Linking the proteins--elucidation of proteome-scale networks using mass spectrometry

Anti-Cancer Roles of Probiotic-Derived P8 Protein in Colorectal Cancer Cell Line DLD-1

A novel probiotics-derived protein, P8, suppresses the growth of colorectal cancer (CRC). P8 can penetrate the cell membrane via endocytosis and cause cell cycle arrest in DLD-1 cells through down-regulation of CDK1/Cyclin B1. However, neither the protein involved in the endocytosis of P8 nor the cell cycle arrest targets of P8 are known. We identified two P8-interacting target proteins [importin subunit alpha-4 (KPNA3) and glycogen synthase kinase-3 beta (GSK3β)] using P8 as a bait in pull-down assays of DLD-1 cell lysates. Endocytosed P8 in the cytosol was found to bind specifically to GSK3β, preventing its inactivation by protein kinases AKT/CK1ε/PKA. The subsequent activation of GSK3β led to strong phosphorylation (S^33,37/T⁴¹) of β-catenin, resulting in its subsequent degradation. P8 in the cytosol was also found to be translocated into the nucleus by KPNA3 and importin. In the nucleus, after its release, P8 binds directly to the intron regions of the GSK3β gene, leading to dysregulation of GSK3β transcription. GSK3β is a key protein kinase in Wnt signaling, which controls cell proliferation during CRC development. P8 can result in a cell cycle arrest morphology in CRC cells, even when they are in the Wnt ON signaling state.

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens

The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic “Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens,” this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.

Bacterial membrane vesicle functions, laboratory methods, and applications

Bacterial membrane vesicles are cupped-shaped structures formed by bacteria in response to environmental stress, genetic alteration, antibiotic exposure, and others. Due to the structural similarities shared with the producer organism, they can retain certain characteristics like stimulating immune responses. They are also able to carry molecules for long distances, without changes in the concentration and integrity of the molecule. Bacteria originally secrete membrane vesicles for gene transfer, excretion, cell to cell interaction, pathogenesis, and protection against phages. These functions are unique and have several innovative applications in the pharmaceutical industry that have attracted both scientific and commercial interest.This led to the development of efficient methods to artificially stimulate vesicle production, purification, and manipulation in the lab at nanoscales. Also, for specific applications, engineering methods to impart pathogen antigens against specific diseases or customization as cargo vehicles to deliver payloads to specific cells have been reported. Many applications of bacteria membrane vesicles are in cancer drugs, vaccines, and adjuvant development with several candidates in clinical trials showing promising results. Despite this, applications in therapy and commercialization stay timid probably due to some challenges one of which is the poor understanding of biogenesis mechanisms. Nevertheless, so far, bacterial membrane vesicles seem to be a reliable and cost-efficient technology with several therapeutic applications. Research toward characterizing more membrane vesicles, genetic engineering, and nanotechnology will enable the scope of applications to widen. This might include solutions to other currently faced medical and healthcare-related challenges.

Serum proteomic analysis of the anti‑arthritic effects of sinomenine on rats with collagen‑induced arthritis

Rheumatoid arthritis (RA) is an autoimmune disease, which is characterized by inflammatory synovitis, and the subsequent destruction of articular cartilage and bone. Sinomenine is a traditional Chinese medicine, which has been employed as a clinical treatment for RA for several years in China. The present study investigated the anti‑arthritic effects of sinomenine on Sprague‑Dawley rats with collagen‑induced arthritis (CIA). The differentially expressed proteins in serum were measured by proteomic analysis in order to generate a differentially expressed protein network. A total of 320 differentially expressed proteins were detected in the drug‑treated group compared with in the control group. In the sinomenine‑treated group, 79 differentially expressed proteins were detected compared with in the model group, and among these, 46 proteins were upregulated. Gene ontology analysis revealed that five functions were affected by sinomenine treatment of CIA rats compared with in the model group. In addition, Ingenuity® Pathway Analysis was used to measure enriched signaling pathways, which revealed nuclear factor‑κB, histones, heat shock proteins and protein kinase B as core proteins, generating ~60 pair associations in the network. To the best of our knowledge, the present study is the first to perform proteomic analysis in sinomenine‑treated CIA rats, and the results revealed that numerous targets were involved in the process. In addition, the present study provided a novel approach and evidence for exploring the biological effects of sinomenine. Therefore, the findings of the present study may provide a novel insight into the anti‑RA mechanisms of sinomenine, and may justify further exploration into its function in other relevant diseases.

Methylated-antibody affinity purification to improve proteomic identification of plant RNA polymerase Pol V complex and the interacting proteins

Affinity purification followed by enzymatic digestion and mass spectrometry has been widely utilized for the sensitive detection of interacting proteins and protein complexes in various organisms. In plants, the method is technically challenging due to the low abundance proteins, non-specific binding and difficulties of eluting interacting proteins from antibody beads. In this report, we describe a strategy to modify antibodies by reductive methylation of lysines without affecting their binding properties, followed by on-bead digestion of bound proteins with endoproteinase Lys-C. By this method, the antibody remains intact and does not interfere with the downstream identification of interacting proteins. Non-specific binding proteins were excluded using ¹⁴N/¹⁵N-metabolic labeling of wild-type and the transgenic plant counterparts. The method was employed to identify 12 co-immunoprecipitated protein subunits in Pol V complex and to discover 17 potential interacting protein targets in Arabidopsis. Our results demonstrated that the modification of antibodies by reductive dimethylation can improve the reliability and sensitivity of identifying low-abundance proteins through on-bead digestion and mass spectrometry. We also show that coupling this technique with chemical crosslinking enables in-depth characterization of endogenous protein complexes and the protein-protein interaction networks including mapping the surface topology and post-translational modifications of interacting proteins.

Undiscovered Physiology of Transcript and Protein Networks

The past two decades have witnessed a rapid evolution in our ability to measure RNA and protein from biological systems. As a result, new principles have arisen regarding how information is processed in cells, how decisions are made, and the role of networks in biology. This essay examines this technological evolution, reviewing (and critiquing) the conceptual framework that has emerged to explain how RNA and protein networks control cellular function. We identify how future investigations into transcriptomes, proteomes, and other cellular networks will enable development of more robust, quantitative models of cellular behavior whilst also providing new avenues to use knowledge of biological networks to improve human health. © 2016 American Physiological Society. Compr Physiol 6:1851-1872, 2016.

Validation of a novel shotgun proteomic workflow for the discovery of protein-protein interactions: focus on ZNF521

The study of protein-protein interactions is increasingly relying on mass spectrometry (MS). The classical approach of separating immunoprecipitated proteins by SDS-PAGE followed by in-gel digestion is long and labor-intensive. Besides, it is difficult to integrate it with most quantitative MS-based workflows, except for stable isotopic labeling of amino acids in cell culture (SILAC). This work describes a fast, flexible and quantitative workflow for the discovery of novel protein-protein interactions. A cleavable cross-linker, dithiobis[succinimidyl propionate] (DSP), is utilized to stabilize protein complexes before immunoprecipitation. Protein complex detachment from the antibody is achieved by limited proteolysis. Finally, protein quantitation is performed via (18)O labeling. The workflow has been optimized concerning (i) DSP concentration and (ii) incubation times for limited proteolysis, using the stem cell-associated transcription cofactor ZNF521 as a model target. The interaction of ZNF521 with the core components of the nuclear remodelling and histone deacetylase (NuRD) complex, already reported in the literature, was confirmed. Additionally, interactions with newly discovered molecular partners of potentially relevant functional role, such as ZNF423, Spt16, Spt5, were discovered and validated by Western blotting.

Identification of novel PAMP-triggered phosphorylation and dephosphorylation events in Arabidopsis thaliana by quantitative phosphoproteomic analysis

Signaling cascades rely strongly on protein kinase-mediated substrate phosphorylation. Currently a major challenge in signal transduction research is to obtain high confidence substrate phosphorylation sites and assign them to specific kinases. In response to bacterial flagellin, a pathogen-associated molecular pattern (PAMP), we searched for rapidly phosphorylated proteins in Arabidopsis thaliana by combining multistage activation (MSA) and electron transfer dissociation (ETD) fragmentation modes, which generate complementary spectra and identify phosphopeptide sites with increased reliability. Of a total of 825 phosphopeptides, we identified 58 to be differentially phosphorylated. These peptides harbor kinase motifs of mitogen-activated protein kinases (MAPKs) and calcium-dependent protein kinases (CDPKs), as well as yet unknown protein kinases. Importantly, 12 of the phosphopeptides show reduced phosphorylation upon flagellin treatment. Since protein abundance levels did not change, these results indicate that flagellin induces not only various protein kinases but also protein phosphatases, even though a scenario of inhibited kinase activity may also be possible.

Mass spectrometry for proteomics-based investigation

Within the past years, we have witnessed a great improvement in mass spectrometry (MS) and proteomics approaches in terms of instrumentation, protein fractionation, and bioinformatics. With the current technology, protein identification alone is no longer sufficient. Both scientists and clinicians want not only to identify proteins but also to identify the protein's posttranslational modifications (PTMs), protein isoforms, protein truncation, protein-protein interaction (PPI), and protein quantitation. Here, we describe the principle of MS and proteomics and strategies to identify proteins, protein's PTMs, protein isoforms, protein truncation, PPIs, and protein quantitation. We also discuss the strengths and weaknesses within this field. Finally, in our concluding remarks we assess the role of mass spectrometry and proteomics in scientific and clinical settings in the near future. This chapter provides an introduction and overview for subsequent chapters that will discuss specific MS proteomic methodologies and their application to specific medical conditions. Other chapters will also touch upon areas that expand beyond proteomics, such as lipidomics and metabolomics.

Recent advances in microarray technologies for proteomics

Proteins are fundamental components of all living systems and critical drivers of biological functions. The large-scale study of proteins, their structures and functions, is defined as proteomics. This systems-wide analysis leads to a more comprehensive view of the intricate signaling transduction pathways that proteins engage in and improves the overall understanding of the complex processes supporting the living systems. Over the last two decades, the development of high-throughput analytical tools, such as microarray technologies, capable of rapidly analyzing thousands of protein-functioning and protein-interacting events, has fueled the growth of this important field. Herein, we review the most recent advancements in microarray technologies, with a special focus on peptide microarray, small molecule microarray, and protein microarray. These technologies have become prominent players in proteomics and have made significant changes to the landscape of life science and biomedical research. We will elaborate on their performance, advantages, challenges, and future directions.

Technical note: proteomic approaches to fundamental questions about neutrophil biology

Proteomics is one of a group of technologies that generates high-throughput, large-scale datasets that can be used to understand cell or organ functions at a systems level. This review will focus on the application of proteomics to the understanding of neutrophil biology. The strengths and weaknesses of common proteomic methods and their application to neutrophils are reviewed, with the goal of evaluating whether the technology is ready to advance our understanding of neutrophil biology.

Proteomic analysis of the NOS2 interactome in human airway epithelial cells

The cytokine-inducible isoform of nitric oxide synthase (NOS2) is constitutively expressed in human respiratory epithelia and is upregulated in inflammatory lung disease. Here, we sought to better define the protein interactions that may be important for NOS2 activity and stability, as well as to identify potential targets of NOS2-derived NO, in the respiratory epithelium. We overexpressed Flag-tagged, catalytically-inactive NOS2 in A549 cells and used mass spectrometry to qualitatively identify NOS2 co-immunoprecipitating proteins. Stable isotope labeling of amino acids in cell culture (SILAC) was used to quantify the coordinate effects of cytokine stimulation on NOS2-protein interactions. Multi-protein networks dominated the NOS2 interactome, and cytokine-inducible interactions with allosteric activators and with the ubiquitin-proteasome system were correlated with cytokine-dependent increases in NO metabolites and in NOS2 ubiquitination. The ubiquitin ligase scaffolding protein, FBXO45, was identified as a novel, direct NOS2 interactor. Similar to the SPRY domain-containing SOCS box (SPSB) proteins, FBXO45 requires Asn27 in the (23)DINNN(27) motif of NOS2 for its interaction. However, FBXO45 is unique from the SPSBs in that it recruits a distinct E3 ligase complex containing MYCBP2 and SKP1. Collectively, these findings demonstrate the general utility of interaction proteomics for defining new aspects of NOS2 physiology.

Applications of Mass Spectrometry in Proteomics

Characterisation of proteins and whole proteomes can provide a foundation to our understanding of physiological and pathological states and biological diseases or disorders. Constant development of more reliable and accurate mass spectrometry (MS) instruments and techniques has allowed for better identification and quantification of the thousands of proteins involved in basic physiological processes. Therefore, MS-based proteomics has been widely applied to the analysis of biological samples and has greatly contributed to our understanding of protein functions, interactions, and dynamics, advancing our knowledge of cellular processes as well as the physiology and pathology of the human body. This review will discuss current proteomic approaches for protein identification and characterisation, including post-translational modification (PTM) analysis and quantitative proteomics as well as investigation of protein–protein interactions (PPIs).

Processing of Mass Spectrometry Data in Clinical Applications

Mass spectrometry-based proteomics has become the leading approach for analyzing complex biological samples at a large-scale level. Its importance for clinical applications is more and more increasing, thanks to the development of high-performing instruments which allow the discovery of disease-specific biomarkers and an automated and rapid protein profiling of the analyzed samples. In this scenario, the large-scale production of proteomic data has driven the development of specific bioinformatic tools to assist researchers during the discovery processes. Here, we discuss the main methods, algorithms, and procedures to identify and use biomarkers for clinical and research purposes. In particular, we have been focused on quantitative approaches, the identification of proteotypic peptides, and the classification of samples, using proteomic data. Finally, this chapter is concluded by reporting the integration of experimental data with network datasets, as valuable instrument for identifying alterations that underline the emergence of specific phenotypes. Based on our experience, we show some examples taking into consideration experimental data obtained by multidimensional protein identification technology (MudPIT) approach.

Identifying true protein complex constituents in interaction proteomics: the example of the DMXL2 protein complex

A typical high-sensitivity antibody affinity purification-mass spectrometry experiment easily identifies hundreds of protein interactors. However, most of these are non-valid resulting from multiple causes other than interaction with the bait protein. To discriminate true interactors from off-target recognition, we propose to differentially include an (peptide) antigen during the antibody incubation in the immuno-precipitation experiment. This contrasts the specific antibody-bait protein interactions, versus all other off-target protein interactions. To exemplify the power of the approach, we studied the DMXL2 interactome. From the initial six immuno-precipitations, we identified about 600 proteins. When filtering for interactors present in all anti-DMXL2 antibody immuno-precipitation experiments, absent in the bead controls, and competed off by the peptide antigen, this hit list is reduced to ten proteins, including known and novel interactors of DMXL2. Together, our approach enables the use of a wide range of available antibodies in large-scale protein interaction proteomics, while gaining specificity of the interactions.

Proteomics and systems biology for understanding diabetic nephropathy

Like many diseases, diabetic nephropathy is defined in a histopathological context and studied using reductionist approaches that attempt to ameliorate structural changes. Novel technologies in mass spectrometry-based proteomics have the ability to provide a deeper understanding of the disease beyond classical histopathology, redefine the characteristics of the disease state, and identify novel approaches to reduce renal failure. The goal is to translate these new definitions into improved patient outcomes through diagnostic, prognostic, and therapeutic tools. Here, we review progress made in studying the proteomics of diabetic nephropathy and provide an introduction to the informatics tools used in the analysis of systems biology data, while pointing out statistical issues for consideration. Novel bioinformatics methods may increase biomarker identification, and other tools, including selective reaction monitoring, may hasten clinical validation.

Analyzing protein-protein interaction networks

The advent of the "omics" era in biology research has brought new challenges and requires the development of novel strategies to answer previously intractable questions. Molecular interaction networks provide a framework to visualize cellular processes, but their complexity often makes their interpretation an overwhelming task. The inherently artificial nature of interaction detection methods and the incompleteness of currently available interaction maps call for a careful and well-informed utilization of this valuable data. In this tutorial, we aim to give an overview of the key aspects that any researcher needs to consider when working with molecular interaction data sets and we outline an example for interactome analysis. Using the molecular interaction database IntAct, the software platform Cytoscape, and its plugins BiNGO and clusterMaker, and taking as a starting point a list of proteins identified in a mass spectrometry-based proteomics experiment, we show how to build, visualize, and analyze a protein-protein interaction network.