In coastal regions, seaweed biomass turnover influences ecosystem functions both at local and global scales. It largely relies on specialized bacteria able to breakdown intact algal tissues and release degradation products in the water column. The ALGAVOR project will explore the ecological and metabolic strategies of such poorly known specialized marine bacteria of the genus Zobellia. We will combine cultivation-dependent and 'omics approaches to (i) evaluate the biodiversity, distribution, abundance, activity and catabolic functions of Zobellia spp in marine environments, (ii) decipher which metabolic pathways they use to degrade fresh seaweed biomass and (iii) study their cooperative interactions with scavenger bacteria that can profit from degradation products. Altogether, ALGAVOR will unveil the strategies of crucial bacteria considered as a bottleneck controlling the fate of organic matter in coastal habitats.

Sulfated biomolecules are widespread in nature and play important roles in biological functions. Among the enzymes responsible for sulfation, ArylSulfate SulfoTransferases (ASSTs) are interesting biocatalysts as they use simple aromatic sulfates such as para-nitrophenyl sulfate as donors in comparison to PAPS-dependent sulfotransferases that use the complex and less stable PAPS as donor. However, very few is known about ASSTs (only one 3D-structure and its molecular mechanism described, tentative assignment into different classes according to their biochemistry or genomic context, only one natural donor and one acceptor substrate identified). According to our preliminary phylogenetic analysis on 2244 sequences of ASSTs genes, we identified 19 clades displaying reasonable boots-trap values. In analogy to CAZY or Sulfatlas databases, each of the actual 19 clades could correspond to a varying substrate specificity or/and mechanism. However, since biochemical and structural data are scarce, this hypothesis cannot be challenged by experimental data today. Moreover many of the branches (clades) coincide with taxonomy, which raises the obvious question that substrate specificity might be a trait which is linked to taxonomy. In the SulfASST project, we uses a combination of complementary approaches in bioinformatics, biochemistry, enzymology, structural biology, molecular modeling and protein engineering to obtain substantial information on the ASST enzymes. Based on the preliminary phylogenetic analysis, one representative of each of the 19 subfamilies (clades) will be expressed and screened for donor and acceptor substrates. Enzyme crystallography of 6-8 soundly selected representatives of ASSTs should provide precious details on molecular aspects of catalysis and selectivity (substrate, regiochemistry). Directed enzyme evolution (KnowVolution) and modeling will allow to obtain tailor-made biocatalysts for biotechnological purposes. Finally, this in-depth characterization of the ASSTs and rationalization of the obtained results will enable to: determine if substrate specificity is correlated to phylogeny; know if the genomic context of ASSTs genes is indicative of substrate or biological activity; decipher the structural determinants of substrate specificity/promiscuity and regioselectivity; define if enzyme mechanism is conserved throughout the different subfamilies (clades); predict substrate selectivity and regioselectivity by molecular modeling.

Brown algae (Phaeophyceae), or brown seaweeds, are multicellular macroalgae that belong to the larger eukaryotic Stramenopile supergroup, which also includes microalgae (e.g., diatoms) as well as heterotrophic protists (e.g., oomycetes). Like plants and animals, brown algae are one of the small number of lineages that evolved complex multicellularity. Yet, the evolutionary process leading to multicellularity in brown algae has been quite distinct, leading to the acquisition of some unique characteristics which are absent in the other lineages. This project aims to deepen the understanding of the molecular processes underlying brown algae development and acquisition of multicellularity through an analysis of the functional regulatory roles of long non-coding RNAs (lncRNAs), key actors in cellular regulation across the Eukarya domain of life. It will employ a multidisciplinary approach, for which the three partners involved have demonstrated expertise, involving comparative genomics and trancriptomics, computational biology, functional biology and epigenomics to characterize how lncRNAs control gene expression programs that define distinct development states in brown algae. Ectocarpus sp. and Saccharina sp. will be used as preferred model systems since they show distinct developmental morphologies but are evolutionary close. On a fundamental level, the BrownLincs project will provide new knowledge on brown algal development regulation and acquisition of complex multicellularity. On a more applied level, this project has the potential to provide novel molecular tools to enable the engineering of high biomass production in brown algae that can be explored to produce new generation, added value materials for food, feed and other industrial applications, in a blue economy context.

The Pacific oyster (Crassostreae gigas) has been introduced from Asia to numerous countries throughout the world (Canada, USA, Australia, New-Zealand, Chile, Mexico, Argentina, South Africa, Namibia and in numerous European countries including France) during the 20th century. C. gigas is currently the main oyster species farmed in the world and represents more than 95% of world production. For decades, C. gigas has been suffering mortalities but the severity of these outbreaks has dramatically increased since 2008. They mainly affect juvenile stages, decimating up to 100% of young oysters in French farms. In recent years, this mortality syndrome, designated Pacific oyster mortality syndrome (POMS), has become panzootic and represents a threat for the oyster industry worldwide. Recently, the consortium of the DECICOMP project overcame a major step towards understanding POMS using a holistic molecular approach developed in mesocosm. We showed that the infection by the Ostreid herpesvirus (OsHV-1 µVar) was the initial step of the infectious process leading to an immune-compromised state, which evolved towards subsequent bacteraemia by opportunistic bacterial pathogens. Nevertheless, by elucidating the mechanisms of the pathogenesis, only a part of the POMS complexity was deciphered. Indeed, this multifactorial disease is tightly controlled by a series of host and environmental factors (temperature, oyster age and diet). However, we still ignore the mechanisms by which these key factors control disease expression. This knowledge is urgently needed to elucidate the whole complexity of the disease and ultimately assess the epidemiological risk. In this context, the first objective of the DECICOMP project is to determine how temperature, oyster age and diet control POMS expression. The second objective is to weight and evaluate the interactions between all the factors controlling POMS in real farming conditions. Finally, our third objective is to model the epidemiological risk of POMS in oyster farms by using the sum of data generated in the project. To address the objectives of the DECICOMP project, we will combine laboratory/field experiments and theoretical approaches. Our multidisciplinary approach (mesoscosm and rationalized infections, integrative omics including epigenomics and metatranscriptomics, physiological/histological/functional validation approaches, modelling) is unique, ambitious and, as we believe, highly original. To reach our objectives, we have put together a consortium of researchers with highly complementary expertises that makes possible the implementation of a multiscale approach for deciphering the functioning of such a complex pathosystem from the finest molecular level to farmed populations. We believe DECICOMP will not only open prospects for substantial scientific knowledge advancement on a complex multifactorial disease but will also help decision-making thanks to tools and applied innovations for a sustainable and integrated management of oyster aquaculture. Indeed, by modelling the epidemiological risk under the influence of the different factors influencing POMS, we will be able to quantify the benefits of different measures that could be conducted by oyster farmers to play on these factors and consequently provide some action-levers to reduce the impact of the disease in farms.

Population genetics methods allow researchers to infer historical events in human and non-human populations, at time scales for which historical records provide no information. Coalescent-based methods have been developed to infer these events. These methods have been successfully applied to many populations, using classical population genetics markers (e.g. microsatellites, DNA sequences). They have allowed us for instance to determine whether populations have undergone events of growth or decline, of migration between surrounding populations, and if some populations result from admixture events between two or more populations. The parameters of these demographic phenomena (e.g. growth rates, migration rates, ancestral population sizes, admixture rates) could be estimated to some extent. The amount of data available on DNA polymorphism is increasing by several orders of magnitude through the recent development of new kind of polymorphism datasets: DNA chips datasets with several hundred of thousands or even a few millions of single nucleotide polymorphisms (SNPs) and full genome sequences. Some of the SNPs are in coding or regulatory regions and may thus be submitted to selection, but others are outside these regions and can thus be used for demographic processes inference. This strong increase in the amount of available data may lead to the logical conclusion that demographic events could be inferred much more precisely thanks to these new datasets. Based on the existing methods, the main problem is to develop new algorithms adapted to such data, as they differ from classical data both by the amount of available polymorphism and also by the occurrence in these datasets of many linked loci, which offers the possibility to use the level of linkage disequilibrium inside the estimation process. The aim of this study is to develop new coalescent-based approaches (ABC and MCMC) for these new data sets and to apply them to human and Drosophila melanogaster polymorphism datasets. The first step will be to develop a simulation program that will be able to generate such large datasets. In a second step, the simulation program will be then used directly to develop ABC methods, but also as a mean to test the validity of the different methods. For the MCMC method, we will focus on how to optimize these methods for large data sets and if a strategy of optimal sub-sampling can be designed to keep a reasonable computing time. In a third step, we will apply these methods to real data on human and Drosophila populations. Regarding humans, the first question will be whether we can infer different demographic history for populations that have been submitted to different lifestyles, namely agriculturalists, herders and hunter-gatherers. In particular do these differences in lifestyle influence their expansion rate? The second question will be whether we can infer the history of migration and admixture of populations in Central Asia. Are these populations the results of admixture events between the neighbouring European and Asian populations, or conversely are they one of the first areas colonised after the emergence of modern humans out of Africa, areas from which other Eurasian area were subsequently colonised? Finally, we will also investigate the possibility to infer a recombination map along the genome in the different population taking into account their demographic history. Regarding D. melanogaster, we will investigate its demographic history in Africa exploiting the data produced by the DPGP project. Two main issue will be tackled, namely the timing and mode of expansion in Africa (particularly the proposed division between East and West African populations) and the time of the out-of–Africa.