Cochineal 16S - Preliminary Results

1 Methodology

Data was processed with version 2.10.0 of the nf-core/ampliseq pipeline (Straub et al. 2020). The pipeline uses DADA2 (Callahan et al. 2016) for Amplicon-sequence variant (ASV) inference, QIIME2 (Bolyen et al. 2019) for calculating diversity indices and PICRUSt2 (Douglas et al. 2020) for predicting the functional potential of bacteria.

2 Results

2.1 Relative Taxonomic Abundance

The full data for the calculated taxonomic abundance can be interacted with in the following page: https://dalmolingroup.github.io/cochineal_16s/qiime2_relative_abundance/. It enables analysis at every taxonomic level, showing the relative frequency of each taxa at that level.

By far the most abundant taxa is Candidatus Uzinura, which has been described previously as an endosymbiont in armored scaled insects (Sabree et al. 2013).

2.2 Alpha Diversity

The Shannon Diversity Index was calculated for each sample, here presented by the group they were assigned to in the sample name.

2.3 Functional Prediction

Below are the 30 most common pathways in all samples, as predicted by PICRUSt2. Pathway data is from the MetaCyc database (Caspi et al. 2018). Values are the log10 transformed predicted abundances from PICRUSt2.

3 Acknowledgements

We would like to thank NPAD/UFRN for computational resources expended.

References

Bolyen, Evan, Jai Ram Rideout, Matthew R. Dillon, Nicholas A. Bokulich, Christian C. Abnet, Gabriel A. Al-Ghalith, Harriet Alexander, et al. 2019. “Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2.” Nature Biotechnology 37 (8): 852–57. https://doi.org/10.1038/s41587-019-0209-9.

Callahan, Benjamin J., Paul J. McMurdie, Michael J. Rosen, Andrew W. Han, Amy Jo A. Johnson, and Susan P. Holmes. 2016. “DADA2: High-Resolution Sample Inference from Illumina Amplicon Data.” Nature Methods 13 (7): 581–83. https://doi.org/10.1038/nmeth.3869.

Caspi, Ron, Richard Billington, Carol A Fulcher, Ingrid M Keseler, Anamika Kothari, Markus Krummenacker, Mario Latendresse, et al. 2018. “The MetaCyc Database of Metabolic Pathways and Enzymes.” Nucleic Acids Research 46 (Database issue): D633–39. https://doi.org/10.1093/nar/gkx935.

Douglas, Gavin M., Vincent J. Maffei, Jesse R. Zaneveld, Svetlana N. Yurgel, James R. Brown, Christopher M. Taylor, Curtis Huttenhower, and Morgan G. I. Langille. 2020. “PICRUSt2 for Prediction of Metagenome Functions.” Nature Biotechnology 38 (6): 685–88. https://doi.org/10.1038/s41587-020-0548-6.

Sabree, Zakee L., Charlie Y. Huang, Akiko Okusu, Nancy A. Moran, and Benjamin B. Normark. 2013. “The Nutrient Supplying Capabilities of Uzinura, an Endosymbiont of Armoured Scale Insects.” Environmental Microbiology 15 (7): 1988–99. https://doi.org/10.1111/1462-2920.12058.

Straub, Daniel, Nia Blackwell, Adrian Langarica-Fuentes, Alexander Peltzer, Sven Nahnsen, and Sara Kleindienst. 2020. “Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline.” Frontiers in Microbiology 11 (October). https://doi.org/10.3389/fmicb.2020.550420.