Amplicon Sequencing Services: High-Resolution Genetic and Microbiome Analysis

Partial results are shown below:

The taxonomy distribution of all sample in Phylum classification level.

Species abundance Heatmap.

Rarefaction curve of the sequenced reads for samples (The above figure) & The depth of the sequencing samples (The below figure).

Boxplot analysis based on bray Curtis (A), binary jaccard (B), unweighted unifrac (C), and weighted unifrac (D).

PCoA analysis based on bray Curtis (A), binary jaccard (B), unweighted unifrac (C), and weighted unifrac (D).

UPGMA clustering tree.

Mean proportion of treated and control group.

Cladogram.

LDA SCORE.

1. What is the difference between targeted sequencing and amplicon sequencing?

Amplicon sequencing involves the PCR amplification of specific genomic regions followed by sequencing, which ensures high specificity and on-target rates due to the precise design of primers. It is particularly suitable for analyzing small, defined regions of the genome, such as in genetic variation analysis and microbial profiling. In contrast, targeted sequencing encompasses methods like hybrid capture and probe-based enrichment to selectively sequence larger genomic regions or multiple genes without prior amplification. This allows for a more comprehensive analysis of selected areas, but may have variable on-target rates depending on the efficiency of the enrichment process. Amplicon sequencing, by its nature, achieves superior on-target rates in contrast to other targeted sequencing methodologies, attributing this efficiency to the precise design of primers. This approach finds particular applicability in tasks like genotyping via sequencing, as well as the discernment of germline single nucleotide polymorphisms (SNPs), insertions and deletions (indels), and known genetic fusions.

2. What are the primary applications of Amplicon Sequencing?

Amplicon sequencing serves as a pivotal tool in diverse scientific domains, encompassing but not limited to the following applications:

• Genetic Variance Analysis: Unveiling single nucleotide polymorphisms (SNPs), insertions, deletions, and other hereditary genetic modifications.
• Microbiome Investigations: Profiling microbial communities by sequencing marker genes like the 16S ribosomal RNA.
• Oncological Inquiry: Spotting somatic mutations and genetic modifications within tumor specimens.
• Hereditary Disease Research: Exploring the genetic underpinnings of inherited disorders.
• Environmental Surveys: Gauging biodiversity and identifying specific organisms within environmental specimens.

3. What is the difference between Amplicon Sequencing and Whole-Genome Sequencing (WGS)?

Amplicon sequencing targets specific genomic regions by amplifying them with PCR before sequencing, allowing for high specificity and depth in analyzing small, defined regions, such as in detecting mutations or profiling microbial communities. In contrast, whole-genome sequencing (WGS) sequences the entire genome without prior selection or amplification, providing a comprehensive view of all genetic information, which is ideal for discovering novel variants and obtaining a complete genetic profile, but it is more resource-intensive and less focused on specific areas of interest.

4. How do you choose the target regions for Amplicon Sequencing?

The selection of target segments in Amplicon Sequencing hinges on the study's objectives and the biological significance of these segments. Pertinent factors include associations with diseases, genetic markers, regions of notable variability, and functional relevance. Collaborating with bioinformaticians and utilizing databases like dbSNP and ClinVar can facilitate precise target region identification.

5. What types of bioinformatic analyses can be performed with Amplicon Sequencing data?

• Variant identification: Recognizing SNPs, insertions, deletions, and diverse genetic variances.
• Analysis of microbial diversity: Evaluating the constitution and prevalence of microbial consortia.
• Phylogenetic investigation: Researching evolutionary connections among sequences.
• Functional elucidation: Associating genetic variances with plausible functional implications.

6. Can Amplicon Sequencing detect rare variants?

Without a doubt, Amplicon Sequencing displays notable sensitivity, allowing the detection of infrequent variants found at low occurrences. This trait renders it applicable for situations like the spotting of mutations in cancer and the evaluation of microbial diversity.

7. How does CD Genomics ensure sequencing success with high-GC content regions?

We use a 3-layer strategy to maximise yield and accuracy:

1. Optimised Library Prep:
   Methylation-adaptive polymerases reduce GC bias during amplification.
2. Enhanced QC Monitoring:
   Real-time nanopore sensing ensures intact, high-quality fragments.
3. Bioinformatics Correction:
   Advanced algorithms compensate for GC-skewed abundance in downstream data.

Customer Publication Highlight

Microbial adaptation and response to high ammonia concentrations and precipitates during anaerobic digestion under psychrophilic and mesophilic conditions

Journal: Water Research

Published: 1 October 2021

DOI: https://doi.org/10.1016/j.watres.2021.117596

Background

High ammonia concentrations (TAN >1.5 g/L) are a major cause of methane inhibition in anaerobic digestion (AD), particularly under mesophilic (37°C) and psychrophilic (22.6°C) conditions. Phosphate precipitates (e.g., struvite) further exacerbate system collapse, reducing methane yield by >50%. This study pioneers the exploration of microbial ammonia adaptation mechanisms in psychrophilic reactors and analyzes the long-term impact of precipitates on methanogenic communities.

Project Objectives

1. Microbial Adaptation: Uncover microbial responses to high TAN (4,000 mg/L) in psychrophilic vs. mesophilic AD.
2. Key Consortium Identification: Identify ammonia-tolerant methanogens and precipitate-sensitive taxa.
3. Functional Dynamics: Link metagenomic shifts to methane metabolism pathways.

CD Genomics’ Services

As the core genomics partner, CD Genomics delivered:

1. 16S rRNA Amplicon Sequencing
   Platform: Illumina MiSeq PE300
   Target Region: V4-V5 hypervariable (optimized for Archaea detection).
   Primers: 515F (5′-GTGYCAGCMGCCGCGGTAA) / 926R (5′-CCGYCAATTYMTTTRAGTTT).
   Data Depth: ~30,000 reads/sample (covering all TAN adaptation phases).
2. Whole-Genome Metagenomic Sequencing
   Platform: Illumina NovaSeq PE150.
   Depth: 6 GB raw data/sample (initial vs. endpoint samples).
   DNA Standards: Concentration ≥50 ng/μL, 260/280 ratio <1.8.
3. Bioinformatics Analysis
   Pipeline: MG-RAST v4.0.3.
   Taxonomy: SILVA (16S classification), GenBank (metagenome classification).
   Functional Annotation: KEGG/Subsystems databases (e-value ≤10⁻²⁵, 90% identity).
   Diversity Metrics: Shannon Index, PCoA (Bray-Curtis distance).

Key Findings

1. Ammonia Adaptation in Psychrophilic Reactor
   • Dominant Methanogen Shift:
     • At TAN >3 g/L, Methanocorpusculum reached 71% relative abundance (metagenome data), becoming the dominant hydrogenotrophic methanogen (Fig. 5a).
     • Bacterial consortium shifted to Enterococcaceae (Firmicutes), surging from 0.1% to 80% abundance (Fig. 4a).
   • Functional Resilience:
     • KEGG analysis revealed enhanced methane metabolism genes (e.g., methyl-compound conversion) under high TAN (Fig. 8).
2. Precipitate-Induced Collapse in Mesophilic Reactors
   • Methane Yield: Declined by >50% post-precipitate formation with no recovery within 50 days (p<0.05).
   • Methanogen Depletion:
     • Annotated methanogen genes collapsed from >300,000 to <2,500 hits (metagenome data, Fig. 5b).
     • Species Replacement: Methanosarcina barkeri displaced M. mazei as the dominant ammonia-tolerant archaeon (Fig. 4b).
3. α Diversity as Stability Indicator
   • Psychrophilic Reactor: Diversity decreased by 40% (Shannon Index) upon successful ammonia adaptation (Fig. 3a).
   • Mesophilic Reactor: Stable diversity masked functional failure, evidenced by persistent methane decline (Fig. 3b).

Figures Referenced

Figure 3. Alpha diversity and methane yield in experimental reactors at different ammonia concentrations (a) psychrophilic reactor (R1-CO) and (b) mesophilic reactor (R2-WW).

Figure 4. Bacteria and Archaea profiles from 16S rRNA amplicon data along with the step-by-step increase in ammonia levels.

Figure 5. (a) Relative abundance of Archaea Domain, and (b) number of hits for Bacteria and Archaea Domain in psychrophilic (R1-CO) and mesophilic (R2-WW) AD.

Figure 8. Number of genes copy for methane metabolism (anabolism and catabolism) using KEGG Database.

Implications

1. Process Optimization: Enriching Methanocorpusculum in psychrophilic AD enhances ammonia tolerance, reducing heating energy demands.
2. Risk Mitigation: Struvite precipitation causes irreversible methanogenic inhibition, necessitating real-time phosphate/pH monitoring.
3. Biotechnological Resource: M. barkeri and Methanocorpusculum serve as candidates for ammonia-resistant inoculum development.