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pmid:21291544

Differential gene expression in liver and small intestine from lactating rats compared to age-matched virgin controls detects increased mRNA of cholesterol biosynthetic genes.

Antony Athippozhy · Liping Huang · Clavia Ruth Wooton-Kee · Tianyong Zhao · Paiboon Jungsuwadee · Arnold J Stromberg · Mary Vore

BMC genomics · 2011 doi:10.1186/1471-2164-12-95

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complete

Experiment

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Data

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:21291544
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2026-11-01T15:14:34Z
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2026-11-01T22:41:53Z
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paper object · 10 fields
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title
Differential gene expression in liver and small intestine from lactating rats compared to age-matched virgin controls detects increased mRNA of cholesterol biosynthetic genes.
doi
10.1186/1471-2164-12-95
pmid
21291544
authors array · 7 items
[0]
Antony Athippozhy
[1]
Liping Huang
[2]
Clavia Ruth Wooton-Kee
[3]
Tianyong Zhao
[4]
Paiboon Jungsuwadee
[5]
Arnold J Stromberg
[6]
Mary Vore
journal
BMC genomics
year
2011
abstract
BACKGROUND: Lactation increases energy demands four- to five-fold, leading to a two- to three-fold increase in food consumption, requiring a proportional adjustment in the ability of the lactating dam to absorb nutrients and to synthesize critical biomolecules, such as cholesterol, to meet the dietary needs of both the offspring and the dam. The size and hydrophobicity of the bile acid pool increases during lactation, implying an increased absorption and disposition of lipids, sterols, nutrients, and xenobiotics. In order to investigate changes at the transcriptomics level, we utilized an exon array and calculated expression levels to investigate changes in gene expression in the liver, duodenum, jejunum, and ileum of lactating dams when compared against age-matched virgin controls. RESULTS: A two-way mixed models ANOVA was applied to detect differentially expressed genes. Significance calls were defined as a p < 0.05 for the overall physiologic state effect (lactation vs. control), and a within tissue pairwise comparison of p < 0.01. The proportion of false positives, an estimate of the ratio of false positives in the list of differentially expressed genes, was calculated for each tissue. The number of differentially expressed genes was 420 in the liver, 337 in the duodenum, 402 in the jejunum, and 523 in the ileum. The list of differentially expressed genes was in turn analyzed by Ingenuity Pathways Analysis (IPA) to detect biological pathways that were overrepresented. In all tissues, sterol regulatory element binding protein (Srebp)-regulated genes involved in cholesterol synthesis showed increased mRNA expression, with the fewest changes detected in the jejunum. We detected increased Scap mRNA in the liver only, suggesting an explanation for the difference in response to lactation between the liver and small intestine. Expression of Cyp7a1, which catalyzes the rate limiting step in the bile acid biosynthetic pathway, was also significantly increased in liver. In addition, decreased levels of mRNA associated with T-cell signaling were found in the jejunum and ileum. Several members of the Solute Carrier (SLC) and Adenosine Triphosphate Binding Cassette (ABC) superfamilies of membrane transporters were found to be differentially expressed; these genes may play a role in differences in nutrient and xenobiotic absorption and disposition. mRNA expression of SLC39a4_predicted, a zinc transporter, was increased in all tissues, suggesting that it is involved in increased zinc uptake during lactation. Microarray data are available through GEO under GSE19175. CONCLUSIONS: We detected differential expression of mRNA from several pathways in lactating dams, including upregulation of the cholesterol biosynthetic pathway in liver and intestine, consistent with Srebp activation. Differential T-Cell signaling in the two most distal regions of the small intestine (ileum and jejunum) was also noted, as well as differential expression of transporters that likely play a key role in nutrient uptake.
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Since all four tissues were taken from each rat, a repeated measures mixed model ANOVA was used to determine if changes in expression were statistically significant for each gene [70].
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sections[24] "Statistical Analysis" paragraph 2
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Since all four tissues were taken from each rat, a repeated measures mixed model ANOVA was used to determine if changes in expression were statistically significant for each gene [70].
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sections[0] "Abstract" paragraph 2
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Microarray data are available through GEO under GSE19175.
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A two-way mixed models ANOVA identified tissue-specific differential gene expression during lactation, and the source microarray data are available through GEO under GSE19175.
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title
Differential gene expression in lactating rats compared against age matched virgin controls
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RT-PCR validation of selected genes from the microarray
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RT-PCR Validation of Microarray Data
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Table 1
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RT-PCR validation of selected genes from the microarray
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[0] array · 6 items
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RT-PCRMeasurements
[1]
TranscriptCluster ID
[2]
RL
[3]
RD
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RJ
[5]
RIL
[1] array · 6 items
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Tmem97
[1]
7080099
[2]
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2.60(p < 0.01)
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1.59(p = 0.42)
[5]
1.73(p = 0.072)
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[1]
-
[2]
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1.27(p = 0.49)
[4]
1.22(p = 0.16)
[5]
0.88(p = 0.72)
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[0]
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Abcb1a
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7250393
[2]
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[3]
0.83(p = 0.77)
[4]
0.72(p = 0.09)
[5]
0.65(p < 0.01)
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[0]
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7336681
[2]
2.98(p = 0.93)
[3]
1.12(p = 0.64)
[4]
2.06(p < 0.01)
[5]
0.78(p = 0.89)
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[0]
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Cyp3a23/3a1
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7100149
[2]
1.83(p = 0.12)
[3]
1.30(p = 1.00)
[4]
BDL
[5]
BDL
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[0]
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7139070
[2]
1.36(p = 0.11)
[3]
2.58(p = 0.22)
[4]
1.02(p = 0.99)
[5]
2.41(p = 0.018)
[12] array · 1 item
[0]
[13] array · 6 items
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Hmgcr
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7202670
[2]
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[3]
1.52(p = 0.30)
[4]
1.14(p = 0.80)
[5]
1.18(p = 0.48)
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[0]
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Slc39a4
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7329323
[2]
2.71(p = 0.59)
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[4]
2.20(p < 0.01)
[5]
1.99(p < 0.01)
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[0]
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7317317
[2]
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[4]
1.71(p = 0.50)
[5]
1.59(p = 0.056)
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[0]
[19] array · 6 items
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[1]
7117373
[2]
0.85(p = 0.047)
[3]
0.76(=0.41)
[4]
0.39(p = 0.21)
[5]
0.62(p = 1.00)
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[0]
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MicroarrayMean Log2Measurements
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[2]
RL
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RD
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RIL
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[0]
[23] array · 6 items
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Tmem97
[1]
7080099
[2]
1.92(p < 0.01)
[3]
2.03(p < 0.01)
[4]
1.82(p < 0.01)
[5]
2.15(p < 0.01)
[24] array · 1 item
[0]
[25] array · 6 items
[0]
Abcb1a
[1]
7250393
[2]
0.63(p < 0.01)
[3]
0.66(p < 0.01)
[4]
0.65(p < 0.01)
[5]
0.70(p = 0.01)
[26] array · 1 item
[0]
[27] array · 6 items
[0]
Cyp1a1
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7336681
[2]
2.29(p < 0.01)
[3]
1.23(p = 0.35)
[4]
1.71(p = 0.02)
[5]
0.67(p = 0.03)
[28] array · 1 item
[0]
[29] array · 6 items
[0]
Cyp3a23/3a1
[1]
7100149
[2]
1.69(p < 0.01)
[3]
0.98(p = 0.81)
[4]
1.05(p = 0.50)
[5]
1.04(p = 0.58)
[30] array · 1 item
[0]
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[0]
Fdft1
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7139070
[2]
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1.98(p < 0.01)
[4]
1.42(p < 0.01)
[5]
1.55(p < 0.01)
[32] array · 1 item
[0]
[33] array · 6 items
[0]
Hmgcr
[1]
7202670
[2]
1.97(p < 0.01)
[3]
1.64(p < 0.01)
[4]
1.17(p = 0.14)
[5]
1.38(p < 0.01)
[34] array · 1 item
[0]
[35] array · 6 items
[0]
Slc39a4
[1]
7329323
[2]
2.81(p < 0.01)
[3]
1.89(p < 0.01)
[4]
1.65(p < 0.01)
[5]
1.68(p < 0.01)
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[0]
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Sqle
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7317317
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1.83(p < 0.01)
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2.27(p < 0.01)
[4]
1.72(p < 0.01)
[5]
1.70(p < 0.01)
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[0]
[39] array · 6 items
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Ugt2b36 (Ugt2b4)
[1]
7117373
[2]
0.96(p = 0.86)
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0.57(p < 0.01)
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0.37(p < 0.01)
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1.00(p = 0.97)
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Genes that displayed increased mRNA during lactation in all tissues
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PMC3045338
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6
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Patterns
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Table 2
caption
Genes that displayed increased mRNA during lactation in all tissues
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[0]
TranscriptCluster ID
[1]
Gene Name
[2]
RL
[3]
RD
[4]
RJ
[5]
RIL
[1] array · 6 items
[0]
7027017
[1]
Acetyl-Coenzyme A acetyltransferase
[2]
1.98(p = 0.0173)
[3]
2.40(p = 0.0049)
[4]
1.80(p = 0.0363)
[5]
2.19(p = 0.0128)
[2] array · 1 item
[0]
[3] array · 6 items
[0]
7080099
[1]
Transmembrane protein 96
[2]
1.92(p = 0.0001)
[3]
2.03(p = 2.8e-05)
[4]
1.82(p = 0.0002)
[5]
2.15(p = 8.7e-06)
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[0]
[5] array · 6 items
[0]
7113785
[1]
Hydroxysteroid(17-beta) dehydrogenase 7
[2]
1.71(p = 2.1e-06)
[3]
1.54(p = 4.7e-05)
[4]
1.19(p = 0.045)
[5]
1.51(p = 0.0001)
[6] array · 1 item
[0]
[7] array · 6 items
[0]
7132836
[1]
PDZ binding kinase predicted*
[2]
1.56(p = 0.0222)
[3]
1.88(p = 0.0031)
[4]
1.66(p = 0.0054)
[5]
1.54(p = 0.0227)
[8] array · 1 item
[0]
[9] array · 6 items
[0]
7139070
[1]
Farnesyl diphosphate farnesyl transferase
[2]
1.38(p = 0.0009)
[3]
1.98(p = 4.7e-08)
[4]
1.42(p = 0.0004)
[5]
1.55(p = 4.1e-05)
[10] array · 1 item
[0]
[11] array · 6 items
[0]
7144691
[1]
Sterol-C4-methyl oxidase-like
[2]
1.41(p = 0.0015)
[3]
2.19(p = 1.9e-07)
[4]
1.45(p = 0.0008)
[5]
1.80(p = 8.2e-06)
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[0]
[13] array · 6 items
[0]
7166170
[1]
Isopentenyl-diphosphate delta isomerase
[2]
1.61(p = 0.0006)
[3]
2.11(p = 2.8e-06)
[4]
1.33(p = 0.0226)
[5]
1.91(p = 2.6e-05)
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[0]
[15] array · 6 items
[0]
7169182
[1]
Kinesin family member 20a_predicted*
[2]
1.46(p = 0.0012)
[3]
1.50(p = 0.0010)
[4]
1.33(p = 0.0104)
[5]
1.45(p = 0.0013)
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[0]
[17] array · 6 items
[0]
7186293
[1]
Mevalonate (diphospho) decarboxylate
[2]
2.65(p = 3.8e-05)
[3]
2.15(p = 0.0004)
[4]
1.49(p = 0.0316)
[5]
1.73(p = 0.0042)
[18] array · 1 item
[0]
[19] array · 6 items
[0]
7199743
[1]
RNA (guanine-9-) methyltransferase domain containing 2
[2]
1.18(p = 0.009)
[3]
1.42(p = 3.6e-06)
[4]
1.19(p = 0.0066)
[5]
1.30(p = 0.002)
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[0]
[21] array · 6 items
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7250653
[1]
CYP51
[2]
1.24(p = 0.0116)
[3]
2.14(p = 1.8e-09)
[4]
1.50(p = 4.2e-05)
[5]
1.89(p = 6.7e-08)
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[0]
[23] array · 6 items
[0]
7280610
[1]
UDP-Galactose-4-epimerase
[2]
1.65(p = 0.0006)
[3]
1.50(p = 0.0042)
[4]
1.36(p = 0.0243)
[5]
1.53(p = 0.0023)
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[0]
[25] array · 6 items
[0]
7291805
[1]
Dehydrodolichyl diphosphate synthase
[2]
1.37(p = 0.0003)
[3]
1.36(p = 0.0005)
[4]
1.27(p = 0.0041)
[5]
1.17(p = 0.0447)
[26] array · 1 item
[0]
[27] array · 6 items
[0]
7317317
[1]
Squalene epoxidase
[2]
1.83(p = 2.4e-05)
[3]
2.27(p = 5.6e-07)
[4]
1.72(p = 0.0001)
[5]
1.70(p = 0002)
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[0]
[29] array · 6 items
[0]
7329323
[1]
Solute carrier 39 (zinc transporter) member 4
[2]
2.81(p = 2.5e-10)
[3]
1.89(p = 9.9e-08)
[4]
1.65(p = 2.1e-06)
[5]
1.68(p = 1.2e-06)
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Top three pathways for overrepresentation in each tissue
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PMC3045338
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Ingenuity Pathways Analysis
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Table 3
caption
Top three pathways for overrepresentation in each tissue
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[3] array · 4 items
[0]
Biosynthesis of steroids
[1]
3.50E-07
[2]
5.80E-05
[3]
↑Dhcr7, ↑Fdft1,↑Hmgcr,↑Idi1, ↑Lss, ↑Mvd, ↑Sqle
[4] array · 1 item
[0]
[5] array · 4 items
[0]
LXR/RXR Activation
[1]
1.57E-04
[2]
1.30E-02
[3]
↓Abcg5, ↓Abcg8, ↑Acaca, ↑Cyp7a1, ↓Hadh, ↑Hmgcr, ↓Lcat
[6] array · 1 item
[0]
[7] array · 4 items
[0]
Pentose/Phosphate Pathway
[1]
3.01E-04
[2]
1.67E-02
[3]
↑Aldoc, ↑G6pd, ↑Gpi, ↓H6pd, ↓Pgm5
[8] array · 1 item
[0]
[9] array · 4 items
[0]
[1]
[2]
[3]
[10] array · 1 item
[0]
[11] array · 4 items
[0]
Duodenum
[1]
[2]
[3]
[12] array · 1 item
[0]
[13] array · 4 items
[0]
Pathway
[1]
[2]
[3]
[14] array · 1 item
[0]
[15] array · 4 items
[0]
Biosynthesis of steroids
[1]
1.13E-10
[2]
1.74E-08
[3]
↑Dhcr7, ↑Fdft1, ↑Fntb, ↑Hmgcr, ↑Idi1, ↑Lss, ↑Mvd, ↑Sc5dl, ↑Sqle
[16] array · 1 item
[0]
[17] array · 4 items
[0]
Androgen and estrogen metabolism
[1]
1.35E-04
[2]
1.04E-02
[3]
↑Ftsj1, ↑Hsd11b1, ↑Hsd17b7, ↑Nsdhl, ↓Srd5a2, ↓Ugt2b7
[18] array · 1 item
[0]
[19] array · 4 items
[0]
Glycerolipid metabolism
[1]
1.46E-03
[2]
7.50E-02
[3]
↓Cel, ↓Clps, ↓Glb1l2, ↓Pnlip, ↓Pnliprp1, ↓Pnliprp2
[20] array · 1 item
[0]
[21] array · 4 items
[0]
[1]
[2]
[3]
[22] array · 1 item
[0]
[23] array · 4 items
[0]
Jejunum
[1]
[2]
[3]
[24] array · 1 item
[0]
[25] array · 4 items
[0]
Pathway
[1]
[2]
[3]
[26] array · 1 item
[0]
[27] array · 4 items
[0]
T-Cell receptor signaling
[1]
1.68E-07
[2]
2.78E-05
[3]
↓Bmx, ↓Cd8b, ↓Grap2, ↓Itk, ↓Malt1, ↓Pik3cg, ↓Pik3r1, ↓Ppp3cc, ↓Prkcq, ↓Ptprc, ↓Rasgrp1, ↓Trb
[28] array · 1 item
[0]
[29] array · 4 items
[0]
CD28 signaling in T helper cells
[1]
1.17E-05
[2]
6.74E-04
[3]
↓Grap2, ↓Itk, ↓Malt1, ↑Mapk9, ↓Pik3cg, ↓Pik3r1, ↓Ppp3cc, ↓Prkcq, ↓Ptprc, ↓Trb
[30] array · 1 item
[0]
[31] array · 4 items
[0]
TR/RXR activation
[1]
1.23E-05
[2]
6.74E-04
[3]
↑Fga, ↑Gh1, ↓Klf9, ↑Ldlr, ↓Pik3cg, ↓Pik3r1, ↓Thra, ↓Thrb, ↓Thrsp
[32] array · 1 item
[0]
[33] array · 4 items
[0]
[1]
[2]
[3]
[34] array · 1 item
[0]
[35] array · 4 items
[0]
Ileum
[1]
[2]
[3]
[36] array · 1 item
[0]
[37] array · 4 items
[0]
Pathway
[1]
[2]
[3]
[38] array · 1 item
[0]
[39] array · 4 items
[0]
Biosynthesis of steroids
[1]
9.42E-08
[2]
1.71E-05
[3]
↑Cyp24a1, ↑Dhcr7, ↑Fdft1, ↑Hmgcr, ↑Idi1, ↑Mvd, ↑Sc5dl, ↑Sqle
[40] array · 1 item
[0]
[41] array · 4 items
[0]
TR/RXR activation
[1]
3.16E-07
[2]
2.86E-05
[3]
↑Fga, ↑Gh1, ↓Klf9, ↑Me1, ↓Nfcor2, ↓Pck1, ↓Pik3c2b, ↓Pik3cg, ↓Pik3r5, ↓Ppargc1a, ↓Thra, ↓Thrb
[42] array · 1 item
[0]
[43] array · 4 items
[0]
Thrombopoietin signaling
[1]
4.85E-05
[2]
2.53E-03
[3]
↓Irs2, ↓Pik3c2b, ↓Pik3cg, ↓Pik3r5, ↓Plcg1, ↓Plcg2, ↓Prkce, ↓Prkcq
locator
section[8]/table-wrap[1]#T3
[5] object · 16 fields
id
asset:table:pmid:21291544:4
role
table
format
jats-table
media_type
application/vnd.jats.table+json
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null
sha256
sha256:8f88b386a46051314b1af75eb6244e4a72ad758eadf51071c1180e4a2f05e699
bytes
1202
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[10]/table-wrap[1]#T4
version
null
note
ATP-Binding Cassette (ABC) transporters detected to be differentially expressed between Control and Lactating dams
structure object · 5 fields
pmcid
PMC3045338
section_index
10
section_title
Cholesterol uptake
row_count
16
column_count
7
table object · 8 fields
label
Table 4
caption
ATP-Binding Cassette (ABC) transporters detected to be differentially expressed between Control and Lactating dams
rows array · 16 items
[0] array · 7 items
[0]
Gene Symbol
[1]
TranscriptCluster ID
[2]
RL
[3]
RD
[4]
RJ
[5]
RIL
[6]
p < 0.01
[1] array · 7 items
[0]
Abca3
[1]
7065486
[2]
0.888(p = 0.138)
[3]
0.931(p = 0.329)
[4]
0.99(p = 0.874)
[5]
0.8(p = 0.007)
[6]
IL
[2] array · 1 item
[0]
[3] array · 7 items
[0]
Abca8a_predicted*
[1]
7083965
[2]
0.567(p = 7.0E-04)
[3]
0.885(p = 0.273)
[4]
0.942(p = 0.744)
[5]
0.763(p = 0.054)
[6]
L
[4] array · 1 item
[0]
[5] array · 7 items
[0]
Abcb1a
[1]
7250393
[2]
0.63(p = 0.001)
[3]
0.663(p = 0.002)
[4]
0.652(p = 0.002)
[5]
0.697(p = 0.005)
[6]
L,D,J,IL
[6] array · 1 item
[0]
[7] array · 7 items
[0]
Abcc5
[1]
7089622
[2]
0.818(p = 0.021)
[3]
1.065(p = 0.476)
[4]
0.938(p = 0.471)
[5]
0.672(p = 8E-05)
[6]
IL
[8] array · 1 item
[0]
[9] array · 7 items
[0]
Abcc6
[1]
7051110
[2]
0.886(p = 0.109)
[3]
0.852(p = 0.03)
[4]
0.884(p = 0.089)
[5]
0.73(p = 2.0E-04)
[6]
IL
[10] array · 1 item
[0]
[11] array · 7 items
[0]
Abcg2
[1]
7254219
[2]
0.949(p = 0.666)
[3]
1.55(p = 6.0E-05)
[4]
1.049(p = 0.613)
[5]
1.203(p = 0.049)
[6]
D
[12] array · 1 item
[0]
[13] array · 7 items
[0]
Abcg5
[1]
7303106
[2]
0.321(p = 3.0E-07)
[3]
0.762(p = 0.095)
[4]
0.901(p = 0.515)
[5]
0.656(p = 0.006)
[6]
L, IL
[14] array · 1 item
[0]
[15] array · 7 items
[0]
Abcg8
[1]
7294657
[2]
0.297(p = 4.0E-06)
[3]
0.67(p = 0.039)
[4]
0.803(p = 0.261)
[5]
0.592(p = 0.005)
[6]
L, IL
locator
section[10]/table-wrap[1]#T4
gaps array · 2 items
[0]
asset:data:geo:GSE19175 not downloaded
[1]
asset:data:geo:GSE19175 licence not read
analysis object · 4 fields
state
complete
methods array · 3 items
[0] object · 10 fields
id
method-1
family
group_comparison
specific
anova
label
Group comparison
dsl_operations array · 4 items
[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
1
band
verbatim
quote
Since all four tissues were taken from each rat, a repeated measures mixed model ANOVA was used to determine if changes in expression were statistically significant for each gene [70].
[1] object · 10 fields
id
method-2
family
meta_analysis
specific
fixed_effects
label
Meta-analysis / evidence synthesis
dsl_operations array · 0 items
missing_operations array · 1 item
[0]
fixed_effects_pool
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
study_level_effect_estimates
evidence object · 3 fields
score
0.8
band
high
quote
fixed effects
[2] object · 10 fields
id
method-3
family
regression_modelling
specific
mixed_effects
label
Regression modelling
dsl_operations array · 0 items
missing_operations array · 1 item
[0]
regression_modelling_mixed_effects
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
predictors_and_outcome
evidence object · 3 fields
score
0.8
band
high
quote
mixed model
targets array · 0 items
gaps array · 0 items
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 4 items
[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: anova.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 1 item
[0]
GSE19175
resolved_accessions array · 1 item
[0]
GSE19175
statements array · 1 item
[0]
Microarray data are available through GEO under GSE19175.
basis
The paper names GSE19175 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 4 items
[0]
group_summary
[1]
welch_t
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bootstrap_mean_difference
[3]
group_comparison_anova
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source:pmid:21291544
[1]
data:geo:GSE19175
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asset:table:pmid:21291544:1
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asset:table:pmid:21291544:2
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asset:table:pmid:21291544:3
[5]
asset:table:pmid:21291544:4
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
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data_gaps array · 1 item
[0]
data:geo:GSE19175
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
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MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-10-31T18:20:55Z
independence
same_provider
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method
verbatim
availability
verbatim
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8
usage object · 3 fields
prompt_tokens
186823
completion_tokens
3599
cached_tokens
140288
stages array · 1 item
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extraction
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complete
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MiniMax-M3.1-Flash
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minimax
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review.independent_reader
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review.machine_critique
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review.human_critique
execution object · 5 fields
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absent
status
not_run
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null
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execution.not_run
provenance object · 7 fields
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pmid:21291544
type
paper
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pmid:21291544
[1] object · 3 fields
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source:pmid:21291544
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manuscript
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PMID 21291544 full text
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data:geo:GSE19175
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dataset
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GEO GSE19175
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activity-reading-sci-2026-11-01
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classification
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pmid:21291544
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paper
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pmid:21291544
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source:pmid:21291544
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manuscript
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PMID 21291544 full text
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data:geo:GSE19175
type
dataset
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GEO GSE19175
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agent:corpus-assay-engine
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activity-read-21291544
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extraction
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source:pmid:21291544
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agent:minimax-reader
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activity-resolve-GSE19175
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accession_resolution
used array · 1 item
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data:geo:GSE19175
agent
agent:corpus-assay-engine
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agent:corpus-assay-engine
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agent:minimax-reader
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343fb30756ba6ec3
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pmid:21291544
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sha256:d9b208ed2d1617558a7dd24e141b463e3eaef2e06b8a427f32f75c088293dbdc
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null
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asset:data:geo:GSE19175 licence not read
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review.human_critique
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[0] object · 11 fields
role
data_generation
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animal_model
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99e1195c7d6f
required_operations array · 0 items
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partial
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99e1195c7d6f
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0.99
evidence array · 4 items
[0]
in liver and small intestine from lactating rats compared to age-matched virgin controls dete
[1]
an and mouse genomes, with many of the genes in the rat Extended dataset identified based on their s
[2]
me is not as well annotated as the human and mouse genomes, with many of the genes in the rat E
[3]
tified based on their similarity to human or mouse genes. Use of the Extended dataset allowed a
order_basis
paper_text_order:29
id
assay:1
[1] object · 11 fields
role
data_generation
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microarray
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99e1195c7d6f
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partial
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99e1195c7d6f
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evidence array · 4 items
[0]
d in increased zinc uptake during lactation. Microarray data are available through GEO under GSE1917
[1]
s instructions by the University of Kentucky Microarray Core Facility (Lexington, KY). Selection of
[2]
es from controls utilizing the untransformed microarray data.Click here for file Additional File 2Be
[3]
nes on Which to Perform Statistical Analysis Affymetrix Expression Console software was used to perf
order_basis
paper_text_order:2674
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assay:2
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amplicon_seq
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99e1195c7d6f
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99e1195c7d6f
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[0]
as additional multiple annotations based on cDNA libraries. Although the Core dataset probes are the be
order_basis
paper_text_order:6060
id
assay:3
[3] object · 11 fields
role
analysis
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group_comparison
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f2132e96ae6c7a4e
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[3]
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supported
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dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Since all four tissues were taken from each rat, a repeated measures mixed model ANOVA was used to determine if changes in expression were statistically significant for each gene [70].
order_basis
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:21291544", "kind": "science-record", "revision": 9, "content_hash": "sha256:e001ec9e93a7aa001b3a3ba6e7a382854a525bdc3995d7c4ebadc9aebdec7738", "created_at": "2026-11-01T15:14:34Z", "updated_at": "2026-11-01T22:41:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Differential gene expression in liver and small intestine from lactating rats compared to age-matched virgin controls detects increased mRNA of cholesterol biosynthetic genes.", "doi": "10.1186/1471-2164-12-95", "pmid": "21291544", "authors": ["Antony Athippozhy", "Liping Huang", "Clavia Ruth Wooton-Kee", "Tianyong Zhao", "Paiboon Jungsuwadee", "Arnold J Stromberg", "Mary Vore"], "journal": "BMC genomics", "year": 2011, "abstract": "BACKGROUND: Lactation increases energy demands four- to five-fold, leading to a two- to three-fold increase in food consumption, requiring a proportional adjustment in the ability of the lactating dam to absorb nutrients and to synthesize critical biomolecules, such as cholesterol, to meet the dietary needs of both the offspring and the dam. The size and hydrophobicity of the bile acid pool increases during lactation, implying an increased absorption and disposition of lipids, sterols, nutrients, and xenobiotics. In order to investigate changes at the transcriptomics level, we utilized an exon array and calculated expression levels to investigate changes in gene expression in the liver, duodenum, jejunum, and ileum of lactating dams when compared against age-matched virgin controls.\nRESULTS: A two-way mixed models ANOVA was applied to detect differentially expressed genes. Significance calls were defined as a p < 0.05 for the overall physiologic state effect (lactation vs. control), and a within tissue pairwise comparison of p < 0.01. The proportion of false positives, an estimate of the ratio of false positives in the list of differentially expressed genes, was calculated for each tissue. The number of differentially expressed genes was 420 in the liver, 337 in the duodenum, 402 in the jejunum, and 523 in the ileum. The list of differentially expressed genes was in turn analyzed by Ingenuity Pathways Analysis (IPA) to detect biological pathways that were overrepresented. In all tissues, sterol regulatory element binding protein (Srebp)-regulated genes involved in cholesterol synthesis showed increased mRNA expression, with the fewest changes detected in the jejunum. We detected increased Scap mRNA in the liver only, suggesting an explanation for the difference in response to lactation between the liver and small intestine. Expression of Cyp7a1, which catalyzes the rate limiting step in the bile acid biosynthetic pathway, was also significantly increased in liver. In addition, decreased levels of mRNA associated with T-cell signaling were found in the jejunum and ileum. Several members of the Solute Carrier (SLC) and Adenosine Triphosphate Binding Cassette (ABC) superfamilies of membrane transporters were found to be differentially expressed; these genes may play a role in differences in nutrient and xenobiotic absorption and disposition. mRNA expression of SLC39a4_predicted, a zinc transporter, was increased in all tissues, suggesting that it is involved in increased zinc uptake during lactation. Microarray data are available through GEO under GSE19175.\nCONCLUSIONS: We detected differential expression of mRNA from several pathways in lactating dams, including upregulation of the cholesterol biosynthetic pathway in liver and intestine, consistent with Srebp activation. Differential T-Cell signaling in the two most distal regions of the small intestine (ileum and jejunum) was also noted, as well as differential expression of transporters that likely play a key role in nutrient uptake.", "claims": [{"id": "claim-method", "statement": "Since all four tissues were taken from each rat, a repeated measures mixed model ANOVA was used to determine if changes in expression were statistically significant for each gene [70].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:21291544", "locator": "sections[24] \"Statistical Analysis\" paragraph 2", "quote": "Since all four tissues were taken from each rat, a repeated measures mixed model ANOVA was used to determine if changes in expression were statistically significant for each gene [70]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Microarray data are available through GEO under GSE19175.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:21291544", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Microarray data are available through GEO under GSE19175."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A two-way mixed models ANOVA identified tissue-specific differential gene expression during lactation, and the source microarray data are available through GEO under GSE19175.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:21291544"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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