ArticlePDF Available

Interferon λ restructures the nasal microbiome and increases susceptibility to Staphylococcus aureus superinfection

Authors:

Abstract

Much of the morbidity and mortality associated with influenza respiratory infection is due to bacterial co-infection with pathogens that colonize the upper respiratory tract such as MRSA and Streptococcus pneumoniae. Several immune mechanisms have been identified that account for the interaction between viruses and bacteria, less well understood is how influenza increases susceptibility to bacterial pneumonia. Here we show that the immune response to infection with influenza causes an increase and restructuring of the upper respiratory microbiota in WT but not Il28r−/− mice, lacking the receptor for the type III interferons. Mice lacking IL-28R fail to induce STAT-1 phosphorylation and its regulation by SOCS1. The Il28r−/− mice have increased expression of IL-22 as well as Ngal and RegIIIg in the nasal cavity, the source of organisms that would be aspirated to cause pneumonia. Proteomic analysis reveals changes in several cytoskeletal proteins that contribute to barrier function in the nasal epithelium that may contribute to the effects of IL-28 signaling on the microbiota. The importance of the effects of IL-28 signaling in the pathogenesis of MRSA pneumonia post influenza were confirmed by showing that WT mice nasally colonized before or after influenza infection had significantly higher levels of infection in the upper airways, as well as significantly greater susceptibility to MRSA pneumonia compared to Il28r−/− mice. Our results suggest that the activation of the type III interferons in response to influenza has a major effect in expanding the upper airway microbiome and increasing susceptibility to lower respiratory tract infection.
Lambda Interferon Restructures the Nasal Microbiome and Increases
Susceptibility to Staphylococcus aureus Superinfection
Paul J. Planet,
a,b
Dane Parker,
a
Taylor S. Cohen,
a
Hannah Smith,
a
Justinne D. Leon, Chanelle Ryan,
a
Tobin J. Hammer,
c
Noah Fierer,
c,d
Emily I. Chen,
e
Alice S. Prince
a
Department of Pediatrics, Division of Pediatric Infectious Diseases, Columbia University, College of Physicians and Surgeons, New York, New York, USA
a
; Sackler Institute
for Comparative Genomics, American Museum of Natural History, New York, New York, USA
b
; Department of Ecology and Evolutionary Biology
c
; Cooperative Institute for
Research in Environmental Sciences
d
, University of Colorado, Boulder, Colorado, USA; Proteomics Shared Resource at the Herbert Irving Comprehensive Cancer Center &
Department of Pharmacology, Columbia University Medical Center, New York, New York, USA
e
P.J.P., D.P., and T.S.C. contributed equally to this article.
ABSTRACT Much of the morbidity and mortality associated with influenza virus respiratory infection is due to bacterial coin-
fection with pathogens that colonize the upper respiratory tract such as methicillin-resistant Staphylococcus aureus (MRSA) and
Streptococcus pneumoniae. A major component of the immune response to influenza virus is the production of type I and III
interferons. Here we show that the immune response to infection with influenza virus causes an increase and restructuring of the
upper respiratory microbiota in wild-type (WT) mice but not in Il28r
/
mutant mice lacking the receptor for type III inter-
feron. Mice lacking the IL-28 receptor fail to induce STAT1 phosphorylation and expression of its regulator, SOCS1. Il28r
/
mutant mice have increased expression of interleukin-22 (IL-22), as well as Ngal and RegIII
, in the nasal cavity, the source of
organisms that would be aspirated to cause pneumonia. Proteomic analysis reveals changes in several cytoskeletal proteins that
contribute to barrier function in the nasal epithelium that may contribute to the effects of IL-28 signaling on the microbiota. The
importance of the effects of IL-28 signaling in the pathogenesis of MRSA pneumonia after influenza virus infection was con-
firmed by showing that WT mice nasally colonized before or after influenza virus infection had significantly higher levels of in-
fection in the upper airways, as well as significantly greater susceptibility to MRSA pneumonia than Il28r
/
mutant mice did.
Our results suggest that activation of the type III interferon in response to influenza virus infection has a major effect in expand-
ing the upper airway microbiome and increasing susceptibility to lower respiratory tract infection.
IMPORTANCE S. aureus and influenza virus are important respiratory pathogens, and coinfection with these organisms is asso-
ciated with significant morbidity and mortality. The ability of influenza virus to increase susceptibility to S. aureus infection is
less well understood. We show here that influenza virus leads to a change in the upper airway microbiome in a type III
interferon-dependent manner. Mice lacking the type III interferon receptor have altered STAT1 and IL-22 signaling. In coinfec-
tion studies, mice without the type III interferon receptor had significantly less nasal S. aureus colonization and subsequent
pneumonia than infected WT mice did. This work demonstrates that type III interferons induced by influenza virus contribute
to nasal colonization and pneumonia due to S. aureus superinfection.
Received 16 November 2015 Accepted 12 January 2016 Published 9 February 2016
Citation Planet PJ, Parker D, Cohen TS, Smith H, Leon JD, Ryan C, Hammer TJ, Fierer N, Chen EI, Prince AS. 2016. Lambda interferon restructures the nasal microbiome and
increases susceptibility to Staphylococcus aureus superinfection. mBio 7(1):e01939-15. doi:10.1128/mBio.01939-15.
Editor Olaf Schneewind, University of Chicago
Copyright © 2016 Planet et al. This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported
license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.
Address correspondence to Alice S. Prince, asp7@columbia.edu.
Bacterial superinfection is a major cause of the morbidity and
mortality associated with influenza (1). Streptococcus pneu-
moniae,Haemophilus influenzae, and Staphylococcus aureus have
been the most prevalent respiratory pathogens; however, highly
virulent methicillin-resistant S. aureus (MRSA) strains common
in the United States are now increasingly frequent (2, 3). These
organisms are often part of the commensal flora colonizing the
nasal cavity, which provides a source for subsequent aspiration
into the lung. While the normal host readily clears the small num-
bers of routinely aspirated flora, in the setting of influenza virus
infection, there is significantly increased susceptibility to severe
bacterial pneumonia (4–6). Substantial epidemiological data have
documented that nasal colonization with S. aureus is a major pre-
disposing factor for subsequent infection (7–10). These data have
spurred screening for nasal carriage of MRSA and universal de-
colonization of intensive care unit patients (11) to prevent infec-
tion, since the carriage of these organisms is so common (12).
Several investigators have demonstrated that the bacterial inocu-
lum required to cause S. pneumoniae (4) or S. aureus pneumonia
(13, 14) in a murine model is significantly decreased in the setting
of antecedent influenza virus infection, and the multiple immune
mechanisms responsible have been recently reviewed (15). The
density of pathogen colonization in the upper respiratory tract is
likely a contributing factor in susceptibility to subsequent pneu-
RESEARCH ARTICLE
crossmark
January/February 2016 Volume 7 Issue 1 e01939-15 ®mbio.asm.org 1
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
monia. Influenza vaccination with live attenuated virus increases
the amounts of both pneumococci and staphylococci in the upper
airway (16), suggesting that the immune response activated by
influenza virus affects the factors that normally regulate the upper
airway microbiome.
A major component of the initial immune response to influ-
enza virus infection is activation of type I and III interferons
(IFNs), resulting in the induction of over 300 genes that comprise
the interferome (17–19). Type I IFN signaling is mediated through
IFNAR, a receptor that responds to 13 IFN-
subtypes, IFN-
,
IFN-, and IFN-
through JAK-STAT signaling and activation of
an autocrine loop (20). The type III IFNs, IFN-
, or IL-28A/B and
IL-29 are sensed through a compound receptor composed of the
IL-28 receptor (IL-28R) and IL-10RB (21), which is not ubiqui-
tous like IFNAR and is expressed predominantly on mucosal sur-
faces (22) and on neutrophils (23). Type I and III IFN signaling
contributes to the pathology elicited by S. aureus pulmonary in-
fection (24, 25) and influenza virus-induced type I IFN contrib-
utes to greater S. aureus-induced lung pathology and poorer prog-
nosis (6). Thus, once these pathogens reach the lower airways,
they are likely to cause more severe disease in the setting of influ-
enza. However, it is not clear whether influenza and IFNs enable
pathogens such as MRSA to colonize and proliferate in the nasal
cavity, which is a major source of subsequent pulmonary infec-
tion.
We postulated that bacterial superinfection after influenza vi-
rus infection requires two factors: first, the accumulation of an
adequate inoculum of potential pathogens in the upper respira-
tory tract, and second, impaired innate immune defenses within
the lung itself, attributed to the type I/III IFNs and other factors (5,
24, 26). The combination of both events results in pneumonia
instead of bacterial clearance. In the studies presented here, we
demonstrate that type III IFN-STAT1 signaling elicited by influ-
enza alters the composition of the nasal microbiome and pro-
teome, increases susceptibility to nasal colonization, and impairs
the clearance of S. aureus from the lower airways.
RESULTS
Influenza virus infection results in expansion and restructuring
of the nasal microbiome. We investigated the influence of mouse-
adapted influenza virus strain PR8 on the upper respiratory mi-
crobiome of wild-type (WT) C57BL/6J mice (Fig. 1). There was a
significant increase in the total numbers of bacteria recovered
from the upper airway following 3 days of infection (P0.002)
(Fig. 1A). Although the overall diversity of the microbial commu-
nity was unchanged, as measured by culture-dependent and
-independent techniques (Fig. 1B and C), we observed specific
changes in the relative abundance of certain bacterial species (Ta-
ble 1; Fig. 1D), including murine commensal staphylococci. In
addition, culture-independent microbial community profiles
clustered on the basis of the presence of influenza virus, suggesting
reproducible shifts in the structure of the commensal microbiome
in response to infection (Fig. 1E and F).
IL-28R signaling has a major effect on the density of the up-
per airway flora. We next addressed the role of type III IFN sig-
naling in the control of the nasal microbiome. Il28 gene expression
in the upper airway was documented to be significantly upregu-
lated in response to PR8 (Fig. 2A). There was no increase in the
abundance or change in diversity of the nasal flora observed in the
Il28r
/
mutant mice after influenza virus infection (Fig. 2B and
C), in contrast to the significant increase in upper airway flora in
WT mice after influenza virus infection. Culture-independent ex-
periments further confirmed a lack of change in diversity after
influenza virus infection (Fig. 2D) and also showed no major
changes in the structure or composition of the microbiome
(Fig. 2E).
To confirm that type III IFN signaling was responsible for al-
terations in flora, recombinant IL-28B (IL-28A and -B are 96%
identical [27]) was instilled into the airways of WT mice, which
resulted in significant increases in the number of bacteria recov-
ered from the upper airway (P0.0011) (Fig. 2F), to levels com-
parable to those observed with influenza virus infection. There
were no significant changes in the overall diversity of the micro-
biota, but as with influenza virus infection, some species increased
disproportionately (Fig. 2G; Table 1; see Tables S1 to S4 in the
supplemental material). These results suggested that IL-28R–
IFN-
signaling is specifically involved in the restructuring of the
microbial ecology of the upper airway and the increase in specific
local flora in response to influenza virus infection. The differences
in flora observed were not due to the severity of influenza virus
infection in the different mouse strains. Weight loss over the
course of infection (Fig. 2I) and levels of viral RNA found in the
lungs of WT and Il28r
/
mutant mice were similar (Fig. 2I).
IL-28R signals through STAT1. The type III IFNs are involved
in multiple downstream signaling cascades that could affect the
abundance of nasal flora through the action of antimicrobial pep-
tides, cytokines that alter barrier function and immune cells re-
cruited across the mucosal surface. The effects of type III IFNs are
mediated by JAK/STAT signaling, initiated by phosphorylation of
STAT1 (28, 29). The relative induction of STAT1 and STAT3
phosphorylation in WT and Il28r
/
mutant mice, exposed to
influenza virus or phosphate-buffered saline (PBS), were com-
pared, and Ifnar
/
mutant mice were included as a control for
the type I IFNs (Fig. 3A). STAT1 and STAT3 phosphorylation was
induced by influenza virus infection in WT and Ifnar
/
mutant
mice at days 3 and 7 postinfection (Fig. 3A). However, minimal
amounts of STAT1 or phosphorylated STAT1 were detected in
Il28r
/
mutant mice. Even at 7 days postinfection, substantially
less phosphorylated STAT1 was detected in Il28r
/
mutant mice
than in WT or Ifnar
/
mutant controls. To determine if these
differences in STAT1 activation were associated with changes in
the upper airway microbiome after influenza virus infection, we
quantified commensal flora in STAT1 null mice before and fol-
lowing influenza virus infection. As was observed in the Il28r
/
mutant mice, in the absence of STAT1, there was no increase in the
abundance or diversity of nasal flora after influenza virus infection
(Fig. 3B). To confirm that the effect of type III IFN on flora was
through STAT1, we applied purified IL-28B to the airways of WT
and Stat1
/
mutant mice. While addition of IL-28B to WT mice
led to expansion of the resident flora and increases in specific
species, Stat1
/
mutant mice had no change in the numbers of
bacteria present (Fig. 3C; see Tables S5 to S8 in the supplemental
material). Thus, IFN-
-dependent regulation of the nasal micro-
biome is mediated by STAT1-dependent signals.
Effects of STAT1-SOCS1 interactions in the upper airway.
STAT1 signaling could affect the abundance of the upper airway
flora through several mechanisms. STAT1 induces and is regu-
lated by SOCS1, which interacts directly with JAK kinase (30, 31).
SOCS1 regulates the expression of antimicrobial peptides that
would control the abundance of commensal flora (32). We pre-
Planet et al.
2®mbio.asm.org January/February 2016 Volume 7 Issue 1 e01939-15
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
dicted that, in the absence of STAT1 signaling, there would be
decreased SOCS1 expression, as was observed in the lung lysates of
Il28r
/
mutant mice compared with those of WT or Ifnar
/
mutant mice, both of which express STAT1 (Fig. 3D). In the nasal
cavities of WT mice, there was significant upregulation of Socs1
expression in response to influenza virus infection, but no in-
crease was observed in the Il28r
/
mutant mice that lacked
P-STAT1 (P0.0101) (Fig. 3E).
SOCS1 involvement in the suppression of NF-
B- and STAT1-
dependent genes could impact the abundance of antimicrobial
peptides in the upper airway. We compared the expression of
selected antimicrobial peptides and cytokines in the nasal cavities
of WT and Il28r
/
mutant mice (Fig. 3F). While Il22 expression
was undetectable in WT mice, it was prominently expressed in
Il28r
/
mutant mice. There was upregulation of the IL-22-
dependent antimicrobial peptide RegIII
in Il28r
/
mutant mice
but no significant differences in S100A8 (33, 34). NGAL (lipoca-
lin), an NF-
B-dependent gene product, was also significantly
upregulated in the absence of IFN-
signaling.
Proteomic analysis of upper airway lavage fluids reflects con-
sequences of IL-28 signaling for epithelial barrier function. A
global analysis of the effects of type III IFN signaling on the pro-
teome of the upper airway was performed. Nasal lavage fluids were
harvested from WT and Il28r
/
mutant mice with and without
influenza virus infection (Fig. 4). At the baseline, we found signif-
icant amounts of the antimicrobial peptide NGAL in Il28r
/
mu-
tant mice, which was likely to have an effect on the overall com-
position of the nasal microbiota (Fig. 4A). Classification of the
families of proteins differentially expressed in WT and Il28r
/
mutant mice indicated major differences in proteins that control
barrier function. Consistent with the observed upregulation of
Il22 in Il28r
/
mutant mice and proposed effects of IL-22 on
mucosal barrier function, we detected differential expression of
proteins that were enriched for actin depolymerization and the
FIG 1 Changes in the upper respiratory microbiome in response to influenza virus infection. WT mice were infected with PR8 or treated with PBS for 3 days.
(a) Total bacterial load (CFU) recovered from the upper airway. n11. (b, c) Shannon-Weiner diversity of bacteria cultured (b) or identified with culture-
independent techniques (c) from the upper airway. n11 (b) or 8 (c). (d) Culture-independent analysis of community composition in the upper airways of WT
mice infected with PR8 or treated with PBS as a control. Relative abundance plots show differences between PR8-infected and PBS-treated mice. The bar plot
shows differences in rarefied reads for the top 10 OTUs (ranked by relative abundance), as indicated by color coding. The top 10 OTUs constitute 99% of the
rarefied reads. n8. (e) Maximum-parsimony tree based on OTU presence in or absence from the upper airways of mice infected with PR8 or treated with PBS.
Branch length is proportional to the number of gain/loss changes in OTUs (scale bar represents 40 changes). Branches representing influenza virus-infected
samples are red, and PBS-inoculated control samples are green. (f) Ordination plot of culture-independent analysis demonstrating segregation of PR8-infected
mice (red) and PBS-treated controls (green); shaded areas are the minimum convex polygons enclosing the data points in each group. P0.004. Data are
representative of at least two independent experiments. Graphs display mean values and standard errors. **, P0.01; *, P0.05.
Type III IFN and Infection
January/February 2016 Volume 7 Issue 1 e01939-15 ®mbio.asm.org 3
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
cytoskeleton, including radixin and moesin, in Il28r
/
mutant
mice (Fig. 4B). Fewer differences were seen in the nasal proteome
of Il28r
/
mutant mice after influenza virus infection, consistent
with the major role of IL-28R in mediation of the local responses
to influenza virus infection (Fig. 4C). One protein differentially
upregulated in Il28r
/
mutant mice in response to influenza vi-
rus infection was AGR2, which interacts with Muc-2 and has an
anti-inflammatory role in the gut (35). Another protein with a
similar expression profile, PP1A, is a phosphatase that plays a role
in HIV-1 transcription (36). Many more differentially expressed
proteins were observed in WT mice following influenza virus in-
fection. These findings suggest a change in the proteome reflecting
consequences of IL-28 signaling in response to viral influenza vi-
rus infection.
Type III IFN signaling promotes MRSA nasal colonization
and pulmonary infection. To determine if the density of the hu-
man commensal/pathogen MRSA USA300 is similarly affected by
IFN-
signaling, we examined the consequences of influenza virus
infection on the nasal carriage of MRSA (Fig. 5A). Following in-
fluenza virus infection, mice were intranasally inoculated with
MRSA in a small volume such that bacteria were not aspirated into
the lungs, animals infected with influenza virus were found to
retain significantly more S. aureus in their nasal tissue, i.e., 67-fold
(at 3 days) and 41-fold (at 7 days) more than PBS-inoculated
controls (Fig. 5A). Influenza virus infection also led to significant
recovery of S. aureus from the lower airways compared to PBS-
inoculated mice that did not aspirate S. aureus into their lungs
(Fig. 5A). Similar results were obtained with mice precolonized
with S. aureus prior to influenza virus, which had a 197-fold
greater nasal bacterial burden (P0.01) and a 21-fold (P0.01)
greater amount of bacteria recovered from the lungs than control
mice (Fig. 5B).
When we repeated the experiment by comparing WT and
Il28r
/
mutant mice, influenza virus-infected Il28r
/
mutant
mice had 40-fold fewer (P0.01) S. aureus bacteria in their nasal
tissue than did infected WT mice (Fig. 5C). Likewise, in their lung
tissue, Il28r
/
mutant mice had 99.7% fewer bacteria than sim-
ilarly infected WT mice (Fig. 5C). Analysis of cytokine production
in bronchoalveolar lavage fluid (BALF) from Il28r
/
mutant
mice showed significant increases in IL-6 and leukemia inhibitory
factor (Fig. 5D) that may have aided in bacterial clearance from
the airway.
To determine if the effects of type III IFN signaling on infection
are specific for the pathogens typically associated with human in-
fection after influenza virus infection, we repeated the influenza
studies by using the opportunistic pathogen Pseudomonas aerugi-
nosa. No increase in subsequent pulmonary infection was ob-
served (Fig. 5E), suggesting that the consequences of IL-28R sig-
naling on the antimicrobial milieu of the upper airway are
somewhat pathogen specific (Fig. 5E). To further confirm that
IL-28 signaling is a major factor in the pathogenesis of pulmonary
MRSA infection after influenza virus infection, PBS-treated or
influenza virus-infected mice were inoculated with a standard in-
tranasal inoculum of S. aureus USA300. As expected, there was
significantly increased susceptibility of WT mice to MRSA pneu-
monia, with a bacterial burden in the lungs 8.3-fold greater than
that of Il28r
/
mutant mice (Fig. 5F).
TABLE 1 Microbiota changes based on culture with influenza and rIL-28
a
Species
No. of mice with bacteria
b
Pvalue
Mean no. of CFU/50
l of BALF
PvalueInfluenza
Influenza
PBS rIL28 Influenza
Influenza
PBS rIL28
Gram positive
Staphylococcus lentus 9 10 1.0000 268 483 0.3910
Staphylococcus xylosus 9 9 1.0000 19 192 0.0264
c
Staphylococcus nepalensis 8 7 1.0000 24 123 0.4698
Enterococcus faecalis 3 4 1.0000 21 21.25 0.3545
Bacillus thuringiensis 4 1 0.3108 8 1 0.2556
Enterococcus gallinarum 2 1 1.0000 3 4 0.9999
Staphylococcus cohnii 2 1 1.0000 11 106 0.9999
Aerococcus urinaeequi 0 2 0.4762 0 13 0.4762
Jeotgalicoccus halotolerans 0 2 0.4762 0 58 0.4762
Gram negative
Klebsiella oxytoca 3 5 0.6594 2 43 0.1883
Enterobacter hormaechei 4 2 0.6351 32 1,224 0.6351
Enterobacter absurie 1 4 0.3108 7 464 0.1454
Gram positive
Staphylococcus xylosus 10 10 1.0000 59 284 0.1639
Staphylococcus lentus 7 9 0.5820 3 230 0.0034
c
Staphylococcus nepalensis 3 9 0.0198
c
4 120 0.0014
c
Staphylococcus cohnii 5 6 1.0000 59 283 0.1948
Enterococcus faecalis 5 5 1.0000 24 244 0.5076
Aerococcus urinaeequi 0 5 0.0325
c
0 289 0.0325
c
Gram negative
Enterobacter hormaechei 4 4 1.0000 57 190 0.8114
Enterobacter absuriae 1 0 1.0000 194 0 0.9999
a
Culture-dependent identification of bacteria recovered from the upper airways of WT mice infected with PR8 or treated with PBS for 3 days or with BSA or rIL28B after 18 h is
shown. Data are representative of at least three independent experiments. For presence data, the Fisher exact test was used. For mean CFU counts, the Mann-Whitney
nonparametric test was used. For the median values and interquartile ranges, see Tables S1 to S4 in the supplemental material.
b
Total n20.
c
Significantly different.
Planet et al.
4®mbio.asm.org January/February 2016 Volume 7 Issue 1 e01939-15
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
DISCUSSION
Influenza virus infection predisposes the host to severe bacterial
pneumonia, which is associated with substantial morbidity and
mortality (1, 3, 37, 38). Numerous mechanisms of immune im-
pairment after influenza virus infection have been described that
contribute to increased severity of infection, as well as increased
susceptibility to smaller inocula of either S. pneumoniae (39, 40) or
S. aureus (13) after influenza virus infection. Our findings suggest
that IFN-
production induced by influenza virus infection is a
major factor contributing to subsequent bacterial pneumonia. In-
duction of IL-28R/STAT1/SOCS1 signaling alters the antimicro-
bial milieu of the upper airway, affecting the expression of antimi-
crobial peptides and cytoskeletal components that regulate the
mucosal barrier function. IFN-
signaling in response to influ-
enza virus infection results in proliferation of the upper airway
microbiota, increasing the likelihood that colonizing pathogens
such as MRSA will be aspirated and cause pneumonia.
Both type I and III IFNs are induced in response to influenza
virus infection, although the type III response, mediated by mu-
cosal IL-28R, has been thought to predominate (41). STAT1 and
STAT3 phosphorylation comprises the major signaling cascade
activated by influenza virus infection contributing to induction of
the expression of over 300 genes that comprise the interferome,
responses that are critical for antiviral activity (17–19). We found
that the induction of STAT1 phosphorylation by influenza virus
was dependent upon IL-28R, consistent with the dominant role of
the type III IFNs in this infection. Our results suggest that, in the
lung during influenza virus infection, the type III IFN pathway
predominantly regulates STAT1 activation. Therefore, while type
I IFN is capable of activating this pathway, perhaps type III IFNs
FIG 2 Effects of type III IFN signaling on upper airway microbiome. (a) WT mice were infected for 3 days with PR8. Nasal lavage fluid was assessed by qRT-PCR
for expression of IFN-
(Il28). n5 (PBS) or 4 (PR8). Il28r
/
mutant mice were infected with PR8 or treated with PBS for 3 days. (b, c) Bacterial load (n8)
(b) and diversity of culture-dependent organisms isolated from upper airways (c). n8 (PBS) or 7 (PR8). (d) Diversity analysis from culture-independent data.
n7. (e) Ordination plot of culture-independent analysis demonstrating lack of segregation of PR8-infected (red) and PBS-treated control (green) Il28r
/
mutant mice. Shaded areas are the minimum convex polygons enclosing the data points in each group. WT mice were treated with 1
g of recombinant murine
IL-28 or with BSA as a control, and the total bacterial load (f) (n10) and diversity of culture-dependent organisms (g) were determined the following day.
n10. (h) Weights of WT and Il28r
/
mutant mice infected with PR8 or treated with PBS. n7. (i) qRT-PCR analysis of viral RNA in the lungs of WT and
Il28r
/
mutant mice infected with PR8 for 3 or 7 days. n6. Data are representative of at least two independent experiments. Graphs display mean values and
standard errors. **, P0.01.
Type III IFN and Infection
January/February 2016 Volume 7 Issue 1 e01939-15 ®mbio.asm.org 5
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
are needed in this model to drive production of the protein. In the
absence of STAT1 signaling and the associated effects on SOCS1,
Il28r
/
mutant mice had significant protection from both en-
dogenous and exogenously acquired organisms, and specifically
from MRSA pneumonia, whether acquired before or after influ-
enza virus infection.
A likely mechanism of the resistance of Il28r
/
mutant mice
to bacterial superinfection is the ability to suppress the nasal mi-
crobiota through increased production of antimicrobial peptides.
IL-22 and IL-22-dependent antimicrobial peptides are important
in the control of the gut microbiota and mucosal barrier integrity
(42). IL-22 functions to ameliorate pulmonary pathology in influ-
enza virus infection and decrease the severity of pneumonia due to
S. pneumoniae superinfection (43). Il28r
/
mutant mice had
markedly increased constitutive IL-22 expression, which likely
contributed to their resistance to superinfection. Increased IL-22
expression may be a compensatory response, as IFN-
and IL-22
have been shown to have synergistic effects in activating the phos-
phorylation of STAT1 (44). Our findings are consistent with this
observation, as in the absence of IL-28R, IL-22 was not sufficient
to activate STAT1. IL-22 itself does not have direct antiviral or
antibacterial effects; thus, much of its beneficial effects are thought
to be due to maintenance of epithelial barrier function (45). We
observed some expected and possible consequences of IL-22 sig-
naling on antimicrobial peptide expression and epithelial barrier
function in the upper airway. There was a substantial increase in
the expression of RegIII
, but not S100A8, both IL-22 dependent
(33, 34), in Il28r
/
mutant mice, consistent with their ability to
control microbial proliferation in the nasal cavity. Additional an-
timicrobial peptides and cytokines with antimicrobial activity
were also found in the nasal secretions of Il28r
/
mutant mice.
Ngal was highly induced in the absence of IFN-
signaling, de-
tected by both quantitative reverse transcription (qRT)-PCR and
proteomic analysis. The suppressive effects of IL-28 signaling on
the expression of these and likely additional antimicrobial pep-
tides help to account for the differences in the microbiotas isolated
from WT and influenza virus-infected mice, differences not ob-
served in Il28r
/
mutant mice.
FIG 3 Mutant (Il28r
/
) mice lack STAT1-associated signaling. (a) Immunoblot assays of P-STAT1, STAT1, P-STAT3, STAT3, and actin in WT and Ifnar
/
and Il28r
/
mutant mice infected with PR8 for 3 or 7 days or treated with PBS as a control. Stat1
/
mutant mice were infected with PR8 or treated with PBS
as a control for 3 days. (b) Bacterial load isolated from the upper airway and Shannon-Weiner analysis of the diversity of recovered bacteria. n4 (WT, PBS),
5 (WT, PR8), 8 (Il28r
/
mutant, PBS), or 7 (Il28r
/
mutant, PR8). (c) WT or Stat1
/
mutant mice were treated with 1
g of recombinant murine IL-28 or
with BSA as a control, and the culture-dependent bacterial load was measured. n6. (d) Immunoblot assays of SOCS1 and actin in WT and Ifnar
/
and
Il28r
/
mutant mice infected with PR8 for 3 days or treated with PBS as a control. (e) qRT-PCR analysis of Socs1 expression in their upper airways. n4 (PBS)
or 5 and 6 (left and right; PR8). (f) qRT-PCR analysis of Il22,Ngal,S100a8, and RegIII
in the upper airways of WT and Il28r
/
mutant mice. Il22 data are shown
for both PBS-treated and PR8-infected mice. Il22,n8; Ngal,n5 and 9, respectively; S100a8,n9 and 8, respectively; RegIII
,n8 and 7, respectively. Data
are representative of at least two independent experiments. Graphs display mean values and standard errors. n.d, not detectable. *, P0.05.
Planet et al.
6®mbio.asm.org January/February 2016 Volume 7 Issue 1 e01939-15
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
The upregulation of several classes of proteins that function in
maintaining the dynamic responses and integrity of the mucosal
barrier were the major changes found in the upper airways of
Il28r
/
mutant mice. In the absence of IL-28R, the nasal mucosal
secretions contained significantly increased amounts of the actin
cytoskeleton and proteins involved in actin depolymerization.
Also significantly increased were the ERM proteins radixin and
moesin that mediate the interactions between the plasma mem-
brane, where pathogens are sensed, and the cytoskeleton (46).
These findings suggest involvement of IL-28R in the suppression
of cytoskeletal dynamics that enable immune cell transmigration,
even in the maintenance of the normal microbiota. Whether these
changes in cytoskeletal proteins are directly due to IL-28R, IL-22,
or some other effectors remains to be established.
The accumulating literature provides a clearer understanding
of the pathogenesis of bacterial superinfection after influenza vi-
rus infection and suggests several targets to prevent this poten-
tially lethal infection. Our data indicate that induction of IFN-
signaling in response to influenza virus infection and the activa-
tion of both STAT1 and its regulator SOCS1, while critical in
regulating the inflammatory response associated with antiviral ac-
tivities, are major factors in enhancing the proliferation of the
FIG 4 Upper airway proteomic changes in WT and Il28r
/
mutant mice. Two micrograms of protein from nasal lavage fluid was proteolytically cleaved by
trypsin and analyzed by LC-MS/MS. (a) The upper airway proteomes of WT and Il28r
/
mutant mice in the resting state were analyzed by an MS-based
approach. Relative protein abundance among biological samples is expressed by spectral counts on a log scale. The color scale bar indicates the range of protein
expression levels. Each column represents an individual mouse. Significantly different proteins (P0.05) are presented and clustered by protein expression
profiles among biological samples. (b) Functional annotation of proteins differentially expressed in WT and Il28r
/
mutant mice. Shown are functional
categories enriched and statistically significant within the data set. Data were analyzed with David. (c) WT and Il28r
/
mutant mice were infected with PR8 or
treated with PBS as a control, and upper airway nasal lavage fluid was analyzed by an MS-based approach. Each column represents an individual mouse.
Representative data are shown.
Type III IFN and Infection
January/February 2016 Volume 7 Issue 1 e01939-15 ®mbio.asm.org 7
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
nasal microbiome, which often includes potential pathogens. Na-
sal IL-22 expression is limited under resting conditions and not
induced by influenza virus infection. Its protective effects in in-
ducing the expression of antimicrobial peptides and in activating
dynamic responses of the mucosal barrier to facilitate signaling
and immune cell recruitment could be exploited therapeutically.
Strategies already in place, such as universal MRSA decolonization
(11), could be more widely implemented in the setting of early
influenza virus infection and, combined with the development of
local immunomodulators of STAT1 or therapeutic delivery of IL-
22, may be useful in preventing bacterial superinfection in influ-
enza.
MATERIALS AND METHODS
Ethics statement. Animal work in this study was carried out in strict
accordance with the recommendations in the Guide for the Care and Use
of Laboratory Animals of the National Institutes of Health, the Animal
Welfare Act, and U.S. federal law. The protocol was approved by the
Institutional Animal Care and Use Committee of Columbia University
(protocol AAAF4851).
FIG 5 Type III IFN signaling contributes to S. aureus colonization of the upper airway and development of bacterial pneumonia. (a) WT C57BL/6J mice
were infected intranasally with 50 PFU of influenza virus PR8 or treated with PBS for 7 days prior to the local intranasal application of 10
8
CFU of S. aureus
USA300. Mice were euthanized 3 and 7 days after S. aureus infection. Bacterial counts in mouse nasal and lung homogenates were assessed. n6 (3-day
data), 4 (PBS, 7 days), or 3 (PR8, 7 days). (b) WT mice were intranasally inoculated locally with 10
8
CFU of S. aureus USA300 7 days before intranasal
infection with 50 PFU of influenza virus PR8 for a further 7 days before euthanasia. Bacterial counts in mouse nasal and lung homogenates were assessed.
n5. (c) WT and Il28r
/
mutant mice were infected with PR8 or treated with PBS in the lungs for 7 days prior to the local administration of S. aureus
(10
8
CFU). Mice were euthanized 3 days later. Bacterial counts in nasal and lung homogenates are shown. n6 (WT and Il28r
/
, PBS) or 4 (Il28r
/
,
PR8). (d) Cytokine differences in BALF from mice nasally infected with PR8 and S. aureus (c). n3 (uninfected [UN]), 6 (WT), or 4 (Il28r
/
). (e) WT
mice were intranasally infected with 10
7
CFU of P. aeruginosa PAK in the lungs for 24 h after 7 days of PR8 infection or treatment with PBS as a control
and 24 h after PAK inoculation. Bacterial counts in the lungs are shown. n7. (f) Counts of S. aureus bacteria in the lungs of WT and Il28r
/
mutant
mice after 7 days of PR8 infection or treatment with PBS as a control and 24 h after intranasal inoculation with S. aureus USA300 (10
7
CFU) under
anesthesia. n13 (WT, PBS), 11 (WT, PR8), or 7 (Il28r
/
). Data are representative of at least two independent experiments. Graphs display mean values
and standard errors. ***, P0.001; **, P0.01; *, P0.05.
Planet et al.
8®mbio.asm.org January/February 2016 Volume 7 Issue 1 e01939-15
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
Viral and bacterial growth. Mouse-adapted influenza virus A/Puerto
Rico/8/34 (PR8; H1N1) was grown on Madin-Darby canine kidney
cells and stored as previously described (13). S. aureus (LAC USA300)
and P. aeruginosa (PAK) were grown to stationary phase overnight at
37°C in Luria-Bertani broth, diluted 1:100 in the morning, and grown
to an optical density at 600 nm of 1.0 (USA300) or 0.50 (PAK) for
infection (25).
Animal models. Seven-week-old C57BL/6J WT mice were bred in
house as previously described (24). Il28r
/
mutant mice were provided
by Bristol Myers Squibb and bred in house, and Ifnar
/
mutant mice
have been described previously (25, 47). Both Ifnar
/
and Il28r
/
mu-
tant mice are bred on the C57BL/6J background. Stat1
/
mutant mice
were generously provided by Christian Schindler, Columbia University
(C57BL/6J background), and from Taconic (129S6/SvEv background).
Mice were anesthetized with ketamine and xylazine and intranasally in-
oculated with influenza virus (50 PFU/mouse) or PBS with 0.1% bovine
serum albumin (BSA) as a control. To determine the effect of IFN-
on
the nasal flora, mice were intranasally treated with 1
g of recombinant
mIL-28B (PBL Assay Science; 1 EU/
g) in 50
l or with PBS-BSA as a
control, resulting in delivery to both the upper and lower respiratory
tracts. Nasal flora, upper airway RNA, and lung tissue were harvested
3 days following infection. Nasal lavage was performed by inserting a
sterile angiocatheter intratracheally, flushing with sterile UV-treated PBS,
and collecting lavage fluid from the nose. Selected animals were reinfected
7 days following initial infection with exponentially growing S. aureus
USA300 (4 10
7
CFU/mouse). These mice were sacrificed 24 h following
bacterial infection, and the numbers of bacteria present in their lungs were
determined as previously described (24). Colonization studies were per-
formed by intranasal inoculation of 10
8
CFU of S. aureus in a small volume
(10
l into the nares without anesthesia) to limit infection to the naso-
pharynx. Colonization studies were performed for 3 or 7 days postinfec-
tion after a 1-week influenza virus infection. S. aureus was applied to the
nares of precolonized mice 7 days prior to influenza virus infection. Bac-
teria were isolated from homogenized nasal septum tissue. Infection of
mice while they were under anesthesia was conducted with 1 10
7
CFU of
exponentially growing P. aeruginosa PAK in 50
l.
Culture-dependent analysis of flora. For each sample, 2 ml of mouse
nasal lavage fluid was centrifuged at 1,400 rpm for 5 min. A 1.7-ml volume
of the supernatant was removed, and the pellet was resuspended in the
remaining 300
l. Fifty microliters of each sample was plated on CHRO-
Magar Orientation and CHROMagar S. aureus plates and incubated over-
night at 37°C. For colony PCR from CHROMagar plates, the 16S rRNA
gene sequence was amplified with primers Bact-8F (5=-AGAGTTTGATC
CTGGCTCAG-3=) and Bact-1391R (5=-GACGGGCGGTGTGTRCA-3=).
At least two colonies per unique color/morphology per plate were picked.
The PCR used Taq 5master mix (New England Biolabs) and 0.5
Mof
forward and reverse primers. The cycling conditions were 95°C for 1 min;
30 cycles of 95°C for 30 s, 60°C for 1 min, and 68°C for 1 min; 68°C for
5 min; and holding at 4°C. The amplified 16S rRNA gene sequences were
sequenced by GeneWiz, Inc., with the Bact-8F primer. The resulting se-
quences were identified by 16S rRNA gene sequence (Bacteria and Ar-
chaea) nucleotide BLAST (basic local alignment search tool) searching at
http://blast.ncbi.nlm.nih.gov/Blast.cgi. The resulting alignments were
viewed, and the highest-scoring alignment with an E (error) value of 0.0
was taken as the species identification. For each sample, the identifying
information recorded for each 16S rRNA gene PCR template colony was
used to identify the assumed species of all colonies of that unique color on
both CHROMagar plates. Using the calculated number of CFU per species
for each nasal lavage fluid sample, the mean total CFU count, taxonomic
richness, Shannon-Weiner diversity index, and species evenness were cal-
culated for each experimental condition, as were the mean CFU count for
each species and the species prevalence.
Culture-independent analysis. Nasal lavage fluid microbial DNA was
isolated with the Powersoil DNA Isolation kit (Mo Bio Laboratories, Inc.,
Carlsbad, CA) according to the manufacturer’s instructions with modifi-
cations. For each sample, 200
l of mouse nasal lavage fluid was used.
Subsequently, the V4 region of the 16S rRNA gene was PCR amplified in
triplicate as described previously (48) with primer pair 515f/806r. PCR
amplicons were sequenced on an Illumina MiSeq at the University of
Colorado Next Generation Sequencing Facility. The raw, paired-end
reads were merged, quality filtered, and clustered into operational taxo-
nomic units (OTUs) at the 97% identity level with the UPARSE pipeline
(49). A taxonomic identity was assigned to each OTU with the QIIME
(50) implementation of the Ribosomal Database Project classifier (51)
and the August 2013 release of the GreenGenes database (52). To account
for variability in sequence depth, communities were rarefied by randomly
selecting 10,000 sequences from each sample. Bray-Curtis dissimilarities
in community structure across our sample set were calculated in the vegan
package in R v. 3.0.0 after log transformation of relative OTU abundances
(53). The resulting DNA sequences and OTU table are available for down-
load at http://dx.doi.org/10.5061/dryad.v1ff0.
Presence/absence analysis. For the PA dendrogram analysis, a pres-
ence/absence matrix was constructed where any number of rarefied reads
for a particular OTU was designated present. Each sample was treated as a
taxon, and each OTU was treated as a binary character. The dendrogram
was then constructed on the basis of a maximum-parsimony algorithm as
implemented in the program PAUP (54). All characters and character
state transformations were given equal weight. We used Mesquite (55) to
define specific OTUs that were exclusively present in or absent from the
cluster of samples defined by influenza virus. All but one virus positive-
sample clustered on the basis of the parsimony analysis. To further ex-
plore characteristic attributes of the virus-positive samples, we performed
the same analysis by forcing all virus-positive samples into the same
group.
ELISA and immunoblotting. BALF was analyzed for cytokine and
chemokine content by enzyme-linked immunosorbent assay (ELISA)
(R&D Biosystems, PBL Assay Science, or eBioscience) and multiplex anal-
ysis (Eve Technologies). Lung homogenates were lysed in radioimmuno-
precipitation assay buffer (20 mM Tris-HCl, 150 mM NaCl, 1 mM EDTA,
1 mM EGTA, 1% Triton X-100) with Halt protease and phosphatase
inhibitor (Pierce). Protein separation, transfer, and immunoblotting were
performed as described previously (26). Anti-phospho-STAT1 (Abcam),
-STAT1, -phospho-STAT3, -STAT3, -SOCS1 (Cell Signaling), and -
-
actin (Sigma) antibodies were used to measure expression.
qRT-PCR. RNA was recovered with the PureLink RNA minikit (Life
Technologies). Briefly, the upper airways were washed with RNA lysis
buffer by inserting a catheter into the trachea and pushing the lysis
buffer through the nares. RNA was purified according to the manufac-
turer’s instructions. cDNA was synthesized with the High Capacity
cDNA RT kit (Applied Biosystems). qRT-PCR was performed with
Power SYBR green PCR master mix in a StepOne Plus thermal cycler
(Applied Biosystems).
-Actin was used to normalize samples. Prim-
ers for actin,Ifnb, and Ifnl have been described previously (24). The
following primers were used: HA,5=-ATGCAGACAATATGTATAGG
C-3=(sense) and 5=-GATACTGAGCTCAATTGCTC (antisense);
mReg3
,5=-ACTCCCTGAAGAATATACCCTCC-3=(sense) and 5=-C
GCTATTGAGCACAGATACGAG-3=(antisense); Lipocalin-2 (Ngal),
5=-TGCAAGTGGCCACCACGGAG-3=(sense) and 5=-GCATTGGTC
GGTGGGGACAGAGA-3=(antisense); S100a8,5=-AAATCACCATGC
CCTCTACAAG-3=(sense) and 5=-CCCACTTTTATCACCATCGCA
A-3=(antisense); SOCS1,5=-CTGCGGCTTCTATTGGGGAC-3=
(sense) and 5=-AAAAGGCAGTCGAAGGTCTCG-3=(antisense); Il22,
5=-ATGAGTTTTTCCCTTATGGGGAC-3=(sense) and 5=-GCTGGAA
GTTGGACACCTCAA-3=(antisense).
Shotgun proteomic analysis. (i) Materials. High-performance liquid
chromatography (HPLC) grade LC buffers, dithiothreitol, acetonitrile
(ACN), ammonium bicarbonate, trifluoroacetic acid, and iodoacetamide
were purchased from Thermo, Fisher Scientific (Waltham, MA). Trypsin
Gold, Mass Spectrometry Grade, was purchased from Promega (Madison,
Type III IFN and Infection
January/February 2016 Volume 7 Issue 1 e01939-15 ®mbio.asm.org 9
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
WI). Nanopure water was prepared with a Milli-Q water purification
system (Millipore, Billerica, MA).
(ii) Sample preparation. Proteins were precipitated from 500
lof
mouse nasal lavage fluids with methanol-chloroform and resuspended in
20
l of 4 M urea in 50 mM ammonium bicarbonate. The protein con-
centration in the mouse lavage fluid was determined by the EZQ Protein
Quantification Assay (Life Technology Corp.). Two micrograms of pro-
tein from mouse lavage fluid was digested with 150 ng of trypsin (1:40)
along with 2 mM CaCl
2
and incubated at 37°C for 16 h. Samples were
centrifuged for 30 min at 14,000 rpm, and the cleared supernatants
were transferred to fresh tubes to be acidified with 90% formic acid
(FA) (2% final) to stop proteolysis. The soluble peptide mixtures were
collected for liquid chromatography-tandem mass spectrometry (LC-
MS/MS) analysis.
(iii) LC-MS/MS analysis. The concentrated peptide mixture was re-
constituted in a solution of 2% ACN–2% FA for MS analysis. Peptides
were loaded with the autosampler directly onto a 2-cm C
18
PepMap pre-
column and eluted from the ID PepMap RSLC C
18
2-
m column (50 cm
by75
m) with a Thermo Dionex 3000 and a 98-min gradient ranging
from 2% buffer B to 30% buffer B (100% acetonitrile, 0.1% FA). The
gradient was switched from 30% to 85% buffer B over 5 min and held
constant for 1 min. Finally, the gradient was changed from 85% buffer B to
98% buffer A (100% water, 0.1% FA) over 2 min and then held constant at
98% buffer A for 25 min. The application of a 2.0-kV distal voltage elec-
trosprayed the eluting peptides directly into the mass spectrometer
equipped with an Easy-Spray Source (Thermo Finnigan, San Jose, CA).
The full mass spectra of the peptides were recorded over an m/zrange of
400 to 1,500 at a 120,000 resolution, followed by MS/MS collision-
induced dissociation events for a total of a 3-s cycle. Charge state-
dependent screening was turned off, and peptides with charge states of 2
to 6 were analyzed. Mass spectrometer scanning functions and HPLC
gradients were controlled by the Xcalibur data system (Thermo Finnigan,
San Jose, CA). Three technical replicates of each sample were run, and
MS/MS data from technical replicates were merged for a subsequent da-
tabase search.
(iv) Database search and interpretation of MS/MS data. Tandem
mass spectra from .raw files were searched against a human protein data-
base with the Proteome discoverer 1.4 (Thermo Finnigan, San Jose, CA).
The Proteome Discoverer application extracts relevant MS/MS spectra
from the .raw file and determines the precursor charge state and the qual-
ity of the fragmentation spectrum. The Proteome Discoverer probability-
based scoring system rates the relevance of the best matches found by the
SEQUEST algorithm (56). The mouse database was downloaded as
FASTA-formatted sequences from the UniProt protein database (released
in December 2014) (57). The peptide mass search tolerance was set to
10 ppm. A minimum sequence length of 7 amino acid residues was re-
quired. Only fully tryptic peptides were considered. To calculate confi-
dence levels and false-positivity rates (FDR), Proteome Discoverer gener-
ates a decoy database containing reverse sequences of the nondecoy
protein database and performs the search against this concatenated data-
base (nondecoy plus decoy) (58). The discriminant score was set at a 1%
FDR determined on the basis of the number of accepted decoy database
peptides to generate protein lists for this study. Spectral counts used to
identify each protein were used for expression profiling analysis. Qlucore
Omics Explorer (Qlucore AB, Sweden) was used to perform statistical
analysis of quantifiable proteins among biological replicates (ttest, P
0.05). Differentially expressed proteins were analyzed with David (59).
Statistics. Significance of data was determined by a nonparametric
Mann-Whitney test. Multiple comparisons were performed by analysis of
variance (ANOVA) with a Bonferroni comparison posttest. These tests
were conducted with GraphPad Prism software, and significance was de-
fined as P0.05.
For culture-independent community analyses, principal-coordinate
analysis was used to visualize pairwise similarities in composition. Permu-
tational multivariate ANOVA (PERMANOVA, with 999 permutations)
was used to test whether influenza virus-infected mice harbored bacterial
communities that were significantly different in composition from those
found in controls. The ordination and PERMANOVA analyses were con-
ducted in R v. 3.0.0.
SUPPLEMENTAL MATERIAL
Supplemental material for this article may be found at http://mbio.asm.org/
lookup/suppl/doi:10.1128/mBio.01939-15/-/DCSupplemental.
Table S1, PPT file, 0.3 MB.
Table S2, PPT file, 0.2 MB.
Table S3, PPT file, 0.2 MB.
Table S4, PPT file, 0.2 MB.
Table S5, PPT file, 0.2 MB.
Table S6, PPT file, 0.2 MB.
Table S7, PPT file, 0.2 MB.
Table S8, PPT file, 0.2 MB.
ACKNOWLEDGMENTS
P.J.P., D.P., T.S.C., and A.S.P. designed the experiments. P.J.P., H.S.,
T.H., N.F., C.R., and D.O. analyzed nasal flora. D.P., T.S.C., and P.J.P
performed in vivo experiments and analyzed host signaling pathways.
E.I.C. conducted proteomic analysis. P.J.P., D.P., T.S.C., and A.S.P. wrote
the manuscript.
FUNDING INFORMATION
HHS | National Institutes of Health (NIH) provided funding to Paul J.
Planet under grant number K08AI101005. HHS | National Institutes of
Health (NIH) provided funding to Alice S. Prince under grant number
HL079395. HHS | National Institutes of Health (NIH) provided funding
to Emily Chen under grant number P30CA013696. HHS | National Insti-
tutes of Health (NIH) provided funding to Dane Parker under grant num-
ber R56HL125653.
This work was supported by funding from the NIH (K08AI101005) and
the John M. Driscoll Jr. MD Childrens Fund Scholarship to PJP, NIH
(R56HL125653) to DP, Parker B. Francis Fellowship to TC, NIH
(P30CA013696-39S3) to EIC, and NIH R01HL079395 to AP.
REFERENCES
1. Morens DM, Taubenberger JK, Fauci AS. 2008. Predominant role of
bacterial pneumonia as a cause of death in pandemic influenza: implica-
tions for pandemic influenza preparedness. J Infect Dis 198:962–970.
http://dx.doi.org/10.1086/591708.
2. Randolph AG, Vaughn F, Sullivan R, Rubinson L, Thompson BT, Yoon
G, Smoot E, Rice TW, Loftis LL, Helfaer M, Doctor A, Paden M, Flori
H, Babbitt C, Graciano AL, Gedeit R, Sanders RC, Giuliano JS, Zim-
merman J, Uyeki TM, Pediatric Acute Lung Injury and Sepsis Investi-
gator’s Network, National Heart Lung and Blood Institute ARDS
Clinical Trials Network. 2011. Critically ill children during the
2009–2010 influenza pandemic in the United States. Pediatrics 128:
e1450-1458. http://dx.doi.org/10.1542/peds.2011-0774.
3. Chertow DS, Memoli MJ. 2013. Bacterial coinfection in influenza: a
grand rounds review. JAMA 309:275–282. http://dx.doi.org/10.1001/
jama.2012.194139.
4. Sun K, Metzger DW. 2008. Inhibition of pulmonary antibacterial defense
by interferon-gamma during recovery from influenza infection. Nat Med
14:558–564. http://dx.doi.org/10.1038/nm1765.
5. Shahangian A, Chow EK, Tian X, Kang JR, Ghaffari A, Liu SY, Belperio
JA, Cheng G, Deng JC. 2009. Type I IFNs mediate development of postin-
fluenza bacterial pneumonia in mice. J Clin Invest 119:1910 –1920. http://
dx.doi.org/10.1172/JCI35412.
6. Kudva A, Scheller EV, Robinson KM, Crowe CR, Choi SM, Slight SR,
Khader SA, Dubin PJ, Enelow RI, Kolls JK, Alcorn JF. 2011. Influenza
A inhibits Th17-mediated host defense against bacterial pneumonia in
mice. J Immunol 186:1666–1674. http://dx.doi.org/10.4049/
jimmunol.1002194.
7. Wertheim HF, Vos MC, Ott A, van Belkum A, Voss A, Kluytmans JA,
van Keulen PH, Vandenbroucke-Grauls CM, Meester MH, Verbrugh
HA. 2004. Risk and outcome of nosocomial Staphylococcus aureus bacte-
Planet et al.
10 ®mbio.asm.org January/February 2016 Volume 7 Issue 1 e01939-15
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
raemia in nasal carriers versus non-carriers. Lancet 364:703–705. http://
dx.doi.org/10.1016/S0140-6736(04)16897-9.
8. Stevens AM, Hennessy T, Baggett HC, Bruden D, Parks D, Klejka J.
2010. Methicillin-resistant Staphylococcus aureus carriage and risk factors
for skin infections, southwestern Alaska, USA. Emerg Infect Dis 16:
797–803. http://dx.doi.org/10.3201/eid1605.091851.
9. Corne P, Marchandin H, Jonquet O, Campos J, Bañuls AL. 2005.
Molecular evidence that nasal carriage of Staphylococcus aureus plays a role
in respiratory tract infections of critically ill patients. J Clin Microbiol
43:3491–3493. http://dx.doi.org/10.1128/JCM.43.7.3491-3493.2005.
10. Tilahun B, Faust AC, McCorstin P, Ortegon A. 2015. Nasal colonization
and lower respiratory tract infections with methicillin-resistant Staphylo-
coccus aureus. Am J Crit Care 24:8–12. http://dx.doi.org/10.4037/
ajcc2015102.
11. Huang SS, Septimus E, Kleinman K, Moody J, Hickok J, Avery TR,
Lankiewicz J, Gombosev A, Terpstra L, Hartford F, Hayden MK,
Jernigan JA, Weinstein RA, Fraser VJ, Haffenreffer K, Cui E, Kaganov
RE, Lolans K, Perlin JB, Platt R, CDC Prevention Epicenters Program,
AHRQ DECIDE Network, Healthcare-Associated Infections Program.
2013. Targeted versus universal decolonization to prevent ICU infection.
N Engl J Med 368:2255–2265. http://dx.doi.org/10.1056/
NEJMoa1207290.
12. Gorwitz RJ, Kruszon-Moran D, McAllister SK, McQuillan G, McDou-
gal LK, Fosheim GE, Jensen BJ, Killgore G, Tenover FC, Kuehnert MJ.
2008. Changes in the prevalence of nasal colonization with Staphylococcus
aureus in the United States, 2001–2004. J Infect Dis 197:1226 –1234. http://
dx.doi.org/10.1086/533494.
13. Lee MH, Arrecubieta C, Martin FJ, Prince A, Borczuk AC, Lowy FD.
2010. A postinfluenza model of Staphylococcus aureus pneumonia. J Infect
Dis 201:508–515. http://dx.doi.org/10.1086/650204.
14. Iverson AR, Boyd KL, McAuley JL, Plano LR, Hart ME, McCullers JA.
2011. Influenza virus primes mice for pneumonia from Staphylococcus
aureus. J Infect Dis 203:880 –888. http://dx.doi.org/10.1093/infdis/jiq113.
15. McCullers JA. 2014. The co-pathogenesis of influenza viruses with bacte-
ria in the lung. Nat Rev Microbiol 12:252–262. http://dx.doi.org/10.1038/
nrmicro3231.
16. Mina MJ, McCullers JA, Klugman KP. 2014. Live attenuated influenza
vaccine enhances colonization of Streptococcus pneumoniae and Staphylo-
coccus aureus in mice. mBio 5:e01040-13. http://dx.doi.org/10.1128/
mBio.01040-13.
17. Der SD, Zhou A, Williams BR, Silverman RH. 1998. Identification of
genes differentially regulated by interferon alpha, beta, or gamma using
oligonucleotide arrays. Proc Natl Acad SciUSA95:15623–15628. http://
dx.doi.org/10.1073/pnas.95.26.15623.
18. Gupta S, Yan H, Wong LH, Ralph S, Krolewski J, Schindler C. 1996. The
SH2 domains of Stat1 and Stat2 mediate multiple interactions in the trans-
duction of IFN-alpha signals. EMBO J 15:1075–1084.
19. Rusinova I, Forster S, Yu S, Kannan A, Masse M, Cumming H,
Chapman R, Hertzog PJ. 2013. Interferome v2.0: an updated database of
annotated interferon-regulated genes. Nucleic Acids Res 41:
D1040–D1046. http://dx.doi.org/10.1093/nar/gks1215.
20. Parker D, Prince A. 2011. Type I interferon response to extracellular
bacteria in the airway epithelium. Trends Immunol 32:582–588. http://
dx.doi.org/10.1016/j.it.2011.09.003.
21. Levy DE, Marié IJ, Durbin JE. 2011. Induction and function of type I and
III interferon in response to viral infection. Curr Opin Virol 1:476486.
http://dx.doi.org/10.1016/j.coviro.2011.11.001.
22. Sommereyns C, Paul S, Staeheli P, Michiels T. 2008. IFN-lambda (IFN-
lambda) is expressed in a tissue-dependent fashion and primarily acts on
epithelial cells in vivo. PLoS Pathog 4:e1000017. http://dx.doi.org/
10.1371/journal.ppat.1000017.
23. Blazek K, Eames HL, Weiss M, Byrne AJ, Perocheau D, Pease JE, Doyle
S, McCann F, Williams RO, Udalova IA. 2015. IFN-
resolves inflam-
mation via suppression of neutrophil infiltration and IL-1
production. J
Exp Med 212:845–853. http://dx.doi.org/10.1084/jem.20140995.
24. Cohen TS, Prince AS. 2013. Bacterial pathogens activate a common in-
flammatory pathway through IFN
regulation of PDCD4. PLoS Pathog
9:e1003682. http://dx.doi.org/10.1371/journal.ppat.1003682.
25. Parker D, Planet PJ, Soong G, Narechania A, Prince A. 2014. Induction
of type I interferon signaling determines the relative pathogenicity of
Staphylococcus aureus strains. PLoS Pathog 10:e1003951. http://
dx.doi.org/10.1371/journal.ppat.1003951.
26. Martin FJ, Gomez MI, Wetzel DM, Memmi G, O’Seaghdha M, Soong
G, Schindler C, Prince A. 2009. Staphylococcus aureus activates type I IFN
signaling in mice and humans through the Xr repeated sequences of pro-
tein A. J Clin Invest 119:1931–1939. http://dx.doi.org/10.1172/JCI35879.
27. Sheppard P, Kindsvogel W, Xu W, Henderson K, Schlutsmeyer S,
Whitmore TE, Kuestner R, Garrigues U, Birks C, Roraback J, Ostrander
C, Dong D, Shin J, Presnell S, Fox B, Haldeman B, Cooper E, Taft D,
Gilbert T, Grant FJ, Tackett M, Krivan W, McKnight G, Clegg C, Foster
D, Klucher KM. 2003. IL-28, IL-29 and their class II cytokine receptor
IL-28R. Nat Immunol 4:63–68. http://dx.doi.org/10.1038/ni873.
28. Leitner NR, Lassnig C, Rom R, Heider S, Bago-Horvath Z, Eferl R,
Müller S, Kolbe T, Kenner L, Rülicke T, Strobl B, Müller M. 2014.
Inducible, dose-adjustable and time-restricted reconstitution of STAT1
deficiency in vivo. PLoS One 9:e86608. http://dx.doi.org/10.1371/
journal.pone.0086608.
29. Odendall C, Dixit E, Stavru F, Bierne H, Franz KM, Durbin AF,
Boulant S, Gehrke L, Cossart P, Kagan JC. 2014. Diverse intracellular
pathogens activate type III interferon expression from peroxisomes. Nat
Immunol 15:717–726. http://dx.doi.org/10.1038/ni.2915.
30. Prêle CM, Woodward EA, Bisley J, Keith-Magee A, Nicholson SE, Hart
PH. 2008. SOCS1 regulates the IFN but not NFkappaB pathway in TLR-
stimulated human monocytes and macrophages. J Immunol 181:
80188026. http://dx.doi.org/10.4049/jimmunol.181.11.8018.
31. Piganis RA, De Weerd NA, Gould JA, Schindler CW, Mansell A,
Nicholson SE, Hertzog PJ. 2011. Suppressor of cytokine signaling (SOCS)
1 inhibits type I interferon (IFN) signaling via the interferon alpha recep-
tor (IFNAR1)-associated tyrosine kinase Tyk2. J Biol Chem 286:
33811–33818. http://dx.doi.org/10.1074/jbc.M111.270207.
32. Tokumaru S, Sayama K, Shirakata Y, Komatsuzawa H, Ouhara K,
Hanakawa Y, Yahata Y, Dai X, Tohyama M, Nagai H, Yang L, Hi-
gashiyama S, Yoshimura A, Sugai M, Hashimoto K. 2005. Induction of
keratinocyte migration via transactivation of the epidermal growth factor
receptor by the antimicrobial peptide LL-37. J Immunol 175:4662–4668.
http://dx.doi.org/10.4049/jimmunol.175.7.4662.
33. Kinnebrew MA, Ubeda C, Zenewicz LA, Smith N, Flavell RA, Pamer
EG. 2010. Bacterial flagellin stimulates Toll-like receptor 5-dependent de-
fense against vancomycin-resistant Enterococcus infection. J Infect Dis
201:534–543. http://dx.doi.org/10.1086/650203.
34. Zindl CL, Lai JF, Lee YK, Maynard CL, Harbour SN, Ouyang W,
Chaplin DD, Weaver CT. 2013. IL-22-producing neutrophils contribute
to antimicrobial defense and restitution of colonic epithelial integrity dur-
ing colitis. Proc Natl Acad SciUSA110:12768–12773. http://dx.doi.org/
10.1073/pnas.1300318110.
35. Park SW, Zhen G, Verhaeghe C, Nakagami Y, Nguyenvu LT, Barczak
AJ, Killeen N, Erle DJ. 2009. The protein disulfide isomerase AGR2 is
essential for production of intestinal mucus. Proc Natl Acad SciUSA
106:69506955. http://dx.doi.org/10.1073/pnas.0808722106.
36. Ammosova T, Jerebtsova M, Beullens M, Voloshin Y, Ray PE, Kumar
A, Bollen M, Nekhai S. 2003. Nuclear protein phosphatase-1 regulates
HIV-1 transcription. J Biol Chem 278:32189–32194. http://dx.doi.org/
10.1074/jbc.M300521200.
37. Hall MW, Geyer SM, Guo CY, Panoskaltsis-Mortari A, Jouvet P,
Ferdinands J, Shay DK, Nateri J, Greathouse K, Sullivan R, Tran T,
Keisling S, Randolph AG, Pediatric Acute Lung Injury and Sepsis
Investigators (PALISI) Network, PICFlu Study Investigators. 2013. In-
nate immune function and mortality in critically ill children with
influenza: a multicenter study. Crit Care Med 41:224–236.
38. Ziakas PD, Anagnostou T, Mylonakis E. 2014. The prevalence and sig-
nificance of methicillin-resistant Staphylococcus aureus colonization at ad-
mission in the general ICU setting: a meta-analysis of published studies.
Crit Care Med 42:433–444. http://dx.doi.org/10.1097/
CCM.0b013e3182a66bb8.
39. Siegel SJ, Roche AM, Weiser JN. 2014. Influenza promotes pneumococ-
cal growth during coinfection by providing host sialylated substrates as a
nutrient source. Cell Host Microbe 16:55–67. http://dx.doi.org/10.1016/
j.chom.2014.06.005.
40. Iwasaki A, Pillai PS. 2014. Innate immunity to influenza virus infection.
Nat Rev Immunol 14:315–328. http://dx.doi.org/10.1038/nri3665.
41. Jewell NA, Cline T, Mertz SE, Smirnov SV, Flaño E, Schindler C,
Grieves JL, Durbin RK, Kotenko SV, Durbin JE. 2010. Lambda inter-
feron is the predominant interferon induced by influenza A virus infection
in vivo. J Virol 84:11515–11522. http://dx.doi.org/10.1128/JVI.01703-09.
42. Shih VF, Cox J, Kljavin NM, Dengler HS, Reichelt M, Kumar P, Rangell
L, Kolls JK, Diehl L, Ouyang W, Ghilardi N. 2014. Homeostatic IL-23
Type III IFN and Infection
January/February 2016 Volume 7 Issue 1 e01939-15 ®mbio.asm.org 11
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
receptor signaling limits Th17 response through IL-22-mediated contain-
ment of commensal microbiota. Proc Natl Acad SciUSA111:
13942–13947. http://dx.doi.org/10.1073/pnas.1323852111.
43. Ivanov S, Renneson J, Fontaine J, Barthelemy A, Paget C, Fernandez
EM, Blanc F, De Trez C, Van Maele L, Dumoutier L, Huerre MR, Eberl
G, Si-Tahar M, Gosset P, Renauld JC, Sirard JC, Faveeuw C, Trottein F.
2013. Interleukin-22 reduces lung inflammation during influenza A virus
infection and protects against secondary bacterial infection. J Virol 87:
6911–6924. http://dx.doi.org/10.1128/JVI.02943-12.
44. Hernández PP, Mahlakõiv T, Yang I, Schwierzeck V, Nguyen N, Gu-
endel F, Gronke K, Ryffel B, Hölscher C, Dumoutier L, Renauld JC,
Suerbaum S, Staeheli P, Diefenbach A. 2015. Interferon-lambda and
interleukin 22 act synergistically for the induction of interferon-
stimulated genes and control of rotavirus infection. Nat Immunol 16:
698–707. http://dx.doi.org/10.1038/ni.3180.
45. Sonnenberg GF, Fouser LA, Artis D. 2010. Functional biology of the
IL-22-IL-22R pathway in regulating immunity and inflammation at bar-
rier surfaces. Adv Immunol 107:1–29. http://dx.doi.org/10.1016/B978-0
-12-381300-8.00001-0.
46. Arpin M, Chirivino D, Naba A, Zwaenepoel I. 2011. Emerging role for
ERM proteins in cell adhesion and migration. Cell Adh Migr 5:199–206.
http://dx.doi.org/10.4161/cam.5.2.15081.
47. Parker D, Prince A. 2012. Staphylococcus aureus induces type I IFN sig-
naling in dendritic cells via TLR9. J Immunol 189:40404046.
48. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer
N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA,
Smith G, Knight R. 2012. Ultra-high-throughput microbial community
analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6:1621–1624.
http://dx.doi.org/10.1038/ismej.2012.8.
49. Edgar RC. 2013. UPARSE: highly accurate OTU sequences from micro-
bial amplicon reads. Nat Methods 10:996 –998. http://dx.doi.org/10.1038/
nmeth.2604.
50. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD,
Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA,
Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D,
Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters
WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. 2010. QIIME
allows analysis of high-throughput community sequencing data. Nat
Methods 7:335–336. http://dx.doi.org/10.1038/nmeth.f.303.
51. Wang Q, Garrity GM, Tiedje JM, Cole JR. 2007. Naive Bayesian classifier
for rapid assignment of rRNA sequences into the new bacterial taxonomy.
Appl Environ Microbiol 73:5261–5267. http://dx.doi.org/10.1128/
AEM.00062-07.
52. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst
A, Andersen GL, Knight R, Hugenholtz P. 2012. An improved Green-
genes taxonomy with explicit ranks for ecological and evolutionary anal-
yses of bacteria and archaea. ISME J 6:610618. http://dx.doi.org/
10.1038/ismej.2011.139.
53. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB,
Simpson GL, Solymos P, Stevens MHH, Wagner H. 2013. Vegan: com-
munity ecology package. R package version 2.0-7. R Foundation for Sta-
tistical Computing, Vienna, Austria. http://CRAN.R-project.org/
packagevegan.
54. Wilgenbusch JC, Swofford D. 2003. Inferring evolutionary trees with
PAUP*. Curr Protoc Bioinformatics Chapter 6:Unit 6.4. http://
dx.doi.org/10.1002/0471250953.bi0604s00.
55. Maddison WP, Maddison DR. 2015. Mesquite: a modular system for
evolutionary analysis, version 2.7. Tangient LLC, San Francisco, CA.
http://mesquiteproject.org.
56. Yates JR III, Eng JK, McCormack AL, Schieltz D. 1995. Method to
correlate tandem mass spectra of modified peptides to amino acid se-
quences in the protein database. Anal Chem 67:1426–1436. http://
dx.doi.org/10.1021/ac00104a020.
57. UniProt Consortium. 2014. Activities at the universal protein resource
(UniProt). Nucleic Acids Res 42:D191–D198. http://dx.doi.org/10.1093/
nar/gkt1140.
58. Elias JE, Gygi SP. 2007. Target-decoy search strategy for increased confi-
dence in large-scale protein identifications by mass spectrometry. Nat
Methods 4:207–214. http://dx.doi.org/10.1038/nmeth1019.
59. Huang da W, Sherman BT, Lempicki RA. 2009. Systematic and integra-
tive analysis of large gene lists using David bioinformatics resources. Nat
Protoc 4:44–57. http://dx.doi.org/10.1038/nprot.2008.211.
Planet et al.
12 ®mbio.asm.org January/February 2016 Volume 7 Issue 1 e01939-15
mbio.asm.org on March 5, 2016 - Published by mbio.asm.orgDownloaded from
... Co-infection with influenza virus and S. pneumoniae increased the expression of many miRNAs, such as miRNA-200a-3p, leading to an inhibition of the JAK-STAT inhibitor SOCS6, and this might increase the production of type I-IFNs exacerbating their detrimental effect [129]. Recent studies demonstrated that type III interferons are crucial in promoting superinfections, with similar mechanisms to type I IFN [137,138]. ...
... Type III IFN induced after influenza infection was associated with a restructuring and expansion of the nasal microbiome, as observed for Klebsiella. Moreover, an increase of secondary infection by S. aureus was also observed after viral infection, suggesting an interplay between microbiome and pathogens during co-infections [137,138]. Until then, studies of co-infections were mainly based on the interaction between two pathogens without considering the lung microbiota. However, it is crucial to consider the respiratory microbiome as it could influence the mechanism leading to co-infections and could be a therapeutic target. ...
Article
Full-text available
Respiratory tract infections constitute a significant public health problem, with a therapeutic arsenal that remains relatively limited and that is threatened by the emergence of antiviral and/or antibiotic resistance. Viral–bacterial co-infections are very often associated with the severity of these respiratory infections and have been explored mainly in the context of bacterial superinfections following primary influenza infection. This review summarizes our current knowledge of the mechanisms underlying these co-infections between respiratory viruses (influenza viruses, RSV, and SARS-CoV-2) and bacteria, at both the physiological and immunological levels. This review also explores the importance of the microbiome and the pathological context in the evolution of these respiratory tract co-infections and presents the different in vitro and in vivo experimental models available. A better understanding of the complex functional interactions between viruses/bacteria and host cells will allow the development of new, specific, and more effective diagnostic and therapeutic approaches.
... Type I IFNs are more proinflammatory when compared to Type III IFNs [10]. Surprisingly, although both Type I and Type III IFNs have antiviral activities, mice infected with influenza virus have increased susceptibility to pneumonia caused by Staphylococcus aureus or Streptococcus pneumoniae that were associated with the increase in IFNλ production or IFNλ treatment [12][13][14][15]. Similarly, IFNλ mRNAs were observed in bronchoalveolar fluid and naso-oropharyngeal samples of SARS-CoV-2 patients and increases in IFNλ mRNA expression were positively associated with increases in COVID-19 disease morbidity [16]. ...
... Deviation from this finely tuned balance can harm the patient through the unleashing of a cytokine storm. The mechanisms that may contribute to this balance could be the viral and host genetic backgrounds, previous infections leading to the presence of pre-existing viral antibodies, autoantibodies against Type I IFN (in particular, IFNa and IFNv) or increased expression of IFNλ associated with poor lung epithelial cell/tissue repair processes [8][9][10][11][12][13][14][15][16][17][18][19][20]. Adding to this complex scenario, IFNg and TNFa combination induced PANaptosis helps to perpetuate the cytokine storm and the disease progression with more lung epithelial cell death, tissue damage and increased susceptibility to bacterial superinfection [44]. ...
Article
Full-text available
Interferons are innate and adaptive cytokines involved in many biological responses, in particular, viral infec-tions. With thefinal response the result of the balance of the different types of Interferons. Cytokine stormsare physiological reactions observed in humans and animals in which the innate immune system causes anuncontrolled and excessive release of pro-inflammatory signaling molecules. The excessive and prolongedpresence of these cytokines can cause tissue damage, multisystem organ failure and death. The role of Inter-ferons in virus clearance, tissue damage and cytokine storms are discussed, in view of COVID-19 caused bySevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The imbalance of Type I, Type II and TypeIII Interferons during a viral infection contribute to the clinical outcome, possibly together with other cyto-kines, in particular, TNFa, with clear implications for clinical interventions to restore their correct balance.© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
... Our team has shown that compared with older children, human infants (<18 months) exhibit increased production of type III IFN-lambda during viral respiratory infections [19]. Type III IFN-lambda is an antiviral molecule produced almost exclusively by the epithelium [20,21] and it has an intriguing role maintaining the bacterial microbiome [22,23]. Our in vivo findings were complemented by an in vitro demonstration of strong type III IFN-lambda production in human infant nasal epithelial cells via the pro-inflammatory activation of NF-kB signaling ...
... Our team has shown that compared with older children, human infants (<18 months) exhibit increased production of type III IFN-lambda during viral respiratory infections [19]. Type III IFNlambda is an antiviral molecule produced almost exclusively by the epithelium [20,21] and it has an intriguing role maintaining the bacterial microbiome [22,23]. Our in vivo findings were complemented by an in vitro demonstration of strong type III IFN-lambda production in human infant nasal epithelial cells via the pro-inflammatory activation of NF-kB signaling induced by either IL-1β or TLR-3 agonist exposure [19]. ...
Article
Full-text available
Over the past two decades, several studies have positioned early-life microbial exposure as a key factor for protection or susceptibility to respiratory diseases. Birth cohorts have identified a strong link between neonatal bacterial colonization of the nasal airway and gut with the risk for respiratory infections and childhood asthma. Translational studies have provided companion mechanistic insights on how viral and bacterial exposures in early life affect immune development at the respiratory mucosal barrier. In this review, we summarize and discuss our current understanding of how early microbial-immune interactions occur during infancy, with a particular focus on the emergent paradigm of "innate immune training". Future human-based studies including newborns and infants are needed to inform the timing and key pathways implicated in the development, maturation, and innate training of the airway immune response, and how early microbiota and virus exposures modulate these processes in the respiratory system during health and disease.
... Studies in mice have shown that interferon contributes to an inflammatory milieu, promoting enteropathogen overgrowth in the gut 34,35 and inhibiting bacterial clearance from the lung 36 . Influenza infection in mice was shown to modulate the nasal microbiota in an interferon-dependent manner, which was related to staphylococcal persistence 37 and heightened susceptibility to secondary staphylococcal infection 38 . These findings align with our data, showing associations between early interferon dynamics and Moraxella enrichment and Haemophilus abundance. ...
Article
Full-text available
The respiratory tract is populated by a specialized microbial ecosystem, which is seeded during and directly following birth. Perturbed development of the respiratory microbial community in early-life has been associated with higher susceptibility to respiratory tract infections (RTIs). Given a consistent gap in time between first signs of aberrant microbial maturation and the observation of the first RTIs, we hypothesized that early-life host–microbe cross-talk plays a role in this process. We therefore investigated viral presence, gene expression profiles and nasopharyngeal microbiota from birth until 12 months of age in 114 healthy infants. We show that the strongest dynamics in gene expression profiles occurred within the first days of life, mostly involving Toll-like receptor (TLR) and inflammasome signalling. These gene expression dynamics coincided with rapid micro- bial niche differentiation. Early asymptomatic viral infection co-occurred with stronger interferon activity, which was related to specific microbiota dynamics following, including early enrichment of Moraxella and Haemophilus spp. These microbial trajec- tories were in turn related to a higher number of subsequent (viral) RTIs over the first year of life. Using a multi-omic approach, we found evidence for species-specific host–microbe interactions related to consecutive susceptibility to RTIs. Although fur- ther work will be needed to confirm causality of our findings, together these data indicate that early-life viral encounters could impact subsequent host–microbe cross-talk, which is linked to later-life infections.
... Sustained uncontrolled IFN production can lead to tissue damage and immunopathology; e.g., type I IFNs cause lymphopenia (244), which has been associated with severe cases of influenza and SARS-CoV-2 infection, increasing susceptibility to secondary infections (245)(246)(247)(248)(249)(250)(251)(252)(253). Furthermore, not only type I but also type III IFNs can impair microbial control during coinfections (254,255). Excessive or prolonged production of IFN-l can interfere with lung repair during influenza recovery, which reduces epithelial proliferation and differentiation, increasing disease severity and susceptibility to coinfections (256,257) (Fig. 2). ...
Article
Individuals suffering from severe viral respiratory tract infections have recently emerged as "at risk" groups for developing invasive fungal infections. Influenza virus is one of the most common causes of acute lower respiratory tract infections worldwide. Fungal infections complicating influenza pneumonia are associated with increased disease severity and mortality, with invasive pulmonary aspergillosis being the most common manifestation. Strikingly, similar observations have been made during the current coronavirus disease 2019 (COVID-19) pandemic. The copathogenesis of respiratory viral and fungal coinfections is complex and involves a dynamic interplay between the host immune defenses and the virulence of the microbes involved that often results in failure to return to homeostasis. In this review, we discuss the main mechanisms underlying susceptibility to invasive fungal disease following respiratory viral infections. A comprehensive understanding of these interactions will aid the development of therapeutic modalities against newly identified targets to prevent and treat these emerging coinfections.
... While IFNλ is important in pulmonary defense against influenza, data from mice lacking the IFNλ receptor show that IFNλ signaling ablation reduces bacterial burden and improves survival during influenza super-infection with methicillin-resistant S. aureus (MRSA) or S. pneumoniae [8,9]. Moreover, overexpression of IFNλ during influenza/MRSA super-infection results in increased MRSA burden [10]. ...
Article
Full-text available
Background: Type III interferon, or interferon lambda (IFNλ) is a crucial antiviral cytokine induced by influenza infection. While IFNλ is important for anti-viral host defense, published data demonstrate that IFNλ is pathogenic during influenza/bacterial super-infection. It is known that polymorphisms in specific IFNλ genes affect influenza responses, but the effect of IFNλ subtypes on bacterial super-infection is unknown. Methods: Using an established model of influenza, Staphylococcus aureus super-infection, we studied IFNλ3-/- and control mice to model a physiologically relevant reduction in IFNλ and to address its role in super-infection. Results: Surprisingly, IFNλ3-/- mice did not have significantly lower total IFNλ than co-housed controls, and displayed no change in viral or bacterial clearance. Importantly, both control and IFNλ3-/- mice displayed a positive correlation between viral burden and total IFNλ in the bronchoalveolar lavage during influenza/bacterial super-infection, suggesting that higher influenza viral burden drives a similar total IFNλ response regardless of IFNλ3 gene integrity. Interestingly, total IFNλ levels positively correlated with bacterial burden, while viral burden and bronchoalveolar lavage cellularity did not. Conclusions: These data suggest IFNλ2 can compensate for IFNλ3 to mount an effective antiviral and defense, revealing a functional redundancy in these highly similar IFNλ subtypes. Further, the IFNλ response to influenza, as opposed to changes in cellular inflammation or viral load, significantly correlates with susceptibility to bacterial super-infection. Moreover, the IFNλ response is regulated and involves redundant subtypes, suggesting it is of high importance to pulmonary pathogen defense.
... These variations may be related to complex interactions between an altered microbiome, virus-induced changes in immune response and growth of pathogenic bacteria as microbial diversity decreases [65]. Diabetes and Chronic respiratory disease also influence the occurrence of Influenza [37] Differences in Acute Respiratory Distress Syndrome (ARDS) incidence and associated mortality were also observed among different racial/ethnic affiliations. Among 96,350 patients studied, discrepancies were found among AAs and CAs for ARDS incidence (0.70% vs. 0.93%) and between LAs and CAs for ARDS-associated mortality (0.27% vs. 0.17%). ...
Article
Full-text available
Acute and chronic upper respiratory illnesses such as asthma, and allergic rhinitis (AR) have been linked to the presence of microorganisms in the nose. Microorganisms can exist in symbiotic or commensal relationships with the human body. However, in certain cases, opportunistic pathogens can take over, leading to altered states (dysbiosis) and causing disease. Thus, the microflora present in a host can be useful to reflect health status. The human body contains 10 trillion to 100 trillion microorganisms. Of these populations, certain pathogens have been identified to promote or undermine wellbeing. Therefore, knowledge of the microbiome is potentially helpful as a diagnostic tool for many diseases. Variations have been recognized in the types of microbes that inhabit various populations based on geography, diet, and lifestyle choices and various microbiota have been shown to modulate immune responses in allergic disease. Interestingly, the diseases affected by these changes are prevalent in certain racial or ethnic populations. These prevalent microbiome variations in these groups suggest that the presence of these microorganisms may be significantly associated with health disparities. We review current research in the search for correlations between ethnic diversity, microbiome communities in the nasal cavity and health outcomes in neurological and respiratory functions.
Article
Full-text available
Emerging evidence indicates that severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) is transmitted through the human nasal mucosa via the principal entry factors angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2), which are highly expressed in the nasal epithelium. Therefore, the biologics targeting host entry factors on human nasal mucosa will be necessary for complete control of SARS-CoV-2. Our data reveal that ACE2 was more abundant in human nasal mucosa than lung tissue. Both ACE2 and TMPRSS2 transcriptions were significantly decreased in nasal epithelium in response to S. epidermidis and were relatively lower in human nasal mucus with large number of S. epidermidis. ACE2 transcription was also reduced in nasal epithelium in response to nasal symbiont S. aureus. This study propose that Staphylococcus species nasal commensals might potentially restrict SARS-CoV-2 entry to the nasal epithelium via down regulation of cellular receptors coupled with reduction of principal host protease.
Article
Full-text available
COVID-19 pandemic has caused more than 3 million deaths globally during the past year. The direct attack from SARS-CoV-2 and hyperactivated immune response contribute to the progress and deterioration of COVID-19. After the virus invades, the activation and release of cytokines/chemokines cause "cytokine storm", leading to acute respiratory distress syndrome (ARDS) and multiple organs dysfunction syndrome (MODS). Eliminating virus and blocking cytokines are important checkpoints of COVID-19 therapy, and several agents targeting immunopathology, including interferons, thymosin, glucocorticoids and immunoglobulin, have shown therapeutic effects in severe patients with COVID-19. Herein, we reviewed the practice evidences and concluded that several agents rounding up the immunopathology of COVID-19 may be the alternative approaches under the scenario of the lacking of effective antiviral drugs.
Article
Full-text available
Background Viruses and bacteria from the nasopharynx are capable of causing community-acquired pneumonia (CAP), which can be difficult to diagnose. We aimed to investigate whether shifts in the composition of these nasopharyngeal microbial communities can be used as diagnostic biomarkers for CAP in adults. Methods We collected nasopharyngeal swabs from adult CAP patients and controls without infection in a prospective multicenter case-control study design. We generated bacterial and viral profiles using 16S ribosomal RNA gene sequencing and multiplex PCR, respectively. Bacterial, viral and clinical data were subsequently used as inputs for extremely randomized trees classification models aiming to distinguish subjects with CAP from healthy controls. Results We enrolled 117 cases and 48 control subjects. Cases displayed significant beta diversity differences in nasopharyngeal microbiota (P=.016, R 2=.01) compared to healthy controls. Our extremely randomized trees classification models accurately discriminated CAP caused by bacteria (area under the curve (AUC) 0.83), viruses (AUC 0.95) or mixed origin (AUC 0.81) from healthy control subjects. We validated this approach using a dataset of nasopharyngeal samples from 140 influenza patients and 38 controls, which yielded highly accurate (AUC 0.93) separation between cases and controls. Conclusions Relative proportions of different bacteria and viruses in the nasopharynx can be leveraged to diagnose CAP and identify etiologic agent(s) in adult patients. Such data can inform the development of a microbiota-based diagnostic panel used to identify CAP patients and causative agents from nasopharyngeal samples, potentially improving diagnostic specificity, efficiency, and antimicrobial stewardship practices.
Article
Full-text available
DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
Article
Full-text available
IL-22 plays an important role in mucosal epithelial cell homeostasis. Using a dextran sodium sulfate-induced mouse model of acute colitis, we observed an IL-23–dependent up-regulation of IL-22 in the middle and distal colon at the onset of epithelial cell damage. This heightened IL-22 correlated with an influx of innate immune cells, suggesting an important role in colonic epithelial protection. Freshly isolated colon-infiltrating neutrophils produced IL-22 contingent upon IL-23 signaling, and IL-22 production was augmented by TNF-α. Importantly, the depletion of neutrophils resulted in diminished IL-22 levels in the colon, and the transfer of IL-22–competent neutrophils to Il22a-deficient mice protected the colonic epithelium from dextran sodium sulfate-induced damage. In addition, IL-22–producing neutrophils targeted colonic epithelial cells to up-regulate the antimicrobial peptides, RegIIIβ and S100A8. This study establishes a role for neutrophils in providing IL-22–dependent mucosal epithelial support that contributes to the resolution of colitis.
Article
Full-text available
The epithelium is the main entry point for many viruses, but the processes that protect barrier surfaces against viral infections are incompletely understood. Here we identified interleukin 22 (IL-22) produced by innate lymphoid cell group 3 (ILC3) as an amplifier of signaling via interferon-λ (IFN-λ), a synergism needed to curtail the replication of rotavirus, the leading cause of childhood gastroenteritis. Cooperation between the receptor for IL-22 and the receptor for IFN-λ, both of which were 'preferentially' expressed by intestinal epithelial cells (IECs), was required for optimal activation of the transcription factor STAT1 and expression of interferon-stimulated genes (ISGs). These data suggested that epithelial cells are protected against viral replication by co-option of two evolutionarily related cytokine networks. These data may inform the design of novel immunotherapy for viral infections that are sensitive to interferons.
Article
Full-text available
The most studied biological role of type III interferons (IFNs) has so far been their antiviral activity, but their role in autoimmune and inflammatory diseases remains largely unexplored. Here, we show that treatment with IFN-λ2/IL-28A completely halts and reverses the development of collagen-induced arthritis (CIA) and discover cellular and molecular mechanisms of IL-28A antiinflammatory function. We demonstrate that treatment with IL-28A dramatically reduces numbers of proinflammatory IL-17-producing Th17 and γδ T cells in the joints and inguinal lymph nodes, without affecting T cell proliferative responses or levels of anticollagen antibodies. IL-28A exerts its antiinflammatory effect by restricting recruitment of IL-1b-expressing neutrophils, which are important for amplification of inflammation. We identify neutrophils as cells expressing high levels of IFN-λ receptor 1 (IFNLR1)-IL-28 receptor α (IL28RA) and targeted by IL-28A. Our data highlight neutrophils as contributors to the pathogenesis of autoimmune arthritis and present IFN-λs or agonists of IFNLR1-IL28RA as putative new therapeutics for neutrophil-driven inflammation. © 2015 Blazek et al.
Article
Background: Both targeted decolonization and universal decolonization of patients in intensive care units (ICUs) are candidate strategies to prevent health care-associated infections, particularly those caused by methicillin-resistant Staphylococcus aureus (MRSA). Methods: We conducted a pragmatic, cluster-randomized trial. Hospitals were randomly assigned to one of three strategies, with all adult ICUs in a given hospital assigned to the same strategy. Group 1 implemented MRSA screening and isolation; group 2, targeted decolonization (i.e., screening, isolation, and decolonization of MRSA carriers); and group 3, universal decolonization (i.e., no screening, and decolonization of all patients). Proportional-hazards models were used to assess differences in infection reductions across the study groups, with clustering according to hospital. Results: A total of 43 hospitals (including 74 ICUs and 74,256 patients during the intervention period) underwent randomization. In the intervention period versus the baseline period, modeled hazard ratios for MRSA clinical isolates were 0.92 for screening and isolation (crude rate, 3.2 vs. 3.4 isolates per 1000 days), 0.75 for targeted decolonization (3.2 vs. 4.3 isolates per 1000 days), and 0.63 for universal decolonization (2.1 vs. 3.4 isolates per 1000 days) (P=0.01 for test of all groups being equal). In the intervention versus baseline periods, hazard ratios for bloodstream infection with any pathogen in the three groups were 0.99 (crude rate, 4.1 vs. 4.2 infections per 1000 days), 0.78 (3.7 vs. 4.8 infections per 1000 days), and 0.56 (3.6 vs. 6.1 infections per 1000 days), respectively (P<0.001 for test of all groups being equal). Universal decolonization resulted in a significantly greater reduction in the rate of all bloodstream infections than either targeted decolonization or screening and isolation. One bloodstream infection was prevented per 54 patients who underwent decolonization. The reductions in rates of MRSA bloodstream infection were similar to those of all bloodstream infections, but the difference was not significant. Adverse events, which occurred in 7 patients, were mild and related to chlorhexidine. Conclusions: In routine ICU practice, universal decolonization was more effective than targeted decolonization or screening and isolation in reducing rates of MRSA clinical isolates and bloodstream infection from any pathogen. (Funded by the Agency for Healthcare Research and the Centers for Disease Control and Prevention; REDUCE MRSA ClinicalTrials.gov number, NCT00980980).
Article
Methicillin-resistant Staphylococcus aureus is a cause of lower respiratory tract infections, particularly health care- and ventilator-associated pneumonia. Although many health systems use nasal screening for this microorganism for infection control, correlation between nasal carriage of the organism and development of infections due to it is not clear. Records of patients admitted to medical intensive care between January 1, 2011, and December 31, 2012, were reviewed retrospectively. Patients' data were included if the patients were 18 years or older, satisfied clinical criteria for pneumonia, and had both nasal swabbing and culturing of respiratory specimens within 24 hours of admission. A total of 165 patients met the inclusion criteria. Most had either community-acquired or health care-associated pneumonia. Of the 28 patients with a nasal swab positive for methicillin-resistant S aureus, 8 (4.8%) also had respiratory tract cultures positive for the microorganism. Among the 165 patients, 2 (1.2%) had negative nasal swabs but positive respiratory cultures. Sensitivity and specificity of nasal colonization with methicillin-resistant S aureus for subsequent infection with the pathogen were 80% and 87.1%, respectively; positive and negative predictive values were 28.6% and 98.5%, respectively. Nasal screening for methicillin-resistant S aureus may be a valuable tool for de-escalation of empiric therapy targeted to the organism, especially in patients admitted for severe community-acquired or health care-associated pneumonia. The high negative predictive value suggests that patients with a negative nasal swab most likely do not have a lower respiratory tract infection caused by the organism. ©2015 American Association of Critical-Care Nurses.