Navarro, P. J. | bioRxiv Science

bioRxiv2026

Large-scale forward genetics reveals genetically separable components of plant cell proliferative capacity

Plant cell cultures underpin regeneration, transformation and emerging applications in biotechnology and plant cell agriculture, yet the genetic determinants of proliferative performance remain poorly characterized. Here, we combined large-scale forward genetics with automated image-based phenotyping to quantify 27,648 calli representing 2,880 Arabidopsis thaliana T-DNA insertion lines. Of these, 2,456 lines produced calli smaller than the WT population mean, whereas only 190 produced larger calli, revealing a strongly asymmetric phenotypic distribution. Enhanced callus growth was not consistently associated with increased vegetative growth, indicating that these traits can be genetically uncoupled. Validation of selected high-growth mutants in suspension culture identified lines with enhanced biomass accumulation, while serial propagation revealed independent variation in sustained proliferative capacity. Thus, callus growth, whole-plant growth, short-term suspension biomass accumulation and sustained proliferation represent distinct, not necessarily coupled components of proliferative performance. These findings establish a scalable framework for identifying genetic determinants relevant to the development of improved plant cell production lines.

plant biology↗

bioRxiv2020

Automatic classification of constitutive and non-constitutive metabolites with gcProfileMakeR

Data analysis in non-targeted metabolomics is extremely time consuming. Genetic factors and environmental cues affect the composition and quantity of present metabolites i.e. the constitutive and non-constitutive metabolites. We developed gcProfileMakeR, an R package that uses standard output files from GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR produces three outputs: a core or constitutive metabolome, a second list of compounds with high quality matches that is non-constitutive and a third set of compounds with low quality matching to MS libraries. As a proof of concept, we defined the floral scent emission of Antirrhinum majus using wild type plants, the floral identity mutants deficiens and compacta as well as RNAi lines of AmLHY. Loss of petal identity was accompanied by appearance of aldehydes typical of green leaf volatile profiles. Decreased levels of AmLHY caused a major increase in volatile complexity, and activated the synthesis of benzyl acetate, absent in WT. Furthermore, some volatiles emitted in a gated fashion in WT such as methyl 3,5-dimethoxybezoate or linalool became constitutive. Using sixteen volatiles of the constitutive profile, all genotypes were classified by Machine Learning with 0% error. gcProfileMakeR may thus help define core and pan-metabolomes. It enhances the quality of data reported in metabolomic profiles as text outputs rely on CAS numbers. This is especially important for FAIR data implementation. One sentence summarygcProfileMakeR allows the automatic annotation of the core metabolome and non-constitutive metabolites, increasing speed and accuracy of non-targeted metabolomics.

bioinformatics↗