Motivation: Lipids are a large and diverse group of biological molecules with roles in membrane formation, energy storage and signaling. Cellular lipidomes may contain tens of thousands of structures, a staggering degree of complexity whose significance is not yet fully understood. High-throughput mass spectrometry-based platforms provide a means to study this complexity, but the interpretation of lipidomic data and its integration with prior knowledge of lipid biology suffers from a lack of appropriate tools to manage the data and extract knowledge from it.Results: To facilitate the description and exploration of lipidomic data and its integration with prior biological knowledge, we have developed a knowledge resource for lipids and their biology—SwissLipids. SwissLipids provides curated knowledge of lipid structures and metabolism which is used to generate an in silico library of feasible lipid structures. These are arranged in a hierarchical classification that links mass spectrometry analytical outputs to all possible lipid structures, metabolic reactions and enzymes. SwissLipids provides a reference namespace for lipidomic data publication, data exploration and hypothesis generation. The current version of SwissLipids includes over 244 000 known and theoretically possible lipid structures, over 800 proteins, and curated links to published knowledge from over 620 peer-reviewed publications. We are continually updating the SwissLipids hierarchy with new lipid categories and new expert curated knowledge.Availability: SwissLipids is freely available at http://www.swisslipids.org/.Contact: alan.bridge@isb-sib.chSupplementary information: Supplementary data are available at Bioinformatics online.

Sleep is essential for optimal brain functioning and health, but the biological substrates through which sleep delivers these beneficial effects remain largely unknown. We used a systems genetics approach in the BXD genetic reference population (GRP) of mice and assembled a comprehensive experimental knowledge base comprising a deep “sleep-wake” phenome, central and peripheral transcriptomes, and plasma metabolome data, collected under undisturbed baseline conditions and after sleep deprivation (SD). We present analytical tools to interactively interrogate the database, visualize the molecular networks altered by sleep loss, and prioritize candidate genes. We found that a one-time, short disruption of sleep already extensively reshaped the systems genetics landscape by altering 60%–78% of the transcriptomes and the metabolome, with numerous genetic loci affecting the magnitude and direction of change. Systems genetics integrative analyses drawing on all levels of organization imply α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor trafficking and fatty acid turnover as substrates of the negative effects of insufficient sleep. Our analyses demonstrate that genetic heterogeneity and the effects of insufficient sleep itself on the transcriptome and metabolome are far more widespread than previously reported.

The reason why most individuals with COVID-19 have relatively limited symptoms while other develop respiratory distress with life-threatening complications remains unknown. Increasing evidence suggests that COVID-19 associated adverse outcomes mainly rely on dysregulated immunity. Here, we compared transcriptomic profiles of blood cells from 103 patients with different severity levels of COVID-19 with that of 27 healthy and 22 influenza-infected individuals. Data provided a complete overview of SARS-CoV-2-induced immune signature, including a dramatic defect in IFN responses, a reduction of toxicity-related molecules in NK cells, an increased degranulation of neutrophils, a dysregulation of T cells, a dramatic increase in B cell function and immunoglobulin production, as well as an important over-expression of genes involved in metabolism and cell cycle in patients infected with SARS-CoV-2 compared to those infected with influenza viruses. These features also differed according to COVID-19 severity. Overall and specific gene expression patterns across groups can be visualized on an interactive website (https://bix.unil.ch/covid/). Collectively, these transcriptomic host responses to SARS-CoV-2 infection are discussed in the context of current studies, thereby improving our understanding of COVID-19 pathogenesis and shaping the severity level of COVID-19.

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration.