False Discovery Rate Calculation Research Articles

Abstract 497: Regulation of CSC lineage and stemness maintenance in progressive neuroblastoma: RD3, a MVP in the league

Abstract Our studies recognized that the retinal degeneration protein 3 (RD3) is constitutively expressed in human adult and fetal tissues beyond retina. Further, we showed that transcriptional/translational loss of RD3 highly associated with advanced disease stage and poor clinical outcomes in neuroblastoma (NB) patients. We identified that RD3 loss is instrumental in NB pathogenesis and, more importantly defined its acquired loss in progressive NB after intensive multimodal clinical therapy. Herein, we investigated the mechanisms, how acquired loss of RD3 orchestrates NB progression. Cell lines derived during diagnosis from stage 4 NB patients off primary (CHLA-15) or metastatic (CHLA-42, SH-SY5Y) site, those with constitutive RD3 expression was used as parental controls. Analogous clones of stable RD3 knockouts were developed (shRNA-RNAi), validated (immunoblotting) and were assayed for whole genome transcriptomic modifications using high-throughput Illumina transcriptome RNA-Seq. Differential gene expression analysis with stringent criteria (log2 fold change) coupled with false discovery rate calculation clearly identified 575 conserved (across three independent cell-lines) genetic determinants that could translate RD3-loss associated disease progression. IPA-functional bioinformatics identified regulation of cellular-lineage directed (stem-cell pluripotency, EMT, Senescence, epithelial adherens junction etc.) signaling coupled with select tumor evolution (NFκB, GPCR, mTOR, Wnt/β-catenin, stat3 etc.) signaling. Individual gene QPCR validation of experimentally observed 13 lead (mean FDR >7.0) candidates affirmed RNA-Seq outcomes. Actuated FAM129A, HAS2, ICAM1, S100A11, TSPAN8, HLA-F, ASIC4, AXL, VCAM1, KLHDC7B, MAGEL2 and, repressed tumor suppressor FOXN4 and NB differentiation factor ROBO2 in RD3-KOs recognized diverted cell lineage (EMT, poor differentiation, stimulated CSCs, CSC stemness maintenance), drug resistance, immune modulation/evasion, and tumor progression (clonal selection and expansion, growth and malignancy, invasion and migration, angiogenesis). For the first time, these results demonstrate the RD3-loss dependent transcriptomic addiction of disease evolution drivers in NB cells. Further, the outcomes imply that RD3 directly regulates key CSC cell lineage and tumor progression/dissemination signaling events. Current investigations are appropriately directed to unveil the functional mechanism(s) that could benefit salvage therapy for progressive NB. Funding: This work was partially or in full, funded by Oklahoma Center for the Advancement of Science and Technology, OCAST-HR19-04; National Institutes of Health, NIH-P20GM103639 and NIH-NCI- Cancer Center Support Grant P30CA225520 Citation Format: Natarajan Aravindan, Dinesh Babu Somasundaram. Regulation of CSC lineage and stemness maintenance in progressive neuroblastoma: RD3, a MVP in the league [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 497.

MS Annika: A New Cross-Linking Search Engine.

Cross-linking mass spectrometry (XL-MS) has become a powerful technique that enables insights into protein structures and protein interactions. The development of cleavable cross-linkers has further promoted XL-MS through search space reduction, thereby allowing for proteome-wide studies. These new analysis possibilities foster the development of new cross-linkers, which not every search engine can deal with out of the box. In addition, some search engines for XL-MS data also struggle with the validation of identified cross-linked peptides, that is, false discovery rate (FDR) estimation, as FDR calculation is hampered by the fact that not only one but two peptides in a single spectrum have to be correct. We here present our new search engine, MS Annika, which can identify cross-linked peptides in MS2 spectra from a wide variety of cleavable cross-linkers. We show that MS Annika provides realistic estimates of FDRs without the need of arbitrary score cutoffs, being able to provide on average 44% more identifications at a similar or better true FDR than comparable tools. In addition, MS Annika can be used on proteome-wide studies due to fast, parallelized processing and provides a way to visualize the identified cross-links in protein 3D structures.

Associations between gut microbiota and genetic risk for rheumatoid arthritis in the absence of disease: a cross-sectional study

SummaryBackgroundRheumatoid arthritis is a chronic inflammatory autoimmune disease that is associated with reduced life expectancy. The disease is heritable and an extensive repertoire of genetic variants have been identified. The gut microbiota might represent an environmental risk factor for rheumatoid arthritis. We aimed to assess whether known rheumatoid arthritis risk alleles were associated with the gut microbiota in a large population who do not have rheumatoid arthritis.MethodsIn this cross-sectional study done in the UK and Switzerland, we used genotyping and microbiota data from previous studies of the TwinsUK cohort, excluding participants who had ever had a diagnosis of rheumatoid arthritis, as well as their unaffected co-twins. We used blood samples for genotyping and stool samples for the assessment of the gut microbiota. We generated a polygenic risk score (PRS) for rheumatoid arthritis in 1650 TwinsUK participants without the disease, based on 233 GWAS-identified single nucleotide polymorphisms associated with rheumatoid arthritis. We validated the PRS using logistic regression against rheumatoid arthritis diagnosis in 2686 UK Biobank individuals with a confirmed diagnosis of rheumatoid arthritis. Amplicon sequence variants (ASVs) were generated from 16S rRNA gene sequencing of stool samples and assessed for association with the PRS for rheumatoid arthritis. We validated the findings in an independent sample comprised of first-degree relatives of patients with rheumatoid arthritis from the SCREEN-RA cohort. Differential abundance of ASVs present in more than 5% of samples, grouped by ASV taxon annotation, against the rheumatoid arthritis PRS as a continuous variable was assessed using fixed-effects covariates. To account for multiple testing, the false discovery rate calculation was applied to all p values to generate q values, with a significance threshold of 0·05 determined a priori.FindingsWe found that presence of Prevotella spp were positively associated with the rheumatoid arthritis PRS in TwinsUK participants (q<1 × 10−7). This finding was validated in SCREEN-RA participants (n=133) carrying established shared epitope risk alleles (q=0·0011). We also found an association between Prevotella spp and presence of preclinical rheumatoid arthritis phases (q=0·021).InterpretationPrevotella spp in the gut microbiota are associated with the rheumatoid arthritis genotype in the absence of rheumatoid arthritis, including in individuals at high risk of developing rheumatoid arthritis. Our findings suggest that host genotype is associated with microbiota profile before disease onset.FundingVersus Arthritis.

Statistics in Proteomics: A Meta-analysis of 100 Proteomics Papers Published in 2019.

We randomly selected 100 journal articles published in five proteomics journals in 2019 and manually examined each of them against a set of 13 criteria concerning the statistical analyses used, all of which were based on items mentioned in the journals' instructions to authors. This included questions such as whether a pilot study was conducted and whether false discovery rate calculation was employed at either the quantitation or identification stage. These data were then transformed to binary inputs, analyzed via machine learning algorithms, and classified accordingly, with the aim of determining if clusters of data existed for specific journals or if certain statistical measures correlated with each other. We applied a variety of classification methods including principal component analysis decomposition, agglomerative clustering, and multinomial and Bernoulli naïve Bayes classification and found that none of these could readily determine journal identity given extracted statistical features. Logistic regression was useful in determining high correlative potential between statistical features such as false discovery rate criteria and multiple testing corrections methods, but was similarly ineffective at determining correlations between statistical features and specific journals. This meta-analysis highlights that there is a very wide variety of approaches being used in statistical analysis of proteomics data, many of which do not conform to published journal guidelines, and that contrary to implicit assumptions in the field there are no clear correlations between statistical methods and specific journals.

Calculation of False Discovery Rate for Peptide and Protein Identification.

Shotgun proteomics is the method of choice for large-scale protein identification. However, the use of a robust statistical workflow to validate such identification is mandatory to minimize false matches, ambiguities, and amplification of error rates from spectra to proteins. In this chapter we emphasize the key concepts to take into account when processing the output of a search engine to obtain reliable peptide or protein identifications. We assume that the reader is already familiar with tandem mass spectrometry so we can focus on the use of statistical confidence methods. After introducing the key concepts we present different software tools and how to use them with an example dataset.

Accurate Estimation of Context-Dependent False Discovery Rates in Top-Down Proteomics

Within the last several years, top-down proteomics has emerged as a high throughput technique for protein and proteoform identification. This technique has the potential to identify and characterize thousands of proteoforms within a single study, but the absence of accurate false discovery rate (FDR) estimation could hinder the adoption and consistency of top-down proteomics in the future. In automated identification and characterization of proteoforms, FDR calculation strongly depends on the context of the search. The context includes MS data quality, the database being interrogated, the search engine, and the parameters of the search. Particular to top-down proteomics-there are four molecular levels of study: proteoform spectral match (PrSM), protein, isoform, and proteoform. Here, a context-dependent framework for calculating an accurate FDR at each level was designed, implemented, and validated against a manually curated training set with 546 confirmed proteoforms. We examined several search contexts and found that an FDR calculated at the PrSM level under-reported the true FDR at the protein level by an average of 24-fold. We present a new open-source tool, the TDCD_FDR_Calculator, which provides a scalable, context-dependent FDR calculation that can be applied post-search to enhance the quality of results in top-down proteomics from any search engine.

A new estimation of protein-level false discovery rate

BackgroundIn mass spectrometry-based proteomics, protein identification is an essential task. Evaluating the statistical significance of the protein identification result is critical to the success of proteomics studies. Controlling the false discovery rate (FDR) is the most common method for assuring the overall quality of the set of identifications. Existing FDR estimation methods either rely on specific assumptions or rely on the two-stage calculation process of first estimating the error rates at the peptide-level, and then combining them somehow at the protein-level. We propose to estimate the FDR in a non-parametric way with less assumptions and to avoid the two-stage calculation process.ResultsWe propose a new protein-level FDR estimation framework. The framework contains two major components: the Permutation+BH (Benjamini–Hochberg) FDR estimation method and the logistic regression-based null inference method. In Permutation+BH, the null distribution of a sample is generated by searching data against a large number of permuted random protein database and therefore does not rely on specific assumptions. Then, p-values of proteins are calculated from the null distribution and the BH procedure is applied to the p-values to achieve the relationship of the FDR and the number of protein identifications. The Permutation+BH method generates the null distribution by the permutation method, which is inefficient for online identification. The logistic regression model is proposed to infer the null distribution of a new sample based on existing null distributions obtained from the Permutation+BH method.ConclusionsIn our experiment based on three public available datasets, our Permutation+BH method achieves consistently better performance than MAYU, which is chosen as the benchmark FDR calculation method for this study. The null distribution inference result shows that the logistic regression model achieves a reasonable result both in the shape of the null distribution and the corresponding FDR estimation result.

Target-Decoy-Based False Discovery Rate Estimation for Large-Scale Metabolite Identification.

Metabolite identification is a crucial step in mass spectrometry (MS)-based metabolomics. However, it is still challenging to assess the confidence of assigned metabolites. We report a novel method for estimating the false discovery rate (FDR) of metabolite assignment with a target-decoy strategy, in which the decoys are generated through violating the octet rule of chemistry by adding small odd numbers of hydrogen atoms. The target-decoy strategy was integrated into JUMPm, an automated metabolite identification pipeline for large-scale MS analysis and was also evaluated with two other metabolomics tools, mzMatch and MZmine 2. The reliability of FDR calculation was examined by false data sets, which were simulated by altering MS1 or MS2 spectra. Finally, we used the JUMPm pipeline coupled to the target-decoy strategy to process unlabeled and stable-isotope-labeled metabolomic data sets. The results demonstrate that the target-decoy strategy is a simple and effective method for evaluating the confidence of high-throughput metabolite identification.

Automatic identification of site-specific glycosylation in proteomics using mass spectrometry and bioinformatics

Protein glycosylation, one of the most prevalent posttranslational modifi cations in proteins, plays important roles in biological systems via various processes, such as adhesion, signaling through cellular recognition, and response to abnormal biological states. However, due to the complexity and heterogeneity of a glycoprotein, current analyses focus mainly on the identification of either glycosites or the released glycans only. In this study, we have developed MS-based high throughput method for intact N-glycopeptides analysis, named GlycoProteomeAnalyzer (GPA) for analysis of N-and O-glycosylation in proteomics, which combines tandem Mass Spectrometry (MS) with a database search and algorithmic suite. We created novel scoring algorithms for confi dent identifi cation of N- and O-glycosylation of proteins with calculation of False Discovery Rate (FDR). In our approach, all amino acid sequence as well as glycosylation site information were obtained from the Uniprot database. From the Swiss-Prot accession number of human protein, our GPA program automatically construct tryptic N- and O-glycopeptide database for the proteins in human plasma sample. It allows automatic identifi cation of site-specifi c N- and O-glycopeptides of protein mixtures using HCD, CID, and ETD MS/MS spectra with GPA-DB from Uniprot with estimated FDR ≤ 1%. GPA has been designed to easily handle high-throughput glycoproteomic data with a graphical user interface and demonstrated on website (https://www.igpa.kr/). It can also be integrated with cloud computing service that eliminates the need for local clusters and increases throughput of data analysis

Improvements in Mass Spectrometry Assay Library Generation for Targeted Proteomics.

In data-independent acquisition mass spectrometry (DIA-MS), targeted extraction of peptide signals in silico using mass spectrometry assay libraries is a successful method for the identification and quantification of proteins. However, it remains unclear if high quality assay libraries with more accurate peptide ion coordinates can improve peptide target identification rates in DIA analysis. In this study, we systematically improved and evaluated the common algorithmic steps for assay library generation and demonstrate that increased assay quality results in substantially higher identification rates of peptide targets from mouse organ protein lysates measured by DIA-MS. The introduced changes are (1) a new spectrum interpretation algorithm, (2) reapplication of segmented retention time normalization, (3) a ppm fragment mass error matching threshold, (4) usage of internal peptide fragments, and (5) a multilevel false discovery rate calculation. Taken together, these changes yielded 14-36% more identified peptide targets at 1% assay false discovery rate and are implemented in three new open source tools, Fraggle, Tramler, and Franklin, available at https://github.com/fickludd/eviltools . The improved algorithms provide ways to better utilize discovery MS data, translating to substantially increased DIA performance and ultimately better foundations for drawing biological conclusions in DIA-based experiments.

A review of methods for interpretation of glycopeptide tandem mass spectral data.

Despite the publication of several software tools for analysis of glycopeptide tandem mass spectra, there remains a lack of consensus regarding the most effective and appropriate methods. In part, this reflects problems with applying standard methods for proteomics database searching and false discovery rate calculation. While the analysis of small post-translational modifications (PTMs) may be regarded as an extension of proteomics database searching, glycosylation requires specialized approaches. This is because glycans are large and heterogeneous by nature, causing glycopeptides to exist as multiple glycosylated variants. Thus, the mass of the peptide cannot be calculated directly from that of the intact glycopeptide. In addition, the chemical nature of the glycan strongly influences product ion patterns observed for glycopeptides. As a result, glycopeptidomics requires specialized bioinformatics methods. We summarize the recent progress towards a consensus for effective glycopeptide tandem mass spectrometric analysis.

Abstract 4582: Enrichment of colorectal cancer and inflammatory bowel disease risk variants in colon expression quantitative trait loci in African Americans

Abstract Background: Genome wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with diseases of the colon including colorectal cancer (CRC) and inflammatory bowel diseases (IBD). However, the functional role of many of these SNPs is largely unknown. Expression quantitative trait loci (eQTL) mapping is a tool to identify target genes of disease-associated SNPs. We have comprehensively assessed eQTLs in the human colon and tested for enrichment of GWAS-associated variants of colon diseases. We also characterized the genetic and functional properties of colonic eQTLs. Methods: Distal colonic biopsies were obtained from 48 healthy African American males and total RNA was extracted. Genomic DNA was obtained from the same patients and genotyped using the Axiom Pan-African array. In total, 8.4 million genotyped and imputed SNPs were included. RNA expression levels were estimated using the Illumina HT-12v4 Expression Beadchip. eQTLs were identified using Matrix eQTL. False discovery rate calculations were performed according to the Benjamini and Hochberg method. Significance thresholds for other associations were determined using permutation-based methods. Results: 1941 significant cis-eQTLs, corresponding to 122 independent signals, were identified at a false discovery rate of 0.01. There was substantial overlap between cis-eQTLs found in liver and ileum and less overlap in lymphoblastoid cells, brain, and skin fibroblasts. Overall, there was enrichment for GWAS variants associated with CRC and IBD, as well as 2 metabolic traits–Type 2 diabetes and body mass index–among colon cis-eQTLs. The CRC-associated eQTL rs3802842 was significantly associated with the expression of C11orf93 (COLCA2). The CRC-associated SNP rs1862748 is associated with 2 cis-eQTLs that are associated with expression differences in the NFATC3 gene, which regulates beta-catenin activation in response to WNT signaling. We also identified UBA7, INPP5E, ERAP2, SFMBT1, NXPE1, REXO2 as target genes for IBD-associated variants. There was significant enrichment of colonic cis-eQTLs near the transcription start sites (TSSs), for active histone marks and for SNPs with high population differentiation. Conclusion: Through this comprehensive study of eQTLs in the human colon, we have identified novel and known target genes for IBD- and CRC-associated genetic variants. The cis-eQTLs are useful in identification of the causal functional variation underlying IBD and CRC associations. The eQTL catalog is now expanded to include the human colon and the functional characteristics of colonic eQTLs. Note: This abstract was not presented at the meeting. Citation Format: Sonia S. Kupfer, Imge Hulur, Eric Gamazon, Andrew Skol, Xavier Llor, Kenan Onel, Nathan A. Ellis. Enrichment of colorectal cancer and inflammatory bowel disease risk variants in colon expression quantitative trait loci in African Americans. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4582. doi:10.1158/1538-7445.AM2015-4582

Computational and Mass-Spectrometry-Based Workflow for the Discovery and Validation of Missing Human Proteins: Application to Chromosomes 2 and 14.

In the framework of the C-HPP, our Franco-Swiss consortium has adopted chromosomes 2 and 14, coding for a total of 382 missing proteins (proteins for which evidence is lacking at protein level). Over the last 4 years, the French proteomics infrastructure has collected high-quality data sets from 40 human samples, including a series of rarely studied cell lines, tissue types, and sample preparations. Here we described a step-by-step strategy based on the use of bioinformatics screening and subsequent mass spectrometry (MS)-based validation to identify what were up to now missing proteins in these data sets. Screening database search results (85,326 dat files) identified 58 of the missing proteins (36 on chromosome 2 and 22 on chromosome 14) by 83 unique peptides following the latest release of neXtProt (2014-09-19). PSMs corresponding to these peptides were thoroughly examined by applying two different MS-based criteria: peptide-level false discovery rate calculation and expert PSM quality assessment. Synthetic peptides were then produced and used to generate reference MS/MS spectra. A spectral similarity score was then calculated for each pair of reference-endogenous spectra and used as a third criterion for missing protein validation. Finally, LC-SRM assays were developed to target proteotypic peptides from four of the missing proteins detected in tissue/cell samples, which were still available and for which sample preparation could be reproduced. These LC-SRM assays unambiguously detected the endogenous unique peptide for three of the proteins. For two of these, identification was confirmed by additional proteotypic peptides. We concluded that of the initial set of 58 proteins detected by the bioinformatics screen, the consecutive MS-based validation criteria led to propose the identification of 13 of these proteins (8 on chromosome 2 and 5 on chromosome 14) that passed at least two of the three MS-based criteria. Thus, a rigorous step-by-step approach combining bioinformatics screening and MS-based validation assays is particularly suitable to obtain protein-level evidence for proteins previously considered as missing. All MS/MS data have been deposited in ProteomeXchange under identifier PXD002131.

Tools (Viewer, Library and Validator) that Facilitate Use of the Peptide and Protein Identification Standard Format, Termed mzIdentML

The Proteomics Standards Initiative has recently released the mzIdentML data standard for representing peptide and protein identification results, for example, created by a search engine. When a new standard format is produced, it is important that software tools are available that make it straightforward for laboratory scientists to use it routinely and for bioinformaticians to embed support in their own tools. Here we report the release of several open-source Java-based software packages based on mzIdentML: ProteoIDViewer, mzidLibrary, and mzidValidator. The ProteoIDViewer is a desktop application allowing users to visualize mzIdentML-formatted results originating from any appropriate identification software; it supports visualization of all the features of the mzIdentML format. The mzidLibrary is a software library containing routines for importing data from external search engines, post-processing identification data (such as false discovery rate calculations), combining results from multiple search engines, performing protein inference, setting identification thresholds, and exporting results from mzIdentML to plain text files. The mzidValidator is able to process files and report warnings or errors if files are not correctly formatted or contain some semantic error. We anticipate that these developments will simplify adoption of the new standard in proteomics laboratories and the integration of mzIdentML into other software tools. All three tools are freely available in the public domain.

Discussion: Why "An estimate of the science-wise false discovery rate and application to the top medical literature" is false

Jager and Leek have tried to estimate a false-discovery rate (FDR) in abstracts of articles published in five medical journals during 2000-2010. Their approach is flawed in sampling, calculations, and conclusions. It uses a tiny portion of select papers in highly select journals. Randomized controlled trials and systematic reviews (designs with the lowest anticipated false-positive rates) are 52% of the analyzed papers, while these designs account for only 4% in PubMed in the same period. The FDR calculations consider the entire published literature as equivalent to a single genomic experiment where all performed analyses are reported without selection or distortion. However, the data used are the P-values reported in the abstracts of published papers; these P-values are a highly distorted, highly select sample. Besides selective reporting biases, all other biases, in particular confounding in observational studies, are also ignored, while these are often the main drivers for high false-positive rates in the biomedical literature. A reproducibility check of the raw data shows that much of the data Jager and Leek used are either wrong or make no sense: most of the usable data were missed by their script, 94% of the abstracts that reported ≥2 P-values had high correlation/overlap between reported outcomes, and only a minority of P-values corresponded to relevant primary outcomes. The Jager and Leek paper exemplifies the dreadful combination of using automated scripts with wrong methods and unreliable data. Sadly, this combination is common in the medical literature.

Identifying differentially expressed proteins in two-dimensional electrophoresis experiments: inputs from transcriptomics statistical tools

Two-dimensional electrophoresis is a crucial method in proteomics that allows the characterization of proteins' function and expression. This usually implies the identification of proteins that are differentially expressed between two contrasting conditions, for example, healthy versus diseased in human proteomics biomarker discovery and stressful conditions versus control in animal experimentation. The statistical procedures that lead to such identifications are critical steps in the 2-DE analysis workflow. They include a normalization step and a test and probability correction for multiple testing. Statistical issues caused by the high dimensionality of the data and large-scale multiple testing have been a more active topic in transcriptomics than proteomics, especially in microarray analysis. We thus propose to adapt innovative statistical tools developed for microarray analysis and incorporate them in the 2-DE analysis pipeline. In this article, we evaluate the performance of different normalization procedures, different statistical tests and false discovery rate calculation methods with both real and simulated datasets. We demonstrate that the use of statistical procedures adapted from microarrays lead to notable increase in power as well as a minimization of false-positive discovery rate. More specifically, we obtained the best results in terms of reliability and sensibility when using the 'moderate t-test' from Smyth in association with classic false discovery rate from Benjamini and Hochberg. The methods discussed are freely available in the 'prot2D' open source R-package from Bioconductor (http://www.bioconductor.org//) under the terms of the GNU General Public License (version 2 or later). sebastien.artigaud@univ-brest.fr or sebastien.artigaud@gmx.com.

Serum protein profiles predict coronary artery disease in symptomatic patients referred for coronary angiography.

BackgroundMore than a million diagnostic cardiac catheterizations are performed annually in the US for evaluation of coronary artery anatomy and the presence of atherosclerosis. Nearly half of these patients have no significant coronary lesions or do not require mechanical or surgical revascularization. Consequently, the ability to rule out clinically significant coronary artery disease (CAD) using low cost, low risk tests of serum biomarkers in even a small percentage of patients with normal coronary arteries could be highly beneficial.MethodsSerum from 359 symptomatic subjects referred for catheterization was interrogated for proteins involved in atherogenesis, atherosclerosis, and plaque vulnerability. Coronary angiography classified 150 patients without flow-limiting CAD who did not require percutaneous intervention (PCI) while 209 required coronary revascularization (stents, angioplasty, or coronary artery bypass graft surgery). Continuous variables were compared across the two patient groups for each analyte including calculation of false discovery rate (FDR ≤ 1%) and Q value (P value for statistical significance adjusted to ≤ 0.01).ResultsSignificant differences were detected in circulating proteins from patients requiring revascularization including increased apolipoprotein B100 (APO-B100), C-reactive protein (CRP), fibrinogen, vascular cell adhesion molecule 1 (VCAM-1), myeloperoxidase (MPO), resistin, osteopontin, interleukin (IL)-1β, IL-6, IL-10 and N-terminal fragment protein precursor brain natriuretic peptide (NT-pBNP) and decreased apolipoprotein A1 (APO-A1). Biomarker classification signatures comprising up to 5 analytes were identified using a tunable scoring function trained against 239 samples and validated with 120 additional samples. A total of 14 overlapping signatures classified patients without significant coronary disease (38% to 59% specificity) while maintaining 95% sensitivity for patients requiring revascularization. Osteopontin (14 times) and resistin (10 times) were most frequently represented among these diagnostic signatures. The most efficacious protein signature in validation studies comprised osteopontin (OPN), resistin, matrix metalloproteinase 7 (MMP7) and interferon γ (IFNγ) as a four-marker panel while the addition of either CRP or adiponectin (ACRP-30) yielded comparable results in five protein signatures.ConclusionsProteins in the serum of CAD patients predominantly reflected (1) a positive acute phase, inflammatory response and (2) alterations in lipid metabolism, transport, peroxidation and accumulation. There were surprisingly few indicators of growth factor activation or extracellular matrix remodeling in the serum of CAD patients except for elevated OPN. These data suggest that many symptomatic patients without significant CAD could be identified by a targeted multiplex serum protein test without cardiac catheterization thereby eliminating exposure to ionizing radiation and decreasing the economic burden of angiographic testing for these patients.

Learning from Decoys to Improve the Sensitivity and Specificity of Proteomics Database Search Results

The statistical validation of database search results is a complex issue in bottom-up proteomics. The correct and incorrect peptide spectrum match (PSM) scores overlap significantly, making an accurate assessment of true peptide matches challenging. Since the complete separation between the true and false hits is practically never achieved, there is need for better methods and rescoring algorithms to improve upon the primary database search results. Here we describe the calibration and False Discovery Rate (FDR) estimation of database search scores through a dynamic FDR calculation method, FlexiFDR, which increases both the sensitivity and specificity of search results. Modelling a simple linear regression on the decoy hits for different charge states, the method maximized the number of true positives and reduced the number of false negatives in several standard datasets of varying complexity (18-mix, 49-mix, 200-mix) and few complex datasets (E. coli and Yeast) obtained from a wide variety of MS platforms. The net positive gain for correct spectral and peptide identifications was up to 14.81% and 6.2% respectively. The approach is applicable to different search methodologies- separate as well as concatenated database search, high mass accuracy, and semi-tryptic and modification searches. FlexiFDR was also applied to Mascot results and showed better performance than before. We have shown that appropriate threshold learnt from decoys, can be very effective in improving the database search results. FlexiFDR adapts itself to different instruments, data types and MS platforms. It learns from the decoy hits and sets a flexible threshold that automatically aligns itself to the underlying variables of data quality and size.

Framework for dissection of complex cytonuclear epistasis by a two-dimensional genome scan

Epistasis between cytoplasmic and nuclear genes is the primary genetic component of complex quantitative traits. Genetic dissection of cytonuclear epistasis is fundamentally important to understand the genetic architecture of complex traits. In this study, a two-dimensional genome scan strategy was employed to evaluate the contribution of cytoplasm, quantitative trait loci (QTL), QTL×QTL interactions and QTL×QTL×cytoplasm interactions to the phenotypic variation. The p-value and parameter value for each genetic effect were calculated by multiple regression analysis. A stepwise approach was suggested to build confidence in candidate QTL on the basis of q-value estimation, false discovery rate calculation and Bonferroni adjustment. A fine-scale grid scan strategy was proposed for further analysis of peaks of interest. Plant height in maize was used as an example to illustrate the efficiency of the two-dimensional genome scan strategy.

Quantitative Proteomics Analysis of Chondrogenic Differentiation of C3H10T1/2 Mesenchymal Stem Cells by iTRAQ Labeling Coupled with On-line Two-dimensional LC/MS/MS

The chondrogenic potential of multipotent mesenchymal stem cells (MSCs) makes them a promising source for cell-based therapy of cartilage defects; however, the exact intracellular molecular mechanisms of chondrogenesis as well as self-renewal of MSCs remain largely unknown. To gain more insight into the underlying molecular mechanisms, we applied isobaric tag for relative and absolute quantitation (iTRAQ) labeling coupled with on-line two-dimensional LC/MS/MS technology to identify proteins differentially expressed in an in vitro model for chondrogenesis: chondrogenic differentiation of C3H10T1/2 cells, a murine embryonic mesenchymal cell line, was induced by micromass culture and 100 ng/ml bone morphogenetic protein 2 treatment for 6 days. A total of 1756 proteins were identified with an average false discovery rate <0.21%. Linear regression analysis of the quantitative data gave strong correlation coefficients: 0.948 and 0.923 for two replicate two-dimensional LC/MS/MS analyses and 0.881, 0.869, and 0.927 for three independent iTRAQ experiments, respectively (p < 0.0001). Among 1753 quantified proteins, 100 were significantly altered (95% confidence interval), and six of them were further validated by Western blotting. Functional categorization revealed that the 17 up-regulated proteins mainly comprised hallmarks of mature chondrocytes and enzymes participating in cartilage extracellular matrix synthesis, whereas the 83 down-regulated were predominantly involved in energy metabolism, chromatin organization, transcription, mRNA processing, signaling transduction, and cytoskeleton; except for a number of well documented proteins, the majority of these altered proteins were novel for chondrogenesis. Finally, the biological roles of BTF3l4 and fibulin-5, two novel chondrogenesis-related proteins identified in the present study, were verified in the context of chondrogenic differentiation. These data will provide valuable clues for our better understanding of the underlying mechanisms that modulate these complex biological processes and assist in the application of MSCs in cell-based therapy for cartilage regeneration.