Amino acids are biologically important organic compounds, consisting of amino (-NH2) and carboxyl (-COOH) functional groups, as well as side chains connected to each amino acid. They are not only the raw material for protein synthesis but also participate in the regulation of various physiological functions of cells, which is essential for maintaining life. Amino acids can be classified into aliphatic amino acids, aromatic amino acids, heterocyclic amino acids, acyclic amino acids, basic amino acids, sulfur-containing amino acids, hydroxyl amino acids, iodine-containing amino acids, etc., according to their different binding groups.
Creative Proteomics developed an amino acid analysis method based on UPLC-MS platform for absolute quantitative detection of 130+ amino acids and their derivatives.
What Can We Provide
Amino Acid Flux Analysis: Quantification of amino acid fluxes within metabolic pathways to understand utilization and conversion.
Amino Acid Metabolite Profiling: Comprehensive profiling of amino acid metabolites to identify and quantify related compounds and intermediates.
Stable Isotope Tracing Studies: Tracking the fate of specific amino acids within metabolic networks using stable isotope tracing techniques.
Amino Acid Metabolism in Disease States: Specialized investigation of altered amino acid metabolism in various disease states, including metabolic disorders and neurodegenerative diseases.
Amino Acid Turnover Rate Determination: Measurement of turnover rates of individual amino acids to assess synthesis and degradation rates.
Customized Amino Acids Metabolic Analysis: Tailored solutions designed in collaboration with clients to meet specific research objectives and project requirements.
Metabolic Pathways Analysis
- Amino acids
- Monoamines
- Polyamines
- Cholamines
- Neurotransmitters
- Cytotoxity markers
- Immunogical biomarkers
- Food consumption biomarkers
Advantages of Our Amino Acids Analysis Service
- Rich experience in sample handling and analysis of more than 130 amino acids
- The ability to analyze amino acids present in a variety of species at the cellular/tissue level
- Efficient quantification of amino acids with LC-MS, UPLC-MS and GC-MS technology
- Flexible statistical analysis and bioinformatics analysis
Amino Acid Metabolism Analysis Platform at Creative Proteomics
| Analytical Technique | Description | Instrument Model |
|---|
| Liquid Chromatography-Mass Spectrometry (LC-MS) | Utilizes liquid chromatography for separation and mass spectrometry for detection. Ideal for complex sample analysis. | Thermo Scientific Q Exactive Plus |
| Gas Chromatography-Mass Spectrometry (GC-MS) | Employs gas chromatography for separation and mass spectrometry for detection. Suited for volatile compounds. | Agilent 7890B GC-MS/5977A MSD |
| High-Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS) | Combines HPLC for separation with mass spectrometry for detection, offering precise analysis even in complex samples. | Waters Xevo TQ-XS |
Workflow for Amino Acid Metabolism Analysis
The Amino Acids We Can Analyze Include But Are Not Limited To
Sample Requirements for Amino Acids Metabolomics
- Tissue: ≥10 mg
- Cell: ≥1×106
- Plasma/serum: ≥10 µL
- Urine: ≥10 µL
- Plant: ≥20 mg
- Feces: ≥10 mg
- DBS: ≥10 µL
- Cell medium: ≥ 100 µL
Amino Acid Metabolism Data Analysis
| Data Analysis Step | Data Analysis Projects | Description |
|---|
| Data Preprocessing | Peak Detection and Integration | Identify and quantify peaks corresponding to amino acids and their derivatives in the raw data. |
| Retention Time Alignment | Correct for variations in retention times across samples to ensure accurate peak alignment. |
| Data Normalization | Adjust data to remove systematic variation, ensuring that samples are directly comparable. |
| Missing Value Imputation | Estimate missing values to ensure complete datasets for downstream analysis. |
| Statistical Analysis | Principal Component Analysis (PCA) | Reduce data dimensionality and visualize sample relationships. |
| Partial Least Squares-Discriminant Analysis (PLS-DA) | Identify differences between sample groups based on metabolite profiles. |
| Analysis of Variance (ANOVA) | Detect significant differences in metabolite levels between sample groups. |
| Student's t-test | Compare the means of two groups to identify significant metabolite changes. |
| Fold Change Analysis | Determine the magnitude of metabolite changes between experimental conditions. |
| Metabolite Identification | Spectral Database Matching | Match experimental mass spectra to spectral databases (e.g., NIST, METLIN) for metabolite identification. |
| Isotopic Pattern Analysis | Analyze the isotopic distribution of metabolites to confirm their elemental composition. |
| Fragmentation Pattern Analysis | Study the fragmentation patterns of metabolites to elucidate their chemical structures. |
| Pathway Analysis | Enrichment Analysis | Identify metabolic pathways enriched with significant metabolite changes. |
| Metabolite Set Enrichment Analysis (MSEA) | Analyze predefined sets of metabolites associated with specific pathways or functions. |
| Pathway Topology Analysis | Consider the connectivity of metabolites within pathways to assess their biological significance. |
| Visualization | Heatmaps | Visualize patterns of metabolite abundance across samples and conditions. |
| Volcano Plots | Highlight significant metabolite changes in a visually intuitive manner. |
| Pathway Diagrams | Illustrate the affected metabolic pathways and the metabolites involved. |
| Scatter Plots | Display relationships between individual metabolites or samples. |
Case: Unlocking Lipid Accumulation in Yarrowia lipolytica: Insights from Metabolic and Transcriptional Analyses under Nitrogen Limitation
Background
The research focuses on the metabolic characteristics of Yarrowia lipolytica, highlighting its potential as a microbial cell factory for biofuel and chemical production. This oleaginous yeast can accumulate a significant portion of its biomass as lipids, making it valuable for advanced biofuel production. The study aims to understand the regulation of lipid accumulation in Y. lipolytica, particularly under conditions of nitrogen limitation.
Samples
The study utilized Y. lipolytica strains, including one overexpressing diacylglycerol acyltransferase (DGA1), and conducted chemostat cultures to create steady-state conditions. Samples were taken from these cultures to analyze biomass composition, lipid content, fatty acid profiles, and transcriptomes.
Technical Methods
Genome-Scale Metabolic Model (GEM) Development: A comprehensive GEM (iYali4) of Y. lipolytica was constructed. It incorporated the latest consensus network data and curated information from previous models. This model provided a framework for understanding metabolic pathways and interactions within the organism.
Physiological Characterization: The high-lipid producing Y. lipolytica strain, overexpressing DGA1, was cultivated in bioreactors under chemostat conditions with either carbon or nitrogen limitation. Various parameters, including glucose consumption rates, biomass yield, and gas exchange rates, were measured to assess the physiological responses.
Lipid and Fatty Acid Analysis: Lipid content and composition were analyzed using a fast microwave-assisted extraction method. LC-CAD allowed quantification of different lipid species, while GC-MS was employed to analyze fatty acid profiles. This provided insights into the lipid metabolism of Y. lipolytica under different nutrient limitations.
RNA Sequencing (RNAseq): Total RNA was extracted from samples, and RNAseq was performed to analyze the transcriptome. The data were aligned to the Y. lipolytica reference genome, and gene expression patterns were assessed under both carbon and nitrogen limitation conditions.
De Novo Assembly: Raw RNA data were processed to remove low-quality reads, and transcripts were de novo assembled. Functional annotation of proteins was conducted to understand their roles in cellular processes.
Gene Set Analysis: Gene sets were generated based on Gene Ontology (GO) annotations, and gene set analysis was performed using normalized RNAseq counts. This analysis helped identify significant changes in gene expression associated with different nutrient limitations.
Experimental design (Kerkhoven et al., 2016).
Results
Lipid Accumulation Response: Under nitrogen limitation, Y. lipolytica exhibited a significant increase in lipid content, affecting the composition of various lipid species. The response was attributed to the redirection of carbon flux from nitrogen-dependent pathways to lipid biosynthesis.
Transcriptional Regulation: While transcriptional regulation played a role in the response to nutrient limitations, lipid metabolism itself exhibited limited transcriptional regulation. This suggested that the metabolic network of Y. lipolytica is adapted to accommodate increased carbon flux toward lipids.
Role of Post-Translational Modifications: Post-translational modifications, such as the phosphorylation of Acc1 by Snf1, likely contribute to the oleaginous phenotype in Y. lipolytica. This indicates that native regulatory mechanisms may influence lipid accumulation.
Importance of Genome-Scale Model: The study emphasizes the significance of the comprehensive genome-scale model (iYali4) for investigating lipid accumulation in Y. lipolytica. It challenges the previously proposed role of Snf1 in lipid accumulation and suggests that lipid accumulation under nitrogen limitation is similar to overflow metabolism observed in other microorganisms.
Reference
- Kerkhoven, Eduard J., et al. "Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica." NPJ systems biology and applications 2.1 (2016): 1-7.
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