Using Global Metagenomes to Evaluate and Improve PCR Primer Coverage and The Application of 3-Domain Amplicon Data to Trait-Based Models

Jesse McNichol¹, Paul M. Berube², Steven J. Biller^2,3,

Sallie W. Chisholm², Jed A. Fuhrman¹

 

¹University of Southern California

²Massachusetts Institute of Technology

³Wellesley College

Image: @claudia_traboni (PhD candidate, Insituto Ciencias Marinas CSIC)

Models do well for phytoplankton

http://darwinproject.mit.edu

 

• Universal Ribosomal PCR Primers target the SSU rRNA of (almost) everything:
  • Quantify the whole cellular community from A-Z (Archaea + Bacteria  + Organelles + Protists + Zooplankton)
  • 16S and 18S on same scale - ratios meaningful
  • Low cost (~$8/sample, 200,000 sequences/ea)
  • Amplicon sequence variants (ASVs) allow mapping, intercomparison

Universal primers as a mapping tool

 

• Detailed protocols available on https://fuhrman-lab.github.io
  • Lab protocol
  • in-silico workflow:
    • Splits 16S and 18S automatically (analyzed separately)
    • Denoises with DADA2/deblur in qiime2 => ASVs
    • Splits data automatically into categories (e.g. chloroplasts)
    • Step-by-step guide on protocols.io, qiime2 visualizations

Universal primers are easy to analyze!

Universal primers complement existing techniques

Universal primers complement existing techniques

 

 

1. Metagenomic validation of universal primer quantification and environmental coverage
2. Use cases for universal primer data (with BioGEOTRACES data)
3. Future work

Talk Outline

Universal primers are quantitative

Also quantitative "in the wild"

Similarly quantitative "in the wild"; 18S less so

Similarly quantitative "in the wild"; 18S less so

PCR amplicon

abundances (18S)

What about primer bias?

MGPrimerEval : a reproducible snakemake pipeline

• Compares primers to unassembled MG reads
• tinyurl.com/titus-workflow-philosophy
• Reproducible by conda/docker integration
• github: tinyurl.com/MGPrimerEval

https://www.physalia-courses.org

MGPrimerEval : datasets

• TARA oceans, BioGEOTRACES, MBARI bloom MG, Malaspina, SPOT/Catalina MG
  • Total = 1,218 metagenomes
  • Globally distributed, mostly oxic, pelagic samples

Results: fraction of MG perfectly matching primer

Conclusions

• Our primers match vast majority of MG sequences perfectly
  • Nearly 100% for 16S, ~90% for 18S
• The glass is also half full! Existing primers can be improved
  • Optimize coverage for biogeochemically-important taxa

 

 

1. Metagenomic validation of universal primer quantification and environmental coverage
2. Use cases for universal primer data (with BioGEOTRACES data)
3. Future work

Talk Outline

2. Use cases for Universal Primers:

 

a) Resolution

 

 

 

b) Sensitivity (detection limit)

 

 

 

c) Modelling

Data source: BioGEOTRACES amplicons

a) Resolution

• Are short amplicons (~373 bp) enough to resolve ecologically-relevant subgroups of Prochlorococcus?

 

b) Sensitivity (detection limit)

 

 

 

c) Modelling

2. Use cases for Universal Primers:

 

*Berube et al. eLife 2019;8:e41043.

Resolution: Prochlorococcus ASVs

Resolution: Prochlorococcus ASVs

Resolution: Prochlorococcus ASVs

Resolution

• Are short amplicons (~373 bp) enough to resolve ecologically-relevant subgroups of Prochlorococcus?

 

Sensitivity (detection limit)

• Can we sequence deeply enough to find rare, but biologically-relevant organisms like diazotrophs?

 

Modelling

2. Use cases for Universal Primers:

 

Sensitivity: Diazotrophs

UCYN-A (diazotroph)

UCYN-A (diazotroph)

qPCR (nifH) data

Sensitivity: Diazotrophs

UCYN-A - Braarudosphaera correlation

UCYN-A (diazotroph)

Resolution

• Are short amplicons (~373 bp) enough to resolve ecologically-relevant subgroups of Prochlorococcus?

 

Sensitivity (detection limit)

• Can we sequence deeply enough to find rare, but biologically-relevant organisms like diazotrophs?

 

Modelling

• How can data be used to improve modelling efforts?

2. Use cases for Universal Primers:

 

Modelling: Rationale

• Microorganisms process up to 50% of PP
• Not yet included in DARWIN model
• Amplicons can constrain microbial biogeography
  • Use to inform and test modelling approaches

Modelling: ASV (~species) depth profiles

Marine actinobacterium

Extreme oligotroph

Very small size

Modelling: Aggregated profiles

Modelling: Defining traits

Modelling: Integrating different data types

3. Future work: Expanding ocean coverage

 

• Simons Collaborative Marine Atlas Project (cmap.readthedocs.io)
• Unify data retrieval and co-localization
• ASVs are stable, and make sense in database

Future work: Public access to data in CMAP

Conclusions: Universal Primers

• Cost-effective tool to map whole cellular community from A-Z (Archaea + Bacteria + Organelles + Protists + Zooplankton)
• Guide more focused studies of same DNA (e.g. meta'omics)
• Straightforward to process and interpret
• Can provide guideposts for modelling efforts

Acknowledgements

• Yi-Chun Yeh (for collaboration on amplicon analysis)
• Shengwei Hou (for help and inspiration on computational analyses)
• Clark Richards (for help with R "oce")
• Ken Youens-Clark (for help with C-microbe-MAP)
• Emily Zakem (for helping me understand modelling)
• Mike Lee (for help separating 16S and 18S in silico)
• Fuhrman lab crew
  • fuhrman-lab.github.io
  • tinyurl.com/MGPrimerEval

NB: 18S usually small fraction of total

% Eukaryotic amplicons

• GP13 (mostly oligotrophic surface waters)
  • AVG = 3.2% (range: ~1-28%)
  • For 200,000 sequences = 6400 18S sequences (2000 - 56,000)
• GA03 (mostly oligotrophic, more deep-sea sampling)
  • AVG = 2.7% (range: 0.17-17%)

DADA2 vs deblur (ASVs vs. MG)

DADA2 vs deblur (ASV vs. ASV)

3. Future work: Expanding ocean coverage