Mass spectrometry-based proteomics in cell biology

Proteomic analysis of granulomas from cattle and pigs naturally infected with Mycobacterium tuberculosis complex by MALDI imaging

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has recently gained prominence for its ability to provide molecular and spatial information in tissue sections. This technology has the potential to uncover novel insights into proteins and other molecules in biological and immunological pathways activated along diseases with a complex host-pathogen interaction, such as animal tuberculosis. Thus, the present study conducted a data analysis of protein signature in granulomas of cattle and pigs naturally infected with the Mycobacterium tuberculosis complex (MTC), identifying biological and immunological signaling pathways activated throughout the disease. Lymph nodes from four pigs and four cattle, positive for the MTC by bacteriological culture and/or real-time PCR, were processed for histopathological examination and MALDI-MSI. Protein identities were assigned using the MaTisse database, and protein-protein interaction networks were visualized using the STRING database. Gene Ontology (GO) analysis was carried out to determine biological and immunological signaling pathways in which these proteins could participate together with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Distinct proteomic profiles between cattle and pig granulomas were displayed. Noteworthy, the GO analysis revealed also common pathways among both species, such as "Complement activation, alternative pathway" and "Tricarboxylic acid cycle", which highlight pathways that are conserved among different species infected by the MTC. In addition, species-specific terms were identified in the current study, such as "Natural killer cell degranulation" in cattle or those related to platelet and neutrophil recruitment and activation in pigs. Overall, this study provides insights into the immunopathogenesis of tuberculosis in cattle and pigs, opening new areas of research and highlighting the importance, among others, of the complement activation pathway and the regulation of natural killer cell- and neutrophil-mediated immunity in this disease.

Substrate and functional characterization of the lysine acetyltransferase MsKat and deacetylase MsCobB in Mycobacterium smegmatis

Tuberculosis (TB) is a serious cause of infectious death worldwide. Recent studies have reported that about 30% of the Mtb proteome was modified post-translationally, indicating that their functions are essential for drug resistance, mycobacterial survival, and pathogenicity. Among them, lysine acetylation, reversibly regulated by acetyltransferase and deacetylase, has important roles involved in energy metabolism, cellular adaptation, and protein interactions. However, the substrate and biological functions of these two important regulatory enzymes remain unclear. Herein, we utilized the non-pathogenic M. smegmatis strain as a model and systematically investigated the dynamic proteome changes in response to the overexpressing of MsKat/MsCobB in mycobacteria. A total of 4179 proteins and 1236 acetylated sites were identified in our data. Further analysis of the dynamic changes involved in proteome and acetylome showed that MsKat/MsCobB played a regulatory role in various metabolic pathways and nucleic acid processes. After that, the quantitative mass spectrometric method was utilized and proved that the AMP-dependent synthetase, Citrate synthase, ATP-dependent specificity component of the Clp protease, and ATP-dependent DNA/RNA helicases were identified to be the substrates of MsKat. Overall, our study provided an important resource underlying the substrates and functions of the acetylation regulatory enzymes in mycobacteria. SIGNIFICANCE: In this study, we systematically analyzed the dynamic molecular changes in response to the MsKat/MsCobB overexpression in mycobacteria at proteome and lysine acetylation level by using a TMT-based quantitative proteomic approach. Pathways related with glycolysis, degradation of branched chain amino acids, phosphotransferase system were affected after disturbance of the two regulates enzymes involved in lysine acetylation. We also proved that AMP-dependent synthetase Clp protease, ATP-dependent DNA/RNA helicases and citrate synthase was the substrate of MsKat according to our proteomic data and biological validation. Together, our study underlined the substrates and functions of the acetylation regulatory enzymes in mycobacteria.

Direct Determination of the Structure of Single Biopolymer Molecules Using Nanopore Sequencing

This review highlights operational principles, features, and modern aspects of the development of third-generation sequencing technology of biopolymers focusing on the nucleic acids analysis, namely the nanopore sequencing system. Basics of the method and technical solutions used for its realization are considered, from the first works showing the possibility of creation of these systems to the easy-to-handle procedure developed by Oxford Nanopore Technologies company. Moreover, this review focuses on applications, which were developed and realized using equipment developed by the Oxford Nanopore Technologies, including assembly of whole genomes, methagenomics, direct analysis of the presence of modified bases.

Mapping the human brain proteome: opportunities, challenges, and clinical potential

INTRODUCTION

Due to the segmented functions and complexity of the human brain, the characterization of molecular profiles within specific areas such as brain structures and biofluids is essential to unveil the molecular basis for structure specialization as well as the molecular imbalance associated with neurodegenerative and psychiatric diseases.

AREAS COVERED

Much of our knowledge about brain functionality derives from neurophysiological, anatomical, and transcriptomic approaches. More recently, laser capture and imaging proteomics, technological and computational developments in LC-MS/MS, as well as antibody/aptamer-based platforms have allowed the generation of novel cellular, spatial, and posttranslational dimensions as well as innovative facets in biomarker validation and druggable target identification.

EXPERT OPINION

Proteomics is a powerful toolbox to functionally characterize, quantify, and localize the extensive protein catalog of the human brain across physiological and pathological states. Brain function depends on multi-dimensional protein homeostasis, and its elucidation will help us to characterize biological pathways that are essential to properly maintain cognitive functions. In addition, comprehensive human brain pathological proteomes may be the basis in computational drug-repositioning methods as a strategy for unveiling potential new therapies in neurodegenerative and psychiatric disorders.

Mass spectrometry-based proteomics for advancing solid organ transplantation research

Scarcity of high-quality organs, suboptimal organ quality assessment, unsatisfactory pre-implantation procedures, and poor long-term organ and patient survival are the main challenges currently faced by the solid organ transplant (SOT) field. New biomarkers for assessing graft quality pre-implantation, detecting, and predicting graft injury, rejection, dysfunction, and survival are critical to provide clinicians with invaluable prediction tools and guidance for personalized patients' treatment. Additionally, new therapeutic targets are also needed to reduce injury and rejection and improve transplant outcomes. Proteins, which underlie phenotypes, are ideal candidate biomarkers of health and disease statuses and therapeutic targets. A protein can exist in different molecular forms, called proteoforms. As the function of a protein depends on its exact composition, proteoforms can offer a more accurate basis for connection to complex phenotypes than protein from which they derive. Mass spectrometry-based proteomics has been largely used in SOT research for identification of candidate biomarkers and therapeutic intervention targets by so-called "bottom-up" proteomics (BUP). However, such BUP approaches analyze small peptides in lieu of intact proteins and provide incomplete information on the exact molecular composition of the proteins of interest. In contrast, "Top-down" proteomics (TDP), which analyze intact proteins retaining proteoform-level information, have been only recently adopted in transplantation studies and already led to the identification of promising proteoforms as biomarkers for organ rejection and dysfunction. We anticipate that the use of top-down strategies in combination with new technological advancements in single-cell and spatial proteomics could drive future breakthroughs in biomarker and therapeutic target discovery in SOT.

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

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

Reverse-ChIP Techniques for Identifying Locus-Specific Proteomes: A Key Tool in Unlocking the Cancer Regulome

A phenotypic hallmark of cancer is aberrant transcriptional regulation. Transcriptional regulation is controlled by a complicated array of molecular factors, including the presence of transcription factors, the deposition of histone post-translational modifications, and long-range DNA interactions. Determining the molecular identity and function of these various factors is necessary to understand specific aspects of cancer biology and reveal potential therapeutic targets. Regulation of the genome by specific factors is typically studied using chromatin immunoprecipitation followed by sequencing (ChIP-Seq) that identifies genome-wide binding interactions through the use of factor-specific antibodies. A long-standing goal in many laboratories has been the development of a 'reverse-ChIP' approach to identify unknown binding partners at loci of interest. A variety of strategies have been employed to enable the selective biochemical purification of sequence-defined chromatin regions, including single-copy loci, and the subsequent analytical detection of associated proteins. This review covers mass spectrometry techniques that enable quantitative proteomics before providing a survey of approaches toward the development of strategies for the purification of sequence-specific chromatin as a 'reverse-ChIP' technique. A fully realized reverse-ChIP technique holds great potential for identifying cancer-specific targets and the development of personalized therapeutic regimens.

Proteomic approaches to study ubiquitinomics

As originally described some 40 years ago, protein ubiquitination was thought to serve primarily as a static mark for protein degradation. In the ensuing years, it has become clear that 'ubiquitination' is a structurally diverse and dynamic post-translational modification and is intricately involved in a myriad of signaling pathways in all eukaryote cells. And like other key pathways in the functional proteome, ubiquitin signaling is often disrupted, sometimes severely so, in human pathophysiology. As a result of its central role in normal physiology and human disease, the ubiquitination field is now represented across the full landscape of biomedical research from fundamental structural and biochemical studies to translational and clinical research. In recent years, mass spectrometry has emerged as a powerful technology for the detection and characterization of protein ubiquitination. Herein we detail qualitative and quantitative proteomic methods using a compare/contrast approach to highlight their strengths and weaknesses.

Yeast Svf1 binds ceramides and contributes to sphingolipid metabolism at the ER cis-Golgi interface

Ceramides are essential precursors of complex sphingolipids and act as potent signaling molecules. Ceramides are synthesized in the endoplasmic reticulum (ER) and receive their head-groups in the Golgi apparatus, yielding complex sphingolipids (SPs). Transport of ceramides between the ER and the Golgi is executed by the essential ceramide transport protein (CERT) in mammalian cells. However, yeast cells lack a CERT homolog, and the mechanism of ER to Golgi ceramide transport remains largely elusive. Here, we identified a role for yeast Svf1 in ceramide transport between the ER and the Golgi. Svf1 is dynamically targeted to membranes via an N-terminal amphipathic helix (AH). Svf1 binds ceramide via a hydrophobic binding pocket that is located in between two lipocalin domains. We showed that Svf1 membrane-targeting is important to maintain flux of ceramides into complex SPs. Together, our results show that Svf1 is a ceramide binding protein that contributes to sphingolipid metabolism at Golgi compartments.

Investigating the effects of glyphosate on the bumblebee proteome and microbiota

Glyphosate is one of the most widely used herbicides globally. It acts by inhibiting an enzyme in an aromatic amino acid synthesis pathway specific to plants and microbes, leading to the view that it poses no risk to other organisms. However, there is growing concern that glyphosate is associated with health effects in humans and an ever-increasing body of evidence that suggests potential deleterious effects on other animals including pollinating insects such as bees. Although pesticides have long been considered a factor in the decline of wild bee populations, most research on bees has focussed on demonstrating and understanding the effects of insecticides. To assess whether glyphosate poses a risk to bees, we characterised changes in survival, behaviour, sucrose solution consumption, the digestive tract proteome, and the microbiota in the bumblebee Bombus terrestris after chronic exposure to field relevant doses of technical grade glyphosate or the glyphosate-based formulation, RoundUp Optima+®. Regardless of source, there were changes in response to glyphosate exposure in important cellular and physiological processes in the digestive tract of B. terrestris, with proteins associated with oxidative stress regulation, metabolism, cellular adhesion, the extracellular matrix, and various signalling pathways altered. Interestingly, proteins associated with endocytosis, oxidative phosphorylation, the TCA cycle, and carbohydrate, lipid, and amino acid metabolism were differentially altered depending on whether the exposure source was glyphosate alone or RoundUp Optima+®. In addition, there were alterations to the digestive tract microbiota of bees depending on the glyphosate source No impacts on survival, behaviour, or food consumption were observed. Our research provides insights into the potential mode of action and consequences of glyphosate exposure at the molecular, cellular and organismal level in bumblebees and highlights issues with the current honeybee-centric risk assessment of pesticides and their formulations, where the impact of co-formulants on non-target organisms are generally overlooked.

Proteomic Comparison of Three Wild-Type Pseudorabies Virus Strains and the Attenuated Bartha Strain Reveals Reduced Incorporation of Several Tegument Proteins in Bartha Virions

Pseudorabies virus (PRV) is a member of the alphaherpesvirus subfamily and the causative agent of Aujeszky's disease in pigs. Driven by the large economic losses associated with PRV infection, several vaccines and vaccine programs have been developed. To this day, the attenuated Bartha strain, generated by serial passaging, represents the golden standard for PRV vaccination. However, a proteomic comparison of the Bartha virion to wild-type (WT) PRV virions is lacking. Here, we present a comprehensive mass spectrometry-based proteome comparison of the attenuated Bartha strain and three commonly used WT PRV strains: Becker, Kaplan, and NIA3. We report the detection of 40 structural and 14 presumed nonstructural proteins through a combination of data-dependent and data-independent acquisition. Interstrain comparisons revealed that packaging of the capsid and most envelope proteins is largely comparable in-between all four strains, except for the envelope protein pUL56, which is less abundant in Bartha virions. However, distinct differences were noted for several tegument proteins. Most strikingly, we noted a severely reduced incorporation of the tegument proteins IE180, VP11/12, pUS3, VP22, pUL41, pUS1, and pUL40 in Bartha virions. Moreover, and likely as a consequence, we also observed that Bartha virions are on average smaller and more icosahedral compared to WT virions. Finally, we detected at least 28 host proteins that were previously described in PRV virions and noticed considerable strain-specific differences with regard to host proteins, arguing that the potential role of packaged host proteins in PRV replication and spread should be further explored. IMPORTANCE The pseudorabies virus (PRV) vaccine strain Bartha-an attenuated strain created by serial passaging-represents an exceptional success story in alphaherpesvirus vaccination. Here, we used mass spectrometry to analyze the Bartha virion composition in comparison to three established WT PRV strains. Many viral tegument proteins that are considered nonessential for viral morphogenesis were drastically less abundant in Bartha virions compared to WT virions. Interestingly, many of the proteins that are less incorporated in Bartha participate in immune evasion strategies of alphaherpesviruses. In addition, we observed a reduced size and more icosahedral morphology of the Bartha virions compared to WT PRV. Given that the Bartha vaccine strain elicits potent immune responses, our findings here suggest that differences in protein packaging may contribute to its immunogenicity. Further exploration of these observations could aid the development of efficacious vaccines against other alphaherpesvirus vaccines such as HSV-1/2 or EHV-1.

Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network

Protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this task, but most of them do not use structural information of proteins. With the advances in protein structure research in recent years, protein contact map prediction has been dramatically enhanced. In this paper, we present GraphLoc, a deep learning model that predicts the localization of proteins at the subcellular level. The cores of the model are a graph convolutional neural network module and a multi-head attention module. The protein topology graph is constructed based on a contact map predicted from protein sequences, which is used as the input of the GCN module to take full advantage of the structural information of proteins. Multi-head attention module learns the weighted contribution of different amino acids to subcellular localization in different feature representation subspaces. Experiments on the benchmark dataset show that the performance of our model is better than others. The code can be accessed at https://github.com/GoodGuy398/GraphLoc . The proposed GraphLoc model consists of three parts. The first part is a graph convolutional network (GCN) module, which utilizes the predicted contact maps to construct protein graph, taking benefit of protein information accordingly. The second part is the multi-head attention module, which learns the weighted contribution of different amino acids in different feature representation subspace, and weighted average the feature map across all amino acid nodes. The last part is a fully connected layer that maps the flatten graph representation vector to another vector with a category number dimension, followed by a softmax layer to predict the protein subcellular localization.

Application of quantitative proteomics to investigate fruit ripening and eating quality

The consumption of fruit and vegetables play an important role in human nutrition, dietary diversity and health. Fruit and vegetable industries impart significant impact on our society, economy, and environment, contributing towards sustainable development in both developing and developed countries. The eating quality of fruit is determined by its appearance, color, firmness, flavor, nutritional components, and the absence of defects from physiological disorders. However, all of these components are affected by many pre- and postharvest factors that influence fruit ripening and senescence. Significant efforts have been made to maintain and improve fruit eating quality by expanding our knowledge of fruit ripening and senescence, as well as by controlling and reducing losses. Innovative approaches are required to gain better understanding of the management of eating quality. With completion of the genome sequence for many horticultural products in recent years and development of the proteomic research technique, quantitative proteomic research on fruit is changing rapidly and represents a complementary research platform to address how genetics and environment influence the quality attributes of various produce. Quantiative proteomic research on fruit is advancing from protein abundance and protein quantitation to gene-protein interactions and post-translational modifications of proteins that occur during fruit development, ripening and in response to environmental influences. All of these techniques help to provide a comprehensive understanding of eating quality. This review focuses on current developments in the field as well as limitations and challenges, both in broad term and with specific examples. These examples include our own research experience in applying quantitative proteomic techniques to identify and quantify the protein changes in association with fruit ripening, quality and development of disorders, as well as possible control mechanisms.

Functional Micropeptides Encoded by Long Non-Coding RNAs: A Comprehensive Review

Long non-coding RNAs (lncRNAs) were originally defined as non-coding RNAs (ncRNAs) which lack protein-coding ability. However, with the emergence of technologies such as ribosome profiling sequencing and ribosome-nascent chain complex sequencing, it has been demonstrated that most lncRNAs have short open reading frames hence the potential to encode functional micropeptides. Such micropeptides have been described to be widely involved in life-sustaining activities in several organisms, such as homeostasis regulation, disease, and tumor occurrence, and development, and morphological development of animals, and plants. In this review, we focus on the latest developments in the field of lncRNA-encoded micropeptides, and describe the relevant computational tools and techniques for micropeptide prediction and identification. This review aims to serve as a reference for future research studies on lncRNA-encoded micropeptides.

Bioinformatic Analysis of the Perilymph Proteome to Generate a Human Protein Atlas

The high complexity of the cellular architecture of the human inner ear and the inaccessibility for tissue biopsy hampers cellular and molecular analysis of inner ear disease. Sampling and analysis of perilymph may present an opportunity for improved diagnostics and understanding of human inner ear pathology. Analysis of the perilymph proteome from patients undergoing cochlear implantation was carried out revealing a multitude of proteins and patterns of protein composition that may enable characterisation of patients into subgroups. Based on existing data and databases, single proteins that are not present in the blood circulation were related to cells within the cochlea to allow prediction of which cells contribute to the individual perilymph proteome of the patients. Based on the results, we propose a human atlas of the cochlea. Finally, druggable targets within the perilymph proteome were identified. Understanding and modulating the human perilymph proteome will enable novel avenues to improve diagnosis and treatment of inner ear diseases.

Mass spectrometry-based proteomics in neurodegenerative lysosomal storage disorders

The major function of the lysosome is to degrade unwanted materials such as lipids, proteins, and nucleic acids; therefore, deficits of the lysosomal system can result in improper degradation and trafficking of these biomolecules. Diseases associated with lysosomal failure can be lethal and are termed lysosomal storage disorders (LSDs), which affect 1 in 5000 live births collectively. LSDs are inherited metabolic diseases caused by mutations in single lysosomal and non-lysosomal proteins and resulting in the subsequent accumulation of macromolecules within. Most LSD patients present with neurodegenerative clinical symptoms, as well as damage in other organs. The discovery of new biomarkers is necessary to understand and monitor these diseases and to track therapeutic progress. Over the past ten years, mass spectrometry (MS)-based proteomics has flourished in the biomarker studies in many diseases, including neurodegenerative, and more specifically, LSDs. In this review, biomarkers of disease pathophysiology and monitoring of LSDs revealed by MS-based proteomics are discussed, including examples from Niemann-Pick disease type C, Fabry disease, neuronal ceroid-lipofuscinoses, mucopolysaccharidosis, Krabbe disease, mucolipidosis, and Gaucher disease.

On-Cell Catalytic Detection of Epithelial-to-Mesenchymal Transition by a Clusterzyme Bioprobe

We construct a peptide-conjugated metal cluster as an enzyme-like catalytic bioprobe to enhance quantitative analysis of a membrane protein biomarker and detect epithelial-to-mesenchymal transition of tumor cells. This bioprobe with atomically precise formula, termed clusterzyme, possesses selective recognition and intrinsic enzyme-like activity. These favorable features facilitate sensitive quantitative analysis of the membrane protein in situ through on-cell catalytic signal amplification. This clusterzyme-based analytical method exhibits excellent compatibility with a traditional enzyme-linked immunosorbent assay and improved detection sensitivity with accuracy and robustness. Further, the expression level of the membrane protein reflects the ability of migration and invasion of model tumor cells, revealing epithelial-to-mesenchymal transition process. This work offers a facile and sensitive approach to monitor tumor cell type evolution at the molecular level, demonstrating a potential application of early cancer diagnosis and therapy assessment.

Shotgun Proteomics as a Powerful Tool for the Study of the Proteomes of Plants, Their Pathogens, and Plant-Pathogen Interactions

The interaction between plants and pathogenic microorganisms is a multifaceted process mediated by both plant- and pathogen-derived molecules, including proteins, metabolites, and lipids. Large-scale proteome analysis can quantify the dynamics of proteins, biological pathways, and posttranslational modifications (PTMs) involved in the plant-pathogen interaction. Mass spectrometry (MS)-based proteomics has become the preferred method for characterizing proteins at the proteome and sub-proteome (e.g., the phosphoproteome) levels. MS-based proteomics can reveal changes in the quantitative state of a proteome and provide a foundation for understanding the mechanisms involved in plant-pathogen interactions. This review is intended as a primer for biologists that may be unfamiliar with the diverse range of methodology for MS-based shotgun proteomics, with a focus on techniques that have been used to investigate plant-pathogen interactions. We provide a summary of the essential steps required for shotgun proteomic studies of plants, pathogens and plant-pathogen interactions, including methods for protein digestion, identification, separation, and quantification. Finally, we discuss how protein PTMs may directly participate in the interaction between a pathogen and its host plant.

Simple but efficacious enrichment of integral membrane proteins and their interactions for in-depth membrane proteomics

Membrane proteins play essential roles in various cellular processes, such as nutrient transport, bioenergetic processes, cell adhesion, and signal transduction. Proteomics is one of the key approaches to exploring membrane proteins comprehensively. Bottom–up proteomics using LC–MS/MS has been widely used in membrane proteomics. However, the low abundance and hydrophobic features of membrane proteins, especially integral membrane proteins, make it difficult to handle the proteins and are the bottleneck for identification by LC–MS/MS. Herein, to improve the identification and quantification of membrane proteins, we have stepwisely evaluated methods of membrane enrichment for the sample preparation. The enrichment methods of membranes consisted of precipitation by ultracentrifugation and treatment by urea or alkaline solutions. The best enrichment method in the study, washing with urea after isolation of the membranes, resulted in the identification of almost twice as many membrane proteins compared with samples without the enrichment. Notably, the method significantly enhances the identified numbers of multispanning transmembrane proteins, such as solute carrier transporters, ABC transporters, and G-protein–coupled receptors, by almost sixfold. Using this method, we revealed the profiles of amino acid transport systems with the validation by functional assays and found more protein–protein interactions, including membrane protein complexes and clusters. Our protocol uses standard procedures in biochemistry, but the method was efficient for the in-depth analysis of membrane proteome in a wide range of samples.

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Fractionation of membranes improves the identification of membrane proteins.

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Membranes washed with urea or alkaline increase identified transmembrane proteins.

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Urea wash increases the detection of multispanning transmembrane proteins.

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Proteomics of urea-washed membranes keeps more protein–protein interactions.

Solid-state and polymer nanopores for protein sensing: A review

In two decades, the solid state and polymer nanopores became attractive method for the protein sensing with high specificity and sensitivity. They also allow the characterization of conformational changes, unfolding, assembly and aggregation as well the following of enzymatic reaction. This review aims to provide an overview of the protein sensing regarding the technique of detection: the resistive pulse and ionic diodes. For each strategy, we report the most significant achievement regarding the detection of peptides and protein as well as the conformational change, protein-protein assembly and aggregation process. We discuss the limitations and the recent strategies to improve the nanopore resolution and accuracy. A focus is done about concomitant problematic such as protein adsorption and nanopore lifetime.

Deep-ultraviolet laser ablation sampling for proteomic analysis of tissue

Deep-ultraviolet laser ablation with a pulsed 193 nm ArF excimer laser was used to remove localized regions from tissue sections from which proteins were extracted for spatially resolved proteomic analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS). The ability to capture intact proteins by ablation at 193 nm wavelength was verified by matrix-assisted laser desorption ionization (MALDI) of the protein standard bovine serum albumin (BSA), which showed that BSA was ablated and captured without fragmentation. A Bradford assay of the ablated and captured proteins indicated 90% efficiency for transfer of the intact protein at a laser fluence of 3 kJ/m². Rat brain tissue sections mounted on quartz microscope slides and ablated in transmission mode yielded 2 μg protein per mm² as quantified by the Bradford assay. Tissue areas ranging from 0.06 mm² to 1 mm² were ablated and the ejected material was collected for proteomic analysis. Extracted proteins were digested and the resulting peptides were analyzed by LC-MS/MS. The proteins extracted from the ablated areas were identified and the average number of identified proteins ranged from 85 in the 0.06 mm² area to 2400 in the 1 mm² area of a 50 μm thick tissue. In comparison to infrared laser ablation of equivalent sampled areas, both the protein mass and number of proteins identified using DUV laser ablation sampling were approximately four times larger.

MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation

Prediction of protein localization plays an important role in understanding protein function and mechanisms. In this paper, we propose a general deep learning-based localization prediction framework, MULocDeep, which can predict multiple localizations of a protein at both subcellular and suborganellar levels. We collected a dataset with 44 suborganellar localization annotations in 10 major subcellular compartments—the most comprehensive suborganelle localization dataset to date. We also experimentally generated an independent dataset of mitochondrial proteins in Arabidopsis thaliana cell cultures, Solanum tuberosum tubers, and Vicia faba roots and made this dataset publicly available. Evaluations using the above datasets show that overall, MULocDeep outperforms other major methods at both subcellular and suborganellar levels. Furthermore, MULocDeep assesses each amino acid’s contribution to localization, which provides insights into the mechanism of protein sorting and localization motifs. A web server can be accessed at http://mu-loc.org.

Human Embryos, Induced Pluripotent Stem Cells, and Organoids: Models to Assess the Effects of Environmental Plastic Pollution

For a long time, animal models were used to mimic human biology and diseases. However, animal models are not an ideal solution due to numerous interspecies differences between humans and animals. New technologies, such as human-induced pluripotent stem cells and three-dimensional (3D) cultures such as organoids, represent promising solutions for replacing, refining, and reducing animal models. The capacity of organoids to differentiate, self-organize, and form specific, complex, biologically suitable structures makes them excellent in vitro models of development and disease pathogenesis, as well as drug-screening platforms. Despite significant potential health advantages, further studies and considerable nuances are necessary before their clinical use. This article summarizes the definition of embryoids, gastruloids, and organoids and clarifies their appliance as models for early development, diseases, environmental pollution, drug screening, and bioinformatics.

Proteomic profiling of the oncogenic septin 9 reveals isoform-specific interactions in breast cancer cells

Septins are a family of multimeric GTP-binding proteins, which are abnormally expressed in cancer. Septin 9 (SEPT9) is an essential and ubiquitously expressed septin with multiple isoforms, which have differential expression patterns and effects in breast cancer cells. It is unknown, however, if SEPT9 isoforms associate with different molecular networks and functions. Here, we performed a proteomic screen in MCF-7 breast cancer cells to identify the interactome of GFP-SEPT9 isoforms 1, 4 and 5, which vary significantly in their N-terminal extensions. While all three isoforms associated with SEPT2 and SEPT7, the truncated SEPT9_i4 and SEPT9_i5 interacted with septins of the SEPT6 group more promiscuously than SEPT9_i1, which bound predominately SEPT8. Spatial mapping and functional clustering of non-septin partners showed isoform-specific differences in interactions with proteins of distinct subcellular organelles (e.g., nuclei, centrosomes, cilia) and functions such as cell signalling and ubiquitination. The interactome of the full length SEPT9_i1 was more enriched in cytoskeletal regulators, while the truncated SEPT9_i4 and SEPT9_i5 exhibited preferential and isoform-specific interactions with nuclear, signalling, and ubiquitinating proteins. These data provide evidence for isoform-specific interactions, which arise from truncations in the N-terminal extensions of SEPT9, and point to novel roles in the pathogenesis of breast cancer.

Mass spectrometry based proteomics for developmental neurobiology in the amphibian Xenopus laevis

The South African clawed frog (Xenopus laevis), a prominent vertebrate model in cell and developmental biology, has been instrumental in studying molecular mechanisms of neural development and disease. Recently, high-resolution mass spectrometry (HRMS), a bioanalytical technology, has expanded the molecular toolbox of protein detection and characterization (proteomics). This chapter overviews the characteristics, advantages, and challenges of this biological model and technology. Discussions are offered on their combined use to aid studies on cell differentiation and development of neural tissues. Finally, the emerging integration of proteomics and other 'omic technologies is reflected on to generate new knowledge, drive and test new hypotheses, and ultimately, advance the understanding of neural development during states of health and disease.

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.

Mapping the SARS-CoV-2-Host Protein-Protein Interactome by Affinity Purification Mass Spectrometry and Proximity-Dependent Biotin Labeling: A Rational and Straightforward Route to Discover Host-Directed Anti-SARS-CoV-2 Therapeutics

Protein-protein interactions (PPIs) are the vital engine of cellular machinery. After virus entry in host cells the global organization of the viral life cycle is strongly regulated by the formation of virus-host protein interactions. With the advent of high-throughput -omics platforms, the mirage to obtain a "high resolution" view of virus-host interactions has come true. In fact, the rapidly expanding approaches of mass spectrometry (MS)-based proteomics in the study of PPIs provide efficient tools to identify a significant number of potential drug targets. Generation of PPIs maps by affinity purification-MS and by the more recent proximity labeling-MS may help to uncover cellular processes hijacked and/or altered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), providing promising therapeutic targets. The possibility to further validate putative key targets from high-confidence interactions between viral bait and host protein through follow-up MS-based multi-omics experiments offers an unprecedented opportunity in the drug discovery pipeline. In particular, drug repurposing, making use of already existing approved drugs directly targeting these identified and validated host interactors, might shorten the time and reduce the costs in comparison to the traditional drug discovery process. This route might be promising for finding effective antiviral therapeutic options providing a turning point in the fight against the coronavirus disease-2019 (COVID-19) outbreak.

Proteomics for Target Identification in Psychiatric and Neurodegenerative Disorders

Psychiatric and neurodegenerative disorders such as schizophrenia (SCZ), Parkinson's disease (PD), and Alzheimer's disease (AD) continue to grow around the world with a high impact on health, social, and economic outcomes for the patient and society. Despite efforts, the etiology and pathophysiology of these disorders remain unclear. Omics technologies have contributed to the understanding of the molecular mechanisms that underlie these complex disorders and have suggested novel potential targets for treatment and diagnostics. Here, we have highlighted the unique and common pathways shared between SCZ, PD, and AD and highlight the main proteomic findings over the last 5 years using in vitro models, postmortem brain samples, and cerebrospinal fluid (CSF) or blood of patients. These studies have identified possible therapeutic targets and disease biomarkers. Further studies including target validation, the use of large sample sizes, and the integration of omics findings with bioinformatics tools are required to provide a better comprehension of pharmacological targets.

Proteomic Approaches to Investigate Regulated Trafficking and Signaling of G Protein-Coupled Receptors

Advances in proteomic methodologies based on quantitative mass spectrometry are now transforming pharmacology and experimental biology more broadly. The present review will discuss several examples based on work in the author's laboratory, which focuses on delineating relationships between G protein-coupled receptor signaling and trafficking in the endocytic network. The examples highlighted correspond to those discussed in a talk presented at the 2019 EB/ASPET meeting, which was organized by Professor Joe Beavo to commemorate his receipt of the Julius Axelrod Award. SIGNIFICANCE STATEMENT: GPCRs are allosteric machines that signal by interacting with other cellular proteins, and this, in turn, is determined by a complex interplay between the biochemical, subcellular localization, and membrane trafficking properties of receptors relative to transducer and regulatory proteins. The present minireview highlights recent advances and challenges in elucidating this dynamic cell biology and toward delineating the cellular basis of drug action at the level of defined GPCR interaction networks using proteomic approaches enabled by quantitative mass spectrometry.

Protocol for the Bottom-Up Proteomic Analysis of Mouse Spleen

This protocol describes the comparative proteomic profiling of the spleen of wild type versus mdx-4cv mouse, a model of dystrophinopathy. We detail sample preparation for bottom-up proteomic mass spectrometry experiments, including homogenization of tissue, protein concentration measurements, protein digestion, and removal of interfering chemicals. We then describe the steps for mass spectrometric analysis and bioinformatic evaluation. For complete details on the use and execution of this protocol, please refer to Dowling et al. (2020).

The antioxidant function of Sco proteins depends on a critical surface-exposed residue

BACKGROUND

Besides their role in copper metabolism, Sco proteins from different organisms have been shown to play a defensive role against oxidative stress. In the present study, we set out to identify crucial amino acid residues for the antioxidant activity.

METHODS

Native and mutated Sco proteins from human, Arabidopsis thaliana and the yeast Kluyveromyces lactis were expressed in the model organism Saccharomyces cerevisiae. The oxidative stress resistance of the respective transformants was determined by growth and lipid peroxidation assays.

RESULTS

A functionally important site, located 15 amino acids downstream of the well-conserved copper binding CxxxC motif, was identified. Mutational analysis revealed that a positive charge at this position has a detrimental effect on the antioxidant capacity. Bioinformatic analysis predicts that this site is surface-exposed, and according to Co-IP data it is required for binding of proteins that are connected to known antioxidant pathways.

CONCLUSION

This study shows that the antioxidant capacity of eukaryotic Sco proteins is conserved and depends on the presence of functional site(s) rather than the extent of overall sequence homology.

GENERAL SIGNIFICANCE

These findings provide an insight into the conserved functional sites of eukaryotic Sco proteins that are crucial for combating oxidative stress. This capacity is probably not due to an enzymatic activity but rather is indirectly mediated by interaction with other proteins.

Proteomic analysis of 2-chloroethanol extracts of rice (Oryza sativa L.) seeds

Ethanol-soluble proteins, including prolamins, are one of the most important seed proteins in rice (Oryza sativa L.). However, little is known about the proteomic profile of ethanol-soluble protein fraction extracted from rice grain. In this work, the differential profile of ethanol-soluble proteins extracted by 2-chloroethanol and ethanol has been documented. Proteome analysis utilizing LC-MS/MS identified a total of 64 unique proteins in the 2-chloroethanol extract of rice seeds. The majority of these proteins had low molecular weight ranging from 10 to 25 kD and isoelectric point (pI) in mid-acidic (pH 5-pH 7) and mid-basic (pH 7-pH 9) ranges. Database searches combined with transmembrane domain (TMD) analysis revealed that >70% of identified proteins were hydrophobic, i.e., had at least one TMD. Gene ontology classification and enrichment analysis showed that the identified proteins were involved in13 types of biological processes, 5 types of cell components, and 17 types of molecular functions. These results were significant based on the hyper p-value of <0.05. The most frequent categories of biological processes, cell components, and molecular functions were, respectively, type I hypersensitivity, extracellular space and extracellular region, and serine-type endopeptidase inhibitor activity. Interestingly, in addition to seed storage proteins such as prolamins and glutelins, certain allergen proteins, protease inhibitors, and lipid transfer proteins were identified in the extracts. Together, the collected data provide novel insights into the protein profile of 2-chloroethanol extract of rice seeds.

Nonstandard conditionally specified models for nonignorable missing data

We consider data-analysis settings where data are missing not at random. In these cases, the two basic modeling approaches are 1) pattern-mixture models, with separate distributions for missing data and observed data, and 2) selection models, with a distribution for the data preobservation and a missing-data mechanism that selects which data are observed. These two modeling approaches lead to distinct factorizations of the joint distribution of the observed-data and missing-data indicators. In this paper, we explore a third approach, apparently originally proposed by J. W. Tukey as a remark in a discussion between Rubin and Hartigan, and reported by Holland in a two-page note, which has been so far neglected.

Data analyses typically rely upon assumptions about the missingness mechanisms that lead to observed versus missing data, assumptions that are typically unassessable. We explore an approach where the joint distribution of observed data and missing data are specified in a nonstandard way. In this formulation, which traces back to a representation of the joint distribution of the data and missingness mechanism, apparently first proposed by J. W. Tukey, the modeling assumptions about the distributions are either assessable or are designed to allow relatively easy incorporation of substantive knowledge about the problem at hand, thereby offering a possibly realistic portrayal of the data, both observed and missing. We develop Tukey’s representation for exponential-family models, propose a computationally tractable approach to inference in this class of models, and offer some general theoretical comments. We then illustrate the utility of this approach with an example in systems biology.

Application of Solid-State Nanopore in Protein Detection

A protein is a kind of major biomacromolecule of life. Its sequence, structure, and content in organisms contains quite important information for normal or pathological physiological process. However, research of proteomics is facing certain obstacles. Only a few technologies are available for protein analysis, and their application is limited by chemical modification or the need for a large amount of sample. Solid-state nanopore overcomes some shortcomings of the existing technology, and has the ability to detect proteins at a single-molecule level, with its high sensitivity and robustness of device. Many works on detection of protein molecules and discriminating structure have been carried out in recent years. Single-molecule protein sequencing techniques based on solid-state nanopore are also been proposed and developed. Here, we categorize and describe these efforts and progress, as well as discuss their advantages and drawbacks.

Hepatitis C Virus Entry: Protein Interactions and Fusion Determinants Governing Productive Hepatocyte Invasion

Hepatitis C virus (HCV) entry is among the best-studied uptake processes for human pathogenic viruses. Uptake follows a spatially and temporally tightly controlled program. Numerous host factors including proteins, lipids, and glycans promote productive uptake of HCV particles into human liver cells. The virus initially attaches to surface proteoglycans, lipid receptors such as the scavenger receptor BI (SR-BI), and to the tetraspanin CD81. After lateral translocation of virions to tight junctions, claudin-1 (CLDN1) and occludin (OCLN) are essential for entry. Clathrin-mediated endocytosis engulfs HCV particles, which fuse with endosomal membranes after pH drop. Uncoating of the viral RNA genome in the cytoplasm completes the entry process. Here we systematically review and classify HCV entry factors by their mechanistic role, relevance, and level of evidence. Finally, we report on more recent knowledge on determinants of membrane fusion and close with an outlook on future implications of HCV entry research.

Mass Spectrometry-Based Analysis of TRPP2 Phosphorylation

Differential phosphorylation of proteins is a key regulatory mechanism in biology. Immunoprecipitation-coupled mass spectrometry facilitates the targeted analysis of transient receptor ion potential channel polycystin-2 (TRPP2) phosphorylation. However, empirical testing is required to optimize experimental conditions for immunoprecipitation and mass spectrometry. Here, we present a detailed workflow for the reliable analysis of endogenous TRPP2 phosphorylation in differentiated renal epithelial cells.

An Integrated Workflow for Global, Glyco-, and Phospho-proteomic Analysis of Tumor Tissues

Recently, the rapid development and application of mass spectrometry (MS)-based technologies have markedly improved the comprehensive proteomic characterization of global proteome and protein post-translational modifications (PTMs). However, the current conventional approach for global proteomic analysis is often carried out separately from PTM analysis. In our study, we developed an integrated workflow for multiplex analysis of global, glyco-, and phospho-proteomics using breast cancer patient-derived xenograft (PDX) tumor samples. Our approach included the following steps: trypsin-digested tumor samples were enriched for phosphopeptides through immobilized metal ion affinity chromatography (IMAC), followed by enrichment of glycopeptides through mixed anion exchange (MAX) method, and then the flow-through peptides were analyzed for global proteomics. Our workflow demonstrated an increased identification of peptides and associated proteins in global proteome, as compared to those using the peptides without PTM depletion. In addition to global proteome, the workflow identified phosphopeptides and glycopeptides from the PTM enrichment. We also found a subset of glycans with unique distribution profiles in the IMAC flow-through, as compared to those enriched directly using the MAX method. Our integrated workflow provided an effective platform for simultaneous global proteomic and PTM analysis of biospecimens.

A BONCAT-iTRAQ method enables temporally resolved quantitative profiling of newly synthesised proteins in Leishmania mexicana parasites during starvation

Adaptation to starvation is integral to the Leishmania life cycle. The parasite can survive prolonged periods of nutrient deprivation both in vitro and in vivo. The identification of parasite proteins synthesised during starvation is key to unravelling the underlying molecular mechanisms facilitating adaptation to these conditions. Additionally, as stress adaptation mechanisms in Leishmania are linked to virulence as well as infectivity, profiling of the complete repertoire of Newly Synthesised Proteins (NSPs) under starvation is important for drug target discovery. However, differential identification and quantitation of low abundance, starvation-specific NSPs from the larger background of the pre-existing parasite proteome has proven difficult, as this demands a highly selective and sensitive methodology. Herein we introduce an integrated chemical proteomics method in L. mexicana promastigotes that involves a powerful combination of the BONCAT technique and iTRAQ quantitative proteomics Mass Spectrometry (MS), which enabled temporally resolved quantitative profiling of de novo protein synthesis in the starving parasite. Uniquely, this approach integrates the high specificity of the BONCAT technique for the NSPs, with the high sensitivity and multiplexed quantitation capability of the iTRAQ proteomics MS. Proof-of-concept experiments identified over 250 starvation-responsive NSPs in the parasite. Our results show a starvation-specific increased relative abundance of several translation regulating and stress-responsive proteins in the parasite. GO analysis of the identified NSPs for Biological Process revealed translation (enrichment P value 2.47e^-35) and peptide biosynthetic process (enrichment P value 4.84e^-35) as extremely significantly enriched terms indicating the high specificity of the NSP towards regulation of protein synthesis. We believe that this approach will find widespread use in the study of the developmental stages of Leishmania species and in the broader field of protozoan biology.

Periodic nutrient scarcity plays crucial roles in the life cycle of the protozoan parasite Leishmania spp. Although adaptation to nutrient stress has a pivotal role in Leishmania biology, the underlying mechanisms remain poorly understood. In a period of nutrient starvation, the parasite responds by decreasing its protein production to conserve nutrient resources and to prevent formation of toxic proteins. However, even during severe starvation, the parasite generates certain essential quality control and rescue proteins. Differential identification of the complete repertoire of these proteins synthesised during starvation from the pre-existing proteins in the parasite holds the key to understanding the starvation adaptation mechanisms. This has been challenging to accomplish due to technical limitations. Using a combination of chemical labelling techniques and protein mass-spectrometry, we selectively identified and measured the proteins generated in the starving Leishmania parasite. Our results show a starvation time-dependent differential expression of important protein synthesis regulators in the parasite. This will serve as an important dataset for a holistic understanding of the starvation adaptation mechanisms in Leishmania. We also believe that this method will find widespread applications in the field of protozoa and other parasites causing Neglected Tropical Diseases.

High throughput discovery of functional protein modifications by Hotspot Thermal Profiling

Mass spectrometry has revolutionized the ability to study posttranslationally modified proteoforms from biologic samples, yet we still lack methods to systematically predict, or even prioritize, which modification sites may perturb protein function. Here we describe a proteomic method to detect the effects of site-specific protein phosphorylation on the thermal stability of thousands of native proteins in live cells. This massively parallel biophysical assay unveiled shifts in overall protein stability in response to site-specific phosphorylation sites, as well as trends related to protein function and structure. This method can detect both intrinsic changes to protein structure as well as extrinsic changes to protein-protein, and protein-metabolite interactions resulting from the diminutive introduction of a phosphate onto large proteins. Finally, we show that functional “hotspot” protein modification sites can be discovered and prioritized for study in a high-throughput and unbiased fashion. This approach is applicable to diverse organisms, cell types and posttranslational modifications.

Single-cell proteomics in complex tissues using microprobe capillary electrophoresis mass spectrometry

Direct measurement of proteins produced by single cells promises to expand our understanding of molecular cell-to-cell differences (heterogeneity) and their contribution to normal and impaired development. High-resolution mass spectrometry (HRMS) is the modern technology of choice for the label-free identification and quantification of proteins, albeit usually in large populations of cells. Recent advances in microscale sample collection and processing, separation, and ionization have extended this powerful technology to single cells. This chapter describes a protocol based on microprobe capillary electrophoresis (CE) HRMS to enable the direct proteomic profiling of single cells embedded in complex tissues without the requirement for dissociation or whole-cell dissection. We here demonstrate the technology for identified individual cells in early developing embryos of Xenopus laevis and zebrafish as well as electrophysiologically identified single neurons in physiologically active brain slices from the mouse substantia nigra. Instructions are provided step-by-step to identify single cells using physiological or morphological cues, collect the content of the cells using microfabricated capillaries, and perform bottom-up proteomics using a custom-built CE electrospray ionization (ESI) mass spectrometer equipped with a quadrupole time-of-flight or orbitrap mass analyzer. Results obtained by this approach have revealed previously unknown differences between the proteomic state of embryonic cells and neurons. The data from single-cell proteomics by microprobe CE-ESI-HRMS complements those from single-cell transcriptomics, thereby opening exciting potentials to deepen our knowledge of molecular mechanisms governing cell and developmental processes.

Accelerating Lipidomic Method Development through in Silico Simulation

Judicious selection of mass spectrometry (MS) acquisition parameters is essential for effectively profiling the broad diversity and dynamic range of biomolecules. Typically, acquisition parameters are individually optimized to maximally characterize analytes from each new sample matrix. This time-consuming process often ignores the synergistic relationship between MS method parameters, producing suboptimal results. Here we detail the creation of an algorithm which accurately simulates LC-MS/MS lipidomic data acquisition performance for a benchtop quadrupole-Orbitrap MS system. By coupling this simulation tool with a genetic algorithm for constrained parameter optimization, we demonstrate the efficient identification of LC-MS/MS method parameter sets individually suited for specific sample matrices. Finally, we utilize the in silico simulation to examine how continued developments in MS acquisition speed and sensitivity will further increase the power of MS lipidomics as a vital tool for impactful biochemical analysis.

Decoding the PTM-switchboard of Notch

The developmentally indispensable Notch pathway exhibits a high grade of pleiotropism in its biological output. Emerging evidence supports the notion of post-translational modifications (PTMs) as a modus operandi controlling dynamic fine-tuning of Notch activity. Although, the intricacy of Notch post-translational regulation, as well as how these modifications lead to multiples of divergent Notch phenotypes is still largely unknown, numerous studies show a correlation between the site of modification and the output. These include glycosylation of the extracellular domain of Notch modulating ligand binding, and phosphorylation of the PEST domain controlling half-life of the intracellular domain of Notch. Furthermore, several reports show that multiple PTMs can act in concert, or compete for the same sites to drive opposite outputs. However, further investigation of the complex PTM crosstalk is required for a complete understanding of the PTM-mediated Notch switchboard. In this review, we aim to provide a consistent and up-to-date summary of the currently known PTMs acting on the Notch signaling pathway, their functions in different contexts, as well as explore their implications in physiology and disease. Furthermore, we give an overview of the present state of PTM research methodology, and allude to a future with PTM-targeted Notch therapeutics.

Proteomics in the World of Induced Pluripotent Stem Cells

Omics approaches have significantly impacted knowledge about molecular signaling pathways driving cell function. Induced pluripotent stem cells (iPSC) have revolutionized the field of biological sciences and proteomics and, in particular, has been instrumental in identifying key elements operating during the maintenance of the pluripotent state and the differentiation process to the diverse cell types that form organisms. This review covers the evolution of conceptual and methodological strategies in proteomics; briefly describes the generation of iPSC from a historical perspective, the state-of-the-art of iPSC-based proteomics; and compares data on the proteome and transcriptome of iPSC to that of embryonic stem cells (ESC). Finally, proteomics of healthy and diseased cells and organoids differentiated from iPSC are analyzed.

Characterization of Contractile Proteins from Skeletal Muscle Using Gel-Based Top-Down Proteomics

The mass spectrometric analysis of skeletal muscle proteins has used both peptide-centric and protein-focused approaches. The term 'top-down proteomics' is often used in relation to studying purified proteoforms and their post-translational modifications. Two-dimensional gel electrophoresis, in combination with peptide generation for the identification and characterization of intact proteoforms being present in two-dimensional spots, plays a critical role in specific applications of top-down proteomics. A decisive bioanalytical advantage of gel-based and top-down approaches is the initial bioanalytical focus on intact proteins, which usually enables the swift identification and detailed characterisation of specific proteoforms. In this review, we describe the usage of two-dimensional gel electrophoretic top-down proteomics and related approaches for the systematic analysis of key components of the contractile apparatus, with a special focus on myosin heavy and light chains and their associated regulatory proteins. The detailed biochemical analysis of proteins belonging to the thick and thin skeletal muscle filaments has decisively improved our biochemical understanding of structure-function relationships within the contractile apparatus. Gel-based and top-down proteomics has clearly established a variety of slow and fast isoforms of myosin, troponin and tropomyosin as excellent markers of fibre type specification and dynamic muscle transition processes.

Mass Spectrometry for Proteomics and Recent Developments in ESI, MALDI and other Ionization Methodologies

Background:

Mass spectrometry is a tool used in analytical chemistry to identify components in a chemical compound and it is of tremendous importance in the field of biology for high throughput analysis of biomolecules, among which protein is of great interest.

Objective:

Advancement in proteomics based on mass spectrometry has led the way to quantify multiple protein complexes, and proteins interactions with DNA/RNA or other chemical compounds which is a breakthrough in the field of bioinformatics.

Methods:

Many new technologies have been introduced in electrospray ionization (ESI) and Matrixassisted Laser Desorption/Ionization (MALDI) techniques which have enhanced sensitivity, resolution and many other key features for the characterization of proteins.

Results:

The advent of ambient mass spectrometry and its different versions like Desorption Electrospray Ionization (DESI), DART and ELDI has brought a huge revolution in proteomics research. Different imaging techniques are also introduced in MS to map proteins and other significant biomolecules. These drastic developments have paved the way to analyze large proteins of >200kDa easily.

Conclusion:

Here, we discuss the recent advancement in mass spectrometry, which is of great importance and it could lead us to further deep analysis of the molecules from different perspectives and further advancement in these techniques will enable us to find better ways for prediction of molecules and their behavioral properties.

Label-free quantification of protein expression in the rainbow trout (Oncorhynchus mykiss) in response to short-term exposure to heat stress

Rainbow trout (Oncorhynchus mykiss) are a cold-water salmonid species that is highly susceptible to heat stress. Summer temperature stress is a common issue in trout aquaculture. To better understand the molecular mechanisms of the heat-stress response in the trout, we used label-free quantitative proteome techniques to identify differentially expressed proteins in the livers of rainbow trout exposed to heat stress. We identified 3362 proteins and 152 differentially expressed proteins (p < 0.05; fold-change >2). Of these, 37 were uniquely expressed in the heat-stress group and 35 were uniquely expressed in the control group. In addition, 42 proteins were significantly upregulated (fold-change >2) and 38 proteins were significantly downregulated (fold-change >2). GO (Gene Ontology) analysis indicated that these differentially expressed proteins were primarily expressed in the nucleus, extracellular matrix, and cytoplasm, and were associated with a variety of functions, including protein binding/bridging and enzyme facilitation. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis of the differentially expressed proteins showed that, during high temperature stress, many biological processes were extensively altered, particularly the estrogen signaling pathway, the complement and coagulation cascades, and the platelet activation pathway. Our study focused on the identification of a systematic approach for the characterization of regulatory networks. Our results provide a framework for further studies of the heat-stress response in fish.

Interferon-stimulated gene 15 modulates cell migration by interacting with Rac1 and contributes to lymph node metastasis of oral squamous cell carcinoma cells

In an effort to understand the underlying mechanisms of lymph node metastasis in oral squamous cell carcinoma (OSCC), through in vivo selection, LN1-1 cells were previously established from OEC-M1 cells and showed enhanced lymphangiogenesis and lymphatic metastasis capabilities. In the current study, we use a stable isotope labeling with amino acids in cell culture (SILAC) and liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic platform to compare LN1-1 to OEC-M1 cells. Interferon-stimulated gene 15 (ISG15) was found highly expressed in LN1-1 cells. Immunohistochemical analysis and meta-analysis of publicly available microarray datasets revealed that the ISG15 level was increased in human OSCC tissues and associated with poor disease outcome. Knockdown of ISG15 had minimal effects on tumor growth but did decrease tumor lymphangiogenesis and lymphatic metastasis of LN1-1 cells. Consistent with the in vivo assay, ISG15 knockdown did not impair cell growth but diminished cell migration, invasion, and transendothelial migration in vitro. ISG15-induced cell migration was independent of ISGylation and associated with membrane protrusion. Ectopic expression of ISG15 increased Rac1 activity and knockdown of Rac1 impaired ISG15-enhanced migration. Furthermore, Rac1 colocalized with ISG15 to a region of membrane protrusion and ISG15 coimmunoprecipitated with Rac1, especially with the Rac1-GDP form. Importantly, as shown by proximity ligation assays, ISG15 and Rac1 physically interacted with each other. Our results indicated that ISG15 affects cell migration by interacting with Rac1 and regulating Rac1 activity and contributes to lymphatic metastasis in OSCC.

Protein identification strategies in MALDI imaging mass spectrometry: a brief review

Matrix assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a powerful technology used to investigate the spatial distributions of thousands of molecules throughout a tissue section from a single experiment. As proteins represent an important group of functional molecules in tissue and cells, the imaging of proteins has been an important point of focus in the development of IMS technologies and methods. Protein identification is crucial for the biological contextualization of molecular imaging data. However, gas-phase fragmentation efficiency of MALDI generated proteins presents significant challenges, making protein identification directly from tissue difficult. This review highlights methods and technologies specifically related to protein identification that have been developed to overcome these challenges in MALDI IMS experiments.