Genomic Hotspots for Adaptation: The Population Genetics of Müllerian Mimicry in the Heliconius melpomene Clade

Wing patterning in Heliconius butterflies is a longstanding example of both Müllerian mimicry and phenotypic radiation under strong natural selection. The loci controlling such patterns are “hotspots” for adaptive evolution with great allelic diversity across different species in the genus. We characterise nucleotide variation, genotype-by-phenotype associations, linkage disequilibrium, and candidate gene expression at two loci and across multiple hybrid zones in Heliconius melpomene and relatives. Alleles at HmB control the presence or absence of the red forewing band, while alleles at HmYb control the yellow hindwing bar. Across HmYb two regions, separated by ∼100 kb, show significant genotype-by-phenotype associations that are replicated across independent hybrid zones. In contrast, at HmB a single peak of association indicates the likely position of functional sites at three genes, encoding a kinesin, a G-protein coupled receptor, and an mRNA splicing factor. At both HmYb and HmB there is evidence for enhanced linkage disequilibrium (LD) between associated sites separated by up to 14 kb, suggesting that multiple sites are under selection. However, there was no evidence for reduced variation or deviations from neutrality that might indicate a recent selective sweep, consistent with these alleles being relatively old. Of the three genes showing an association with the HmB locus, the kinesin shows differences in wing disc expression between races that are replicated in the co-mimic, Heliconius erato, providing striking evidence for parallel changes in gene expression between Müllerian co-mimics. Wing patterning loci in Heliconius melpomene therefore show a haplotype structure maintained by selection, but no evidence for a recent selective sweep. The complex genetic pattern contrasts with the simple genetic basis of many adaptive traits studied previously, but may provide a better model for most adaptation in natural populations that has arisen over millions rather than tens of years

Eicosapentaenoic and Docosahexaenoic Acid-Enriched High Fat Diet Delays Skeletal Muscle Degradation in Mice

Low-grade chronic inflammatory conditions such as ageing, obesity and related metabolic disorders are associated with deterioration of skeletal muscle (SkM). Human studies have shown that marine fatty acids influence SkM function, though the underlying mechanisms of action are unknown. As a model of diet-induced obesity, we fed C57BL/6J mice either a high fat diet (HFD) with purified marine fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (HFD-ED), a HFD with corn oil, or normal mouse chow for 8 weeks; and used transcriptomics to identify the molecular effects of EPA and DHA on SkM. Consumption of ED-enriched HFD modulated SkM metabolism through increased gene expression of mitochondrial β-oxidation and slow-fiber type genes compared with HFD-corn oil fed mice. Furthermore, HFD-ED intake increased nuclear localization of nuclear factor of activated T-cells (Nfatc4) protein, which controls fiber-type composition. This data suggests a role for EPA and DHA in mitigating some of the molecular responses due to a HFD in SkM. Overall, the results suggest that increased consumption of the marine fatty acids EPA and DHA may aid in the prevention of molecular processes that lead to muscle deterioration commonly associated with obesity-induced low-grade inflammation

High-throughput metabolomics for the design and validation of a diauxic shift model.

Funder: The Wallenberg AI, Autonomous Systems and Software ProgramSaccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput

High-throughput metabolomics for the design and validation of a diauxic shift model.

Acknowledgements: This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and the Swedish Research Council Formas (2020-01690).Funder: The Wallenberg AI, Autonomous Systems and Software Program (WASP)Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput

High-throughput metabolomics for the design and validation of a diauxic shift model

Abstract Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io

Sympatric speciation in palms on an oceanic island.

The origin of species diversity has challenged biologists for over two centuries. Allopatric speciation, the divergence of species resulting from geographical isolation, is well documented. However, sympatric speciation, divergence without geographical isolation, is highly controversial. Claims of sympatric speciation must demonstrate species sympatry, sister relationships, reproductive isolation, and that an earlier allopatric phase is highly unlikely. Here we provide clear support for sympatric speciation in a case study of two species of palm (Arecaceae) on an oceanic island. A large dated phylogenetic tree shows that the two species of Howea, endemic to the remote Lord Howe Island, are sister taxa and diverged from each other well after the island was formed 6.9 million years ago. During fieldwork, we found a substantial disjunction in flowering time that is correlated with soil preference. In addition, a genome scan indicates that few genetic loci are more divergent between the two species than expected under neutrality, a finding consistent with models of sympatric speciation involving disruptive/divergent selection. This case study of sympatric speciation in plants provides an opportunity for refining theoretical models on the origin of species, and new impetus for exploring putative plant and animal examples on oceanic islands