Emerging and Resistant Infections

Abstract

The lungs are a major target for infection and a key battleground in the fight against the development of antimicrobial drug–resistant pathogens. Ventilator-associated pneumonia (VAP) is associated with mortality rates of 24–50%. The optimal duration of antibiotic therapy against VAP is unknown, but prolonged courses are associated with the emergence of bacterial resistance. De-escalation strategies in which treatment is discontinued based on signs of clinical resolution, fixed durations of therapy (generally 7–8 d), or serum procalcitonin responses have been shown to decrease antibiotic consumption. Outcomes are comparable to longer treatment courses, with the possible exception of VAP due to nonfermenting, gram-negative bacilli such as Pseudomonas aeruginosa. Staphylococcus aureus is a leading cause of VAP and other infections. Outcomes after S. aureus infection are shaped by the interplay between environmental, bacterial, and host genetic factors. It is increasingly clear that mechanisms of pathogenesis vary in different types of S. aureus infections. Genome-scale studies of S. aureus strains, host responses, and host genetics are redefining our understanding of the pathogenic mechanisms underlying VAP. Genome-sequencing technologies are also revolutionizing our understanding of the molecular epidemiology, evolution, and transmission of influenza. Deep sequencing using next-generation technology platforms is defining the remarkable genetic diversity of influenza strains within infected hosts. Investigators have demonstrated that antiviral drug-resistant influenza may be present prior to the initiation of treatment. Moreover, drug-resistant minor variant influenza strains can be transmitted from person to person in the absence of selection pressure. Studies of lung infections and the causative pathogens will remain at the cutting edge of clinical and basic medical research.

The emergence of antimicrobial drug-resistant infections is one of the great challenges facing modern medicine (1). In this article, we review three presentations from the 2013 Pittsburgh International Lung Conference, which was devoted to acute and chronic lung infections. As a major site of infection, the lungs are a key battleground in the fight against the development of antimicrobial drug-resistant pathogens. Resistance is a particular problem in infections with high microbial burdens at tissue sites that limit antimicrobial penetration, such as ventilator-associated pneumonia (VAP), and among common pathogens that have significant genetic plasticity or achieve high genetic diversity within infected hosts, such as Staphylococcus aureus and influenza type A, respectively. The topics covered in this review include recent research on antibiotic de-escalation strategies for VAP, host determinants of Staphylococcus susceptibility, and the tracking of influenza transmission (including antiviral drug-resistant influenza) by deep genome sequencing.

Antibiotic De-escalation for Ventilator-associated Pneumonia: Why, When, and How?

Health-care-associated pneumonia (HCAP), in particular VAP, is the leading cause of death due to nosocomial infections (2). The mortality rate attributable to VAP ranges from 24–50% (3). The optimal duration of therapy for VAP remains controversial. Despite a lack of data regarding efficacy, physicians most commonly treat VAP for 8–21 days. Studies have demonstrated that antibiotic exposure for ≥7 days may be linked to the emergence of resistant bacteria and superinfections (4, 5). In a recent meta-analysis of patients who received antibiotics in primary care settings, a greater number or longer duration of antibiotic courses was associated with an increased risk for the emergence of resistance (6). These data provide a compelling argument for rational antibiotic de-escalation strategies.

Antibiotic de-escalation may involve decreasing the number of agents, changing agents (for example, switching from a less active to a more active agent or narrowing from a broad-spectrum to a narrow-spectrum agent), and/or shortening the duration of therapy. Prior to implementing a strategy, a decision must be made about the mechanism that will be used to initiate de-escalation. Options to be discussed herein include triggers based on clinical resolution, microbiologic diagnostic precision, fixed antibiotic duration, and biomarker-driven variable treatment duration. Other approaches that have been investigated include the use of bundle protocols, antibiotic stewardship and restriction, antibiotic cycling, and combination regimens. A second decision to be made is how to measure the efficacy of de-escalation. Researchers have employed a wide range of study endpoints, including mortality at a predetermined time point, intensive care unit (ICU) mortality, hospital mortality, length of stay, days on ventilation, days on antibiotics, antibiotic-free days, clinical and microbiologic cures, antibiotic resistance, recurrence or superinfections, side effects of antibiotics, and costs. This list of parameters highlights the complexity of designing and interpreting clinical trials conducted to investigate drug-resistant infections.

Antibiotic De-escalation Based on Clinical Resolution

Researchers in two randomized clinical trials have assessed de-escalation based on clinical resolution of HCAP. In one study, 81 ICU patients with low probability of pneumonia at a single Veterans Affairs medical center were enrolled (5). Patients with pulmonary infiltrates who had a Clinical Pulmonary Infection Score (CPIS) <7 on day 1 were randomized to “standard” therapy or ciprofloxacin monotherapy. Ciprofloxacin was discontinued if a patient’s CPIS remained <7 at day 3. The mean age and CPIS of the patients were 67 years and 4.9, respectively. Overall, antibiotics were continued beyond 3 days in 90% and 28% of patients in the standard therapy and ciprofloxacin monotherapy arms, respectively (P = 0.0001). Mortality at 30 days and mean ICU length of stay were 31% vs. 13% (P = not significant) and 14 days vs. 9 days (P = 0.04), respectively. Antimicrobial resistance and/or superinfections were diagnosed in 35% vs. 15% (P = 0.02). In another single-center study, 290 patients with VAP were randomized to “standard” treatment vs. an antibiotic discontinuation policy (7). The patients’ mean age and CPIS were 60 years and 7.1, respectively. The duration of antibiotic therapy was significantly shorter in the discontinuation arm (6 ± 4.9 vs. 8 ± 5.6 days; P = 0.001). There were no differences in secondary episodes of VAP, hospital mortality, or mean ICU stay. Taken together, the results of these studies indicate that antibiotic discontinuation strategies based on documented absence or resolution of HCAP reduce antibiotic consumption without negatively impacting outcomes.

Antibiotic De-escalation Based on Microbiologic Diagnostic Precision

Researchers in a Cochrane Collaboration meta-analysis have studied the impact of quantitative versus qualitative respiratory cultures in patients with VAP. In the meta-analysis (8), there were no significant differences in mortality, antibiotic changes, duration of mechanical ventilation, or ICU stays.

Antibiotic De-escalation Based on Fixed Duration of Treatment

In three randomized clinical trials, investigators have studied strategies based on a fixed duration of treatment. The first study enrolled 401 patients with VAP at multiple centers in France. The patients were randomized to an 8-day or 15-day antibiotic treatment arm (4). Patients were included only if they received appropriate therapy. There were no significant differences in mortality, recurrent infections, ventilator-free days, organ failure-free days, or lengths of stay. The short-duration arm was associated with more antibiotic-free days (mean = 13.1 vs. 8.7 d; P < 0.001) and fewer multi-drug-resistant pathogens among patients with recurrent infections (42.1% vs. 62%; P = 0.04). Recurrence rates were higher in the short-course arm among patients infected with nonfermenting, gram-negative bacilli (NF-GNB), including Pseudomonas aeruginosa (40.6% vs. 25.4%). In an unpublished, single-center study conducted in Uruguay, 77 patients were randomized to an 8-day or 12-day treatment arm. The researchers found no significant difference in hospital mortality, but they noted a nonsignificant trend toward more recurrent infections in the short-course arm (62% vs. 43%; P = 0.11). In the other study, 30 patients at a single center in Tunisia were randomized to a 7-day or 10-day treatment arm. The investigators in that study found no differences in hospital mortality, mean ICU stay, or recurrent infections (9).

Researchers who conducted a Cochrane Collaboration meta-analysis considered eight randomized clinical trials of HCAP in which researchers compared fixed durations of antibiotic therapy or protocols intended to limit the duration of therapy with standard care (10). Short-course therapy (7–8 d) significantly increased antibiotic-free days and reduced recurrence of multi-drug-resistant VAP compared with longer courses (10–15 d) without adversely affecting other outcomes. However, the recurrence of VAP was greater among the patients treated with short-course therapy for NF-GNB (OR, 2.18 [95% CI, 1.14–4.16]). Therefore, the authors concluded that short-course, fixed duration regimens may be more appropriate than longer courses for the treatment of patients with non–NF-GNB VAP.

Antibiotic De-escalation Based on Biomarker-driven Variable Treatment Duration

The biomarker that has been best studied is procalcitonin, a precursor of calcitonin that is upregulated in sepsis and severe bacterial infections (11). Researchers in three randomized clinical trials have investigated the use of procalcitonin as a guide for antibiotic de-escalation among patients in ICUs, including those with VAP. One of these studies was described in an abstract but has not been published yet. It included 81 patients at multiple centers in Uruguay who were randomized to a standard discontinuation arm (based on a negative bronchoalveolar lavage result) or to an experimental arm (discontinuation based on serum procalcitonin <0.5). The duration of antibiotic therapy was significantly shorter in the procalcitonin arm (7.9 d vs. 11.9 d in the standard discontinuation arm), but recurrent infections were more common (45.2% vs. 28.6%). In a multicenter, multinational study, 101 patients with suspected VAP were randomized to standard and procalcitonin-guided discontinuation arms (12). Antibiotic treatment duration was shorter in the experimental arm (10 d vs. 15 d in the control arm; P = 0.038). There were no significant differences in 28-day mortality, mean hospital stay, or recurrent infections. In the largest study, 630 patients at centers in France were randomized to either the procalcitonin or the control arm (13). The researchers' anticipation was for patients to stay in the ICU for more than 3 days. Once again, antibiotic duration was shorter in the procalcitonin arm (7.3 d vs. 9.4 d in the control arm) without a significant impact on mortality, length of stay, or recurrent infections.

Other investigators performed a meta-analysis using data derived from 14 clinical trials of patients with acute respiratory infections (N = 4,221) who received antibiotic treatment based on a procalcitonin algorithm or standard care and/or published guidelines (14, 15). Procalcitonin-guided treatment resulted in significantly shorter antibiotic regimen durations among all patients (7 d vs. 10 d; P < 0.0001), ICU patients (8 d vs. 12 d; P < 0.0001), and patients with VAP (11 d vs. 14 d; P = 0.02). Treatment failures were lower in the procalcitonin arm among all patients, but differences were not evident among ICU patients or patients with VAP. There were no differences in 30-day mortality between any of the groups. The authors concluded that the use of procalcitonin was effective in reducing antibiotic consumption without worsening outcomes across clinical settings and types of respiratory infection. Nevertheless, there are important caveats to interpreting the data which suggest that caution should be exercised so that conclusions are not applied too broadly. Most of the analyzed studies were performed in primary care or emergency department settings in Europe (mostly in France and Switzerland) and included patients with medical rather than surgical conditions who had not previously received broad-spectrum antibiotics. Most infections were community-acquired, not health-care-associated. Therefore, more data are needed to address these shortcomings before procalcitonin-guided strategies can be advocated for patients admitted to ICUs or those with VAP.

Future studies are also needed to establish the cost-effectiveness of procalcitonin-based strategies. Clearly, shorter courses of antibiotic therapy should result in direct pharmacy cost savings. Further indirect cost reductions may be realized if procalcitonin-guided treatment algorithms reduce antibiotic toxicity, superinfections such as Clostridium difficile colitis, antibiotic resistance, and the need for additional microbiologic tests such as blood cultures. In the end, cost reductions will depend upon local charges for drugs and testing, the frequency of testing, the standard duration of antibiotic therapy at a center, the magnitude of reduction in treatment duration, and the adherence to treatment protocols (16, 17).

When Is Antibiotic De-escalation Not Appropriate?

The data described above demonstrate that carefully designed and implemented antibiotic de-escalation strategies have a place in the treatment of patients with HCAP and other serious infections. However, these approaches are not appropriate in all instances. Examples of situations in which antibiotic de-escalation should not be pursued are summarized in Table 1. As is true throughout the practice of medicine, de-escalation algorithms do not take the place of sound clinical judgment and discretion.

Table 1.

Clinical settings in which antibiotic de-escalation strategies for health-care-associated pneumonia are inappropriate

Failure to Resolve Shock	Mediastinitis
Failure to extubate	Pulmonary septic emboli
Failure to discharge from ICU	Metastatic infection (e.g., endocarditis)
Empyema	Other uncontrolled sources of infection
Lung abscess	Staphylococcus aureus or Pseudomonas aeruginosa bacteremia

Definition of abbreviation: ICU = intensive care unit.

Host Determinants of Staphylococcus Susceptibility

Two questions have long stood at the center of infectious disease clinical practice and research. Why are some patients susceptible to infection by a particular pathogen but others are not? Why do some patients, once infected, respond well and others develop complications and fare poorly? S. aureus is among the most versatile of human pathogens, capable of developing resistance to antimicrobial agents and causing diverse types of infection that are associated with a wide range of outcomes. Diseases such as S. aureus bacteremia, infective endocarditis, pneumonia, and myonecrosis and necrotizing fasciitis result in high mortality rates despite antimicrobial therapy. In the absence of systemic dissemination, diseases such as skin and soft-tissue infections and osteomyelitis do not typically result in death, but may lead to significant morbidity. In some instances, S. aureus colonizes a body site such as the upper respiratory tract without causing disease. The predisposition to S. aureus infections and clinical outcomes are influenced by the interplay between environmental, bacterial, and host genetic factors. Research conducted during the past decade has provided important insights into these factors and serves as a model for the study of other pathogens.

Environment Is the Most Important Risk Factor for S. aureus Infections

The role of environmental factors in the development of S. aureus infections is well-recognized, particularly with regard to the importance of contact with the health-care system. Indeed, 80% and 40% of S. aureus bacteremia and endocarditis, respectively, are health care–associated (18, 19). These data are important because environmental risk factors are more amenable to modification than bacterial or genetic factors. In general surgical populations, for example, decolonization of S. aureus in nasal carriers reduces the rate of postoperative S. aureus infections by 40–60% (20, 21). A recent study of ICUs in 43 hospitals demonstrated that reduced rates of methicillin-resistant S. aureus infections were achieved with both (1) screening-based mupirocin and chlorhexidine decolonization strategies and (2) universal mupirocin and chlorhexidine strategies (22). At the same time, environmental factors are not the only determinants of susceptibility to S. aureus infections, nor do they individually dictate the course of infection. Indeed, approximately 15% of patients with complicated S. aureus bacteremia have no identifiable environmental risk factors or distinguishing clinical characteristics (18).

Bacterial Characteristics Influence Outcomes after S. aureus Infection

S. aureus strain-specific characteristics that influence outcomes include the elaboration of virulence factors and genetic variations. Different S. aureus genes contribute to the pathogenesis of particular clinical syndromes. Classic examples include genes encoding surface proteins and toxins (23). During exponential growth, cells express surface proteins such as fibronectin-, elastin-, and collagen-binding proteins; clumping factor; protein A; and coagulase. These proteins facilitate adhesion-mediated diseases such as endocarditis, osteomyelitis, and septic arthritis. Toxin-mediated diseases such as toxic shock syndrome and gastroenteritis result from the expression of TSST-1 (toxic shock syndrome toxin 1), α-toxin, enterotoxin B, and other proteins secreted by cells during the stationary phase.

Genetic variations between S. aureus strains that impact outcomes may reflect clonal differences or single-nucleotide polymorphisms (SNPs). In a study of 331 patients with colonization or a range of complicated and uncomplicated S. aureus infections, investigators assigned 379 strains to 1 of 18 clonal complexes by genotyping (24, 25). Most genotypes exhibited the capacity to cause invasive disease, but strains from clonal complexes 5 and 30 were associated with more frequent hematogenous complications. In a subsequent multicenter international collaboration, methicillin-susceptible S. aureus strains from clonal complex 30 were confirmed to cause endocarditis disproportionately (25). Moreover, strains causing endocarditis were more likely than strains causing soft-tissue infections to contain three specific adhesins (cflB, can, and map/eap) and five enterotoxins (tst, sea, sed, see, and sei). In a study of 80 S. aureus clinical strains, 3 SNPs in fibronectin-binding protein A (E652D, H782Q, and K786N) were associated with infections of cardiac devices (26). The SNPs were also linked to higher binding forces and energy as measured by atomic force microscopy, as well as the presence of extra hydrogen bonds with fibronectin as demonstrated by in silico molecular dynamics simulations. These findings are noteworthy because device infections take the form of biofilms, which are initiated by adherence to fibronectin and other proteins that can coat endovascular prostheses (27).

Host Genetic Characteristics Influence Outcomes after S. aureus Infection

Multiple lines of investigation support the importance of host genetics in shaping outcomes of S. aureus infections. Clinical studies have demonstrated elevated S. aureus infection rates in genetically distinct populations, including New Zealand Māori (28), Canadian First Nations (29), Australian Aboriginals (30), and African Americans (31). Increased predisposition also exists among patients with rare genetic conditions such as Chediak-Higashi syndrome and Job syndrome (32, 33). In a sense, these diseases constitute “experiments of nature,” in which specific host defense defects can be linked to susceptibility to infection (Table 2). To date, however, much of our understanding of host genetic factors comes from studies that have used nonhuman animal models.

Table 2.

Genetic conditions that increase susceptibility to Staphylococcus aureus infection

Signaling	Genetic Conditions
Defects in IL-1R or TLR signaling	MYDS8 deficiency, IRAK4 deficiency
Neutrophils
Neutropenia	Severe congenital neutropenia
Defective oxidative burst	Chronic granulomatous disease, myeloperoxidase deficiency
Defective chemotaxis	Leukocyte adhesion deficiency type I, Wiskott-Aldrich syndrome, RAC2 deficiency
Granulocyte disorders	Chediak-Higashi syndrome, neutrophil-specific granule deficiency
T cells
Decreased Th17 cells	Job syndrome (hyperimmunoglobulin E syndrome) due to STAT3 and DOCKS mutations
IL-17 and IL-17RA deficiency	Chronic mucocutaneous candidiasis

Data from References 32 and 33.

Definition of abbreviations: IL = interleukin; IL-17RA = interleukin 17A receptor; Th17 = T helper 17 cell; TLR = Toll-like receptor.

Numerous nonhuman animal species such as cattle, sheep, and inbred mice are particularly susceptible to S. aureus infections (34, 35). In a study of several inbred mouse strains, C57BL/6 mice were most resistant to infection following intravenous inoculation of a virulent S. aureus strain (34). Resistance was dependent upon neutrophil function. In contrast, A/J mice were highly susceptible to infection. Neutrophil function is not inhibited in these mice, but the expression of the neutrophil chemoattractants KC and MIP-2 (macrophage inflammatory protein 2) was delayed in the A/J mice compared with C57BL/6 mice. Therefore, delays in neutrophil recruitment may play an important role in S. aureus susceptibility. In a subsequent study that employed combined microarray and quantitative trait loci analyses, two genes of A/J mice (Tnfaip8 and Seh11) were associated with susceptibility to S. aureus infections, likely through roles in the regulation of macrophage cytokine expression (35). Although these genes and others identified through mouse models may represent promising candidates for human genetic susceptibility studies, it is increasingly evident that genomic responses in mice poorly mimic human inflammatory diseases (36). In an alternative approach to identifying host genetic factors that confer microbial resistance, investigators used gene expression data derived from patients with S. aureus and E. coli bacteremia (37). A S. aureus classifier based on gene expression patterns from a cohort of 94 human subjects distinguished S. aureus bacteremia from healthy subjects and E. coli bacteremia. The classifier was validated in an independent human cohort. In addition to identifying candidate target genes important in defense against S. aureus infections, the data suggest that gene expression profiling may be a useful tool for developing new diagnostic assays.

In a number of ongoing genome-wide association studies, researchers are attempting to exploit the power of large repositories and databases to identify genetic variants that influence patient outcomes. In one study, investigators are comparing 331 patients with health care–associated S. aureus bacteremia and 699 cardiac patients without S. aureus infection, with the goal of implicating common genetic variants in disease acquisition and severity (38). Preliminary analysis did not identify a common variant of large effect size that had genome-wide significance for association with either the risk of acquiring bacteremia or the severity of bacteremia. Nevertheless, the variant most significantly associated with the severity of infection was located in a biologically plausible candidate gene (CDON, a member of the immunoglobulin family) that may warrant further study. In another case-control genome-wide association study, researchers have enrolled an aggregate total of more than 53,000 patients with community-acquired S. aureus soft-tissue infections and control subjects matched for age and sex.

The Importance of Choosing Phenotypes Carefully

As the tools for investigating susceptibility to S. aureus infections become more powerful and studies become larger and more complex, it is incumbent upon investigators to choose disease phenotypes carefully. S. aureus clinical syndromes are diverse, distinct, and complex, and the pathogenesis of various types of disease differs. Indeed, “virulence” and “susceptibility” factors are likely to be syndrome-specific. Even at the same site of infection, mechanisms are likely to vary for different disease manifestations (for example, necrotizing vs. non-necrotizing pneumonias). Therefore, “invasive S. aureus infection” is an inadequate phenotype for genetic association studies. Grouping strains or patients with different syndromes runs the risk of regression toward the mean and producing results that are inconclusive or misleading.

Tracking Influenza Transmission Networks by Deep Sequencing

Influenza A is an RNA virus that is responsible for seasonal infections worldwide, as well as epidemics and pandemics of historical significance. A characteristic of RNA viruses is their genetic diversity, which is attributable to high replication rates and the absence of proofreading activity of RNA-dependent RNA polymerase (39, 40). One genomic mutation is expected to be introduced in each influenza A replication cycle. Diversity is also attained through a genomic mixing process, as influenza’s 13.5-kb negative-sense, segmented RNA genome allows for reassortment during mixed infections and coinfection of cells (39). The rapidity of mutation, replication, and reassortment and/or recombination means that each infected host is likely to carry influenza populations with high genetic diversity. To date, viral diversity within a given host has been studied primarily in chronic infections such as HIV and hepatitis C. Intrahost viral diversity has been more difficult to define for acute infections such as influenza.

Large-scale genomic sequencing technologies are revolutionizing our understanding of the molecular epidemiology and evolution of influenza and other viruses. First-generation sequencing platforms have proven to be powerful tools for studying circulating influenza. Consensus sequencing using these technologies, however, provides a relatively homogeneous representation of a virus population and does not capture the extensive genetic diversity that exists within a host (39). Deep sequencing with next-generation technologies ensures that genomes are sequenced multiple times. As such, the use of deep sequencing enables researchers to characterize genetic diversity with much greater sensitivity than consensus sequencing. Recent deep sequencing studies have afforded unique insights into influenza evolution within hosts, the emergence of drug resistance, and transmission between patients.

Defining the Molecular Epidemiology of Influenza Type A Based on First-Generation Sequencing

First-generation whole-genome sequencing and phylogenetic analyses of seasonal A/H1N1 and A/H3N2 influenza viruses from around the world gave the first comprehensive picture of influenza virus evolution, including its pattern of transmission through human populations (39). The data derived from these studies demonstrate that seasonal influenza transmission and evolution are highly dynamic, characterized by extensive reassortment and adaptive evolution of multiple cocirculating viral lineages (41). Study results have led to the formation of competing models of viral ecology. In one model, new lineages are believed to be seeded from a reservoir in the tropics to populations in temperate zones (42). In the other, A/H3N2 viruses exist as a migrating metapopulation and epidemics stem from viruses in multiple locations, but not in the tropics (43). Within an epidemic, multiple clades of seasonal A/H1N1 and A/H3N2 viruses have been shown to cocirculate and express novel patterns of spatial spread (44). In a retrospective study, researchers discovered that, since the Spanish influenza pandemic of 1918, intrasubtype reassortment of seasonal and pandemic A/H1N1 viruses has been a more important evolutionary process than previously realized and may have played a role in the antigenic shifting of the virus (45).

First-generation sequencing technologies have been particularly valuable for studying the 2009 A/H1N1 pandemic (39). The virus emerged after reassortment between two swine flu viruses circulating in North America and Eurasia (46). Within months of its introduction into the United States, the virus diversified into distinct lineages (47). The first wave of the pandemic exhibited defined spatial patterns, followed by extensive viral migration and mixing in the second wave (48). At the community level, pandemic A/H1N1 was introduced independently on multiple occasions, suggesting that community-based infection control methods may have limited efficacy in future pandemics (49). Studies of A/H3N2 viruses collected in 2009 between influenza seasons demonstrated that seasonal viruses were still introduced, transmitted, and cocirculated, emphasizing the importance of year-round molecular surveillance (50).

Demonstrating Transmission of Oseltamivir-resistant Minor Variants of Influenza through Deep Sequencing

The power of deep sequencing for studying the emergence of influenza drug resistance and viral transmission patterns was highlighted by the study of two sets of serial nasopharyngeal specimens that were positive for influenza A/H1N1 (51). The first set was collected over a >6-week period from an immunocompromised child who was treated with oseltamivir and zanamivir. The second set was collected from an index case (a young boy) and a household contact (his father) who was diagnosed 8 days later despite postexposure prophylaxis with oseltamivir (52). The most striking result was that the mutation most commonly associated with oseltamivir resistance (H275Y) was present prior to oseltamivir treatment as a minor variant in the viral population of both the immunocompromised child (Case 1) and the son (Case 2). The minor drug-resistant variants represented <0.1% and >2.4% of initial viral populations, respectively, and were not revealed by conventional methods such as phenotypic resistance tests or consensus sequencing. Moreover, deep sequencing clearly demonstrated transmission of multiple variants between the son and his father. Indeed, 60 mutational variants were passed between these individuals, including the oseltamivir-resistant mutant.

In addition to demonstrating how deep sequencing of intrahost viral populations provides an unprecedented ability to dissect the mutational spectrum and emergence of drug resistance, the data have important public health and clinical implications (51). The prior existence of drug-resistant variants means that the selection for drug resistance will proceed much more rapidly after the institution of treatment than it would if only wild-type virus were present. Furthermore, the H275Y mutation is not strongly deleterious to viral fitness, as mutants were maintained in the absence of selection pressure. Therefore, oseltamivir resistance would be predicted to spread rapidly as soon as selection pressure was applied. The passage of H275Y mutants between individuals argues against severe population bottlenecks during the interhost transmission of influenza. Coinfection with major and minor variants has also been observed in patients with human rhinovirus infections (53). These transmission dynamics differ from those of HIV, for example, in which infection is initiated by a small number of viral particles and most variants are produced after replication in the new host (54).

Using Deep Sequencing to Study Intrahost Evolution and Reconstruct Chains of Transmission for an Epidemic

Researchers in ongoing deep sequencing studies are addressing two questions that were previously unanswerable: (1) How does influenza evolve within a host over the course of infection? (2) Can chains of transmission in an epidemic be reconstructed using minor variants? To answer the first question, investigators are studying serial samples collected over the course of almost 2 years from a child with severe combined immunodeficiency who was infected with influenza A/H3N2 (55). During this time period, the child was initially treated with oseltamivir and then with amantadine and zanamivir. Using first-generation consensus sequencing methods, the investigators previously demonstrated the emergence and persistence of multi-drug-resistant virus even after the discontinuation of antiviral therapy (55). Deep sequencing will allow these researchers to characterize the roles of minor variants in the emergence of resistance and determine whether the child was reinfected over time.

To answer the second question, influenza A strains that were collected in 2009 as part of a prospective study of household transmission in Hong Kong are being studied with the use of deep sequencing (56). In an earlier study, investigators described sequence variations in pandemic H1N1 and seasonal H3N2 viruses within an individual, a household, and the community. They concluded that family clusters of influenza were predominantly the result of secondary transmission within a household (56). The results also suggest that intrahost viral sequence variation is more common than previously realized. On the scale that influenza evolution happens, there are not enough changes in viral sequences to reconstruct transmission networks from consensus sequencing. Deep sequencing will allow researchers to deduce transmission networks by tracking significant changes in the distribution of variants within intrahost populations.

Future Directions

In the future, the detection of minor variants by deep sequencing will have numerous public health applications. Using deep sequencing data, researchers may be able to predict the widespread emergence of antigenic drift variants or to design early warning systems for new evolutionary trends. Deep sequencing data may also aid in vaccine selection. As powerful as next-generation sequencing technology platforms are, they still have shortcomings that limit their utility as evolutionary biology tools (39). For example, the platforms generate sequence reads that capture genetic diversity, but it may not be possible to unambiguously assemble the data into individual genomic segments. Many of these shortcomings will be addressed by third-generation sequencing platforms currently in development that promise single-molecule sequencing capabilities.

Conclusions

In this review, we highlight how the fight against antimicrobial resistance depends upon improving the use of antimicrobial agents in the clinic and increasing understanding of the genetic mechanisms of pathogenesis, the emergence of resistance, and the transmission of pathogens. Studies of lung infections and the pathogens that cause them will remain at the cutting edge of research in these areas.