The human CNOT1-CNOT10-CNOT11 complex forms a structural platform for protein-protein interactions

The evolutionary conserved CCR4-NOT complex functions in the cytoplasm as the main mRNA deadenylase in both constitutive mRNA turnover and regulated mRNA decay pathways. The versatility of this complex is underpinned by its modular multi-subunit organization, with distinct structural modules actuating different functions. The structure and function of all modules are known, except for that of the N-terminal module. Using different structural approaches, we obtained high-resolution data revealing the architecture of the human N-terminal module composed of CNOT1, CNOT10, and CNOT11. The structure shows how two helical domains of CNOT1 sandwich CNOT10 and CNOT11, leaving the most conserved domain of CNOT11 protruding into solvent as an antenna. We discovered that GGNBP2, a protein identified as a tumor suppressor and spermatogenic factor, is a conserved interacting partner of the CNOT11 antenna domain. Structural and biochemical analyses thus pinpoint the N-terminal CNOT1-CNOT10-CNOT11 module as a conserved protein-protein interaction platform

IDEFIX: a versatile performance-portable Godunov code for astrophysical flows

Exascale super-computers now becoming available rely on hybrid energy-efficient architectures that involve an accelerator such as Graphics Processing Units (GPU). Leveraging the computational power of these machines often means a significant rewrite of the numerical tools each time a new architecture becomes available. To address these issues, we present Idefix, a new code for astrophysical flows that relies on the Kokkos meta-programming library to guarantee performance portability on a wide variety of architectures while keeping the code as simple as possible for the user. Idefix is based on a Godunov finite-volume method that solves the non-relativistic HD and MHD equations on various grid geometries. Idefix includes a wide choice of solvers and several additional modules (constrained transport, orbital advection, non-ideal MHD) allowing users to address complex astrophysical problems. Idefix has been successfully tested on Intel and AMD CPUs (up to 131 072 CPU cores on Irene-Rome at TGCC) as well as NVidia and AMD GPUs (up to 1024 GPUs on Adastra at CINES). Idefix achieves more than 1e8 cell/s in MHD on a single NVidia V100 GPU and 3e11 cell/s on 256 Adastra nodes (1024 GPUs) with 95% parallelization efficiency (compared to a single node). For the same problem, Idefix is up to 6 times more energy efficient on GPUs compared to Intel Cascade Lake CPUs. Idefix is now a mature exascale-ready open-source code that can be used on a large variety of astrophysical and fluid dynamics applications.Comment: 18 pages, 18 figures, 3 tables, accepted for publication in Astronomy & Astrophysic

Rapid identification of causal mutations in tomato EMS populations via mapping-by-sequencing

The tomato is the model species of choice for fleshy fruit development and for the Solanaceae family. Ethyl methanesulfonate (EMS) mutants of tomato have already proven their utility for analysis of gene function in plants, leading to improved breeding stocks and superior tomato varieties. However, until recently, the identification of causal mutations that underlie particular phenotypes has been a very lengthy task that many laboratories could not afford because of spatial and technical limitations. Here, we describe a simple protocol for identifying causal mutations in tomato using a mapping-by-sequencing strategy. Plants displaying phenotypes of interest are first isolated by screening an EMS mutant collection generated in the miniature cultivar Micro-Tom. A recombinant F2 population is then produced by crossing the mutant with a wild-type (WT; non-mutagenized) genotype, and F2 segregants displaying the same phenotype are subsequently pooled. Finally, whole-genome sequencing and analysis of allele distributions in the pools allow for the identification of the causal mutation. The whole process, from the isolation of the tomato mutant to the identification of the causal mutation, takes 6-12 months. This strategy overcomes many previous limitations, is simple to use and can be applied in most laboratories with limited facilities for plant culture and genotyping

IDEFIX: a versatile performance-portable Godunov code for astrophysical flows

International audienceExascale super-computers now becoming available rely on hybrid energy-efficient architectures that involve an accelerator such as Graphics Processing Units (GPU). Leveraging the computational power of these machines often means a significant rewrite of the numerical tools each time a new architecture becomes available. To address these issues, we present Idefix, a new code for astrophysical flows that relies on the Kokkos meta-programming library to guarantee performance portability on a wide variety of architectures while keeping the code as simple as possible for the user. Idefix is based on a Godunov finite-volume method that solves the non-relativistic HD and MHD equations on various grid geometries. Idefix includes a wide choice of solvers and several additional modules (constrained transport, orbital advection, non-ideal MHD) allowing users to address complex astrophysical problems. Idefix has been successfully tested on Intel and AMD CPUs (up to 131 072 CPU cores on Irene-Rome at TGCC) as well as NVidia and AMD GPUs (up to 1024 GPUs on Adastra at CINES). Idefix achieves more than 1e8 cell/s in MHD on a single NVidia V100 GPU and 3e11 cell/s on 256 Adastra nodes (1024 GPUs) with 95% parallelization efficiency (compared to a single node). For the same problem, Idefix is up to 6 times more energy efficient on GPUs compared to Intel Cascade Lake CPUs. Idefix is now a mature exascale-ready open-source code that can be used on a large variety of astrophysical and fluid dynamics applications

IDEFIX: a versatile performance-portable Godunov code for astrophysical flows

International audienceExascale super-computers now becoming available rely on hybrid energy-efficient architectures that involve an accelerator such as Graphics Processing Units (GPU). Leveraging the computational power of these machines often means a significant rewrite of the numerical tools each time a new architecture becomes available. To address these issues, we present Idefix, a new code for astrophysical flows that relies on the Kokkos meta-programming library to guarantee performance portability on a wide variety of architectures while keeping the code as simple as possible for the user. Idefix is based on a Godunov finite-volume method that solves the non-relativistic HD and MHD equations on various grid geometries. Idefix includes a wide choice of solvers and several additional modules (constrained transport, orbital advection, non-ideal MHD) allowing users to address complex astrophysical problems. Idefix has been successfully tested on Intel and AMD CPUs (up to 131 072 CPU cores on Irene-Rome at TGCC) as well as NVidia and AMD GPUs (up to 1024 GPUs on Adastra at CINES). Idefix achieves more than 1e8 cell/s in MHD on a single NVidia V100 GPU and 3e11 cell/s on 256 Adastra nodes (1024 GPUs) with 95% parallelization efficiency (compared to a single node). For the same problem, Idefix is up to 6 times more energy efficient on GPUs compared to Intel Cascade Lake CPUs. Idefix is now a mature exascale-ready open-source code that can be used on a large variety of astrophysical and fluid dynamics applications