Screening of wild coffee (coffea spp.) for resistance to meloidogyne incognita race 1

International audienceOne hundred and forty six cuttings representing duplicates of 73 wild accessions from 16 coffee species were evaluated for resistance to Meloidogyne incognita race 1. Five species were subdivided on the asis of geographical origin because morphological differences were previously observed. Two wellcharacterized susceptible and resistant cultivars were used as comparative controls. The experiments were conducted in a greenhouse using a clonal population of M. incognita from Brazil. The reproduction factor (RF) was used to evaluate the resistance (RF1) to the nematode infection. Plants of both controls were discriminated on the basis of RF values. Both duplicate cuttings of the wild accessions were identically classified as resistant or susceptible. Eight species displayed a resistant reaction, onespecies was considered to be susceptible, and seven species presented both susceptible and resistant accessions. Resistance to M. incognita appeared to be a more frequent character than susceptibility within the gene pool of wild coffee. These results provide coffee breeders with material whose resistance can be transferred into commercial cultivars.Ciento curaenta y seis estacas de 73 accesiones silvestres perteneciendo a 16 especies de café fueronevaluadas para su resistencia a Meloidogyne incognita raza 1. Se subdividió cinco especies según su origengeográfico en base a diferencias morfológicas previamente observadas. Los experimentos fueron realizadosen invernadero con una población clonal de M. incognita colectada en Brasil. Las resistencia ysusceptibilidad a este nematodo fueron evaluadas mediante el factor de reproducción (FR). Las plantas delos dos testigos fueron discriminadas en base a los valores del FR. Se observó siempre la mismaclasificación como resistente o susceptible de las dos estacas de cada una de las accesiones silvestresevaluadas. Ocho especies mostraron una respuesta de resistencia; una especie fue considerada comosusceptible y siete especies presentaron algunas accesiones susceptibles y otras resistentes. La resistencia aM. incognita parece ser más frecuente que el carácter de susceptibilidad dentro de la base genética de loscafetos silvestres. Los presentes resultados proveen a los fitomejoradores de café material vegetal cuyaresistencia puede ser transferida a cultivares comerciale

A gene-based map of the Nod factor-independent Aeschynomene evenia sheds new light on the evolution of nodulation and legume genomes

International audienceAeschynomene evenia has emerged as a new model legume for the deciphering of the molecular mechanisms of an alternative symbiotic process that is independent of the Nod factors. Whereas most of the research on nitrogen-fixing symbiosis, legume genetics and genomics has so far fo-cused on Galegoid and Phaseolid legumes, A. evenia falls in the more basal and understudied Dalbergioid clade along with peanut (Arachis hypogaea). To provide insights into the symbiotic genes content and the structure of the A. evenia genome, we established a gene-based genetic map for this species. Firstly, an RNAseq analysis was performed on the two parental lines selected to generate a F 2 mapping population. The transcriptomic data were used to develop molecular markers and they allowed the identification of most symbiotic genes. The resulting map comprised 364 markers arranged in 10 linkage groups (2n ¼ 20). A comparative analysis with the sequenced genomes of Arachis duranensis and A. ipaensis, the diploid ancestors of peanut, indicated blocks of conserved macrosynteny. Altogether, these results provided important clues regarding the evolution of symbiotic genes in a Nod factor-independent context. They provide a Downloaded from https://academic.oup.com/dnaresearch/article-abstract/23/4/365/2469965 by BIU Montpellier user on 12 March 2020 basis for a genome sequencing project and pave the way for forward genetic analysis of symbio-sis in A. evenia

The complete genome sequence of Orobanche cumana (sunflower broomrape)

Trabajo presentado en el 14th World Congress on Parasitic Plants (From genome to field), celebrado en Asilomar (California) el 24 y 25 de junio de 2017.Orobanche cumana (sunflower broomrape) is an obligate parasitic plant that specifically infects sunflower (Helianthus annuus). It is one of the main limiting factors of sunflower crop in Eastern Europe, Spain and Asia. In 2007, the first infested fields have been reported in France. Breeding for resistance in sunflower was successful but new more virulent races of O. cumana often overcame the resistance genes. The first developmental stages of O. cumana occur underground. The germination of the seeds is first stimulated by sunflower root exudates before entering the host root through a haustorium. Without roots nor chlorophyll, O. cumana depends on sunflower for water and nutrients supply. It connects to the vascular system of the sunflower root and store metabolites in a tubercle before emerging a flowering shoot. The inactivation of these developmental stages is a key resistance mechanism in sunflower. A better understanding of the biology of O. cumana will help to identify new resistance processes and resistance genes in sunflower. In the frame of a collaborative project between French and Spanish research institutes, we have produced a first version of the 1.42 Gb genome sequence of O. cumana by combining PacBio sequencing, optical mapping and genetic map. More than twenty transcriptomic RNA-seq experiments from O. cumana were used for annotating the genome sequence. This first sequence assembly (622 scaffolds, 1.38Gb, N50=5.9Mb) and its annotation will be provided through a Web Genome Browser to the public research community. Our strategy to obtain and finalize the genome assembly as well as results on population diversity will be presented. The genome sequence of O. cumana will enable the characterization of its physiology and development. Avirulence genes should be identified more efficiently and, as putative interactor with sunflower proteins, should help in identifying new resistance genes in sunflower. This resource will help in understanding parasitic plants’ biology and evolution, like parasitism capacity acquisition.N

The DeepFaune initiative: a collaborative effort towards the automatic identification of the French fauna in camera-trap images

Abstract Camera-traps have revolutionized the way ecologists monitor biodiversity and population abundances. Their full potential is however only realized when the hundreds of thousands of images collected can be rapidly classified with minimal human intervention. Machine learning approaches, and in particular deep learning methods, have allowed extraordinary progress towards this end. Trained classification models remain rare however, and for instance are only emerging for the European fauna. This can be explained by the technical expertise they require but also by the limited availability of large datasets of annotated pictures, which are key to obtaining successful recognition models. In this context, we set-up the DeepFaune initiative ( https://deepfaune.cnrs.fr ), a large-scale collaboration between dozens of partners involved in research, conservation and management of wildlife in France. The aim of DeepFaune is to aggregate individual datasets of annotated pictures to train species classification models based on convolutional neural networks, an established deeplearning approach. Here we report on our first milestone, a two-step pipeline built upon the MegaDetector algorithm for detection (discarding empty pictures and cropping the animal) and a classification model for 18 species or higher-level taxa as well as people and vehicles. The classification model achieved 92% validation accuracy and showed > 90% sensitivity and specificity for many classes. Most importantly, these performances were generally conserved when tested on an independent out-of-sample dataset. In addition, we developed a cross-platform graphical-user-interface that allows running the pipeline on images stored locally on a personal computer. In conclusion, the DeepFaune initiative provides a freely available (for non-commercial purposes) toolbox with high performance to classify the French fauna in camera-trap images

The DeepFaune initiative: a collaborative effort towards the automatic identification of Europeanfauna in camera trap images

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep-learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed aclassification model trained to recognize 26 species or higher-level taxa that are common in Europe, with an emphasis on mammals. The classification model achieved 0.97 validation accuracy and often >0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequences of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly and high-performance tool for wildlife practitioners to automatically classify camera trap images. The DeepFaune initiative is an ongoing project, with new partners joining regularly,which allows us to continuously add new species to the classification model