Metabolome, Plant Hormone, and Transcriptome Analyses Reveal the Mechanism of Spatial Accumulation Pattern of Anthocyanins in Peach Flesh.

Anthocyanins are important secondary metabolites in fruits, and anthocyanin accumulation in the flesh of peach exhibits a spatial pattern, but the relevant mechanism is still unknown. In this study, the yellow-fleshed peach, cv. 'Jinxiu', with anthocyanin accumulation in the mesocarp around the stone was used as the experimental material. Red flesh (RF) and yellow flesh (YF) were sampled separately for flavonoid metabolite (mainly anthocyanins), plant hormone, and transcriptome analyses. The results showed that the red coloration in the mesocarp was due to the accumulation of cyanidin-3-O-glucoside, with an up-regulation of anthocyanin biosynthetic genes (F3H, F3'H, DFR, and ANS), transportation gene GST, and regulatory genes (MYB10.1 and bHLH3). Eleven ERFs, nine WRKYs, and eight NACs were also defined as the candidate regulators of anthocyanin biosynthesis in peach via RNA-seq. Auxin, cytokinin, abscisic acid (ABA), salicylic acid (SA), and 1-aminocyclopropane-1-carboxylic acid (ACC, ethylene precursor) were enriched in the peach flesh, with auxin, cytokinin, ACC, and SA being highly accumulated in the RF, but ABA was mainly distributed in the YF. The activators and repressors in the auxin and cytokinin signaling transduction pathways were mostly up-regulated and down-regulated, respectively. Our results provide new insights into the regulation of spatial accumulation pattern of anthocyanins in peach flesh.

Postharvest light-induced flavonoids accumulation in mango (Mangifera indica L.) peel is associated with the up-regulation of flavonoids-related and light signal pathway genes.

Introduction: Flavonoids are important secondary metabolites in plants and light is a crucial environmental factor regulating flavonoids biosynthesis. However, effect of light on the different flavonoids compositions accumulation in mango and the relevant molecular mechanism still need to be clarified. Methods: In this study, green-mature fruits of red mango cultivar 'Zill' were subjected to postharvest light treatment, and fruit peel color, total soluble solids content, total organic acid, and firmness of flesh were measured. The flavonoids metabolites profile, and the expression of flavonoids-related genes and light signal pathway genes were also analyzed. Results: Results showed that light treatment promoted the red coloration of fruit peel and increased the total soluble solids content and firmness of flesh. The concentration of flavonols, proanthocyanidins and anthocyanins, and expression of key flavonoids biosynthetic genes including MiF3H, MiFLS, MiLAR, MiANS, MiUFGT1, and MiUFGT3 were significantly induced by light. The MYBs regulating flavonols and proanthocyanidins, i.e. MiMYB22 and MiMYB12, as well as the key light signal pathway transcription factors (TFs) MiHY5 and MiHYH, were identified in mango. The transcription of MiMYB1, MiMYB12, MiMYB22, MiHY5 and MiHYH was up-regulated by light. Discussion: Our results provide a postharvest technology to improve mango fruit appearance quality, and are helpful to reveal the molecular mechanism of light-induced flavonoids biosynthesis in mango.

Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso.

In clinical trials, identification of prognostic and predictive biomarkers has became essential to precision medicine. Prognostic biomarkers can be useful for the prevention of the occurrence of the disease, and predictive biomarkers can be used to identify patients with potential benefit from the treatment. Previous researches were mainly focused on clinical characteristics, and the use of genomic data in such an area is hardly studied. A new method is required to simultaneously select prognostic and predictive biomarkers in high dimensional genomic data where biomarkers are highly correlated. We propose a novel approach called PPLasso, that integrates prognostic and predictive effects into one statistical model. PPLasso also takes into account the correlations between biomarkers that can alter the biomarker selection accuracy. Our method consists in transforming the design matrix to remove the correlations between the biomarkers before applying the generalized Lasso. In a comprehensive numerical evaluation, we show that PPLasso outperforms the traditional Lasso and other extensions on both prognostic and predictive biomarker identification in various scenarios. Finally, our method is applied to publicly available transcriptomic and proteomic data.

RNA-Seq reveals the key pathways and genes involved in the light-regulated flavonoids biosynthesis in mango (Mangifera indica L.) peel.

Introduction: Flavonoids are important water soluble secondary metabolites in plants, and light is one of the most essential environmental factors regulating flavonoids biosynthesis. In the previous study, we found bagging treatment significantly inhibited the accumulation of flavonols and anthocyanins but promoted the proanthocyanidins accumulation in the fruit peel of mango (Mangifera indica L.) cultivar 'Sensation', while the relevant molecular mechanism is still unknown. Methods: In this study, RNA-seq was conducted to identify the key pathways and genes involved in the light-regulated flavonoids biosynthesis in mango peel. Results: By weighted gene co-expression network analysis (WGCNA), 16 flavonoids biosynthetic genes were crucial for different flavonoids compositions biosynthesis under bagging treatment in mango. The higher expression level of LAR (mango026327) in bagged samples might be the reason why light inhibits proanthocyanidins accumulation in mango peel. The reported MYB positively regulating anthocyanins biosynthesis in mango, MiMYB1, has also been identified by WGCNA in this study. Apart from MYB and bHLH, ERF, WRKY and bZIP were the three most important transcription factors (TFs) involved in the light-regulated flavonoids biosynthesis in mango, with both activators and repressors. Surprisingly, two HY5 transcripts, which are usually induced by light, showed higher expression level in bagged samples. Discussion: Our results provide new insights of the regulatory effect of light on the flavonoids biosynthesis in mango fruit peel.

C1431T Variant of PPARγ Is Associated with Preeclampsia in Pregnant Women.

Peroxisome proliferator-activated receptor γ (PPARγ) is essential for placental development, whose SNPs have shown increased susceptibility to pregnancy-related diseases, such as preeclampsia. Our aim was to investigate the association between preeclampsia and three PPARγ SNPs (Pro12Ala, C1431T, and C681G), which together with nine clinical factors were used to build a pragmatic model for preeclampsia prediction. Data were collected from 1648 women from the EDEN cohort, of which 35 women had preeclamptic pregnancies, and the remaining 1613 women had normal pregnancies. Univariate analysis comparing preeclamptic patients to the control resulted in the SNP C1431T being the only factor significantly associated with preeclampsia (p < 0.05), with a confidence interval of 95% and odds ratio ranging from 4.90 to 8.75. On the other hand, three methods of multivariate feature selection highlighted seven features that could be potential predictors of preeclampsia: maternal C1431T and C681G variants, obesity, body mass index, number of pregnancies, primiparity, cigarette use, and education. These seven features were further used as input into eight different machine-learning algorithms to create predictive models, whose performances were evaluated based on metrics of accuracy and the area under the receiver operating characteristic curve (AUC). The boost tree-based model performed the best, with respective accuracy and AUC values of 0.971 ± 0.002 and 0.991 ± 0.001 in the training set and 0.951 and 0.701 in the testing set. A flowchart based on the boost tree model was constructed to depict the procedure for preeclampsia prediction. This final decision tree showed that the C1431T variant of PPARγ is significantly associated with susceptibility to preeclampsia. We believe that this final decision tree could be applied in the clinical prediction of preeclampsia in the very early stages of pregnancy.

Age and Sex-Related Changes in Human First-Trimester Placenta Transcriptome and Insights into Adaptative Responses to Increased Oxygen.

Physiological oxygen tension rises dramatically in the placenta between 8 and 14 weeks of gestation. Abnormalities in this period can lead to gestational diseases, whose underlying mechanisms remain unclear. We explored the changes at mRNA level by comparing the transcriptomes of human placentas at 8-10 gestational weeks and 12-14 gestational weeks. A total of 20 samples were collected and divided equally into four groups based on sex and age. Cytotrophoblasts were isolated and sequenced using RNAseq. Key genes were identified using two different methods: DESeq2 and weighted gene co-expression network analysis (WGCNA). We also constructed a local database of known targets of hypoxia-inducible factor (HIF) subunits, alpha and beta, to investigate expression patterns likely linked with changes in oxygen. Patterns of gene enrichment in and among the four groups were analyzed based on annotations of gene ontology (GO) and KEGG pathways. We characterized the similarities and differences between the enrichment patterns revealed by the two methods and the two conditions (age and sex), as well as those associated with HIF targets. Our results provide a broad perspective of the processes that are active in cytotrophoblasts during the rise in physiological oxygen, which should benefit efforts to discover possible drug-targeted genes or pathways in the human placenta.

A variable selection approach for highly correlated predictors in high-dimensional genomic data.

MOTIVATION: In genomic studies, identifying biomarkers associated with a variable of interest is a major concern in biomedical research. Regularized approaches are classically used to perform variable selection in high-dimensional linear models. However, these methods can fail in highly correlated settings. RESULTS: We propose a novel variable selection approach called WLasso, taking these correlations into account. It consists in rewriting the initial high-dimensional linear model to remove the correlation between the biomarkers (predictors) and in applying the generalized Lasso criterion. The performance of WLasso is assessed using synthetic data in several scenarios and compared with recent alternative approaches. The results show that when the biomarkers are highly correlated, WLasso outperforms the other approaches in sparse high-dimensional frameworks. The method is also illustrated on publicly available gene expression data in breast cancer. AVAILABILITYAND IMPLEMENTATION: Our method is implemented in the WLasso R package which is available from the Comprehensive R Archive Network (CRAN). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mining of combined human placental gene expression data across pregnancy, applied to PPAR signaling pathway.

INTRODUCTION: To date, we have only an incomplete understanding of how gene expression in the human placenta changes at the genome-wide scale from very early in gestation to term. Our aim was to investigate the dynamic changes in gene expression throughout placentation. METHODS: In our study, gene expression profiles were collected of human placentas from 4 to 40 gestational weeks of age. Simple linear regression and weighted correlation network analysis were applied to identify genes of interest. Analyses of gene enrichment, including gene ontology and pathways from the Kyoto Encyclopedia of Genes and Genomes, were performed using clusterProfiler. Finally, dynamic changes in the expression of individual genes were represented using line graphs of scaled and adjusted gene expression. RESULTS: Our results highlighted a total of 5173 genes that are involved in different periods of placentation. Downstream annotation of these genes revealed the biological processes and pathways involved, from which we chose to further investigate the PPAR signaling pathway. We were able to detect changes over time in many genes involved in lipid storage/metabolism, including members of the FABP family and LPL. These patterns were corroborated by lipid staining of placental sections, which revealed a significant decrease in lipid droplet content in placentas from early in the first trimester to term. CONCLUSION: Our study provides detailed information on the dynamics of biological processes and pathways across human placentation. These findings give us new clues for deciphering the normal functions of placentation and the ways in which the mis-regulation of these pathways may be linked to pregnancy-related diseases. As an example, our results show that the PPAR signaling pathway mediates a constant decrease in placental lipid content over the course of pregnancy.

Comparative Study of PPARγ Targets in Human Extravillous and Villous Cytotrophoblasts.

Trophoblasts, as the cells that make up the main part of the placenta, undergo cell differentiation processes such as invasion, migration, and fusion. Abnormalities in these processes can lead to a series of gestational diseases whose underlying mechanisms are still unclear. One protein that has proven to be essential in placentation is the peroxisome proliferator-activated receptor γ (PPARγ), which is expressed in the nuclei of extravillous cytotrophoblasts (EVCTs) in the first trimester and villous cytotrophoblasts (VCTs) throughout pregnancy. Here, we aimed to explore the genome-wide effects of PPARγ on EVCTs and VCTs via treatment with the PPARγ-agonist rosiglitazone. EVCTs and VCTs were purified from human chorionic villi, cultured in vitro, and treated with rosiglitazone. The transcriptomes of both types of cells were then quantified using microarray profiling. Differentially expressed genes (DEGs) were filtered and submitted for gene ontology (GO) annotation and pathway analysis with ClueGO. The online tool STRING was used to predict PPARγ and DEG protein interactions, while iRegulon was used to predict the binding sites for PPARγ and DEG promoters. GO and pathway terms were compared between EVCTs and VCTs with clusterProfiler. Visualizations were prepared in Cytoscape. From our microarray data, 139 DEGs were detected in rosiglitazone-treated EVCTs (RT-EVCTs) and 197 DEGs in rosiglitazone-treated VCTs (RT-VCTs). Downstream annotation analysis revealed the similarities and differences between RT-EVCTs and RT-VCTs with respect to the biological processes, molecular functions, cellular components, and KEGG pathways affected by the treatment, as well as predicted binding sites for both protein-protein interactions and transcription factor-target gene interactions. These results provide a broad perspective of PPARγ-activated processes in trophoblasts; further analysis of the transcriptomic signatures of RT-EVCTs and RT-VCTs should open new avenues for future research and contribute to the discovery of possible drug-targeted genes or pathways in the human placenta.