From raw data to biological discoveries: a computational analysis pipeline for mass spectrometry-based proteomics

In vivo mapping of protein-protein interactions of schizophrenia risk factors generates an interconnected disease network

Genetic analyses of Schizophrenia (SCZ) patients have identified thousands of risk factors. In silico protein-protein interaction (PPI) network analysis has provided strong evidence that disrupted PPI networks underlie SCZ pathogenesis. In this study, we performed in vivo PPI analysis of several SCZ risk factors in the rodent brain. Using endogenous antibody immunoprecipitations coupled to mass spectrometry (MS) analysis, we constructed a SCZ network comprising 1612 unique PPI with a 5% FDR. Over 90% of the PPI were novel, reflecting the lack of previous PPI MS studies in brain tissue. Our SCZ PPI network was enriched with known SCZ risk factors, which supports the hypothesis that an accumulation of disturbances in selected PPI networks underlies SCZ. We used Stable Isotope Labeling in Mammals (SILAM) to quantitate phencyclidine (PCP) perturbations in the SCZ network and found that PCP weakened most PPI but also led to some enhanced or new PPI. These findings demonstrate that quantitating PPI in perturbed biological states can reveal alterations to network biology.

Activity-Induced Cortical Glutamatergic Neuron Nascent Proteins

Neuronal activity initiates signaling cascades that culminate in diverse outcomes including structural and functional neuronal plasticity, and metabolic changes. While studies have revealed activity-dependent neuronal cell type-specific transcriptional changes, unbiased quantitative analysis of cell-specific activity-induced dynamics in newly synthesized proteins (NSPs) synthesis in vivo has been complicated by cellular heterogeneity and a relatively low abundance of NSPs within the proteome in the brain. Here we combined targeted expression of mutant MetRS (methionine tRNA synthetase) in genetically defined cortical glutamatergic neurons with tight temporal control of treatment with the noncanonical amino acid, azidonorleucine, to biotinylate NSPs within a short period after pharmacologically induced seizure in male and female mice. By purifying peptides tagged with heavy or light biotin-alkynes and using direct tandem mass spectrometry detection of biotinylated peptides, we quantified activity-induced changes in cortical glutamatergic neuron NSPs. Seizure triggered significant changes in ∼300 NSPs, 33% of which were decreased by seizure. Proteins mediating excitatory and inhibitory synaptic plasticity, including SynGAP1, Pak3, GEPH1, Copine-6, and collybistin, and DNA and chromatin remodeling proteins, including Rad21, Smarca2, and Ddb1, are differentially synthesized in response to activity. Proteins likely to play homeostatic roles in response to activity, such as regulators of proteastasis, intracellular ion control, and cytoskeleton remodeling proteins, are activity induced. Conversely, seizure decreased newly synthetized NCAM, among others, suggesting that seizure induced degradation. Overall, we identified quantitative changes in the activity-induced nascent proteome from genetically defined cortical glutamatergic neurons as a strategy to discover downstream mediators of neuronal plasticity and generate hypotheses regarding their function.SIGNIFICANCE STATEMENT Activity-induced neuronal and synaptic plasticity are mediated by changes in the protein landscape, including changes in the activity-induced newly synthesized proteins; however, identifying neuronal cell type-specific nascent proteome dynamics in the intact brain has been technically challenging. We conducted an unbiased proteomic screen from which we identified significant activity-induced changes in ∼300 newly synthesized proteins in genetically defined cortical glutamatergic neurons within 20 h after pharmacologically induced seizure. Bioinformatic analysis of the dynamic nascent proteome indicates that the newly synthesized proteins play diverse roles in excitatory and inhibitory synaptic plasticity, chromatin remodeling, homeostatic mechanisms, and proteasomal and metabolic functions, extending our understanding of the diversity of plasticity mechanisms.

Proteomic screen reveals diverse protein transport between connected neurons in the visual system

Intercellular transfer of toxic proteins between neurons is thought to contribute to neurodegenerative disease, but whether direct interneuronal protein transfer occurs in the healthy brain is not clear. To assess the prevalence and identity of transferred proteins and the cellular specificity of transfer, we biotinylated retinal ganglion cell proteins in vivo and examined biotinylated proteins transported through the rodent visual circuit using microscopy, biochemistry, and mass spectrometry. Electron microscopy demonstrated preferential transfer of biotinylated proteins from retinogeniculate inputs to excitatory lateral geniculate nucleus (LGN) neurons compared with GABAergic neurons. An unbiased mass spectrometry-based screen identified 200 transneuronally transported proteins (TNTPs) isolated from the visual cortex. The majority of TNTPs are present in neuronal exosomes, and virally expressed TNTPs, including tau and β-synuclein, were detected in isolated exosomes and postsynaptic neurons. Our data demonstrate transfer of diverse endogenous proteins between neurons in the healthy intact brain and suggest that TNTP transport may be mediated by exosomes.

Schiapparelli et al. show that diverse endogenous proteins are transported anterogradely across synapses in the rat visual system. About 200 transneuronally transported proteins (TNTPs) were identified by MS/MS, and selected TNTPs, including β-synuclein and tau, were validated using biochemical and histological methods. TNTP transport may be mediated by exosomes.

Hypothetical Proteins as Predecessors of Long Non-coding RNAs

Hypothetical Proteins [HP] are the transcripts predicted to be expressed in an organism, but no evidence of it exists in gene banks. On the other hand, long non-coding RNAs [lncRNAs] are the transcripts that might be present in the 5’ UTR or intergenic regions of the genes whose lengths are above 200 bases. With the known unknown [KU] regions in the genomes rapidly existing in gene banks, there is a need to understand the role of open reading frames in the context of annotation. In this commentary, we emphasize that HPs could indeed be the predecessors of lncRNAs.

Quantitative analysis of global protein stability rates in tissues

Protein degradation is an essential mechanism for maintaining proteostasis in response to internal and external perturbations. Disruption of this process is implicated in many human diseases. We present a new technique, QUAD (Quantification of Azidohomoalanine Degradation), to analyze the global degradation rates in tissues using a non-canonical amino acid and mass spectrometry. QUAD analysis reveals that protein stability varied within tissues, but discernible trends in the data suggest that cellular environment is a major factor dictating stability. Within a tissue, different organelles and protein functions were enriched with different stability patterns. QUAD analysis demonstrated that protein stability is enhanced with age in the brain but not in the liver. Overall, QUAD allows the first global quantitation of protein stability rates in tissues, which will allow new insights and hypotheses in basic and translational research.

PINE: An Automation Tool to Extract and Visualize Protein-Centric Functional Networks

Recent surges in mass spectrometry-based proteomics studies demand a concurrent rise in speedy and optimized data processing tools and pipelines. Although several stand-alone bioinformatics tools exist that provide protein-protein interaction (PPI) data, we developed Protein Interaction Network Extractor (PINE) as a fully automated, user-friendly, graphical user interface application for visualization and exploration of global proteome and post-translational modification (PTM) based networks. PINE also supports overlaying differential expression, statistical significance thresholds, and PTM sites on functionally enriched visualization networks to gain insights into proteome-wide regulatory mechanisms and PTM-mediated networks. To illustrate the relevance of the tool, we explore the total proteome and its PTM-associated relationships in two different nonalcoholic steatohepatitis (NASH) mouse models to demonstrate different context-specific case studies. The strength of this tool relies in its ability to (1) perform accurate protein identifier mapping to resolve ambiguity, (2) retrieve interaction data from multiple publicly available PPI databases, and (3) assimilate these complex networks into functionally enriched pathways, ontology categories, and terms. Ultimately, PINE can be used as an extremely powerful tool for novel hypothesis generation to understand underlying disease mechanisms.

pepFunk: a tool for peptide-centric functional analysis of metaproteomic human gut microbiome studies

Motivation

Enzymatic digestion of proteins before mass spectrometry analysis is a key process in metaproteomic workflows. Canonical metaproteomic data processing pipelines typically involve matching spectra produced by the mass spectrometer to a theoretical spectra database, followed by matching the identified peptides back to parent-proteins. However, the nature of enzymatic digestion produces peptides that can be found in multiple proteins due to conservation or chance, presenting difficulties with protein and functional assignment.

Results

To combat this challenge, we developed pepFunk, a peptide-centric metaproteomic workflow focused on the analysis of human gut microbiome samples. Our workflow includes a curated peptide database annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG) terms and a gene set variation analysis-inspired pathway enrichment adapted for peptide-level data. Analysis using our peptide-centric workflow is fast and highly correlated to a protein-centric analysis, and can identify more enriched KEGG pathways than analysis using protein-level data. Our workflow is open source and available as a web application or source code to be run locally.

Availability and implementation

pepFunk is available online as a web application at https://shiny.imetalab.ca/pepFunk/ with open-source code available from https://github.com/northomics/pepFunk.

Contact

dfigeys@uottawa.ca

Supplementary information

Supplementary data are available at Bioinformatics online.

Structural Analysis of Hippocampal Kinase Signal Transduction

Kinases are a major clinical target for human diseases. Identifying the proteins that interact with kinases in vivo will provide information on unreported substrates and will potentially lead to more specific methods for therapeutic kinase regulation. Here, endogenous immunoprecipitations of evolutionally distinct kinases (i.e., Akt, ERK2, and CAMK2) from rodent hippocampi were analyzed by mass spectrometry to generate three highly confident kinase protein-protein interaction networks. Proteins of similar function were identified in the networks, suggesting a universal model for kinase signaling complexes. Protein interactions were observed between kinases with reported symbiotic relationships. The kinase networks were significantly enriched in genes associated with specific neurodevelopmental disorders providing novel structural connections between these disease-associated genes. To demonstrate a functional relationship between the kinases and the network, pharmacological manipulation of Akt in hippocampal slices was shown to regulate the activity of potassium/sodium hyperpolarization-activated cyclic nucleotide-gated channel(HCN1), which was identified in the Akt network. Overall, the kinase protein-protein interaction networks provide molecular insight of the spatial complexity of in vivo kinase signal transduction which is required to achieve the therapeutic potential of kinase manipulation in the brain.

Screening the Molecular Framework Underlying Local Dendritic mRNA Translation

In the last decade, bioinformatic analyses of high-throughput proteomics and transcriptomics data have enabled researchers to gain insight into the molecular networks that may underlie lasting changes in synaptic efficacy. Development and utilization of these techniques have advanced the field of learning and memory significantly. It is now possible to move from the study of activity-dependent changes of a single protein to modeling entire network changes that require local protein synthesis. This data revolution has necessitated the development of alternative computational and statistical techniques to analyze and understand the patterns contained within. Thus, the focus of this review is to provide a synopsis of the journey and evolution toward big data techniques to address still unanswered questions regarding how synapses are modified to strengthen neuronal circuits. We first review the seminal studies that demonstrated the pivotal role played by local mRNA translation as the mechanism underlying the enhancement of enduring synaptic activity. In the interest of those who are new to the field, we provide a brief overview of molecular biology and biochemical techniques utilized for sample preparation to identify locally translated proteins using RNA sequencing and proteomics, as well as the computational approaches used to analyze these data. While many mRNAs have been identified, few have been shown to be locally synthesized. To this end, we review techniques currently being utilized to visualize new protein synthesis, a task that has proven to be the most difficult aspect of the field. Finally, we provide examples of future applications to test the physiological relevance of locally synthesized proteins identified by big data approaches.

Cardiovascular proteomics in the era of big data: experimental and computational advances

Proteomics plays an increasingly important role in our quest to understand cardiovascular biology. Fueled by analytical and computational advances in the past decade, proteomics applications can now go beyond merely inventorying protein species, and address sophisticated questions on cardiac physiology. The advent of massive mass spectrometry datasets has in turn led to increasing intersection between proteomics and big data science. Here we review new frontiers in technological developments and their applications to cardiovascular medicine. The impact of big data science on cardiovascular proteomics investigations and translation to medicine is highlighted.

Using PSEA-Quant for Protein Set Enrichment Analysis of Quantitative Mass Spectrometry-Based Proteomics

PSEA-Quant analyzes quantitative mass spectrometry-based proteomics datasets to identify enrichments of annotations contained in repositories such as the Gene Ontology and Molecular Signature databases. It allows users to identify the annotations that are significantly enriched for reproducibly quantified high abundance proteins. PSEA-Quant is available on the Web and as a command-line tool. It is compatible with all label-free and isotopic labeling-based quantitative proteomics methods. This protocol describes how to use PSEA-Quant and interpret its output. The importance of each parameter as well as troubleshooting approaches are also discussed. © 2016 by John Wiley & Sons, Inc.