Microbiomics I August 24th, Introduction. Robert Kraaij, PhD Erasmus MC, Internal Medicine

Transcription

1 Microbiomics I August 24th, 2017 Introduction Robert Kraaij, PhD Erasmus MC, Internal Medicine r.kraaij@erasmusmc.nl

2 Welcome to Microbiomics I Infection & Immunity MSc students Only first day no practicals MolMed and NIHES programs Two days Microbiomics I technical approaches I&I Summer Course 2 August Microbiomics II health and disease I&I Winter course 2 February

3 Metagenomics - terminology the study of metagenomes genetic material recovered from environmental samples ecological community of microorganisms symbiosis commensal mutual parasitic microbiota microbiome community of microorganisms genomes of the microbiota

4 Metagenomics - terminology the study of metagenomes genetic material recovered from environmental samples ecological community of microorganisms symbiosis commensal mutual parasitic microbiota metagenome microbiome collection of microorganisms genomes of the microbiota community of microorganisms and host

5 Microbiota Archaea Bacteria Protozoa Viruses Fungi molds yeasts

6 The human gut microbiota - the forgotten organ bacterial cells vs body cells ~10 6 bacterial genes vs ~20,000 human genes many functions involved in health and disease nose mouth bladder (urine) vagina skin etc.

7 Microbiota GENOTYPE ENVIRONMENT DIET MICROBIOME PHENOTYPE LIFE-STYLE

8 Gut microbiome and disease associations obesity Crohn s disease ulcerative colitis eczema asthma diabetes depression etc hype cycle

9 Overview Microbiota profiling

10 Microbiota profiling Who are they? What do they do?

11 Microbiota profiling culture-based techniques 16S rrna amplicon arrays (hitchip) sequencing shotgun sequencing (metagenomics)

12 Microbiota profiling culture-based techniques 16S rrna amplicon arrays (hitchip) sequencing Affymetrix array shotgun sequencing

13 Affymetrix Axiom Microbiome array

14 Affymetrix Axiom Microbiome array Launch in July 2016 Costs $80 per sample In 2016 only quantitative measurements In 2017 also qualitative measurements Affymetrix says

15 Microbiota profiling Who are they? 16S taxonomy & Metagenomics What can they do? Metagenomics What are they doing? Metatranscriptomics & Metaproteomics What have they done? Metabolomics Are they involved in health and disease? Multivariate analyses

16 Overview Microbiota profiling An example: metagenomics

17 Shotgun metagenomics Direct sequencing of DNA

18 High output sequencing 2 x 100 bp reads are too short for annotation de novo assembly need for compute power 2 x 100 bp paired-reads de novo assembly ~1 kbp contigs

19 Metagenomics technology push MetaHIT European FP7 project Human Microbiome Project (HMP) NIH-sponsored project

20 Profiling of shotgun data phylotyping databases metagenomic species (MGS) ~7000 MGS specified gene catalogue 8.1 million genes from 760 samples functional databases

21 Phylotyping of shotgun data Arumugam et al., 2011 MetaHIT

22 Functional analysis of shotgun data Arumugam et al., 2011 MetaHIT

23 Taxonomic vs functional profiling large taxonomic differences are not reflected in functional profiles Samples ordered by taxonomic profiles Samples ordered by functional profiles The Human Microbiome Project Consortium (2012)

24 Overview Microbiota profiling An example: metagenomics Data analysis

25 Complex multi-dimensional data no normal or mean how to analyze?

26 OTU table OTU id sample_01 sample_02 sample_03 sample_04 OTU_ OTU_ OTU_ Total 5,975 4,952 6,735 5,374

27 From profile to point estimate OTU id sample_01 sample_02 sample_03 sample_04 OTU_ OTU_ OTU_ Total 5,975 4,952 6,735 5,374 number of species richness

28 Alpha diversity measures Richness Shannon index (Inverse) Simpson index Chao1 Fisher alpha Etc. diversity = richness x evenness

29 Shannon vs Inverse Simpson The change to pick twice in a row the same type of bacteria Shannon index -Σ abundance * ln(abundance) (Inverse) Simpson index Σ abundance 2

30 Richness table sample id richness sample_ sample_ sample_ sample_ Formula Estimate (β) Adjusted p-value BMD *** BMD + Sex ** BMD + Sex + Age ** BMD + Sex + Age + Smoking ** BMD + Sex + Age + Smoking + Mail *** BMD + Sex + Age + Smoking + Mail + BMI

31 From profile to single OTU OTU id sample_01 sample_02 sample_03 sample_04 OTU_ OTU_ OTU_ Total 5,975 4,952 6,735 5,374

32 2-step analysis per OTU Fu et al Circulation Research 117:

33 From profiles to distances OTU id sample_01 sample_02 sample_03 sample_04 OTU_ OTU_ OTU_ Total 5,975 4,952 6,735 5,374 (dis)similarity

34 Beta diversity measures Correlation Bray-Curtis dissimilarity Does not take into account 0, 0 values UniFrac Takes phylogenetic tree into account Unweighted (present/absent) Weighted (abundancies) Etc.

35 From profiles to distances OTU id sample_01 sample_02 sample_03 sample_04 OTU_ OTU_ OTU_ Total 5,975 4,952 6,735 5,374 sample_01 sample_02 sample_03 sample_04 sample_01 0 sample_ sample_ sample_

36 Cluster analyses 1D ordination - Dendrogram Bray-Curtis dissimilarity distances

37 PCoA 2 Principal coordinate analysis (PCoA) 2D ordination PCoA 1

38 PNG vs US humans an example Martinez et al. (Cell, 2015)

39 PNG vs US humans α-diversity β-diversity Martinez et al. (Cell, 2015)

40 PNG vs US humans unweighted profiling Martinez et al. (Cell, 2015)

41 Unweighted principal component analyses - UniFrac-based Gevers et al. (Cell Host Microbe, 2015)

42 Principal component analysis of shotgun data - enterotypes Arumugam et al., 2011 MetaHIT

43 It is time for large scale studies case-control studies many have been initiated population-based studies metahit HMP LifeLines (n = 1,300) Generation R (n = 2,700) The Rotterdam Study (RSIII subcohort, n = 1,700)

44 Overview Microbiota profiling An example: metagenomics Data analysis The Rotterdam Study

45 ERGO: The Rotterdam Study A single-center, prospective population-based cohort study Started in 1990 Base-line cohort = 7,983 men and women of age 55 years Determinants and prevalence/incidence of chronic and disabling disease in the elderly: cardiovascular disease neurodegenerative disease locomotor disease (osteoporosis, osteoarthritis) macular degenerative disease

46 16S RNA analysis pipeline DNA isolation (NorDiag Arrow) 16S rrna amplicon Sequencing (Illumina MiSeq) Analysis (QIIME)

47 Sample collection started in May 2012

48 Sample collection started in May 2012

49 Effects of room temperature on sample quality - experimental design directly frozen -80ºC replicate Bulk Sample 4x room temperature (RT) various days RT + stabilizer various days NorDiag Arrow DNA isolation

50 QIIME/UPARSE-based analysis pipeline cutadapt primer trimming PEAR read-pair merging QIIME demultiplexing UPARSE bit chimera filtering read QC OTU calling QIIME taxonomy phylogeny OTU abundancy filtering SILVA database 119 Max Planck Institute July 2014 >6,000,000 entries OTU table Phylogenetic tree

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52 DAY 1 INTRODUCTION TO MICROBIOMICS Introduction Robert Kraaij Introduction to microbiota John Hays Study design Robert Kraaij Lunch Profiling techniques I Djawad Radjabzadeh Profiling techniques II Robert Kraaij Microbiota analyses Djawad Radjabzadeh

53 DAY 2 IN DEPTH PROFILING AND ANALYSIS Morning: Lecture: 16S rrna profiling pipeline Practical: 16S rrna profiling Djawad Radjabzadeh Afternoon: Lecture: 16S rrna analyses Practical: data analysis on microbiome data using VEGAN (R) Robert Kraaij

54 Learning Goals general understanding of different microbiota profiling techniques in depth understanding of 16S rrna profiling general understanding of data analyses

55 Questions??