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182 results for “Catabolism”

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zenodo48/100

Comparative proteomics analysis of whole-cell catalyst of K. rhizophila strain SA117 catabolism of SMX

<p><span>Sulfamethoxazole (SMX), an oral sulfonamide antibiotic, presents significant environmental challenges due to its persistence and potential role in promoting antibiotic resistance. The bacterial strain <em><span>Kocuria rhizophila</span></em> SA117, isolated from polluted soils, has demonstrated a remarkable capability to metabolize SMX. Proteomic analysis revealed the presence of various enzymes and metabolic pathways that may contribute to SMX degradation, including those involved in para-aminobenzoate condensation and protocatechuate metabolism. Notably, the genome of SA117 harbors eight monooxygenase genes, including those related to antibiotic biosynthesis and flavin family monooxygenases. Additionally, several cytochrome c-encoding genes, known for their role in respiratory versatility and potential application in bioremediation, were identified. Genes associated with sulfur metabolism, including an iron-sulfur cluster gene cluster (SufB, C, D, R, E) linked to oxidative stress response, were also found. A comparative proteomic study under SMX exposure highlighted significant upregulation of stress-related proteins. These findings underscore the metabolic adaptability of <em><span>Kocuria rhizophila</span></em> SA117 and its potential application in the bioremediation of SMX-contaminated environments.</span></p>

opencc-by-4.0Aug 2024View details →
zenodo44/100

Methylated Amine Gene Inventory of Catabolism database (MAGICdb)

<p>MAGICdb is the&nbsp;Methylated Amine Gene Inventory of Catabolism database, an analysis of 6,341 gut-derived microbial genomes:</p> <p><strong>DataFileS2_FinalGeneCalling.xlsx</strong>- inventory of genes and genomes in MAGICdb</p> <p><strong>6341_genomes.tar.gz</strong>- methylated amine catabolism encoding genomes in MAGICdb (fasta format)</p> <p><strong>DataFileS3_ALL_NUCL_MethylamineGenes.fna</strong>- methylated amine genes in MAGICdb&nbsp;(fasta format)</p> <p><strong>predict_cvd_from_MAGICdb_mapping.html</strong>- input data, code, and models for predicting cardiovascular disease from human metagenomic datasets using MAGICdb</p> <p><strong>metaP and metaT text files</strong>- contain output tables from mapping Abu-Ali, et al. metatranscriptome and Lloyd-Price, et al. metaproteome data to MAGICdb.</p> <p><strong>Node_table_042722_ANNOTATED.csv</strong>- EFI network node file with sequences for MAGIC database (Fig. 6).&nbsp;&nbsp;</p>

opencc-by-4.0Apr 2021View details →
zenodo40/100

In Vitro Reconstitution of a Five-Step Pathway for Bacterial Ergothioneine Catabolism

<p>Data underlying the figures in the publication &ldquo;In Vitro Reconstitution of a Five-Step Pathway for Bacterial Ergothioneine Catabolism&rdquo;, published in <em>ACS Chem. Biol.,</em> <strong>2021</strong>, 16, 2, 397&ndash;403.</p> <p><a href="https://pubs.acs.org/doi/10.1021/acschembio.0c00968">https://pubs.acs.org/doi/10.1021/acschembio.0c00968</a></p> <p>Table of contents:</p> <p><strong>1. Table 1</strong>; Text document containing the Michaelis - Menten kinetic data for thiourocaate hydratase, obtained with UV-Vis spectrophotometer as described in the supporting information of the publication.</p> <p><strong>2. Figure 3</strong>; Text document containing the numerical data for <em>Figure 3</em>: Reverse phase HPLC traces (wavelength 220 nm) for each metabolite in ergothioneine degradation pathway.</p> <p><strong>3. Figure 4</strong>; Data and model validation report of X-ray crystallography diffraction data of thiouricanate hydratase collected at the swiss light source at 100K. Data processed and refined according to standard procedure.</p>

opencc-by-4.0Jul 2021View details →
dryad32/100

Plant litter chemistry drives long-lasting changes in the catabolic capacities of soil microbial communities

<p><span>Although </span><span>microbial communities play an important role in explaining plant litter decomposition rates, whether and how litter chemistry may alter catabolic capacities of soil microbial communities remains poorly studied.</span></p> <p><span>During a one-year litter decomposition experiment of twelve herbaceous species with contrasting litter chemistry, we examined the effect of plant litter type (roots vs leaves) and litter chemical traits on the resulting capacity of soil microbial communities to degrade a wide range of carbon substrates of variable complexity (MicroRespTM method).</span></p> <p><span>Litter chemistry impacted both the total catabolic activity as well as specific catabolic capacities of microbial communities. In early stages of litter decomposition total catabolic activity was mainly influenced by the amount of C and N in litter leachates, and litter N, P and Mg, then, later, by lignin concentrations. Some specific catabolic capacities could also be related to litter initial chemistry. Overall, litter trait effects on soil microbial communities decreased over time and the relative importance of traits shifted during the decomposition process.</span></p> <p><span>Our results highlight that litter chemistry is a strong driver of catabolic capacities of microbial decomposers and, whilst its effect fades with time, it remains substantial throughout the litter decomposition process. These long-lasting effects of litter chemistry suggest a persistent control on microbial catabolic capacities in ecosystems with recurrent litter production. Soil microbial catabolic activities were driven by broadly the same chemical traits across leaf and root litters. </span></p> <p><span>Synthesis.</span><span> Such long-lasting effects of litter chemistry on catabolic capacities of microbial communities may represent a substantial indirect driver of the decomposition process. Disentangling the relative importance of this overlooked effect of litter chemistry on decomposition represents the next challenge. We argue that such research line should open ground-breaking perspectives for reconsidering our current understanding of the mechanistic links between litter traits and decomposition rate. </span></p>

opencc-zeroMay 2023View details →
zenodo32/100

Fig. 8 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 8. Reaction scheme for the formation of BOH (6) and BCOOH (8) from reactive benzaldehyde through a Cannizzaro type reaction, catalyzed by a benzaldehyde dehydrogenase (based on Wuensch et al., 2013). The proposed scheme would require only one enzymatic activity for both the oxidative and reductive half-reactions.

opennotspecifiedNov 2020View details →
zenodo32/100

Fig. 7 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 7. Proposed scheme for reactions taking place during the maca drying. The sequence is based on the main hydrolytic metabolites observed during the drying process. Step A describes reactions generating the initial hydrolysis products and including BITC (2), BCN (4) and BIOC (7). Step B is the formation of BNH2 (3), the main accumulation product, from BITC (2). Step C describes deamination of BNH2 (3) to generate various deaminated benzenoids. Step D corresponds to the disproportionation reactions leading to pools of BCHO (5), BOH (6) and BCOOH (8) through the action of an aldehyde dehydrogenase. Step E is the condensation of BNH2 (3) with free fatty acids to produce macamides (MAC 9–13) and step F corresponds to esterification or glycosylation of BCOOH (8) as a detoxification mechanism. Numbers correspond to those in Fig. 1. Dark lines show favored reactions according to our results. Compounds in light gray are minor transient products.

opennotspecifiedNov 2020View details →
zenodo32/100

Fig. 4. Pearson correlation values for glucosinolate hydrolytic products. Panel A in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 4. Pearson correlation values for glucosinolate hydrolytic products. Panel A shows the correlation of metabolites for the early stage of drying, where reactions are caused by direct damage to the tissue by shredding. Panel B shows the correlation between intermediaries as a result of the late stage tissue dehydration. BCOOR- 2, BCHO-2 and BCOOH-2 shown in panel B correspond to data points in Fig. 5 shown as part of the red solid line while BCOOR (8a), BCHO (5) and BCOOH (8) in panel A correspond to the early stage in the figure shown in solid black lines. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opennotspecifiedNov 2020View details →
zenodo32/100

Fig. 6 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 6. Time course for the concentration of ammonium ion in maca tissues during drying. Deamination of benzyl amine and amino acids by amine oxidases are potential sources of ammonia under our short (72 h) drying conditions. Peak values of NH+ are 25 μmol g 1 dry wt.. Total amount of deaminated benzenoids 4 accounts for only 0.75 μmol g 1 dry wt. indicating other major sources of ammonium in the process. Curve adjusted by nonlinear regression (R2 = 0.9472).

opennotspecifiedNov 2020View details →
zenodo32/100

Fig. 3 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 3. Tissue concentrations during oven drying for benzyl glucosinolate (BGL) and its metabolites. Drying maca tissue was extracted in solvent and the levels of benzyl glucosinolate, its primary hydrolytic products (BITC, BCN) and two products that accumulate in the flour, benzyl amine (BNH2) and macamides (MAC) were analyzed. RW represents residual humidity. Hydrolytic and final accumulation product patterns match those previously reported (Esparza et al., 2015), although for this study, a 72 h drying period and 35 ◦ C constant temperature were employed. Values are expressed in molar fraction of initial glucosinolate concentration, where 1 = 36 ± 5 μmol g 1 dry wt. (N = 6). Nonlinear regression coefficients for the compounds were: R2 = 0.941 (BGL, 1), R2 = 0.971 (BITC, 2), R2 = 0.777 (BCN, 4), R2 = 0.955 (BNH, 3), R2 = 0.985 (MAC, 9–13) and R2 = 0.993 (RW).

opennotspecifiedNov 2020View details →
zenodo32/100

Fig. 2 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 2. Time course of major VOCs from glucosinolate hydrolysis measured by headspace SPME-GC-MS. Values are expressed as percentage of average peak total ion current (TIC) for each compound. Compounds, from top left are: benzyl isothiocyanate (BITC, 2), benzyl nitrile (BCN, 4), benzaldehyde (BCHO, 5), benzyl alcohol (BOH, 6), benzyl isocyanate (BIOC, 7), and average relative humidity (RH) inside the chamber (N = 9). Peak TIC values (x 107) for the compounds were: 238.42 (2, 100%); 2.88 (4, 1.2%); 34.9 (5,14.6%); 28.17 (6, 11.8%) and 3.75 (7, 1.6%). Values in parenthesis after the compound number denote the relative intensity of the peak in relation to the isothiocyanate peak. RH = relative humidity in the headspace.

opennotspecifiedNov 2020View details →
zenodo32/100

Fig. 5 in Glucosinolate catabolism during postharvest drying determines the ratio of bioactive macamides to deaminated benzenoids in Lepidium meyenii (maca) root flour

Fig. 5. Time course profiles for transient and final deaminated benzenoid products in drying maca tissues. Kinetic profiles show two stages, early "fast" kinetics (solid black lines) that take place in the initial 12 h, corresponding to damage to the tissues done by shredding to reduce size for drying and a second "slow" stage (solid red lines) that shows the dehydration of the tissues in the later part of the drying process. Data points corresponding to the initial and the late phases have been subjected to nonlinear regression separately. The nonlinear regression coefficients for the metabolites were for BCHO (5): R2 = 0.9962 (early phase), R2 = 0.9880 (late phase), for BOH (6): R2 = 0.9999 (early), R2 = 0.9522 (late), BCOOH (8): R2 = 0.9999 (early), R2 = 0.9526 (late), BCOOR (8a): R2 = 0.9677. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opennotspecifiedNov 2020View details →
ClinicalTrials.gov32/100

Indirect Calorimetry Versus Urea-creatinine Ratio to Evaluate Catabolism in Critically Ill Patients

ClinicalTrials.gov study NCT06244381. IPD Sharing: NO. Countries: 1. Publications: 6.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

The Role of Lactate in Lipolysis and Catabolism in Humans

ClinicalTrials.gov study NCT04710875. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Influence of Severe Heart Failure to Function and Molecular Biological Parameters of Catabolism in the Human Diaphragm and Peripheral Skeletal Muscle

ClinicalTrials.gov study NCT02663115. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Can Beta-Hydroxy-beta-Methylbutyrate Supplementation Counteract Muscle Catabolism in Critically Ill Patients?

ClinicalTrials.gov study NCT03628365. IPD Sharing: Not stated. Countries: 1. Publications: 8.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Catabolic Marker Levels After Platelet Poor Plasma (PPP) Lavage

ClinicalTrials.gov study NCT03120988. IPD Sharing: NO. Countries: 1. Publications: 9.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

The Anabolic and Catabolic Effect of Loaded and Unloaded Exercise on Articular Cartilage Health

ClinicalTrials.gov study NCT05925244. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Study of Ocean Rowing Muscle Metabolism. 1. What Effect Does Rowing 3000 Miles Across Atlantic Ocean Have on Calf Muscle Size? 2. In the Catabolic State of Extreme Endurance, is Muscle Loss Uniform or

ClinicalTrials.gov study NCT05729841. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Influence of Tightly Glucose Control on Hyperglycemic Toxicity and Protein Catabolism in Critically Ill Patients

ClinicalTrials.gov study NCT01227148. IPD Sharing: Not stated. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →
dryad32/100

Plant litter chemistry drives long-lasting changes in the catabolic capacities of soil microbial communities

Open the record for dataset details and reuse information.

publicMay 2023View details →

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