Sricharan Bandhakavi

Insulin signalling to mTOR mediated by the Akt/PKB substrate PRAS40

Insulin stimulates protein synthesis and cell growth by activation of the protein kinases Akt (also known as protein kinase B, PKB) and mammalian target of rapamycin (mTOR). It was reported that Akt activates mTOR by phosphorylation and inhibition of tuberous sclerosis complex 2 (TSC2). However, in recent studies the physiological requirement of Akt phosphorylation of TSC2 for mTOR activation has been questioned. Here, we identify PRAS40 (proline-rich Akt/PKB substrate 40 kDa) as a novel mTOR binding partner that mediates Akt signals to mTOR. PRAS40 binds the mTOR kinase domain and its interaction with mTOR is induced under conditions that inhibit mTOR signalling, such as nutrient or serum deprivation or mitochondrial metabolic inhibition. Binding of PRAS40 inhibits mTOR activity and suppresses constitutive activation of mTOR in cells lacking TSC2. PRAS40 silencing inactivates insulin-receptor substrate-1 (IRS-1) and Akt, and uncouples the response of mTOR to Akt signals. Furthermore, PRAS40 phosphorylation by Akt and association with 14-3-3, a cytosolic anchor protein, are crucial for insulin to stimulate mTOR. These findings identify PRAS40 as an important regulator of insulin sensitivity of the Akt–mTOR pathway and a potential target for the treatment of cancers, insulin resistance and hamartoma syndromes.

PRR5, a Novel Component of mTOR Complex 2, Regulates Platelet-derived Growth Factor Receptor β Expression and Signaling

The protein kinase mammalian target of rapamycin (mTOR) plays an important role in the coordinate regulation of cellular responses to nutritional and growth factor conditions. mTOR achieves these roles through interacting with raptor and rictor to form two distinct protein complexes, mTORC1 and mTORC2. Previous studies have been focused on mTORC1 to elucidate the central roles of the complex in mediating nutritional and growth factor signals to the protein synthesis machinery. Functions of mTORC2, relative to mTORC1, have remained little understood. Here we report identification of a novel component of mTORC2 named PRR5 (PRoline-Rich protein 5), a protein encoded by a gene located on a chromosomal region frequently deleted during breast and colorectal carcinogenesis (Johnstone, C. N., Castellvi-Bel, S., Chang, L. M., Sung, R. K., Bowser, M. J., Pique, J. M., Castells, A., and Rustgi, A. K. (2005) Genomics 85, 338–351). PRR5 interacts with rictor, but not raptor, and the interaction is independent of mTOR and not disturbed under conditions that disrupt the mTOR-rictor interaction. PRR5, unlike Sin1, another component of mTORC2, is not important for the mTOR-rictor interaction and mTOR activity toward Akt phosphorylation. Despite no significant effect of PRR5 on mTORC2-mediated Akt phosphorylation, PRR5 silencing inhibits Akt and S6K1 phosphorylation and reduces cell proliferation rates, a result consistent with PRR5 roles in cell growth and tumorigenesis. The inhibition of Akt and S6K1 phosphorylation by PRR5 knock down correlates with reduction in the expression level of platelet-derived growth factor receptor β (PDGFRβ). PRR5 silencing impairs PDGF-stimulated phosphorylation of S6K1 and Akt but moderately reduces epidermal growth factor- and insulin-stimulated phosphorylation. These findings propose a potential role of mTORC2 in the cross-talk with the cellular machinery that regulates PDGFRβ expression and signaling.

A dynamic range compression and three-dimensional peptide fractionation analysis platform expands proteome coverage and the diagnostic potential of whole saliva

Comprehensive identification of proteins in whole human saliva is critical for appreciating its full diagnostic potential. However, this is challenged by the large dynamic range of protein abundance within the fluid. To address this problem, we used an analysis platform that coupled hexapeptide libraries for dynamic range compression (DRC) with three-dimensional (3D) peptide fractionation. Our approach identified 2340 proteins in whole saliva and represents the largest saliva proteomic dataset generated using a single analysis platform. Three-dimensional peptide fractionation involving sequential steps of preparative isoelectric focusing (IEF), strong cation exchange, and capillary reversed-phase liquid chromatography was essential for maximizing gains from DRC. Compared to saliva not treated with hexapeptide libraries, DRC substantially increased identified proteins across physicochemical and functional categories. Approximately 20% of total salivary proteins are also seen in plasma, and proteins in both fluids show comparable functional diversity and disease-linkage. However, for a subset of diseases, saliva has higher apparent diagnostic potential. These results expand the potential for whole saliva in health monitoring/diagnostics and provide a general platform for improving proteomic coverage of complex biological samples.

Deep metaproteomic analysis of human salivary supernatant

The human salivary proteome is extremely complex, including proteins from salivary glands, serum, and oral microbes. Much has been learned about the host component, but little is known about the microbial component. Here we report a metaproteomic analysis of salivary supernatant pooled from six healthy subjects. For deep interrogation of the salivary proteome, we combined protein dynamic range compression (DRC), multidimensional peptide fractionation, and high‐mass accuracy MS/MS with a novel two‐step peptide identification method using a database of human proteins plus those translated from oral microbe genomes. Peptides were identified from 124 microbial species as well as uncultured phylotypes such as TM7. Streptococcus, Rothia, Actinomyces, Prevotella, Neisseria, Veilonella, Lactobacillus, Selenomonas, Pseudomonas, Staphylococcus, and Campylobacter were abundant among the 65 genera from 12 phyla represented. Taxonomic diversity in our study was broadly consistent with metagenomic studies of saliva. Proteins mapped to 20 KEGG pathways, with carbohydrate metabolism, amino acid metabolism, energy metabolism, translation, membrane transport, and signal transduction most represented. The communities sampled appear to be actively engaged in glycolysis and protein synthesis. This first deep metaproteomic catalog from human salivary supernatant provides a baseline for future studies of shifts in microbial diversity and protein activities potentially associated with oral disease.

Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework.

Proteogenomics combines large-scale genomic and transcriptomic data with mass-spectrometry-based proteomic data to discover novel protein sequence variants and improve genome annotation. In contrast with conventional proteomic applications, proteogenomic analysis requires a number of additional data processing steps. Ideally, these required steps would be integrated and automated via a single software platform offering accessibility for wet-bench researchers as well as flexibility for user-specific customization and integration of new software tools as they emerge. Toward this end, we have extended the Galaxy bioinformatics framework to facilitate proteogenomic analysis. Using analysis of whole human saliva as an example, we demonstrate Galaxy’s flexibility through the creation of a modular workflow incorporating both established and customized software tools that improve depth and quality of proteogenomic results. Our customized Galaxy-based software includes automated, batch-mode BLASTP searching and a Peptide Sequence Match Evaluator tool, both useful for evaluating the veracity of putative novel peptide identifications. Our complex workflow (approximately 140 steps) can be easily shared using built-in Galaxy functions, enabling their use and customization by others. Our results provide a blueprint for the establishment of the Galaxy framework as an ideal solution for the emerging field of proteogenomics.

Hexapeptide Libraries for Enhanced Protein PTM Identification and Relative Abundance Profiling in Whole Human Saliva

Dynamic range compression (DRC) by hexapeptide libraries increases MS/MS-based identification of lower-abundance proteins in complex mixtures. However, two unanswered questions impede fully realizing DRCs potential in shotgun proteomics. First, does DRC enhance identification of post-translationally modified proteins? Second, can DRC be incorporated into a workflow enabling relative protein abundance profiling? We sought to answer both questions analyzing human whole saliva. Addressing question one, we coupled DRC with covalent glycopeptide enrichment and MS/MS. With DRC we identified ∼2 times more N-linked glycoproteins and their glycosylation sites than without DRC, dramatically increasing the known salivary glycoprotein catalog. Addressing question two, we compared differentially stable isotope-labeled saliva samples pooled from healthy and metastatic breast cancer women using a multidimensional peptide fractionation-based workflow, analyzing in parallel one sample portion with DRC and one portion without. Our workflow categorizes proteins with higher absolute abundance, whose relative abundance ratios are altered by DRC, from proteins of lower absolute abundance detected only after DRC. Within each of these salivary protein categories, we identified novel abundance changes putatively associated with breast cancer, demonstrating feasibility and benefits of DRC for relative abundance profiling. Collectively, our results bring us closer to realizing the full potential of DRC for proteomic studies.

Quantitative Nuclear Proteomics Identifies mTOR Regulation of DNA Damage Response

Cellular nutritional and energy status regulates a wide range of nuclear processes important for cell growth, survival, and metabolic homeostasis. Mammalian target of rapamycin (mTOR) plays a key role in the cellular responses to nutrients. However, the nuclear processes governed by mTOR have not been clearly defined. Using isobaric peptide tagging coupled with linear ion trap mass spectrometry, we performed quantitative proteomics analysis to identify nuclear processes in human cells under control of mTOR. Within 3 h of inhibiting mTOR with rapamycin in HeLa cells, we observed down-regulation of nuclear abundance of many proteins involved in translation and RNA modification. Unexpectedly, mTOR inhibition also down-regulated several proteins functioning in chromosomal integrity and up-regulated those involved in DNA damage responses (DDRs) such as 53BP1. Consistent with these proteomic changes and DDR activation, mTOR inhibition enhanced interaction between 53BP1 and p53 and increased phosphorylation of ataxia telangiectasia mutated (ATM) kinase substrates. ATM substrate phosphorylation was also induced by inhibiting protein synthesis and suppressed by inhibiting proteasomal activity, suggesting that mTOR inhibition reduces steady-state (abundance) levels of proteins that function in cellular pathways of DDR activation. Finally, rapamycin-induced changes led to increased survival after radiation exposure in HeLa cells. These findings reveal a novel functional link between mTOR and DDR pathways in the nucleus potentially operating as a survival mechanism against unfavorable growth conditions.

Hsf1 Activation Inhibits Rapamycin Resistance and TOR Signaling in Yeast Revealed by Combined Proteomic and Genetic Analysis

TOR kinases integrate environmental and nutritional signals to regulate cell growth in eukaryotic organisms. Here, we describe results from a study combining quantitative proteomics and comparative expression analysis in the budding yeast, S. cerevisiae, to gain insights into TOR function and regulation. We profiled protein abundance changes under conditions of TOR inhibition by rapamycin treatment, and compared this data to existing expression information for corresponding gene products measured under a variety of conditions in yeast. Among proteins showing abundance changes upon rapamycin treatment, almost 90% of them demonstrated homodirectional (i.e., in similar direction) transcriptomic changes under conditions of heat/oxidative stress. Because the known downstream responses regulated by Tor1/2 did not fully explain the extent of overlap between these two conditions, we tested for novel connections between the major regulators of heat/oxidative stress response and the TOR pathway. Specifically, we hypothesized that activation of regulator(s) of heat/oxidative stress responses phenocopied TOR inhibition and sought to identify these putative TOR inhibitor(s). Among the stress regulators tested, we found that cells (hsf1-R206S, F256S and ssa1-3 ssa2-2) constitutively activated for heat shock transcription factor 1, Hsf1, inhibited rapamycin resistance. Further analysis of the hsf1-R206S, F256S allele revealed that these cells also displayed multiple phenotypes consistent with reduced TOR signaling. Among the multiple Hsf1 targets elevated in hsf1-R206S, F256S cells, deletion of PIR3 and YRO2 suppressed the TOR-regulated phenotypes. In contrast to our observations in cells activated for Hsf1, constitutive activation of other regulators of heat/oxidative stress responses, such as Msn2/4 and Hyr1, did not inhibit TOR signaling. Thus, we propose that activated Hsf1 inhibits rapamycin resistance and TOR signaling via elevated expression of specific target genes in S. cerevisiae. Additionally, these results highlight the value of comparative expression analyses between large-scale proteomic and transcriptomic datasets to reveal new regulatory connections.

Novel In Situ Collection of Tumor Interstitial Fluid from a Head and Neck Squamous Carcinoma Reveals a Unique Proteome with Diagnostic Potential

IntroductionTumors lack normal drainage of secreted fluids and consequently build up tumor interstitial fluid (TIF). Unlike other bodily fluids, TIF likely contains a high proportion of tumor-specific proteins with potential as biomarkers.MethodsHere, we evaluated a novel technique using a unique ultrafiltration catheter for in situ collection of TIF and used it to generate the first catalog of TIF proteins from a head and neck squamous cell carcinoma (HNSCC). To maximize proteomic coverage, TIF was immunodepleted for high abundance proteins and digested with trypsin, and peptides were fractionated in three dimensions prior to mass spectrometry.ResultsWe identified 525 proteins with high confidence. The HNSCC TIF proteome was distinct compared to proteomes of other bodily fluids. It contained a relatively high proportion of proteins annotated by Gene Ontology as “extracellular” compared to other secreted fluid and cellular proteomes, indicating minimal cell lysis from our in situ collection technique. Several proteins identified are putative biomarkers of HNSCC, supporting our catalog’s value as a source of potential biomarkers.ConclusionsIn all, we demonstrate a reliable new technique for in situ TIF collection and provide the first HNSCC TIF protein catalog with value as a guide for others seeking to develop tumor biomarkers.

Workflow for analysis of high mass accuracy salivary data set using MaxQuant and ProteinPilot search algorithm

LTQ Orbitrap data analyzed with ProteinPilot can be further improved by MaxQuant raw data processing, which utilizes precursor‐level high mass accuracy data for peak processing and MGF creation. In particular, ProteinPilot results from MaxQuant‐processed peaklists for Orbitrap data sets resulted in improved spectral utilization due to an improved peaklist quality with higher precision and high precursor mass accuracy (HPMA). The output and postsearch analysis tools of both workflows were utilized for previously unexplored features of a three‐dimensional fractionated and hexapeptide library (ProteoMiner) treated whole saliva data set comprising 200 fractions. ProteinPilots ability to simultaneously predict multiple modifications showed an advantage from ProteoMiner treatment for modified peptide identification. We demonstrate that complementary approaches in the analysis pipeline provide comprehensive results for the whole saliva data set acquired on an LTQ Orbitrap. Overall our results establish a workflow for improved protein identification from high mass accuracy data.