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Biology subjects

Aury, J. M.

Publications and source records attributed to Aury, J. M..

2 recordsLinked to original sources

Water stress-associated isolation barriers between two sympatric oak species

Drought and waterlogging impede tree growth and may even lead to tree death. With climate change, these environmental factors are a growing source of concern, particularly for temperate forests. Oaks, an emblematic group of tree species, have evolved a range of adaptations to cope with these constraints. The two most widely distributed European species -- pedunculate oak (PO) and sessile oaks (SO) -- have overlapping ranges, but are highly constrained locally by soil water content variation. These differences in local ecological requirements provide a powerful biological model for studying the role of ecological barriers in speciation. We used an experimental set-up mimicking the ecological preferences of these species, in which seedlings were subjected to waterlogging and drought. We studied gene expression in roots by RNA-seq and identified genes differentially expressed between treatments with different outcomes depending on species. These "species x environment"-responsive genes revealed adaptive molecular strategies involving adventitious and lateral root formation, aerenchyma formation in PO, and osmoregulation and ABA regulation in SO. With this experimental design, we also identified genes with expression profiles presenting a "species" effect regardless of imposed constraints with important roles in intrinsic reproductive barriers. Finally, we compared our findings with those for a genome scan of species divergence and found that the candidate genes were enriched in highly differentiated SNPs. This suggests that many of the genes involved in the contrasting transcriptomic responses are subject to natural selection and that gene regulation helps to maintain these two different oak species in sympatry.

plant biology↗

De novo Clustering Nanopore Long Reads of Transcriptomics Data by Gene

Long-read sequencing currently provides sequences of several thousand base pairs. This allows to obtain complete transcripts, which offers an un-precedented vision of the cellular transcriptome.\n\nHowever the literature is lacking tools to cluster such data de novo, in particular for Oxford Nanopore Technologies reads, because of the inherent high error rate compared to short reads.\n\nOur goal is to process reads from whole transcriptome sequencing data accurately and without a reference genome in order to reliably group reads coming from the same gene. This de novo approach is therefore particularly suitable for non-model species, but can also serve as a useful pre-processing step to improve read mapping. Our contribution is both to propose a new algorithm adapted to clustering of reads by gene and a practical and free access tool that permits to scale the complete processing of eukaryotic transcriptomes.\n\nWe sequenced a mouse RNA sample using the MinION device, this dataset is used to compare our solution to other algorithms used in the context of biological clustering. We demonstrate its is better-suited for transcriptomics long reads. When a reference is available thus mapping possible, we show that it stands as an alternative method that predicts complementary clusters.

bioinformatics↗