Trends in Molecular Medicine
ReviewClinical applications of pathogen phylogenies
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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Cited by (10)
Molecular epidemiology of HIV-1 in Iceland: Early introductions, transmission dynamics and recent outbreaks among injection drug users
2017, Infection, Genetics and EvolutionCitation Excerpt :HIV-1 has been intensely studied in the recent years from a molecular epidemiology perspective which was facilitated by the wide availability of polymerase gene (pol) sequences that are generated as a by-product of antiviral drug resistance testing, (Hirsch et al., 2003) along with proving the suitability of this region of HIV-1 genome to resolve epidemiologic linkages (Hue et al., 2004). The advances in sequencing technology and the improved computational power have also augmented the yield of HIV-1 epidemiologic research (Hartfield et al., 2014). The initial use of phylogenetic analysis on HIV-1 was justified by getting forensic insights into transmission patterns in suspected criminal cases (Albert et al., 1994; de Oliveira et al., 2006).
Integrating molecular epidemiology and social network analysis to study infectious diseases: Towards a socio-molecular era for public health
2016, Infection, Genetics and EvolutionTransmission of hepatitis C virus infection among younger and older people who inject drugs in Vancouver, Canada
2016, Journal of HepatologyCitation Excerpt :A phylogeny is represented by a tree structure containing tips (observed data), nodes (inferred ancestors) and branches (connections between tips and nodes) [6]. Phylogenetic methods approximate the full transmission chain of an outbreak based on the available data, with additional information such as clinical or behavioural data informing the dynamics of infection and outcomes in the population (reviewed in [7]). Bayesian Markov Chain Monte Carlo (MCMC) inference accounts for uncertainty during the process of phylogenetic reconstruction by determining the probability of the tree given the data and modifiable model parameters.
Hospital-associated microbiota and implications for nosocomial infections
2015, Trends in Molecular MedicineCitation Excerpt :Phylogenetic studies of S. aureus isolates from the lungs of a chronically infected cystic fibrosis patient taking heavy doses of antibiotics found strong evidence for local adaptation of a single isolating strain that became heterogeneously insensitive to antibiotic treatment [51]. These types of longitudinal studies are critical for documenting the emergence of AMR in vivo so that antibiotic-treatment failure can be more specifically explained [52]. Although Gram-positive pathogens have dominated research into AMR evolution, in vivo AMR transfer has also been documented in Gram-negative strains such as Klebsiella pneumoniae and E. coli [46].
A new method for inferring timetrees from temporally sampled molecular sequences
2020, PLoS Computational Biology