Virus evolution and the spread of infectious disease

Richard Neher
Biozentrum, University of Basel

slides at neherlab.org/202211_Ringvorlesung.html

Viruses

tobacco mosaic virus (Thomas Splettstoesser, wikipedia)

bacteria phage (adenosine, wikipedia)

influenza virus wikipedia

human immunodeficiency virus wikipedia

rely on host to replicate
little more than genome + capsid
genomes typically 5-200k bases (+exceptions)
most abundant organisms on earth $\sim 10^{31}$

Lifecycle of animal viruses

By GrahamColm at English Wikipedia

Development of sequencing technologies

We can now sequence...

thousands of bacterial isolates
thousands of single cells
populations of viruses, bacteria or flies
diverse ecosystems

Virus genomes change rapidly through time

A/Brisbane/100/2014
GGATAATTCTATTAACCATGAAGACTATCATTGCTTT...

A/Brisbane/1000/2015
GGATAATTCTATTAACCATGAAGACTATTATTGCTTT...

A/Brisbane/1/2017
GGATAATTCTATTAACCATGAAGACTATCATTGCTTT...

... hundreds of thousands of sequences...

Phylogenetic analysis of viral sequences

RNA viruses have a high mutation rate. New mutations arise every few weeks.

Some viruses evolve a million times faster than animals

Animal haemoglobin

HIV protein

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

Immune escape in early HIV infection

Selective sweeps

Viruses carrying a beneficial mutation have more offspring: on average $1+s$ instead of $1$
$s$ is called selection coefficient
Fraction $x$ of viruses carrying the mutation changes as $$x(t+1) = \frac{(1+s)x(t)}{(1+s)x(t) + (1-x(t))}$$
In continuous time → logistic differential equation: $$\frac{dx}{dt} = sx(1-x) \Rightarrow x(t) = \frac{e^{s(t-t_0)}}{1+ e^{s(t-t_0)}}$$

Population sequencing to track all mutations above 1%

diverge at 0.1-1% per year
almost whole genome coverage in 10 patients
full data set at hiv.tuebingen.mpg.de

Zanini et al, eLife, 2015; antibody data from Richman et al, 2003

Human Influenza A viruses

slide by Trevor Bedford

Weekly numbers of positive influenza tests in the US by subtype

Data by the US CDC

Influenza virus

Surface proteins hemagglutinin (HA) and neuraminidase (NA)
Influenza A virus

Common in birds and mammals
Many different subtypes defined by surface proteins
H3N2, H1N1, H7N9, H5N1

Influenza B virus

infects mainly humans
two lineages that split 30-40y ago
B/Victoria vs B/Yamagata

nextflu.org

joint work with Trevor Bedford & his lab

nextstrain.org

joint work with Trevor Bedford & his lab

Clonal interference and traveling waves

RN, Annual Reviews, 2013; Desai & Fisher; Brunet & Derrida; Kessler & Levine

Typical tree

Bolthausen-Sznitman Coalescent

RN, Hallatschek, PNAS, 2013; see also Brunet and Derrida, PRE, 2007

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

Validation on simulated data

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

Real-time tracking of SARS-CoV-2

thousands of new sequences every day
more than >14M sequences right now
comprehensive analysis require hours to days to complete

→ requires continuous analysis and easy dissemination

→ interpretable and intuitive visualization

nextstrain.org

joint project with Trevor Bedford & his lab

Emergence and dominance of SARS-CoV-2 variants

Gradual shift from selection on transmission to immune escape

Until early-2021, seroprevalence was low to moderate
Delta infections and vaccination resulted in high seroprevalence in 2021
Variant success now is dominated by immune escape

Kleynhans et al, ourworldindata.org

So far, independent variants have dominated sequentially

Variant emergence likely through chronic infections
Strong dichotomy (until 2022): dramatic changes between variants, slow and steady within variants
Omicron variants are more dynamic
With BA.4/5 and BA.2 subvariants, we start to see second generation variants

What is driving this?

Can we predict?

nextstrain.org/ncov/gisaid/global

To predict, we need to quantify selection by immunity

Given a population immunity "landscape", how much escape is caused by which mutation?
How variable are individual immune responses?
How does exposure history shape neutralization of different variants?
What is the contribution of chronic vs acute infections?
Does escape during chronic infection mediate inter-individual escape? It did for Omicron, but will this stay that way?
What is the contribution of chronic infections in other viruses?