General statistical modeling of data from protein relative expression isobaric tags

Widespread dysregulation of mRNA splicing implicates RNA processing in the development and progression of Huntington's disease

BACKGROUND

In Huntington's disease (HD), a CAG repeat expansion mutation in the Huntingtin (HTT) gene drives a gain-of-function toxicity that disrupts mRNA processing. Although dysregulation of gene splicing has been shown in human HD post-mortem brain tissue, post-mortem analyses are likely confounded by cell type composition changes in late-stage HD, limiting the ability to identify dysregulation related to early pathogenesis.

METHODS

To investigate gene splicing changes in early HD, we performed alternative splicing analyses coupled with a proteogenomics approach to identify early CAG length-associated splicing changes in an established isogenic HD cell model.

FINDINGS

We report widespread neuronal differentiation stage- and CAG length-dependent splicing changes, and find an enrichment of RNA processing, neuronal function, and epigenetic modification-related genes with mutant HTT-associated splicing. When integrated with a proteomics dataset, we identified several of these differential splicing events at the protein level. By comparing with human post-mortem and mouse model data, we identified common patterns of altered splicing from embryonic stem cells through to post-mortem striatal tissue.

INTERPRETATION

We show that widespread splicing dysregulation in HD occurs in an early cell model of neuronal development. Importantly, we observe HD-associated splicing changes in our HD cell model that were also identified in human HD striatum and mouse model HD striatum, suggesting that splicing-associated pathogenesis possibly occurs early in neuronal development and persists to later stages of disease. Together, our results highlight splicing dysregulation in HD which may lead to disrupted neuronal function and neuropathology.

FUNDING

This research is supported by the Lee Kong Chian School of Medicine, Nanyang Technological University Singapore Nanyang Assistant Professorship Start-Up Grant, the Singapore Ministry of Education under its Singapore Ministry of Education Academic Research Fund Tier 1 (RG23/22), the BC Children's Hospital Research Institute Investigator Grant Award (IGAP), and a Scholar Award from the Michael Smith Health Research BC.

Proteomic alteration of endometrial tissues during secretion in polycystic ovary syndrome may affect endometrial receptivity

Embryo implantation is a complex developmental process that requires coordinated interactions among the embryo, endometrium, and the microenvironment of endometrium factors. Even though the impaired endometrial receptivity of patients with polycystic ovary syndrome (PCOS) is known, understanding of endometrial receptivity is limited. A proteomics study in three patients with PCOS and 3 fertile women was performed to understand the impaired endometrial receptivity in patients with PCOS during luteal phases. Through isobaric tags for relative and absolute quantitation (iTRAQ) analyses, we identified 232 unique proteins involved in the metabolism, inflammation, and cell adhesion molecules. Finally, our results suggested that energy metabolism can affect embryo implantation, whereas inflammation and cell adhesion molecules can affect both endometrial conversion and receptivity. Our results showed that endometrial receptive damage in patients with PCOS is not a single factor. It is caused by many proteins, pathways, systems, and abnormalities, which interact with each other and make endometrial receptive research more difficult.

VIQoR: a web service for visually supervised protein inference and protein quantification

MOTIVATION

In quantitative bottom-up mass spectrometry (MS)-based proteomics, the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization.

RESULTS

In this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the crucial components protein inference and summarization and interactive visualization modules, including the novel VIQoR plot. We implemented two different parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well-established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle shared peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in datasets with replicated design.

AVAILABILITY AND IMPLEMENTATION

VIQoR is accessible at: http://computproteomics.bmb.sdu.dk/Apps/VIQoR/. The source code is available at: https://bitbucket.org/veitveit/viqor/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

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

Analysis of Astroglial Secretomic Profile in the Mecp2-Deficient Male Mouse Model of Rett Syndrome

Mutations in the X-linked MECP2 gene are responsible for Rett syndrome (RTT), a severe neurological disorder. MECP2 is a transcriptional modulator that finely regulates the expression of many genes, specifically in the central nervous system. Several studies have functionally linked the loss of MECP2 in astrocytes to the appearance and progression of the RTT phenotype in a non-cell autonomous manner and mechanisms are still unknown. Here, we used primary astroglial cells from Mecp2-deficient (KO) pups to identify deregulated secreted proteins. Using a differential quantitative proteomic analysis, twenty-nine proteins have been identified and four were confirmed by Western blotting with new samples as significantly deregulated. To further verify the functional relevance of these proteins in RTT, we tested their effects on the dendritic morphology of primary cortical neurons from Mecp2 KO mice that are known to display shorter dendritic processes. Using Sholl analysis, we found that incubation with Lcn2 or Lgals3 for 48 h was able to significantly increase the dendritic arborization of Mecp2 KO neurons. To our knowledge, this study, through secretomic analysis, is the first to identify astroglial secreted proteins involved in the neuronal RTT phenotype in vitro, which could open new therapeutic avenues for the treatment of Rett syndrome.

Quantitative Proteomic Approach Reveals Altered Metabolic Pathways in Response to the Inhibition of Lysine Deacetylases in A549 Cells under Normoxia and Hypoxia

Growing evidence is showing that acetylation plays an essential role in cancer, but studies on the impact of KDAC inhibition (KDACi) on the metabolic profile are still in their infancy. Here, we analyzed, by using an iTRAQ-based quantitative proteomics approach, the changes in the proteome of KRAS-mutated non-small cell lung cancer (NSCLC) A549 cells in response to trichostatin-A (TSA) and nicotinamide (NAM) under normoxia and hypoxia. Part of this response was further validated by molecular and biochemical analyses and correlated with the proliferation rates, apoptotic cell death, and activation of ROS scavenging mechanisms in opposition to the ROS production. Despite the differences among the KDAC inhibitors, up-regulation of glycolysis, TCA cycle, oxidative phosphorylation and fatty acid synthesis emerged as a common metabolic response underlying KDACi. We also observed that some of the KDACi effects at metabolic levels are enhanced under hypoxia. Furthermore, we used a drug repositioning machine learning approach to list candidate metabolic therapeutic agents for KRAS mutated NSCLC. Together, these results allow us to better understand the metabolic regulations underlying KDACi in NSCLC, taking into account the microenvironment of tumors related to hypoxia, and bring new insights for the future rational design of new therapies.

Free Open Source Software for Protein and Peptide Mass Spectrometry- based Science

In the field of biology, and specifically in protein and peptide science, the power of mass spectrometry is that it is applicable to a vast spectrum of applications. Mass spectrometry can be applied to identify proteins and peptides in complex mixtures, to identify and locate post-translational modifications, to characterize the structure of proteins and peptides to the most detailed level or to detect protein-ligand non-covalent interactions. Thanks to the Free and Open Source Software (FOSS) movement, scientists have limitless opportunities to deepen their skills in software development to code software that solves mass spectrometric data analysis problems. After the conversion of raw data files into open standard format files, the entire spectrum of data analysis tasks can now be performed integrally on FOSS platforms, like GNU/Linux, and only with FOSS solutions. This review presents a brief history of mass spectrometry open file formats and goes on with the description of FOSS projects that are commonly used in protein and peptide mass spectrometry fields of endeavor: identification projects that involve mostly automated pipelines, like proteomics and peptidomics, and bio-structural characterization projects that most often involve manual scrutiny of the mass data. Projects of the last kind usually involve software that allows the user to delve into the mass data in an interactive graphics-oriented manner. Software projects are thus categorized on the basis of these criteria: software libraries for software developers vs desktop-based graphical user interface, software for the end-user and automated pipeline-based data processing vs interactive graphics-based mass data scrutiny.

Intracellular and Extracellular Markers of Lethality in Osteogenesis Imperfecta: A Quantitative Proteomic Approach

Osteogenesis imperfecta (OI) is a heritable disorder that mainly affects the skeleton. The inheritance is mostly autosomal dominant and associated to mutations in one of the two genes, COL1A1 and COL1A2, encoding for the type I collagen α chains. According to more than 1500 described mutation sites and to outcome spanning from very mild cases to perinatal-lethality, OI is characterized by a wide genotype/phenotype heterogeneity. In order to identify common affected molecular-pathways and disease biomarkers in OI probands with different mutations and lethal or surviving phenotypes, primary fibroblasts from dominant OI patients, carrying COL1A1 or COL1A2 defects, were investigated by applying a Tandem Mass Tag labeling-Liquid Chromatography-Tandem Mass Spectrometry (TMT LC-MS/MS) proteomics approach and bioinformatic tools for comparative protein-abundance profiling. While no difference in α1 or α2 abundance was detected among lethal (type II) and not-lethal (type III) OI patients, 17 proteins, with key effects on matrix structure and organization, cell signaling, and cell and tissue development and differentiation, were significantly different between type II and type III OI patients. Among them, some non-collagenous extracellular matrix (ECM) proteins (e.g., decorin and fibrillin-1) and proteins modulating cytoskeleton (e.g., nestin and palladin) directly correlate to the severity of the disease. Their defective presence may define proband-failure in balancing aberrances related to mutant collagen.

Utility of Proteomics in Emerging and Re-Emerging Infectious Diseases Caused by RNA Viruses

Emerging and re-emerging infectious diseases due to RNA viruses cause major negative consequences for the quality of life, public health, and overall economic development. Most of the RNA viruses causing illnesses in humans are of zoonotic origin. Zoonotic viruses can directly be transferred from animals to humans through adaptation, followed by human-to-human transmission, such as in human immunodeficiency virus (HIV), severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and, more recently, SARS coronavirus 2 (SARS-CoV-2), or they can be transferred through insects or vectors, as in the case of Crimean-Congo hemorrhagic fever virus (CCHFV), Zika virus (ZIKV), and dengue virus (DENV). At the present, there are no vaccines or antiviral compounds against most of these viruses. Because proteins possess a vast array of functions in all known biological systems, proteomics-based strategies can provide important insights into the investigation of disease pathogenesis and the identification of promising antiviral drug targets during an epidemic or pandemic. Mass spectrometry technology has provided the capacity required for the precise identification and the sensitive and high-throughput analysis of proteins on a large scale and has contributed greatly to unravelling key protein-protein interactions, discovering signaling networks, and understanding disease mechanisms. In this Review, we present an account of quantitative proteomics and its application in some prominent recent examples of emerging and re-emerging RNA virus diseases like HIV-1, CCHFV, ZIKV, and DENV, with more detail with respect to coronaviruses (MERS-CoV and SARS-CoV) as well as the recent SARS-CoV-2 pandemic.

MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures

Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.

The Greatwall kinase safeguards the genome integrity by affecting the kinome activity in mitosis

Progression through mitosis is balanced by the timely regulation of phosphorylation and dephosphorylation events ensuring the correct segregation of chromosomes before cytokinesis. This balance is regulated by the opposing actions of CDK1 and PP2A, as well as the Greatwall kinase/MASTL. MASTL is commonly overexpressed in cancer, which makes it a potential therapeutic anticancer target. Loss of Mastl induces multiple chromosomal errors that lead to the accumulation of micronuclei and multilobulated cells in mitosis. Our analyses revealed that loss of Mastl leads to chromosome breaks and abnormalities impairing correct segregation. Phospho-proteomic data for Mastl knockout cells revealed alterations in proteins implicated in multiple processes during mitosis including double-strand DNA damage repair. In silico prediction of the kinases with affected activity unveiled NEK2 to be regulated in the absence of Mastl. We uncovered that, RAD51AP1, involved in regulation of homologous recombination, is phosphorylated by NEK2 and CDK1 but also efficiently dephosphorylated by PP2A/B55. Our results suggest that MastlKO disturbs the equilibrium of the mitotic phosphoproteome that leads to the disruption of DNA damage repair and triggers an accumulation of chromosome breaks even in noncancerous cells.

Proteomic Characterization of the Olfactory Molecular Imbalance in Dementia with Lewy Bodies

Olfactory dysfunction is one of the prodromal symptoms in dementia with Lewy bodies (DLB). However, the molecular pathogenesis associated with decreased smell function remains largely undeciphered. We generated quantitative proteome maps to detect molecular alterations in olfactory bulbs (OB) derived from DLB subjects compared to neurologically intact controls. A total of 3214 olfactory proteins were quantified, and 99 proteins showed significant alterations in DLB cases. Protein interaction networks disrupted in DLB indicated an imbalance in translation and the synaptic vesicle cycle. These alterations were accompanied by alterations in AKT/MAPK/SEK1/p38 MAPK signaling pathways that showed a distinct expression profile across the OB–olfactory tract (OT) axis. Taken together, our data partially reflect the missing links in the biochemical understanding of olfactory dysfunction in DLB.

Multi-faceted epigenetic dysregulation of gene expression promotes esophageal squamous cell carcinoma

Epigenetic landscapes can shape physiologic and disease phenotypes. We used integrative, high resolution multi-omics methods to delineate the methylome landscape and characterize the oncogenic drivers of esophageal squamous cell carcinoma (ESCC). We found 98% of CpGs are hypomethylated across the ESCC genome. Hypo-methylated regions are enriched in areas with heterochromatin binding markers (H3K9me3, H3K27me3), while hyper-methylated regions are enriched in polycomb repressive complex (EZH2/SUZ12) recognizing regions. Altered methylation in promoters, enhancers, and gene bodies, as well as in polycomb repressive complex occupancy and CTCF binding sites are associated with cancer-specific gene dysregulation. Epigenetic-mediated activation of non-canonical WNT/β-catenin/MMP signaling and a YY1/lncRNA ESCCAL-1/ribosomal protein network are uncovered and validated as potential novel ESCC driver alterations. This study advances our understanding of how epigenetic landscapes shape cancer pathogenesis and provides a resource for biomarker and target discovery.

Progress and pitfalls of using isobaric mass tags for proteome profiling

Introduction: Quantitative proteomics using mass spectrometry is performed via label-free or label-based approaches. Labeling strategies rely on the incorporation of stable heavy isotopes by metabolic, enzymatic, or chemical routes. Isobaric labeling uses chemical labels of identical masses but of different fragmentation behaviors to allow the relative quantitative comparison of peptide/protein abundances between biological samples.Areas covered: We have carried out a systematic review on the use of isobaric mass tags in proteomic research since their inception in 2003. We focused on their quantitative performances, their multiplexing evolution, as well as their broad use for relative quantification of proteins in pre-clinical models and clinical studies. Current limitations, primarily linked to the quantitative ratio distortion, as well as state-of-the-art and emerging solutions to improve their quantitative readouts are discussed.Expert opinion: The isobaric mass tag technology offers a unique opportunity to compare multiple protein samples simultaneously, allowing higher sample throughput and internal relative quantification for improved trueness and precision. Large studies can be performed when shared reference samples are introduced in multiple experiments. The technology is well suited for proteome profiling in the context of proteomic discovery studies.

Placental growth factor regulates the pentose phosphate pathway and antioxidant defense systems in human retinal endothelial cells

The molecular mechanisms whereby placental growth factor (PlGF) mediates its effects in nonproliferative diabetic retinopathy (DR) are unknown. To better understand the role of PlGF in DR, we used tandem mass tags (TMT)-labeled quantitative proteomics to human retinal endothelial cells (HRECs), treated anti-PlGF antibody, and PBS as a control. Functional annotation and pathway enrichments were performed, which suggested that the differentially expressed proteins (DEPs) were involved in key metabolic processes, protein binding, and membrane, pentose phosphate pathway (PPP) and adherens junction. We conducted integrated gene profiles of our previously published transcriptomic data to the TMT-labeled proteomics data. The results showed the sixty genes were found to be changed at the proteome level. The functional annotation conducted for the sixty proteins suggested that 58.3% of proteins were involved in PPP, 25% of proteins were in interleukin-12 singling and 16.7% of proteins were involved in glycolysis and gluconeogenesis pathway. Mass spectrometry results were validated by transendothelial electrical resistance measurement by an electrical cell-impedance sensing (ECIS) and western blot analysis of VE-cadherin, G6PD. These findings suggest that the PPP proteins and antioxidants may act as a downstream target of PlGF and may play a decisive role in HREC biological functions in DR. SIGNIFICANCE: PlGF (Placental growth factor) is known to play a pivotal role in pathological angiogenesis and inflammation by stimulating endothelial cell migration and by recruiting pericytes and inflammatory cells such as microglia and macrophages. Despite the well-defined pathophysiological roles of PlGF, the underlying molecular and cellular mechanisms are not completely understood, especially the exact relationships between biochemical events and molecular pathways regulated by PlGF, whose inhibition exhibits a protective role in DR. This study provides new insights into protein expression patterns and enables the identification of many attractive candidates for investigation of PPP pathway role in the activation of the antioxidant defense system in DR. Our findings suggest that the PPP proteins and antioxidants (PRDX6, HMOX1, NQO1 and YES1) may act as downstream targets of PlGF and may play a decisive role in HREC biological functions in DR.

Methods and Algorithms for Quantitative Proteomics by Mass Spectrometry

Protein quantitation by mass spectrometry has always been a resourceful technique in protein discovery, and more recently it has leveraged the advent of clinical proteomics. A single mass spectrometry analysis experiment provides identification and quantitation of proteins as well as information on posttranslational modifications landscape. By contrast, protein array technologies are restricted to quantitation of targeted proteins and their modifications. Currently, there are an overwhelming number of quantitative mass spectrometry methods for protein and peptide quantitation. The aim here is to provide an overview of the most common mass spectrometry methods and algorithms used in quantitative proteomics and discuss the computational aspects to obtain reliable quantitative measures of proteins, peptides and their posttranslational modifications. The development of a pipeline using commercial or freely available software is one of the main challenges in data analysis of many experimental projects. Recent developments of R statistical programming language make it attractive to fully develop pipelines for quantitative proteomics. We discuss concepts of quantitative proteomics that together with current R packages can be used to build highly customizable pipelines.

Type I Interferon Signaling Disrupts the Hepatic Urea Cycle and Alters Systemic Metabolism to Suppress T Cell Function

Infections induce complex host responses linked to antiviral defense, inflammation, and tissue damage and repair. We hypothesized that the liver, as a central metabolic hub, may orchestrate systemic metabolic changes during infection. We infected mice with chronic lymphocytic choriomeningitis virus (LCMV), performed RNA sequencing and proteomics of liver tissue, and integrated these data with serum metabolomics at different infection phases. Widespread reprogramming of liver metabolism occurred early after infection, correlating with type I interferon (IFN-I) responses. Viral infection induced metabolic alterations of the liver that depended on the interferon alpha/beta receptor (IFNAR1). Hepatocyte-intrinsic IFNAR1 repressed the transcription of metabolic genes, including Otc and Ass1, which encode urea cycle enzymes. This led to decreased arginine and increased ornithine concentrations in the circulation, resulting in suppressed virus-specific CD8+ T cell responses and ameliorated liver pathology. These findings establish IFN-I-induced modulation of hepatic metabolism and the urea cycle as an endogenous mechanism of immunoregulation. VIDEO ABSTRACT.

Modulation of muscle protein synthesis by amino acids: what consequences for the secretome? A preliminary in vitro study

The modulation by amino acids of muscle secretome is largely unknown. In this study, we investigate the effect of hyperaminoacidemia or specific amino acids (citrulline or leucine) on protein synthesis and secretome in myotubes. All conditions stimulate muscle protein synthesis, and secretome is differently modulated depending of the amino acids considered. In conclusion, the activation of protein synthesis by amino acids induces different modulations of the muscle secretome, proposing a new role of amino acids in the regulation of muscle function.

B cells sustain inflammation and predict response to immune checkpoint blockade in human melanoma

Tumor associated inflammation predicts response to immune checkpoint blockade in human melanoma. Current theories on regulation of inflammation center on anti-tumor T cell responses. Here we show that tumor associated B cells are vital to melanoma associated inflammation. Human B cells express pro- and anti-inflammatory factors and differentiate into plasmablast-like cells when exposed to autologous melanoma secretomes in vitro. This plasmablast-like phenotype can be reconciled in human melanomas where plasmablast-like cells also express T cell-recruiting chemokines CCL3, CCL4, CCL5. Depletion of B cells in melanoma patients by anti-CD20 immunotherapy decreases tumor associated inflammation and CD8+ T cell numbers. Plasmablast-like cells also increase PD-1+ T cell activation through anti-PD-1 blockade in vitro and their frequency in pretherapy melanomas predicts response and survival to immune checkpoint blockade. Tumor associated B cells therefore orchestrate and sustain melanoma inflammation and may represent a predictor for survival and response to immune checkpoint blockade therapy.

Proteomic Response to Rising Temperature in the Marine Cyanobacterium Synechococcus Grown in Different Nitrogen Sources

Synechococcus is one of the most important contributors to global primary productivity, and ocean warming is predicted to increase abundance and distribution of Synechococcus in the ocean. Here, we investigated molecular response of an oceanic Synechococcus strain WH8102 grown in two nitrogen sources (nitrate and urea) under present (25°C) and predicted future (28°C) temperature conditions using an isobaric tag (IBT)-based quantitative proteomic approach. Rising temperature decreased growth rate, contents of chlorophyll a, protein and sugar in the nitrate-grown cells, but only decreased protein content and significantly increased zeaxanthin content of the urea-grown cells. Expressions of CsoS2 protein involved in carboxysome formation and ribosomal subunits in both nitrate- and urea-grown cells were significantly decreased in rising temperature, whereas carbohydrate selective porin and sucrose-phosphate synthase (SPS) were remarkably up-regulated, and carbohydrate degradation associated proteins, i.e., glycogen phosphorylase kinase, fructokinase and glucose-6-phosphate dehydrogenase, were down-regulated in the urea-grown cells. Rising temperature also increased expressions of three redox-sensitive enzymes (peroxiredoxin, thioredoxin, and CP12) in both nitrate- and urea-grown cells. Our results indicated that rising temperature did not enhance cell growth of Synechococcus; on the contrary, it impaired cell functions, and this might influence cell abundance and distribution of Synechococcus in a future ocean.

MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes

Isotope-labeling-based mass spectrometry (MS) is widely used in quantitative proteomic studies. With this technique, the relative abundance of thousands of proteins can be efficiently profiled in parallel, greatly facilitating the detection of proteins differentially expressed across samples. However, this task remains computationally challenging. Here we present a new approach, termed Model-based Analysis of Proteomic data (MAP), for this task. Unlike many existing methods, MAP does not require technical replicates to model technical and systematic errors, and instead utilizes a novel step-by-step regression analysis to directly assess the significance of observed protein abundance changes. We applied MAP to compare the proteomic profiles of undifferentiated and differentiated mouse embryonic stem cells (mESCs), and found it has superior performance compared with existing tools in detecting proteins differentially expressed during mESC differentiation. A web-based application of MAP is provided for online data processing at http://bioinfo.sibs.ac.cn/shaolab/MAP.

Quantitative proteomics reveals the key molecular events occurring at different cell cycle phases of the in situ blooming dinoflagellate cells

Dinoflagellate blooms are the results of rapid cell proliferation governed by cell cycle, a highly-ordered series of events that culminates in cell division. However, little is known about cell cycle progression of the in situ bloom cells. Here, we compared proteomes of the in situ blooming cells of a dinoflagellate Prorocentrum donghaiense collected at different cell cycle phases. The blooming P. donghaiense cells completed a cell cycle within 24 h with a high synchronization rate of 82.7%. Proteins associated with photosynthesis, porphyrin and chlorophyll synthesis, carbon, nitrogen and amino acid metabolisms exhibited high expressions at the G₁ phase; DNA replication and mismatch repair related proteins were more abundant at the S phase; while protein synthesis and oxidative phosphorylation were highly enriched at the G₂/M phase. Cell cycle proteins presented similar periodic diel patterns to other eukaryotic cells, and higher expressions of proliferating cell nuclear antigen and cyclin dependent kinase 2 at the S phase ensured the smooth S-G₂/M transition. Strikingly, four histones were first identified in P. donghaiense and highly expressed at the G₂/M phase, indicating their potential roles in regulating cell cycle. This study presents the first quantitative survey, to our knowledge, of proteome changes at different cell cycle phases of the in situ blooming cells in natural environment and provides insights into cell cycle regulation of the blooming dinoflagellate cells.

Inferring Protein-Protein Interaction Networks From Mass Spectrometry-Based Proteomic Approaches: A Mini-Review

Studying protein-protein interaction networks provide key evidence for the underlying molecular mechanisms. Mass spectrometry-based proteomic approaches have been playing a pivotal role in deciphering these interaction networks, along with precise quantification for individual interactions. In this mini-review we discuss the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks. We then summarize the down-stream computational strategies for identification and quantification of interactions from those techniques. Finally, we highlight the challenges and limitations of current computational pipelines in eliminating false positive interactors, followed by a summary of the innovative algorithms to address these issues, along with the scope for future improvements.

IsoProt: A Complete and Reproducible Workflow To Analyze iTRAQ/TMT Experiments

Reproducibility has become a major concern in biomedical research. In proteomics, bioinformatic workflows can quickly consist of multiple software tools each with its own set of parameters. Their usage involves the definition of often hundreds of parameters as well as data operations to ensure tool interoperability. Hence, a manuscript’s methods section is often insufficient to completely describe and reproduce a data analysis workflow. Here we present IsoProt: A complete and reproducible bioinformatic workflow deployed on a portable container environment to analyze data from isobarically labeled, quantitative proteomics experiments. The workflow uses only open source tools and provides a user-friendly and interactive browser interface to configure and execute the different operations. Once the workflow is executed, the results including the R code to perform statistical analyses can be downloaded as an HTML document providing a complete record of the performed analyses. IsoProt therefore represents a reproducible bioinformatics workflow that will yield identical results on any computer platform.

A large scale proteome analysis of the gefitinib primary resistance overcome by KDAC inhibition in KRAS mutated adenocarcinoma cells overexpressing amphiregulin

KDAC inhibitors (KDACi) overcome gefitinib primary resistance in non-small cell lung cancer (NSCLC) including mutant-KRAS lung adenocarcinoma. To identify which proteins are involved in the restoration of this sensitivity and to provide new therapeutic targets for mutant-KRAS lung adenocarcinoma, we performed an iTRAQ quantitative proteomic analysis after subcellular fractionation of H358-NSCLC treated with gefitinib and KDACi (TSA/NAM) versus gefitinib alone. The 86 proteins found to have been significantly dysregulated between the two conditions, were mainly involved in cellular metabolism and cell transcription processes. As expected, the pathway related to histone modifications was affected by the KDACi. Pathways known for controlling tumor development and (chemo)-resistance (miRNA biogenesis/glutathione metabolism) were affected by the KDACi/gefitinib treatment. Moreover, 57 dysregulated proteins were upstream of apoptosis (such as eEF1A2 and STAT1) and hence provide potential therapeutic targets. The inhibition by siRNA of eEF1A2 expression resulted in a slight decrease in H358-NSCLC viability. In addition, eEF1A2 and STAT1 siRNA transfections suggested that both STAT1 and eEF1A2 prevent AKT phosphorylation known for enhancing gefitinib resistance in NSCLC. Therefore, altogether our data provide new insights into proteome regulations in the context of overcoming the NSCLC resistance to gefitinib through KDACi in H358 KRAS mutated and amphiregulin-overexpressing NSCLC cells.

A Chemoproteomic Strategy for Direct and Proteome-Wide Covalent Inhibitor Target-Site Identification

Despite recent clinical successes for irreversible drugs, potential toxicities mediated by unpredictable modification of off-target cysteines represents a major hurdle for expansion of covalent drug programs. Understanding the proteome-wide binding profile of covalent inhibitors can significantly accelerate their development; however, current mass spectrometry strategies typically do not provide a direct, amino acid level readout of covalent activity for complex, selective inhibitors. Here we report the development of CITe-Id, a novel chemoproteomic approach that employs covalent pharmacologic inhibitors as enrichment reagents in combination with an optimized proteomic platform to directly quantify dose-dependent binding at cysteine-thiols across the proteome. CITe-Id analysis of our irreversible CDK inhibitor THZ1 identified dose-dependent covalent modification of several unexpected kinases, including a previously unannotated cysteine (C840) on the understudied kinase PKN3. These data streamlined our development of JZ128 as a new selective covalent inhibitor of PKN3. Using JZ128 as a probe compound, we identified novel potential PKN3 substrates, thus offering an initial molecular view of PKN3 cellular activity. CITe-Id provides a powerful complement to current chemoproteomic platforms to characterize the selectivity of covalent inhibitors, identify new, pharmacologically addressable cysteine-thiols, and inform structure-based drug design programs.

Reagents for Isobaric Labeling Peptides in Quantitative Proteomics

Currently, the commercial reagents for isobaric peptides labeling (TMT and iTRAQ) have some drawbacks, such as high cost in experiments, especially in quantitation for the modified peptides, and inconvenient handling for variable sizes of samples. Herein, we developed a set of 10-plex isobaric tags (IBT) with high stability and low cost. The labeled peptides were sensitively detected on Orbitrap Q Exactive MS with an MS2 resolution of 35 000 at 30% NCE, while the peptides were efficiently labeled over 97% by IBT at a ratio of 10:1 of reagent/peptide (w/w) in 200 mM TEAB buffer for 2 h. The IBT labeling was demonstrated with a wide dynamic range of 50-fold without obvious matrix effects on quantification. Importantly, there was little quantification bias found among the individual IBT tags, indicating that the peptides labeled by different tags were quantitatively comparable. The IBT 10-plex reagents were applied for dynamically monitoring the quantitative responses of phosphoproteome stimulated by EGF treatment in HeLa cells. In total, 5 361 unique phosphopeptides were identified, which reached a similar conclusion as others reported. The IBT reagents were therefore experimentally proven as a new type of reagents for isobaric peptides labeling and useful in a large quantity peptides of quantitative proteomics.

Palmitate-Induced Insulin Hypersecretion and Later Secretory Decline Associated with Changes in Protein Expression Patterns in Human Pancreatic Islets

In obese children with high circulating concentrations of free fatty acid palmitate, we have observed that insulin levels at fasting and in response to a glucose challenge were several times higher than in obese children with low concentrations of the fatty acid as well as in lean controls. Declining and even insufficient insulin levels were observed in obese adolescents with high levels of the fatty acid. In isolated human islets exposed to palmitate we have observed insulin hypersecretion after 2 days exposure. In contrast, insulin secretion from the islets was reduced after 7 days culture in the presence of the fatty acid. This study aims at identifying islet-related biological events potentially linked with the observed insulin hypersecretion and later secretory decline in these obese children and adolescents using the islet model. We analyzed protein expression data obtained from human islets exposed to elevated palmitate levels for 2 and 7 days by an improved methodology for statistical analysis of differentially expressed proteins. Protein profiling of islet samples by liquid chromatography-tandem mass spectrometry identified 115 differentially expressed proteins (DEPs). Several DEPs including sorcin were associated with increased glucose-stimulated insulin secretion in islets after 2 days of exposure to palmitate. Similarly, several metabolic pathways including altered protein degradation, increased autophagy, altered redox condition, and hampered insulin processing were coupled to the functional impairment of islets after 7 days of culture in the presence of palmitate. Such biological events, once validated in the islets, may give rise to novel treatment strategies aiming at normalizing insulin levels in obese children with high palmitate levels, which may reduce or even prevent obesity-related type 2 diabetes mellitus.

Data for Tandem Mass Tag (TMT) proteomic analysis of the pancreas during the early phase of experimental pancreatitis

The quantitative proteomics data reported here pertain to the research article entitled “A Tandem Mass Tag (TMT) proteomic analysis during the early phase of experimental pancreatitis reveals new insights in the disease pathogenesis” (García-Hernández et al., 2018) [1].

The development of acute pancreatitis (AP, an important pathological inflammatory state of the exocrine pancreas) would be based on early changes in protein expression and signaling pathways whose unmasking would be crucial for deciphering AP at the molecular level. We reported here a Tandem Mass Tag (TMT)-based proteomics analysis of rat subcellular fractions of the pancreas during the early phase of experimental AP, using a sixplex isobaric chemical labeling technique. We identified 997 unique proteins, of which 353 were significantly different (22, 276 or 55 in both, the soluble or the membrane fractions, respectively).

Accordingly, using TMT proteomics and bioinformatic tools, in García-Hernández et al., 2018- [1] we were able to detect significant changes in protein expression related to many pathobiological pathways of AP as from the early phase of the disease, including some changes never described before in this disease. Proteomics data are publicly available in ProteomeXchange via PRIDE through the identifier PXD007096.

Angiogenin and Osteoprotegerin are type II muscle specific myokines protecting pancreatic beta-cells against proinflammatory cytokines

Tissue cross-talk is emerging as a determinant way to coordinate the different organs implicated in glucose homeostasis. Among the inter-organ communication factors, muscle-secreted myokines can modulate the function and survival of pancreatic beta-cells. Using primary human myotubes from soleus, vastus lateralis and triceps brachii muscles, we report here that the impact of myokines on beta-cells depends on fiber types and their metabolic status. We show that Type I and type II primary myotubes present specific mRNA and myokine signatures as well as a different sensitivity to TNF-alpha induced insulin resistance. Finally, we show that angiogenin and osteoprotegerin are triceps specific myokines with beta-cell protective actions against proinflammatory cytokines. These results suggest that type I and type II muscles could impact insulin secretion and beta-cell mass differentially in type 2 diabetes through specific myokines secretion.

A tandem mass tag (TMT) proteomic analysis during the early phase of experimental pancreatitis reveals new insights in the disease pathogenesis

Changes in the protein expression occurring within the initiation phase of acute pancreatitis (AP) might be vital in the development of this complex disease. However, the exact mechanisms involved in the onset of AP remains elusive and most of our knowledge about the pathobiology of AP comes from animal models. We performed in a rat pancreatitic model a high-throughput shotgun proteomic profiling of the soluble and whole membrane fractions from the pancreas during the early phase of cerulein (Cer)-induced AP. We identified 997 proteins, of which 353 were significantly different (22, 276 or 55 in both, the soluble or the membrane fractions, respectively). Gene Ontology and KEGG PATHWAY analyses revealed that these proteins were implicated in molecular mechanisms relevant to AP pathogenesis, including vesicle-mediated and protein transport, lysosomal and mitochondrial impairment or proteolysis. Numerous metabolic processes were downregulated apparently to reduce energy consumption, and a remarkable increase in inflammatory and stress responses was also highlighted. The proteomic data were verified by immunoblotting of 11 and 7 different soluble or membrane-associated proteins, either novel (VPS29 and MCTS1) or known factors in AP. Also, our first observation of the imbalance of some COP proteins during AP early phase deserves further characterization.

BIOLOGICAL SIGNIFICANCE

AP is one of the most important pathological inflammatory states of the exocrine pancreas but its pathophysiology remains incompletely understood, especially the early acinar events. Proteomic analysis of pancreatic subcellular fractions simplifies protein maps and helps in the identification of new protein alterations and biomarkers characterizing pancreatic tissue damage. Our shotgun approach has not been previously used to profile the early proteomic alterations of the disease, which are considered crucial for its development and for the founding of clinical procedures. Furthermore, our subcellular fractionation protocol allowed us to detect changes in membrane proteins so far overlooked in the proteomic study of AP. Accordingly, using TMT proteomics and bioinformatic tools, we were able to detect significant changes in protein expression related to many pathobiological pathways of acute pancreatitis as from the early phase of the disease. To our knowledge, some of these changes, such as the imbalance of some COP proteins, have never been described in this disease.

Lymphatic exosomes promote dendritic cell migration along guidance cues

Inflammation stimulates lymphatic endothelial cells to release exosomes, which accumulate in the perivascular stroma. Brown et al. show that these exosomes promote the directional migration of dendritic cells along guidance cues in complex environments by enhancing dynamic cellular protrusions in a CX3CL1-dependent manner.

Lymphatic endothelial cells (LECs) release extracellular chemokines to guide the migration of dendritic cells. In this study, we report that LECs also release basolateral exosome-rich endothelial vesicles (EEVs) that are secreted in greater numbers in the presence of inflammatory cytokines and accumulate in the perivascular stroma of small lymphatic vessels in human chronic inflammatory diseases. Proteomic analyses of EEV fractions identified >1,700 cargo proteins and revealed a dominant motility-promoting protein signature. In vitro and ex vivo EEV fractions augmented cellular protrusion formation in a CX3CL1/fractalkine-dependent fashion and enhanced the directional migratory response of human dendritic cells along guidance cues. We conclude that perilymphatic LEC exosomes enhance exploratory behavior and thus promote directional migration of CX3CR1-expressing cells in complex tissue environments.

A slow-cycling subpopulation of melanoma cells with highly invasive properties

Melanoma is a heterogeneous tumor with different subpopulations showing different proliferation rates. Slow-cycling cells were previously identified in melanoma, but not fully biologically characterized. Using the label-retention method, we identified a subpopulation of slow-cycling cells, defined as label-retaining cells (LRC), with strong invasive properties. We demonstrate through live imaging that LRC are leaving the primary tumor mass at a very early stage and disseminate to peripheral organs. Through global proteome analyses, we identified the secreted protein SerpinE2/protease nexin-1 as causative for the highly invasive potential of LRC in melanomas.

Effects of Alanyl-Glutamine Treatment on the Peritoneal Dialysis Effluent Proteome Reveal Pathomechanism-Associated Molecular Signatures

Peritoneal dialysis (PD) is a modality of renal replacement therapy in which the high volumes of available PD effluent (PDE) represents a rich source of biomarkers for monitoring disease and therapy. Although this information could help guide the management of PD patients, little is known about the potential of PDE to define pathomechanism-associated molecular signatures in PD.

We therefore subjected PDE to a high-performance multiplex proteomic analysis after depletion of highly-abundant plasma proteins and enrichment of low-abundance proteins. A combination of label-free and isobaric labeling strategies was applied to PDE samples from PD patients (n = 20) treated in an open-label, randomized, two-period, cross-over clinical trial with standard PD fluid or with a novel PD fluid supplemented with alanyl-glutamine (AlaGln).

With this workflow we identified 2506 unique proteins in the PDE proteome, greatly increasing coverage beyond the 171 previously-reported proteins. The proteins identified range from high abundance plasma proteins to low abundance cellular proteins, and are linked to larger numbers of biological processes and pathways, some of which are novel for PDE. Interestingly, proteins linked to membrane remodeling and fibrosis are overrepresented in PDE compared with plasma, whereas the proteins underrepresented in PDE suggest decreases in host defense, immune-competence and response to stress. Treatment with AlaGln-supplemented PD fluid is associated with reduced activity of membrane injury-associated mechanisms and with restoration of biological processes involved in stress responses and host defense.

Our study represents the first application of the PDE proteome in a randomized controlled prospective clinical trial of PD. This novel proteomic workflow allowed detection of low abundance biomarkers to define pathomechanism-associated molecular signatures in PD and their alterations by a novel therapeutic intervention.

Multiple testing corrections in quantitative proteomics: A useful but blunt tool

Multiple testing corrections are a useful tool for restricting the FDR, but can be blunt in the context of low power, as we demonstrate by a series of simple simulations. Unfortunately, in proteomics experiments low power can be common, driven by proteomics-specific issues like small effects due to ratio compression, and few replicates due to reagent high cost, instrument time availability and other issues; in such situations, most multiple testing corrections methods, if used with conventional thresholds, will fail to detect any true positives even when many exist. In this low power, medium scale situation, other methods such as effect size considerations or peptide-level calculations may be a more effective option, even if they do not offer the same theoretical guarantee of a low FDR. Thus, we aim to highlight in this article that proteomics presents some specific challenges to the standard multiple testing corrections methods, which should be employed as a useful tool but not be regarded as a required rubber stamp.

Quantitative Proteomics Reveals Potential Crosstalk between a Small RNA CoaR and a Two-Component Regulator Slr1037 in Synechocystis sp. PCC6803

Bacterial small RNAs (sRNAs) and two-component systems (TCSs) were two vital regulatory mechanisms employed by microorganisms to respond to environmental changes and stresses. As a promising "autotrophic cell factory", photosynthetic cyanobacteria have attracted a lot of attention these years. Although most studies focused on studying the roles of sRNAs or TCS regulators in stress response in photosynthetic cyanobacteria, limited work has elucidated their potential crosstalk. Our previous work has identified a negative sRNA regulator CoaR and a positive response regulator Slr1037 both related to 1-butanol stress regulation in Synechocystis sp. PCC6803. In this work, the potential crosstalk between CoaR and Slr1307 (i.e., the coregulated genes mediated by CoaR and Slr1037) was identified and validated through quantitative proteomics and quantitative real-time PCR (qRT-PCR), respectively. The results showed that the sensitive phenotype to 1-butanol of Δslr1037 could be rescued by suppressing coaR in Δslr1037, probably due to the fact that some target genes of Slr1037 could be reactivated by repression of CoaR. Twenty-eight coregulated proteins mediated by CoaR and Slr1037 were found through quantitative proteomics, and 10 of the annotated proteins were validated via qRT-PCR. This study proved the existence of crosstalk between sRNAs and response regulators and provided new insights into the coregulation of biofuel resistance in cyanobacteria.

Proteomic discovery and verification of serum amyloid A as a predictor marker of patients at risk of post-stroke infection: a pilot study

Background

Post-stroke infections occur in 20–36% of stroke patients and are associated with high morbidity and mortality rates. Early identification of patients at risk of developing an infection could improve care via an earlier treatment leading to a better outcome. We used proteomic tools in order to discover biomarkers able to stratify patients at risk of post-stroke infection.

Methods

The post hoc analysis of a prospective cohort study including 40 ischemic stroke patients included 21 infected and 19 non-infected participants. A quantitative, isobaric labeling, proteomic strategy was applied to the plasma samples of 5 infected and 5 non-infected patients in order to highlight any significantly modulated proteins. A parallel reaction monitoring (PRM) assay was applied to 20 additional patients (10 infected and 10 non-infected) to verify discovery results. The most promising protein was pre-validated using an ELISA immunoassay on 40 patients and at different time points after stroke onset.

Results

Tandem mass analysis identified 266 proteins, of which only serum amyloid A (SAA1/2) was significantly (p = 0.007) regulated between the two groups of patients. This acute-phase protein appeared to be 2.2 times more abundant in infected patients than in non-infected ones. These results were verified and validated using PRM and ELISA immunoassays, which showed that infected patients had significantly higher concentrations of SAA1/2 than non-infected patients at hospital admission, but also at 1, 3, and 5 days after admission.

Conclusions

The present study demonstrated that SAA1/2 is a promising predictor, at hospital admission, of stroke patients at risk of developing an infection. Further large, multicenter validation studies are needed to confirm these results. If confirmed, SAA1/2 concentrations could be used to identify the patients most at risk of post-stroke infections and therefore implement treatments more rapidly, thus reducing mortality.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-017-9162-0) contains supplementary material, which is available to authorized users.

Tubulin Beta-3 Chain as a New Candidate Protein Biomarker of Human Skin Aging: A Preliminary Study

Skin aging is a complex process, and a lot of efforts have been made to identify new and specific targets that could help to diagnose, prevent, and treat skin aging. Several studies concerning skin aging have analyzed the changes in gene expression, and very few investigations have been performed at the protein level. Moreover, none of these proteomic studies has used a global quantitative labeled proteomic offgel approach that allows a more accurate description of aging phenotype. We applied such an approach on human primary keratinocytes obtained from sun-nonexposed skin biopsies of young and elderly women. A total of 517 unique proteins were identified, and 58 proteins were significantly differentially expressed with 40 that were downregulated and 18 upregulated with aging. Gene ontology and pathway analysis performed on these 58 putative biomarkers of skin aging evidenced that these dysregulated proteins were mostly involved in metabolism and cellular processes such as cell cycle and signaling pathways. Change of expression of tubulin beta-3 chain was confirmed by western blot on samples originated from several donors. Thus, this study suggested the tubulin beta-3 chain has a promising biomarker in skin aging.

Combined lipidomic and proteomic analysis of isolated human islets exposed to palmitate reveals time-dependent changes in insulin secretion and lipid metabolism

Studies on the pathophysiology of type 2 diabetes mellitus (T2DM) have linked the accumulation of lipid metabolites to the development of beta-cell dysfunction and impaired insulin secretion. In most in vitro models of T2DM, rodent islets or beta-cell lines are used and typically focus is on specific cellular pathways or organs. Our aim was to, firstly, develop a combined lipidomics and proteomics approach for lipotoxicity in isolated human islets and, secondly, investigate if the approach could delineate novel and/ or confirm reported mechanisms of lipotoxicity. To this end isolated human pancreatic islets, exposed to chronically elevated palmitate concentrations for 0, 2 and 7 days, were functionally characterized and their levels of multiple targeted lipid and untargeted protein species determined. Glucose-stimulated insulin secretion from the islets increased on day 2 and decreased on day 7. At day 7 islet insulin content decreased and the proinsulin to insulin content ratio doubled. Amounts of cholesterol, stearic acid, C16 dihydroceramide and C24:1 sphingomyelin, obtained from the lipidomic screen, increased time-dependently in the palmitate-exposed islets. The proteomic screen identified matching changes in proteins involved in lipid biosynthesis indicating up-regulated cholesterol and lipid biosynthesis in the islets. Furthermore, proteins associated with immature secretory granules were decreased when palmitate exposure time was increased despite their high affinity for cholesterol. Proteins associated with mature secretory granules remained unchanged. Pathway analysis based on the protein and lipid expression profiles implicated autocrine effects of insulin in lipotoxicity. Taken together the study demonstrates that combining different omics approaches has potential in mapping of multiple simultaneous cellular events. However, it also shows that challenges exist for effectively combining lipidomics and proteomics in primary cells. Our findings provide insight into how saturated fatty acids contribute to islet cell dysfunction by affecting the granule maturation process and confirmation in human islets of some previous findings from rodent islet and cell-line studies.

Synergistic effects of citrulline supplementation and exercise on performance in male rats: evidence for implication of protein and energy metabolisms

Background: Exercise and citrulline (CIT) are both regulators of muscle protein metabolism. However, the combination of both has been under-studied yet may have synergistic effects on muscle metabolism and performance. Methods: Three-month-old healthy male rats were randomly assigned to be fed ad libitum for 4 weeks with either a citrulline-enriched diet (1 g·kg^-1·day^-1) (CIT) or an isonitrogenous standard diet (by addition of nonessential amino acid) (Ctrl) and trained (running on treadmill 5 days·week^-1) (ex) or not. Maximal endurance activity and body composition were assessed, and muscle protein metabolism (protein synthesis, proteomic approach) and energy metabolism [energy expenditure, mitochondrial metabolism] were explored. Results: Body composition was affected by exercise but not by CIT supplementation. Endurance training was associated with a higher maximal endurance capacity than sedentary groups (P<0.001), and running time was 14% higher in the CITex group than the Ctrlex group (139±4 min versus 122±6 min, P<0.05). Both endurance training and CIT supplementation alone increased muscle protein synthesis (by +27% and +33%, respectively, versus Ctrl, P<0.05) with an additive effect (+48% versus Ctrl, P<0.05). Mitochondrial metabolism was modulated by exercise but not directly by CIT supplementation. However, the proteomic approach demonstrated that CIT supplementation was able to affect energy metabolism, probably due to activation of pathways generating acetyl-CoA. Conclusion: CIT supplementation and endurance training in healthy male rats modulates both muscle protein and energy metabolisms, with synergic effects on an array of parameters, including performance and protein synthesis.

Artemisinins Target GABAA Receptor Signaling and Impair α Cell Identity

Type 1 diabetes is characterized by the destruction of pancreatic β cells, and generating new insulin-producing cells from other cell types is a major aim of regenerative medicine. One promising approach is transdifferentiation of developmentally related pancreatic cell types, including glucagon-producing α cells. In a genetic model, loss of the master regulatory transcription factor Arx is sufficient to induce the conversion of α cells to functional β-like cells. Here, we identify artemisinins as small molecules that functionally repress Arx by causing its translocation to the cytoplasm. We show that the protein gephyrin is the mammalian target of these antimalarial drugs and that the mechanism of action of these molecules depends on the enhancement of GABA_A receptor signaling. Our results in zebrafish, rodents, and primary human pancreatic islets identify gephyrin as a druggable target for the regeneration of pancreatic β cell mass from α cells.

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Artemisinins inhibit ARX function and impair α cell identity

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Compounds act by stabilizing gephyrin, thus enhancing GABA_A receptor signaling

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Artemisinins increase β cell mass in zebrafish and rodent models

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Functional and transcriptional data indicate a conserved phenotype in human islets

A time-resolved molecular map of the macrophage response to VSV infection

Studying the relationship between virus infection and cellular response is paradigmatic for our understanding of how perturbation changes biological systems. Immune response, in this context is a complex yet evolutionarily adapted and robust cellular change, and is experimentally amenable to molecular analysis. To visualize the full cellular response to virus infection, we performed temporal transcriptomics, proteomics, and phosphoproteomics analysis of vesicular stomatitis virus (VSV)-infected mouse macrophages. This enabled the understanding of how infection-induced changes in host gene and protein expression are coordinated with post-translational modifications by cells in time to best measure and control the infection process. The vast and complex molecular changes measured could be decomposed in a limited number of clusters within each category (transcripts, proteins, and protein phosphorylation) each with own kinetic parameter and characteristic pathways/processes, suggesting multiple regulatory options in the overall sensing and homeostatic program. Altogether, the data underscored a prevalent executive function to phosphorylation. Resolution of the molecular events affecting the RIG-I pathway, central to viral recognition, reveals that phosphorylation of the key innate immunity adaptor mitochondrial antiviral-signaling protein (MAVS) on S328/S330 is necessary for activation of type-I interferon and nuclear factor κ B (NFκB) pathways. To further understand the hierarchical relationships, we analyzed kinase–substrate relationships and found RAF1 and, to a lesser extent, ARAF to be inhibiting VSV replication and necessary for NFκB activation, and AKT2, but not AKT1, to be supporting VSV replication. Integrated analysis using the omics data revealed co-regulation of transmembrane transporters including SLC7A11, which was subsequently validated as a host factor in the VSV replication. The data sets are predicted to greatly empower future studies on the functional organization of the response of macrophages to viral challenges.

Human tissue engineering allows the identification of active miRNA regulators of glioblastoma aggressiveness

Glioblastoma multiforme (GBM) is among the most aggressive cancers associated with massive infiltration of peritumoral parenchyma by migrating tumor cells. The infiltrative nature of GBM cells, the intratumoral heterogeneity concomitant with redundant signaling pathways likely underlie the inability of conventional and targeted therapies to achieve long-term remissions. In this respect, microRNAs (miRNAs), which are endogenous small non-coding RNAs that play a role in cancer aggressiveness, emerge as possible relevant prognostic biomarkers or therapeutic targets for treatment of malignant gliomas. We previously described a tissue model of GBM developing into a stem cell-derived human Engineered Neural Tissue (ENT) that allows the study of tumor/host tissue interaction. Combined with high throughput sequencing analysis, we took advantage of this human and integrated tissue model to understand miRNAs regulation. Three miRNAs (miR-340, -494 and -1293) active on cell proliferation, adhesion to extracellular matrix and tumor cell invasion were identified in GBM cells developing within ENT, and also confirmed in GBM biopsies. The components of miRNAs regulatory network at the transcriptional and the protein level have been also revealed by whole transcriptome analysis and Tandem Mass Tag in transfected GBM cells. Notably, miR-340 has a clinical relevance and modulates the expression of miR-494 and -1293, emphasizing its biological significance. Altogether, these findings demonstrate that human tissue engineering modeling GBM development in neural host tissue is a suitable tool to identify active miRNAs. Collectively, our study identified miR-340 as a strong modulator of GBM aggressiveness which may constitute a therapeutic target for treatment of malignant gliomas.

On the Statistical Significance of Compressed Ratios in Isobaric Labeling: A Cross-Platform Comparison

Isobaric labeling is gaining popularity in proteomics due to its multiplexing capacity. However, copeptide fragmentation introduces a bias that undermines its accuracy. Several strategies have been shown to partially and, in some cases, completely solve this issue. However, it is still not clear how ratio compression affects the ability to identify a protein's change of abundance as statistically significant. Here, by using the "two proteomes" approach (E. coli lysates with fixed 2.5 ratios in the presence or absence of human lysates acting as the background interference) and manipulating isolation width values, we were able to model isobaric data with different levels of accuracy and precision in three types of mass spectrometers: LTQ Orbitrap Velos, Impact, and Q Exactive. We determined the influence of these variables on the statistical significance of the distorted ratios and compared them to the ratios measured without impurities. Our results confirm previous findings1-4 regarding the importance of optimizing acquisition parameters in each instrument in order to minimize interference without compromising precision and identification. We also show that, under these experimental conditions, the inclusion of a second replicate increases statistical sensitivity 2-3-fold and counterbalances to a large extent the issue of ratio compression.

Discovery of serum protein biomarkers in drug-free patients with major depressive disorder

OBJECTIVE

Major depressive disorder (MDD) is a systemic and multifactorial disorder involving complex interactions between genetic predisposition and disturbances of various molecular pathways. Its underlying molecular pathophysiology remains unclear, and no valid and objective diagnostic tools for the condition are available.

METHODS

We performed large-scale proteomic profiling to identify novel peripheral biomarkers implicated in the pathophysiology of MDD in 25 drug-free female MDD patients and 25 healthy controls. First, quantitative serum proteome profiles were obtained and analyzed by liquid chromatography-tandem mass spectrometry using serum samples from 10 MDD patients and 10 healthy controls. Next, candidate biomarker sets, including differentially expressed proteins from the profiling experiment and those identified in the literature, were verified using multiple-reaction monitoring in 25 patients and 25 healthy controls. The final panel of potential biomarkers was selected using multiparametric statistical analysis.

RESULTS

We identified a serum biomarker panel consisting of six proteins: apolipoprotein D, apolipoprotein B, vitamin D-binding protein, ceruloplasmin, hornerin, and profilin 1, which could be used to distinguish MDD patients from controls with 68% diagnostic accuracy. Our results suggest that modulation of the immune and inflammatory systems and lipid metabolism are involved in the pathophysiology of MDD.

CONCLUSIONS

Our findings of functional proteomic changes in the peripheral blood of patients with MDD further clarify the molecular biological pathway underlying depression. Further studies using larger, independent cohorts are needed to verify the role of these candidate biomarkers for the diagnosis of MDD.

CONSTANd : A Normalization Method for Isobaric Labeled Spectra by Constrained Optimization

In quantitative proteomics applications, the use of isobaric labels is a very popular concept as they allow for multiplexing, such that peptides from multiple biological samples are quantified simultaneously in one mass spectrometry experiment. Although this multiplexing allows that peptide intensities are affected by the same amount of instrument variability, systematic effects during sample preparation can also introduce a bias in the quantitation measurements. Therefore, normalization methods are required to remove this systematic error. At present, a few dedicated normalization methods for isobaric labeled data are at hand. Most of these normalization methods include a framework for statistical data analysis and rely on ANOVA or linear mixed models. However, for swift quality control of the samples or data visualization a simple normalization technique is sufficient. To this aim, we present a new and easy-to-use data-driven normalization method, named CONSTANd. The CONSTANd method employs constrained optimization and prior information about the labeling strategy to normalize the peptide intensities. Further, it allows maintaining the connection to any biological effect while reducing the systematic and technical errors. As a result, peptides can not only be compared directly within a multiplexed experiment, but are also comparable between other isobaric labeled datasets from multiple experimental designs that are normalized by the CONSTANd method, without the need to include a reference sample in every experimental setup. The latter property is especially useful when more than six, eight or ten (TMT/iTRAQ) biological samples are required to detect differential peptides with sufficient statistical power and to optimally make use of the multiplexing capacity of isobaric labels.

Visualization of proteomics data using R and bioconductor

Data visualization plays a key role in high-throughput biology. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience. Here, we provided a short overview of the application of the R software to the visualization of proteomics data. We present a summary of R's plotting systems and how they are used to visualize and understand raw and processed MS-based proteomics data.