Argonaut: A web platform for collaborative multi-omic data visualization and exploration

Researchers now generate large multi-omic datasets using increasingly mature mass spectrometry techniques at an astounding pace, facing new challenges of Big Data dissemination, visualization, and exploration. Conveniently, web-based data portals accommodate the complexity of multi-omic experiments and the many experts involved. However, developing these tailored companion resources requires programming expertise and knowledge of web server architecture-a substantial burden for most. Here, we describe Argonaut, a simple, code-free, and user-friendly platform for creating customizable, interactive data-hosting websites. Argonaut carries out real-time statistical analyses of the data, which it organizes into easily sharable projects. Collaborating researchers worldwide can explore the results, visualized through popular plots, and modify them to streamline data interpretation. Increasing the pace and ease of access to multi-omic data, Argonaut aims to propel discovery of new biological insights. We showcase the capabilities of this tool using a published multi-omics dataset on the large mitochondrial protease deletion collection

Fast and deep phosphoproteome analysis with the Orbitrap Astral mass spectrometer.

Owing to its roles in cellular signal transduction, protein phosphorylation plays critical roles in myriad cell processes. That said, detecting and quantifying protein phosphorylation has remained a challenge. We describe the use of a novel mass spectrometer (Orbitrap Astral) coupled with data-independent acquisition (DIA) to achieve rapid and deep analysis of human and mouse phosphoproteomes. With this method, we map approximately 30,000 unique human phosphorylation sites within a half-hour of data collection. The technology is benchmarked to other state-of-the-art MS platforms using both synthetic peptide standards and with EGF-stimulated HeLa cells. We apply this approach to generate a phosphoproteome multi-tissue atlas of the mouse. Altogether, we detect 81,120 unique phosphorylation sites within 12 hours of measurement. With this unique dataset, we examine the sequence, structural, and kinase specificity context of protein phosphorylation. Finally, we highlight the discovery potential of this resource with multiple examples of phosphorylation events relevant to mitochondrial and brain biology

Controlled and Synchronised Vascular Regeneration upon the Implantation of Iloprost- and Cationic Amphiphilic Drugs-Conjugated Tissue-Engineered Vascular Grafts into the Ovine Carotid Artery: A Proteomics-Empowered Study

Implementation of small-diameter tissue-engineered vascular grafts (TEVGs) into clinical practice is still delayed due to the frequent complications, including thrombosis, aneurysms, neointimal hyperplasia, calcification, atherosclerosis, and infection. Here, we conjugated a vasodilator/platelet inhibitor, iloprost, and an antimicrobial cationic amphiphilic drug, 1,5-bis-(4-tetradecyl-1,4-diazoniabicyclo [2.2.2]octan-1-yl) pentane tetrabromide, to the luminal surface of electrospun poly(ε-caprolactone) (PCL) TEVGs for preventing thrombosis and infection, additionally enveloped such TEVGs into the PCL sheath to preclude aneurysms, and implanted PCLIlo/CAD TEVGs into the ovine carotid artery (n = 12) for 6 months. The primary patency was 50% (6/12 animals). TEVGs were completely replaced with the vascular tissue, free from aneurysms, calcification, atherosclerosis and infection, completely endothelialised, and had clearly distinguishable medial and adventitial layers. Comparative proteomic profiling of TEVGs and contralateral carotid arteries found that TEVGs lacked contractile vascular smooth muscle cell markers, basement membrane components, and proteins mediating antioxidant defense, concurrently showing the protein signatures of upregulated protein synthesis, folding and assembly, enhanced energy metabolism, and macrophage-driven inflammation. Collectively, these results suggested a synchronised replacement of PCL with a newly formed vascular tissue but insufficient compliance of PCLIlo/CAD TEVGs, demanding their testing in the muscular artery position or stimulation of vascular smooth muscle cell specification after the implantation

Chemical Proteomics Strategies for Analyzing Protein Lipidation Reveal the Bacterial O-Mycoloylome.

Protein lipidation dynamically controls protein localization and function within cellular membranes. A unique form of protein O-fatty acylation in Corynebacterium, termed protein O-mycoloylation, involves the attachment of mycolic acids─unusually large and hydrophobic fatty acids─to serine residues of proteins in these organisms outer mycomembrane. However, as with other forms of protein lipidation, the scope and functional consequences of protein O-mycoloylation are challenging to investigate due to the inherent difficulties of enriching and analyzing lipidated peptides. To facilitate the analysis of protein lipidation and enable the comprehensive profiling and site mapping of protein O-mycoloylation, we developed a chemical proteomics strategy integrating metabolic labeling, click chemistry, cleavable linkers, and a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method employing LC separation and complementary fragmentation methods tailored to the analysis of lipophilic, MS-labile O-acylated peptides. Using these tools in the model organism Corynebacterium glutamicum, we identified approximately 30 candidate O-mycoloylated proteins, including porins, mycoloyltransferases, secreted hydrolases, and other proteins with cell envelope-related functions─consistent with a role for O-mycoloylation in targeting proteins to the mycomembrane. Site mapping revealed that many of the proteins contained multiple spatially proximal modification sites, which occurred predominantly at serine residues surrounded by conformationally flexible peptide motifs. Overall, this study (i) discloses the putative protein O-mycoloylome for the first time, (ii) yields new insights into the undercharacterized proteome of the mycomembrane, which is a hallmark of important pathogens (e.g., Corynebacterium diphtheriae, Mycobacterium tuberculosis), and (iii) provides generally applicable chemical strategies for the proteomic analysis of protein lipidation

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response

<div><p>Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in <i>Saccharomyces cerevisiae</i>, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.</p></div

Rck2 is a hub in the osmotic stress-signaling network.

<p>A manually chosen section of the network capturing source regulators, Hog pathway components, and Rck2 shown, as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.g004" target="_blank">Fig 4</a> and the key. The figure also shows three constituent proteins that are predicted targets of our method and whose physical interaction with source regulators we validated by co-immunoprecipitation (dashed lines).</p

Overview of the inference method.

<p>The method consists of three main steps (see text for details). In the first step, stress-altered phospho-peptides are partitioned into submodules of peptides likely to be co-regulated. This is accomplished by <b>(A)</b> clustering phospho-peptides based on their pattern of phosphorylation change in wild type cells, then <b>(B)</b> further partitioning peptides into submodules if they share the same phospho-motif and <b>(C)</b> if they share the same defect in each of three interrogated mutant strains. Yellow and blue filled submodules are comprised of peptides with increased or decreased phosphorylation, respectively, in response to NaCl. <b>(D)</b> We then identify ‘Shared Interactors’ (SIs, green circles) as proteins that show more physical interaction with submodule constituent proteins than expected by chance–identified SIs are connected to each submodule with a new directional edge. <b>(E)</b> A background network of previously measured protein-protein (undirected dashed line) and kinase-substrate (directed arrow) interactions, represented here with Proteins A–L, is augmented by <b>(F)</b> adding SI-submodule units as well as outgoing edges (ball and stick) between each submodule and its constituent proteins whose phospho-peptides belong to the submodule. <b>(G)</b> The ILP method then enumerates all paths of a given length from each source regulator (red) to its dependent submodules (grey boxes), traversing through SIs (green) and other proteins in the augmented PPI background network. In this cartoon, submodules 1, 2, and 3 consist of phospho-peptides whose salt responsiveness depends on interrogated source regulator Protein F. Submodules without a source dependency (white boxes) can be incorporated as pathway intermediates. <b>(H)</b> The ILP connects the units using a multi-stage objective function to reveal the subnetwork inferred to regulate phosphoproteome changes.</p