Most proteins function by forming complexes within a dynamic interconnected network that underlies various biological mechanisms. To systematically investigate such interactomes, high-throughput techniques, including CF-MS, have been developed to capture, identify, and quantify protein-protein interactions (PPIs) on a large scale. Compared to other techniques, CF-MS allows the global identification and quantification of native protein complexes in one setting, without genetic manipulation. Furthermore, quantitative CF-MS can potentially elucidate the distribution of a protein in multiple co-elution features, informing the stoichiometries and dynamics of a target protein complex. In this issue, Youssef et al. (Proteomics 2023, 00, e2200404) combined multiplex CF-MS and a new algorithm to study the dynamics of the PPI network for Escherichia coli grown under ten different conditions. Although the results demonstrated that most proteins remained stable, the authors were able to detect disrupted interactions that were growth condition specific. Further bioinformatics analyses also revealed the biophysical properties and structural patterns that govern such a response.
Affinity purification coupled to mass spectrometry (AP-MS) is a powerful method to analyze protein-protein interactions (PPIs). The AP-MS approach provides an unbiased analysis of the entire protein complex and is useful to identify indirect interactors. However, reliable protein identification from the complex AP-MS experiments requires appropriate control of false identifications and rigorous statistical analysis. Another challenge that can arise from AP-MS analysis is to distinguish bona fide interacting proteins from the non-specifically bound endogenous proteins or the "background contaminants" that co-purified by the bait experiments. In this chapter, we will first describe the protocol for performing in-solution trypsinization for the samples from the AP experiment followed by LC-MS/MS analysis. We will then detail the MaxQuant workflow for protein identification and quantification for the PPI data derived from the AP-MS experiment. Finally, we describe the CRAPome interface to process the data by filtering against contaminant lists, score the interactions and visualize the protein interaction networks.
Proteins often interact with each other to form complexes and play functional roles in almost all cellular processes. The study of protein-protein interactions is therefore critical to understand protein function and biological pathways. Affinity Purification coupled with Mass Spectrometry (AP-MS) is an invaluable technique for identifying the interaction partners in protein complexes. In this approach, the protein of interest is fused to an affinity tag, followed by the expression and purification of the fusion protein. The affinity-purified sample is then analyzed by mass spectrometry to identify the interaction partners of the bait proteins. In this chapter, we detail the protocol for tandem affinity purification (TAP) based on the use of the FLAG (a fusion tag with peptide sequence DYKDDDDK) and hemagglutinin (HA) peptide epitopes. The immunoprecipitation using dual-affinity tags offers the advantage of increasing the specificity of the purification with lower nonspecific-background interactions.
The life span of cancer patients can be prolonged with appropriate therapies if detected early. Mass screening for early detection of cancer, however, requires sensitive and specific biomarkers obtainable from body fluids such as blood or urine. To date, most biomarker discovery programs focus on the proteome rather than the endogenous peptidome. It has been long-established that tumor cells and stromal cells produce tumor resident proteases (TRPs) to remodel the surrounding tumor microenvironment in support of tumor progression. In fact, proteolytic products of TRPs have been shown to correlate with malignant behavior. Being of low molecular weight, these unique peptides can pass through the endothelial barrier of the vasculature into the bloodstream. As such, the cancer peptidome has increasingly become a focus for biomarker discovery. In this review, we discuss on the various aspects of the peptidome in cancer biomarker research.
Two-dimensional gel electrophoresis (2-DE) is a technique that has been widely applied in a variety of proteomics studies. It is capable of resolving complex protein mixtures into individual protein spots based on their isoelectric point and molecular weight, enabling large-scale analysis of protein expression patterns for deciphering their changes in different biological conditions. 2-DE is a powerful tool that empowers researchers to perform differential qualitative and quantitative proteome analysis and is particularly advantageous for characterizing protein isoforms and post-translationally modified proteins. Despite its popularity as the workhorse for proteomics in the past few decades, it has been gradually displaced by the more sophisticated and high-performance mass spectrometry-based methods. However, there are several variations of the 2-DE technique that have emerged as promising approaches that shine new light on specific niches that 2-DE could still contribute. In this review, we first provide an overview of the applications of 2-DE, its merits and pitfalls in the current proteomic research arena, followed by a discussion on several alternative approaches for potential future applications.
Kinases are key regulatory signalling proteins governing numerous essential biological processes and cellular functions. Dysregulation of many protein kinases is associated with cancer initiation and progression. Given their crucial roles, there has been increasing interest in harnessing kinases as prospective drug targets for cancer. In recent decades, numerous small-molecule kinase inhibitors have been developed and revolutionized the cancer treatment landscape. Despite their great potential, challenges remain in developing highly selective and effective kinase inhibitors, with toxicity and resistance issues frequently arising. In this review, we first provide an overview of the role of kinases in carcinogenesis and describe the current progress with small-molecule kinase inhibitors that have been approved for clinical use. We then discuss the application of mass spectrometry (MS)-based proteomics strategies to help in the design of kinase inhibitors. Finally, we discuss the challenges and outlook concerning MS-based proteomics techniques for kinase drug research.
cAMP is a second messenger that regulates a myriad of cellular functions in response to multiple extracellular stimuli. New developments in the field have provided exciting insights into how cAMP utilizes compartmentalization to ensure specificity when the message conveyed to the cell by an extracellular stimulus is translated into the appropriate functional outcome. cAMP compartmentalization relies on the formation of local signaling domains where the subset of cAMP signaling effectors, regulators and targets involved in a specific cellular response cluster together. These domains are dynamic in nature and underpin the exacting spatiotemporal regulation of cAMP signaling. In this review, we focus on how the proteomics toolbox can be utilized to identify the molecular components of these domains and to define the dynamic cellular cAMP signaling landscape. From a therapeutic perspective, compiling data on compartmentalized cAMP signaling in physiological and pathological conditions will help define the signaling events underlying disease and may reveal domain-specific targets for the development of precision medicine interventions.
Epithelial cell adhesion molecule (EpCAM) is a cell surface protein that was discovered as a tumour marker of epithelial origins nearly four decades ago. EpCAM is expressed at basal levels in the basolateral membrane of normal epithelial cells. However, EpCAM expression is upregulated in solid epithelial cancers and stem cells. EpCAM can also be found in disseminated tumour cells and circulating tumour cells. Various OMICs studies have demonstrated that EpCAM plays roles in several key biological processes such as cell adhesion, migration, proliferation and differentiation. Additionally, EpCAM can be detected in the bodily fluid of cancer patients suggesting that EpCAM is a pathophysiologically relevant anti-tumour target as well as being utilized as a diagnostic/prognostic agent for a variety of cancers. This review will focus on the structure-features of EpCAM protein and discuss recent evidence on the pathological and physiological roles of EpCAM in modulating cell adhesion and signalling pathways in cancers as well as deliberating the clinical implication of EpCAM as a therapeutic target.
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Biomarkers that can facilitate better clinical management of CRC are in high demand to improve patient outcome and to reduce mortality. In this regard, proteomic analysis holds a promising prospect in the hunt of novel biomarkers for CRC and in understanding the mechanisms underlying tumorigenesis. This review aims to provide an overview of the current progress of proteomic research, focusing on discovery and validation of diagnostic biomarkers for CRC. We will summarize the contributions of proteomic strategies to recent discoveries of protein biomarkers for CRC and also briefly discuss the potential and challenges of different proteomic approaches in biomarker discovery and translational applications.
Understanding the relationship between genotypes and phenotypes is essential to disentangle biological mechanisms and to unravel the molecular basis of diseases. Genes and proteins are closely linked in biological systems. However, genomics and proteomics have developed separately into two distinct disciplines whereby crosstalk among scientists from the two domains is limited and this constrains the integration of both fields into a single data modality of useful information. The emerging field of proteogenomics attempts to address this by building bridges between the two disciplines. In this review, how genomics and transcriptomics data in different formats can be utilized to assist proteogenomics application is briefly discussed. Subsequently, a much larger part of this review focuses on proteogenomics research articles that are published in the last five years that answer two important questions. First, how proteogenomics can be applied to tackle biological problems is discussed, covering genome annotation and precision medicine. Second, the latest developments in analytical technologies for data acquisition and the bioinformatics tools to interpret and visualize proteogenomics data are covered.
Plasma and serum are widely used for proteomics-based biomarker discovery. However, analysis of these biofluids is highly challenging due to the complexity and wide dynamic range of their proteomes. Notably, highly abundant proteins tend to obscure the detection of potential biomarkers that are usually of lower concentrations. Among the strategies to resolve this problem are: depletion of high-abundance proteins, enrichment of low abundant proteins of interest and prefractionation. In this review, we focus on current and emerging depletion techniques used to enhance the detection and identification of the less abundant proteins in plasma and serum. We discuss the applications and contributions of these methods to proteomics analysis of plasma and serum alongside their limitations and future perspectives.
Resistance to anti-cancer treatments is a critical and widespread health issue that has brought serious impacts on lives, the economy and public policies. Mounting research has suggested that a selected spectrum of patients with advanced colorectal cancer (CRC) tend to respond poorly to both chemotherapeutic and targeted therapeutic regimens. Drug resistance in tumours can occur in an intrinsic or acquired manner, rendering cancer cells insensitive to the treatment of anti-cancer therapies. Multiple factors have been associated with drug resistance. The most well-established factors are the emergence of cancer stem cell-like properties and overexpression of ABC transporters that mediate drug efflux. Besides, there is emerging evidence that signalling pathways that modulate cell survival and drug metabolism play major roles in the maintenance of multidrug resistance in CRC. This article reviews drug resistance in CRC as a result of alterations in the MAPK, PI3K/PKB, Wnt/β-catenin and Notch pathways.
During the last few decades, the treatment options available for patients with metastatic colorectal cancer (mCRC) have undergone continuous improvements, transitioning from conventional chemotherapy to targeted therapy. These therapeutic innovations have led to significant improvements in patient clinical outcomes. However, there remains a need to improve the outcome for many CRC patients. Chemotherapy remains a cornerstone of CRC treatment, but the wide variability in tumour response and adverse reactions to chemotherapy poses a challenge to cancer treatment management. As a result, there is an unmet need to identify predictive biomarkers of chemotherapeutic response to guide treatment decisions. In this review, we summarise the conventional biomarkers used to predict chemotherapy responses in CRC and provide an overview of emerging predictive biomarkers based on the current understanding of the molecular biology of treatment response. Finally, we explore the challenges and future prospects of biomarker discovery to improve the prediction of patient response and ensure optimal treatment management for patients with metastatic CRC.
Cells encounter a myriad of endogenous and exogenous stresses that could perturb cellular physiological processes. Therefore, cells are equipped with several adaptive and stress-response machinery to overcome and survive these insults. One such machinery is the heat shock response (HSR) program that is governed by the heat shock factors (HSFs) family in response towards elevated temperature, free radicals, oxidants, and heavy metals. HSF4 is a member of this HSFs family that could exist in two predominant isoforms, either the transcriptional repressor HSFa or transcriptional activator HSF4b. HSF4 is constitutively active due to the lack of oligomerization negative regulator domain. HSF4 has been demonstrated to play roles in several physiological processes and not only limited to regulating the classical heat shock- or stress-responsive transcriptional programs. In this review, we will revisit and delineate the recent updates on HSF4 molecular properties. We also comprehensively discuss the roles of HSF4 in health and diseases, particularly in lens cell development, cataract formation, and cancer pathogenesis. Finally, we will posit the potential direction of HSF4 future research that could enhance our knowledge on HSF4 molecular networks as well as physiological and pathophysiological functions.
Cancer harbours extensive proteomic heterogeneity. Inspired by the prior success of single-cell RNA sequencing (scRNA-seq) in characterizing minute transcriptomics heterogeneity in cancer, researchers are now actively searching for information regarding the proteomics counterpart. Therefore recently, single-cell proteomics by mass spectrometry (SCP) has rapidly developed into state-of-the-art technology to cater the need. This review aims to summarize application of SCP in cancer research, while revealing current development progress of SCP technology. The review also aims to contribute ideas into research gaps and future directions, ultimately promoting the application of SCP in cancer research.
Anterior gradient-2 (AGR2) protein mediates the formation, breakage and isomerization of disulphide bonds during protein maturation in the endoplasmic reticulum (ER) and contributes to the homoeostasis of the secretory pathway. AGR2 promotes tumour development and metastasis and its elevated expression is almost completely restricted to malignant tumours. Interestingly, this supposedly ER-resident protein can be localised to other compartments of cancer cells and can also be secreted into the extracellular milieu. There are emerging evidences that describe the gain-of-function activities of the extracellular AGR2, particularly in cancer development. Here, we reviewed studies detailing the expression, pathological and physiological roles associated with AGR2 and compared the duality of localization, intracellular and extracellular, with special emphasis on the later. We also discussed the possible mechanisms of AGR2 secretion as well as deliberating the functional impacts of AGR2 in cancer settings. Last, we deliberate the current therapeutic strategies and posit the potential use AGR2, as a prognosis and diagnosis marker in cancer.
Dysbiosis of gut microbiome can contribute to inflammation, and subsequently initiation and progression of colorectal cancer (CRC). Throughout these stages, various proteins and metabolites are secreted to the external environment by microorganisms or the hosts themselves. Studying these proteins may help enhance our understanding of the host-microorganism relationship or they may even serve as useful biomarkers for CRC. However, secretomic studies of gut microbiome of CRC patients, until now, are scarcely performed. In this review article, the focus is on the roles of gut microbiome in CRC, the current findings on CRC secretome are highlighted, and the emerging challenges and strategies to drive forward this area of research are addressed.
Aim: Micro and macro vascular invasion (VI) are known as independent predictors of tumor recurrence and poor survival after surgical treatment of hepatocellular carcinoma (HCC). Here, we aimed to re-analyze The Cancer Genome Atlas of liver hepatocellular carcinoma datasets to identify the VI-expression signatures. Materials & methods: We filtered The Cancer Genome Atlas liver hepatocellular carcinoma (LIHC) datasets into three groups: no VI (NVI = 198); micro VI (MIVI = 89) and macro VI (MAVI = 16). We performed differential gene expression, methylation and microRNA analyses. Results & conclusion: We identified 12 differentially expressed genes and 55 differentially methylated genes in MAVI compared with no VI. The GPD1L gene appeared in all of the comparative analyses. Higher GPD1L expression was associated with VI and poor outcomes in the HCC patients.