Probing the physiological role of the plastid outer-envelope membrane using the oemiR plasmid collection.

Plastids are the site of complex biochemical pathways, most prominently photosynthesis. The organelle evolved through endosymbiosis with a cyanobacterium, which is exemplified by the outer envelope membrane that harbors more than 40 proteins in Arabidopsis. Their evolutionary conservation indicates high significance for plant cell function. While a few proteins are well-studied as part of the protein translocon complex the majority of outer envelope protein functions is unclear. Gaining a deeper functional understanding has been complicated by the lack of observable loss-of-function mutant phenotypes, which is often rooted in functional genetic redundancy. Therefore, we designed outer envelope-specific artificial micro RNAs (oemiRs) capable of downregulating transcripts from several loci simultaneously. We successfully tested oemiR function by performing a proof-of-concept screen for pale and cold-sensitive mutants. An in-depth analysis of pale mutant alleles deficient in the translocon component TOC75 using proteomics provided new insights into putative compensatory import pathways. The cold stress screen not only recapitulated 3 previously known phenotypes of cold-sensitive mutants but also identified 4 mutants of additional oemiR outer envelope loci. Altogether our study revealed a role of the outer envelope to tolerate cold conditions and showcasts the power of the oemiR collection to research the significance of outer envelope proteins.

Predictive Models of Genetic Redundancy in Arabidopsis thaliana.

Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.