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11,869 results for “metabolic”
Processed data to regenerate figures in Noecker et al, "Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe Eggerthella lenta"
<p>This archive contains the processed source data for the publication by Noecker et al, "Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe <em>Eggerthella lenta</em>" (2023, in review). Data tables underlying each figure panel are included, except for the following panels:</p> <ul> <li>Figure S1A: Source data is in Table S1 of the publication</li> <li>Figure 6D: Source data can be found at NCBI GEO accession GSE212420 (supplementary counts data matrix)</li> </ul> <p>Raw metabolomics data can also be found at Metabolomics Workbench accession PR001620.</p> <p>Methods used to summarize these data and generate the figures are described in the manuscript Materials and Methods and figure captions. Code to generate all figures is also available at www.github.com/turnbaughlab/2022_Noecker_ElentaMetabolism and 10.5281/zenodo.7779454.</p>
Repeatability of energy metabolism and resistance to dehydration in the invasive slug Limax maximus
<p>Dataset from the paper "Repeatability of energy metabolism and resistance to dehydration in the invasive slug <em>Limax maximus"</em></p> <p>It contains metabolic rates and body mass assessed on 30 individuals of L. maximus in three different trials. Metabolic rates are in CO2 ml/m</p>
Genome-scale community modelling reveals key metabolic cross-feedings in epipelagic bacterioplankton communities (Supplementary Materials)
<p>A comprehensive catalog of 19,791 marine prokaryotic isolates (WGS), single-amplified genomes (SAGs) and metagenomic-assembled genomes (MAGs) compiled from MarRef v4.0 (N=943, mostly high-quality WGS), MarDB v4.0 (N=12,963), and the aquatic representative genomes from the ProGenomes database v1.0 (N=566). This collection of well-documented genomes was complemented by 5,319 MAGs assembled from four distinct studies, namely: Parks et al. 2017 (<a href="https://doi.org/10.1038/s41564-017-0012-7">DOI</a>; N=1,765; downloaded from EBI), Tully et al. 2017/2018 (<a href="https://doi.org/10.7717/peerj.3558">DOI</a> and <a href="https://10.1038/sdata.2017.203">DOI</a>; N=2,597; downloaded from EBI), and Delmont et al. 2018 (<a href="https://doi.org/10.1038/s41564-018-0176-9">DOI</a>; N=957; downloaded from FIGSHARE). The Parks et al. study contained genomes reconstructed from non-marine biomes. Thus, a selection of 1,765 genomes was extracted by searching for specific keywords: “tara|marine|sea|ocean|mediterranean” (case insensitive). Note that depending on their study of origin, included MAGs may have been reconstructed using different assembling and binning methods.</p> <p>The archive includes:</p> <ul> <li>a metadata file describing the quality and redundancy of the genomes named `EcoSysMic_metadata.tsv`</li> <li>sequences of the 19,791 (redundant) genomes in `All/WGS`</li> <li>companion files in `All/Data` and `dRep95/Data` (see Methods in the associated paper), including <ul> <li>predicted CDS and EggNOG functional annotations</li> <li>predicted GTDB taxonomy</li> <li>CarveMe reconstructed metabolic models and their MEMOTE quality</li> </ul> </li> </ul> <p>The 7,658 non-redundant species-level genomes (delineated by a 95% ANI threshold over 60% of genome length) that were used in the associated paper are defined by the column `is_drep95` in the metadata file.</p>
Data for the study of Posidonia microbial nitrogen metabolism manuscript
<p>These are data related to the study of the seagrass microbiome and function. <em>Posidonia oceanica</em>, an endemic seagrass species that dominates the Mediterranean Sea, achieves high abundances in seawater with relatively low concentrations of dissolved inorganic nitrogen. Here we test whether microbial metabolisms associated with <em>P. oceanica</em> and surrounding seawater enhance seagrass access to nitrogen. Using stable isotope enrichments of intact seagrass with amino acids, we show that ammonification by free-living and seagrass-associated microbes produce ammonium that is likely used by seagrass and surrounding particulate organic matter. Metagenomic analysis of the epiphytic biofilm on the blades and rhizomes support the ubiquity of microbial ammonification genes in this system. Further, we leveraged the presence of natural carbon dioxide vents and show that microbial ammonification was reduced at lower pH. The presence of <em>P. oceanica</em> enhanced the uptake of nitrogen by water column particulate organic matter, increasing carbon fixation by a factor of 8.6 to 17.4 with the greatest effect at CO<sub>2</sub> vent sites. Seagrass and its microbial associates thus enhanced water column productivity and were a locus for nutrient cycling.</p>
Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 2
<p>Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al.</p> <p>The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.</p> <p>The dataset contains the following files:</p> <p>1. param_fixing.zip - self-explanatory (Figure 4 & 5); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv).</p> <p>2. scripts.zip - scripts to generate figure 2-5 on toy data</p>
Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 1
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387).</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.</p> <p>The dataset contains the following files:</p> <p>1. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of <em>E. coli </em>metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4).</p> <p>2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b)</p> <p>3. ODE_solutions.zip - self-explanatory (Figure 2c)</p> <p>4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d)</p> <p>5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a)</p> <p>6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g</p> <p><strong>The remaining datasets are published in the following links</strong></p> <p><em> - https://doi.org/10.5281/zenodo.7930084</em></p> <p><em> - https://doi.org/10.5281/zenodo.10391802</em></p>
Lake Mendota metabolism model and data set
<p>This is a zipped file that includes the model code, written in R, as well as the input and output data for the model. This publication accompanies the manuscript entitled, Legacy phosphorus and ecosystem memory control future water quality in a eutrophic lake</p>
Metabolic profiling and antibacterial activity of tree wood extracts obtained under variable extraction conditions
<p>Scripts and dataset for Vinchira-Villarraga et al., 2024 "Metabolic profiling and antibacterial activity of tree wood extracts obtained under variable extraction conditions". The files label as A1, C6, H2/HCN2 and O1 correspond to the dataset obtained for Ash, Cherry, Horse-chestnut and Oak respectively. </p> <p>_batch.xml files contains the script for the pre-processing of .mzML mass spectrometry data created on MzMine 3.2.8. The file can be uploaded to newer versions of MzMine, but due to the addition of new modules, some parameters name have changed.</p> <p>_SIRIUS.mfg files contains the mass spectrometry data of each dataset for analysis in SIRIUS. The files were used for chemical formula prediction, classification and compound annotation using SIRIUS 5.0</p> <p>_quant.csv correspond to the output matrix as it was obtained from MzMine without noise, adducts and highly variable features filtering.</p> <p>_abundance.csv correspond to the output matrix after noise, adducts and highly variable features filtering.</p>
Lake ecosystem metabolism estimates from 3 locations in Lake Sunapee, NH, USA during the summer stratified period from June to September 2018
Surface water lake ecosystem metabolism daily estimates during the 2018 summer stratified period (04 June - 22 Sept) at three locations within Lake Sunapee (NH, USA). Estimates at each site used previously published data from high-frequency temperature and dissolved oxygen sensors deployed in the lake: the Deep Site (LSPA et al., 2021a: full citation in Methods) and the Herrick Cove and Georges Mills sites (Ward et al., 2021: full citation in methods). The Deep Site was located near Loon Island in the main basin of the lake with 12 m total depth and the dissolved oxygen sensor was deployed 1 m below surface. The Herrick Cove site was in the north east cove of the lake with 6.5 m total depth at site and the dissolved oxygen sensor was deployed 1.75 m below surface. The Georges Mills site was in the northwest cove of the lake with 7 m total depth at site and the dissolved oxygen sensor deployed 1.75 m below surface. We used an inverse modeling approach, where the lake ecosystem model predicted diel changes in dissolved oxygen to estimate daily volumetric rates of gross primary production (GPP), respiration (R), and net ecosystem metabolism (NEM) using the in-lake buoy measurements at each site and wind and surface PAR from the meteorological station at the Deep Site buoy (LSPA et al., 2021b). Raw metabolism estimates were QA/QC'd to generate this final metabolism estimate dataset following protocols described in the Methods section of this dataset.
Metabolism and decomposition rates from 5 Lake Superior Tributaries, 2018-2019
Ecosystem respiration (ER), and decomposition are fundamental processes driving carbon cycling in streams. Most studies examine rates of autotrophic respiration (AR) and heterotrophic respiration (HR) together as ecosystem respiration (ER), even though these two processes are carried out by different groups of organisms, and these processes, alongside decomposition, may respond differently to ongoing changes in environmental factors. We measured metabolism (gross primary production and ER) and decomposition at eight sites in four streams in the Upper Peninsula of Michigan across gradients of canopy cover and DOC concentrations. We estimated AR and HR using quantile regression and used predictive modeling to determine the environmental drivers that predicted variation in these processes across the study streams. This data archive includes (1) continuous dissolved oxygen and temperature data used to model metabolism, (2) modeled rates of gross primary production, ecosystem respiration, autotrophic respiration and heterotrophic respiration, (3) estimates of decomposition rates determined using cotton strip assays, and (4) environmental model used for predictive modeling
Metabolism estimates from dissolved oxygen and inorganic carbon in the Upper Clark Fork River, MT, USA.
This package provides necessary supporting data and models for the manuscript titled "Divergent metabolism estimates from dissolved oxygen and inorganic carbon: implications for river carbon cycling". The entire dataset consists of sensor data collected at three reaches and metabolism estimates from different models. The sensor data include partial pressure of carbon dioxide in water, dissolved oxygen and temperature. At each reach, we established a two station approach, meaning at least one pair of sensor suits were distributed upstream and downstream. Results for metabolism estimates differ by solutes (i.e., oxygen or carbon based) and modelling approaches (i.e., single station or two station approach). In addition to data products, we also provide R packages for metabolism models.
Study of wildfire smoke effects on ecosystem metabolism in 10 California lakes (2018, 2020, 2021)
This dataset was collected as part of a large-scale study to assess impacts of smoke cover on gross primary production (GPP) and ecosystem respiration (R) in California lakes. The 10 study lakes span large gradients in elevation, size, nutrient concentrations, and water clarity. They include 5 ponds and lakes in Sequoia National Park, Lake Tahoe, Dulzura Lake, Clear Lake, Castle Lake, and a site in the Sacramento-San Joaquin River Delta. Metabolic rates in the upper mixed layer of each lake was estimated from hourly in-situ sensor data during the three smokiest years in California since 2006 (2018, 2020, 2021). The dataset includes daily estimates of GPP and R, mean daily values of variables used in metabolism models (water temperature, dissolved oxygen, mixed layer depth, photosynthetically active radiation, wind speed), and mean daily values of metrics related to smoke cover (shortwave radiation, PM2.5, smoke density derived from remote-sensing).
Nitrogen cycling and metabolic rates of aquacultured and wild Acropora coral from Guam in response to ammonium loading rates during 2020-2022
Rates from aquacultured corals and coral fragments collected in Guam in response to ammonium loading. These data are from two separate experiments, one using aquacultured corals and artificial seawater and light. The second was done with wild collected Acropora pistillata from two reef sites in Guam. The first site was West Hagåtña Bay (N13.479650, E144.741750; N13.479833, E144.741733) which had more nearby urban influences and was near the sewage outfall for the city (Redding et al. 2013). The second site was Luminao Reef (N13.4652417, E144.6477483; N13.465467, E144.648050) which was a more isolated reef on the seaward side of the breakwater for Guam’s major port. We measured respiration, gross primary production, 15N ammonium uptake to corals, related nitrogen cycling fluxes in the tanks (reminerization, net uptake, nitrification).
High-frequency water temperature and dissolved oxygen data and derived stability and metabolism metrics for nine lakes in northeastern North America for months before and after Tropical Cyclone Irene, Fall 2011
This dataset is used in the analysis published in the following manuscript: Klug, J.L., D.C. Richardson, H.A. Ewing, B.R.Hargreaves, N. R. Samal, D. Vachon, D.C. Pierson, A. E. Lindsey, D. O'Donnell, S.W. Effler, and K.C. Weathers. 2012. Ecosystem effects of a tropical cyclone on a network of lakes in northeastern North America. Environmental Science and Technology 46(21): 11693–11701. We include Quality Assurance Quality Controlled (QAQC) high-frequency dissolved oxygen, wind speed, and water temperature data from nine lakes and reservoirs in northeastern North America which were near the track of Tropical Cyclone Irene in August 2011. These data were collected using a set of in situ, automated monitoring systems associated with the Global Lake Ecological Observatory Network (GLEON) that record data at high frequency (10 min to 6 h). These sensor data were the basis for the derived measures of Schmidt stability, net ecosystem production, respiration, and gross primary production included in the dataset. We also include daily rainfall data collected at on-site or nearby weather stations. All data cover the period from 01 August through 15 October 2011.
Ecosystem metabolism estimates from Lake Sunapee, NH, USA and meteorological driver data at the Newport, NH, USA NOAA NCDC weather station from August 2007 – December 2008
In August 2007, the Lake Sunapee Protective Association (LSPA) deployed a buoy in Lake Sunapee, NH with a meteorological station, dissolved oxygen sensor at 1 m, and a string of thermistor water temperature sensors collecting data every 10 minutes. For this dataset, the Lake Sunapee buoy was located at a location near Loon Island all year (August 2007–December 2008) including during the winter. We used high-frequency dissolved oxygen, photosynthetically active radiation (PAR), wind speed, and water temperature profile data to estimate daily rates of gross primary production, respiration, and net ecosystem production as metrics of ecosystem metabolism and carbon cycling. This dataset included under-ice data, and we compared seasonal changes in ecosystem metabolism rates for an entire lake year from the start of fall mixing in 2007 through the end of summer stratification in 2008. Under-ice and mixing periods are infrequently sampled due to logistical challenges and assumptions of low biological activity, resulting in limited understanding of the contribution of winter epilimnetic metabolism to annual carbon cycling. Using a continuous year of high-frequency dissolved oxygen data, we found that on average, under-ice net ecosystem production (NEP) was negative, in contrast to positive NEP for the spring and summer periods. Importantly, under-ice respiration was 1.2 times higher than summer respiration. Gross primary production was low but not absent under ice and increased during the last under-ice phase. Including winter metabolism estimates flipped annual NEP from autotrophy to heterotrophy, highlighting the importance of estimating metabolism year-round. Our methods for the data analysis are described in Brentrup et al. (accepted at Inland Waters) and were similar to Richardson et al. (2016) with some modifications described in the methods. This dataset also includes derived thermocline depth calculations from the water temperature thermistor data on the Lake Sunape
Stream Metabolism in Marsh Creek, Idaho, USA 2016-2017
Most research on stream metabolism uses a single reach to characterize a stream. We aimed to characterize variation in stream metabolism rates and the controls on metabolism along a 75-km agricultural stream. We continuously measured dissolved oxygen, discharge, conductivity, temperature, light, and turbidity using a variety of sensors deployed at 6 locations along Marsh Creek, a stream in southeastern Idaho. Monthly grab samples were collected for water chemistry throughout the study period, and aquatic vegetation was surveyed from May through September. Data were collected from October 2016 through October 2017.
Baltimore Ecosystem Study: Stream metabolism data for core sites in Gwynns Falls
An ongoing component of the Baltimore urban long-term ecological research (LTER) project (Baltimore Ecosystem Study, BES) is the use of the watershed approach and monitoring of stream water quality to evaluate the integrated ecosystem functioning of Baltimore. The LTER research has focused on the Gwynns Falls watershed, which spans a gradient from highly urban, urban-residential, and suburban zones. In addition, a forested watershed serves as a reference. The long-term sampling network includes four longitudinal sampling sites along the Gwynns Falls mainstem, as well as several small (40-100 ha) watershed within or near the Gwynns Falls, providing data on water quality in different land use zones of the watersheds. Each study site is continuously monitored for discharge and is sampled weekly for water chemistry. Those data are available elsewhere on the BES website. We are interested in studying the bioreactivity of streams in our watersheds in an attempt to quantify how streams themselves may affect or be affected by water quality. To assess the bioreactivity of streams, we measure whole stream metabolism, which is an integrative metric which quantifies the production and consumption of energy by a stream ecosystem. Stream metabolism represents how energy is created (primary production) and used (respiration) within a stream; it can be thought of as a stream breathing, with primary production being similar to an inhale, and respiration as an exhale. We are monitoring stream metabolism in each of our long-term water quality monitoring stations by deploying sensors that record dissolve oxygen and temperature of the stream every five minutes, and we also have deployed light sensors to record irradiance every five minutes at long-term BES water chemistry streams, which is needed for metabolism modeling. In addition, each dissolved oxygen sensor is located near a USGS gage which estimates discharge every 15 minutes. We used USGS manual discharge estimations linked with
Physical, chemical, and metabolic leaf characteristics within sugar maple in the MELNHE study at Bartlett Experimental Forest, central NH USA, 2017
The MELNHE study looks at patterns of resource limitation through nutrient manipulations in three study sites in New Hampshire: Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook, located in the White Mountain National Forest. The investigation is monitoring stem diameter, leaf area, sap flow, foliar chemistry, leaf litter production and chemistry, foliar nutrient resorption, root biomass and production, mycorrhizal associations, soil respiration, heterotrophic respiration, N and P availability, N mineralization, soil phosphatase activity, soil carbon and nitrogen, nutrient uptake capacity of roots, and mineral weathering. Applications of N and P began in June 2011 and continue at the rate of 30 kg N/ha/yr (as NH4NO3) and 10 kg P/ha/yr (as NaH2PO4). This data set includes physical, chemical, and metabolic leaf characteristics collected in a vertical transect within individual tree crowns. Because trees varied in crown depth, not all trees have the same number of samples collected. Depth from the top of the crown was measured and climbing ropes and a pole pruner were used to collect a population of leaves. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
MCR LTER: Coral Reef: Coral Larval Metabolism in pH and Temperature Treatments
In this study, we explored plasticity of physiological responses of coral larvae from a reef in French Polynesia. The study was motivated by these questions: (1) what is the present-day exposure of Pocillopora damicornis to natural variability of pH and temperature on the natal reef?, (2) what is the response of P. damicornis larvae to conditions of decreased pH and warming, measured via two indices of metabolism - rates of oxygen consumption and citrate synthase activity?, and (3) are there differences in larval sensitivity to environmental change between cohorts that are released from adult colonies at different times? P. damicornis larvae were incubated for 6 hours in seawater containing combinations of CO2 concentration (450 and 950 μatm) and temperature (28 and 30C). Rates of larval oxygen consumption were higher at elevated temperatures. In contrast, high CO2 levels elicited depressed metabolic rates, most dramatically for larvae released later in the spawning period. Rates of citrate synthase, a rate-limiting enzyme in aerobic metabolism, suggested a biochemical limit for increasing oxidative capacity in a warming, acidifying ocean. Biological responses were also compared between cohorts, larvae released from adult colonies on the same day. The metabolic physiology of P. damicornis larvae varied significantly by day of release. Without adaptation or acclimatization, only a portion of naive P. damicornis larvae may have suitable metabolic phenotypes for maintaining function and fitness in an end-of-the century ocean. These data are published in Rivest, E.B. and G.E. Hofmann. 2014. Responses of the metabolism of the larvae of Pocillopora damicornis to ocean acidification and warming. PLoS ONE DOI:10.1371/journal.pone.0096172
Supplementary data and scripts for "An Atlas of Human Metabolism"
<p>This repository contains the models, data, and scripts associated with the publication "An Atlas of Human Metabolism".</p> <p>The content is divided into three main directories, each containing their own README file containing instructions.</p> <ol> <li>tINIT_GEMs - Contains the genome-scale metabolic models (GEMs) generated in the study using the tINIT algorithm, as well as many scripts and datasets necessary to reproduce model generation, analyses, and figures.</li> <li>ec_GEMs - Contains the enzyme-constrained models (ecGEMs) generated in the study using the GECKO framework, as well as many scripts and datasets necessary to reproduce model generation, analyses, and figures.</li> <li>GEM_PRO - Contains the GEM-PRO dataframe with the protein structure information associated with the Human1 model.</li> </ol>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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
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