Silicon nanoparticles confer hypoxia tolerance in citrus rootstocks by modulating antioxidant activities and carbohydrate metabolism.

Citrus is a remarkable fruit crop, extremely sensitive to flooding conditions, which frequently trigger hypoxia stress and cause severe damage to citrus plants. Silicon nanoparticles (SiNPs) are beneficial and have the potential to overcome this problem. Therefore, the present study aimed to investigate the effect of silicon nanoparticles to overcome hypoxia stress through modulating antioxidant enzyme activity and carbohydrate metabolism. Three citrus rootstocks (Carrizo citrange, Roubidoux, and Rich 16-6) were exposed to flooding (with and without oxygen) through different SiNP treatments via foliar and root zone. SiNPs applied treatment plants showed a significant increase in photosynthesis, leaf greenness, antioxidant enzymes, and carbohydrate metabolic activities, besides the higher accumulation of proline and glycine betaine. The rate of lipid peroxidation was drastically higher in flooded plants; however, SiNPs application reduced it significantly, ultimately reducing oxidative damage. Overall, Rich16-6 rootstock showed good performance via root zone application compared to other rootstocks, possibly due to genotypical variation in silicon uptake. Our outcomes demonstrate that SiNPs significantly affect plant growth during hypoxia stress conditions, and their use is an optimal strategy to overcome this issue. This study laid the foundation for future research to use at the commercial level to overcome hypoxia stress and a potential platform for future research.

CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer.

MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising diagnostic and prognostic biomarkers for human cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive biomarkers for early cancer detection. Novel and user-friendly tools are desperately needed to facilitate data mining of the vast amount of miRNA expression data from The Cancer Genome Atlas (TCGA) and large-scale circulating miRNA profiling studies. To fill this void, we developed CancerMIRNome, a comprehensive database for the interactive analysis and visualization of miRNA expression profiles based on 10 554 samples from 33 TCGA projects and 28 633 samples from 40 public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and machine learning algorithms have been packaged in CancerMIRNome, allowing for the pan-cancer analysis of a miRNA of interest across multiple cancer types and the comprehensive analysis of miRNome profiles to identify dysregulated miRNAs and develop diagnostic or prognostic signatures. The data analysis and visualization modules will greatly facilitate the exploit of the valuable resources and promote translational application of miRNA biomarkers in cancer. The CancerMIRNome database is publicly available at http://bioinfo.jialab-ucr.org/CancerMIRNome.

Boosting predictabilities of agronomic traits in rice using bivariate genomic selection.

The multivariate genomic selection (GS) models have not been adequately studied and their potential remains unclear. In this study, we developed a highly efficient bivariate (2D) GS method and demonstrated its significant advantages over the univariate (1D) rival methods using a rice dataset, where four traditional traits (i.e. yield, 1000-grain weight, grain number and tiller number) as well as 1000 metabolomic traits were analyzed. The novelty of the method is the incorporation of the HAT methodology in the 2D BLUP GS model such that the computational efficiency has been dramatically increased by avoiding the conventional cross-validation. The results indicated that (1) the 2D BLUP-HAT GS analysis generally produces higher predictabilities for two traits than those achieved by the analysis of individual traits using 1D GS model, and (2) selected metabolites may be utilized as ancillary traits in the new 2D BLUP-HAT GS method to further boost the predictability of traditional traits, especially for agronomically important traits with low 1D predictabilities.

Extended application of genomic selection to screen multiomics data for prognostic signatures of prostate cancer.

Prognostic tests using expression profiles of several dozen genes help provide treatment choices for prostate cancer (PCa). However, these tests require improvement to meet the clinical need for resolving overtreatment, which continues to be a pervasive problem in PCa management. Genomic selection (GS) methodology, which utilizes whole-genome markers to predict agronomic traits, was adopted in this study for PCa prognosis. We leveraged The Cancer Genome Atlas (TCGA) database to evaluate the prediction performance of six GS methods and seven omics data combinations, which showed that the Best Linear Unbiased Prediction (BLUP) model outperformed the other methods regarding predictability and computational efficiency. Leveraging the BLUP-HAT method, an accelerated version of BLUP, we demonstrated that using expression data of a large number of disease-relevant genes and with an integration of other omics data (i.e. miRNAs) significantly increased outcome predictability when compared with panels consisting of a small number of genes. Finally, we developed a novel stepwise forward selection BLUP-HAT method to facilitate searching multiomics data for predictor variables with prognostic potential. The new method was applied to the TCGA data to derive mRNA and miRNA expression signatures for predicting relapse-free survival of PCa, which were validated in six independent cohorts. This is a transdisciplinary adoption of the highly efficient BLUP-HAT method and its derived algorithms to analyze multiomics data for PCa prognosis. The results demonstrated the efficacy and robustness of the new methodology in developing prognostic models in PCa, suggesting a potential utility in managing other types of cancer.

Inference of Chromosome-Length Haplotypes Using Genomic Data of Three or a Few More Single Gametes.

Compared with genomic data of individual markers, haplotype data provide higher resolution for DNA variants, advancing our knowledge in genetics and evolution. Although many computational and experimental phasing methods have been developed for analyzing diploid genomes, it remains challenging to reconstruct chromosome-scale haplotypes at low cost, which constrains the utility of this valuable genetic resource. Gamete cells, the natural packaging of haploid complements, are ideal materials for phasing entire chromosomes because the majority of the haplotypic allele combinations has been preserved. Therefore, compared with the current diploid-based phasing methods, using haploid genomic data of single gametes may substantially reduce the complexity in inferring the donor's chromosomal haplotypes. In this study, we developed the first easy-to-use R package, Hapi, for inferring chromosome-length haplotypes of individual diploid genomes with only a few gametes. Hapi outperformed other phasing methods when analyzing both simulated and real single gamete cell sequencing data sets. The results also suggested that chromosome-scale haplotypes may be inferred by using as few as three gametes, which has pushed the boundary to its possible limit. The single gamete cell sequencing technology allied with the cost-effective Hapi method will make large-scale haplotype-based genetic studies feasible and affordable, promoting the use of haplotype data in a wide range of research.

Offsetting Expression Profiles of Prognostic Markers in Prostate Tumor vs. Its Microenvironment.

Diagnosis of the presence of tumors and subsequent prognosis based on tumor microenvironment becomes more clinically practical because tumor-adjacent tissues are easy to collect and they are more genetically homogeneous. The purpose of this study was to identify new prognostic markers in prostate stroma that are near the tumor. We have demonstrated the prognostic features of FGFR1, FRS2, S6K1, LDHB, MYPT1, and P-LDHA in prostate tumors using tissue microarrays (TMAs) which consist of 241 patient samples from Massachusetts General Hospital (MGH). In this study, we investigated these six markers in the tumor microenvironment using an Aperio Imagescope system in the same TMAs. The joint prognostic power of markers was further evaluated and classified using a new algorithm named Weighted Dichotomizing. The classifier was verified via rigorous 10-fold cross validation. Statistical analysis of the protein expression indicated that in tumor-adjacent stroma FGFR1 and MYPT1 were significantly correlated with patient outcomes and LDHB showed the outcome-association tendency. More interestingly, these correlations were completely opposite regarding tumor tissue as previously reported. The results suggest that prognostic testing should utilize either tumor-enriched tissue or stroma with distinct signature profiles rather than using mixture of both tissue types. The new classifier based on stroma tissue has potential value in the clinical management of prostate cancer patients.

Identification of optimal prediction models using multi-omic data for selecting hybrid rice.

Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting breeding values for agronomically important traits. In this study, the best prediction strategies were determined for yield, 1000 grain weight, number of grains per panicle, and number of tillers per plant of hybrid rice (derived from recombinant inbred lines) by comprehensively evaluating all possible combinations of omic datasets with different prediction methods. It was demonstrated that, in rice, the predictions using a combination of genomic and metabolomic data generally produce better results than single-omics predictions or predictions based on other combined omic data. Best linear unbiased prediction (BLUP) appears to be the most efficient prediction method compared to the other commonly used approaches, including least absolute shrinkage and selection operator (LASSO), stochastic search variable selection (SSVS), support vector machines with radial basis function and epsilon regression (SVM-R(EPS)), support vector machines with radial basis function and nu regression (SVM-R(NU)), support vector machines with polynomial kernel and epsilon regression (SVM-P(EPS)), support vector machines with polynomial kernel and nu regression (SVM-P(NU)) and partial least squares regression (PLS). This study has provided guidelines for selection of hybrid rice in terms of which types of omic datasets and which method should be used to achieve higher trait predictability. The answer to these questions will benefit academic research and will also greatly reduce the operative cost for the industry which specializes in breeding and selection.

Quantification of punicalagins in commercial preparations and pomegranate cultivars, by liquid chromatography-mass spectrometry.

BACKGROUND: Pomegranate (Punica granatum L.) - a delicious fruit once used in Ayurvedic medicine - is now largely known for the antioxidant properties of its juice, which has also been considered to have health benefits against diseases such as cancer and cardiovascular diseases. These beneficial effects are associated with the fruit's high content of polyphenolic compounds. High demand and lower production levels drive pomegranate prices up, which leads to the possibility of pomegranate products being adulterated, diluted or substituted. To ensure the presence of pomegranate in various preparations labeled as containing pomegranate, a simple method was developed to screen and quantify the specific punicalagins by mass spectrometry. RESULTS: The present method was used to analyze several pure and mixed beverages from the US market, and also to quantify punicalagins in the juice of 14 pomegranate cultivars. Punicalagins were detected in all cultivars, with higher concentrations in whole fruit juices compared with aril juices. Amongst the 20 commercial beverages, punicalagins were not detected in four preparations. CONCLUSION: The liquid chromatographic-mass spectrometric method presented herein enables an easy and rapid quantification of the specific punicalagins. The latter was detected in all cultivar samples, thus supporting that punicalagin is a suitable marker of these 14 pomegranate cultivars in commercial juices. Absence of the specific marker in four commercial preparations shows the necessity of having simple and rapid methods to evaluate the presence of pomegranate in preparations. © 2019 Society of Chemical Industry.

Effects of Site and Cultivar on Consumer Acceptance of Pomegranate.

Pomegranate (Punica granatum L.) is an important fruit in many cultures. The fruit and juice have risen in popularity as it was discovered that pomegranate has relatively high antioxidant activity compared to most other fruits. In this study, six cultivars were utilized to determine consumer acceptance compared to the industry standard, 'Wonderful,' which comprises 90% to 95% of commercial production in the United States. Fruit were sourced from 2 cultivar field trials, one in inland Riverside, California, and one in coastal Ventura County, California. Cultivars selected for the study included 'Eversweet,' 'Green Globe,' 'Haku Botan,' 'Loffani,' 'Phoenicia,' 'Wonderful,' and 'cv. 857,' an heirloom cultivar from Ventura County, CA, U.S.A. Pomegranate arils were subject to sensory evaluation by 87 untrained consumer panelists in late 2016. Panelists were given pomegranate arils and asked to score the samples using a 9-point Hedonic scale for the following fruit quality traits: aril color, sweetness, tartness, seed hardness, bitterness, and overall desirability. There were significant differences among cultivars for all traits assessed by the sensory panelists. There were differences in acceptance among consumers for 'Wonderful' depending on if it was grown on the coast versus inland, and consumers preferred inland- versus coastal-grown 'Wonderful.' 'Wonderful' pomegranate was associated with cultivars that consumers scored low on desirability for bitterness. Cultivars that scored well in overall desirability compared with 'Wonderful' were 'cv. 857,' 'Eversweet,' 'Green Globe,' and 'Phoenicia.' PRACTICAL APPLICATION: Consumer sensory panels are important to determine scientifically which cultivars are desired by the public. These panels allowed for the determination of which pomegranate cultivars are liked or disliked by consumers and why. If the pomegranate growers know the most desirable cultivars for consumers, they are more likely to adopt and plant them, thus potentially increasing the diversity in the marketplace, as has been with apples, peaches, plums, pears, mangoes, strawberries, raspberries, blueberries, and citrus.