The abundance of homoeologue transcripts is disrupted by hybridization and is partially restored by genome doubling in synthetic hexaploid wheat.

BACKGROUND: The formation of an allopolyploid is a two step process, comprising an initial wide hybridization event, which is later followed by a whole genome doubling. Both processes can affect the transcription of homoeologues. Here, RNA-Seq was used to obtain the genome-wide leaf transcriptome of two independent Triticum turgidum × Aegilops tauschii allotriploids (F1), along with their spontaneous allohexaploids (S1) and their parental lines. The resulting sequence data were then used to characterize variation in homoeologue transcript abundance. RESULTS: The hybridization event strongly down-regulated D-subgenome homoeologues, but this effect was in many cases reversed by whole genome doubling. The suppression of D-subgenome homoeologue transcription resulted in a marked frequency of parental transcription level dominance, especially with respect to genes encoding proteins involved in photosynthesis. Singletons (genes where no homoeologues were present) were frequently transcribed at both the allotriploid and allohexaploid plants. CONCLUSIONS: The implication is that whole genome doubling helps to overcome the phenotypic weakness of the allotriploid, restoring a more favourable gene dosage in genes experiencing transcription level dominance in hexaploid wheat.

A relative ordering-based predictor for tamoxifen-treated estrogen receptor-positive breast cancer patients: multi-laboratory cohort validation.

Current predictors for estrogen receptor-positive (ER-positive) breast cancer patients receiving tamoxifen are often invalid in inter-laboratory validation. We aim to develop a robust predictor based on the relative ordering of expression measurement (ROE) in gene pairs. Using a large integrated dataset of 420 normal controls and 1,129 ER-positive breast tumor samples, we identified the gene pairs with stable ROEs in normal control and significantly reversed ROEs in ER-positive tumor. Using these gene pairs, we characterized each sample of a cohort of 292 ER-positive patients who received tamoxifen monotherapy for 5 years and then identified relapse risk-associated gene pairs. We extracted a gene pair subset that resulted in the largest positive and negative predictive values for predicting 10-year relapse-free survival (RFS) using a genetic algorithm. A predictor was developed based on the gene pair subset and was validated in 2 large multi-laboratory cohorts (N = 250 and 248, respectively) of ER-positive patients who received 5-year tamoxifen alone. In the first validation cohort, the patients predicted to be tamoxifen sensitive had a 10-year RFS of 91 % (95 % confidence interval [CI] 85-97 %) with an absolute risk reduction of 34 % (95 % CI 17-51 %). The patients predicted to be tamoxifen insensitive had a significantly higher relapse risk than the patients predicted to be tamoxifen sensitive (hazard ratio = 4.99, 95 % CI 2.45-10.17, P = 9.13 × 10(-7)). Similar performance was achieved for the second validation cohort. The predictor performed well in both node-negative and node-positive subsets and added significant predictive power to the clinical parameters. In contrast, 2 previously proposed predictors did not achieve significantly better performances than the baselines of the validation cohorts. In summary, the proposed predictor can accurately and robustly predict tamoxifen sensitivity of ER-positive breast cancer patients and identified patients with a high probability of 10-year RFS following tamoxifen monotherapy.

Construction of high-density genetic linkage map and identification of flowering-time QTLs in orchardgrass using SSRs and SLAF-seq.

Orchardgrass (Dactylis glomerata L.) is one of the most economically important perennial, cool-season forage species grown and pastured worldwide. High-density genetic linkage mapping is a valuable and effective method for exploring complex quantitative traits. In this study, we developed 447,177 markers based on SLAF-seq and used them to perform a comparative genomics analysis. Perennial ryegrass sequences were the most similar (5.02%) to orchardgrass sequences. A high-density linkage map of orchardgrass was constructed using 2,467 SLAF markers and 43 SSRs, which were distributed on seven linkage groups spanning 715.77 cM. The average distance between adjacent markers was 0.37 cM. Based on phenotyping in four environments, 11 potentially significant quantitative trait loci (QTLs) for two target traits-heading date (HD) and flowering time (FT)-were identified and positioned on linkage groups LG1, LG3, and LG5. Significant QTLs explained 8.20-27.00% of the total phenotypic variation, with the LOD ranging from 3.85-12.21. Marker167780 and Marker139469 were associated with FT and HD at the same location (Ya'an) over two different years. The utility of SLAF markers for rapid generation of genetic maps and QTL analysis has been demonstrated for heading date and flowering time in a global forage grass.

Similar source of differential blood mRNAs in lung cancer and pulmonary inflammatory diseases: calls for improved strategy for identifying cancer-specific biomarkers.

BACKGROUND: Many studies try to identify cancer diagnostic biomarkers by comparing peripheral whole blood (PWB) of cancer samples and healthy controls, explicitly or implicitly assuming that such biomarkers are potential candidate biomarkers for distinguishing cancer from nonmalignant inflammation-associated diseases. METHODS: Multiple PWB gene expression profiles for lung cancer/inflammation-associated pulmonary diseases were used for differential mRNAs identification and comparison and for proportion estimation of PWB cell subtypes. RESULTS: The differentially expressed genes (DE genes) between lung cancer/inflammation-associated pulmonary patients and healthy controls were reproducibly identified in different datasets. For these DE genes observed in lung cancer/inflammation-associated pulmonary diseases, more than 90.2% were differentially expressed between myeloid cells and lymphoid cells, with at least 96.8% having consistent directions of regulation (up- or down-regulations) in myeloid cells compared to lymphoid cells, explainable by the shifted populations of PWB cell subtypes under the disease conditions. The comparison of DE genes for lung cancer and inflammation-associated pulmonary diseases showed that the overlapping genes were 100% consistent in the sense of direction of regulation. CONCLUSIONS: The differential blood mRNAs observed in lung cancer and in inflammation-associated pulmonary diseases were similar, both mainly reflecting the difference between myeloid cells and lymphoid cells predominantly determined by PWB cell population shifts. Thus, the strategy of comparing cancer with healthy controls may provide little information of the ability of the identified candidate biomarkers in discriminating cancer from inflammation-associated pulmonary diseases.

Genes dysregulated to different extent or oppositely in estrogen receptor-positive and estrogen receptor-negative breast cancers.

BACKGROUND: Directly comparing gene expression profiles of estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancers cannot determine whether differentially expressed genes between these two subtypes result from dysregulated expression in ER+ cancer or ER- cancer versus normal controls, and thus would miss critical information for elucidating the transcriptomic difference between the two subtypes. PRINCIPAL FINDINGS: Using microarray datasets from TCGA, we classified the genes dysregulated in both ER+ and ER- cancers versus normal controls into two classes: (i) genes dysregulated in the same direction but to a different extent, and (ii) genes dysregulated to opposite directions, and then validated the two classes in RNA-sequencing datasets of independent cohorts. We showed that the genes dysregulated to a larger extent in ER+ cancers than in ER- cancers enriched in glycerophospholipid and polysaccharide metabolic processes, while the genes dysregulated to a larger extent in ER- cancers than in ER+ cancers enriched in cell proliferation. Phosphorylase kinase and enzymes of glycosylphosphatidylinositol (GPI) anchor biosynthesis were upregulated to a larger extent in ER+ cancers than in ER- cancers, whereas glycogen synthase and phospholipase A2 were downregulated to a larger extent in ER+ cancers than in ER- cancers. We also found that the genes oppositely dysregulated in the two subtypes significantly enriched with known cancer genes and tended to closely collaborate with the cancer genes. Furthermore, we showed the possibility that these oppositely dysregulated genes could contribute to carcinogenesis of ER+ and ER- cancers through rewiring different subpathways. CONCLUSIONS: GPI-anchor biosynthesis and glycogenolysis were elevated and hydrolysis of phospholipids was depleted to a larger extent in ER+ cancers than in ER- cancers. Our findings indicate that the genes oppositely dysregulated in the two subtypes are potential cancer genes which could contribute to carcinogenesis of both ER+ and ER- cancers through rewiring different subpathways.

An integrated approach to uncover driver genes in breast cancer methylation genomes.

BACKGROUND: Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play "driver" roles in tumorigenesis, whereas others are only "passengers". RESULTS: Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer. CONCLUSION: The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy.