Untargeted Metabolomics Service

Untargeted metabolomics, namely discovery metabolomics, involves the comparison of the metabolome between the control and test groups, to identify differences between their metabolite profiles which may be relevant to specific biological conditions.

Untargeted Metabolomics Workflow

There are usually three steps in the experimental workflow:

1. Profiling in order to seek the metabolites with statistically significant variations in abundance within a set of experimental and control samples;
2. Determination of metabolite ID, including the chemical structure;
3. Comprehensions as the last step which makes connections between the identified metabolites and the biological processes.

In the workflow of discovery metabolomics, analytical reproducibility is critical for expression profiling work; annotation is a tentative identification based on an accurate mass match to a database or a spectral match to a library; the collected data can be interpreted for biomarker discovery, biological signature/fingerprint selection and pathway mapping. Above are the most important parts in untargeted metabolomic research. The tech panel in Creative Proteomics is experienced in sample preparation, data interpretations. It also can provide you reliable metabolite identification.

What Untargeted Metabolomics services we provide?

With integrated a set of separation, characterization, identification and quantification systems featured with excellent robustness & reproducibility, high and ultra-sensitivity, Creative Proteomics provides reliable, rapid and cost-effective Untargeted Metabolomics as below.

• Serum Untargeted Metabolomics
• Urine Untargeted Metabolomics
• Cerebrospinal Fluid Untargeted Metabolomics
• Plant Untargeted Metabolomics
• Exosome Untargeted Metabolomics
• GC–MS Untargeted Metabolomic Analysis
• LC-MS/MS Untargeted Metabolomics Services
• Gut Flora Metabolomics Research Solution

Advantages

• Based on advanced liquid phase tandem mass spectrometry technology, it can detect up to thousands of pg-level compounds, and can be competent for the detection of thermally unstable, non-volatile, and non-volatile substances.
• Lower cost

Analysis Content

1. Data pre-processing
2. Univariate analysis
3. PCA analysis, PLS-DA analysis
4. Difference m/z value screening and identification
5. Cluster analysis
6. Differential metabolite metabolic pathway annotation analysis

Sample Requirement

• Cells, tissues, urine, whole blood, serum, plasma, etc.
• Plasma or serum > 300 μL, urine > 5 mL, tissue > 100 mg, and cells > 10⁷.

Q1: What databases for metabolite identification?

A: Public databases such as KEGG, METLIN, etc., have an accuracy of less than 10ppm, and identification can be more accurate if the corresponding secondary spectra are available inside the database. If you want to verify, you can buy standard products. We also supply high quality standard products.

Q2: Is there a requirement for the number of samples of untargeted metabolomes?

A: For untargeted metabolomes whose goal is to find differential metabolites, the number of clinical samples is recommended to be no less than 30 per group. For model organisms and plant and animal samples, no less than 10 are recommended.

Q3: What is the difference between untargeted metabolomic analysis and lipid metabolomic analysis?

A: untargeted metabolome analysis is a research method to quantitatively analyze all metabolites in the organism and find the relative relationship between metabolites and physiological and pathological changes. The research object is small molecular substances with a relative molecular mass less than 1000Da, such as lipids , ketones, organic acids, etc. As we all know, genomics and proteomics explore life activities at the gene and protein levels respectively, but in fact many life activities in cells occur at the metabolite level, such as cell signal release, energy transfer and intercellular communication, etc. are all affected by metabolites regulation. Untargeted metabolome analysis is to discover differentially expressed metabolite information through univariate and multivariate analysis, thereby reflecting the environment in which cells are located and the interaction between them and external factors.

Lipidomics is a discipline that studies the lipid composition, lipid metabolism and lipid interactions of organisms, and is the most important branch of metabolomics. Lipids have a variety of important biological functions, such as material transport, energy metabolism, information transmission, and metabolic regulation. Abnormal lipid metabolism can cause many human diseases, including Alzheimer's disease, diabetes, obesity, and atherosclerosis. hardening, etc.

Untargeted metabolomics reveals the role of AQP9 in nonalcoholic fatty liver disease in a mice model

Journal: International Journal of Biological Macromolecules
Published: 2022

Research Background

Non-alcoholic fatty liver disease (NAFLD) is recognized as a major chronic liver disease. NAFLD leads to an increased incidence and mortality of liver-related conditions such as liver cancer and cirrhosis. However, the pathogenesis of NAFLD remains largely unknown, and no drug therapy has been approved for the prevention or treatment of NAFLD.

Aquaporin 9 (AQP9) is a water channel protein (AQP) highly expressed in both human and mouse livers, playing a role in the transport of glycerol and other small solutes. Knockout of AQP9 has been shown to alleviate high-fat diet (HFD)-induced NAFLD.

The aim of this study is to investigate the role of AQP9 in the development of NAFLD and its therapeutic effects in NAFLD treatment using non-targeted metabolomics. The study results suggest that AQP9 may serve as a novel molecular target for NAFLD treatment by downregulating lipid-related metabolites.

Research Results

Impact of AQP9 Gene Knockout on Body Weight

The influence of AQP9 gene knockout on body weight was evaluated in this study. Body weight measurements were taken at the 12th-week endpoint for each experimental group of mice. The results demonstrated significant variations in body weight among the groups. Notably, the ko-control group exhibited a substantial reduction in body weight compared to the wt-control group after the 12-week period.

Figure 1: All four groups of patients showed changes in body weight, with significant differences in body weight at the end of the experiment.

Impact of AQP9 Gene Knockout on Serum Indexes

By contrasting the serum levels of ALT, AST, TG, TC, HDL-C, and LDL-C in the four patient groups, the therapeutic effectiveness of AQP9 deletion was evaluated. When compared to the WT-control group, the WT-HFD group showed considerably increased ALT, AST, TG, TC, and LDL-C levels as well as significantly lower HDL-C levels. In contrast, when compared to the WT-HFD group, the KO-HFD group showed notable decreases in all biochemical markers, with the exception of HDL-C. These results imply that AQP9 deletion may lessen the harmful metabolic changes related to NAFLD.

Figure 2: Serum Indexes Among Groups

Antioxidant and Anti-inflammatory Effects of AQP9 Gene Knockout

Previous studies have indicated that AQP9 knockout can inhibit oxidative stress and inflammatory responses, which play important roles in the progression of NAFLD. Researchers further investigated whether AQP9 knockout inhibits oxidative stress and inflammatory responses in NAFLD mice. The results showed that SOD and GSH-Px activities in the WT-HFD group were lower than in the WT-control group, while MDA levels were higher. Compared to the WT-HFD group, the KO-HFD group exhibited elevated levels of GSH-Px and SOD, along with reduced MDA levels.

Moreover, AQP9 knockout led to a decrease in the levels of inflammatory factors compared to the WT-HFD group.

Figure 3: Oxidative Stress and Pro-inflammatory Factors Among the Four Groups of Patients

Impact of AQP9 Gene Knockout on Liver Morphology

The results depicted in the figure demonstrate that all groups subjected to a high-fat diet (HFD) exhibited different levels of fat infiltration and hepatomegaly. Notably, these alterations were more significant in the WT-HFD group as opposed to the KO-HFD group, where AQP9 had been knocked out. Although the liver lobular structure was not distinctly visible in the tissue slices, all four groups displayed minimal hepatocellular steatosis, indicating the presence of fatty deposits within the liver cells.

Based on these compelling findings, the researchers reached the conclusion that AQP9 knockout can ameliorate the mouse model of hepatic steatosis induced by the HFD. This suggests that AQP9 may play a crucial role in the development of hepatic steatosis, and its absence can lead to an improvement in the condition, potentially offering a promising avenue for therapeutic interventions targeting hepatic steatosis.

Figure 4: Lipid Deposition in the Liver

Figure 5: Quality Control of Metabolomics Data

Metabolic Changes between WT-Control and WT-HFD Groups

Using non-targeted metabolomics, 220 metabolites were detected in the liver tissues of the WT-Control and WT-HFD groups, filtered based on the criteria of p1. Hierarchical clustering analysis of differentially expressed metabolites helped categorize metabolites with similar characteristics and identify their features. From the figure, it can be observed that fatty acids and lipids are differentially expressed metabolites according to the KEGG classification, indicating successful modeling of NAFLD. Glycerophospholipid metabolism and cholesterol metabolism are related to the development of NAFLD. The differential abundance score plot shows an increase in metabolites related to glycerophospholipid metabolism in the WT-HFD group.

Figure 6: Metabolic Changes between the WT-Control and WT-HFD Groups

Metabolic Changes between the KO-HFD and WT-HFD Groups

A total of 209 differentially expressed metabolites were identified between the KO-HFD and WT-HFD groups. Hierarchical clustering analysis of the top 50 metabolites revealed that 9 metabolites (18%) associated with lipids, fatty acids, and sterols showed significant changes. Pathway analysis indicated significant alterations in fatty acid biosynthesis, including 5 metabolites, after AQP9 knockout in the HFD condition. As shown in the figure, most of the differentially expressed metabolites were related to fatty acid metabolism, suggesting that AQP9 plays an important role in lipid metabolism in NAFLD.

Figure 7: Metabolic Changes between the KO-HFD and WT-HFD Groups

Metabolic Changes among the KO-HFD, WT-HFD, and WT-Control Groups

Through one-way ANOVA, a total of 190 differentially expressed metabolites were observed among the three groups. The OPLS-DA model demonstrated significant metabolic changes among the three groups, with R2 and Q2 values of 0.994 and 0.773, respectively. Using the Key Means method, AQP9 knockout was found to have an impact on 122 metabolites related to NAFLD.

Figure 8: Metabolic Changes among the KO-HFD, WT-HFD, and WT-Control Groups

Conclusion

In this study, researchers focused on the fatty acid biosynthesis pathway, which plays a pivotal role in disrupted lipid homeostasis in the liver leading to NAFLD. By examining six biomarkers related to fatty acid metabolism, they confirmed the successful establishment of the NAFLD model.

The study investigated the role of AQP9 in NAFLD by employing AQP9-/- mice and NAFLD models. The results showed that AQP9 knockout mitigates NAFLD progression by reducing inflammation and oxidative stress. Non-targeted metabolomics analysis suggested that fatty acid biosynthesis and insulin resistance pathways may be affected by AQP9 gene knockout. These findings provide valuable insights into the potential therapeutic effects of AQP9 in NAFLD. However, the precise mechanisms through which AQP9 influences lipid accumulation in NAFLD warrant further investigation.