Funding support was provided by the National Institutes of Health (NIH) (R01AG047928, R01AG053987, RF1AG064909, RF1AG068581, and U54NS110435) and ALSAC (American Lebanese Syrian Associated Charities). The MS analysis was carried out in St. Jude Children&#39;s Research Hospital&#39;s Center of Proteomics and Metabolomics, which was partially supported by NIH Cancer Center Support Grant (P30CA021765). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

The authors have nothing to disclose.

Here we introduced our JUMPn software and its protocol, which have been applied in multiple projects for dissecting molecular mechanisms using deep quantitative proteomics data25,26,27,30,64. The JUMPn software and protocol have been fully optimized, including consideration of DE proteins for co-expression network analysis, a compilation of comprehensive and h...

Mass spectrometry-based shotgun proteomics has become the key approach for analyzing proteome diversity of complex samples1. With recent advances in mass spectrometry instrumentation2,3, chromatography4,5, ion mobility detection6, acquisition methods (data-independent7 and data-dependent acquisition8), quantification approaches (multi-plex isobaric peptide labeling method, e.g., TMT9,10, and label-free quantification11,12) and data analysis strategies/software development13,14,15,16,17,18, quantification of the whole proteome (e.g., over 10,000 proteins) is now routine19,20,21. However, how to gain mechanistic insights from such deep quantitative datasets is still challenging22. Initial attempts for investigating these datasets relied predominantly upon the annotation of individual elements of the data, treating each component (protein) independently. However, biological systems and their behavior cannot be solely explained by examining individual components23. Therefore, a systems approach that places the quantified biomolecules in the context of interaction networks is essential for the understanding of complex systems and the associated processes such as embryogenesis, immune response, and pathogenesis of human diseases24.
Network-based systems biology has emerged as a powerful paradigm for analyzing large-scale quantitative proteomics data25,26,27,28,29,30,31,32,33. Conceptually, complex systems such as mammalian cells could be modeled as a hierarchical network34,35, in which the whole system is represented in tiers: first by a number of large components, each of which then iteratively modeled by smaller subsystems. Technically, the structure of proteome dynamics can be presented by inter-connected networks of co-expressed protein clusters (because co-expressed genes/proteins often share similar biological functions or mechanisms of regulation36) and physically interacting PPI modules37. As a recent example25, we generated temporal profiles of whole proteome and phosphoproteome during T cell activation and used integrative co-expression networks with PPIs to identify functional modules that mediate T-cell quiescence exit. Multiple bioenergetic-related modules were highlighted and experimentally validated (e.g., the mitoribosome and complex IV modules25, and the one-carbon module38). In another example26, we further extended our approach to study the pathogenesis of Alzheimer&#39;s disease, and successfully prioritized disease progression associated protein modules and molecules. Importantly, many of our unbiased discoveries were validated by independent patient cohorts26,29 and/or disease mouse models26. These examples illustrated the power of the systems biology approach for dissecting molecular mechanisms with quantitative proteomics and other omics integrations.
Here we introduce JUMPn, a streamlined software that explores quantitative proteomics data using network-based systems biology approaches. JUMPn serves as the downstream component of the established JUMP proteomics software suite13,14,39, and aims to fill the gap from individual protein quantifications to biologically meaningful pathways and protein modules using the systems biology approach. By taking the quantification matrix of differentially expressed (or the most variable) proteins as input, JUMPn aims to organize the proteome into a tiered hierarchy of protein clusters co-expressed across samples and densely connected PPI modules (e.g., protein complexes), which are further annotated with public pathway databases by over-representation (or enrichment) analysis (Figure 1). JUMPn is developed with the R/Shiny platform40 for a user-friendly interface and integrates three major functional modules: co-expression clustering analysis, pathway enrichment analysis, and PPI network analysis (Figure 1). After each analysis, results are automatically visualized and are adjustable via the R/shiny widget functions and readily downloadable as publication tables in Microsoft Excel format. In the following protocol, we use quantitative whole proteome data as an example and describe the major steps of using JUMPn, including installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, co-expression network analysis, and PPI module analysis, result visualization and interpretation, and trouble shootings. JUMPn software is freely available on GitHub41.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>MacBook Pro with a 2.3 GHz Quad-Core Processor running OS 10.15.7.</td>
			<td>Apple Inc.</td>
			<td>MacBook Pro 13&#39;&#39;</td>
			<td>Hardware used for software development and testing</td>
		</tr>
		<tr>
			<td>Anoconda</td>
			<td>Anaconda, Inc.</td>
			<td>version 4.9.2</td>
			<td>https://docs.anaconda.com/anaconda/install/</td>
		</tr>
		<tr>
			<td>miniconda</td>
			<td>Anaconda, Inc.</td>
			<td>version 4.9.2</td>
			<td>https://docs.conda.io/en/latest/miniconda.html</td>
		</tr>
		<tr>
			<td>RStudio</td>
			<td>RStudio Public-benefit corporation</td>
			<td>version 4.0.3</td>
			<td>https://www.rstudio.com/products/rstudio/download/</td>
		</tr>
		<tr>
			<td>Shiny Server</td>
			<td>RStudio Public-benefit corporation</td>
			<td></td>
			<td>https://shiny.rstudio.com/articles/shinyapps.html</td>
		</tr>
	</tbody>
</table>

jumpn: a streamlined application for protein co-expression clustering and network analysis in proteomics

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.

We used our published deep proteomics datasets25,26,27,30 (Figures 5 and Figure 6) as well as data simulations57 (Table 1) to optimize and evaluate JUMPn performance. For co-expression protein clustering analysis via WGCNA, we recommend utilizing proteins significantly changed across samples as ...

Watch this Scientific Journal Video about JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics at JoVE.com

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network analysis.

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. ...

jumpn-streamlined-application-for-protein-co-expression-clustering

Research

JoVE Journal

Biochemistry

3.0K Views.  St. Jude Children’s Research Hospital. We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network analysis.

Video: JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Method for Identifying Small Molecule Inhibitors of the Protein-protein Interaction Between HCN1 and TRIP8b

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the excitability of neurons. The subcellular distribution of these channels in pyramidal neurons of hippocampal area CA1 is regulated by tetratricopeptide repeat-containing Rab8b interacting protein (TRIP8b), an auxiliary subunit. Genetic knockout of HCN pore forming subunits or TRIP8b, both lead to an increase in antidepressant-like behavior, suggesting that limiting the function of HCN channels may be useful as a treatment for Major Depressive Disorder (MDD). Despite significant therapeutic interest, HCN channels are also expressed in the heart, where they regulate rhythmicity. To circumvent off-target issues associated with blocking cardiac HCN channels, our lab has recently proposed targeting the protein-protein interaction between HCN and TRIP8b in order to specifically disrupt HCN channel function in the brain. TRIP8b binds to HCN pore forming subunits at two distinct interaction sites, although here the focus is on the interaction between the tetratricopeptide repeat (TPR) domains of TRIP8b and the C terminal tail of HCN1. In this protocol, an expanded description of a method for purifying TRIP8b and executing a high throughput screen to identify small molecule inhibitors of the interaction between HCN and TRIP8b, is described. The method for high throughput screening utilizes a Fluorescence Polarization (FP) -based assay to monitor the binding of a large TRIP8b fragment to a fluorophore-tagged eleven amino acid peptide corresponding to the HCN1 C terminal tail. This method allows &#39;hit&#39; compounds to be identified based on the change in the polarization of emitted light. Validation assays are then performed to ensure that &#39;hit&#39; compounds are not artifactual.

The interaction between HCN channels and their auxiliary subunit has been identified as a therapeutic target in Major Depressive Disorder. Here, a fluorescence polarization-based method for identifying small molecule inhibitors of this protein-protein interaction, is presented.

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the ...

Lipid Droplet Isolation for Quantitative Mass Spectrometry Analysis

Lipid droplets are vital to the replication of a variety of different pathogens, most prominently the Hepatitis C Virus (HCV), as the putative site of virion morphogenesis. Quantitative lipid droplet proteome analysis can be used to identify proteins that localize to or are displaced from lipid droplets under conditions such as virus infections. Here, we describe a protocol that has been successfully used to characterize the changes in the lipid droplet proteome following infection with HCV. We use Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) and thus label the complete proteome of one population of cells with &#34;heavy&#34; amino acids to quantitate the proteins by mass spectrometry. For lipid droplet isolation, the two cell populations (i.e.&#160;HCV-infected/&#34;light&#34; amino acids and uninfected control/&#34;heavy&#34; amino acids) are mixed 1:1 and lysed mechanically in hypotonic buffer. After removing the nuclei and cell debris by low speed centrifugation, lipid droplet-associated proteins are enriched by two subsequent ultracentrifugation steps followed by three washing steps in isotonic buffer. The purity of the lipid droplet fractions is analyzed by western blotting with antibodies recognizing different subcellular compartments. Lipid droplet-associated proteins are then separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) followed by Coomassie staining. After tryptic digest, the peptides are quantified by liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Using this method, we identified proteins recruited to lipid droplets upon HCV infection that might represent pro- or antiviral host factors. Our method can be applied to a variety of different cells and culture conditions, such as infection with pathogens, environmental stress, or drug treatment.

Lipid droplets are important organelles for the replication of several pathogens, including the Hepatitis C Virus (HCV). We describe a method to isolate lipid droplets for quantitative mass spectrometry of associated proteins; it can be used under a variety of conditions, such as virus infection, environmental stress, or drug treatment.

Lipid droplets are vital to the replication of a variety of different pathogens, most prominently the Hepatitis C Virus (HCV), as the putative site of ...

Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions

Secreted factors, membrane-tethered receptors, and their interacting partners are main regulators of cellular communication and initiation of signaling cascades during homeostasis and disease, and as such represent prime therapeutic targets. Despite their relevance, these interaction networks remain significantly underrepresented in current databases; therefore, most extracellular proteins have no documented binding partner. This discrepancy is primarily due to the challenges associated with the study of the extracellular proteins, including expression of functional proteins, and the weak, low affinity, protein interactions often established between cell surface receptors. The purpose of this method is to describe the printing of a library of extracellular proteins in a microarray format for screening of protein-protein interactions. To enable detection of weak interactions, a method based on multimerization of the query protein under study is described. Coupled to this microbead-based multimerization approach for increased multivalency, the protein microarray allows robust detection of transient protein-protein interactions in high throughput. This method offers a rapid and low sample consuming-approach for identification of new interactions applicable to any extracellular protein. Protein microarray printing and screening protocol are described. This technology will be useful for investigators seeking a robust method for discovery of protein interactions in the extracellular space.

Here, we present a protocol to screen extracellular protein microarrays for identification of novel receptor-ligand interactions in high throughput. We also describe a method to enhance detection of transient protein-protein interactions by using protein-microbead complexes.

Secreted factors, membrane-tethered receptors, and their interacting partners are main regulators of cellular communication and initiation of signaling ...

Preparation of Chloroplast Sub-compartments from Arabidopsis for the Analysis of Protein Localization by Immunoblotting or Proteomics

Chloroplasts are major components of plant cells. Such plastids fulfill many crucial functions, such as assimilation of carbon, sulfur and nitrogen as well as synthesis of essential metabolites. These organelles consist of the following three key sub-compartments. The envelope, characterized by two membranes, surrounds the organelle and controls the communication of the plastid with other cell compartments. The stroma is the soluble phase of the chloroplast and the main site where carbon dioxide is converted into carbohydrates. The thylakoid membrane is the internal membrane network consisting of grana (flat compressed sacs) and lamellae (less dense structures), where oxygenic photosynthesis takes place. The present protocol describes step by step procedures required for the purification, using differential centrifugations and Percoll gradients, of intact chloroplasts from Arabidopsis, and their fractionation, using sucrose gradients, in three sub-compartments (i.e., envelope, stroma, and thylakoids). This protocol also provides instructions on how to assess the purity of these fractions using markers associated to the various chloroplast sub-compartments. The method described here is valuable for subplastidial localization of proteins using immunoblotting, but also for subcellular and subplastidial proteomics and other studies.

Here, we describe a method to purify intact chloroplasts from Arabidopsis leaves and their three main sub-compartments (envelope, stroma, and thylakoids), using a combination of differential centrifugations, continuous Percoll gradients, and discontinuous sucrose gradients. The method is valuable for subplastidial and subcellular localization of proteins by immunoblotting and proteomics.

Chloroplasts are major components of plant cells. Such plastids fulfill many crucial functions, such as assimilation of carbon, sulfur and nitrogen as ...

Analysis of Protein Folding, Transport, and Degradation in Living Cells by Radioactive Pulse Chase

Radioactive pulse-chase labeling is a powerful tool for studying the conformational maturation, the transport to their functional cellular location, and the degradation of target proteins in live cells. By using short (pulse) radiolabeling times (&lt;30 min) and tightly controlled chase times, it is possible to label only a small fraction of the total protein pool and follow its folding. When combined with nonreducing/reducing SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and immunoprecipitation with (conformation-specific) antibodies, folding processes can be examined in great detail. This system has been used to analyze the folding of proteins with a huge variation in properties such as soluble proteins, single and multi-pass transmembrane proteins, heavily N- and O-glycosylated proteins, and proteins with and without extensive disulfide bonding. Pulse-chase methods are the basis of kinetic studies into a range of additional features, including co- and posttranslational modifications, oligomerization, and polymerization, essentially allowing the analysis of a protein from birth to death. Pulse-chase studies on protein folding are complementary with other biochemical and biophysical methods for studying proteins in vitro by providing increased temporal resolution and physiological information. The methods as described within this paper are adapted easily to study the folding of almost any protein that can be expressed in mammalian or insect-cell systems.

Here we describe a protocol for a general pulse-chase method that allows the kinetic analysis of folding, transport, and degradation of proteins to be followed in live cells.

Radioactive pulse-chase labeling is a powerful tool for studying the conformational maturation, the transport to their functional cellular location, and ...

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ubiquitinated proteins, peptides with a diglycine remnant conjugated to the epsilon amino group of lysine (&#39;K-&#949;-diglycine&#39; or simply &#39;diGly&#39;) can be used to track back the original modification site. Efficient immunopurification of diGly peptides combined with sensitive detection by mass spectrometry has resulted in a huge increase in the number of ubiquitination sites identified up to date. We have made several improvements to this workflow, including offline high pH reverse-phase fractionation of peptides prior to the enrichment procedure, and the inclusion of more advanced peptide fragmentation settings in the ion routing multipole. Also, more efficient cleanup of the sample using a filter-based plug in order to retain the antibody beads results in a greater specificity for diGly peptides. These improvements result in the routine detection of more than 23,000 diGly peptides from human cervical cancer cells (HeLa) cell lysates upon proteasome inhibition in the cell. We show the efficacy of this strategy for in-depth analysis of the ubiquitinome profiles of several different cell types and of in vivo samples, such as brain tissue. This study presents an original addition to the toolbox for protein ubiquitination analysis to uncover the deep cellular ubiquitinome.

We present a method for the purification, detection, and identification of diGly peptides that originate from ubiquitinated proteins from complex biological samples. The presented method is reproducible, robust, and outperforms published methods with respect to the level of depth of the ubiquitinome analysis.

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ...

Application of Biolayer Interferometry (BLI) for Studying Protein-Protein Interactions in Transcription

A transcription factor&#160;(TF) is a protein that regulates gene expression by interacting with the RNA polymerase, another TF, and/or template DNA. GrgA is a novel transcription activator found specifically in the obligate intracellular bacterial pathogen Chlamydia. Protein pulldown assays using affinity beads have revealed that GrgA binds two &#963; factors, namely &#963;66 and &#963;28, which recognize different sets of promoters for genes whose products are differentially required at developmental stages. We have used BLI to confirm and further characterize the interactions. BLI demonstrates several advantages over pulldown: 1) It reveals real-time association and dissociation between binding partners, 2) It generates quantitative kinetic parameters, and 3) It can detect bindings that pulldown assays often fail to detect. These characteristics have enabled us to deduce the physiological roles of GrgA in gene expression regulation in Chlamydia, and possible detailed interaction mechanisms. We envision that this relatively affordable technology can be extremely useful for studying transcription and other biological processes.

Application of Biolayer Interferometry BLI for Studying Protein-Protein Interactions in Transcription

Interactions of transcription factors (TFs) with the RNA polymerase are usually studied using pulldown assays. We apply a Biolayer Interferometry (BLI) technology to characterize the interaction of GrgA with the chlamydial RNA polymerase. Compared to pulldown assays, BLI detects real-time association and dissociation, offers higher sensitivity, and is highly quantitative.

A transcription factor&#160;(TF) is a protein that regulates gene expression by interacting with the RNA polymerase, another TF, and/or template DNA. GrgA ...

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.

Quantitative Multiplex Immunoprecipitation (QMI) uses flow cytometry for sensitive detection of differences in the abundance of targeted protein-protein interactions between two samples. QMI can be performed using a small amount of biomaterial, does not require genetically engineered tags, and can be adapted for any previously defined protein interaction network.

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic ...

PeptiQuick, a One-Step Incorporation of Membrane Proteins into Biotinylated Peptidiscs for Streamlined Protein Binding Assays

Membrane proteins, including transporters, channels, and receptors, constitute nearly one-fourth of the cellular proteome and over half of current drug targets. Yet, a major barrier to their characterization and exploitation in academic or industrial settings is that most biochemical, biophysical, and drug screening strategies require these proteins to be in a water-soluble state. Our laboratory recently developed the peptidisc, a membrane mimetic offering a &#8220;one-size-fits-all&#8221; approach to the problem of membrane protein solubility. We present here a streamlined protocol that combines protein purification and peptidisc reconstitution in a single chromatographic step. This workflow, termed PeptiQuick, allows for bypassing dialysis and incubation with polystyrene beads, thereby greatly reducing exposure to detergent, protein denaturation, and sample loss. When PeptiQuick is performed with biotinylated scaffolds, the preparation can be directly attached to streptavidin-coated surfaces. There is no need to biotinylate or modify the membrane protein target. PeptiQuick is showcased here with the membrane receptor FhuA and antimicrobial ligand colicin M, using biolayer interferometry to determine the precise kinetics of their interaction. It is concluded that PeptiQuick is a convenient way to prepare and analyze membrane protein-ligand interactions within one&#160;day in a detergent-free environment.

We present a method that combines membrane protein purification and reconstitution into peptidiscs in a single chromatographic step. Biotinylated scaffolds are used for direct surface attachment and measurement of protein-ligand interactions via biolayer interferometry.

Membrane proteins, including transporters, channels, and receptors, constitute nearly one-fourth of the cellular proteome and over half of current drug ...

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view of the impact of a disease, treatment, or other condition on the proteome as a whole. This report provides a detailed protocol for the extraction, quantification, precipitation, digestion, labeling, and subsequent data analysis of protein samples. Our optimized TMT labeling protocol requires a lower tag-label concentration and achieves consistently reliable data. We have used this protocol to evaluate protein expression profiles in a variety of mouse tissues (i.e., heart, skeletal muscle, and brain) as well as cells cultured in vitro. In addition, we demonstrate how to evaluate thousands of proteins from the resulting dataset.

We present an optimized tandem mass tag (TMT) labeling protocol that includes detailed information for each of the following steps: protein extraction, quantification, precipitation, digestion, labeling, submission to a proteomics facility, and data analyses.

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view ...