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Carbohydrate Metabolism

Carbohydrate Metabolism refers to the biological processes involved in the utilization and regulation of carbohydrates within the body.
This encompasses the digestion, absorption, and cellular metabolism of sugars, starches, and other carbohydrate compounds.
The study of carbohydrate metabolism is crucial for understanding energy production, storage, and utilization, as well as its role in various physiological and pathological conditions, such as diabetes, obesity, and metabolic disorders.
Researchers in this field investigate the enzymes, pathways, and regulatory mechanisms that govern carbohydrate homeostasis, with the aim of developing therapies and interventions to optimize carbohydrate metabolism and improve human health.

Most cited protocols related to «Carbohydrate Metabolism»

Complementary DNA preparations were prepared for sequencing using the Roche Library Preparation Kit (http://www.roche.com/), Roche Emulsion PCR kit and PicoTiterPlates. Sequencing of B. napus was performed with the Roche GS FLX Titanium and for other species with Roche GS FLX.
Reads were trimmed to remove low-quality and primer sequences using Seq-Clean and assembled with CAP3 (Huang and Madan, 1999 (link)). Initially, 5% of the data were assembled to identify and remove abundant ESTs from the full dataset using BLAT (Kent, 2002 (link)). The reduced dataset then underwent two rounds of assembly with CAP3. First-round CAP3 parameter settings for percentage match, overlap length, maximum over-hang percentage, gap penalty, and base quality cut-off for clipping were p90, o50, h15, g2, and c17, respectively. For the second round, overlap length was changed to 100. The resultant contigs were annotated with a translated BLAST against the TAIR8 database (E-value cut-off 10−10) and further annotated based on information at http://aralip.plantbiology.msu.edu/. The number of ESTs/100 000 ESTs was used as a measure for gene expression. The EST levels and annotations for the oilseed orthologs of >350 Arabidopsis proteins related to lipid and carbohydrate metabolism are provided in Table S1a and S1b. The DNA sequences from this study are deposited at the GenBank Short Read Archive (SRA) with accession numbers provided in Table S4. The EST level data for all orthologs of Arabidopsis proteins (>10 ESTs) are provided in Table S5. Contig nucleotide sequences for R. communis, B. napus and E. alatus are provided as fasta files (RcContigSeq, BnContigSeq and EaContigSeq, respectively).
We used SOMs to evaluate temporal EST expression patterns of 228 proteins related to lipid and carbohydrate metabolism. Expression data were centered and normalized for each protein using adjust methods in Cluster 3.0 (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm) and SOM clusters were generated using Gene Cluster 2.0 (http://www.broadinstitute.org/cancer/software/genecluster2/gc2.html).
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Publication 2011
Arabidopsis Proteins Base Sequence Carbohydrate Metabolism cDNA Library DNA, Complementary DNA Sequence Emulsions Expressed Sequence Tags Gene Clusters Gene Expression Lipids Malignant Neoplasms Oligonucleotide Primers Proteins Titanium
A total of 208 consecutive patients diagnosed with NAFLD seen at Bambino Gesù Children’s Hospital from January 2005 to January 2010 were included in the study. The study was approved by the Ethics Committee of the Bambino Gesù Children’s Hospital and Research Institute, Rome, Italy. Informed consent was obtained from each patient or responsible guardian.
Inclusion criteria were liver biopsy consistent with the diagnosis of NAFLD (8 (link), 9 (link)). Exclusion criteria were the presence of hepatic virus infections, alcohol consumption (≥140gm/week), history of parenteral nutrition, and use of drugs known to induce steatosis (e.g. valproate, amiodarone or prednisone) or to affect body weight and carbohydrate metabolism. Autoimmune liver disease, metabolic liver disease, Wilson’s disease, and α-1-antitrypsin-associated liver disease were ruled out using standard clinical, laboratory and histological criteria.
The body mass index (BMI) and BMI Z-score were calculated (10 (link), 11 (link)). Metabolic syndrome (MS) was defined as the presence of ≥ 3 of the following 5 criteria (12 (link)): abdominal obesity (defined by waist circumference ≥ 90th percentile for age) (13 (link)); hypertriglyceridemia as TG > 95th percentile for age, gender and race (14 (link)); low HDL cholesterol as concentrations < 5th percentile for age and sex (14 (link)); elevated blood pressure (BP) as systolic or diastolic BP > 95th percentile for age and sex (15 (link)); and impaired fasting glucose or known type 2 diabetes mellitus (16 (link), 17 (link)). The degrees of insulin resistance (IR) and sensitivity were determined by the homeostasis model assessment for insulin resistance (HOMA-IR) using the formula: insulin resistance = (insulin × glucose)/22.5, by the insulin sensitivity index (ISI) derived from oral glucose tolerance test using the formula: ISI = [10,000/square root of (fasting glucose × fasting insulin) × (mean glucose × mean insulin during OGTT) and by the quantitative insulin sensitivity check index (QUICKI) using the formula: insulin sensitivity = 1/(log of fasting insulin + log of fasting glucose) (18 (link)-20 (link)).
Publication 2011
Amiodarone Autoimmune Diseases Biopsy Body Weight Carbohydrate Metabolism Child Clinical Laboratory Services Diabetes Mellitus, Non-Insulin-Dependent Diagnosis Ethics Committees, Clinical Glucose Hepatolenticular Degeneration High Density Lipoprotein Cholesterol Homeostasis Hypersensitivity Hypertriglyceridemia Index, Body Mass Insulin Insulin Resistance Insulin Sensitivity Legal Guardians Liver Liver Diseases Metabolic Diseases Metabolic Syndrome X Non-alcoholic Fatty Liver Disease Oral Glucose Tolerance Test Parenteral Nutrition Patients Pharmaceutical Preparations Plant Roots Prednisone Pressure, Diastolic SERPINA1 protein, human Steatohepatitis Systolic Pressure Valproate Virus Diseases Vision Waist Circumference
Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to analyze expression of the genes involved in sugar metabolism and accumulation (Tables S1, S2, S3, S4, S5, 6). Total RNA was extracted from samples by the modified CTAB method [29] , and DNase was used to clean out DNA before reverse-transcription. After analysis of sequence similarities, gene-specific primers (Table S7) were designed, using Primer5 software. Primer specificity was determined by RT-PCR and Melt Curve analysis. qRT-PCR was performed with a iScript cDNA Synthesis Kit (Bio-Rad) according to the manufacturer's protocol. The amplified PCR products were quantified by an iQ5 Multicolor Real-Time PCR Detection System (Bio-Rad Laboratories, Hercules, CA, USA), with iQ SYBR Green Supermix kit (Bio-Rad). Actin (CN938023) transcripts were used to standardize the different gene cDNA samples throughout the test. For all samples, five tubes of total RNA were extracted from five replicates, respectively, and then mixed in a tube used for reverse-transcription. qRT-PCR experiments were done with 3 technical replicates. The data were analyzed using the ddCT method in iQ5 2.0 standard optical system analysis software.
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Publication 2012
Actins Anabolism Carbohydrate Metabolism Cetrimonium Bromide Deoxyribonuclease I DNA, Complementary Gene Expression Genes Genetic Testing Oligonucleotide Primers Reverse Transcriptase Polymerase Chain Reaction Reverse Transcription Sequence Analysis SYBR Green I
Potato leaf samples (∼150 mg) were homogenized using a TissueLyzer (Qiagen, Germany) and the total RNA was extracted using RNeasy Plant Mini kits (Qiagen), as described by Baebler et al. (2009) (link). The total RNA from the larval midgut tissue was isolated using TRIzol (Invitrogen, USA), as described by Petek et al. (2012) (link). The RNA concentrations and integrity were validated using a NanoDrop ND-1000 spectrometer (NanoDrop Products, USA) and agarose gel electrophoresis. DNase treatment (DNase I; Invitrogen) and reverse transcription (High Capacity cDNA RT kits; Invitrogen) were performed as described by Baebler et al. (2009) (link). The samples were analysed using a LightCycler 480 real-time PCR system (Roche Applied Science, USA), as described by Petek et al. (2010) (link).
For the potato plants, the analysis included the expression of 26 genes involved in JA biosynthesis and signalling, ET, SA and auxin signalling, phenylpropanoid biosynthesis, gene silencing, photosynthesis, sugar metabolism and genes regulated by phytohormonal pathways. Cytochrome oxidase (COX), elongation factor 1 (EF-1) and 18S rRNA were used as the reference genes for data normalization. In the CPB larvae, 11 midgut-expressed genes involved in larval response to potato defences were assayed, and Ld_smt3 and 18S rRNA were used as the reference genes for data normalization. Detailed descriptions of the assay design and analysis are given in the Supporting Information and in Table S1 (Supporting information). The standard curve approach described by Petek et al. (2012) (link) was used for quantification.
Publication 2014
Anabolism Auxins Biological Assay Carbohydrate Metabolism Deoxyribonuclease I DNA, Complementary Electrophoresis, Agar Gel Gene Expression Genes Larva Oxidase, Cytochrome-c Peptide Elongation Factor 1 Photosynthesis Plant Growth Regulators Plant Leaves Plants Reverse Transcription RNA, Ribosomal, 18S Solanum tuberosum Tissues trizol
The metabolism of several carbohydrates by sourdough LAB strains was determined by using API 50 CHL galleries (BioMérieux, Marcy-l’Etoile, France) according to the manufacturer instructions. Gas production was detected by Durham tube method [37 ] in MRS broth (Biolife, Milan, Italy) for 24 h at 30 °C. The growth performance of strains was monitored at 10, 30, 37 and 45 °C for 24 h in an MRS broth using a Thermo Bioscreen C automatic turbidometer (Labsystems, Helsinki, Finland). The viability of the isolated strains to grow in acidic environments was evaluated in MRS broth acidified to a final pH of 2.5 with HCl (Biolife, Milan, Italy) in tubes, according to Lee et al. [38 (link)]. Total viable counts were determined by using standard plate count techniques [39 ]. The results were expressed as log of colony-forming units (CFU) per milliliter. All phenotype analyses were carried out in triplicate.
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Publication 2019
Acids Carbohydrate Metabolism Phenotype Strains

Most recents protocols related to «Carbohydrate Metabolism»

In order to determine the secretory signal peptides, SignalP v4.1 (Nielsen, 2017 (link))4 was used to examine the 16,179 predicted proteins of F. udum. Further, TMHMM v2.0 was used to predict the protein sets with the existence of transmembrane domains (Krogh et al., 2001 (link)) and GPI (glycosylphosphatidyl inositol)-anchor using PredGPI (Pierleoni et al., 2008 (link)). Proteins including one transmembrane domain situated within the N-terminal signal peptide and no transmembrane domain overall were chosen. The predicted secretory proteins’ cysteine content was examined. In order to functionally annotate the predicted secretome, BLAST2GO was used to assign GO keywords (Altschul et al., 1990 (link)). The dbCAN HMMs 5.0 (Yin et al., 2012 (link)) was used to find carbohydrate metabolism active enzymes (CAZymes) based on the CAZy database in the F. udum secretome.
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Publication 2023
Carbohydrate Metabolism Cysteine Enzymes Glycosylphosphatidylinositols Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Proteins secretion Secretome SET Domain Signal Peptides
BlastKOALA for KEGG orthology was used for deeper functional annotation57 (link), while biosynthetic gene clusters (BGCs) were automatically searched and analysed by AntiSMASH v6.058 (link). The carbohydrate active enzymes (CAZymes) involved in carbohydrate metabolism were identified through the dbCAN2 meta web server59 (link), which includes SignalP v4.060 (link) for putative secreted proteins identification. Prediction of transmembrane proteins was performed with DeepTMTHMM61 (link), while the Pathogen Host Interactions (PHI) database was used to identify pathogenicity and virulence related genes19 (link).
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Publication 2023
Anabolism Carbohydrate Metabolism Carbohydrates Enzymes Gene Clusters Host-Pathogen Interactions Integral Membrane Proteins Pathogenicity Proteins Virulence
For cDNA synthesis, 1000 ng of intact RNA was reversed to cDNA with a reverse transcription kit (YEASEN, Shanghai, China) with the concentration detected. The synthesized cDNA was diluted to 200 ng/μL as a template for RT-qPCR. To detect carbohydrate metabolism, the expression of genes related to glycogen synthesis (ugp2b, gys2), glycogen degradation (pygl), gluconeogenesis (pck1, pcxb), glycolysis (gck), TCA cycle pathway (idh), and pentose phosphate pathway (g6pd) were evaluated. The expression of genes related to lipid synthesis (fasn, acaca, aclyb) and decomposition (acadl, acaa1, lpl) were determined to illustrate the influence on lipid metabolism, and the expression of genes related to the urea cycle (gs, cps3, otc, ass, asl, and arg1) was also detected. The qPCR was performed in Jena qTOWER3G system using the real-time quantitative PCR detection kit EvaGreen 2 × qPCR Master mix (YEASEN, Shanghai, China): pre-denaturation at 95 °C for 5 min; denaturation at 95 °C for 10 s; annealing and extension at 60 °C for 30 s; and PCR reaction step running 40 cycles. After RT-qPCR, melting curves were analyzed to ensure the specificity of the reaction. Using 18s as the internal reference gene, the relative quantitative data analysis was performed by the 2−ΔΔCt method. RT-qPCR data were analyzed using GraphPad Prism 6. The primers used in the present study are shown in Table 1.
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Publication 2023
ACACA protein, human Anabolism arginase-1, human Carbohydrate Metabolism Citric Acid Cycle DNA, Complementary FASN protein, human Gene Expression Genes Gluconeogenesis Glucosephosphate Dehydrogenase Glycogen Glycogenolysis Glycolysis Lipid Metabolism Lipogenesis Oligonucleotide Primers Pentose Phosphate Pathway prisma Real-Time Polymerase Chain Reaction Reverse Transcription Urea
In the present study, the oxidation reaction of glucose, palmitic acid, and an average of 20 amino acids was chosen to represent the metabolism of carbohydrates, lipids, and protein, respectively, and calculations were performed on the basis of reactions Equations (2)–(4). All the calculations for NAFLD patients with different Child–Pugh Scores and healthy people were performed by using the data given in Table 4 based on the planned diet plans shown in Table 5. According to the procedure described by Öngel et al. [10 (link)], the entropy generation rate and lifespan estimation of the patients were calculated. It is assumed that oxidation of protein, fat, and carbohydrate is 28%, 46%, and 88% for the patient in Child–Pugh Score A, 31%, 55%, and 86% for the patients in Child–Pugh Score B, and 24%, 59%, and 88% for the patient with Child–Pugh Score C, respectively [56 (link)]. However, it was considered that during the disease, the patients in Child–Pugh Scores A, B, and C lost 5 kg, 10 kg, and 20 kg weight, respectively, due to complications. For a healthy person, these rates are 92%, 95%, and 99% for protein, fat, and carbohydrate, respectively [54 (link)]. On the basis of the entropy balance equation (Equation (7)) by Özilgen and Sorgüven [57 ] and Kuddusi [45 (link)], the entropy generation rate and lifespan estimation were found. It was assumed that d[m s]systemdt equals zero because the thermodynamic system, the body, is in a quasi-steady-state condition. To calculate the entropy generation rate during NAFLD (Scirrhotic), it was assumed that patients maintained a healthy life until they reached age 40 and consumed the diet of a healthy person, and then they became NAFLD patients; therefore, they started consuming a special diet for each Child–Pugh Score (Table 5). The lifespan entropy generation limit was 11,404 kJ/K kg in the calculations of Kuddusi [45 (link)]. The one-year survival rate is approximately 95%, 80%, and 44% for the patients with Child–Pugh Scores of A, B, and C, respectively [57 ]. Öngel et al. [10 (link)] estimated the disease-related entropy generation for 19 different varieties of cancer as:
The same expression is employed in this study for the NAFLD, and the remaining average lifetime (tavg) was estimated for each Child–Pugh Score patient and listed in Table 7. If persons are diagnosed with Child–Pugh Score C NAFLD at the age of 40, they would have already generated 4796 kJ/kg K entropy and may generate 6604 kJ/kg K of more entropy during the rest of their lifespan. Pinter et al. [58 (link)] indicated that 95%, 80%, and 44% of the NAFLD patients with Child–Pugh Score A, B, and C will survive the first year of the disease; therefore, when compared with the 119.9 kJ/kg K year of entropy generation rate of an obese (otherwise healthy) person, we may estimate that, on average, a Child–Pugh Score A patient may generate (119.9 kJ/kg K) [1/(0.95)]= 126.2 kJ/kg K of entropy annually. Similarly, Child–Pugh Score B and C patients may generate 149.9 and 272.5 kJ/kg K of entropy annually, respectively (Table 8).
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Publication 2023
Amino Acids Carbohydrate Metabolism Carbohydrates Child Diet Entropy Glucose Human Body Infantile Neuroaxonal Dystrophy Lipids Malignant Neoplasms Non-alcoholic Fatty Liver Disease Obesity Palmitic Acid Patients Proteins
A total of 240 1-day-old male Arbor Acre broilers (body weight, 42.62 ± 0.82 g) were randomly allocated to three groups. Each group consisted of eight replicates (pens) with 10 broilers per pen. Two phase non-medicated basal diets in mashed form were formulated based on the nutrient requirements of the National Research Council (1994) ; (Table 1). The three groups included basal diet (CT, n = 8), and basal diet with a dose of 1.0 × 108 CFU/kg (BCG1, n = 8) and 1.0 × 109 CFU/kg (BCG2, n = 8) B. licheniformis BCG, respectively (Wang et al., 2017 (link); Kan et al., 2021 (link); Xu et al., 2021 (link)). All broilers were feed in wire-floored cages in a one-level battery on their respective diets. The study lasted 42 days, during which time broilers had ad libitum to feed and fresh water. Broilers were housed in an environmentally controlled room and temperature was gradually reduced from 35°C on day 1 to 26°C at day 21 and then kept roughly constant. A 20 h light-4 h dark cycle was carried out throughout the experimental period. B. licheniformis BCG was isolated from humus soil in the northeast forest area and preserved in the Key Laboratory of Feed Biotechnology of Ministry of Agriculture and Rural Affairs. It presents great biological characteristics in carbohydrate metabolism enzymes and stress tolerance through the whole genome sequencing and in vitro evaluation. B. licheniformis BCG with viable count = 1.08 × 1010 CFU/g was used and mixed in the basal diet, which was prepared in bacterial mashed form after processed in activation, culture, centrifugation, freeze-drying, and grinding.
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Publication 2023
Bacteria Biopharmaceuticals Body Weight Carbohydrate Metabolism Centrifugation Diet Enzymes Forests Immune Tolerance Males Nutrients

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More about "Carbohydrate Metabolism"

Carbohydrate metabolism refers to the biological processes involved in the utilization and regulation of carbohydrates within the body, including the digestion, absorption, and cellular metabolism of sugars, starches, and other carbohydrate compounds.
This field of study is crucial for understanding energy production, storage, and utilization, as well as its role in various physiological and pathological conditions, such as diabetes, obesity, and metabolic disorders.
Researchers in this area investigate the enzymes, pathways, and regulatory mechanisms that govern carbohydrate homeostasis, with the aim of developing therapies and interventions to optimize carbohydrate metabolism and improve human health.
This includes the study of key processes like glycolysis, gluconeogenesis, glycogenesis, and glycogenolysis, as well as the role of insulin, glucagon, and other hormones in regulating carbohydrate metabolism.
The study of carbohydrate metabolism is also closely linked to the regulation of energy balance, as carbohydrates are a primary source of energy for the body.
Imbalances in carbohydrate metabolism can lead to conditions like diabetes, where the body is unable to properly regulate blood sugar levels, or obesity, where excess carbohydrate intake and storage can contribute to weight gain.
To support research in this field, various tools and techniques are available, such as the FitAmp™ Blood and Cultured Cell DNA Extraction Kit for DNA isolation, the MethylFlash Methylated DNA Quantification Kit for epigenetic analysis, and the FastQuant RT Kit for reverse transcription and qPCR.
Additionally, the API 50 CHB/E medium can be used for carbohydrate fermentation profiling, while the Power SYBR Green PCR Master Mix and Multiscan spectrophotometer can be utilized for gene expression analysis and quantification.
By leveraging these resources and the insights gained from the MeSH term description, researchers can enhance the reproducibility and accuracy of their carbohydrate metabolism studies, ultimately leading to a better understanding of this critical aspect of human physiology and the development of more effective therapies and interventions.