Carbohydrate Metabolism Analysis Service

What is Carbohydrate Metabolism?

Carbohydrate metabolism is the set of biochemical processes that cells use to manage the synthesis, breakdown, and transformation of carbohydrates—essential biomolecules that serve as a primary energy source for nearly all forms of life. These processes are tightly regulated and interconnected, ensuring that cells can respond to energy demands, maintain homeostasis, and synthesize vital compounds such as nucleotides, glycoproteins, and cell wall components.

At the heart of carbohydrate metabolism is glucose, a simple sugar that is metabolized through multiple, highly coordinated pathways to produce energy, intermediates for biosynthesis, and reducing equivalents that support cellular growth and function. Carbohydrate metabolism can be broken down into several key pathways, each playing a distinct role in maintaining cellular health:

• Glycolysis: This is the primary pathway for glucose breakdown. In glycolysis, glucose is converted into pyruvate, producing ATP (adenosine triphosphate) and NADH (nicotinamide adenine dinucleotide), which are essential for cellular energy. Glycolysis is crucial because it provides energy in the absence of oxygen (anaerobic conditions), making it a fundamental process for fast-growing cells, including cancer cells.
• Gluconeogenesis: In contrast to glycolysis, gluconeogenesis is the process of synthesizing glucose from non-carbohydrate sources such as lactate, glycerol, and amino acids. This pathway is vital for maintaining blood glucose levels during fasting or intense physical activity and is particularly active in the liver and kidneys.
• Glycogenesis and Glycogenolysis: These pathways regulate the storage and release of glucose. Glycogenesis is the process by which glucose is stored in the form of glycogen, a multi-branched polysaccharide, primarily in liver and muscle cells. Glycogenolysis, on the other hand, breaks down glycogen into glucose-1-phosphate when the body needs energy between meals or during exercise.
• Pentose Phosphate Pathway (PPP): This parallel pathway to glycolysis generates NADPH, which is crucial for reductive biosynthesis (e.g., fatty acid synthesis) and maintaining the redox balance in cells. The PPP also produces ribose-5-phosphate, a precursor for nucleotide synthesis, essential for DNA and RNA production.
• Tricarboxylic Acid (TCA) Cycle: Also known as the Krebs Cycle or Citric Acid Cycle, the TCA cycle takes place in the mitochondria and is the final common pathway for the oxidation of carbohydrates, fats, and proteins. It plays a critical role in the production of high-energy compounds such as ATP, FADH2 (flavin adenine dinucleotide), and NADH, which drive various cellular processes.

Together, these pathways ensure that cells can efficiently convert carbohydrates into energy, store excess glucose for future use, and produce key metabolic intermediates for biosynthesis and regulation. Dysregulation in carbohydrate metabolism can lead to a range of metabolic disorders, including diabetes, obesity, and certain types of cancer. Therefore, understanding these pathways is essential for diagnosing diseases, developing targeted therapies, and enhancing biotechnological processes.

Carbohydrate Metabolism Analysis in Creative Proteomics

At Creative Proteomics, we provide comprehensive carbohydrate metabolism analysis using cutting-edge platforms designed to meet the highest scientific standards. Our approach enables detailed profiling of the metabolites involved in carbohydrate metabolism, providing critical insights for a wide range of applications—from disease biomarker discovery to industrial fermentation optimization.

Our expertise spans the full range of metabolomics services, and we work with a wide variety of sample types, including biological tissues, biofluids, cell cultures, and more. By combining the power of modern analytical tools with in-depth biological knowledge, we offer a complete solution tailored to the needs of researchers in academic, clinical, and industrial sectors.

Targeted Metabolomics: We use targeted approaches to quantify specific metabolites of interest in the carbohydrate metabolic pathways, ensuring high sensitivity and precision.

Untargeted Metabolomics: For a broader analysis, our untargeted metabolomics approach enables comprehensive profiling of all detectable metabolites, providing a holistic view of metabolic alterations.

Metabolic Flux Analysis (MFA): By incorporating stable isotope tracers, we can quantify the rates of metabolic reactions, providing insights into the dynamics of carbohydrate metabolism.

Quantitative and Qualitative Analysis: Our high-throughput LC-MS and GC-MS platforms offer both quantitative and qualitative analysis, allowing us to identify and quantify a wide range of metabolites simultaneously.

Bioinformatics Integration: We provide extensive bioinformatics support, including pathway mapping and data visualization, enabling researchers to interpret their results in the context of known metabolic pathways.

List of Carbohydrate Metabolism Metabolites We Can Analyze

Technology Platforms Used for Carbohydrate Metabolism Analysis

1. Liquid Chromatography-Mass Spectrometry (LC-MS)

We use the Agilent 1290 Infinity II LC coupled with Agilent 6545 Q-TOF LC-MS for high-sensitivity analysis of metabolites like glucose, pyruvate, and lactate.

2. Gas Chromatography-Mass Spectrometry (GC-MS)

The Thermo Scientific Trace 1310 GC with TSQ 8000 Evo Triple Quadrupole MS is ideal for volatile metabolites such as mannitol and sorbitol, and isotope-labeled metabolite studies.

3. Nuclear Magnetic Resonance (NMR) Spectroscopy

Using the Bruker Avance III 600 MHz NMR, we analyze carbohydrate structures and metabolic derivatives, like glucose and mannose, with high precision.

4. Capillary Electrophoresis-Mass Spectrometry (CE-MS)

Our SCIEX PA 800 Plus with TripleTOF 5600+ System provides high-resolution analysis of charged sugar derivatives such as glucose-6-phosphate and fructose-6-phosphate.

5. High-Performance Liquid Chromatography (HPLC)

The Shimadzu Prominence HPLC system is used for separating and quantifying complex carbohydrates like maltose and raffinose, with precise detection.

6. Isotope Ratio Mass Spectrometry (IRMS)

The Thermo Scientific DELTA V Advantage IRMS enables tracing of metabolic flux using stable isotope-labeled compounds like [13C] glucose.

Sample Requirements for Carbohydrate Metabolism Analysis

Multiomics of a rice population identifies genes and genomic regions that bestow low glycemic index and high protein content.

Journal: Proceedings of the National Academy of Sciences

Published: 2023

Background

The world faces rising rates of obesity, undernutrition, and hidden hunger, with diabetes being a leading health issue, causing 6.7 million deaths and significant economic losses in 2021. Type 2 diabetes is prevalent, especially in Southeast Asia and the Western Pacific.

Rice, a staple food with a high glycemic index (GI) due to its digestible starch, contributes to diabetes risk. Genetic advancements have enabled the creation of rice varieties with lower GI and higher protein. The study focuses on using QTL mapping and metabolomic analysis to develop rice with low GI, high amylose, and high protein content, aiming to improve health outcomes and provide diabetic-friendly nutrition.

Materials & Methods

Plant Materials and Experimental Design

A cross between the IR36 amylose extender mutant (IR36ae) and Samba Mahsuri was used to develop F3 populations. These were categorized into high amylose and high protein (HAHP) and low amylose and low protein (LALP) lines for analysis.

QTL Mapping

Bulk segregant analysis sequencing (BSA-Seq) was performed to identify QTLs associated with amylose content (AC) and protein content (PC). Significant QTL peaks on chromosomes 1, 2, and 6 were identified and linked to genes involved in starch branching, protein storage, and grain development.

Metabolomic Analysis

Metabolite extraction from HAHP and LALP lines was conducted using HPLC-MS. Pathway enrichment analysis identified key metabolic pathways distinguishing the lines, focusing on amino acids and lipids.

Machine Learning Classification

Principal component analysis (PCA) and an artificial neural network (ANN) model were used to classify rice samples based on GI, AC, and PC. The model achieved an accuracy of 74.47% in predicting GI classes.

CRISPR/Cas9 Gene Editing

CRISPR/Cas9 was employed to edit the OsSBEIIb gene in IRRI 154. Mutant lines were analyzed for glycemic index (GI) and resistant starch (RS) content. Notable mutations included 1-bp insertions and deletions.

Statistical Analysis

Data were analyzed for significance in QTLs, metabolomic profiles, and machine learning results. Phenotypic variance explained (PVE) by SNPs was calculated to assess the impact on traits.

Validation

Candidate genes identified were functionally validated through gene editing and multitrial experiments, focusing on the development of low GI and high protein rice varieties.

Results

Identification of Genetic Regions Influencing Traits

Through BSA-Seq, several QTLs associated with amylose content (AC) and protein content (PC) were identified. Significant peaks were detected on chromosomes 1, 2, and 6. The most impactful QTLs included:

• qseqAC2.1 and qseqAC2.2 on chromosome 2, involving genes related to starch branching (OsSBEIIb), sucrose degradation (OsCIN1), and grain size (LARGE GRAIN1).
• qseqPC2.1 and qseqPC2.2, which overlapped with qseqAC2.1, included genes such as Glutelin B6 and those involved in sugar transport and meiotic recombination.

Metabolomic Profiles

Metabolomic analysis highlighted distinct profiles between HAHP and LALP lines. Key findings included:

• HAHP Lines: Higher levels of dipeptides and essential amino acids, with lower levels of fatty acids. Increased protein yield was observed, with a higher concentration of essential amino acids like lysine.
• LALP Lines: Lower levels of amino acids and higher accumulation of lipids such as phosphatidylcholines and phosphatidylethanolamines.

Metabolomic analysis of HAHP and LALP groups. (A) Hierarchical clustering of 275 metabolites distinguishes HAHP (red) from LALP (green) samples. (B) Pathway enrichment analysis highlights significant differences in amino acid and fatty acid pathways between HAHP and LALP. (C) Top 25 metabolites from enriched pathways with node size and color indicating enrichment ratio and P-values. (D) Specific metabolites accumulating in HAHP with significance shown and boxplots comparing HAHP and LALP. (E) Protein concentrate from HAHP_101 shows higher yield compared to Samba Mahsuri. (F) HAHP_101 protein powder has higher essential amino acid levels than Samba Mahsuri.

Machine Learning Classification

PCA and ANN models classified rice lines into different GI categories (ultralow, low, intermediate, high) with 74.47% accuracy. The model effectively distinguished between ultralow and high GI lines, though performance was less accurate for intermediate GI classes.

CRISPR/Cas9-Mediated Gene Editing

Gene editing of OsSBEIIb led to significant reductions in GI and increases in resistant starch (RS). Notable mutations were observed, including 1-bp insertions and deletions. Edited lines exhibited lower GI values and higher RS content compared to wild-type controls.

Reference

1. Badoni, Saurabh, et al. "Multiomics of a rice population identifies genes and genomic regions that bestow low glycemic index and high protein content." Proceedings of the National Academy of Sciences 121.36 (2024): e2410598121.