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11,869 results for “metabolic”
Proccessed data for Trend Validation of Metabolic Models Against Measurements Using Indirect Calorimetry
<p>A cleaned data set used to validate metabolism models in a muscuskeletal modeling software.<br> The dataset contains 240 rows and 18 columns. </p> <p>Labels:</p> <ul> <li>AnyMet = Metabolic output by the modelling software. Calculated as the mean energy cost per repetition [J] .</li> <li>VynMet = Metabolic output by the indirect calorimetry system (Vyntus CPX). Calculated as the mean energy cost per repetition [J].</li> <li>rest_energy = total energy cost during rest [J]. Measured with Indirect caliometry</li> <li>rest_time = total time of rest [min]</li> <li>Work = Energy cost times the displacement per rep [J].</li> <li>watt = Work divided by total duration of a repetition [J/s]</li> <li>extension time = duration of the extension part of the movement [s]</li> <li>flexion time = duration of the flexion part of the movement [s]</li> <li>bw = bodyweight [kg]</li> <li>height [m]</li> <li>CV = coefficient of variation for the measured rest_energy. </li> <li>model = model type used for AnyMet. </li> <li>Subject </li> <li>Contraction = Contraction type performed</li> <li>intensity = Intensity to overcome created by the dynamometer. </li> <li>mech_watt_kg = mechcanical watt, watt divided by bodyweight</li> <li>any_met_watt_kg = watt pr kg: (AnyMet / bw) / (extension time + flexion time)</li> <li>vyn_met_watt_kg = watt pr kg: (VynMet / bw) / (extension time + flexion time)<br> <br> There is also a zip file containing the raw data from the dynanometer and the Vyntus PGE system.</li> </ul>
Data_Figure4_Loss of Claudin-3 Impairs Hepatic Metabolism, Biliary Barrier Function, and Cell Proliferation in the Murine Liver
<p>Data of Figure 4 from “Loss of Claudin-3 Impairs Hepatic Metabolism, Biliary Barrier Function, and Cell Proliferation in the Murine Liver”</p> <p>Dataset (doi: 10.1016/j.jcmgh.2021.04.003) contains the original publication as PDF-format (10.1038_s41598-020-59701-0). Corresponding raw data obtained from LC-MS/MS analysis provided as two files in CSV format (31003A-179400_10.1016j.jcmgh.2021.04.003_CGC_4-1.csv, 31003A-179400_10.1016j.jcmgh.2021.04.003_CGC_4-2.csv). All further experiment related information provided as two meta-data-files (31003A-179400_10.1016j.jcmgh.2021.04.003_CGC _4_1-2_M_1.PDF, 31003A-179400_10.1016j.jcmgh.2021.04.003_CGC _4_1-2_M_2.PDF) as PDF format.</p>
Data from: Additive effects of developmental acclimation and physiological syndromes on lifetime metabolic and water loss rates of a dry-skinned ectotherm
<p>Data sets from the paper: "Additive effects of developmental acclimation and physiological syndromes on lifetime metabolic and water loss rates of a dry-skinned ectotherm" by Dezetter et al. in Functional Ecology.</p> <p> </p> <p> </p>
Evolutionary responses of energy metabolism, development, and reproduction to artificial selection for increasing heat tolerance in Drosophila subobscura
<p><span>Adaptation to warming conditions involves increased heat tolerance and metabolic changes to reduce maintenance costs and maximize biological functions close to fitness. Evidence shows that energy metabolism evolves in response to warming conditions, but we know little about how heat stress intensity determines the evolutionary responses of metabolism and life history traits. Here, we evaluated the evolutionary responses of energy metabolism and life-history traits to artificial selection for increasing heat tolerance in Drosophila subobscura, using two protocols to measure and select heat tolerance: slow and fast ramping protocols. We found that the increase in heat tolerance was associated with reduced activity of the enzymes involved in the glucose-6-phosphate branchpoint, but no changes in the metabolic rate in selected lines. We also found that the evolution of increased heat tolerance increased the early fecundity in selected lines and increased the egg-to-adult viability only in the slow-ramping selected lines. This work shows heat tolerance can evolve under different thermal scenarios but with different evolutionary outcomes on associated traits depending on the heat stress intensity. Therefore, spatial and temporal variability of thermal stress intensity should be taken into account to understand and predict the adaptive response to ongoing and future climatic conditions.</span></p>
single-cell RNAseq data (data set 1) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset1) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CRC samples downloaded from the GEO website (<strong>GSE81861). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
Why does the metabolic cost of walking increase on compliant substrates?
<p>Walking on compliant substrates requires more energy than walking on hard substrates, but the biomechanical factors that contribute to this increase are debated. Previous studies suggest various causative mechanical factors, including disruption to pendular energy recovery, increased muscle work, decreased muscle efficiency and increased gait variability. We test each of these hypotheses simultaneously by collecting a large kinematic and kinetic data set of human walking on foams of differing thickness. This allowed us to systematically characterise changes in gait with substrate compliance, and, by combining data with mechanical substrate testing, drive the very first subject-specific computer simulations of human locomotion on compliant substrates to estimate the internal kinetic demands on the musculoskeletal system. Negative changes to pendular energy exchange or ankle mechanics are not supported by our analyses. Instead, we find that the mechanistic causes of increased energetic costs on compliant substrates are more complex than captured by any single previous hypothesis. We present a model in which elevated activity and mechanical work by muscles crossing the hip and knee are required to support the changes in joint (greater excursion and maximum flexion) and spatiotemporal kinematics (longer stride lengths, stride times and stance times, and duty factors) on compliant substrates.</p>
Dataset: A quantitative 1H NMR approach for evaluating the metabolic response of Saccharomyces cerevisiae to mild heat stress
<p>In this study, the effect of growth temperature on the yeast (<em>Saccharomyces cerevisiae</em>) metabolome has been analyzed by one-dimensional proton NMR spectroscopy (<sup>1</sup>H NMR).</p> <p>Yeast cells were grown either at 30 or 37°C. A non-targeted chemometric evaluation of the spectra was performed in order to detect potential biomarkers. Moreover, an exhaustive assignment for most of the detected NMR signals was carried out, corresponding to 38 identified metabolites. Resonances from these identified metabolites were integrated, and univariate and multivariate data analyses were applied on the matrices of these relative concentrations. Observed changes in metabolite concentrations were consistent with the expected process of temperature acclimation, showing alterations in amino acid cellular pools, nucleotide metabolism and lipid composition.</p>
Supplementary material for Metabolic constraints on the body size scaling of extreme population densities
<p>Data for the analysis of the paper entitled</p> <p>Metabolic constraints on the body size scaling of extreme population densities. Ecology letters.</p> <p><a href="https://zenodo.org/api/files/a60c335d-e6ca-4bd9-a17f-fc98ca9703e2/air_sea_outputs_L4_nsw.txt?versionId=1416dceb-47d0-45b1-99a8-baa833df8f76">air_sea_outputs_L4_nsw.txt </a></p> <p>This data set contains 8693 rows and 13 variables</p> <p>From colum 1 to 13 the following names apply:</p> <p>Julian- Day of the year (1rst January =1)</p> <p>day- Day number</p> <p>month- Month number</p> <p>year- Year number</p> <p>lat- Latitude</p> <p>lon- Longitude</p> <p>wind_speed_m_s- 1m wind speed in meters per second </p> <p>presure_mB- Air pressure in milibars</p> <p>Temp_°C- Air temperature in degrees Celsius </p> <p>Temp_algo- Other temperature</p> <p>Cloud_cover%- Percentage of cloud cover</p> <p>SST_°C- sea surface temperature in degree Celsius</p> <p>SW_Radiation_W_m2- Short wave radiation in Watts per square meter.</p> <p><a href="https://zenodo.org/api/files/a60c335d-e6ca-4bd9-a17f-fc98ca9703e2/SurfaceTemperature_L4_Merged.csv?versionId=9738e9d6-53ed-49c4-ab0d-136b74ddb16e">SurfaceTemperature_L4_Merged.csv </a></p> <p>This data set has 1064 rows and two columns</p> <p>Fechas_tempAll is the date of measurement formated as (Year-Month-Day Hour)</p> <p>e.g. 2013-05-20 09:00:00</p> <p>TEMP_EC- Surface water Temperature in degree Celsius</p> <p><a href="https://zenodo.org/record/7374166/files/Nutrients.csv?download=1">Nutrients.csv</a></p> <p>The data set sas 445 rows and 8 Variables</p> <p>Sample.Date- Date when surface water sample was taken.</p> <p>NITRITE.µM, - Nitrite (NO2) concentration in micromolar</p> <p>NITRATE.NITRITE.µM"- Nitrate (NO3) concentration in micromolar</p> <p>AMMONIA.µM, - Ammonia (NH4) concentration in micromolar</p> <p>SILICATE.µM, - Silica (Si) concentration in micromolar</p> <p>PHOSPHATE.µM- - Posphate (PO4) concentration in micromolar<br> FLAG- Quality flag (TRUE/FALSE).</p> <p> </p> <p> </p>
Data from: Balanced replacement of fish meal with Hermetia illucens meal allows efficient hepatic nutrient metabolism and increased fillet lipid quality in gilthead sea bream (Sparus aurata) juveniles
<p>In the present study, gilthead sea bream (<em>Sparus aurata</em>) juveniles were reared using sustainable feeds containing insect meal from <em>Hermetia illucens</em> larvae and poultry by-products meal. Proteomics and Proton Nuclear Magnetic Resonance-based metabolomics analysis were used to assess the metabolic impact of tested dietary formulations in sea bream liver, whereas the composition of muscle fillet was characterized by means of metabolomics and gas chromatography of fatty acids methyl esters. Replacing fish meal with insect meal in a 5% fish meal diet did not substantially alter metabolism of dietary nutrients, leading to small but statistically detectable effects solely on lauric acid content of sea bream fillet, and few alterations in some markers of immune response, such as leukocyte elastase inhibitor-like, granzyme B (G,H)-like, and two associated ortholog groups namely serpin B, and chymase). Liver morphology confirmed the absence of structural damage or inflammation in the insect meal-fed group, which showed a lower amount of hepatic lipid deposition and accumulation, too.</p>
Inferring and comparing metabolisms across heterogeneous sets of annotated genomes using AuCoMe
<p>CONTENT OF THIS ARCHIVE</p> <p>The Zenodo archive is composed of one file and four main directories:<br> * <strong>analyses</strong> gathers three subdirectories: algae, bacteria, and fungi. It includes all files used to create the figures, supplemental figures, and results of the paper.</p> <p>* <strong>code</strong> contains all AuCoMe and PADMET codes.</p> <p> – <strong>aucome_v0.5.1</strong> this directory gathers the code of AuCoMe used to run the three datasets.</p> <p> – <strong>padmet_v5.0.1</strong> this directory contains the code of PADMET used to run AuCoMe.</p> <p>* <strong>datasets</strong> this directory gathers all datasets on which AuCoMe was run: the bacterial, fungal, and algal datasets, and the 32 synthetic datasets, which contain an <em>E. coli</em> K–12 MG1655 genome to which various degradations were applied, together with 28 other bacterial genomes. It also encompasses the version 23.5 of MetaCyc database.</p> <p>* <strong>scripts_analyses</strong> this directory contains several scripts to generate the figures, supplemental figures and a script to degrade the <em>E. coli</em> K–12 MG1655 genome.</p> <p> </p> <p> </p> <p>1/ Content of the <strong>analyses</strong> repertory<br> It is composed of three subdirectories: <strong>algae</strong>, <strong>bacteria</strong>, and <strong>fungi</strong>.</p> <p>1.1/ Content of the <strong>algae</strong> subdirectory<br> It encompasses 9 files.</p> <p>* <strong>Figure_2_algal_nb_reactions.tsv</strong> for each species of the algal dataset, this file gives the number of reactions at each AuCoMe step. It was used to create figure 2D.</p> <p>* <strong>Figure_S10_Deepec_algal.tsv</strong> for each species of the algal dataset, at each AuCoMe step (robust orthology, non-robust orthology, and annotation or orthology), several measures were computed, i.e.: the number of reactions, the number of ECs, the number of ECs validated by DeepEC, and the ratio number of ECs valided by DeepEC / number of ECs. It was used to design figure S10(b).</p> <p>* <strong>Table_S6_50_random_reactions_found.xlsx</strong> contains manual validation of 50 randomly chosen reactions found in any of the species (is the Supplemental Table S6).</p> <p>* <strong>Table_S7_50_random reactions absent.xlsx</strong> includes manual validation of 50 reactions absent from a species and randomly chosen (is the Supplemental Table S7).</p> <p>* <strong>Table_S8_reactions_common_only_Cokamuranus_Sjaponica.xlsx</strong> encompasses reactions common to <em>Saccharina japonica</em> and <em>Cladosiphon okamuranus</em> but not found in other brown algae (is the Supplemental Table S8).</p> <p>* <strong>Table_S9_homologues_Esiliculosus_Sjaponica.xlsx</strong> contains additional homologs in <em>E. siliculosus</em> found by BLASTP searches for sequences inferred to be present only in <em>C. okamuranus</em> and <em>S. japonica</em> (is the Supplemental Table S9).</p> <p>* <strong>Table_S10_o-aminophenol_Esiliculosus_holomogues.xlsx</strong> includes additional o-aminophenol oxidases from <em>E. siliculosus</em> and their homologs in other stramenopiles. It is the Supplemental Table S10 with more detail (like the amino acid sequences).</p> <p>* <strong>Table_S11_reactions_cryptophytes_haptophytes_stramenopiles_archeplastida.xlsx</strong> encompasses reactions distinguishing the cryptophyte, haptophyte, stramenopile, and archeplastida groups (is the Supplemental Table S11).</p> <p>* <strong>Table_S12_pathways_cryptophytes_haptophytes_stramenopiles_archeplastida.xlsx</strong> contains shared metabolic pathways as well as the absence of pathways between chryptophytes, haptophytes, stramenopiles, and archaeplastida (is the Supplemental Table S12).</p> <p> </p> <p>1.2/ Content of the <strong>bacteria</strong> subdirectory<br> It gathers 12 files and 9 repertories.</p> <p>* <strong>aucome_final.tsv</strong> output file of the figure S4 comparison bacteria.py script, for each of the 29 bacterial metabolic networks produced with AuCoMe, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>carveme_stat.tsv</strong> output file of the figure S4 comparison bacteria.py script, for each of the 29 bacterial metabolic networks produced with CarveMe, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>ecocyc.padmet</strong> contains the EcoCyc database version 23.5 at the PADMet, is used to generate the Supplemental Fig. S5.</p> <p>* <strong>Figure_2_bacterial_nb_reactions.tsv</strong> for each species of the bacterial dataset, this file gives the number of reactions at each AuCoMe step. It was used to create figure 2B.</p> <p>* <strong>Figure_3_nb_reactions_step.tsv</strong> for each dataset of the 32 synthetic bacterial datasets, this file enumerates the number of reactions at each AuCoMe step. It was used to create figure 3A.</p> <p>* <strong>Figure_3_fmeasure_steps.tsv</strong> for each dataset of the 32 synthetic bacterial datasets, this file indicates the values of the F-measures resulting of the comparison of the GSMNs recovered for each <em>E. coli</em> K–12 MG1655 genome replicate with the gold-standard network EcoCyc. It was used to create figure 3B.</p> <p>* <strong>Figure_S4_output</strong> contains 3 output files of the figure_S4_comparison_bacteria.py script:</p> <p> – <strong>Figure_S4_boxplot_networks.svg</strong> is the Supplemental Figure S4 in high resolution.</p> <p> – <strong>Figure_S4_boxplot_networks.tsv</strong> contains the number of reactions, the type of reactions (All, Reactions with genes, ...), and the used software. Thes data were produced and used in the figure_S4_comparison_bacteria.py script.</p> <p> – <strong>Figure_S4_barplot_time_networks.svg</strong> for each software, shows the required time in seconds used to reconstruct these bacterial metabolic networks.</p> <p>* <strong>Figure_S5_output</strong> encompasses 3 output files of figure S5 reference catalog.py script:</p> <p> – <strong>Figure_S5_ec_union.svg</strong> is the Supplemental Figure S5 in high resolution.</p> <p> – <strong>Figure_S5_ec_union_venn.svg</strong> another visualisation of presenting the results of the Supplemental Fig. S5.</p> <p> – <strong>Figure_S5_refence_ec_catalog_K12MG1655.tsv</strong> contains an EC catalog to <em>E. coli</em> K-12 MG1655 from the BIGG, EcoCyc, KEGG, and ModelSEED databases. This file is used to produce the Supplemental Figure S5.</p> <p>* <strong>Figure_S6_output</strong> includes 2 output files of the figure_S6.py script:</p> <p> – <strong>Figure_S6_comparison_all.svg</strong> is the Supplemental Figure S6 in high resolution.</p> <p> – <strong>Figure_S6_comparison_all.tsv</strong> contains data used to produce the Supplemental Figure S6.</p> <p>* <strong>gapseq_stat.tsv</strong> output file of the figure_S4_comparison_bacteria.py script, for each of the 29 bacterial metabolic networks produced with gapseq, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>jsons_bigg</strong> todate contains the five metabolic networks of <em>E. coli</em> K–12 MG1655 that can find in BIGG at JSON format. These files correspond to the BIGG reference metabolic network on the Supplemental Figure S5.</p> <p>* <strong>jsons_modelseed</strong> todate includes the metabolic network of <em>E. coli</em> K–12 MG1655 that can find in ModelSEED at JSON format. It is the ModelSEED reference metabolic network on the Supplemental Figure S5.</p> <p>* <strong>kegg_ecs.txt</strong> input file of the figure_S5_reference_catalog.py script, it contains matches between EC numbers and all the entries of <em>E. coli</em> K–12 MG1655 in the KEGG database.</p> <p>* <strong>mapping_modelseed_ec.tsv</strong> input file of the figure_S4_comparison_bacteria.py script, it encompasses matches between ModelSEED reactions and EC numbers.</p> <p>* <strong>modelseed_stat.tsv</strong> output file of the figure_S4_comparison_bacteria.py script, for each of the 29 bacterial metabolic networks produced with ModelSEED, this table contains the number of ECs, the number of unique ECs, the number of total reactions, the number of enzymatic reactions with genes, the number of enzymatic reactions without genes, and the number of spontaneous reactions.</p> <p>* <strong>networks_aucome</strong> for each of the 29 bacteria, contains a metabolic networks at the PADMet format obtained with AuCoMe.</p> <p>* <strong>networks_carveme</strong> for each of the 29 bacteria, contains a metabolic networks at the SBML format got to CarveMe.</p> <p>* <strong>networks_gapseq</strong> composes of 29 subdirectories (one for each bacterium). All these subdirectories contain 10 files about the metabolic networks a obtained with gapseq:</p> <p> – <strong>species-all-Pathways.tbl</strong> encompasses data on pathways at TBL format.</p> <p> – <strong>species-all-Reactions.tbl</strong> includes data on reactions at TBL format.</p> <p> – <strong>species-draft.RDS</strong> is a draft metabolic network at RDS (R Data Format).</p> <p> – <strong>species-draft.xml</strong> is a draft metabolic network at SBML format.</p> <p> – <strong>species-medium.csv</strong> encompasses all the metabolites allow the default medium.</p> <p> – <strong>species.RDS</strong> is the final metabolic network at RDS (R Data Format).</p> <p> – <strong>species-rxnWeights.RDS</strong> is a temporary file nedeed to gapseq fill at RDS (R Data Format).</p> <p> – <strong>species-rxnXgenes.RDS</strong> is a temporary file nedeed to gapseq fill at RDS (R Data Format).</p> <p> – <strong>species-Transporter.tbl</strong> includes data on transporters at TBL format.</p> <p> – <strong>species.xml</strong> is the final metabolic network at SBML format.</p> <p>* <strong>networks_modelseed</strong> includes two subdirectories:</p> <p> – <strong>sbml</strong> for each of the 29 bacteria, encompasses a metabolic networks at the SBML format got to ModelSEED.</p> <p> – <strong>tsv</strong> for each of the 29 bacteria, contains two TSV files:</p> <p> – <strong>genomeset__species.gbk_genome.fbamodel-compounds.tsv</strong> includes data on compounds at TSV format.</p> <p> – <strong>genomeset__species.gbk_genome.fbamodel-reactions.tsv</strong> encompasses data on reactions at TSV format.</p> <p>* <strong>time_carveme.txt</strong> input file of the figure S4 comparison bacteria.py script, for each of the 29 bacteria it stores the running time of CarveMe (in seconds) to reconstruct a metabolic network.</p> <p>* <strong>time_gapseq.txt</strong> input file of the figure S4 comparison bacteria.py script, for each of the 29 bacteria it stores the running time of gapseq (in seconds) to reconstruct a metabolic network.</p> <p> </p> <p>1.3/ Content of the <strong>fungi</strong> repertory<br> It contains three files and five directories.</p> <p>* <strong>All-pathways-of-S.-cerevisiae-S288c.txt</strong> encompasses all the YeastCyc pathways.</p> <p>* <strong>Figure_2_fungal_nb_reactions.tsv</strong> for each species of the fungal dataset, this file gives the number of reactions at each AuCoMe step. It was used to create figure 2C.</p> <p>* <strong>Figure_S7_output</strong> contains 11 output files of the figure S7 comparison pathway fungi.py script:</p> <p> – <strong>completion_pathway_species.svg</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), contains a subfigure of the Supplemental Fig. S7.</p> <p> – <strong>fungi_stats.tsv</strong> is the Supplemental Table S5.</p> <p> – <strong>pathway_venn_species.png</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), includes a Venn diagram about all the pathways found with the 3 software (AuCoMe, gapseq, and ModelSEED).</p> <p>* <strong>Figures_S8_S9_output</strong> contains 11 files, in all these files, a comparison of all pathways of metabolic networks of <em>S. cerevisiae</em> S288C obtained with AuCoMe and gapseq to those of YeastCyc was released.</p> <p> – <strong>comparison_yeastcyc.png</strong> is a picture about number of pathways true positive, false positive, and false negative are found, according the used method (AuCoMe and gapseq).</p> <p> – <strong>completion_pathway_gapseq.svg</strong> includes the number of pathways common or specific to YeastCyc and gapseq with their completeness ratio predicted by gapseq.</p> <p> – <strong>Figure_S8_completion_pathway_aucome.svg</strong> contanis the number of pathways common or specific to YeastCyc and AuCoMe with their completeness ratio predicted by AuCoMe, is the Supplemental Figure S8.</p> <p> – <strong>Figure_S9_venn_diagram_70_100.svg</strong> is the Supplemental Figure S9. All pathways of AuCoMe, gapseq and YeastCyc with a completion rate between 50% and 70% are compared.</p> <p> – <strong>venn_diagram.svg</strong> in this picture, all pathways are compared.</p> <p> – <strong>venn_diagram 50.svg</strong> all pathways of AuCoMe, gapseq and YeastCyc with a completion rate less than 50% are compared.</p> <p> – <strong>venn_diagram_50_gapseq.svg</strong> all pathways of gapseq whatever their completion rate are compared to the AuCoMe and YeastCyc pathways with a completion rate less than 50%.</p> <p> – <strong>venn_diagram_50_70.svg</strong> all pathways of AuCoMe, gapseq, and YeastCyc with a completion rate between 50% and 70% are compared.</p> <p> – <strong>venn_diagram_50_70_gapseq.svg</strong> all pathways of gapseq whatever their completion rate are compared to the AuCoMe and YeastCyc pathways with a completion rate between 50% and 70%.</p> <p> – <strong>venn diagram_70_100_gapseq.svg</strong> all pathways of gapseq whatever their completion rate are compared to the AuCoMe and YeastCyc pathways with a completion rate between 70% and 100%.</p> <p> – <strong>yeast_cyc_comparison.tsv</strong> contains the number of pathways true positive, false positive, and false negative are found, according the used method (AuCoMe and gapseq).</p> <p>* <strong>Figure_S10_Deepec_fungal.tsv</strong> for each species of the fungal dataset, at each AuCoMe step (robust orthology, non-robust orthology, and annotation or orthology), several measures were computed, i.e.: the number of reactions, the number of ECs, the number of ECs valided by DeepEC, and ratio number of ECs validated by DeepEC / number of ECs. It was used to design figure S10(a).</p> <p>* <strong>networks_aucome</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), contains a metabolic networks at the PADMet format obtained with AuCoMe.</p> <p>* <strong>networks_gapseq</strong> is composed of 5 subdirectories (one for each fungus). All these subdirectories contain two files about the metabolic networks a obtained with gapseq:</p> <p> – <strong>species-all-Pathways.tbl</strong> encompasses data on pathways at TBL format.</p> <p> – <strong>species-all-Reactions.tbl</strong> includes data on reactions at TBL format.</p> <p>* <strong>networks_modelseed</strong> for each of the 5 fungi (<em>L. bicolor</em>, <em>N. crassa</em>, <em>R. oryzae</em>, <em>S. cerevisiae</em> S288C, and <em>S. pombe</em>), contains two TSV files:</p> <p> – <strong>species.gbk_genome.draftModel-compounds.tsv</strong> includes data on compounds at TSV format.</p> <p> – <strong>species.gbk_genome.draftModel-reactions.tsv</strong> encompasses data on reactions at TSV format.</p> <p> </p> <p> </p> <p>2/ Content of the <strong>code</strong> repertory<br> It gathers two directories <strong>aucome v0.5.1</strong> and <strong>padmet_v5.0.1</strong>.</p> <p>2.1/ Content of the <strong>aucome v0.5.1</strong> subdirectory<br> This directory contains a copy of the AuCoMe project on the GitHub site: <a href="https://github.com/AuReMe/aucome">https://github.com/AuReMe/aucome</a> (downloaded the 15/11/2022). It is composed of two subdirectories and five files:<br> * <strong>LICENCE</strong> licence of the AuCoMe software.</p> <p>* <strong>README.rst</strong> README of the AuCoMe software.</p> <p>* <strong>requirements.txt</strong> contains the list of requires Python packages.</p> <p>* <strong>setup.cfg</strong> contains metadata about AuCoMe package and is used with setup.py to distribute AuCoMe.</p> <p>* <strong>setup.py</strong> contains various information relevant to the AuCoMe package including options and metadata. Then, it is used to distribute AuCoMe with PyPI. It is also used to create an entrypoint when installing it with pip.</p> <p>* <strong>recipes</strong> this subdirectory contains two files:<br> – <strong>Dockerfile</strong> contains instructions to run AuCoMe in a Docker environment.<br> <br> – <strong>Singularity</strong> contains instructions to run AuCoMe in a Singularity container.</p> <p>* <strong>aucome</strong> this directory contains 11 Python files:<br> – <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>__main__.py</strong> contains the functions implementing the command-line interface of AuCoMe.</p> <p> – <strong>analysis.py</strong> contains the functions to analyse the AuCoMe results.</p> <p> – <strong>check.py</strong> contains the functions to check the input files.</p> <p> – <strong>compare.py</strong> contains the functions to compare the AuCoMe results between two distinct subgroups.</p> <p> – <strong>orthology.py</strong> contains the functions to propagate reaction through orthology.</p> <p> – <strong>reconstruction.py</strong> contains the functions to perform the reconstruction of draft GSMNs by using Pathway Tools in a parallel implementation.<br> <br> – <strong>spontaneous.py</strong> contains the functions to add spontaneous reactions to some GSMNs if it completes MetaCyc metabolic pathway.</p> <p> – <strong>structural.py</strong> contains the functions to check that no reactions are missing due to missing gene structures. A genomic search is performed for all reactions present in one organism but not in another.<br> <br> – <strong>utils.py</strong> contains a function to analyse the configuration file.</p> <p> – <strong>workflow.py</strong> contains functions to run all the steps of AuCoMe.</p> <p> </p> <p>2.2/ Content of the <strong>padmet_v5.0.1</strong> subdirectory<br> This directory contains a copy of the PADMET project on the GitHub site: <a href="https://github.com/AuReMe/padmet/">https://github.com/AuReMe/padmet/</a> (downloaded the 15/11/2022). It is composed of two subdirectories and six files:<br> * <strong>CHANGELOG.md </strong>records of all notable changes made in the PADMET project.</p> <p>* <strong>docs</strong> this repertory contains all the documentation files of PADMET package in the RST format.</p> <p>* <strong>LICENCE</strong> licence of the PADMET package.</p> <p>* <strong>README.md</strong> manual of the PADMET package.</p> <p>* <strong>requirements.txt</strong> contains the list of requires Python packages.</p> <p>* <strong>setup.cfg</strong> contains metadata about PADMET package and is used with setup.py to distribute PADMET.</p> <p>* <strong>setup.py</strong> contains various information relevant to the PADMET package including options and metadata. Then, it is used to distribute PADMET with PyPI. It is also used to create an entrypoint when installing it with pip.<br> <br> * <strong>padmet</strong> this repertory grathers two files and two subdirectories:<br> – <strong>__init__.py</strong> indicates the version of PADMET.</p> <p> – <strong>__main__.py</strong> contains the functions implementing the command-line interface of PADMET.</p> <p> – <strong>classes</strong> contains 7 files.</p> <p> – <strong>utils</strong> contains 4 files and 3 subdirectories.</p> <p><br> 2.2.1/ Content of the <strong>class</strong> subdirectory<br> The class repertory contains 7 files.<br> * <strong>__init__.py </strong>indicates the directory as a python module.</p> <p>* <strong>instantiation.py</strong> contains a function to instantiate padmet object.</p> <p>* <strong>node.py</strong> contains a class defining a Node object which is representing an element in a metabolic network (e.g: compound, reaction).</p> <p>* <strong>padmetRef.py</strong> contains a class defining a PadmetRef object which is representing a database of metabolic network.</p> <p>* <strong>padmetSpec.py</strong> creates a PadmetSpec object which is representing the metabolic network of a species/organism based on a reference database PadmetRef.</p> <p>* <strong>policy.py</strong> contains a class defining a Policy object that is defining the types of Relations and Nodes of a network.</p> <p>* <strong>relation.py</strong> contains a class defining a Relation object which is representing a link between two elements (Node) in a metabolic network.</p> <p><br> 2.2.2/ Content of the <strong>utils</strong> subdirectory<br> The utils directory contains 4 files and 3 subdirectories.<br> * <strong>__init__.py</strong> indicates the directory as a python module.</p> <p>* <strong>gbr.py</strong> implements a lexical analysis to handle genes relationship associated with a reaction, either a complex (with and relation between genes) or isozyme (with or relation between genes).</p> <p>* <strong>sbmlPlugin.py</strong> contains functions to handle SBML element (ex: species or reaction), then it returns all the sections named notes in a dictionary.<br> <br> * <strong>utils.py</strong> contains a function that checks paths of file.</p> <p>* <strong>connection</strong> this subdirectory contains 22 files:<br> - <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>biggAPI_to_padmet.py</strong> allows to extract the BIGG database from the API to create a padmet. An Internet access is required.</p> <p> – <strong>check_orthology_input.py</strong> is written to check if the metabolic network and the proteome of the model organism use the same identifiers for genes (or at least more than a given cutoff), before running orthology based reconstruction.</p> <p> – <strong>enhanced_meneco_output.py</strong> extracts the results from Meneco gap-filling to add more information to the gap-filled reactions. Then it returns a PADMET file with more information for each reaction.</p> <p> – <strong>extract_orthofinder.py</strong> after running Orthofinder on n FASTA files, it reads the output file ’Orthogroups.tsv’ to identify the orthologous genes. It is used by AuCoMe to extract the orthologous genes.<br> <br> – <strong>extract_rxn_with_gene_assoc.py</strong> from a given SBML file, it creates a SBML with only the reactions associated to a gene.<br> <br> – <strong>gbk_to_faa.py</strong> extracts protein sequence from a GenBank into a FASTA file with Biopython package.</p> <p> – <strong>gene_to_targets.py</strong> from a list of genes, it gets the products associated with the reactions linked to the genes. For example: R1 is linked to G1, R1 produces M1 and M2, this script outputs: M1, M2.</p> <p> – <strong>get_metacyc_ontology.py</strong> from the PadmetRef of MetaCyc, it creates the MetaCyc ontology.</p> <p> – <strong>metexploreviz_export.py</strong> converts a PADMET object representing a metabolic network into a JSON compatible with MetExplore.<br> <br> – <strong>modelSeed_to_padmet.py</strong> from ModelSEED reactions and pathways files, it creates a PADMET.<br> <br> – <strong>network_to_gnn.py</strong> creates input for GNN (Graph Neural Networks) from PADMET or SBML.</p> <p> – <strong>padmet_to_asp.py</strong> converts PADMET to Answer Set Programming.</p> <p> – <strong>padmet_to_matrix.py</strong> creates a stoichiometry matrix from a PADMET file, in which the columns represent the reactions and rows represent metabolites.</p> <p> – <strong>padmet_to_padmet.py</strong> allows to merge 1-n PADMET.<br> <br> – <strong>padmet_to_tsv.py</strong> converts a PADMET representing a database (PadmetRef) and/or a PADMET representing a model (PadmetSpec) to TSV files.</p> <p> – <strong>pgdb_to_padmet.py</strong> reads a PGDB folder (from BIOCYC/Pathway Tools) and creates a PADMET. It is used by AuCoMe to create PADMET files from PGDB in the annotation-based step.</p> <p> – <strong>sbmlGenerator.py</strong> contains functions to generate SBML files from PADMET and TXT files usign the libsbml package. It is used by AuCoMe to create SBML files at the annotation-based, orthology and final steps.</p> <p> – <strong>sbml_to_curation_form.py</strong> extracts one or several reactions from a SBML file to the form used in AuReMe for curation.</p> <p> – <strong>sbml_to_padmet.py</strong> converts a SBML file into a PADMET file (with or without a reference database).</p> <p> – <strong>sbml_to_sbml.py</strong> creates a SBML file from another one. Use it to change the SBML level.</p> <p> – <strong>wikiGenerator.py</strong> contains all necessary functions to generate wiki pages from a PADMET file and update a wiki online. It requires WikiManager module (with wikiMate, Vendor).</p> <p>* <strong>exploration</strong> this subdirectory contains 15 files:<br> - <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>compare_padmet.py</strong> compares 1-n PADMET files, and creates a folder with 4 output files (compounds.tsv, genes.tsv, pathways.tsv and reactions.tsv). It is used by AuCoMe to create these files to analyse the metabolic networks.</p> <p> – <strong>compare_sbml.py</strong> compares 2 or 1-n SBML, then it creates two output files reactions.tsv and metabolites.tsv with the reactions/metabolites in each SBML files.</p> <p> – <strong>compare_sbml_padmet.py</strong> compares reaction identifiers in SBML versus PADMET, then returns the number of reactions in both, and reaction identifiers not in SBML or not in PADMET.</p> <p> – <strong>convert_sbml_db.py</strong> uses the MetaNetX database to check or convert a SBML. Flat files from MetaNetx are required to run this script. They can be found in the AuReMe workflow or from the MetaNetx website.</p> <p> – <strong>dendrogram_reactions_distance.py</strong> uses the reactions.tsv file from compare_padmet.py to create a dendrogram using the R package pvclust. It has been used in the article to create the metabolic dendrogram.</p> <p> – <strong>flux_analysis.py</strong> runs the flux balance analyse with cobra package on an already defined reaction. It needs to set in the SBML the value ’objective_coefficient’ to 1.</p> <p> – <strong>get_pwy_from_rxn.py</strong> from a file containing a list of reaction, it returns the pathways where these reactions are involved.</p> <p> – <strong>padmet_stats.py</strong> creates a PADMET stats file (named padlet_stats.tsv) containing the number of pathways, reactions, genes and compounds inside the one or several PADMET files.</p> <p> – <strong>pathway_production.py</strong> compares 1-n PADMET objects to show the pathway input/output for them.</p> <p> – <strong>prot2genome.py</strong> contains function to search a genome using protein sequences and Gene-Protein-Reaction associations. It is used in the structural search step of AuCoMe.</p> <p> – <strong>report_network.py</strong> creates reports of a PADMET file, and it writes three TSV files (all metabolites.tsv, all_pathways.tsv, and all_reactions.tsv).</p> <p> – <strong>visu_network.py</strong> allows to visualize a metabolic network on a compounds perspectives.</p> <p> – <strong>visu_path.py</strong> allows to visualize a pathway in PADMET network.<br> <br> – <strong>visu_similarity_gsmn.py</strong> visualize similarity between metabolic networks using MDS.</p> <p>* <strong>management</strong> this subdirectory contains 5 files:<br> – <strong>__init__.py</strong> indicates the directory as a python module.</p> <p> – <strong>manual_curation.py</strong> updates a PadmetSpec object by filling specific forms. It either creates new reaction(s) to PADMET file, or it adds/removes reaction(s) from a PadmetRef.</p> <p> – <strong>padmet_compart.py</strong> for a given PADMET file, it checks and updates compartment.</p> <p> – <strong>padmet_medium.py</strong> for a given set of compounds representing the growth medium (or seeds), it creates two reactions in order to maintain consistency of the network for flux analysis.</p> <p> – <strong>relation_curation.py</strong> for a given PADMET file, it adds or removes relations between nodes.</p> <p> </p> <p> </p> <p>3/ Content of the <strong>datasets</strong> subdirectory<br> It contains a files and four repertories.</p> <p>* <strong>metacyc 23.5.padmet</strong> the version 23.5 of the <a href="https://metacyc.org/">MetaCyc</a> database in the PADMET format. It was used by AuCoMe to reconstruct all the metabolic networks. Hence metacyc 23.5.padmet is required to reproduce the article results.</p> <p>3.1/ Content of the <strong>algal</strong>, <strong>bacterial</strong>, and <strong>fungal</strong> directories<br> These three directories are composed of 8 subdirectories and a Supplemental Table (respectively Table S1, Table S2 and Table S3 in bacterial, fungal and algal directories):<br> * <strong>FASTA</strong> contains the proteome of each species as a FASTA file.</p> <p>* <strong>cleaned_GBKs</strong> for each species, it contains the annotated genome, with the protein sequences in a GenBank format file.</p> <p>* <strong>dictionaries</strong> for some species, genes needed to be renamed for compatibility reasons. This folder contains CSV files with the mapping between the old names of genes and the new ones.</p> <p>* <strong>annotated_DATs</strong> contains a subdirectory per species with all the output files from Pathway Tools v23.5, without any post-treatment, in the DAT format.</p> <p>* <strong>annotated_PADMETs</strong> for each species, it contains a metabolic network of the draft reconstruction step of AuCoMe, in the PADMET format.<br> <br> * <strong>final_PADMETs</strong> for each species, it contains a metabolic network generated by the AuCoMe workflow, at the PADMET format.</p> <p>* <strong>final_SBMLs</strong> for each species, it contains a metabolic network generated by the AuCoMe workflow, in the SBML format.</p> <p>* <strong>panmetabolism</strong> is composed of 7 files describing the final metabolic networks:<br> – <strong>genes.tsv</strong> contains, for each organism, the list of genes and the associated reactions.</p> <p> – <strong>metabolites.tsv</strong> contains the list of metabolites present in the panmetabolism. Then, for each metabolite and for each organism, it lists the reactions that produced this compound and the reactions that consumed it.<br> <br> – <strong>pathways.tsv</strong> contains the list of pathways present in the panmetabolism. For each pathway and for each organism, it indicates the number of reactions present in this pathway, and the names of these reactions.<br> <br> – <strong>reactions.tsv</strong> contains the list of reactions present in the panmetabolism. Then for each reaction, it indicates whether or not it belongs to an organism. If a reaction is found in a species, the genes associated with the reaction are also listed.</p> <p> – <strong>pvclust_reaction_dendrogram.png</strong> based on the presence/absence matrix of reactions in different species of the dataset, it computes the Jaccard distances between these species, and it applies a hierarchical clustering on these data with a complete linkage to create a dendrogram. The R package pvclust is used to create the dendrogram, with bootstrap resampling. For each node, a p-value indicates how strong the cluster is supported by data. This dendrogram is provided as a PNG picture.</p> <p><br> 3.2/ Content of the <strong>synthetic_bacterial</strong> repertory<br> The synthetic_bacterial repertory contains the Supplemental Table S4 and 32 subdirectories named Run_00, Run_01, . . . , etc, Run 31. Each subdirectory is composed of 9 files:<br> * <strong>K_12_MG1655.gbk</strong> the annotated genome of <em>E. coli K–12 MG1655</em> to which degradation of the functional and/or structural annotations was applied.</p> <p>* <strong>annotated_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the draft reconstruction step of AuCoMe in the SBML format.</p> <p>* <strong>annotated_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the draft reconstruction step of AuCoMe in the PADMET format.</p> <p>* <strong>orthology_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the orthology propagation step of AuCoMe in the SBML format.</p> <p>* <strong>orthology_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the orthology propagation step of AuCoMe in the PADMET format.<br> <br> * <strong>structural_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the structural verification step of AuCoMe in the SBML format.<br> <br> * <strong>structural_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the structural verification step of AuCoMe in the PADMET format.</p> <p>* <strong>final_K_12_MG1655.sbml</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the AuCoMe workflow in the SBML format.</p> <p>* <strong>final_K_12_MG1655.padmet</strong> the metabolic network of <em>E. coli K–12 MG1655</em> output of the AuCoMe worflow in the PADMET format.</p> <p> </p> <p> </p> <p>4/ Content of the <strong>scripts_analyses</strong> subdirectory<br> The scripts repertory contains 12 files:</p> <p>* <strong>bacteria_random_degradation.py</strong> was used to degrade the <em>E. coli</em> K–12 MG1655 genome. The procedure for the genome degradation is described in the algorithm 1.</p> <p>* <strong>figure_2_algal_dataset.py</strong> for each species of the algal dataset, and at each AuCoMe step. This script allows to generate the figure 2D.</p> <p>* <strong>figure_2_bacterial_dataset.py</strong> for each species of the bacterial dataset, and at each AuCoMe step. This script allows to generate the figure 2B.</p> <p>* <strong>figure_2_fungal_dataset.py</strong> for each species of the fungal dataset, and at each AuCoMe step. This script allows to generate the figure 2C.</p> <p>* <strong>figure_3_degradation.py</strong> allows to generate the figure 3B from the figure_3_fmeasure_steps.tsv file (described above).</p> <p>* <strong>figure_5_mds.py</strong> allows to generate the figure 5A from two reactions.tsv files of the algal dataset (annotation-based and final).</p> <p>* <strong>figure_S4_comparison_bacteria.py</strong> computes statistics on all the 29 bacterial metabolic networks reconstructed with AuCoMe, CarveMe, gapseq and ModelSEED, it uses the mapping_modelseed_ec.tsv, soft_stat.tsv files and bacteria/networks soft directories, then it creates the Supplemental Fig. S4 and files inside the analyses/bacteria/Figure_S4_output repertory.</p> <p>* <strong>figure_S5_reference_catalog.py</strong> reads the ecocyc.padmet, kegg ecs.txt files, and the jsons bigg/, jsons modelseed/ directories, then it creates the Figure_S5_refence_ec_catalog_K12MG1655.tsv and the Supplemental Fig. S5.</p> <p>* <strong>figure_S6.py</strong> reads the Figure_S5_refence_ec_catalog_K12MG1655.tsv file and those inside the analyses/bacteria/networks_soft directories about <em>E. coli</em> K–12 MG1655, it generates the Supplemental Fig. S6.</p> <p>* <strong>figure_S7_comparison_pathway_fungi.py</strong> reads metacyc_23.5.padmet file and those inside the analyses/fungi/networks_soft directories, then it create five completion pathway species.svg pictures which composed the Supplemental Fig. S7.</p> <p>* <strong>figures_S8_S9.py</strong> reads the metacyc_23.5.padmet, and metabolic networks of <em>S. cerevisiae</em> S288 reconstructed with AuCoMe, gapseq, and YeastCyc. For all pathways of both AuCoMe and gapseq networks, it also computes their completion rates. Then it compares the results obtained with AuCoMe and gapseq on <em>S. cerevisiae</em> S288C to YeastCyc according to the completion rates of their pathways. It generates Supplemental Fig. S8 and S9.</p> <p>* <strong>figure_S12_supervenn.py</strong> allows to generate the figure S12, it reads the reactions.tsv file of the algal dataset at the final AuCoMe step, and another tabular file that contains abbreviated names of species.</p>
Output files of the computationally reviewed literature data on enantiomeric metabolism
<p>The computationally reviewed literature data on enantiomeric metabolism. The chiral drug selection based on quantum chemistry's role in the enantiomeric metabolism.</p>
Spatial variation in the evolutionary potential and constraints of basal metabolic rate and body mass in a wild bird
<p><span>Organismal energy budget is strongly related to resource consumption, performance, and fitness. Hence, understanding the evolution of key energetic traits, such as basal metabolic rate (BMR), in natural populations is central for understanding life-history evolution and ecological processes. Here we used quantitative genetic analyses to study evolutionary potential of BMR in two insular populations of the house sparrow <em>(Passer domesticus</em>). We obtained measurements of BMR and body mass (M<sub>b</sub>) from 911 house sparrows on the islands of Leka and Vega along the coast of Norway</span><span>.</span><span> These two populations were the source populations for translocations to create an additional third, admixed "common garden" population in 2012. With the use of a novel genetic group animal model concomitant with a genetically determined pedigree, we differentiate genetic and environmental sources of variation, thereby providing insight into the effects of spatial population structure on evolutionary potential. We found that the evolutionary potential of BMR was similar in the two source populations, whereas the Vega population had a somewhat higher evolutionary potential of M<sub>b</sub> than the Leka population. BMR was genetically correlated with M<sub>b</sub> in both populations, and the conditional evolutionary potential of BMR (independent of body mass) was 41% (Leka) and 53% (Vega) lower than unconditional estimates. Overall, our results show that there is potential for BMR to evolve independently of M<sub>b</sub>, but that selection on BMR and/or M<sub>b</sub> may have different evolutionary consequences in different populations of the same species.</span></p>
Perturbations in fatty acid metabolism and collagen production infer pathogenicity of a novel MBTPS2 variant in Osteogenesis imperfecta
<p>(i) PCR-sequencing: Chromatograms were generated by PCR-sequencing of a region within exon 4 of MBTPS2 using DNA extracted from a healthy control and the proband's fibroblasts</p> <p>(ii) Gene expression was quantified by qRT-PCR using RNA extracted from fibroblasts. Transcript levels of each gene of interest was calculated using the 2^-deltaCt method with normalization to the average Ct values of endogenous control genes GAPDH, IPO8 and TBP.</p> <p>(iii) Cellular fatty acid content was quantified by GC-MS/MS. Each table represents one technical replicate. Absolute values of each fatty acid are listed in the tables; relative ratios of various fatty acids are calculated at the bottom of each table. </p> <p>(iv) Immunocytochemistry images of ECM proteins (COL1 = collagen type I; COL4 = collagen type IV; COL5 = collagen type V; a2b1 = integrin a2b1) and binding of collagen-hybridising peptide (R-CHP).</p>
Data from: Population divergence in heat and drought responses of a coastal plant: from metabolic phenotypes to plant morphology and growth
<p>This dataset supports the article "Population divergence in heat and drought responses of a coastal plant: from metabolic phenotypes to plant morphology and growth", which is under minor revision in Journal of Experimental Botany. The study addresses the combined effects of and plant population origin, drought and heat stress on plant growth, plant morphology and the leaf metabolome. The data were assessed in Northern and Southern European individuals of <em>Cakile maritma</em> (See Rocket). An R-script containing all statistical analyses that have been implemented with these data is also provided.</p>
Dominant nitrogen metabolisms of a tropical monomictic lake identified using genome resolved metatranscriptomics
<p><strong>Dataset for manuscript entitled "Dominant nitrogen metabolisms of a tropical monomictic lake identified using genome resolved metatranscriptomics"</strong></p> <p><strong>Abstract: </strong>Reactive nitrogen (N) is one of the principal drivers of primary productivity across aquatic ecosystems. However, N cycling in tropical inland waters is less well understood relative to temperate ecosystems. In particular, N cycling in monomictic tropical lakes may be distinct from that of their temperate counterparts due to the warm (>20 °C) temperatures of their anoxic hypolimnions (deepest portion of a stratified water column). These warm anoxic conditions have the potential to dramatically alter ecosystem biogeochemistry by releasing anaerobic metabolic pathways from temperature limitations that are present in anoxic strata of lake ecosystems at higher latitudes. To explore dominant N transformation pathways under warm anoxic conditions, we combined measurements of geochemistry and water column thermophysical structure with genome resolved metatranscriptomic analyses of the water column microbiome in Lake Yojoa, Honduras. Lake Yojoa is characterized by a pronounced accumulation of ammonium in the hypolimnions during stratification, beginning in May, and an annual mixing event that releases reactive N to the photic zone, in November. We sampled Lake Yojoa both above (1m) and below (16m) the thermocline at three locations, in June 2021, when the water column was stratified, and again at the same depths and locations in January 2022, when the water column was mixed. We identified 335 different lineages and significantly different microbiome membership between seasons and, in June, between depths. Expression of genes associated with N metabolic pathways was highest in June below the thermocline. In particular, we found <em>nrfA</em>, along with other respiratory NO<sub>2</sub><sup>-</sup> reductases, was upregulated relative to other N metabolism genes in the June hypolimnions, suggesting that dissimilatory nitrate reduction to ammonium (DNRA) was partially responsible for the previously observed ammonium accumulation. DNRA is infrequently identified as a dominant N transformation pathway in natural aquatic ecosystems. This work highlights the need to better define N biogeochemistry in the poorly understood warm anoxic hypolimnions of tropical lake ecosystems. Defining the dominant anaerobic pathways when temperature is not limiting is one key to providing a more complete understanding of how rapidly occurring global change is altering tropical inland waters.</p> <p><strong>Files described below: </strong></p> <p><em>Yojoa_572_MAGs_scaffolds.fna.zip </em>scaffolds for 572 dereplicated MAGs </p> <p><em>Yojoa_572_MAGs_annotations.tsv.zip</em> DRAM annotations for 572 dereplicated MAGs </p> <p><em>bipartitionsBranchLabels.nxr_nar_for_tree_aligned.fasta_mode_low.renamed </em>phylogenetic tree for nar/nxr genes</p> <p><em>bipartitionsBranchLabels.amo_pmo_fortree_aligned.fasta_mode_low.renamed </em>phylogenetic tree for amo/pmo genes</p> <p><em>YojoaMasterSheet_final.xls</em> MAG inventory with accession numbers and quality stats</p> <p><em>MetaT_norm.counts.rpk_edger.bins_mean.csv </em>mean geTMM values per bin expressed in metatranscriptomic data</p> <p><em>MetaT_geTMM_norm.counts.rpk_edger_genes.csv</em> geTMM values per gene expressed in metranscriptomic data</p> <p><em>geochem_data_Yojoa.csv</em> geochemical data from each location including NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, total phosphorus (TP), and dissolved organic carbon (DOC)</p> <p><em>profile_data_Yojoa.csv</em> depth profile data at each location including temperature and dissolved oxygen measurements</p>
Estimates of ecosystem metabolism for 59 rivers in North America, 2008-2021
<p>This dataset is companion with the github repository https://github.com/nmarzolf91/usgs_metabolism_project that accesses USGS water quality and hydrologic data to estimate ecosystem metabolism in 59 rivers across the US from 2007-2021. Contained in this data citation are information about sites, raw time-series data at sub-daily intervals, prepared time series for modeling metabolism, metabolism outputs in raw form, processed and QAQC'd metabolism estimates, and supplementary information for the associated manuscript(s).</p>
Trans-acting genotypes drive mRNA expression affecting metabolic and thermal tolerance traits
<p>Evolutionary processes driving physiological trait variation depend on the underlying genomic mechanisms. Evolution of these mechanisms depends on whether traits are genetically complex (involving many genes) and how gene expression that impacts the traits is converted to phenotype. Yet, genomic mechanisms that impact physiological traits are diverse and context-dependent (e.g., vary by environment or among tissues), making them difficult to discern. Here we examine the relationships between genotype, mRNA expression, and physiological traits to discern the genetic complexity and whether the gene expression affecting the physiological traits is primarily cis or trans-acting. We use low-coverage whole genome sequencing and tissue-specific mRNA expression among individuals to identify polymorphisms directly associated with physiological traits and expressed quantitative trait loci (eQTL) driving variation in six temperature-specific physiological traits (standard metabolic rate, thermal tolerance, and four substrate-specific cardiac metabolic rates). Not surprisingly, there were few, only five, SNPs directly associated with physiological traits. Yet, by focusing on a select set of mRNAs belonging to co-expression modules that explain up to 82% of temperature specific (12°C or 28°C) metabolism and thermal tolerance, we identified hundreds of significant eQTL for mRNA whose expression affects physiological traits. Surprisingly, most eQTL (97.4% for heart and 96.7% for brain) of eQTL were trans-acting. This could be due to higher effect size or greater importance of trans versus cis-acting eQTLs for mRNAs that are central to co-expression modules. That is, we may have enhanced the identification of trans-acting factors by looking for SNPs associated with mRNAs in co-expression modules that are known to be correlated with the expression of 10s or 100s of other genes, and thus have identified eQTLs with widespread effects on broad gene expression patterns. Overall, these data indicate that the genomic mechanism driving physiological variation across environments is driven by trans-acting tissue-specific mRNA expression.</p>
Data from: Leaf metabolic traits reveal hidden dimensions of plant form and function
<p>In this study, we interpreted leaf metabolome variation among 457 tropical and 339 temperate plant species to understand how the metabolome contributes to macroecological variation in plant functioning. Metabolome data were generated using liquid chromatography mass spectrometry, annotated with compound names (where possible), and cross-referenced against chemoinformatics databases to derive metabolite chemical properties. We then compared variation in leaf metabolite chemical properties among species with variation in classical plant functional traits.</p>
Cystine/glutamate antiporter system Xc- deficiency impairs macrophage glutathione metabolism and cytokine production.
<p>Raw Data for Publication "Cystine/glutamate antiporter system Xc- deficiency impairs macrophage glutathione metabolism and cytokine production."</p>
Linking the metabolic rate of individuals to species ecology and life history in key Arctic copepods
<p>This folder contains data and code for the manuscript "Linking the metabolic rate of individuals to species ecology <br> and life history in key Arctic copepods"</p> <p>The first script to run is the "rolling regression and adding covariates to MR data.R", this will read in all the<br> files with oxygen measurements, dry weight, and species and life stage information. The code will fit and predict <br> estimates for each individual, calculate O2 from calibration data, subtract background respiration, run the rolling regression,<br> and add the covariates DW, species and life stage to the metabolic rate data, and write the resulting data fame to a .txt file.</p> <p>The second script "lme4 Analysis and figures 5 6 7.R" will read in the data from the first script. <br> Here the AMR RMR and aerobic scope is estimated by Density Estimation via Model-Based Clustering. <br> The resulting data is fitted with a mixed model 'lmer'. <br> The remaining part of the script make the predictions that are presented in the text of the manuscript<br> and that are shown in figure 5, 6, and 7.</p> <p>Further information is annotated in the scripts</p>
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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.