Review
Clinical applications of pathogen phylogenies

https://doi.org/10.1016/j.molmed.2014.04.002Get rights and content

Highlights

  • Phylogenetic methods are not being used to their potential in clinical studies.

  • Recent advances can link pathogen genetics to infection outcomes.

  • These developments can determine internal infection spread, and detecting CTL escape mutations.

  • A more thorough use of these tools will aid infections disease research.

Innovative sequencing techniques now allow the routine access of whole genomes of pathogens, generating vast amounts of data. Phylogenetic trees are a common method for synthesizing this information. Unfortunately, these trees are often seen only as a visual support to guide researchers, thus neglecting the value of employing phylogenetic techniques to perform hypothesis testing on clinical questions. These include investigating how a pathogen spreads within a patient, or whether the infection severity (often measured by virus load) is controlled by viral genetics. Advances in methodology mean the time is ripe for combining phylogenies with clinical data to better understand and fight infectious diseases.

Section snippets

Phylogenies and their current use in clinical research

Several human RNA viruses, including HIV, hepatitis C virus (HCV), and influenza, are characterized by high mutation rates, so that samples taken from different patients will differ vastly from one another 1, 2. This fact even remains true for isolates taken from different locations within a single patient [3]. Phylogenetic analyses (see Glossary) are now a common tool in clinical studies that aim to illustrate, and then determine, the impact of this inherent diversity.

Phylogenetic analysis is

Study design and data collection

Before phylogenetic analysis can proceed, researchers need to decide what genomic data is needed, including what regions of the pathogen to sequence, and whether to obtain isolates at specific time intervals. Phylogenies can be created at both within- and between-host levels. Determining the data to be analysed will specify what software needs to be used. For example, if one wants to investigate the genetic basis behind different infection outcomes, then there is one marker value (infection

Analysis of phylogenetic trees

After phylogenetic trees have been generated, they can be used for further study and analysis. Typically there is only one output from a ML analysis, and several hundred for an MCMC analysis. However, the single ML output can differ depending on the parameters used, so several trees can be inferred and analysed as with MCMC analyses. Most phylogenetic software (Table 1) can read phylogenetic outputs, as well as some statistical packages (e.g., the APE package for R [28]); stand-alone software

Measuring how pathogen genetic variation affects pathogenesis

The genomics revolution has led clinical researchers to investigate human genetic variations associated with increased susceptibility or resistance to infectious diseases, with many successes [29]. This is particularly pertinent since some virulent human pathogens cause large variations in infection outcomes. For example, some HIV patients can live for nearly 25 years in the absence of treatment, whereas others die within a year [30]. Similarly, HCV infections clear spontaneously from the host

Concluding remarks and future perspectives

Phylogenetic inferences are increasingly being used in clinical research. These tools are well worn in ecology and evolution, where they are described as ‘phylogenetic comparative methods’ [67]. Despite their power, their clinical applications are still at a nascent stage, and stronger inferences are possible.

Here we have highlighted clinical research that uses phylogenetics to link pathogen genetics to infectious disease traits. Given the flexibility of phylogenetic methods, and the potential

Acknowledgements

M.H. is funded by an ATIP-Avenir grant from CNRS and INSERM to S.A.; S.A. and M.H. acknowledge additional support from the CNRS and the IRD. C.L.M. is funded by the Office for Science and Technology at the Embassy of France in Canada.

Glossary

Ancestral state
the trait status of the founding individual, either for the entire phylogeny or for a subclade, from which all other samples are descended (see Figure 3 in main text).
Burn-in (for MCMC)
the first few results produced by an MCMC algorithm (generally set at 10–20%). These outputs are usually discarded because they produce unrealistic estimates.
Clades, subclades
a collection of tips on a phylogeny, representing a branch and its related individuals. The clade size is simply its size,

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