The bacterium Staphylococcus aureus lives harmlessly on our skin and noses. However, occasionally, it gets into our blood and internal organs, such as our bones and joints, where it causes severe, long-lasting infections that are difficult to treat.

Over time, S. aureus acquire characteristics that help them to adapt to different locations, such as transitioning from the nose to the blood, and avoid being killed by antibiotics. Previous studies have identified changes, or ‘mutations’, in genes that are likely to play an important role in this evolutionary process. One of these genes, called accessory gene regulator (or agr for short), has been shown to control the mechanisms S. aureus use to infect cells and disseminate in the body. However, it is unclear if there are changes in other genes that also help S. aureus adapt to life inside the human body.

To help resolve this mystery, Giulieri et al. collected 2,500 samples of S. aureus from almost 400 people. This included bacteria harmlessly living on the skin or in the nose, as well as strains that caused an infection. Gene sequencing revealed a small number of genes, referred to as ‘adaptive genes’, that often acquire mutations during infection. Of these, agr was the most commonly altered. However, mutations in less well-known genes were also identified: some of these genes are related to resistance to antibiotics, while others are involved in chemical processes that help the bacteria to process nutrients.

Most mutations were caused by random errors being introduced in to the bacteria’s genetic code which stopped genes from working. However, in some cases, genes were turned off by small fragments of DNA moving around and inserting themselves into different parts of the genome.

This study highlights a group of genes that help S. aureus to thrive inside the body and cause severe and prolonged infections. If these results can be confirmed, it may help to guide which antibiotics are used to treat different infections. Furthermore, understanding which genes are important for infection could lead to new strategies for eliminating this dangerous bacterium.

While Staphylococcus aureus is one of the most important human pathogens (Tong et al., 2015), its common interaction with the human host is colonisation, usually of the anterior nares (Wertheim et al., 2005). Comparatively, severe, life-threatening infections such as bacteraemia or osteomyelitis occur very rarely. This suggests that at the macro-evolutionary level, S. aureus is primarily adapted to its natural ecological niche (the nasal cavity) and to specific selective pressures arising in this environment, such as competition with the resident microbiota (Krismer et al., 2017). By contrast, during invasive infection, a new fitness trade-off needs to be achieved to adjust to environmental challenges that include innate and acquired immune responses (Proctor, 2019), high-dose antibiotics (Kuehl et al., 2020), and nutrient starvation (Hood and Skaar, 2012). These trade-offs could occur across three potentially distinctive dynamics of micro-evolution during colonisation and infection (within the colonising population, from colonising to invasive, and within the invasive population), leading to nose-adapted, early infection-adapted, and late infection-adapted strains. Identifying infection-adapted strains might assist precision medicine strategies for infection prevention and management and refine the understanding of S. aureus pathogenesis versatility, as mutational footprints of selection mirror functions that are critically important for bacterial survival during invasion.

Emerging genomic approaches for analysis of within-host evolution are among the most powerful means to study bacterial host adaptation (Marvig et al., 2015; Kennemann et al., 2011; Lieberman et al., 2016). Studies have shown the remarkable diversity and evolution of colonising populations of Streptococcus pneumoniae (Chaguza et al., 2020) and S. aureus (Paterson et al., 2015). In S. aureus, Enterococcus faecalis and Enterococcus faecium it has been shown that transition from colonisation to invasion favours strains with specific adaptive signatures (Young et al., 2017; Van Tyne et al., 2019; Chilambi et al., 2020), while evidence of niche adaptation was noted in a within-host study of bacterial meningitis due to S. pneumoniae (Lees et al., 2017b). Furthermore, phenotypic and genomic adaptation (often in response to antibiotic pressure) has been investigated during selected episodes of persistent invasive infections due to S. aureus (Giulieri et al., 2020; Howden et al., 2006; Giulieri et al., 2018), Pseudomonas aeruginosa (Wheatley et al., 2021), Salmonella enterica (Klemm et al., 2016), and Mycobacterium tuberculosis (Vargas et al., 2021). To increase power, bacterial within-host evolution studies have leveraged on large collections of paired samples coupled with statistical models of genome-wide mutation rates (Marvig et al., 2015; Lees et al., 2017b; Gatt and Margalit, 2021) and extended the analysis to include chromosomal structural variation (Giulieri et al., 2018; Lees et al., 2017a; Gao et al., 2015) as well as intergenic mutations (Lees et al., 2017a; Khademi et al., 2019).

Convergent evolution among separated (independent) episode of colonisation or infection is a key indication of adaptation in evolution analyses. However, with the notable exception of one study of cystic fibrosis (Long et al., 2020), the convergence has generally been weak in within-host studies of S. aureus infections, with no convergence at all (Giulieri et al., 2018) or significant enrichment limited to the S. aureus master regulator agr (Young et al., 2017). We hypothesised that in addition to the small sample size, the extended range of bacterial functions potentially under selective pressure (each function being potentially targeted by diverse pathoadaptive mutations) has hampered the identification of important adaptation mechanisms. To overcome the limitations of studies to date, we have pooled all publicly available S. aureus within-host evolution studies, and complemented this with a new dataset from a recent S. aureus clinical trial (Tong et al., 2020), in a single large-scale analysis. Rather than focussing on point mutations and small insertions/deletions alone, we leveraged multiple layers of genome annotation (encompassing coding regions, operons, intergenic regions, and functional categories) and included chromosome structural variants to compile a comprehensive catalogue of bacterial genetic variation arising during host infection. This strategy enabled the detection of convergent adaptation patterns at an unprecedented resolution. We also outline distinctive signatures of adaptation during colonisation, upon transition from colonisation to infection and during invasive infection.

Within-host evolution of bacterial pathogens such as S. aureus is thought to be governed by a combination of positive selection for variants that confers an advantage within the host and random fixation of mutations (genetic drift) (Klemm et al., 2016; Didelot et al., 2016). Sudden changes in the effective population size (bottlenecks) can cause genetic drift, for example, when a single or few bacterial cells invade the bloodstream or when a secondary infection foci is established in tissues and organs. Consistent with this view, animal studies have shown that after infecting the blood with a polyclonal population, bacteraemic infection is established stochastically by a single clone (McVicker et al., 2014; Guerillot et al., 2018), although estimating the bottleneck size at invasion from clinical sequences has been more challenging (Abel et al., 2015). On the other hand, several lines of evidence support the role of positive selection and adaptive evolution during S. aureus infection. First, adaptive phenotypic features appear to be acquired during infection. The most obvious adaptative phenotype is secondary resistance to antistaphylococcal antibiotics such as rifampicin, vancomycin (Howden et al., 2006), daptomycin (Peleg et al., 2012), and oxacillin (Giulieri et al., 2020). Crucially, these resistance phenotypes can be associated with pleiotropic, pathoadaptative phenotypes such as small colony variant and immune evasion (Guérillot et al., 2018; Jiang et al., 2019; Guérillot et al., 2019). Furthermore, phenotypic adaptation (e.g. loss of toxicity) has been observed upon transition from colonisation to infection (Laabei et al., 2015), supporting the concept that invasive infection is linked to pathoadapted strains. At the molecular level, an excess of protein-truncating mutations in invasive strains (Giulieri et al., 2018) and in late colonising strains leading to infection (Young et al., 2012) have been noted. While this observation alone could be explained by relaxed constraint resulting from reduced population size (Didelot et al., 2016), it has been suggested that loss of gene function might be a common adaptation mechanism of within-host evolution (Gatt and Margalit, 2021), as supported by evidence of gene- or pathway-specific enrichment of mutations across independent infection episodes (Young et al., 2017).

Despite support for adaptive evolution from previous studies, it has been difficult to identify specific molecular signatures of adaptation during infection, due to the limited power of previous within-host studies of bacteraemia and other serious S. aureus infections that were often limited to a restricted number of episodes. To increase our ability to identify signatures of adaptation and find significantly enriched loci, we analysed multiple sources of genetic variation (point mutations, large deletions, IS insertions, and copy number variants) in a large collection of independent episodes of S. aureus colonisation and infection from 25 studies. We predicted that the main advantage of our approach would be to increase the ability of detecting convergence of genetic variants arising during invasive infections as opposed to those detected during the colonisation and upon transition from colonisation to infection. To test this hypothesis, we classified within-host variants based on their likely position in the within-host phylogeny (Figure 1D).

Bacterial adaptation is promoted by genomic plasticity; however, within-host evolution is characterised by low genetic variation (Giulieri et al., 2018). Based on our previous genomic studies of S. aureus bacteraemia, we reasoned that chromosome structural variants may provide an additional mechanism to increase genetic variation during infection. Here, we found that new insertions of IS are strongly enriched during invasive infection. However, despite this 20-fold enrichment, IS insertions remained a rare source of variation even within invasive strains and appeared to have a selective contribution to adaptation (i.e. limited to specific loci such as agrC). Similarly, large deletions and copy number variants appeared to play a less prominent role in adaptation, although we did not include them in our enrichment analysis.

Together with the enrichment for LOF mutations, which is another feature of evolution within the invasive population in our analysis, these IS movements suggest that a pattern of reductive evolution (genome degradation through loss of genes or accumulation of LOF mutations) emerges during within-host evolution of invasive S. aureus. This genome degradation might be related to less effective purifying selection (loss of deleterious alleles) in the invasive population due to a decrease in effective population size and a shorter evolutionary timescale (Didelot et al., 2016). However, our data indicate that these changes converge to specific genes, operons, and pathways, suggesting an adaptative benefit. Reductive evolution has been described in several ‘commensal-to-pathogen’ settings (Toft and Andersson, 2010). Although niche adaptation through reductive evolution has been described in extracellular pathogens (Stinear et al., 2007), a smaller (reduced) genome is a hallmark of obligate intracellular endosymbiotic bacteria (Batut et al., 2014). Since it appears that invasive S. aureus is able to reside intracellularly (promoting dissemination through mobile phagocytes during S. aureus sepsis [Thwaites and Gant, 2011; Surewaard et al., 2016], and immune evasion), it is plausible that this pattern of reductive evolution reflects an adaptation of invasive S. aureus to an invasive and intracellular lifestyle, as it has been shown for other facultative intracellular pathogens such as non-typhoidal Salmonella (Klemm et al., 2016). However, it is possible that these signatures of reductive evolution might be only temporary, as genome degradation might be present only in a minority of strains or be reversible; moreover, LOF mutations are expected to be more likely than gain of function mutations.

Beside reductive evolution, another distinctive feature of within-host evolution during invasive infection was intergenic mutations (both point mutations and IS insertions). In a within-host evolution study of S. pneumoniae colonisation, it was shown that intergenic sites were under convergent evolution (Chaguza et al., 2020). Mutations in promoter sequences of some core genes can play an important role in antibiotic resistance as it was repeatedly shown for pbp4 and resistance to beta-lactam antibiotics (Basuino et al., 2018). The role of intergenic mutations in within-host evolution was shown in a study of P. aeruginosa infection, where convergent evolution targeted several intergenic regulatory regions including upstream of antibiotic resistance genes (Khademi et al., 2019).

Previous work on within-host evolution by our group (included in this analysis) has established that agrA mutations are significantly enriched upon transition from colonisation to infection (Young et al., 2017). In addition, we have shown through genomics and targeted mutagenesis that mutations in key genes such as walKR (Howden et al., 2011) and rpoB (Gao et al., 2013) play a key pathoadaptive role in selected cases of persistent S. aureus infections. In this study, we increased our ability to discover potential targets of adaptation by analysing several mechanisms of genetic variation and applying several layers of annotation. As compared to previous work on S. aureus, this approach provides a higher-resolution picture of within-host evolution and adaptation. Importantly, this analysis remains robust after removing more than 1000 sequences from the largest within-host study included (Figure 3—figure supplement 7). We increase here the generalisability of our findings. We expand the list of genes targeted by convergent evolution and show that there are distinctive adaptation pathways in colonising and invasive populations. We confirm that the dominantly mutated loci belonged to the agr locus, in particular agrA and agrC. This finding is consistent with a large body of literature that predated the genomic era (Novick and Geisinger, 2008) that supports the role of the agr locus as the master regulator of gene expression in S. aureus. Agr-mediated adaptation was so important that we found a highly significant enrichment of agr mutations arros all type variants, including within colonising strains (type C>C variants). Shopsin et al. showed that ~10% of healthy S. aureus carriers held an agr-defective strain and that prior hospitalisation was significantly associated with agr-defective status, suggesting prior adaptive pressures (Shopsin et al., 2008).

Consistent with the distinctive general patterns of evolution displayed during invasive infection, some genes and loci were specifically mutated within invasive strains. Some of these genes were emerging targets of S. aureus pathogenesis in vivo, such as purR and yjbH that were not singled out in previous within-host evolution investigations. Others were known determinants of antibiotic resistance, including mprF, rpoB, and the walKR operon. This underscores the crucial role of antibiotic exposure in shaping adaptive evolution during invasive infection. A recent study of within-host evolution during cystic fibrosis found that resistance genes were hotspots of convergent evolution in this population, which is frequently treated with antibiotics and shows features of phenotypic adaptation (Long et al., 2020).

While most mutations in adaptive loci were substitutions within the coding sequence, about 40% of walKR operon mutations were located outside of the coding regions of walR and walk, emphasising the need to study intergenic mutations and mutations throughout an operon to capture adaptive signatures. This observation highlights the importance of expanding the analysis of intergenic mutations for the detection of adaptive mutations, in particular those linked to antibiotic resistance.

If within-host evolution represents adaptative evolution, it is possible that adaptation involved an accumulation of mutations and possibly epistatic mechanisms. Our data show that some mutations are specific for invasive strains; these mutations may reflect late adaptation, occurring after evolution during colonisation in the nose and upon transition from colonisation to infection, and thus occurring after adaptive mutations were acquired during earlier stages. This evolutionary pattern of stepwise adaptation (or adaptive continuum) encompassing the entire within-host evolutionary arch has been well described for cancer (Abbosh et al., 2017) and has been also investigated in a study of transition from colonisation to infection (Young et al., 2012). One way to capture this is mutation co-occurrence analysis. Here, we show mutation co-occurrence within the agr operon but also co-occurrence of the same mutations in two uncharacterised proteins in S. aureus. Our network of mutation co-occurrence linked to agrAC mutations might suggest a potential pathway of stepwise adaptation following initial mutations acquisition in the agr locus, an hypothesis that has been explored in one of studies included in our analysis (Altman et al., 2018).

While combining multiple studies allowed us to increase statistical power in order to detect genome-wide convergent signals of adaptation, this approach has some limitations. The quality of the publicly available sequences and metadata can be heterogeneous, despite performing quality control assessment, for example, due to different read coverage across studies. Lack of consistent metadata might have impacted the clinical categorisations used here, including the distinction between colonising and invasive strains (however, 83% of the strains could be classified unambiguously based on the site of collection). In addition, detection of structural variants from short reads is not as accurate as from long reads; for example, chromosomal inversions can be missed if their inversion site span is larger than the insert site of the paired-end reads (Guérillot et al., 2019). Furthermore, the majority of the episodes included had low sampling density. More contemporary strategies leverage a multiplicity of samples and deep sequencing strategies to capture within-host diversity, allowing to detect minority variants that could be relevant for adaptation and to obtain a more accurate understanding of evolutionary dynamics within the host, including estimating bottlenecks (Hall et al., 2019) and tracking the fall and rise of within-host lineages (Bryant et al., 2021). Finally, while several adaptive loci identified here have been previously assessed experimentally, the functional impact of adaptive variants in less characterised genes and intergenic mutations warrants further exploration using targeted mutagenesis (Monk et al., 2015).

Ultimately, the goal of detecting adaptive signals is to identify new mechanisms of pathogenesis or resistance to therapeutic targets and to inform prediction of clinical outcomes. So far, studies have failed to show consistent associations between specific clinical outcomes and genetic features of infecting (or colonising) S. aureus strains. This might be related to the dominance of host/environmental factors, but it could also be linked to the limited power of studies performed so far. By contrast, within-host evolution studies are an elegant approach to identify signatures of adaptation that might be candidate markers of important clinical outcomes, such as infection risk in case of colonisation or treatment failure in case of infection. Here, we show that adaptation signatures are at least partially specific to colonisation, infection, and upon transition from colonisation to infection and that adaptive changes are more frequent in distinctive infection episodes (complicated bacteraemia and endocarditis). These findings suggest that adaptive signatures might be indicative of important clinical outcomes. In the future, precision medicine in infectious diseases could follow the lead of cancer genomics, where within-host evolution studies have tracked the evolution of cancer clones and enabled the detection of high risk mutations early.

All data generated or analysed during this study are included in the manuscript and supporting file 1-6. The code to call, filter and annotated within-host variants and to perform the enrichment analysis is available on github at https://github.com/stefanogg/staph_adaptation_paper, (copy archived at swh:1:rev:6ec5132855405e1c759fedadb4c70e295c1c1974).

1. 1. Giulieri SG
   2. Daniel D
   3. Seemann T
   4. Davis JS
   5. Tong SYC
   6. Stinear TP
   7. Howden BP

   (2022) European Nucleotide Archive

   ID PRJEB50796. Within-host evolution analysis of S. aureus bacteraemias included in the CAMERA-2 trial.

   https://github.com/stefanogg/staph_adaptation_paper

1. 1. Gao W
   2. Monk IR
   3. Tobias NJ
   4. Gladman SL
   5. Seemann T
   6. Stinear TP
   7. Howden BP

   (2015) NCBI BioProject

   ID PRJEB9193. Large tandem chromosome duplications facilitate niche adaptation during persistent infection with drug-resistant Staphylococcus aureus.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJEB9193
2. 1. Howden BP

   (2011) NCBI Sequence Read Archive

   ID SRA027352. Evolution of Multidrug Resistance during Staphylococcus aureus Infection Involves Mutation of the Essential Two Component Regulator WalKR.

   https://www.ncbi.nlm.nih.gov/sra/?term=SRA027352
3. 1. Howden BP

   (2011) NCBI BioProject

   ID PRJNA29567. Evolution of Multidrug Resistance during Staphylococcus aureus Infection Involves Mutation of the Essential Two Component Regulator WalKR.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA29567
4. 1. Howden BP
   2. McEvoy CR
   3. Allen DL
   4. Chua K
   5. Gao W
   6. Harrison PF
   7. Bell J
   8. Coombs G
   9. Bennett-Wood V
   10. Porter JL
   11. Robins-Browne R
   12. Davies JK
   13. Seemann T
   14. Stinear TP

   (2011) NCBI BioProject

   ID PRJNA29569. Evolution of Multidrug Resistance during Staphylococcus aureus Infection Involves Mutation of the Essential Two Component Regulator WalKR.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA29569
5. 1. Young BC
   2. Golubchik T
   3. Batty EM
   4. Fung R
   5. Larner-Svensson H
   6. Votintseva AA
   7. Miller RR
   8. Godwin H
   9. Knox K
   10. Everitt RG
   11. Iqbal Z
   12. Rimmer AJ
   13. Cule M
   14. Ip CLC
   15. Didelot X
   16. Harding RM
   17. Donnelly P
   18. Peto TE
   19. Crook DW
   20. Bowden R
   21. Wilson DJ

   (2012) NCBI BioProject

   ID PRJEB2862. Evolutionary dynamics of Staphylococcus aureus during progression from carriage to disease.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJEB2862
6. 1. Golubchik T
   2. Batty EM
   3. Miller RR
   4. Farr H
   5. Young BC
   6. Larner-Svensson H
   7. Fung R
   8. Godwin H
   9. Knox K
   10. Votintseva A
   11. Everitt RG
   12. Street T
   13. Cule M
   14. CL Ip
   15. Didelot X
   16. Peto TE
   17. Harding RM
   18. Wilson DJ
   19. Crook DW
   20. Bowden R

   (2013) NCBI BioProject

   ID PRJEB2881. Within-host evolution of Staphylococcus aureus during asymptomatic carriage.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJEB2881
7. 1. Burd EM
   2. Alam MT
   3. Passalacqua KD
   4. Kalokhe AS
   5. Eaton ME
   6. Satola SW
   7. Kraft CS
   8. Read TD

   (2014) NCBI BioProject

   ID PRJNA248678. Development of oxacillin resistance in a patient with recurrent Staphylococcus aureus bacteremia.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA248678
8. 1. Rishishwar L
   2. Kraft CS
   3. Jordan IK

   (2016) NCBI BioProject

   ID PRJNA259799. Population Genomics of Reduced Vancomycin Susceptibility in Staphylococcus aureus.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA259799
9. 1. Trouillet-Assant S
   2. Lelièvre L
   3. Martins-Simões P
   4. Gonzaga L
   5. Tasse J
   6. Valour F
   7. Rasigade JP
   8. Vandenesch F
   9. Muniz Guedes RL
   10. Ribeiro de Vasconcelos AT
   11. Caillon J
   12. Lustig S
   13. Ferry T
   14. Jacqueline C
   15. Loss de Morais G
   16. Laurent F

   (2016) NCBI BioProject

   ID PRJNA298748. Adaptive processes of Staphylococcus aureus isolates during the progression from acute to chronic bone and joint infections in patients.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA298748
10. 1. Rouard C
    2. Garnier F
    3. Leraut J
    4. Lepainteur M
    5. Rahajamananav L
    6. Languepin J
    7. Ploy MC
    8. Bourgeois-Nicolaos N
    9. Doucet-Populaire F

    (2018) NCBI BioProject

    ID PRJNA434495. Emergence and Within-Host Genetic Evolution of Methicillin-Resistant Staphylococcus aureus Resistant to Linezolid in a Cystic Fibrosis Patient.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA434495
11. 1. Langhanki L
    2. Berger P
    3. Treffon J
    4. Catania F
    5. Kahl BC
    6. Mellmann A

    (2018) NCBI BioProject

    ID PRJEB22600. In vivo competition and horizontal gene transfer among distinct Staphylococcus aureus lineages as major drivers for adaptational changes during long-term persistence in humans.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJEB22600
12. 1. Altman DR

    (2018) NCBI BioProject

    ID PRJNA393749. Genome Plasticity of agr-Defective Staphylococcus aureus during Clinical Infection.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA393749
13. 1. Giulieri SG

    (2018) NCBI BioProject

    ID PRJEB27932. Genomic exploration of sequential clinical isolates reveals a distinctive molecular signature of persistent Staphylococcus aureus bacteraemia.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJEB27932
14. 1. Benoit JB
    2. Frank DN
    3. Bessesen MT

    (2018) NCBI BioProject

    ID PRJNA414752. Genomic evolution of Staphylococcus aureus isolates colonizing the nares and progressing to bacteremia.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA414752
15. 1. Suligoy CM
    2. Lattar SM
    3. Noto Llana M
    4. González CD
    5. Alvarez LP
    6. Robinson DA
    7. Gómez MI
    8. Buzzola FR
    9. Sordelli DO

    (2018) NCBI BioProject

    ID PRJNA414566. Mutation of Agr Is Associated with the Adaptation of Staphylococcus aureus to the Host during Chronic Osteomyelitis.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA414566
16. 1. Harkins CP
    2. Pettigrew KA
    3. Oravcová K
    4. Gardner J
    5. Hearn RMR
    6. Rice D
    7. Mather AE
    8. Parkhill J
    9. Brown SJ
    10. Proby CM
    11. Holden MTG

    (2018) NCBI BioProject

    ID PRJEB20148. The Microevolution and Epidemiology of Staphylococcus aureus Colonization during Atopic Eczema Disease Flare.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJEB20148
17. 1. Tan X

    (2019) NCBI BioProject

    ID PRJNA446073. Chronic Staphylococcus aureus Lung Infection Correlates With Proteogenomic and Metabolic Adaptations Leading to an Increased Intracellular Persistence.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA446073
18. 1. Loss G
    2. Simões PM
    3. Valour F
    4. Cortês MF
    5. Gonzaga L
    6. Bergot M
    7. Trouillet-Assant S
    8. Josse J
    9. Diot A
    10. Ricci E
    11. Vasconcelos AT
    12. Laurent F

    (2019) NCBI BioProject

    ID PRJNA497214. Staphylococcus aureus Small Colony Variants (SCVs): News From a Chronic Prosthetic Joint Infection.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA497214
19. 1. Kuroda M
    2. Sekizuka T
    3. Matsui H
    4. Ohsuga J
    5. Ohshima T
    6. Hanaki H

    (2019) NCBI BioProject

    ID PRJDB8056. IS256-Mediated Overexpression of the WalKR Two-Component System Regulon Contributes to Reduced Vancomycin Susceptibility in a Staphylococcus aureus Clinical Isolate.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJDB8056
20. 1. Azarian T
    2. Ridgway JP
    3. Yin Z
    4. David MZ

    (2019) NCBI BioProject

    ID PRJNA527261. Long-Term Intrahost Evolution of Methicillin Resistant Staphylococcus aureus Among Cystic Fibrosis Patients With Respiratory Carriage.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA527261
21. 1. Wüthrich D
    2. Cuénod A
    3. Hinic V
    4. Morgenstern M
    5. Khanna N
    6. Egli A
    7. Kuehl R

    (2019) NCBI BioProject

    ID PRJNA488707. Genomic characterization of inpatient evolution of MRSA resistant to daptomycin, vancomycin and ceftaroline.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA488707
22. 1. Ji S
    2. Jiang S
    3. Wei X
    4. Sun L
    5. Wang H
    6. Zhao F
    7. Chen Y
    8. Yu Y

    (2020) NCBI BioProject

    ID PRJNA577181. In-Host Evolution of Daptomycin Resistance and Heteroresistance in Methicillin-Resistant Staphylococcus aureus Strains From Three Endocarditis Patients.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA577181
23. 1. Miller CR
    2. Dey S
    3. Smolenski PD
    4. Kulkarni PS
    5. Monk JM
    6. Szubin R
    7. Sakoulas G
    8. Berti AD

    (2020) NCBI BioProject

    ID PRJNA544229. Distinct Subpopulations of Intravalvular Methicillin-Resistant Staphylococcus aureus with Variable Susceptibility to Daptomycin in Tricuspid Valve Endocarditis.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA544229
24. 1. Petrovic Fabijan A
    2. Lin RCY
    3. Ho J
    4. Maddocks S
    5. Ben Zakour NL
    6. Iredell JR

    (2020) NCBI BioProject

    ID PRJNA541589. Safety of bacteriophage therapy in severe Staphylococcus aureus infection.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA541589

1. 1. Abbosh C
   2. Birkbak NJ
   3. Wilson GA
   4. Jamal-Hanjani M
   5. Constantin T
   6. Salari R

   (2017) Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution

   Nature 545:446–451.

   https://doi.org/10.1038/nature22364

   • Google Scholar
2. 1. Abel S
   2. Abel zur Wiesch P
   3. Davis BM
   4. Waldor MK

   (2015) Analysis of Bottlenecks in Experimental Models of Infection

   PLOS Pathogens 11:e1004823.

   https://doi.org/10.1371/journal.ppat.1004823

   • Google Scholar
3. 1. Altman DR
   2. Sullivan MJ
   3. Chacko KI
   4. Balasubramanian D
   5. Pak TR
   6. Sause WE
   7. Kumar K
   8. Sebra R
   9. Deikus G
   10. Attie O
   11. Rose H
   12. Lewis M
   13. Fulmer Y
   14. Bashir A
   15. Kasarskis A
   16. Schadt EE
   17. Richardson AR
   18. Torres VJ
   19. Shopsin B
   20. van Bakel H

   (2018) Genome Plasticity of agr-Defective Staphylococcus aureus during Clinical Infection

   Infection and Immunity 86:e00331-18.

   https://doi.org/10.1128/IAI.00331-18

   • PubMed
   • Google Scholar
4. 1. Aseev LV
   2. Boni IV

   (2011) Extraribosomal functions of bacterial ribosomal proteins

   Molekuliarnaia Biologiia 45:805–816.

   https://doi.org/10.1134/S0026893311050025

   • PubMed
   • Google Scholar
5. 1. Austin CM
   2. Garabaglu S
   3. Krute CN
   4. Ridder MJ
   5. Seawell NA
   6. Markiewicz MA

   (2019) Contribution of YjbIH to Virulence Factor Expression and Host Colonization in Staphylococcus aureus

   Infection and Immunity 87:e00155-19.

   https://doi.org/10.1128/IAI.00155-19

   • Google Scholar
6. 1. Azarian T
   2. Ridgway JP
   3. Yin Z
   4. David MZ

   (2019) Long-Term Intrahost Evolution of Methicillin Resistant Staphylococcus aureus Among Cystic Fibrosis Patients With Respiratory Carriage

   Frontiers in Genetics 10:546.

   https://doi.org/10.3389/fgene.2019.00546

   • Google Scholar
7. 1. Bailey SF
   2. Blanquart F
   3. Bataillon T
   4. Kassen R

   (2017) What drives parallel evolution?: How population size and mutational variation contribute to repeated evolution

   BioEssays : News and Reviews in Molecular, Cellular and Developmental Biology 39:1–9.

   https://doi.org/10.1002/bies.201600176

   • PubMed
   • Google Scholar
8. 1. Basuino L
   2. Jousselin A
   3. Alexander JAN
   4. Strynadka NCJ
   5. Pinho MG
   6. Chambers HF

   (2018) PBP4 activity and its overexpression are necessary for PBP4-mediated high-level β-lactam resistance

   The Journal of Antimicrobial Chemotherapy 73:1177–1180.

   https://doi.org/10.1093/jac/dkx531

   • Google Scholar
9. 1. Batut B
   2. Knibbe C
   3. Marais G
   4. Daubin V

   (2014) Reductive genome evolution at both ends of the bacterial population size spectrum

   Nature Reviews. Microbiology 12:841–850.

   https://doi.org/10.1038/nrmicro3331

   • Google Scholar
10. 1. Behe MJ

    (2010) Experimental evolution, loss-of-function mutations, and “the first rule of adaptive evolution.”

    The Quarterly Review of Biology 85:419–445.

    https://doi.org/10.1086/656902

    • PubMed
    • Google Scholar
11. 1. Benoit JB
    2. Frank DN
    3. Bessesen MT

    (2018) Genomic evolution of Staphylococcus aureus isolates colonizing the nares and progressing to bacteremia

    PLOS ONE 13:e0195860.

    https://doi.org/10.1371/journal.pone.0195860

    • Google Scholar
12. 1. Both A
    2. Huang J
    3. Qi M
    4. Lausmann C
    5. Weißelberg S
    6. Büttner H

    (2021) Distinct clonal lineages and within-host diversification shape invasive Staphylococcus epidermidis populations

    PLOS Pathogens 17:e1009304.

    https://doi.org/10.1371/journal.ppat.1009304

    • Google Scholar
13. 1. Bryant JM
    2. Brown KP
    3. Burbaud S
    4. Everall I
    5. Belardinelli JM
    6. Rodriguez-Rincon D
    7. Grogono DM
    8. Peterson CM
    9. Verma D
    10. Evans IE
    11. Ruis C
    12. Weimann A
    13. Arora D
    14. Malhotra S
    15. Bannerman B
    16. Passemar C
    17. Templeton K
    18. MacGregor G
    19. Jiwa K
    20. Fisher AJ
    21. Blundell TL
    22. Ordway DJ
    23. Jackson M
    24. Parkhill J
    25. Floto RA

    (2021) Stepwise pathogenic evolution of Mycobacterium abscessus

    Science (New York, N.Y.) 372:eabb8699.

    https://doi.org/10.1126/science.abb8699

    • PubMed
    • Google Scholar
14. 1. Brynildsrud O
    2. Snipen LG
    3. Bohlin J

    (2015) CNOGpro: detection and quantification of CNVs in prokaryotic whole-genome sequencing data

    Bioinformatics (Oxford, England) 31:1708–1715.

    https://doi.org/10.1093/bioinformatics/btv070

    • Google Scholar
15. 1. Burd EM
    2. Alam MT
    3. Passalacqua KD
    4. Kalokhe AS
    5. Eaton ME
    6. Satola SW
    7. Kraft CS
    8. Read TD

    (2014) Development of oxacillin resistance in a patient with recurrent Staphylococcus aureus bacteremia

    Journal of Clinical Microbiology 52:3114–3117.

    https://doi.org/10.1128/JCM.00615-14

    • PubMed
    • Google Scholar
16. 1. Cameron DR
    2. Ward DV
    3. Kostoulias X
    4. Howden BP
    5. Moellering RC J
    6. Eliopoulos GM

    (2012) Serine/threonine phosphatase Stp1 contributes to reduced susceptibility to vancomycin and virulence in Staphylococcus aureus

    The Journal of Infectious Diseases 205:1677–1687.

    https://doi.org/10.1093/infdis/jis252

    • Google Scholar
17. 1. Chaguza C
    2. Senghore M
    3. Bojang E
    4. Gladstone RA
    5. Lo SW
    6. Tientcheu PE

    (2020) Within-host microevolution of Streptococcus pneumoniae is rapid and adaptive during natural colonisation

    Nature Communications 11:3442.

    https://doi.org/10.1038/s41467-020-17327-w

    • Google Scholar
18. 1. Chilambi GS
    2. Nordstrom HR
    3. Evans DR
    4. Ferrolino JA
    5. Hayden RT
    6. Marón GM
    7. Vo AN
    8. Gilmore MS
    9. Wolf J
    10. Rosch JW
    11. Van Tyne D

    (2020) Evolution of vancomycin-resistant Enterococcus faecium during colonization and infection in immunocompromised pediatric patients

    PNAS 117:11703–11714.

    https://doi.org/10.1073/pnas.1917130117

    • PubMed
    • Google Scholar
19. 1. Choi Y
    2. Sims GE
    3. Murphy S
    4. Miller JR
    5. Chan AP

    (2012) Predicting the functional effect of amino acid substitutions and indels

    PLOS ONE 7:e46688.

    https://doi.org/10.1371/journal.pone.0046688

    • PubMed
    • Google Scholar
20. 1. Cingolani P
    2. Platts A
    3. Wang LL
    4. Coon M
    5. Nguyen T
    6. Wang L

    (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff

    Fly 6:80–92.

    https://doi.org/10.4161/fly.19695

    • Google Scholar
21. 1. Dehal PS
    2. Joachimiak MP
    3. Price MN
    4. Bates JT
    5. Baumohl JK
    6. Chivian D
    7. Friedland GD
    8. Huang KH
    9. Keller K
    10. Novichkov PS
    11. Dubchak IL
    12. Alm EJ
    13. Arkin AP

    (2010) MicrobesOnline: an integrated portal for comparative and functional genomics

    Nucleic Acids Research 38:D396–D400.

    https://doi.org/10.1093/nar/gkp919

    • PubMed
    • Google Scholar
22. 1. Didelot X
    2. Walker AS
    3. Peto TE
    4. Crook DW
    5. Wilson DJ

    (2016) Within-host evolution of bacterial pathogens

    Nature Reviews. Microbiology 14:150–162.

    https://doi.org/10.1038/nrmicro.2015.13

    • Google Scholar
23. 1. Engman J
    2. Rogstam A
    3. Frees D
    4. Ingmer H
    5. von Wachenfeldt C

    (2012) The YjbH adaptor protein enhances proteolysis of the transcriptional regulator Spx in Staphylococcus aureus

    Journal of Bacteriology 194:1186–1194.

    https://doi.org/10.1128/JB.06414-11

    • Google Scholar
24. 1. Ernst CM
    2. Peschel A

    (2019) MprF-mediated daptomycin resistance

    International Journal of Medical Microbiology 309:359–363.

    https://doi.org/10.1016/j.ijmm.2019.05.010

    • Google Scholar
25. 1. Feldgarden M
    2. Brover V
    3. Haft DH
    4. Prasad AB
    5. Slotta DJ
    6. Tolstoy I
    7. Tyson GH
    8. Zhao S
    9. Hsu C-H
    10. McDermott PF
    11. Tadesse DA
    12. Morales C
    13. Simmons M
    14. Tillman G
    15. Wasilenko J
    16. Folster JP
    17. Klimke W

    (2019) Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates

    Antimicrobial Agents and Chemotherapy 63:e00483-19.

    https://doi.org/10.1128/AAC.00483-19

    • PubMed
    • Google Scholar
26. 1. Fuchs S
    2. Mehlan H
    3. Bernhardt J
    4. Hennig A
    5. Michalik S
    6. Surmann K
    7. Pané-Farré J
    8. Giese A
    9. Weiss S
    10. Backert L
    11. Herbig A
    12. Nieselt K
    13. Hecker M
    14. Völker U
    15. Mäder U

    (2018) AureoWiki ̵ The repository of the Staphylococcus aureus research and annotation community

    International Journal of Medical Microbiology 308:558–568.

    https://doi.org/10.1016/j.ijmm.2017.11.011

    • PubMed
    • Google Scholar
27. 1. Gao W
    2. Cameron DR
    3. Davies JK
    4. Kostoulias X
    5. Stepnell J
    6. Tuck KL
    7. Yeaman MR
    8. Peleg AY
    9. Stinear TP
    10. Howden BP

    (2013) The RpoB H₄₈₁Y rifampicin resistance mutation and an active stringent response reduce virulence and increase resistance to innate immune responses in Staphylococcus aureus

    The Journal of Infectious Diseases 207:929–939.

    https://doi.org/10.1093/infdis/jis772

    • PubMed
    • Google Scholar
28. 1. Gao W
    2. Monk IR
    3. Tobias NJ
    4. Gladman SL
    5. Seemann T
    6. Stinear TP
    7. Howden BP

    (2015) Large tandem chromosome expansions facilitate niche adaptation during persistent infection with drug-resistant Staphylococcus aureus

    Microbial Genomics 1:e000026.

    https://doi.org/10.1099/mgen.0.000026

    • PubMed
    • Google Scholar
29. 1. Gatt YE
    2. Margalit H

    (2021) Common Adaptive Strategies Underlie Within-Host Evolution of Bacterial Pathogens

    Molecular Biology and Evolution 38:1101–1121.

    https://doi.org/10.1093/molbev/msaa278

    • PubMed
    • Google Scholar
30. Preprint

    1. Gerasimavicius L
    2. Livesey BJ
    3. Marsh JA

    (2021) Loss-of-Function, Gain-of-Function and Dominant-Negative Mutations Have Profoundly Different Effects on Protein Structure: Implications for Variant Effect Prediction

    bioRxiv.

    https://doi.org/10.1101/2021.10.23.465554

    • Google Scholar
31. 1. Giulieri SG
    2. Baines SL
    3. Guerillot R
    4. Seemann T
    5. Gonçalves da Silva A
    6. Schultz M

    (2018) Genomic exploration of sequential clinical isolates reveals a distinctive molecular signature of persistent Staphylococcus aureus bacteraemia

    Genome Medicine 10:65.

    https://doi.org/10.1186/s13073-018-0574-x

    • Google Scholar
32. 1. Giulieri SG
    2. Guérillot R
    3. Kwong JC
    4. Monk IR
    5. Hayes AS
    6. Daniel D
    7. Baines S
    8. Sherry NL
    9. Holmes NE
    10. Ward P
    11. Gao W
    12. Seemann T
    13. Stinear TP
    14. Howden BP

    (2020) Comprehensive Genomic Investigation of Adaptive Mutations Driving the Low-Level Oxacillin Resistance Phenotype in Staphylococcus aureus

    MBio 11:e02882-20.

    https://doi.org/10.1128/mBio.02882-20

    • PubMed
    • Google Scholar
33. 1. Göhring N
    2. Fedtke I
    3. Xia G
    4. Jorge AM
    5. Pinho MG
    6. Bertsche U

    (2011) New role of the disulfide stress effector YjbH in β-lactam susceptibility of Staphylococcus aureus

    Antimicrobial Agents and Chemotherapy 55:5452–5458.

    https://doi.org/10.1128/AAC.00286-11

    • Google Scholar
34. 1. Golubchik T
    2. Batty EM
    3. Miller RR
    4. Farr H
    5. Young BC
    6. Larner-Svensson H

    (2013) Within-host evolution of Staphylococcus aureus during asymptomatic carriage

    PLOS ONE 8:e61319.

    https://doi.org/10.1371/journal.pone.0061319

    • Google Scholar
35. 1. Goncheva MI
    2. Flannagan RS
    3. Sterling BE
    4. Laakso HA
    5. Friedrich NC
    6. Kaiser JC

    (2019) Stress-induced inactivation of the Staphylococcus aureus purine biosynthesis repressor leads to hypervirulence

    Nature Communications 10:775.

    https://doi.org/10.1038/s41467-019-08724-x

    • Google Scholar
36. 1. Goncheva MI
    2. Flannagan RS
    3. Heinrichs DE

    (2020) De Novo Purine Biosynthesis Is Required for Intracellular Growth of Staphylococcus aureus and for the Hypervirulence Phenotype of a purR Mutant

    Infection and Immunity 88:e00104-20.

    https://doi.org/10.1128/IAI.00104-20

    • Google Scholar
37. 1. Griffith DM
    2. Veech JA
    3. Marsh CJ

    (2016) cooccur: Probabilistic Species Co-Occurrence Analysis in R

    Journal of Statistical Software, Code Snippets 69:1–17.

    https://doi.org/10.18637/jss.v069.c02

    • Google Scholar
38. 1. Guérillot R
    2. Gonçalves da Silva A
    3. Monk I
    4. Giulieri S
    5. Tomita T
    6. Alison E
    7. Porter J
    8. Pidot S
    9. Gao W
    10. Peleg AY
    11. Seemann T
    12. Stinear TP
    13. Howden BP

    (2018) Convergent Evolution Driven by Rifampin Exacerbates the Global Burden of Drug-Resistant Staphylococcus aureus

    MSphere 3:e00550-17.

    https://doi.org/10.1128/mSphere.00550-17

    • PubMed
    • Google Scholar
39. 1. Guerillot R
    2. Li L
    3. Baines S
    4. Howden B
    5. Schultz MB
    6. Seemann T

    (2018) Comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (RM-seq

    Genome Medicine 10:63.

    https://doi.org/10.1186/s13073-018-0572-z

    • Google Scholar
40. 1. Guérillot R
    2. Kostoulias X
    3. Donovan L
    4. Li L
    5. Carter GP
    6. Hachani A
    7. Vandelannoote K
    8. Giulieri S
    9. Monk IR
    10. Kunimoto M
    11. Starrs L
    12. Burgio G
    13. Seemann T
    14. Peleg AY
    15. Stinear TP
    16. Howden BP

    (2019) Unstable chromosome rearrangements in Staphylococcus aureus cause phenotype switching associated with persistent infections

    PNAS 116:20135–20140.

    https://doi.org/10.1073/pnas.1904861116

    • PubMed
    • Google Scholar
41. 1. Hall MD
    2. Holden MT
    3. Srisomang P
    4. Mahavanakul W
    5. Wuthiekanun V
    6. Limmathurotsakul D
    7. Fountain K
    8. Parkhill J
    9. Nickerson EK
    10. Peacock SJ
    11. Fraser C

    (2019) Improved characterisation of MRSA transmission using within-host bacterial sequence diversity

    eLife 8:e46402.

    https://doi.org/10.7554/eLife.46402

    • PubMed
    • Google Scholar
42. 1. Harkins CP
    2. Pettigrew KA
    3. Oravcova K
    4. Gardner J
    5. Hearn RMR
    6. Rice D

    (2018) The Microevolution and Epidemiology of Staphylococcus aureus Colonization during Atopic Eczema Disease Flare

    The Journal of Investigative Dermatology 138:336–343.

    https://doi.org/10.1016/j.jid.2017.09.023

    • Google Scholar
43. 1. Hawkey J
    2. Monk JM
    3. Billman-Jacobe H
    4. Palsson B
    5. Holt KE

    (2020) Impact of insertion sequences on convergent evolution of Shigella species

    PLOS Genetics 16:e1008931.

    https://doi.org/10.1371/journal.pgen.1008931

    • Google Scholar
44. 1. Hood MI
    2. Skaar EP

    (2012) Nutritional immunity: transition metals at the pathogen-host interface

    Nature Reviews. Microbiology 10:525–537.

    https://doi.org/10.1038/nrmicro2836

    • PubMed
    • Google Scholar
45. 1. Howden BP
    2. Johnson PD
    3. Ward PB
    4. Stinear TP
    5. Davies JK

    (2006) Isolates with low-level vancomycin resistance associated with persistent methicillin-resistant Staphylococcus aureus bacteremia

    Antimicrobial Agents and Chemotherapy 50:3039–3047.

    https://doi.org/10.1128/AAC.00422-06

    • Google Scholar
46. 1. Howden BP
    2. McEvoy CR
    3. Allen DL
    4. Chua K
    5. Gao W
    6. Harrison PF

    (2011) Evolution of multidrug resistance during Staphylococcus aureus infection involves mutation of the essential two component regulator WalKR

    PLOS Pathogens 7:e1002359.

    https://doi.org/10.1371/journal.ppat.1002359

    • Google Scholar
47. 1. Ji S
    2. Jiang S
    3. Wei X
    4. Sun L
    5. Wang H
    6. Zhao F
    7. Chen Y
    8. Yu Y

    (2020) In-Host Evolution of Daptomycin Resistance and Heteroresistance in Methicillin-Resistant Staphylococcus aureus Strains From Three Endocarditis Patients

    The Journal of Infectious Diseases 221:S243–S252.

    https://doi.org/10.1093/infdis/jiz571

    • PubMed
    • Google Scholar
48. 1. Jiang JH
    2. Dexter C
    3. Cameron DR
    4. Monk IR
    5. Baines SL
    6. Abbott IJ

    (2019) Evolution of Daptomycin Resistance in Coagulase-Negative Staphylococci Involves Mutations of the Essential Two-Component Regulator WalKR

    Antimicrobial Agents and Chemotherapy 63:e01926-18.

    https://doi.org/10.1128/AAC.01926-18

    • Google Scholar
49. 1. Kennemann L
    2. Didelot X
    3. Aebischer T
    4. Kuhn S
    5. Drescher B
    6. Droege M
    7. Reinhardt R
    8. Correa P
    9. Meyer TF
    10. Josenhans C
    11. Falush D
    12. Suerbaum S

    (2011) Helicobacter pylori genome evolution during human infection

    PNAS 108:5033–5038.

    https://doi.org/10.1073/pnas.1018444108

    • PubMed
    • Google Scholar
50. Preprint

    1. Key FM
    2. Khadka VD
    3. Romo-González C
    4. Blake KJ
    5. Deng L
    6. Lynn TC
    7. Lee JC
    8. Chiu IM
    9. García-Romero MT
    10. Lieberman TD

    (2021) On-Person Adaptive Evolution of Staphylococcus aureus during Treatment for Atopic Dermatitis

    bioRxiv.

    https://doi.org/10.1101/2021.03.24.436824

    • Google Scholar
51. 1. Khademi SMH
    2. Sazinas P
    3. Jelsbak L

    (2019) Within-Host Adaptation Mediated by Intergenic Evolution in Pseudomonas aeruginosa

    Genome Biology and Evolution 11:1385–1397.

    https://doi.org/10.1093/gbe/evz083

    • PubMed
    • Google Scholar
52. 1. Klemm EJ
    2. Gkrania-Klotsas E
    3. Hadfield J
    4. Forbester JL
    5. Harris SR
    6. Hale C

    (2016) Emergence of host-adapted Salmonella Enteritidis through rapid evolution in an immunocompromised host

    Nature Microbiology 1:e15023.

    https://doi.org/10.1038/nmicrobiol.2015.23

    • Google Scholar
53. 1. Krismer B
    2. Weidenmaier C
    3. Zipperer A
    4. Peschel A

    (2017) The commensal lifestyle of Staphylococcus aureus and its interactions with the nasal microbiota

    Nature Reviews. Microbiology 15:675–687.

    https://doi.org/10.1038/nrmicro.2017.104

    • Google Scholar
54. 1. Kuehl R
    2. Morata L
    3. Meylan S
    4. Mensa J
    5. Soriano A

    (2020) When antibiotics fail: a clinical and microbiological perspective on antibiotic tolerance and persistence of Staphylococcus aureus

    The Journal of Antimicrobial Chemotherapy 75:1071–1086.

    https://doi.org/10.1093/jac/dkz559

    • Google Scholar
55. 1. Kuroda M
    2. Sekizuka T
    3. Matsui H
    4. Ohsuga J
    5. Ohshima T
    6. Hanaki H

    (2019) IS256-Mediated Overexpression of the WalKR Two-Component System Regulon Contributes to Reduced Vancomycin Susceptibility in a Staphylococcus aureus Clinical Isolate

    Frontiers in Microbiology 10:1882.

    https://doi.org/10.3389/fmicb.2019.01882

    • PubMed
    • Google Scholar
56. 1. Laabei M
    2. Uhlemann AC
    3. Lowy FD
    4. Austin ED
    5. Yokoyama M
    6. Ouadi K

    (2015) Evolutionary Trade-Offs Underlie the Multi-faceted Virulence of Staphylococcus aureus

    PLOS Biology 13:e1002229.

    https://doi.org/10.1371/journal.pbio.1002229

    • Google Scholar
57. 1. Langhanki L
    2. Berger P
    3. Treffon J
    4. Catania F
    5. Kahl BC
    6. Mellmann A

    (2018) In vivo competition and horizontal gene transfer among distinct Staphylococcus aureus lineages as major drivers for adaptational changes during long-term persistence in humans

    BMC Microbiology 18:152.

    https://doi.org/10.1186/s12866-018-1308-3

    • Google Scholar
58. 1. Lees JA
    2. Croucher NJ
    3. Goldblatt D
    4. Nosten F
    5. Parkhill J
    6. Turner C
    7. Turner P
    8. Bentley SD

    (2017a) Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration

    eLife 6:e26255.

    https://doi.org/10.7554/eLife.26255

    • PubMed
    • Google Scholar
59. 1. Lees JA
    2. Kremer PHC
    3. Manso AS
    4. Croucher NJ
    5. Ferwerda B
    6. Serón MV
    7. Oggioni MR
    8. Parkhill J
    9. Brouwer MC
    10. van der Ende A
    11. van de Beek D
    12. Bentley SD

    (2017b) Large scale genomic analysis shows no evidence for pathogen adaptation between the blood and cerebrospinal fluid niches during bacterial meningitis

    Microbial Genomics 3:e000103.

    https://doi.org/10.1099/mgen.0.000103

    • PubMed
    • Google Scholar
60. Preprint

    1. Lemoine F
    2. Gascuel O

    (2021) Gotree/Goalign: Toolkit and Go API to Facilitate the Development of Phylogenetic Workflows

    bioRxiv.

    https://doi.org/10.1101/2021.06.09.447704

    • Google Scholar
61. 1. Levin-Reisman I
    2. Brauner A
    3. Ronin I
    4. Balaban NQ

    (2019) Epistasis between antibiotic tolerance, persistence, and resistance mutations

    PNAS 116:14734–14739.

    https://doi.org/10.1073/pnas.1906169116

    • PubMed
    • Google Scholar
62. Preprint

    1. Li H

    (2013) Aligning Sequence Reads, Clone Sequences and Assembly CContigs with BWA-MEM

    arXiv.

    https://doi.org/10.48550/arXiv.1303.3997

    • Google Scholar
63. 1. Lieberman TD
    2. Wilson D
    3. Misra R
    4. Xiong LL
    5. Moodley P
    6. Cohen T

    (2016) Genomic diversity in autopsy samples reveals within-host dissemination of HIV-associated Mycobacterium tuberculosis

    Nature Medicine 22:1470–1474.

    https://doi.org/10.1038/nm.4205

    • Google Scholar
64. 1. Liu J
    2. Gefen O
    3. Ronin I
    4. Bar-Meir M
    5. Balaban NQ

    (2020) Effect of tolerance on the evolution of antibiotic resistance under drug combinations

    Science (New York, N.Y.) 367:200–204.

    https://doi.org/10.1126/science.aay3041

    • PubMed
    • Google Scholar
65. 1. Long DR
    2. Wolter DJ
    3. Lee M
    4. Precit M
    5. McLean K
    6. Holmes E

    (2020) Polyclonality, Shared Strains, and Convergent Evolution in Chronic CF S. aureus Airway Infection

    American Journal of Respiratory and Critical Care Medicine 203:1127–1137.

    https://doi.org/10.1164/rccm.202003-0735OC

    • Google Scholar
66. 1. Lopatkin AJ
    2. Bening SC
    3. Manson AL
    4. Stokes JM
    5. Kohanski MA
    6. Badran AH

    (2021) Clinically relevant mutations in core metabolic genes confer antibiotic resistance

    Science (New York, N.Y.) 371:eaba0862.

    https://doi.org/10.1126/science.aba0862

    • Google Scholar
67. 1. Loss G
    2. Simoes PM
    3. Valour F
    4. Cortes MF
    5. Gonzaga L
    6. Bergot M

    (2019) Staphylococcus aureus Small Colony Variants (SCVs): News From a Chronic Prosthetic Joint Infection

    Frontiers in Cellular and Infection Microbiology 9:363.

    https://doi.org/10.3389/fcimb.2019.00363

    • Google Scholar
68. 1. Lüdecke D
    2. Ben-Shachar MS
    3. Patil I
    4. Waggoner P
    5. Makowski D

    (2021) performance: An R Package for Assessment, Comparison and Testing of Statistical Models

    J. Journal of Open Source Software 6:3139.

    https://doi.org/10.21105/joss.03139

    • Google Scholar
69. 1. Machado H
    2. Seif Y
    3. Sakoulas G
    4. Olson CA
    5. Hefner Y
    6. Anand A

    (2021) Environmental conditions dictate differential evolution of vancomycin resistance in Staphylococcus aureus

    Communications Biology 4:e793.

    https://doi.org/10.1038/s42003-021-02339-z

    • Google Scholar
70. 1. Mäder U
    2. Nicolas P
    3. Depke M
    4. Pané-Farré J
    5. Debarbouille M
    6. van der Kooi-Pol MM
    7. Guérin C
    8. Dérozier S
    9. Hiron A
    10. Jarmer H
    11. Leduc A
    12. Michalik S
    13. Reilman E
    14. Schaffer M
    15. Schmidt F
    16. Bessières P
    17. Noirot P
    18. Hecker M
    19. Msadek T
    20. Völker U
    21. van Dijl JM

    (2016) Staphylococcus aureus Transcriptome Architecture: From Laboratory to Infection-Mimicking Conditions

    PLOS Genetics 12:e1005962.

    https://doi.org/10.1371/journal.pgen.1005962

    • PubMed
    • Google Scholar
71. 1. Martincorena I
    2. Raine KM
    3. Gerstung M
    4. Dawson KJ
    5. Haase K
    6. Van Loo P

    (2017) Universal Patterns of Selection in Cancer and Somatic Tissues

    Cell 171:1029–1041.

    https://doi.org/10.1016/j.cell.2017.09.042

    • Google Scholar
72. 1. Marvig RL
    2. Sommer LM
    3. Molin S
    4. Johansen HK

    (2015) Convergent evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis

    Nature Genetics 47:57–64.

    https://doi.org/10.1038/ng.3148

    • Google Scholar
73. 1. McEvoy CRE
    2. Tsuji B
    3. Gao W
    4. Seemann T
    5. Porter JL
    6. Doig K
    7. Ngo D
    8. Howden BP
    9. Stinear TP

    (2013) Decreased vancomycin susceptibility in Staphylococcus aureus caused by IS256 tempering of WalKR expression

    Antimicrobial Agents and Chemotherapy 57:3240–3249.

    https://doi.org/10.1128/AAC.00279-13

    • PubMed
    • Google Scholar
74. 1. McVicker G
    2. Prajsnar TK
    3. Williams A
    4. Wagner NL
    5. Boots M
    6. Renshaw SA

    (2014) Clonal expansion during Staphylococcus aureus infection dynamics reveals the effect of antibiotic intervention

    PLOS Pathogens 10:e1003959.

    https://doi.org/10.1371/journal.ppat.1003959

    • Google Scholar
75. 1. Miller CR
    2. Dey S
    3. Smolenski PD
    4. Kulkarni PS
    5. Monk JM
    6. Szubin R
    7. Sakoulas G
    8. Berti AD

    (2020) Distinct Subpopulations of Intravalvular Methicillin-Resistant Staphylococcus aureus with Variable Susceptibility to Daptomycin in Tricuspid Valve Endocarditis

    Antimicrobial Agents and Chemotherapy 64:e01593-19.

    https://doi.org/10.1128/AAC.01593-19

    • PubMed
    • Google Scholar
76. 1. Minh BQ
    2. Schmidt HA
    3. Chernomor O
    4. Schrempf D
    5. Woodhams MD
    6. von Haeseler A
    7. Lanfear R

    (2020) IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

    Molecular Biology and Evolution 37:1530–1534.

    https://doi.org/10.1093/molbev/msaa015

    • PubMed
    • Google Scholar
77. 1. Monk IR
    2. Tree JJ
    3. Howden BP
    4. Stinear TP
    5. Foster TJ

    (2015) Complete Bypass of Restriction Systems for Major Staphylococcus aureus Lineages

    MBio 6:e00308-15.

    https://doi.org/10.1128/mBio.00308-15

    • PubMed
    • Google Scholar
78. 1. Monk IR
    2. Shaikh N
    3. Begg SL
    4. Gajdiss M
    5. Sharkey LKR
    6. Lee JYH
    7. Pidot SJ
    8. Seemann T
    9. Kuiper M
    10. Winnen B
    11. Hvorup R
    12. Collins BM
    13. Bierbaum G
    14. Udagedara SR
    15. Morey JR
    16. Pulyani N
    17. Howden BP
    18. Maher MJ
    19. McDevitt CA
    20. King GF
    21. Stinear TP

    (2019) Zinc-binding to the cytoplasmic PAS domain regulates the essential WalK histidine kinase of Staphylococcus aureus

    Nature Communications 10:3067.

    https://doi.org/10.1038/s41467-019-10932-4

    • PubMed
    • Google Scholar
79. 1. Morvan C
    2. Halpern D
    3. Kénanian G
    4. Hays C
    5. Anba-Mondoloni J
    6. Brinster S

    (2016) Environmental fatty acids enable emergence of infectious Staphylococcus aureus resistant to FASII-targeted antimicrobials

    Nature Communications 7:12944.

    https://doi.org/10.1038/ncomms12944

    • Google Scholar
80. 1. Novick RP
    2. Geisinger E

    (2008) Quorum sensing in staphylococci

    Annual Review of Genetics 42:541–564.

    https://doi.org/10.1146/annurev.genet.42.110807.091640

    • Google Scholar
81. 1. Page AJ
    2. Taylor B
    3. Delaney AJ
    4. Soares J
    5. Seemann T
    6. Keane JA
    7. Harris SR

    (2016) SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments

    Microbial Genomics 2:e000056.

    https://doi.org/10.1099/mgen.0.000056

    • PubMed
    • Google Scholar
82. 1. Paterson GK
    2. Harrison EM
    3. Murray GGR
    4. Welch JJ
    5. Warland JH
    6. Holden MTG

    (2015) Capturing the cloud of diversity reveals complexity and heterogeneity of MRSA carriage, infection and transmission

    Nature Communications 6:6560.

    https://doi.org/10.1038/ncomms7560

    • Google Scholar
83. 1. Paudel A
    2. Panthee S
    3. Hamamoto H
    4. Grunert T
    5. Sekimizu K

    (2021) YjbH regulates virulence genes expression and oxidative stress resistance in Staphylococcus aureus

    Virulence 12:470–480.

    https://doi.org/10.1080/21505594.2021.1875683

    • PubMed
    • Google Scholar
84. 1. Peleg AY
    2. Miyakis S
    3. Ward DV
    4. Earl AM
    5. Rubio A
    6. Cameron DR
    7. Pillai S
    8. Moellering RC
    9. Eliopoulos GM

    (2012) Whole genome characterization of the mechanisms of daptomycin resistance in clinical and laboratory derived isolates of Staphylococcus aureus

    PLOS ONE 7:e28316.

    https://doi.org/10.1371/journal.pone.0028316

    • PubMed
    • Google Scholar
85. 1. Petrovic Fabijan A
    2. Lin RCY
    3. Ho J
    4. Maddocks S
    5. Ben Zakour NL
    6. Iredell JR
    7. Westmead Bacteriophage Therapy Team

    (2020) Safety of bacteriophage therapy in severe Staphylococcus aureus infection

    Nature Microbiology 5:465–472.

    https://doi.org/10.1038/s41564-019-0634-z

    • PubMed
    • Google Scholar
86. 1. Phaneuf PV
    2. Yurkovich JT
    3. Heckmann D
    4. Wu M
    5. Sandberg TE
    6. King ZA

    (2020) Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity

    BMC Genomics 21:514.

    https://doi.org/10.1186/s12864-020-06920-4

    • Google Scholar
87. 1. Phillips PC

    (2008) Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems

    Nature Reviews Genetics 9:855–867.

    https://doi.org/10.1038/nrg2452

    • Google Scholar
88. 1. Prados J
    2. Linder P
    3. Redder P

    (2016) TSS-EMOTE, a refined protocol for a more complete and less biased global mapping of transcription start sites in bacterial pathogens

    BMC Genomics 17:849.

    https://doi.org/10.1186/s12864-016-3211-3

    • Google Scholar
89. 1. Price MN
    2. Arkin AP
    3. Langille MGI

    (2017) PaperBLAST: Text Mining Papers for Information about Homologs

    MSystems 2:e00039-17.

    https://doi.org/10.1128/mSystems.00039-17

    • Google Scholar
90. 1. Proctor RA

    (2019) Immunity to Staphylococcus aureus: Implications for Vaccine Development

    Microbiology Spectrum 7:e0037-2018.

    https://doi.org/10.1128/microbiolspec.GPP3-0037-2018

    • PubMed
    • Google Scholar
91. 1. Quinlan AR
    2. Hall IM

    (2010) BEDTools: a flexible suite of utilities for comparing genomic features

    Bioinformatics (Oxford, England) 26:841–842.

    https://doi.org/10.1093/bioinformatics/btq033

    • Google Scholar
92. 1. Renzoni A
    2. Andrey DO
    3. Jousselin A
    4. Barras C
    5. Monod A
    6. Vaudaux P

    (2011) Whole genome sequencing and complete genetic analysis reveals novel pathways to glycopeptide resistance in Staphylococcus aureus

    PLOS ONE 6:e21577.

    https://doi.org/10.1371/journal.pone.0021577

    • Google Scholar
93. 1. Rishishwar L
    2. Kraft CS
    3. Jordan IK

    (2016) Population Genomics of Reduced Vancomycin Susceptibility in Staphylococcus aureus

    MSphere 1:e00094.

    https://doi.org/10.1128/mSphere.00094-16

    • Google Scholar
94. 1. Rouard C
    2. Garnier F
    3. Leraut J
    4. Lepainteur M
    5. Rahajamananav L
    6. Languepin J
    7. Ploy M-C
    8. Bourgeois-Nicolaos N
    9. Doucet-Populaire F

    (2018) Emergence and Within-Host Genetic Evolution of Methicillin-Resistant Staphylococcus aureus Resistant to Linezolid in a Cystic Fibrosis Patient

    Antimicrobial Agents and Chemotherapy 62:e00720-18.

    https://doi.org/10.1128/AAC.00720-18

    • PubMed
    • Google Scholar
95. 1. Sause WE
    2. Balasubramanian D
    3. Irnov I
    4. Copin R
    5. Sullivan MJ
    6. Sommerfield A

    (2019) The purine biosynthesis regulator PurR moonlights as a virulence regulator in Staphylococcus aureus

    PNAS 116:13563–13572.

    https://doi.org/10.1073/pnas.1904280116

    • Google Scholar
96. 1. Schreiber F
    2. Szekat C
    3. Josten M
    4. Sahl HG
    5. Bierbaum G

    (2013) Antibiotic-induced autoactivation of IS256 in Staphylococcus aureus

    Antimicrobial Agents and Chemotherapy 57:6381–6384.

    https://doi.org/10.1128/AAC.01585-13

    • PubMed
    • Google Scholar
97. 1. Seemann T

    (2014) Prokka: rapid prokaryotic genome annotation

    Bioinformatics (Oxford, England) 30:2068–2069.

    https://doi.org/10.1093/bioinformatics/btu153

    • Google Scholar
98. Software

    1. Seemann T

    (2022a) ABRicate, version 955d402

    GitHub.

    https://github.com/tseemann/abricate
99. Software

    1. Seemann T

    (2022b) mlst, version 09851e9

    GitHub.

    https://github.com/tseemann/mlst
100. Software

     1. Seemann T

     (2022c) Shovill, version f64d19e

     GitHub.

     https://github.com/tseemann/shovill
101. Software

     1. Seemann T

     (2022d) snippy, version 2af674c

     Github.

     https://github.com/tseemann/snippy
102. 1. Shopsin B
     2. Drlica-Wagner A
     3. Mathema B
     4. Adhikari RP
     5. Kreiswirth BN
     6. Novick RP

     (2008) Prevalence of agr dysfunction among colonizing Staphylococcus aureus strains

     The Journal of Infectious Diseases 198:1171–1174.

     https://doi.org/10.1086/592051

     • PubMed
     • Google Scholar
103. 1. Skwark MJ
     2. Croucher NJ
     3. Puranen S
     4. Chewapreecha C
     5. Pesonen M

     (2017) Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis

     PLOS Genetics 13:e1006508.

     https://doi.org/10.1371/journal.pgen.1006508

     • Google Scholar
104. 1. Smyth DS
     2. Kafer JM
     3. Wasserman GA
     4. Velickovic L
     5. Mathema B
     6. Holzman RS

     (2012) Nasal carriage as a source of agr-defective Staphylococcus aureus bacteremia

     The Journal of Infectious Diseases 206:1168–1177.

     https://doi.org/10.1093/infdis/jis483

     • Google Scholar
105. 1. Stinear TP
     2. Seemann T
     3. Pidot S
     4. Frigui W
     5. Reysset G
     6. Garnier T

     (2007) Reductive evolution and niche adaptation inferred from the genome of Mycobacterium ulcerans, the causative agent of Buruli ulcer

     Genome Research 17:192–200.

     https://doi.org/10.1101/gr.5942807

     • Google Scholar
106. 1. Stoletzki N
     2. Eyre-Walker A

     (2011) Estimation of the neutrality index

     Molecular Biology and Evolution 28:63–70.

     https://doi.org/10.1093/molbev/msq249

     • PubMed
     • Google Scholar
107. 1. Suligoy CM
     2. Lattar SM
     3. Noto Llana M
     4. Gonzalez CD
     5. Alvarez LP
     6. Robinson DA

     (2018) Mutation of Agr Is Associated with the Adaptation of Staphylococcus aureus to the Host during Chronic Osteomyelitis

     Frontiers in Cellular and Infection Microbiology 8:18.

     https://doi.org/10.3389/fcimb.2018.00018

     • Google Scholar
108. 1. Suligoy CM
     2. Díaz RE
     3. Gehrke AK
     4. Ring N
     5. Yebra G
     6. Alves J

     (2020) Acapsular Staphylococcus aureus with a non-functional agr regains capsule expression after passage through the bloodstream in a bacteremia mouse model

     Scientific Reports 10:14108.

     https://doi.org/10.1038/s41598-020-70671-1

     • Google Scholar
109. 1. Surewaard BGJ
     2. Deniset JF
     3. Zemp FJ
     4. Amrein M
     5. Otto M
     6. Conly J
     7. Omri A
     8. Yates RM
     9. Kubes P

     (2016) Identification and treatment of the Staphylococcus aureus reservoir in vivo

     The Journal of Experimental Medicine 213:1141–1151.

     https://doi.org/10.1084/jem.20160334

     • PubMed
     • Google Scholar
110. 1. Tan X
     2. Coureuil M
     3. Ramond E
     4. Euphrasie D
     5. Dupuis M
     6. Tros F
     7. Meyer J
     8. Nemazanyy I
     9. Chhuon C
     10. Guerrera IC
     11. Ferroni A
     12. Sermet-Gaudelus I
     13. Nassif X
     14. Charbit A
     15. Jamet A

     (2019) Chronic Staphylococcus aureus Lung Infection Correlates With Proteogenomic and Metabolic Adaptations Leading to an Increased Intracellular Persistence

     Clinical Infectious Diseases 69:1937–1945.

     https://doi.org/10.1093/cid/ciz106

     • PubMed
     • Google Scholar
111. 1. Thwaites GE
     2. Gant V

     (2011) Are bloodstream leukocytes Trojan Horses for the metastasis of Staphylococcus aureus?

     Nature Reviews. Microbiology 9:215–222.

     https://doi.org/10.1038/nrmicro2508

     • Google Scholar
112. 1. Toft C
     2. Andersson SG

     (2010) Evolutionary microbial genomics: insights into bacterial host adaptation

     Nature Reviews. Genetics 11:465–475.

     https://doi.org/10.1038/nrg2798

     • Google Scholar
113. 1. Tong SYC
     2. Davis JS
     3. Eichenberger E
     4. Holland TL
     5. Fowler VG

     (2015) Staphylococcus aureus infections: epidemiology, pathophysiology, clinical manifestations, and management

     Clinical Microbiology Reviews 28:603–661.

     https://doi.org/10.1128/CMR.00134-14

     • PubMed
     • Google Scholar
114. 1. Tong SYC
     2. Lye DC
     3. Yahav D
     4. Sud A
     5. Robinson JO
     6. Nelson J
     7. Archuleta S
     8. Roberts MA
     9. Cass A
     10. Paterson DL
     11. Foo H
     12. Paul M
     13. Guy SD
     14. Tramontana AR
     15. Walls GB
     16. McBride S
     17. Bak N
     18. Ghosh N
     19. Rogers BA
     20. Ralph AP
     21. Davies J
     22. Ferguson PE
     23. Dotel R
     24. McKew GL
     25. Gray TJ
     26. Holmes NE
     27. Smith S
     28. Warner MS
     29. Kalimuddin S
     30. Young BE
     31. Runnegar N
     32. Andresen DN
     33. Anagnostou NA
     34. Johnson SA
     35. Chatfield MD
     36. Cheng AC
     37. Fowler VG
     38. Howden BP
     39. Meagher N
     40. Price DJ
     41. van Hal SJ
     42. O’Sullivan MVN
     43. Davis JS
     44. Australasian Society for Infectious Diseases Clinical Research Network

     (2020) Effect of Vancomycin or Daptomycin With vs Without an Antistaphylococcal β-Lactam on Mortality, Bacteremia, Relapse, or Treatment Failure in Patients With MRSA Bacteremia: A Randomized Clinical Trial

     JAMA 323:527–537.

     https://doi.org/10.1001/jama.2020.0103

     • PubMed
     • Google Scholar
115. 1. Törönen P
     2. Medlar A
     3. Holm L

     (2018) PANNZER2: a rapid functional annotation web server

     Nucleic Acids Research 46:W84–W88.

     https://doi.org/10.1093/nar/gky350

     • Google Scholar
116. 1. Trong HNG
     2. Prunier AL
     3. Leclercq R

     (2005) Hypermutable and fluoroquinolone-resistant clinical isolates of Staphylococcus aureus

     Antimicrobial Agents and Chemotherapy 49:2098–2101.

     https://doi.org/10.1128/AAC.49.5.2098-2101.2005

     • PubMed
     • Google Scholar
117. 1. Trouillet-Assant S
     2. Lelièvre L
     3. Martins-Simões P
     4. Gonzaga L
     5. Tasse J
     6. Valour F
     7. Rasigade JP
     8. Vandenesch F
     9. Muniz Guedes RL
     10. Ribeiro de Vasconcelos AT
     11. Caillon J
     12. Lustig S
     13. Ferry T
     14. Jacqueline C
     15. Loss de Morais G
     16. Laurent F

     (2016) Adaptive processes of Staphylococcus aureus isolates during the progression from acute to chronic bone and joint infections in patients

     Cellular Microbiology 18:1405–1414.

     https://doi.org/10.1111/cmi.12582

     • PubMed
     • Google Scholar
118. 1. Van Tyne D
     2. Manson AL
     3. Huycke MM
     4. Karanicolas J
     5. Earl AM
     6. Gilmore MS

     (2019) Impact of antibiotic treatment and host innate immune pressure on enterococcal adaptation in the human bloodstream

     Science Translational Medicine 11:eaat8418.

     https://doi.org/10.1126/scitranslmed.aat8418

     • PubMed
     • Google Scholar
119. 1. Vargas R
     2. Freschi L
     3. Marin M
     4. Epperson LE
     5. Smith M
     6. Oussenko I

     (2021) In-host population dynamics of Mycobacterium tuberculosis complex during active disease

     eLife 10:e61805.

     https://doi.org/10.7554/eLife.61805

     • Google Scholar
120. 1. Wadsworth CB
     2. Arnold BJ
     3. Sater MRA
     4. Grad YH

     (2018) Azithromycin Resistance through Interspecific Acquisition of an Epistasis-Dependent Efflux Pump Component and Transcriptional Regulator in Neisseria gonorrhoeae

     MBio 9:e01419-18.

     https://doi.org/10.1128/mBio.01419-18

     • Google Scholar
121. 1. Wang Y
     2. Bojer MS
     3. George SE
     4. Wang Z
     5. Jensen PR
     6. Wolz C
     7. Ingmer H

     (2018) Inactivation of TCA cycle enhances Staphylococcus aureus persister cell formation in stationary phase

     Scientific Reports 8:10849.

     https://doi.org/10.1038/s41598-018-29123-0

     • PubMed
     • Google Scholar
122. 1. Wertheim HF
     2. Melles DC
     3. Vos MC
     4. van Leeuwen W
     5. van Belkum A
     6. Verbrugh HA

     (2005) The role of nasal carriage in Staphylococcus aureus infections

     The Lancet. Infectious Diseases 5:751–762.

     https://doi.org/10.1016/S1473-3099(05)70295-4

     • Google Scholar
123. 1. Wheatley R
     2. Diaz Caballero J
     3. Kapel N
     4. de Winter FHR
     5. Jangir P
     6. Quinn A
     7. Del Barrio-Tofiño E
     8. López-Causapé C
     9. Hedge J
     10. Torrens G
     11. Van der Schalk T
     12. Xavier BB
     13. Fernández-Cuenca F
     14. Arenzana A
     15. Recanatini C
     16. Timbermont L
     17. Sifakis F
     18. Ruzin A
     19. Ali O
     20. Lammens C
     21. Goossens H
     22. Kluytmans J
     23. Kumar-Singh S
     24. Oliver A
     25. Malhotra-Kumar S
     26. MacLean C

     (2021) Rapid evolution and host immunity drive the rise and fall of carbapenem resistance during an acute Pseudomonas aeruginosa infection

     Nature Communications 12:2460.

     https://doi.org/10.1038/s41467-021-22814-9

     • PubMed
     • Google Scholar
124. 1. Wheeler NE
     2. Gardner PP
     3. Barquist L

     (2018) Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica

     PLOS Genetics 14:e1007333.

     https://doi.org/10.1371/journal.pgen.1007333

     • Google Scholar
125. 1. Wood DE
     2. Lu J
     3. Langmead B

     (2019) Improved metagenomic analysis with Kraken 2

     Genome Biology 20:257.

     https://doi.org/10.1186/s13059-019-1891-0

     • PubMed
     • Google Scholar
126. 1. Wuthrich D
     2. Cuenod A
     3. Hinic V
     4. Morgenstern M
     5. Khanna N
     6. Egli A

     (2019) Genomic characterization of inpatient evolution of MRSA resistant to daptomycin, vancomycin and ceftaroline

     The Journal of Antimicrobial Chemotherapy 74:1452–1454.

     https://doi.org/10.1093/jac/dkz003

     • Google Scholar
127. 1. Yokoyama M
     2. Stevens E
     3. Laabei M
     4. Bacon L
     5. Heesom K
     6. Bayliss S

     (2018) Epistasis analysis uncovers hidden antibiotic resistance-associated fitness costs hampering the evolution of MRSA

     Genome Biology 19:94.

     https://doi.org/10.1186/s13059-018-1469-2

     • Google Scholar
128. 1. Young BC
     2. Golubchik T
     3. Batty EM
     4. Fung R
     5. Larner-Svensson H
     6. Votintseva AA
     7. Miller RR
     8. Godwin H
     9. Knox K
     10. Everitt RG
     11. Iqbal Z
     12. Rimmer AJ
     13. Cule M
     14. Ip CLC
     15. Didelot X
     16. Harding RM
     17. Donnelly P
     18. Peto TE
     19. Crook DW
     20. Bowden R
     21. Wilson DJ

     (2012) Evolutionary dynamics of Staphylococcus aureus during progression from carriage to disease

     PNAS 109:4550–4555.

     https://doi.org/10.1073/pnas.1113219109

     • PubMed
     • Google Scholar
129. 1. Young BC
     2. Gordon NC
     3. Cole K
     4. Price JR
     5. Liu E

     (2017) Severe infections emerge from commensal bacteria by adaptive evolution

     eLife 6:e30637.

     https://doi.org/10.7554/eLife.30637

     • Google Scholar
130. 1. Yu G
     2. Wang L-G
     3. Han Y
     4. He Q-Y

     (2012) clusterProfiler: an R package for comparing biological themes among gene clusters

     OMICS : A Journal of Integrative Biology 16:284–287.

     https://doi.org/10.1089/omi.2011.0118

     • PubMed
     • Google Scholar

Author details

1. Stefano G Giulieri

   1. Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
   2. Department of Infectious Diseases, Austin Health, Heidelberg, Australia
   3. Victorian Infectious Diseases Service, Royal Melbourne Hospital, Melbourne, Australia

   Contribution

   Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

2. Romain Guérillot

   Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia

   Contribution

   Conceptualization, Formal analysis, Methodology, Software, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

3. Sebastian Duchene

   Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia

   Contribution

   Resources, Software, Writing – review and editing

   Competing interests

   No competing interests declared

4. Abderrahman Hachani

   Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8032-2154

5. Diane Daniel

   1. Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
   2. Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at the Doherty Institute for Infection and Immunity, Melbourne, Australia

   Contribution

   Data curation, Investigation, Project administration

   Competing interests

   No competing interests declared

6. Torsten Seemann

   Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at the Doherty Institute for Infection and Immunity, Melbourne, Australia

   Contribution

   Methodology, Software, Supervision

   Competing interests

   No competing interests declared

7. Joshua S Davis

   1. Department of Infectious Diseases, John Hunter Hospital, Newcastle, New South Wales, Australia
   2. Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia

   Contribution

   Investigation, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

8. Steven YC Tong

   1. Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
   2. Victorian Infectious Disease Service, Royal Melbourne Hospital, and University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia

   Contribution

   Conceptualization, Investigation, Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1368-8356

9. Bernadette C Young

   Nuffield Department of medicine, Oxford, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6071-6770

10. Daniel J Wilson

    Big Data Institute, Nuffield Department of Population Health, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, University of Oxford, Oxford, United Kingdom

    Contribution

    Methodology, Software, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0940-3311

11. Timothy P Stinear

    Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia

    Contribution

    Conceptualization, Methodology, Supervision, Writing – review and editing

    For correspondence

    tstinear@unimelb.edu.au

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0150-123X

12. Benjamin P Howden

    1. Department of Microbiology and Immunology at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
    2. Department of Infectious Diseases, Austin Health, Heidelberg, Australia
    3. Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at the Doherty Institute for Infection and Immunity, Melbourne, Australia

    Contribution

    Conceptualization, Funding acquisition, Supervision, Writing – review and editing

    For correspondence

    bhowden@unimelb.edu.au

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0237-1473

• 2,271

  views

• 521

  downloads

• 18

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.