Microbiome Profiling via 16S rRNA Variable Region Sequencing
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Solution Overview
Problem
Current methods for classifying microbiomes face challenges due to the complexity of metagenomic analysis and the limitations of 'second-generation' sequencing technologies, which often result in inaccurate bacterial classification, especially in complex samples, due to incomplete coverage of 16S rRNA variable regions.
Innovation Solution
The method involves obtaining nucleic acid sequences of 16S or 23S ribosomal subunits from a biological sample, comparing them to reference sequences, and identifying microbes at the strain or sub-strain level, using long read sequencing platforms to achieve accurate classification and profiling of microbiomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If second-generation sequencing technologies are used to sequence 16S rRNA subunit, then the sequencing cost is reduced and throughput is increased, but the read length is insufficient to cover variable regions completely resulting in inaccurate bacterial classification
Solution Approach 1:
The patent segments the 16S rRNA sequencing task into multiple overlapping regions that can be covered by shorter reads. By designing primers that target different variable regions (V1-V9) and using multiple sequencing reactions, the complete 16S sequence can be reconstructed even with limited read lengths, thereby maintaining classification accuracy while using high-throughput second-generation sequencing technologies.
Solution Approach 2:
The patent introduces the dimension of multiple sequencing targets by simultaneously sequencing different variable regions of the 16S rRNA gene. Instead of relying on a single long read, the approach uses multiple shorter reads from different regions, combining information across multiple dimensions to achieve accurate bacterial classification.
2Adaptability or versatility
If 16S rRNA subunit sequencing is used for taxonomic classification, then universal bacterial identification is achieved, but misclassification occurs when bacteria share the same variable regions
Solution Approach 1:
The patent applies local quality by focusing on multiple different variable regions (V1-V9) of the 16S rRNA gene rather than relying on a single region. Each variable region has different discriminatory power for different bacterial taxa, and by sequencing multiple regions with region-specific primers, the method captures local variations that improve classification accuracy while maintaining universal applicability.
Solution Approach 2:
The patent creates a composite classification approach by combining sequence data from multiple variable regions. Instead of relying on a single sequencing target, the method integrates information from multiple regions to form a composite profile for each bacterium, which improves classification accuracy by compensating for similarities in individual regions.
3Measurement precision
If full 16S rRNA sequencing is performed to achieve accurate classification, then classification accuracy is improved, but the sequencing complexity and cost increase significantly
Solution Approach 1:
The patent extracts only the necessary variable regions (V1-V9) of the 16S rRNA gene that are most informative for bacterial classification, rather than sequencing the entire 16S gene or whole genomes. By using PCR primers that specifically amplify these variable regions, the method reduces sequencing complexity and data processing requirements while maintaining high classification accuracy.
Solution Approach 2:
The patent uses partial sequencing of the 16S rRNA gene by targeting specific variable regions rather than the complete sequence. This partial action approach sequences only the most discriminatory regions needed for classification, reducing the overall sequencing burden, computational complexity, and cost while achieving sufficient accuracy for taxonomic identification.
Data Source
AI summary
The present disclosure provides methods for profiling a microbiome and therapeutic compositions for treatment. Additionally, the methods, systems, compositions and kits provided herein are directed to assessing or predicting health status in a subject. Some of the embodiments include generating a report.