Stress-induced expression is enriched for evolutionarily young genes in diverse budding yeasts

The Saccharomycotina subphylum (budding yeasts) spans 400 million years of evolution and includes species that thrive in diverse environments. To study niche-adaptation, we identify changes in gene expression in three divergent yeasts grown in the presence of various stressors. Duplicated and non-conserved genes are significantly more likely to respond to stress than genes that are conserved as single-copy orthologs. Next, we develop a sorting method that considers evolutionary origin and duplication timing to assign an evolutionary age to each gene. Subsequent analysis reveals that genes that emerged in recent evolutionary time are enriched amongst stress-responsive genes for each species. This gene expression pattern suggests that budding yeasts share a stress adaptation mechanism, whereby selective pressure leads to functionalization of young genes to improve growth in adverse conditions. Further characterization of young genes from species that thrive in harsh environments can inform the design of more robust strains for biotechnology.</p

Author Correction: A consensus <i>S. cerevisiae</i> metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism (Nature Communications, (2019), 10, 1, (3586), 10.1038/s41467-019-11581-3)

An amendment to this paper has been published and can be accessed via a link at the top of the paper

SysBioChalmers/RAVEN: v2.8.5

&lt;ul&gt; &lt;li&gt;feat:&lt;ul&gt; &lt;li&gt;Distribute SoPlex binary for Windows with RAVEN, as another open-source alternative, slower than glpk, but suitable for MILP. Installation instructions are &lt;a href="https://github.com/SysBioChalmers/RAVEN/wiki/Installation#solvers"&gt;updated&lt;/a&gt;.&lt;/li&gt; &lt;li&gt;&lt;code&gt;readYAMLmodel&lt;/code&gt; should always make model.c-field, even if no objective function is specified (solves #509).&lt;/li&gt; &lt;li&gt;&lt;code&gt;standardizeGrRules&lt;/code&gt; should throw error if no grRules field is found.&lt;/li&gt; &lt;/ul&gt; &lt;/li&gt; &lt;li&gt;refactor:&lt;ul&gt; &lt;li&gt;&lt;code&gt;addRxnsGenesMets&lt;/code&gt; more detailed error message if reactions cannot be found.&lt;/li&gt; &lt;li&gt;&lt;code&gt;getAllowedBounds&lt;/code&gt; use progressbar for reporting status.&lt;/li&gt; &lt;/ul&gt; &lt;/li&gt; &lt;/ul&gt

SysBioChalmers/RAVEN: v2.8.6

&lt;ul&gt; &lt;li&gt;fix:&lt;ul&gt; &lt;li&gt;&lt;code&gt;checkInstallation&lt;/code&gt; (via &lt;code&gt;solverTests&lt;/code&gt;) will not report soplex as &lt;code&gt;fail&lt;/code&gt; on Windows when using RAVEN-provided binary (solves #513).&lt;/li&gt; &lt;/ul&gt; &lt;/li&gt; &lt;/ul&gt

An atlas of human metabolism

Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease