Biology subjects

Naegele, R.

Publications and source records attributed to Naegele, R..

3 recordsLinked to original sources

bioRxiv2026

Genomic compartmentalization of pervasive sex-biased gene expression in the vine mealybug Planococcus ficus

The vine mealybug, Planococcus ficus, is a globally invasive pest of grapevine and a vector of leafroll viruses. Like other mealybugs, it reproduces through paternal genome elimination, a sex-determination system that operates without sex chromosomes and is associated with extreme sexual dimorphism. To characterize genome organization and sex-biased expression in this species, we generated a long-read reference genome spanning 369 Mb with 23,489 annotated genes and macrosynteny conserved with the citrus mealybug, Planococcus citri. Resequencing of four California field individuals yielded a first whole-genome estimate of nucleotide diversity and 132 microsatellite markers for population monitoring. Among 2,129 candidate secreted proteins, a conserved core is shared with P. citri, but each species carries a distinct set of lineage-specific effectors. Comparing adult male and female transcriptomes, we found sex-biased expression to be pervasive and skewed toward females: 41% of tested genes differed between the sexes, with female-biased genes both more numerous and showing larger fold changes. These female-biased genes were not randomly distributed but concentrated in discrete blocks of coordinately expressed, tandemly duplicated gene families, a pattern not previously described in a mealybug. Male- and female-biased secreted proteins also differed in origin, with male-biased proteins drawn from a conserved repertoire shared with P. citri and female-biased proteins spanning a more lineage-specific pool. Together, these results reveal a female-skewed, spatially clustered architecture of sex-biased expression in a mealybug that lacks sex chromosomes, and provide genomic resources for managing an invasive vineyard pest.

genomics↗

bioRxiv2026

Evolutionary and Deep Learning Models Highlight Deleterious Mutations Behind the History of Sugar Beet Breeding

Sugar beet (Beta vulgaris ssp. vulgaris) is a major global source of sucrose. Domestication and a history of breeding have shaped the distribution of deleterious mutations across the genome. Accurately resolving these variants offers a compelling avenue to accelerate crop improvement through breeding or genetic engineering. We combine three scales of evolutionary time: protein conservation defined by conservation across all living organisms (SIFT), angiosperm-wide DNA language deep learning model (PlantCaduceus), and fine-scale evolutionary rates from Amaranthaceae plant family sequence alignments to define a high-confidence set of deleterious variants enriched for alleles under negative selection. We then evaluate these deleterious mutations across a multi-study panel of more than 1,900 whole-genome sequenced wild and cultivated beet accessions. We found that domesticated beet accessions exhibit higher deleterious load relative to wild maritima, yet sugar beet carries significantly fewer deleterious variants than other cultivated beet crops. Historical trends show a decline in genetic load across more than a century of sugar beet entries in the U.S. NPGS, reflecting sustained purging during modern breeding. This combinatorial method of leveraging evolutionary conservation reveals how deleterious mutations have shaped sugar beet breeding history and provides potential targets for future selection, causal-variant discovery, and precision genome engineering.

genomics↗

bioRxiv2025

Procrustean pseudo-landmark methods in Python to measure massive quantities of leaf shape data

PremiseWhen examining leaf shapes that are different from one another, it can be difficult to compare both the overall leaf shape and points along the leaf margin in biologically and statistically meaningful ways. MethodTo address this problem, we present a simple and user-friendly leaf shape analysis in Jupyter Notebook and Python that uses pseudo-landmarks and Generalized Procrustes Analysis to measure and compare the shape of any leaf. To demonstrate our analysis, we created a repository of real leaves gathered from eight experimental datasets. ResultsUsing our leaf repository, we explain how we can use pseudo-landmarks to compare all leaf shapes both within and between species using dimension reduction techniques like Principal Component Analysis and can predict leaf shapes using pseudo-landmarks through Linear Discriminant Analysis. Our leaf shape analysis also maps differences in shape as leaves grew around a rosette, showing the transition of shape across development (phyllotaxy). Finally, we showed how we can investigate the relationship between leaf shape variation and genetic diversity by combining shape with genetic data. DiscussionThrough the use of Generalized Procrustes Analysis and pseudo-landmarks, our leaf shape analysis presents a powerful tool for examining the shape of any leaf across multiple biological, ecological, evolutionary, and developmental scales.

plant biology↗