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Measuring and interpreting microbiome

Measuring and interpreting microbiome. Carolina Medina-Gomez Department of Internal Medicine, ErasmusMC 45th  Congress of the European Calcified Tissue Society May 27th, 2018. Outline of the Presentation. Brief Introduction Phylogenetic resolution Analysis of grouped data

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Measuring and interpreting microbiome

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  1. Measuring and interpreting microbiome Carolina Medina-Gomez Department of Internal Medicine, ErasmusMC 45th Congress of the European Calcified Tissue Society May 27th, 2018

  2. Outline of the Presentation • Brief Introduction • Phylogenetic resolution • Analysis of grouped data • Reverse causal reasoning • The right transformation

  3. Humans as Holobionts • Gut microbiota influences the immune system, energy biogenesis, biosynthesis of vitamins and hormones, and our metabolism. Diseases as diabetes, Cohn’s disease, ulcerative colitis and multiple sclerosis are associated with gut microbiome. • The gut microbiomeprovidesanattractive target fortherapeuticintervention.

  4. Humans as Holobionts Stool samples: adequate representation of gut content *Sender et al. PLOS Biology. Aug.2016

  5. Biogeography of the intestine *Perera and Berry et al. Environmental Microbiology. 2017

  6. Microbiome Profiling

  7. Comparison metagenomics VS. 16SrRNA METAGENOMICS METAGENOMICS 16SrRNA SEQUENCING Bacterial Genome Genome fragments Genome fragments Alignments of sequences Alignments of sequences Amplicon library Entire communities (viruses and fungi) Only bacteria and Archaea Relatively expensive Relatively cheap – large sample sizes Highly variability, higher resolution Taxonomic classification to Genus level? Direct assessment of genes and pathways Genes and pathways based on classification Entire communities (viruses and fungi) Only bacteria and Archaea Relatively expensive Relatively cheap – large sample sizes Highly variability, higher resolution Taxonomic classification to Genus level? Direct assessment of genes and pathways Genes and pathways based on classification

  8. MetaGenomics

  9. 16SrRNA R packages dada2, phyloseq, DESeq2 MaAsLin

  10. 1. Phylogenetic resolution

  11. OTUs associated with pediatric bone accrual

  12. Enterococcus sp. Phylogenetic tree Enterococcus faecium 2004: 28 sp 2018: 52 sp. J. Clin. Microbiol. 2004. 42 (3): 1192-1198 Braz. J. Biol. 2015. 75 (4)

  13. Taxonomic Classification of Bacterial Sequences NEXTflex V4 16S amplicon Nextera XT Jovel at al. 2016. Frontiers of Microbiology

  14. Segre J. The rise of whole genome microbial sequencing: A new era for human microbiome analysis Originally aired: Wednesday, October 4, 2017

  15. Genes gains and losses at subspecies level

  16. New routines for 16rRNA strain-level resolution

  17. 2. Analysis of group data

  18. OTU vs. Taxonomy Classification Analysis • Several OTUs can be assigned to the same genus • Depending on my research question I can decide type of analysis (also consider FDR adj). (What does it mean ChristensenellaceaeR7group?) • We aggregated the data at different taxonomy classifications (excluding OTUs with < 0.8 Score at each different level). • OUT_315 wont be included in the ChristensenellaceaeR7group Genus aggregation , nor in the Christensenellaceae Family aggregation.

  19. Taxonomical analysis: Group based analyses

  20. Ecological fallacy: Group based analyses

  21. 3. Reverse causal reasoning

  22. ? Boulange, Genome Medicine 2016

  23. MaAsLin: Multivariate Association with Linear Models OTU ~ trait/disease https://huttenhower.sph.harvard.edu/maaslin-- Department of Biostatistics, Harvard T.H. Chan School of Public Health

  24. 3. The correct transformation

  25. Relative Abundances– Arcsine transformation Not perfectly normal but much better ArcSin transformation is default to MaAslin but you have an option to change this default transformation

  26. Relative Abundances– Arcsine transformation Zero inflated models!

  27. Y= Trait or disease X= Microbiome species confounders

  28. Compositional data Gloor et al. 2017. Frontiers of Microbiology

  29. The additive log-ratio transformation Aitchison’s work

  30. Pearson correlation

  31. Takehome messages! • As no “gold standard” methods are still available yet for the analysis of microbiome, there is a lot of flexibility in the methods to be used. However, you need to be conscious of the pros/contras of this decision. • New methodologies will surely emerge as, in other fields, the advances to acquire more and better data are going faster than the development of analysis algorithms. Therefore, in such an emergent field you need to update yourself constantly to keep in the cutting-edge loop

  32. Different variableregions of 16S rRNAsequencing * https://chunlab.wordpress.com/16s-rrna-and-16s-rrna-gene/

  33. Methodology in a nutshell

  34. Microbiome Profile of our studies

  35. Compositional data

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