Click here to load reader
Upload
haxuyen
View
216
Download
0
Embed Size (px)
Citation preview
Mukherjee et al., 2016
Online Methods:
All ELISAs for sputum and animal samples were developed in our laboratory using the reagents
from KPL Inc., (Gaithersburg, MA, USA), unless otherwise stated.
Sputum Ig Isotyping (IgA, IgM, IgG, IgE): For detecting the total IgG, IgA and IgM quantity in
each sputum sample, 96-well MaxiSorb (Nunc) plates were coated with the respective sputum
IP-Igs at a dilution of 1:50 (in PBS), separately for each subtype. ChromPure Human IgG, IgM
and IgA (Jackson ImmunoResearch Laboratories, PA, USA) were used for constructing the
standard curve. The Igs were allowed to attach at 37˚C for 1.5 hrs, followed by a thorough-wash
and 1 hr of blocking (1% BSA) at RT. Biotinylated secondary antibodies (BD Biosciences, ON,
Canada) against human IgG (1:2000 dilution), IgM (1:1000) and IgA (1:1000) were added to the
respective wells for 1 hr at RT, and subsequently the plates were developed and absorbance read
at 600 nm. A 4PL-standard curve was used to analyse the unknowns. In addition, since Protein
A/G does not bind IgE, the total IgE levels in all 79 sputum supernatants were tested with a
Human IgE SinglePlex kit (Eve Technologies, Calgary, Canada).
Immunostaining of eosinophils with Sputum Igs
Human peripheral blood eosinophils were isolated from the venous blood of atopic donors, as
previously described (E1). Cells were re-suspended in serum-free RPMI 1640 at 1 x 106/mL, and
coated onto glass coverslips for 15 min at 37°C, and fixed with 4% PFA for 20 min. Cells were
then permeabilized with 0.1% Triton X-100 in PBS for 3 min prior to blocking with 2% bovine
serum albumin and 2% goat serum in PBS to reduce nonspecific binding by antibodies. Sputum
Igs were diluted in blocking buffer and added to permeabilized eosinophils and incubated at RT
for 1 hr. For co-localization studies, mouse anti-EPX monoclonal antibody (clone MM25-
429.1.1) was used. Secondary antibody labelling was carried out with Alexa 488-conjugated
E1Supplementary material
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Mukherjee et al., 2016
anti-human IgG and Cy3-conjugated anti-mouse IgG secondary antibodies (Life Technologies,
Inc.). Images were captured on a Deltavision OMX super resolution microscope using a 60X
objective, and fluorescence was quantified on a Leica SP5 confocal microscope. Image analysis
was carried out using Fiji by ImageJ to determine total cellular fluorescence values.
Mice models of airway degranulation
The double transgenic model of severe respiratory inflammation (I5/hE2) was created from a
cross of NJ.1638 mice (a transgenic line of mice generated by driving constitutive expression of
a cDNA/genomic fusion gene of mouse IL-5 using a CD3δ promoter/enhancer construct (E2, E3)
with another transgenic strain constitutively expressing the human eotaxin 2 gene in central
airway epithelial cells (i.e., Clara cells) using a rat secretory protein CC10 promoter (E4). These
individual transgenic lines were created on a C57BL/6J background and subsequent generations
of transgenic animals were the result of backcrosses (every 5th generation) onto inbred strain
C57BL/6J and have been in this breeding program for >35 generations. The studies described in
this report were performed exclusively with mice hemizygous for both transgenes. Transgene
negative littermates and/or C57BL/6J mice purchased from The Jackson Laboratory (Bar Harbor,
ME) were used as control animals. The mice were maintained in ventilated micro-isolator cages
housed in the specific pathogen-free animal facility at Mayo Clinic Arizona. The sentinel cages
within the animal colony surveyed negative for the presence of known mouse pathogens.
Protocols and studies involving animals were carried out in accordance with NIH and Mayo
Foundation institutional guidelines.
Bronchoalveolar lavage fluid cellularity, anti-EPX IgG detection and Immunohistochemical
detection of B-cell clusters
E2Supplementary material
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
Mukherjee et al., 2016
BAL fluid was recovered following instillation of 1mL of 2% fetal calf serum (FCS) in
phosphate-buffered saline. The BAL fluid was initially centrifuged at low speed (10 min at
400xg, 4°C) for cell isolation. However, cell-free BAL fluid for assessment of Ig levels and the
presence of free eosinophil peroxidase (i.e., eosinophil degranulation) was prepared by a second
high speed centrifugation (10 minutes, 10,000xg) at 4°C, which was necessary to clear all
remaining cells from the fluid. For the anti-EPX reactivity in mice BAL and sera,
immunoprecipitated Igs were incubated overnight in EPX-coated (1µg/ml LEE Biosolutions,
MO, USA) Maxisorb plates and probed with biotinylated secondary antibodies against mouse
IgG. ,Mouse anti-EPX monoclonal (clone MM25-429.1.1) antibody was used for constructing
the standard curve, and values were normalised to the total protein content measured by Bio-rad
DC kit. Lung and lymph node infiltrating B220+ mononuclear cells (pan-B cell marker) was
identified via immunohistochemistry using a rat monoclonal antibody recognizing mouse B220
(BD Pharmingen, California) as per manufacturer’s instructions.
Statistical Analysis:
All experimental data were analysed with GraphPad Prism (Version 6.05, La Jolla, CA, USA).
Statistical comparison between the groups were performed by Analysis of Variance (ANOVA) /
Kruskal-Wallis non-parametric tests, and associations were determined by Spearman’s rank/
Pearson correlation test based on the distribution of the respective data-sets (D'Agostino &
Pearson omnibus normality tests). IBM® SPSS® Statistical software (version 23.0) was used for
multivariate regression analysis. P values ≤ 0.01 were considered to be significant, unless
otherwise stated.
Online Results:
E3Supplementary material
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
Mukherjee et al., 2016
Development of local autoimmune responses in the airways of mice with heightened airway
degranulation
To reiterate the importance of airway degranulation we investigated the presence autoimmune
phenomenon in two unique murine models of eosinophilic asthma: (i) double transgenic mice
with severe respiratory inflammation (I5/hE2) and airway eosinophil degranulation (REF: E4),
showing detectable EPX exclusively in the bronchoalveolar lavage (BAL) (Figure E5, A) vs. (ii)
lung-specific IL-5 transgenic mice (l5) with no demonstrable airway degranulation (REF: E3).
We exploited this distinction and investigated the concurrent presence of anti-EPX
autoantibodies and B-cell clusters. Anti-EPX IgG was detectable in the BAL of l5/hE2 mice
otherwise undetectable in l5 and WT; and their respective matched sera (Figure E5, B-D). The
lung histology of the l5/hE2 mice exhibited large clusters of B220+ B-cells with surrounding
zones of cells resembling T cell morphology (Figure E5, E-L) and could be considered to be the
potential sites for in situ autoantibody generation. As mentioned earlier l5 mice with ectopic
expression of IL-5 showed organized iBALT in lungs (REF: E3), but the lack of an autoimmune
response in this model can be explained by the absence of airway degranulation in these mice
(Figure E5).
E-References:
E1. Lacy P, Latif DA, Steward M, Musat-Marcu S, Man SFP, Moqbel R. Divergence of
Mechanisms Regulating Respiratory Burst in Blood and Sputum Eosinophils and
Neutrophils from Atopic Subjects. The Journal of Immunology 2003; 170: 2670-2679.
E2. Lee NA, Loh DY, Lacy E. CD8 surface levels alter the fate of alpha/beta T cell receptor-
expressing thymocytes in transgenic mice. The Journal of Experimental Medicine 1992;
175: 1013-1025.
E4Supplementary material
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
Mukherjee et al., 2016
E3. Lee JJ, McGarry MP, Farmer SC, Denzler KL, Larson KA, Carrigan PE, Brenneise IE,
Horton MA, Haczku A, Gelfand EW, Leikauf GD, Lee NA. Interleukin-5 Expression in
the Lung Epithelium of Transgenic Mice Leads to Pulmonary Changes Pathognomonic of
Asthma. The Journal of Experimental Medicine 1997; 185: 2143-2156.
E4. Ochkur SI, Jacobsen EA, Protheroe CA, Biechele TL, Pero RS, McGarry MP, Wang H,
O’Neill KR, Colbert DC, Colby TV, Shen H, Blackburn MR, Irvin CC, Lee JJ, Lee NA.
Coexpression of IL-5 and Eotaxin-2 in Mice Creates an Eosinophil-Dependent Model of
Respiratory Inflammation with Characteristics of Severe Asthma. The Journal of
Immunology 2007; 178: 7879-7889.
Supplementary figure legends:
Figure E1: Sputum IgGs from asthmatics show anti-eosinophil reactivity: Representative
image panels showing Immunostaining of eosinophils fixed and permeabilized on cover-slips,
and reacted with Igs isolated from a healthy volunteer with no anti-EPX IgG and ANA, along
with two patients positive for anti-EPX IgG and ANAs. The green channel represents the
reactivity of the sputum Igs counterstained by secondary antibodies to human IgG conjugated
with Alexa Fluor 488. The red channel represents EPX staining with Cy3-conjugated mouse
secondary antibodies. DAPI (blue) was used to stain the nuclei. Yellow regions in Overlay
images represent localisation, indicative of an anti-EPX specificity of sputum IgGs. Bars
represent 10 µM.
Figure E2: Sputum autoantibodies associated with indices of airway degranulation:
Association studies between anti-EPX reactivity (represented as closed red circles) and ANA
index (open black circles) within the diseased population (n = 64, excluding 15 healthy controls)
with respect to the documented (A) sputum eosinophil% (counted as intact eosinophils on
E5Supplementary material
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
Mukherjee et al., 2016
stained cytospin slides) and markers of luminal eosinophil activity - (B) measured sputum EPX
content, (C) extent of free eosinophil granules, FEG, in the sputum. The respective Spearman’s
correlation coefficient (ρ) and the P values are presented next to their respective correlation
plots.
Figure E3: Sputum Igs from severe asthmatics cause eosinophil degranulation in vitro: (A)
LDH activity measured as a function of detected NADH (nmol) in the supernatants collected
from eosinophils incubated with immobilised sputum Igs from n = 5 asthmatics with anti-EPX
IgG and ANAs (2-way ANOVA, post Tukey multiple comparison test). Data presented as mean ±
SD (B) total IgG content between asthmatic and healthy sputum samples used for experiment
(Wilcoxon paired test, P = 0.125) (C) Representative images of immunostaining with anti-
histone monoclonal antibody (counterstained with anti-human IgG Alexa Fluor 488) on fixed,
non-permeabilized eosinophils stimulated with sputum Igs at different time-points and uncoated
negative control. 2 µM A23817 at t = 3 hr was used as positive control for eosinophil cytolysis.
Figure E4: Distribution of Immunoglobulin subtypes in sputum: Distribution of sputum (A)
IgA, (B) IgG, (C) IgM, (D) IgE in tested population. Kruskal-Wallis with Dunn multiple
comparison.
Figure E5: Mice with airway degranulation have anti-EPX autoantibodies and organised
B-cell clusters: (A) Double transgenic l5/hE2 mice show detectable EPX in the BAL, not serum,
indicative of eosinophil degranulation localised to the lungs. (B) Significant increase in anti-EPX
antibodies (IgG) detected in the BAL of l5/hE2 mice, otherwise undetectable in wild type (WT)
and negligible in l5 mice, normalised to the protein content. (C-D) the comparative anti-EPX
optical density (OD) values measured for three dilutions of BAL and serum.*indicates
E6Supplementary material
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
Mukherjee et al., 2016
significant difference, P <0.05, two-way ANOVA, post Tukey test. Immunostaining lung tissue
sections with anti-B220 antibody showing no evident B cells in WT mice(E-F), with a few
positive B cells infiltrated into the airways with OVA challenge (G-H), and evidence of B220+ B
cell clusters in l5/hE2 mice lungs (I-J) indicated with arrows. 200x and 400x magnification (K,
L) shows organised B (designated as iB) and T cell zones (designated as T), indicative of
functional iBALTs.
E7Supplementary material
136
137
138
139
140
141
Mukherjee et al., 2016
Supplementary Tables:
Table E1: Subject characteristics of the validation cohort
Groups Healthy Non-Eos Asthma
Eos Asthma
Subjects, n 6 4 10
Sputum Eos% 0.2 ± 0.4 0.15 ± 0.17
33 ± 28*
Sputum Neu % 28 ± 15 76 ± 24 47 ± 33
Sputum TCC (106/g)
5 ± 2 25 ± 26 10 ± 7
OCS (median) 0 7.5 11.225ICS (median) 0 1500 1350
Data presented as Mean ± SD; ICS - mcg daily equivalent dose of fluticasone propionate; OCS:
prednisone, mg/daily; *P < 0.01
Table E2: Non-parametric ranking of clinical parameters and inflammatory mediators
[A] Clinical Parameters
Independent Samples Mann-Whitney U TestSignificance (P values)
Anti-EPX signature ANA signatureAge 0.046 0.200Atopy 0.230 0.310Blood_Abs_Eosinophils 0.832 0.231Blood_Abs_Lymphocytes 0.273 0.583Blood_Abs_Monocytes 0.381 0.390Blood_Abs_neutrophils 0.885 0.095Blood_platelet 0.274 0.710BMI 0.004 0.025CRS 0.330 0.047FEG_index 0.000 0.000FEV1 (L) 0.001 0.021FEV1% 0.001 0.006FEV1/FVC ratio 0.011 0.007Hx_Lymphopenia 0.016 0.251ICS 0.000 0.001OCS 0.000 0.019Smoking_Hx 0.520 0.210Sp_Lymphocyte% 0.068 0.046
E8Supplementary material
142
143
144
145
146
Mukherjee et al., 2016
Sputum_Eosinophil% 0.001 0.000Sputum_EPX_ng/ml 0.018 0.003Sputum_Neutrophil% 0.428 0.951Sputum_TCC 0.031 0.487
[B] Cytokines/ mediators
Independent Samples Mann-Whitney U TestSignificance P values
Anti-EPX signature ANA signature6CKine 0.386 0.932BAFF 0.000 0.000BCA-1 0.017 0.037CTACK 0.330 0.083EGF 0.122 0.197ENA-78 0.021 0.020Eotaxin-1 0.008 0.139Eotaxin-2 0.001 0.000Eotaxin-3 0.380 0.008FGF-2 0.012 0.226Flt-3L 0.254 0.179Fractalkine 0.148 0.575G-CSF 0.163 0.311GM-CSF 0.003 0.407GRO pan 0.451 0.572I-309 0.847 0.752IFNa2 0.179 0.519IFNy 0.137 0.558IL-10 0.966 0.828IL-12P40 0.789 0.594IL-12P70 0.327 0.171IL-13 0.000 0.000IL-15 0.120 0.089IL-16 0.001 0.007IL-17A 0.419 0.906IL-18 0.010 0.039IL-1a 0.979 0.528IL-1B 0.233 0.664IL-1RA 0.016 0.608IL-2 0.504 0.792IL-20 0.727 0.792IL-21 0.914 0.464IL-23 0.043 0.812IL-28A 0.090 0.947
E9Supplementary material
Mukherjee et al., 2016
IL-3 0.140 0.579IL-33 0.064 0.088IL-4 0.095 0.874IL-5 0.001 0.009IL-6 0.157 0.469IL-7 0.611 0.289IL-8 0.007 0.043IL-9 0.011 0.218IP-10 0.160 0.174LIF 0.638 0.485MCP-1 0.022 0.124MCP-2 0.542 0.457MCP-3 0.314 0.916MCP-4 0.632 0.693MDC 0.002 0.004MIP-1a 0.009 0.050MIP-1B 0.002 0.034MIP-1d 0.075 0.453PDGF-AA 0.024 0.013PDGF-BB 0.021 0.234RANTES 0.347 0.449sCD40L 0.251 0.678SCF 0.451 0.843SDF-1a+B 0.317 0.169TARC 0.003 0.002TGF-a 0.125 0.502TNFa 0.242 0.511TNFB 0.835 0.138TPO 0.592 0.995TRAIL 0.176 0.317TSLP 0.636 0.797VEGF-A 0.116 0.150
BMI- body mass index; TCC- total cell count; FEV1 – forced expiratory volume in 1 second; FVC- forced
vital capacity; ICS – inhaled corticosteroid; CRSwNP- chronic rhinosinusitis with nasal polyposis; Abs-
absolute; Hx – history; ; IL-interleukin; Chemokine (C-C motif); EGF- epithelial growth factor; FGF-
fibroblast growth factor; GM-CSF-Granulocyte macrophage colony-stimulating factor; IFN- interferon;
GRO pan comprises of CXCL1/2/3; MCP- Monocyte Chemoattractant Protein ; MDC- Macrophage-
derived chemokine ; TNF- Tumour Necrosis Factor ; VEGF- Vascular endothelial growth factor; TARC-
E10Supplementary material
147
148
149
150
151
152
Mukherjee et al., 2016
Thymus- and activation-regulated chemokine (CCL17) ; LIF- Leukemia inhibitory factor ; TPO-
Thrombopoietin; SCF- Stem cell factor; TSLP- Thymic stromal lymphopoetin; TRAIL- TNF-related
apoptosis-inducing ligand; CTACK- Cutaneous T-cell-attracting chemokine; SDF- Stromal cell-derived
factor; ENA- epithelial-derived neutrophil-activating peptide; MIP- Macrophage inflammatory protein.
E11Supplementary material
153
154
155
156
Mukherjee et al., 2016
Table E3: Model summary of regression analyses for assessing clinical predictors
A. Regression Model for Prediction of Anti-EPX signature:(Dependent Variable: Anti-EPX_sig: Cut-off threshold: Absorbance value 0.76)
ModelSum of
Squares dfMean
Square F Sig.Regression 3.146 2 1.573 7.992 .001b
Residual 10.236 52 .197Total 13.382 54
Model Unstandardized CoefficientsStandardized Coefficients
t Sig.B Std. Error Beta(Constant) .071 .106 .670 .506FEG index .943 .246 .493 3.833 .000Sputum_TCC .003 .001 .301 2.344 .023
Excluded Variables
Beta In t Sig.
Partial Correlatio
n
Collinearity Statistics
Tolerance VIFMinimum Tolerance
BMI .182b 1.466 .149 .201 .934 1.071 .842FEV1(L) -.039b -.300 .766 -.042 .897 1.115 .816FEV1 % -.041b -.319 .751 -.045 .926 1.080 .843FEV1/FVC .055b .452 .653 .063 .995 1.005 .887Sputum Eosinophil -.036b -.272 .786 -.038 .865 1.156 .819Sp EPX ng/ml -.136b -1.110 .272 -.154 .969 1.032 .864ICS .043b .329 .744 .046 .876 1.141 .795OCS .075b .577 .567 .080 .873 1.146 .780Hx_Lymphopenia -.036b -.277 .783 -.039 .885 1.130 .806
a. Dependent Variable: Anti-EPX_sigb. Predictors in the Model: (Constant), FEG index, Sputum_TCC
B. Regression Model for Prediction of ANA signature:(Dependent Variable: ANA sig : cut-off threshold ANA Index: 0.08)
ModelSum of
Squares dfMean
Square F Sig.Regression 1.381 1 1.381 6.070 .018b
Residual 10.236 45 .227Total 11.617 46
Coefficientsa
E12Supplementary material
157
158159
160
161
162
163164
165
Mukherjee et al., 2016
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.B Std. Error Beta(Constant) .377 .100 3.788 .000FEG index .630 .256 .345 2.464 .018
a. Dependent Variable: ANA_signature
Excluded Variablesa
Model Beta In t Sig.Partial
Correlation
Collinearity Statistics
Tolerance VIFMinimum Tolerance
BMI .014b .097 .923 .015 .980 1.021 .980FEV1(L) .039b .274 .785 .041 .991 1.009 .991FEV1 % .007b .050 .960 .008 .980 1.021 .980FEV1/FVC .045b .319 .751 .048 .999 1.001 .999Sputum_TCC .016b .105 .917 .016 .883 1.133 .883Sputum Eosinophil -.037b -.243 .809 -.037 .874 1.144 .874
Sp EPX ng/ml .102b .710 .481 .106 .969 1.032 .969ICS -.097b -.690 .494 -.103 1.000 1.000 1.000OCS -.132b -.901 .372 -.135 .914 1.094 .914Sp_Lymphocyte -.052b -.365 .717 -.055 .987 1.013 .987
a. Dependent Variable: ANA_signatureb. Predictors in the Model: (Constant), FEG index
Table E4: Correlation coefficient and non-parametric ranking for all sputum inflammatory mediator levels with autoantibody signature
Autoantibody signatureCorrelation
matrixMann
Whitney
r P PBAFF 0.457 0.000 0.000
BCA-1 0.312 0.003 0.015
CTACK 0.006 0.479 0.469
EGF 0.206 0.037 0.183
ENA-78 0.224 0.026 0.031
Eotaxin-1 0.149 0.099 0.065
Eotaxin-2 0.426 0.000 0.000
Eotaxin-3 0.153 0.093 0.231
FGF-2 0.201 0.041 0.065
Flt-3 Ligand 0.217 0.030 0.196
Fractalkine 0.154 0.092 0.460
E13Supplementary material
166
167
168169
Mukherjee et al., 2016
G-CSF 0.093 0.211 0.473
GM-CSF 0.171 0.070 0.012
GRO pan 0.035 0.383 0.771
I-309 0.043 0.357 0.751
IFNa2 0.158 0.087 0.444
IFNy 0.082 0.241 0.603
IL-10 -0.035 0.381 0.681
IL-12P40 -0.013 0.457 0.692
IL-12P70 -0.187 0.053 0.217
IL-13 0.444 0.000 0.000
IL-15 0.242 0.018 0.069
IL-16 0.207 0.036 0.002
IL-17A 0.010 0.465 0.525
IL-18 0.213 0.032 0.006
IL-1alpha -0.061 0.300 0.492
IL-1beta 0.124 0.143 0.571
IL-1RA 0.193 0.048 0.067
IL-2 -0.050 0.333 0.791
IL-20 -0.001 0.497 0.838
IL-21 -0.105 0.184 0.193
IL-23 0.109 0.174 0.398
IL-28A 0.107 0.178 0.386
IL-3 0.032 0.392 0.312
IL-33 0.146 0.104 0.269
IL-4 0.175 0.066 0.338
IL-5 0.292 0.005 0.002
IL-6 0.082 0.241 0.574
IL-7 0.118 0.155 0.320
IL-8 0.220 0.028 0.027
IL-9 0.224 0.026 0.053
IP-10 0.176 0.064 0.224
LIF -0.026 0.413 0.539
MCP-1 0.166 0.075 0.212
MCP-2 0.137 0.120 0.382
MCP-3 -0.007 0.477 0.542
MCP-4 -0.013 0.456 0.696
MDC 0.283 0.007 0.001
MIP-1a 0.098 0.201 0.056
MIP-1B 0.194 0.046 0.011
MIP-1d 0.069 0.278 0.293
PDGF-AA 0.135 0.123 0.035
PDGF-BB -0.016 0.446 0.093
RANTES -0.020 0.433 0.621
E14Supplementary material
Mukherjee et al., 2016
sCD40L 0.082 0.241 0.823
SCF 0.143 0.108 0.677
SDF-1a+B -0.122 0.148 0.126
TARC 0.387 0.000 0.003
TGF-alpha -0.106 0.180 0.614
TNFa 0.075 0.260 0.921
TNFB -0.080 0.245 0.799
TPO -0.069 0.276 0.758
TRAIL -0.169 0.072 0.720
TSLP 0.058 0.311 0.707
VEGF-A 0.071 0.272 0.168
*r - correlation coefficient, P < 0.01 is considered significant
Table E5: Model summary of regression analyses for assessing molecular predictors
A. Regression Model for Prediction of Anti-EPX signature: (Dependent Variable: Anti-EPX_sig: Cut-off threshold: Absorbance value 0.76)
ModelSum of
Squares dfMean
Square F Sig.Regressio
n 8.207 2 4.103 30.792 .000b
Residual 9.728 73 .133
Total 17.934 75
ModelUnstandardized
CoefficientsStandardized Coefficients
t Sig.BStd. Error Beta
(Constant) .058 .059 .977 .332
BAFF .001 .000 .465 5.341 .000
IL-13 .165 .033 .431 4.955 .000
Excluded Variables
Model Beta In t Sig.
Partial Correlatio
n
Collinearity Statistics
Tolerance VIF
Minimum Toleranc
eTARC 0.204 1.811 .074 .209 .566 1.768 .560
IL-16 0.128 1.391 .169 .162 .860 1.163 .851Eotaxin-
2 0.071 .635 .528 .075 .604 1.656 .600
MIP-1B 0.087 .900 .371 .105 .791 1.265 .775
E15Supplementary material
170
171
172
173174
175
176
Mukherjee et al., 2016
IL-5 0.168 1.408 .163 .164 .513 1.950 .503
MDC 0.034 .334 .740 .039 .719 1.391 .705
GM-CSF 0.056 .587 .559 .069 .832 1.203 .825
a. Dependent Variable: Anti-EPX_sigb. Predictors in the Model: (Constant), BAFF, IL-13
B. Regression Model for Prediction of ANA signature:(Dependent Variable: ANA sig: cut-off threshold ANA Index: 0.08)
ModelSum of
Squares dfMean
Square F Sig.2 Regression
4.785 2 2.393 13.048 .000b
Residual 11.369 62 .183Total 16.154 64
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.B Std. Error Beta(Constant)
.214 .074 2.902 .005
Eotaxin-2 .001 .000 .467 4.340 .000BAFF .001 .000 .223 2.071 .043
Excluded Variablesa
Model Beta In t Sig.
Partial Correlatio
n
Collinearity Statistics
Tolerance VIF
Minimum Toleranc
eTARC 0.106 .495 .622 .063 .249 4.013 .249
IL-5 0.065 .486 .629 .062 .638 1.568 .626
IL-13 0.174 1.237 .221 .156 .569 1.758 .568
MDC -0.128 -.800 .427 -.102 .447 2.237 .439Eotaxin-
3 -0.078 -.545 .588 -.070 .558 1.791 .549
IL-16 -0.002 -.020 .984 -.003 .959 1.042 .948PDGF-
AA -0.199 -1.451 .152 -.183 .594 1.684 .583
a. Dependent Variable: ANA_signatureb. Predictors in the Model: (Constant), Eotaxin-2 , BAFF
E16Supplementary material
177
178179
180
181
182