Stephen R. Pennington

Guidelines for the next 10 years of proteomics

In the last ten years, the field of proteomics has expanded at a rapid rate. A range of exciting new technology has been developed and enthusiastically applied to an enormous variety of biological questions. However, the degree of stringency required in proteomic data generation and analysis appears to have been underestimated. As a result, there are likely to be numerous published findings that are of questionable quality, requiring further confirmation and/or validation. This manuscript outlines a number of key issues in proteomic research, including those associated with experimental design, differential display and biomarker discovery, protein identification and analytical incompleteness. In an effort to set a standard that reflects current thinking on the necessary and desirable characteristics of publishable manuscripts in the field, a minimal set of guidelines for proteomics research is then described. These guidelines will serve as a set of criteria which editors of PROTEOMICS will use for assessment of future submissions to the Journal.

Protein and peptides in pictures: Imaging with MALDI mass spectrometry

Imaging using MS has the potential to deliver highly parallel, multiplexed data on the specific localization of molecular ions in tissue samples directly, and to measure and map the variations of these ions during development and disease progression or treatment. There is an intrinsic potential to be able to identify the biomarkers in the same experiment, or by relatively simple extension of the technique. Unlike many other imaging techniques, no a priori knowledge of the markers being sought is necessary. This review concentrates on the use of MALDI‐MS for MS imaging (MSI) of proteins and peptides, with an emphasis on mammalian tissue. We discuss the methodologies used, their potential limitations, overall experimental considerations and progress that has been made towards establishing MALDI‐MSI as a routine technique for the spatially resolved measurement of peptides and proteins. As well as determining the local abundance of individual molecular ions, there is the potential to determine their identity within the same experiment using relatively simple extensions of the basic techniques. In this way MSI offers an important opportunity for biomarker discovery and identification.

Arrays for protein expression profiling: Towards a viable alternative to two-dimensional gel electrophoresis?

Two‐dimensional gel electrophoresis (2‐DE) is used as a platform method for the measurement of protein expression patterns within cells, tissues or organisms. This approach can support expression profiling of several thousand proteins in multiple samples and as such it is currently unrivalled as a tool for the analysis of protein expression, which is a key component of the rapidly expanding field of proteomics. However, 2‐DE has a number of significant limitations and as a consequence, alternative approaches for the measurement of expression of proteins within complex samples are actively being explored. Here we review some existing and emerging methods for protein expression analysis. In particular, we review a range of technologies that might be integrated to support the development of ‘arrays’ or ‘chips’ for rapid, high‐throughput analysis of protein expression in a manner analogous to the current use of DNA arrays for mRNA expression analysis. We conclude that such separation‐independent platforms may ultimately supersede two‐dimensional (2‐D) gel‐based analyses for global protein expression analysis but that before this the technologies might provide important new platforms for diagnostic and prognostic monitoring of diseases.

In vitro nuclear interactome of the HIV-1 Tat protein

BackgroundOne facet of the complexity underlying the biology of HIV-1 resides not only in its limited number of viral proteins, but in the extensive repertoire of cellular proteins they interact with and their higher-order assembly. HIV-1 encodes the regulatory protein Tat (86–101aa), which is essential for HIV-1 replication and primarily orchestrates HIV-1 provirus transcriptional regulation. Previous studies have demonstrated that Tat function is highly dependent on specific interactions with a range of cellular proteins. However they can only partially account for the intricate molecular mechanisms underlying the dynamics of proviral gene expression. To obtain a comprehensive nuclear interaction map of Tat in T-cells, we have designed a proteomic strategy based on affinity chromatography coupled with mass spectrometry.ResultsOur approach resulted in the identification of a total of 183 candidates as Tat nuclear partners, 90% of which have not been previously characterised. Subsequently we applied in silico analysis, to validate and characterise our dataset which revealed that the Tat nuclear interactome exhibits unique signature(s). First, motif composition analysis highlighted that our dataset is enriched for domains mediating protein, RNA and DNA interactions, and helicase and ATPase activities. Secondly, functional classification and network reconstruction clearly depicted Tat as a polyvalent protein adaptor and positioned Tat at the nexus of a densely interconnected interaction network involved in a range of biological processes which included gene expression regulation, RNA biogenesis, chromatin structure, chromosome organisation, DNA replication and nuclear architecture.ConclusionWe have completed the in vitro Tat nuclear interactome and have highlighted its modular network properties and particularly those involved in the coordination of gene expression by Tat. Ultimately, the highly specialised set of molecular interactions identified will provide a framework to further advance our understanding of the mechanisms of HIV-1 proviral gene silencing and activation.

Identification of Ca2+-dependent binding partners for the neuronal calcium sensor protein neurocalcin δ: interaction with actin, clathrin and tubulin

The neuronal calcium sensors are a family of EF-hand-containing Ca(2+)-binding proteins expressed predominantly in retinal photoreceptors and neurons. One of the family members is neurocalcin delta, the function of which is unknown. As an approach to elucidating the protein interactions made by neurocalcin delta, we have identified brain cytosolic proteins that bind to neurocalcin delta in a Ca(2+)-dependent manner. We used immobilized recombinant myristoylated neurocalcin delta combined with protein identification using MS. We demonstrate a specific interaction with clathrin heavy chain, alpha- and beta-tubulin, and actin. These interactions were dependent upon myristoylation of neurocalcin delta indicating that the N-terminal myristoyl group may be important for protein-protein interactions in addition to membrane association. Direct binding of neurocalcin delta to clathrin, tubulin and actin was confirmed using an overlay assay. These interactions were also demonstrated for endogenous neurocalcin delta by co-immunoprecipitation from rat brain cytosol. When expressed in HeLa cells, neurocalcin delta was cytosolic at resting Ca(2+) levels but translocated to membranes, including a perinuclear compartment (trans-Golgi network) where it co-localized with clathrin, following Ca(2+) elevation. These data suggest the possibility that neurocalcin delta functions in the control of clathrin-coated vesicle traffic.

Interaction of Developmental Transcription Factor HOX11 with Steroid Receptor Coactivator SRC-1 Mediates Resistance to Endocrine Therapy in Breast Cancer

Mechanisms of acquired resistance to endocrine therapy in breast cancer, a major clinical challenge, are poorly understood. We have used a mass spectrometry-based screen to identify proteins that are associated with the endocrine-resistant phenotype. In this study, we report the identification of a novel pathway of resistance to endocrine therapy involving interactions of the developmental transcription HOXC11 with the steroid receptor coactivator protein SRC-1, which is a strong predictor of reduced disease-free survival in breast cancer patients. HOXC11 and SRC-1 cooperate to regulate expression of the calcium-binding protein S100beta in resistant breast cancer cells. Nuclear HOXC11 and S100beta were found to strongly predict poor disease-free survival in breast cancer patients (n = 560; hazard ratios: 5.79 and 5.82, respectively; P < 0.0001). Elevated serum levels of S100beta detected in patients also predicted reduced disease-free survival (n = 80; hazard ratio: 5.3; P = 0.004). Our findings define a biomolecular interaction network that drives an adaptive response to endocrine therapy with negative consequences for survival in breast cancer.

Urinary markers for prostate cancer

Prostate cancer is the commonest solid‐organ malignancy to affect men in Europe and the USA; it is estimated that one in six men will develop this cancer in their lifetime. Current screening relies on a digital rectal examination with a serum prostate‐specific antigen test. Novel urinary diagnostic tests are potentially interesting screening tools for this disease. We examined published reports assessing the use of urinary markers for the diagnosis of prostate cancer. Using a PubMed‐based search we identified studies of urinary markers for prostate cancer published from 1985 to February 2006 using the search terms ‘urine’, ‘marker’ and ‘prostate cancer’. Studies to date have used small cohorts and relied on prostatic biopsies to provide histology. The sensitivity and specificity of markers are wide ranging but with only a few studies published on each putative marker it is difficult to assess their potential impact. Using urinary biomarkers for prostate cancer is a relatively novel diagnostic approach; they are appealing as a screening test because they are not invasive. Further work is needed to identify and validate ‘signature markers’ indicative of prostatic malignancy. The newer proteomic platforms are promising biomarker discovery tools that might uncover the next generation of urinary biomarkers.

Application of proteomic strategies to the identification of urinary biomarkers for prostate cancer: A review

Abstract In the post-genomic era, genes and proteins are now studied on a more comprehensive scale. Studying disease processes at only the genetic or transcriptomic level will give an incomplete amount of information. A proteomic approach potentially allows for a more global overview of how disease processes affect the proteins present in cells, tissues and organisms. The challenge arises in determining which proteins are affected in specific diseases and establishing which of these changes are unique to a particular disease. Existing and emerging proteomic technologies allow for high throughput analysis of proteins in a variety of sample types. Prostate cancer is a significant male health problem in the Western world. It is widely accepted that more specific prognostic and diagnostic markers of prostate cancer are urgently required. The present paper suggests that urine may be an attractive biofluid in which to pursue the identification of novel biomarkers of prostate cancer. This review introduces some proteomic techniques including mass spectrometry and the newer, quantitative proteomic strategies. It focuses on the potential application of these platforms to novel urinary biomarker identification in prostate malignancy. It also includes a synopsis of the current literature on urinary proteomics.

Why have so few proteomic biomarkers “survived” validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC‐MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC‐MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.

Biomarkers in rheumatology, now and in the future

This review examines the biomarker development process by using rheumatic disorders as the disease model for discussion. We evaluate the current role of biomarkers in the practice of rheumatology and discuss their likely role in the future. We define the essential components of the biomarker development pipeline and discuss the issue of fitness for purpose, i.e. what the biomarker(s) might offer in a clinical setting. As a component of this review we also highlight several emerging technologies that are beginning to provide practical solutions to support biomarker validation. In the process, we highlight some scenarios where additional biomarkers would add considerable value to clinical practice, and we review appropriate methods for each. We also emphasize some important but infrequently discussed considerations, including the need for protein variant verification. Ultimately, the adroit application of the methods of proteomics will transform the practice rheumatology and allow personalized clinical practice to become a reality.