Quantitative Proteomics Using Isobaric Labeling: A Practical Guide

Ganoderic acid A ameliorates depressive-like behaviors in CSDS mice: Insights from proteomic profiling and molecular mechanisms

OBJECTIVE

Ganoderic Acid A (GAA), a primary bioactive component in Ganoderma, has demonstrated ameliorative effects on depressive-like behaviors in a Chronic Social Defeat Stress (CSDS) mouse model. This study aims to elucidate the underlying molecular mechanisms through proteomic analysis.

METHODS

C57BL/6 J mice were allocated into control (CON), chronic social defeat stress (CSDS), GAA, and imipramine (IMI) groups. Post-depression induction via CSDS, the GAA and IMI groups received respective treatments of GAA (2.5 mg/kg) and imipramine (10 mg/kg) for five days. Behavioral assessments utilized standardized tests. Proteins from the prefrontal cortex were analyzed using LC-MS, with further examination via bioinformatics and PRM for differential expression. Western blot analysis confirmed protein expression levels.

RESULTS

Chronic social defeat stress (CSDS) induced depressive-like behaviors in mice, which were significantly alleviated by GAA treatment, comparably to imipramine (IMI). Proteomic analysis identified distinct proteins in control (305), GAA-treated (949), and IMI-treated (289) groups. Enrichment in mitochondrial and synaptic proteins was evident from GO and PPI analyses. PRM analysis revealed significant expression changes in proteins crucial for mitochondrial and synaptic functions (namely, Naa30, Bnip1, Tubgcp4, Atxn3, Carmil1, Nup37, Apoh, Mrpl42, Tprkb, Acbd5, Dcx, Erbb4, Ppp1r2, Fam3c, Rnf112, and Cep41). Western blot validation in the prefrontal cortex showed increased levels of Mrpl42, Dcx, Fam3c, Ppp1r2, Rnf112, and Naa30 following GAA treatment.

CONCLUSION

GAA exhibits potential antidepressant properties, with its action potentially tied to the modulation of synaptic functions and mitochondrial activities.

Bioorthogonal non-canonical amino acid tagging to track transplanted human induced pluripotent stem cell-specific proteome

BACKGROUND

Human induced pluripotent stem cells (hiPSCs) and their differentiated cell types have a great potential for tissue repair and regeneration. While the primary focus of using hiPSCs has historically been to regenerate damaged tissue, emerging studies have shown a more potent effect of hiPSC-derived paracrine factors on tissue regeneration. However, the precise contents of the transplanted hiPSC-derived cell secretome are ambiguous. This is mainly due to the lack of tools to distinguish cell-specific secretome from host-derived proteins in a complex tissue microenvironment in vivo.

METHODS

In this study, we present the generation and characterization of a novel hiPSC line, L274G-hiPSC, expressing the murine mutant methionyl-tRNA synthetase, L274GMmMetRS, which can be used for tracking the cell specific proteome via biorthogonal non-canonical amino acid tagging (BONCAT). We assessed the trilineage differentiation potential of the L274G-hiPSCs in vitro and in vivo. Furthermore, we assessed the cell-specific proteome labelling in the L274G-hiPSC derived cardiomyocytes (L274G-hiPSC-CMs) in vitro following co-culture with wild type human umbilical vein derived endothelial cells and in vivo post transplantation in murine hearts.

RESULTS

We demonstrated that the L274G-hiPSCs exhibit typical hiPSC characteristics and that we can efficiently track the cell-specific proteome in their differentiated progenies belonging to the three germ lineages, including L274G-hiPSC-CMs. Finally, we demonstrated cell-specific BONCAT in transplanted L274G-hiPSC-CMs.

CONCLUSION

The novel L274G-hiPSC line can be used to study the cell-specific proteome of hiPSCs in vitro and in vivo, to delineate mechanisms underlying hiPSC-based cell therapies for a variety of regenerative medicine applications.

Progression from healthy periodontium to gingivitis and periodontitis: Insights from bioinformatics-driven proteomics - A systematic review with meta-analysis

AIM

The current study aimed to: (1) systematically review the published literature regarding the proteomics analyses of saliva and gingival crevicular fluid (GCF) in healthy humans and gingivitis and/or periodontitis patients; and (2) to identify the differentially expressed proteins (DEPs) based on the systematic review, and comprehensively conduct meta-analyses and bioinformatics analyses.

METHODS

An online search of Web of Science, Scopus, and PubMed was performed without any restriction on the year and language of publication. After the identification of the DEPs reported by the included human primary studies, gene ontology (GO), the Kyoto encyclopedia of genes and genomes pathway (KEGG), protein-protein interaction (PPI), and meta-analyses were conducted. The risk of bias among the included studies was evaluated using the modified Newcastle-Ottawa quality assessment scale.

RESULTS

The review identified significant differences in protein expression between healthy individuals and those with gingivitis and periodontitis. In GCF, 247 proteins were upregulated and 128 downregulated in periodontal diseases. Saliva analysis revealed 79 upregulated and 70 downregulated proteins. There were distinct protein profiles between gingivitis and periodontitis, with 159 and 31 unique upregulated proteins in GCF, respectively. Meta-analyses confirmed significant upregulation of various proteins in periodontitis, including ALB and MMP9, while CSTB and GSTP1 were downregulated. AMY1A and SERPINA1 were upregulated in periodontitis saliva. HBD was upregulated in gingivitis GCF, while DEFA3 was downregulated. PPI analysis revealed complex networks of interactions among DEPs. GO and KEGG pathway analyses provided insights into biological processes and pathways associated with periodontal diseases.

CONCLUSION

The ongoing MS-based proteomics studies emphasize the need for a highly sensitive and specific diagnostic tool for periodontal diseases. Clinician acceptance of the eventual diagnostic method relies on its ability to provide superior or complementary information to current clinical assessment procedures. Future research should prioritize the multiplex measurement of multiple biomarkers simultaneously to enhance diagnostic accuracy and large study cohorts are necessary to ensure the validity and reliability of research findings.

Welfare Assessment in Pigs Using the Salivary Proteome

Identifying the potential presence of stress at the pig farm is fundamental since it affects pig welfare. As a result, a reliable and straightforward tool to monitor stress could record the welfare status of the animals. Although numerous methods to assess the welfare of pigs have been developed in the past, no gold standard has been established yet. Recently, the value of saliva as a tool to identify chronic stress in piglets was explored, as it can be collected fast and non-invasively. Since the protein composition, i.e., the proteome of porcine saliva, responds to stress, the affected proteins could be used as salivary stress biomarkers. The present review first defines stress and its relationship with welfare. Next, the porcine gland-specific salivary proteome is characterized. Finally, six potential salivary biomarkers for stress are proposed, i.e., odorant-binding protein, vomeromodulin-like protein, chitinase, lipocalin-1, long palate lung and nasal epithelium protein, and alpha-2-HS-glycoprotein.

Advancing Nanomaterial Toxicology Screening Through Efficient and Cost-Effective Quantitative Proteomics

Proteomic investigations yield high-dimensional datasets, yet their application to large-scale toxicological assessments is hindered by reproducibility challenges due to fluctuating measurement conditions. To address these limitations, this study introduces an advanced tandem mass tag (TMT) labeling protocol. Although labeling approaches shorten data acquisition time by multiplexing samples compared to traditional label-free quantification (LFQ) methods in general, the associated costs may surge significantly with large sample sets, for example, in toxicological screenings. However, the introduced advanced protocol offers an efficient, cost-effective alternative, reducing TMT reagent usage (by a factor of ten) and requiring minimal biological material (1 µg), while demonstrating increased reproducibility compared to LFQ. To demonstrate its effectiveness, the advanced protocol is employed to assess the toxicity of nine benchmark nanomaterials (NMs) on A549 lung epithelial cells. While LFQ measurements identify 3300 proteins, they proved inadequate to reveal NM toxicity. Conversely, despite detecting 2600 proteins, the TMT protocol demonstrates superior sensitivity by uncovering alterations induced by NM treatment. In contrast to previous studies, the introduced advanced protocol allows simultaneous and straightforward assessment of multiple test substances, enabling prioritization, ranking, and grouping for hazard evaluation. Additionally, it fosters the development of New Approach Methodologies (NAMs), contributing to innovative methodologies in toxicological research.

Proteomics profiling of extracellular vesicle for identification of potential biomarkers in Alzheimer's disease: A comprehensive review

The intricate origins and diverse symptoms of Alzheimer's disease (AD) pose significant challenges for both diagnosis and treatment. Exosomes and microvesicles, which carry disease-specific cargo from a variety of central nervous system cell types, have emerged as promising reservoirs of biomarkers for AD. Research on the screening of possible biomarkers in Alzheimer's disease using proteomic profiling of EVs is systematically reviewed in this comprehensive review. We highlight key methodologies employed in EV isolation, characterization, and proteomic analysis, elucidating their advantages and limitations. Furthermore, we summarize the evolving landscape of EV-associated biomarkers implicated in AD pathogenesis, including proteins involved in amyloid-beta metabolism, tau phosphorylation, neuroinflammation, synaptic dysfunction, and neuronal injury. The literature review highlights the necessity for robust validation strategies and standardized protocols to effectively transition EV-based biomarkers into clinical use. In the concluding section, this review delves into potential future avenues and technological advancements pivotal in crafting EV-derived biomarkers applicable to AD diagnostics and prognostics. This review contributes to our comprehension of AD pathology and the advancement of precision medicine in neurodegenerative diseases, hinting at a promising era in AD precision medicine.

A Mass Spectrometry Strategy for Protein Quantification Based on the Differential Alkylation of Cysteines Using Iodoacetamide and Acrylamide

Mass spectrometry has become the most prominent yet evolving technology in quantitative proteomics. Today, a number of label-free and label-based approaches are available for the relative and absolute quantification of proteins and peptides. However, the label-based methods rely solely on the employment of stable isotopes, which are expensive and often limited in availability. Here we propose a label-based quantification strategy, where the mass difference is identified by the differential alkylation of cysteines using iodoacetamide and acrylamide. The alkylation reactions were performed under identical experimental conditions; therefore, the method can be easily integrated into standard proteomic workflows. Using high-resolution mass spectrometry, the feasibility of this approach was assessed with a set of tryptic peptides of human serum albumin. Several critical questions, such as the efficiency of labeling and the effect of the differential alkylation on the peptide retention and fragmentation, were addressed. The concentration of the quality control samples calculated against the calibration curves were within the ±20% acceptance range. It was also demonstrated that heavy labeled peptides exhibit a similar extraction recovery and matrix effect to light ones. Consequently, the approach presented here may be a viable and cost-effective alternative of stable isotope labeling strategies for the quantification of cysteine-containing proteins.

Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

Updates of the current strategies of labeling for N-glycan analysis

This mini review summarizes the current methods used for screening N-glycosylation of glycoproteins, with a specific focus on therapeutic proteins and on techniques involving the release of N-glycans. With the continuous development of biopharmaceuticals, particularly monoclonal antibodies (mAbs), which are N-glycosylated proteins, monitoring has gained importance in recent decades. Glycosylation of therapeutic glycoproteins is considered a critical quality attribute because it can impact the efficacy and safety of these therapeutic drugs. The protocols and instrumentation have evolved with the advancement of technologies. Nowadays, methods are becoming increasingly robust, rapid, and sensitive. For the release of N-glycans, the most commonly used method is enzymatic release using PNGase F. The latter is discussed in light of the advent of rapid release that is now possible. The strategy for separating N-glycans using either liquid chromatography (LC) with hydrophilic interaction liquid chromatography (HILIC) chemistry or capillary electrophoresis will be discussed. The selection of the labeling agent is a crucial step in sample preparation for the analysis of released N-glycans. This review also discusses labeling agents that are compatible with and dependent on the separation and detection techniques employed. The emergence of multiplex labeling agents is also summarized. The latter enables the analysis of multiple samples in a single run, but it requires MS analysis.

Shaping the future of oral cancer diagnosis: advances in salivary proteomics

INTRODUCTION

Saliva has gained increasing attention in the quest for disease biomarkers. Because it is a biological fluid that can be collected is an easy, painless, and safe way, it has been increasingly studied for the identification of oral cancer biomarkers. This is particularly important because oral cancer is often diagnosed at late stages with a poor prognosis.

AREAS COVERED

The review addresses the evolution of the experimental approaches used in salivary proteomics studies of oral cancer over the years and outlines advantages and pitfalls related to each one. In addition, examines the current landscape of oral cancer biomarker discovery and translation focusing on salivary proteomic studies. This discussion is based on an extensive literature search (PubMed, Scopus and Google Scholar).

EXPERT OPINION

The introduction of mass spectrometry has revolutionized the study of salivary proteomics. In the future, the focus will be on refining existing methods and introducing powerful experimental techniques such as mass spectrometry with selected reaction monitoring, which, despite their effectiveness, are still underutilized due to their high cost. In addition, conducting studies with larger cohorts and establishing standardized protocols for salivary proteomics are key challenges that need to be addressed in the coming years.

An ELISA-based method for Galleria mellonella apolipophorin-III quantification

Mammalian models, such as murine, are used widely in pathophysiological studies because they have a high degree of similarity in body temperature, metabolism, and immune response with humans. However, non-vertebrate animal models have emerged as alternative models to study the host-pathogen interaction with minimal ethical concerns. Galleria mellonella is an alternative model that has proved useful in studying the interaction of the host with either bacteria or fungi, performing drug testing, and assessing the immunological response to different microorganisms. The G. mellonella immune response includes cellular and humoral components with structural and functional similarities to the immune effectors found in higher vertebrates, such as humans. An important humoral effector stimulated during infections is apolipophorin III (apoLp-III), an opsonin characterized by its lipid and carbohydrate-binding properties that participate in lipid transport, as well as immunomodulatory activity. Despite some parameters, such as the measurement of phenoloxidase activity, melanin production, hemocytes counting, and expression of antimicrobial peptides genes are already used to assess the G. mellonella immune response to pathogens with different virulence degrees, the apoLp-III quantification remains to be a parameter to assess the immune response in this invertebrate. Here, we propose an immunological tool based on an enzyme-linked immunosorbent assay that allows apoLp-III quantification in the hemolymph of larvae challenged with pathogenic agents. We tested the system with hemolymph coming from larvae infected with Escherichia coli, Candida albicans, Sporothrix schenckii, Sporothrix globosa, and Sporothrix brasiliensis. The results revealed significantly higher concentrations of apoLp-III when each microbial species was inoculated, in comparison with untouched larvae, or inoculated with phosphate-buffered saline. We also demonstrated that the apoLp-III levels correlated with the strains' virulence, which was already reported. To our knowledge, this is one of the first attempts to quantify apoLp-III, using a quick and easy-to-use serological technique.

Characterizing Age-related Changes in Intact Mitochondrial Proteoforms in Murine Hearts using Quantitative Top-Down Proteomics

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, and the prevalence of CVDs increases markedly with age. Due to the high energetic demand, the heart is highly sensitive to mitochondrial dysfunction. The complexity of the cardiac mitochondrial proteome hinders the development of effective strategies that target mitochondrial dysfunction in CVDs. Mammalian mitochondria are composed of over 1000 proteins, most of which can undergo post-translational protein modifications (PTMs). Top-down proteomics is a powerful technique for characterizing and quantifying all protein sequence variations and PTMs. However, there are still knowledge gaps in the study of age-related mitochondrial proteoform changes using this technique. In this study, we used top-down proteomics to identify intact mitochondrial proteoforms in young and old hearts and determined changes in protein abundance and PTMs in cardiac aging.

METHODS

Intact mitochondria were isolated from the hearts of young (4-month-old) and old (24-25-month-old) mice. The mitochondria were lysed, and mitochondrial lysates were subjected to denaturation, reduction, and alkylation. For quantitative top-down analysis, there were 12 runs in total arising from 3 biological replicates in two conditions, with technical duplicates for each sample. The collected top-down datasets were deconvoluted and quantified, and then the proteoforms were identified.

RESULTS

From a total of 12 LC-MS/MS runs, we identified 134 unique mitochondrial proteins in the different sub-mitochondrial compartments (OMM, IMS, IMM, matrix). 823 unique proteoforms in different mass ranges were identified. Compared to cardiac mitochondria of young mice, 7 proteoforms exhibited increased abundance and 13 proteoforms exhibited decreased abundance in cardiac mitochondria of old mice. Our analysis also detected PTMs of mitochondrial proteoforms, including N-terminal acetylation, lysine succinylation, lysine acetylation, oxidation, and phosphorylation.

CONCLUSION

By combining mitochondrial protein enrichment using mitochondrial fractionation with quantitative top-down analysis using ultrahigh-pressure liquid chromatography (UPLC)-MS and label-free quantitation, we successfully identified and quantified intact proteoforms in the complex mitochondrial proteome. Using this approach, we detected age-related changes in abundance and PTMs of mitochondrial proteoforms in the heart.

How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

The role of proteomics in acute pancreatitis: new and old knowledge

INTRODUCTION

Around 20% of individuals diagnosed with acute pancreatitis (AP) may develop severe acute pancreatitis (SAP), possibly resulting in a mortality rate ranging from 15% to 35%. There is an urgent need to thoroughly understand the molecular phenotypes of SAP resulting from diverse etiologies. The field of translational research on AP has seen the use of several innovative proteomic methodologies via the ongoing improvement of isolation, tagging, and quantification methods.

AREAS COVERED

This paper provides a comprehensive overview of differentially abundant proteins (DAPs) identified in AP by searching the PubMed/MEDLINE database (2003-2023) and adds significantly to the current theoretical framework.

EXPERT OPINION

DAPs for potentially diagnosing AP based on proteomic identification need to be confirmed by multi-center studies that include larger samples. The discovery of DAPs in various organs at different AP stages via proteomic technologies is essential better to understand the pathophysiology of AP-related multiple organ dysfunction syndrome. Regarding the translational research of AP, novel approaches like single-cell proteomics and imaging using mass spectrometry may be used as soon as they become available.

Deep Learning-Assisted Analysis of Immunopeptidomics Data

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.

Concentrated ionic liquids for proteomics: Caveat emptor!

The use of concentrated ionic liquids (ILs) in the bioanalytical chemistry of proteins is sparse; typically, dilute aqueous IL solutions are used. Concentrated ILs have unique properties that may allow researchers to dissolve previously insoluble protein analytes, to increase the depth and robustness of sample preparation and the analysis of proteins. Previous research using concentrated ILs for this purpose is sparse and there is a need to systematically investigate the structure-activity relationship between the IL structure and its capacity to solubilise proteins. Here, bovine serum albumin was dissolved in various ionic liquids and monitored over time by light microscopy and SDS-PAGE. While qualitative, these measures provide a good estimate of, respectively, the dissolving power of an IL towards the given protein and the retained integrity of the protein. Hydrophilic ILs show the best solubilisation capacity and higher temperatures (in a restricted sense) improve the solubility of the protein. Higher temperatures and longer reaction times reduce the molecular weight of the protein, which could inhibit their applicability in proteomics, unless the conditions are judiciously controlled. Researchers should exercise caution when using concentrated ILs for protein analysis until the full scope and limitations are known, an aspect we are presently investigating.

Inhibiting Protein Aggregation Using Cellulose Nanocrystal in MALDI-TOF MS Analysis: Improving the Sensitivity and Repeatability of Intact Protein in Pueraria

Protein aggregation can induce low sensitivity and poor repeatability of matrix-assisted laser desorption/ionization time-of-fight mass spectrometry (MALDI-TOF MS) analysis for intact protein. Herein, we introduced a strategy to decrease protein aggregation in the sample solution by using cellulose nanocrystal (CNC). The results indicated that protein granule size was effectively reduced by adding CNC to the sample solution. Through MALDI-TOF MS analysis, the signal-to-noise ratio of [M + H]+ peak increased 2-fold, and the detection of limit was <10 μg/mL for intact protein. The CNC also contributed to excellent point-to-point repeatability for MALDI-TOF MS analysis with the coefficient of variation (CV) of 10.0% with CNC vs 48.9% without CNC in Hb solution. Also, the repeatability of Pueraria protein ion signals was improved by using CNC, and the CV with and without CNC was 16.1% and 39.6%, respectively. Moreover, protein ion intensity exhibited great linear relationship (y = 53.04x - 3.474, R2 = 0.9936) with the concentrations (ranging from 0.1 to 10 mg/mL) when using CNC. Further investigation revealed that m/z 19,000 and m/z 21,000 peaks of Pueraria could be used for the adulteration analysis and post-translational modification research, demonstrating our method has the potential for broad applications.

Food Safety-Transcriptomics and Proteomics

Food safety is a critical aspect of public health and involves the handling, preparation, and storage of food to avoid contamination and foodborne illnesses [...].

Proteomic advance of ischemic stroke: preclinical, clinical, and intervention

Ischemic stroke (IS) is the most common type of stroke and is characterized by high rates of mortality and long-term injury. The prediction and early diagnosis of IS are therefore crucial for optimal clinical intervention. Proteomics has provided important techniques for exploring protein markers associated with IS, but there has been no systematic evaluation and review of research that has used these techniques. Here, we review the differential proteins that have been found in cell- and animal- based studies and clinical trials of IS in the past 10 years; determine the key pathological proteins that have been identified in clinical trials; summarize the target proteins affected by interventions aimed at treating IS, with a focus on traditional Chinese medicine treatments. Overall, we clarify findings and problems that have been identified in recent proteomics research on IS and provide suggestions for improvements in this area. We also suggest areas that could be explored for determining the pathogenesis and developing interventions for IS.

An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic analysis

Multiplexed quantitative proteomics using tandem mass tag (TMT) is increasingly used in -omic study of complex samples. While TMT-based proteomics has the advantages of the higher quantitative accuracy, fewer missing values, and reduced instrument analysis time, it is limited by the additional reagent cost. In addition, current TMT labeling workflows involve repeated small volume pipetting of reagents in volatile solvents, which may increase the sample-to-sample variations and is not readily suitable for high throughput applications. In this study, we demonstrated that the TMT labeling procedures could be streamlined by using pre-aliquoted dry TMT reagents in a 96 well plate or 12-tube strip. As little as 50 μg dry TMT per channel was used to label 6-12 μg peptides, yielding high TMT labeling efficiency (∼99%) in both microbiome and mammalian cell line samples. We applied this workflow to analyze 97 samples in a study to evaluate whether ice recrystallization inhibitors improve the cultivability and activity of frozen microbiota. The results demonstrated tight sample clustering corresponding to groups and consistent microbiome responses to prebiotic treatments. This study supports the use of TMT reagents that are pre-aliquoted, dried, and stored for robust quantitative proteomics and metaproteomics in high throughput applications.

An integrated investigation of 16S rRNA gene sequencing and proteomics to elucidate the mechanism of Corydalis bungeana Turcz. on dextran sulfate sodium-induced colitis

Corydalis bungeana Turcz. (CBT) is frequently used to treat inflammatory illnesses, the mechanisms underlying its use to ulcerative colitis (UC) remain unclear. A dextran sulfate sodium (DSS)-induced UC mice model was established. The disease activity index (DAI), colonic length, histological inspection by hematoxylin-eosin staining, the cytokines levels in the colon, proteomics and intestinal flora in mice were investigated to evaluate the effect of CBT. The results showed that CBT can significantly reduce the DAI, increase the length of colon, improve the pathological injury of colon tissue, decrease the level of TNF-α, IL-6, IL-1β and increase the level of IL-10 in UC mice. Gut microbe sequencing showed that CBT could enhance the abundance of the intestinal microbiome, decrease possibly harmful bacteria and promote potentially helpful microbes. Proteomics investigation showed that 20 overlapping differentially expressed proteins (DEPs) were discovered in the control, model, and CBT administration groups. The DEPs in the CBT administration group were connected to biological procedures mainly involving detoxification. Extracellular matrix (ECM) receptor-associated proteins such as Col6a1 and CD36 may be important targets for CBT treatment of UC. Overall, this integrated methodology identified a comprehensive multi-omics network, composed of a certain set of gut microbiota and proteins, which may be potential targets for CBT treatment with UC.

Mass Spectrometry-Based Proteomic Technology and Its Application to Study Skeletal Muscle Cell Biology

Voluntary striated muscles are characterized by a highly complex and dynamic proteome that efficiently adapts to changed physiological demands or alters considerably during pathophysiological dysfunction. The skeletal muscle proteome has been extensively studied in relation to myogenesis, fiber type specification, muscle transitions, the effects of physical exercise, disuse atrophy, neuromuscular disorders, muscle co-morbidities and sarcopenia of old age. Since muscle tissue accounts for approximately 40% of body mass in humans, alterations in the skeletal muscle proteome have considerable influence on whole-body physiology. This review outlines the main bioanalytical avenues taken in the proteomic characterization of skeletal muscle tissues, including top-down proteomics focusing on the characterization of intact proteoforms and their post-translational modifications, bottom-up proteomics, which is a peptide-centric method concerned with the large-scale detection of proteins in complex mixtures, and subproteomics that examines the protein composition of distinct subcellular fractions. Mass spectrometric studies over the last two decades have decisively improved our general cell biological understanding of protein diversity and the heterogeneous composition of individual myofibers in skeletal muscles. This detailed proteomic knowledge can now be integrated with findings from other omics-type methodologies to establish a systems biological view of skeletal muscle function.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Serum a-1 antitrypsin as a novel biomarker in chronic heart failure

AIMS

Chronic heart failure (CHF) remains a major health issue worldwide. In the present study, we aimed to identify novel circulating biomarkers for CHF using serum proteomics technology and to validate the biomarker in three independent cohorts.

METHODS AND RESULTS

The isobaric tags for relative and absolute quantitation technology was utilized to identify the potential biomarkers of CHF. The validation was conducted in three independent cohort. Cohort A included 223 patients with ischaemic heart disease (IHD) and 321 patients with ischaemic heart failure (IHF) from the CORFCHD-PCI study. Cohort B recruited 817 patients with IHD and 1139 patients with IHF from the PRACTICE study. Cohort C enrolled 559 non-ischaemic heart disease patients with CHF (n = 316) or without CHF (n = 243). We found the expression of a-1 antitrypsin (AAT) was elevated significantly in patients with CHF compared with that in the patients with stable IHD using statistical and bioinformatics analyses. In a validation study, there was a significant difference between patients with stable IHD and patients with IHF in AAT concentration either in cohort A (1.35 ± 0.40 vs. 1.64 ± 0.56, P < 0.001) or in cohort B (1.37 ± 0.42 vs. 1.70 ± 0.48, P < 0.001). The area under the receiver operating characteristic curve was 0.70 [95% confidence interval (CI): 0.66 to 0.74, P < 0.001] in cohort A and 0.74 (95% CI: 0.72 to 0.76, P < 0.001) in cohort B. Furthermore, AAT was negative correlated with left ventricular ejection fraction (r = -0.261, P < 0.001). After adjusting for confounders using a multivariate logistic regression analysis, AAT remained an independent association with CHF in both cohort A (OR = 3.14, 95% CI: 1.667 to 5.90, P < 0.001) and cohort B (OR = 4.10, 95% CI: 2.97 to 5.65, P < 0.001). This association was also validated in cohort C (OR = 1.86, 95% CI: 1.02 to 3.38, P = 0.043).

CONCLUSIONS

The present study suggests that serum AAT is a reliable biomarker for CHF in a Chinese population.

Omics approaches for the assessment of biological responses to nanoparticles

Nanotechnology has enabled the development of innovative therapeutics, diagnostics, and drug delivery systems. Nanoparticles (NPs) can influence gene expression, protein synthesis, cell cycle, metabolism, and other subcellular processes. While conventional methods have limitations in characterizing responses to NPs, omics approaches can analyze complete sets of molecular entities that change upon exposure to NPs. This review discusses key omics approaches, namely transcriptomics, proteomics, metabolomics, lipidomics and multi-omics, applied to the assessment of biological responses to NPs. Fundamental concepts and analytical methods used for each approach are presented, as well as good practices for omics experiments. Bioinformatics tools are essential to analyze, interpret and visualize large omics data, and to correlate observations in different molecular layers. The authors envision that conducting interdisciplinary multi-omics analyses in future nanomedicine studies will reveal integrated cell responses to NPs at different omics levels, and the incorporation of omics into the evaluation of targeted delivery, efficacy, and safety will improve the development of nanomedicine therapies.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Endometrial proteomic profile of patients with repeated implantation failure

Introduction

Successful embryo implantation, is the initiating step of pregnancy, relies on not only the high quality of the embryo but also the synergistic development of a healthy endometrium. Characterization and identification of biomarkers for the receptive endometrium is an effective method for increasing the probability of successful embryo implantation.

Methods

Endometrial tissues from 22 women with a history of recurrent implantation failure (RIF) and 19 fertile controls were collected using biopsy catheters on 7-9 days after the peak of luteinizing hormone. Differentially expressed proteins (DEPs) were identified in six patients with RIF and six fertile controls using isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomics analysis.

Results

Two hundred and sixty-three DEPs, including proteins with multiple bioactivities, such as protein translation, mitochondrial function, oxidoreductase activity, fatty acid and amino acid metabolism, were identified from iTRAQ. Four potential biomarkers for receptive endometrium named tubulin polymerization-promoting protein family member 3 TPPP3, S100 Calcium Binding Protein A13 (S100A13), 17b-hydroxysteroid dehydrogenase 2 (HSD17B2), and alpha-2-glycoprotein 1, zinc binding (AZGP1) were further verified using ProteinSimple Wes and immunohistochemical staining in all included samples (n=22 for RIF and n=19 for controls). Of the four proteins, the protein levels of TPPP3 and HSD17B2 were significantly downregulated in the endometrium of patients with RIF.

Discussion

Poor endometrial receptivity is considered the main reason for the decrease in pregnancy success rates in patients suffering from RIF. iTRAQ techniques based on isotope markers can identify and quantify low abundance proteomics, and may be suitable for identifying differentially expressed proteins in RIF. This study provides novel evidence that TPPP3 and HSD17B2 may be effective targets for the diagnosis and treatment of non-receptive endometrium and RIF.

Extracellular Matrix Proteomics: The mdx-4cv Mouse Diaphragm as a Surrogate for Studying Myofibrosis in Dystrophinopathy

The progressive degeneration of the skeletal musculature in Duchenne muscular dystrophy is accompanied by reactive myofibrosis, fat substitution, and chronic inflammation. Fibrotic changes and reduced tissue elasticity correlate with the loss in motor function in this X-chromosomal disorder. Thus, although dystrophinopathies are due to primary abnormalities in the DMD gene causing the almost-complete absence of the cytoskeletal Dp427-M isoform of dystrophin in voluntary muscles, the excessive accumulation of extracellular matrix proteins presents a key histopathological hallmark of muscular dystrophy. Animal model research has been instrumental in the characterization of dystrophic muscles and has contributed to a better understanding of the complex pathogenesis of dystrophinopathies, the discovery of new disease biomarkers, and the testing of novel therapeutic strategies. In this article, we review how mass-spectrometry-based proteomics can be used to study changes in key components of the endomysium, perimysium, and epimysium, such as collagens, proteoglycans, matricellular proteins, and adhesion receptors. The mdx-4cv mouse diaphragm displays severe myofibrosis, making it an ideal model system for large-scale surveys of systematic alterations in the matrisome of dystrophic fibers. Novel biomarkers of myofibrosis can now be tested for their appropriateness in the preclinical and clinical setting as diagnostic, pharmacodynamic, prognostic, and/or therapeutic monitoring indicators.

Biochemical and proteomic insights into sarcoplasmic reticulum Ca2+-ATPase complexes in skeletal muscles

INTRODUCTION

Skeletal muscles contain large numbers of high-molecular-mass protein complexes in elaborate membrane systems. Integral membrane proteins are involved in diverse cellular functions including the regulation of ion handling, membrane homeostasis, energy metabolism and force transmission.

AREAS COVERED

The proteomic profiling of membrane proteins and large protein assemblies in skeletal muscles are outlined in this article. This includes a critical overview of the main biochemical separation techniques and the mass spectrometric approaches taken to study membrane proteins. As an illustrative example of an analytically challenging large protein complex, the proteomic detection and characterization of the Ca2+-ATPase of the sarcoplasmic reticulum is discussed. The biological role of this large protein complex during normal muscle functioning, in the context of fiber type diversity and in relation to mechanisms of physiological adaptations and pathophysiological abnormalities is evaluated from a proteomics perspective.

EXPERT OPINION

Mass spectrometry-based muscle proteomics has decisively advanced the field of basic and applied myology. Although it is technically challenging to study membrane proteins, innovations in protein separation methodology in combination with sensitive mass spectrometry and improved systems bioinformatics has allowed the detailed proteomic detection and characterization of skeletal muscle membrane protein complexes, such as Ca2+-pump proteins of the sarcoplasmic reticulum.

Mapping the Arabidopsis thaliana proteome in PeptideAtlas and the nature of the unobserved (dark) proteome; strategies towards a complete proteome

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected PTMs, and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for building the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome - the 'dark' proteome. This dark proteome is highly enriched for certain ( e.g. CLE, CEP, IDA, PSY) but not other ( e.g. THIONIN, CAP,) signaling peptides families, E3 ligases, TFs, and other proteins with unfavorable physicochemical properties. A machine learning model trained on RNA expression data and protein properties predicts the probability for proteins to be detected. The model aids in discovery of proteins with short-half life ( e.g. SIG1,3 and ERF-VII TFs) and completing the proteome. PeptideAtlas is linked to TAIR, JBrowse, PPDB, SUBA, UniProtKB and Plant PTM Viewer.

Insights into the Lysine Acetylome of the Haloarchaeon Haloferax volcanii during Oxidative Stress by Quantitative SILAC-Based Proteomics

Oxidative stress adaptation strategies are important to cell function and are linked to cardiac, neurodegenerative disease, and cancer. Representatives of the Archaea domain are used as model organisms based on their extreme tolerance to oxidants and close evolutionary relationship with eukaryotes. A study of the halophilic archaeon Haloferax volcanii reveals lysine acetylation to be associated with oxidative stress responses. The strong oxidant hypochlorite: (i) stimulates an increase in lysine acetyltransferase HvPat2 to HvPat1 abundance ratios and (ii) selects for lysine deacetylase sir2 mutants. Here we report the dynamic occupancy of the lysine acetylome of glycerol-grown H. volcanii as it shifts in profile in response to hypochlorite. These findings are revealed by the: (1) quantitative multiplex proteomics of the SILAC-compatible parent and Δsir2 mutant strains and (2) label-free proteomics of H26 'wild type' cells. The results show that lysine acetylation is associated with key biological processes including DNA topology, central metabolism, cobalamin biosynthesis, and translation. Lysine acetylation targets are found conserved across species. Moreover, lysine residues modified by acetylation and ubiquitin-like sampylation are identified suggesting post-translational modification (PTM) crosstalk. Overall, the results of this study expand the current knowledge of lysine acetylation in Archaea, with the long-term goal to provide a balanced evolutionary perspective of PTM systems in living organisms.

Technologies for Direct Detection of Covalent Protein—Drug Adducts

In the past two decades, drug candidates with a covalent binding mode have gained the interest of medicinal chemists, as several covalent anticancer drugs have successfully reached the clinic. As a covalent binding mode changes the relevant parameters to rank inhibitor potency and investigate structure-activity relationship (SAR), it is important to gather experimental evidence on the existence of a covalent protein–drug adduct. In this work, we review established methods and technologies for the direct detection of a covalent protein–drug adduct, illustrated with examples from (recent) drug development endeavors. These technologies include subjecting covalent drug candidates to mass spectrometric (MS) analysis, protein crystallography, or monitoring intrinsic spectroscopic properties of the ligand upon covalent adduct formation. Alternatively, chemical modification of the covalent ligand is required to detect covalent adducts by NMR analysis or activity-based protein profiling (ABPP). Some techniques are more informative than others and can also elucidate the modified amino acid residue or bond layout. We will discuss the compatibility of these techniques with reversible covalent binding modes and the possibilities to evaluate reversibility or obtain kinetic parameters. Finally, we expand upon current challenges and future applications. Overall, these analytical techniques present an integral part of covalent drug development in this exciting new era of drug discovery.

Mass Spectrometry-Based Untargeted Approaches to Reveal Diagnostic Signatures of Male Infertility in Seminal Plasma: A New Laboratory Perspective for the Clinical Management of Infertility?

Male infertility has been recognized as a global health problem. Semen analysis, although considered the golden standard, may not provide a confident male infertility diagnosis alone. Hence, there is the urgent request for an innovative and reliable platform to detect biomarkers of infertility. The rapid expansion of mass spectrometry (MS) technology in the field of the 'omics' disciplines, has incredibly proved the great potential of MS-based diagnostic tests to revolutionize the future of pathology, microbiology and laboratory medicine. Despite the increasing success in the microbiology area, MS-biomarkers of male infertility currently remain a proteomic challenge. In order to address this issue, this review encompasses proteomics investigations by untargeted approaches with a special focus on experimental designs and strategies (bottom-up and top-down) for seminal fluid proteome profiling. The studies reported here witness the efforts of the scientific community to address these investigations aimed at the discovery of MS-biomarkers of male infertility. Proteomics untargeted approaches, depending on the study design, might provide a great plethora of biomarkers not only for a male infertility diagnosis, but also to address a new MS-biomarkers classification of infertility subtypes. From the early detection to the evaluation of infertility grade, new MS-derived biomarkers might also predict long-term outcomes and clinical management of infertility.

Fiber-Type Shifting in Sarcopenia of Old Age: Proteomic Profiling of the Contractile Apparatus of Skeletal Muscles

The progressive loss of skeletal muscle mass and concomitant reduction in contractile strength plays a central role in frailty syndrome. Age-related neuronal impairments are closely associated with sarcopenia in the elderly, which is characterized by severe muscular atrophy that can considerably lessen the overall quality of life at old age. Mass-spectrometry-based proteomic surveys of senescent human skeletal muscles, as well as animal models of sarcopenia, have decisively improved our understanding of the molecular and cellular consequences of muscular atrophy and associated fiber-type shifting during aging. This review outlines the mass spectrometric identification of proteome-wide changes in atrophying skeletal muscles, with a focus on contractile proteins as potential markers of changes in fiber-type distribution patterns. The observed trend of fast-to-slow transitions in individual human skeletal muscles during the aging process is most likely linked to a preferential susceptibility of fast-twitching muscle fibers to muscular atrophy. Studies with senescent animal models, including mostly aged rodent skeletal muscles, have confirmed fiber-type shifting. The proteomic analysis of fast versus slow isoforms of key contractile proteins, such as myosin heavy chains, myosin light chains, actins, troponins and tropomyosins, suggests them as suitable bioanalytical tools of fiber-type transitions during aging.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Optimization of Higher-Energy Collisional Dissociation Fragmentation Energy for Intact Protein-Level Tandem Mass Tag Labeling

Isobaric chemical tag labeling (e.g., TMT) is a commonly used approach in quantitative proteomics, and quantification is enabled through detection of low-mass reporter ions generated after MS2 fragmentation. Recently, we have introduced and optimized an intact protein-level TMT labeling platform that demonstrated >90% labeling efficiency in complex samples with top-down proteomics. Higher-energy collisional dissociation (HCD) is commonly utilized for isobaric tag-labeled peptide fragmentation because it produces accurate reporter ion intensities and avoids loss of low mass ions. HCD energies have been optimized for isobaric tag labeled-peptides but have not been systematically evaluated for isobaric tag-labeled intact proteins. In this study, we report a systematic evaluation of normalized HCD fragmentation energies (NCEs) on TMT-labeled HeLa cell lysate using top-down proteomics. Our results suggested that reporter ions often result in higher ion intensities at higher NCEs. Optimal fragmentation of intact proteins for identification, however, required relatively lower NCE. We further demonstrated that a stepped NCE scheme with energies from 30% to 50% resulted in optimal quantification and identification of TMT-labeled HeLa proteins. These parameters resulted in an average reporter ion intensity of ∼4E4 and average proteoform spectrum matches (PrSMs) of >1000 per RPLC-MS/MS run with a 1% false discovery rate (FDR) cutoff.

ITRAQ-based quantitative proteomics analysis of forest musk deer with pneumonia

Pneumonia can seriously threaten the life of forest musk deer (FMD, an endangered species). To gain a comprehensive understanding of pneumonia pathogenesis in FMD, iTRAQ-based proteomics analysis was performed in diseased (Pne group) lung tissues of FMD that died of pneumonia and normal lung tissues (Ctrl group) of FMD that died from fighting against each other. Results showed that 355 proteins were differentially expressed (fold change ≥ 1.2 and adjusted P-value &lt; 0.05) in Pne vs. Ctrl. GO/KEGG annotation and enrichment analyses showed that dysregulated proteins might play vital roles in bacterial infection and immunity. Given the close association between bacterial infection and pneumonia, 32 dysregulated proteins related to Staphylococcus aureus infection, bacterial invasion of epithelial cells, and pathogenic Escherichia coli infection were screened out. Among these 32 proteins, 13 proteins were mapped to the bovine genome. Given the close phylogenetic relationships of FMD and bovine, the protein-protein interaction networks of the above-mentioned 13 proteins were constructed by the String database. Based on the node degree analysis, 5 potential key proteins related to pneumonia-related bacterial infection in FMD were filtered out. Moreover, 85 dysregulated proteins related to the immune system process were identified given the tight connection between immune dysregulation and pneumonia pathogenesis. Additionally, 12 proteins that might function as crucial players in pneumonia-related immune response in FMD were screened out using the same experimental strategies described above. In conclusion, some vital proteins, biological processes, and pathways in pneumonia development were identified in FMD.

Interspecies Isobaric Labeling-Based Quantitative Proteomics Reveals Protein Changes in the Ovary of Aedes aegypti Coinfected With ZIKV and Wolbachia

Zika is a vector-borne disease caused by an arbovirus (ZIKV) and overwhelmingly transmitted by Ae. aegypti. This disease is linked to adverse fetal outcomes, mostly microcephaly in newborns, and other clinical aspects such as acute febrile illness and neurologic complications, for example, Guillain-Barré syndrome. One of the most promising strategies to mitigate arbovirus transmission involves releasing Ae. aegypti mosquitoes carrying the maternally inherited endosymbiont bacteria Wolbachia pipientis. The presence of Wolbachia is associated with a reduced susceptibility to arboviruses and a fitness cost in mosquito life-history traits such as fecundity and fertility. However, the mechanisms by which Wolbachia influences metabolic pathways leading to differences in egg production remains poorly known. To investigate the impact of coinfections on the reproductive tract of the mosquito, we applied an isobaric labeling-based quantitative proteomic strategy to investigate the influence of Wolbachia wMel and ZIKV infection in Ae. aegypti ovaries. To the best of our knowledge, this is the most complete proteome of Ae. aegypti ovaries reported so far, with a total of 3913 proteins identified, were also able to quantify 1044 Wolbachia proteins in complex sample tissue of Ae. aegypti ovary. Furthermore, from a total of 480 mosquito proteins modulated in our study, we discuss proteins and pathways altered in Ae. aegypti during ZIKV infections, Wolbachia infections, coinfection Wolbachia/ZIKV, and compared with no infection, focusing on immune and reproductive aspects of Ae. aegypti. The modified aspects mainly were related to the immune priming enhancement by Wolbachia presence and the modulation of the Juvenile Hormone pathway caused by both microorganism’s infection.

Application of Proteomics in the Discovery of Radiosensitive Cancer Biomarkers

Radiation therapy remains an important component of cancer treatment. Gene-encoded proteins were the actual executors of cellular functions. Proteomic was a novel technology that can systematically analysis protein composition and measure their levels of change, this was a high throughput method, and were the import tools in the post genomic era. In recent years, rapid progress of proteomic have been made in the study of cancer mechanism, diagnosis, and treatment. This article elaborates current advances and future directions of proteomics in the discovery of radiosensitive cancer biomarkers.