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Table 2 Limma results for 10 proteins with lowest p values

From: Systemic inflammatory proteins in offspring following maternal probiotic supplementation for atopic dermatitis prevention

Proteins

(Top 10)

Average NPX (SD)

Fold Change

p-value

Probiotics

Placebo

IL-17C

1.18 (0.56)

1.49 (0.86)

0.807

0.002

CCL11

6.82 (0.54)

6.96 (0.47)

0.904

0.04

FGF19

5.71 (0.92)

5.96 (1.04)

0.838

0.06

MCP1

10.37 (0.41)

10.47 (0.43)

0.930

0.09

CXCL6

8.07 (0.55)

8.19 (0.47)

0.920

0.1

CDCP1

0.68 (0.34)

0.60 (0.29)

1.053

0.11

TNFRSF9

5.80 (0.48)

5.91 (0.44)

0.930

0.11

CCL23

6.52 (0.54)

6.63 (0.51)

0.922

0.11

CX3CL1

1.35 (0.45)

1.45 (0.56)

0.930

0.15

IL-8

4.23 (0.35)

4.31 (0.48)

0.942

0.15

Proteins

(Top 10)

Average NPX (SD)

Fold Change

p-value

AD (n = 49)

Without AD

(n = 153)

IL-17C

1.59 (0.92)

1.26 (0.65)

1.258

0.005

MCP4

13.55 (0.67)

13.29 (0.59)

1.195

0.01

uPA

8.32 (0.42)

8.17 (0.37)

1.113

0.02

CD6

5.64 (0.65)

5.45 (0.47)

1.144

0.02

CASP8

1.65 (0.70)

1.46 (0.54)

1.137

0.05

CST5

3.93 (0.84)

3.70 (0.72)

1.173

0.06

TNFSF14

3.58 (0.59)

3.44 (0.45)

1.103

0.08

SIRT2

3.30 (0.99)

3.00 (1.11)

1.227

0.09

IL-10RB

3.88 (0.52)

3.76 (4.40)

1.088

0.11

IL-8

4.35 (0.44)

4.25 (0. 41)

1.079

0.12

  1. In bold: p-values < 0.05 were considered statistically significant. NPX normalised protein expression. Limma Linear Models for Microarray Data. IL –interleukin, CCL C–C motif chemokine ligand. FGF fibroblast growth factor, MCP – monocyte chemotactic protein, CXCL C-X-C motif chemokine ligand, Complement C1r/C1s, Uegf, Bmp1 (CUB) Domain Containing Protein. TNFRSF tumour necrosis factor receptor superfamily member. uPA urokinase-type plasminogen activator. CD cluster of differentiation. CASP caspase. CST cystatin-D. TNFSF tumour necrosis factor ligand superfamily member. SIRT sirtuin
  2. Limma is a package for the analysis of gene expression microarray data, especially the use of linear models for analysing designed experiments and the assessment of differential expression. Empirical Bayesian methods are used to provide stable results even when the number of arrays is small. The linear model and differential expression functions apply to all gene expression technologies, including microarrays, RNA-seq and quantitative PCR