Quantitative, high-resolution proteomics for data-driven systems biology

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

Characterization of the plasma proteome from healthy adult dogs

Introduction

Bloodwork is a widely used diagnostic tool in veterinary medicine, as diagnosis and therapeutic interventions often rely on blood biomarkers. However, biomarkers available in veterinary medicine often lack sensitivity or specificity. Mass spectrometry-based proteomics technology has been extensively used in the analysis of biological fluids. It offers excellent potential for a more comprehensive characterization of the plasma proteome in veterinary medicine.

Methods

In this study, we aimed to identify and quantify plasma proteins in a cohort of healthy dogs and compare two techniques for depleting high-abundance plasma proteins to enable the detection of lower-abundance proteins via label-free quantification liquid chromatography-mass spectrometry. We utilized surplus lithium-heparin plasma from 30 healthy dogs, subdivided into five groups of pooled plasma from 6 randomly selected individuals each. Firstly, we used a commercial kit to deplete high-abundance plasma proteins. Secondly, we employed an in-house method to remove albumin using Blue-Sepharose.

Results and discussion

Among all the samples, some of the most abundant proteins identified were apolipoprotein A and B, albumin, alpha-2-macroglobulin, fibrinogen beta chain, fibronectin, complement C3, serotransferrin, and coagulation factor V. However, neither of the depletion techniques achieved significant depletion of highly abundant proteins. Despite this limitation, we could detect and quantify many clinically relevant proteins. Determining the healthy canine proteome is a crucial first step in establishing a reference proteome for canine plasma. After enrichment, this reference proteome can later be utilized to identify protein markers associated with different diseases, thereby contributing to the diagnosis and prognosis of various pathologies.

Protein Signatures of Parathyroid Adenoma according to Tumor Volume and Functionality

BACKGRUOUND

Parathyroid adenoma (PA) is a common endocrine disease linked to multiple complications, but the pathophysiology of the disease remains incompletely understood. The study aimed to identify the key regulator proteins and pathways of PA according to functionality and volume through quantitative proteomic analyses.

METHODS

We conducted a retrospective study of 15 formalin-fixed, paraffin-embedded PA samples from tertiary hospitals in South Korea. Proteins were extracted, digested, and the resulting peptides were analyzed using liquid chromatography-tandem mass spectrometry. Pearson correlation analysis was employed to identify proteins significantly correlated with clinical variables. Canonical pathways and transcription factors were analyzed using Ingenuity Pathway Analysis.

RESULTS

The median age of the participants was 52 years, and 60.0% were female. Among the 8,153 protein groups analyzed, 496 showed significant positive correlations with adenoma volume, while 431 proteins were significantly correlated with parathyroid hormone (PTH) levels. The proteins SLC12A9, LGALS3, and CARM1 were positively correlated with adenoma volume, while HSP90AB2P, HLA-DRA, and SCD5 showed negative correlations. DCPS, IRF2BPL, and FAM98A were the main proteins that exhibited positive correlations with PTH levels, and SLITRK4, LAP3, and AP4E1 had negative correlations. Canonical pathway analysis demonstrated that the RAN and sirtuin signaling pathways were positively correlated with both PTH levels and adenoma volume, while epithelial adherence junction pathways had negative correlations.

CONCLUSION

Our study identified pivotal proteins and pathways associated with PA, offering potential therapeutic targets. These findings accentuate the importance of proteomics in understanding disease pathophysiology and the need for further research.

RNA polymerase II promotes the organization of chromatin following DNA replication

Understanding how chromatin organisation is duplicated on the two daughter strands is a central question in epigenetics. In mammals, following the passage of the replisome, nucleosomes lose their defined positioning and transcription contributes to their re-organisation. However, whether transcription plays a greater role in the organization of chromatin following DNA replication remains unclear. Here we analysed protein re-association with newly replicated DNA upon inhibition of transcription using iPOND coupled to quantitative mass spectrometry. We show that nucleosome assembly and the re-establishment of most histone modifications are uncoupled from transcription. However, RNAPII acts to promote the re-association of hundreds of proteins with newly replicated chromatin via pathways that are not observed in steady-state chromatin. These include ATP-dependent remodellers, transcription factors and histone methyltransferases. We also identify a set of DNA repair factors that may handle transcription-replication conflicts during normal transcription in human non-transformed cells. Our study reveals that transcription plays a greater role in the organization of chromatin post-replication than previously anticipated.

Fast proteomics with dia-PASEF and analytical flow-rate chromatography

Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition, and data analysis with the DIA-NN software suite, to achieve high-quality proteomic experiments from limited sample amounts, at a throughput of up to 400 samples per day. For instance, when benchmarking our workflow using a 500-μL/min flow rate and 3-min chromatographic gradients, we report the quantification of 5211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used this platform to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-min chromatographic gradient and alternating column regeneration on a dual pump system. The method delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates.

Improved in situ characterization of protein complex dynamics at scale with thermal proximity co-aggregation

Cellular activities are carried out vastly by protein complexes but large repertoire of protein complexes remains functionally uncharacterized which necessitate new strategies to delineate their roles in various cellular processes and diseases. Thermal proximity co-aggregation (TPCA) is readily deployable to characterize protein complex dynamics in situ and at scale. We develop a version termed Slim-TPCA that uses fewer temperatures increasing throughputs by over 3X, with new scoring metrics and statistical evaluation that result in minimal compromise in coverage and detect more relevant complexes. Less samples are needed, batch effects are minimized while statistical evaluation cost is reduced by two orders of magnitude. We applied Slim-TPCA to profile K562 cells under different duration of glucose deprivation. More protein complexes are found dissociated, in accordance with the expected downregulation of most cellular activities, that include 55S ribosome and respiratory complexes in mitochondria revealing the utility of TPCA to study protein complexes in organelles. Protein complexes in protein transport and degradation are found increasingly assembled unveiling their involvement in metabolic reprogramming during glucose deprivation. In summary, Slim-TPCA is an efficient strategy for characterization of protein complexes at scale across cellular conditions, and is available as Python package at https://pypi.org/project/Slim-TPCA/ .

A Critical Analysis of the FDA's Omics-Driven Pharmacodynamic Biomarkers to Establish Biosimilarity

Demonstrating biosimilarity entails comprehensive analytical assessment, clinical pharmacology profiling, and efficacy testing in patients for at least one medical indication, as required by the U.S. Biologics Price Competition and Innovation Act (BPCIA). The efficacy testing can be waived if the drug has known pharmacodynamic (PD) markers, leaving most therapeutic proteins out of this concession. To overcome this, the FDA suggests that biosimilar developers discover PD biomarkers using omics technologies such as proteomics, glycomics, transcriptomics, genomics, epigenomics, and metabolomics. This approach is redundant since the mode-action-action biomarkers of approved therapeutic proteins are already available, as compiled in this paper for the first time. Other potential biomarkers are receptor binding and pharmacokinetic profiling, which can be made more relevant to ensure biosimilarity without requiring biosimilar developers to conduct extensive research, for which they are rarely qualified.

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.

Redefining serological diagnostics with immunoaffinity proteomics

Serological diagnostics is generally defined as the detection of specific human immunoglobulins developed against viral, bacterial, or parasitic diseases. Serological tests facilitate the detection of past infections, evaluate immune status, and provide prognostic information. Serological assays were traditionally implemented as indirect immunoassays, and their design has not changed for decades. The advantages of straightforward setup and manufacturing, analytical sensitivity and specificity, affordability, and high-throughput measurements were accompanied by limitations such as semi-quantitative measurements, lack of universal reference standards, potential cross-reactivity, and challenges with multiplexing the complete panel of human immunoglobulin isotypes and subclasses. Redesign of conventional serological tests to include multiplex quantification of immunoglobulin isotypes and subclasses, utilize universal reference standards, and minimize cross-reactivity and non-specific binding will facilitate the development of assays with higher diagnostic specificity. Improved serological assays with higher diagnostic specificity will enable screenings of asymptomatic populations and may provide earlier detection of infectious diseases, autoimmune disorders, and cancer. In this review, we present the major clinical needs for serological diagnostics, overview conventional immunoassay detection techniques, present the emerging immunoassay detection technologies, and discuss in detail the advantages and limitations of mass spectrometry and immunoaffinity proteomics for serological diagnostics. Finally, we explore the design of novel immunoaffinity-proteomic assays to evaluate cell-mediated immunity and advance the sequencing of clinically relevant immunoglobulins.

Advances in proteomics: characterization of the innate immune system after birth and during inflammation

Proteomics is the characterization of the protein composition, the proteome, of a biological sample. It involves the large-scale identification and quantification of proteins, peptides, and post-translational modifications. This review focuses on recent developments in mass spectrometry-based proteomics and provides an overview of available methods for sample preparation to study the innate immune system. Recent advancements in the proteomics workflows, including sample preparation, have significantly improved the sensitivity and proteome coverage of biological samples including the technically difficult blood plasma. Proteomics is often applied in immunology and has been used to characterize the levels of innate immune system components after perturbations such as birth or during chronic inflammatory diseases like rheumatoid arthritis (RA) and inflammatory bowel disease (IBD). In cancers, the tumor microenvironment may generate chronic inflammation and release cytokines to the circulation. In these situations, the innate immune system undergoes profound and long-lasting changes, the large-scale characterization of which may increase our biological understanding and help identify components with translational potential for guiding diagnosis and treatment decisions. With the ongoing technical development, proteomics will likely continue to provide increasing insights into complex biological processes and their implications for health and disease. Integrating proteomics with other omics data and utilizing multi-omics approaches have been demonstrated to give additional valuable insights into biological systems.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Methods to Discover and Validate Biofluid-Based Biomarkers in Neurodegenerative Dementias

Neurodegenerative dementias are progressive diseases that cause neuronal network breakdown in different brain regions often because of accumulation of misfolded proteins in the brain extracellular matrix, such as amyloids or inside neurons or other cell types of the brain. Several diagnostic protein biomarkers in body fluids are being used and implemented, such as for Alzheimer's disease. However, there is still a lack of biomarkers for co-pathologies and other causes of dementia. Such biofluid-based biomarkers enable precision medicine approaches for diagnosis and treatment, allow to learn more about underlying disease processes, and facilitate the development of patient inclusion and evaluation tools in clinical trials. When designing studies to discover novel biofluid-based biomarkers, choice of technology is an important starting point. But there are so many technologies to choose among. To address this, we here review the technologies that are currently available in research settings and, in some cases, in clinical laboratory practice. This presents a form of lexicon on each technology addressing its use in research and clinics, its strengths and limitations, and a future perspective.

piRNA processing by a trimeric Schlafen-domain nuclease

Transposable elements are genomic parasites that expand within and spread between genomes1. PIWI proteins control transposon activity, notably in the germline2,3. These proteins recognize their targets through small RNA co-factors named PIWI-interacting RNAs (piRNAs), making piRNA biogenesis a key specificity-determining step in this crucial genome immunity system. Although the processing of piRNA precursors is an essential step in this process, many of the molecular details remain unclear. Here, we identify an endoribonuclease, precursor of 21U RNA 5'-end cleavage holoenzyme (PUCH), that initiates piRNA processing in the nematode Caenorhabditis elegans. Genetic and biochemical studies show that PUCH, a trimer of Schlafen-like-domain proteins (SLFL proteins), executes 5'-end piRNA precursor cleavage. PUCH-mediated processing strictly requires a 7-methyl-G cap (m7G-cap) and a uracil at position three. We also demonstrate how PUCH interacts with PETISCO, a complex that binds to piRNA precursors4, and that this interaction enhances piRNA production in vivo. The identification of PUCH concludes the search for the 5'-end piRNA biogenesis factor in C. elegans and uncovers a type of RNA endonuclease formed by three SLFL proteins. Mammalian Schlafen (SLFN) genes have been associated with immunity5, exposing a molecular link between immune responses in mammals and deeply conserved RNA-based mechanisms that control transposable elements.

BLM helicase protein negatively regulates stress granule formation through unwinding RNA G-quadruplex structures

Bloom's syndrome (BLM) protein is a known nuclear helicase that is able to unwind DNA secondary structures such as G-quadruplexes (G4s). However, its role in the regulation of cytoplasmic processes that involve RNA G-quadruplexes (rG4s) has not been previously studied. Here, we demonstrate that BLM is recruited to stress granules (SGs), which are cytoplasmic biomolecular condensates composed of RNAs and RNA-binding proteins. BLM is enriched in SGs upon different stress conditions and in an rG4-dependent manner. Also, we show that BLM unwinds rG4s and acts as a negative regulator of SG formation. Altogether, our data expand the cellular activity of BLM and shed light on the function that helicases play in the dynamics of biomolecular condensates.

Utilising the precision nutrition toolkit in the path towards precision medicine

The overall aim of precision nutrition is to replace the 'one size fits all' approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more 'precision' dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene-diet or microbiota-diet interactions. Third, precision nutrition can be used for 'personalised nutrition' advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet-disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.

Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review

Peptide sequencing is of great significance to fundamental and applied research in the fields such as chemical, biological, medicinal and pharmaceutical sciences. With the rapid development of mass spectrometry and sequencing algorithms, de-novo peptide sequencing using tandem mass spectrometry (MS/MS) has become the main method for determining amino acid sequences of novel and unknown peptides. Advanced algorithms allow the amino acid sequence information to be accurately obtained from MS/MS spectra in short time. In this review, algorithms from exhaustive search to the state-of-art machine learning and neural network for high-throughput and automated de-novo sequencing are introduced and compared. Impacts of datasets on algorithm performance are highlighted. The current limitations and promising direction of de-novo peptide sequencing are also discussed in this review.

LC-MS/MS based characterisation and differential expression of proteins in Himalayan snow trout, Schizothorax labiatus using LFQ technique

Molecular characterization of fish muscle proteins are nowadays considered as a key component to understand the role of specific proteins involved in various physiological and metabolic processes including their up and down regulation in the organisms. Coldwater fish specimens including snow trouts hold different types of proteins which help them to survive in highly diversified temperatures fluctuating from 0 to 20 °C. So, in current study, the liquid chromatography mass spectrometry using label free quantification technique has been used to investigate the muscle proteome profile of Schizothorax labiatus. For proteomic study, two weight groups of S. labiatus were taken from river Sindh. The proteomic analysis of group 1 revealed that a total of 235 proteins in male and 238 in female fish were recorded. However, when male and female S. labiatus were compared with each other on the basis of spectral count and abundance of peptides by ProteinLynx Global Server software, a total of 14 down-regulated and 22 up-regulated proteins were noted in this group. The highly down-regulated ones included homeodomain protein HoxA2b, retinol-binding protein 4, MHC class II beta chain and proopiomelanocortin while as the highly expressed up-regulated proteins comprised of gonadotropin I beta subunit, NADH dehydrogenase subunit 4, manganese superoxide dismutase, recombinase-activating protein 2, glycosyltransferase, chymotrypsin and cytochrome b. On the other hand, the proteomic characterisation of group 2 of S. labiatus revealed that a total of 227 proteins in male and 194 in female fish were recorded. When male and female S. labiatus were compared with each other by label free quantification, a total of 20 down-regulated and 18 up-regulated proteins were recorded. The down-regulated protein expression of group 2 comprised hepatic lipase, allograft inflammatory factor-1, NADH dehydrogenase subunit 4 and myostatin 1 while the highly expressed up-regulated proteins included glycogen synthase kinase-3 beta variant 2, glycogen synthase kinase-3 beta variant 5, cholecystokinin, glycogen synthase kinase-3 beta variant 3 and cytochrome b. Significant (P < 0.05) difference in the expression of down-regulated and up-regulated proteins was also noted between the two sexes of S. labiatus in each group. According to MS analysis, the proteins primarily concerned with the growth, skeletal muscle development and metabolism were down-regulated in river Sindh, which indicates that growth of fish during the season of collection i.e., winter was slow owing to less food availability, gonad development and low metabolic activity. While, the proteins related to immune response of fish were also noted to be down-regulated thereby signifying that the ecosystem has less pollution loads, microbial, pathogenic and anthropogenic activities. It was also found that the proteins involved in glycogen metabolism, reproductive and metabolic processes, particularly lipid metabolism were up-regulated in S. labiatus. The significant expression of these proteins may be connected to pre-spawning, gonad development and use of stored food as source of energy. The information generated in this study can be applied to future research aimed at enhancing food traceability, food safety, risk management and authenticity analysis.

Proteomic approaches to study ubiquitinomics

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

Analysis and prediction of protein stability based on interaction network, gene ontology, and KEGG pathway enrichment scores

Metabolic stability of proteins plays a vital role in various dedicated cellular processes. Traditional methods of measuring the metabolic stability are time-consuming and expensive. Therefore, we developed a more efficient computational approach to understand the protein dynamic action mechanisms in biological process networks. In this study, we collected 341 short-lived proteins and 824 non-short-lived proteins from U2OS; 342 short-lived proteins and 821 non-short-lived proteins from HEK293T; 424 short-lived proteins and 1153 non-short-lived proteins from HCT116; and 384 short-lived proteins and 992 non-short-lived proteins from RPE1. The proteins were encoded by GO and KEGG enrichment scores based on the genes and their neighbors in STRING, resulting in 20,681 GO term features and 297 KEGG pathway features. We also incorporated the protein interaction information from STRING into the features and obtained 19,247 node features. Boruta and mRMR methods were used for feature filtering, and IFS method was used to obtain the best feature subsets and create the models with the highest performance. The present study identified 42 features that did not appear in previous studies and classified them into eight groups according to their functional annotation. By reviewing the literature, we found that the following three functional groups were critical in determining the stability of proteins: synaptic transmission, post-translational modifications, and cell fate determination. These findings may serve as a valuable reference for developing drugs that target protein stability.

The mitochondrial protease OMA1 acts as a metabolic safeguard upon nuclear DNA damage

The metabolic plasticity of mitochondria ensures cell development, differentiation, and survival. The peptidase OMA1 regulates mitochondrial morphology via OPA1 and stress signaling via DELE1 and orchestrates tumorigenesis and cell survival in a cell- and tissue-specific manner. Here, we use unbiased systems-based approaches to show that OMA1-dependent cell survival depends on metabolic cues. A metabolism-focused CRISPR screen combined with an integrated analysis of human gene expression data found that OMA1 protects against DNA damage. Nucleotide deficiencies induced by chemotherapeutic agents promote p53-dependent apoptosis of cells lacking OMA1. The protective effect of OMA1 does not depend on OMA1 activation or OMA1-mediated OPA1 and DELE1 processing. OMA1-deficient cells show reduced glycolysis and accumulate oxidative phosphorylation (OXPHOS) proteins upon DNA damage. OXPHOS inhibition restores glycolysis and confers resistance against DNA damage. Thus, OMA1 dictates the balance between cell death and survival through the control of glucose metabolism, shedding light on its role in cancerogenesis.

A comprehensive molecular profiling approach reveals metabolic alterations that steer bone tissue regeneration

Bone regeneration after fracture is a complex process with high and dynamic energy demands. The impact of metabolism on bone healing progression and outcome, however, is so far understudied. Our comprehensive molecular profiling reveals that central metabolic pathways, such as glycolysis and the citric acid cycle, are differentially activated between rats with successful or compromised bone regeneration (young versus aged female Sprague-Dawley rats) early in the inflammatory phase of bone healing. We also found that the citric acid cycle intermediate succinate mediates individual cellular responses and plays a central role in successful bone healing. Succinate induces IL-1β in macrophages, enhances vessel formation, increases mesenchymal stromal cell migration, and potentiates osteogenic differentiation and matrix formation in vitro. Taken together, metabolites-here particularly succinate-are shown to play central roles as signaling molecules during the onset of healing and in steering bone tissue regeneration.

Proteomic profiling reveals distinct phases to the restoration of chromatin following DNA replication

Chromatin organization must be maintained during cell proliferation to preserve cellular identity and genome integrity. However, DNA replication results in transient displacement of DNA-bound proteins, and it is unclear how they regain access to newly replicated DNA. Using quantitative proteomics coupled to Nascent Chromatin Capture or isolation of Proteins on Nascent DNA, we provide time-resolved binding kinetics for thousands of proteins behind replisomes within euchromatin and heterochromatin in human cells. This shows that most proteins regain access within minutes to newly replicated DNA. In contrast, 25% of the identified proteins do not, and this delay cannot be inferred from their known function or nuclear abundance. Instead, chromatin organization and G1 phase entry affect their reassociation. Finally, DNA replication not only disrupts but also promotes recruitment of transcription factors and chromatin remodelers, providing a significant advance in understanding how DNA replication could contribute to programmed changes of cell memory.

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.

A targeted multi-proteomics approach generates a blueprint of the ciliary ubiquitinome

Establishment and maintenance of the primary cilium as a signaling-competent organelle requires a high degree of fine tuning, which is at least in part achieved by a variety of post-translational modifications. One such modification is ubiquitination. The small and highly conserved ubiquitin protein possesses a unique versatility in regulating protein function via its ability to build mono and polyubiquitin chains onto target proteins. We aimed to take an unbiased approach to generate a comprehensive blueprint of the ciliary ubiquitinome by deploying a multi-proteomics approach using both ciliary-targeted ubiquitin affinity proteomics, as well as ubiquitin-binding domain-based proximity labelling in two different mammalian cell lines. This resulted in the identification of several key proteins involved in signaling, cytoskeletal remodeling and membrane and protein trafficking. Interestingly, using two different approaches in IMCD3 and RPE1 cells, respectively, we uncovered several novel mechanisms that regulate cilia function. In our IMCD3 proximity labeling cell line model, we found a highly enriched group of ESCRT-dependent clathrin-mediated endocytosis-related proteins, suggesting an important and novel role for this pathway in the regulation of ciliary homeostasis and function. In contrast, in RPE1 cells we found that several structural components of caveolae (CAV1, CAVIN1, and EHD2) were highly enriched in our cilia affinity proteomics screen. Consistently, the presence of caveolae at the ciliary pocket and ubiquitination of CAV1 specifically, were found likely to play a role in the regulation of ciliary length in these cells. Cilia length measurements demonstrated increased ciliary length in RPE1 cells stably expressing a ubiquitination impaired CAV1 mutant protein. Furthermore, live cell imaging in the same cells revealed decreased CAV1 protein turnover at the cilium as the possible cause for this phenotype. In conclusion, we have generated a comprehensive list of cilia-specific proteins that are subject to regulation via ubiquitination which can serve to further our understanding of cilia biology in health and disease.

Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways

Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)-also known as multiple reaction monitoring (MRM)-and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.

In-Depth Blood Proteome Profiling by Extensive Fractionation and Multiplexed Quantitative Mass Spectrometry

Blood in the circulatory system carries information of physiological and pathological status of the human body, so blood proteins are often used as biomarkers for diagnosis, prognosis, and therapy. Human blood proteome can be explored by the latest technologies in mass spectrometry (MS), creating an opportunity of discovering new disease biomarkers. The extreme dynamic range of protein concentrations in blood, however, poses a challenge to detect proteins of low abundance, namely, tissue leakage proteins. Here, we describe a strategy to directly analyze undepleted blood samples by extensive liquid chromatography (LC) fractionation and 18-plex tandem-mass-tag (TMT) mass spectrometry. The proteins in blood specimens (e.g., plasma or serum) are isolated by acetone precipitation and digested into peptides. The resulting peptides are TMT-labeled, separated by basic pH reverse-phase (RP) LC into at least 40 fractions, and analyzed by acidic pH RPLC and high-resolution MS/MS, leading to the quantification of ~3000 unique proteins. Further increase of basic pH RPLC fractions and adjustment of the fraction concatenation strategy can enhance the proteomic coverage (up to ~5000 proteins). Finally, the combination of multiple batches of TMT experiments allows the profiling of hundreds of blood samples. This TMT-MS-based method provides a powerful platform for deep proteome profiling of human blood samples.

Top-Down Proteomics and Comparative 2D-DIGE Analysis

The combination of large-scale protein separation techniques, sophisticated mass spectrometry, and systems bioinformatics has led to the establishment of proteomics as a distinct discipline within the wider field of protein biochemistry. Both discovery proteomics and targeted proteomics are widely used in biological and biomedical research, whereby the analytical approaches can be broadly divided into proteoform-centric top-down proteomics versus peptide-centric bottom-up proteomics. This chapter outlines the scientific value of top-down proteomics and describes how fluorescence two-dimensional difference gel electrophoresis can be combined with the systematic analysis of crucial post-translational modifications. The concept of on-membrane digestion following the electrophoretic transfer of proteins and the usefulness of comparative two-dimensional immunoblotting are discussed.

Functional impairment of cortical AMPA receptors in schizophrenia

Clinical and preclinical studies suggest that some of the behavioral alterations observed in schizophrenia (SZ) may be mechanistically linked to synaptic dysfunction of glutamatergic signaling. Recent genetic and proteomic studies suggest alterations of cortical glutamate receptors of the AMPA-type (AMPARs), which are the predominant ligand-gated ionic channels of fast transmission at excitatory synapses. The impact of gene and protein alterations on the electrophysiological activity of AMPARs is not known in SZ. In this proof of principle work, using human postmortem brain synaptic membranes isolated from the dorsolateral prefrontal cortex (DLPFC), we combined electrophysiological analysis from microtransplanted synaptic membranes (MSM) with transcriptomic (RNA-Seq) and label-free proteomics data in 10 control and 10 subjects diagnosed with SZ. We observed in SZ a reduction in the amplitude of AMPARs currents elicited by kainate, an agonist of AMPARs that blocks the desensitization of the receptor. This reduction was not associated with protein abundance but with a reduction in kainate's potency to activate AMPARs. Electrophysiologically-anchored dataset analysis (EDA) was used to identify synaptosomal proteins that linearly correlate with the amplitude of the AMPARs responses, gene ontology functional annotations were then used to determine protein-protein interactions. Protein modules associated with positive AMPARs current increases were downregulated in SZ, while protein modules that were upregulated in SZ were associated with decreased AMPARs currents. Our results indicate that transcriptomic and proteomic alterations, frequently observed in the DLPFC in SZ, converge at the synaptic level producing a functional electrophysiological impairment of AMPARs.

Biomarkers discovery for endometrial cancer: A graph convolutional sample network method

BACKGROUND

Endometrial carcinoma is the sixth most common cancer in women worldwide. Importantly, endometrial cancer is among the few types of cancers with patient mortality that is still increasing, which indicates that the improvement in its diagnosis and treatment is still urgent. Moreover, biomarker discovery is essential for precise classification and prognostic prediction of endometrial cancer.

METHODS

A novel graph convolutional sample network method was used to identify and validate biomarkers for the classification of endometrial cancer. The sample networks were first constructed for each sample, and the gene pairs with high frequencies were identified to construct a subtype-specific network. Putative biomarkers were then screened using the highest degrees in the subtype-specific network. Finally, simplified sample networks are constructed using the biomarkers for the graph convolutional network (GCN) training and prediction.

RESULTS

Putative biomarkers (23) were identified using the novel bioinformatics model. These biomarkers were then rationalised with functional analyses and were found to be correlated to disease survival with network entropy characterisation. These biomarkers will be helpful in future investigations of the molecular mechanisms and therapeutic targets of endometrial cancers.

CONCLUSIONS

A novel bioinformatics model combining sample network construction with GCN modelling is proposed and validated for biomarker discovery in endometrial cancer. The model can be generalized and applied to biomarker discovery in other complex diseases.

Unveiling the Secretome of the Fungal Plant Pathogen Neofusicoccum parvum Induced by In Vitro Host Mimicry

Neofusicoccum parvum is a fungal plant pathogen of a wide range of hosts but knowledge about the virulence factors of N. parvum and host-pathogen interactions is rather limited. The molecules involved in the interaction between N. parvum and Eucalyptus are mostly unknown, so we used a multi-omics approach to understand pathogen-host interactions. We present the first comprehensive characterization of the in vitro secretome of N. parvum and a prediction of protein-protein interactions using a dry-lab non-targeted interactomics strategy. We used LC-MS to identify N. parvum protein profiles, resulting in the identification of over 400 proteins, from which 117 had a different abundance in the presence of the Eucalyptus stem. Most of the more abundant proteins under host mimicry are involved in plant cell wall degradation (targeting pectin and hemicellulose) consistent with pathogen growth on a plant host. Other proteins identified are involved in adhesion to host tissues, penetration, pathogenesis, or reactive oxygen species generation, involving ribonuclease/ribotoxin domains, putative ricin B lectins, and necrosis elicitors. The overexpression of chitosan synthesis proteins during interaction with the Eucalyptus stem reinforces the hypothesis of an infection strategy involving pathogen masking to avoid host defenses. Neofusicoccum parvum has the molecular apparatus to colonize the host but also actively feed on its living cells and induce necrosis suggesting that this species has a hemibiotrophic lifestyle.

Deep Single-Shot NanoLC-MS Proteome Profiling with a 1500 Bar UHPLC System, Long Fully Porous Columns, and HRAM MS

This study demonstrates how the latest ultrahigh-performance liquid chromatography (UHPLC) technology can be combined with high-resolution accurate-mass (HRAM) mass spectrometry (MS) and long columns packed with fully porous particles to improve bottom-up proteomics analysis with nanoflow liquid chromatography–mass spectrometry (nanoLC-MS) methods. The increased back pressures from the UHPLC system enabled the use of 75 μm I.D. × 75 cm columns packed with 2 μm particles at a typical 300 nL/min flow rate as well as elevated and reduced flow rates. The constant pressure pump operation at 1500 bar reduced sample loading and column washing/equilibration stages and overall overhead time, which maximizes MS utilization time. The versatility of flow rate optimization to balance the sensitivity, throughput with sample loading amount, and capability of using longer gradients contributes to a greater number of peptide and protein identifications for single-shot bottom-up proteomics experiments. The routine proteome profiling and precise quantification of >7000 proteins with single-shot nanoLC-MS analysis open possibilities for large-scale discovery studies with a deep dive into the protein level alterations. Data are available via ProteomeXchange with identifier PXD035665.

Cross-linking reactions in food proteins and proteomic approaches for their detection

Various protein cross-linking reactions leading to molecular polymerization and covalent aggregates have been described in processed foods. They are an undesired side effect of processes designed to reduce bacterial load, extend shelf life, and modify technological properties, as well as being an expected result of treatments designed to modify raw material texture and function. Although the formation of these products is known to affect the sensory and technological properties of foods, the corresponding cross-linking reactions and resulting protein polymers have not yet undergone detailed molecular characterization. This is essential for describing how their generation can be related to food processing conditions and quality parameters. Due to the complex structure of cross-linked species, bottom-up proteomic procedures developed to characterize various amino acid modifications associated with food processing conditions currently offer a limited molecular description of bridged peptide structures. Recent progress in cross-linking mass spectrometry for the topological characterization of protein complexes has facilitated the development of various proteomic methods and bioinformatic tools for unveiling bridged species, which can now also be used for the detailed molecular characterization of polymeric cross-linked products in processed foods. We here examine their benefits and limitations in terms of evaluating cross-linked food proteins and propose future scenarios for application in foodomics. They offer potential for understanding the protein cross-linking formation mechanisms in processed foods, and how the inherent beneficial properties of treated foodstuffs can be preserved or enhanced.

Functional Diversity and Evolution of the Drosophila Sperm Proteome

Spermatozoa are central to fertilization and the evolutionary fitness of sexually reproducing organisms. As such, a deeper understanding of sperm proteomes (and associated reproductive tissues) has proven critical to the advancement of the fields of sexual selection and reproductive biology. Due to their extraordinary complexity, proteome depth-of-coverage is dependent on advancements in technology and related bioinformatics, both of which have made significant advancements in the decade since the last Drosophila sperm proteome was published. Here, we provide an updated version of the Drosophila melanogaster sperm proteome (DmSP3) using improved separation and detection methods and an updated genome annotation. Combined with previous versions of the sperm proteome, the DmSP3 contains a total of 3176 proteins, and we provide the first label-free quantitation of the sperm proteome for 2125 proteins. The top 20 most abundant proteins included the structural elements α- and β-tubulins and sperm leucyl-aminopeptidases. Both gene content and protein abundance were significantly reduced on the X chromosome, consistent with prior genomic studies of X chromosome evolution. We identified 9 of the 16 Y-linked proteins, including known testis-specific male fertility factors. We also identified almost one-half of known Drosophila ribosomal proteins in the DmSP3. The role of this subset of ribosomal proteins in sperm is unknown. Surprisingly, our expanded sperm proteome also identified 122 seminal fluid proteins (Sfps), proteins originally identified in the accessory glands. We show that a significant fraction of 'sperm-associated Sfps' are recalcitrant to concentrated salt and detergent treatments, suggesting this subclass of Sfps are expressed in testes and may have additional functions in sperm, per se. Overall, our results add to a growing landscape of both sperm and seminal fluid protein biology and in particular provides quantitative evidence at the protein level for prior findings supporting the meiotic sex-chromosome inactivation model for male-specific gene and X chromosome evolution.

Interspecies Isobaric Labeling-Based Quantitative Proteomics Reveals Protein Changes in the Ovary of Aedes aegypti Coinfected With ZIKV and Wolbachia

Zika is a vector-borne disease caused by an arbovirus (ZIKV) and overwhelmingly transmitted by Ae. aegypti. This disease is linked to adverse fetal outcomes, mostly microcephaly in newborns, and other clinical aspects such as acute febrile illness and neurologic complications, for example, Guillain-Barré syndrome. One of the most promising strategies to mitigate arbovirus transmission involves releasing Ae. aegypti mosquitoes carrying the maternally inherited endosymbiont bacteria Wolbachia pipientis. The presence of Wolbachia is associated with a reduced susceptibility to arboviruses and a fitness cost in mosquito life-history traits such as fecundity and fertility. However, the mechanisms by which Wolbachia influences metabolic pathways leading to differences in egg production remains poorly known. To investigate the impact of coinfections on the reproductive tract of the mosquito, we applied an isobaric labeling-based quantitative proteomic strategy to investigate the influence of Wolbachia wMel and ZIKV infection in Ae. aegypti ovaries. To the best of our knowledge, this is the most complete proteome of Ae. aegypti ovaries reported so far, with a total of 3913 proteins identified, were also able to quantify 1044 Wolbachia proteins in complex sample tissue of Ae. aegypti ovary. Furthermore, from a total of 480 mosquito proteins modulated in our study, we discuss proteins and pathways altered in Ae. aegypti during ZIKV infections, Wolbachia infections, coinfection Wolbachia/ZIKV, and compared with no infection, focusing on immune and reproductive aspects of Ae. aegypti. The modified aspects mainly were related to the immune priming enhancement by Wolbachia presence and the modulation of the Juvenile Hormone pathway caused by both microorganism’s infection.

Dual roles of drug or its metabolite-protein conjugate: Cutting-edge strategy of drug discovery using shotgun proteomics

Many drugs can bind directly to proteins or be bioactivated by metabolizing enzymes to form reactive metabolites (RMs) that rapidly bind to proteins to form drug-protein conjugates or metabolite-protein conjugates (DMPCs). The close relationship between DMPCs and idiosyncratic adverse drug reactions (IADRs) has been recognized; drug discovery teams tend to avoid covalent interactions in drug discovery projects. Covalent interactions in DMPCs can provide high potency and long action duration and conquer the intractable targets, inspiring drug design, and development. This forms the dual role feature of DMPCs. Understanding the functional implications of DMPCs in IADR control and therapeutic applications requires precise identification of these conjugates from complex biological samples. While classical biochemical methods have contributed significantly to DMPC detection in the past decades, the low abundance and low coverage of DMPCs have become a bottleneck in this field. An emerging transformation toward shotgun proteomics is on the rise. The evolving shotgun proteomics techniques offer improved reproducibility, throughput, specificity, operability, and standardization. Here, we review recent progress in the systematic discovery of DMPCs using shotgun proteomics. Furthermore, the applications of shotgun proteomics supporting drug development, toxicity mechanism investigation, and drug repurposing processes are also reviewed and prospected.

Innovative in Silico Approaches for Characterization of Genes and Proteins

Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.

Systematic Review and Meta-Analysis on MS-Based Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Bipolar Disorder

Bipolar disorder (BD) is a clinically heterogeneous condition, presenting a complex underlying etiopathogenesis that is not sufficiently characterized. Without molecular biomarkers being used in the clinical environment, several large screen proteomics studies have been conducted to provide valuable molecular information. Mass spectrometry (MS)-based techniques can be a powerful tool for the identification of disease biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids to assess BD biomarkers and identify relevant networks of biological pathways. Following PRISMA guidelines, we searched for studies using MS proteomics to identify proteomic differences between BD patients and healthy controls (PROSPERO database: CRD42021264955). Fourteen articles fulfilled the inclusion criteria, allowing the identification of 266 differentially expressed proteins. Gene ontology analysis identified complement and coagulation cascades, lipid and cholesterol metabolism, and focal adhesion as the main enriched biological pathways. A meta-analysis was performed for apolipoproteins (A-I, C-III, and E); however, no significant differences were found. Although the proven ability of MS proteomics to characterize BD, there are several confounding factors contributing to the heterogeneity of the findings. In the future, we encourage the scientific community to use broader samples and validation cohorts, integrating omics with bioinformatics tools towards providing a comprehensive understanding of proteome alterations, seeking biomarkers of BD, and contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.

Bayesian Inference of Stochastic Dynamic Models Using Early-Rejection Methods Based on Sequential Stochastic Simulations

Stochastic modelling is an important method to investigate the functions of noise in a wide range of biological systems. However, the parameter inference for stochastic models is still a challenging problem partially due to the large computing time required for stochastic simulations. To address this issue, we propose a novel early-rejection method by using sequential stochastic simulations. We first show that a large number of stochastic simulations are required to obtain reliable inference results. Instead of generating a large number of simulations for each parameter sample, we propose to generate these simulations in a number of stages. The simulation process will go to the next stage only if the accuracy of simulations at the current stage satisfies a given error criterion. We propose a formula to determine the error criterion and use a stochastic differential equation model to examine the effects of different criteria. Three biochemical network models are used to evaluate the efficiency and accuracy of the proposed method. Numerical results suggest the proposed early-rejection method achieves substantial improvement in the efficiency for the inference of stochastic models.

Characterization of a Thermostable and Surfactant-Tolerant Chondroitinase B from a Marine Bacterium Microbulbifer sp. ALW1

Chondroitinase plays an important role in structural and functional studies of chondroitin sulfate (CS). In this study, a new member of chondroitinase B of PL6 family, namely ChSase B6, was cloned from marine bacterium Microbulbifer sp. ALW1 and subjected to enzymatic and structural characterization. The recombinant ChSase B6 showed optimum activity at 40 °C and pH 8.0, with enzyme kinetic parameters of Km and Vmax against chondroitin sulfate B (CSB) to be 7.85 µg/mL and 1.21 U/mg, respectively. ChSase B6 demonstrated thermostability under 60 °C for 2 h with about 50% residual activity and good pH stability under 4.0-10.0 for 1 h with above 60% residual activity. In addition, ChSase B6 displayed excellent stability against the surfactants including Tween-20, Tween-80, Trion X-100, and CTAB. The degradation products of ChSase B6-treated CSB exhibited improved antioxidant ability as a hydroxyl radical scavenger. Structural analysis and site-directed mutagenesis suggested that the conserved residues Lys248 and Arg269 were important for the activity of ChSase B6. Characterization, structure, and molecular dynamics simulation of ChSase B6 provided a guide for further tailoring for its industrial application for chondroitin sulfate bioresource development.

Whole-genome sequencing reveals that variants in the Interleukin 18 Receptor Accessory Protein 3'UTR protect against ALS

The noncoding genome is substantially larger than the protein-coding genome but has been largely unexplored by genetic association studies. Here, we performed region-based rare variant association analysis of >25,000 variants in untranslated regions of 6,139 amyotrophic lateral sclerosis (ALS) whole genomes and the whole genomes of 70,403 non-ALS controls. We identified interleukin-18 receptor accessory protein (IL18RAP) 3' untranslated region (3'UTR) variants as significantly enriched in non-ALS genomes and associated with a fivefold reduced risk of developing ALS, and this was replicated in an independent cohort. These variants in the IL18RAP 3'UTR reduce mRNA stability and the binding of double-stranded RNA (dsRNA)-binding proteins. Finally, the variants of the IL18RAP 3'UTR confer a survival advantage for motor neurons because they dampen neurotoxicity of human induced pluripotent stem cell (iPSC)-derived microglia bearing an ALS-associated expansion in C9orf72, and this depends on NF-κB signaling. This study reveals genetic variants that protect against ALS by reducing neuroinflammation and emphasizes the importance of noncoding genetic association studies.

Protein signatures to distinguish aggressive from indolent prostate cancer

BACKGROUND

Distinguishing men with aggressive from indolent prostate cancer is critical to decisions in the management of clinically localized prostate cancer. Molecular signatures of aggressive disease could help men overcome this major clinical challenge by reducing unnecessary treatment and allowing more appropriate treatment of aggressive disease.

METHODS

We performed a mass spectrometry-based proteomic analysis of normal and malignant prostate tissues from 22 men who underwent surgery for prostate cancer. Prostate cancer samples included Grade Groups (3-5), with 8 patients experiencing recurrence and 14 without evidence of recurrence with a mean of 6.8 years of follow-up. To better understand the biological pathways underlying prostate cancer aggressiveness, we performed a systems biology analysis and gene enrichment analysis. Proteins that distinguished recurrent from nonrecurrent cancer were chosen for validation by immunohistochemical analysis on tissue microarrays containing samples from a larger cohort of patients with recurrent and nonrecurrent prostate cancer.

RESULTS

In all, 24,037 unique peptides (false discovery rate < 1%) corresponding to 3,313 distinct proteins were identified with absolute abundance ranges spanning seven orders of magnitude. Of these proteins, 115 showed significantly (p < 0.01) different levels in tissues from recurrent versus nonrecurrent cancers. Analysis of all differentially expressed proteins in recurrent and nonrecurrent cases identified several protein networks, most prominently one in which approximately 24% of the proteins in the network were regulated by the YY1 transcription factor (adjusted p < 0.001). Strong immunohistochemical staining levels of three differentially expressed proteins, POSTN, CALR, and CTSD, on a tissue microarray validated their association with shorter patient survival.

CONCLUSIONS

The protein signatures identified could improve understanding of the molecular drivers of aggressive prostate cancer and be used as candidate prognostic biomarkers.

BRCA1 mutations in high-grade serous ovarian cancer are associated with proteomic changes in DNA repair, splicing, transcription regulation and signaling

Despite recent advances in the management of BRCA1 mutated high-grade serous ovarian cancer (HGSC), the physiology of these tumors remains poorly understood. Here we provide a comprehensive molecular understanding of the signaling processes that drive HGSC pathogenesis with the addition of valuable ubiquitination profiling, and their dependency on BRCA1 mutation-state directly in patient-derived tissues. Using a multilayered proteomic approach, we show the tight coordination between the ubiquitination and phosphorylation regulatory layers and their role in key cellular processes related to BRCA1-dependent HGSC pathogenesis. In addition, we identify key bridging proteins, kinase activity, and post-translational modifications responsible for molding distinct cancer phenotypes, thus providing new opportunities for therapeutic intervention, and ultimately advance towards a more personalized patient care.

Sensitive Plant N-Terminome Profiling with HUNTER

Protein N-termini provide unique and distinguishing information on proteolytically processed or N-terminally modified proteoforms. Also splicing, use of alternative translation initiation sites, and a variety of co- and post-translational N-terminal modifications generate distinct proteoforms that are unambiguously identified by their N-termini. However, N-terminal peptides are only a small fraction among all peptides generated in a shotgun proteome digest, are often of low stoichiometric abundance, and therefore require enrichment. Various protocols for enrichment of N-terminal peptides have been established and successfully been used for protease substrate discovery and profiling of N-terminal modification, but often require large amounts of proteome. We have recently established the High-efficiency Undecanal-based N-Termini EnRichment (HUNTER) as a fast and sensitive method to enable enrichment of protein N-termini from limited sample sources with as little as a few microgram proteome. Here we present our current HUNTER protocol for sensitive plant N-terminome profiling, including sample preparation, enrichment of N-terminal peptides, and mass spectrometry data analysis.

Differential Translation Activity Analysis Using Bioorthogonal Noncanonical Amino Acid Tagging (BONCAT) in Archaea

The study of protein production and degradation in a quantitative and time-dependent manner is a major challenge to better understand cellular physiological response. Among available technologies bioorthogonal noncanonical amino acid tagging (BONCAT) is an efficient approach allowing for time-dependent labeling of proteins through the incorporation of chemically reactive noncanonical amino acids like L-azidohomoalanine (L-AHA). The azide-containing amino-acid derivative enables a highly efficient and specific reaction termed click chemistry, whereby the azide group of the L-AHA reacts with a reactive alkyne derivate, like dibenzocyclooctyne (DBCO) derivatives, using strain-promoted alkyne-azide cycloaddition (SPAAC). Moreover, available DBCO containing reagents are versatile and can be coupled to fluorophore (e.g., Cy7) or affinity tag (e.g., biotin) derivatives, for easy visualization and affinity purification, respectively.Here, we describe a step-by-step BONCAT protocol optimized for the model archaeon Haloferax volcanii , but which is also suitable to harness other biological systems. Finally, we also describe examples of downstream visualization, affinity purification of L-AHA-labeled proteins and differential expression analysis.In conclusion, the following BONCAT protocol expands the available toolkit to explore proteostasis using time-resolved semiquantitative proteomic analysis in archaea .

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Deep N-terminomics of Mycobacterium tuberculosis H37Rv extensively correct annotated encoding genes

Mycobacterium tuberculosis (MTB) is a severe causing agent of tuberculosis (TB). Although H37Rv, the type strain of M. tuberculosis was sequenced in 1998, annotation errors of encoding genes have been frequently reported in hundreds of papers. This phenomenon is particularly severe at the 5' end of the genes. Here, we applied a TMPP [(N-Succinimidyloxycarbonylmethyl) tris (2,4,6-trimethoxyphenyl) phosphonium bromide] labeling combined with StageTip separating strategy on M. tuberculosis H37Rv to characterize the N-terminal start sites of its annotated encoding genes. Totally, 1047 proteins were identified with 2058 TMPP labeled N-terminal peptides from all the 2625 mass spectrometer (MS) sequenced proteins. Comparative genomics analysis allowed the re-annotation of 43 proteins' N-termini in H37Rv and 762 proteins in Mycobacteriaceae. All revised N-termini start sites were distributed in 5'-UTR of annotated genes due to over-annotation of previous N-terminal initiation codon, especially the ATG. In addition, we identified and verified a novel gene Rv1078A in +3 frame different from the annotated gene Rv1078 in +2 frame. Altogether, our findings contribute to the better understanding of N-terminal of H37Rv and other species from Mycobacteriaceae that can assist future studies on biological study.