The case for well-conducted experiments to validate statistical protocols for 2D gels: different pre-processing = different lists of significant proteins

Multi-Omics Methods Applied to Flower Development

Developmental processes in multicellular organisms depend on the proficiency of cells to orchestrate different gene expression programs. Over the past years, several studies of reproductive organ development have considered genomic analyses of transcription factors and global gene expression changes, modeling complex gene regulatory networks. Nevertheless, the dynamic view of developmental processes requires, as well, the study of the proteome in its expression, complexity, and relationship with the transcriptome. In this chapter, we describe a dual extraction method-for protein and RNA-for the characterization of genome expression at proteome level and its correlation to transcript expression data. We also present a shotgun proteomic procedure (LC-MS/MS) followed by a pipeline for the imputation of missing values in mass spectrometry results.

Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics

Analysis of differential abundance in proteomics data sets requires careful application of missing value imputation. Missing abundance values widely vary when performing comparisons across different sample treatments. For example, one would expect a consistent rate of “missing at random” (MAR) across batches of samples and varying rates of “missing not at random” (MNAR) depending on the inherent difference in sample treatments within the study. The missing value imputation strategy must thus be selected that best accounts for both MAR and MNAR simultaneously. Several important issues must be considered when deciding the appropriate missing value imputation strategy: (1) when it is appropriate to impute data; (2) how to choose a method that reflects the combinatorial manner of MAR and MNAR that occurs in an experiment. This paper provides an evaluation of missing value imputation strategies used in proteomics and presents a case for the use of hybrid left-censored missing value imputation approaches that can handle the MNAR problem common to proteomics data.

Urinary peptide panel for prognostic assessment of bladder cancer relapse

Non-invasive tools stratifying bladder cancer (BC) patients according to the risk of relapse are urgently needed to guide clinical intervention. As a follow-up to the previously published study on CE-MS-based urinary biomarkers for BC detection and recurrence monitoring, we expanded the investigation towards BC patients with longitudinal data. Profiling datasets of BC patients with follow-up information regarding the relapse status were investigated. The peptidomics dataset (n = 98) was split into training and test set. Cox regression was utilized for feature selection in the training set. Investigation of the entire training set at the single peptide level revealed 36 peptides being strong independent prognostic markers of disease relapse. Those features were further integrated into a Random Forest-based model evaluating the risk of relapse for BC patients. Performance of the model was assessed in the test cohort, showing high significance in BC relapse prognosis [HR = 5.76, p-value = 0.0001, c-index = 0.64]. Urinary peptide profiles integrated into a prognostic model allow for quantitative risk assessment of BC relapse highlighting the need for its incorporation in prospective studies to establish its value in the clinical management of BC.

A Urinary Fragment of Mucin-1 Subunit α Is a Novel Biomarker Associated With Renal Dysfunction in the General Population

INTRODUCTION

Sequencing peptides included in the urinary proteome identifies the parent proteins and may reveal mechanisms underlying the pathophysiology of chronic kidney disease.

METHODS

In 805 randomly recruited Flemish individuals (50.8% women; mean age, 51.1 years), we determined the estimated glomerular filtration rate (eGFR) from serum creatinine using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. We categorized eGFR according to the National Kidney Foundation Kidney Disease Outcomes Quality Initiative guideline. We analyzed 74 sequenced urinary peptides with a detectable signal in more than 95% of participants. Follow-up measurements of eGFR were available in 597 participants.

RESULTS

In multivariable analyses, baseline eGFR decreased (P ≤ 0.022) with urinary fragments of mucin-1 (standardized association size expressed in ml/min/1.73 m², -4.48), collagen III (-2.84), and fibrinogen (-1.70) and was bi-directionally associated (P ≤ 0.0006) with 2 urinary collagen I fragments (+2.28 and -3.20). The eGFR changes over 5 years (follow-up minus baseline) resulted in consistent estimates (P ≤ 0.025) for mucin-1 (-1.85), collagen (-1.37 to 1.43) and fibrinogen (-1.45) fragments. Relative risk of having or progressing to eGFR <60 ml/min/1.73 m² was associated with mucin-1. Partial least-squares analysis confirmed mucin-1 as the strongest urinary marker associated with decreased eGFR, with a score of 2.47 compared with 1.80 for a collagen I fragment as the next contender. Mucin-1 predicted eGFR decline to <60 ml/min/1.73 m² over and above microalbuminuria (P = 0.011) and retained borderline significance (P = 0.05) when baseline eGFR was accounted for.

DISCUSSION

In the general population, mucin-1 subunit α, an extracellular protein that is shed from renal tubular epithelium, is a novel biomarker associated with renal dysfunction.

Vascular Endothelial Growth Factor (VEGF) Promotes Assembly of the p130Cas Interactome to Drive Endothelial Chemotactic Signaling and Angiogenesis

p130Cas is a polyvalent adapter protein essential for cardiovascular development, and with a key role in cell movement. In order to identify the pathways by which p130Cas exerts its biological functions in endothelial cells we mapped the p130Cas interactome and its dynamic changes in response to VEGF using high-resolution mass spectrometry and reconstruction of protein interaction (PPI) networks with the aid of multiple PPI databases. VEGF enriched the p130Cas interactome in proteins involved in actin cytoskeletal dynamics and cell movement, including actin-binding proteins, small GTPases and regulators or binders of GTPases. Detailed studies showed that p130Cas association of the GTPase-binding scaffold protein, IQGAP1, plays a key role in VEGF chemotactic signaling, endothelial polarization, VEGF-induced cell migration, and endothelial tube formation. These findings indicate a cardinal role for assembly of the p130Cas interactome in mediating the cell migratory response to VEGF in angiogenesis, and provide a basis for further studies of p130Cas in cell movement.

Diastolic Left Ventricular Function in Relation to Urinary and Serum Collagen Biomarkers in a General Population

Current knowledge on the pathogenesis of diastolic heart failure predominantly rests on case-control studies involving symptomatic patients with preserved ejection fraction and relying on invasive diagnostic procedures including endomyocardial biopsy. Our objective was to gain insight in serum and urinary biomarkers reflecting collagen turnover and associated with asymptomatic diastolic LV dysfunction. We randomly recruited 782 Flemish (51.3% women; 50.5 years). We assessed diastolic LV function from the early and late diastolic peak velocities of the transmitral blood flow and of the mitral annulus. By sequencing urinary peptides, we identified 70 urinary collagen fragments. In serum, we measured carboxyterminal propeptide of procollagen type 1 (PICP) as marker of collagen I synthesis and tissue inhibitor of matrix metalloproteinase type 1 (TIMP-1), an inhibitor of collagen-degrading enzymes. In multivariable-adjusted analyses with Bonferroni correction, we expressed effect sizes per 1-SD in urinary collagen I (uCI) or collagen III (uCIII) fragments. In relation to uCI fragments, e’ decreased by 0.183 cm/s (95% confidence interval, 0.017 to 0.350; p = 0.025), whereas E/e’ increased by 0.210 (0.067 to 0.353; p = 0.0012). E/e’ decreased with uCIII by 0.168 (0.021 to 0.316; p = 0.018). Based on age-specific echocardiographic criteria, 182 participants (23.3%) had subclinical diastolic LV dysfunction. Partial least squares discriminant analysis contrasting normal vs. diastolic LV dysfunction confirmed the aforementioned associations with the uCI and uCIII fragments. PICP and TIMP-1 increased in relation to uCI (p<0.0001), whereas these serum markers decreased with uCIII (p≤0.0006). Diastolic LV dysfunction was associated with higher levels of TIMP-1 (653 vs. 696 ng/mL; p = 0.013). In a general population, the non-invasively assessed diastolic LV function correlated inversely with uCI and serum markers of collagen I deposition, but positively with uCIII. These observations generalise previous studies in patients to randomly recruited people, in whom diastolic LV function ranged from normal to subclinical impairment, but did not encompass overt diastolic heart failure.

Tuber proteome comparison of five potato varieties by principal component analysis

BACKGROUND

Data analysis of omics data should be performed by multivariate analysis such as principal component analysis (PCA). The way data are clustered in PCA is of major importance to develop some classification systems based on multivariate analysis, such as soft independent modeling of class analogy (SIMCA). In a previous study a one-class classifier based on SIMCA was built using microarray data from a set of potatoes. The PCA grouped the transcriptomic data according to varieties. The present work aimed to use PCA to verify the clustering of the proteomic profiles for the same potato varieties.

RESULTS

Proteomic profiles of five potato varieties (Biogold, Fontane, Innovator, Lady Rosetta and Maris Piper) were evaluated by two-dimensional gel electrophoresis (2-DE) performed on two immobilized pH gradient (IPG) strip lengths, 13 and 24 cm, both under pH range 4-7. For each strip length, two gels were prepared from each variety; in total there were ten gels per analysis. For 13 cm strips, 199-320 spots were detected per gel, and for 24 cm strips, 365-684 spots.

CONCLUSION

All four PCAs performed with these datasets presented clear grouping of samples according to the varieties. The data presented here showed that PCA was applicable for proteomic analysis of potato and was able to separate the samples by varieties. © 2016 Society of Chemical Industry.

Combined proteomic and transcriptomic analysis identifies differentially expressed pathways associated to Pinus radiata needle maturation

Needle differentiation is a very complex process that leads to the formation of a mature photosynthetic organ from pluripotent needle primordia. The proteome and transcriptome of immature and fully developed needles of Pinus radiata D. Don were compared to described changes in mRNA and protein species that characterize the needle maturation developmental process. A total of 856 protein spots were analyzed, defining a total of 280 spots as differential between developmental stages, from which 127 were confidently identified. A suppressive subtractive library (2048 clones, 274 non redundant contigs) was built, and 176 genes showed to be differentially expressed. The Joint data analysis of proteomic and transcriptomic results provided a broad overview of differentially expressed pathways associated with needle maturation and stress-related pathways. Proteins and genes related to energy metabolism pathways, photosynthesis, and oxidative phosphorylation were overexpressed in mature needles. Amino acid metabolism, transcription, and translation pathways were overexpressed in immature needles. Interestingly, stress related proteins were characteristic of immature tissues, a fact that may be linked to defense mechanisms and the higher growth rate and morphogenetic competence exhibited by these needles. Thus, this work provides an overview of the molecular changes affecting proteomes and transcriptomes during P. radiata needle maturation, having an integrative vision of the functioning and physiology of this process.

Feature detection techniques for preprocessing proteomic data

Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures. Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations. Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information. In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data. This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis. Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript, and thus the discussed methods are likewise applicable.

Proteomic profiling of cancer of the gingivo-buccal complex: Identification of new differentially expressed markers

Tobacco-related oral cancer is the most common cancer among Indian males, gingivo-buccal complex (GBC) being the most affected subsite due to the habit of chewing tobacco. Proteins from the lysates of microdissected normal and transformed epithelium from clinically well-characterized tissue samples of the GBC were separated by two-dimensional gel electrophoresis to identify differentially expressed proteins. Eleven protein spots showed differential expression, which could withstand the stringency of statistical evaluation. The observations were confirmed with additional tissues. Nine of these differentiators were identified by MS as lactate dehydrogenase B, α-enolase, prohibitin, cathepsin D, apolipoprotein A-I, tumor protein translationally controlled-1, an SFN family protein, 14-3-3σ and tropomyosin. Cluster analysis indicated that these proteins, as a coexpressed set, could distinguish normal and transformed epithelium. Functionally, these differentiator molecules are relevant to the pathways and processes that have been previously implicated in oral carcinogenesis and could therefore be investigated further as a panel of markers for management of cancer of the GBC.

Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: assessing experimental effects in a melanoma cell study

MOTIVATION

Difference in-gel electrophoresis (DIGE)-based protein expression analysis allows assessing the relative expression of proteins in two biological samples differently labeled (Cy5, Cy3 CyDyes). In the same gel, a reference sample is also used (Cy2 CyDye) for spot matching during image analysis and volume normalization. The standard statistical techniques to identify differentially expressed (DE) proteins are the calculation of fold-changes and the comparison of treatment means by the t-test. The analyses rarely accounts for other experimental effects, such as CyDye and gel effects, which could be important sources of noise while detecting treatment effects.

RESULTS

We propose to identify DIGE DE proteins using a two-stage linear mixed model. The proposal consists of splitting the overall model for the measured intensity into two interconnected models. First, we fit a normalization model that accounts for the general experimental effects, such as gel and CyDye effects as well as for the features of the associated random term distributions. Second, we fit a model that uses the residuals from the first step to account for differences between treatments in protein-by-protein basis. The modeling strategy was evaluated using data from a melanoma cell study. We found that a heteroskedastic model in the first stage, which also account for CyDye and gel effects, best normalized the data, while allowing for an efficient estimation of the treatment effects. The Cy2 reference channel was used as a covariate in the normalization model to avoid skewness of the residual distribution. Its inclusion improved the detection of DE proteins in the second stage.

Proteomics in liver fibrosis is more than meets the eye

Liver fibrosis is a serious health issue for many liver patients and is currently diagnosed using liver biopsy. The erroneous nature of this technique urges the search for better, noninvasive alternatives. In this regard, proteomics has been described as a useful biomarker discovery tool and has become increasingly applied in the study of liver fibrosis. Experimental and clinical studies have already provided deeper insights in the molecular pathways of liver fibrosis and even confirmed previous findings. Recent advances in proteomic strategies and tools enable multiple fractionation, multiple protein identifications and parallel analyses of multiple samples. Despite its increasing popularity, proteomics still faces certain pitfalls concerning preanalytical variability, protein coverage and statistic reliability. Proteomics is still evolving, but will undoubtedly contribute to a better understanding of the basics of the pathology and certainly offer opportunities in liver fibrosis diagnostics and therapeutics.

Comparison of DIGE and post-stained gel electrophoresis with both traditional and SameSpots analysis for quantitative proteomics

2-DE is an important tool in quantitative proteomics. Here, we compare the deep purple (DP) system with DIGE using both a traditional and the SameSpots approach to gel analysis. Missing values in the traditional approach were found to be a significant issue for both systems. SameSpots attempts to address the missing value problem. SameSpots was found to increase the proportion of low volume data for DP but not for DIGE. For all the analysis methods applied in this study, the assumptions of parametric tests were met. Analysis of the same images gave significantly lower noise with SameSpots (over traditional) for DP, but no difference for DIGE. We propose that SameSpots gave lower noise with DP due to the stabilisation of the spot area by the common spot outline, but this was not seen with DIGE due to the co-detection process which stabilises the area selected. For studies where measurement of small abundance changes is required, a cost-benefit analysis highlights that DIGE was significantly cheaper regardless of the analysis methods. For studies analysing large changes, DP with SameSpots could be an effective alternative to DIGE but this will be dependent on the biological noise of the system under investigation.

Discovering robust protein biomarkers for disease from relative expression reversals in 2-D DIGE data

This study assesses the ability of a novel family of machine learning algorithms to identify changes in relative protein expression levels, measured using 2-D DIGE data, which support accurate class prediction. The analysis was done using a training set of 36 total cellular lysates comprised of six normal and three cancer biological replicates (the remaining are technical replicates) and a validation set of four normal and two cancer samples. Protein samples were separated by 2-D DIGE and expression was quantified using DeCyder-2D Differential Analysis Software. The relative expression reversal (RER) classifier correctly classified 9/9 training biological samples (p<0.022) as estimated using a modified version of leave one out cross validation and 6/6 validation samples. The classification rule involved comparison of expression levels for a single pair of protein spots, tropomyosin isoforms and alpha-enolase, both of which have prior association as potential biomarkers in cancer. The data was also analyzed using algorithms similar to those found in the extended data analysis package of DeCyder software. We propose that by accounting for sources of within- and between-gel variation, RER classifiers applied to 2-D DIGE data provide a useful approach for identifying biomarkers that discriminate among protein samples of interest.

A Probabilistic Treatment of the Missing Spot Problem in 2D Gel Electrophoresis Experiments

Two-dimensional SDS-PAGE gel electrophoresis using post-run staining is widely used to measure the abundances of thousands of protein spots simultaneously. Usually, the protein abundances of two or more biological groups are compared using biological and technical replicates. After gel separation and staining, the spots are detected, spot volumes are quantified, and spots are matched across gels. There are almost always many missing values in the resulting data set. The missing values arise either because the corresponding proteins have very low abundances (or are absent) or because of experimental errors such as incomplete/over focusing in the first dimension or varying run times in the second dimension as well as faulty spot detection and matching. In this study, we show that the probability for a spot to be missing can be modeled by a logistic regression function of the logarithm of the volume. Furthermore, we present an algorithm that takes a set of gels with technical and biological replicates as input and estimates the average protein abundances in the biological groups from the number of missing spots and measured volumes of the present spots using a maximum likelihood approach. Confidence intervals for abundances and p-values for differential expression between two groups are calculated using bootstrap sampling. The algorithm is compared to two standard approaches, one that discards missing values and one that sets all missing values to zero. We have evaluated this approach in two different gel data sets of different biological origin. An R-program, implementing the algorithm, is freely available at http://bioinfo.thep .lu.se/MissingValues2Dgels.html.

Biomarker discovery using high-dimensional lipid analysis

PURPOSE OF REVIEW

High-dimensional lipid analysis technologies (lipidomics) provide researchers with an opportunity to measure lipids on an unprecedented scale. They do not, however, guarantee a fast track to new knowledge. The vast amount of data produced by these platforms presents a major hurdle to assembling valid knowledge and to the discovery of mechanistic biomarkers. This review examines strategies for improving the quality of high-dimensional lipid data and streamlining data analysis to increase the value of lipidomics platforms to research and commercial applications.

RECENT FINDINGS

Recent articles focus on careful study design and data analysis protocols. Authors offer detailed descriptions of study populations, analytical methods and data analysis, and highlight the use of practical data preprocessing and the incorporation of biological knowledge into data analysis.

SUMMARY

The field is moving towards more methodical and structured approaches to biomarker identification. Experimental designs focusing on well-defined outcomes have a better chance of producing biologically relevant results. The high-dimensional lipid analysis techniques available are varied, have different strengths and weaknesses, and must be chosen carefully depending on the experimental design and application. Many techniques for data analysis are available, but the most successful are those incorporating existing biological knowledge into the statistical analysis.

Arginine deficiency in preconfluent intestinal Caco-2 cells modulates expression of proteins involved in proliferation, apoptosis, and heat shock response

Arginine is classified as a conditionally essential amino acid required exogenously during catabolic disease states and periods of rapid growth, both characterized by increased arginine utilization. Arginine plays an important role in the intestine, where it is extensively metabolized, and enhances its immune-supportive function and mucosal repair. Cell proliferation is important for the latter process. This study aimed for a better molecular insight in the response to arginine deprivation/supplementation of preconfluent and 5-day-confluent, differentiated Caco-2 intestinal cells. The potential of citrulline to counteract the effects of arginine deprivation was investigated in preconfluent cells. 2-DE combined with MALDI-TOF-MS and the antibody microarray technology were applied. Evidence is provided that arginine deficiency modulates the protein expression profiles of preconfluent Caco-2 cells differently than that of postconfluent differentiated cells. In preconfluent cells, certain proteins changed in direct response to arginine deficiency, whereas other proteins did not, but instead responded during the recovery phase after an arginine/citrulline resupplementation. The protein changes suggest that arginine deprivation decreases cell proliferation and heat shock protein expression, and enhances the cells susceptibility to apoptosis. These processes are critical for proper cell function, and hence a state of arginine deficiency can be detrimental for intestinal cells which proliferate actively in vivo.

2D gel proteomics: an approach to study age-related differences in protein abundance or isoform complexity in biological samples

This chapter describes protocols for two-dimensional (2D) gel electrophoresis (isoelectric focusing [IEF] followed by sodium-dodecyl sulfate (SDS)-polyacrylamide gel electro-phoresis [PAGE]), staining of gels with the fluorescent dye Sypro Ruby, 2D gel image analysis, peptide mass fingerprint (PMF) analysis using matrix-assisted laser desorption ionization (MALDI)-time-of-flight (TOF) mass spectrometry (MS), liquid chromatography (LC)-tandem mass spectrometry (MS/MS), Western blot analysis of protein oxidations, and mass spectrometric mapping of sites of protein oxidations. Many of these methods were used to identify proteins affected in rat brain following ingestion of grape seed extract (GSE), a dietary supplement touted for anti-oxidant activity. Although beneficial actions in cell and animal models of chronic disease have been described for GSE, it has not been shown whether specific proteins were affected, or the nature of the effects. Applying 2D gel proteomics technology allowed discovery of proteins targeted by GSE without a priori knowledge of which one(s) might be affected. The newer 2D blue native (BN) electrophoresis methodology, which resolves protein complexes in a nondenaturing first dimension and then the components of these complexes in a denaturing second dimension, is discussed as a complementary approach. Analysis of protein oxidations and protein-protein interactions have special relevance to aging-related research, since oxidative stress and altered protein interactions may be at the heart of aging-related diseases. Finally, quality control issues related to implementation of high throughput technologies are addressed, to underscore the importance of minimizing bias and randomizing human and technical error in generating large datasets that are expensive and time-consuming to repeat.

New nutrition, proteomics, and how both can enhance studies in cancer prevention and therapy

The increased application of MS technologies to nutrition and cancer prevention research has enabled unique insights into the health benefits of polyphenols. Polyphenols are phytochemicals that appear to have chemical properties that provide valuable health benefits when ingested. In particular, experiments have suggested that grape seed proanthocyanidins, oligomers of the catechin family of polyphenols, may have health benefits, possibly due to their capacity to be oxidized. Two-dimensional gel proteomics technology identified specific rat brain proteins that were differentially affected after ingestion of grape seed extract. Beneficial changes in the expression of these proteins were observed relative to changes seen in the brains of Alzheimer disease patients at autopsy and of transgenic mouse models of dementia. These findings were consistent with the hypothesis that grape seed polyphenols may have neuroprotective activity. Previous experiments showed that grape seed extract was significantly chemopreventive in a rat model of breast cancer, but this depended on the specific diet in which the grape seed was administered. Thus, phytochemicals such as polyphenols may have health benefits in mammalian tissues unrelated to classical nutritional deficiency models. This report illustrates how experimental approaches that combine proteomics technologies with a dietary intervention with specific phytochemicals in normal animals can enhance studies on cancer prevention and treatment.