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Metabolism

Q: What is metabolism and why is it important?

Metabolism refers to the set of chemical reactions and processes that occur within living organisms to maintain life. It involves the breakdown and conversion of nutrients into energy, as well as the synthesis of essential molecules. Metabolism plays a crucial role in various physiological functions, including energy production, growth, and repair. Optimizing metabolism research is essential for understanding and addressing metabolic disorders, developing targeted therapies, and improving overall health.

Q: What are some common challenges in metabolism research?

One of the key challenges in metabolism research is the complexity of the metabolic system and the vast number of variables that can affect it. Researchers often struggle to identify the most effective protocols and products for their specific research goals, which can impact the reproducibility and accuracy of their studies. Additionally, the sheer volume of literature, preprints, and patents available can make it difficult to efficiently screen and identify the most relevant information.

Q: How does PubCompare.ai address these challenges?

PubCompare.ai addresses these challenges by providing a centralized platform that allows researchers to easily access and compare relevant protocols from literature, preprints, and patents. The platform's AI-driven analysis capabilities can help identify the most effective protocols by highlighting key differences in their effectiveness, enabling researchers to choose the best option for their metabolism studies. This can significantly improve the reproducibility and accuracy of their research, ultimately leading to better understanding and advancements in the field of metabolism.

Q: What are the different types or variations of metabolism?

Metabolism can be broadly categorized into two main types: cataboli*sm and anabolism. Catabolism refers to the breakdown of complex molecules into simpler ones, releasing energy in the process. This includes the breakdown of carbohydrates, lipids, and proteins to produce ATP, the primary energy currency of the cell. Anabolism, on the other hand, involves the synthesis of complex molecules from simpler ones, using energy to build up and store essential molecules. These two processes work in tandem to maintain the delicate balance of the metabolic system.

Metabolism is the set of chemical reactions and processes that occur within living organisms to maintain life.
It involves the breakdown and conversion of nutrients into energy, as well as the synthesis of essential molecules.
Metabolism plays a crucial role in various physiological functions, including energy production, growth, and repair.
Optimizing metabolism research is crucial for understanding and addressing metabolic disorders, developing targeted therapies, and improving overall health.
PubCompare.ai, an AI-driven platform, can enhance the reproducibility and accuracy of metabolism studies by providing easy access to relevant protocols from literatuure, preprints, and patents, and enabling AI-driven comparisons to identify the best protocols and products for your research.
Streamline your metabolism studies with this powerful tool and unlock new insights into this fundamental biological process.

Most cited protocols related to «Metabolism»

Microbiome Analysis of Crohn's Disease

Inverse Simpson diversity, Chao1 richness (using the R fossil package), and Pielou evenness were calculated for clade abundance, KEGG pathway and module abundance, and Gene Ontology term abundance [94 -97 ]. Next, data were pre-processed for quality control before modeling. Clinical metadata were removed when more than 10% of data were missing, or when they did not vary in value over the available samples. Clades, pathways, and features of very low abundance (< 0.001 in ≥ 90% of samples) and feature outliers outside of the lower or upper outer fence (3× interquartile range) were removed. Missing data were imputed for significance testing with the mean abundance of the sample; missing factor metadata were imputed with a 'NA' factor level using the na.gam.replace function from the R package [98 ]. Unless stated otherwise, all subsequent analyses and calculations were performed using these processed data. After processing, 228 and 231 samples passed quality control for clade abundance and functional abundance analyses, respectively.
Finally, clades and functions were tested for statistically significant associations with clinical metadata of interest by using a novel multivariate algorithm. Each clade (excluding ecological measures) was normalized with a variance-stabilizing arcsine square-root transformation and evaluated with a general linear model (in R using the glm package). Model selection for sparse data was performed per clade using boosting (gbm package [99 (link)]). A multivariate linear model associating all available metadata with each clade independently was boosted, and any metadata selected in at least 1% of these iterations was finally tested for significance in a standard generalized linear model. This composite model was thus of the form:

arcsin (\sqrt{y_{i}})) = β_{0} + \sum_{p} β_{p} X_{i, p} + ε_{i}, i = 1, . . ., n

where p are the clinical metadata selected from boosting.
Within each metadatum/clade association independently, multiple comparisons over factor levels were adjusted using a Bonferonni correction; multiple hypothesis tests over all clades and metadata were adjusted to produce a final Benjamini and Hochberg false discovery rate [100 ]. Unless otherwise indicated, significant association was considered below a q-value threshold of 0.25; the KEGG pathway sulfur metabolism (ko00920) had an average q-value of 0.26 for association with Crohn's disease. Multiple factor analysis was performed to visualize the relationships within heterogeneous factor data as well as with a select group of taxa found to be significantly associated with metadata (using the FactoMineR R package [101 ]). Total abundances and significant associations between metadata, taxa, and functions are listed in Additional files 1 and 11.

Morgan X.C., Tickle T.L., Sokol H., Gevers D., Devaney K.L., Ward D.V., Reyes J.A., Shah S.A., LeLeiko N., Snapper S.B., Bousvaros A., Korzenik J., Sands B.E., Xavier R.J, & Huttenhower C. (2012). Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biology, 13(9), R79.

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Publication 2012

Crohn Disease factor A Genetic Heterogeneity Metabolism Plant Roots Sulfur

Systematic review of glycaemic index data

We conducted a literature search of MEDLINE from January 1981 through December 2007 using the terms “glyc(a)emic index” and “glyc(a)emic load.” We restricted the search to human studies published in English using standardized methodology. We performed a manual search of relevant citations and contacted experts in the field. Unpublished values from our laboratory and elsewhere were included. Values listed in previous tables (6 (link),7 ) were not automatically entered but reviewed first. Final data were divided into two lists. Values derived from groups of eight or more healthy subjects were included in the first list. Data derived from testing individuals with diabetes or impaired glucose metabolism, from studies using too few subjects (n ≤ 5), or showing wide variability (SEM > 15) were included in the second list. Some foods were tested in only six or seven normal subjects but otherwise appeared reliable and were included in the first list. Two columns of GI values were created because both glucose and white bread continue to be used as reference foods. The conversion factor 100/70 or 70/100 was used to convert from one scale to the other. In instances where other reference foods (e.g., rice) were used, this was accepted provided the conversion factor to the glucose scale had been established. To avoid confusion, the glucose scale is recommended for final reporting. GL values were calculated as the product of the amount of available carbohydrate in a specified serving size and the GI value (using glucose as the reference food), divided by 100. Carbohydrate content was obtained from the reference paper or food composition tables (8 ). The relationship between GI values determined in normal subjects versus diabetic subjects was tested by linear regression. Common foods (n = 20), including white bread, cornflakes, rice, oranges, corn, apple juice, sucrose, and milk were used for this analysis.

Atkinson F.S., Foster-Powell K, & Brand-Miller J.C. (2008). International Tables of Glycemic Index and Glycemic Load Values: 2008. Diabetes Care, 31(12), 2281-2283.

Publication 2008

Bread Carbohydrates Corns Diabetes Mellitus Food Glucose Healthy Volunteers Homo sapiens Metabolism Milk, Cow's Oryza sativa Sucrose

Comprehensive Molecular Catalog Compilation

We loaded catalogs from over 320 commercial vendors and 130 annotated catalogs. Some sources such as HMDB and DrugBank were loaded as several distinct catalogs in ZINC allowing us to leverage the curation of metabolite origin such as plant metabolites in HMDB or drug status such as investigational drugs in DrugBank. All catalogs in ZINC are categorized by their biogenic and bioactivity status, if any.⁹⁵ Only descriptions that characterized the entire catalog contents were applied. For instance, the “Approved” subset of DrugBank was categorized as “World Drugs” since it contains over 100 drugs approved in other countries but not by FDA, and the “Endogenous” subset of HMDB was categorized as having a biogenic type of “endogenous human metabolite”. Molecules inherit biogenic and bioactive properties from the catalogs they are found in. These values are computed and stored, and are accessible in the interface as molecular features. There are four biogenic catalog levels: 1) Endogenous human metabolites, i.e. compounds that are synthesized in man. Interestingly, this may include compounds produced by our bacterial flora; 2) Metabolites of any species, i.e. small molecules that are involved in metabolism, development and reproduction, but not metabolites of xenobiotics; 3) Biogenic compounds, often called natural products; 4) Unknown biogenic status. Likewise, ZINC supports seven levels of bioactivity annotation as follows. 1) FDA approved; 2) World drugs; 3) Investigational, compounds reported to be used in clinical trials; 4) In Man, which including nutraceuticals, for instance; 5) In vivo, which includes DrugBank experimental compounds that have been in animals; 6) In cells, which includes compounds reported active in cell based assays; 7) In vitro, compounds active or assumed active at 10 μM or better in a direct binding assay. All other compounds are marked as having unknown biological activity. The categories are ordered to be progressively inclusive within each series, thus all FDA approved drugs are also world drugs and all compounds active in cells are also active in vitro. We annotate as building blocks those catalogs of compounds available in preparative quantities, typically 250 mg or more. Commercial vendors are categorized by the speed and cost of compound acquisition, allowing the best purchasability of every compound to be computed based on its current catalog membership. Catalog categorizations are refined continually by purchasing experience in our lab and reports from colleagues, as follows:⁹⁵ 1) In stock, delivery in under two weeks, 95% typical acquisition success rate; 2) Procurement agent, in stock, delivery in 2 weeks, 95% typical acquisition success rate; 3) Make-on-demand, delivery typically within 8 to 10 weeks, 70% typical acquisition success rate; 4) Boutique, where the cost may be high, but still likely cheaper than making it yourself, 70% typical acquisition success rate.

Sterling T, & Irwin J.J. (2015). ZINC 15 – Ligand Discovery for Everyone. Journal of Chemical Information and Modeling, 55(11), 2324-2337.

Publication 2015

Anabolism Animals Bacteria Biological Assay Biopharmaceuticals Cells Inclusion Bodies Investigational New Drugs Metabolism Natural Products Nutraceuticals Obstetric Delivery Pharmaceutical Preparations Plants Reproduction Xenobiotics Zinc

Comprehensive Metagenomic Metabolic Reconstruction

Here, we describe the methodology employed in this study in two parts: first, the computational pipeline for metagenomic metabolic reconstruction implemented in HUMAnN, and second its application to the 741 microbial community samples of the Human Microbiome Project. HUMAnN inputs metagenomic DNA sequences and infers community-wide gene and pathway abundances through a process of seven steps (Figure 1):
HUMAnN has additionally adapted ecological diversity metrics in order to provide functional diversity and richness profiles for each sample, and we validated its gene- and pathway-level accuracy using four synthetic communities of varying complexity.
To assess microbial community function and metabolism in the human microbiome, we applied this process to the metagenomic data generated by the HMP [9] , comprising >3.5 Tbp of microbial DNA from 7 body sites spanning 102 individuals (Table 1). We identified modules over- or under-represented in individual body sites using the LEfSe [23] (link) biomarker detection system, as well as associating the resulting gene and module abundances with subject clinical metadata and with external data including CAZy [21] abundances using standard nonparametric Spearman correlation.

Abubucker S., Segata N., Goll J., Schubert A.M., Izard J., Cantarel B.L., Rodriguez-Mueller B., Zucker J., Thiagarajan M., Henrissat B., White O., Kelley S.T., Methé B., Schloss P.D., Gevers D., Mitreva M, & Huttenhower C. (2012). Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome. PLoS Computational Biology, 8(6), e1002358.

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Publication 2012

Biological Markers DNA Sequence Genes Human Body Human Microbiome Metabolism Metagenome Microbial Community Reconstructive Surgical Procedures

Genome-Wide Metabolic Profiling of Serum

Metabolic profiling was done on fasting serum from participants of the German KORA F4 study (n=1,768) and the British TwinsUK study (n=1,052) using ultrahigh performance liquid-phase chromatography and gas chromatography separation coupled with tandem mass spectrometry ^{5 (link)-7 (link)}. We achieved highly efficient profiling (24 minutes/sample) with low median process variability (<12%) of more than 250 metabolites, covering over 60 biochemical pathways of human metabolism (Supplemental Table 1). Based on our previous observation that ratios between metabolite concentrations can strengthen the association signal and provide new information about possible metabolic pathways ^{4 (link),8 (link)}, we included all pairs of ratios between these metabolites in the genome-wide statistical analysis. To reduce the computational and data storage burden associated with meta-analyzing over 37,000 metabolites and ratios, we applied a staged approach for selection of promising association signals (Supplemental Figure 1). In the initial screening stage we assessed associations of approximately 600,000 genotyped SNPs with over 37,000 metabolic traits (concentrations and their ratios) by fitting linear models separately in both cohorts to log-transformed metabolic traits, adjusting for age, gender and family structure (Supplemental Figure 2 & Supplemental Table 2). Next, we selected all association signals having suggestive evidence for association with a metabolic trait in both cohorts (p<10⁻⁶ in both cohorts or p<10⁻³ in one and p<10⁻⁹ in the other). For each of these loci, we then re-assessed the amount of association signals through fixed-effects inverse variance meta-analysis of the two cohorts for all 37,000 available traits using imputed SNPs relative to HapMap2 data (see Online Methods for details). The SNP/trait combination yielding the smallest P-value in this meta-analysis was finally selected for each locus. To account for multiple testing we applied conservative Bonferroni correction leading to an adjusted threshold for genome-wide significance of p < 2.0×10⁻¹².

Suhre K., Shin S.Y., Petersen A.K., Mohney R.P., Meredith D., Wägele B., Altmaier E., Deloukas P., Erdmann J., Grundberg E., Hammond C.J., de Angelis M.H., Kastenmüller G., Köttgen A., Kronenberg F., Mangino M., Meisinger C., Meitinger T., Mewes H.W., Milburn M.V., Prehn C., Raffler J., Ried J.S., Römisch-Margl W., Samani N.J., Small K.S., Wichmann H.E., Zhai G., Illig T., Spector T.D., Adamski J., Soranzo N, & Gieger C. (2011). Human metabolic individuality in biomedical and pharmaceutical research. Nature, 477(7362), 10.1038/nature10354.

Publication 2011

Family Structure Gas Chromatography Genome Homo sapiens Liquid Chromatography Metabolism Serum Tandem Mass Spectrometry

Most recents protocols related to «Metabolism»

Characterization of Oral [14C]Vorasidenib Metabolism in Humans

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Example 8

Characterization of Absorption, Distribution, Metabolism, and Excretion of Oral [¹⁴C]Vorasidenib with Concomitant Intravenous Microdose Administration of [¹³C₃¹⁵N₃]Vorasidenib in Humans

Metabolite profiling and identification of vorasidenib (AG-881) was performed in plasma, urine, and fecal samples collected from five healthy subjects after a single 50-mg (100 μCi) oral dose of [14C]AG-881 and concomitant intravenous microdose of [¹³C3 ¹⁵N3]AG-881.

Plasma samples collected at selected time points from 0 through 336 hour postdose were pooled across subjects to generate 0—to 72 and 96-336-hour area under the concentration-time curve (AUC)-representative samples. Urine and feces samples were pooled by subject to generate individual urine and fecal pools. Plasma, urine, and feces samples were extracted, as appropriate, the extracts were profiled using high performance liquid chromatography (HPLC), and metabolites were identified by liquid chromatography-mass spectrometry (LC-MS and/or LC-MS/MS) analysis and by comparison of retention time with reference standards, when available.

Due to low radioactivity in samples, plasma metabolite profiling was performed by using accelerator mass spectrometry (AMS). In plasma, AG-881 was accounted for 66.24 and 29.47% of the total radioactivity in the pooled AUC_{0-72 h}and AUC_{96-336 h}plasma, respectively. The most abundant radioactive peak (P7; M458) represented 0.10 and 43.92% of total radioactivity for pooled AUC_0-72and AUC_{96-336 h}plasma, respectively. All other radioactive peaks accounted for less than 6% of the total plasma radioactivity and were not identified.

The majority of the radioactivity recovered in feces was associated with unchanged AG-881 (55.5% of the dose), while no AG-881 was detected in urine. In comparison, metabolites in excreta accounted for approximately 18% of dose in feces and for approximately 4% of dose in urine. M515, M460-1, M499, M516/M460-2, and M472/M476 were the most abundant metabolites in feces, and each accounted for approximately 2 to 5% of the radioactive dose, while M266 was the most abundant metabolite identified in urine and accounted for a mean of 2.54% of the dose. The remaining radioactive components in urine and feces each accounted for <1% of the dose.

Overall, the data presented indicate [¹⁴C]AG-881 underwent moderate metabolism after a single oral dose of 50-mg (100 μCi) and was eliminated in humans via a combination of metabolism and excretion of unchanged parent. AG-881 metabolism involved the oxidation and conjugation with glutathione (GSH) by displacement of the chlorine at the chloropyridine moiety. Subsequent biotransformation of GSH intermediates resulted in elimination of both glutamic acid and glycine to form the cysteinyl conjugates (M515 and M499). The cysteinyl conjugates were further converted by a series of biotransformation reactions such as oxidation, S-dealkylation, S-methylation, S-oxidation, S-acetylation and N-dealkylation resulting in the formation multiple metabolites.

A summary of the metabolites observed is included in Table 2

TABLE 2

Retention

ComponentTimeMatrix

designation(Minutes)[M + H]⁺Type of BiotransformationPlasmaUrineFeces

Unidentified 17.00UnknownX

M2667.67^a267N-dealkylationX

Unidentified 2UnknownX

Unidentified 3UnknownX

Unidentified 4UnknownX

Unidentified 5UnknownX

M51519.79^b516OxidationX

M460-120.76^b461OxidationX

M49921.22^b500Dechloro-glutathioneXX

conjugation + hydrolysis

M51621.89^b517Oxidative-deaminationX

M460-221.98^b461OxidationX

M47222.76^b473S-dealkylation + S-X

acetylation + reduction

M47622.76^b477OxidationX

Unidentified 6UnknownX

M47423.63^b475OxidationX

Unidentified 7UnknownX

M43025.88^b431AG-881-oxidationX

M42630.62^b427S-dealkylation + methylationX

M45831.03^c459AG-69460X*

AG-88139.41^b415AG-881XX

M42847.40^b429S-dealkylation + oxidationX

Table 3 contains a summary of protonated molecular ions and characteristic product ions for AG-881 and identified metabolites

TABLE 3

RetentionCharacteristic

MetaboliteTimeProposed MetaboliteProduct Ions

designation(Minutes)[M + H]⁺Identification(m/z)Matrix

M266 7.88^a267[Figure (not displayed)]
188, 187Urine

M51519.79^b516[Figure (not displayed)]
429, 260, 164, 153Feces

M460-120.76^b461[Figure (not displayed)]
379, 260, 164Feces

M49921.22^b500[Figure (not displayed)]
437, 413, 260, 164, 137Urine Feces

M51621.89^b517[Figure (not displayed)]
427, 260, 164, 153Feces

M460-221.98^b461[Figure (not displayed)]
369, 260, 164, 139, 121, 93Feces

M47222.76^b473[Figure (not displayed)]
429, 260, 179, 164, 153Feces

M47622.76^b477[Figure (not displayed)]
395, 260, 164, 139, 119Feces

M47423.63^b475[Figure (not displayed)]
260, 164, 68Feces

M43025.88^b431[Figure (not displayed)]
260, 164, 155, 68Feces

M42630.62^b427[Figure (not displayed)]
260, 164, 151Feces

M45831.03^b459[Figure (not displayed)]
380, 311, 260, 183, 164, 130Plasma Feces^d

AG-88139.41^b415[Figure (not displayed)]
319, 277, 260, 240, 164, 139, 119, 68Plasma Feces^d

M42847.40^b429[Figure (not displayed)]
260, 164, 153Feces

Notes

^aRetention time from analysis of a urine sample

^bRetention time from analysis of a feces sample

^cRetention time from analysis of a plasma sample

^dM458 was only detected in feces by mass spectrometry, not by radioprofiling.

The proposed (theoretical) biotransformation pathways leading to the observed metabolites are shown in FIG. 1.

US11865079B2. Therapeutically active compounds and their methods of use (2024-01-09). Servier Pharmaceuticals LLC [US]. Inventors: Chandra Agarwal Prakash [US].

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Patent 2024

Acetylation AG 30 Biotransformation Chlorine Dealkylation Deamination Elements, Radioactive Feces Glutamic Acid Glutathione Glycine Healthy Volunteers High-Performance Liquid Chromatographies Homo sapiens Hydrolysis Intravenous Infusion Ions Liquid Chromatography Mass Spectrometry Metabolism Methylation Parent Plasma Radioactivity Retention (Psychology) Tandem Mass Spectrometry Urinalysis Urine vorasidenib

Targeting dsRNA Pathways Enhances Immunotherapy

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Example 3

Deletion of novel candidate immunotherapy targets was found to increase sensitivity of tumor cells to immunotherapy. sgRNAs targeting genes involved in dsRNA editing, sensing, and/or metabolism (e.g., Adar) were markedly depleted in mice treated with GVAX and PD-1 blockade (FIG. 2) relative to growth in TCRα^−/− mice. In many cases, multiple members of the same pathway or even the same multi-protein complex were depleted under immune selective pressure, underscoring the importance of diverse biological pathways, such as the dsRNA editing, sensing, and/or metabolism pathway (FIGS. 3-7).

US11873486B2. Modulating dsRNA editing, sensing, and metabolism to increase tumor immunity and improve the efficacy of cancer immunotherapy and/or modulators of intratumoral interferon (2024-01-16). Dana-Farber Cancer Institute, Inc. [US]. Inventors: Matthew Meyerson [US], William N. Haining [US], Jeffrey Ishizuka [US], Robert Manguso [US], Hugh Gannon [US].

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Patent 2024

Candidate Gene Identification Cells Deletion Mutation Figs Genes Hypersensitivity Immunotherapy Metabolism Mus Neoplasms Pressure Proteins RNA, Double-Stranded

Cholesterol Metabolism in STAT1 Mice

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Example 9

CH25H was originally known to regulate cholesterol metabolism. However, when we compared the body weight, lipid deposition in liver and key enzymes involved in lipid metabolism, there was no significant change between WT and STAT1−/− mice (FIGS. 19A-19F). As shown in FIG. 19A, there was no significant difference of the body weight between APP/PS1 and APP/PS1/STAT1−/− mice. As shown in FIG. 19B, there was no significant difference in lipid deposition in liver cells between APP/PS1 and APP/PS1/STAT1−/− mice. Further, as shown in FIGS. 19C-19F, there were no significant difference of the expression of key enzymes including LPL, ABCA1, APOE, HMGCR between APP/PS1 and APP/PS1/STAT1−/− mice.

US11873321B2. Compositions and methods for treating alzheimer's disease (2024-01-16). Generos Biopharma Ltd. [CN]. Inventors: Xin-Yuan Fu [US], Yi Zhou [SG].

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Patent 2024

ABCA1 protein, human ApoE protein, human Body Weight Cholesterol Enzymes Figs Hepatocyte HMGCR protein, human Lipid Metabolism Lipids Liver Metabolism Mice, Laboratory STAT1 protein, human

Herbicide-Tolerant Plant Regeneration

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Example 8

Tolerant tissue was regenerated and characterized molecularly for PPO gene sequence mutations and/or biochemically for altered PPO activity in the presence of the selective agent. In addition, genes involved directly and/or indirectly in tetrapyrrole biosynthesis and/or metabolism pathways were also sequenced to characterize mutations. Finally, enzymes that change the fate (e.g. metabolism, translocation, transportaion) were also sequence to characterized mutations. Following herbicide selection, calli were regenerated using a media regime of R025M for 10-14 days, R026M for ca. 2 weeks, R327M until well formed shoots were developed, and R008S until shoots were well rooted for transfer to the greenhouse. Regeneration was carried out in the light. No selection agent was included during regeneration. Once strong roots were established, MO regenerants were transplant to the greenhouse in square or round pots. Transplants were maintained under a clear plastic cup until they were adapted to greenhouse conditions. The greenhouse was set to a day/night cycle of 27° C./21° C. (80° F./70° F.) with 600 W high pressure sodium lights supplementing light to maintain a 14 hour day length. Plants were watered according to need, depending in the weather and fertilized daily.

US11866720B2. Transgenic or non-transgenic plants with mutated protoporphyrinogen oxidase having increased tolerance to herbicides (2024-01-09). BASF AGRO B.V. [NL]. Inventors: Raphael Aponte [DE], Stefan Tresch [DE], Matthias Witschel [DE], Jens Lerchl [DE], Dario Massa [DE], Tobias Seiser [DE], Thomas Mietzner [DE], Jill Marie Paulik [US], Chad Brommer [US].

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Patent 2024

Anabolism Callosities Enzymes Genes Grafts Herbicides Light Marijuana Abuse Metabolism Mutation Plant Roots Plants Pressure Regeneration Sodium Tetrapyrroles Tissues Translocation, Chromosomal

Herbicide-Resistant Plant Regeneration

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Example 8

Tolerant tissue is regenerated and characterized molecularly for PPO gene sequence mutations and/or biochemically for altered PPO activity in the presence of the selective agent. In addition, genes involved directly and/or indirectly in tetrapyrrole biosynthesis and/or metabolism pathways are also sequenced to characterize mutations. Finally, enzymes that change the fate (e.g. metabolism, translocation, transportation) are also sequence to characterized mutations. Following herbicide selection, calli are regenerated using a media regime of R025M for 10-14 days, R026M for ca. 2 weeks, R327M until well formed shoots are developed, and R008S until shoots are well rooted for transfer to the greenhouse. Regeneration is carried out in the light. No selection agent is included during regeneration. Once strong roots are established, M0 regenerants are transplant to the greenhouse in square or round pots. Transplants are maintained under a clear plastic cup until they are adapted to greenhouse conditions. The greenhouse is set to a day/night cycle of 27° C./21° C. (80° F./70° F.) with 600 W high pressure sodium lights supplementing light to maintain a 14-hour day length. Plants are watered according to need, depending in the weather and fertilized daily.

US11879132B2. Plants having increased tolerance to herbicides (2024-01-23). BASF AGRO B.V. [NL]. Inventors: Raphael Aponte [DE], Stefan Tresch [DE], Dario Massa [DE], Matthias Witschel [DE], Tobias Seiser [DE], Jill Marie Paulik [US].

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Patent 2024

Anabolism Callosities Enzymes Genes Grafts Herbicides Light Marijuana Abuse Metabolism Mutation Plant Roots Plants Pressure Regeneration Sodium Tetrapyrroles Tissues Translocation, Chromosomal

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TRIzol is a monophasic solution of phenol and guanidine isothiocyanate that is used for the isolation of total RNA from various biological samples. It is a reagent designed to facilitate the disruption of cells and the subsequent isolation of RNA.

What is metabolism and why is it important?

Metabolism refers to the set of chemical reactions and processes that occur within living organisms to maintain life. It involves the breakdown and conversion of nutrients into energy, as well as the synthesis of essential molecules. Metabolism plays a crucial role in various physiological functions, including energy production, growth, and repair. Optimizing metabolism research is essential for understanding and addressing metabolic disorders, developing targeted therapies, and improving overall health.

How can PubCompare.ai help with metabolism research?

PubCompare.ai, an AI-driven platform, can enhance the reproducibility and accuracy of metabolism studies by providing easy access to relevant protocols from literature, preprints, and patents, and enabling AI-driven comparisons to identify the best protocols and products for your research. The platform allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights that can help researchers identify the most effective protocols related to metabolism for their specific research goals. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuaracy.

What are some common challenges in metabolism research?

One of the key challenges in metabolism research is the complexity of the metabolic system and the vast number of variables that can affect it. Researchers often struggle to identify the most effective protocols and products for their specific research goals, which can impact the reproducibility and accuracy of their studies. Additionally, the sheer volume of literature, preprints, and patents available can make it difficult to efficiently screen and identify the most relevant information.

How does PubCompare.ai address these challenges?

PubCompare.ai addresses these challenges by providing a centralized platform that allows researchers to easily access and compare relevant protocols from literature, preprints, and patents. The platform's AI-driven analysis capabilities can help identify the most effective protocols by highlighting key differences in their effectiveness, enabling researchers to choose the best option for their metabolism studies. This can significantly improve the reproducibility and accuracy of their research, ultimately leading to better understanding and advancements in the field of metabolism.

What are the different types or variations of metabolism?

Metabolism can be broadly categorized into two main types: cataboli*sm and anabolism. Catabolism refers to the breakdown of complex molecules into simpler ones, releasing energy in the process. This includes the breakdown of carbohydrates, lipids, and proteins to produce ATP, the primary energy currency of the cell. Anabolism, on the other hand, involves the synthesis of complex molecules from simpler ones, using energy to build up and store essential molecules. These two processes work in tandem to maintain the delicate balance of the metabolic system.

More about "Metabolism"

Metabolism is the fundamental biological process that encompasses the chemical reactions and transformations within living organisms.
This complex system involves the breakdown and conversion of nutrients, such as carbohydrates, lipids, and proteins, into energy, as well as the synthesis of essential molecules required for growth, repair, and various physiological functions.
Optimizing metabolism research is crucial for understanding and addressing metabolic disorders, developing targeted therapies, and improving overall health.
Techniques like TRIzol reagent, RT2 First Strand Kit, and RNeasy Mini Kit are commonly used in metabolism studies to extract and analyze RNA, while tools like QikProp, MTT, and RT2 Profiler PCR Array aid in the assessment of metabolic activity and gene expression.
The integration of computational power, through software like MATLAB, and statistical analysis tools like Prism 8, further enhances the ability to interpret and extrapolate insights from metabolism research.
Additionally, the use of fetal bovine serum (FBS) in cell culture models provides a physiologically relevant environment for studying metabolic processes.
To streamline and optimize your metabolism research, consider using PubCompare.ai, an AI-driven platform that can enhance the reproducibility and accuracy of your studies.
This powerful tool provides easy access to relevant protocols from literature, preprints, and patents, and enables AI-driven comparisons to identify the best protocols and products for your specific research needs.
Leveraging the capabilities of PubCompare.ai can unlock new insights into this fundamental biological process and contribute to advancements in the field of metabolism.