metwarebio

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Applications

Metabolomics Offers Solutions For You

Metabolomics is widely applicable across different industries from agriculture to medicine. Understanding the chemical profile from various samples will help resolve what is currently happening and what is being observed. Metware Biotechnology has built its processes to handle different types of samples that can help industries with their metabolomics experiments.

 Agriculture & Breeding

Yield and nutrition is important in agriculture, thus understand nutritional value and agronomic traits directly through metabolomics is a critical component in agricultural breeding.

 Microbiome

Gut microbiome is considered a host's second genome and the metabolism of gut microbiota is important for the host's defense against pathogens, nutrient breakdown and uptake, and neuronal signaling. Study the metabolic interactions between host and microbiome through metabolomics.

 Cancer

Metabolism is an essential component to understand the complexity of cancer and human diseases. Many metabolites from body fluids can be use for diagnosis and instruct proper medication or therapy. Metabolomics is the best technology providing these insights.

 Infectious Diseases

Infectious diseases can happen quickly. The 2019 global pandemic of COVID-19 showed that multi-omics examination and rapid pharmaceutical development are needed to combat infectious diseases.

 Metabolic Disorders

Human diseases, from chronic pain to acute infectious diseases, often have metabolic readouts that indicate the state of the disease. Metabolomics has been applied to metabolic disorders such as diabetes and obesity to infectious diseases such as tuberculosis and COVID-19.

 https://www.metwarebio.com/widely-applicable-metabolomics-service/

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Untargeted Metabolomics

The LC-MS untargeted metabolomics is used for unbiased detection of metabolites in samples by liquid chromatography-mass spectrometry and to obtain their qualitative and quantitative information.

 Technology Introduction

The main application is for comparing the case group with the control group to find the metabolites and metabolic pathways that show significant differences between groups, which can provide clues and directions for the research in biomarker development, disease pathogenesis, and drug treatment mechanisms.

 Technical Features of Untargeted Metabolomics Service

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Large Curated Database

Collected ultra-high sensitinity data of over 280,000 metabolites. Each sample can typically identify 1500-3000 metabolites.

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Comprehensive Identification Strategy

A comprehensive identification of metabolites was performed using four qualitative methods:

 Matching to in-house standard database;

 Matching to integrated public database;

 Matching to AI database;

 Identify using the metDNA algorithm.

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Rigorous Quality Control

A mature quality control system monitoring all aspects of experimentation from sample preparetion to data collection.

 https://www.metwarebio.com/untargeted-metabolomics-service/

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Transcriptome+Metabolome

The transcriptome is an important method to obtain gene expression in a biological system, and the metabolome is the basis and direct readout of the system’s phenotype. The metabolites are the final result of gene transcription under internal and external regulation and are the material basis of the observed phenotype. In the era of systems biology research, biological processes and gene regulatory networks are complex and dynamic. It is often insufficient to use a single dataset to study systems biology. Correlating transcriptomic data that has a large number of differentially expressed genes with differential metabolites detected by metabolomics can pinpoint key genes, metabolites, and metabolic pathways that are closely associated with internal changes in the system, and thereby explain biological problems in a more holistic approach.

 Technology Introduction of Transcriptome+Metabolome Services

Correlating transcriptomic data that has a large number of differentially expressed genes with differential metabolites detected by metabolomics can pinpoint key genes, metabolites, and metabolic pathways that are closely associated with internal changes in the system, and thereby explain biological problems in a more holistic approach.

 Case Study: The Cardamine Enshiensis Genome Reveals Whole Genome Duplication And Insight Into Selenium Hyperaccumulation And Tolerance (Huang Et Al., Cell Discovery 2021)

 To better understand the mechanisms of Se tolerance in Cardamine enshiensis, the authors constructed the 443Mb genome for C. enshiensis and performed RNA-seq and Widely-Targeted Metabolomics on seedlings treated with water (control) or 400uM sodium selenite.

 Mechanisms of Se tolerance and hyperaccumulation in C. enshiensis

 In total, 29,671 differentially expressed genes were identified and a total of 558 metabolites were identified in the leaf tissue of C. enshiensis, of which 127 were differential metabolites. The authors found 10 flavone-related metabolites were altered between the two groups, indicating that flavones play a pivotal role in Se tolerance. KEGG analyses showed the significantly enriched pathways were associated with the biosynthesis of secondary metabolites and flavone/flavonoid/flavonol compounds. A Pearson’s correlation coefficient threshold of r > 0.8 was used to identify the metabolites that were significantly correlated with each gene and the results revealed that 175 transcripts were highly correlated (R2 > 0.96) with tricetin O-malonylhexoside and amentoflavone.

 https://www.metwarebio.com/transcripto-memetabolome-services/

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TM Widely-Targeted Metabolomics

TM Widely-targeted metabolomics is the 'Untargeted metabolomics + Targeted Metabolomics' process that achieves the perfect combination of high resolution, wide-coverage, high sensitivity, and precise quantification. It is a great starting point to examine the global metabolite profile. Metware's curated database of 280,000 metabolites, which includes 3000 metabolites not found in public databases, allows more annotations from high-resolution mass spec data.

 Technology Introduction

"TM" Widely-Targeted Metabolomics combines Untargeted metabolomics and Targeted metabolomics to achieve the perfect combination of high-resolution, wide-coverage, high-sensitivity, and precise quantification. Metware's patented Widely-Targeted Metabolomics technology stands out from many others with features such as:

 Using high-resolution mass spectrometers to allow the unbiased collection of MS/MS spectrum data;

 Highly curated metabolomics database with over 7000+ in-house metabolite database providing more metabolite annotation;

 Using MRM analysis from QQQ to accurately quantify metabolites in each sample.

 List of Metabolites

Our curated database contains over 280,000 metabolites. Our in-house standard database has over 3,000 metabolites. The integrated public database has over 150,000 metabolites, and the AI predictive structural database has over 130,000 metabolites. The integrated public databases include KEGG, HMDB, Metlin, Mass Bank, Lipid Search, and other commonly used databases.

 https://www.metwarebio.com/tm-widely-targeted-metabolomics-service/

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Resequencing+Metabolome

The types and quantities of metabolites vary greatly in different species and tissues. The mGWAS (metabolome Genome-Wide Association Study) approach uses population materials, based on resequencing genetic information and metabolomics data to carry out genome-wide association analysis between genomic variations and metabolite quantities.

 Case Study: Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism (Chen et al., Nature Genetics 2014)

 Subspecies specific metabolites between two rice subspecies

 Using 529 rice leaf materials, a total of 840 compounds were detected by Widely-Targeted Metabolomics. Cluster analysis of these metabolites identified C-glycosylated and malonylated flavonoids accumulated more in the O. indica, while the O. japonica contained large amounts of phenolamides and arabidopyl alcohol derivative compounds. These subspecies-specific metabolites reflect the differentiation of O. indica and O. japonica.

 SNP analysis and metabolite association of the rice population

 Using NGS on the 529 materials, 6,428,770 SNPs were screened for further analysis. Simple linear regression (LR) and linear mixed models (LMM) were used for mGWAS analysis and a total of 2,947 major SNPs were identified, corresponding to 634 loci in at least one population.

  161 significant loci shown on the Manhattan plot correspond to flavonoids, phenolic compounds, amino acids and their derivatives, terpenoids, nucleic acids and their derivatives, and other known metabolites. In addition, there are 195 loci corresponding to unknown metabolites.

 genome-wide-association-study-analysis.jpg

 mGWAS assists with annotation of unknown metabolites

 mGWAS facilitates the annotation of metabolites by associating unknown metabolites with functionally relevant genes. For example, SNP sf1207801034 in the NOMT gene encoding naringenin 7-O-methyltransferase correlated with mr1002 levels, suggesting that this metabolite may be sakuranetin. Subsequently, the mr1002 metabolite was proven to be sakuranetin by comparing the retention time and fragmentation pattern with the chemical standard of this compound. In this study, GWAS was able to annotate 166 unknown metabolites.

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 Biochemical and functional interpretation of GWAS results

 In addition to providing insight into the genetic basis of metabolic changes, mGWAS also provide insight into the biochemistry and function of underlying pathways. We can examine previously unidentified candidate genes by (i) finding genes or gene clusters associated with the relevant metabolic signatures encoded by these loci; (ii) clustering candidate genes with homologous genes with known functions; (iii) cross-reference genetic map results. We hope to uncover candidate genes or SNPs valuable for plant physiological or human nutrition.

 For instance, trigonelline, an N-methyl conjugate of nicotinic acid, has long been reported to regulate various processes, especially abiotic stress in plants, but the enzyme that catalyzes niacin to trigonelline was unknown at the time. Trigonelline levels were significantly correlated with SNP sf0235317720 on chromosome 2, which was in linkage disequilibrium with the Os02g57760 gene encoding an O-methyltransferase protein, indicating that Os02g57760 encodes a key methyltransferase that catalyzes trigonelline biosynthesis. When N-terminal His-tagged Os02g57760 was expressed in E. coli BL-21, we were able to detect niacin:N-methyltransferase activity in soluble protein extracts. Consistent with the in vitro activity, overexpression of Os02g57760 in ZH11 (an O. japonica cultivar with low trigonelline content) resulted in a greater accumulation of trigonelline in the transgenic lines compared to control plants, confirming that Os02g57760 catalyzes trigonelline biosynthesis.

 https://www.metwarebio.com/resequencing-metabolome-services/

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Quantitative Lipidomics for Plants

Metware Biotechnology uses Widely Targeted Metabolomics technology to build a database specific for plant lipids and performs qualitative analysis from the mass spectrometry data of many biological materials, followed by absolute quantification using QqQ and  MRM to provide a comprehensive analysis of lipids in plant samples. This process is applicable to many plant research questions in growth and development as well as stress response.

 Technology Introduction of Quantitative Lipidomics Service for Plants

A class of organic compounds known as lipids are easily soluble in non-polar solvents but insoluble in water. The lipidome, as a subtype of metabolome, focuses on annotating and quantifying lipids in samples in order to understand their functions in plant growth and development. Important cellular processes, such as material transport, energy metabolism, information transfer, and metabolic regulation, all depend on lipids. With the help of Widely Targeted Metabolomics and a specific database of more than 2500 plant lipids, our Quantitative Lipidomics Service for Plants allows high throughput analysis of plant lipids in all types of plant materials.

 https://www.metwarebio.com/quantitative-lipidomics-service-for-plants/