Nature Biotechnology journal featuring biotechnology articles and science research papers of commercial interest in pharmaceutical, medical, and environmental sciences.

Steroid hormones regulate many physiological processes in vertebrates, nematodes, and arthropods through binding to nuclear receptors (NR), a metazoan-specific family of ligand-activated transcription factors. The main steps controlling the diversification of this family are now well-understood. In contrast, the origin and evolution of steroid ligands remain mysterious, although this is crucial for understanding the emergence of modern endocrine systems. Using a comparative genomic approach, we analyzed complete metazoan genomes to provide a comprehensive view of the evolution of major enzymatic players implicated in steroidogenesis at the whole metazoan scale. Our analysis reveals that steroidogenesis has been independently elaborated in the 3 main bilaterian lineages, and that steroidogenic cytochrome P450 enzymes descended from those that detoxify xenobiotics.

Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from “à la carte” pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway. Genome-scale metabolic models describe an organism’s metabolism. Building good models requires the integration of all relevant available information, obtained by exploring different data types and biological databases. This process is not straightforward and choices are made along the way, for example, which data is analyzed, with what tools. It matters that all reconstruction steps are well documented so that models can be fully exploited by the community. Having this metadata allows other researchers to reproduce, improve or reuse a model as a blueprint to create new ones. Sadly, this information is usually scattered and its proper distribution is the exception rather than the norm when using “à la carte” pipelines that combine main platforms and individual tools. We created a platform for “à la carte” metabolic model generation that responds to the need of transparency and data-connection in the field. It includes a battery of tools to exploit heterogeneous data through customizable pipelines. At each step, relevant information is stored, ensuring reproducibility and documentation of processes. Furthermore, exploration of models and metadata during the reconstruction process is facilitated through the automatic generation of local wikis. This view offers a user-friendly solution to iteratively explore genome-scale metabolic models produced with personalized pipelines and poorly interoperable tools. We highlight these benefits by building models for organisms with various input data. Among them, we show why the combination of heterogeneous information is necessary to elucidate specificities of Tisochrysis lutea, a eukaryotic microalga, for anti-oxidant production.

Brown seaweeds are keystone species of coastal ecosystems, often forming extensive underwater forests, and are under considerable threat from climate change. In this study, analysis of multiple genomes has provided insights across the entire evolutionary history of this lineage, from initial emergence, through later diversification of the brown algal orders, down to microevolutionary events at the genus level. Emergence of the brown algal lineage was associated with a marked gain of new orthologous gene families, enhanced protein domain rearrangement, increased horizontal gene transfer events, and the acquisition of novel signaling molecules and key metabolic pathways, the latter notably related to biosynthesis of the alginate-based extracellular matrix, and halogen and phlorotannin biosynthesis. We show that brown algal genome diversification is tightly linked to phenotypic divergence, including changes in life cycle strategy and zoid flagellar structure. The study also showed that integration of large viral genomes has had a significant impact on brown algal genome content throughout the emergence of the lineage.

Biological rhythms are a fundamental property of life. The deep ocean covers 66% of our planet surface and is one of the largest biomes. The deep sea has long been considered as an arrhythmic environment because sunlight is totally absent below 1,000 m depth. In the present study, we have sequenced the temporal transcriptomes of a deep-sea species, the ecosystem-structuring vent mussel Bathymodiolus azoricus. We reveal that tidal cycles predominate in the transcriptome and physiology of mussels fixed directly at hydrothermal vents at 1,688 m depth at the Mid-Atlantic Ridge, whereas daily cycles prevail in mussels sampled after laboratory acclimation. We identify B. azoricus canonical circadian clock genes, and show that oscillations observed in deep-sea mussels could be either a direct response to environmental stimulus, or be driven endogenously by one or more biological clocks. This work generates in situ insights into temporal organisation in a deep-sea organism.