Biology subjects

Thomas Derrien

Publications and source records attributed to Thomas Derrien.

3 recordsLinked to original sources

bioRxiv2020

Interest of circulating tumor DNA as a biomarker for canine cancers: illustration in histiocytic sarcoma, oral malignant melanoma and multicentric lymphoma

Circulating tumor DNA (ctDNA) has become an attractive biomarker in human oncology and may be informative in cancer-affected dogs. By performing ddPCR or PARR methods, we detected tumor-specific point mutations, copy number alterations and chromosomal rearrangements in the plasma of cancer-affected dogs. It allowed the detection of ctDNA in 2/8 (25%) oral malignant melanoma cases, 12/13 (92.3%) lymphoma cases and 21/23 (91.3%) histiocytic sarcoma (HS) cases. The value of ctDNA to diagnose HS was explored in 133 dogs including 49 with HS. In this cohort, screening recurrent PTPN11 mutations in plasma had a specificity of 98.8%, and a sensitivity between 42.8-77% according to HS clinical presentation, being higher in internal forms, especially with pulmonary location. Regarding lymphoma, the follow-up of four dogs showed that the minimal residual disease detection by targeting lymphoma-specific antigen receptor rearrangement in the plasma was concordant with the clinical evaluation. Moreover, ctDNA analysis appeared interesting to assess treatment response and to predict relapse. This study shows that ctDNA is detectable in the plasma of cancer-affected dogs and is a relevant biomarker for diagnosis and clinical follow-up. With a growing interest in integrating natural canine tumors to explore new therapies, this biomarker appears promising in comparative oncology research.

cancer biology↗

bioRxiv2016

A clonally reproducing generalist aphid pest colonises diverse host plants by rapid transcriptional plasticity of duplicated gene clusters

BackgroundThe prevailing paradigm of host-parasite evolution is that arms races lead to increasing specialisation via genetic adaptation. Insect herbivores are no exception, and the majority have evolved to colonise a small number of closely related host species. Remarkably, the green peach aphid, Myzus persicae, colonises plant species across 40 families and single M. persicae clonal lineages can colonise distantly related plants. This remarkable ability makes M. persicae a highly destructive pest of many important crop species.\n\nResultsTo investigate the exceptional phenotypic plasticity of M. persicae, we sequenced the M. persicae genome and assessed how one clonal lineage responds to host plant species of different families. We show that genetically identical individuals are able to colonise distantly related host species through the differential regulation of genes belonging to aphid-expanded gene families. Multigene clusters collectively up-regulate in single aphids within two days upon host switch. Furthermore, we demonstrate the functional significance of this rapid transcriptional change using RNA interference (RNAi)-mediated knock-down of genes belonging to the cathepsin B gene family. Knock-down of cathepsin B genes reduced aphid fitness, but only on the host that induced up-regulation of these genes.\n\nConclusionsPrevious research has focused on the role of genetic adaptation of parasites to their hosts. Here we show that the generalist aphid pest M. persicae is able to colonise diverse host plant species in the absence of genetic specialisation. This is achieved through rapid transcriptional plasticity of genes that have duplicated during aphid evolution.

Genomics↗

bioRxiv2016

FEELnc: A tool for Long non-coding RNAs annotation and its application to the dog transcriptome

Whole transcriptome sequencing (RNA-seq) has become a standard for cataloguing and monitoring RNA populations. Among the plethora of reconstructed transcripts, one of the main bottlenecks consists in correctly identifying the different classes of RNAs, particularly those that will be translated (mRNAs) from the class of long non-coding RNAs (lncRNAs). Here, we present FEELnc (FlExible Extraction of LncRNAs), an alignment-free program which accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE datasets. The program also provides several specific modules that enable to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to annotate lncRNAs even in the absence of training set of noncoding RNAs. We used FEELnc on a real dataset comprising 20 new canine RNA-seq samples produced in the frame of the European LUPA consortium to expand the canine genome annotation and classified 10,374 novel lncRNAs and 58,640 new mRNA transcripts. FEELnc represents a standardized protocol for identifying and annotating lncRNAs and is freely accessible at https://github.com/tderrien/FEELnc.

Bioinformatics↗