High-throughput sequencing and latent variable modelling of within-host parasite communities
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The document discusses using high-throughput DNA sequencing and statistical modeling to study parasite and microbiota communities within hosts. Large data sets on hundreds of host samples and thousands of species can identify within-host communities and determine how environmental and host variables affect composition. Statistical approaches allow modeling potential species interactions and generating testable hypotheses. Case studies of mouse lemurs and ticks demonstrate how longitudinal data collection reveals extensive within-individual variation in parasites and microbiota over time and across environments.
High-throughput sequencing and latent variable modelling of within-host parasite communities
- 1. High-throughput sequencing and latent variable modelling of within-host parasite communities Tuomas Aivelo, ICOPA2018 Daegu, South Korea 21.8.2018
- 2. Thanks: • Jukka Jernvall, Juha Laakkonen • Barbara Tschirren • Alan Medlar, Ari Löytynoja • Anna Norberg, Otso Ovaskainen • Herman Rafalinirina, Patricia Wright Research Foundation of University of Helsinki
- 3. Metabarcoding brings big data • High-throughput sequencing allows to identify species communities in remarkable speed • Within-gut communities can be identified with certain accuracy Aivelo et al. 2018
- 4. Community ecology on steroids • Large data sets can have hundreds of samples, thousands of ”species” and large numbers of known variables • Requires statistical approach which are also computationally light
- 5. Research questions • Which environmental or host-related variables affect within-host community composition? • Can we suggest potential interactions between symbionts based on occurrence modelling?
- 6. Joint species modelling opportunities Hierarchical Modelling of Species Communities (HMSC) Parasite and pathogen phylogeny life cycle, endosymbiont, human pathogen geographical coordinates, genetic similarity parasites and microbiota as presence/absence environmental and host related variables Ovaskainen et al, 2017a; Ovaskainen et al., 2017b; Tikhonov et al., 2017
- 7. System 1: Free-living mouse lemurs • Eastern Madagascar • Small, long-lived, territorial • Easily captured, marked and retrapped • Nematodes & microbiota barcoded
- 8. Benefits of longitudinal data collection • Within-individual variation extensive in both parasites and microbiota! Aivelo et al., 2015
- 9. Aivelo et al., 2016
- 10. Aivelo & Norberg, 2016 Rafalinirina et al. 2015 Also: better body condition correlates with higher fecal egg counts! Environmental variables Eimeria H.diminuta H.nana Nematode1 Nematode2 Nematode3 Nematode4 Nematode5 Nematode6 Tick Lice Sex (male) Age Testis Size Body condition Aggression Time
- 11. System 2: Free-living ticks • Replicated transects on altitudinal gradient in Swiss Alps • Collection of questing nymphs, adults • Metabarcoding microbiota
- 12. Female Nymph Tickindheterozyg Tickabundance Exptickheterozyg Voleabundance Voleratio Elevation Temperature,mean Temperature,days>7 Precipitation Forestcover Slope Aspect R. helvetica R. monacensis Rickettsia sp. Anaplasma Neoehrlichia B. miyamotoi B. valaisiana B. afzelii B. garinii Tick Environment
- 13. Possible interactions within communities B.valaisiana R ickettsia sp. B. garinii Rickettsia helvetica R . m onacensis Anaplasma Lariskella Spiroplasm a Rickettsiella B. miyamotoi C a. N eoehrlichia Borreliaafzelii Aivelo & Norberg, 2018
- 14. • Powerful stastistical approaches can create testable hypothesis for species occurrence, including species interactions • Parasites and pathogen occurrence determined by a range of variables • Closely related species might have different interactions • Endosymbionts may play role in pathogen invasion success In nutshell
- 15. References • Aivelo et al. 2015 https://doi.org/10.1017/S0031182015000438 • Aivelo et al. 2016 http://doi.org/10.1128/AEM.00559-16 • Aivelo et al. 2018 https://doi.org/10.1007/s10764-017-0010-x • Aivelo & Medlar, 2018 https://doi.org/10.1017/S0031182017000610 • Aivelo & Norberg, 2016 https://doi.org/10.1101/076059 • Aivelo & Norberg, 2018 https://doi.org/10.1111/1365-2656.12708 • Medlar et al. 2014 https://doi.org/10.1186/s12862-014-0235-7 • Rafalinirina et al. 2015 https://doi.org/10.4314/mcd.v10i2.4 • Ovaskainen et al., 2017a: https://doi.org/10.1111/ele.12757 • Ovaskainen et al., 2017b: https://doi.org/10.1098/rspb.2017.0768 • Tikhonov et al., 2017: https://doi.org/10.1111/2041-210X.12723 •DOIhttps://doi.org/10.1007/s10764-017-0010-x •DOIhttps://doi.org/10.1007/s10764-017-0010-x
Editor's Notes
- #13: We did a variable selection, that is, fitted a model on which regression parameters where further modelled with each variable being 0 or 1. As a result, this builds a number of models with different sets of models. Here are pathogen OTUs and how different variables affect their prevalence, or specifically, the possibility of these OTUs being in a tick. Effects shown here are a combination of most common variables used in models The strange part here is that B. valaisiana and B. garinii seem to be vole-affected.