Temporal proteomic analysis of IGF-1R signalling in MCF-7 breast adenocarcinoma cells

Anticancer effect of HOTTIP regulates human pancreatic cancer via the metabotropic glutamate receptor 1 pathway

The present study aimed to determine how the expression and function of HOTTIP modifies, and regulates the metabotropic glutamate receptor 1 (mGluR1) to affect human pancreatic cancer cell viability. HOTTIP expression was higher in human pancreatic cancer tissue compared with in para-carcinoma tissue. However, downregulation of HOTTIP expression was revealed to significantly reduce cell viability, induce apoptosis, promote caspase-3 and caspase-8 activities and increase Bax expression in pancreatic cancer cells. Additionally, downregulation of HOTTIP expression significantly suppressed mGluR1 and mitigated activation of the phosphoinositide 3-kinase (PI3K)/Akt/mechanistic target of rapamycin (mTOR) pathway in pancreatic cancer cells. To the best of our knowledge, the present study is the first to identify that the anticancer effect of HOTTIP against human pancreatic cancer functions the mGluR1 pathway.

The proximity-labeling technique BioID identifies sorting nexin 6 as a member of the insulin-like growth factor 1 (IGF1)-IGF1 receptor pathway

The insulin-like growth factor 1 receptor (IGF1R) is a receptor tyrosine kinase with critical roles in various biological processes. Recent results from clinical trials targeting IGF1R indicate that IGF1R signaling pathways are more complex than previously thought. Moreover, it has become increasingly clear that the function of many proteins can be understood only in the context of a network of interactions. To that end, we sought to profile IGF1R-protein interactions with the proximity-labeling technique BioID. We applied BioID by generating a HEK293A cell line that stably expressed the BirA* biotin ligase fused to the IGF1R. Following stimulation by IGF1, biotinylated proteins were analyzed by MS. This screen identified both known and previously unknown interactors of IGF1R. One of the novel interactors was sorting nexin 6 (SNX6), a protein that forms part of the retromer complex, which is involved in intracellular protein sorting. Using co-immunoprecipitation, we confirmed that IGF1R and SNX6 physically interact. SNX6 knockdown resulted in a dramatic diminution of IGF1-mediated ERK1/2 phosphorylation, but did not affect IGF1R internalization. Bioluminescence resonance energy transfer experiments indicated that the SNX6 knockdown perturbed the association between IGF1R and the key adaptor proteins insulin receptor substrate 1 (IRS1) and SHC adaptor protein 1 (SHC1). Intriguingly, even in the absence of stimuli, SNX6 overexpression significantly increased Akt phosphorylation. Our study confirms the utility of proximity-labeling methods, such as BioID, to screen for interactors of cell-surface receptors and has uncovered a role of one of these interactors, SNX6, in the IGF1R signaling cascade.

A Timely Shift from Shotgun to Targeted Proteomics and How It Can Be Groundbreaking for Cancer Research

The fact that cancer is a leading cause of death all around the world has naturally sparked major efforts in the pursuit of novel and more efficient biomarkers that could better serve as diagnostic tools, prognostic predictors, or therapeutical targets in the battle against this type of disease. Mass spectrometry-based proteomics has proven itself as a robust and logical alternative to the immuno-based methods that once dominated the field. Nevertheless, intrinsic limitations of classic proteomic approaches such as the natural gap between shotgun discovery-based methods and clinically applicable results have called for the implementation of more direct, hypothesis-based studies such as those made available through targeted approaches, that might be able to streamline biomarker discovery and validation as a means to increase survivability of affected patients. In fact, the paradigm shifting potential of modern targeted proteomics applied to cancer research can be demonstrated by the large number of advancements and increasing examples of new and more useful biomarkers found during the course of this review in different aspects of cancer research. Out of the many studies dedicated to cancer biomarker discovery, we were able to devise some clear trends, such as the fact that breast cancer is the most common type of tumor studied and that most of the research for any given type of cancer is focused on the discovery diagnostic biomarkers, with the exception of those that rely on samples other than plasma and serum, which are generally aimed toward prognostic markers. Interestingly, the most common type of targeted approach is based on stable isotope dilution-selected reaction monitoring protocols for quantification of the target molecules. Overall, this reinforces that notion that targeted proteomics has already started to fulfill its role as a groundbreaking strategy that may enable researchers to catapult the number of viable, effective, and validated biomarkers in cancer clinical practice.

Targeted proteomics of solid cancers: from quantification of known biomarkers towards reading the digital proteome maps

The concept of personalized medicine includes novel protein biomarkers that are expected to improve the early detection, diagnosis and therapy monitoring of malignant diseases. Tissues, biofluids, cell lines and xenograft models are the common sources of biomarker candidates that require verification of clinical value in independent patient cohorts. Targeted proteomics - based on selected reaction monitoring, or data extraction from data-independent acquisition based digital maps - now represents a promising mass spectrometry alternative to immunochemical methods. To date, it has been successfully used in a high number of studies answering clinical questions on solid malignancies: breast, colorectal, prostate, ovarian, endometrial, pancreatic, hepatocellular, lung, bladder and others. It plays an important role in functional proteomic experiments that include studying the role of post-translational modifications in cancer progression. This review summarizes verified biomarker candidates successfully quantified by targeted proteomics in this field and directs the readers who plan to design their own hypothesis-driven experiments to appropriate sources of methods and knowledge.

A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data

Time course ‘omics’ experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. ‘Omics’ technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course ‘omics’ experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course ‘omics’ data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course ‘omics’ studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.

Quantification of proteins using (13)C7-labeled and unlabeled iodoacetanilide by nano liquid chromatography/nanoelectrospray ionization and by selected reaction monitoring mass spectrometry

The combination of cysteine-specific modifiers, iodoacetanilide (IAA) and (13)C7-labeled iodoacetanilide ((13)C7-IAA), has been applied to absolute quantification of proteins. The selected reaction monitoring (SRM) with the use of nano liquid chromatography/nanoelectrospray ionization ion trap mass spectrometry (nano LC/nano-ESI-IT-MS) analysis was applied to precise quantification of three commercial proteins. Good correlation was observed between the theoretical ratios and observed ratios for all these proteins both in a simple buffer solution and in a complex protein environment. Due to efficient tagging, this method does not require separate synthesis of isotope-labeled peptides for the SRM studies. Therefore, this method is expected to be a useful tool for proteomics research.

Highly multiplexed and reproducible ion-current-based strategy for large-scale quantitative proteomics and the application to protein expression dynamics induced by methylprednisolone in 60 rats

A proteome-level time-series study of drug effects (i.e., pharmacodynamics) is critical for understanding mechanisms of action and systems pharmacology, but is challenging, because of the requirement of a proteomics method for reliable quantification of many biological samples. Here, we describe a highly reproducible strategy, enabling a global, large-scale investigation of the expression dynamics of corticosteroid-regulated proteins in livers from adrenalectomized rats over 11 time points after drug dosing (0.5–66 h, N = 5/point). The analytical advances include (i) exhaustive tissue extraction with a Polytron/sonication procedure in a detergent cocktail buffer, and a cleanup/digestion procedure providing very consistent protein yields (relative standard deviation (RSD%) of 2.7%–6.4%) and peptide recoveries (4.1–9.0%) across the 60 animals; (ii) an ultrahigh-pressure nano-LC setup with substantially improved temperature stabilization, pump-noise suppression, and programmed interface cleaning, enabling excellent reproducibility for continuous analyses of numerous samples; (iii) separation on a 100-cm-long column (2-μm particles) with high reproducibility for days to enable both in-depth profiling and accurate peptide ion-current match; and (iv) well-controlled ion-current-based quantification. To obtain high-quality quantitative data necessary to describe the 11 time-points protein expression temporal profiles, strict criteria were used to define “quantifiable proteins”. A total of 323 drug-responsive proteins were revealed with confidence, and the time profiles of these proteins provided new insights into the diverse temporal changes of biological cascades associated with hepatic metabolism, response to hormone stimuli, gluconeogenesis, inflammatory responses, and protein translation processes. Most profile changes persisted well after the drug was eliminated. The developed strategy can also be broadly applied in preclinical and clinical research, where the analysis of numerous biological replicates is crucial.

αNAC inhibition of the FADD-JNK axis plays anti-apoptotic role in multiple cancer cells

Nascent polypeptide-associated complex α (αNAC) is reportedly overexpressed in several types of cancers and regulates cell apoptosis under hypoxic conditions in HeLa cells. The aim of our study was to investigate the apoptotic function of αNAC in cancer progression. First, we observed the cellular effects of αNAC depletion. Mouse αNAC was used to restore the protein level and verify the effect. An Annexin V assay, a caspase activity reporter assay, an apoptotic molecular marker, and a colony formation assay were used as markers to investigate the mechanisms of cell death caused by αNAC depletion. The Cancer 10-pathway reporter assay was used to screen downstream pathways. PCR site-directed deletion based on the functional domains of αNAC was used to construct deletion mutants. Those functional domain deletion mutants were used to recover the apoptotic phenotype caused by αNAC depletion. Finally, the role of αNAC in TNF-related apoptosis-inducing ligand (TRAIL) treatment was investigated in vitro. We found that depletion of αNAC in multiple types of cancer cells induce typical apoptotic cell death. This anti-apoptotic function is mediated by the FADD/c-Jun N-terminal kinase pathway. Intact αNAC is required for the direct binding of FADD as well as its anti-apoptosis function. Either αNAC depletion or the deletion of the ubiquitin-associated domain of αNAC sensitizes L929 cancer cells to mTRAIL treatment. Our study revealed a αNAC anti-apoptotic function in multiple types of cancer cells and suggested its potential in cancer therapy.

Quantitative protein analysis using (13)C7-labeled iodoacetanilide and d5-labeled N-ethylmaleimide by nano liquid chromatography/nanoelectrospray ionization ion trap mass spectrometry

We have developed a methodology for quantitative analysis and concurrent identification of proteins by the modification of cysteine residues with a combination of iodoacetanilide (IAA, 1) and (13)C7-labeled iodoacetanilide ((13)C7-IAA, 2), or N-ethylmaleimide (NEM, 3) and d5-labeled N-ethylmaleimide (d5-NEM, 4), followed by mass spectrometric analysis using nano liquid chromatography/nanoelectrospray ionization ion trap mass spectrometry (nano LC/nano-ESI-IT-MS). The combinations of these stable isotope-labeled and unlabeled modifiers coupled with LC separation and ESI mass spectrometric analysis allow accurate quantitative analysis and identification of proteins, and therefore are expected to be a useful tool for proteomics research.

Proteomics analysis suggests broad functional changes in potato leaves triggered by phosphites and a complex indirect mode of action against Phytophthora infestans

Phosphite (salts of phosphorous acid; Phi)-based fungicides are increasingly used in controlling oomycete pathogens, such as the late blight agent Phytophthora infestans. In plants, low amounts of Phi induce pathogen resistance through an indirect mode of action. We used iTRAQ-based quantitative proteomics to investigate the effects of phosphite on potato plants before and after infection with P. infestans. Ninety-three (62 up-regulated and 31 down-regulated) differentially regulated proteins, from a total of 1172 reproducibly identified proteins, were identified in the leaf proteome of Phi-treated potato plants. Four days post-inoculation with P. infestans, 16 of the 31 down-regulated proteins remained down-regulated and 42 of the 62 up-regulated proteins remained up-regulated, including 90% of the defense proteins. This group includes pathogenesis-related, stress-responsive, and detoxification-related proteins. Callose deposition and ultrastructural analyses of leaf tissues after infection were used to complement the proteomics approach. This study represents the first comprehensive proteomics analysis of the indirect mode of action of Phi, demonstrating broad effects on plant defense and plant metabolism. The proteomics data and the microscopy study suggest that Phi triggers a hypersensitive response that is responsible for induced resistance of potato leaves against P. infestans.

BIOLOGICAL SIGNIFICANCE

Phosphie triggers complex functional changes in potato leaves that are responsible for the induced resistance against Phytophthora infestans. This article is part of a Special Issue entitled: Translational Plant Proteomics.

Widespread protein aggregation as an inherent part of aging in C. elegans

Several hundred proteins become insoluble and aggregation-prone as a consequence of aging in Caenorhabditis elegans. The data indicate that these proteins influence disease-related protein aggregation and toxicity.

Aberrant protein aggregation is a hallmark of many age-related diseases, yet little is known about whether proteins aggregate with age in a non-disease setting. Using a systematic proteomics approach, we identified several hundred proteins that become more insoluble with age in the multicellular organism Caenorhabditis elegans. These proteins are predicted to be significantly enriched in β-sheets, which promote disease protein aggregation. Strikingly, these insoluble proteins are highly over-represented in aggregates found in human neurodegeneration. We examined several of these proteins in vivo and confirmed their propensity to aggregate with age. Different proteins aggregated in different tissues and cellular compartments. Protein insolubility and aggregation were significantly delayed or even halted by reduced insulin/IGF-1-signaling, which also slows aging. We found a significant overlap between proteins that become insoluble and proteins that influence lifespan and/or polyglutamine-repeat aggregation. Moreover, overexpressing one aggregating protein enhanced polyglutamine-repeat pathology. Together our findings indicate that widespread protein insolubility and aggregation is an inherent part of aging and that it may influence both lifespan and neurodegenerative disease.

In neurodegenerative diseases, such as Alzheimer's disease and Huntington's disease, specific proteins escape the cell's quality-control system and associate together, forming insoluble aggregates. Until now, little was known about whether proteins aggregate in a non-disease context. In this study, we discovered that the aging process itself, in the absence of disease, leads to the insolubilization and increased aggregation propensity of several hundred proteins in the roundworm Caenorhabditis elegans. These aggregation-prone proteins have distinct structural and functional proprieties. We asked if this inherent age-dependent protein aggregation impacts neurodegenerative diseases. We found that proteins similar to those aggregating in old worms have also been identified as minor components of human disease aggregates. In addition, we showed that higher levels of inherent protein aggregation aggravated toxicity in a C. elegans Huntington's disease model. Inherent protein aggregation is a new biomarker of aging. Understanding how to modulate it will lead to important insights into the mechanisms that underlie aging and protein aggregation diseases.

GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm

As one of the most important and ubiquitous post-translational modifications (PTMs) of proteins, S-nitrosylation plays important roles in a variety of biological processes, including the regulation of cellular dynamics and plasticity. Identification of S-nitrosylated substrates with their exact sites is crucial for understanding the molecular mechanisms of S-nitrosylation. In contrast with labor-intensive and time-consuming experimental approaches, prediction of S-nitrosylation sites using computational methods could provide convenience and increased speed. In this work, we developed a novel software of GPS-SNO 1.0 for the prediction of S-nitrosylation sites. We greatly improved our previously developed algorithm and released the GPS 3.0 algorithm for GPS-SNO. By comparison, the prediction performance of GPS 3.0 algorithm was better than other methods, with an accuracy of 75.80%, a sensitivity of 53.57% and a specificity of 80.14%. As an application of GPS-SNO 1.0, we predicted putative S-nitrosylation sites for hundreds of potentially S-nitrosylated substrates for which the exact S-nitrosylation sites had not been experimentally determined. In this regard, GPS-SNO 1.0 should prove to be a useful tool for experimentalists. The online service and local packages of GPS-SNO were implemented in JAVA and are freely available at: http://sno.biocuckoo.org/.