Statistical identification of differentially labeled peptides from liquid chromatography tandem mass spectrometry

A Pathway Proteomic Profile of Ischemic Stroke Survivors Reveals Innate Immune Dysfunction in Association with Mild Symptoms of Depression - A Pilot Study

Depression after stroke is a common occurrence, raising questions as to whether depression could be a long-term biological and immunological sequela of stroke. Early explanations for post-stroke depression (PSD) focused on the neuropsychological/psychosocial effects of stroke on mobility and quality of life. However, recent investigations have revealed imbalances of inflammatory cytokine levels in association with PSD, though to date, there is only one published proteomic pathway analysis testing this hypothesis. Thus, we examined the serum proteome of stroke patients (n = 44, mean age = 63.62 years) and correlated these with the Montgomery–Åsberg Depression Rating Scale (MADRS) scores at 3 months post-stroke. Overall, the patients presented with mild depression symptoms on the MADRS, M = 6.40 (SD = 7.42). A discovery approach utilizing label-free relative quantification was employed utilizing an LC-ESI–MS/MS coupled to a LTQ-Orbitrap Elite (Thermo-Scientific). Identified peptides were analyzed using the gene set enrichment approach on several different genomic databases that all indicated significant downregulation of the complement and coagulation systems with increasing MADRS scores. Complement and coagulation systems are traditionally thought to play a key role in the innate immune system and are established precursors to the adaptive immune system through pro-inflammatory cytokine signaling. Both systems are known to be globally affected after ischemic or hemorrhagic stroke. Thus, our results suggest that lowered complement expression in the periphery in conjunction with depressive symptoms post-stroke may be a biomarker for incomplete recovery of brain metabolic needs, homeostasis, and inflammation following ischemic stroke damage. Further proteomic investigations are now required to construct the temporal profile, leading from acute lesion damage to manifestation of depressive symptoms. Overall, the findings provide support for the involvement of inflammatory and immune mechanisms in PSD symptoms and further demonstrate the value and feasibility of the proteomic approach in stroke research.

Identification of 7 Proteins in Sera of RA Patients with Potential to Predict ETA/MTX Treatment Response

Objective: The recent growth of innovating biologics has opened fascinating avenues for the management of patients. In rheumatoid arthritis, many biologics are currently available, the choice of which being mostly determined empirically. Importantly, a given biologic may not be active in a fraction of patients and may even provoke side effects. Here, we conducted a comparative proteomics study in attempt to identify a predictive theranostic signature of non-response in patients with rheumatoid arthritis treated by etanercept/methotrexate combination.

Methods: A serum sample was collected prior to treatment exposure from a cohort of 22 patients with active RA. A proteomic “label free” approach was then designed to quantitate protein biomarkers using mass spectrometry. To verify these results, a relative quantification followed by an absolute quantification of interesting protein candidates were performed on a second cohort. The criterion of judgment was the response to etanercept/methotrexate combination according to the EULAR criteria assessed at 6 months of treatment.

Results: These investigations led to the identification of a set of 12 biomarkers with capacity to predict treatment response. A targeted quantitative analysis allowed to confirm the potential of 7 proteins from the latter combination on a new cohort of 16 patients. Two highly discriminating proteins, PROS and CO7, were further evaluated by ELISA on this second cohort. By combining the concentration threshold of each protein associated to a right classification (responders vs non-responders), the sensitivity and specificity reached 88.9 % and 100 %, respectively.

Conclusion: Prior to methotrexate/etanercept treatment, abundance of several sera proteins, notably PROS and CO7, were associated to response status of RA patients 6 month after treatment initiation.

Computational and statistical methods for high-throughput analysis of post-translational modifications of proteins

The investigation of post-translational modifications (PTMs) represents one of the main research focuses for the study of protein function and cell signaling. Mass spectrometry instrumentation with increasing sensitivity improved protocols for PTM enrichment and recently established pipelines for high-throughput experiments allow large-scale identification and quantification of several PTM types. This review addresses the concurrently emerging challenges for the computational analysis of the resulting data and presents PTM-centered approaches for spectra identification, statistical analysis, multivariate analysis and data interpretation. We furthermore discuss the potential of future developments that will help to gain deep insight into the PTM-ome and its biological role in cells. This article is part of a Special Issue entitled: Computational Proteomics.

Online quantitative proteomics p-value calculator for permutation-based statistical testing of peptide ratios

The utility of high-throughput quantitative proteomics to identify differentially abundant proteins en-masse relies on suitable and accessible statistical methodology, which remains mostly an unmet need. We present a free web-based tool, called Quantitative Proteomics p-value Calculator (QPPC), designed for accessibility and usability by proteomics scientists and biologists. Being an online tool, there is no requirement for software installation. Furthermore, QPPC accepts generic peptide ratio data generated by any mass spectrometer and database search engine. Importantly, QPPC utilizes the permutation test that we recently found to be superior to other methods for analysis of peptide ratios because it does not assume normal distributions.1 QPPC assists the user in selecting significantly altered proteins based on numerical fold change, or standard deviation from the mean or median, together with the permutation p-value. Output is in the form of comma separated values files, along with graphical visualization using volcano plots and histograms. We evaluate the optimal parameters for use of QPPC, including the permutation level and the effect of outlier and contaminant peptides on p-value variability. The optimal parameters defined are deployed as default for the web-tool at http://qppc.di.uq.edu.au/ .

Volcano plots in analyzing differential expressions with mRNA microarrays

A volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log(10)(p-value) from the t-test). We review the basic and interactive use of the volcano plot and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide a unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility of applying volcano plots to other fields beyond microarray.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

Cellular phenotype switching and microvesicles

Cell phenotype alteration by cell-derived vesicles presents a new aspect for consideration of cell fate. Accumulating data indicates that vesicles from many cells interact with or enter different target cells from other tissues, altering their phenotype toward that of the cell releasing the vesicles. Cells may be changed by direct interactions, transfer of cell surface receptors or epigenetic reprogramming via transcriptional regulators. Induced epigenetic changes appear to be stable and result in significant functional effects. These data force a reconsideration of the cellular context in which transcription regulates the proliferative and differentiative fate of tissues and suggests a highly plastic cellular system, which might underlay a relatively stable tissue system. The capacity of marrow to convert to non-hematopoietic cells related to vesicle cross-communication may underlie the phenomena of stem cell plasticity. Additionally, vesicles have promise in the clinical arenas of disease biomarkers, tissue restoration and control of neoplastic cell growth.

MALDI mass spectrometric imaging using the stretched sample method to reveal neuropeptide distributions in aplysia nervous tissue

Neuropeptides are a diverse set of complex cell-cell signaling molecules that modulate behavior, learning, and memory. Their spatially heterogeneous distributions, large number of post-translational modifications, and wide range of physiologically active concentrations make their characterization challenging. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometric imaging is well-suited to characterizing and mapping neuropeptides in the central nervous system. Because matrix application can cause peptide migration within tissue samples, application parameters for MALDI typically represent a compromise between attaining the highest signal quality and preserving native spatial distributions. The stretched sample approach minimizes this trade-off by fragmenting the tissue section into thousands of spatially isolated islands, each approximately 40 mum in size. This inhibits analyte migration between the pieces and, at the same time, reduces analyte-salt adduct formation. Here, we present methodological improvements that enable the imaging of stretched tissues and reveal neuropeptide distributions in nervous tissue from Aplysia californica. The distributions of known neuropeptides are shown to correspond with previous immunohistochemical results, demonstrating that the stretched imaging method is well-suited for working with easily redistributed molecules and heterogeneous tissues and reduces adducts from physiological salts.

Comparison of small n statistical tests of differential expression applied to microarrays

Background

DNA microarrays provide data for genome wide patterns of expression between observation classes. Microarray studies often have small samples sizes, however, due to cost constraints or specimen availability. This can lead to poor random error estimates and inaccurate statistical tests of differential expression. We compare the performance of the standard t-test, fold change, and four small n statistical test methods designed to circumvent these problems. We report results of various normalization methods for empirical microarray data and of various random error models for simulated data.

Results

Three Empirical Bayes methods (CyberT, BRB, and limma t-statistics) were the most effective statistical tests across simulated and both 2-colour cDNA and Affymetrix experimental data. The CyberT regularized t-statistic in particular was able to maintain expected false positive rates with simulated data showing high variances at low gene intensities, although at the cost of low true positive rates. The Local Pooled Error (LPE) test introduced a bias that lowered false positive rates below theoretically expected values and had lower power relative to the top performers. The standard two-sample t-test and fold change were also found to be sub-optimal for detecting differentially expressed genes. The generalized log transformation was shown to be beneficial in improving results with certain data sets, in particular high variance cDNA data.

Conclusion

Pre-processing of data influences performance and the proper combination of pre-processing and statistical testing is necessary for obtaining the best results. All three Empirical Bayes methods assessed in our study are good choices for statistical tests for small n microarray studies for both Affymetrix and cDNA data. Choice of method for a particular study will depend on software and normalization preferences.

Mitochondrial DNA mutation stimulates prostate cancer growth in bone stromal environment

BACKGROUND AND OBJECTIVES

Mitochondrial DNA (mtDNA) mutations, inherited and somatically acquired, are common in clinical prostate cancer. We have developed model systems designed to study specific mtDNA mutations in controlled experiments. Because prostate cancer frequently metastasizes to bone we tested the hypothesis that mtDNA mutations enhance prostate cancer growth and survival in the bone microenvironment.

METHODS

The pathogenic nucleotide position (np) 8993 mDNA mutation was introduced into PC3 prostate cancer cells by cybrid formation. Wild-type and mutant cybrids were grown as nude mouse subcutaneous xenografts with or without bone stromal cell co-inoculation. Cybrids were also grown in the intratibial space. Tumor growth was assayed by direct tumor measurement and luciferase chemiluminescence. Gene expression was assayed using cDNA microarrays confirmed by real time PCR, western blot analysis and immunohistochemistry.

RESULTS

Cybrids with the 8,993 mtDNA mutation grew faster than wild-type cybrids. Further growth acceleration was demonstrated in the bone microenvironment. A 37 gene molecular signature characterized the growth advantage conferred by the mtDNA mutation and bone microenvironment. Two genes of known importance in clinical prostate cancer, FGF1 and FAK, were found to be substantially upregulated only when both mtDNA mutation and bone stromal cell were present.

CONCLUSIONS

The ATP6 np 8,993 mtDNA mutation confers a growth advantage to human prostate cancer that is most fully manifest in the bone microenvironment. The identification of specific molecular alterations associated with mtDNA mutation and growth in bone may allow new understanding of prostate cancer bone metastasis.

The paradoxical dynamism of marrow stem cells: considerations of stem cells, niches, and microvesicles

Marrow stem cell regulation represents a complex and flexible system. It has been assumed that the system was intrinsically hierarchical in nature, but recent data has indicated that at the progenitor/stem cell level the system may represent a continuum with reversible alterations in phenotype occurring as the stem cells transit cell cycle. Short and long-term engraftment, in vivo and in vitro differentiation, gene expression, and progenitor numbers have all been found to vary reversibly with cell cycle. In essence, the stem cells appear to show variable potential, probably based on transcription factor access, as they proceed through cell cycle. Another critical component of the stem cell regulation is the microenvironment, so-called niches. We propose that there are not just several unique niche cells, but a wide variety of niche cells which continually change phenotype to appropriately interact with the continuum of stem cell phenotypes. A third component of the regulatory system is microvesicle transfer of genetic information between cells. We have shown that marrow cells can express the genetic phenotype of pulmonary epithelial cells after microvesicle transfer from lung to marrow cells. Similar transfers of tissue specific mRNA occur between liver, brain, and heart to marrow cells. Thus, there would appear to be a continuous genetic modulation of cells through microvesicle transfer between cells. We propose that there is an interactive triangulated Venn diagram with continuously changing stem cells interacting with continuously changing areas of influence, both being modulated by transfer of genetic information by microvesicles.

A correction for estimating error when using the Local Pooled Error Statistical Test

Jain et al. introduced the Local Pooled Error (LPE) statistical test designed for use with small sample size microarray gene-expression data. Based on an asymptotic proof, the test multiplicatively adjusts the standard error for a test of differences between two classes of observations by pi/2 due to the use of medians rather than means as measures of central tendency. The adjustment is upwardly biased at small sample sizes, however, producing fewer than expected small P-values with a consequent loss of statistical power. We present an empirical correction to the adjustment factor which removes the bias and produces theoretically expected P-values when distributional assumptions are met. Our adjusted LPE measure should prove useful to ongoing methodological studies designed to improve the LPE's; performance for microarray and proteomics applications and for future work for other high-throughput biotechnologies.

AVAILABILITY

The software is implemented in the R language and can be downloaded from the Bioconductor project website (http://www.bioconductor.org).

Protein and lysate array technologies in cancer research

Capturing quantitative proteomic information provides new insights for enhancing the understanding of cancer biology. There have been several protein microarray formats, and each has an advantage depending on what is being detected. However, in contrast to nucleotide printing, the production of protein arrays generally requires the capability of handling viscous solutions, and the mishandling of various factors, such as temperature and humidity, adversely affect protein status. The requirement for such specifications is critical when increasing the throughput for monitoring a large number of samples for rigorous quantitation. In particular, a new solid pin arrayer has been extremely powerful when highly viscous cell lysates printed for high-density, "reverse-phase" protein arrays, and acquired data allows for theoretical models of protein signaling networks to be constructed. In this review, applications of currently available protein microarray technology to cancer research are discussed including the advantages of the new solid pin architecture for opening up powerful proteomic applications.

Comparative LC-MS: a landscape of peaks and valleys

Quantitative proteomics approaches using stable isotopes are well-known and used in many labs nowadays. More recently, high resolution quantitative approaches are reported that rely on LC-MS quantitation of peptide concentrations by comparing peak intensities between multiple runs obtained by continuous detection in MS mode. Characteristic of these comparative LC-MS procedures is that they do not rely on the use of stable isotopes; therefore the procedure is often referred to as label-free LC-MS. In order to compare at comprehensive scale peak intensity data in multiple LC-MS datasets, dedicated software is required for detection, matching and alignment of peaks. The high accuracy in quantitative determination of peptide abundance provides an impressive level of detail. This approach also requires an experimental set-up where quantitative aspects of protein extraction and reproducible separation conditions need to be well controlled. In this paper we will provide insight in the critical parameters that affect the quality of the results and list an overview of the most recent software packages that are available for this procedure.