A decision theory paradigm for evaluating identifier mapping and filtering methods using data integration.

BACKGROUND: In bioinformatics, we pre-process raw data into a format ready for answering medical and biological questions. A key step in processing is labeling the measured features with the identities of the molecules purportedly assayed: "molecular identification" (MI). Biological meaning comes from identifying these molecular measurements correctly with actual molecular species. But MI can be incorrect. Identifier filtering (IDF) selects features with more trusted MI, leaving a smaller, but more correct dataset. Identifier mapping (IDM) is needed when an analyst is combining two high-throughput (HT) measurement platforms on the same samples. IDM produces ID pairs, one ID from each platform, where the mapping declares that the two analytes are associated through a causal path, direct or indirect (example: pairing an ID for an mRNA species with an ID for a protein species that is its putative translation). Many competing solutions for IDF and IDM exist. Analysts need a rigorous method for evaluating and comparing all these choices. RESULTS: We describe a paradigm for critically evaluating and comparing IDF and IDM methods, guided by data on biological samples. The requirements are: a large set of biological samples, measurements on those samples from at least two high-throughput platforms, a model family connecting features from the platforms, and an association measure. From these ingredients, one fits a mixture model coupled to a decision framework. We demonstrate this evaluation paradigm in three settings: comparing performance of several bioinformatics resources for IDM between transcripts and proteins, comparing several published microarray probeset IDF methods and their combinations, and selecting optimal quality thresholds for tandem mass spectrometry spectral events. CONCLUSIONS: The paradigm outlined here provides a data-grounded approach for evaluating the quality not just of IDM and IDF, but of any pre-processing step or pipeline. The results will help researchers to semantically integrate or filter data optimally, and help bioinformatics database curators to track changes in quality over time and even to troubleshoot causes of MI errors.

Identifier mapping performance for integrating transcriptomics and proteomics experimental results.

BACKGROUND: Studies integrating transcriptomic data with proteomic data can illuminate the proteome more clearly than either separately. Integromic studies can deepen understanding of the dynamic complex regulatory relationship between the transcriptome and the proteome. Integrating these data dictates a reliable mapping between the identifier nomenclature resultant from the two high-throughput platforms. However, this kind of analysis is well known to be hampered by lack of standardization of identifier nomenclature among proteins, genes, and microarray probe sets. Therefore data integration may also play a role in critiquing the fallible gene identifications that both platforms emit. RESULTS: We compared three freely available internet-based identifier mapping resources for mapping UniProt accessions (ACCs) to Affymetrix probesets identifications (IDs): DAVID, EnVision, and NetAffx. Liquid chromatography-tandem mass spectrometry analyses of 91 endometrial cancer and 7 noncancer samples generated 11,879 distinct ACCs. For each ACC, we compared the retrieval sets of probeset IDs from each mapping resource. We confirmed a high level of discrepancy among the mapping resources. On the same samples, mRNA expression was available. Therefore, to evaluate the quality of each ACC-to-probeset match, we calculated proteome-transcriptome correlations, and compared the resources presuming that better mapping of identifiers should generate a higher proportion of mapped pairs with strong inter-platform correlations. A mixture model for the correlations fitted well and supported regression analysis, providing a window into the performance of the mapping resources. The resources have added and dropped matches over two years, but their overall performance has not changed. CONCLUSIONS: The methods presented here serve to achieve concrete context-specific insight, to support well-informed decisions in choosing an ID mapping strategy for "omic" data merging.

Gender differences in stimulated cytokine production following acute psychological stress.

Emerging evidence suggests that acute psychological stress modulates inflammatory competence; however, not all findings are consistent. Gender is one factor that may impact magnitude of response. To explore this possibility, we examined the effects of acute mental stress on lipopolysaccharide-induced production of pro-inflammatory cytokines interleukin (IL)-1beta, IL-6, and tumor necrosis factor (TNF)-alpha among a relatively healthy sample of midlife men (n=28) and women (n=34). Blood samples for the assessment of cytokine production were drawn before, immediately after, and 30min following subjects' performance of an evaluative speech task. Relative to baseline evaluations, the speech stressor elicited a significant increase in stimulated production of all 3 pro-inflammatory cytokines, as measured 30min following the end of the task. There were no gender differences in the magnitude of this effect. However, men showed a significant decrease in cytokine production from before to immediately following the stressor, whereas women showed no change across this period. Menopausal status partially accounted for these gender differences, with post-menopausal women displaying greater increases in IL-6 and TNF-alpha production from baseline-to-post-task when compared to men. These data provide further evidence that acute psychological stress primes the immune system to mount larger inflammatory responses and initial support for gender differences in the patterning of stress-related cytokine activity. In addition, this study presents novel evidence that post-menopausal women may be particularly susceptible to stress-related inflammatory responses. The possibility that this contributes to the increased risk of inflammatory disease observed among older women warrants investigation.

Impact of TGFbeta1 gene polymorphisms on acute and chronic rejection in pediatric heart transplant allografts.

BACKGROUND: The aim of this study was to assess the influence of IL-10 and TGFbeta1 gene polymorphisms on the development of acute rejection and coronary disease in pediatric heart transplant recipients. METHODS: Patients were classified as either Rejectors or Nonrejectors. Coronary artery disease (CAD) was diagnosed by angiography or on macroscopic examination. Genotyping PCR-SSP were performed for IL-10 and TGFbeta1 (codon 10 and 25) in 111 patients. Thirty-nine were Rejectors and 31 developed CAD. RESULTS: The proportion of IL-10 low-producers was higher in Rejectors than in Nonrejectors (respectively 46% versus 22%, P=0.009). IL-10 gene polymorphism was not associated with CAD. TGFbeta1-codon10-25 high-producers were 92.3% in Rejectors and 75% in Nonrejectors (P=0.026), 93.5% in patients with CAD and 76.2% in patients free from CAD (P=0.037). TGFbeta1-codon25 high-production separately analyzed correlated with CAD (31/31 high-producers in CAD=100% versus 69/80 in noCAD patients=86.2%, P=0.03). TGFbeta1-codon10 gene polymorphisms were not associated with CAD. CONCLUSION: IL-10 low-producers have an increased risk of acute rejection. High-expressors of TGFbeta1-codon10-25 have an increased risk of acute rejection and CAD, while TGFbeta1-codon25 high-production is associated with coronary disease. Genetic polymorphism may reveal patients at high-risk in whom therapies and monitoring should be adjusted.

Disparate distribution of 16 candidate single nucleotide polymorphisms among racial and ethnic groups of pediatric heart transplant patients.

BACKGROUND: Allograft failure in African-Americans remains higher than in Caucasians. Single nucleotide polymorphisms (SNPs) have been associated with altered allograft outcomes. METHODS: In this multi-center study we compared SNP frequencies in 364 pediatric heart recipients from three ethnic/racial groups: Caucasian (n = 243), African-American (n = 39), and Hispanic (n = 82). The target genes were: tumor necrosis factor-alpha, interleukin (IL)-10, IL-6, interferon (IFN)-gamma, vascular endothelial growth factor (VEGF), transforming growth factor-beta1, Fas, FasL, granzyme B, ABCB1, CYP3A5. RESULTS: Compared to Caucasians, African-Americans exhibited a higher prevalence of genotypes associated with low expression of IFN-gamma (24% vs. 45.7%, P < 0.001) and IL-10 (33% vs. 57.1%, P = 0.052). African-Americans also exhibited an increased prevalence of high IL-6 (82.9% vs. 38.1%; P < 0.001). VEGF -2578 C/C and -460 C/C genotypes were found more frequently in African-Americans and Hispanics as compared to Caucasians (P < 0.001). G/G genotype of Fas and T/T genotype of FasL were expressed more often by African-American recipients. The prevalence of Granzyme B (-295A/G) genotype was differentially distributed in the three groups. Compared with Caucasians, African-Americans were twice as likely to carry the ABCB1 2677 G/G genotype (78.6% vs. 33.7%, P < 0.0025), and they were more frequent carriers of the CYP3A5 *1/*1 genotype (35.7% vs. 0.6% in Caucasians and 7.2% in Hispanics; P < 0.001). CONCLUSION: African-Americans have a genetic background that may predispose to proinflammatory/lower regulatory environment, reduced drug exposure and immunosuppressive efficacy. In this ongoing multicenter study, these gene polymorphisms differences among ethnic/racial groups are being documented so that therapeutic strategies can be devised to optimize outcomes for pediatric transplant recipients.

Impact of TGFbeta1 gene polymorphisms on late renal function in pediatric heart transplantation.

Late renal dysfunction may affect long-term outcome of nonrenal transplant recipients. We hypothesized that transforming growth factor beta1 (TGFbeta1) might play a role in the fibrogenic mechanisms leading to renal dysfunction. The aim was to determine whether TGFbeta1 gene polymorphisms are associated with renal outcome in pediatric heart recipients. Eighty-eight patients underwent a first heart transplantation at the age of 7.1 +/- 6.5 years, received tacrolimus-based immunosuppression, and were followed for > or =1 year (6.7 +/- 3.2 years). Creatinine clearance (CrCl; ml/mn/1.73 m2) was calculated (Schwartz) before transplant, then at 1 month, 6 months, and 1 year, and yearly up to 7 years. Impaired function was defined as CrCl <80 ml/mn/1.73 m2. Mean CrCl decreased from 120 +/- 53 ml/mn/1.73 m2 before transplant to 98 +/- 40, 96 +/- 37, 102 +/- 30, and 101 +/- 38 ml/mn/1.73 m2 at, respectively, 6 months and 1, 5 (n = 58), and 7 years (n = 33). The TGFbeta1 high-producer genotype had worse CrCl than intermediate and low producers at every time point, despite similar pretransplant CrCl (pretransplant = 120 +/- 53 vs 118 +/- 55 ml/mn/1.73 m2 [p = 0.8], 1 year = 92 +/- 38 vs 113 +/- 30 ml/mn/1.73 m2 [p = 0.03]) and similar tacrolimus levels. The TGFbeta1 high-producer genotype was associated with CrCl < 80 ml/mn/1.73 m2. The TGFbeta1 high-producer genotype is associated with renal dysfunction in pediatric heart recipients.

Improving Cancer Gene Expression Data Quality through a TCGA Data-Driven Evaluation of Identifier Filtering.

Data quality is a recognized problem for high-throughput genomics platforms, as evinced by the proliferation of methods attempting to filter out lower quality data points. Different filtering methods lead to discordant results, raising the question, which methods are best? Astonishingly, little computational support is offered to analysts to decide which filtering methods are optimal for the research question at hand. To evaluate them, we begin with a pair of expression data sets, transcriptomic and proteomic, on the same samples. The pair of data sets form a test-bed for the evaluation. Identifier mapping between the data sets creates a collection of feature pairs, with correlations calculated for each pair. To evaluate a filtering strategy, we estimate posterior probabilities for the correctness of probesets accepted by the method. An analyst can set expected utilities that represent the trade-off between the quality and quantity of accepted features. We tested nine published probeset filtering methods and combination strategies. We used two test-beds from cancer studies providing transcriptomic and proteomic data. For reasonable utility settings, the Jetset filtering method was optimal for probeset filtering on both test-beds, even though both assay platforms were different. Further intersection with a second filtering method was indicated on one test-bed but not the other.