Plant hormones regulate growth and responses to environmental change. Hormone action ultimately modifies cellular physiological processes and gene activity. To facilitate transcriptome evaluation of novel mutants and environmental responses, there is a need to rapidly assess the possible contribution of hormone action to changes in the levels of gene transcripts. We developed a vector-based algorithm that rapidly compares lists of transcripts yielding correlation values. The application as described here, called HORMONOMETER, was used to analyze hormone-related activity in a transcriptome of Arabidopsis (Arabidopsis thaliana). The veracity of the resultant analysis was established by comparison with cognate and noncognate hormone transcriptomes as well as with mutants and selected plant-environment interactions. The HORMONOMETER accurately predicted correlations between hormone action and biosynthetic mutants for which transcriptome data are available. A high degree of correlation was detected between many hormones, particularly at early time points of hormone action. Unforeseen complexity was detected in the analysis of mutants and in plant-herbivore interactions. The HORMONOMETER provides a diagnostic tool for evaluating the physiological state of being of the plant from the point of view of transcripts regulated by hormones and yields biological insight into the multiple response components that enable plant adaptation to the environment. A Web-based interface has been developed to facilitate external interfacing with this platform.

This report uses WES to uncover rare genetic variants in AMD. A null-variant in HMCN1 has been identified in one AMD family, and a missense variant in CFI was discovered in two other families. These variants confirm the genetic complexity and significance of rare genetic variants in the pathogenesis of AMD.

Introduction: Aging is characterized by the progressive loss of physiological capacity. Changes in gene expression can alter activity in defined age-related molecular pathways leading to cellular aging and increased aging disease susceptibility. The aim of the current study was to evaluate whether hyperbaric oxygen therapy (HBOT) affects gene expression in normal, non-pathological, aging adults. Methods: Thirty-five healthy independently living adults, aged 64 and older, were enrolled to receive 60 daily HBOT exposures. Whole blood samples were collected at baseline, at the 30th and 60th HBOT session, and 1–2 weeks following the last session. Differential gene expression analysis was performed. Results: Following 60 sessions of HBOT, 1342 genes and 570 genes were differently up- and downregulated (1912 total), respectively ( p < 0.01 FDR), compared to baseline. Out of which, five genes were downregulated with a >1.5-fold change: ABCA13 (FC = −2.28), DNAJ6 (FC = −2.16), HBG2 (FC = −1.56), PDXDC1 (FC = −1.53), RANBP17 (FC = −1.75). Two weeks post-HBOT, ABCA13 expression was significantly downregulated with a >1.5fold change (FC = −1.54, p = 0.008). In conclusion, for the first time in humans, the study provides direct evidence of HBOT is associated with transcriptome changes in whole-blood samples. Our results demonstrate significant changes in gene expression of normal aging population.

Objective:Circulating tumor DNA is a promising noninvasive tool for cancer monitoring. One of the challenges in applying this tool is the detection of low-frequency mutations. The detection limit of these mutations varies between different molecular methods. The aim of this study is to characterize the factors affecting the limit of detection for epidermal growth factor receptor p.T790M mutation in circulating tumor DNA of patients with lung adenocarcinoma.Methods:DNA was extracted from plasma samples of 102 patients. For sequencing the DNA, we used 2 different next-generation sequencing–based platforms: Ion Torrent Personal Genome Machine (56 cases) and Roche/454 (46 cases). Serially diluted synthetic DNA samples carrying the p.T790M mutation were sequenced using the Ion Torrent Personal Genome Machine for validation. Limit of detection was determined through the analysis of non-hot-spot nonreference reads, which were regarded as sequencing artifacts.Results:The frequency of the non-hot-spot nonreference reads was higher in Ion Torrent Personal Genome Machine compared to Roche/454 (0.07% ± 0.08% and 0.03% ± 0.06%, respectively, P < .001). We found that different base type substitutions occur with different frequency. Since the base substitution leading to p.T790M mutation is C>T transition, its frequency was used to determine the limit of detection for the assay. Based on the C>T non-hot-spot nonreference allele frequency, we found that the limit of detection is 0.18% in Ion Torrent Personal Genome Machine and 0.1% in Roche/454. Based on these values, 48% and 56% of the cases were positive for T790M mutation in Ion Torrent Personal Genome Machine and Roche/454 groups, respectively. Agreement between duplicates was 76% in Ion Torrent Personal Genome Machine and 72% in Roche/454. Using serially diluted synthetic DNA samples carrying the p.T790M mutation, we could identify mutations with allele frequency of 0.18% or more using the Ion Torrent Personal Genome Machine, supporting our approach to determine the detection limit.Conclusion:Both the sequencing platform and the specific nucleotide change affect the limit of detection and should therefore be determined in the validation process of new assays.