Repeatability and predictability in microbial evolution

Richard Neher
Biozentrum, University of Basel

slides at

  • Repeatability in replicated experiments
    • same exact mutations?
    • same genes?
    • same pathways?
    • same phenotypes?
  • Predictability
    • future frequency trajectories of existing alleles?
    • future mutations?

Colistin resistance evolution in Pseudomonas aeruginosa

  • polymyxin, active against gram negatives
  • interacts with outer membrane
  • old antibiotic, discontinued because of nephrotoxicity
  • today: last-resort antibiotic
  • How fast?
  • Which mutations?
  • Which order?
  • Genetic background?

Morbidostat by Toprak et al.

  • Computer measures OD
  • Controls pumps to add medium or AB
  • Waste is removed
  • Morbidostat→ growth rate is kept constant
  • Chemostat → dilution is constant
  • Turbidostat → OD is constant

Our morbidostat

  • more flexible software
  • more compact design
  • cheaper pumps and controllers

Colistin resistance emerges within 2 weeks

Mutation trajectories in strain PA77

  • Whole genome deep sequencing ($>200x$) with Illumina.
  • Track rare mutations, no matter where they are
  • Mutation frequencies to about 5% accuracy

Mutation trajectories in strain PA83

Recurrent mutations PA77

Genelocus tag PAO1 v01 v02 v03 v04 v05 v05a v08a v10a v11a
pmrB PA4777 V9A,L17Q L90Q,E320K V9A P216Q P254L P169X,M292I S257N N41I,P169X H261Y
pmrE PA2043 Y28N Y28C Y28N Y28C Y28N Y28C Y28C Y28N Y28N
lptDPA3559 Y803XL538R
  • pmrE: Most PA strains are 28C → reversion
  • pmrB: Many mutations that constitutively activate the gene
    → canonical colistin resistance gene
  • lptD: code for outer membrane protein, LPS transport.
    → has been associated with colistin resistance in Acinetobacter

Recurrent mutations in PA83

locus tag PAO1 v02 v03 v05 v06 v08 v11 v12 v14 v15
lpxC PA4406 P101S V222A,S106G V222A V164G,A107T A107T,G21W,F176S A107T,I131F M103I D232E,D232G,V217F,V217A V222A,S106G
pmrB PA4777 L96R L171P L87P F51L S8P,E320K V9A G123S E320K,A248T,L167P R259H,V361M
putative tranferase PA3853 C226G Y3C,G62S V34A,Y155C C226G R60C,Y216C,E185G C226G V122A,E185G
asparagine synthetase L365P frameshift L425P G32S frameshift W153* L365P,W153*,V286M
migA PA0705 H219P C25R,N27S D106G Q191R,V22A T196P,H123P H219P A168T
mutS PA3620 T51P T51P T51P T51P T51P T51P,T287P
lpxO2 PA0936 D163A D163N W209* D163A frameshift in-frame deletion
pmrA PA4776 L11Q L11P R159L,G15V,N172D
cupB5 PA4082 G260X,R26C P139P
pdtA PA0690 A3885V,A3885A G1527X
morA PA4601 R1199H G143D
lpxA PA3644 R96S R191C
priA PA5050 L38L R689R
traN W773* G912D
wbpM PA3141 E273K E273G
mscL PA4614 V86I S35P
  • lpxC: lipid A biosynthesis
  • pmrB: Many mutations that constitutively activate the gene
    → canonical colistin resistance gene
  • lptD: code for outer membrane protein, LPS transport.
    → has been associated with colistin resistance in Acinetobacter

Mutations in pmrAB

Previously observed and new mutations in pmrAB (blue: PA83, red: PA77)
Olaitan et al. Front. Micro., 2014

Evolution of HIV

  • Chimp → human transmission around 1900 gave rise to HIV-1 group M
  • ~100 million infected people since
  • subtypes differ at 10-20% of their genome
  • HIV-1 evolves ~0.1% per year
image: Sharp and Hahn, CSH Persp. Med.

HIV infection

  • $10^8$ cells are infected every day
  • the virus repeatedly escapes immune recognition
  • integrates into T-cells as latent provirus
image: wikipedia

HIV-1 evolution within one individual

silouhette:, Zanini at al, 2015. Collaboration with Jan Albert and his group

Population sequencing to track all mutations above 1%

Zanini et al, eLife, 2015

Minor diversity accumulation is predictable

Zanini et al, eLife, 2015

Divergence at increasingly conserved positions

  • Six categories from high to low conservation
  • mutation away from preferred state with rate $\mu$
  • selection against non-preferred state with strength $s$
  • variant frequency dynamics: $\frac{d x}{dt} = \mu -s x $
  • equilibrium frequency: $\bar{x} = \mu/s $
  • fitness cost: $s = \mu/\bar{x}$
  • Fit model of minor variation to categories of conservation
  • $\Rightarrow$ harmonic average fitness cost in category

Fitness landscape of HIV-1

Zanini et al, biorxiv, 2017

Selection on RNA structures and regulatory sites

Zanini et al, biorxiv, 2017

  • Influenza viruses evolve to avoid human immunity
  • Vaccines need frequent updates

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


  • Colistin resistance evolution predictable at the gene and pathway level
  • Intra-host HIV evolution is governed by a universal fitness landscape, modulated by host-specific immune response
  • Reversion and and predictable diversity patterns
  • Landscape of fitness costs can be estimated from intra-host diversity
  • Tree shape contains fitness information -- estimate derivatives from a snapshot

Thank you for your attention!

Acknowledgements -- Colistin

  • Bianca Regenbogen, now Uni Hohenheim
  • Silke Peter, UKT Tübingen
  • Matthias Willmann, UKT Tübingen

Acknowledgments -- HIV

  • Fabio Zanini
  • Jan Albert
  • Johanna Brodin
  • Christa Lanz
  • Göran Bratt
  • Lina Thebo
  • Vadim Puller

Influenza and Theory acknowledgments

  • Boris Shraiman
  • Colin Russell
  • Trevor Bedford
  • Oskar Hallatschek