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. 2010 Oct 19;2010:325183. doi: 10.1155/2010/325183

Table 2.

Modulation of gene expression in human liver cell models after treatment with PPARγ agonists.

Ref. [34] [35] [65] Rogue et al. unpublished [74] [75] [76] [77] [78] [79] [80] [81]
model PHH PHH DONOR 1 PHH DONOR 2 PHH DONOR 3 PHH DONOR 4 PHH DONOR 5 HepaRG cells HepaRG cells Hep3b Huh7 Huh7 Hepg2 HLF HLF, HAK, HuH-7
treatment TRO 24 h 5,50,100
μM-		 TRO 24 h 25 μM- TRO 24 h 10 μM- TRO 24 h 5 μM- TRO 24 h 20 μM- TRO 24 h 5 μM- TRO 24 h 20 μM- TRO 24 h 5 μM- TRO 24 h 20 μM- TRO 24 h 5 μM- TRO 24 h 20 μM- TRO 24 h 40 μM- TRO 24 h 5 μM- TRO 24 h 20 μM- TRO 24 h 40 μM- TRO 24 h 5 μM- TRO 24 h 20 μM- TRO 24 h 40 μM- TRO 24 h 0,024 μM–25 μM TRO04
8 h -
50 μM-		 TRO-ROSI 1–8–
24 h 50 μM-		 TRO 6 h 30 μM- TRO-4 h 10–30 μ M TRO-ROSI 48 h 25–100 μM- TRO for up 48  h 50 μM TRO 24 h 50 μM
method q-PCR Amer-sham Agilent Agilent q-PCR Super Array Bioscience qPCR q-PCR/ northern blotting q-PCR
ABCB1 + 0 + 0 0 + + 0 0 + + + + 0 0 0
Transporters ABCC2 + 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0
ABCC3 + + 0 0 0 0 0 0 0 0 0 0 0 0 0 0
SLC10A1 − 0 0 0 0 0 0 0 0 0 0 0 − − − −
ABCB4 + 0 0 0 0 + + 0 + + + + + 0 0 0
SLCO1B1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 −
SLCO1B3 − − − − − − − 0 − − 0 − − 0 0 0
CCND1 + 0 + + + 0 + 0 0 0 0 0 0 0 0 0
Cell cycle, proliferation, death and differentiation CDKN1A 0 0 0 + + + + 0 0 0 0 0 0 0 0 0 + +
GADD45G + 0 + 0 0 0 + 0 0 0 + + + 0 0 0
AFP − 0 − 0 − 0 0 0 − − 0 0 0 0 0 0
TGFA + 0 0 0 − − − 0 0 0 0 0 0 0 0 0
CCNE1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0
ALB 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 −
CDKN1B 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
JUND 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
CCNG1 0 0 0 − − 0 − 0 0 0 0 0 0 0 0 0
MYC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
TGFB1 0 0 0 + + 0 + 0 0 0 0 0 0 0 0 0
ALPL 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 −
GADD45A + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
IGFBP1 0 0 0 − − − 0 0 0 0 + 0 0 0 + +
SKP2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + −
FABP1 + + + + + + + 0 + + + + + + 0 −
Lipid metabolism FASN + 0 0 0 0 0 0 0 0 0 + + 0 0 0 0
CPT1A + 0 + + + 0 0 0 + + + + + 0 0 0
SREBF2 + 0 0 + 0 0 0 0 0 0 0 0 0 0 0 −
HMGCR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 − −
INSIG1 0 0 0 0 0 0 0 0 0 + + + 0 0 0 nd
LDLR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 −
INSIG2 0 0 0 0 0 − 0 0 0 0 0 0 0 0 0 +
CYP1A1 0 + + 0 0 0 + 0 0 0 0 0 0 0 − 0 0
Xenobiotic metabolism CYP1A2 + 0 0 0 − 0 0 0 0 0 + 0 0 + 0 0 0 0
CYP2B6 + + + 0 0 + + 0 + + 0 0 + 0 0 0 +
CYP2C9 + 0 0 0 0 0 0 0 0 + + + + + 0 0
CYP2E1 + 0 0 − − − − 0 0 0 0 0 0 0 0 −
CYP3A4 + + 0 0 0 0 + + 0 + + + + + + + + +
UGT1A10 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GSTP1 + 0 0 0 0 − − 0 0 0 0 0 0 0 0 0
GSTA1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 −
G6PC 0 0 0 0 − − 0 0 0 0 0 0 0 − −
Carbohydrate metabolism PDK4 − 0 − − − − 0 + + + + + 0 + +
PEPCK 0 0 0 0 − − 0 0 0 0 0 + + + +
FBP1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 −
HMOX1 + + + + + + + + + + 0 0 0 0 + +
Oxidative stres PTGS2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 − −
HSPA1A + + + 0 0 + + 0 0 0 0 0 0 0 0 0
TXN + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
COX-2 −
CAT − 0 0 − − 0 0 0 0 0 0 0 0 0 0 +
HNF4A + + 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Transcription factors PPARG 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0
CEBPA 0 0 + + + + + 0 0 0 0 0 0 0 0 0
CEBPB 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0
NR1I2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
NR1I3 0 0 0 0 − 0 0 0 0 0 0 0 0 0 0 −
GSN 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Fbrosis/
senescence		 TIMP1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
CDH1 + + + 0 0 0 0 0 0 0 0 0 − 0 0 0
RGN 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 −
PDIA4 0 0 − − 0 0 0 0 0 0 0 0 0 0 0 +
Miscellaneous ACTA1 0 + 0 + + 0 0 0 0 0 0 0 − 0 0 0

+: up-regulated

−: down-regulated

0: not modulated

The case is empty when the gene has not been studied.

Differentiated HepaRG cells from three different passages and 2-day human hepatocyte cultures from 5 donors were treated for 24 h with different concentrations of TRO. 500 ng of RNA samples from control and treated cultures were separately reverse transcribed and amplified using Quick Amplification Labeling Kit (Agilent). Then they were hybridized using 4×44 K Agilent microarrays satisfying Minimum Information About a Microarray Experiment (MIAME) requirements as previously described [82]. Normalization algorithms and background subtractions were automatically applied to each array to reduce systematic errors and to adjust effects due to technological rather than biological variations using FE and Resolver softwares. The combination of technical and biological replicates uses the error-weighted log ratio average and an estimated error method of the Rosetta Resolver system.