Tracking and predicting the spread of pathogens and resistance
Richard Neher
Biozentrum, University of Basel
slides at neherlab.org/201903_HMS.html
Human seasonal influenza viruses
slide by Trevor Bedford
Sequences record the spread of pathogens
images by Trevor Bedford
Influenza viruses evolve to avoid human immunity
Vaccines need frequent updates
Beyond tracking: can we predict?
Fitness variation in rapidly adapting populations
Speed of adaptation is logarithmic in population size
Environment (fitness landscape), not mutation supply, determines adaptation
Different models have universal emerging properties
RN, Annual Reviews, 2013; Desai & Fisher; Brunet & Derride; Kessler & Levine
Neutral/Kingman coalescent
strong selection
Bolthausen-Sznitman Coalescent
RN, Hallatschek, PNAS, 2013; see also Brunet and Derrida, PRE, 2007; Desai, Walczak, Fisher, Genetics, 2013
Bursts in a tree ↔ high fitness genotypes
Can we read fitness of a tree?
Predicting evolution
Given the branching pattern:
can we predict fitness?
pick the closest relative of the future?
RN, Russell, Shraiman, eLife, 2014
Fitness inference from trees
$$P(\mathbf{x}|T) = \frac{1}{Z(T)} p_0(x_0) \prod_{i=0}^{n_{int}} g(x_{i_1}, t_{i_1}| x_i, t_i)g(x_{i_2}, t_{i_2}| x_i, t_i)$$
RN, Russell, Shraiman, eLife, 2014
Prediction of the dominating H3N2 influenza strain
no influenza specific input
how can the model be improved? (see model by Luksza & Laessig)
what other context might this apply?
RN, Russell, Shraiman, eLife, 2014
Enterovirus D68 -- with Robert Dyrdak, Emma Hodcroft & Jan Albert
Non-polio enterovirus
Almost everybody has antibodies against EV-D68
Large outbreak in 2014 with severe neurological symptoms in young children (acute flaccid myelitis)
Another outbreak in 2016
Outbreaks tend to start in late summer/fall
Several reports of EV-D68 outbreaks last fall (201 AFM cases in the US in 2018)
EV-D68 whole genome deep sequencing project across Europe
Infections with multiple variants
A set of iSNVs at very similar frequencies in full linkage
Suggest infection with two related variants
3 out of 50 samples: Implies high prevalence
Dyrdak et al, biorxiv
Carbapenemase producing bacteria
Reserve antibiotics used to treat MDR bacteria
Introduced in the 1980ies
Resistance spread rapidly
Resistance is mediated by several distinct beta-lactamases
→ pressing public health problem
→ fascinating instance of genes sweeping the globe by horizontal transfer
Resistance is horizontally acquired → tracking the core genome doesn't do it
Need to track genes on there mobilizing genetic background
Tracking bacteria by sequencing
Illumina → millions of short reads (<500bp)
Too short to bridge repetitive elements
→ assemblies are fragmented into 100s of "contigs"
Problem: all the important bits are flanked by repetitive/mobile elements
(really terrible example)
Images: illumina.com, github.com/rrwick
Long-read sequencing of Carbapenemase producing bacteria
Contigs with drug resistance genes ~1-6 genes → no phylogenetic resolution
long-read assemblies give full length plasmids
tracking via synteny and structural diversity, not SNPs
→ we need to reconstruct spread from genome structure evolution
Noll et al, biorxiv, 2018
Synteny alignments of Carbapenemase containing loci
Structural changes can resolve evolutionary relationships
Different KPC alleles are found on the same background
Identical KPC alleles are found on different backgrounds
Similar plasmids are spread across MLSTs and species boundaries
Noll et al, biorxiv, 2018
Summary
Timely data sharing + automated analysis allows near real-time tracking of influenza
Such analyses provide important input for vaccine strain selection
Sequencing, analysis, and dissemination can be rapidly set-up for emerging pathogens
Bacterial pathogens come with a special set of challenges
Fascinating evolutionary process beyond mutations in homologous sequence
Acknowledgments -- nextstrain
Trevor Bedford
Colin Megill
Pavel Sagulenko
Sidney Bell
James Hadfield
Wei Ding
Emma Hodcroft
Sanda Dejanic
John Huddleston
Barney Potter
Influenza and Theory acknowledgments
Boris Shraiman
Colin Russell
Trevor Bedford
Oskar Hallatschek
Acknowledgments -- Enterovirus
Robert Dyrdak
Jan Albert
Lina Thebo
Emma Hodcroft
Bert Niesters (Groningen)
Randy Poelman (Groningen)
Elke Wollants (Leuven)
Adrian Egli (Basel)
Andrés Antón Pagarolas (Barcelona)
Acknowledgments -- CPE
Wei Ding
Nicholas Noll
Eric Ulrich
Adrian Egli (at USB)
With-in host diversity
Above 0.5%, iSNVs are biological
Most samples have few iSNVs, three had more than 20
Dyrdak et al, biorxiv