· Web view[Online repository] Dysregulation of lipidomic profile and antiviral immunity in response to hyaluronan in severe asthma Milena Sokolowska, MD, PhD1,#, Li-Yuan Chen, PhD1,

Sokolowska M. et al 1

[Online repository]

Dysregulation of lipidomic profile and antiviral immunity in response to hyaluronan in severe asthma

Milena Sokolowska, MD, PhD1,#, Li-Yuan Chen, PhD1, Yueqin Liu, PhD1, Asuncion Martinez-Anton, PhD1,

Carolea Logun, MSc1, Sara Alsaaty, MSc1, Rosemarie A. Cuento, MSc2, Rongman Cai, PhD1, Junfeng Sun,

PhD1, Oswald Quehenberger, PhD3, Aaron M. Armado, MS4, Edward A. Dennis, PhD4, Stewart J. Levine,

MD2, James H. Shelhamer, MD1

1Critical Care Medicine Department, Clinical Center, NIH, Bethesda, MD, USA

2Laboratory of Asthma and Lung Inflammation, Cardiovascular and Pulmonary Branch, National Heart, Lung

and Blood Institute, NIH, Bethesda, MD

3Department of Medicine, Department of Pharmacology, San Diego, La Jolla, CA, USA

4Department of Chemistry and Biochemistry, San Diego, La Jolla, CA, USA

# Current address: Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos,

Switzerland; CK-CARE, Davos, Switzerland

Corresponding author:

James H Shelhamer

Critical Care Medicine Department, Clinical Center, NIH

9000 Rockville Pike, Bethesda, Maryland 20892

phone: 301-402-4846; fax: 301-480-3389; email: [email protected]


ONLINE METHODS

Participants

The subjects were enrolled under an institutional review board approved protocol (99-H-0076) of the National

Heart, Lung, and Blood Institute and all participants provided written, informed consent prior to the study.

Severe asthma was defined according to the American Thoracic Society (ATS) Workshop on

Refractory Asthma 2000 reportE1 and confirmed by the 2013 European Respiratory Society

(ERS)/ATS guidelines.E2 A patient was included to the severe asthma group if he/she

fulfilled one or both major and at least two minor ATS 2000 criteria. Patients who did

not fulfil these criteria were enrolled to mild-to-moderate asthma group. Atopy was

defined as one or more positive reactions in the skin prick tests, based on the

information from the outside physicians or performed at the NIH with cat dander,

Dermatophagoides farinae, short ragweed and Timothy grass (HollisterStier, Spokane,

WA). Sensitivity to aspirin was reported based on the patient’s history. The doses of

inhaled corticosteroids were calculated as the budesonide equivalent; the doses of long

acting -agonist as a salmeterol equivalent. The sex and age matched control individuals had a

negative family history of asthma, a normal chest X-ray and spirometry. All participants had a 1 pack-year or

less smoking history. Spirometry was performed after withholding bronchodilatator if symptoms permitted.

All asthmatic patients presented bronchodilatator reversibility in at least one spirometry. Subjects were not

asked to withhold medications before the blood collection. The levels of corticosteroids or nonsteroidal anti-

inflammatory drugs were not measured in the blood of participants at the time of sample collection.

Participants’ demographic and detailed phenotypic characteristics are presented in Table E1. Many

underlying medical conditions E3-E8 such as nasal polyps, obesity, diabetes, autoimmune disorders,

hypertension or cancer might influence the results of our study, thus we examined the differences in their

prevalence in our participants (Table E1). Obesity was defined as BMI ≥ 30. We found no differences in

comorbidities, except those connected to asthma, such as atopy and allergic rhinitis. Interestingly, there were


more subjects with the history of pneumonia or other severe infections in our severe asthma group. Complete

blood count with differential (Table E2) and serum IgE were analyzed for phenotypic characteristics. Blood

samples were obtained between June and October. The majority of patients in our cohort were allergic to

several allergens, including the perennial allergens such as house dust mites, molds and cat or dog. Some of

them were additionally allergic to grass and ragweed.

Peripheral blood mononuclear cells (PBMCs) collection and experimental procedures

Blood specimens from patients with asthma and control subjects were drawn after obtaining informed

consent. Complete blood count with differential of all subjects is presented in Table E2. Blood samples were

processed immediately using lymphocyte separation medium (LSM) (Lonza, Walkersville, MD), according to

the manufacturer’s protocol to obtain PBMCs, a mixture of lymphocytes and monocytes, and a small amount

of platelets. After isolation of PBMCs, samples were subjected to cytospins and stained by Diff-Quik

(Siemens HealthCare Diagnostics, Newark, DE) to ensure homogenous cellularity between subjects. 4x106

PBMCs were resuspended in 1 ml of RPMI 1640 medium with 2 mM of L-glutamine, supplemented with

10% heat-inactivated FBS (Life Technologies, Thermo Scientific, Waltham, MA) and treated with purified

LMW HA (MP Biomedicals; Solon, OH), vehicle, cPLA2 inhibitor (N-{(2S,4R)-4-(Biphenyl-2-ylmethyl-

isobutyl-amino)-1-[2-(2,4-difluorobenzoyl)-benzoyl]-pyrrolidin-2-ylmethyl-3-[4-(2,4-dioxothiazolidin-5-

ylidenemethyl)-phenyl] acrylamide, HCl; Calbiochem, EMD4Biosciences, Gibbstown, NJ) or cPLA2

inhibitor with LMW HA for 6 h. Each experiment was performed in duplicates. LMW HA used in this study

was a purified mixture of fragments between 50 kDa to 600 kDa, as we described previously, together with

the detailed quality control.E9 Each experiment was performed in the presence of polymyxin B sulfate

(Calbiochem) (10 g/ml) to exclude an effect of LPS contamination. The dose and time of the LMW HA

stimulation was chosen based on the dose-response and time-course performed in the previous study.E9

Supernatants were collected and kept frozen in -80oC before further analyses. Cell pellets were lysed in RLT


buffer, homogenized using QIAshredder columns (Qiagen, Valencia, CA) and stored at -80C before RNA

extraction.

Lipidomic profiling by UPLC Mass Spectrometry

All baseline and LMW HA treated samples, as well as 3 random samples with cPLA 2 inhibitor from each

phenotype were subjected to full lipidomics analysis. For extraction, 200 ul of cell medium was

supplemented with a cocktail consisting of 26 deuterated internal standards, and purified by solid phase

extraction on Strata-X columns (Phenomenex, Torrance, CA), following the activation procedure provided by

the distributor. Samples were eluted with 1 ml of 100% methanol, the eluent was dried under vacuum and

dissolved in 50 µl of buffer A consisting of 60/40/0.02 water/acetonitrile/acetic acid = 60/40/0.02 (v/v/v) and

immediately used for analysis.

Eicosanoids were analyzed by ultra-high pressure liquid chromatography and mass spectrometry essentially

as previously described.E10-E12 Briefly, eicosanoids were separated by reverse phase chromatography using a

1.7uM 2.1x100 mm BEH Shield Column (Waters, Milford, MA) and an Acquity UPLC system (Waters,

Milford, MA). The column was equilibrated with buffer A and 5 µl of sample was injected via the

autosampler. Samples were eluted with a step gradient to 99% buffer B consisting of acetonitrile/isopropanol

= 50/50 (v/v). Gradient elution was carried out for 5 min at a flow rate of 0.5 mL/min. Gradient conditions

were as follows: 0-4.0 min, 0.1-55% B; 4.0-4.5 min, 55-99% B; 4.5-5.0 min, 99% B;

The liquid chromatography effluent is interfaced with a mass spectrometer and mass spectral analysis was

performed on an AB SCIEX 6500 QTrap mass spectrometer equipped with an IonDrive Turbo V source (AB

SCIEX, Framingham, MA). Eicosanoids were measured using multiple reaction monitoring (MRM) pairs

with the instrument operating in the negative ion mode. The electrospray voltage was -4.5 kV, and the turbo

ion spray source temperature was 525 °C. Collisional activation of the eicosanoid precursor ions was achieved

with nitrogen as the collision gas, and other mass spectrometer parameters including the declustering


potentials and collision energies were optimized for each analyte. The eicosanoids were identified by

matching their MRM signal and chromatographic retention time with those of pure identical standards.E13

Eicosanoids were quantitated by the stable isotope dilution method. Briefly, identical amounts of deuterated

internal standards were added to each sample and to all the primary standards used to generate standard

curves. To calculate the amount of eicosanoids in a sample, ratios of peak areas between endogenous

eicosanoids and matching deuterated internal eicosanoids were calculated. Ratios were converted to absolute

amounts by linear regression analysis of standard curves generated under identical conditions. Currently, we

can quantitate over 150 eicosanoids at sub-fmole levels. Data acquisitions were performed using Analyst

1.6.2 (Applied Biosystems, Foster City, CA) and Multiquant 2.0 (Applied Biosystems) was used for data

analysis.

RNA extraction

Total RNA was extracted from cells using QIAshredder columns and RNeasy mini kit and treated with DNase

(Qiagen). Total RNA concentration was measured by the Nanodrop ND-1000 spectophotometer (NanoDrop

Technologies, Wilmington, DE) and the quality of RNA was confirmed by OD 260/280 ratio. The purified

total RNA was stored at -80C until all samples were collected.

Gene arrays analysis

Total RNA (100 ng) was processed for GeneChip analysis using the 3’ IVT Express kit and the U133 plus 2.0

microarray chip (Affymetrix, Santa Clara, CA) following the manufacturer’s directions. The entire data set

has been submitted to the National Center for Biotechnology Information Gene Expression Omnibus (NCBI

GEO; http://www.ncbi.nlm.nih.gov/projects/geo/index.cgi), accession number GSE59019.


Gene expression was preprocessed by the Robust Multichip Analysis (RMA) algorithm using Affymetrix

Expression Console. Statistical analyses were performed using the Mathematical and Statistical Computing

Laboratory (MSCL) Analyst's Toolbox (http://abs.cit.nih.gov/MSCLtoolbox/) written by P. J. Munson and J.

Barb in the JMP scripting language (SAS Institute, Cary, NC). One way ANOVA and post-hoc tests were

performed for comparisons between groups, and resulting p-values were converted to False Discovery Rate

(FDR) to adjust for multiple testing. Hierarchical cluster analysis was done using JMP version 9. Networks

and functional analyses were generated through the use of Ingenuity Pathway Analysis (IPA) (Ingenuity

Systems, www.ingenuity.com) and verified by Gene Set Enrichment Analysis (GSEA, Broad Institute,

http://www.broadinstitute.org/gsea) bioinformatics resources. E14, E15 IPA-generated canonical and

biofunctional pathways were rank ordered according to their significance score. Significance scores of those

pathways are indicated by the Benjamini Hochberg P-value, which represents results adjusted for multiple

comparisons (FDR).

In detail, we analyzed first the effect of LMW HA on the global gene expression in all subjects enrolled in the

study. Principal component analysis and hierarchical clustering revealed that there was a strong effect of HA

stimulation in all samples. Separation by HA stimulation was much stronger than separation by the disease

phenotype. Therefore, we first compared the HA-treated group to the vehicle-treated group in all subjects. We

found that HA stimulation significantly changed expression of 2,273 genes (FDR ≤ 0.001 and FC ≥1.5 or

FC≤0.67), among which 1,030 were up-regulated and 1,243 down-regulated (Table E3). Each phenotype

analyzed separately for the effect of HA stimulation also yielded a large number of genes significantly

regulated by HA (Table E3). The list of significantly changed genes (by FDR ≤ 0.001 and FC ≥1.5 or

FC≤0.67) was then analyzed by IPA to find enriched canonical pathways, associated diseases and bio

functions. IPA transforms FC < 1 into the negative values and thus all values are presented accordingly,

being called FC for simplicity. Analysis of the effects of HA stimulation in combined phenotypes yielded a

comparable rank-order list of the most significant pathways and bio functions as the similar analysis in each

phenotype separately. Thus, we first report here the results of overall effect of HA in all subjects (Fig. E3, A,


B), followed by the significant differences in HA effect between phenotypes. Stimulation of PBMCs with

LMW HA led to a significant enrichment of genes in 177 canonical pathways (Table E4). The 12 most

significant pathways are presented in Fig. E3, A. In these pathways, HA stimulation led to an overall increase

of gene expression (indicated by red color in the stacked bar in Fig E3, A; in detail presented in Tables E5-

E9). Profound changes occurred in the gene expression in the Granulocyte Adhesion and Diapedesis pathway

(Fig E3, A, Table E5), Triggering Receptor Expression on Myeloid Cells 1 (TREM1) Signaling pathway (Fig

E3, A, Table E6) and Role of Pattern Recognition Receptors of Bacteria and Viruses pathway (Fig E3, A,

Table E7). There was also a strong enrichment in pathways associated with IL-10 (Fig E3, A, Table E8) and

IL-17 signaling (Fig E3, A, Table E9). Overall, the data suggest an association of HA signaling with immune

cells trafficking and activation. Indeed, further IPA analysis showed the most significant involvement of HA-

induced genes in Immune Cell Trafficking from the Physiological System Development and Function

category, Cellular Function and Maintenance from the Molecular and Cellular Functions category as well as

Inflammatory Response from the Diseases and Disorders category (Table E10). Not surprisingly, in light of

the lipidomics results, Lipid Metabolism was also among the significantly enriched functional categories,

with eicosanoid metabolism as one of the main subcategories (Fig. E3, B). We confirmed the LMW HA-

induced changes in the expression of selected genes by real-time PCR (Table E11) and by their translation to

protein by Multiplex (Fig. E4).

Next, we analyzed the specific effect of LMW HA on gene expression in asthma. We compared the global

gene expression changes induced by LMW HA, corrected by the baseline gene expression in each individual

(paired difference), in mild-to-moderate asthma and in severe asthma compared to non-asthmatic controls.

The clustering relationship in PCA did not show a clear separation among those phenotypic groups. Likewise,

by applying one-way ANOVA and post-hoc tests, we did not find any genes that passed the criteria of FDR <

0.05 and FC ≥1.5 or FC≤0.67, which is a common observation in gene expression studies in heterogenic

phenotypes. Therefore, we applied further IPA analysis on genes which passed the criteria of p<0.05 and FC

≥1.5 or FC≤0.67 to find any changed pathways and bio functions, which were further corrected for multiple


comparisons. Applying these conditions, we found 6 canonical pathways significantly changed in severe

asthma vs controls (Fig. 2, A), while there were no significantly changed pathways in mild-to-moderate

asthma vs controls. Surprisingly, these pathways were created from significantly less upregulated genes as

compared to their expression after HA treatment in control subjects; therefore, they are designated by green

color in the stacked bar graph (Fig. 2, B). These pathways include genes involved in interferon signaling,

antiviral innate immunity, pattern recognition receptors, cell movement and apoptosis. Downstream analysis

of bio functions and diseases revealed that the Infectious Disease category was significantly changed and

contained several subcategories with the highest prediction score for increase of function (Table E12) in the

entire data set. This further corresponded to the significantly changed Antimicrobial Response category,

containing a predicted decrease of antiviral response (p=2.94E-7; z-sore=-1.94) and antibacterial response

(p=1.66E-6; z-score=-1.94), as well as decrease in cell movement of lymphocytes (6.12E-3; z-score=-2.02)

and homing T lymphocytes (5.11E-3; z-score=-1.99). Detailed analysis of these categories and functions led

to establishing associations with the canonical pathways and building the model of those associations in

severe asthma (Fig. 2, C). This model predicts that less upregulated expression of genes by LMW HA in the

Interferon signaling and other canonical pathways in severe asthmatics might lead to impaired activation of

antiviral and antimicrobial responses and allow for a subsequent increase in Viral Infection signaling (Fig. 2,

C). Surprisingly, the Lipid Metabolism category, including eicosanoid synthesis was not significantly

enriched in severe asthma, suggesting that posttranscriptional or posttranslational changes (e.g.

phosphorylation) are responsible for the significant increases in the designated lipid species.

Control analyses

We performed several analyses to control for the baseline (unstimulated) phenotype influence on the analysis

results. First, as mentioned above, the comparison of LMW HA effect between phenotypes was performed

after paired analysis and transformation to fold change as compared to baseline expression in the same


subject. Therefore, the potential baseline phenotypic differences should be eliminated. Second, to additionally

control whether baseline down regulation or pre-activation of genes could lead to their subsequent less potent

activation by LMW HA, we reanalyzed separately baseline, non-stimulated expression of a subset of 84 genes

involved in significantly changed canonical pathways and bio functions between controls and severe

asthmatics after LMW HA stimulation. We did not find any changes in the basal expression of those genes

between different phenotypes (p>0.05 in each gene, data not shown). Third, there were no significant

differences of the baseline expression of any analyzed proteins (Fig 2D, Fig. E4). Finally, to exclude the

effects of corticosteroids on the analysis results we examined the effect on the Glucocorticosteroid Receptor

Signaling pathway. We found that this pathway was unchanged in severe asthma as compared to controls.

These results suggest that the significantly less upregulated pathways after LMW HA treatment in severe

asthmatics were not likely due to a systemic effect of medications or baseline down or up regulated

expression of those genes. Additionally, we performed confirmatory real-time PCR to re-analyze mRNA

expression of cPLA2 (PLA2G4A) and various hyaluronan receptors (TLR4, CD44, TLR2, Rhamm (HMMR)

and stabilin 2 (STAB2)) with/without LMW HA stimulation. We studied whether the differences in their

expression might be responsible for the observed differences in the lipid mediators and/or gene expression

profiles between severe asthmatics and controls. Our real-time PCR results confirmed that PLA2G4A, TLR4,

CD44, TLR2 and HMMR mRNA expression increased upon LMW HA stimulation, but there was no

difference between studied groups (Figure E5). The expression of STAB2 was very low or undetectable in

several patients. Therefore, these data suggest that posttranscriptional changes in the signaling pathways

might be responsible for the observed differences between severe asthmatics and controls.

Involvement of several eicosanoids is very important in the pathogenesis of AERD (Aspirin Exacerbated

Respiratory Disease). Thus, even though we enrolled to our study only 2 patients with aspirin (acetyl salicylic

acid; ASA) sensitivity, based on their medical history, as stated in Table E1, we reanalyzed our lipidomic

data, including ASA-sensitivity to highlight those patients on the graph (Figure E2). There was no statistical

difference between patients with and without aspirin sensitivity (p>0.05, Mann-Whitney test) in severe


asthma group. Even though, one of the patients with aspirin sensitivity had the highest level of eicosanoids

within the severe asthma group, the other one was grouped differently, depending on which mediator was

analyzed. Moreover, often in the mild-to-moderate asthma group, there were patients with higher values of

several mediators. We think that this issue, not robust and hugely underpowered in this report, requires further

studies in the larger population of subjects.

Real-Time PCR

Reverse transcription was performed using an iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA). Gene

expression was assessed using RT-PCR performed on an ABI Prism ViiA7 sequence detection system

(Applied Biosystems) using commercially available probe and primers sets (Applied Biosystems) as follows:

PTGS2 (COX2), Hs00153133_m1; PTGES, Hs01115610_m1; PLA2G4A (cPLA2a), Hs00233352_m1 ;

INHBA, Hs01081598_m1; IL36G, Hs00219742_m1; ALOX5, Hs01095330_m1; FN1, Hs00365052_m1;

LEP, Hs00174877_m1; CD44, Hs01075862_m1; TLR4, Hs00152939_m1; TLR2, Hs01014511_m1; HMMR

(Rhamm), Hs00234864_m1; STAB2 (stabilin-2), Hs00213948_m1; and iTaq Universal Probes Supermix

(Bio-Rad). Gene expression was normalized to GAPDH transcripts and represented as a relative

quantification (RQ) compared with vehicle treated control.

Cytokines and chemokines quantitation by Multiplex and ELISA

Multiplex quantitation of TNF, IL-1, IL-1, IL-6, IL-15, G-CSF, INF, IL12 (p40), IL-10, CXCL10,

CXCL11, CCL8, CXCL9, CCL20 was conducted using the Bioplex (Bio-Rad) according to manufacturer

directions. IL-27 ELISA (Bio-Legend, San Diego, CA) was performed according to the manufacturer manual.

Statistical analysis


Data were analyzed by one-way ANOVA with Holm-Sidak post hoc test, Kruskall-Wallis ANOVA on ranks

with Dunn’s post hoc test, Mann–Whitney U test or Student t test, as appropriate. Differences were

considered significant when p < 0.05.

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Sokolowska M. et al

ONLINE REPOSITORY TABLES

Control subjects Patients with mild to moderate asthma

Patients with severe asthma P value

6 7 6

Age (y) 53 (38-62) 49 (38-66) 51.5 (28-63) .978

Sex, female/male 3/3 4/3 5/1 .594

Ethnicity 4W/2B 6W/1B 5W/1A .642

BMI 27.35 (23.9-42.5) 29.5 (19.7-48.3) 29.25 (23.6-32.5) 0.596Active smoking history (1 pack-year or less), yes/no 0/6 1/6 1/5 0.754

Atopy, yes/no 1/5 6/1 6/0 .006

Sensitivity to aspirin (history), yes/no 0/6 0/7 2/4 .175

Nasal polyps, yes/no 0/6 1/6 2/4 .613

Allergic rhinitis, yes/no 1/5 5/2 6/0 .008

Atopic dermatitis, yes/no 0/6 1/6 3/3 .115History of pneumonia/severe infection, yes/no 0/6 2/5 4/2 .043

Gastroesophageal reflux disease, yes/no 0/6 2/5 1/5 .610

Obesity, yes/no 1/5 3/4 3/3 .510

Diabetes, yes/no 0/6 0/7 0/6 1.00

Hypertension, yes/no 0/6 3/4 2/4 0.287

Autoimmune disease, yes/no 1/5 0/7 0/6 0.316

Chronic musculoskeletal disorder, yes/no 0/6 3/4 2/4 0.287

Cancer, yes/no 0/6 0/7 0/6 1.00Dose of inhaled budesonide equivalent (mg/day) n/a 256 (0-800) 1600 (640-2240) <.001

Dose of inhaled salmeterol equivalent (mg/day) n/a 0 (0-100) 100 (74.9-124.9) 0.009

Montelukast use, yes/no n/a 3/4 2/4 1.00

Oral glucocorticosteroids, yes/no n/a 0/7 1/5 .462

NSAIDs usage, yes/no 1/5 1/6 1/5 1.00

FEV1 (L) 3.01 (2.25-4.18) 2.81 (1.54-3.82) 1.49 (0.98-2.87) .017

FEV1 (%) 98 (84-126) 88 (45-121) 63 (33-71) .006

FEV1/FVC (%) 85.5 (67-101) 68 (44-75) 52.5 (31-67) <.001

FeNO (ppb) 16.40 (8.2-40.4) 26.2 (10.2-91.7) 22.3 (10.2-60.1) .426

Peripheral blood eosinophils (%) 1.25 (0.5-4) 3 (1.5-4.6) 6.25 (0.1-13.9) .027

Serum IgE level (IU/mL) 16.8 (2.8-407) 90.3 (14.5-725) 554 (15.5-21836) .222TABLE E1.PARTICIPANTS' DEMOGRAPHIC AND ASTHMA PHENOTYPIC CHARACTERISTICS

Data are presented as median and range. W-White, B-Black, A-Asian. FeNO-fraction of exhaled nitric oxide. NSAIDs-nonsteroidal anti-inflammatory drugs. Data were analyzed by ANOVA and Fisher exact test.

Sokolowska M. et al

TABLE E2. COMPLETE BLOOD COUNT RESULTS

Control subjects

(n=6)

Patients with mild to

moderate asthma (n=7)

Patients with severe

asthma (n=6)

P value

WBC (x10E3/mL) 4.08 (3.49-7.65) 6.66 (5.33-10.54) 8.54 (4.48-15.95) .022

RBC (x10E6/mL) 4.41 (3.7-5.1) 4.74 (4.47-5.25) 4.74 (3.99-5.99) .439

Hemoglobin (g/dL) 13.95 (10.9-16.1) 14.1 (13.1-15.5) 13.8 (12.8-14.3) .839

Hematocrit (%) 41.45 (33.7-46.8) 41.8 (39.2-44) 42.25 (38.6-43) .825

MCV (fL) 91.45 (83.7-96.3) 88.2 (83.8-89) 89.55 (71.8-96.7) .437

MCH (pg) 30.05 (27.6-33.4) 29.5 (28.7-30.2) 28.95 (23.4-32.1) .306

MCHC (g/dL) 33.5 (32.3-34.9) 33.7 (32.3-35.4) 32.8 (31.6-33.7) .146

RDW (%) 13.4 (13-14.3) 12.8 (12.2-13.7) 14.45 (12.6-16.2) .017

Platelets (x10E3/mL) 204 (170-291) 237 (204-290) 302.5 (235-447) .019

Neutrophils (%) 57.65 (45.3-66) 59.9 (46.4-74.7) 58.75 (48.4-85.7) .719

Lymphocytes (%) 27.75 (23.2-43.4) 26.6 (13.3-42.8) 21.65 (10.4-35.3) .397

Monocytes (%) 10.15 (5.6-12.8) 7.8 (6.6-10.6) 7.55 (3.1-9) .102

Eos (%) 1.25 (0.5-4) 3 (1.5-4.6) 6.25 (0.1-13.9) .027

Basophils (%) 0.5 (0.3-1.1) 0.6 (0.3-0.8) 0.25 (0-0.6) .035

Neutrophils (Absolute) 2.28 (1.93-4.89) 3.92 (3.1-7.87) 5.25 (2.39-11.42) .054

Lymphocytes (Absolute) 1.22 (0.81-2.08) 1.58 (1.4-3.32) 1.69 (1.39-2.74) .206

Monocytes (Absolute) 0.43 (0.22-0.58) 0.54 (0.45-0.7) 0.525 (0.34-1.2) .269

Eos (Absolute) 0.065 (0.02-0.18) 0.2 (0.08-0.49) 0.495 (0.02-2.21) .058

Basophils (Absolute) 0.02 (0.02-0.04) 0.04 (0.02-0.06) 0.03 (0-0.05) .107

Data are presented as median and range

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TABLE E3. SIGNIFICANTLY CHANGED GENES AFTER LMW HA TREATMENT

Subjects (N) FDR≤0.001

FC≥2

FDR≤0.001

FC≤0.5 (-2)

Total FDR≤0.001

FC≥1.5

FDR≤0.001

FC≤0.67 (-1.5)

Total FC_Max FC_min

All (19) 560 488 1048 1030 1243 2273 131.14 -20

Control (6) 417 339 756 574 727 1301 238.7 -20

Mild-to-

moderate

asthma (7)

494 408 902 762 818 1580 117.37 -33.3

Severe

asthma (6)

356 317 673 513 667 1180 194.98 -25

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TABLE E4. CANONICAL PATHWAYS ASSOCIATED WITH LMW HA SIGNALING

Ingenuity Canonical Pathways -log(B-H p-value) FDR Ratio Downregulated UpregulatedGranulocyte Adhesion and Diapedesis 1.07E+01 2.00E-11 0.31 17/182 (9%) 39/182 (21%)TREM1 Signaling 1.07E+01 2.00E-11 0.38 15/90 (17%) 19/90 (21%)IL-10 Signaling 9.07E+00 8.51E-10 0.40 14/78 (18%) 17/78 (22%)Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses

7.99E+00 1.02E-08 0.32 16/109 (15%) 19/109 (17%)

Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis

7.75E+00 1.78E-08 0.21 35/342 (10%) 38/342 (11%)

Differential Regulation of Cytokine Production in Macrophages and T Helper Cells by IL-17A and IL-17F

7.72E+00 1.91E-08 0.78 0/18 (0%) 14/18 (78%)

Altered T Cell and B Cell Signaling in Rheumatoid Arthritis

7.21E+00 6.17E-08 0.30 7/100 (7%) 23/100 (23%)

T Helper Cell Differentiation 6.82E+00 1.51E-07 0.36 6/72 (8%) 20/72 (28%)Differential Regulation of Cytokine Production in Intestinal Epithelial Cells by IL-17A and IL-17F

6.82E+00 1.51E-07 0.65 0/23 (0%) 15/23 (65%)

Hepatic Fibrosis / Hepatic Stellate Cell Activation

6.80E+00 1.58E-07 0.27 16/155 (10%) 26/155 (17%)

Role of Cytokines in Mediating Communication between Immune Cells

6.57E+00 2.69E-07 0.42 0/55 (0%) 23/55 (42%)

Role of IL-17F in Allergic Inflammatory Airway Diseases

6.52E+00 3.02E-07 0.42 6/48 (13%) 14/48 (29%)

Agranulocyte Adhesion and Diapedesis 6.41E+00 3.89E-07 0.25 13/192 (7%) 35/192 (18%)Role of IL-17A in Arthritis 6.29E+00 5.13E-07 0.36 8/64 (13%) 15/64 (23%)Glucocorticoid Receptor Signaling 5.98E+00 1.05E-06 0.21 30/299 (10%) 32/299 (11%)Dendritic Cell Maturation 5.89E+00 1.29E-06 0.21 18/211 (9%) 27/211 (13%)IL-17A Signaling in Fibroblasts 5.50E+00 3.16E-06 0.43 6/40 (15%) 11/40 (28%)Type I Diabetes Mellitus Signaling 5.49E+00 3.24E-06 0.26 10/121 (8%) 22/121 (18%)IL-6 Signaling 5.49E+00 3.24E-06 0.28 14/124 (11%) 21/124 (17%)HMGB1 Signaling 5.32E+00 4.79E-06 0.28 15/109 (14%) 15/109 (14%)IL-8 Signaling 5.27E+00 5.37E-06 0.21 27/225 (12%) 20/225 (9%)Communication between Innate and Adaptive Immune Cells

5.24E+00 5.75E-06 0.23 6/112 (5%) 20/112 (18%)

PPAR Signaling 5.17E+00 6.76E-06 0.27 11/107 (10%) 18/107 (17%)Cholecystokinin/Gastrin-mediated Signaling

5.16E+00 6.92E-06 0.29 18/106 (17%) 13/106 (12%)

Role of Osteoblasts, Osteoclasts and Chondrocytes in Rheumatoid Arthritis

5.08E+00 8.32E-06 0.21 25/250 (10%) 27/250 (11%)

Acute Phase Response Signaling 4.89E+00 1.29E-05 0.24 16/181 (9%) 27/181 (15%)Role of JAK1 and JAK3 in γc Cytokine Signaling

4.89E+00 1.29E-05 0.32 8/68 (12%) 14/68 (21%)

iNOS Signaling 4.88E+00 1.32E-05 0.34 9/53 (17%) 9/53 (17%)Colorectal Cancer Metastasis Signaling 4.80E+00 1.58E-05 0.20 29/268 (11%) 25/268 (9%)Aryl Hydrocarbon Receptor Signaling 4.76E+00 1.74E-05 0.21 22/171 (13%) 14/171 (8%)JAK/Stat Signaling 4.62E+00 2.40E-05 0.32 10/71 (14%) 13/71 (18%)Molecular Mechanisms of Cancer 4.60E+00 2.51E-05 0.18 44/388 (11%) 26/388 (7%)IL-17A Signaling in Airway Cells 4.56E+00 2.75E-05 0.29 8/76 (11%) 14/76 (18%)Hepatic Cholestasis 4.48E+00 3.31E-05 0.20 17/183 (9%) 19/183 (10%)Apoptosis Signaling 4.48E+00 3.31E-05 0.27 9/100 (9%) 18/100 (18%)TNFR1 Signaling 4.32E+00 4.79E-05 0.33 6/54 (11%) 12/54 (22%)Tec Kinase Signaling 4.29E+00 5.13E-05 0.21 17/184 (9%) 21/184 (11%)IL-15 Signaling 4.27E+00 5.37E-05 0.29 10/72 (14%) 11/72 (15%)

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Role of Tissue Factor in Cancer 4.27E+00 5.37E-05 0.23 16/130 (12%) 14/130 (11%)PDGF Signaling 4.12E+00 7.59E-05 0.28 14/86 (16%) 10/86 (12%)TNFR2 Signaling 4.12E+00 7.59E-05 0.38 3/34 (9%) 10/34 (29%)Death Receptor Signaling 4.12E+00 7.59E-05 0.29 2/68 (3%) 18/68 (26%)Atherosclerosis Signaling 4.12E+00 7.59E-05 0.23 10/139 (7%) 22/139 (16%)G-Protein Coupled Receptor Signaling 4.12E+00 7.59E-05 0.20 36/276 (13%) 19/276 (7%)Interferon Signaling 4.12E+00 7.59E-05 0.39 2/36 (6%) 12/36 (33%)NF-κB Signaling 4.12E+00 7.59E-05 0.22 17/181 (9%) 23/181 (13%)p38 MAPK Signaling 4.11E+00 7.76E-05 0.26 13/120 (11%) 18/120 (15%)Role of Hypercytokinemia/hyperchemokinemia in the Pathogenesis of Influenza

4.03E+00 9.33E-05 0.35 1/46 (2%) 15/46 (33%)

Role of NFAT in Regulation of the Immune Response

4.03E+00 9.33E-05 0.20 28/200 (14%) 11/200 (6%)

IL-17A Signaling in Gastric Cells 4.01E+00 9.77E-05 0.43 6/28 (21%) 6/28 (21%)Xenobiotic Metabolism Signaling 4.01E+00 9.77E-05 0.19 35/288 (12%) 19/288 (7%)Toll-like Receptor Signaling 3.98E+00 1.05E-04 0.30 9/64 (14%) 10/64 (16%)Acute Myeloid Leukemia Signaling 3.91E+00 1.23E-04 0.27 12/84 (14%) 11/84 (13%)RANK Signaling in Osteoclasts 3.84E+00 1.45E-04 0.26 16/97 (16%) 9/97 (9%)Oncostatin M Signaling 3.83E+00 1.48E-04 0.40 4/35 (11%) 10/35 (29%)CD40 Signaling 3.73E+00 1.86E-04 0.28 9/71 (13%) 11/71 (15%)Graft-versus-Host Disease Signaling 3.73E+00 1.86E-04 0.29 3/51 (6%) 12/51 (24%)B Cell Receptor Signaling 3.72E+00 1.91E-04 0.21 21/175 (12%) 16/175 (9%)Prolactin Signaling 3.66E+00 2.19E-04 0.26 11/84 (13%) 11/84 (13%)STAT3 Pathway 3.66E+00 2.19E-04 0.28 12/80 (15%) 10/80 (13%)Production of Nitric Oxide and Reactive Oxygen Species in Macrophages

3.66E+00 2.19E-04 0.19 25/212 (12%) 16/212 (8%)

Chemokine Signaling 3.66E+00 2.19E-04 0.28 16/75 (21%) 5/75 (7%)Role of PKR in Interferon Induction and Antiviral Response

3.62E+00 2.40E-04 0.31 4/49 (8%) 11/49 (22%)

Role of IL-17A in Psoriasis 3.61E+00 2.45E-04 0.57 1/14 (7%) 7/14 (50%)Crosstalk between Dendritic Cells and Natural Killer Cells

3.61E+00 2.45E-04 0.23 5/106 (5%) 19/106 (18%)

PPARα/RXRα Activation 3.45E+00 3.55E-04 0.19 23/200 (12%) 15/200 (8%)PKCθ Signaling in T Lymphocytes 3.42E+00 3.80E-04 0.19 18/144 (13%) 10/144 (7%)LXR/RXR Activation 3.37E+00 4.27E-04 0.22 13/139 (9%) 17/139 (12%)Role of JAK family kinases in IL-6-type Cytokine Signaling

3.33E+00 4.68E-04 0.39 4/28 (14%) 7/28 (25%)

IL-17 Signaling 3.30E+00 5.01E-04 0.28 9/75 (12%) 12/75 (16%)NRF2-mediated Oxidative Stress Response

3.25E+00 5.62E-04 0.20 20/195 (10%) 19/195 (10%)

IL-9 Signaling 3.25E+00 5.62E-04 0.33 3/40 (8%) 10/40 (25%)Renal Cell Carcinoma Signaling 3.21E+00 6.17E-04 0.25 14/79 (18%) 6/79 (8%)PI3K Signaling in B Lymphocytes 3.19E+00 6.46E-04 0.21 20/143 (14%) 10/143 (7%)cAMP-mediated signaling 3.11E+00 7.76E-04 0.20 30/226 (13%) 15/226 (7%)Activation of IRF by Cytosolic Pattern Recognition Receptors

3.00E+00 1.00E-03 0.25 1/73 (1%) 17/73 (23%)

UVA-Induced MAPK Signaling 2.96E+00 1.10E-03 0.24 13/98 (13%) 10/98 (10%)Role of NFAT in Cardiac Hypertrophy 2.95E+00 1.12E-03 0.18 27/209 (13%) 11/209 (5%)GM-CSF Signaling 2.91E+00 1.23E-03 0.27 9/68 (13%) 9/68 (13%)CD27 Signaling in Lymphocytes 2.91E+00 1.23E-03 0.27 6/59 (10%) 10/59 (17%)Erythropoietin Signaling 2.85E+00 1.41E-03 0.24 11/79 (14%) 8/79 (10%)HIF1α Signaling 2.85E+00 1.41E-03 0.22 17/112 (15%) 8/112 (7%)Tumoricidal Function of Hepatic Natural Killer Cells

2.85E+00 1.41E-03 0.37 2/27 (7%) 8/27 (30%)

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Role of JAK1, JAK2 and TYK2 in Interferon Signaling

2.85E+00 1.41E-03 0.36 2/28 (7%) 8/28 (29%)

ERK5 Signaling 2.85E+00 1.41E-03 0.27 13/68 (19%) 5/68 (7%)Semaphorin Signaling in Neurons 2.83E+00 1.48E-03 0.30 9/54 (17%) 7/54 (13%)TWEAK Signaling 2.81E+00 1.55E-03 0.31 3/39 (8%) 9/39 (23%)Growth Hormone Signaling 2.80E+00 1.58E-03 0.24 13/78 (17%) 6/78 (8%)Macropinocytosis Signaling 2.80E+00 1.58E-03 0.25 14/77 (18%) 5/77 (6%)Type II Diabetes Mellitus Signaling 2.78E+00 1.66E-03 0.16 13/171 (8%) 14/171 (8%)IL-12 Signaling and Production in Macrophages

2.76E+00 1.74E-03 0.19 14/157 (9%) 16/157 (10%)

IL-2 Signaling 2.75E+00 1.78E-03 0.26 10/61 (16%) 6/61 (10%)Phospholipase C Signaling 2.67E+00 2.14E-03 0.17 29/265 (11%) 15/265 (6%)Allograft Rejection Signaling 2.67E+00 2.14E-03 0.13 5/97 (5%) 8/97 (8%)Fcγ Receptor-mediated Phagocytosis in Macrophages and Monocytes

2.66E+00 2.19E-03 0.22 14/106 (13%) 9/106 (8%)

HGF Signaling 2.58E+00 2.63E-03 0.22 14/111 (13%) 10/111 (9%)Glycolysis I 2.53E+00 2.95E-03 0.22 9/41 (22%) 0/41 (0%)Germ Cell-Sertoli Cell Junction Signaling

2.53E+00 2.95E-03 0.20 20/169 (12%) 13/169 (8%)

Leukocyte Extravasation Signaling 2.52E+00 3.02E-03 0.19 27/210 (13%) 12/210 (6%)4-1BB Signaling in T Lymphocytes 2.51E+00 3.09E-03 0.31 4/36 (11%) 7/36 (19%)LPS-stimulated MAPK Signaling 2.43E+00 3.72E-03 0.23 13/83 (16%) 6/83 (7%)Glioma Invasiveness Signaling 2.41E+00 3.89E-03 0.24 11/66 (17%) 5/66 (8%)Gαq Signaling 2.37E+00 4.27E-03 0.18 20/171 (12%) 11/171 (6%)GNRH Signaling 2.35E+00 4.47E-03 0.18 19/153 (12%) 9/153 (6%)LPS/IL-1 Mediated Inhibition of RXR Function

2.34E+00 4.57E-03 0.16 23/245 (9%) 17/245 (7%)

Airway Pathology in Chronic Obstructive Pulmonary Disease

2.31E+00 4.90E-03 0.46 1/11 (9%) 4/11 (36%)

Endothelin-1 Signaling 2.28E+00 5.25E-03 0.18 19/192 (10%) 15/192 (8%)Inhibition of Matrix Metalloproteases 2.27E+00 5.37E-03 0.30 5/40 (13%) 7/40 (18%)IL-22 Signaling 2.25E+00 5.62E-03 0.36 4/25 (16%) 5/25 (20%)Lymphotoxin β Receptor Signaling 2.23E+00 5.89E-03 0.24 7/62 (11%) 8/62 (13%)P2Y Purigenic Receptor Signaling Pathway

2.20E+00 6.31E-03 0.18 17/144 (12%) 9/144 (6%)

FLT3 Signaling in Hematopoietic Progenitor Cells

2.20E+00 6.31E-03 0.23 12/79 (15%) 6/79 (8%)

IL-4 Signaling 2.20E+00 6.31E-03 0.23 12/80 (15%) 6/80 (8%)Thrombopoietin Signaling 2.16E+00 6.92E-03 0.23 10/64 (16%) 5/64 (8%)Fc Epsilon RI Signaling 2.15E+00 7.08E-03 0.21 15/117 (13%) 9/117 (8%)fMLP Signaling in Neutrophils 2.15E+00 7.08E-03 0.18 16/132 (12%) 8/132 (6%)Renin-Angiotensin Signaling 2.10E+00 7.94E-03 0.19 17/126 (13%) 7/126 (6%)Sphingosine-1-phosphate Signaling 2.10E+00 7.94E-03 0.20 16/123 (13%) 8/123 (7%)Hematopoiesis from Pluripotent Stem Cells

2.09E+00 8.13E-03 0.18 2/63 (3%) 9/63 (14%)

CCR5 Signaling in Macrophages 2.06E+00 8.71E-03 0.17 10/97 (10%) 6/97 (6%)Cardiac Hypertrophy Signaling 2.06E+00 8.71E-03 0.16 28/250 (11%) 13/250 (5%)IL-1 Signaling 2.05E+00 8.91E-03 0.19 10/109 (9%) 11/109 (10%)OX40 Signaling Pathway 2.04E+00 9.12E-03 0.13 8/97 (8%) 5/97 (5%)iCOS-iCOSL Signaling in T Helper Cells

2.00E+00 1.00E-02 0.18 12/126 (10%) 10/126 (8%)

Retinoic acid Mediated Apoptosis Signaling

1.95E+00 1.12E-02 0.21 3/73 (4%) 12/73 (16%)

TGF-β Signaling 1.95E+00 1.12E-02 0.21 15/94 (16%) 5/94 (5%)IL-15 Production 1.91E+00 1.23E-02 0.29 2/31 (6%) 7/31 (23%)MIF-mediated Glucocorticoid 1.90E+00 1.26E-02 0.24 4/42 (10%) 6/42 (14%)

Sokolowska M. et al

RegulationSAPK/JNK Signaling 1.90E+00 1.26E-02 0.19 11/105 (10%) 9/105 (9%)Induction of Apoptosis by HIV1 1.89E+00 1.29E-02 0.22 2/67 (3%) 13/67 (19%)PI3K/AKT Signaling 1.89E+00 1.29E-02 0.16 13/152 (9%) 12/152 (8%)Gαs Signaling 1.87E+00 1.35E-02 0.18 14/125 (11%) 9/125 (7%)PEDF Signaling 1.86E+00 1.38E-02 0.22 9/79 (11%) 8/79 (10%)Protein Kinase A Signaling 1.84E+00 1.45E-02 0.15 37/409 (9%) 24/409 (6%)April Mediated Signaling 1.83E+00 1.48E-02 0.25 7/44 (16%) 4/44 (9%)ErbB2-ErbB3 Signaling 1.78E+00 1.66E-02 0.22 9/63 (14%) 5/63 (8%)Amyotrophic Lateral Sclerosis Signaling 1.75E+00 1.78E-02 0.17 11/126 (9%) 10/126 (8%)Neurotrophin/TRK Signaling 1.75E+00 1.78E-02 0.21 12/76 (16%) 4/76 (5%)ErbB Signaling 1.75E+00 1.78E-02 0.21 13/90 (14%) 6/90 (7%)Ceramide Signaling 1.75E+00 1.78E-02 0.20 11/91 (12%) 7/91 (8%)Natural Killer Cell Signaling 1.72E+00 1.91E-02 0.19 18/118 (15%) 4/118 (3%)Fatty Acid β-oxidation I 1.72E+00 1.91E-02 0.20 5/45 (11%) 4/45 (9%)MIF Regulation of Innate Immunity 1.67E+00 2.14E-02 0.21 6/52 (12%) 5/52 (10%)B Cell Activating Factor Signaling 1.67E+00 2.14E-02 0.24 7/46 (15%) 4/46 (9%)Glycogen Degradation III 1.67E+00 2.14E-02 0.28 3/18 (17%) 2/18 (11%)Antioxidant Action of Vitamin C 1.66E+00 2.19E-02 0.18 8/111 (7%) 12/111 (11%)Uracil Degradation II (Reductive) 1.66E+00 2.19E-02 0.27 2/11 (18%) 1/11 (9%)Thymine Degradation 1.66E+00 2.19E-02 0.27 2/11 (18%) 1/11 (9%)Hypoxia Signaling in the Cardiovascular System

1.66E+00 2.19E-02 0.22 6/68 (9%) 9/68 (13%)

Aldosterone Signaling in Epithelial Cells

1.66E+00 2.19E-02 0.17 12/169 (7%) 17/169 (10%)

Nur77 Signaling in T Lymphocytes 1.65E+00 2.24E-02 0.19 9/64 (14%) 3/64 (5%)G Beta Gamma Signaling 1.62E+00 2.40E-02 0.16 13/121 (11%) 6/121 (5%)Mouse Embryonic Stem Cell Pluripotency

1.58E+00 2.63E-02 0.20 12/99 (12%) 8/99 (8%)

Granzyme B Signaling 1.57E+00 2.69E-02 0.33 1/18 (6%) 5/18 (28%)Putrescine Degradation III 1.57E+00 2.69E-02 0.20 4/30 (13%) 2/30 (7%)IL-3 Signaling 1.55E+00 2.82E-02 0.21 12/75 (16%) 4/75 (5%)Small Cell Lung Cancer Signaling 1.55E+00 2.82E-02 0.17 6/94 (6%) 10/94 (11%)Role of MAPK Signaling in the Pathogenesis of Influenza

1.55E+00 2.82E-02 0.21 5/72 (7%) 10/72 (14%)

PTEN Signaling 1.55E+00 2.82E-02 0.17 14/139 (10%) 9/139 (6%)Cytotoxic T Lymphocyte-mediated Apoptosis of Target Cells

1.55E+00 2.82E-02 0.21 2/39 (5%) 6/39 (15%)

CNTF Signaling 1.52E+00 3.02E-02 0.21 9/57 (16%) 3/57 (5%)Role of JAK2 in Hormone-like Cytokine Signaling

1.47E+00 3.39E-02 0.24 2/37 (5%) 7/37 (19%)

Paxillin Signaling 1.46E+00 3.47E-02 0.17 16/117 (14%) 4/117 (3%)γ-linolenate Biosynthesis II (Animals) 1.46E+00 3.47E-02 0.25 2/24 (8%) 4/24 (17%)Ethanol Degradation IV 1.46E+00 3.47E-02 0.21 5/29 (17%) 1/29 (3%)Docosahexaenoic Acid (DHA) Signaling

1.44E+00 3.63E-02 0.20 5/50 (10%) 5/50 (10%)

Pancreatic Adenocarcinoma Signaling 1.40E+00 3.98E-02 0.16 10/128 (8%) 11/128 (9%)Estrogen-Dependent Breast Cancer Signaling

1.40E+00 3.98E-02 0.19 10/73 (14%) 4/73 (5%)

EGF Signaling 1.40E+00 3.98E-02 0.20 9/64 (14%) 4/64 (6%)Fatty Acid Activation 1.38E+00 4.17E-02 0.26 1/19 (5%) 4/19 (21%)Gαi Signaling 1.36E+00 4.37E-02 0.17 14/135 (10%) 9/135 (7%)T Cell Receptor Signaling 1.36E+00 4.37E-02 0.17 13/109 (12%) 6/109 (6%)Prostanoid Biosynthesis 1.33E+00 4.68E-02 0.27 2/15 (13%) 2/15 (13%)Folate Transformations I 1.33E+00 4.68E-02 0.12 1/33 (3%) 3/33 (9%)

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CD28 Signaling in T Helper Cells 1.32E+00 4.79E-02 0.15 14/136 (10%) 7/136 (5%)Regulation of IL-2 Expression in Activated and Anergic T Lymphocytes

1.32E+00 4.79E-02 0.18 10/89 (11%) 6/89 (7%)

14-3-3-mediated Signaling 1.31E+00 4.90E-02 0.18 16/121 (13%) 6/121 (5%)

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TABLE E5. GENES PRESENTING CHANGES IN GRANULOCYTE ADHESION AND DIAPEDESIS PATHWAY IN PBMC STIMULATED WITH LMW HA COMPARED TO UNSTIMULATED PBMC IN ALL SUBJECTS

Entrez Gene ID

Entrez Gene Name Gene Symbol Fold Change

728 complement component 5a receptor 1 C5AR1 -4.26346 chemokine (C-C motif) ligand 1 CCL1 1.96347 chemokine (C-C motif) ligand 2 CCL2 9.76348 chemokine (C-C motif) ligand 3 CCL3 16.46351 chemokine (C-C motif) ligand 4 CCL4 8.26354 chemokine (C-C motif) ligand 7 CCL7 4.76359 chemokine (C-C motif) ligand 15 CCL15 4.06362 chemokine (C-C motif) ligand 18 (pulmonary and activation-regulated) CCL18 23.86363 chemokine (C-C motif) ligand 19 CCL19 2.66364 chemokine (C-C motif) ligand 20 CCL20 30.56367 chemokine (C-C motif) ligand 22 CCL22 2.56368 chemokine (C-C motif) ligand 23 CCL23 23.66349|414062

chemokine (C-C motif) ligand 3-like 1 CCL3L1/CCL3L3

18.3

9560|388372

chemokine (C-C motif) ligand 4-like 1 CCL4L1/CCL4L2

17.1

51192 chemokine-like factor CKLF -2.81440 colony stimulating factor 3 (granulocyte) CSF3 7.51441 colony stimulating factor 3 receptor (granulocyte) CSF3R -2.32919 chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating

activity, alpha)CXCL1 21.3

2920 chemokine (C-X-C motif) ligand 2 CXCL2 8.92921 chemokine (C-X-C motif) ligand 3 CXCL3 7.46374 chemokine (C-X-C motif) ligand 5 CXCL5 13.76372 chemokine (C-X-C motif) ligand 6 CXCL6 10.110563 chemokine (C-X-C motif) ligand 13 CXCL13 3.958191 chemokine (C-X-C motif) ligand 16 CXCL16 -1.73579 chemokine (C-X-C motif) receptor 2 CXCR2 -3.02358 formyl peptide receptor 2 FPR2 5.92359 formyl peptide receptor 3 FPR3 -13.12734 golgi glycoprotein 1 GLG1 -1.62771 guanine nucleotide binding protein (G protein), alpha inhibiting activity

polypeptide 2GNAI2 -1.6

3269 histamine receptor H1 HRH1 4.63383 intercellular adhesion molecule 1 ICAM1 3.03576 interleukin 8 IL8 2.53552 interleukin 1, alpha IL1A 28.63553 interleukin 1, beta IL1B 7.83554 interleukin 1 receptor, type I IL1R1 2.17850 interleukin 1 receptor, type II IL1R2 -7.43556 interleukin 1 receptor accessory protein IL1RAP -2.23557 interleukin 1 receptor antagonist IL1RN 9.656300 interleukin 36, gamma IL36G 110.726525 interleukin 36 receptor antagonist IL36RN 1.93683 integrin, alpha L (antigen CD11A (p180), lymphocyte function-

associated antigen 1; alpha polypeptide)ITGAL -2.2

3684 integrin, alpha M (complement component 3 receptor 3 subunit) ITGAM -3.6

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3689 integrin, beta 2 (complement component 3 receptor 3 and 4 subunit) ITGB2 -3.24312 matrix metallopeptidase 1 (interstitial collagenase) MMP1 12.74313 matrix metallopeptidase 2 (gelatinase A, 72kDa gelatinase, 72kDa type

IV collagenase)MMP2 -1.6

4316 matrix metallopeptidase 7 (matrilysin, uterine) MMP7 24.04319 matrix metallopeptidase 10 (stromelysin 2) MMP10 29.14323 matrix metallopeptidase 14 (membrane-inserted) MMP14 14.679148 matrix metallopeptidase 28 MMP28 -1.85175 platelet/endothelial cell adhesion molecule 1 PECAM1 -14.35962 radixin RDX 1.86383 syndecan 2 SDC2 2.16385 syndecan 4 SDC4 2.36404 selectin P ligand SELPLG -1.77124 tumor necrosis factor TNF 10.57133 tumor necrosis factor receptor superfamily, member 1B TNFRSF1B 1.9

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TABLE E6. GENES PRESENTING CHANGES IN TREM1 SIGNALING IN PBMC STIMULATED WITH LMW HA COMPARED TO UNSTIMULATED PBMC IN ALL SUBJECTS

Entrez Gene ID

Entrez Gene Name Symbol Fold Change

838 caspase 5, apoptosis-related cysteine peptidase CASP5 17.16347 chemokine (C-C motif) ligand 2 CCL2 9.76348 chemokine (C-C motif) ligand 3 CCL3 16.46354 chemokine (C-C motif) ligand 7 CCL7 4.7958 CD40 molecule, TNF receptor superfamily member 5 CD40 2.59308 CD83 molecule CD83 2.5942 CD86 molecule CD86 -4.81437 colony stimulating factor 2 (granulocyte-macrophage) CSF2 2.72921 chemokine (C-X-C motif) ligand 3 CXCL3 7.42213 Fc fragment of IgG, low affinity IIb, receptor (CD32) FCGR2B -5.53383 intercellular adhesion molecule 1 ICAM1 3.03569 interleukin 6 (interferon, beta 2) IL6 122.93576 interleukin 8 IL8 2.53586 interleukin 10 IL10 13.63553 interleukin 1, beta IL1B 7.83687 integrin, alpha X (complement component 3 receptor 4 subunit) ITGAX -1.87462 linker for activation of T cells family, member 2 LAT2 -1.95594 mitogen-activated protein kinase 1 MAPK1 -1.85595 mitogen-activated protein kinase 3 MAPK3 -1.94671 NLR family, apoptosis inhibitory protein NAIP -2.44790 nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 NFKB1 2.84791 nuclear factor of kappa light polypeptide gene enhancer in B-cells 2

(p49/p100)NFKB2 2.5

197358 NLR family, CARD domain containing 3 NLRC3 -1.758484 NLR family, CARD domain containing 4 NLRC4 -3.422861 NLR family, pyrin domain containing 1 NLRP1 -1.8114548 NLR family, pyrin domain containing 3 NLRP3 1.991662 NLR family, pyrin domain containing 12 NLRP12 -8.364127 nucleotide-binding oligomerization domain containing 2 NOD2 -3.76774 signal transducer and activator of transcription 3 (acute-phase response

factor)STAT3 1.7

6776 signal transducer and activator of transcription 5A STAT5A 1.97096 toll-like receptor 1 TLR1 -1.910333 toll-like receptor 6 TLR6 -1.97124 tumor necrosis factor TNF 10.57305 TYRO protein tyrosine kinase binding protein TYROBP -2.7

Sokolowska M. et al

TABLE E7. GENES PRESENTING CHANGES IN ROLE OF PATTERN RECOGNITION RECEPTORS IN RECOGNITION OF BACTERIA AND VIRUSES IN PBMC STIMULATED WITH LMW HA COMPARED TO UNSTIMULATED PBMC IN ALL SUBJECTS

Entrez Gene ID


472 ataxia telangiectasia mutated ATM -1.7718 complement component 3 C3 2.9713 complement component 1, q subcomponent, B chain C1QB 2.0728 complement component 5a receptor 1 C5AR1 -4.293978 C-type lectin domain family 6, member A CLEC6A -1.864581 C-type lectin domain family 7, member A CLEC7A -15.223586 DEAD (Asp-Glu-Ala-Asp) box polypeptide 58 DDX58 2.45610 eukaryotic translation initiation factor 2-alpha kinase 2 EIF2AK2 3.164135 interferon induced with helicase C domain 1 IFIH1 2.83569 interleukin 6 (interferon, beta 2) IL6 122.93586 interleukin 10 IL10 13.63593 interleukin 12B (natural killer cell stimulatory factor 2, cytotoxic

lymphocyte maturation factor 2, p40)IL12B 131.1

3553 interleukin 1, beta IL1B 7.83665 interferon regulatory factor 7 IRF7 2.15594 mitogen-activated protein kinase 1 MAPK1 -1.85595 mitogen-activated protein kinase 3 MAPK3 -1.94790 nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 NFKB1 2.84791 nuclear factor of kappa light polypeptide gene enhancer in B-cells 2

(p49/p100)NFKB2 2.5

58484 NLR family, CARD domain containing 4 NLRC4 -3.4114548 NLR family, pyrin domain containing 3 NLRP3 1.964127 nucleotide-binding oligomerization domain containing 2 NOD2 -3.74938 2'-5'-oligoadenylate synthetase 1, 40/46kDa OAS1 2.24939 2'-5'-oligoadenylate synthetase 2, 69/71kDa OAS2 3.24940 2'-5'-oligoadenylate synthetase 3, 100kDa OAS3 7.05291 phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit beta PIK3CB -2.95294 phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit

gammaPIK3CG -1.6

5578 protein kinase C, alpha PRKCA -1.65579 protein kinase C, beta PRKCB -1.65806 pentraxin 3, long PTX3 12.98767 receptor-interacting serine-threonine kinase 2 RIPK2 2.56041 ribonuclease L (2',5'-oligoisoadenylate synthetase-dependent) RNASEL -1.56850 spleen tyrosine kinase SYK -2.27096 toll-like receptor 1 TLR1 -1.910333 toll-like receptor 6 TLR6 -1.97124 tumor necrosis factor TNF 10.5

Sokolowska M. et al

TABLE E8. GENES PRESENTING CHANGES IN IL-10 SIGNALING IN PBMC STIMULATED WITH LMW HA COMPARED TO UNSTIMULATED PBMC IN ALL SUBJECTS

Entrez Gene


384 arginase 2 ARG2 -2.149645 biliverdin reductase B (flavin reductase (NADPH)) BLVRB -1.8911230 chemokine (C-C motif) receptor 1 CCR1 -4.341929 CD14 molecule CD14 -32213 Fc fragment of IgG, low affinity IIb, receptor (CD32) FCGR2B -5.5029103 Fc fragment of IgG, low affinity IIc, receptor for (CD32)

(gene/pseudogene)FCGR2C -1.783

2353 FBJ murine osteosarcoma viral oncogene homolog FOS -3.6083162 heme oxygenase (decycling) 1 HMOX1 -2.3213569 interleukin 6 (interferon, beta 2) IL6 122.8713586 interleukin 10 IL10 13.6233552 interleukin 1, alpha IL1A 28.6013553 interleukin 1, beta IL1B 7.763554 interleukin 1 receptor, type I IL1R1 2.0767850 interleukin 1 receptor, type II IL1R2 -7.3563556 interleukin 1 receptor accessory protein IL1RAP -2.2183557 interleukin 1 receptor antagonist IL1RN 9.6456300 interleukin 36, gamma IL36G 110.73726525 interleukin 36 receptor antagonist IL36RN 1.9033566 interleukin 4 receptor IL4R 1.7943725 jun proto-oncogene JUN -1.6275606 mitogen-activated protein kinase kinase 3 MAP2K3 1.7789448 mitogen-activated protein kinase kinase kinase kinase 4 MAP4K4 1.5675594 mitogen-activated protein kinase 1 MAPK1 -1.7791432 mitogen-activated protein kinase 14 MAPK14 -1.6354790 nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 NFKB1 2.7784791 nuclear factor of kappa light polypeptide gene enhancer in B-cells 2

(p49/p100)NFKB2 2.519

4792 nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha

NFKBIA 2.988

9021 suppressor of cytokine signaling 3 SOCS3 8.3516774 signal transducer and activator of transcription 3 (acute-phase response

factor)STAT3 1.684

7124 tumor necrosis factor TNF 10.5417297 tyrosine kinase 2 TYK2 -1.697

Sokolowska M. et al

TABLE E9. GENES PRESENTING CHANGES IN DIFFERENTIAL REGULATION OF CYTOKINE PRODUCTION IN MACROPHAGES AND T HELPER CELLS BY IL17A AND IL17F CANONICAL PATHWAY IN PBMC STIMULATED WITH LMW HA COMPARED TO UNSTIMULATED PBMC IN ALL SUBJECTS

Entrez Gene ID


6347 chemokine (C-C motif) ligand 2 CCL2 9.76348 chemokine (C-C motif) ligand 3 CCL3 16.46351 chemokine (C-C motif) ligand 4 CCL4 8.21437 colony stimulating factor 2 (granulocyte-macrophage) CSF2 2.71440 colony stimulating factor 3 (granulocyte) CSF3 7.52919 chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity,

alpha)CXCL1 21.3

3569 interleukin 6 (interferon, beta 2) IL6 122.93586 interleukin 10 IL10 13.63596 interleukin 13 IL13 1.63593 interleukin 12B (natural killer cell stimulatory factor 2, cytotoxic lymphocyte

maturation factor 2, p40)IL12B 131.1

3605 interleukin 17A IL17A 2.0112744 interleukin 17F IL17F 1.73553 interleukin 1, beta IL1B 7.87124 tumor necrosis factor TNF 10.5

Sokolowska M. et al

TABLE E10. SIGNIFICANTLY ENRICHED BIO FUNCTIONS AND DESEASES IN LMW HA STIMULATED PBMC COMPARED TO UNSTIMULATED PBMC IN ALL SUBJECTS

Physiological System Development and Function

Category B-H p-value (FDR)Immune Cell Trafficking 1.15E-58-3.43E-10Hematological System Development and Function 1.17E-56-3.64E-10Tissue Morphology 1.17E-56-4.74E-10Organismal Survival 1.67E-32-2.68E-22Hematopoiesis 3.09E-30-3.64E-10Lymphoid Tissue Structure and Development 8.47E-27-3.28E-10Tissue Development 3.1E-26-4.52E-10Humoral Immune Response 8.09E-25-3.54E-10Organ Morphology 1.41E-23-2.65E-11Cell-mediated Immune Response 1.97E-23-3.28E-10Tumor Morphology 3.6E-23-1.57E-10Organismal Development 2.26E-19-4.52E-10Skeletal and Muscular System Development and Function 5.09E-18-4.52E-10Cardiovascular System Development and Function 5.28E-18-1.78E-10Digestive System Development and Function 1.48E-17-2.48E-11Hepatic System Development and Function 1.48E-17-1.48E-17Organ Development 1.48E-17-4.52E-10Embryonic Development 1.94E-17-4.52E-10Connective Tissue Development and Function 3.98E-15-4.52E-10

Molecular and Cellular Functions

Category B-H p-value (FDR)Cellular Function and Maintenance 6.56E-72-3.43E-10Cellular Movement 1.15E-58-3.43E-10Cellular Growth and Proliferation 3.63E-52-3.12E-10Cellular Development 4.32E-51-4.72E-10Cell Death and Survival 1.88E-47-4.35E-10Cell-To-Cell Signaling and Interaction 1.43E-46-2.97E-10Protein Synthesis 8.09E-25-3.54E-10Cell Morphology 1.62E-21-4.96E-10Free Radical Scavenging 6.16E-21-4.93E-17Cellular Compromise 1E-20-3.86E-12Gene Expression 7.28E-19-4.16E-10Cell Signaling 1.07E-15-1.31E-11Small Molecule Biochemistry 1.07E-15-2.72E-10Lipid Metabolism 5.99E-15-2.72E-10Molecular Transport 1.94E-14-1.01E-10Vitamin and Mineral Metabolism 1.94E-14-1.31E-11Carbohydrate Metabolism 1.76E-12-1.76E-12Post-Translational Modification 1.23E-11-1.23E-11Cell Cycle 1.81E-11-1.85E-10Drug Metabolism 2.72E-10-2.72E-10

Sokolowska M. et al

Diseases and Disorders

Category B-H p-value (FDR)Inflammatory Response 3.68E-48-4.76E-10Immunological Disease 9.1E-45-4.7E-10Connective Tissue Disorders 8.25E-37-3.73E-11Inflammatory Disease 8.25E-37-5.72E-11Skeletal and Muscular Disorders 8.25E-37-5.56E-12Infectious Disease 2.25E-34-1.55E-10Neurological Disease 4.88E-33-6.56E-11Dermatological Diseases and Conditions 6.13E-33-5.72E-11Respiratory Disease 1.27E-28-1.08E-11Cancer 1.67E-27-2.41E-10Endocrine System Disorders 2.04E-23-5.95E-22Gastrointestinal Disease 2.04E-23-1.67E-12Metabolic Disease 2.04E-23-6.95E-20Cardiovascular Disease 1.56E-22-1.04E-10Organismal Injury and Abnormalities 2.7E-22-4.99E-10Hepatic System Disease 2.85E-20-8.27E-14Renal and Urological Disease 6.62E-14-2.37E-11Reproductive System Disease 8.71E-14-8.71E-14Ophthalmic Disease 1.43E-13-1.43E-13Hematological Disease 1.83E-13-4.7E-10Developmental Disorder 3.4E-12-3.4E-12Hypersensitivity Response 3.72E-12-1.37E-10Antimicrobial Response 4.52E-12-4.52E-12

Sokolowska M. et al

TABLE E11. CONFIRMATORY EXPERIMENTS

LMW HA vs vehicle

FC (95% CI)

Gene name Gene

Symbol

Entrez

Gene

RT PCR Array

prostaglandin-endoperoxide synthase 2

(prostaglandin G/H synthase and

cyclooxygenase)

PTGS2 5743 332 (235.8; 428.3) 35.26 (22.47; 55.33)

prostaglandin E synthase PTGES 9536 13.01 (9.5; 16.6) 1.28 (1.16; 1.42)

phospholipase A2, group IVA

(cytosolic, calcium-dependent)

PLA2G4A 5321 4.7 (3.4; 6.1) 2.36 (1.78; 3.14)

inhibin, beta A INHBA 3624 266.2 (157.3; 375.1) 28.44 (20.25; 39.67)

interleukin 36, gamma IL36G 56300 4428.4 (1591.8; 7265) 110.66 (72.5; 170.07)

arachidonate 5-lipoxygenase ALOX5 240 -1.42 (-1.84; -1.16) -3.45 (-4.55; -2.7)

fibronectin 1 FN1 2335 -5.2 (-12.9; -3.3) -5.56 (-8.33; -3.57)

leptin LEP 3952 -36.3 (-120; -21.4) -6.67 (-11.11; -4)

Sokolowska M. et al

TABLE E12. SIGNIFICANTLY ENRICHED GENES IN THE INFECTIOUS DISEASE CATEGORY IN SEVERE ASTHMA

Diseases or Functions Annotation p-Value Activation

z-score

Bias-

corrected z-

score

#

Molecules

Replication of vesicular stomatitis

virus

1.02E-07 2.875 1.666 9

Replication of RNA virus 3.70E-07 2.968 4.216 20

Viral Infection 4.20E-06 3.279 5.388 35

Infection of mammalia 2.43E-05 1.876 0.585 14

Replication of Herpesviridae 3.00E-05 1.673 0.781 7

Replication of Murine herpesvirus 4 4.15E-04 2.000 4

Replication of HIV-1 6.00E-03 1.154 1.391 6

Bacterial Infection 6.88E-03 1.607 1.027 11

Replication of Flaviviridae 3.20E-02 2.000 1.902 4

Replication of Influenza A virus 4.45E-02 1.276 2.531 7

P-value is calculated based on the experimental data set. Positive activation z-score predicts increase of function; negative activation z-score predicts decrease of function. # of molecules presented in the experimental data set that are enriched in this bio function.

Sokolowska M. et al

ONLINE FIGURE LEGENDS

Figure E1. LMW HA causes profound changes in the global lipidomic profile. A, Bar graph presenting

all cyclooxygenase (COX) metabolites as a percentage of total lipid mediators in each phenotype. Each bar

presents average and SD from all subjects (n=6, controls; n=7, mild-to-moderate asthma; n=6, severe asthma).

* P<.05 as assessed by one-way ANOVA with Dunn’s post hoc test. B, Heat map representing the actual

levels (pmol/ml) of lipid species, significantly upregulated by LMW HA (* P<.05 as assessed by Kruskal-

Wallis ANOVA on ranks with Dunn’s post hoc test) in each phenotype; AA, arachidonic acid; DHA,

docosahexaenoic acid; COX, cyclooxygenase; LOX, lipoxygenase; CYP 450, cytochrome P450; 15-HETE,

15-hydroxyeicosatetraenoic acid; 13-HODE, 13-hydroxyoctadecadienoic acid; 13-HOTRE(y), 13-

hydroxyoctadecatrienoic acid; 15-HETrE, 15-hydroxyeicosatrienoic acid; 11(12)-EET, 11, 12-

epoxyeicosatrienoic acid; 14(15)-EET, 14, 15-epoxyeicosatrienoic acid; 16(17) EpDPE, 16(17)-

epoxydocosapentaenoic acid; 13 HDoHE, 13-hydroxydocosahexaenoic acid; 12-HHTrE, 12-

hydroxyheptadecatrienoic acid; 11-HETE, 11-hydroxyeicosatetraenoic acid; PGE2, D2,F2, prostaglandin

E2, D2, F2; TxB2, thromboxane B2;

Figure E2. LMW HA-induced lipid mediators, significantly upregulated in severe asthmatics. Each dot

represents average levels of experimental duplicates in each individual; line represents the median. Larger and

half-filled dots in the severe asthma group symbolize patients with ASA sensitivity. * P<.05 as indicated, as

assessed by Kruskal-Wallis ANOVA on ranks with Dunn’s post hoc test and subsequent Bonferroni

correction.

Figure E3. LMW HA effect on global gene expression in PBMCs. A, Twelve most significantly enriched

canonical pathways after LMW HA compared to vehicle in all subjects. Left hand side graph X axis presents -

log(B-H p-value), which is a Benjamini Hochberg false discovery rate (FDR) transformation, with indicated

threshold 1.3 equal to FDR < 0.05. Orange dots, connected with line present ratio of number of genes

presented in the experimental data set to the entire number of genes assigned to this pathway. Right hand side

Sokolowska M. et al

stacked bar graph X axis presents percentage of genes presented in the data set creating enrichment in the

indicated pathway, with the number on right stating the entire number of genes assigned to this pathway.

Green indicates down regulation and red up regulation. Orange dots connected with the line indicate -log(B-H

p-value). B, Heat map of significantly enriched genes in eicosanoid metabolism biofunction.

Figure E4. Expression of proteins, not involved in the LMW HA-dependent signaling impairment in

severe asthma, is similar in each phenotype. Dot blots, presenting the LMW HA-induced expression of

CCL20, IL-6, IL-1, IL-1 and IL-10. Each dot represents average levels of experimental duplicates in each

individual; line represents median. * P<.05 as compared to vehicle control in similar phenotype as assessed by

Kruskal-Wallis ANOVA on ranks with Dunn’s post hoc test. If proteins were not expressed at baseline,

statistical significance was evaluated as compared to the value of the detection level.

Figure E5. cPLA2 and different hyaluronan receptors mRNA expression is increased upon LMW HA

treatment, but similar in each phenotype. Dot blots, presenting the LMW HA-induced mRNA expression

of A, PLA2G4A (cPLA2); B, TLR4; C, CD44; D, TLR2; E, HMMR (Rhamm) mRNA. * P<.05 as compared

to vehicle control in similar phenotype as assessed by Kruskal-Wallis ANOVA on ranks with Dunn’s post hoc

test.

Documents

· Web view[Online repository] Dysregulation of lipidomic profile and antiviral immunity in response to hyaluronan in severe asthma Milena Sokolowska, MD, PhD1,#, Li-Yuan Chen, PhD1,