SNP discovery and chromosome anchoring provide the first physically-anchored hexaploid oat map and reveal synteny with model species.

A physically anchored consensus map is foundational to modern genomics research; however, construction of such a map in oat (Avena sativa L., 2n = 6x = 42) has been hindered by the size and complexity of the genome, the scarcity of robust molecular markers, and the lack of aneuploid stocks. Resources developed in this study include a modified SNP discovery method for complex genomes, a diverse set of oat SNP markers, and a novel chromosome-deficient SNP anchoring strategy. These resources were applied to build the first complete, physically-anchored consensus map of hexaploid oat. Approximately 11,000 high-confidence in silico SNPs were discovered based on nine million inter-varietal sequence reads of genomic and cDNA origin. GoldenGate genotyping of 3,072 SNP assays yielded 1,311 robust markers, of which 985 were mapped in 390 recombinant-inbred lines from six bi-parental mapping populations ranging in size from 49 to 97 progeny. The consensus map included 985 SNPs and 68 previously-published markers, resolving 21 linkage groups with a total map distance of 1,838.8 cM. Consensus linkage groups were assigned to 21 chromosomes using SNP deletion analysis of chromosome-deficient monosomic hybrid stocks. Alignments with sequenced genomes of rice and Brachypodium provide evidence for extensive conservation of genomic regions, and renewed encouragement for orthology-based genomic discovery in this important hexaploid species. These results also provide a framework for high-resolution genetic analysis in oat, and a model for marker development and map construction in other species with complex genomes and limited resources.

Consistency of a two clinical site sample collection: a proteomics study.

PURPOSE: We investigated the ability to perform a clinical proteomic study using samples collected at different times from two independent clinical sites. EXPERIMENTAL DESIGN: Label-free 2-D-LC-MS proteomic analysis was used to differentially quantify tens of thousands of peptides from human plasma. We have asked whether samples collected from two sites, when analyzed by this type of peptide profiling, reproducibly contain detectable peptide markers that are differentially expressed in the plasma of disease (advanced renal cancer) patients relative to healthy normals. RESULTS: We have demonstrated that plasma proteins enriched in disease patients are indeed detected reproducibly in both clinical collections. Regression analysis, unsupervised hierarchical clustering and PCA detected no systematic bias in the data related to site of sample collection and processing. Using a genetic algorithm, support vector machine classification method, we were able to correctly classify disease samples at 88% sensitivity and 94% specificity using the second site as an independent validation set. CONCLUSIONS AND CLINICAL RELEVANCE: We conclude that multiple site collection, when analyzed by label-free 2-D-LC-MS, generates data that are sufficiently reproducible to guide reliable biomarker discovery.

Confirming human antibody responses to a therapeutic monoclonal antibody using a statistical approach.

Confirmatory assays for immunogenicity testing typically involve testing a sample in the presence or absence of excess drug. A decrease in assay signal in the presence of drug is taken to indicate the presence of anti-drug antibodies (ADAb) and the sample is confirmed positive. While there is widespread acceptance of the principle, there are currently no published guidelines for determining how much the signal should be reduced for a sample to be confirmed positive. In this report we address this issue using a novel approach employing a Student's t-test. The basic premise is to assess if an observed decrease in signal in the presence of drug is greater than what might be expected to occur as a result of the normal variation in the system. A key component of the method involves being able to capture and measure all of the normal variation. This requires a modification of commonly employed methods of sample preparation. We validated the method and tested samples from a clinical study. In addition, we reanalyzed the data to see what would have been the outcome had we used two other common approaches for confirmatory assays, one based on a minimum percent decrease in signal to confirm positivity (arbitrarily set at 25%), and one requiring a minimum drop in signal, set by a low quality control (QC) sample. The t-test approach proved superior over a wide range of assay signals and appeared to result in fewer false negatives.

Activation in dorsolateral prefrontal cortex in response to maternal criticism and praise in recovered depressed and healthy control participants.

BACKGROUND: High family levels of expressed emotion reliably predict relapse in patients with schizophrenia and mood disorders; however, the neural mechanisms linking expressed emotion and relapse are unexplored. Dysfunctional activity in the dorsolateral prefrontal cortex (DLPFC) has been implicated in the pathophysiology of depression. Functional magnetic resonance imaging (fMRI) was used to assess focal activation changes in DLPFC in response to a novel psychosocial challenge stimulus developed from the expressed emotion construct. METHODS: Healthy control subjects and fully remitted unipolar depressed participants completed blood oxygen level-dependent fMRI while they heard their own mothers making critical and praising comments about them. RESULTS: Relative to control subjects, participants with a history of depression failed to activate DLPFC when they heard critical remarks. There were no differences between the two groups in their DLPFC responses to maternal praise. CONCLUSIONS: Even if fully well at the time of testing, participants with a known vulnerability to depression respond differently to the psychosocial challenge of being criticized. These findings might have implications for our understanding of vulnerability to depression and to depressive relapse.

Survival analysis of genome-wide gene expression profiles of prostate cancers identifies new prognostic targets of disease relapse.

Current models of prostate cancer classification are poor at distinguishing between tumors that have similar histopathological features but vary in clinical course and outcome. Here, we applied classical survival analysis to genome-wide gene expression profiles of prostate cancers and preoperative prostate-specific antigen (PSA) levels from each patient, to identify prognostic markers of disease relapse that provide additional predictive value relative to PSA concentration. Three of approximately 200 probesets showing strongest correlation with relapse were identified as the gene for the putative calcium channel protein, trp-p8, with loss of trp-p8 mRNA expression associated with a significantly shorter time to PSA relapse-free survival. We observed subsequently that trp-p8 is lost in the transition to androgen independence in a prostate cancer xenograft model and in prostate cancer tissue from patients treated preoperatively with antiandrogen therapy, suggesting that trp-p8 is androgen regulated, and its loss may be associated with more advanced disease. The identification of trp-p8 and other proteins implicated in the phosphatidylinositol signal transduction pathway that are associated with prostate cancer outcome, both here and in other published work, suggests an integral role for this pathway in prostate carcinogenesis. Thus, our findings demonstrate that multivariable survival analysis can be applied to gene expression profiles of prostate cancers with censored follow-up data and used to identify molecular markers of prostate cancer relapse with strong predictive power and relevance to the etiology of this disease.