Multidimensional proteomics for cell biology

Poly-pharmacology of existing drugs: How to crack the code?

Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner.

Recent Contributions of Proteomics to Our Understanding of Reversible Nε-Lysine Acylation in Bacteria

Post-translational modifications (PTMs) have been extensively studied in both eukaryotes and prokaryotes. Lysine acetylation, originally thought to be a rare occurrence in bacteria, is now recognized as a prevalent and important PTM in more than 50 species. This expansion in interest in bacterial PTMs became possible with the advancement of mass spectrometry technology and improved reagents such as acyl-modification specific antibodies. In this Review, we discuss how mass spectrometry-based proteomic studies of lysine acetylation and other acyl modifications have contributed to our understanding of bacterial physiology, focusing on recently published studies from 2018 to 2023. We begin with a discussion of approaches used to study bacterial PTMs. Next, we discuss newly characterized acylomes, including acetylomes, succinylomes, and malonylomes, in different bacterial species. In addition, we examine proteomic contributions to our understanding of bacterial virulence and biofilm formation. Finally, we discuss the contributions of mass spectrometry to our understanding of the mechanisms of acetylation, both enzymatic and nonenzymatic. We end with a discussion of the current state of the field and possible future research avenues to explore.

Canine Mammary Tumors: Classification, Biomarkers, Traditional and Personalized Therapies

In recent years, many studies have focused their attention on the dog as a proper animal model for human cancer. In dogs, mammary tumors develop spontaneously, involving a complex interplay between tumor cells and the immune system and revealing several molecular and clinical similarities to human breast cancer. In this review, we summarized the major features of canine mammary tumor, risk factors, and the most important biomarkers used for diagnosis and treatment. Traditional therapy of mammary tumors in dogs includes surgery, which is the first choice, followed by chemotherapy, radiotherapy, or hormonal therapy. However, these therapeutic strategies may not always be sufficient on their own; advancements in understanding cancer mechanisms and the development of innovative treatments offer hope for improved outcomes for oncologic patients. There is still a growing interest in the use of personalized medicine, which should play an irreplaceable role in the research not only in human cancer therapy, but also in veterinary oncology. Moreover, immunotherapy may represent a novel and promising therapeutic option in canine mammary cancers. The study of novel therapeutic approaches is essential for future research in both human and veterinary oncology.

How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.

Modern Organometallic C-H Functionalizations with Earth-Abundant Iron Catalysts: An Update

Iron-catalyzed C-H activation has recently emerged as an increasingly powerful synthetic method for the step- and atom- economical direct C-H functionalizations of otherwise inert C-H bonds. Iron's low-cost and toxicity along with its catalytic versatility have encouraged the scientific community to elect this metal for the development of new C-H activation methodologies. Within this review, we aim to present a collection of the most recent examples of iron-catalyzed C-H functionalizations with a particular emphasis on modern synthetic strategies and mechanistic aspects.

Leveraging transformers-based language models in proteome bioinformatics

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.

Physiological and omics-based insights for underpinning the molecular regulation of secondary metabolite production in medicinal plants: UV stress resilience

Despite complex phytoconstituents, the commercial potential of medicinal plants under ultraviolet (UV) stress environment hasn't been fully comprehended. Due to sessile nature, these plants are constantly exposed to damaging radiation, which disturbs their natural physiological and biochemical processes. To combat with UV stress, plants synthesized several small organic molecules (natural products of low molecular mass like alkaloids, terpenoids, flavonoids and phenolics, etc.) known as plant secondary metabolites (PSMs) that come into play to counteract the adverse effect of stress. Plants adapted a stress response by organizing the expression of several genes, enzymes, transcription factors, and proteins involved in the synthesis of chemical substances and by making the signaling cascade (a series of chemical reactions induced by a stimulus within a biological cell) flexible to boost the defensive response. To neutralize UV exposure, secondary metabolites and their signaling network regulate cellular processes at the molecular level. Conventional breeding methods are time-consuming and difficult to reveal the molecular pattern of the stress tolerance medicinal plants. Acquiring in-depth knowledge of the molecular drivers behind the defensive mechanism of medicinal plants against UV radiation would yield advantages (economical and biological) that will bring prosperity to the burgeoning world's population. Thus, this review article emphasized the comprehensive information and clues to identify several potential genes, transcription factors (TFs), proteins, biosynthetic pathways, and biological networks which are involved in resilience mechanism under UV stress in medicinal plants of high-altitudes.

Phospho-Analyst: An Interactive, Easy-to-Use Web Platform To Analyze Quantitative Phosphoproteomics Data

Phosphoproteomics is nowadays the method of choice to comprehensively identify and quantify thousands of phosphorylated peptides and their associated proteins with the goal of interrogating changes in signal transduction pathways and other cellular processes. One of the most popular software suites to analyze phosphoproteomic data sets is MaxQuant, which converts mass spectrometric raw data into quantitative information on phosphopeptides and proteins. However, despite the increased utilization of phosphoproteomics in biomedical research, simple and user-friendly tools supporting downstream statistical analysis and interpretation of these highly complex outputs are still lacking. We have therefore developed Phospho-Analyst, which─similar to its sibling LFQ-Analyst─is an easy-to-use, interactive web application specifically designed to reproducibly perform differential expression analyses with "one click" and to visualize phosphoproteomic results in a meaningful and practical manner. Furthermore, if quantitative total proteomic information is available for the same samples, Phospho-Analyst automatically normalizes all phosphoproteomic results to underlying protein abundance levels, thereby ensuring that only genuine changes in phosphorylation events are considered. As such, Phospho-Analyst can not only be used by experienced proteomic veterans but also by researchers without any prior knowledge in (phospho)proteomics, statistics, or bioinformatics. Phospho-Analyst, including a detailed manual, is freely available at https://analyst-suites.org/apps/phospho-analyst/.

A feature extraction free approach for protein interactome inference from co-elution data

Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

Combining offline high performance liquid chromatography fractionation of peptides and intact proteins to enhance proteome coverage in bottom-up proteomics

Offline peptide separation (PS) using high-performance liquid chromatography (HPLC) is currently used to enhance liquid chromatography-tandem mass spectrometry (LC-MS/MS) detection of proteins. In search of more effective methods for enhancing MS proteome coverage, we developed a robust method for intact protein separation (IPS), an alternative first-dimension separation technique, and explored additional benefits that it offers. Comparing IPS to the traditional PS method, we found that both enhance detection of unique protein IDs to a similar magnitude, though in diverse ways. IPS was especially effective in serum, which has a small number of extremely high abundance proteins. PS was more effective in tissues with fewer dominating high-abundance proteins and was more effective in enhancing detection of post-translational modifications (PTMs). Combining the IPS and PS methods together (IPS+PS) was especially beneficial, enhancing proteome detection more than either method could independently. The comparison of IPS+PS versus six PS fractionation pools increased total number of proteins IDs by nearly double, while also significantly increasing number of unique peptides detected per protein, percent peptide sequence coverage of each protein, and detection of PTMs. This IPS+PS combined method requires fewer LC-MS/MS runs than current PS methods would need to obtain similar improvements in proteome detection, and it is robust, time- and cost-effective, and generally applicable to various tissue and sample types.

An insight into omics analysis and metabolic pathway engineering of lignin-degrading enzymes for enhanced lignin valorization

Lignin, a highly heterogeneous polymer of lignocellulosic biomass, is intricately associated with cellulose and hemicellulose, responsible for its strength and rigidity. Lignin decomposition is carried out through certain enzymes derived from microorganisms to promote the hydrolysis of lignin. Analyzing multi-omics data helps to emphasize the probable value of fungal-produced enzymes to degrade the lignocellulosic material, which provides them an advantage in their ecological niches. This review focuses on lignin biodegrading microorganisms and associated ligninolytic enzymes, including lignin peroxidase, manganese peroxidase, versatile peroxidase, laccase, and dye-decolorizing peroxidase. Further, enzymatic catalysis, lignin biodegradation mechanisms, vital factors responsible for lignin modification and degradation, and the design and selection of practical metabolic pathways are also discussed. Highlights were made on metabolic pathway engineering, different aspects of omics analyses, and its scope and applications to ligninase enzymes. Finally, the advantages and essential steps of successfully applying metabolic engineering and its path forward have been addressed.

Multiplex Protein Imaging through PACIFIC: Photoactive Immunofluorescence with Iterative Cleavage

Multiplex protein imaging technologies enable deep phenotyping and provide rich spatial information about biological samples. Existing methods have shown great success but also harbored trade-offs between various pros and cons, underscoring the persisting necessity to expand the imaging toolkits. Here we present PACIFIC: photoactive immunofluorescence with iterative cleavage, a new modality of multiplex protein imaging methods. PACIFIC achieves iterative multiplexing by implementing photocleavable fluorophores for antibody labeling with one-step spin-column purification. PACIFIC requires no specialized instrument, no DNA encoding, or chemical treatments. We demonstrate that PACIFIC can resolve cellular heterogeneity in both formalin-fixed paraffin-embedded (FFPE) samples and fixed cells. To further highlight how PACIFIC assists discovery, we integrate PACIFIC with live-cell tracking and identify phosphor-p70S6K as a critical driver that governs U87 cell mobility. Considering the cost, flexibility, and compatibility, we foresee that PACIFIC can confer deep phenotyping capabilities to anyone with access to traditional immunofluorescence platforms.

Age-Related Changes in Post-Translational Modifications of Proteins from Whole Male and Female Skeletal Elements

One of the key questions in forensic cases relates to some form of age inference, whether this is how old a crime scene is, when in time a particular crime was committed, or how old the victim was at the time of the crime. These age-related estimations are currently achieved through morphological methods with varying degrees of accuracy. As a result, biomolecular approaches are considered of great interest, with the relative abundances of several protein markers already recognized for their potential forensic significance; however, one of the greatest advantages of proteomic investigations over genomics ones is the wide range of post-translational modifications (PTMs) that make for a complex but highly dynamic resource of information. Here, we explore the abundance of several PTMs including the glycosylation, deamidation, and oxidation of several key proteins (collagen, fetuin A, biglycan, serum albumin, fibronectin and osteopontin) as being of potential value to the development of an age estimation tool worthy of further evaluation in forensic contexts. We find that glycosylations lowered into adulthood but deamidation and oxidation increased in the same age range.

DNA-framework-based multidimensional molecular classifiers for cancer diagnosis

A molecular classification of diseases that accurately reflects clinical behaviour lays the foundation of precision medicine. The development of in silico classifiers coupled with molecular implementation based on DNA reactions marks a key advance in more powerful molecular classification, but it nevertheless remains a challenge to process multiple molecular datatypes. Here we introduce a DNA-encoded molecular classifier that can physically implement the computational classification of multidimensional molecular clinical data. To produce unified electrochemical sensing signals across heterogeneous molecular binding events, we exploit DNA-framework-based programmable atom-like nanoparticles with n valence to develop valence-encoded signal reporters that enable linearity in translating virtually any biomolecular binding events to signal gains. Multidimensional molecular information in computational classification is thus precisely assigned weights for bioanalysis. We demonstrate the implementation of a molecular classifier based on programmable atom-like nanoparticles to perform biomarker panel screening and analyse a panel of six biomarkers across three-dimensional datatypes for a near-deterministic molecular taxonomy of prostate cancer patients.

Functional and Clinical Proteomic Exploration of Pancreatic Cancer

Pancreatic cancer, most cases being pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal cancers with a median survival time of less than 6 months. Therapeutic options are very limited for PDAC patients, and surgery is still the most effective treatment, making improvements in early diagnosis critical. One typical characteristic of PDAC is the desmoplastic reaction of its stroma microenvironment, which actively interacts with cancer cells to orchestrate key components in tumorigenesis, metastasis, and chemoresistance. Global exploration of cancer-stroma crosstalk is essential to decipher PDAC biology and design intervention strategies. Over the past decade, the dramatic improvement of proteomics technologies has enabled profiling of proteins, post-translational modifications (PTMs), and their protein complexes at unprecedented sensitivity and dimensionality. Here, starting with our current understanding of PDAC characteristics, including precursor lesions, progression models, tumor microenvironment, and therapeutic advancements, we describe how proteomics contributes to the functional and clinical exploration of PDAC, providing insights into PDAC carcinogenesis, progression, and chemoresistance. We summarize recent achievements enabled by proteomics to systematically investigate PTMs-mediated intracellular signaling in PDAC, cancer-stroma interactions, and potential therapeutic targets revealed by these functional studies. We also highlight proteomic profiling of clinical tissue and plasma samples to discover and verify useful biomarkers that can aid early detection and molecular classification of patients. In addition, we introduce spatial proteomic technology and its applications in PDAC for deconvolving tumor heterogeneity. Finally, we discuss future prospects of applying new proteomic technologies in comprehensively understanding PDAC heterogeneity and intercellular signaling networks. Importantly, we expect advances in clinical functional proteomics for exploring mechanisms of cancer biology directly by high-sensitivity functional proteomic approaches starting from clinical samples.

Functional proteomics based on protein microarray technology for biomedical research

This chapter traces a route through Proteomics from its origins to the present day. The different proteomics applications are discussed with a focus on microarray technology. Analytical microarrays, functional microarrays and reverse phase microarrays and their different applications are discussed. Several studies are mentioned where the great versatility of this approach is shown. Finally, the advantages and future challenges of microarray technology are outlined.

Technological development of multidimensional liquid chromatography-mass spectrometry in proteome research

Liquid chromatography-mass spectrometry (LC-MS) is the method of choice for high-throughput proteomic research. Limited by the peak capacity, the separation performance of conventional single-dimensional LC hampers the development of proteomics. Combining different separation modes orthogonally, multidimensional liquid chromatography (MDLC) with high peak capacity was developed to address this challenge. MDLC has evolved rapidly since its establishment, and the progress of proteomics has been greatly facilitated by the advent of novel MDLC-MS-based methods. In this paper, we will review the advances of MDLC-MS-based methodologies and technologies in proteomics studies, from different perspectives including novel application scenarios and proteomic targets, automation, miniaturization, and the improvement of the classic methods in recent years. In addition, attempts regarding new MDLC-MS models are also mentioned together with the outlook of MDLC-MS-based proteomics methods.

Characteristics and expression profiles of MHC class Ⅰ molecules in Carassius auratus

Major histocompatibility complex class Ⅰ (MHC Ⅰ) molecules play a vital role in adaptive immune systems in vertebrates by presenting antigens to effector T cells. Understanding the expression profiling of MHC Ⅰ molecules in fish is essential for improving our knowledge of the relationship between microbial infection and adaptive immunity. In this study, we conducted a comprehensive analysis of MHC Ⅰ gene characteristics in Carassius auratus, an important freshwater aquaculture fish in China that is susceptible to Cyprinid herpesvirus 2 (CyHV-2) infection. We identified approximately 20 MHC Ⅰ genes discussed, including U, Z, and L lineage genes. However, only U and Z lineage proteins were identified in the kidney of Carassius auratus using high pH reversed-phase chromatography and mass spectrometry. The L lineage proteins were either not expressed or present at an extremely low level in the kidneys of Carassius auratus. We also used targeted proteomics to analyze changes in protein MHC Ⅰ molecules abundance in healthy and CyHV-2-infected Carassius auratus. We observed that five MHC Ⅰ molecules were upregulated, and Caau-UFA was downregulated in the diseased group. This study is the first to reveal the expression of MHC Ⅰ molecules at a large scale in Cyprinids, which enhances our understanding of fish adaptive immune systems.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Chemical isotope labeling for quantitative proteomics

Advancements in liquid chromatography and mass spectrometry over the last decades have led to a significant development in mass spectrometry-based proteome quantification approaches. An increasingly attractive strategy is multiplex isotope labeling, which significantly improves the accuracy, precision and throughput of quantitative proteomics in the data-dependent acquisition mode. Isotope labeling-based approaches can be classified into MS1-based and MS2-based quantification. In this review, we give an overview of approaches based on chemical isotope labeling and discuss their principles, benefits, and limitations with the goal to give insights into fundamental questions and provide a useful reference for choosing a method for quantitative proteomics. As a perspective, we discuss the current possibilities and limitations of multiplex, isotope labeling approaches for the data-independent acquisition mode, which is increasing in popularity.

Plant Hormonomics: A Key Tool for Deep Physiological Phenotyping to Improve Crop Productivity

Agriculture is particularly vulnerable to climate change. To cope with the risks posed by climate-related stressors to agricultural production, global population growth, and changes in food preferences, it is imperative to develop new climate-smart crop varieties with increased yield and environmental resilience. Molecular genetics and genomic analyses have revealed that allelic variations in genes involved in phytohormone-mediated growth regulation have greatly improved productivity in major crops. Plant science has remarkably advanced our understanding of the molecular basis of various phytohormone-mediated events in plant life. These findings provide essential information for improving the productivity of crops growing in changing climates. In this review, we highlight the recent advances in plant hormonomics (multiple phytohormone profiling) and discuss its application to crop improvement. We present plant hormonomics as a key tool for deep physiological phenotyping, focusing on representative plant growth regulators associated with the improvement of crop productivity. Specifically, we review advanced methodologies in plant hormonomics, highlighting mass spectrometry- and nanosensor-based plant hormone profiling techniques. We also discuss the applications of plant hormonomics in crop improvement through breeding and agricultural management practices.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Multi-Dimensional Structure and Dynamics Landscape of Proteins in Mammalian Cells Revealed by In-Cell NMR

Governing function, half-life and subcellular localization, the 3D structure and dynamics of proteins are in nature constantly changing in a tightly regulated manner to fulfill the physiological and adaptive requirements of the cells. To find evidence for this hypothesis, we applied in-cell NMR to three folded model proteins and propose that the splitting of cross peaks constitutes an atomic fingerprint of distinct structural states that arise from multiple target binding co-existing inside mammalian cells. These structural states change upon protein loss of function or subcellular localisation into distinct cell compartments. In addition to peak splitting, we observed NMR signal intensity attenuations indicative of transient interactions with other molecules and dynamics on the microsecond to millisecond time scale.

Complexity and self-organization in the evolution of cell polarization

Cellular life exhibits order and complexity, which typically increase over the course of evolution. Cell polarization is a well-studied example of an ordering process that breaks the internal symmetry of a cell by establishing a preferential axis. Like many cellular processes, polarization is driven by self-organization, meaning that the macroscopic pattern emerges as a consequence of microscopic molecular interactions at the biophysical level. However, the role of self-organization in the evolution of complex protein networks remains obscure. In this Review, we provide an overview of the evolution of polarization as a self-organizing process, focusing on the model species Saccharomyces cerevisiae and its fungal relatives. Moreover, we use this model system to discuss how self-organization might relate to evolutionary change, offering a shift in perspective on evolution at the microscopic scale.

Tear proteomic analysis of young glasses, orthokeratology, and soft contact lens wearers

Contact lens-related ocular surface complications occur more often in teenagers and young adults. The purpose of this study was to determine changes in tear proteome of young patients wearing glasses (GL), orthokeratology lenses (OK), and soft contact lenses (SCL). Twenty-two young subjects (10-26 years of age) who were established GL, OK, and SCL wearers were recruited. Proteomic data were collected using a data-independent acquisition-parallel accumulation serial fragmentation workflow. In total, 3406 protein groups were identified, the highest number of proteins identified in Schirmer strip tears to date. Eight protein groups showed higher abundance, and 11 protein groups showed lower abundance in the SCL group compared to the OK group. In addition, the abundance of 82 proteins significantly differed in children compared to young adult GL wearers, among which 67 proteins were higher, and 15 proteins were lower in children. These 82 proteins were involved in inflammation, immune, and glycoprotein metabolic biological processes. In summary, this work identified over 3000 proteins in Schirmer Strip tears. The results indicated that tear proteomes were altered by orthokeratology and soft contact wear and age, which warrants further larger-scale study on the ocular surface responses of teenagers and young adults separately to contact lens wear. SIGNIFICANCE: In this work, we examined the tear proteomes of young patients wearing glasses, orthokeratology lenses, and soft contact lenses using a data-independent acquisition-parallel accumulation serial fragmentation (diaPASEF) workflow and identified 3406 protein groups in Schirmer strip tears. Nineteen protein groups showed significant abundance changes between orthokeratology and soft contact lens wearers. Moreover, eighty-two protein groups significantly differed in abundance in children and young adult glasses wearers. As a pilot study, this work provides a deep coverage of tear proteome and suggests the need to investigate ocular responses to contact lens wear separately for children and young adults.

Genomic medicine in Africa: a need for molecular genetics and pharmacogenomics experts

The large-scale implementation of genomic medicine in Africa has not been actualized. This overview describes how routine molecular genetics and advanced protein engineering/structural biotechnology could accelerate the implementation of genomic medicine. By using data-mining and analysis approaches, we analyzed relevant information obtained from public genomic databases on pharmacogenomics biomarkers and reviewed published studies to discuss the ideas. The results showed that only 68 very important pharmacogenes currently exist, while 867 drug label annotations, 201 curated functional pathways, and 746 annotated drugs have been catalogued on the largest pharmacogenomics database (PharmGKB). Only about 5009 variants of the reported ∼25,000 have been clinically annotated. Predominantly, the genetic variants were derived from 43 genes that contribute to 2318 clinically relevant variations in 57 diseases. Majority (∼60%) of the clinically relevant genetic variations in the pharmacogenes are missense variants (1390). The enrichment analysis showed that 15 pharmacogenes are connected biologically and are involved in the metabolism of cardiovascular and cancer drugs. The review of studies showed that cardiovascular diseases are the most frequent non-communicable diseases responsible for approximately 13% of all deaths in Africa. Also, warfarin pharmacogenomics is the most studied drug on the continent, while CYP2D6, CYP2C9, DPD, and TPMT are the most investigated pharmacogenes with allele activities indicated in African and considered to be intermediate metaboliser for DPD and TPMT (8.4% and 11%). In summary, we highlighted a framework for implementing genomic medicine starting from the available resources on ground.

Proteomic Insights into Cardiac Fibrosis: From Pathophysiological Mechanisms to Therapeutic Opportunities

Cardiac fibrosis is a common pathophysiologic process in nearly all forms of heart disease which refers to excessive deposition of extracellular matrix proteins by cardiac fibroblasts. Activated fibroblasts are the central cellular effectors in cardiac fibrosis, and fibrotic remodelling can cause several cardiac dysfunctions either by reducing the ejection fraction due to a stiffened myocardial matrix, or by impairing electric conductance. Recently, there is a rising focus on the proteomic studies of cardiac fibrosis for pathogenesis elucidation and potential biomarker mining. This paper summarizes the current knowledge of molecular mechanisms underlying cardiac fibrosis, discusses the potential of imaging and circulating biomarkers available to recognize different phenotypes of this lesion, reviews the currently available and potential future therapies that allow individualized management in reversing progressive fibrosis, as well as the recent progress on proteomic studies of cardiac fibrosis. Proteomic approaches using clinical specimens and animal models can provide the ability to track pathological changes and new insights into the mechanisms underlining cardiac fibrosis. Furthermore, spatial and cell-type resolved quantitative proteomic analysis may also serve as a minimally invasive method for diagnosing cardiac fibrosis and allowing for the initiation of prophylactic treatment.

Global Landscape of Native Protein Complexes in Synechocystis sp. PCC 6803

Synechocystis sp. PCC 6803 (hereafter: Synechocystis) is a model organism for studying photosynthesis, energy metabolism, and environmental stress. Although known as the first fully sequenced phototrophic organism, Synechocystis still has almost half of its proteome without functional annotations. In this study, by using co-fractionation coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS), we define 291 multi-protein complexes, encompassing 24,092 protein-protein interactions (PPIs) among 2062 distinct gene products. This information not only reveals the roles of photosynthesis in metabolism, cell motility, DNA repair, cell division, and other physiological processes, but also shows how protein functions vary from bacteria to higher plants due to changes in interaction partners. It also allows us to uncover the functions of hypothetical proteins, such as Sll0445, Sll0446, and Sll0447 involved in photosynthesis and cell motility, and Sll1334 involved in regulation of fatty acid biogenesis. Here we present the most extensive PPI data for Synechocystis so far, which provide critical insights into fundamental molecular mechanisms in cyanobacteria.

The expression pattern of Immune checkpoints after chemo/radiotherapy in the tumor microenvironment

As a disease with the highest disease-associated burden worldwide, cancer has been the main subject of a considerable proportion of medical research in recent years, intending to find more effective therapeutic approaches with fewer side effects. Combining conventional methods with newer biologically based treatments such as immunotherapy can be a promising approach to treating different tumors. The concept of "cancer immunoediting" that occurs in the field of the tumor microenvironment (TME) is the aspect of cancer therapy that has not been at the center of attention. One group of the role players of the so-called immunoediting process are the immune checkpoint molecules that exert either co-stimulatory or co-inhibitory effects in the anti-tumor immunity of the host. It involves alterations in a wide variety of immunologic pathways. Recent studies have proven that conventional cancer therapies, such as chemotherapy, radiotherapy, or a combination of them, i.e., chemoradiotherapy, alter the "immune compartment" of the TME. The mentioned changes encompass a wide range of variations, including the changes in the density and immunologic type of the tumor-infiltrating lymphocytes (TILs) and the alterations in the expression patterns of the different immune checkpoints. These rearrangements can have either anti-tumor immunity empowering or immune attenuating sequels. Thus, recognizing the consequences of various chemo(radio)therapeutic regimens in the TME seems to be of great significance in the evolution of therapeutic approaches. Therefore, the present review intends to summarize how chemo(radio)therapy affects the TME and specifically some of the most important, well-known immune checkpoints' expressions according to the recent studies in this field.

A review of suspension trapping digestion method in bottom-up proteomics

The standard bottom-up proteomic workflow is comprised of sample preparation, data acquisition, and data analysis. While the latter two parts have made considerable advances in the last decade, sample preparation has remained an important challenge within the workflow due to the multi-step nature of complex biological samples, and still requires much development. Several sample preparation methods have been developed and used in the last two decades, including in-gel, in-solution, on-bead, filter-aided sample preparation, and suspension trapping, to improve reproducibility, efficiency, scalability, and reduce handling time of this process. One of the most recent methods developed and applied in proteomics studies in recent years is suspension trapping, which combines rapid detergent removal, reactor-type protein digestion, and peptide clean-up in a tip or spin column. Suspension trapping is a simple, rapid, and reproducible digestion method that can effectively handle proteins in low microgram or sub-microgram amounts. This review discusses the benefits of the suspension trapping digestion method in relation to its development and application in bottom-up proteomics studies. We also discuss recent applications of suspension trapping digestion to different sample types and the features of the suspension trapping digestion method compared with other sample preparation methods. This article is protected by copyright. All rights reserved.

Low Temperature Stress Tolerance: An Insight Into the Omics Approaches for Legume Crops

The change in climatic conditions is the major cause for decline in crop production worldwide. Decreasing crop productivity will further lead to increase in global hunger rate. Climate change results in environmental stress which has negative impact on plant-like deficiencies in growth, crop yield, permanent damage, or death if the plant remains in the stress conditions for prolonged period. Cold stress is one of the main abiotic stresses which have already affected the global crop production. Cold stress adversely affects the plants leading to necrosis, chlorosis, and growth retardation. Various physiological, biochemical, and molecular responses under cold stress have revealed that the cold resistance is more complex than perceived which involves multiple pathways. Like other crops, legumes are also affected by cold stress and therefore, an effective technique to mitigate cold-mediated damage is critical for long-term legume production. Earlier, crop improvement for any stress was challenging for scientific community as conventional breeding approaches like inter-specific or inter-generic hybridization had limited success in crop improvement. The availability of genome sequence, transcriptome, and proteome data provides in-depth sight into different complex mechanisms under cold stress. Identification of QTLs, genes, and proteins responsible for cold stress tolerance will help in improving or developing stress-tolerant legume crop. Cold stress can alter gene expression which further leads to increases in stress protecting metabolites to cope up the plant against the temperature fluctuations. Moreover, genetic engineering can help in development of new cold stress-tolerant varieties of legume crop. This paper provides a general insight into the "omics" approaches for cold stress in legume crops.

High-Throughput Mass Spectrometry-Based Proteomics with dia-PASEF

Ion mobility separation is becoming an integral part in mass spectrometry-based proteomics. Here we describe the use of a trapped ion mobility-quadrupole time-of-flight (TIMS-QTOF) mass spectrometer for high-throughput label-free quantification with data-independent acquisition. The parallel accumulation-serial fragmentation (PASEF) operation mode positions the mass-selecting quadrupole as a function of the TIMS separation, which allows highly efficient data-independent acquisition schemes (dia-PASEF), but also increases complexity in the method design. We provide a step-by-step protocol for instrument setup, method design, data acquisition and ion mobility-aware, library-based data analysis with Spectronaut. We highlight key acquisition parameters and illustrate their optimization for short gradients. Using the EvosepOne liquid chromatography system, we demonstrate expected results for the analysis of a human cancer cell line at a throughput of 60 samples per day, leading to the quantification of about 6000 protein groups with very high reproducibility. Importantly, the protocol can be readily adapted to other gradients and sample types such as modified peptides.

A1 Ions: Peptide-Specific and Intensity-Enhanced Fragment Ions for Accurate and Multiplexed Proteome Quantitation

Accurate proteome quantitation is of great significance to deeply understand various cellular and physiological processes. Since a1 ions, generated from dimethyl-labeled peptides, exhibited high formation efficiency (up to 99%) and enhanced intensities (2.34-fold by average) in tandem mass spectra, herein, we proposed an a1 ion-based proteome quantitation (APQ) method, which showed high quantitation accuracy (relative errors < 7%) and precision (median coefficients of variation ≤ 11%) even in a 20-fold dynamic range. Notably, due to the mass differences of a1 ions from peptides with different N-terminal amino acids, APQ demonstrated interference-free capacity by distinguishing target peptides from the coisolated ones. By designing an isobaric dimethyl labeling strategy, we achieved simultaneous proteome-wide measurements across up to eight samples. Using APQ to quantify the time-resolved proteomic profiles during a TGF-β-induced epithelial-mesenchymal transition, we found many differentially expressed proteins associated with fatty acid degradation, indicating that fatty acid metabolism reprogramming occurred during the process. The APQ method combines high quantitation accuracy with multiplexing capacity, which is suitable for deep mining and understanding of dynamic biological processes.

Proteomic Investigation of Molecular Mechanisms in Response to PEG-Induced Drought Stress in Soybean Roots

Roots are generally the critical drought sensors, but little is known about their molecular response to drought stress. We used the drought-tolerant soybean variety ‘Jiyu 47’ to investigate the differentially expressed proteins (DEPs) in soybean roots during the seedling stage based on the tandem mass tag (TMT) proteomics analysis. Various expression patterns were observed in a total of six physiological parameters. A total of 468 DEPs (144 up-regulated and 324 down-regulated) among a total of 8687 proteins were identified in response to drought stress in 24 h. The expression of DEPs was further validated based on quantitative real-time PCR of a total of five genes (i.e., GmGSH, GmGST1, GmGST2 k GmCAT, and Gm6PGD) involved in the glutathione biosynthesis. Results of enrichment analyses revealed a coordinated expression pattern of proteins involved in various cellular metabolisms responding to drought stress in soybean roots. Our results showed that drought stress caused significant alterations in the expression of proteins involved in several metabolic pathways in soybean roots, including carbohydrate metabolism, metabolism of the osmotic regulation substances, and antioxidant defense system (i.e., the glutathione metabolism). Increased production of reduced glutathione (GSH) enhanced the prevention of the damage caused by reactive oxygen species and the tolerance of the abiotic stress. The glutathione metabolism played a key role in modifying the antioxidant defense system in response to drought stress in soybean roots. Our proteomic study suggested that the soybean plants responded to drought stress by coordinating their protein expression during the vegetative stage, providing novel insights into the molecular mechanisms regulating the response to abiotic stress in plants.

Molecular and cellular mechanisms that regulate human erythropoiesis

To enable effective oxygen transport, ∼200 billion red blood cells (RBCs) need to be produced every day in the bone marrow through the fine-tuned process of erythropoiesis. Erythropoiesis is regulated at multiple levels to ensure that defective RBC maturation or overproduction can be avoided. Here, we provide an overview of different layers of this control, ranging from cytokine signaling mechanisms that enable extrinsic regulation of RBC production to intrinsic transcriptional pathways necessary for effective erythropoiesis. Recent studies have also elucidated the importance of posttranscriptional regulation and highlighted additional gatekeeping mechanisms necessary for effective erythropoiesis. We additionally discuss the insights gained by studying human genetic variation affecting erythropoiesis and highlight the discovery of BCL11A as a regulator of hemoglobin switching through genetic studies. Finally, we provide an outlook of how our ability to measure multiple facets of this process at single-cell resolution, while accounting for the impact of human variation, will continue to refine our knowledge of erythropoiesis and how this process is perturbed in disease. As we learn more about this intricate and important process, additional opportunities to modulate erythropoiesis for therapeutic purposes will undoubtedly emerge.

Spatial proteomics reveals subcellular reorganization in human keratinocytes exposed to UVA light

The effects of UV light on the skin have been extensively investigated. However, systematic information about how the exposure to ultraviolet-A (UVA) light, the least energetic but the most abundant UV radiation reaching the Earth, shapes the subcellular organization of proteins is lacking. Using subcellular fractionation, mass-spectrometry-based proteomics, machine learning algorithms, immunofluorescence, and functional assays, we mapped the subcellular reorganization of the proteome of human keratinocytes in response to UVA light. Our workflow quantified and assigned subcellular localization for over 1,600 proteins, of which about 200 were found to redistribute upon UVA exposure. Reorganization of the proteome affected modulators of signaling pathways, cellular metabolism, and DNA damage response. Strikingly, mitochondria were identified as one of the main targets of UVA-induced stress. Further investigation demonstrated that UVA induces mitochondrial fragmentation, up-regulates redox-responsive proteins, and attenuates respiratory rates. These observations emphasize the role of this radiation as a potent metabolic stressor in the skin.

Mass Spectrometry for Neurobiomarker Discovery: The Relevance of Post-Translational Modifications

Neurodegenerative diseases are incurable, heterogeneous, and age-dependent disorders that challenge modern medicine. A deeper understanding of the pathogenesis underlying neurodegenerative diseases is necessary to solve the unmet need for new diagnostic biomarkers and disease-modifying therapy and reduce these diseases’ burden. Specifically, post-translational modifications (PTMs) play a significant role in neurodegeneration. Due to its proximity to the brain parenchyma, cerebrospinal fluid (CSF) has long been used as an indirect way to measure changes in the brain. Mass spectrometry (MS) analysis in neurodegenerative diseases focusing on PTMs and in the context of biomarker discovery has improved and opened venues for analyzing more complex matrices such as brain tissue and blood. Notably, phosphorylated tau protein, truncated α-synuclein, APP and TDP-43, and many other modifications were extensively characterized by MS. Great potential is underlying specific pathological PTM-signatures for clinical application. This review focuses on PTM-modified proteins involved in neurodegenerative diseases and highlights the most important and recent breakthroughs in MS-based biomarker discovery.

The quantitative proteomic analysis of rare minnow, Gobiocypris rarus, infected with virulent and attenuated isolates of grass carp reovirus genotype Ⅱ

Grass carp reovirus genotype Ⅱ (GCRV II) causes severe hemorrhagic disease in grass carp and affects the aquaculture industry in China. GCRV Ⅱ isolates have been collected from different epidemic areas in China, and these isolates can lead to different degrees of hemorrhagic symptoms in grass carp. Rare minnow (Gobiocypris rarus) is widely used as a model fish to study the mechanism of hemorrhagic disease because of its high sensitivity to GCRV. In this study, the protein levels in the spleen of rare minnow after infection with GCRV virulent isolate JZ809 and attenuated isolate XT422 were investigated using isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomics. 109 and 50 differentially expressed proteins (DEPs) in the virulent and attenuated infection groups were obtained, respectively, among which 40 DEPs were identified in both groups. Combining protein expression profiling with gene ontology (GO) annotation, the responses of rare minnow to the two genotypes GCRV Ⅱ in terms of upregulated proteins were similar, focusing on ATP synthesis, in which ATP can serve as a "danger" signal to activate an immunoreaction in eukaryotes. Meanwhile, the virulent genotype JZ809 induced more immunoproteins and increased the levels of ubiquitin-proteasome system members to adapt to virus infection. However, together with a persistent and excessive inflammatory response and declining carbon metabolism, rare minnow presented more severe hemorrhagic disease and mortality after infection with virulent JZ809 than with attenuated XT422. The results provide a valuable information that will increase our understanding of the pathogenesis of viruses with different levels of virulence and the mechanism of interaction between the virus and host. Furthermore, the 6 proteins that were only significantly upregulated in the XT422 infection group all belonged to cluster 2, and 28 of 30 proteins that were only upregulated in JZ809 infection group were clustered into cluster 1. For the downregulated proteins, all DEPs in the XT422 infection group were clustered into cluster 4, and 25 of 39 proteins that were only significantly downregulated in the JZ809 infection group belonged to cluster 3. The results indicated that the DEPs in the attenuated XT422 infection group might be sensitive and their abundance changed more quickly when fish experienced virus infection.

The emerging role of mass spectrometry-based proteomics in drug discovery

Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.

Proximity-Dependent Biotinylation Approaches to Explore the Dynamic Compartmentalized Proteome

In recent years, proximity-dependent biotinylation approaches, including BioID, APEX, and their derivatives, have been widely used to define the compositions of organelles and other structures in cultured cells and model organisms. The associations between specific proteins and given compartments are regulated by several post-translational modifications (PTMs); however, these effects have not been systematically investigated using proximity proteomics. Here, we discuss the progress made in this field and how proximity-dependent biotinylation strategies could elucidate the contributions of PTMs, such as phosphorylation, to the compartmentalization of proteins.

Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation

Single‐cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in‐depth characterization of individual cells by mass spectrometry (MS)‐based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow‐rate chromatography, and a novel trapped ion mobility mass spectrometer, resulting in a more than 10‐fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS‐isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single‐cell proteomes to transcriptome data revealed a stable‐core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra‐high sensitivity analyses of tissue material, posttranslational modifications, and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease.

Application of Proteomics in Apical Periodontitis

Apical periodontitis is an inflammatory reaction of the periradicular tissues as a consequence of multispecies microbial communities organized as biofilms within the root canal system. Periradicular tissue changes at the molecular level initiate and orchestrate the inflammatory process and precede the presentation of clinical symptoms. Inflammatory mediators have been studied at either the proteomic, metabolomic, or transcriptomic levels. Analysis at the protein level is the most common approach used to identify and quantify analytes from diseased periradicular tissues during root canal treatment, since it is more representative of definitive and active periradicular inflammatory mediator than its transcript expression level. In disease, proteins expressed in an altered manner could be utilized as biomarkers. Biomarker proteins in periradicular tissues have been qualitatively and quantitatively assessed using antibodies (immunoassays and immunostaining) or mass spectrometry-based approaches. Herein, we aim to provide a comprehensive understanding of biomarker proteins identified in clinical studies investigating periradicular lesions and pulp tissue associated with apical periodontitis using proteomics. The high throughput mass spectrometry-based proteomics has the potential to improve the current methods of monitoring inflammation while distinguishing between progressive, stable, and healing lesions for the identification of new diagnostic and therapeutic targets. This method would provide more objective tools to (a) discover biomarkers related to biological processes for better clinical case selection, and (b) determine tissue response to novel therapeutic interventions for more predictable outcomes in endodontic treatment.

Multidimensional dynamics of the proteome in the neurodegenerative and aging mammalian brain

The amount of any given protein in the brain is determined by the rates of its synthesis and destruction, which are regulated by different cellular mechanisms. Here, we combine metabolic labeling in live mice with global proteomic profiling to simultaneously quantify both the flux and amount of proteins in mouse models of neurodegeneration. In multiple models, protein turnover increases were associated with increasing pathology. This method distinguishes changes in protein expression mediated by synthesis from those mediated by degradation. In the App^NL-F knockin mouse model of Alzheimer’s disease, increased turnover resulted from imbalances in both synthesis and degradation, converging on proteins associated with synaptic vesicle recycling (Dnm1, Cltc, Rims1) and mitochondria (Fis1, Ndufv1). In contrast to disease models, aging in wild-type mice caused a widespread decrease in protein recycling associated with a decrease in autophagic flux. Overall, this simple multidimensional approach enables a comprehensive mapping of proteome dynamics and identifies affected proteins in mouse models of disease and other live animal test settings.

•

Multidimensional proteomic screen to detect imbalances in mouse models of disease.

•

Increased proteome turnover in multiple symptomatic neurodegeneration mouse models.

•

Healthy aging is associated with a global decrease in protein turnover.

Localization of Organelle Proteins by Isotope Tagging: Current status and potential applications in drug discovery research

Spatial proteomics has provided important insights into the relationship between protein function and subcellular location. Localization of Organelle Proteins by Isotope Tagging (LOPIT) and its variants are proteome-wide techniques, not matched in scale by microscopy-based or proximity tagging-based techniques, allowing holistic mapping of protein subcellular location and re-localization events downstream of cellular perturbations. LOPIT can be a powerful and versatile tool in drug discovery for unlocking important information on disease pathophysiology, drug mechanism of action, and off-target toxicity screenings. Here, we discuss technical concepts of LOPIT with its potential applications in drug discovery and development research.

CellFIE: CRISPR- and Cell Fusion-based Two-hybrid Interaction Mapping of Endogenous Proteins

Numerous genetic methods facilitate the detection of binary protein-protein interactions (PPIs) by exogenous overexpression, which can lead to false results. Here, we describe CellFIE, a CRISPR- and cell fusion-based PPI detection method, which enables the mapping of interactions between endogenously tagged two-hybrid proteins. We demonstrate the specificity and reproducibility of CellFIE in a matrix mapping approach, validating the interactions of VCP with ASPL and UBXD1, and the self-interaction of TDP-43 under endogenous conditions. Furthermore, we show that CellFIE can be used to quantify changes of endogenous PPIs upon stress induction or drug treatment. For the first time, CellFIE facilitates systematic mapping of interactions between endogenously tagged proteins and represents a novel tool to characterize PPIs in live cells under dynamic conditions.

Pathogenesis and virulence of flavivirus infections

The Flavivirus genus consists of >70 members including several that are considered significant human pathogens. Flaviviruses display a broad spectrum of diseases that can be roughly categorised into two phenotypes - systemic disease involving haemorrhage exemplified by dengue and yellow Fever virus, and neurological complications associated with the likes of West Nile and Zika viruses. Attempts to develop vaccines have been variably successful against some. Besides, mosquito-borne flaviviruses can be vertically transmitted in the arthropods, enabling long term persistence and the possibility of re-emergence. Therefore, developing strategies to combat disease is imperative even if vaccines become available. The cellular interactions of flaviviruses with their human hosts are key to establishing the viral lifecycle on the one hand, and activation of host immunity on the other. The latter should ideally eradicate infection, but often leads to immunopathological and neurological consequences. In this review, we use Dengue and Zika viruses to discuss what we have learned about the cellular and molecular determinants of the viral lifecycle and the accompanying immunopathology, while highlighting current knowledge gaps which need to be addressed in future studies.

Human bronchial-pulmonary proteomics in coronavirus disease 2019 (COVID-19) pandemic: applications and implications

INTRODUCTION

The outbreak of the newly discovered human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has disrupted the normal life of almost every civilization worldwide. Studies have shown that the coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 can affect multiple human organs and physiological systems, but the respiratory system remains the primary location for viral infection.

AREAS COVERED

We summarize how omics technologies are used in SARS-CoV-2 research and specifically review the current knowledge of COVID-19 from the aspect of human bronchial-pulmonary proteomics. Also, knowledge gaps in COVID-19 that can be fulfilled by proteomics are discussed.

EXPERT OPINION

Overall, human bronchial-pulmonary proteomics plays an important role in revealing the dynamics, functions, tropism, and pathogenicity of SARS-CoV-2, which is crucial for COVID-19 biomarker and therapeutic target discoveries. To more fully understand the impact of COVID-19, research from various angles using multi-omics approaches should also be conducted on the lungs as well as other organs.