Molecular Epidemiology of Bordetella pertussis

Bordetella pertussis is the causative agent of whooping cough, a highly contagious infection of the upper respiratory tract that can lead to particularly severe disease in infants and young children, including death. A whole cell vaccine was introduced in the 1940s leading to a rapid decline in the number of cases; however adverse events from the vaccine led to the development and release of a safer acellular vaccine in the 1990s. Since the introduction of the acellular vaccine, pertussis cases began to rise. The past few years have seen a particularly large resurgence in cases; in 2012 the number of reported cases in the United States was the highest since 1955. Reasons for this resurgence are not entirely clear. As a large proportion of cases are in fully vaccinated individuals, we hypothesized that interactions with host microbiota through coaggregation interactions may play a role in who gets infected. We further hypothesized that vaccination selected for B. pertussis strains without the antigens included in the vaccine. To explore the ability of B. pertussis to coaggregate with common commensals of the nasopharynx we developed a high-throughput method for its detection. I applied this method to screen for coaggregation between 10 B. pertussis strains with 20 nasopharygeal commensal strains. We also used whole genome sequencing and phylogenetic analysis of 100 B. pertussis isolates randomly selected from 8 vaccination time periods to test whether vaccination produced a bottleneck in the B. pertussis genome. There was apparent coaggregation between B. pertussis and strains of H. influenzae, P. aeriginosa, S. aureus, S. pyogenes, and S. pneumoniae; however visual examination using the FlowCam™ runs and confocal microscopy suggested induction of autoaggregation in B. pertussis by S. aureus and P. aeruginosa, and no interaction between B. pertussis and the other strains. By inducing autoaggregation in B. pertussis, S. aureus and P. aeriginosa may be able to prevent B. pertussis from colonizing the host. Analysis of the genetic sequence data suggests that pre-vaccine era isolates are distinct from post-vaccination strains (p<0.0001) and that B. pertussis underwent a bottleneck.PHDEpidemiological ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135794/1/lizlevin_1.pd

Characterization of circulating RSV strains among subjects in the OUTSMART-RSV surveillance program during the 2016-17 winter viral season in the United States

<div><p>Background</p><p>Respiratory syncytial virus (RSV) is an established cause of serious lower respiratory disease in infants, elderly and high-risk populations. The OUTSMART surveillance program aims to characterize patient populations and currently circulating RSV strains, and monitor temporal and geographic evolution of RSV F and G proteins in the U.S.</p><p>Methods</p><p>The OUTSMART 2016–17 study collected RSV-positive samples from 25 RSVAlert^® laboratories from 4 U.S. regions and Puerto Rico during November 2016 through March 2017. Frequencies of A and B subtypes and genotypes were determined for several demographic and geographic variables. To gauge the representativeness of the OUTSMART patients, results were compared to discharge data from the NEDS and NIS databases.</p><p>Results</p><p>A total of 1,041 RSV-positive samples with associated demographic data were obtained and the RSV F gene and second variable region of the G gene were sequenced. The majority of samples (76.0%) came from children under 2 years old: <1 year (48.4%), 1–2 years (27.6%). The OUTSMART patient sample was similar to NEDS and NIS for age, gender, and geographic location. Both OUTSMART and national RSV cases peaked in January. Of OUTSMART samples, 45.3% were subtype A, 53.7% were subtype B and 1.0% were mixed A and B. The percentage of RSV B cases increased with increasing age. Hospitalization (length of hospital stay, LOS, >24 hrs) occurred in 29.0% of patients of which 52.0% had RSV B. Outpatients (LOS <24 hrs) were 64.4% of total of which 73.3% were diagnosed in the ER and discharged, while only 6% were diagnosed in other outpatient settings.</p><p>Conclusions</p><p>The OUTSMART 2016–17 study was representative of the U.S. RSV experience. Geographic and temporal information from the RSV surveillance program will be used to establish a molecular baseline of RSV F and G sequence variability and to help inform development of novel agents for RSV prophylaxis and treatment.</p></div

Map of participating OUTSMART laboratories during the 2016–2017 season.

<p>Pie-charts represent proportions of RSV A (blue), RSV B (orange), RSV A+B (red) and QNS (yellow) samples per lab. Numbers within the pie charts represent the total number of samples per lab.</p

Temporal distributions of RSV positive tests.

<p>(A) OUTSMART 2016–17 RSV positive tests by RSV subtype. (B) All RSV positive tests in OUTSMART—participating laboratories, and RSV in NEDS and NIS.</p

Comparison of OUTSMART November 2016—March 2017 RSV positive tests with RSV in NEDS and NIS November 2013-March 2014 by age, gender and region.

<p>Comparison of OUTSMART November 2016—March 2017 RSV positive tests with RSV in NEDS and NIS November 2013-March 2014 by age, gender and region.</p

OUTSMART November 2016—March 2017 RSV positive inpatient and Emergency Room cases compared with NIS and NEDS RSV positive cases during November 2013-March 2014 by age group.

<p>OUTSMART November 2016—March 2017 RSV positive inpatient and Emergency Room cases compared with NIS and NEDS RSV positive cases during November 2013-March 2014 by age group.</p

OUTSMART 2016–17 RSV-positive tests by LOS, referring department and subtype.

<p>OUTSMART 2016–17 RSV-positive tests by LOS, referring department and subtype.</p

OUTSMART 2016–17 RSV-positive tests by LOS, age and subtype.

<p>OUTSMART 2016–17 RSV-positive tests by LOS, age and subtype.</p