Growth conditions trigger genotype-specific metabolic responses that affect the nutritional quality of kale cultivars.

Kales (Brassica oleracea convar acephala) are fast-growing, nutritious leafy vegetables ideal for year-round indoor farming. However, selection of best cultivars for growth under artificial lighting necessitates a deeper understanding of leaf metabolism in different kale types. Here we examined a curly leaved cultivar Half Tall and a lacinato type cultivar Black Magic under moderate growth light (130 µmol photons m-1s-1/22°C) and high light (800 µmol photons m-1s-1/26°C) conditions. These conditions induced genotype-dependent differences in nutritionally important metabolites, especially anthocyanins and glucosinolates (GSLs), in the kale cultivars. In the pale green Half Tall, growth under high light conditions did not induce changes in either pigmentation or total GSL content. In contrast, the purple pigmentation of Black Magic intensified due to increased anthocyanin accumulation. Black Magic showed reduced amounts of indole GSLs and increased amounts of aliphatic GSLs under high light conditions, with notable cultivar-specific adjustments in individual GSL species. Correlation analysis of metabolite profiles suggested cultivar-specific metabolic interplay between serine biosynthesis and the production of indole GSLs. RNA sequencing identified candidate genes encoding metabolic enzymes and regulatory components behind anthocyanin and GSL biosynthesis. These findings improve the understanding of leaf metabolism and its effects on the nutritional quality of kale cultivars.

Simultaneous Ozone and High Light Treatments Reveal an Important Role for the Chloroplast in Co-ordination of Defense Signaling.

Plants live in a world of changing environments, where they are continuously challenged by alternating biotic and abiotic stresses. To transfer information from the environment to appropriate protective responses, plants use many different signaling molecules and pathways. Reactive oxygen species (ROS) are critical signaling molecules in the regulation of plant stress responses, both inside and between cells. In natural environments, plants can experience multiple stresses simultaneously. Laboratory studies on stress interaction and crosstalk at regulation of gene expression, imply that plant responses to multiple stresses are distinctly different from single treatments. We analyzed the expression of selected marker genes and reassessed publicly available datasets to find signaling pathways regulated by ozone, which produces apoplastic ROS, and high light treatment, which produces chloroplastic ROS. Genes related to cell death regulation were differentially regulated by ozone versus high light. In a combined ozone + high light treatment, the light treatment enhanced ozone-induced cell death in leaves. The distinct responses from ozone versus high light treatments show that plants can activate stress signaling pathways in a highly precise manner.

Reproducibility-optimized detection of differential DNA methylation.

Aim: DNA methylation is a key epigenetic mechanism regulating gene expression. Identifying differentially methylated regions is integral to DNA methylation analysis and there is a need for robust tools reliably detecting regions with significant differences in their methylation status. Materials & methods: We present here a reproducibility-optimized test statistic (ROTS) for detection of differential DNA methylation from high-throughput sequencing or array-based data. Results: Using both simulated and real data, we demonstrate the ability of ROTS to identify differential methylation between sample groups. Conclusion: Compared with state-of-the-art methods, ROTS shows competitive sensitivity and specificity in detecting consistently differentially methylated regions.

Hypoxia-inducible factor (HIF)-prolyl hydroxylase 3 (PHD3) maintains high HIF2A mRNA levels in clear cell renal cell carcinoma.

Most clear cell renal cell carcinomas (ccRCCs) have inactivation of the von Hippel-Lindau tumor suppressor protein (pVHL), resulting in the accumulation of hypoxia-inducible factor α-subunits (HIF-α) and their downstream targets. HIF-2α expression is particularly high in ccRCC and is associated with increased ccRCC growth and aggressiveness. In the canonical HIF signaling pathway, HIF-prolyl hydroxylase 3 (PHD3) suppresses HIF-2α protein by post-translational hydroxylation under sufficient oxygen availability. Here, using immunoblotting and immunofluorescence staining, qRT-PCR, and siRNA-mediated gene silencing, we show that unlike in the canonical pathway, PHD3 silencing in ccRCC cells leads to down-regulation of HIF-2α protein and mRNA. Depletion of other PHD family members had no effect on HIF-2α expression, and PHD3 knockdown in non-RCC cells resulted in the expected increase in HIF-2α protein expression. Accordingly, PHD3 knockdown decreased HIF-2α target gene expression in ccRCC cells and expression was restored upon forced HIF-2α expression. The effect of PHD3 depletion was pinpointed to HIF2A mRNA stability. In line with these in vitro results, a strong positive correlation of PHD3 and HIF2A mRNA expression in ccRCC tumors was detected. Our results suggest that in contrast to the known negative regulation of HIF-2α in most cell types, high PHD3 expression in ccRCC cells maintains elevated HIF-2α expression and that of its target genes, which may enhance kidney cancer aggressiveness.

Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study.

BACKGROUND: Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. METHODS AND RESULTS: A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P<0.0001) and validation data (AUC=0.769 versus AUC=0.747, P=0.026). WGRS97 improved the accuracy in training (AUC=0.782 versus AUC=0.744, P<0.0001) but not in validation data (AUC=0.749 versus AUC=0.747, P=0.785). Higher WGRS19 associated with higher body mass index at 9 years and WGRS97 at 6 years. Replication in BHS confirmed our findings that WGRS19 and WGRS97 are associated with body mass index. CONCLUSIONS: WGRS19 improves prediction of adulthood obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity.

End-of-life chemotherapy use at a Finnish university hospital: a retrospective cohort study.

BACKGROUND: Recent trends in the end-of-life (EOL) cancer care have suggested that the levels of treatment are becoming more aggressive. The aim of this single-center study was to evaluate the time from the last intravenous (IV) chemotherapy treatment to death and identify factors correlating with treatment closer to death. MATERIAL AND METHODS: The study included all patients diagnosed with cancer at Turku University Central Hospital between the years 2005 and 2013 (N = 38,982) who received IV chemotherapy during the last year of life (N = 3285). The cohort of patients and their respective clinical information were identified from electronic medical records. Statistical analysis was performed to assess and compare the treatment strategies, taking into account the patient's age, the year they were treated, and the type of cancer they were diagnosed with. RESULTS: A total of 11,250 cancer patients died during the observation time and one-third (N = 3285, 29.2%) of them had received IV chemotherapy during the last year of life. The time from the last IV chemotherapy regimen to death remained consistent across the follow-up time. During the last month of life, every third patient under the age of 50 years and only one-tenth of patients over the age of 80 years received IV chemotherapy. Hematological malignancies and lymphomas were treated closer to death when compared to other diagnostic groups. CONCLUSIONS: During the period of 9 years, the pattern of EOL IV chemotherapy treatment remained stable. Every third patient died at tertiary care. Only 7.2% of patients who received IV chemotherapy during the last year of life were treated 14 days before death, which is in line with international recommendations. However, significant variation in EOL treatment strategies between different diagnosis and age groups were identified.

Assessing the utilization of radiotherapy near end of life at a Finnish University Hospital: a retrospective cohort study.

BACKGROUND: Palliative radiotherapy can improve quality of life for cancer patients during the last months of life. However, very short life expectancy may devastate the benefit of the treatment. This single center study assesses the utilization of radiotherapy during the last weeks of life. MATERIAL AND METHODS: All cancer patients (N = 38,982) treated with radiotherapy (N = 11,395) in Turku University Central Hospital during 2005-2013 were identified in the database consisting of electronic patient records. One fourth (N = 2904, 25.5%) of the radiotherapy treatments were given during the last year of life. The last radiotherapy treatments and the time from the last radiotherapy treatment to death were assessed in regards to patients' age, cancer diagnosis, domicile, place of death and the treatment year. Treatments given during the last two weeks of life were also assessed regarding the goal of treatment and the reason for possible discontinuation. RESULTS: The median time from the last fraction of radiotherapy to death was 84 d. During the last two weeks before death (N = 340), pain (29.4%) was the most common indication for radiotherapy. Treatment was discontinued in 40.6% of the patients during the last two weeks of life, and worsening of general condition was the most common reason for discontinuity (70.3%). The patients receiving radiotherapy during the last weeks of life were more likely to die in tertiary care unit. During the last year of life single-fraction treatment was used only in 7% of all therapy courses. There was a statistically significant (p < .05) decrease in the median number of fractions in the last radiotherapy treatment between 2005-2007 (8 fractions) and 2011-2013 (6 fractions). CONCLUSIONS: Up to 70% of the treatments during the last two weeks of life were not delivered to alleviate pain and utilization of single fraction radiotherapy during the last year of life was infrequent. These observations suggest that practice of radiotherapy during the last weeks of life should be revisited.

ROTS: An R package for reproducibility-optimized statistical testing.

Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).

Comparison of methods to detect differentially expressed genes between single-cell populations.

We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications.

A DREAM Challenge to Build Prediction Models for Short-Term Discontinuation of Docetaxel in Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge. PATIENTS AND METHODS: The comparator arms of four phase III clinical trials in first-line mCRPC were collected, annotated, and compiled, with a total of 2,070 patients. Early discontinuation was defined as treatment stoppage within 3 months as a result of adverse treatment effects; 10% of patients discontinued treatment. We designed an open-data, crowd-sourced DREAM Challenge for developing models with which to predict early discontinuation of docetaxel treatment. Clinical features for all four trials and outcomes for three of the four trials were made publicly available, with the outcomes of the fourth trial held back for unbiased model evaluation. Challenge participants from around the world trained models and submitted their predictions. Area under the precision-recall curve was the primary metric used for performance assessment. RESULTS: In total, 34 separate teams submitted predictions. Seven models with statistically similar area under precision-recall curves (Bayes factor ≤ 3) outperformed all other models. A postchallenge analysis of risk prediction using these seven models revealed three patient subgroups: high risk, low risk, or discordant risk. Early discontinuation events were two times higher in the high-risk subgroup compared with the low-risk subgroup. Simulation studies demonstrated that use of patient discontinuation prediction models could reduce patient enrollment in clinical trials without the loss of statistical power. CONCLUSION: This work represents a successful collaboration between 34 international teams that leveraged open clinical trial data. Our results demonstrate that routinely collected clinical features can be used to identify patients with mCRPC who are likely to discontinue treatment because of adverse events and establishes a robust benchmark with implications for clinical trial design.

Longitudinal modeling of ultrasensitive and traditional prostate-specific antigen and prediction of biochemical recurrence after radical prostatectomy.

Ultrasensitive prostate-specific antigen (u-PSA) remains controversial for follow-up after radical prostatectomy (RP). The aim of this study was to model PSA doubling times (PSADT) for predicting biochemical recurrence (BCR) and to capture possible discrepancies between u-PSA and traditional PSA (t-PSA) by utilizing advanced statistical modeling. 555 RP patients without neoadjuvant/adjuvant androgen deprivation from the Turku University Hospital were included in the study. BCR was defined as two consecutive PSA values >0.2 ng/mL and the PSA measurements were log2-transformed. One third of the data was reserved for independent validation. Models were first fitted to the post-surgery PSA measurements using cross-validation. Major trends were then captured using linear mixed-effect models and a predictive generalized linear model effectively identified early trends connected to BCR. The model generalized for BCR prediction to the validation set with ROC-AUC of 83.6% and 95.1% for the 1 and 3 year follow-up censoring, respectively. A web-based tool was developed to facilitate its use. Longitudinal trends of u-PSA did not display major discrepancies from those of t-PSA. The results support that u-PSA provides useful information for predicting BCR after RP. This can be beneficial to avoid unnecessary adjuvant treatments or to start them earlier for selected patients.

ROTS: reproducible RNA-seq biomarker detector-prognostic markers for clear cell renal cell cancer.

Recent comprehensive assessments of RNA-seq technology support its utility in quantifying gene expression in various samples. The next step of rigorously quantifying differences between sample groups, however, still lacks well-defined best practices. Although a number of advanced statistical methods have been developed, several studies demonstrate that their performance depends strongly on the data under analysis, which compromises practical utility in real biomedical studies. As a solution, we propose to use a data-adaptive procedure that selects an optimal statistic capable of maximizing reproducibility of detections. After demonstrating its improved sensitivity and specificity in a controlled spike-in study, the utility of the procedure is confirmed in a real biomedical study by identifying prognostic markers for clear cell renal cell carcinoma (ccRCC). In addition to identifying several genes previously associated with ccRCC prognosis, several potential new biomarkers among genes regulating cell growth, metabolism and solute transport were detected.

A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer.

Metastatic castration resistant prostate cancer (mCRPC) is one of the most common cancers with a poor prognosis. To improve prognostic models of mCRPC, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) Consortium organized a crowdsourced competition known as the Prostate Cancer DREAM Challenge. In the competition, data from four phase III clinical trials were utilized. A total of 1600 patients' clinical information across three of the trials was used to generate prognostic models, whereas one of the datasets (313 patients) was held out for blinded validation. The previously introduced prognostic model of overall survival of chemotherapy-naive mCRPC patients treated with docetaxel or prednisone (so called Halabi model) was used as a performance baseline. This paper presents the model developed by the team TYTDreamChallenge and its improved version to predict the prognosis of mCRPC patients within the first 30 months after starting the treatment based on available clinical features of each patient. In particular, by replacing our original larger set of eleven features with a smaller more carefully selected set of only five features the prediction performance on the independent validation cohort increased up to 5.4 percent. While the original TYTDreamChallenge model (iAUC=0.748) performed similarly as the performance-baseline model developed by Halabi et al. (iAUC=0.743), the improved post-challenge model (iAUC=0.779) showed markedly improved performance by using only PSA, ALP, AST, HB, and LESIONS as features. This highlights the importance of the selection of the clinical features when developing the predictive models.

Comparison of software packages for detecting differential expression in RNA-seq studies.

RNA-sequencing (RNA-seq) has rapidly become a popular tool to characterize transcriptomes. A fundamental research problem in many RNA-seq studies is the identification of reliable molecular markers that show differential expression between distinct sample groups. Together with the growing popularity of RNA-seq, a number of data analysis methods and pipelines have already been developed for this task. Currently, however, there is no clear consensus about the best practices yet, which makes the choice of an appropriate method a daunting task especially for a basic user without a strong statistical or computational background. To assist the choice, we perform here a systematic comparison of eight widely used software packages and pipelines for detecting differential expression between sample groups in a practical research setting and provide general guidelines for choosing a robust pipeline. In general, our results demonstrate how the data analysis tool utilized can markedly affect the outcome of the data analysis, highlighting the importance of this choice.