Food Microbiome Analysis

As we all known, Food may be a driver of various human microbiome variation. Microorganisms and their communities on foods are important determinants and indicators of food safety and quality, affecting our ability to detect and identify pathogens during foodborne outbreaks. Microbiome sequencing is useful to characterize complex food microbial communities and uncover characteristics about the associated microbial content. 16S amplicon sequencing (microbial diversity), Metagenomics (microbial community) and metatranscriptomics approaches will surely advance our understanding of how to effectively use the invaluable microbial resources to improve food quality and safety.

Food Microbiome Analysis

Potential Research Directions of Food Microbiota Analysis

• Investigate pathogenic and spoilage microorganisms to study microbial origin and population dynamics of the complex process
• Detect multiple foodborne pathogens and monitor their relative abundances
• Profile particular microbiome constituents such as antimicrobial resistance genes (ARGs), or resistomes
• Monitor the microbiome for food safety and quality using deep shotgun sequencing

Technical Platforms

With our next-generation sequencing (NGS) technology, PacBio SMRT sequencing, and Nanopore sequencing platforms, we have the tool to identify, quantify and analysis the food microbiota. With this high-throughput technology we can perform complete metagenomics studies capable of surveying the genomes of entire communities, including those of unculturable organisms.

Project Workflow

High-throughput sequencing analysis process

Fig.1 High-throughput sequencing analysis process

High-throughput sequencing analysis process

Fig.2 PCR-DGGE analysis process

Service Specifications

Sample requirements and preparation

• Sample: Genomic DNA
• Quality requirements: ≥ 300 ng, ≥ 10 ng/μL, OD260/280=1.8-2.0, non-degradative
• Shipment: Dry ice or ice packs
• Repeated freeze-thaw cycles should be avoided

Detectable Objects
Food surface swabs, milk, yogurt

Detectable Microorganisms

• Bacteria
• Fungi
• Viruses

Basic analysis

• Sequence Filtering and Trimming
• Sequence Length Distribution
• OTU Clustering and Species Annotation
• Diversity Index
• Shannon-Wiener Curve
• Rank-Abundance Curve
• Rarefraction Curve
• Multiple Contrast
• Heatmap
• Principal Components Analysis (PCA)

Routine analysis

• Heatmap
• VENN
• Principal Components Analysis (PCA)
• Microbial Community Structure Analysis
• α Diversity Index Analysis
• Matastats Analysis
• Weighted Unifrac test
• CCA/RDA Analysis

Advanced data analysis

• Phylogenetic Tree
• LDA-Effect Size (LEfSe)
• Network Analysis
• Correlation Analysis

References
1. Li, S., Mann, D. A., Zhang, S., Qi, Y., Meinersmann, R. J., & Deng, X. (2020). Microbiome-Informed Food Safety and Quality: Longitudinal Consistency and Cross-Sectional Distinctiveness of Retail Chicken Breast Microbiomes. mSystems, 5(5).
2. Johnson, A. J., Zheng, J. J., Kang, J. W., Saboe, A., Knights, D., & Zivkovic, A. M. (2020). A Guide to Diet-Microbiome Study Design.Frontiers in nutrition, 7, 79.
3. De Filippis, F., Parente, E., & Ercolini, D. (2018). Recent Past, Present, and Future of the Food Microbiome. Annual Review of Food Science and Technology, 9(1), 589–608.

For research use only.

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