Mass spectrometry-based proteomics and network biology

Mass-spectrometric analysis of APOB polymorphism rs1042031 (G/T) and its influence on serum proteome of coronary artery disease patients: genetic-derived proteomics consequences

Genetic polymorphisms of apolipoprotein B gene (APOB) may result into serum proteomic perturbance in Coronary Artery Disease (CAD). The current case-control cohort of Pakistani subjects was designed to analyze the genetic influence of APOB rs1042031, (G/T) genotype on serum proteome. Subjects were categorized into two groups: CAD patients (n = 480) and healthy individuals (n = 220). For genotyping, tetra ARMS-PCR was carried out and validated through sequencing, whereas LC/MS-based proteomic analysis of serum samples was performed through label-free quantification. In initial step of genotyping, the frequencies of each genotype GG, GT, and TT were 70%, 27%, and 30% in CAD patients, while in control group, the subjects were 52%, 43%, and 5%, respectively, in CAD patients. The genotypic frequencies in patients vs. control groups found significantly different (p = 0.004), and a strong association of dominant alleles GG with the CAD was observed in both dominant (OR: 2.4 (1.71-3.34), p = 0.001) and allelic genetic models (OR: 2.0 (1.45-2.86), p = 0.001). In second step of label-free quantitation, a total of 40 significant proteins were found with altered expression in CAD patients. The enriched Gene Ontology (GO) terms of molecular functions and pathways of these protein showed upregulated pathways as follows: chylomicron remodeling and assembly, complement cascade activation, plasma lipoprotein assembly, apolipoprotein-A receptor binding, and metabolism of fat-soluble vitamins in G allele carrier of rs1042031 (G > T) vs. mutant T-allele carriers. This study provides better understanding of CAD pathobiology by proteogenomics of APOB. It evidences the influence of APOB rs1042031-dominant (GG) genotype with CAD patients.

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.

Pan-Omics in Sheep: Unveiling Genetic Landscapes

Multi-omics-integrated analysis, known as panomics, represents an advanced methodology that harnesses various high-throughput technologies encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Sheep, playing a pivotal role in agricultural sectors due to their substantial economic importance, have witnessed remarkable advancements in genetic breeding through the amalgamation of multiomics analyses, particularly with the evolution of high-throughput technologies. This integrative approach has established a robust theoretical foundation, enabling a deeper understanding of sheep genetics and fostering improvements in breeding strategies. The comprehensive insights obtained through this approach shed light on diverse facets of sheep development, including growth, reproduction, disease resistance, and the quality of livestock products. This review primarily focuses on the application of principal omics analysis technologies in sheep, emphasizing correlation studies between multiomics data and specific traits such as meat quality, wool characteristics, and reproductive features. Additionally, this paper anticipates forthcoming trends and potential developments in this field.

Proteomics and Metabolomics Analysis Reveal the Regulation Mechanism of Linoleate Isomerase Activity and Function in Propionibacterium acnes

Conjugated linoleic acid (CLA) holds significant application prospects due to its anticancer, anti-atherosclerosis, lipid-lowering, weight-loss, and growth-promoting functions. The key to its efficient production lies in optimizing the biocatalytic performance of linoleic acid isomerase (LAI). Here, we constructed a Propionibacterium acnes mutant library and screened positive mutants with high linoleate isomerase activity. The proteomics and metabolomics were used to explore the mechanism in the regulation of linoleic acid isomerase activity. High-throughput proteomics revealed 104 differentially expressed proteins unique to positive mutant strains of linoleic acid isomerase of which 57 were upregulated and 47 were downregulated. These differentially expressed proteins were primarily involved in galactose metabolism, the phosphotransferase system, starch metabolism, and sucrose metabolism. Differential metabolic pathways were mainly enriched in amino acid biosynthesis, including glutamate metabolism, the Aminoacyl-tRNA biosynthesis pathway, and the ABC transporter pathway. The upregulated metabolites include dl-valine and Acetyl coA, while the downregulated metabolites include Glutamic acid and Phosphoenolpyruvate. Overall, the activity of linoleic acid isomerase in the mutant strain was increased by the regulation of key proteins involved in galactose metabolism, sucrose metabolism, and the phosphotransferase system. This study provides a theoretical basis for the development of high-yield CLA food.

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/ .

The interplay of post-translational protein modifications in Arabidopsis leaves during photosynthesis induction

Diurnal dark to light transition causes profound physiological changes in plant metabolism. These changes require distinct modes of regulation as a unique feature of photosynthetic lifestyle. The activities of several key metabolic enzymes are regulated by light-dependent post-translational modifications (PTM) and have been studied at depth at the level of individual proteins. In contrast, a global picture of the light-dependent PTMome dynamics is lacking, leaving the response of a large proportion of cellular function undefined. Here, we investigated the light-dependent metabolome and proteome changes in Arabidopsis rosettes in a time resolved manner to dissect their kinetic interplay, focusing on phosphorylation, lysine acetylation, and cysteine-based redox switches. Of over 24 000 PTM sites that were detected, more than 1700 were changed during the transition from dark to light. While the first changes, as measured 5 min after onset of illumination, occurred mainly in the chloroplasts, PTM changes at proteins in other compartments coincided with the full activation of the Calvin-Benson cycle and the synthesis of sugars at later timepoints. Our data reveal connections between metabolism and PTM-based regulation throughout the cell. The comprehensive multiome profiling analysis provides unique insight into the extent by which photosynthesis reprograms global cell function and adds a powerful resource for the dissection of diverse cellular processes in the context of photosynthetic function.

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.

Quartet protein reference materials and datasets for multi-platform assessment of label-free proteomics

BACKGROUND

Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories.

RESULTS

Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics.

CONCLUSION

Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.

CACO: A Core-Attachment Method With Cross-Species Functional Ortholog Information to Detect Human Protein Complexes

Protein complexes play an essential role in living cells. Detecting protein complexes is crucial to understand protein functions and treat complex diseases. Due to high time and resource consumption of experiment approaches, many computational approaches have been proposed to detect protein complexes. However, most of them are only based on protein-protein interaction (PPI) networks, which heavily suffer from the noise in PPI networks. Therefore, we propose a novel core-attachment method, named CACO, to detect human protein complexes, by integrating the functional information from other species via protein ortholog relations. First, CACO constructs a cross-species ortholog relation matrix and transfers GO terms from other species as a reference to evaluate the confidence of PPIs. Then, a PPI filter strategy is adopted to clean the PPI network and thus a weighted clean PPI network is constructed. Finally, a new effective core-attachment algorithm is proposed to detect protein complexes from the weighted PPI network. Compared to other thirteen state-of-the-art methods, CACO outperforms all of them in terms of F-measure and Composite Score, showing that integrating ortholog information and the proposed core-attachment algorithm are effective in detecting protein complexes.

Targeting amyloid proteins for clinical diagnosis of neurodegenerative diseases

Abnormal aggregation and accumulation of pathological amyloid proteins such as amyloid-β, Tau, and α-synuclein play key pathological roles and serve as histological hallmarks in different neurodegenerative diseases (NDs) such as Alzheimer's disease (AD) and Parkinson's disease (PD). In addition, various post-translational modifications (PTMs) have been identified on pathological amyloid proteins and are subjected to change during disease progression. Given the central role of amyloid proteins in NDs, tremendous efforts have been made to develop amyloid-targeting strategies for clinical diagnosis and molecular classification of NDs. In this review, we summarize two major strategies for targeting amyloid aggregates, with a focus on the trials in AD diagnosis. The first strategy is a positron emission tomography (PET) scan of protein aggregation in the brain. We mainly focus on introducing the development of small-molecule PET tracers for specifically recognizing pathological amyloid fibrils. The second strategy is the detection of PTM biomarkers on amyloid proteins in cerebrospinal fluid and plasma. We discuss the pathological roles of different PTMs in diseases and how we can use the PTM profile of amyloid proteins for clinical diagnosis. Finally, we point out the potential technical challenges of these two strategies, and outline other potential strategies, as well as a combination of multiple strategies, for molecular diagnosis of NDs.

Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode

The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein-protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein-protein interaction predictors, the Protein-protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a "guilt by association" in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean.

Simultaneous quantification and structural characterization of monoclonal antibodies after administration using capillary zone electrophoresis-tandem mass spectrometry

Monoclonal antibodies (mAbs) are demonstrating major success in various therapeutic areas such as oncology and the treatment of immune disorders. Over the past two decades, novel analytical methodologies allowed to address the challenges of mAbs characterization in the context of their production. However, after administration only their quantification is performed and insights regarding their structural evolution remain limited. For instance, clinical practice has recently highlighted significant inter-patient differences in mAb clearance and unexpected clinical responses, without providing alternative interpretations. Here, we report the development of a novel analytical strategy based on capillary zone electrophoresis coupled to tandem mass spectrometry (CE-MS/MS) for the simultaneous absolute quantification and structural characterization of infliximab (IFX) in human serum. CE-MS/MS quantification was validated over the range 0.4-25 µg·mL^-1 corresponding to the IFX therapeutic window and achieved a LOQ of 0.22 µg·mL^-1 (1.5 nM) while demonstrating outstanding specificity compared to the ELISA assay. CE-MS/MS allowed structural characterization and estimation of the relative abundance of the six major N-glycosylations expressed by IFX. In addition, the results allowed characterization and determination of the level of modification of post-translational modifications (PTMs) hotspots including deamidation of 4 asparagine and isomerization of 2 aspartate. Concerning N-glycosylation and PTMs, a new normalization strategy was developed to measure the variation of modification levels that occur strictly during the residence time of IFX in the patient's system, overcoming artefactual modifications induced by sample treatment and/or storage. The CE-MS/MS methodology was applied to the analysis of samples from patients with Crohn's disease. The data identified a gradual deamidation of a particular asparagine residue located in the complementary determining region that correlated with IFX residence time, while the evolution of IFX concentration showed significant variability among patients.

The proteomic landscape of genome-wide genetic perturbations

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

MAPKAP Kinase-2 phosphorylation of PABPC1 controls its interaction with 14-3-3 proteins after DNA damage: A combined kinase and protein array approach

14-3-3 proteins play critical roles in controlling multiple aspects of the cellular response to stress and DNA damage including regulation of metabolism, cell cycle progression, cell migration, and apoptotic cell death by binding to protein substrates of basophilic protein kinases following their phosphorylation on specific serine/threonine residues. Although over 200 mammalian proteins that bind to 14-3-3 have been identified, largely through proteomic studies, in many cases the relevant protein kinase responsible for conferring 14-3-3-binding to these proteins is not known. To facilitate the identification of kinase-specific 14-3-3 clients, we developed a biochemical approach using high-density protein filter arrays and identified the translational regulatory molecule PABPC1 as a substrate for Chk1 and MAPKAP Kinase-2 (MK2) in vitro, and for MK2 in vivo, whose phosphorylation results in 14-3-3-binding. We identify Ser-470 on PABPC1 within the linker region connecting the RRM domains to the PABC domain as the critical 14-3-3-binding site, and demonstrate that loss of PABPC1 binding to 14-3-3 results in increased cell proliferation and decreased cell death in response to UV-induced DNA damage.

Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics

Major aims of single-cell proteomics include increasing the consistency, sensitivity and depth of protein quantification, especially for proteins and modifications of biological interest. Here, to simultaneously advance all these aims, we developed prioritized Single-Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while maximizing instrument time spent analyzing identifiable peptides, thus increasing proteome depth. These strategies increased the sensitivity, data completeness and proteome coverage over twofold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport, similarly across both treatment conditions. This covariation is coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at http://scp.slavovlab.net/pSCoPE .

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

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

Precise diagnosis and typing of early-stage renal immunoglobulin-derived amyloidosis by label-free quantification of parallel reaction monitoring-based targeted proteomics

BACKGROUND

Early diagnosis and typing are crucial for improving the prognosis of patients with renal amyloidosis. Currently, Untargeted proteomics based precise diagnosis and typing of amyloid deposits are crucial for guiding patient management. Although untargeted proteomics achieve ultra-high-throughput by selecting the most abundant eluting cationic peptide precursors in series for tandem MS events, it lacks in sensitivity and reproducibility, which may not be suitable for early-stage renal amyloidosis with minor damages. Here, we aimed to develop parallel reaction monitoring (PRM)-based targeted proteomics to achieve high sensitivity and specificity by determining absolute abundances and codetecting all transitions of highly repeatable peptides of preselected amyloid signature and typing proteins in identifying early-stage renal immunoglobulin-derived amyloidosis.

METHODS AND RESULTS

In 10 discovery cohort cases, Congo red-stained FFPE slices were micro-dissected and analyzed by data-dependent acquisition-based untargeted proteomics for preselection of typing specific proteins and peptides. Further, a list of proteolytic peptides from amyloidogenic proteins and internal standard proteins were quantified by PRM-based targeted proteomics to validate performance for diagnosis and typing in 26 validation cohort cases. The diagnosis and typing effectiveness of PRM-based targeted proteomics in 10 early-stage renal amyloid cases was assessed via a comparison with untargeted proteomics. A peptide panel of amyloid signature proteins, immunoglobulin light chain and heave chain in PRM-based targeted proteomics showed significantly distinguishing ability and amyloid typing performance in patients. The diagnostic algorithm of targeted proteomics with a low amount of amyloid deposits in early-stage renal immunoglobulin-derived amyloidosis showed better performance than untargeted proteomics in amyloidosis typing.

CONCLUSIONS

This study demonstrates that the utility of these prioritized peptides in PRM-based targeted proteomics ensure high sensitivity and reliability for identifying early-stage renal amyloidosis. Owing to the development and clinical application of this method, rapid acceleration of the early diagnosis, and typing of renal amyloidosis is expected.

Allergy, asthma, and proteomics: opportunities with immediate impact

Allergy is widely discussed by researchers due to its complex mechanism that leads to disorders and injuries, but the reason behind the allergic status remains unclear. Current treatments are insufficient to improve the patient's quality of life significantly. New technologies in scientific and technological development are emerging. For instance, the union between allergy and peptidomics and bioinformatics tools may help fill the gaps in this field, diagnosis, and treatment. In this review, we look at peptidomics and address some findings, such as target proteins or biomarkers that help better understand mechanisms that lead to inflammation, organ damage, and, consequently, poor quality of life or even death.

Glycoinformatics in the Artificial Intelligence Era

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

A Quantitative Proteomic Approach Explores the Possible Mechanisms by Which the Small Molecule Stemazole Promotes the Survival of Human Neural Stem Cells

Neurodegenerative disorders have become a serious healthcare problem worldwide and there is no efficacious cure. However, regulating the fate of stem cells is an effective way to treat these neurological diseases. In previous work, stemazole was reported to maintain the survival of human neural stem cells in the absence of growth factors and to have therapeutic effects on neurodegenerative diseases. However, although it is a promising small molecule, the molecular mechanisms against apoptosis are ambiguous. In this study, tandem mass tag (TMT)-based proteomics were performed to obtain whole protein expression profiles of human neural stem cells in different groups under extreme conditions. Bioinformatics analysis based on protein–protein interaction (PPI) network construction, gene ontology (GO) and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis were adopted to explore crucial proteins and possible pharmacological mechanisms. A total of 77 differentially expressed proteins were identified, comprising 38 upregulated proteins and 39 downregulated proteins. Combined with a diseases database of Alzheimer’s disease (AD), caspase-2 (CASP2), PKA C-alpha (PRKACA), fibronectin (FN1), large neutral amino acid transporter small subunit 1 (SLC7A5), which are involved in cell proliferation and apoptosis, this was further validated by enzyme activity assay and molecular docking, and regarded as putative targets regulated by stemazole. The present results give an insight into this small molecule and a better understanding for further elucidating the underlying mechanisms in the treatment of stem cells and neurodegenerative diseases.

Mass Spectrometry-Based Techniques to Elucidate the Sugar Code

Cells encode information in the sequence of biopolymers, such as nucleic acids, proteins, and glycans. Although glycans are essential to all living organisms, surprisingly little is known about the "sugar code" and the biological roles of these molecules. The reason glycobiology lags behind its counterparts dealing with nucleic acids and proteins lies in the complexity of carbohydrate structures, which renders their analysis extremely challenging. Building blocks that may differ only in the configuration of a single stereocenter, combined with the vast possibilities to connect monosaccharide units, lead to an immense variety of isomers, which poses a formidable challenge to conventional mass spectrometry. In recent years, however, a combination of innovative ion activation methods, commercialization of ion mobility-mass spectrometry, progress in gas-phase ion spectroscopy, and advances in computational chemistry have led to a revolution in mass spectrometry-based glycan analysis. The present review focuses on the above techniques that expanded the traditional glycomics toolkit and provided spectacular insight into the structure of these fascinating biomolecules. To emphasize the specific challenges associated with them, major classes of mammalian glycans are discussed in separate sections. By doing so, we aim to put the spotlight on the most important element of glycobiology: the glycans themselves.

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.

Secretomics—A Key to a Comprehensive Picture of Unconventional Protein Secretion

For a long time, leaderless secreted proteins (LLSP) were neglected as artifacts derived from dying cells. It is now generally accepted that secretion of LLSP–as a part of the collective term unconventional protein secretion (UPS) - is an evolutionarily conserved process and that these LLSP are actively and selectively secreted from living cells bypassing the classical endoplasmic reticulum-Golgi pathway. However, the mechanism of UPS pathways, as well as the number of LLSP and which part of a protein is involved in the selection of LLSPs for secretion, are still enigmatic and await clarification. Secretomics-a proteomics-based approach to identify and quantify all proteins secreted by a cell-is inherently unbiased toward a particular secretion pathway and offers the opportunity to shed light on the UPS. Here, we will evaluate and present recent results of proteomic workflows allowing to obtain high-confident secretome data. Additionally, we address that cell culture conditions largely affect the composition of the secretome. This has to be kept in mind to control cell culture induced artifacts and adaptation stress in serum free conditions. Evaluation of click chemistry for secretome analysis of cells under serum-containing conditions showed a significant change in the cellular proteome with longer incubation time upon treatment with non-canonical amino acid azidohomoalanine. Finally, we showed that the number of LLSP far exceeds the number of secreted proteins annotated in Uniprot and ProteinAtlas. Thus, secretomics in combination with sophisticated microbioanalytical and sample preparation methods is well suited to provide a comprehensive picture of UPS.

Cocoa bean fingerprinting via correlation networks

Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa.

Effect of APOB polymorphism rs562338 (G/A) on serum proteome of coronary artery disease patients: a "proteogenomic" approach

In the current study, APOB (rs1052031) genotype-guided proteomic analysis was performed in a cohort of Pakistani population. A total of 700 study subjects, including Coronary Artery Disease (CAD) patients (n = 480) and healthy individuals (n = 220) as a control group were included in the study. Genotyping was carried out by using tetra primer-amplification refractory mutation system-based polymerase chain reaction (T-ARMS-PCR) whereas mass spectrometry (Orbitrap MS) was used for label free quantification of serum samples. Genotypic frequency of GG genotype was found to be 90.1%, while 6.4% was for GA genotype and 3.5% was for AA genotypes in CAD patients. In the control group, 87.2% healthy subjects were found to have GG genotype, 11.8% had GA genotype, and 0.9% were with AA genotypes. Significant (p = 0.007) difference was observed between genotypic frequencies in the patients and the control group. The rare allele AA was found to be strongly associated with the CAD [OR: 4 (1.9-16.7)], as compared to the control group in recessive genetic model (p = 0.04). Using label free proteomics, altered expression of 60 significant proteins was observed. Enrichment analysis of these protein showed higher number of up-regulated pathways, including phosphatidylcholine-sterol O-acyltransferase activator activity, cholesterol transfer activity, and sterol transfer activity in AA genotype of rs562338 (G>A) as compared to the wild type GG genotype. This study provides a deeper insight into CAD pathobiology with reference to proteogenomics, and proving this approach as a good platform for identifying the novel proteins and signaling pathways in relation to cardiovascular diseases.

Quantitative Proteomics Using Isobaric Labeling: A Practical Guide

In the past decade, relative proteomic quantification using isobaric labeling technology has developed into a key tool for comparing the expression of proteins in biological samples. Although its multiplexing capacity and flexibility make this a valuable technology for addressing various biological questions, its quantitative accuracy and precision still pose significant challenges to the reliability of its quantification results. Here, we give a detailed overview of the different kinds of isobaric mass tags and the advantages and disadvantages of the isobaric labeling method. We also discuss which precautions should be taken at each step of the isobaric labeling workflow, to obtain reliable quantification results in large-scale quantitative proteomics experiments. In the last section, we discuss the broad applications of the isobaric labeling technology in biological and clinical studies, with an emphasis on thermal proteome profiling and proteogenomics.

Protein complexes and neighborhoods driving autophagy

Autophagy summarizes evolutionarily conserved, intracellular degradation processes targeting cytoplasmic material for lysosomal degradation. These encompass constitutive processes as well as stress responses, which are often found dysregulated in diseases. Autophagy pathways help in the clearance of damaged organelles, protein aggregates and macromolecules, mediating their recycling and maintaining cellular homeostasis. Protein-protein interaction networks contribute to autophagosome biogenesis, substrate loading, vesicular trafficking and fusion, protein translocations across membranes and degradation in lysosomes. Hypothesis-free proteomic approaches tremendously helped in the functional characterization of protein-protein interactions to uncover molecular mechanisms regulating autophagy. In this review, we elaborate on the importance of understanding protein-protein-interactions of varying affinities and on the strengths of mass spectrometry-based proteomic approaches to study these, generating new mechanistic insights into autophagy regulation. We discuss in detail affinity purification approaches and recent developments in proximity labeling coupled to mass spectrometry, which uncovered molecular principles of autophagy mechanisms.Abbreviations: AMPK: AMP-activated protein kinase; AP-MS: affinity purification-mass spectrometry; APEX2: ascorbate peroxidase-2; ATG: autophagy related; BioID: proximity-dependent biotin identification; ER: endoplasmic reticulum; GFP: green fluorescent protein; iTRAQ: isobaric tag for relative and absolute quantification; MS: mass spectrometry; PCA: protein-fragment complementation assay; PL-MS: proximity labeling-mass spectrometry; PtdIns3P: phosphatidylinositol-3-phosphate; PTM: posttranslational modification; PUP-IT: pupylation-based interaction tagging; RFP: red fluorescent protein; SILAC: stable isotope labeling by amino acids in cell culture; TAP: tandem affinity purification; TMT: tandem mass tag.

Cost-Effectiveness of a Proteomic Test for Preterm Birth Prediction

Background

Preterm birth (PTB) carries increased risk of short- and long-term health problems as well as higher healthcare costs. Current strategies using clinically accepted maternal risk factors (prior PTB, short cervix) can only identify a minority of singleton PTBs.

Objective

We modeled the cost-effectiveness of a risk-screening-and-treat strategy versus usual care for commercially insured pregnant US women without clinically accepted PTB risk factors. The risk-screening-and-treat strategy included use of a novel PTB prognostic blood test (PreTRM^®) in the 19th–20th week of pregnancy, followed by treatment with a combined regimen of multi-component high-intensity-case-management and pharmacologic interventions for the remainder of the pregnancy for women assessed as higher-risk by the test, and usual care in women without higher risk.

Methods

We built a cost-effectiveness model using a combined decision-tree/Markov approach and a US payer perspective. We modeled 1-week cycles of pregnancy from week 19 to birth (preterm or term) and assessed costs throughout the pregnancy, and further to 12-months post-delivery in mothers and 30-months in infants. PTB rates and costs were based on >40,000 mothers and infants from the HealthCore Integrated Research Database^® with birth events in 2016. Estimates of test performance, treatment effectiveness, and other model inputs were derived from published literature.

Results

In the base case, the risk-screening-and-treat strategy dominated usual care with an estimated 870 fewer PTBs (20% reduction) and $54 million less in total cost ($863 net savings per pregnant woman). Reductions were projected for neonatal intensive care admissions (10%), overall length-of-stay (7%), and births <32 weeks (33%). Treatment effectiveness had the strongest influence on cost-effectiveness estimates. The risk-screening-and-treat strategy remained dominant in the majority of probabilistic sensitivity analysis simulations and model scenarios.

Conclusion

Use of a novel prognostic test during pregnancy to identify women at risk of PTB combined with evidence-based treatment is estimated to reduce total costs while preventing PTBs and their consequences.

Comprehensive analysis of lysine crotonylation modification in patients with chronic renal failure

BACKGROUND

Post-translational modifications (PTMs) are at the heart of many cellular signaling events, which changes the function of protein. Crotonylation, one of the most important and common PTMs, plays a crucial role in the regulation of various biological processes. However, no study has evaluated the role of lysine crotonylation modification in chronic renal failure (CRF) patients.

METHODS

Here, we comparatively evaluated the crotonylation proteome of normal controls and chronic renal failure patients using liquid chromatography-tandem mass spectrometry (LC-MS/MS) coupled with highly sensitive immune-affinity purification.

RESULTS

A total of 1109 lysine modification sites were identified, of which 772 sites were up-regulated and 69 sites were down-regulated. This suggested that crotonylation modification maintains high levels in the patients with chronic renal failure. Gene ontology(GO) enrichment analysis showed that the crotonylated proteins were significantly enriched in the platelet alpha granule lumen, platelet degradulation, and cell adhesion molecule binding. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG)-based functional enrichment analysis in the Kyoto encyclopedia showed that crotonylated protein was enriched in CD36, which is closely linked to renal failure.

CONCLUSIONS

This is the first report of the global crotonylation proteome in chronic renal failure patients. Crotonylation of histone and non-histone may play important roles in delaying the continuous deterioration of renal function in patients with chronic renal failure.

Fuzzy protein theory for disordered proteins

Why proteins are fuzzy? Constant adaptation to the cellular environment requires a wide range of changes in protein structure and interactions. Conformational ensembles of disordered proteins in particular exhibit large shifts to activate or inhibit alternative pathways. Fuzziness is critical for liquid–liquid phase separation and conversion of biomolecular condensates into fibrils. Interpretation of these phenomena presents a challenge for the classical structure-function paradigm. Here I discuss a multi-valued formalism, based on fuzzy logic, which can be applied to describe complex cellular behavior of proteins.

A Comparative Proteomic Analysis of Praziquantel-Susceptible and Praziquantel-Resistant Schistosoma mansoni Reveals Distinct Response Between Male and Female Animals

Schistosomiasis is a chronic neglected tropical disease saddling millions of people in the world, mainly children living in poor rural areas. Praziquantel (PZQ) is currently the only drug used for the treatment and control of this disease. However, the extensive use of this drug has brought concern about the emergence of PZQ-resistance/tolerance by Schistosoma mansoni. Studies of Schistosoma spp. genome, transcriptome, and proteome are crucial to better understand this situation. In this in vitro study, we compare the proteomes of a S. mansoni variant strain stably resistant to PZQ and isogenic to its fully susceptible parental counterpart, identifying proteins from male and female adult parasites of PZQ-resistant and PZQ-susceptible strains, exposed and not exposed to PZQ. A total of 60 Schistosoma spp. proteins were identified, some of which present or absent in either strain, which may putatively be involved in the PZQ-resistance phenomenon. These proteins were present in adult parasites not exposed to PZQ, but some of them disappeared when these adult parasites were exposed to the drug. Understanding the development of PZQ-resistance in S. mansoni is crucial to prolong the efficacy of the current drug and develop markers for monitoring the potential emergence of drug resistance.

Exploring the Potential of Neuroproteomics in Alzheimer's Disease

Alzheimer's disease (AD) is progressive brain amyloidosis that damages brain regions associated with memory, thinking, behavioral and social skills. Neuropathologically, AD is characterized by intraneuronal hyperphosphorylated tau inclusions as neurofibrillary tangles (NFTs), and buildup of extracellular amyloid-beta (Aβ) peptide as senile plaques. Several biomarker tests capturing these pathologies have been developed. However, for the full clinical expression of the neurodegenerative events of AD, there exist other central molecular pathways. In terms of understanding the unidentified underlying processes for the progression and development of AD, a complete comprehension of the structure and composition of atypical aggregation of proteins is essential. Presently, to aid the prognosis, diagnosis, detection, and development of drug targets in AD, neuroproteomics is elected as one of the leading essential tools for the efficient exploratory discovery of prospective biomarker candidates estimated to play a crucial role. Therefore, the aim of this review is to present the role of neuroproteomics to analyze the complexity of AD.

Proteomic comparison between different tissue preservation methods for identification of promising biomarkers of urothelial bladder cancer

Samples in biobanks are generally preserved by formalin-fixation and paraffin-embedding (FFPE) and/or optimal cutting temperature compound (OCT)-embedding and subsequently frozen. Mass spectrometry (MS)-based analysis of these samples is now available via developed protocols, however, the differences in results with respect to preservation methods needs further investigation. Here we use bladder urothelial carcinoma tissue of two different tumor stages (Ta/T1-non-muscle invasive bladder cancer (NMIBC), and T2/T3-muscle invasive bladder cancer (MIBC)) which, upon sampling, were divided and preserved by FFPE and OCT. Samples were parallel processed from the two methods and proteins were analyzed with label-free quantitative MS. Over 700 and 1200 proteins were quantified in FFPE and OCT samples, respectively. Multivariate analysis indicates that the preservation method is the main source of variation, but also tumors of different stages could be differentiated. Proteins involved in mitochondrial function were overrepresented in OCT data but missing in the FFPE data, indicating that these proteins are not well preserved by FFPE. Concordant results for proteins such as HMGCS2 (uniquely quantified in Ta/T1 tumors), and LGALS1, ANXA5 and plastin (upregulated in T2/T3 tumors) were observed in both FFPE and OCT data, which supports the use of MS technology for biobank samples and encourages the further evaluation of these proteins as biomarkers.

An overview of the human brain myelin proteome and differences associated with schizophrenia

OBJECTIVES

Disturbances in the myelin sheath drive disruptions in neural transmission and brain connectivity as seen in schizophrenia. Here, the myelin proteome was characterised in schizophrenia patients and healthy controls to visualise differences in proteomic profiles.

METHODS

A liquid chromatography tandem mass spectrometry-based shotgun proteomic analysis was performed of a myelin-enriched fraction of postmortem brain samples from schizophrenia patients (n = 12) and mentally healthy controls (n = 8). In silico pathway analyses were performed on the resulting data.

RESULTS

The present characterisation of the human myelinome led to the identification of 480 non-redundant proteins, of which 102 proteins are newly annotated to be associated with the myelinome. Levels of 172 of these proteins were altered between schizophrenia patients and controls. These proteins were mainly associated with glial cell differentiation, metabolism/energy, synaptic vesicle function and neurodegeneration. The hub proteins with the highest degree of connectivity in the network included multiple kinases and synaptic vesicle transport proteins.

CONCLUSIONS

Together these findings suggest disruptive effects on synaptic activity and therefore neural transmission and connectivity, consistent with the dysconnectivity hypothesis of schizophrenia. Further studies on these proteins may lead to the identification of potential drug targets related to the synaptic dysconnectivity in schizophrenia and other psychiatric and neurodegenerative disorders.

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

Proteomics Complementation of the Rat Uterotrophic Assay for Estrogenic Endocrine Disruptors: A Roadmap of Advancing High Resolution Mass Spectrometry-Based Shotgun Survey to Targeted Biomarker Quantifications

The widely used rat uterotrophic assay to assess known and potential estrogenic compounds only considers uterine weight gain as endpoint measurement. To complement this method with an advanced technology that reveals molecular targets, we analyzed changes in protein expression using label-free quantitative proteomics by nanoflow liquid chromatography coupled with high-resolution mass spectrometry and tandem mass spectrometry from uterine protein extracts of ovariectomized rats after daily 17β-estradiol exposure for five days in comparison with those of vehicle-treated control animals. Our discovery-driven study revealed 165 uterine proteins significantly regulated by estrogen treatment and mapped by pathway analyses. Estrogen-regulated proteins represented cell death, survival and development, cellular growth and proliferation, and protein synthesis as top molecular and cellular functions, and a network found with the presence of nuclear estrogen receptor(s) as a prominent molecular node confirmed the relevance of our findings to hormone-associated events. An exploratory application of targeted proteomics to bisphenol A as a well-known example of an estrogenic endocrine disruptor is also presented. Overall, the results of this study have demonstrated the power of combining untargeted and targeted quantitative proteomic strategies to identify and verify candidate molecular markers for the evaluation of endocrine-disrupting chemicals to complement a conventional bioassay.

A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies

Comparative functional genomics offers a powerful approach to study species evolution. To date, the majority of these studies have focused on the transcriptome in mammalian and yeast phylogenies. Here, we present a novel multi-species proteomic dataset and a computational pipeline to systematically compare the protein levels across multiple plant species. Globally we find that protein levels diverge according to phylogenetic distance but is more constrained than the mRNA level. Module-level comparative analysis of groups of proteins shows that proteins that are more highly expressed tend to be more conserved. To interpret the evolutionary patterns of conservation and divergence, we develop a novel network-based integrative analysis pipeline that combines publicly available transcriptomic datasets to define co-expression modules. Our analysis pipeline can be used to relate the changes in protein levels to different species-specific phenotypic traits. We present a case study with the rhizobia-legume symbiosis process that supports the role of autophagy in this symbiotic association.

Virus systems biology: Proteomics profiling of dynamic protein networks during infection

The host cell proteome undergoes a variety of dynamic changes during viral infection, elicited by the virus itself or host cell defense mechanisms. Studying these changes on a global scale by integrating functional and physical interactions within protein networks during infection is an important tool to understand pathology. Indeed, proteomics studies dissecting protein signaling cascades and interaction networks upon infection showed how global information can significantly improve understanding of disease mechanisms of diverse viral infections. Here, we summarize and give examples of different experimental designs, proteomics approaches and bioinformatics analyses that allow profiling proteome changes and host-pathogen interactions to gain a molecular systems view of viral infection.

ConCysFind: a pipeline tool to predict conserved amino acids of protein sequences across the plant kingdom

Background

Post-translational modifications (PTM) of amino acid (AA) side chains in peptides control protein structure and functionality. PTMs depend on the specific AA characteristics. The reactivity of cysteine thiol-based PTMs are unique among all proteinaceous AA. This pipeline aims to ease the identification of conserved AA of polypeptides or protein families based on the phylogenetic occurrence in the plant kingdom. The tool is customizable to include any species. The degree of AA conservation is taken as indicator for structural and functional significance, especially for PTM-based regulation. Further, this pipeline tool gives insight into the evolution of these potentially regulatory important peptides.

Results

The web-based or stand-alone pipeline tool Conserved Cysteine Finder (ConCysFind) was developed to identify conserved AA such as cysteine, tryptophan, serine, threonine, tyrosin and methionine. ConCysFind evaluates multiple alignments considering the proteome of 21 plant species. This exemplar study focused on Cys as evolutionarily conserved target for multiple redox PTM. Phylogenetic trees and tables with the compressed results of the scoring algorithm are generated for each Cys in the query polypeptide. Analysis of 33 translation elongation and release factors alongside of known redox proteins from Arabidopsis thaliana for conserved Cys residues confirmed the suitability of the tool for identifying conserved and functional PTM sites. Exemplarily, the redox sensitivity of cysteines in the eukaryotic release factor 1-1 (eRF1-1) was experimentally validated.

Conclusion

ConCysFind is a valuable tool for prediction of new potential protein PTM targets in a broad spectrum of species, based on conserved AA throughout the plant kingdom. The identified targets were successfully verified through protein biochemical assays. The pipeline is universally applicable to other phylogenetic branches by customization of the database.

The tissue proteome in the multi-omic landscape of kidney disease

Kidney research is entering an era of 'big data' and molecular omics data can provide comprehensive insights into the molecular footprints of cells. In contrast to transcriptomics, proteomics and metabolomics generate data that relate more directly to the pathological symptoms and clinical parameters observed in patients. Owing to its complexity, the proteome still holds many secrets, but has great potential for the identification of drug targets. Proteomics can provide information about protein synthesis, modification and degradation, as well as insight into the physical interactions between proteins, and between proteins and other biomolecules. Thus far, proteomics in nephrology has largely focused on the discovery and validation of biomarkers, but the systematic analysis of the nephroproteome can offer substantial additional insights, including the discovery of mechanisms that trigger and propagate kidney disease. Moreover, proteome acquisition might provide a diagnostic tool that complements the assessment of a kidney biopsy sample by a pathologist. Such applications are becoming increasingly feasible with the development of high-throughput and high-coverage technologies, such as versatile mass spectrometry-based techniques and protein arrays, and encourage further proteomics research in nephrology.

Bacillus cereus Decreases NHE and CLO Exotoxin Synthesis to Maintain Appropriate Proteome Dynamics During Growth at Low Temperature

Cellular proteomes and exoproteomes are dynamic, allowing pathogens to respond to environmental conditions to sustain growth and virulence. Bacillus cereus is an important food-borne pathogen causing intoxication via emetic toxin and/or multiple protein exotoxins. Here, we compared the dynamics of the cellular proteome and exoproteome of emetic B. cereus cells grown at low (16 °C) and high (30 °C) temperature. Tandem mass spectrometry (MS/MS)-based shotgun proteomics analysis identified 2063 cellular proteins and 900 extracellular proteins. Hierarchical clustering following principal component analysis indicated that in B. cereus the abundance of a subset of these proteins—including cold-stress responders, and exotoxins non-hemolytic enterotoxin (NHE) and hemolysin I (cereolysin O (CLO))—decreased at low temperature, and that this subset governs the dynamics of the cellular proteome. NHE, and to a lesser extent CLO, also contributed significantly to exoproteome dynamics; with decreased abundances in the low-temperature exoproteome, especially in late growth stages. Our data therefore indicate that B. cereus may reduce its production of secreted protein toxins to maintain appropriate proteome dynamics, perhaps using catabolite repression to conserve energy for growth in cold-stress conditions, at the expense of virulence.

Alternative proteins are functional regulators in cell reprogramming by PKA activation

It has been recently shown that many proteins are lacking from reference databases used in mass spectrometry analysis, due to their translation templated on alternative open reading frames. This questions our current understanding of gene annotation and drastically expands the theoretical proteome complexity. The functions of these alternative proteins (AltProts) still remain largely unknown. We have developed a large-scale and unsupervised approach based on cross-linking mass spectrometry (XL-MS) followed by shotgun proteomics to gather information on the functional role of AltProts by mapping them back into known signalling pathways through the identification of their reference protein (RefProt) interactors. We have identified and profiled AltProts in a cancer cell reprogramming system: NCH82 human glioma cells after 0, 16, 24 and 48 h Forskolin stimulation. Forskolin is a protein kinase A activator inducing cell differentiation and epithelial–mesenchymal transition. Our data show that AltMAP2, AltTRNAU1AP and AltEPHA5 interactions with tropomyosin 4 are downregulated under Forskolin treatment. In a wider perspective, Gene Ontology and pathway enrichment analysis (STRING) revealed that RefProts associated with AltProts are enriched in cellular mobility and transfer RNA regulation. This study strongly suggests novel roles of AltProts in multiple essential cellular functions and supports the importance of considering them in future biological studies.

Fluorescent protein expression in the ectomycorrhizal fungus Laccaria bicolor: a plasmid toolkit for easy use of fluorescent markers in basidiomycetes

For long time, studies on ectomycorrhiza (ECM) have been limited by inefficient expression of fluorescent proteins (FPs) in the fungal partner. To convert this situation, we have evaluated the basic requirements of FP expression in the model ECM homobasidiomycete Laccaria bicolor and established eGFP and mCherry as functional FP markers. Comparison of intron-containing and intronless FP-expression cassettes confirmed that intron-processing is indispensable for efficient FP expression in Laccaria. Nuclear FP localization was obtained via in-frame fusion of FPs between the intron-containing genomic gene sequences of Laccaria histone H2B, while cytosolic FP expression was produced by incorporating the intron-containing 5' fragment of the glyceraldehyde-3-phosphate dehydrogenase encoding gene. In addition, we have characterized the consensus Kozak sequence of strongly expressed genes in Laccaria and demonstrated its boosting effect on transgene mRNA accumulation. Based on these results, an Agrobacterium-mediated transformation compatible plasmid set was designed for easy use of FPs in Laccaria. The four cloning plasmids presented here allow fast and highly flexible construction of C-terminal in-frame fusions between the sequences of interest and the two FPs, expressed either from the endogenous gene promoter, allowing thus evaluation of the native regulation modes of the gene under study, or alternatively, from the constitutive Agaricus bisporus gpdII promoter for enhanced cellular protein localization assays. The molecular tools described here for cell-biological studies in Laccaria can also be exploited in studies of other biotrophic or saprotrophic basidiomycete species susceptible to genetic transformation.

Deciphering flavivirus-host interactions using quantitative proteomics

Flaviviruses are a group of important emerging and re-emerging human pathogens that cause worldwide epidemics with thousands of deaths annually. Flaviviruses are small, enveloped, positive-sense, single-stranded RNA viruses that are obligate intracellular pathogens, relying heavily on host cell machinery for productive replication. Proteomic approaches have become an increasingly powerful tool to investigate the mechanisms by which viruses interact with host proteins and manipulate cellular processes to promote infection. Here, we review recent advances in employing quantitative proteomics techniques to improve our understanding of the complex interplay between flaviviruses and host cells. We describe new findings on our understanding of how flaviviruses impact protein-protein interactions, protein-RNA interactions, protein abundance, and post-translational modifications to modulate viral infection.

Advances of proteomics technologies for multidrug-resistant mechanisms

Multidrug resistance (MDR) is a vital issue in cancer treatment. Drug resistance can be developed through a variety of mechanisms, including increased drug efflux, activation of detoxifying systems and DNA repair mechanisms, and escape of drug-induced apoptosis. Identifying the exact mechanism related in a particular case is a difficult task. Proteomics is the large-scale study of proteins, particularly their expression, structures and functions. In recent years, comparative proteomic methods have been performed to analyze MDR mechanisms in drug-selected model cancer cell lines. In this paper, we review the recent developments and progresses by comparative proteomic approaches to identify potential MDR mechanisms in drug-selected model cancer cell lines, which may help understand and design chemical sensitizers.

Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform

For mass spectrometry-based peptide and protein quantification, label-free quantification (LFQ) based on precursor mass peak (MS1) intensities is considered reliable due to its dynamic range, reproducibility, and accuracy. LFQ enables peptide-level quantitation, which is useful in proteomics (analyzing peptides carrying post-translational modifications) and multi-omics studies such as metaproteomics (analyzing taxon-specific microbial peptides) and proteogenomics (analyzing non-canonical sequences). Bioinformatics workflows accessible via the Galaxy platform have proven useful for analysis of such complex multi-omic studies. However, workflows within the Galaxy platform have lacked well-tested LFQ tools. In this study, we have evaluated moFF and FlashLFQ, two open-source LFQ tools, and implemented them within the Galaxy platform to offer access and use via established workflows. Through rigorous testing and communication with the tool developers, we have optimized the performance of each tool. Software features evaluated include: (a) match-between-runs (MBR); (b) using multiple file-formats as input for improved quantification; (c) use of containers and/or conda packages; (d) parameters needed for analyzing large datasets; and (e) optimization and validation of software performance. This work establishes a process for software implementation, optimization, and validation, and offers access to two robust software tools for LFQ-based analysis within the Galaxy platform.