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Wang et al. BMC Medical Genomics 2012, 5:21http://www.biomedcentral.com/1755-8794/5/21

RESEARCH ARTICLE Open Access

Genes associated with MUC5AC expression insmall airway epithelium of human smokers andnon-smokersGuoqing Wang1,5*, Zhibo Xu1,2, Rui Wang1, Mohammed Al-Hijji1, Jacqueline Salit1, Yael Strulovici-Barel1,Ann E Tilley1,3, Jason G Mezey1,4 and Ronald G Crystal1,3

Abstract

Background: Mucus hypersecretion contributes to the morbidity and mortality of smoking-related lung diseases,especially chronic obstructive pulmonary disease (COPD), which starts in the small airways. Despite progress inanimal studies, the genes and their expression pattern involved in mucus production and secretion in humanairway epithelium are not well understood. We hypothesized that comparison of the transcriptomes of the smallairway epithelium of individuals that express high vs low levels of MUC5AC, the major macromolecular componentof airway mucus, could be used as a probe to identify the genes related to human small airway mucus production/secretion.

Methods: Flexible bronchoscopy and brushing were used to obtain small airway epithelium (10th to 12th orderbronchi) from healthy nonsmokers (n=60) and healthy smokers (n=72). Affymetrix HG-U133 plus 2.0 microarrayswere used to assess gene expression. Massive parallel sequencing (RNA-Seq) was used to verify gene expression ofsmall airway epithelium from 5 nonsmokers and 6 smokers.

Results: MUC5AC expression varied 31-fold among the healthy nonsmokers. Genome-wide comparison betweenhealthy nonsmokers (n = 60) grouped as “high MUC5AC expressors” vs “low MUC5AC expressors” identified 528genes significantly up-regulated and 15 genes significantly down-regulated in the high vs low expressors. Thisstrategy identified both mucus production and secretion related genes under control of a network composed ofmultiple transcription factors. Based on the literature, genes in the up-regulated list were used to identify a 73“MUC5AC-associated core gene” list with 9 categories: mucus component; mucus-producing cell differentiation-related transcription factor; mucus-producing cell differentiation-related pathway or mediator; post-translationalmodification of mucin; vesicle transport; endoplasmic reticulum stress-related; secretory granule-associated; mucussecretion-related regulator and mucus hypersecretory-related ion channel. As a validation cohort, we assessed theMUC5AC-associated core gene list in the small airway epithelium of an independent set of healthy smokers (n = 72).There was up-regulation of MUC5AC in the small airway epithelium of smokers (2.3-fold, p< 10-8) associated with acoordinated up-regulation of MUC5AC-associated core gene expression pattern in the small airway epithelium ofsmokers (p< 0.01). Deep sequencing confirmed these observations.

Conclusion: The identification of the genes associated with increased airway mucin production in humans shouldbe useful in understanding the pathogenesis of airway mucus hypersecretion and identifying therapeutic targets.

* Correspondence: [email protected] of Genetic Medicine, Weill Cornell Medical College, New York,NY, USA5Department of Genetic Medicine, Weill Cornell Medical College, 1300 YorkAvenue, Box 9610065, New York, NY, USAFull list of author information is available at the end of the article

© 2012 Wang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.

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Wang et al. BMC Medical Genomics 2012, 5:21 of 16http://www.biomedcentral.com/1755-8794/5/21

Author summary

Mucus hypersecretion contributes to the morbidity and mortality of smoking-related lung diseases, especiallychronic obstructive pulmonary disease (COPD), which starts in the small airways. Little is known about the genenetworks associated with the synthesis and secretion of mucins in the human small airway epithelium. Takingadvantage of the knowledge that MUC5AC is a major mucin secreted by the small airway epithelium, the expressionof MUC5AC in small airway epithelium is highly regulated at the transcriptional level and our observation thathealthy nonsmokers have variable numbers of MUC5AC+ secretory cells in the human small airway epithelium, wecompared genome-wide gene expression of the small airway epithelium of high vs low MUC5AC expressors from 60nonsmokers to identify the genes associated with MUC5AC expression. This novel strategy enabled identification ofa 73 “MUC5AC-associated core gene” list with 9 categories, which control a series of processes from mucinbiosynthesis to mucus secretion. The coordinated gene expression pattern of MUC5AC-associated core genes werecorroborated in an independent cohort of 72 healthy smokers. Deep sequencing of small airway epithelium RNAconfirmed these observations. This finding will be useful in identifying therapeutic targets to treat small airwaymucus hypersecretion.

BackgroundThe process of mucus production and secretion is centralto the normal defense of the lung, with mucus an importantcomponent of the airway mucociliary escalator that con-tinuously cleanses the lung of inhaled particulates, patho-gens and xenobiotics [1]. Mucus is comprised of severalcomponents, but is dominated by the secretory mucins:high molecular weight, disulfide linked, heavily glycosylatedproteins [2]. There has been considerable progress in defin-ing the mucin composition of airway mucus in health anddisease [3], and regulation of mucin synthesis and secretionin the mouse lung [4-9]. However, little is known about thegene networks associated with the synthesis and secretionof mucins in the human small airway epithelium, the cellpopulation exhibiting the first abnormalities associated withsmoking [10-14], and the major site of pathology in chronicobstructive pulmonary disease (COPD) [13,15].One successful approach to understanding mucin syn-

thesis/secretion in the human lung has been to use infor-mation from gene manipulation in murine models toidentify genes associated with mucin synthesis/secretion,and to evaluate these genes and their associated genes insamples of human airways or in air-liquid interface cul-tures of human airway epithelium [3-9,16-22]. In order toexpand this knowledge base on a genome wide level, wehave developed a novel strategy to define the network ofgenes linked to MUC5AC expression in the human smallairway epithelium. Our approach is based on the under-standing that there are no mucus glands in the small air-ways of humans, and the secretory mucins are producedsolely by surface secretory cells [1]. Taking advantage ofthe knowledge that MUC5AC is a major mucin secretedby the small airway epithelium, the expression ofMUC5AC in small airway epithelium is highly regulated atthe transcriptional level [17] and our observation thathealthy nonsmokers have variable numbers of MUC5AC+

secretory cells in the human small airway epithelium, we

hypothesized that we could group healthy nonsmokersinto small airway epithelium “high” and “low”.MUC5AC expressors. With this strategy, we compared

genome-wide gene expression of the small airway epi-thelium of high vs low MUC5AC expressors to identifythe genes associated with MUC5AC expression. Anno-tating this list of genes with the literature relating tomucus production/secretion enabled identification of asmall airway epithelium “MUC5AC-associated coregene” list. As a validation strategy for the MUC5AC-associated core gene list, based on the knowledge thatcigarette smoking is an environmental stressor asso-ciated with increased mucus production [1-3,17,18,22,23], we assessed MUC5AC and MUC5AC-associatedcore gene expression in an independent cohort ofhealthy smokers. Not only was MUC5AC up-regulatedin the smoker small airway epithelium, but many of theMUC5AC-associated core genes were as well, confirm-ing the validity of the core gene list as being co-regulated with MUC5AC expression.

ResultsMUC5AC expression in the small airway epithelium ofhealthy nonsmokersConsistent with prior studies of airway mucin gene ex-pression in humans [2,3], only 3 types of mucins (secre-tion-polymeric, tethered-shed in mucus and tethered)were expressed (P call % >80%) in the small airway epi-thelium (Table 1). Among these 8 mucin genes, we fo-cused on MUC5AC (214385_s_at), a secreted-polymericmucin, as it is highly expressed by airway surfacemucus-producing cells [2,3,18].To assess the proportion of MUC5AC positive cells in

the small airway epithelium, MUC5AC staining was car-ried out on cytospin slides prepared from 10-12th orderbrushed airway epithelial cells from healthy nonsmokers(Figure 1). Consistent with the literature [24,25], there

Table 1 Mucin gene expression in small airwayepithelium of healthy nonsmokers

Category Mucin gene Probeset ID1 %P call Normalizedexpression2

Secreted,nonpolymeric

MUC7 217059_at 0 N/A3

MUC8 217295_at 0 N/A

Secreted,polymeric

MUC6 214133_at 8 N/A

MUC19 1553436_at 0 N/A

MUC2 204673_at 25 N/A

MUC5AC 214385_s_at 100 54 ± 44

MUC5B 213432_at 100 48 ± 29

Tethered, shedin mucus

MUC1 213693_s_at 100 70 ± 18

MUC16 220196_at 100 46 ± 18

MUC4 217109_at 100 81 ± 36

Tethered MUC12 226654_at 6 N/A

MUC13 218687_s_at 100 5 ± 4

MUC15 227238_at 100 65 ± 18

MUC17 232321_at 0 N/A

MUC20 226622_at 100 46 ± 23

MUC3A 217117_x_at 50 N/A

MUC3B 214898_x_at 2 N/A1 If one gene had multiple probe sets, the one with highest % P was used.2 Gene expression data was normalized “per chip” in Genespring 7.0, which isnormalized to the median expression level of all the genes on the chip. Data isexpressed as mean ± standard deviation.3 N/A, the expression is not shown because % P< 80%.

Figure 1 Variability of proportions of surface MUC5AC+ cells inthe small airway epithelium of healthy nonsmokers.Immunofluorescence of MUC5AC staining (red) was processed oncytospin slides prepared from 10th-12th order brushed airwayepithelial cells. Shown are examples from 6 individuals. Not shown,IgG irrelevant control, negative for MUC5AC staining. Blue - DAPI.Bar = 20 μm.


were MUC5AC positive cells in the small airway epithe-lium of healthy nonsmokers. Importantly, relevant to thepresent study, there was significant variability from indi-vidual to individual in the number of MUC5AC positivecells. In agreement with the histologic findings, healthynonsmokers showed significant variation in the degreeof MUC5AC gene expression. Compared to the house-keeping genes ACTB (CV= 23.9%), GAPDH (CV=38.1%,), B2M (CV= 15.2%), RPLP0 (CV= 23.0%) andPPIA (CV= 31.4%), the variability of MUC5AC was >2-fold higher (CV= 81.6%; Figure 2; Ansari-Bradley test,p< 10-8, all comparisons).

Identification of MUC5AC-associated core genesIn order to identify genes associated with MUC5AC bio-synthesis and secretion, we compared the gene expres-sion differences between healthy nonsmoker highMUC5AC expressors vs low MUC5AC expressors. Toavoid confusion of the dominant transcriptome responseto oxidative stress in smokers, only nonsmokers wereused to identify genes associated with MUC5AC expres-sion. After controlling false discovery rate by Benjamini-Hochberg correction (p< 0.05), microarray analysisrevealed 528 genes were up-regulated and 15 genes were

down-regulated in MUC5AC high expressors comparedto the MUC5AC low expressors (Figure 3; Additionalfile 1: Table S2).To explore the function of the up-regulated genes, the

online annotation term enrichment tool, DAVID, wasused. Genes with functional annotation of Golgi (p< 10-9),endoplasmic reticulum (ER; p< 10-5), and ER-Golgitransport (p< 2x10-4) were enriched in these genes(Additional file 1: Table S3). This is consistent withthe knowledge that Golgi, ER apparatus, and transportbetween ER and Golgi are essential to the secretion path-way [26]. Based on these associations, further literaturemining was carried out to identify which genes wereknown to be associated with or potentially involved inmucus production or secretion.From 528 up-regulated genes, we found 73 genes with

literature supported roles or potential roles in mucusproduction/secretion. The putative roles of these 73“MUC5AC-core genes” were grouped into 9 categories:(1) mucus components; (2) mucus-producing celldifferentiation-related transcription factors; (3) mucus-producing cell differentiation-related pathways or media-tors; (4) post-translational modification of mucin; (5)

-2-4-8-16 1 16842

Fold-change (nonsmokers-high MUC5AC expressors vs nonsmokers-low MUC5AC expressors

p v

alu

e

Not significant

Significant

Probesets=17

Genes=15

Probesets=591

Genes=528

p=0.05

100

10-1

10-2

10-3

10-4

10-5

10-6

10-7

Figure 3 Identification of MUC5AC-associated core genes. Shown isa volcano plot comparing the normalized expression of gene probe setsin the 10-12th order brushed airway epithelial cells of nonsmoker-highMUC5AC expressors vs low MUC5AC expressors; y-axis - negative log ofp value; x-axis - log2-transformed fold-change; red dots - probe sets withsignificant p value; blue dots - probe sets with non-significant p value.Differentially expressed genes were determined by unequal varianceStudent’s t-test followed by Benjamini-Hochberg correction (p< 0.05).There were 528 genes identified genome-wide that were significantlycorrelated with MUC5AC high expressors. Of these, 73 genes wereidentified as MUC5AC-associated core genes based on literature mining(Table 1, Additional file 1: Table S1, S2).

0

1

2

3

4

5

Nonsmoker-high MUC5ACexpressors

Nonsmoker-low MUC5AC expressors

No

rmal

ized

gen

e ex

pre

ssio

n

Healthy nonsmokers

GAPDH

ACTB B2M

81.6%

RPLP0

PPIA

MUC5AC

23.9% 38.1% 15.2% 23.0% 31.4%

Figure 2 Healthy nonsmokers were grouped based on MUC5ACgene expression in 10-12th order brushed airway epithelialcells. Shown is data for n = 60 nonsmokers divided into nonsmoker“high MUC5AC expressors” (MUC5AC expression, highest quartile ofall healthy nonsmokers, n = 15, upper shaded region) andnonsmoker “low MUC5AC expressors” (MUC5AC expression, lowestquartile of all healthy non-smokers, n = 15, lower shaded region).Expression of 5 endogenous control genes (ACTB, GAPDH, B2M,RPLP0 and PPIA) in the same samples are used as controls to assessthe expression variability in nonsmokers of housekeeping genescompared to MUC5AC. The coefficients of variation (CV) of MUC5ACand endogenous control gene expression are shown.


vesicle transport; (6) endoplasmic reticulum stress-related;(7) secretory granule-associated; (8) mucus secretion-related regulators and (9) mucus hypersecretory-relatedion channels (Table 2; see Additional file 1: Table S1 forall related references; see Additional file 1: Figure S1, S2for the likely roles of the products of these genes in airwaymucus production and secretion). Within each category,the literature regarding the relevance of each gene to smallairway epithelial MUC5AC production and secretion werecatalogued as to links in the literature based on non-human models, human cells in vitro, human airway epi-thelium in vivo and “functional association”, i.e., likelyfunction in the airway epithelium based on the knownfunction of the gene product.From the assignments based on the literature, 27 of the

73 MUC5AC-core genes have been previously identifiedas being associated with mucus production or secretion,including those associated with mucus components(TFF3, TFF1); mucus-producing cell differentiationrelated-transcription factors (SPDEF, FOXA3, SOX2 andKLF4); mucus-producing cell differentiation-related path-ways or mediators (HES1 and TSTA3, Notch pathway;RPS6KA3 and KRAS, MAPK pathway; PLA2G4A; CTSC;SERPINB4); post-translational modification of mucin(AGR2, GNE, GALNT4, GALNT7, GALNT12); secretory

granule-associated (SYTL2, RAB27B, RAB3D, SCIN,MYO5B, MYO5C); mucus secretion-related regulators(ITPR3, PRKCD); and a mucus hypersecretory-relatedion channel (SLC12A2). Of these 27 MUC5AC-coregenes known to be strongly linked to mucus produc-tion and secretion, most have been linked to airwaymucus production and secretion by murine orin vitro studies; only 9 have been previously linked toMUC5AC production/secretion in human in vivostudies (Additional file 1: Table S1).Of the remaining 46 MUC5AC-core genes, although

they have not been strongly linked to airway mucus pro-duction/secretion, there is literature supporting theirroles in mucus production/secretion in other cell types,including mucus-producing cell differentiation-relatedpathways or mediators (Wnt pathway gene, LRRFIP2and MAPK pathway gene, MAPK13); post-translationalmodification of mucin (FUT3, FUT6, ST6GAL1,ST8SIA1, CHST6, GALNT5, GALNT6); vesicle trans-port (MIA3, SURF4, KDELR2, KDELR3, ITSN1,ERGIC1, CKAP4, GOSR1, SYNJ2BP, MPPE1, SEC31A,ARF4, VPS13D, SEC22B and TPD52); ER stress-related(EIF2AK3, EDEM3, XBP1 and CREB3L1); secretory

Table 2 Human small airway epithelium MUC5AC-associated core genes1,2

Mucuscomponents

Mucin producing celldifferentiation-related

Post-translationalmodificationof mucin

Vesicletransport

Endoplasmicreticulumstress-

associated

Secretorygranule-associated

Mucussecretion-related

regulators

Mucushypersecretory-

related ionchannels

Transcriptionfactors

Pathways ormeditators

TFF3 SPDEF HES1 AGR2 MIA3 CREB3L1 SYTL2 PRSS23 SLC12A2

TFF1 FOXA3 TSTA3 GNE SURF4 EDEM3 RAB3D PLCE1 CLCA2

SOX2 LRRFIP2 GALNT4 KDELR2 XBP1 SCIN DGKA SCNN1A

KLF4 KRAS GALNT7 KDELR3 EIF2AK3 STXBP6 ITPR3 GABRP

MAPK13 GALNT12 ITSN1 RAB27B PRKCD

RPS6KA3 PDIA5 ERGIC1 SYTL4

CTSC FUT3 CKAP4 SYTL5

SERPINB4 FUT6 GOSR1 GSN

PLA2G4A ST6GAL1 SYNJ2BP RIMS1

ST8SIA1 MPPE1 CASK

CHST6 SEC31A MYO5B

GALNT5 ARF4 MYO5C

GALNT6 VPS13D PCLO

SEC22B PAM

TPD52 ATP6V0A4KIF5B

CDC42EP51 Genes with a role in mucus production or mucus secretion (based on the literature) were selected from the significantly up-regulated genes in nonsmokerhigh-MUC5AC expressors compared to nonsmoker low-MUC5AC expressors (see Supplemental Methods for complete criteria).2 References for the functions of each gene are in Additional file 1: Table S1. See Additional file 1: Figure S1, S2 for the schematic illustration of the functions ofthese genes relevant to MUC5AC-production/secretion.


granule-associated (RIMS, PCLO, PAM, ATP6V0A4,KIF5B, CDC42EP5); mucus secretion-related regulators(PRSS23, DGKA, PLCE1); and mucus hypersecretory-related ion channels (CLCA2, SCNN1B and GABAP)(Table 2, Additional file 1: Table S1).Together, the MUC5AC-core genes suggest that mul-

tiple molecular events involving the nucleus, endoplas-mic reticulum, Golgi, vesicles and plasma membranework coordinately in the human small airway epitheliumto promote mucus production and secretion inMUC5AC-producing cells.

MUC5AC-core genes in smokersChronic cigarette smoking, with its 4000 xenobioticsand >1014 oxidants per puff, is a complicated stress tothe airway epithelium of smokers [22,27]. Based on this,and the knowledge that smoking induces metaplasia ofMUC5AC positive cells in the airway epithelium [1-3,22], and prior literature detailing increased expressionof MUC5AC in the airway epithelium of smokers[3,28,29], we used small airway epithelium of chronicsmokers as a validation set, i.e., if the human MUC5AC-core genes identified in the nonsmoker small airwayepithelium are truly associated with MUC5AC synthesis/secretion, then the MUC5AC-core genes should be up-regulated in the small airway epithelium of smokers. As

expected, healthy smokers had 2.3-fold higher MUC5ACgene expression than nonsmokers (Figure 4A). Likethe nonsmokers, there was considerable variability inMUC5AC expression among smokers [MUC5ACcoefficient of variation among smokers 47.1%, comparedto 25.3 ± 3.1% for the 5 housekeeping genes, ACTB,GAPDH, B2M, RPLP0 and PPIA (Ansari-Bradley test, allp< 10-11 for comparison between MUC5AC and eachhousekeeping gene)]. Comparison of the expression pat-terns of MUC5AC-core genes between healthy smokersand healthy nonsmokers demonstrated that 28 of the 73(38%) MUC5AC-core genes were up-regulated insmokers (Figure 4B, Additional file 1: Table S4). Interest-ingly, 6 out of the 73 MUC5AC-core genes were down-regulated by smoking, suggesting smoking has a variableeffect on some components of the orchestratedMUC5AC-production/secretion system in the small air-way epithelium.To assess these global smoking-related modifications of

the MUC5AC core genes, we created an overall index ofthe MUC5AC-core genes. The index analysis supportedthe concept that healthy smokers had more activeMUC5AC-core gene expression (up- or down-regulation)compared to the nonsmokers (p< 0.01, Figure 4C).Clustering analysis comparing the high and low MUC5ACexpression in nonsmokers and smokers globally

B.

Nu

mb

ers

of

sub

ject

s

MUC5AC, normalized expression

Healthy smokers

Healthy nonsmokers

A.

Ind

ex o

f M

UC

5AC

-co

re g

enes

(%

)

Healthynonsmokers

Healthysmokers

0

10

20

30

40Fold=1.4p<0.01

Smoker-high MUC5ACexpressors

MU

C5A

C-c

ore

gen

es

Nonsmoker-lowMUC5AC expressors

High expression

Low expression

Nonsmoker-highMUC5AC expressors

Smoker-lowMUC5AC expressors

C. D.

10-14

Smoking up-regulated MUC5AC-core genes

Smoking down-regulatedMUC5AC-core genes

Fold-change (smokers vsnonsmokers)

p v

alu

e

n=6

Unchanged MUC5AC-core genes

10-10

10-8

10-6

10-4

10-2

100

10-12

-8 -4 -2 1 2 4 8

p=0.05

n=28

n=39

Figure 4 cMUC5AC expression and MUC5AC-core genes in the small airway epithelium of healthy smokers. A. MUC5AC gene expressiondistribution in healthy smokers (blue, n = 60) compared to healthy nonsmokers (red, n = 72). Y-axis, number of subjects; X-axis, normalizedMUC5AC expression. B. Volcano plot of MUC5AC-core genes comparing healthy smokers and healthy nonsmokers. Differentially expressed genesbetween smokers (n = 72) and nonsmokers (n = 60) were determined by unequal variance Student’s t-test followed by Benjamini-Hochbergcorrection (p< 0.05). Only MUC5AC-core genes were plotted. y-axis - negative log of p value; x-axis - log2-transformed fold-change; red dots –smoking up-regulated MUC5AC-core genes; blue dots – smoking down-regulated MUC5AC-core genes; grey dots – smoking unchangedMUC5AC-core genes. Numbers of each group are shown. C. Index of expression of the MUC5AC-core genes (% of abnormally expressedMUC5AC-core genes beyond that of healthy nonsmoker MUC5AC low-expressors in healthy nonsmokers (blue) compared to healthy smokers(red). Fold-change represents the average index difference between the groups, p value indicates significant differences between the groups.D. Unsupervised hierarchical clustering analysis of expression of MUC5AC-core genes in nonsmoker-high MUC5AC expressors (black, highestquartile of all healthy non-smokers, n = 15), nonsmoker-low MUC5AC expressors (green, lowest quartile of all healthy non-smokers, n = 15),smoker-high MUC5AC expressors (red, highest quartile of all healthy smokers, n = 18) and smoker-low MUC5AC expressors (blue, lowest quartileof all healthy smokers, n = 18). Genes expressed above the average are represented in red, below average in blue, and average in white.


demonstrated that there were 2 major groups among smo-kers and nonsmokers, based on expression of theMUC5AC-core genes, with nonsmoker-high MUC5ACexpressors having a similar expression pattern ofMUC5AC-core genes to smoker-high MUC5AC expres-sors (Figure 4D). Together, these results suggest that mostMUC5AC-core genes are shared by smokers and nonsmo-kers in association with high MUC5AC expression.

To confirm the microarray findings, RNA sequencingwas used to assess the gene expression profile from airwayepithelium of 5 nonsmokers and 6 smokers. Consistentwith microarray data, RNA-Seq data showed thatMUC5AC-core genes had a coordinated expression pattern(Figure 5). As with the microarray data, in general, smokershad more active MUC5AC-core gene expression, althoughsimilar to the microarray data, there was overlap, with some

102435805264465228952 79974083194258350 4252

NS3NS4 NS1NS2 S5NS5 S6S4 S2S3 S1

High expressionLow expression

MU

C5A

C-c

ore

gen

es

Figure 5 MUC5AC-core genes assessed by RNA-Seq analysis in healthy smokers and healthy nonsmokers. Shown is a hierarchical clusteranalysis of RNA-Seq data of expression of the MUC5AC-associated core genes from 5 healthy nonsmokers and 6 healthy smokers. Smoking statuswas represented by different colors (nonsmoker, blue; smoker, brown). In the heat map, gene expression above the average is represented in red,below average in blue, and average in white. The color schemes represent the relative expression level across each row (each gene). Dendrogramcolor denotes there are two different groups independent of smoking status. The expression level (RPKM) of MUC5AC is shown under the labelof each individual (NS = nonsmoker; S = smoker).


nonsmokers having high levels of MUC5AC-core geneexpression.

MUC5AC-core genes in asthmaAsthma is known to be associated with airway mucusoverproduction. To assess the effect of asthma onMUC5AC-core gene expression, the data set from astudy by Woodruff et al. [30] was used. Comparison of

the expression patterns of MUC5AC-core genes betweenasthma patients at baseline and healthy nonsmokersdemonstrated that 6 of the 73 (8%) MUC5AC-core geneswere up-regulated and 1 MUC5AC-core gene wasdown-regulated in asthma (Additional file 1: Table S6).To assess whether expression of MUC5AC-core geneswere correlated with MUC5AC in this asthma study, weperformed genome-wide correlation analysis to


MUC5AC gene expression. Interestingly, 10 of 73MUC5AC-core genes are among the top 300 genescorrelated with MUC5AC (p< 10-4, Additional file 1:Table S7).

Gene networks linking the MUC5AC-core genesTo help understand the underlying mechanism of thecoordinate expression of the MUC5AC-core genes, agene network was built with the focus on transcrip-tional control. Based on the knowledge from murinestudies or in vitro studies that inhibition of the tran-scription factors Spdef, Sox2 and Klf4 or perturbation ofWnt, Notch and MAPK pathway affects differentiation

Notchpathway

CREB3L1

EDEM2

KLF4

HES1

SEC31A

EIF2AK3

XBP1

KDELR3

MAPK13

TSTA3

Figure 6 Proposed gene network of the MUC5AC-core genes. From thconnection to other MUC5AC-core genes were selected (see Methods for dis affected by the other gene; the data is derived from literature with in vittransgenic mice data (see Additional file 1: Table S5 for references). Proteincircle include, transcription factors (SPDEF, SOX2, KLF4) and the Wnt, Notchdifferentiation. A red dashed circle was used to emphasize their importantMUC5AC-core gene list with literature supported potential role in MUC5ACfactors in the MUC5AC-core gene list. Arrows indicate upstream gene candata [31], KLF4 can affect mucus-producing cell differentiation, but there issolid line linked to pathway indicates that MAPK13, KRAS and RPS6KA3 belLRRFIP2 belong to “Wnt pathway”. The “T” like symbol indicates a potential

of mucus-producing cells or expression of MUC5AC[4-9,31], these genes were used as the backbone of thenetwork (Figure 6). We manually screened each of 73human small airway MUC5AC-core genes for theirtranscriptional connections with other MUC5AC-coregenes. Only those genes with connections with otherMUC5AC-core genes were integrated into the network.These known interactions among MUC5AC-core genessuggest that the findings based on the comparison be-tween high and low MUC5AC expressors were not justby chance. The network was centered on MUC5AC,but, interestingly, involved multiple transcription factorsand regulatory pathways. Downstream genes of these

SPDEF

GALNT7

MUC5AC

FOXA3

AGR2

GALNT4

TFF3

MAPKpathway

ST8SIA1

GNE

SOX2

RPS6KA3

KRAS

Wntpathway

LRRFIP2

e MUC5AC-core gene list, genes with at least one literature supportedetail). The connection denotes that the expression level of one genero gene overexpression/activation/knockdown experiments orto protein interactions are not included. Genes in the brown solid, MAPK pathways, which can affect MUC5AC-producing cellroles. Genes in green solid circle represent transcription factors in the-producing cells. Genes in green rectangles represent non-transcriptionregulate downstream gene expression. Dashed arrow, based on mouseno direct evidence showing KLF4 can affect MUC5AC expression. Theong to “MAPK pathway”; HES1 and TSTA3 belong to “Notch pathway”;blocking effect.


transcription factors or pathways are linked to differentsteps of mucus production, which imply the coordi-nated expression patterns of MUC5AC-core genes areorchestrated by these transcription factors or pathways.Notably, SPDEF and CREB3L1 seem to be two major“driving forces” in this network. The gene network alsoprovides clues to understand the function of otherMUC5AC-associated genes in mucus production.The EGF receptor family related signaling pathway

plays an import role in mucus production [32]. However,our approach to identify MUC5AC-core genes did notidentify EGF receptor family genes as MUC5AC-associated genes. Interestingly, overexpression of EGFreceptor 2 (ERBB2, HER2) has been demonstrated inprimary lung mucinous adenocarcinomas, which werecharacterized by KRAS activating mutations as well asvery high levels of MUC5AC production [33,34]. To in-vestigate the possible relation between ERBB2 andMUC5AC at the transcriptome level, we performedgenome-wide correlation analysis for ERBB2 in bothhealthy nonsmokers and healthy smokers. AlthoughEGF receptor-associated pathways were enriched inERBB2 correlated genes (Additional file 1: Table S8), noclose relation between ERBB2 and mucus production-related genes were found.

DiscussionMucus hypersecretion is a cardinal feature of chronicairway diseases and mucins are the major component ofairway mucus [1-4,17,18,23]. Despite the significant pro-gress in understanding the transcriptional control, bio-synthesis and secretion of mucins using murine models,advances in understanding the coordinated gene expres-sion relevant to producing and secreting mucins in thehuman airway have been limited due to the lack ofmethods to isolate and culture pure populations of pri-mary human airway mucin-producing cells. Taking ad-vantage of the knowledge that MUC5AC is the majorsecreted mucin of the small airway epithelium, and ourobservation that there is variable small airway epitheliumMUC5AC expression among healthy nonsmokers, wehypothesized that genome-wide comparison of small air-way gene expression of healthy individuals with “high”MUC5AC expression to those with “low” MUC5AC ex-pression would identify genes whose regulation paral-leled that of MUC5AC.From a list of 528 genes up-regulated in healthy high

MUC5AC expressors compared to healthy lowMUC5AC expressors, we identified 73 MUC5AC coregenes that had literature support for a role in mucinproduction/secretion, from murine studies of the air-ways, immunohistochemical or in situ hybridizationstudies of human airway epithelium, studies frommucin-secreting cell lines, or studies relating to mucin

production/secretion in organs other than lung. Thesegenes included those categorized as mucus components,mucus-producing cell differentiation-related transcrip-tion factors, mucus-producing cell differentiation-relatedpathways or mediators, post-translational modificationof mucin, vesicle transport, ER stress-associated,secretory granule-associated, mucus secretion-relatedregulators and mucus hypersecretory related ion chan-nels. While some of these 73 human small airway epi-thelium core MUC5AC genes have been previouslyassociated with human airway MUC5AC expression, formost, this is the first demonstration that, in the humanairways, these genes are coordinately up-regulated alongwith MUC5AC expression. As an independent validationset, we assessed the expression of these 73 coreMUC5AC genes in the small airway epithelium ofhealthy smokers, where there is a stress on the airwayepithelium associated with up-regulation of MUC5ACexpression [1-4,17,18,22,23]. The data demonstrates that,as in the “high” vs “low” healthy nonsmokers, the healthysmokers up-regulate 38% of the MUC5AC-core genes.Finally, and consistent with studies in knockout andtransgenic mice, MUC5AC expression in the humansmall airway epithelium was linked to several mucusproduction/secretion-related transcription factors(SOX2, KLF4, SPDEF, XBP1 and CREB3L1), as well asthe Notch, Wnt and MAPK pathways, that, in turn, con-trol coordinated expression of many of the MUC5AC-core genes identified in the present study. These findingsexpand the knowledge regarding molecular eventsunderlying human airway MUC5AC-related production/secretion and provide several potential targets for med-ical intervention for mucus hypersecretion.

MUC5AC expression in the human small airwayepitheliumMUC5AC is one of the major secretory mucinsexpressed by surface airway epithelial cells, and repre-sents a marker for the airway surface mucus-producingcells [1-4,17,18,22,23]. As demonstrated in the presentstudy and in several other studies [25,28,29], there isup-regulation of MUC5AC expression in the airway epi-thelium of smokers compared to nonsmokers. This ob-servation is consistent with the knowledge that, on theaverage, smoking is associated with increased airwaymucus production [1-4,17,18,22,23]. However, novel tothe present study is the observation that MUC5AC ex-pression in the small airway epithelium, assessed atboth the mRNA and protein levels, is highly variableamong healthy nonsmokers, implying that there aresome individuals more prone to airway mucus produc-tion than others. We capitalized on this observation tocategorize healthy nonsmokers into “high” and “low”MUC5AC expressors. Then, using genome wide


assessment of gene expression, we compared “high” vs“low” MUC5AC expressors to identify genes, the ex-pression of which correlates with MUC5AC expression.Using the literature to identify genes already known tobe associated with the airway epithelium (mostly frommurine studies), as well as to identify genes associatedwith mucin expression in other organs, we were able todefine “MUC5AC-core” genes i.e., the genes plausiblylinked to MUC5AC gene expression in the human smallairway epithelium. To test the validity of the MUC5AC-core genes, we hypothesized that many of thesegenes would be up-regulated further in the smallairway epithelium of smokers, since smokers have, onthe average, up-regulation of MUC5AC. This proved tobe correct.

Mucus-producing cell differentiation-related transcriptionfactors, pathways and mediatorsStudies of MUC5AC expression in experimental non-human models have identified several transcription fac-tors that modulate mucus-producing cell differentiation,including Stat6, Spdef, Klf4, Sox2 and Foxa2[4,5,20,31,35]. In addition to these transcription factors,murine studies have identified several signaling path-ways, including Wnt [6], Notch [7,8] and MAPK [9]pathways, which have been implicated in differentiationof MUC5AC-producing cells.Consistent with these observations, the MUC5AC-

core genes linked to MUC5AC expression in the humansmall airway epithelium in the present study include themucus-producing cell differentiation-related transcrip-tion factors, SPDEF, KLF4, SOX2 and FOXA3. Up-regulation of FOXA3 and SPDEF have been reportedduring mucus overproduction in human airways cellsin vitro and in vivo [4]. SPDEF can also drive differenti-ation of MUC5AC-producing cells in primary humanairway epithelial cells in vitro [4]. The demonstrationthat SOX2 and KLF4 are linked to MUC5AC in thehuman airway epithelium is novel to the present study.The MUC5AC-core genes also included the Wnt path-way gene LRRFIP2, Notch pathway genes HES1 andTSTA3 [19], and the MAPK pathways genes MAPK13,KRAS [36], and RPS6KA3 (RSK2) [37] as linked toMUC5AC expression in the human small airway epithe-lium. Together, this suggests that a gene network, in-stead of single gene or pathway, controls differentiationof MUC5AC-producting cells.The EGF receptor family mediated signaling pathway

is involved in mucus production [32]. One of the EGFreceptor family members, ERBB2, has been shown to beoverexpressed in lung mucinous adenocarcinomas[33,34]. However, no correlation between ERBB2 andMUC5AC at transcription level was found in the currentstudy using normal airway epithelium. These results

suggest that MUC5AC expression is not dependent onthe expression level of EGF receptors in the normal air-way epithelium. Instead, activation status of EGF recep-tors might be more important, while in mucinousadenocarcinomas, the constitutive activation of theERBB2 pathway, which is caused by persistent up-regulation of ERBB2 and ERBB2 mutations, might con-tribute to the MUC5AC overexpression.In addition to these transcription factors and path-

ways, it is known that several enzyme-related genes canmediate mucus-producing cell differentiation [38-40].Consistent with these observations, we found that SER-PINB4 (serpin peptidase inhibitor), CTSC (peptidase)and PLA2G4A (phospholipase) are up-regulated in highMUC5AC expressors. Serpinb3a (SERPINB4 homolog)deletion markedly attenuates mucus-producing cellhyperplasia in mouse airway after allergen challenge[38]; ozone and Staphylococcal enterotoxin B exposureis associated with CTSC-driven mucus production in themouse [39]; and PLA2G4A activation is associated withmucus overproduction in murine airway [40]. Both SER-PINB4 and CTSC are up-regulated in IL13 treatedhuman primary bronchial epithelial cells on air liquidinterface culture [16], and PLA2G4A is more active inbronchial explants from individuals with cystic fibrosis[40]. It is a novel observation that these genes are linkedto small airway MUC5AC production/secretion inhealthy humans.

MUC5AC biosynthesisMUC5AC is a polymeric mucin which is heavily glycosy-lated and thus the ER and Golgi apparatus play a centralrole in biosynthesis of MUC5AC. To form the matureMUC5AC protein, a stepwise posttranslational modifica-tion is needed, including N-glycosylation, dimer formation,O-glycosylation, fucose/sialic acid/sulfate modification andpolymer formation, processes involving several specificenzymes [2,3]. Among these, several were identified amongthe MUC5AC core genes.Consistent with the findings from mouse or human

in vitro studies, we found that AGR2 (protein disulfide iso-merases) [41], GALNT4 and GALNT7 (GalNAc-T mem-bers) [4] and GNE (required for normal sialylation) [42]were up-regulated in high-MUC5AC expressors. Besidesthese genes, we found some novel enzyme genes that arelikely involved in human MUC5AC posttranslational modi-fications, including PDIA5 (disulfide isomerase), GALNT5,GALNT6 and GALNT12 (GalNAc-T members), FUT3and FUT6 (fucosyltransferase), ST6GAL1 and ST8SIA1(sialic acid transferase). Interestingly, PDIA5 is also up-regulated in Barrett’s esophagus, a disorder in whichmucus-producing cells are a basic pathologic change [43].Along the secretory pathway, vesicle-mediated trans-

portation is important to carry newly synthesized


proteins from the ER to the Golgi [17]. To maintain thefunction of the ER, reverse transportation is also active,which balances the loss of protein embedded in the vesi-cles from the ER [44]. Consistent with this concept, wefound 15 vesicle transport related genes associated withhigh MUC5AC expression, including MIA3, SURF4,KDELR2, KDELR3, ITSN1, ERGIC1, CKAP4, GOSR1,SYNJ2BP, MPPE1, SEC31A, ARF4, VPS13D, SEC22B,and TPD52. None have been associated with mucus pro-duction in the literature.Mucin biosynthesis places a large metabolic burden on

the cell, with potential ER and Golgi apparatus stress[45]. In agreement with the concept that mucus deregu-lation might induce ER stress, EIF2AK3 and XBP1 (clas-sical unfolded protein response genes), EDEM3(endoplasmic reticulum-associated degradation gene),and CREB3L1 (non-canonic unfolded protein responsegene) were up-regulated in high-MUC5AC expressors.EIF2AK3 can repress global protein synthesis [46], XBP1can induce chaperone synthesis [47], and EDEM3 canpromote the degradation of poor-quality proteins [48].Finally, the Drosophila ortholog of CREB3L1, CrebA,has been shown to be the major and direct regulator ofsecretory capacity in the salivary gland [49]. Our findingsraise the possibility that the human airway surfaceMUC5AC-producing cells likely use an ER stress-asso-ciated signal as feedback to finely control mucusproduction.

MUC5AC secretionMUC5AC secretion is highly regulated [17]. As shownin the “secretory granule-associated” and “mucussecretion-related regulator” categories, activation of themechanisms underlying mucin secretion was reflected inthe function of MUC5AC-core genes. For example, wefound PRSS23, PLCE, PRKCD, DKGA, ITPR3, RAB3D,RAB27B, MYO5B, MYO5C, SYTL2, SYTL4, SYTL5,SCIN, CDC42EP5, ATP6V0A4, STXBP6, PAM, RIMS1,PLCO and CASK were up-regulated in high-MUC5ACexpressors. PRSS23 is a serine protease, which canstimulate mucus glycoprotein release from hamster tra-cheal ring organ culture [50]. PLCE is a PLC familymember that might be involved in generating the intra-cellular second messengers, IP3 and DAG, in mucus-producing cells [1]. ITPR3 is the receptor of IP3 [1].PRKCD is a protein kinase C family member. RAB3Dand RAB27B belong to RAB3 and RAB27 family, re-spectively [17]. ATP6V0A4 and PAM are associated withmaturation of secretion granules [51]. Both MYO5B andMYO5C are type V myosins, and SYTL2 (SLP2A) [52],SYTL4 and SYTL5 are granulophilins [17]. KIF5B isassociated with granule movement [53]. SCIN (scin-derin) is an enzyme that is needed for actin disruption[17] and gelsolin can affect actin remodeling [54].

RIMS1 (RIM), PCLO (Piccolo), and CASK can helpsecretory granule tethering and docking [17]. STXBP6belongs to Munc18 family. Among this group ofMUC5AC-core genes, SYTL4, SYTL5, RIMS1, PCLO,CASK, KIF5B, CDC42EP5, ATP6V0A4, STXBP6, PAM,DKGA, and PLCE have not been previously identified inassociation with mucus-producing cells.

Mucus hypersecretion-related ion channelsSeveral studies have suggested that ion channels areinvolved in mucus production, including CFTR [55],SLC26A4 (pendrin) [56], SCNN1B (ENaC subunit) [21],CLCA family [57], GABAergic system receptors [58] andSLC12A2 [59]. In the current study, we found SCNN1A(ENaC alpha subunit), CLCA2 (CLCA family member),GABAP (a subunit of the GABAergic system receptor),SLC12A2 (Slc12a2 homolog) were up-regulated in highMUC5AC expressors. Among them, SLC12A2 expres-sion is known to be restricted to human airway mucus-producing cells [59]. The links between SCNN1A,CLCA2, GABAP and MUC5AC expression in humanairway are novel. Since multiple ion channel genes arefound to be associated with MUC5AC expression, theseobservations suggest an active water/ion exchange in theairway epithelial cell membrane during human airwaymucus production.Several limitations of this study should be pointed out.

First, all MUC5AC-core genes were identified based onthe literature, and thus, important genes might beignored because we are lacking clues from currentknowledge. Second, our strategy was based on the tran-scriptome level, so regulation mechanisms depending onprotein level modulation are underestimated.

MethodsNomenclatureThe terminology associated with mucus-producing cellsin the airway epithelium varies in the literature, includ-ing the terms “goblet cells” and “mucous cells” [1,23].Since we are focusing on genes correlating with the ex-pression of MUC5AC, to avoid confusion, we use theterm “MUC5AC-producing cells” as the surface airwayepithelium cells (the cells that are sampled with bron-choscopy and brushing) expressing MUC5AC. When re-ferring to the literature, if the mucus-related gene/protein is not clear, the term “mucus-producing cells”will be used.

Ethics statementAll individuals were evaluated at the Weill Cornell NIHClinical and Translational Science Center and Depart-ment of Genetic Medicine Clinical Research Facility,using Institutional Review Board-approved clinicalprotocols.


Study populationHealthy nonsmokers and healthy smokers were recruitedfrom the general population in New York City. Thenonsmokers were used as the “test” set to identify theMUC5AC-core genes; the smokers were used as a“validation” set to determine if the MUC5AC-core genescorrelate with MUC5AC gene expression under condi-tions (smoking) where MUC5AC gene expression isknown to be up-regulated.The criteria for “healthy” was based on a history, phys-

ical exam, complete blood count, coagulation studies, liverfunction tests, urine studies, chest X-ray, EKG and pul-monary function tests (Table 3; see Supplemental Meth-ods for inclusion/exclusion criteria). All subjects werenegative for HIV1 and had normal α1-antitrypsin levels.Urine nicotine and cotinine levels were measured using li-quid chromatography-tandem mass spectrometry (ARUP

Table 3 Study population and biologic samples1

Parameter Healthynonsmokers

Healthysmokers

n 60 72

Gender (male/female) 38/22 51/21

Age 41± 12 43 ± 8

Race (B/W/O)2 27/23/10 45/16/11

Smoking history (pack-yr) 0 27 ± 16

Urine nicotine (ng/ml) negative 1098 ± 1312

Urine cotinine (ng/ml) negative 1228 ± 939

Pulmonary function parameters4

FVC (% predicted) 106 ± 13 110 ± 14

FEV1 (% predicted) 106 ± 14 110 ± 15

FEV1/FVC (% observed) 82 ± 6 81 ± 4

TLC (% predicted) 99 ± 13 100 ± 12

DLCO (% predicted) 98 ± 14 94 ± 11

Epithelial cells4

Number recovered x106 6.5 ± 2.9 7.4 ± 3.3

% epithelial cells5 99.1 ± 1.2 99.0 ± 1.3

% inflammatory cells 0.9 ± 1.2 1.0 ± 1.3

Differential cell count6

Ciliated (%) 72.0 ± 8.9 64.0 ± 12

Secretory (%) 6.7 ± 3.7 8.4 ± 4.4

Basal (%) 12.6 ± 6.6 14.5 ± 8.0

Intermediate (%) 7.8 ± 3.4 12.2 ± 6.81 Data are presented as mean ± standard deviation.2 B = Black, W=White, O=Other.3 Pulmonary function testing parameters are given as % of predicted valuewith the exception of FEV1/FVC, which is reported as % observed; FVC - forcedvital capacity; FEV1 - forced expiratory volume in 1 sec; TLC - total lungcapacity; DLCO - diffusing capacity.4 To quantify the percentage of epithelial and inflammatory cells and theproportions of ciliated, basal, secretory, and intermediate epithelial cells,aliquots of 2x104 cells were prepared by centrifugation and stained withDiffQuik.5 Of total cells recovered.6 Of total epithelial cells recovered.

laboratories, Salt Lake City, UT). “Nonsmokers” (n = 60)were defined as self-reported life-long nonsmokers, withnon-detectable urine nicotine (<2 ng/ml) and cotinine(<5 ng/ml); “Smokers” (n = 72) were defined as self-reported current smokers with urine nicotine >2 ng/mland/or urine cotinine >5 ng/ml. No individuals smokedwithin 12 hr prior to the bronchoscopy procedure to col-lect the airway epithelium.

Epithelial sampling, cDNA preparation and microarrayprocessingAirway epithelium (10th to12th order) was collected byfiberoptic bronchoscopy by brushing as previouslydescribed [14]. The expression of genes encoding surfac-tant and Clara cell secretory proteins confirmed thesamples were derived from the small airway epithelium[14]. Cytopreparations were prepared by centrifugation(Cytospin 11, Shandon Instruments, Pittsburgh, PA) andstained with Diff-Quik (Dade Behring, Newark, NJ). ForMUC5AC staining in small airway epithelium, cytospinslides of brushed cells were stained with mouse anti-human MUC5AC antibody (Vector, Burlingame, CA)and detected by Cy3 labeled goat anti-mouse antibody(Jackson, West Grove, PA). Nuclei were counterstainedwith DAPI (Invitrogen, Carlsbad, CA).RNA in the airway epithelium samples was processed for

microarray analysis as previously described [14,60]. TotalRNA was extracted from the small airway epithelium usinga modification of the TRIzol method (Invitrogen, Carlsbad,CA) and processed to generate cDNA. Genome-wide geneexpression analysis was performed using HG-U133 Plus 2.0array (Affymetrix, Santa Clara, CA) according to Affymetrixprotocols, hardware and software. Overall microarray qual-ity was verified by the criteria: (1) 3'/5' ratio for GAPDH≤3; and (2) scaling factor ≤10.0 [61]. The captured imagedata from the HG-U133 Plus 2.0 arrays was processedusing the MAS5 algorithm. The raw data are available atthe Gene Expression Omnibus (GEO) site (http://www.ncbi.nlm.nih.gov/geo/); accession number for this dataset isGSE34450.

Mucin gene expression in the small airway epitheliumThe mucin genes represented on the HG-U133 Plus 2.0array were grouped as: secretion-nonpolymeric; secretion-polymeric; tethered-shed in mucus; and tethered [2,3]. Forgenes with multiple probe sets, the one with highest expres-sion level in nonsmokers was chosen. The percentage ofAffymetrix “P call” was calculated for each probeset. Tocompare expression levels among mucin genes, Genespringdata normalized “per chip” (normalized to the median of allthe genes on the same chip) was used.To evaluate the expression variability of MUC5AC, en-

dogenous controls were used as references. Five en-dogenous control genes were randomly selected from

http://www.ncbi.nlm.nih.gov/geo/

http://www.ncbi.nlm.nih.gov/geo/


literature (ACTB, GAPDH, B2M, RPLP0 and PPIA) [62]with “per chip” normalized gene expression level >99%genes (probesets) on the HG-U133 Plus 2.0 array. Coef-ficient of variation (CV) of expression of MUC5AC andthe control genes were calculated. For control genes withmultiple probesets, the one with the highest CV waschosen. Genespring “per chip” and “per gene” (normalizedto the median of the same gene across all samples) nor-malizations were used. Comparison of the variance ofMUC5AC vs control gene expression (“per chip” and “pergene” normalized data) was done using the Ansari-Bradleytest in R language (http://www.r-project.org/).

Identification of MUC5AC-associated core genesTo identify the MUC5AC-associated core genes,MUC5AC expression of the healthy nonsmokers wasquantified using probeset 214385_s_at, the probesetgenerating the strongest signal among the 3 ofMUC5AC probesets on the microarray. Healthy non-smokers were categorized as high MUC5AC expres-sors (MUC5AC expression highest quartile, n = 15),medium MUC5AC expressors (the 2nd and 3rd quar-tiles; n = 15 and n = 15, respectively) and lowMUC5AC expressors (MUC5AC expression lowestquartile, n = 15). Genome-wide analysis of the genesassociated with high and low MUC5AC expressorswere filtered by the criteria: (1) Affymetrix “P call”>80% in nonsmokers; and (2) genes with knownannotations. Significant differences of gene expres-sion between high MUC5AC expressors and lowMUC5AC expressors were determined by an unequalvariance Student’s t-test followed by Benjamini-Hochberg correction (B-H correction, p< 0.05). Theprobe sets of MUC5AC were not included. For genesrepresented by more than one probeset, the probesetwith the lowest p value was used. The Database forAnnotation, Visualization and Integrated Discovery(DAVID) was used to do gene function annotationterm enrichment analysis of the significant up-regulated genes [63].The role of each up-regulatedgene in mucus production and secretion wasassessed by manual literature mining. Genes with lit-erature supporting roles in mucus production or se-cretion were defined as the “MUC5AC-core genes”(see Supplemental methods for complete criteria).

Effects of smoking on expression of MUC5AC andMUC5AC-core genesAs a validation cohort to assess the role of smokingon MUC5AC expression and the MUC5AC-coregenes, MUC5AC expression (probeset 214385_s_at)was compared in the small airway epithelium ofhealthy nonsmokers (n = 60) and healthy smokers(n = 72). The comparison of variability of MUC5AC

to the 5 housekeeping genes in smokers was analyzedwith the Ansari-Bradley test. To determine if theMUC5AC-core genes identified in nonsmokers werealso up-regulated in smokers, genome-wide transcrip-tome comparison between healthy smokers (n = 72)and healthy nonsmokers (n = 60) was based on the fil-tered probesets (using same filter criteria used toidentify the MUC5AC-associated core genes). Signifi-cant differences of gene expression between smokersand nonsmokers were determined by an unequal vari-ance Student’s t-test followed by Benjamini-Hochbergcorrection (p<0.05). The results of MUC5AC-coregenes (fold-change and Benjamini-Hochberg correctedp value) were then extracted.Cluster analysis was performed on microarray data (“per

chip” and “per gene” normalization) of the MUC5AC-coregenes in nonsmokers and smokers using settings withSpearman correlation as similarity measure and theaverage linkage clustering algorithm. To reduce the vari-ability associated with medium MUC5AC expressors, onlynonsmoker-high MUC5AC expressors (highest quartileof all healthy nonsmokers, n = 15), nonsmoker-lowMUC5AC expressors (lowest quartile of all healthy non-smokers, n = 15), smoker-high MUC5AC expressors (high-est quartile of all healthy smokers, n = 18) and smoker-lowMUC5AC expressors (lowest quartile of all healthy smo-kers, n = 18) were used.

Asthma studyGEO4302 datasets [30] were used to evaluate the ef-fect of asthma on MUC5AC-core genes, using datafrom 42 asthmatics and 28 healthy subjects in thisdata set. Microarray data were processed by RMA al-gorithm in Genespring 7.0. Significant differences ofgene expression between asthma and healthy subjectswere determined by an unequal variance Student’s t-test followed by Benjamini-Hochberg correction (p< 0.05). Genome-wide correlations to MUC5AC(214385_s_at) were assessed by Spearman’s rank cor-relation coefficient.

MUC5AC-core gene indexA “MUC5AC-core gene index” was created as a glo-bal measure of the coordinate MUC5AC-enhancedexpression-associated changes by integrating infor-mation on expression levels of all MUC5AC-coregenes using the conceptual framework described byTilley et al. [64]. For each gene, the mean andstandard deviation were calculated from the valuesin nonsmoker-low MUC5AC expressors. The “nor-mal range” was defined as within 2 standard devia-tions (SD) of the mean. The MUC5AC-core geneindex for each nonsmoker and smoker was definedas a percentage of MUC5AC-core genes with

http://www.r-project.org/


expression levels outside the normal range, using theformula:

MUC5ACð%Þ ¼Xn

n¼1

cEn

where E1 has a value of 1 if the expression level forgene 1 was >2 SD beyond that of nonsmoker-lowMUC5AC expressors or a value of 0 if the expres-sion level was ≤2 SD below that of nonsmoker-lowMUC5AC expressors; E2 is the index for gene 2,etc., and the constant c [c = 100 divided by the num-ber of MUC5AC-core genes (n) normalizes the indexto the percent of MUC5AC-core genes that are out-side of the range of healthy nonsmoker-low expres-sors]. The statistical significance of differences inindex between the groups was determined using theMann Whitney U-test.

RNA-seq data analysisAnalysis of the MUC5AC-core genes was also carried outusing a database of massive parallel sequencing (RNA-Seq) of the transcriptome of healthy nonsmokers (n= 5)and healthy smokers (n= 6) [65]. Because there is noMUC5AC sequence in the reference genome build UCSChg19, the resultant reads were aligned to Homo sapienshigh coverage assembly GRCh37 using Bowtie v 0.12.Reads per kilobase of exon model per million mappedreads (RPKM) was used to quantify transcript levels ofeach gene. The sequence read data have been submittedto the NCBI SRA database (SRA accession #SRP005411).Of the 73 MUC5AC-core genes, 72 were used for cluster-ing analysis (GALNT4 does not have an ensembl se-quence). Log2 transformed gene level RNA-Seq data wasused for hierarchical clustering analysis, which wasperformed in “HierarchicalClustering” of Genepattern(http://genepattern.broadinstitute.org) with the default set-ting (Distance measure: Pearson correlation; Clusteringmethod: pairwise complete-linkage). To visualize the heat-map, “HierarchicalClusteringViewer” of Genepattern wasused with the default setting (View Options are “relativecolor schemes” and “gradient color”).

Gene network analysisA gene network analysis was built on the human smallairway epithelium MUC5AC-core genes by manuallydata mining the literature. The network focused on tran-scription control, using transcription factors/pathwaysknown to be associated with mucin biosynthesis (SPDEF,SOX2 and KLF4 and Wnt, Notch and MAPK pathways)as the network backbone. MUC5AC-core genes withtranscriptional/pathway level connections with otherMUC5AC-core genes were selected to expand the net-work. The interconnections among genes were based on

literature with gene overexpression/activation/knock-down experiments in vitro or data from genetically engi-neered mice showing that the expression level of onegene is affected by another gene. Protein-protein interac-tions were not included (See Additional file 1: Table S5for details).

Additional file

Additional file 1: Inclusion and Exclusion Criteria for HealthyNonsmokers and Healthy Smokers [1,2,4-6,8,9,16,17,19,21,30,31,36-43,46-54,57-59,66-76].

Competing interestsThe authors have no competing interests.

Authors’ contributionsGW, ZX, RW and MAH conceptualized and designed the study. YSB, AET andRGC oversaw patient recruitment, sample acquisition and experimentalprotocols. JGM contributed to the design of the analytic strategy. JS and GWperformed the statistical and computational analyses. GW and RWinterpreted the results, and wrote the manuscript. RGC supervised theanalyses and edited the manuscript. All authors read and approved the finalmanuscript.

AcknowledgmentsWe thank B. Ferris and B. Witover for help with immunohistochemistryanalysis: N. Mohamed and D.N. McCarthy for help in preparing thismanuscript. These studies were supported, in part, by P50 HL084936 andUL1-RR024996. GW and RW are supported, in part, by T32 HL094284. AET issupported, in part, by 1 K23 HL103837.

Author details1Department of Genetic Medicine, Weill Cornell Medical College, New York,NY, USA. 2Chengdu Second People’s Hospital, Chengdu, China. 3Departmentof Medicine, Division of Pulmonary and Critical Care Medicine, Weill CornellMedical College, New York, NY, USA. 4Department of Biological Statistics andComputational Biology, Cornell University, Ithaca, NY, USA. 5Department ofGenetic Medicine, Weill Cornell Medical College, 1300 York Avenue, Box9610065, New York, NY, USA.

Received: 16 February 2012 Accepted: 1 May 2012Published: 7 June 2012

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doi:10.1186/1755-8794-5-21Cite this article as: Wang et al.: Genes associated with MUC5ACexpression in small airway epithelium of human smokers andnon-smokers. BMC Medical Genomics 2012 5:21.

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