software (beta release) installs on a PC and processes data in protXML format obtained from files parsed through the Trans-Proteomic Pipeline. The easy-to-use interface allows examination of results at protein, peptide, and PTM levels, and the overall design offers tremendous flexibility that provides proteomics insight beyond simple assignment and counting.