Quantitative proteome analysis using isotope-coded affinity tags and mass spectrometry

Pyruvate Kinase M1/2 Proteoformics for Accurate Insights into Energy Metabolism Abnormity to Promote the Overall Management of Ovarian Cancer Towards Predictive, Preventive, and Personalized Medicine Approaches

Ovarian cancer (OC) is a global health problem that frequently presents at advanced stages, is predisposed to recurrence, readily develops resistance to platinum-based drugs, and has a low survival rate. Predictive, preventive, and personalized medicine (PPPM/3PM) offers an integrated solution with the use of genetic, proteomic, and metabolic biomarkers to identify high-risk individuals for early detection. Metabolic reprogramming is one of the key strategies employed by tumor cells to adapt to the microenvironment and support unlimited proliferation. Pyruvate kinases M1 and M2 (PKM1/2) are encoded by the PKM gene, a pivotal enzyme in the last step of the glycolytic pathway, which is at the crossroads of aerobic oxidation and the Warburg effect to serve as a potential regulator of glucose metabolism and influence cellular energy production and metabolic reprogramming. Commonly, the ratio of PKM1-to-PKM2 is changed in tumors compared to normal controls, and PKM2 is highly expressed in OC to induce a high glycolysis rate and participate in the malignant invasion and metastatic characteristics of cancer cells with epithelial/mesenchymal transition (EMT). PKM2 inhibitors suppress the migration and growth of OC cells by interfering with the Warburg effect. Proteoforms are the final structural and functional forms of a gene/protein, and the canonical protein PKM contains all proteoforms encoded by the same PKM gene. The complexity of PKM can be elucidated by proteoformics. The OC-specific PKM proteoform might represent a specific target for therapeutic interventions against OC. In the framework of PPPM/3PM, the OC-specific PKM proteoform might be the early warning and prognosis biomarker. It is important to clarify the molecular mechanisms of PKM proteoforms in cancer metabolism. This review analyzes the expression, function, and molecular mechanisms of PKM proteoforms in OC, which help identify specific biomarkers for OC.

Deciphering Cancer Complexity: Integrative Proteogenomics and Proteomics Approaches for Biomarker Discovery

Proteomics has revolutionized the field of cancer biology because the use of a large number of in vivo (SILAC), in vitro (iTRAQ, ICAT, TMT, stable-isotope Dimethyl, and 18O) labeling techniques or label-free methods (spectral counting or peak intensities) coupled with mass spectrometry enables us to profile and identify dysregulated proteins in diseases such as cancer. These proteome and genome studies have led to many challenges, such as the lack of consistency or correlation between copy numbers, RNA, and protein-level data. This review covers solely mass spectrometry-based approaches used for cancer biomarker discovery. It also touches on the emerging role of oncoproteogenomics or proteogenomics in cancer biomarker discovery and how this new area is attracting the integration of genomics and proteomics areas to address some of the important questions to help impinge on the biology and pathophysiology of different malignancies to make these mass spectrometry-based studies more realistic and relevant to clinical settings.

Gut dysbiosis and neurological modalities: An engineering approach via proteomic analysis of gut-brain axis

The human gut microbiota is a complex and dynamic community of microorganisms, that influence metabolic, neurodevelopmental, and immune pathways. Microbial dysbiosis, characterized by changes in microbial diversity and relative abundances, is implicated in the development of various chronic neurological and neurodegenerative disorders. These disorders are marked by the accumulation of pathological protein aggregates, leading to the progressive loss of neurons and behavioural functions. Dysregulations in protein-protein interaction networks and signalling complexes, critical for normal brain function, are common in neurological disorders but challenging to unravel, particularly at the neuron and synapse-specific levels. To advance therapeutic strategies, a deeper understanding of neuropathogenesis, especially during the progressive disease phase, is needed. Biomarkers play a crucial role in identifying disease pathophysiology and monitoring disease progression. Proteomics, a powerful technology, shows promise in accelerating biomarker discovery and aiding in the development of novel treatments. In this chapter, we provide an in-depth overview of how proteomic techniques, utilizing various biofluid samples from patients with neurological conditions and diverse animal models, have contributed valuable insights into the pathogenesis of numerous neurological disorders. We also discuss the current state of research, potential challenges, and future directions in proteomic approaches to unravel neuro-pathological conditions.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Ameliorating the effects of multiple stresses on agronomic traits in crops: modern biotechnological and omics approaches

While global climate change poses a significant environmental threat to agriculture, the increasing population is another big challenge to food security. To address this, developing crop varieties with increased productivity and tolerance to biotic and abiotic stresses is crucial. Breeders must identify traits to ensure higher and consistent yields under inconsistent environmental challenges, possess resilience against emerging biotic and abiotic stresses and satisfy customer demands for safer and more nutritious meals. With the advent of omics-based technologies, molecular tools are now integrated with breeding to understand the molecular genetics of genotype-based traits and develop better climate-smart crops. The rapid development of omics technologies offers an opportunity to generate novel datasets for crop species. Identifying genes and pathways responsible for significant agronomic traits has been made possible by integrating omics data with genetic and phenotypic information. This paper discusses the importance and use of omics-based strategies, including genomics, transcriptomics, proteomics and phenomics, for agricultural and horticultural crop improvement, which aligns with developing better adaptability in these crop species to the changing climate conditions.

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Proteomics-Driven Biomarkers in Pancreatic Cancer

Pancreatic cancer is a devastating disease that has a grim prognosis, highlighting the need for improved screening, diagnosis, and treatment strategies. Currently, the sole biomarker for pancreatic ductal adenocarcinoma (PDAC) authorized by the U.S. Food and Drug Administration is CA 19-9, which proves to be the most beneficial in tracking treatment response rather than in early detection. In recent years, proteomics has emerged as a powerful tool for advancing our understanding of pancreatic cancer biology and identifying potential biomarkers and therapeutic targets. This review aims to offer a comprehensive survey of proteomics' current status in pancreatic cancer research, specifically accentuating its applications and its potential to drastically enhance screening, diagnosis, and treatment response. With respect to screening and diagnostic precision, proteomics carries the capacity to augment the sensitivity and specificity of extant screening and diagnostic methodologies. Nonetheless, more research is imperative for validating potential biomarkers and establishing standard procedures for sample preparation and data analysis. Furthermore, proteomics presents opportunities for unveiling new biomarkers and therapeutic targets, as well as fostering the development of personalized treatment strategies based on protein expression patterns associated with treatment response. In conclusion, proteomics holds great promise for advancing our understanding of pancreatic cancer biology and improving patient outcomes. It is essential to maintain momentum in investment and innovation in this arena to unearth more groundbreaking discoveries and transmute them into practical diagnostic and therapeutic strategies in the clinical context.

A Proteomics Insight into Advancements in the Rice-Microbe Interaction

Rice is one of the most-consumed foods worldwide. However, the productivity and quality of rice grains are severely constrained by pathogenic microbes. Over the last few decades, proteomics tools have been applied to investigate the protein level changes during rice-microbe interactions, leading to the identification of several proteins involved in disease resistance. Plants have developed a multi-layered immune system to suppress the invasion and infection of pathogens. Therefore, targeting the proteins and pathways associated with the host's innate immune response is an efficient strategy for developing stress-resistant crops. In this review, we discuss the progress made thus far with respect to rice-microbe interactions from side views of the proteome. Genetic evidence associated with pathogen-resistance-related proteins is also presented, and challenges and future perspectives are highlighted in order to understand the complexity of rice-microbe interactions and to develop disease-resistant crops in the future.

Lipidomics and proteomics: An integrative approach for early diagnosis of dementia and Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder and considered to be responsible for majority of worldwide prevalent dementia cases. The number of patients suffering from dementia are estimated to increase up to 115.4 million cases worldwide in 2050. Hence, AD is contemplated to be one of the major healthcare challenge in current era. This disorder is characterized by impairment in various signaling molecules at cellular and nuclear level including aggregation of Aβ protein, tau hyper phosphorylation altered lipid metabolism, metabolites dysregulation, protein intensity alteration etc. Being heterogeneous and multifactorial in nature, the disease do not has any cure or any confirmed diagnosis before the onset of clinical manifestations. Hence, there is a requisite for early diagnosis of AD in order to downturn the progression/risk of the disorder and utilization of newer technologies developed in this field are aimed to provide an extraordinary assistance towards the same. The lipidomics and proteomics constitute large scale study of cellular lipids and proteomes in biological matrices at normal stage or any stage of a disease. The study involves high throughput quantification and detection techniques such as mass spectrometry, liquid chromatography, nuclear mass resonance spectroscopy, fluorescence spectroscopy etc. The early detection of altered levels of lipids and proteins in blood or any other biological matrices could aid in preventing the progression of AD and dementia. Therefore, the present review is designed to focus on the recent techniques and early diagnostic criteria for AD, revealing the role of lipids and proteins in this disease and their assessment through different techniques.

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.

The Need for Biomarkers in the ALS-FTD Spectrum: A Clinical Point of View on the Role of Proteomics

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are severely debilitating and progressive neurodegenerative disorders. A distinctive pathological feature of several neurodegenerative diseases, including ALS and FTD, is the deposition of aberrant protein inclusions in neuronal cells, which leads to cellular dysfunction and neuronal damage and loss. Despite this, to date, the biological process behind developing these protein inclusions must be better clarified, making the development of disease-modifying treatment impossible until this is done. Proteomics is a powerful tool to characterize the expression, structure, functions, interactions, and modifications of proteins of tissue and biological fluid, including plasma, serum, and cerebrospinal fluid. This protein-profiling characterization aims to identify disease-specific protein alteration or specific pathology-based mechanisms which may be used as markers of these conditions. Our narrative review aims to highlight the need for biomarkers and the potential use of proteomics in clinical practice for ALS-FTD spectrum disorders, considering the emerging rationale in proteomics for new drug development. Certainly, new data will emerge in the near future in this regard and support clinicians in the development of personalized medicine.

Computational analysis of 4-1BB-induced NFκB signaling suggests improvements to CAR cell design

BACKGROUND

Chimeric antigen receptor (CAR)-expressing cells are a powerful modality of adoptive cell therapy against cancer. The potency of signaling events initiated upon antigen binding depends on the costimulatory domain within the structure of the CAR. One such costimulatory domain is 4-1BB, which affects cellular response via the NFκB pathway. However, the quantitative aspects of 4-1BB-induced NFκB signaling are not fully understood.

METHODS

We developed an ordinary differential equation-based mathematical model representing canonical NFκB signaling activated by CD19scFv-4-1BB. After a global sensitivity analysis on model parameters, we ran Monte Carlo simulations of cell population-wide variability in NFκB signaling and quantified the mutual information between the extracellular signal and different levels of the NFκB signal transduction pathway.

RESULTS

In response to a wide range of antigen concentrations, the magnitude of the transient peak in NFκB nuclear concentration varies significantly, while the timing of this peak is relatively consistent. Global sensitivity analysis showed that the model is robust to variations in parameters, and thus, its quantitative predictions would remain applicable to a broad range of parameter values. The model predicts that overexpressing NEMO and disabling IKKβ deactivation can increase the mutual information between antigen levels and NFκB activation.

CONCLUSIONS

Our modeling predictions provide actionable insights to guide CAR development. Particularly, we propose specific manipulations to the NFκB signal transduction pathway that can fine-tune the response of CD19scFv-4-1BB cells to the antigen concentrations they are likely to encounter. Video Abstract.

Evaluation of acquisition modes for semi-quantitative analysis by targeted and untargeted mass spectrometry

RATIONALE

Analyte quantitation by mass spectrometry underpins a diverse range of scientific endeavors. The fast-growing field of mass spectrometer development has resulted in several targeted and untargeted acquisition modes suitable for these applications. By characterizing the acquisition methods available on an ion mobility (IM)-enabled orthogonal acceleration time-of-flight (oa-ToF) instrument, the optimum modes for analyte semi-quantitation can be deduced.

METHODS

Serial dilutions of commercial metabolite, peptide, or cross-linked peptide analytes were prepared in matrices of human urine or Escherichia coli digest. Each analyte dilution was introduced into an IM separation-enabled oa-ToF mass spectrometer by reversed-phase liquid chromatography and electrospray ionization. Data were acquired for each sample in duplicate using nine different acquisition modes, including four IM-enabled acquisitions modes, available on the mass spectrometer.

RESULTS

Five (metabolite) or seven (peptide/cross-linked peptide) point calibration curves were prepared for analytes across each of the acquisition modes. A nonlinear response was observed at high concentrations for some modes, attributed to saturation effects. Two correction methods, one MS1 isotope-correction and one MS2 ion intensity-correction, were applied to address this observation, resulting in an up to twofold increase in dynamic range. By averaging the semi-quantitative results across analyte classes, two parameters, linear dynamic range (LDR) and lower limit of quantification (LLOQ), were determined to evaluate each mode.

CONCLUSION

A comparison of the acquisition modes revealed that data-independent acquisition and parallel reaction monitoring methods are most robust for semi-quantitation when considering achievable LDR and LLOQ. IM-enabled modes exhibited sensitivity increases, but a simultaneous reduction in dynamic range required correction methods to recover. These findings will assist users in identifying the optimum acquisition mode for their analyte quantitation needs, supporting a diverse range of applications and providing guidance for future acquisition mode developments.

Multiomics Studies Investigating Recurrent Pregnancy Loss: An Effective Tool for Mechanism Exploration

Patients with recurrent pregnancy loss (RPL) account for approximately 1%-5% of women aiming to achieve childbirth. Although studies have shown that RPL is associated with failure of endometrial decidualization, placental dysfunction, and immune microenvironment disorder at the maternal-fetal interface, the exact pathogenesis remains unknown. With the development of high-throughput technology, more studies have focused on the genomics, transcriptomics, proteomics and metabolomics of RPL, and new gene mutations and new biomarkers of RPL have been discovered, providing an opportunity to explore the pathogenesis of RPL from different biological processes. Bioinformatics analyses of these differentially expressed genes, proteins and metabolites also reflect the biological pathways involved in RPL, laying a foundation for further research. In this review, we summarize the findings of omics studies investigating decidual tissue, villous tissue and blood from patients with RPL and identify some possible limitations of current studies.

Proteomics in Multiple Sclerosis: The Perspective of the Clinician

Multiple sclerosis (MS) is the inflammatory demyelinating and neurodegenerative disease of the central nervous system (CNS) that affects approximately 2.8 million people worldwide. In the last decade, a new era was heralded in by a new phenotypic classification, a new diagnostic protocol and the first ever therapeutic guideline, making personalized medicine the aim of MS management. However, despite this great evolution, there are still many aspects of the disease that are unknown and need to be further researched. A hallmark of these research are molecular biomarkers that could help in the diagnosis, differential diagnosis, therapy and prognosis of the disease. Proteomics, a rapidly evolving discipline of molecular biology may fulfill this dire need for the discovery of molecular biomarkers. In this review, we aimed to give a comprehensive summary on the utility of proteomics in the field of MS research. We reviewed the published results of the method in case of the pathogenesis of the disease and for biomarkers of diagnosis, differential diagnosis, conversion of disease courses, disease activity, progression and immunological therapy. We found proteomics to be a highly effective emerging tool that has been providing important findings in the research of MS.

Proteomic perspectives on thermotolerant microbes: an updated review

INTRODUCTION

Thermotolerant microbes are a group of microorganisms that survive in elevated temperatures. The thermotolerant microbes, which are found in geothermal heat zones, grow at temperatures of or above 45°C. The proteins present in such microbes are optimally active at these elevated temperatures. Hence, therefore, serves as an advantage in various biotechnological applications. In the last few years, scientists have tried to understand the molecular mechanisms behind the maintenance of the structural integrity of the cell and to study the stability of various thermotolerant proteins at extreme temperatures. Proteomic analysis is the solution for this search. Applying novel proteomic tools determines the proteins involved in the thermostability of microbes at elevated temperatures.

METHODS

Advanced proteomic techniques like Mass spectrometry, nano-LC-MS, protein microarray, ICAT, iTRAQ, and SILAC could enable the screening and identification of novel thermostable proteins.

RESULTS

This review provides up-to-date details on the protein signature of various thermotolerant microbes analyzed through advanced proteomic tools concerning relevant research articles. The protein complex composition from various thermotolerant microbes cultured at different temperatures, their structural arrangement, and functional efficiency of the protein was reviewed and reported.

CONCLUSION

This review provides an overview of thermotolerant microbes, their enzymes, and the proteomic tools implemented to characterize them. This article also reviewed a comprehensive view of the current proteomic approaches for protein profiling in thermotolerant microbes.

Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects

Omics technologies, namely genomics, transcriptomics, proteomics, metabolomics, and phenomics, are becoming an integral part of virtually every commercial cereal crop breeding program, as they provide substantial dividends per unit time in both pre-breeding and breeding phases. Continuous advances in omics assure time efficiency and cost benefits to improve cereal crops. This review provides a comprehensive overview of the established omics methods in five major cereals, namely rice, sorghum, maize, barley, and bread wheat. We cover the evolution of technologies in each omics section independently and concentrate on their use to improve economically important agronomic as well as biotic and abiotic stress-related traits. Advancements in the (1) identification, mapping, and sequencing of molecular/structural variants; (2) high-density transcriptomics data to study gene expression patterns; (3) global and targeted proteome profiling to study protein structure and interaction; (4) metabolomic profiling to quantify organ-level, small-density metabolites, and their composition; and (5) high-resolution, high-throughput, image-based phenomics approaches are surveyed in this review.

Automation of On-Resin Enrichment of S-Nitrosylated Proteins for Oxidized Cysteine-Selective cPILOT

S-Nitrosylation (SNO) is a cysteine post-translational modification that increases with normal aging and is present in Alzheimer's disease and other aging-related illnesses. Detection of SNO-modified proteins can be challenging; however, we previously developed a robust quantitative proteomics approach termed "Oxidized Cysteine-Selective combined precursor isobaric labeling and isobaric tagging (OxcyscPILOT)" that allows for detection of endogenous SNO-modified proteins. OxcyscPILOT involves enrichment of SNO-modified proteins using a thiol-based resin. This enrichment is performed manually, and wash steps with the resin require numerous stages and buffer reagents. The goal of this study is to transfer the manual protocol to an automated liquid handler system in order to reduce wash steps, increase sample throughput, and minimize experimental error. In order to accomplish this, we evaluated the Biomek i7 liquid handler automated workstation and a Positive Pressure ALP (PPA) apparatus to conduct automated on-resin enrichment. Our findings provide starting pressure conditions for the use of PPA in an automated OxcyscPILOT proteomics workflow that could be transferred to other robotic liquid handling systems.

Proteome analysis using machine learning approaches and its applications to diseases

With the tremendous developments in the fields of biological and medical technologies, huge amounts of data are generated in the form of genomic data, images in medical databases or as data on protein sequences, and so on. Analyzing this data through different tools sheds light on the particulars of the disease and our body's reactions to it, thus, aiding our understanding of the human health. Most useful of these tools is artificial intelligence and deep learning (DL). The artificially created neural networks in DL algorithms help extract viable data from the datasets, and further, to recognize patters in these complex datasets. Therefore, as a part of machine learning, DL helps us face all the various challenges that come forth during protein prediction, protein identification and their quantification. Proteomics is the study of such proteins, their structures, features, properties and so on. As a form of data science, Proteomics has helped us progress excellently in the field of genomics technologies. One of the major techniques used in proteomics studies is mass spectrometry (MS). However, MS is efficient with analysis of large datasets only with the added help of informatics approaches for data analysis and interpretation; these mainly include machine learning and deep learning algorithms. In this chapter, we will discuss in detail the applications of deep learning and various algorithms of machine learning in proteomics.

Phosphoproteomics Meets Chemical Genetics: Approaches for Global Mapping and Deciphering the Phosphoproteome

Protein kinases are important enzymes involved in the regulation of various cellular processes. To function properly, each protein kinase phosphorylates only a limited number of proteins among the thousands present in the cell. This provides a rapid and dynamic regulatory mechanism that controls biological functions of the proteins. Despite the importance of protein kinases, most of their substrates remain unknown. Recently, the advances in the fields of protein engineering, chemical genetics, and mass spectrometry have boosted studies on identification of bona fide substrates of protein kinases. Among the various methods in protein kinase specific substrate identification, genetically engineered protein kinases and quantitative phosphoproteomics have become promising tools. Herein, we review the current advances in the field of chemical genetics in analog-sensitive protein kinase mutants and highlight selected strategies for identifying protein kinase substrates and studying the dynamic nature of protein phosphorylation.

A quantitative thiol reactivity profiling platform to analyze redox and electrophile reactive cysteine proteomes

Cysteine is unique among all protein-coding amino acids, owing to its intrinsically high nucleophilicity. The cysteinyl thiol group can be covalently modified by a broad range of redox mechanisms or by various electrophiles derived from exogenous or endogenous sources. Measuring the response of protein cysteines to redox perturbation or electrophiles is critical for understanding the underlying mechanisms involved. Activity-based protein profiling based on thiol-reactive probes has been the method of choice for such analyses. We therefore adapted this approach and developed a new chemoproteomic platform, termed 'QTRP' (quantitative thiol reactivity profiling), that relies on the ability of a commercially available thiol-reactive probe IPM (2-iodo-N-(prop-2-yn-1-yl)acetamide) to covalently label, enrich and quantify the reactive cysteinome in cells and tissues. Here, we provide a detailed and updated workflow of QTRP that includes procedures for (i) labeling of the reactive cysteinome from cell or tissue samples (e.g., control versus treatment) with IPM, (ii) processing the protein samples into tryptic peptides and tagging the probe-modified peptides with isotopically labeled azido-biotin reagents containing a photo-cleavable linker via click chemistry reaction, (iii) capturing biotin-conjugated peptides with streptavidin beads, (iv) identifying and quantifying the photo-released peptides by mass spectrometry (MS)-based shotgun proteomics and (v) interpreting MS data by a streamlined informatic pipeline using a proteomics software, pFind 3, and an automatic post-processing algorithm. We also exemplified here how to use QTRP for mining H₂O₂-sensitive cysteines and for determining the intrinsic reactivity of cysteines in a complex proteome. We anticipate that this protocol should find broad applications in redox biology, chemical biology and the pharmaceutical industry. The protocol for sample preparation takes 3 d, whereas MS measurements and data analyses require 75 min and <30 min, respectively, per sample.

Technological Advances in Preclinical Drug Evaluation: The Role of -Omics Methods

Preclinical drug development is an essential step in the drug development process where the evaluation of new chemical entities occurs. In particular, preclinical drug development phases include deep analysis of drug candidates' interactions with biomolecules/targets, their safety, toxicity, pharmacokinetics, metabolism by use of assays in vitro and in vivo animal assays. Legal aspects of the required procedures are well-established. Herein, we present a comprehensive summary of current state-of-the art approaches and techniques used in preclinical studies. In particular, we will review the potential of new, -omics methods and platforms for mechanistic evaluation of drug candidates and speed-up of the preclinical evaluation steps.

An LC-MS/MS assay with a label-free internal standard for quantitation of serum cystatin C

Cystatin C is considered as an alternative to the evaluation of glomerular filtration rate. In this study, we highlighted an LC-MS/MS approach for the absolute quantitation of serum cystatin C based on label-free internal standards. A tryptic peptide (ALDFAVGEYNK) was selected as the surrogate whilst analogue (ALDFAVGEYQK) served as an internal standard. After denaturation, reduction, alkylation, digestion and concentration, the target peptides were separated on an LC column and monitored under MRM. The calibration range was from 0.25 mg/L to 15 mg/L with LLOQ of 0.05 mg/L and LOD of 0.03 mg/L, respectively. The certified reference material (ERM-DA471) was determined at 5.12 mg/L with bias of 6.57%. The recovery was between 89.68% and 92.43%. The RSD of intra- and inter-assay imprecision were both <10%. Good stability was also observed. The assay also demonstrated that the quantification of native cystatin C in human serum could be achieved using label-free internal standards. The assay was robust, cheap and sensitive.

Redox Proteomes in Human Physiology and Disease Mechanisms

Redox proteomics is a field of proteomics that is concerned with the characterization of the oxidation state of proteins to gain information about their modulated structure, function, activity, and involvement in different physiological pathways. Oxidative modifications of proteins have been shown to be implicated in normal physiological processes of cells as well as in pathomechanisms leading to the development of cancer, diabetes, neurodegenerative diseases, and some rare hereditary metabolic diseases, like classic galactosemia. Reactive oxygen species generate a variety of reversible and irreversible modifications in amino acid residue side chains and within the protein backbone. These oxidative post-translational modifications (Ox-PTMs) can participate in the activation of signal transduction pathways and mediate the toxicity of harmful oxidants. Thus the application of advanced redox proteomics technologies is important for gaining insights into molecular mechanisms of diseases. Mass-spectrometry-based proteomics is one of the most powerful methods that can be used to give detailed qualitative and quantitative information on protein modifications and allows us to characterize redox proteomes associated with diseases. This Review illustrates the role and biological consequences of Ox-PTMs under basal and oxidative stress conditions by focusing on protein carbonylation and S-glutathionylation, two abundant modifications with an impact on cellular pathways that have been intensively studied during the past decade.

Proteomics and Leishmaniasis: Potential Clinical Applications

Leishmaniases are diseases caused by protozoan parasites of the genus Leishmania. They are endemic in 98 countries, affect around 12 million people worldwide and may present several distinct clinical forms. Unfortunately, there are only a few drugs available for treatment of leishmaniasis, which are toxic and not always effective. Different parasite species and different clinical forms require optimization of the treatment or more specific therapies, which are not available. The emergence of resistance is also a matter of concern. Besides, diagnosis can sometimes be complicated due to atypical manifestations and associations with other pathologies. In this review, proteomic data are presented and discussed in terms of their application in important issues in leishmaniasis such as parasite resistance to chemotherapy, diagnosis of active disease in patients and dogs, markers for different clinical forms, identification of virulence factors, and their potential use in vaccination. It is shown that proteomics has contributed to the discovery of potential biomarkers for prognosis, diagnosis, therapeutics, monitoring of disease progression, treatment follow-up and identification of vaccine candidates for specific diseases. However, the authors believe its capabilities have not yet been fully explored for routine clinical analysis for several reasons, which will be presented in this review.

Recent insights into Mycobacterium tuberculosis through proteomics and implications for the clinic

This review aimed at providing an update on the application of proteomics-based approaches to gain recent insights of Mycobacterium tuberculosis (M.tb) and its relevance to clinic. Proteomics and bioinformatics approaches helped in the identification and characterization of novel proteins. Studying M.tb, causative agent of tuberculosis (TB), at the proteomic level can contribute to the identification of proteins which can be considered as potential targets for developed drugs and can help us in better understanding the pathogen physiology. Areas covered: In this review we have presented a comprehensive literature pertaining to role of proteomics in understanding M.tb. We have also focused on how the development and advancement in technology in the field of proteomics has augmented the research and played a pivotal role in answering many unexplored questions. Lastly, the application of proteomics to clinic has also been discussed. Expert commentary: We envisage that proteomics has gained remarkable momentum over the years. Proteomics can play an important role in the discovery of biomarkers for TB and other diseases. Also, it can aid in development of effective vaccines and simple, rapid and cost-effective test for the diagnosis of TB which is crucial for the management and control of the disease.

Proteomic analysis of synovial fluid: current and potential uses to improve clinical outcomes

INTRODUCTION

Synovial fluid (SF) is in close proximity to tissues which are primarily altered during articular disease and has significant potential to better understand the underlying disease pathogeneses of articular pathologies and biomarker discovery. Although development of mass spectrometry-based methods has allowed faster and higher sensitivity techniques, interrogation of the SF proteome has been hindered by its large protein concentration dynamic range, impeding quantification of lower abundant proteins. Areas covered: Recent advances have developed methodologies to reduce the large protein concentration dynamic range of SF and subsequently allow deeper exploration of the SF proteome. This review concentrates on methods to overcome biofluid complexity, mass spectrometry proteomics methodologies, extracellular vesicles proteomics and the application of advances within the field in clinical disease, including osteoarthritis, rheumatoid arthritis, spondyloarthritis and juvenile arthritis. A narrative review was conducted with articles searched using PubMed, 1991-2018. Expert opinion: The SF proteomics field faces various challenges, including the requirement for rigorous and standardised methods of sample collection/storage, the sensitivity and specificity of proteomic assays, techniques to combat the large protein concentration dynamic range and comprehensive data analysis to reduce falsely identified markers. Additionally, there are challenges in developing multi 'omic' integration techniques, with computational integration enhancing analysis.

Recent Trends in System-Scale Integrative Approaches for Discovering Protective Antigens Against Mycobacterial Pathogens

Mycobacterial infections are one of the deadliest infectious diseases still posing a major health burden worldwide. The battle against these pathogens needs to focus on novel approaches and key interventions. In recent times, availability of genome scale data has revolutionized the fields of computational biology and immunoproteomics. Here, we summarize the cutting-edge ‘omics’ technologies and innovative system scale strategies exploited to mine the available data. These may be targeted using high-throughput technologies to expedite the identification of novel antigenic candidates for the rational next generation vaccines and serodiagnostic development against mycobacterial pathogens for which traditional methods have been failing.

Investigation of post-translational modifications in type 2 diabetes

The investigation of post-translational modifications (PTMs) plays an important role for the study of type 2 diabetes. The importance of PTMs has been realized with the advancement of analytical techniques. The challenging detection and analysis of post-translational modifications is eased by different enrichment methods and by high throughput mass spectrometry based proteomics studies. This technology along with different quantitation methods provide accurate knowledge about the changes happening in disease conditions as well as in normal conditions. In this review, we have discussed PTMs such as phosphorylation, N-glycosylation, O-GlcNAcylation, acetylation and advanced glycation end products in type 2 diabetes which have been characterized by high throughput mass spectrometry based proteomics analysis.

Dual Maleimide Tagging for Relative and Absolute Quantitation of Cysteine-Containing Peptides by MALDI-TOF MS

A dual maleimide (DuMal) tagging method has been developed for both relative and absolute quantitation of cysteine-containing peptides (CCPs) in combination with MALDI-TOF mass spectrometry. A pair of maleimides with minimal differences in their chemical structures, N-methylmaleimide and Nethylmaleimide, have been chosen to allow for the rapid (≈minutes) tagging of CCPs in the Michael addition reaction with high efficiency. It has been validated that the DuMal tagging technique is sensitive and reliable in the quantitative analysis of CCPs. Absolute quantitation of CCPs can be achieved with a detection limit as low as 7.3 nm. Relative quantitation of CCPs can be performed in various sample mixtures with consistent results (coefficient of variation <5 %). The DuMal tagging technique provides a sensitive and accurate approach for the quantitation of biomolecules containing thiol reactive sites; thus it is promising for protein detection, disease diagnosis, and biomarker discovery associated with post-translational modifications of cysteines.

Deuterium- and Tritium-Labelled Compounds: Applications in the Life Sciences

Hydrogen isotopes are unique tools for identifying and understanding biological and chemical processes. Hydrogen isotope labelling allows for the traceless and direct incorporation of an additional mass or radioactive tag into an organic molecule with almost no changes in its chemical structure, physical properties, or biological activity. Using deuterium-labelled isotopologues to study the unique mass-spectrometric patterns generated from mixtures of biologically relevant molecules drastically simplifies analysis. Such methods are now providing unprecedented levels of insight in a wide and continuously growing range of applications in the life sciences and beyond. Tritium (³ H), in particular, has seen an increase in utilization, especially in pharmaceutical drug discovery. The efforts and costs associated with the synthesis of labelled compounds are more than compensated for by the enhanced molecular sensitivity during analysis and the high reliability of the data obtained. In this Review, advances in the application of hydrogen isotopes in the life sciences are described.

Chemical Proteomic Approaches Targeting Cancer Stem Cells: A Review of Current Literature

Cancer stem cells (CSCs) have been proposed as central drivers of tumor initiation, progression, recurrence, and therapeutic resistance. Therefore, identifying stem-like cells within cancers and understanding their properties is crucial for the development of effective anticancer therapies. Recently, chemical proteomics has become a powerful tool to efficiently determine protein networks responsible for CSC pathophysiology and comprehensively elucidate molecular mechanisms of drug action against CSCs. This review provides an overview of major methodologies utilized in chemical proteomic approaches. In addition, recent successful chemical proteomic applications targeting CSCs are highlighted. Future direction of potential CSC research by integrating chemical genomic and proteomic data obtained from a single biological sample of CSCs are also suggested in this review.

Recent advances in quantitative and chemical proteomics for autophagy studies

Macroautophagy/autophagy is an evolutionarily well-conserved cellular degradative process with important biological functions that is closely implicated in health and disease. In recent years, quantitative mass spectrometry-based proteomics and chemical proteomics have emerged as important tools for the study of autophagy, through large-scale unbiased analysis of the proteome or through highly specific and accurate analysis of individual proteins of interest. At present, a variety of approaches have been successfully applied, including (i) expression and interaction proteomics for the study of protein post-translational modifications, (ii) investigating spatio-temporal dynamics of protein synthesis and degradation, and (iii) direct determination of protein activity and profiling molecular targets in the autophagic process. In this review, we attempted to provide an overview of principles and techniques relevant to the application of quantitative and chemical proteomics methods to autophagy, and outline the current landscape as well as future outlook of these methods in autophagy research.

Photo-affinity labeling (PAL) in chemical proteomics: a handy tool to investigate protein-protein interactions (PPIs)

Protein-protein interactions (PPIs) trigger a wide range of biological signaling pathways that are crucial for biomedical research and drug discovery. Various techniques have been used to study specific proteins, including affinity chromatography, activity-based probes, affinity-based probes and photo-affinity labeling (PAL). PAL has become one of the most powerful strategies to study PPIs. Traditional photocrosslinkers are used in PAL, including benzophenone, aryl azide, and diazirine. Upon photoirradiation, these photocrosslinkers (Pls) generate highly reactive species that react with adjacent molecules, resulting in a direct covalent modification. This review introduces recent examples of chemical proteomics study using PAL for PPIs.

The oxidized thiol proteome in aging and cataractous mouse and human lens revealed by ICAT labeling

Age‐related cataractogenesis is associated with disulfide‐linked high molecular weight (HMW) crystallin aggregates. We recently found that the lens crystallin disulfidome was evolutionarily conserved in human and glutathione‐depleted mouse (LEGSKO) cataracts and that it could be mimicked by oxidation in vitro (Mol. Cell Proteomics, 14, 3211‐23 (2015)). To obtain a comprehensive blueprint of the oxidized key regulatory and cytoskeletal proteins underlying cataractogenesis, we have now used the same approach to determine, in the same specimens, all the disulfide‐forming noncrystallin proteins identified by ICAT proteomics. Seventy‐four, 50, and 54 disulfide‐forming proteins were identified in the human and mouse cataracts and the in vitro oxidation model, respectively, of which 17 were common to all three groups. Enzymes with oxidized cysteine at critical sites include GAPDH (hGAPDH, Cys247), glutathione synthase (hGSS, Cys294), aldehyde dehydrogenase (hALDH1A1, Cys126 and Cys186), sorbitol dehydrogenase (hSORD, Cys140, Cys165, and Cys179), and PARK7 (hPARK7, Cys46 and Cys53). Extensive oxidation was also present in lens‐specific intermediate filament proteins, such as BFSP1 and BFSP12 (hBFSP1 and hBFSP12, Cys167, Cys65, and Cys326), vimentin (mVim, Cys328), and cytokeratins, as well as microfilament and microtubule filament proteins, such as tubulin and actins. While the biological impact of these modifications for lens physiology remains to be determined, many of these oxidation sites have already been associated with either impaired metabolism or cytoskeletal architecture, strongly suggesting that they have a pathogenic role in cataractogenesis. By extrapolation, these findings may be of broader significance for age‐ and disease‐related dysfunctions associated with oxidant stress.

Clinical Proteomics: Liquid Chromatography-Mass Spectrometry (LC-MS) Purification Systems

Liquid chromatography-mass spectrometry (LC-MS) has become a routine powerful technology in clinical proteomic studies for protein identification, protein characterization and the discovery of biomarkers. In this chapter, we describe two protocol methods to analyze clinical patient samples using a resin based depletion column followed by either protein In-gel enzymatic digestion or protein in-solution enzymatic digestion and then analysis by one-dimensional reverse-phase chromatography or two-dimensional strong cation exchange (SCX)-reverse-phase chromatography (RPC).

Peptides Labeled with Pyridinium Salts for Sensitive Detection and Sequencing by Electrospray Tandem Mass Spectrometry

Mass spectrometric analysis of trace amounts of peptides may be problematic due to the insufficient ionization efficiency resulting in limited sensitivity. One of the possible ways to overcome this problem is the application of ionization enhancers. Herein we developed new ionization markers based on 2,4,6-triphenylpyridinium and 2,4,6-trimethylpyridinium salts. Using of inexpensive and commercially available pyrylium salt allows selective derivatization of primary amino groups, especially those sterically unhindered, such as ε-amino group of lysine. The 2,4,6-triphenylpyridinium modified peptides generate in MS/MS experiments an abundant protonated 2,4,6-triphenylpyridinium ion. This fragment is a promising reporter ion for the multiple reactions monitoring (MRM) analysis. In addition, the fixed positive charge of the pyridinium group enhances the ionization efficiency. Other advantages of the proposed ionization enhancers are the simplicity of derivatization of peptides and the possibility of convenient incorporation of isotopic labels into derivatized peptides.

Proteomics: Technologies and Their Applications

Proteomics involves the applications of technologies for the identification and quantification of overall proteins present content of a cell, tissue or an organism. It supplements the other "omics" technologies such as genomic and transcriptomics to expound the identity of proteins of an organism, and to cognize the structure and functions of a particular protein. Proteomics-based technologies are utilized in various capacities for different research settings such as detection of various diagnostic markers, candidates for vaccine production, understanding pathogenicity mechanisms, alteration of expression patterns in response to different signals and interpretation of functional protein pathways in different diseases. Proteomics is practically intricate because it includes the analysis and categorization of overall protein signatures of a genome. Mass spectrometry with LC-MS-MS and MALDI-TOF/TOF being widely used equipment is the central among current proteomics. However, utilization of proteomics facilities including the software for equipment, databases and the requirement of skilled personnel substantially increase the costs, therefore limit their wider use especially in the developing world. Furthermore, the proteome is highly dynamic because of complex regulatory systems that control the expression levels of proteins. This review efforts to describe the various proteomics approaches, the recent developments and their application in research and analysis.

Approaches for targeted proteomics and its potential applications in neuroscience

An extensive guide on practicable and significant quantitative proteomic approaches in neuroscience research is important not only because of the existing overwhelming limitations but also for gaining valuable understanding into brain function and deciphering proteomics from the workbench to the bedside. Early methodologies to understand the functioning of biological systems are now improving with high-throughput technologies, which allow analysis of various samples concurrently, or of thousand of analytes in a particular sample. Quantitative proteomic approaches include both gel-based and non-gel-based methods that can be further divided into different labelling approaches. This review will emphasize the role of existing technologies, their advantages and disadvantages, as well as their applications in neuroscience. This review will also discuss advanced approaches for targeted proteomics using isotope-coded affinity tag (ICAT) coupled with laser capture microdissection (LCM) followed by liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis. This technology can further be extended to single cell proteomics in other areas of biological sciences and can be combined with other 'omics' approaches to reveal the mechanism of a cellular alterations. This approach may lead to further investigation in basic biology, disease analysis and surveillance, as well as drug discovery. Although numerous challenges still exist, we are confident that this approach will increase the understanding of pathological mechanisms involved in neuroendocrinology, neuropsychiatric and neurodegenerative disorders by delivering protein biomarker signatures for brain dysfunction.

Ion Mobility Separation of Peptide Isotopomers

Differential or field asymmetric waveform ion mobility spectrometry (FAIMS) operating at high electric fields fully resolves isotopic isomers for a peptide with labeled residues. The naturally present isotopes, alone and together with targeted labels, also cause spectral shifts that approximately add for multiple heavy atoms. Separation qualitatively depends on the gas composition. These findings may enable novel strategies in proteomic and metabolomic analyses using stable isotope labeling.

Multiplexed Isobaric Tagging Protocols for Quantitative Mass Spectrometry Approaches to Auditory Research

Modern biologists have at their disposal a large array of techniques used to assess the existence and relative or absolute quantity of any molecule of interest in a sample. However, implementing most of these procedures can be a daunting task for the first time, even in a lab with experienced researchers. Just choosing a protocol to follow can take weeks while all of the nuances are examined and it is determined whether a protocol will (a) give the desired results, (b) result in interpretable and unbiased data, and (c) be amenable to the sample of interest. We detail here a robust procedure for labeling proteins in a complex lysate for the ultimate differential quantification of protein abundance following experimental manipulations. Following a successful outcome of the labeling procedure, the sample is submitted for mass spectrometric analysis, resulting in peptide quantification and protein identification. While we will concentrate on cells in culture, we will point out procedures that can be used for labeling lysates generated from tissues, along with any minor modifications required for such samples. We will also outline, but not fully document, other strategies used in our lab to label proteins prior to mass spectrometric analysis, and describe under which conditions each procedure may be desirable. What is not covered in this chapter is anything but the most brief introduction to mass spectrometry (instrumentation, theory, etc.), nor do we attempt to cover much in the way of software used for post hoc analysis. These two topics are dependent upon one's resources, and where applicable, one's collaborators. We strongly encourage the reader to seek out expert advice on topics not covered here.

A multi-omic analysis of human naïve CD4+ T cells

Background

Cellular function and diversity are orchestrated by complex interactions of fundamental biomolecules including DNA, RNA and proteins. Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel and unbiased measurements. Such high-throughput technologies have been extensively used to carry out broad, unbiased studies, particularly in the context of human diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome and proteome of a single human cell type to obtain a coherent view of the complex interplay between various biomolecules has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells isolated from a single individual.

Results

Integrating multi-omics datasets allowed us to investigate genome-wide methylation and its effect on mRNA/protein expression patterns, extent of RNA editing under normal physiological conditions and allele specific expression in naïve CD4+ T cells. In addition, we carried out a multi-omic comparative analysis of naïve with primary resting memory CD4+ T cells to identify molecular changes underlying T cell differentiation. This analysis provided mechanistic insights into how several molecules involved in T cell receptor signaling are regulated at the DNA, RNA and protein levels. Phosphoproteomics revealed downstream signaling events that regulate these two cellular states. Availability of multi-omics data from an identical genetic background also allowed us to employ novel proteogenomics approaches to identify individual-specific variants and putative novel protein coding regions in the human genome.

Conclusions

We utilized multiple high-throughput technologies to derive a comprehensive profile of two primary human cell types, naïve CD4+ T cells and memory CD4+ T cells, from a single donor. Through vertical as well as horizontal integration of whole genome sequencing, methylation arrays, RNA-Seq, miRNA-Seq, proteomics, and phosphoproteomics, we derived an integrated and comparative map of these two closely related immune cells and identified potential molecular effectors of immune cell differentiation following antigen encounter.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0225-4) contains supplementary material, which is available to authorized users.