Biology subjects

Duc, C.

Publications and source records attributed to Duc, C..

5 recordsLinked to original sources

bioRxiv2024

Identification of novel H2A histone variants across diverse clades of algae

Histones are recognized as one of the most conserved proteins across eukaryotes. They not only ensure DNA compaction in the nucleus but also participate in epigenetic regulation of genome expression. These key epigenetic players are divided into canonical histones, expressed during the S-phase, and variants, expressed throughout the cell cycle. Compared with other core histones, H2A proteins exhibit a high level of variability but the characterization of algal H2A variants remains very limited. In this study, we exploited genome data from 21 species to perform a phylogenetic analysis of brown seaweeds. We identified new H2A variants that are specific to the Phaeophyceae and closely-related sister group species. For each identified H2A protein, we characterized motifs present in the histone fold domain as well as in the N- and C-terminal tails. This approach enabled us to classify the different isoforms and to identify three new variants (H2A.N, H2A.O and H2A.E). Based on mass spectrometry and RNA-Seq data, we identified distinct epigenetics marks and specific expression patterns of the H2A variants in both vegetative and reproductive tissues as well as in response to various environmental conditions. These analyses identified sperm-specific variants and inferred putative roles for the newly discovered variants.

evolutionary biology↗

bioRxiv2024

Evolutionary genomics of the emergence of brown algae as key components of coastal ecosystems

Brown seaweeds are keystone species of coastal ecosystems, often forming extensive underwater forests, that are under considerable threat from climate change. Despite their ecological and evolutionary importance, this phylogenetic group, which is very distantly related to animals and land plants, is still poorly characterised at the genome level. Here we analyse 60 new genomes that include species from all the major brown algal orders. Comparative analysis of these genomes indicated the occurrence of several major events coinciding approximately with the emergence of the brown algal lineage. These included marked gain of new orthologous gene families, enhanced protein domain rearrangement, horizontal gene transfer events and the acquisition of novel signalling molecules and metabolic pathways. The latter include enzymes implicated in processes emblematic of the brown algae such as biosynthesis of the alginate-based extracellular matrix, and halogen and phlorotannin biosynthesis. These early genomic innovations enabled the adaptation of brown algae to their intertidal habitats. The subsequent diversification of the brown algal orders tended to involve loss of gene families, and genomic features were identified that correlated with the emergence of differences in life cycle strategy, flagellar structure and halogen metabolism. Analysis of microevolutionary patterns within the genus Ectocarpus indicated that deep gene flow between species may be an important factor in genome evolution on more recent timescales. Finally, we show that integration of large viral genomes has had a significant impact on brown algal genome content and propose that this process has persisted throughout the evolutionary history of the lineage.

genomics↗

bioRxiv2019

Trapping a somatic endogenous retrovirus into a germline piRNA cluster immunizes the germline against further invasion

BackgroundFor species survival, the germline must faithfully transmit the genetic information to the progeny. Transposable elements (TEs), which are major components of eukaryotic genomes, constitute a significant threat to genome stability due to their mobility. In the metazoan germline, their mobilization is limited by a class of small RNAs that are called PIWI-interacting RNAs (piRNAs) and are produced by dedicated genomic loci called piRNA clusters. Although the piRNA pathway is an adaptive genomic immunity system, it remains unclear how the germline is protected from transposon invasion. To address this question, we used Drosophila melanogaster lines harboring a deletion within flamenco, a major piRNA cluster that is specifically expressed in somatic follicular cells. This deletion leads to derepression of the retrotransposon ZAM and to germline genome invasion.\n\nResultsIn this mutant line that express ZAM in somatic follicular cells, we identified de novo production of sense and antisense ZAM-derived piRNAs that displayed a germinal molecular signature. These piRNAs originated from a new ZAM insertion into a germline dual-strand piRNA cluster and silenced ZAM expression specifically in germ cells. Finally, we found that ZAM trapping in a germinal piRNA cluster is a frequent event that occurs early during the isolation of the mutant line.\n\nConclusionsTransposons can hijack the host developmental process to propagate whenever their silencing is lost. Here, we show that the germline can protect itself by trapping invading somatic-specific TEs into germline piRNA clusters. This is the first demonstration of \"auto-immunization\" of the germline endangered by mobilization of a surrounding somatic TE.

genetics↗

bioRxiv2018

Detection and Mitigation of Spurious Antisense RNA-seq Reads with RoSA

MotivationAntisense transcription is known to have a range of impacts on sense gene expression, including (but not limited to) impeding transcription initiation, disrupting post-transcriptional processes, and enhancing, slowing, or even preventing transcription of the sense gene. Strand-specific RNA-Seq protocols preserve the strand information of the original RNA in the data, and so can be used to identify where antisense transcription may be implicated in regulating gene expression. However, our analysis of 199 strand-specific RNA-Seq experiments reveals that spurious antisense reads are often present in these datasets at levels greater than 1% of sense gene expression levels. Furthermore, these levels can vary substantially even between replicates in the same experiment, potentially disrupting any downstream analysis, if the incorrectly assigned antisense counts dominate the set of genes with high antisense transcription levels. Currently, no tools exist to detect or correct for this spurious antisense signal.\n\nResultsOur tool, RoSA (Removal of Spurious Antisense), detects the presence of high levels of spurious antisense read alignments in strand-specific RNA-Seq datasets. It uses incorrectly spliced reads on the antisense strand and/or ERCC spike-ins (if present in the data) to calculate both global and gene-specific antisense correction factors. We demonstrate the utility of our tool to filter out spurious antisense transcript counts in an Arabidopsis thaliana RNA-Seq experiment.\n\nAvailabilityRoSA is open source software available under the GPL licence via the Barton Group GitHub page https://github.com/bartongroup.\n\nContactg.j.barton@dundee.ac.uk, g.g.simpson@dundee.ac.uk

bioinformatics↗

bioRxiv2016

How well do RNA-Seq differential gene expression tools perform in higher eukaryotes?

RNA-seq experiments are usually carried out in three or fewer replicates. In order to work well with so few samples, Differential Gene Expression (DGE) tools typically assume the form of the underlying distribution of gene expression. A recent highly replicated study revealed that RNA-seq gene expression measurements in yeast are best represented as being drawn from an underlying negative binomial distribution. In this paper, the statistical properties of gene expression in the higher eukaryote Arabidopsis thaliana are shown to be essentially identical to those from yeast despite the large increase in the size and complexity of the transcriptome: Gene expression measurements from this model plant species are consistent with being drawn from an underlying negative binomial or log-normal distribution and the false positive rate performance of nine widely used DGE tools is not strongly affected by the additional size and complexity of the A. thaliana transcriptome. For RNA-seq data, we therefore recommend the use of DGE tools that are based on the negative binomial distribution.

genomics↗