Guzzo, F. | Search bioRxiv

bioRxiv2023

Role of the metallo-reductase FADING and vacuolinos in anthocyanin degradation in flowers and fruits.

Anthocyanins are vacuolar pigments that confer red-violet colors to plant tissues. Pigmentation patterns result from spatio-temporally regulated anthocyanin synthesis and degradation. Mutational inactivation of a conserved MYB-bHLH-WDrepeat-WRKY transcriptional complex (MBWW) causes degradation of anthocyanins and fading of flower color via a pathway that involves FADING (FA). Here we show that FA encodes a vacuolar membrane Fe-reductase-oxidase that promotes anthocyanin degradation. In wild type petals anthocyanins in the central vacuole (CV) are stable, because FA-GFP is upheld in small vacuoles (vacuolinos) and kept away from the CV, indicating that vacuolinos act as gatekeepers in protein trafficking. In cells lacking vacuolinos, including mbww- mutant petals, FA-GFP reaches the CV and triggers anthocyanin degradation. Virus-induced gene silencing (VIGS) of an FA-homolog in pepper fruits prevented the "fading" of anthocyanins during fruit maturation. These findings provide new insights to breed ornamental and food crops with increased anthocyanin-content and enhanced nutritional value of edible parts.

plant biology↗

bioRxiv2022

The ENCODE Imputation Challenge: A critical assessment of methods for cross-cell type imputation of epigenomic profiles

Functional genomics experiments are invaluable for understanding mechanisms of gene regulation. However, comprehensively performing all such experiments, even across a fixed set of sample and assay types, is often infeasible in practice. A promising alternative to performing experiments exhaustively is to, instead, perform a core set of experiments and subsequently use machine learning methods to impute the remaining experiments. However, questions remain as to the quality of the imputations, the best approaches for performing imputations, and even what performance measures meaningfully evaluate performance of such models. In this work, we address these questions by comprehensively analyzing imputations from 23 imputation models submitted to the ENCODE Imputation Challenge. We find that measuring the quality of imputations is significantly more challenging than reported in the literature, and is confounded by three factors: major distributional shifts that arise because of differences in data collection and processing over time, the amount of available data per cell type, and redundancy among performance measures. Our systematic analyses suggest several steps that are necessary, but also simple, for fairly evaluating the performance of such models, as well as promising directions for more robust research in this area.

bioinformatics↗