Coinfections of the Respiratory Tract: Viral Competition for Resources

Effect of Pullet Vaccination on Development and Longevity of Immunity

Avian respiratory disease causes significant economic losses in commercial poultry. Because of the need to protect long-lived poultry against respiratory tract pathogens from an early age, vaccination programs for pullets typically involve serial administration of a variety of vaccines, including infectious bronchitis virus (IBV), Newcastle disease virus (NDV), and infectious laryngotracheitis virus (ILTV). Often the interval between vaccinations is only a matter of weeks, yet it is unknown whether the development of immunity and protection against challenge when vaccines are given in short succession occurs in these birds, something known as viral interference. Our objective was to determine whether serially administered, live attenuated vaccines against IBV, NDV, and ILTV influence the development and longevity of immunity and protection against challenge in long-lived birds. Based on a typical pullet vaccination program, specific-pathogen-free white leghorns were administered multiple live attenuated vaccines against IBV, NDV, and ILTV until 16 weeks of age (WOA), after which certain groups were challenged with IBV, NDV, or ILTV at 20, 24, 28, 32, and 36 WOA. Five days post-challenge, viral load, clinical signs, ciliostasis, tracheal histopathology, and antibody titers in serum and tears were evaluated. We demonstrate that pullets serially administered live attenuated vaccines against IBV, NDV, and ILTV were protected against homologous challenge with IBV, NDV, or ILTV for at least 36 weeks, and conclude that the interval between vaccinations used in this study (at least 2 weeks) did not interfere with protection. This information is important because it shows that a typical pullet vaccination program consisting of serially administered live attenuated vaccines against multiple respiratory pathogens can result in the development of protective immunity against each disease agent.

Superinfection and cell regeneration can lead to chronic viral coinfections

•

Clinical researchers have found that coinfection of the respiratory tract can cause distinct disease outcome, sometimes leading to long-lasting infection, compared to single viral infection.

•

The impact of coinfections in human respiratory tract have not yet been evaluated in either theoretical or experimental studies on a large scale.

•

A few experiments confirm that different respiratory viruses can infect the same cell (superinfection).

•

Superinfection alone cannot cause long-lasting viral coinfections.

•

The combined mechanism of superinfection and cell regeneration provides a plausible mechanism for chronic viral coinfections.

Molecular diagnostic techniques have revealed that approximately 43% of the patients hospitalized with influenza-like illness are infected by more than one viral pathogen, sometimes leading to long-lasting infections. It is not clear how the heterologous viruses interact within the respiratory tract of the infected host to lengthen the duration of what are usually short, self-limiting infections. We develop a mathematical model which allows for single cells to be infected simultaneously with two different respiratory viruses (superinfection) to investigate the possibility of chronic coinfections. We find that a model with superinfection and cell regeneration has a stable chronic coinfection fixed point, while superinfection without cell regeneration produces only acute infections. This analysis suggests that both superinfection and cell regeneration are required to sustain chronic coinfection via this mechanism since coinfection is maintained by superinfected cells that allow slow-growing infections a chance to infect cells and continue replicating. This model provides a possible mechanism for chronic coinfection independent of any viral interactions via the immune response.

Invasion reproductive numbers for discrete-time models

Although invasion reproductive numbers (IRNs) are utilized frequently in continuous-time models with multiple interacting pathogens, they are yet to be explored in discrete-time systems. Here, we extend the concept of IRNs to discrete-time models by showing how to calculate them for a set of two-pathogen SIS models with coinfection. In our exploration, we address how sequencing events impacts the basic reproductive number (BRN) and IRN. As an illustrative example, our models are applied to rhinovirus and respiratory syncytial virus co-circulation. Results show that while the BRN is unaffected by variations in the order of events, the IRN differs. Furthermore, our models predict copersistence of multiple pathogen strains under cross-immunity, which is atypical of analogous continuous-time models. This investigation shows that sequencing events has important consequences for the IRN and can drastically alter competition dynamics.

Validated Models of Immune Response to Virus Infection

Viruses are a main cause of disease worldwide and many are without effective therapeutics or vaccines. A lack of understanding about how host responses work to control viral spread is one factor limiting effective management. How different immune components regulate infection dynamics is beginning to be better understood with the help of mathematical models. These models have been key in discriminating between hypotheses and in identifying rates of virus growth and clearance, dynamical control by different host factors and antivirals, and synergistic interactions during multi-pathogen infections. A recent focus in evaluating model predictions in the laboratory and clinic has illuminate the accuracy of models for a variety of viruses and highlighted the critical nature of theoretical approaches in virology. Here, I discuss recent model-driven exploration of host-pathogen interactions that have illustrated the importance of model validation in establishing the model's predictive capability and in defining new biology.

Host-pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

Influenza virus infections are a leading cause of morbidity and mortality worldwide. This is due in part to the continual emergence of new viral variants and to synergistic interactions with other viruses and bacteria. There is a lack of understanding about how host responses work to control the infection and how other pathogens capitalize on the altered immune state. The complexity of multi-pathogen infections makes dissecting contributing mechanisms, which may be non-linear and occur on different time scales, challenging. Fortunately, mathematical models have been able to uncover infection control mechanisms, establish regulatory feedbacks, connect mechanisms across time scales, and determine the processes that dictate different disease outcomes. These models have tested existing hypotheses and generated new hypotheses, some of which have been subsequently tested and validated in the laboratory. They have been particularly a key in studying influenza-bacteria coinfections and will be undoubtedly be useful in examining the interplay between influenza virus and other viruses. Here, I review recent advances in modeling influenza-related infections, the novel biological insight that has been gained through modeling, the importance of model-driven experimental design, and future directions of the field.

Influenza Virus Infection Model With Density Dependence Supports Biphasic Viral Decay

Mathematical models that describe infection kinetics help elucidate the time scales, effectiveness, and mechanisms underlying viral growth and infection resolution. For influenza A virus (IAV) infections, the standard viral kinetic model has been used to investigate the effect of different IAV proteins, immune mechanisms, antiviral actions, and bacterial coinfection, among others. We sought to further define the kinetics of IAV infections by infecting mice with influenza A/PR8 and measuring viral loads with high frequency and precision over the course of infection. The data highlighted dynamics that were not previously noted, including viral titers that remain elevated for several days during mid-infection and a sharp 4–5 log₁₀ decline in virus within 1 day as the infection resolves. The standard viral kinetic model, which has been widely used within the field, could not capture these dynamics. Thus, we developed a new model that could simultaneously quantify the different phases of viral growth and decay with high accuracy. The model suggests that the slow and fast phases of virus decay are due to the infected cell clearance rate changing as the density of infected cells changes. To characterize this model, we fit the model to the viral load data, examined the parameter behavior, and connected the results and parameters to linear regression estimates. The resulting parameters and model dynamics revealed that the rate of viral clearance during resolution occurs 25 times faster than the clearance during mid-infection and that small decreases to this rate can significantly prolong the infection. This likely reflects the high efficiency of the adaptive immune response. The new model provides a well-characterized representation of IAV infection dynamics, is useful for analyzing and interpreting viral load dynamics in the absence of immunological data, and gives further insight into the regulation of viral control.

Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology

Background

Health care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy.

Objective

The aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems.

Methods

We describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States.

Results

The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present.

Conclusions

Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.

Epidemiology and clinical characteristics of acute respiratory tract infections among hospitalized infants and young children in Chengdu, West China, 2009-2014

Background

Acute respiratory infection (ARI) is the leading cause of morbidity and mortality in pediatric patients worldwide and imposes an intense pressure on health care facilities. Data on the epidemiology profiles of ARIs are scarce in the western and rural areas of China. The purpose of the current study is to provide data on the presence of potential pathogens of ARIs in hospitalized children in Chengdu, west China.

Methods

Respiratory specimens were obtained from hospitalized patients (under 6 years old) with ARIs in a local hospital in Chengdu. Eight respiratory viruses were identified by PCR and 6 respiratory bacteria by biochemical reactions and Analytical Profile Index (API). Pathogens profiles, clinical characteristics and seasonality were analyzed.

Results

Fifty-one percent of patients were identified with at least one respiratory pathogen. Human rhinovirus (HRV) (23%), Respiratory syncytial virus (RSV) (22.7%) was the most commonly identified viruses, with Klebsiella pneumoniae (11.5%) the most commonly identified bacterium in the study. The presences of more than one pathogen were found, and multiple viral, bacterial, viral/bacterial combinations were identified in 14.9, 3.3 and 13.9% of patients respectively. Respiratory viruses were identified throughout the year with a seasonal peak in December–February. Pathogens profiles and clinical associations were different between infants (< 1 year of age) and older children (> 1 year of age). Infants with ARIs were more likely to have one or more viruses than older children. Infants identified with multiple pathogens had significantly higher proportions of tachypnea than infants that were not.

Conclusions

This study demonstrated that viral agents were frequently found in hospitalized children with ARI in Chengdu during the study period. This study gives us better information on the pathogen profiles, clinical associations, co-infection combinations and seasonal features of ARIs in hospitalized children, which is important for diagnoses and treatment of ARIs, as well as implementation of vaccines in this area. Moreover, future efforts in reducing the impact of ARIs will depend on programs in which available vaccines, especially vaccines on RSV, HRV and S. pneumoniae could be employed in this region and new vaccines could be developed against common pathogens.

Virological and Immunological Outcomes of Coinfections

Coinfections involving viruses are being recognized to influence the disease pattern that occurs relative to that with single infection. Classically, we usually think of a clinical syndrome as the consequence of infection by a single virus that is isolated from clinical specimens. However, this biased laboratory approach omits detection of additional agents that could be contributing to the clinical outcome, including novel agents not usually considered pathogens. The presence of an additional agent may also interfere with the targeted isolation of a known virus. Viral interference, a phenomenon where one virus competitively suppresses replication of other coinfecting viruses, is the most common outcome of viral coinfections. In addition, coinfections can modulate virus virulence and cell death, thereby altering disease severity and epidemiology. Immunity to primary virus infection can also modulate immune responses to subsequent secondary infections. In this review, various virological mechanisms that determine viral persistence/exclusion during coinfections are discussed, and insights into the isolation/detection of multiple viruses are provided. We also discuss features of heterologous infections that impact the pattern of immune responsiveness that develops.

Analysis of genetic variability of respiratory syncytial virus groups A and B in Kuwait

Respiratory syncytial virus (RSV) is the most frequently identified viral agent in infants, children, and elderly people with acute respiratory tract infections (ARTIs). This study is the only one of its kind in Kuwait, and its purpose was to investigate the genetic variability of the G protein gene in RSV strains prevalent in Kuwait. Respiratory samples were collected from patients with ARTIs in various hospitals in Kuwait and subjected to reverse transcription PCR (RT-PCR) amplifying a fragment of the G gene of RSV. A total of 305 samples were collected between January and mid-December 2016, and 77 (25.2%) were positive for RSV. Group A viruses were predominant over group B viruses; the RSV-A group was detected in 52 (67.5%) of the positive samples, while the RSV-B group was detected in 25 (32.5%) of the positive samples. Phylogenetic analysis showed that all RSV-A strains grouped into eight clusters of identical sequences of untyped strains. Twelve RSV-B strains, on the other hand, belonged to the RSV-B/BA10 genotype, while the rest were untyped. These data suggest that new and untyped strains of RSV-A group likely predominated in Kuwait and that the BA10 genotype of the RSV-B group became the dominant genotype in the 2016 season.

MERS-CoV: Understanding the Latest Human Coronavirus Threat

Human coronaviruses cause both upper and lower respiratory tract infections in humans. In 2012, a sixth human coronavirus (hCoV) was isolated from a patient presenting with severe respiratory illness. The 60-year-old man died as a result of renal and respiratory failure after admission to a hospital in Jeddah, Saudi Arabia. The aetiological agent was eventually identified as a coronavirus and designated Middle East respiratory syndrome coronavirus (MERS-CoV). MERS-CoV has now been reported in more than 27 countries across the Middle East, Europe, North Africa and Asia. As of July 2017, 2040 MERS-CoV laboratory confirmed cases, resulting in 712 deaths, were reported globally, with a majority of these cases from the Arabian Peninsula. This review summarises the current understanding of MERS-CoV, with special reference to the (i) genome structure; (ii) clinical features; (iii) diagnosis of infection; and (iv) treatment and vaccine development.

A comparison of RSV and influenza in vitro kinetic parameters reveals differences in infecting time

Influenza and respiratory syncytial virus (RSV) cause acute infections of the respiratory tract. Since the viruses both cause illnesses with similar symptoms, researchers often try to apply knowledge gleaned from study of one virus to the other virus. This can be an effective and efficient strategy for understanding viral dynamics or developing treatment strategies, but only if we have a full understanding of the similarities and differences between the two viruses. This study used mathematical modeling to quantitatively compare the viral kinetics of in vitro RSV and influenza virus infections. Specifically, we determined the viral kinetics parameters for RSV A2 and three strains of influenza virus, A/WSN/33 (H1N1), A/Puerto Rico/8/1934 (H1N1), and pandemic H1N1 influenza virus. We found that RSV viral titer increases at a slower rate and reaches its peak value later than influenza virus. Our analysis indicated that the slower increase of RSV viral titer is caused by slower spreading of the virus from one cell to another. These results provide estimates of dynamical differences between influenza virus and RSV and help provide insight into the virus-host interactions that cause observed differences in the time courses of the two illnesses in patients.

Influenza interaction with cocirculating pathogens and its impact on surveillance, pathogenesis, and epidemic profile: A key role for mathematical modelling

Evidence is mounting that influenza virus interacts with other pathogens colonising or infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. This is particularly true for mathematical modelling studies, which have become critical in public health decision-making. Yet models usually focus on influenza virus acquisition and infection alone, thereby making broad oversimplifications of pathogen ecology. Herein, we report evidence of influenza virus interactions with bacteria and viruses and systematically review the modelling studies that have incorporated interactions. Despite the many studies examining possible associations between influenza and Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitidis, respiratory syncytial virus (RSV), human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. The notable exception is the pneumococcus-influenza interaction, for which several recent modelling studies demonstrate the power of dynamic modelling as an approach to test biological hypotheses on interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and possible misinterpretation, and we illustrate the impact of interactions on public health surveillance using simple transmission models. We demonstrate that the development of multipathogen models is essential to assessing the true public health burden of influenza and that it is needed to help improve planning and evaluation of control measures. Finally, we identify the public health, surveillance, modelling, and biological challenges and propose avenues of research for the coming years.

Peach RNA viromes in six different peach cultivars

Many recent studies have demonstrated that several known and unknown viruses infect many horticultural plants. However, the elucidation of a viral population and the understanding of the genetic complexity of viral genomes in a single plant are rarely reported. Here, we conducted metatranscriptome analyses using six different peach trees representing six individual peach cultivars. We identified six viruses including five viruses in the family Betaflexiviridae and a novel virus belonging to the family Tymoviridae as well as two viroids. The number of identified viruses and viroids in each transcriptome ranged from one to six. We obtained 18 complete or nearly complete genomes for six viruses and two viroids using transcriptome data. Furthermore, we analyzed single nucleotide variations for individual viral genomes. In addition, we analyzed the amount of viral RNA and copy number for identified viruses and viroids. Some viruses or viroids were commonly present in different cultivars; however, the list of infected viruses and viroids in each cultivar was different. Taken together, our study reveals the viral population in a single peach tree and a comprehensive overview for the diversities of viral communities in different peach cultivars.

Molecular and clinical characterization of human respiratory syncytial virus in South Korea between 2009 and 2014

Respiratory syncytial virus (RSV) can cause serious respiratory infections, second only to influenza virus. In order to know RSV's genetic changes we examined 4028 respiratory specimens from local hospital outpatients in Gyeonggi Province, South Korea over six consecutive years by real-time one-step RT-PCR; 183 patients were positive for RSV infection. To investigate the specific distribution of RSV genotypes, we performed partial sequencing of the glycoprotein gene. Of the 131 RSV-A specimens sequenced, 61 (43·3%) belonged to the ON1 genotype, 66 (46·8%) were NA1 genotype, 3 (2·1%) were GA5 genotype, and 1 (0·7%) belonged to the GA1 genotype. Of the 31 RSV-B specimens sequenced, 29 were BA9 genotype (87·9%) and 2 were BA10 genotype (6·1%). The most common clinical symptoms were fever, cough, nasal discharge, and phlegm; multiple logistic regression analysis showed that RSV-positive infection on pediatric patients was strongly associated with cough (OR = 2·8, 95% CI 1·6-5·1) and wheezing (OR = 2·8, 95% CI 1·7-4·4). The ON1 genotype was significantly associated with phlegm (OR = 11·8, 95% CI 3·8-46·7), while the NA1 genotype was associated with the pediatric patients' gender (males, OR = 2·4, 95% CI 1·1-5·4) and presence of chills (OR = 5·1, 95% CI 1·1-27·2). RSV subgroup B was showed association with nasal obstruction (OR = 4·6, 95% CI 1·2-20·0). The majority of respiratory virus coinfections with RSV were human rhinovirus (47·2%). This study contributes to our understanding of the molecular epidemiological characteristics of RSV, which promotes the potential for improving RSV vaccines.

The impact of cell regeneration on the dynamics of viral coinfection

Many mathematical models of respiratory viral infections do not include regeneration of cells within the respiratory tract, arguing that the infection is resolved before there is significant cellular regeneration. However, recent studies have found that ∼40% of patients hospitalized with influenza-like illness are infected with at least two different viruses, which could potentially lead to longer-lasting infections. In these longer infections, cell regeneration might affect the infection dynamics, in particular, allowing for the possibility of chronic coinfections. Several mathematical models have been used to describe cell regeneration in infection models, though the effect of model choice on the predicted time course of viral coinfections is not clear. We investigate four mathematical models incorporating different mechanisms of cell regeneration during respiratory viral coinfection to determine the effect of cell regeneration on infection dynamics. We perform linear stability analysis for each of the models and find the steady states analytically. The analysis suggests that chronic illness is possible but only with one viral species; chronic coexistence of two different viral species is not possible with the regeneration models considered here.