Case Study: Prolonged Infectious SARS-CoV-2 Shedding from an Asymptomatic Immunocompromised Individual with Cancer.

Long-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding was observed from the upper respiratory tract of a female immunocompromised individual with chronic lymphocytic leukemia and acquired hypogammaglobulinemia. Shedding of infectious SARS-CoV-2 was observed up to 70 days, and of genomic and subgenomic RNA up to 105 days, after initial diagnosis. The infection was not cleared after the first treatment with convalescent plasma, suggesting a limited effect on SARS-CoV-2 in the upper respiratory tract of this individual. Several weeks after a second convalescent plasma transfusion, SARS-CoV-2 RNA was no longer detected. We observed marked within-host genomic evolution of SARS-CoV-2 with continuous turnover of dominant viral variants. However, replication kinetics in Vero E6 cells and primary human alveolar epithelial tissues were not affected. Our data indicate that certain immunocompromised individuals may shed infectious virus longer than previously recognized. Detection of subgenomic RNA is recommended in persistently SARS-CoV-2-positive individuals as a proxy for shedding of infectious virus.

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host.

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

Within-host evolutionary dynamics and tissue compartmentalization during acute SARS-CoV-2 infection.

The global evolution of SARS-CoV-2 depends in part upon the evolutionary dynamics within individual hosts with varying immune histories. To characterize the within-host evolution of acute SARS-CoV-2 infection, we sequenced saliva and nasal samples collected daily from vaccinated and unvaccinated individuals early during infection. We show that longitudinal sampling facilitates high-confidence genetic variant detection and reveals evolutionary dynamics missed by less-frequent sampling strategies. Within-host dynamics in both unvaccinated and vaccinated individuals appeared largely stochastic; however, in rare cases, minor genetic variants emerged to frequencies sufficient for forward transmission. Finally, we detected significant genetic compartmentalization of viral variants between saliva and nasal swab sample sites in many individuals. Altogether, these data provide a high-resolution profile of within-host SARS-CoV-2 evolutionary dynamics.IMPORTANCEWe detail the within-host evolutionary dynamics of SARS-CoV-2 during acute infection in 31 individuals using daily longitudinal sampling. We characterized patterns of mutational accumulation for unvaccinated and vaccinated individuals, and observed that temporal variant dynamics in both groups were largely stochastic. Comparison of paired nasal and saliva samples also revealed significant genetic compartmentalization between tissue environments in multiple individuals. Our results demonstrate how selection, genetic drift, and spatial compartmentalization all play important roles in shaping the within-host evolution of SARS-CoV-2 populations during acute infection.

Within-Host Rhinovirus Evolution in Upper and Lower Respiratory Tract Highlights Capsid Variability and Mutation-Independent Compartmentalization.

BACKGROUND: Rhinovirus (RV) infections can progress from the upper (URT) to lower (LRT) respiratory tract in immunocompromised individuals, causing high rates of fatal pneumonia. Little is known about how RV evolves within hosts during infection. METHODS: We sequenced RV complete genomes from 12 hematopoietic cell transplant patients with infection for up to 190 days from both URT (nasal wash, NW) and LRT (bronchoalveolar lavage, BAL). Metagenomic and amplicon next-generation sequencing were used to track the emergence and evolution of intrahost single nucleotide variants (iSNVs). RESULTS: Identical RV intrahost populations in matched NW and BAL specimens indicated no genetic adaptation is required for RV to progress from URT to LRT. Coding iSNVs were 2.3-fold more prevalent in capsid over nonstructural genes. iSNVs modeled were significantly more likely to be found in capsid surface residues, but were not preferentially located in known RV-neutralizing antibody epitopes. Newly emergent, genotype-matched iSNV haplotypes from immunocompromised individuals in 2008-2010 could be detected in Seattle-area community RV sequences in 2020-2021. CONCLUSIONS: RV infections in immunocompromised hosts can progress from URT to LRT with no specific evolutionary requirement. Capsid proteins carry the highest variability and emergent mutations can be detected in other, including future, RV sequences.

Differences in virus and immune dynamics for SARS-CoV-2 Delta and Omicron infections by age and vaccination histories.

Vaccination against COVID-19 was integral to controlling the pandemic that persisted with the continuous emergence of SARS-CoV-2 variants. Using a mathematical model describing SARS-CoV-2 within-host infection dynamics, we estimate differences in virus and immunity due to factors of infecting variant, age, and vaccination history (vaccination brand, number of doses and time since vaccination). We fit our model in a Bayesian framework to upper respiratory tract viral load measurements obtained from cases of Delta and Omicron infections in Singapore, of whom the majority only had one nasopharyngeal swab measurement. With this dataset, we are able to recreate similar trends in URT virus dynamics observed in past within-host modelling studies fitted to longitudinal patient data.We found that Omicron had higher R0,within values than Delta, indicating greater initial cell-to-cell spread of infection within the host. Moreover, heterogeneities in infection dynamics across patient subgroups could be recreated by fitting immunity-related parameters as vaccination history-specific, with or without age modification. Our model results are consistent with the notion of immunosenescence in SARS-CoV-2 infection in elderly individuals, and the issue of waning immunity with increased time since last vaccination. Lastly, vaccination was not found to subdue virus dynamics in Omicron infections as well as it had for Delta infections.This study provides insight into the influence of vaccine-elicited immunity on SARS-CoV-2 within-host dynamics, and the interplay between age and vaccination history. Furthermore, it demonstrates the need to disentangle host factors and changes in pathogen to discern factors influencing virus dynamics. Finally, this work demonstrates a way forward in the study of within-host virus dynamics, by use of viral load datasets including a large number of patients without repeated measurements.

Within-host genomic evolution of methicillin-resistant Staphylococcus aureus in long-term carriers.

Assessing the genomic evolution of Staphylococcus aureus can help us understand how the bacteria adapt to its environment. In this study, we aimed to assess the mutation rate within 144 methicillin-resistant Staphylococcus aureus (MRSA) carriers with a carriage time from 4 to 11 years, including some carriers who belonged to the same households. We found that 23 of the 144 individuals had completely different MRSA types over time and were therefore not long-term carriers of the same MRSA. From the remaining 121 individuals, we performed whole-genome sequencing (WGS) on 424 isolates and then compared these pairwise using core genome multilocus sequence typing (cgMLST) and single-nucleotide polymorphism (SNP) analyses. We found a median within-host mutation rate in long-term MRSA carriers of 4.9 (3.4-6.9) SNPs/genome/year and 2.7 (1.8-4.2) allelic differences/genome/year, when excluding presumed recombination. Furthermore, we stratified the cohort into subgroups and found no significant difference between the median mutation rate of members of households, individuals with presumed continued exposure, e.g., from travel and persons without known continued exposure. Finally, we found that SNPs occurred at random within the genes in our cohort. KEY POINTS: • Median mutation rate within long-term MRSA carriers of 4.9 (3.4-6.9) SNPs/genome/year • Similar median mutation rates in subgroups (households, travelers) • No hotspots for SNPs within the genome.

Global dynamics of a time-delayed nonlocal reaction-diffusion model of within-host viral infections.

In this paper, we study a time-delayed nonlocal reaction-diffusion model of within-host viral infections. We introduce the basic reproduction number R 0 and show that the infection-free steady state is globally asymptotically stable when R 0 ≤ 1 , while the disease is uniformly persistent when R 0 > 1 . In the case where all coefficients and reaction terms are spatially homogeneous, we obtain an explicit formula of R 0 and the global attractivity of the positive constant steady state. Numerically, we illustrate the analytical results, conduct sensitivity analysis, and investigate the impact of drugs on curtailing the spread of the viruses.

Strong within-host selection in a maternally inherited obligate symbiont: Buchnera and aphids.

Numerous animal lineages have maternally inherited symbionts that are required for host reproduction and growth. Endosymbionts also pose a risk to their hosts because of the mutational decay of their genomes through genetic drift or to selfish mutations that favor symbiont fitness over host fitness. One model for heritable endosymbiosis is the association of aphids with their obligate bacterial symbiont, Buchnera We experimentally established heteroplasmic pea aphid matrilines containing pairs of closely related Buchnera haplotypes and used deep sequencing of diagnostic markers to measure haplotype frequencies in successive host generations. These frequencies were used to estimate the effective population size of Buchnera within hosts (i.e., the transmission bottleneck size) and the extent of within-host selection. The within-host effective population size was in the range of 10 to 20, indicating a strong potential for genetic drift and fixation of deleterious mutations. Remarkably, closely related haplotypes were subject to strong within-host selection, with selection coefficients as high as 0.5 per aphid generation. In one case, the direction of selection depended on the thermal environment and went in the same direction as between-host selection. In another, a new mutant haplotype had a strong within-host advantage under both environments but had no discernible effect on host-level fitness under laboratory conditions. Thus, within-host selection can be strong, resulting in a rapid fixation of mutations with little impact on host-level fitness. Together, these results show that within-host selection can drive evolution of an obligate symbiont, accelerating sequence evolution.

Efficient Estimates of Surface Diffusion Parameters for Spatio-Temporally Resolved Virus Replication Dynamics.

Advanced methods of treatment are needed to fight the threats of virus-transmitted diseases and pandemics. Often, they are based on an improved biophysical understanding of virus replication strategies and processes in their host cells. For instance, an essential component of the replication of the hepatitis C virus (HCV) proceeds under the influence of nonstructural HCV proteins (NSPs) that are anchored to the endoplasmatic reticulum (ER), such as the NS5A protein. The diffusion of NSPs has been studied by in vitro fluorescence recovery after photobleaching (FRAP) experiments. The diffusive evolution of the concentration field of NSPs on the ER can be described by means of surface partial differential equations (sufPDEs). Previous work estimated the diffusion coefficient of the NS5A protein by minimizing the discrepancy between an extended set of sufPDE simulations and experimental FRAP time-series data. Here, we provide a scaling analysis of the sufPDEs that describe the diffusive evolution of the concentration field of NSPs on the ER. This analysis provides an estimate of the diffusion coefficient that is based only on the ratio of the membrane surface area in the FRAP region to its contour length. The quality of this estimate is explored by a comparison to numerical solutions of the sufPDE for a flat geometry and for ten different 3D embedded 2D ER grids that are derived from fluorescence z-stack data of the ER. Finally, we apply the new data analysis to the experimental FRAP time-series data analyzed in our previous paper, and we discuss the opportunities of the new approach.

Niche theory for within-host parasite dynamics: Analogies to food web modules via feedback loops.

Why do parasites exhibit a wide dynamical range within their hosts? For instance, why does infecting dose either lead to infection or immune clearance? Why do some parasites exhibit boom-bust, oscillatory dynamics? What maintains parasite diversity, that is coinfection v single infection due to exclusion or priority effects? For insights on parasite dose, dynamics and diversity governing within-host infection, we turn to niche models. An omnivory food web model (IGP) blueprints one parasite competing with immune cells for host energy (PIE). Similarly, a competition model (keystone predation, KP) mirrors a new coinfection model (2PIE). We then drew analogies between models using feedback loops. The following three points arise: first, like in IGP, parasites oscillate when longer loops through parasites, immune cells and resource regulate parasite growth. Shorter, self-limitation loops (involving resources and enemies) stabilise those oscillations. Second, IGP can produce priority effects that resemble immune clearance. But, despite comparable loop structure, PIE cannot due to constraints imposed by production of immune cells. Third, despite somewhat different loop structure, KP and 2PIE share apparent and resource competition mechanisms that produce coexistence (coinfection) or priority effects of prey or parasites. Together, this mechanistic niche framework for within-host dynamics offers new perspective to improve individual health.

Case Commentary: The hidden side of oxacillin resistance in Staphylococcus aureus.

Acquisition of PBP2a (encoded by the mec gene) is the key resistance mechanism to ß-lactams in Staphylococcus aureus. The mec gene can be easily detected by PCR assays; however, these tools will miss mec-independent oxacillin resistance. This phenotype is mediated by mutations in cell wall metabolism genes that can be acquired during persistent infections under prolonged antibiotic exposure. The complex case presented by Hess et al. (Antimicrob Agents Chemother 67:e00437-23, 2023, https://doi.org/10.1128/aac.00437-23) highlights the diagnostic and therapeutic challenges in the management of mec-independent oxacillin resistance.

Novel Multilocus Sequence Typing and Global Sequence Clustering Schemes for Characterizing the Population Diversity of Streptococcus mitis.

Streptococcus mitis is a common oral commensal and an opportunistic pathogen that causes bacteremia and infective endocarditis; however, the species has received little attention compared to other pathogenic streptococcal species. Effective and easy-to-use molecular typing tools are essential for understanding bacterial population diversity and biology, but schemes specific for S. mitis are not currently available. We therefore developed a multilocus sequence typing (MLST) scheme and defined sequence clusters or lineages of S. mitis using a comprehensive global data set of 322 genomes (148 publicly available and 174 newly sequenced). We used internal 450-bp sequence fragments of seven housekeeping genes (accA, gki, hom, oppC, patB, rlmN, and tsf) to define the MLST scheme and derived the global S. mitis sequence clusters using the PopPUNK clustering algorithm. We identified an initial set of 259 sequence types (STs) and 258 global sequence clusters. The schemes showed high concordance (100%), capturing extensive S. mitis diversity with strains assigned to multiple unique STs and global sequence clusters. The tools also identified extensive within- and between-host S. mitis genetic diversity among isolates sampled from a cohort of healthy individuals, together with potential transmission events, supported by both phylogeny and pairwise single nucleotide polymorphism (SNP) distances. Our novel molecular typing and strain clustering schemes for S. mitis allow for the integration of new strain data, are electronically portable at the PubMLST database (https://pubmlst.org/smitis), and offer a standardized approach to understanding the population structure of S. mitis. These robust tools will enable new insights into the epidemiology of S. mitis colonization, disease and transmission.

Simplified within-host and Dose-response Models of SARS-CoV-2.

Understanding the mechanistic dynamics of transmission is key to designing more targeted and effective interventions to limit the spread of infectious diseases. A well-described within-host model allows explicit simulation of how infectiousness changes over time at an individual level. This can then be coupled with dose-response models to investigate the impact of timing on transmission. We collected and compared a range of within-host models used in previous studies and identified a minimally-complex model that provides suitable within-host dynamics while keeping a reduced number of parameters to allow inference and limit unidentifiability issues. Furthermore, non-dimensionalised models were developed to further overcome the uncertainty in estimates of the size of the susceptible cell population, a common problem in many of these approaches. We will discuss these models, and their fit to data from the human challenge study (see Killingley et al. (2022)) for SARS-CoV-2 and the model selection results, which has been performed using ABC-SMC. The parameter posteriors have then used to simulate viral-load based infectiousness profiles via a range of dose-response models, which illustrate the large variability of the periods of infection window observed for COVID-19.

A target-cell limited model can reproduce influenza infection dynamics in hosts with differing immune responses.

We consider a hierarchy of ordinary differential equation models that describe the within-host viral kinetics of influenza infections: the IR model explicitly accounts for an immune response to the virus, while the simpler, target-cell limited TEIV and TV models do not. We show that when the IR model is fitted to pooled experimental murine data of the viral load, fraction of dead cells, and immune response levels, its parameters values can be determined. However, if, as is common, only viral load data are available, we can estimate parameters of the TEIV and TV models but not the IR model. These results are substantiated by a structural and practical identifiability analysis. We then use the IR model to generate synthetic data representing infections in hosts whose immune responses differ. We fit the TV model to these synthetic datasets and show that it can reproduce the characteristic exponential increase and decay of viral load generated by the IR model. Furthermore, the values of the fitted parameters of the TV model can be mapped from the immune response parameters in the IR model. We conclude that, if only viral load data are available, a simple target-cell limited model can reproduce influenza infection dynamics and distinguish between hosts with differing immune responses.

A mathematical model of the within-host kinetics of SARS-CoV-2 neutralizing antibodies following COVID-19 vaccination.

Compelling evidence continues to build to support the idea that SARS-CoV-2 Neutralizing Antibody (NAb) levels in an individual can serve as an important indicator of the strength of protective immunity against infection. It is not well understood why NAb levels in some individuals remain high over time, while in others levels decline rapidly. In this work, we present a two-state mathematical model of within-host NAb dynamics in response to vaccination. By fitting only four host-specific parameters, the model is able to capture individual-specific NAb levels over time as measured by the AditxtScore™ for NAbs. The model can serve as a foundation for predicting NAb levels in the long-term, understanding connections between NAb levels, protective immunity, and breakthrough infections, and potentially guiding decisions about whether and when a booster vaccination may be warranted.

Evidence for exposure dependent carriage of malaria parasites across the dry season: modelling analysis of longitudinal data.

BACKGROUND: In malaria endemic regions, transmission of Plasmodium falciparum parasites is often seasonal with very low transmission during the dry season and high transmission in the wet season. Parasites survive the dry season within some individuals who experience prolonged carriage of parasites and are thought to 'seed' infection in the next transmission season. METHODS: Dry season carriers and their role in the subsequent transmission season are characterized using a combination of mathematical simulations and data analysis of previously described data from a longitudinal study in Mali of individuals aged 3 months-12 years (n = 579). RESULTS: Simulating the life-history of individuals experiencing repeated exposure to infection predicts that dry season carriage is more likely in the oldest, most exposed and most immune individuals. This hypothesis is supported by the data from Mali, which shows that carriers are significantly older, experience a higher biting rate at the beginning of the transmission season and develop clinical malaria later than non-carriers. Further, since the most exposed individuals in a community are most likely to be dry season carriers, this is predicted to enable a more than twofold faster spread of parasites into the mosquito population at the start of the subsequent wet season. CONCLUSIONS: Carriage of malaria parasites over the months-long dry season in Mali is most likely in the older, more exposed and more immune children. These children may act as super-spreaders facilitating the fast spread of parasites at the beginning of the next transmission season.

Environment pollutants exposure affects the endogenous activation of within-host Mycobacterium tuberculosis.

OBJECTIVE: Epidemiological studies have linked ambient pollutants with tuberculosis (TB) risk, but the association has not been fully understood. Here, for the first time, we applied whole-genome sequencing (WGS) to assess the reproductive state of Mycobacterium tuberculosis (MTB) by profiling the mutation rate of MTB (MTBMR) during within-host endogenous reactivated progression, intending to dissect the actual effects of ambient pollutants on the endogenous reactivation. METHODS: We conducted a retrospective cohort study on bacteriologically confirmed TB patients and followed them for relapse in Jiangsu and Sichuan Province, China. Endogenous and exogenous activation were distinguished by WGS of the pathogen. The average concentration of air pollution was estimated by considering a lag of 0-1 to 0-12 months. We applied a generalized additive model with a Poisson function to evaluate the relationships between ambient pollutants exposure and MTBMR. RESULTS: In the single-pollutant adjusted models, the maximum effect for PM10 (MTBMR increase: 81.87%, 95% CI: 38.38, 139.03) and PM2.5 (MTBMR increase: 73.91%, 95% CI: 22.17, 147.55) was observed at a lag of 0-12 months for every 10 µg/m³ increase. For SO2, the maximum effect was observed at lag 0-8 months, with MTBMR increasing by 128.06% (95% CI: 45.92, 256.44); and for NO2, the maximum effect was observed at lag 0-9 months, with MTBMR increasing by 124.02% (95% CI: 34.5, 273.14). In contrast, the O3 concentration was inversely associated with MTBMR, and the maximum reduction of MTBMR was 6.18% (95% CI: -9.24, -3.02) at a lag of 0-9 months. Similar results were observed for multi-pollutant models. CONCLUSIONS: Increased exposure to ambient pollutants (PM10, PM2.5, SO2, and NO2) contributed to a faster MTBMR, indicating that MTB exhibits increased reproductive activity, thus accelerating within-host endogenous reactivation. O3 exposure could decrease the MTBMR, suggesting that MTB exerts low reproductive activity by inhibiting within-host endogenous activation.

Within-Host Evolution of SARS-CoV-2 in a B-Cell Depleted Patient With Successful Treatment.

Prolonged viral shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in an immunocompromised host is a challenge as the treatment and infection control for chronic coronavirus disease 2019 infection is not well established and there is a potential risk of new variants emerging. A 48-year-old woman who underwent chemotherapy, including rituximab and steroid, had reactivation of SARS-CoV-2 68 days after the virus was first detected. She successfully recovered after receiving convalescent plasma and intravenous immunoglobulin. Genomic analysis demonstrated that viruses collected from the nasopharyngeal specimens at day 0 and day 68 had 18 different nucleotide mutations, implying within-host evolution after in-depth epidemiologic investigation.

Convergent Evolution of Antibiotic Tolerance in Patients with Persistent Methicillin-Resistant Staphylococcus aureus Bacteremia.

Severe infections caused by methicillin-resistant Staphylococcus aureus (MRSA) are often complicated by persistent bacteremia (PB) despite active antibiotic therapy. Antibiotic resistance rarely contributes to MRSA-PB, suggesting an important role for antibiotic tolerance pathways. To identify bacterial factors associated with PB, we sequenced the whole genomes of 206 MRSA isolates derived from 20 patients with PB and looked for genetic signatures of adaptive within-host evolution. We found that genes involved in the tricarboxylic acid cycle (citZ and odhA) and stringent response (rel) bore repeated, independent, protein-altering mutations across multiple infections, indicative of convergent evolution. Both pathways have been linked previously to antibiotic tolerance. Mutations in citZ were identified most frequently, and further study showed they caused antibiotic tolerance through the loss of citrate synthase activity. Isolates harboring mutant alleles (citZ, odhA, and rel) were sampled at a low frequency from each patient but were detected in 10 (50%) of the patients. These results suggest that subpopulations of antibiotic-tolerant mutants emerge commonly during MRSA-PB. Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of hospital-acquired infection. In severe cases, bacteria invade the bloodstream and cause bacteremia, a condition associated with high mortality. We analyzed the genomes of serial MRSA isolates derived from patients with bacteremia that persisted through active antibiotic therapy and found a frequent evolution of pathways leading to antibiotic tolerance. Antibiotic tolerance is distinct from antibiotic resistance, and the role of tolerance in clinical failure of antibiotic therapy is defined poorly. Our results show genetic evidence that perturbation of specific metabolic pathways plays an important role in the ability of MRSA to evade antibiotics during severe infection.

Common Adaptive Strategies Underlie Within-Host Evolution of Bacterial Pathogens.

Within-host adaptation is a hallmark of chronic bacterial infections, involving substantial genomic changes. Recent large-scale genomic data from prolonged infections allow the examination of adaptive strategies employed by different pathogens and open the door to investigate whether they converge toward similar strategies. Here, we compiled extensive data of whole-genome sequences of bacterial isolates belonging to miscellaneous species sampled at sequential time points during clinical infections. Analysis of these data revealed that different species share some common adaptive strategies, achieved by mutating various genes. Although the same genes were often mutated in several strains within a species, different genes related to the same pathway, structure, or function were changed in other species utilizing the same adaptive strategy (e.g., mutating flagellar genes). Strategies exploited by various bacterial species were often predicted to be driven by the host immune system, a powerful selective pressure that is not species specific. Remarkably, we find adaptive strategies identified previously within single species to be ubiquitous. Two striking examples are shifts from siderophore-based to heme-based iron scavenging (previously shown for Pseudomonas aeruginosa) and changes in glycerol-phosphate metabolism (previously shown to decrease sensitivity to antibiotics in Mycobacterium tuberculosis). Virulence factors were often adaptively affected in different species, indicating shifts from acute to chronic virulence and virulence attenuation during infection. Our study presents a global view on common within-host adaptive strategies employed by different bacterial species and provides a rich resource for further studying these processes.