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69 results for “metabolic evolution”
RDF Representation of RNA Metabolism Evolution data - version 3 (diagrammed in https://zenodo.org/deposit/47641/)
<p>Version 3 (replaces http://doi.org/10.5281/zenodo.50496)<br> <br> Protein complexes involved in RNA Metabolism; individual proteins and their orthologues through a wide range of fungal species spanning much of the kingdom (using yeast as the primary seed for orthology search, and using the EMBL-EBI orthologue database to identify orthologues). For each family of orthologues, the protein domain structure is determined, and then the presence/absence of that domain is evaluated in each of the species. The data is presented in RDF, and is visualized in the form of Heat Maps in http://doi.org/10.5281/zenodo.47641</p>
Data from: Padfield et al. (2016) Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecology letters.
<p>This repository provides the data from the TPC and logistic growth curves from the paper:</p> <p>Padfield, D., Yvon‐Durocher, G., Buckling, A., Jennings, S., & Yvon‐Durocher, G. (2016). Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecology letters, 19(2), 133-142.</p> <p>metadata.pdf gives a more detailed explanation of the data.</p>
Code and Data associated with "Idiosyncratic purifying selection on metabolic enzymes in the long-term evolution experiment with Escherichia coli"
<p>Code and data sufficient to reproduce analyses in "Idiosyncratic purifying selection on metabolic enzymes in the long-term evolution experiment with <em>Escherichia coli</em>".</p>
The evolution of multi-gene families and metabolic pathways in the evening primroses (Oenothera: Onagraceae): a comparative transcriptomics approach
<p>The plant genus <em>Oenothera</em> has played an important role in the study of genome evolution and plant defense and reproduction. Here, we built on the 1kp transcriptomic dataset and developed a molecular resource of 63 transcriptomes and present a large-scale comparative study across 29 <em>Oenothera</em> species. We produced 2.3 million transcripts and 25.4 Mb of total length assembly per individual. We used this transcriptome resource to examine genome-wide evolutionary patterns and functional diversification by searching for orthologous genes and performed gene family evolution analysis. We found wide heterogeneity in gene family evolution across the genus, with section <em>Oenothera </em>exhibiting the most pronounced evolutionary changes. Overall, more significant expansions occurred than contractions. We also analyzed the molecular evolution of phenolic metabolism by retrieving proteins annotated for phenolic enzymatic complexes. We identified 1,568 phenolic genes arranged into 83 multigene families that varied widely across the genus. All taxa experienced rapid phenolic evolution involving 33 gene families, which exhibited large expansions, gaining about 2-fold more genes than they lost. Upstream enzymes phenylalanine ammonia-lyase (PAL) and 4-coumaroyl: CoA ligase (4CL) accounted for most of the significant expansions and contractions. Our results suggest that adaptive responses to environmental stress coupled with non-adaptive evolutionary forces have contributed to <em>Oenothera </em>diversification and rapid gene family evolution.</p>
Data from: Phenotypic plasticity of antibiotic resistance, metabolism byproduct utilization and the evolution of mutually beneficial cooperation in Escherichia coli
<p><span>Although tag-based donation and recognition have well explained how the cooperative individuals are positively assorted if the cooperative individuals possess some signals and are also able to detect such signals, an additional mechanism is required to explain why some individuals pay the costs of evolving such a tag that may not be rewarded subsequently, and how such tag-based cooperative individuals will meet other similar individuals with a very low mutation rate. Here, we show that many and even all<em> Escherichia coli </em>bacteria cells in the increased antibiotic concentration will plastically evolve to be antibiotic resistant individuals who could protect antibiotic sensitive strain from the attack of antibiotics, and the antibiotic resistant strain could reversibly evolve to be antibiotic sensitive in non-antibiotic supplement medium but in a harsher environment with low glucose. A further experiment showed that antibiotic-sensitive <em>E. coli </em>strain could in turn help reduce the concentration of indole produced by the resistant strain. This metabolic product is harmful to the growth of the antibiotic-resistant strain but benefits the antibiotic-sensitive strain by helping turn on the multi-drug exporter to discharge the antibiotic. The utilization of metabolism byproduct indole produced by antibiotic-resistant cells benefits antibiotic-sensitive cells, while the indole-absorbing service of antibiotic sensitive cells unconsciously help in nullifying the indole side effect on antibiotic resistant strain, and a mutual benefit cooperation could therefore evolve.</span></p>
Microgeographic evolution of metabolic physiology in a salamander metapopulation
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Data from: Phenotypic plasticity of antibiotic resistance, metabolism byproduct utilization and the evolution of mutually beneficial cooperation in Escherichia coli
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Metabolic byproduct utilization and the evolution of mutually beneficial cooperation in Escherichia coli
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The evolution of multi-gene families and metabolic pathways in the evening primroses (Oenothera: Onagraceae): a comparative transcriptomics approach
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Evolution and regulation of microbial secondary metabolism
Microbes have disproportionate impacts on the macroscopic world. This is in part due to their ability to grow to large groups and cooperatively secrete massive amounts of secondary metabolites that impact their environment. Yet, the conditions enabling secondary metabolism without compromising primary needs remain unclear. Here we investigated the biosynthesis of thamnolipids, a secondary metabolite that Pseudomonas aeruginosa makes to decrease the surface tension of surrounding liquid. Using a combination of genomics, metabolomics, transcriptomics, and mathematical modeling we show that biosynthesis of rhamnolipids from glycerol varies inconsistently across the phylogenetic tree; instead, non-producer lineages are also those worse at reducing the oxidative stress of primary glycerol metabolism. The link to oxidative stress explains the inconsistent distribution across the P. aeruginosa tree, adding a new layer to the regulation of rhamnolipids—a microbial secondary metabolite important for fitness in natural and clinical settings.
Plasticity and evolution shape the scaling of metabolism and excretion along a geothermal temperature gradient
<p>Physiological rates are heavily dependent on temperature and body size. Most current predictions of organisms' response to environmental warming are based on the assumption that key physiological rates like metabolism and excretion scale independently with body size and temperature and will not evolve. However, temperature is a significant driver for phenotypic variability in the allometric scaling and thermal sensitivity of physiological rates within ectotherm species, suggesting that evolution may play a role in shaping these parameters.</p> <p>We common-reared six populations of western mosquitofish that have recently established (~100 years ago) in geothermal springs along a broad thermal gradient (19-33°C) to determine whether these scaling parameters are affected by evolutionary and/or plastic responses to warming over ecological timescales. Each population was reared at four different temperatures (23, 26, 30 and 32°C). We measured routine metabolic and nitrogen excretion rates on mosquitofish across a wide body size range.</p> <p>We found evidence for plasticity, but not evolution, increasing the allometric scaling of metabolic rate with temperature. Plasticity in metabolism allometry reflected a decrease in thermal sensitivity at smaller body sizes.</p> <p>We found evidence for evolution of phenotypic plasticity on the allometry of excretion rate, reflecting evolutionary differences in how thermal sensitivity varies with body size across different populations.</p> <p>Evolutionary differences in excretion rate scaling did not influence the relationship between excretion and metabolism across rearing temperatures, suggesting that warming does not affect the balance between mosquitofish energy demands and nutrient recycling rates.</p>
Figure 5 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 5. Gene family and time-calibrated evolution analyses of 28 litostomateans and two spirotricheans. Ages are given as million years ago (Mya). Calibrated nodes are indicated with a star. Numbers asser "+" and "-" represent the expanded or contracted gene families in each branch* respectively. MRCA* the most recent common ancestor; H* subclass Haptoria; R* subclass Rhynchostomatia; º* subclass ºrichostomatia; CZ* Cenozoic; MZ* Mesozoic; NP* Neoproterozoic; PZ* Palaeozoic.
Figure 6 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 6. Heatmap showing the bias of stop codon usage among 28 litostomatean ciliates. H* subclass Haptoria; R* subclass Rhynchostomatia; º* subclass ºrichostomatia.
Figure 3. Phylogenomic tree estimated from a in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 3. Phylogenomic tree estimated from a concatenated dataset of 1680 orthogroups of 28 litostomateans and two spirotricheans
Figure 3. Phylogenomic tree estimated from a in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 3. Phylogenomic tree estimated from a concatenated dataset of 1680 orthogroups of 28 litostomateans and two spirotricheans by maximum likelihood (ML) and Bayesian inference (BI) methods. Sequences from the present study are in bold. Ŋe numbers at the nodes are the bootstrap values of ML out of 1000 pseudoreplicates and the posterior probability of Bayesian analysis* respectively. Ŋe black dots represent full support values both in the ML and in the BI trees. º* subclass ºrichostomatia; H* subclass Haptoria; R* subclass Rhynchostomatia.
Figure 2 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 2. Comparative analysis of 28 litostomatean ciliates. A* UpSet plot of shared orthogroups among the three litostomatean subclasses. B* GO enrichment analysis of conserved orthogroups in Litostomatea. C* heatmap showing the number of shared genes among 28 litostomatean ciliates. º* subclass ºrichostomatia; H* subclass Haptoria; R* subclass Rhynchostomatia.
Figure 7 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 7. Ŋe length distribution (A–C) and motif (C–E) of the introns detected in Monodinium sp.* Myriokaryon sp.* and Apodileptus visscheri.
Figure 1 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 1. Maps showing the location of the sampling sites and photomicrographs showing the in vivo morphology of the 14 litostomatean ciliates for which omics' data were newly obtained.
Figure 4 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 4. Carbohydrate-Active Enzymes Database (CAZy) annotation results of 28 litostomatean ciliates. A* comparison of the number of CAZymes in 28 litostomatean ciliates. B* CAZy function classification diagrams of three newly sequenced litostomateans (Didinium sp.1* Myriokaryon sp.* and Apodileptus visscheri). º* subclass ºrichostomatia; H* subclass Haptoria; R* subclass Rhynchostomatia.
Figure 2 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 2. Comparative analysis of 28 litostomatean ciliates. A* UpSet plot of shared orthogroups among the three litostomatean subclasses. B*
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.