Organellar Maps Through Proteomic Profiling - A Conceptual Guide

UNC-45A: A potential therapeutic target for malignant tumors

Uncoordinated mutant number-45 myosin chaperone A (UNC-45A), a protein highly conserved throughout evolution, is ubiquitously expressed in somatic cells. It is correlated with tumorigenesis, proliferation, metastasis, and invasion of multiple malignant tumors. The current understanding of the role of UNC-45A in tumor progression is mainly related to the regulation of non-muscle myosin II (NM-II). However, many studies have suggested that the mechanisms by which UNC-45A is involved in tumor progression are far greater than those of NM-II regulation. UNC-45A can also promote tumor cell proliferation by regulating checkpoint kinase 1 (ChK1) phosphorylation or the transcriptional activity of nuclear receptors, and induces chemoresistance to paclitaxel in tumor cells by destabilizing microtubule activity. In this review, we discuss the recent advances illuminating the role of UNC-45A in tumor progression. We also put forward therapeutic strategies targeting UNC-45A, in the hope of paving the way the development of UNC-45A-targeted therapies for patients with malignant tumors.

Mapping protein-protein interactions by mass spectrometry

Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.

Proteomics Impact on Cell Biology to Resolve Cell Structure and Function

The acceleration of advances in proteomics has enabled integration with imaging at the EM and light microscopy levels, cryo-EM of protein structures, and artificial intelligence with proteins comprehensively and accurately resolved for cell structures at nanometer to subnanometer resolution. Proteomics continues to outpace experimentally based structural imaging, but their ultimate integration is a path toward the goal of a compendium of all proteins to understand mechanistically cell structure and function.

Cross-link assisted spatial proteomics to map sub-organelle proteomes and membrane protein topologies

The functions of cellular organelles and sub-compartments depend on their protein content, which can be characterized by spatial proteomics approaches. However, many spatial proteomics methods are limited in their ability to resolve organellar sub-compartments, profile multiple sub-compartments in parallel, and/or characterize membrane-associated proteomes. Here, we develop a cross-link assisted spatial proteomics (CLASP) strategy that addresses these shortcomings. Using human mitochondria as a model system, we show that CLASP can elucidate spatial proteomes of all mitochondrial sub-compartments and provide topological insight into the mitochondrial membrane proteome. Biochemical and imaging-based follow-up studies confirm that CLASP allows discovering mitochondria-associated proteins and revising previous protein sub-compartment localization and membrane topology data. We also validate the CLASP concept in synaptic vesicles, demonstrating its applicability to different sub-cellular compartments. This study extends the scope of cross-linking mass spectrometry beyond protein structure and interaction analysis towards spatial proteomics, and establishes a method for concomitant profiling of sub-organelle and membrane proteomes.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

Deep and fast label-free Dynamic Organellar Mapping

The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we enhance the performance of DOMs through data-independent acquisition (DIA) mass spectrometry. DIA-DOMs achieve twice the depth of our previous workflow in the same mass spectrometry runtime, and substantially improve profiling precision and reproducibility. We leverage this gain to establish flexible map formats scaling from high-throughput analyses to extra-deep coverage. Furthermore, we introduce DOM-ABC, a powerful and user-friendly open-source software tool for analyzing profiling data. We apply DIA-DOMs to capture subcellular localization changes in response to starvation and disruption of lysosomal pH in HeLa cells, which identifies a subset of Golgi proteins that cycle through endosomes. An imaging time-course reveals different cycling patterns and confirms the quantitative predictive power of our translocation analysis. DIA-DOMs offer a superior workflow for label-free spatial proteomics as a systematic phenotype discovery tool.

Genomics, Proteomics, and Metabolomics Approaches to Improve Abiotic Stress Tolerance in Tomato Plant

To explore changes in proteins and metabolites under stress circumstances, genomics, proteomics, and metabolomics methods are used. In-depth research over the previous ten years has gradually revealed the fundamental processes of plants' responses to environmental stress. Abiotic stresses, which include temperature extremes, water scarcity, and metal toxicity brought on by human activity and urbanization, are a major cause for concern, since they can result in unsustainable warming trends and drastically lower crop yields. Furthermore, there is an emerging reliance on agrochemicals. Stress is responsible for physiological transformations such as the formation of reactive oxygen, stomatal opening and closure, cytosolic calcium ion concentrations, metabolite profiles and their dynamic changes, expression of stress-responsive genes, activation of potassium channels, etc. Research regarding abiotic stresses is lacking because defense feedbacks to abiotic factors necessitate regulating the changes that activate multiple genes and pathways that are not properly explored. It is clear from the involvement of these genes that plant stress response and adaptation are complicated processes. Targeting the multigenicity of plant abiotic stress responses caused by genomic sequences, transcripts, protein organization and interactions, stress-specific and cellular transcriptome collections, and mutant screens can be the first step in an integrative approach. Therefore, in this review, we focused on the genomes, proteomics, and metabolomics of tomatoes under abiotic stress.

Labeling of Phospho-Specific Antibodies with oYo-Link® Epitope Tags for Multiplex Immunostaining

Spatial proteomics has recently garnered significant interest, as it offers to provide unprecedented insight into biological processes in both health and disease, by connecting protein expression patterns from the subcellular level to the tissue or even organism level. These high-content approaches generally rely on a high degree of multiplexing, whereby multiple proteins can be detected simultaneously. The most versatile multiplexing approaches utilize antibodies to confer specificity for various intracellular proteins of interest. Therefore, these methods must be able to differentiate many antibodies at once. In this chapter, we describe a simple and rapid approach to labeling antibodies with distinct epitope tags in a site-specific manner. This allows multiple antibodies, even from the same host species, to be uniquely identified and detected and offers a simple approach for spatial proteomic applications.

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

Membrane compartmentalisation of the ubiquitin system

We now have a comprehensive inventory of ubiquitin system components. Understanding of any system also needs an appreciation of how components are organised together. Quantitative proteomics has provided us with a census of their relative populations in several model cell types. Here, by examining large scale unbiased data sets, we seek to identify and map those components, which principally reside on the major organelles of the endomembrane system. We present the consensus distribution of > 50 ubiquitin modifying enzymes, E2s, E3s and DUBs, that possess transmembrane domains. This analysis reveals that the ER and endosomal compartments have a diverse cast of resident E3s, whilst the Golgi and mitochondria operate with a more restricted palette. We describe key functions of ubiquitylation that are specific to each compartment and relate this to their signature complement of ubiquitin modifying components.

Spatial Proteomics Reveals Differences in the Cellular Architecture of Antibody-Producing CHO and Plasma Cell-Derived Cells

Most of the recombinant biotherapeutics employed today to combat severe illnesses, for example, various types of cancer or autoimmune diseases, are produced by Chinese hamster ovary (CHO) cells. To meet the growing demand of these pharmaceuticals, CHO cells are under constant development in order to enhance their stability and productivity. The last decades saw a shift from empirical cell line optimization toward rational cell engineering using a growing number of large omics datasets to alter cell physiology on various levels. Especially proteomics workflows reached new levels in proteome coverage and data quality because of advances in high-resolution mass spectrometry instrumentation. One type of workflow concentrates on spatial proteomics by usage of subcellular fractionation of organelles with subsequent shotgun mass spectrometry proteomics and machine learning algorithms to determine the subcellular localization of large portions of the cellular proteome at a certain time point. Here, we present the first subcellular spatial proteome of a CHO-K1 cell line producing high titers of recombinant antibody in comparison to the spatial proteome of an antibody-producing plasma cell-derived myeloma cell line. Both cell lines show colocalization of immunoglobulin G chains with chaperones and proteins associated in protein glycosylation within the endoplasmic reticulum compartment. However, we report differences in the localization of proteins associated to vesicle-mediated transport, transcription, and translation, which may affect antibody production in both cell lines. Furthermore, pairing subcellular localization data with protein expression data revealed elevated protein masses for organelles in the secretory pathway in plasma cell-derived MPC-11 (Merwin plasma cell tumor-11) cells. Our study highlights the potential of subcellular spatial proteomics combined with protein expression as potent workflow to identify characteristics of highly efficient recombinant protein-expressing cell lines. Data are available via ProteomeXchange with identifier PXD029115.

Centrosome heterogeneity in stem cells regulates cell diversity

Stem cells are at the source of creating cellular diversity. Multiple mechanisms, including basic cell biological processes, regulate their fate. The centrosome is at the core of many stem cell functions and recent work highlights the association of distinct proteins at the centrosome in stem cell differentiation. As showcased by a novel centrosome protein regulating neural stem cell differentiation, it is timely to review the heterogeneity of the centrosome at protein and RNA levels and how this impacts their function in stem and progenitor cells. Together with evidence for heterogeneity of other organelles so far considered as similar between cells, we call for exploring the cell type-specific composition of organelles as a way to expand protein function in development with relevance to regenerative medicine.

Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.

Systems Biology of Virus-Host Protein Interactions: From Hypothesis Generation to Mechanisms of Replication and Pathogenesis

As obligate intracellular parasites, all viruses must co-opt cellular machinery to facilitate their own replication. Viruses often co-opt these cellular pathways and processes through physical interactions between viral and host proteins. In addition to facilitating fundamental aspects of virus replication cycles, these virus-host protein interactions can also disrupt physiological functions of host proteins, causing disease that can be advantageous to the virus or simply a coincidence. Consequently, unraveling virus-host protein interactions can serve as a window into molecular mechanisms of virus replication and pathogenesis. Identifying virus-host protein interactions using unbiased systems biology approaches provides an avenue for hypothesis generation. This review highlights common systems biology approaches for identification of virus-host protein interactions and the mechanistic insights revealed by these methods. We also review conceptual innovations using comparative and integrative systems biology that can leverage global virus-host protein interaction data sets to more rapidly move from hypothesis generation to mechanism. Expected final online publication date for the Annual Review of Virology, Volume 9 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A Method to Estimate the Distribution of Proteins across Multiple Compartments Using Data from Quantitative Proteomics Subcellular Fractionation Experiments

Knowledge of cellular location is key to understanding the biological function of proteins. One commonly used large-scale method to assign cellular locations is subcellular fractionation, followed by quantitative mass spectrometry to identify proteins and estimate their relative distribution among centrifugation fractions. In most of such subcellular proteomics studies, each protein is assigned to a single cellular location by comparing its distribution to those of a set of single-compartment reference proteins. However, in many cases, proteins reside in multiple compartments. To accurately determine the localization of such proteins, we previously introduced constrained proportional assignment (CPA), a method that assigns each protein a fractional residence over all reference compartments (Jadot Mol. Cell Proteomics 2017, 16(2), 194-212. 10.1074/mcp.M116.064527). In this Article, we describe the principles underlying CPA, as well as data transformations to improve accuracy of assignment of proteins and protein isoforms, and a suite of R-based programs to implement CPA and related procedures for analysis of subcellular proteomics data. We include a demonstration data set that used isobaric-labeling mass spectrometry to analyze rat liver fractions. In addition, we describe how these programs can be readily modified by users to accommodate a wide variety of experimental designs and methods for protein quantitation.

A lysosomal biogenesis map reveals the cargo spectrum of yeast vacuolar protein targeting pathways

The lysosome is the major catabolic organelle in the cell that has been established as a key metabolic signaling center. Mutations in many lysosomal proteins have catastrophic effects and cause neurodegeneration, cancer, and age-related diseases. The vacuole is the lysosomal analog of Saccharomyces cerevisiae that harbors many evolutionary conserved proteins. Proteins reach vacuoles via the Vps10-dependent endosomal vacuolar protein sorting pathway, via the alkaline phosphatase (ALP or AP-3) pathway, and via the cytosol-to-vacuole transport (CVT) pathway. A systematic understanding of the cargo spectrum of each pathway is completely lacking. Here, we use quantitative proteomics of purified vacuoles to generate the yeast lysosomal biogenesis map. This dataset harbors information on the cargo-receptor relationship of almost all vacuolar proteins. We map binding motifs of Vps10 and the AP-3 complex and identify a novel cargo of the CVT pathway under nutrient-rich conditions. Our data show how organelle purification and quantitative proteomics can uncover fundamental insights into organelle biogenesis.

BioID organelle mapping: you are the company you keep

In a recent study, Go, Knight et al. combined a panel of protein markers with BioID proximity-dependent labeling to profile the composition of 20 distinct subcellular compartments. Comparison with similar global datasets acquired using imaging or fractionation-based approaches confirmed the consistency of the results while highlighting unique advantages.

Methodologies for Measuring Protein Trafficking across Cellular Membranes

Nearly all proteins are synthesized in the cytosol. The majority of this proteome must be trafficked elsewhere, such as to membranes, to subcellular compartments, or outside of the cell. Proper trafficking of nascent protein is necessary for protein folding, maturation, quality control and cellular and organismal health. To better understand cellular biology, molecular and chemical technologies to properly characterize protein trafficking (and mistrafficking) have been developed and applied. Herein, we take a biochemical perspective to review technologies that enable spatial and temporal measurement of protein distribution, focusing on both the most widely adopted methodologies and exciting emerging approaches.

Proximity-dependent biotinylation approaches to study apicomplexan biology

In the last 10 years, proximity-dependent biotinylation (PDB) techniques greatly expanded the ability to study protein environments in the living cell that range from specific protein complexes to entire compartments. This is achieved by using enzymes such as BirA* and APEX that are fused to proteins of interest and biotinylate proteins in their proximity. PDB techniques are now also increasingly used in apicomplexan parasites. In this review, we first give an overview of the main PDB approaches and how they compare with other techniques that address similar questions. PDB is particularly valuable to detect weak or transient protein associations under physiological conditions and to study cellular structures that are difficult to purify or have a poorly understood protein composition. We also highlight new developments such as novel smaller or faster-acting enzyme variants and conditional PDB approaches, providing improvements in both temporal and spatial resolution which may offer broader application possibilities useful in apicomplexan research. In the second part, we review work using PDB techniques in apicomplexan parasites and how this expanded our knowledge about these medically important parasites.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

Microbial pathogens have evolved numerous mechanisms to hijack host's systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one's analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.

Multiplexed complexome profiling using tandem mass tags

Complexome profiling is a rapidly spreading, powerful technique to gain insight into the nature of protein complexes. It identifies and quantifies protein complexes separated into multiple fractions of increasing molecular mass using mass spectrometry-based, label-free bottom-up proteomics. Complexome profiling enables a sophisticated and thorough characterization of the composition, molecular mass, assembly, and interactions of protein complexes. However, in practice, its application is limited by the large number of samples it generates and the related time of mass spectrometry analyses. Here, we report an improved process workflow that implements tandem mass tags for multiplexing complexome profiling. This workflow substantially reduces the number of samples and measuring time without compromising protein identification or quantification reliability. In profiles from mitochondrial fractions of cells recovering from chloramphenicol treatment, tandem mass tags-multiplexed complexome profiling exhibited migration patterns of mature ATP synthase (complex V) and assembly intermediates that were consistent in composition and abundance with profiles obtained by the label-free approach. Reporter ion quantifications of proteins and complexes unaffected by the chloramphenicol treatment presented less variation in comparison to the label-free method. Incorporation of tandem mass tags enabled an efficient and robust complexome profiling analysis and may foster broader application for protein complex profiling in biomedical research and diagnostics.

Subcellular proteomics

The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.

Beyond the Extracellular Vesicles: Technical Hurdles, Achieved Goals and Current Challenges When Working on Adipose Cells

Adipose tissue and its crosstalk with other organs plays an essential role in the metabolic homeostasis of the entire body. Alteration of this communication (i.e., due to obesity) is related to the development of several comorbidities including type 2 diabetes, cardiovascular diseases, or cancer. Within the adipose depot, adipocytes are the main cell type and thus the main source of secreted molecules, which exert modulating effects not only at a local but also at a systemic level. Extracellular vesicles (EVs) have recently emerged as important mediators in cell–cell communication and account for part of the cellular secretome. In recent years, there has been a growing body of research on adipocyte-derived extracellular vesicles (Ad-EVs). However, there is still a lack of standardized methodological approaches, especially regarding primary adipocytes. In this review, we will provide an outline of crucial aspects when working on adipose-derived material, with a special focus on primary adipocytes. In parallel, we will point out current methodological challenges in the EV field and how they impact the transcriptomic, proteomic and functional evaluations of Ad-EVs.

Unbiased proteomic profiling of host cell extracellular vesicle composition and dynamics upon HIV-1 infection

Cells release diverse types of extracellular vesicles (EVs), which transfer complex signals to surrounding cells. Specific markers to distinguish different EVs (e.g. exosomes, ectosomes, enveloped viruses like HIV) are still lacking. We have developed a proteomic profiling approach for characterizing EV subtype composition and applied it to human Jurkat T cells. We generated an interactive database to define groups of proteins with similar profiles, suggesting release in similar EVs. Biochemical validation confirmed the presence of preferred partners of commonly used exosome markers in EVs: CD81/ADAM10/ITGB1, and CD63/syntenin. We then compared EVs from control and HIV-1-infected cells. HIV infection altered EV profiles of several cellular proteins, including MOV10 and SPN, which became incorporated into HIV virions, and SERINC3, which was re-routed to non-viral EVs in a Nef-dependent manner. Furthermore, we found that SERINC3 controls the surface composition of EVs. Our workflow provides an unbiased approach for identifying candidate markers and potential regulators of EV subtypes. It can be widely applied to in vitro experimental systems for investigating physiological or pathological modifications of EV release.

Crosslinking mass spectrometry: A link between structural biology and systems biology

Protein structure underpins functional roles in all biological processes; therefore, improved understanding of protein structures is of fundamental importance in nearly all biological and biomedical research areas. Traditional techniques such as X-ray crystallography and more recently, cryo-EM, can reveal structural features on isolated proteins/protein complexes at atomic resolution level and have become indispensable tools for structural biology. Crosslinking mass spectrometry (XL-MS), on the other hand, is an emerging technique capable of capturing transient and dynamic information on protein interactions and assemblies in their native environment. The combination of XL-MS with traditional techniques holds potential for bridging the gap between structural biology and systems biology approaches. Such a combination will enable visualization of protein structures and interactions within the crowded macromolecular environment in living systems that can dramatically increase understanding of biological functions. In this review, we first discuss general strategies of XL-MS and then survey recent examples to show how qualitative and quantitative XL-MS studies can be integrated with available protein structural data to better understand biological function at systems level.

Protein Subcellular Localization Prediction

The elucidation of the subcellular localization of proteins is very important in order to deeply understand their functions. In fact, proteins activities are strictly correlated to the cellular compartment and microenvironment in which they are present.In recent years, several effective and reliable proteomics techniques and computational methods have been developed and implemented in order to identify the proteins subcellular localization. This process is often time-consuming and expensive, but the recent technological and bioinformatics progress allowed the development of more accurate and simple workflows to determine the localization, interactions, and functions of proteins.In the following chapter, a brief introduction on the importance of knowing subcellular localization of proteins will be presented. Then, sample preparation protocols, proteomic methods, data analysis strategies, and software for the prediction of proteins localization will be presented and discussed. Finally, the more recent and advanced spatial proteomics techniques will be shown.

Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). Thus, it is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released. Even in this era of proteomic atlas at the single-cell level, researchers continue to develop new algorithms, aiming at accessing the impact of disease-causing mutations/cell type-specific alternative splicing, for example. In this article, we overview the entire field and discuss its future direction.

Proteomics of host-bacterial interactions: new insights from dual perspectives

Mass-spectrometry (MS)-based proteomics is a powerful and robust platform for studying the interactions between biological systems during health and disease. Bacterial infections represent a significant threat to global health and drive the pursuit of novel therapeutic strategies to combat emerging and resistant pathogens. During infection, the interplay between a host and pathogen determines the ability of the microbe to survive in a hostile environment and promotes an immune response by the host as a protective measure. It is the protein-level changes from either biological system that define the outcome of infection, and MS-based proteomics provides a rapid and effective platform to identify such changes. In particular, proteomics detects alterations in protein abundance, quantifies protein secretion and (or) release, measures an array of post-translational modifications that influence signaling cascades, and profiles protein-protein interactions through protein complex and (or) network formation. Such information provides new insight into the role of known and novel bacterial effectors, as well as the outcome of host cell activation. In this Review, we highlight the diverse applications of MS-based proteomics in profiling the relationship between bacterial pathogens and the host. Our work identifies a plethora of strategies for exploring mechanisms of infection from dual perspectives (i.e., host and pathogen), and we suggest opportunities to extrapolate the current knowledgebase to other biological systems for applications in therapeutic discovery.

Small Molecule Enhancers of Endosome-to-Cytosol Import Augment Anti-tumor Immunity

Cross-presentation of antigens by dendritic cells (DCs) is critical for initiation of anti-tumor immune responses. Yet, key steps involved in trafficking of antigens taken up by DCs remain incompletely understood. Here, we screen 700 US Food and Drug Administration (FDA)-approved drugs and identify 37 enhancers of antigen import from endolysosomes into the cytosol. To reveal their mechanism of action, we generate proteomic organellar maps of control and drug-treated DCs (focusing on two compounds, prazosin and tamoxifen). By combining organellar mapping, quantitative proteomics, and microscopy, we conclude that import enhancers undergo lysosomal trapping leading to membrane permeation and antigen release. Enhancing antigen import facilitates cross-presentation of soluble and cell-associated antigens. Systemic administration of prazosin leads to reduced growth of MC38 tumors and to a synergistic effect with checkpoint immunotherapy in a melanoma model. Thus, inefficient antigen import into the cytosol limits antigen cross-presentation, restraining the potency of anti-tumor immune responses and efficacy of checkpoint blockers.

Grease on-Perspectives in lipid droplet biology

Lipid droplets (LDs) are now recognized as omnipresent and dynamic subcellular organelles of amazing morphological and functional diversity. Beyond the obvious benefit of having molecules full of chemical energy stored in a dedicated structural entity, LDs may also be viewed as a safe harbor for potentially damaging metabolites. This protective function might in many cases even supersede the relevance of lipid storage for eventual energy gain and membrane biogenesis. Furthermore, the LD surface constitutes a unique membrane environment, creating a platform for hosting specific proteins and thus enabling their interactions. These metabolic hotspots would contribute decisively to compartmentalized metabolism in the cytosol. LDs are also communicating extensively with other subcellular organelles in directing and regulating lipid metabolism. Deciphering the relevance of LD storage and regulation at the organismic level will be essential for the understanding of widespread and serious metabolic complications in humans. Increasing attention is also devoted to pathogens appropriating LDs for their own benefit. LD biology is still considered an emerging research area in rapid and vibrant development, attracting scientists from all disciplines of the life sciences and beyond, which is mirrored by the accompanying review collection. Here, we present our personal views on areas we believe are especially exciting and hold great potential for future developments. Particularly, we address issues relating to LD biogenesis and heterogeneity, required technological advances, and the complexity of human physiology.