Mass-spectrometric exploration of proteome structure and function

Dissecting the Immune System through Gene Regulation

The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.

Integrative Proteogenomics for Differential Expression and Splicing Variation in a DM1 Mouse Model

Dysregulated mRNA splicing is involved in the pathogenesis of many diseases including cancer, neurodegenerative diseases, and muscular dystrophies such as myotonic dystrophy type 1 (DM1). Comprehensive assessment of dysregulated splicing on the transcriptome and proteome level has been methodologically challenging, and thus investigations have often been targeting only few genes. Here, we performed a large-scale coordinated transcriptomic and proteomic analysis to characterize a DM1 mouse model (HSALR) in comparison to wild type. Our integrative proteogenomics approach comprised gene- and splicing-level assessments for mRNAs and proteins. It recapitulated many known instances of aberrant mRNA splicing in DM1 and identified new ones. It enabled the design and targeting of splicing-specific peptides and confirmed the translation of known instances of aberrantly spliced disease-related genes (e.g., Atp2a1, Bin1, Ryr1), complemented by novel findings (Flnc and Ywhae). Comparative analysis of large-scale mRNA and protein expression data showed quantitative agreement of differentially expressed genes and splicing patterns between disease and wild type. We hence propose this work as a suitable blueprint for a robust and scalable integrative proteogenomic strategy geared toward advancing our understanding of splicing-based disorders. With such a strategy, splicing-based biomarker candidates emerge as an attractive and accessible option, as they can be efficiently asserted on the mRNA and protein level in coordinated fashion.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

High-throughput proteomics uncovers exercise training and type 2 diabetes-induced changes in human white adipose tissue

White adipose tissue (WAT) is important for metabolic homeostasis. We established the differential proteomic signatures of WAT in glucose-tolerant lean and obese individuals and patients with type 2 diabetes (T2D) and the response to 8 weeks of high-intensity interval training (HIIT). Using a high-throughput and reproducible mass spectrometry-based proteomics pipeline, we identified 3773 proteins and found that most regulated proteins displayed progression in markers of dysfunctional WAT from lean to obese to T2D individuals and were highly associated with clinical measures such as insulin sensitivity and HbA1c. We propose that these distinct markers could serve as potential clinical biomarkers. HIIT induced only minor changes in the WAT proteome. This included an increase in WAT ferritin levels independent of obesity and T2D, and WAT ferritin levels were strongly correlated with individual insulin sensitivity. Together, we report a proteomic signature of WAT related to obesity and T2D and highlight an unrecognized role of human WAT iron metabolism in exercise training adaptations.

OMICs approaches and technologies for understanding low-high feed efficiency traits in chicken: implication to breeding

In poultry production, there has been a trend of continuous increase in cost of feed ingredients which represents the major proportion of the production costs. Feed costs can be reduced by improving feed efficiency traits which increase the possibility of using various indigestible feed sources and decrease the environmental impact of the enhanced poultry production. Therefore, feed efficiency has been used as one of the most important economic traits of selection in the breeding program of chickens. Recently, many OMICs experimental studies have been designed to characterize biological differences between the high and low feed efficiency chicken phenotypes. Biological complexity cannot be fully captured by main individual OMICs such as genomics, transcriptomics, proteomics and metabolomics. Therefore, researchers have combined multiple assays from the same set of samples to create multi-OMICs datasets. OMICs findings are crucial in improving existing approaches to poultry breeding. The current review aimed to highlight the components of feed efficiency and general OMICs approaches and technologies. Besides, individual and multi-OMICs based understanding of chicken feed efficiency traits and the application of the acquired knowledge in the chicken breeding program were addressed.

Multitissue Circadian Proteome Atlas of WT and Per1-/-/Per2-/- Mice

The molecular basis of circadian rhythm, driven by core clock genes such as Per1/2, has been investigated on the transcriptome level, but not comprehensively on the proteome level. Here we quantified over 11,000 proteins expressed in eight types of tissues over 46 h with an interval of 2 h, using WT and Per1/Per2 double knockout mouse models. The multitissue circadian proteome landscape of WT mice shows tissue-specific patterns and reflects circadian anticipatory phenomena, which are less obvious on the transcript level. In most peripheral tissues of double knockout mice, reduced protein cyclers are identified when compared with those in WT mice. In addition, PER1/2 contributes to controlling the anticipation of the circadian rhythm, modulating tissue-specific cyclers as well as key pathways including nucleotide excision repair. Severe intertissue temporal dissonance of circadian proteome has been observed in the absence of Per1 and Per2. The γ-aminobutyric acid might modulate some of these temporally correlated cyclers in WT mice. Our study deepens our understanding of rhythmic proteins across multiple tissues and provides valuable insights into chronochemotherapy. The data are accessible at https://prot-rhythm.prottalks.com/.

Subcellular localization and relevant mechanisms of human cancer-related micropeptides

Rapid advances in high-quality sequencing and bioinformatics have invalidated the argument that noncoding RNAs (ncRNAs) are junk transcripts that do not encode proteins. Increasing evidence suggests that small open reading frames (sORFs) in ncRNAs can encode micropeptides and polypeptides within 100 amino acids in length. Several micropeptides have been characterized and proven to have various functions in human physiology and pathology, particularly in cancer. The present review mainly highlights the latest studies on ncRNA-encoded micropeptides in different cancers and categorizes them based on their subcellular localization, thereby providing a theoretical basis for micropeptide applications in the early diagnosis and prognosis of cancer and as therapeutic targets. However, considering the inherent characteristics of micropeptides and the limitations of the assay technology methods, more detailed information is warranted.

Guidelines for mouse and human DC functional assays

This article is part of the Dendritic Cell Guidelines article series, which provides a collection of state-of-the-art protocols for the preparation, phenotype analysis by flow cytometry, generation, fluorescence microscopy, and functional characterization of mouse and human dendritic cells (DC) from lymphoid organs and various non-lymphoid tissues. Recent studies have provided evidence for an increasing number of phenotypically distinct conventional DC (cDC) subsets that on one hand exhibit a certain functional plasticity, but on the other hand are characterized by their tissue- and context-dependent functional specialization. Here, we describe a selection of assays for the functional characterization of mouse and human cDC. The first two protocols illustrate analysis of cDC endocytosis and metabolism, followed by guidelines for transcriptomic and proteomic characterization of cDC populations. Then, a larger group of assays describes the characterization of cDC migration in vitro, ex vivo, and in vivo. The final guidelines measure cDC inflammasome and antigen (cross)-presentation activity. While all protocols were written by experienced scientists who routinely use them in their work, this article was also peer-reviewed by leading experts and approved by all co-authors, making it an essential resource for basic and clinical DC immunologists.

The social and structural architecture of the yeast protein interactome

Cellular functions are mediated by protein-protein interactions, and mapping the interactome provides fundamental insights into biological systems. Affinity purification coupled to mass spectrometry is an ideal tool for such mapping, but it has been difficult to identify low copy number complexes, membrane complexes and complexes that are disrupted by protein tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive high-throughput method using highly reproducible affinity enrichment coupled to mass spectrometry combined with a quantitative two-dimensional analysis strategy to comprehensively map the interactome of Saccharomyces cerevisiae. Thousand-fold reduced volumes in 96-well format enabled replicate analysis of the endogenous GFP-tagged library covering the entire expressed yeast proteome1. The 4,159 pull-downs generated a highly structured network of 3,927 proteins connected by 31,004 interactions, doubling the number of proteins and tripling the number of reliable interactions compared with existing interactome maps2. This includes very-low-abundance epigenetic complexes, organellar membrane complexes and non-taggable complexes inferred by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 16 interactors. Similar to social networks between humans, the average shortest distance between proteins is 4.2 interactions. AlphaFold-Multimer provided novel insights into the functional roles of previously uncharacterized proteins in complexes. Our web portal ( www.yeast-interactome.org ) enables extensive exploration of the interactome dataset.

Small proteins in bacteria - Big challenges in prediction and identification

Proteins with up to 100 amino acids have been largely overlooked due to the challenges associated with predicting and identifying them using traditional methods. Recent advances in bioinformatics and machine learning, DNA sequencing, RNA and Ribo-seq technologies, and mass spectrometry (MS) have greatly facilitated the detection and characterisation of these elusive proteins in recent years. This has revealed their crucial role in various cellular processes including regulation, signalling and transport, as toxins and as folding helpers for protein complexes. Consequently, the systematic identification and characterisation of these proteins in bacteria have emerged as a prominent field of interest within the microbial research community. This review provides an overview of different strategies for predicting and identifying these proteins on a large scale, leveraging the power of these advanced technologies. Furthermore, the review offers insights into the future developments that may be expected in this field.

Using proteomics for stratification and risk prediction in patients with solid tumors

Proteomics, the study of proteins and their functions, has greatly evolved due to advances in analytical chemistry and computational biology. Unlike genomics or transcriptomics, proteomics captures the dynamic and diverse nature of proteins, which play crucial roles in cellular processes. This is exemplified in cancer, where genomic and transcriptomic information often falls short in reflecting actual protein expression and interactions. Liquid chromatography-mass spectrometry (LC-MS) is pivotal in proteomic data generation, enabling high-throughput analysis of protein samples. The MS-based workflow involves protein digestion, chromatographic separation, ionization, and fragmentation, leading to peptide identification and quantification. Computational biostatistics, particularly using tools in R (R Foundation for Statistical Computing, Vienna, Austria; www.R-project.org ), aid in data analysis, revealing protein expression patterns and correlations with clinical variables. Proteomic studies can be explorative, aiming to characterize entire proteomes, or targeted, focusing on specific proteins of interest. The integration of proteomics with genomics addresses database limitations and enhances peptide identification. Case studies in intrahepatic cholangiocarcinoma, glioblastoma multiforme, and pancreatic ductal adenocarcinoma highlight proteomics' clinical applications, from subtyping cancers to identifying diagnostic markers. Moreover, proteomic data augment molecular tumor boards by providing deeper insights into pathway activities and genomic mutations, supporting personalized treatment decisions. Overall, proteomics contributes significantly to advancing our understanding of cellular biology and improving clinical care.

Integrated untargeted and targeted proteomics to unveil plasma prognostic markers for patients with acute paraquat poisoning: A pilot study

Paraquat (PQ) is a widely used but strongly toxic herbicide, which can induce multiple organ failure. The overall survival rate of the poisoned patients was only 54.4% due to lack of specific antidotes. Besides, the definite pathogenic mechanism of PQ is still not fully understood. In this pilot study, untargeted and targeted proteomics were integrated to explore the expression characteristics of plasma protein in PQ poisoned patients, and identify the differentially expressed proteins between survivors and non-survivors. A total of 494 plasma proteins were detected, and of which 47 were upregulated and 44 were downregulated in PQ poisoned patients compared to healthy controls. Among them, five differential plasma proteins (S100A9, S100A8, MB, ACTB and RAB11FIP3) were further validated by multiple reaction monitoring (MRM)-based targeted proteomic approach, and three of them (S100A9, S100A8 and ACTB) were confirmed to be correlated with PQ poisoning. Meanwhile, 84 dysregulated plasma proteins were identified in non-survivors compared with survivors. Moreover, targeted proteomic and ROC analysis suggested that ACTB had a good performance in predicting the prognosis of PQ poisoned patients. These findings highlighted the value of label-free and mass spectrometry-based proteomics in screening prognostic biomarkers of PQ poisoning and studying the mechanism of PQ toxicity.

Ropivacaine inhibits the malignant behavior of lung cancer cells by regulating retinoblastoma-binding protein 4

Background

Ropivacaine is a local anesthetic commonly used in regional nerve blocks to manage perioperative pain during lung cancer surgery. Recently, the antitumor potential of ropivacaine has received considerable attention. Our previous study showed that ropivacaine treatment inhibits the malignant behavior of lung cancer cells in vitro. However, the potential targets of ropivacaine in lung cancer cells have not yet been fully identified. This study aimed to explore the antitumor effects and mechanisms of action of ropivacaine in lung cancer.

Methods

Lung cancer A549 cells were treated with or without 1 mM ropivacaine for 48 h. Quantitative proteomics was performed to identify the differentially expressed proteins (DEPs) triggered by ropivacaine treatment. STRING and Cytoscape were used to construct protein-protein interaction (PPI) networks and analyze the most significant hub genes. Overexpression plasmids and small interfering RNA were used to modulate the expression of key DEPs in A549 and H1299 cells. MTS, transwell assays, and flow cytometry were performed to determine whether the key DEPs were closely related to the anticancer effect of ropivacaine on the malignant behavior of A549 and H1299 cells.

Results

Quantitative proteomic analysis identified 327 DEPs (185 upregulated and 142 downregulated proteins) following ropivacaine treatment. Retinoblastoma-binding protein 4 (RBBP4) was one of the downregulated DEPs and was selected as the hub protein. TCGA database showed that RBBP4 was significantly upregulated in lung cancer and was associated with poor patient prognosis. Inhibition of RBBP4 by siRNA resulted in a significant decrease in the proliferation and invasive capacity of lung cancer cells and the induction of cell cycle arrest. Additionally, the results indicated RBBP4 knockdown enhanced antitumor effect of ropivacaine on A549 and H1299 cells. Conversely, the overexpression of RBBP4 using plasmids reversed the inhibitory effects of ropivacaine.

Conclusion

Our data suggest that ropivacaine suppresses lung cancer cell malignancy by downregulating RBBP4 protein expression, which may help clarify the mechanisms underlying the antitumor effects of ropivacaine.

Deep topographic proteomics of a human brain tumour

The spatial organisation of cellular protein expression profiles within tissue determines cellular function and is key to understanding disease pathology. To define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping proteomes within tissue structures. Here, we present a workflow for spatially-resolved, quantitative proteomics of tissue that generates maps of protein abundance across tissue slices derived from a human atypical teratoid-rhabdoid tumour at three spatial resolutions, the highest being 40 µm, to reveal distinct abundance patterns of thousands of proteins. We employ spatially-aware algorithms that do not require prior knowledge of the fine tissue structure to detect proteins and pathways with spatial abundance patterns and correlate proteins in the context of tissue heterogeneity and cellular features such as extracellular matrix or proximity to blood vessels. We identify PYGL, ASPH and CD45 as spatial markers for tumour boundary and reveal immune response-driven, spatially-organised protein networks of the extracellular tumour matrix. Overall, we demonstrate spatially-aware deep proteo-phenotyping of tissue heterogeneity, to re-define understanding tissue biology and pathology at the molecular level.

Mass spectrometry-based proteomics for advancing solid organ transplantation research

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

The proteomic landscape of synaptic diversity across brain regions and cell types

Neurons build synaptic contacts using different protein combinations that define the specificity, function, and plasticity potential of synapses; however, the diversity of synaptic proteomes remains largely unexplored. We prepared synaptosomes from 7 different transgenic mouse lines with fluorescently labeled presynaptic terminals. Combining microdissection of 5 different brain regions with fluorescent-activated synaptosome sorting (FASS), we isolated and analyzed the proteomes of 18 different synapse types. We discovered ∼1,800 unique synapse-type-enriched proteins and allocated thousands of proteins to different types of synapses (https://syndive.org/). We identify shared synaptic protein modules and highlight the proteomic hotspots for synapse specialization. We reveal unique and common features of the striatal dopaminergic proteome and discover the proteome signatures that relate to the functional properties of different interneuron classes. This study provides a molecular systems-biology analysis of synapses and a framework to integrate proteomic information for synapse subtypes of interest with cellular or circuit-level experiments.

Extracellular vesicle-based liquid biopsy biomarkers and their application in precision immuno-oncology

While the field of precision oncology is rapidly expanding and more targeted options are revolutionizing cancer treatment paradigms, therapeutic resistance particularly to immunotherapy remains a pressing challenge. This can be largely attributed to the dynamic tumor-stroma interactions that continuously alter the microenvironment. While to date most advancements have been made through examining the clinical utility of tissue-based biomarkers, their invasive nature and lack of a holistic representation of the evolving disease in a real-time manner could result in suboptimal treatment decisions. Thus, using minimally-invasive approaches to identify biomarkers that predict and monitor treatment response as well as alert to the emergence of recurrences is of a critical need. Currently, research efforts are shifting towards developing liquid biopsy-based biomarkers obtained from patients over the course of disease. Liquid biopsy represents a unique opportunity to monitor intercellular communication within the tumor microenvironment which could occur through the exchange of extracellular vesicles (EVs). EVs are lipid bilayer membrane nanoscale vesicles which transfer a plethora of biomolecules that mediate intercellular crosstalk, shape the tumor microenvironment, and modify drug response. The capture of EVs using innovative approaches, such as microfluidics, magnetic beads, and aptamers, allow their analysis via high throughput multi-omics techniques and facilitate their use for biomarker discovery. Artificial intelligence, using machine and deep learning algorithms, is advancing multi-omics analyses to uncover candidate biomarkers and predictive signatures that are key for translation into clinical trials. With the increasing recognition of the role of EVs in mediating immune evasion and as a valuable biomarker source, these real-time snapshots of cellular communication are promising to become an important tool in the field of precision oncology and spur the recognition of strategies to block resistance to immunotherapy. In this review, we discuss the emerging role of EVs in biomarker research describing current advances in their isolation and analysis techniques as well as their function as mediators in the tumor microenvironment. We also highlight recent lung cancer and melanoma studies that point towards their application as predictive biomarkers for immunotherapy and their potential clinical use in precision immuno-oncology.

A framework for ultra-low-input spatial tissue proteomics

Spatial proteomics combining microscopy-based cell phenotyping with ultrasensitive mass-spectrometry-based proteomics is an emerging and powerful concept to study cell function and heterogeneity in (patho)physiology. However, optimized workflows that preserve morphological information for phenotype discovery and maximize proteome coverage of few or even single cells from laser microdissected tissue are currently lacking. Here, we report a robust and scalable workflow for the proteomic analysis of ultra-low-input archival material. Benchmarking in murine liver resulted in up to 2,000 quantified proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions. Applied to human tonsil, we profiled 146 microregions including T and B lymphocyte niches and quantified cell-type-specific markers, cytokines, and transcription factors. These data also highlighted proteome dynamics within activated germinal centers, illuminating sites undergoing B cell proliferation and somatic hypermutation. This approach has broad implications in biomedicine, including early disease profiling and drug target and biomarker discovery. A record of this paper's transparent peer review process is included in the supplemental information.

Data-Driven Tool for Cross-Run Ion Selection and Peak-Picking in Quantitative Proteomics with Data-Independent Acquisition LC-MS/MS

Proteomics provides molecular bases of biology and disease, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a platform widely used for bottom-up proteomics. Data-independent acquisition (DIA) improves the run-to-run reproducibility of LC-MS/MS in proteomics research. However, the existing DIA data processing tools sometimes produce large deviations from true values for the peptides and proteins in quantification. Peak-picking error and incorrect ion selection are the two main causes of the deviations. We present a cross-run ion selection and peak-picking (CRISP) tool that utilizes the important advantage of run-to-run consistency of DIA and simultaneously examines the DIA data from the whole set of runs to filter out the interfering signals, instead of only looking at a single run at a time. Eight datasets acquired by mass spectrometers from different vendors with different types of mass analyzers were used to benchmark our CRISP-DIA against other currently available DIA tools. In the benchmark datasets, for analytes with large content variation among samples, CRISP-DIA generally resulted in 20 to 50% relative decrease in error rates compared to other DIA tools, at both the peptide precursor level and the protein level. CRISP-DIA detected differentially expressed proteins more efficiently, with 3.3 to 90.3% increases in the numbers of true positives and 12.3 to 35.3% decreases in the false positive rates, in some cases. In the real biological datasets, CRISP-DIA showed better consistencies of the quantification results. The advantages of assimilating DIA data in multiple runs for quantitative proteomics were demonstrated, which can significantly improve the quantification accuracy.

Elegant Nematodes Improve Our Understanding of Human Neuronal Diseases, the Role of Pollutants and Strategies of Resilience

The prevalence of neurodegenerative disorders such as Alzheimer's and Parkinson's disease are rising globally. The role of environmental pollution in neurodegeneration is largely unknown. Thus, this perspective advocates exposome research in C. elegans models of human diseases. The models express amyloid proteins such as Aβ, recapitulate the degeneration of specifically vulnerable neurons and allow for correlated neurobehavioral phenotyping throughout the entire life span of the nematode. Neurobehavioral traits like locomotion gaits, rigidity, or cognitive decline are quantifiable and carefully mimic key aspects of the human diseases. Underlying molecular pathways of neurodegeneration are elucidated in pollutant-exposed C. elegans Alzheimer's or Parkinson's models by transcriptomics (RNA-seq), mass spectrometry-based proteomics and omics addressing other biochemical traits. Validation of the identified disease pathways can be achieved by genome-wide association studies in matching human cohorts. A consistent One Health approach includes isolation of nematodes from contaminated sites and their comparative investigation by imaging, neurobehavioral profiling and single worm proteomics. C. elegans models of neurodegenerative diseases are likewise well-suited for high throughput methods that provide a promising strategy to identify resilience pathways of neurosafety and keep up with the number of pollutants, nonchemical exposome factors, and their interactions.

Exercise, healthy ageing, and the potential role of small extracellular vesicles

Extracellular vesicles (EVs) can be released from most cells in the body and act as intercellular messengers transferring information in their cargo to affect cellular function. A growing body of evidence suggests that a subset of EVs, referred to here as 'small extracellular vesicles' (sEVs), can accelerate or slow the processes of ageing and age-related diseases dependent on their molecular cargo and cellular origin. Continued exploration of the vast complexity of the sEV cargo aims to further characterise these systemic vehicles that may be targeted to ameliorate age-related pathologies. Marked progress in the development of mass spectrometry-based technologies means that it is now possible to characterise a significant proportion of the proteome of sEVs (surface and cargo) via unbiased proteomics. This information is vital for identifying biomarkers and the development of sEV-based therapeutics in the context of ageing. Although exercise and physical activity are prominent features in maintaining health in advancing years, the mechanisms responsible are unclear. A potential mechanism by which plasma sEVs released during exercise could influence ageing and senescence is via the increased delivery of cargo proteins that function as antioxidant enzymes or inhibitors of senescence. These have been observed to increase in sEVs following acute and chronic exercise, as identified via independent interrogation of high coverage, publicly available proteomic datasets. Establishing tropism and exchange of functionally active proteins by these processes represents a promising line of enquiry in implicating sEVs as biologically relevant mediators of the ageing process.

A snapshot of Bothrops jararaca snake venom gland subcellular proteome

Snake venom protein synthesis undergoes finely regulated processes in the specialized secretory epithelium within the venom gland. Such processes occur within a defined period in the cell and at specific cellular locations. Thus, the determination of subcellular proteomes allows the characterization of protein groups for which the site may be relevant to their biological roles, thereby allowing the deconvolution of complex biological circuits into functional information. In this regard, we performed subcellular fractionation of proteins from B. jararaca venom gland, focusing on nuclear proteins since this cellular compartment comprises key effectors that shape gene expression. Our results provided a snapshot of B. jararaca's subcellular venom gland proteome and pointed to a 'conserved' proteome core among different life stages (newborn and adult) and between sexes (adult male and female). Overall, the top 15 highly abundant proteins identified in B. jararaca venom glands mirrored the panel of highly expressed genes in human salivary glands. Therefore, the expression profile observed for such a protein set could be considered a conserved core signature of salivary gland secretory epithelium. Moreover, the newborn venom gland displayed a unique expression signature of transcription factors involved in regulating transcription and biosynthetic processes and may mirror biological constraints of the ontogenetic development of B. jararaca, contributing to venom proteome diversity.

50 shades of oxidative stress: A state-specific cysteine redox pattern hypothesis

Oxidative stress is biochemically complex. Like primary colours, specific reactive oxygen species (ROS) and antioxidant inputs can be mixed to create unique "shades" of oxidative stress. Even a minimal redox module comprised of just 12 (ROS & antioxidant) inputs and 3 outputs (oxidative damage, cysteine-dependent redox-regulation, or both) yields over half a million "shades" of oxidative stress. The present paper proposes the novel hypothesis that: state-specific shades of oxidative stress, such as a discrete disease, are associated with distinct tell-tale cysteine oxidation patterns. The patterns are encoded by many parameters, from the identity of the oxidised proteins, the cysteine oxidation type, and magnitude. The hypothesis is conceptually grounded in distinct ROS and antioxidant inputs coalescing to produce unique cysteine oxidation outputs. And considers the potential biological significance of the holistic cysteine oxidation outputs. The literature supports the existence of state-specific cysteine oxidation patterns. Measuring and manipulating these patterns offer promising avenues for advancing oxidative stress research. The pattern inspired hypothesis provides a framework for understanding the complex biochemical nature of state-specific oxidative stress.

[Omics to serve myology]

Despite efforts in biomedical research, pathophysiological mechanisms and therapeutic targets of diseases remain difficult to identify. The development of high-throughput techniques led to the advent of innovatve technologies called omics. They aim at characterizing as exhaustively as possible a set of molecules: genes, RNAs, proteins, metabolites, etc. These a priori methods allow a precise molecular characterization of diseases and a better understanding of complex pathophysiological mechanisms. In this paper, we will review most omics approaches, their integration and their applications in the context of myology.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

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

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

Blood-Based Biomarkers for Managing Workload in Athletes: Perspectives for Research on Emerging Biomarkers

At present, various blood-based biomarkers have found their applications in the field of sports medicine. This current opinion addresses biomarkers that warrant consideration in future research for monitoring the athlete training load. In this regard, we identified a variety of emerging load-sensitive biomarkers, e.g., cytokines (such as IL-6), chaperones (such as heat shock proteins) or enzymes (such as myeloperoxidase) that could improve future athlete load monitoring as they have shown meaningful increases in acute and chronic exercise settings. In some cases, they have even been linked to training status or performance characteristics. However, many of these markers have not been extensively studied and the cost and effort of measuring these parameters are still high, making them inconvenient for practitioners so far. We therefore outline strategies to improve knowledge of acute and chronic biomarker responses, including ideas for standardized study settings. In addition, we emphasize the need for methodological advances such as the development of minimally invasive point-of-care devices as well as statistical aspects related to the evaluation of these monitoring tools to make biomarkers suitable for regular load monitoring.

Anemonefishes: A model system for evolutionary genomics

Anemonefishes are an iconic group of coral reef fish particularly known for their mutualistic relationship with sea anemones. This mutualism is especially intriguing as it likely prompted the rapid diversification of anemonefish. Understanding the genomic architecture underlying this process has indeed become one of the holy grails of evolutionary research in these fishes. Recently, anemonefishes have also been used as a model system to study the molecular basis of highly complex traits such as color patterning, social sex change, larval dispersal and life span. Extensive genomic resources including several high-quality reference genomes, a linkage map, and various genetic tools have indeed enabled the identification of genomic features controlling some of these fascinating attributes, but also provided insights into the molecular mechanisms underlying adaptive responses to changing environments. Here, we review the latest findings and new avenues of research that have led to this group of fish being regarded as a model for evolutionary genomics.

E. coli outer membrane protein T (OmpT) nanopore for peptide sensing

Peptide detection methods with facility and high sensitivity are essential for diagnosing disease associated with peptide biomarkers. Nanopore sensing technology had emerged as a low cost, high-throughput, and scalable tool for peptide detection. The omptins family proteins which can form β-barrel pores have great potentials to be developed as nanopore biosensor. However, there are no study about the channel properties of E. coli OmpT and the development of OmpT as a nanopore biosensor. In this study, the OmpT biological nanopore channel was constructed with a conductance of 1.49 nS in 500 mM NaCl buffer and a three-step gating phenomenon under negative voltage higher than 100 mV and then was developed as a peptide biosensor which can detect peptide without the interfere of ssDNA and dNTPs. The OmpT constructed in this study has potential application in peptide detection, and also provides a new idea for the detection of peptides using the specific binding ability of protease.

A comprehensive spectral assay library to quantify the Halobacterium salinarum NRC-1 proteome by DIA/SWATH-MS

Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E. coli and few other organisms. However, a spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, a model organism that contributed to several notable discoveries, is not publicly available yet. Here, we report a comprehensive spectral assay library to measure 2,563 of 2,646 annotated H. salinarum NRC-1 proteins. We demonstrate the utility of this library by measuring global protein abundances over time under standard growth conditions. The H. salinarum NRC-1 library includes 21,074 distinct peptides representing 97% of the predicted proteome and provides a new, valuable resource to confidently measure and quantify any protein of this archaeon. Data and spectral assay libraries are available via ProteomeXchange (PXD042770, PXD042774) and SWATHAtlas (SAL00312-SAL00319).

MALDI MS Imaging of Chickpea Seeds (Cicer arietinum) and Crab's Eye Vine (Abrus precatorius) after Tryptic Digestion Allows Spatially Resolved Identification of Plant Proteins

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) imaging following in situ enzymatic digestion is a versatile analytical method for the untargeted investigation of protein distributions, which has rarely been used for plants so far. The present study describes a workflow for in situ tryptic digestion of plant seed tissue for MALDI MS imaging. Substantial modifications to the sample preparation procedure for mammalian tissues were necessary to cater to the specific properties of plant materials. For the first time, distributions of tryptic peptides were successfully visualized in plant tissue using MS imaging with accurate mass detection. Sixteen proteins were visualized and identified in chickpea seeds showing different distribution patterns, e.g., in the cotyledons, radicle, or testa. All tryptic peptides were detected with a mass resolution higher than 60,000 as well as a mass accuracy better than 1.5 ppm root-mean-square error and were matched to results from complementary liquid chromatography-MS/MS (LC-MS/MS) data. The developed method was also applied to crab's eye vine seeds for targeted MS imaging of the toxic protein abrin, showing the presence of abrin-a in all compartments. Abrin (59 kDa), as well as the majority of proteins visualized in chickpeas, was larger than 50 kDa and would thus not be readily accessible by top-down MS imaging. Since antibodies for plant proteins are often not readily available, in situ digestion MS imaging provides unique information, as it makes the distribution and identification of larger proteins in plant tissues accessible in an untargeted manner. This opens up new possibilities in the field of plant science as well as to assess the nutritional quality and/or safety of crops.

Top-Down Protein Analysis by Tandem-Trapped Ion Mobility Spectrometry/Mass Spectrometry (Tandem-TIMS/MS) Coupled with Ultraviolet Photodissociation (UVPD) and Parallel Accumulation/Serial Fragmentation (PASEF) MS/MS Analysis

"Top-down" proteomics analyzes intact proteins and identifies proteoforms by their intact mass as well as the observed fragmentation pattern in tandem mass spectrometry (MS/MS) experiments. Recently, hybrid ion mobility spectrometry-mass spectrometry (IM/MS) methods have gained traction for top-down experiments, either by allowing top-down analysis of individual isomers or alternatively by improving signal/noise and dynamic range for fragment ion assignment. We recently described the construction of a tandem-trapped ion mobility spectrometer/mass spectrometer (tandem-TIMS/MS) coupled with an ultraviolet (UV) laser and demonstrated a proof-of-principle for top-down analysis by UV photodissociation (UVPD) at 2-3 mbar. The present work builds on this with an exploration of a top-down method that couples tandem-TIMS/MS with UVPD and parallel-accumulation serial fragmentation (PASEF) MS/MS analysis. We first survey types and structures of UVPD-specific fragment ions generated in the 2-3 mbar pressure regime of our instrument. Notably, we observe UVPD-induced fragment ions with multiple conformations that differ from those produced in the absence of UV irradiation. Subsequently, we discuss how MS/MS spectra of top-down fragment ions lend themselves ideally for probability-based scoring methods developed in the bottom-up proteomics field and how the ability to record automated PASEF-MS/MS spectra resolves ambiguities in the assignment of top-down fragment ions. Finally, we describe the coupling of tandem-TIMS/MS workflows with UVPD and PASEF-MS/MS analysis for native top-down protein analysis.

Localization and Quantification of Post-Translational Modifications of Proteins Using Electron Activated Dissociation Fragmentation on a Fast-Acquisition Time-of-Flight Mass Spectrometer

Protein post-translational modifications (PTMs) are crucial and dynamic players in a large variety of cellular processes and signaling. Proteomic technologies have emerged as the method of choice to profile PTMs. However, these analyses remain challenging due to potential low PTM stoichiometry, the presence of multiple PTMs per proteolytic peptide, PTM site localization of isobaric peptides, and neutral losses. Collision-induced dissociation (CID) is commonly used to characterize PTMs, but the application of collision energy can lead to neutral losses and incomplete peptide sequencing for labile PTM groups. In this study, we assessed the performance of an alternative fragmentation, electron activated dissociation (EAD), to characterize, site localize, and quantify peptides with labile modifications in comparison to CID, both operated on a recently introduced fast-scanning quadrupole-time-of-flight (QqTOF) mass spectrometer. We analyzed biologically relevant phosphorylated, succinylated, malonylated, and acetylated synthetic peptides using targeted parallel reaction monitoring (PRM or MRMHR) assays. We report that electron-based fragmentation preserves the malonyl group from neutral losses. The novel tunable EAD kinetic energy maintained labile modification integrity and provided better peptide sequence coverage with strong PTM-site localization fragment ions. Activation of a novel trap-and-release technology significantly improves the duty cycle and provided significant MS/MS sensitivity gains by an average of 6-11-fold for EAD analyses. Evaluation of the quantitative EAD PRM workflows revealed high reproducibility with coefficients of variation of ∼2-7%, as well as very good linearity and quantification accuracy. This novel workflow combining EAD and trap-and-release technology provides high sensitivity, alternative fragmentation information to achieve confident PTM characterization and quantification.

Compendium of Chromatographic Behavior of Post-translationally and Chemically Modified Peptides in Bottom-Up Proteomic Experiments

We have systematically evaluated the chromatographic behavior of post-translationally/chemically modified peptides using data spanning over 70 of the most relevant modifications. These retention properties were measured for standard bottom-up proteomic settings (fully porous C18 separation media, 0.1% formic acid as ion-pairing modifier) using collections of modified/nonmodified peptide pairs. These pairs were generated by spontaneous degradation, chemical or enzymatic treatment, analysis of synthetic peptides, or the cotranslational incorporation of noncanonical proline analogues. In addition, these measurements were validated using external data acquired for synthetic peptides and enzymatically induced citrullination. Working in units of hydrophobicity index (HI, % ACN) and evaluating the average retention shifts (ΔHI) represent the simplest approach to describe the effect of modifications from a didactic point of view. Plotting HI values for modified (y-axis) vs nonmodified (x-axis) counterparts generates unique slope and intercept values for each modification defined by the chemistry of the modifying moiety: its hydrophobicity, size, pKa of ionizable groups, and position of the altered residue. These composition-dependent correlations can be used for coarse incorporation of PTMs into models for prediction of peptide retention. More accurate predictions would require the development of specific sequence-dependent algorithms to predict ΔHI values.

Proteomic mapping reveals dysregulated angiogenesis in the cerebral arteries of rats with early-onset hypertension

Hypertension is associated with the presence of vascular abnormalities, including remodeling and rarefaction. These processes play an important role in cerebrovascular disease development; however, the mechanistic changes leading to these diseases are not well characterized. Using data-independent acquisition-based mass spectrometry analysis, here we determined the protein changes in cerebral arteries in pre- and early-onset hypertension from the spontaneously hypertensive rat (SHR), a model that resembles essential hypertension in humans. Our analysis identified 125 proteins with expression levels that were significantly upregulated or downregulated in 12-week-old spontaneously hypertensive rats compared to normotensive Wistar Kyoto rats. Using an angiogenesis enrichment analysis, we further identified a critical imbalance in angiogenic proteins that promoted an anti-angiogenic profile in cerebral arteries at early onset of hypertension. In a comparison to previously published data, we demonstrate that this angiogenic imbalance is not present in mesenteric and renal arteries from age-matched SHRs. Finally, we identified two proteins (Fbln5 and Cdh13), whose expression levels were critically altered in cerebral arteries compared to the other arterial beds. The observation of an angiogenic imbalance in cerebral arteries from the SHR reveals critical protein changes in the cerebrovasculature at the early onset of hypertension and provides novel insights into the early pathology of cerebrovascular disease.

Proteomics of the haemolymph of the terrestrial crustacean Porcellio scaber reveals components of its innate immunity under baseline conditions

The terrestrial crustacean Porcellio scaber is an established test organism in environmental research. We analysed the haemolymph proteome of P. scaber using a classical proteomic approach based on one-dimensional gel electrophoresis and tandem mass spectrometry. Using a publicly available protein database and our P. scaber transcriptome data, we have identified 76 proteins involved in cytoskeleton formation, protein degradation, vesicular transport, genetic information processing, detoxification, carbohydrate and lipid metabolism reflecting haemocyte metabolic activity, active intracellular transport, and intercellular communication. Compared with the data reported for other crustaceans, 28 of these P. scaber proteins have been linked to its immunity, among them hemocyanin, α-2-macroglobulin, phenoloxidase 3, superoxide dismutase, glutathione S-transferase, haemolymph clottable protein, and histones H4 and H2B. Our results thus provide a firm base for studying the innate immune response of P. scaber at the level of the haemolymph proteome. This knowledge is of particular importance in ecotoxicity studies with various environmental stressors where understanding physiological changes is important to reveal possible modes of action.

Proteomic analysis to unravel the biochemical mechanisms triggered by Bacillus toyonensis SFC 500-1E under chromium(VI) and phenol stress

Bacillus toyonensis SFC 500-1E is a member of the consortium SFC 500-1 able to remove Cr(VI) and simultaneously tolerate high phenol concentrations. In order to elucidate mechanisms utilized by this strain during the bioremediation process, the differential expression pattern of proteins was analyzed when it grew with or without Cr(VI) (10 mg/L) and Cr(VI) + phenol (10 and 300 mg/L), through two complementary proteomic approaches: gel-based (Gel-LC) and gel-free (shotgun) nanoUHPLC-ESI-MS/MS. A total of 400 differentially expressed proteins were identified, out of which 152 proteins were down-regulated under Cr(VI) and 205 up-regulated in the presence of Cr(VI) + phenol, suggesting the extra effort made by the strain to adapt itself and keep growing when phenol was also added. The major metabolic pathways affected include carbohydrate and energetic metabolism, followed by lipid and amino acid metabolism. Particularly interesting were also ABC transporters and the iron-siderophore transporter as well as transcriptional regulators that can bind metals. Stress-associated global response involving the expression of thioredoxins, SOS response, and chaperones appears to be crucial for the survival of this strain under treatment with both contaminants. This research not only provided a deeper understanding of B. toyonensis SFC 500-1E metabolic role in Cr(VI) and phenol bioremediation process but also allowed us to complete an overview of the consortium SFC 500-1 behavior. This may contribute to an improvement in its use as a bioremediation strategy and also provides a baseline for further research.

Foodomics-Based Approaches Shed Light on the Potential Protective Effects of Polyphenols in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic and progressive inflammatory disorder affecting the gastrointestinal tract (GT) caused by a wide range of genetic, microbial, and environmental factors. IBD is characterized by chronic inflammation and decreased gut microbial diversity, dysbiosis, with a lower number of beneficial bacteria and a concomitant increase in pathogenic species. It is well known that dysbiosis is closely related to the induction of inflammation and oxidative stress, the latter caused by an imbalance between reactive oxygen species (ROS) production and cellular antioxidant capacity, leading to cellular ROS accumulation. ROS are responsible for intestinal epithelium oxidative damage and the increased intestinal permeability found in IBD patients, and their reduction could represent a potential therapeutic strategy to limit IBD progression and alleviate its symptoms. Recent evidence has highlighted that dietary polyphenols, the natural antioxidants, can maintain redox equilibrium in the GT, preventing gut dysbiosis, intestinal epithelium damage, and radical inflammatory responses. Here, we suggest that the relatively new foodomics approaches, together with new technologies for promoting the antioxidative properties of dietary polyphenols, including novel delivery systems, chemical modifications, and combination strategies, may provide critical insights to determine the clinical value of polyphenols for IBD therapy and a comprehensive perspective for implementing natural antioxidants as potential IBD candidate treatment.

Serum APOC1 levels are decreased in young autoantibody positive children who rapidly progress to type 1 diabetes

Better understanding of the early events in the development of type 1 diabetes is needed to improve prediction and monitoring of the disease progression during the substantially heterogeneous presymptomatic period of the beta cell damaging process. To address this concern, we used mass spectrometry-based proteomics to analyse longitudinal pre-onset plasma sample series from children positive for multiple islet autoantibodies who had rapidly progressed to type 1 diabetes before 4 years of age (n = 10) and compared these with similar measurements from matched children who were either positive for a single autoantibody (n = 10) or autoantibody negative (n = 10). Following statistical analysis of the longitudinal data, targeted serum proteomics was used to verify 11 proteins putatively associated with the disease development in a similar yet independent and larger cohort of children who progressed to the disease within 5 years of age (n = 31) and matched autoantibody negative children (n = 31). These data reiterated extensive age-related trends for protein levels in young children. Further, these analyses demonstrated that the serum levels of two peptides unique for apolipoprotein C1 (APOC1) were decreased after the appearance of the first islet autoantibody and remained relatively less abundant in children who progressed to type 1 diabetes, in comparison to autoantibody negative children.

Recent applications of quantitative mass spectrometry in biopharmaceutical process development and manufacturing

Biopharmaceutical products have seen rapid growth over the past few decades and continue to dominate the global pharmaceutical market. Aligning with the quality by design (QbD) framework and realization, recent advances in liquid chromatography-mass spectrometry (LC-MS) instrumentation and related techniques have enhanced biopharmaceutical characterization capabilities and have supported an increased development of biopharmaceutical products. Beyond its routine qualitative characterization, the quantitative feature of LC-MS has unique applications in biopharmaceutical process development and manufacturing. This review describes the recent applications and implications of the advancement of quantitative MS methods in biopharmaceutical process development, and characterization of biopharmaceutical product, product-related variants, and process-related impurities. We also provide insights on the emerging applications of quantitative MS in the lifecycle of biopharmaceutical product development including quality control in the Good Manufacturing Practice (GMP) environment and process analytical technology (PAT) practices during process development and manufacturing. Through collaboration with instrument and software vendors and regulatory agencies, we envision broader adoption of phase-appropriate quantitative MS-based methods for the analysis of biopharmaceutical products, which in turn has the potential to enable manufacture of higher quality products for patients.

Assessment and Prediction of Human Proteotypic Peptide Stability for Proteomics Quantification

Mass spectrometry coupled to liquid chromatography is one of the most powerful technologies for proteome quantification in biomedical samples. In peptide-centric workflows, protein mixtures are enzymatically digested to peptides prior their analysis. However, proteome-wide quantification studies rarely identify all potential peptides for any given protein, and targeted proteomics experiments focus on a set of peptides for the proteins of interest. Consequently, proteomics relies on the use of a limited subset of all possible peptides as proxies for protein quantitation. In this work, we evaluated the stability of the human proteotypic peptides during 21 days and trained a deep learning model to predict peptide stability directly from tryptic sequences, which together constitute a resource of broad interest to prioritize and select peptides in proteome quantification experiments.

Engineering Biological Nanopore Approaches toward Protein Sequencing

Biotechnological innovations have vastly improved the capacity to perform large-scale protein studies, while the methods we have for identifying and quantifying individual proteins are still inadequate to perform protein sequencing at the single-molecule level. Nanopore-inspired systems devoted to understanding how single molecules behave have been extensively developed for applications in genome sequencing. These nanopore systems are emerging as prominent tools for protein identification, detection, and analysis, suggesting realistic prospects for novel protein sequencing. This review summarizes recent advances in biological nanopore sensors toward protein sequencing, from the identification of individual amino acids to the controlled translocation of peptides and proteins, with attention focused on device and algorithm development and the delineation of molecular mechanisms with the aid of simulations. Specifically, the review aims to offer recommendations for the advancement of nanopore-based protein sequencing from an engineering perspective, highlighting the need for collaborative efforts across multiple disciplines. These efforts should include chemical conjugation, protein engineering, molecular simulation, machine-learning-assisted identification, and electronic device fabrication to enable practical implementation in real-world scenarios.

Quantitative multiorgan proteomics of fatal COVID-19 uncovers tissue-specific effects beyond inflammation

SARS-CoV-2 may directly and indirectly damage lung tissue and other host organs, but there are few system-wide, untargeted studies of these effects on the human body. Here, we developed a parallelized mass spectrometry (MS) proteomics workflow enabling the rapid, quantitative analysis of hundreds of virus-infected FFPE tissues. The first layer of response to SARS-CoV-2 in all tissues was dominated by circulating inflammatory molecules. Beyond systemic inflammation, we differentiated between systemic and true tissue-specific effects to reflect distinct COVID-19-associated damage patterns. Proteomic changes in the lungs resembled those of diffuse alveolar damage (DAD) in non-COVID-19 patients. Extensive organ-specific changes were also evident in the kidneys, liver, and lymphatic and vascular systems. Secondary inflammatory effects in the brain were related to rearrangements in neurotransmitter receptors and myelin degradation. These MS-proteomics-derived results contribute substantially to our understanding of COVID-19 pathomechanisms and suggest strategies for organ-specific therapeutic interventions.

Resolving the Temporal Splenic Proteome during Fungal Infection for Discovery of Putative Dual Perspective Biomarker Signatures

Fungal pathogens are emerging threats to global health with the rise of incidence associated with climate change and increased geographical distribution; factors also influencing host susceptibility to infection. Accurate detection and diagnosis of fungal infections is paramount to offer rapid and effective therapeutic options. For improved diagnostics, the discovery and development of protein biomarkers presents a promising avenue; however, this approach requires a priori knowledge of infection hallmarks. To uncover putative novel biomarkers of disease, profiling of the host immune response and pathogen virulence factor production is indispensable. In this study, we use mass-spectrometry-based proteomics to resolve the temporal proteome of Cryptococcus neoformans infection of the spleen following a murine model of infection. Dual perspective proteome profiling defines global remodeling of the host over a time course of infection, confirming activation of immune associated proteins in response to fungal invasion. Conversely, pathogen proteomes detect well-characterized C. neoformans virulence determinants, along with novel mapped patterns of pathogenesis during the progression of disease. Together, our innovative systematic approach confirms immune protection against fungal pathogens and explores the discovery of putative biomarker signatures from complementary biological systems to monitor the presence and progression of cryptococcal disease.

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields

MOTIVATION

Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning.

RESULTS

To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena.

AVAILABILITY AND IMPLEMENTATION

All source code and data are freely available at https://github.com/zpliulab/ActivePPI.

Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis

The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package ( https://github.com/InfectionMedicineProteomics/BINN ).

Dynamic lipidome alterations associated with human health, disease and ageing

Lipids can be of endogenous or exogenous origin and affect diverse biological functions, including cell membrane maintenance, energy management and cellular signalling. Here, we report >800 lipid species, many of which are associated with health-to-disease transitions in diabetes, ageing and inflammation, as well as cytokine-lipidome networks. We performed comprehensive longitudinal lipidomic profiling and analysed >1,500 plasma samples from 112 participants followed for up to 9 years (average 3.2 years) to define the distinct physiological roles of complex lipid subclasses, including large and small triacylglycerols, ester- and ether-linked phosphatidylethanolamines, lysophosphatidylcholines, lysophosphatidylethanolamines, cholesterol esters and ceramides. Our findings reveal dynamic changes in the plasma lipidome during respiratory viral infection, insulin resistance and ageing, suggesting that lipids may have roles in immune homoeostasis and inflammation regulation. Individuals with insulin resistance exhibit disturbed immune homoeostasis, altered associations between lipids and clinical markers, and accelerated changes in specific lipid subclasses during ageing. Our dataset based on longitudinal deep lipidome profiling offers insights into personalized ageing, metabolic health and inflammation, potentially guiding future monitoring and intervention strategies.

Longitudinal Serum Proteomics Characterization of CD19-CAR-T Cell Therapy for B-Cell Malignancies

Chimeric antigen receptor (CAR)-modified T cells have demonstrated remarkable efficacy in treating B-cell leukemia. However, treated patients may potentially develop side effects, such as cytokine release syndrome (CRS), the mechanisms of which remain unclear. Here, we collected 43 serum samples from eight patients with B-cell acute lymphoblastic leukemia (B-ALL) before and five time points after CD19-specific CAR-T cell treatment. Using TMTpro 16-plex-based quantitative proteomics, we quantified 1151 proteins and profiled the longitudinal proteomes analysis of each patient. Seven days after therapy, we found the most dysregulated inflammatory proteins. Lipid metabolism proteins, including APOA1, decreased after therapy, reached their minimum after 7 days, and then gradually recovered. Hence, APOA1 has been selected as a potential biomarker of the CRS disease progression. Furthermore, we identified CD163 as a potential biomarker of CRS severity. These two biomarkers were successfully validated using targeted proteomics in an independent cohort. Our study provides new insights into CAR-T cell therapy-induced CRS. The biomarkers we identified may help develop targeted drugs and monitoring strategies.

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry

Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.

Proteomic Profiling and Tumor Microenvironment Characterization Reveal Molecular and Immunological Hallmarks of Left-Sided and Right-Sided Colon Cancer Tumorigenesis

Left-sided and right-sided colon cancer (LSCC and RSCC) display different biological and clinical characteristics. However, the differences in their tumorigenesis and tumor microenvironment remain unclear. In this study, we profiled the proteomic landscapes of LSCC and RSCC with data-independent acquisition mass spectrometry (DIA-MS) using fresh tumor and adjacent normal tissues from 24 patients. A total of 7403 proteingroups were primarily identified with DIA-MS. After quality control, 7212 proteingroups were used for further analysis. Through comparing the difference in proteomic profiles between LSCC and RSCC samples, 2556 commonly and 1982 region-type-specific regulated proteingroups were characterized. During the development of LSCC and RSCC, metabolic, growth, cell division, cell adhesion, and migration pathways were found to be significantly dysregulated (P < 0.05), which was further confirmed by transcriptome data from TCGA. Compared to RSCC, most parts of the immune-related signatures, immune cell infiltration scores, and overall immune scores of LSCC were higher. The systematic elucidation of proteomic and transcriptomic profiles in this work improves our understanding of tumorigenesis and immune microenvironment characteristics of LSCC and RSCC.