Regulation of pancreatic islet gene expression in mouse islets by pregnancy

in Journal of Endocrinology
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  • 1 Division of Endocrinology, Northwestern University Biomedical Informatics Center, Division of Transplantation Surgery, Genomics Core, Medical Biotechnology Center, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, 303 East Chicago Avenue, Tarry 15, Chicago, Illinois 60611, USA

Pancreatic β cells adapt to pregnancy-induced insulin resistance by unclear mechanisms. This study sought to identify genes involved in β cell adaptation during pregnancy. To examine changes in global RNA expression during pregnancy, murine islets were isolated at a time point of increased β cell proliferation (E13.5), and RNA levels were determined by two different assays (global gene expression array and G-protein-coupled receptor (GPCR) array). Follow-up studies confirmed the findings for select genes. Differential expression of 110 genes was identified and follow-up studies confirmed the changes in select genes at both the RNA and protein level. Surfactant protein D (SP-D) mRNA and protein levels exhibited large increases, which were confirmed in murine islets. Cytokine-induced expression of SP-D in islets was also demonstrated, suggesting a possible role as an anti-inflammatory molecule. Complementing these studies, an expression array was performed to define pregnancy-induced changes in expression of GPCRs that are known to impact islet cell function and proliferation. This assay, the results of which were confirmed using real-time reverse transcription-PCR assays, demonstrated that free fatty acid receptor 2 and cholecystokinin receptor A mRNA levels were increased at E13.5. This study has identified multiple novel targets that may be important for the adaptation of islets to pregnancy.

Abstract

Pancreatic β cells adapt to pregnancy-induced insulin resistance by unclear mechanisms. This study sought to identify genes involved in β cell adaptation during pregnancy. To examine changes in global RNA expression during pregnancy, murine islets were isolated at a time point of increased β cell proliferation (E13.5), and RNA levels were determined by two different assays (global gene expression array and G-protein-coupled receptor (GPCR) array). Follow-up studies confirmed the findings for select genes. Differential expression of 110 genes was identified and follow-up studies confirmed the changes in select genes at both the RNA and protein level. Surfactant protein D (SP-D) mRNA and protein levels exhibited large increases, which were confirmed in murine islets. Cytokine-induced expression of SP-D in islets was also demonstrated, suggesting a possible role as an anti-inflammatory molecule. Complementing these studies, an expression array was performed to define pregnancy-induced changes in expression of GPCRs that are known to impact islet cell function and proliferation. This assay, the results of which were confirmed using real-time reverse transcription-PCR assays, demonstrated that free fatty acid receptor 2 and cholecystokinin receptor A mRNA levels were increased at E13.5. This study has identified multiple novel targets that may be important for the adaptation of islets to pregnancy.

Introduction

Pancreatic β cells within the islets of Langerhans play a critical role in the regulation of glucose homeostasis by sensing glucose levels and secreting insulin. Compensation in response to increased demands for insulin occurs through changes in both insulin synthesis and release, as well as changes in β cell mass. These changes observed in β cells play an important role in regulating glucose homeostasis during different physiological and pathological states such as the insulin resistant states of pregnancy and obesity (Butler et al. 2007). Studying states in which insulin needs change can provide insight into pathways mediating β cell adaptations.

In mice, β cell mass increases about twofold during pregnancy, with a maximum rate of β cell proliferation near day 14 post coitum. This response is dynamic, as β cell mass decreases in the postpartum period (Karnik et al. 2007). Although the activation of the prolactin receptor (PRLR) by prolactin and placental lactogens contributes to the regulation of β cell mass during pregnancy (Brelje et al. 1993), the mechanisms controlling the expansion of β cell mass during pregnancy remain incompletely defined, and it is likely that additional signaling pathways contribute to or are directly involved in the PRLR-mediated proliferative response (Karnik et al. 2007, Sorenson & Brelje 2009).

This study was designed to begin to elucidate the molecular mechanisms responsible for pregnancy-induced changes in β cell mass. This was accomplished by comparing the results of whole genome RNA expression analysis using islets from pregnant mice on day E13.5 and control mice. We identified 110 genes that are differentially expressed in pregnant islets, including the gene encoding surfactant protein D (SP-D). Furthermore, the impact of pregnancy on the expression of G-protein-coupled receptors (GPCRs), a family of proteins involved in the regulation of β cell function and mass and effectors for a number of therapeutic agents (Vasavada et al. 1996, Holz & Chepurny 2005, Xiao et al. 2008), was examined in detail using a separate but complementary microarray approach. Together, the results of our studies suggest multiple novel targets that will facilitate further studies seeking to define molecular changes characteristic of the pregnancy-induced increase in β cell mass.

Materials and Methods

Islet isolation

C57BL/6 pregnant (day 13.5 of pregnancy) and control (age- and sex-matched) mice were purchased from Jackson Laboratories (Bar Harbor, ME, USA). Islets were isolated using standard protocols as described previously (Chen et al. 2006), and RNA was isolated using RNeasy (Qiagen). For RNA analyses, islets isolated from three mice were pooled for each sample to reduce interindividual variability in the RNA expression analyses. All procedures performed on animals were approved by the Northwestern University Animal Care and Use Committee.

Microarray analysis

RNA expression analysis was performed using the Illumina MouseWG-6 BeadChip, which provides coverage of over 45 200 genes and expressed sequence tags. Islet RNA from pregnant and control mice was labeled using a commercial kit (TargetAmp 1-Round Aminoallyl-aRNA Ki; Epicentre, Madison, WI, USA). To reduce changes related to interindividual variability, each sample of islet RNA was prepared using pooled islets from three pregnant or control mice. Labeled RNA was hybridized to microarrays. Raw signal intensities of each probe were obtained and processed as described previously (Du et al. 2008). Differentially expressed genes were identified using an ANOVA model with empirical Bayesian variance estimation. The problem of multiple comparisons was corrected using the false discovery rate (FDR). Genes were identified as being differentially expressed on the basis of a statistical significance (P value <0.01), FDR <5%, and 1.5-fold change in expression level in pregnant compared with control islets. Functional classification was performed using the ingenuity pathway analysis (IPA) software, based on statistical significance (IPA pathway test, P<0.01) and biological importance.

GPCR RNA array

A commercially available GPCR-specific 384 well TaqMan real-time reverse transcription (RT)-PCR microarray (Applied Biosystems, Carlsbad, CA, USA), which allows for the detection of mRNAs encoding 343 GPCRs (not including the odorant, olfactory, gustatory, and pheromone receptors), was used with RNA extracted as described above. cDNA was prepared using the high-capacity reverse transcriptase kit (Applied Biosystems) from the RNA that had contaminating DNA removed by DNAase I (Ambion, Applied Biosystems, Austin, TX, USA), and each port was loaded with cDNA (from 1 μg RNA) and the TaqMan Gene Expression Master Mix (Applied Biosystems). The resulting plate was analyzed on the 7900HT ABI PRISM, and the data were analyzed using SDS 2.3 and RQ Manager 1.2 software provided by Applied Biosystems. The results for each individual GPCR were examined, and if the sample had a calculated threshold <0.1, it was defined as undetectable (this generally indicated no discernible melt curve) and for data processing, the cycle time (Ct) was set at 40.0. A comparative Ct approach was utilized to quantify the relative levels of mRNA. Cts were normalized to the geometric mean of three housekeeping (HK) genes. Each HK gene on the microarray was evaluated for stability by geNorm (Vandesompele et al. 2002), and the three most stable HK genes were used. This experiment was performed on two independent samples, where each sample consisted of islets pooled from three mice.

Real-time RT-PCR

RNA was isolated and cDNA was prepared as described above. cDNA samples were examined by real-time RT-PCR and were normalized to the geometric mean of the three most stable HK genes as determined by geNorm (Vandesompele et al. 2002). These were hypoxanthine–guanine phosphoribosyltransferase (HPRT), TATA-binding protein (TBP), and peptidylprolyl isomerase A (PPIA). Sequences of primers used in the different assays are listed in Supplementary Table 1, see section on supplementary data given at the end of this article. A comparative Ct analysis was utilized to determine fold change in RNA expression based on the following approach: (Fleige et al. 2006). For each real-time RT-PCR experiment, three independent replicates were used.

Western blot

Islets from the control or pregnant mice (day 13.5 of pregnancy) were isolated as described above, and proteins were extracted in RIPA cell lysis buffer containing a cocktail of protease inhibitors (Calbiochem, LaJolla, CA, USA). Primary antibodies for free fatty acid receptor 2 (FFAR2) and PRLR were from Santa Cruz (Santa Cruz, CA, USA) and for tryptophan hydroxylase 1 (TPH1) from Thermo Scientific (Rockford, IL, USA). Mouse monoclonal antibodies to SP-D (hyb-246) were prepared as described earlier (Sorensen et al. 2009). The protein content of the lysate was determined using the Coomassie blue protein assay. Twenty micrograms of protein were separated by 10% SDS–PAGE and transferred to a polyvinylidene fluoride membrane in a semidry apparatus. Western blot analyses were performed as previously described (El Muayed et al. 2010). For SP-D samples, both reduced and nonreduced protein samples were examined. For reduced samples for SP-D, protein preparations were prepared by mixing the protein samples 1:2 with Laemmli sample buffer with 5% 2-mercaptoethanol and heating at 100 °C for 1 min (nonreduced preparations were heated in sample buffer with the addition of iodoacetamide, final concentration of 90 mM) as previously described (Madsen et al. 2000, Sørensen et al. 2009). Each assay was repeated with two or three independent samples.

Cytokine treatment

For studies with murine islets, purified islets were incubated for 24 h in RPMI 1640 (ATCC, Manassas, VA, USA) supplemented with 1% nonheat inactivated FBS. The medium was supplemented with or without 5 ng/ml interleukin 1β (IL1β) or 10 ng/ml tumor necrosis factor-α (TNF-α, R&D Systems, Minneapolis, MN, USA), and RNA was isolated as described above. Three independent experiments were performed.

Statistical analysis

P values were calculated by Student's t-test (one tailed) with a significance level at P<0.05 using Excel software. To determine the statistical significance for a change in the real-time RT-PCR studies, the fold change was used relative to the control condition, which was set at 1.0. For TPH1, statistical analysis was performed using logarithmic transformed values.

Results

Global RNA expression analysis of mouse islets during pregnancy

Pregnancy-induced changes in islet gene expression were examined by RNA expression analysis using RNA prepared from islets isolated from pregnant (day 13.5) and control (age- and sex-matched) mice. In all, 110 differentially expressed transcripts were identified (Fig. 1A), as defined by a statistically significant (P<0.01, FDR <5%)>1.5-fold change in expression. Table 1 lists the 110 differentially regulated transcripts using a classification scheme from ingenuity (see Materials and Methods). In Fig. 1B, these 110 differentially regulated genes are categorized into seven groups. An overrepresentation P test, which is a hypergeometric test and provides a representation of the enrichment of the genes in each category compared with the chip as a whole, demonstrated that the genes in each of the categories were overrepresented relative to the genes on the chip as a whole.

Figure 1
Figure 1

Global gene expression analysis of pancreatic islets at day 13.5 of pregnancy. (A) Heat map of differentially regulated pancreatic islet genes in pooled islets from either pregnant mice on day 13.5 (E1–E3) or control mice. E1, E2, C1, and C2 represent independent samples prepared from separate pools of pregnant (E1 and 2) and control (C1 and 2) islets. E3 and C3 are data obtained using the same RNA as E1/C1 respectively but prepared for microarray analysis independently. Scale bar: relative RNA expression (mean centered, standardized). (B) Graphical representation of the 110 differentially regulated genes from islets of pregnant mice compared with those of control mice using a functional classification scheme from ingenuity. Genes could be included in more than one category. The number of genes in each category is shown. P values for each category were determined by a category overrepresentation P test to examine the relative proportion of the genes in each category among the differentially regulated genes compared with the chip as a whole. The P values were as follows: cellular movement (P=2.9×10−3), molecular transport (P=4.5×10−4), cell-to-cell signaling and interaction (P=4.5×10−4), cell death (P=9.3×10−4), cellular growth and proliferation (P=4.5×10−4), inflammation (P=1.5×10−2), and metabolism (P=3.4×10−3).

Citation: Journal of Endocrinology 207, 3; 10.1677/JOE-10-0298

Table 1

Global expression analysis of RNA isolated from pregnant mice (E13.5) compared with that from control mice (age- and sex-matched)

GenesDescriptionFoldP value
Cell-to-cell signaling and interaction
SftpdSurfactant-associated protein D12.8<0.0005
Tph1Tryptophan hydroxylase 16.1<0.0005
Tnfrsf11bOsteoprotegerin3.7<0.0005
VipVasoactive intestinal polypeptide2.90.002
Tac1Tachykinin 12.6<0.0005
PrlrProlactin receptor2.2<0.0005
C3Complement component 32.1<0.0005
CartptCART prepropeptide2.1<0.0005
Glycam1Glycosylation-dependent cell adhesion molecule 12.10.002
GrpGastrin-releasing peptide2.10.001
C1qaComplement component 1, q subcomponent, alpha polypeptide1.7<0.0005
Ttyh1Tweety homolog 1 (Drosophila)1.7<0.0005
Ffar2Free fatty acid receptor 21.6<0.0005
PtpreProtein tyrosine phosphatase, receptor type, E1.60.003
Ntrk2Neurotrophic tyrosine kinase, receptor, type 21.5<0.0005
LplLipoprotein lipase−1.5<0.0005
Pcsk9Proprotein convertase subtilisin/kexin type 9−1.50.0001
Rap2aRAS-related protein 2a−1.60.003
IbspIntegrin-binding sialoprotein−1.70.002
Spon2Spondin 2, extracellular matrix protein−1.7<0.0005
Cellular growth and proliferation
SftpdSurfactant-associated protein D12.8<0.0005
CishCytokine-inducible SH2-containing protein7.5<0.0005
Tnfrsf11bOsteoprotegerin3.7<0.0005
VipVasoactive intestinal polypeptide2.90.002
Nupr1Nuclear protein 12.90.001
Ovol2Ovo-like 2 (Drosophila)2.7<0.0005
Tac1Tachykinin 12.6<0.0005
Socs2Suppressor of cytokine signaling 22.6<0.0005
Igfbp-5Insulin-like growth factor-binding protein-52.5<0.0005
HopxHOP homeobox2.3<0.0005
Enpp2Ectonucleotide pyrophosphatase/phosphodiesterase 22.3<0.0005
PrlrProlactin receptor2.2<0.0005
C3Complement component 32.1<0.0005
GrpGastrin-releasing peptide2.10.001
Gadd45αGrowth arrest and DNA-damage-inducible 45 alpha2.0<0.0005
Apobec1Apolipoprotein B editing complex 11.70.0003
Dnmt3bDNA methyltransferase 3B1.7<0.0005
Mcfd2Multiple coagulation factor deficiency 21.70.0001
Eif4ebp-1Eukaryotic translation initiation factor 4E-binding protein-11.60.003
Cited2Cbp/p300-interacting transactivator, w/Glu/Asp carb.-terminaldom.21.60.0008
PtpreProtein tyrosine phosphatase, receptor type, E1.60.003
Fgl2Fibrinogen-like protein 21.6<0.0005
FhitFragile histidine triad gene1.6<0.0005
Isg20Interferon-stimulated protein1.60.0002
Ntrk2Neurotrophic tyrosine kinase, receptor, type 21.5<0.0005
Defb1Defensin beta 1−1.50.0002
ToxThymocyte selection-associated high-mobility group box−1.5<0.0005
FosbFBJ osteosarcoma oncogene B−1.5<0.0005
Tm4sf4Transmembrane 4 superfamily member 4−1.7<0.0005
Molecular transport
SftpdSurfactant-associated protein D12.8<0.0005
Tnfrsf11bOsteoprotegerin3.7<0.0005
VipVasoactive intestinal polypeptide2.90.002
Tac1Tachykinin 12.6<0.0005
Igfbp-5Insulin-like growth factor-binding protein-52.5<0.0005
Enpp2Ectonucleotide pyrophosphatase/phosphodiesterase 22.3<0.0005
PrlrProlactin receptor2.2<0.0005
C3Complement component 32.1<0.0005
GrpGastrin-releasing peptide2.10.001
CartptCART prepropeptide2.1<0.0005
Slc6a8Solute carrier family 6 (neurotransmitter transporter, creatine), member 81.8<0.0005
Apobec1Apolipoprotein B editing complex 11.70.0003
Hba-α1Hemoglobin alpha, adult chain 11.60.0008
Hbb-β1Hemoglobin, beta adult major chain1.6<0.0005
LplLipoprotein lipase−1.5<0.0005
Pcsk9Proprotein convertase subtilisin/kexin type 9−1.50.0001
Adh1Alcohol dehydrogenase 1 (class I)−1.70.0002
IbspIntegrin-binding sialoprotein−1.70.002
Cell death
SftpdSurfactant-associated protein D12.8<0.0005
Tnfrsf11bOsteoprotegerin3.7<0.0005
VipVasoactive intestinal polypeptide2.90.002
Nupr1Nuclear protein 12.90.001
Tac1Tachykinin 12.6<0.0005
Socs2Suppressor of cytokine signaling 22.6<0.0005
Igfbp-5Insulin-like growth factor-binding protein-52.5<0.0005
PrlrProlactin receptor2.2<0.0005
C3Complement component 32.1<0.0005
Gadd45αGrowth arrest and DNA-damage-inducible 45 alpha2.0<0.0005
Dnmt3bDNA methyltransferase 3B1.7<0.0005
C1qaComplement component 1, q subcomponent, alpha polypeptide1.7<0.0005
Fgl2Fibrinogen-like protein 21.6<0.0005
FhitFragile histidine triad gene1.6<0.0005
PtpreProtein tyrosine phosphatase, receptor type, E1.60.003
EzrEzrin1.6<0.0005
Ern1Endoplasmic reticulum (ER) to nucleus signaling 11.60.0003
Eif4ebp1Eukaryotic translation initiation factor 4E binding protein 11.60.003
Ntrk2Neurotrophic tyrosine kinase, receptor, type 21.5<0.0005
Pcsk9Proprotein convertase subtilisin/kexin type 9−1.50.0001
Defb1Defensin beta 1−1.50.0002
AnpepAlanyl (membrane) aminopeptidase−1.5<0.0005
FosbFBJ osteosarcoma oncogene B−1.5<0.0005
Cellular movement
SftpdSurfactant-associated protein D12.8<0.0005
Tnfrsf11bOsteoprotegerin3.7<0.0005
VipVasoactive intestinal polypeptide2.90.002
Tac1Tachykinin 12.6<0.0005
Socs2Suppressor of cytokine signaling 22.6<0.0005
Enpp2Ectonucleotide pyrophosphatase/phosphodiesterase 22.3<0.0005
CartptCART prepropeptide2.1<0.0005
GrpGastrin-releasing peptide2.10.001
C3Complement component 32.1<0.0005
Gadd45αGrowth arrest and DNA-damage-inducible 45 alpha2.0<0.0005
Dnmt3bDNA methyltransferase 3B1.7<0.0005
EzrEzrin1.6<0.0005
Fgl2Fibrinogen-like protein 21.6<0.0005
FhitFragile histidine triad gene1.6<0.0005
Ntrk2Neurotrophic tyrosine kinase, receptor, type 21.5<0.0005
Defb1Defensin beta 1−1.50.0002
AnpepAlanyl (membrane) aminopeptidase−1.5<0.0005
Rap2aRAS-related protein 2a−1.60.003
Metabolism
Tph2Tryptophan hydroxylase 23.9<0.0005
IvdIsovaleryl coenzyme A dehydrogenase3.7<0.0005
Tnfrsf11bOsteoprotegerin3.7<0.0005
Tac1Tachykinin 12.6<0.0005
Igfbp-5Insulin-like growth factor-binding protein-52.5<0.0005
PrlrProlactin receptor2.2<0.0005
PrlrProlactin receptor2.2<0.0005
GrpGastrin-releasing peptide2.10.001
C3Complement component 32.1<0.0005
CartptCART prepropeptide2.1<0.0005
Slc40a1Solute carrier family 40, member 12.1<0.0005
Grhl1Grainyhead-like 1 (Drosophila)2.0<0.0005
Slc2a13Solute carrier family 2 (facilitated glucose transporter), 132.0<0.0005
Enox1Ecto-NOX disulfide-thiol exchanger 11.8<0.0005
Slc6a8Solute carrier family 6 (neurotransmitter transporter, creatine), member 81.8<0.0005
Cdh7Cadherin 7, type 21.7<0.0005
Matn2Matrilin 21.7<0.0005
GcdhGlutaryl-coenzyme A dehydrogenase1.6<0.0005
FhitFragile histidine triad gene1.6<0.0005
PtpreProtein tyrosine phosphatase, receptor type, E1.60.003
Slc39a11Solute carrier family 39 (metal ion transporter), member 111.6<0.0005
Uhrf2Ubiquitin-like, containing PHD and RING finger, 21.6<0.0005
Ntrk2Neurotrophic tyrosine kinase, receptor, type 21.5<0.0005
Atp8a1ATPase, aminophospholipid transporter1.5<0.0005
Edem2ER degradation enhancer, mannosidase alpha-like 21.50.0003
Tmem2Transmembrane protein 21.5<0.0005
LplLipoprotein lipase−1.5<0.0005
Pcsk9Proprotein convertase subtilisin/kexin type 9−1.50.0001
Neurog3Neurogenin 3−1.5<0.0005
ToxThymocyte selection-associated high-mobility group box−1.5<0.0005
DdcDOPA decarboxylase−1.6<0.0005
Hmgn3High-mobility group nucleosomal-binding domain 3−1.6<0.0005
Scg3Secretogranin III−1.6<0.0005
Inflammation
SftpdSurfactant-associated protein D12.8<0.0005
Tph2Tryptophan hydroxylase 23.9<0.0005
Tnfrsf11bOsteoprotegerin3.7< 0.0005
ChgbChromogranin B2.9<0.0005
Tac1Tachykinin 12.6<0.0005
HopxHOP homeobox2.3<0.0005
Enpp2Ectonucleotide pyrophosphatase/phosphodiesterase 22.3<0.0005
PrlrProlactin receptor2.2<0.0005
C3Complement component 32.1<0.0005
GrpGastrin-releasing peptide2.10.001
Slc2a13Solute carrier family 2 (facilitated glucose transporter), 132.0<0.0005
Sgip1SH3-domain GRB2-like (endophilin) interacting protein 12.0<0.0005
Gadd45αGrowth arrest and DNA-damage-inducible 45 alpha2.0<0.0005
Enox1Ecto-NOX disulfide-thiol exchanger 11.8<0.0005
C1qaComplement component 1, q subcomponent, alpha polypeptide1.7<0.0005
Matn2Matrilin 21.7<0.0005
Hba-α1Hemoglobin alpha, adult chain 11.60.0008
Hbb-β1Hemoglobin, beta adult major chain1.6<0.0005
Slc39a11Solute carrier family 39 (metal ion transporter), member 111.6<0.0005
FhitFragile histidine triad gene1.6<0.0005
Ern1Endoplasmic reticulum (ER) to nucleus signaling 11.60.0003
Atp8a1ATPase, aminophospholipid transporter1.5<0.0005
Tspan14Tetraspanin 141.5<0.0005
Ntrk2Neurotrophic tyrosine kinase, receptor, type 21.5<0.0005
Edem2ER degradation enhancer, mannosidase alpha-like 21.50.0003
ToxThymocyte selection-associated high-mobility group box−1.5<0.0005
AnpepAlanyl (membrane) aminopeptidase−1.5<0.0005
FosbFBJ osteosarcoma oncogene B−1.5<0.0005
Acpl2Acid phosphatase-like 2−1.6<0.0005
SphkapSPHK1 interactor, AKAP domain containing−1.60.0005
DdcDOPA decarboxylase−1.6<0.0005
AardAlanine and arginine-rich domain-containing protein−1.7<0.0005
Miscellaneous
Fmo1Flavin-containing monooxygenase 110.7<0.0005
B3galnt1UDP-beta 1, 3-galactosaminyltransferase, polypep-12.90.001
EG244911Predicted gene, EG2449112.8<0.0005
H21RikRIKEN cDNA 64 305 50H21 gene2.8<0.0005
Hsbp-1Heat shock factor-binding protein-12.70.0007
Wipi1WD repeat domain, phosphoinositide interacting 12.6<0.0005
Znrf2Zinc and ring finger 22.40.0009
Car15Carbonic anhydrase 152.40.003
Chac1ChaC, cation transport regulator-like 1 (Escherichia coli)2.20.002
Aqp4Aquaporin 42.20.0009
Txnrd2Thioredoxin reductase 22.0<0.0005
Fkbp-11FK506-binding protein-112.00.001
EhhadhEnoyl-Coenzyme A, hydratase/3-hydroxyacyl CoA dehydrogenase1.9<0.0005
IgfalsInsulin-like growth factor-binding protein, acid labile subunit1.90.0005
Rag1ap1Recombination activating gene 1 activating protein 11.90.02
Aldh1l2Aldehyde dehydrogenase 1 family, member L21.90.002
Pycr1Pyrroline-5-carboxylate reductase 11.80.001
BC006662cDNA sequence BC0066621.70.0005
C ldn8Claudin 81.70.0005
Gnβ1lG-protein, beta polypeptide 1-like1.70.0009
Rnf182Ring finger protein 1821.70.001
Hapln4Hyaluronan and proteoglycan link protein 41.70.006
Tmem28Transmembrane protein 281.70.006
Lsm6LSM6 homolog, U6 small nuclear RNA associated1.6<0.0005
Ugt1a7cUDP glucuronosyltransferase 1 family, polypeptide A7C1.60.006
Ugt1a6bUDP glucuronosyltransferase 1 family, polypeptide A6B1.50.001
Edem1ER degradation enhancer, mannosidase alpha-like 11.50.002
Uck2Uridine-cytidine kinase 21.50.01
D01RikRIKEN cDNA 22 104 12D01 gene−1.50.002
H3f3bH3 histone, family 3B−1.50.04
MlxiplMLX interacting protein-like−1.50.001
Slc30a8Solute carrier family 30 (zinc transporter), member 8−1.50.0006
Mdm1Transformed mouse 3T3 cell double minute 1−1.5<0.0005
Slc29a4Solute carrier family 29 (nucleoside transporters), member 4−1.60.001
Nt5dc25′-nucleotidase domain-containing 2−1.70.02
Sult1d1Sulfotransferase family 1D, member 1−1.70.01
Msx3Homeobox, msh-like 3−1.80.04
Cib-3Calcium and integrin-binding family member-3−1.80.01
CryβA2Crystallin, beta A2−1.80.0009

Shown are the 110 unique total transcripts that were identified, where each of these genes demonstrated at least 1.5-fold change, being statistically significant (P value <0.01) with false discovery rate (P<0.05). These 110 genes were categorized based on a classification scheme from ingenuity, and could be classified into more than one category. The miscellaneous category corresponds to genes that did not appear in any category shown here.

To confirm the results of the RNA expression analysis, real-time RT-PCR was performed on a select group of the differentially regulated genes (rationale for their selection is provided below). The gene encoding SP-D was examined as it demonstrated the greatest pregnancy-induced change in expression in the global expression array (12.8-fold increase in SP-D mRNA levels). This finding was verified by real-time RT-PCR (Fig. 2A) where the level of SP-D mRNA increased 22.4±1.7-fold (mean±s.e.m., n=3, P<0.05) in pregnant compared with that in control islets. Real-time RT-PCR studies were performed on additional genes that demonstrated significant pregnancy-induced increases in mRNA levels and have been directly or indirectly implicated in islet function: osteoprotegrin (Schrader et al. 2007), TPH1 (Paulmann et al. 2009), TPH2 (Paulmann et al. 2009), cytokine-inducible SH2-containing protein (Dif et al. 2001), and isovaleryl-CoA dehydrogenase (MacDonald 2007). The pregnancy-induced changes in the expression were confirmed for each of these genes (Fig. 2A). In addition, the expression of two ligands for GPCRs, gastrin-releasing peptide (GRP), and vasoactive intestinal polypeptide (VIP) was increased; however, in follow-up real-time RT-PCR analyses, the changes in GRP and VIP mRNA levels, while trending in the same direction, did not reach statistical significance (Fig. 2A). In addition to the above mRNAs, the levels of mRNA encoding the PRLR, which has a well described role in β cells during pregnancy, as well as suppressor of cytokine signaling 2, which is known to attenuate PRLR signaling (Sutherland et al. 2007), were also increased by pregnancy in the global expression analysis and confirmed in real-time RT-PCR studies.

Figure 2
Figure 2

Pregnancy induced changes in expression of select genes by real-time RT-PCR and western blot analysis. (A and B) Fold change in the level of select mRNAs in islets isolated from pregnant mice at day E13.5 compared with that in age- and sex-matched control mice. (A) Shows the results for genes with increased mRNA levels and (B) shows the results for genes with deceased mRNA levels in pregnant compared with the control islets. The change in the level of each of the transcripts in both (A) and (B) was statistically significant at P<0.05 (indicated by *), except for GRP (P=0.13) and VIP (P=0.13). Values are the mean fold change±s.e.m. from three independently prepared samples (each individual sample was a pooled sample from three mice). Fold change was calculated by a comparative cycle time (Ct) approach (see Materials and Methods) where the housekeeping genes (HPRT, TBP, and PPIA) were determined by geNORM (Vandesompele et al. 2002) using an approach defined in section ‘Materials and Methods’. The housekeeping Cts were used to derive the geometric means for the comparative Ct analyses. (C) Changes in the level of proteins encoded by three genes that exhibited pregnancy-induced changes in expression. Proteins were extracted from isolated islets from pregnant mice at day E13.5 and age- and sex-matched control mice. Representative immunoblots for prolactin receptor (PRLR), FFAR2, and TPH1 are shown. Shown are the results of a single experiment. Three independent experiments were performed. Each experiment was performed using islets prepared from a different control and pregnant mouse. After hybridization, the blots were stripped and re-probed with antibody against GAPDH.

Citation: Journal of Endocrinology 207, 3; 10.1677/JOE-10-0298

Among the mRNAs with a decreased level of expression in pregnant compared with that in control islets were mRNAs encoding zinc transporter-8 (ZnT8), neurogenin 3 (NGN3), and MLX interacting protein-like 1 (Mlxipl). ZnT8 and NGN3 are important for islet cell function and/or development (Gu et al. 2002, Lemaire et al. 2009), while the role of Mlxip1 in islets is unclear, although an association with NGN3 has been reported (Soyer et al. 2010). The pregnancy-induced changes in the expression for each of these genes were confirmed by real-time RT-PCR (Fig. 2B).

Figure 2C shows the results of western blot analyses that examined changes in the protein level of select genes in islets from pregnant mice compared with those in control mice. The expression of each of the proteins was increased in pregnant compared with that in control islets, consistent with the pregnancy-induced change in the level of their mRNA.

A potential role of SP-D in pancreatic β cells

The transcript encoding SP-D exhibited the largest pregnancy-induced increase in expression in the global expression analysis, which was confirmed by the real-time RT-PCR studies. As the expression of SP-D in adult islets has not been observed before, we examined SP-D expression in islets during pregnancy. Using mouse monoclonal antibodies, SP-D was detected in the mouse lung tissue (Fig. 3A, lane 2, at ∼40–45 kDa) but not in the brain (lane 1), consistent with the known pattern of SP-D expression (Motwani et al. 1995). The immunoreactive SP-D detected in mouse lung was consistent with monomeric SP-D, which is ∼45 kDa (Sørensen et al. 2009). However, SP-D is commonly present in large complex structures (Sørensen et al. 2009). In unreduced preparations, the antibody typically recognizes proteins with a molecular weight of ∼120 kDa and above, which likely represent trimers and other multimers. In our studies, using unreduced protein preparations, the anti-SP-D antibodies recognized proteins of ∼120 and 180 kDa (see Fig. 3A, lane 3) consistent with previous reports (Sørensen et al. 2009). Interestingly, in murine islets, a single band of ∼100 kDa was detected and in the nonreduced state, a band of ∼180 kDa was observed (Fig. 3A, lanes 4 and 5). It has been previously described that differences in covalent binding patterns between SP-D monomers exist and can result in the formation of nonreducible dimers (Crouch et al. 1993, Guo et al. 2008, Matalon et al. 2009, Sørensen et al. 2009) that are detected by western blots. Therefore, this band at 100 kDa likely represents a dimer. To further explore this, we examined SP-D expression in the lung, pancreas, spleen, and islets (Fig. 3A, lanes 6–9). All tissues exhibited immunoreactive bands at both 45 and 100 kDa, although the relative intensities of the bands varied widely between tissues. These data are consistent with differences in SP-D complexes between the tissues. Finally, we compared the level of SP-D in islets from control and pregnant mice, and found increased expression in pregnant islets, consistent with the pregnancy-induced increase in the level of SP-D mRNA (Fig. 3B).

Figure 3
Figure 3

Surfactant protein D (SP-D) expression in mouse islets. (A) Expression of SP-D in the lungs and islets. Proteins were extracted from mouse lung and islets. Samples from the brain (lane 1), lung (lane 2), mouse lung – not reduced (lane 3), islets (lane 4), islets – not reduced (lane 5), lung (lane 6), pancreas (lane 7), spleen (lane 8), and islets (lane 9) were either reduced or not reduced prior to separation by PAGE. The results are representative of the results of two to three independent experiments. (B) SP-D expression in pregnant islets. Protein extracts were prepared from islets isolated from pregnant mice at day E13.5 and age- and sex-matched control mice. Western blot analysis was performed as described in section ‘Materials and Methods.’ C, control; P, pregnant. After hybridization, the blots were stripped and re-probed with antibody against GAPDH. The findings are representative of the results of three independent experiments. (C) Effect of cytokines on SP-D mRNA levels in murine islets. RNA was prepared from murine islets treated for 48 h with 5 ng/ml IL1β or 10 ng/ml TNF-α. The fold change in SP-D mRNA was determined by real-time RT-PCR, using the comparative method and the housekeeping gene, 18S. Values represent the mean±s.e.m. (three independent samples, n=3) and are the relative level of SP-D mRNA compared with the level in control islets not treated with cytokines. *P<0.05 compared with control mice.

Citation: Journal of Endocrinology 207, 3; 10.1677/JOE-10-0298

In addition to its role in surfactant biology, SP-D may have an anti-inflammatory role in the lung tissue (Sørensen et al. 2007). As pregnancy is characterized by increased levels of pro-inflammatory peptides (Richardson & Carpenter 2007), we hypothesized that SP-D mRNA levels may be regulated by pro-inflammatory molecules in islets. To address this possibility, the impact of treating isolated native murine islets for 48 h with IL1β or TNF-α on SP-D expression was examined. As can be seen (Fig. 3C), IL1β and TNF-α each increased SP-D mRNA levels in isolated murine islets.

Analysis of GPCR-specific expression in mouse islets during pregnancy

In the global RNA expression analysis, one GPCR, FFAR2, was differentially regulated by pregnancy (>1.5-fold change in expression, P<0.01). As the genes for GPCRs tend to be underrepresented in the global RNA expression analyses, potentially leading to missed targets (Fredriksson & Schioth 2005), further studies were performed to determine whether pregnancy induced changes in the expression of genes encoding members of this receptor family. To do this, a secondary approach, a real-time RT-PCR array, was used to examine the expression of transcripts encoding 343 different GPCRs. The expression of GPCRs in the mouse islets during pregnancy at E13.5 was compared with the expression in islets isolated from nonpregnant female mice. Of the 343 GPCRs probed in the real-time array, the mRNAs encoding 216 of these GPCRs were detectable in the islets, as defined by the detection of a discernible melt curve in the control islets. Of the 343 GPCR transcripts that were examined in the real-time RT-PCR array, 92 were detected in the global RNA expression array. For this study, we focused on the 100 most highly expressed GPCR mRNAs in control islets (Table 2). Three of these GPCRs (FFAR2; GAL3R, galanin receptor 3; and CCKAR, cholecystokinin receptor A) exhibited a >2.5-fold change in the expression in pregnant compared with those in control islets. In the global RNA expression analysis, the mRNA encoding CCKAR was not detected. In contrast, FFAR2 mRNA exhibited a significant 1.5-fold increase in expression in the global RNA expression analysis and GAL3R mRNA was increased 1.3-fold (P=0.19). Follow-up real-time RT-PCR studies of these three GPCRs were performed (Fig. 4), and a significant pregnancy-induced increase in mRNA levels was observed for only FFAR2 and CCKAR (P<0.05).

Table 2

Comparison of the 100 most highly expressed G-protein-coupled receptors (GPCRs) in the GPCR-specific real-time reverse transcripton-PCR array compared with the global expression array for RNA from islets isolated from pregnant mice (E13.5) compared with control mice (age- and sex-matched)

GPCR arrayGlobal array
Relative expressionaFold changeaFold changeaP<value
GPCRs
Glp1r32 9881.71.20.01
Sstr311 6631.1−1.10.03
Adra2a94731.1−1.00.59
Gabbr254412.1ND
Gpr5654411.4−1.00.89
Gipr50771.3ND
Gpr11650770.6ND
Lphn150770.9ND
Casr47370.8−1.00.29
Ffar144191.1−1.10.14
Gpr10844191.41.20.01
Gpr11941232.0ND
Vipr141230.71.00.52
Adora127200.7−1.10.10
Gpr137b26830.9ND
Chrm325381.3−1.00.40
Galr123681.0−1.10.04
Gpr13722101.2ND
P2ry620621.61.00.56
Crcp19240.8−1.10.34
F2rl117950.7−1.37.04×10−5
P2ry517951.11.20.004
Ptger313600.9ND
Bai312690.5−1.00.31
Tm7sf312691.3ND
Eltd111840.9ND
Gabbr111840.9ND
Gcgr11841.2−1.20.01
Gprc5c11841.6ND
Lgr411841.2ND
Adcyap1r111050.7ND
Fzd411050.91.00.58
Fzd310311.4ND
Fzd610311.2−1.10.01
Gpr12510311.1−1.00.12
Fzd79621.0−1.10.07
Taar19621.0−1.10.03
F2r8970.9−1.10.07
Edg28370.6−1.10.06
Ednra8370.6ND
Lphn38370.81.00.54
Ccrl27291.2−1.00.40
Calcrl6801.1ND
Cd976801.2ND
Gpr1466801.2−1.00.10
Chrm46350.6ND
Fzd56351.0ND
Gpr1206351.1−1.32.67×10−5
Oxtr6351.6ND
Celsr35921.3−1.20.04
Ednrb5921.41.00.53
Gprc5b5920.8−1.10.01
Lphn25921.1ND
Fzd85521.3ND
Gpr855520.8−1.20.03
Cnr15150.7−1.00.58
Galr35156.51.10.01
Gpr395150.9ND
P2ry15150.8ND
Gpr1614811.1ND
Gpr754490.7ND
Gpr814490.7ND
Ffar24192.81.63.96×10−5
Mrgpre3641.8ND
Ghsr3170.7−1.10.06
Agtrl12962.2−1.00.66
Gpr412961.5ND
Npy1r2960.6−1.00.93
Adora2b2580.7−1.10.26
Gpr1242580.9−1.00.20
Gpr632580.8ND
Cckar24011.1ND
Adora2a2240.7ND
Gpr192241.11.10.03
Admr2091.2ND
Avpr1b2090.9ND
Cx3cr12091.1−1.00.51
Edg12091.7ND
Gprc5a2090.6−1.00.82
Ebi21950.8ND
Gpr41951.7ND
Sstr11951.0ND
Celsr11822.21.10.03
Cxcr31821.3ND
Gpr1531821.2ND
Avpr1a1700.3−1.00.61
Chrm11700.5ND
Crhr11700.7−1.00.13
Gprc6a1700.61.00.50
P2ry21590.4ND
Ccr21481.1ND
Gpr1351481.11.00.46
Tbxa2r1481.4−1.10.01
Chrm21380.6ND
Emr11381.6−1.00.24
Fzd11291.4ND
Gpr109a1291.41.00.30
Gpr651290.8−1.00.28
Galr21291.3ND
Celsr21290.6ND

Indicated in bold are the G-protein-coupled receptors that demonstrated a fold change of >2.5.

Shown are the 100 GPCRs with the greatest expression and their corresponding fold change in pregnant islets compared with those in control. Relative expression was calculated by . Fold change for the GPCR array and global array were calculated as outlined in section ‘Materials and Methods’.

Figure 4
Figure 4

Real-time RT-PCR analysis of GPCR mRNAs regulated by pregnancy in mouse islets at day 13.5 of pregnancy. The fold change in the level of mRNA encoding the following GPCRs, CCKAR, FFAR2, and GAL3R, in islets isolated from pregnant mice (day E13.5) compared with that in age- and sex-matched control mice was determined by real-time RT-PCR. Values are the mean±s.e.m. from three independent experiments. The change in the level of each transcript was statistically significant at P<0.05 (indicated by *), except for GAL3R (P=0.19).

Citation: Journal of Endocrinology 207, 3; 10.1677/JOE-10-0298

Discussion

The global RNA expression analysis and the confirmatory real-time RT-PCR assays demonstrated a marked increase in the level of the mRNA encoding SP-D, a protein best known for its role in lung function (Botas et al. 1998). A clear role for SP-D in lung and surfactant homeostasis has been described (Sørensen et al. 2007), but expression of this gene in the adult islets has not been previously reported. However, a recent study identified SP-D as a marker of new β cell formation during the perinatal period (Aye et al. 2010). A global SP-D knockout has been created (Botas et al. 1998), although alterations in glucose metabolism and/or islet biology in these mice have not been reported. A possible metabolic role of SP-D is suggested by the observation that SP-D impacts systemic lipid levels and atherosclerosis (Sørensen et al. 2006b). Consistent with that is the observation that circulating SP-D levels are inversely associated with obesity in humans (Zhao et al. 2007). Furthermore, mice with a null mutation of the SP-D gene maintained on a normal diet exhibited more weight gain and increased adiposity compared with control mice (Sørensen et al. 2006a).

The novel observation that SP-D mRNA is expressed in β cells was confirmed by western blot and immunohistochemical analyses (see Fig. 3A and B). Through a combination of studies, it is apparent that SP-D in islets exists in a form that leads to nonreducible dimers. This is not surprising, considering the complex nature of this protein. First, SP-D ranges in size from 37 to 50 kDa in the monomeric form because of different glycosylation patterns (Leth-Larsen et al. 1999, Sørensen et al. 2009). Furthermore, SP-D trimers contain multiple disulfide bonds between cysteine residues as well as nondisulfide covalent bonds, and these nondisulfide covalent bonds result in dimers resistant to reduction (Crouch et al. 1993, Guo et al. 2008, Matalon et al. 2009, Sørensen et al. 2009). As the function of SP-D is dependent on these modifications (Leth-Larsen et al. 1999, Sørensen et al. 2009), the modifications of SP-D in islets may have important implications for its function.

If SP-D indeed has a role in islet function, it could be hypothesized that, similar to endothelial cells where SP-D has an anti-inflammatory role (Snyder et al. 2008), it may function as an anti-inflammatory molecule in islets. As pregnancy is characterized by increased levels of circulating pro-inflammatory molecules (Richardson & Carpenter 2007), we examined the impact of the pro-inflammatory cytokines, IL1β and TNF-α, on SP-D expression in islets. We demonstrated that not only SP-D mRNA is expressed in islets but also its expression is regulated by cytokines. This is consistent with the possibility that SP-D serves as an anti-inflammatory factor in islets during pregnancy, although future studies will be needed to more fully examine this possibility. As with another anti-inflammatory molecule, cytokine IL1 receptor antagonist, which protects mice fed with high-fat diets from hyperglycemia (Sauter et al. 2008), this type of molecule could have unique therapeutic potential.

Recent genome-wide association studies have reported association of a missense mutation in SLC30A8, the gene that encodes the ZnT8, with type 2 diabetes (Chimienti et al. 2006, Sladek et al. 2007). ZnT8 may also be an autoantigen in type 1 diabetes (Wenzlau et al. 2007). Expression of SLC30A8 is largely localized to islets and, more specifically, β cells (Chimienti et al. 2006). ZnT8 is thought to play an important role in zinc transport into the insulin secretory granules (Nicolson et al. 2009). Thus, the decrease in ZnT8 mRNA levels in islets from pregnant compared with those in control mice was unexpected given the increased insulin secretory demands in pregnancy. However, pregnancy is a pro-inflammatory state, and recent observations (Egefjord et al. 2009, El Muayed et al. 2010) have shown that cytokines decrease the level of ZnT8 mRNA in islet cells. This may explain, in part, the pregnancy-induced decrease in ZnT8 mRNA levels.

In addition to the global RNA expression analysis, we examined genes encoding the GPCRs in greater detail. Multiple GPCRs are known to be expressed in islets (Winzell & Ahren 2007), but the impact of pregnancy on GPCR expression has not been previously examined. These receptors are well-known targets for multiple medicinal therapies. Moreover, given the important role of some GPCRs (e.g. the glucagon-like peptide 1 and parathyroid hormone-1 receptors) in the regulation of islet function and mass (De Leon et al. 2003), we explored the impact of pregnancy on the expression of mRNAs encoding GPCRs in islets using a real-time RT-PCR array. This array examined the level of mRNAs encoding 343 GPCRs in pregnant compared with that in control islets and was complementary to the global RNA expression analyses. Low levels of GPCR mRNA (Fredriksson & Schioth 2005) and the stringent requirements for statistical significance can lead to missed targets with the global RNA expression analysis. A recent study reported a role for serotonin in the regulation of β cell mass in pregnancy (Kim et al. 2010). The expression of mRNA encoding the serotonin receptor, Htr2b, was increased at mid-gestation while that encoding Htr1d was increased at the end of gestation. In our studies, Htr2b was only weakly expressed in islets from both pregnant and control mice and a clear pregnancy-induced change in expression was not observed; Htr1d mRNA was detectable on day E13.5 but a pregnancy-induced change in the expression was not evident at that time point during pregnancy.

Our studies did exhibit a reproducible pregnancy-induced change in two GPCRs, FFAR2 and CCKAR. Of the two, the role of CCKAR in islets has been more fully examined, with initial studies suggesting that CCKAR plays a role in mediating insulin secretion through CCK binding (Winzell & Ahren 2007). FFAR2, which exhibited a pregnancy-induced increase in mRNA and protein levels in islets, is a member of a recently identified family of GPCRs that are activated by free fatty acids. This family can be divided by the chain length (long versus short) of the free fatty acids that bind to the receptors. GPCRs that are specific for long-chain free fatty acids include GPR40 (FFAR1) and GPR120. In contrast, FFAR2 binds specifically to short-chain fatty acids (Kebede et al. 2009). Pregnancy-induced changes in the expression of the mRNAs encoding GPR40 and GPR120 were not observed. The role of FFAR2 in islets is unclear, although a previous study has demonstrated the expression of FFAR2 in islets (Regard et al. 2007). Moreover, evidence exists that FFAR2 is expressed in β cells, at least in pancreatic β cell lines (Kebede et al. 2009). Taken together, these data suggest that FFAR2 may be involved in the regulation of islet cell function or mass in pregnancy, although future studies will be required to define its role in islet cell biology.

During the preparation of this manuscript, a study exploring the global RNA expression analysis in islets from pregnant mice was reported (Rieck et al. 2009). These investigators identified 1907 genes that exhibited differential regulation compared with 110 in our study. This difference relates to the use of different analytic approaches. Using an approach similar to that of Rieck et al., we also detected ∼1500 differentially regulated genes. Of the 110 differentially regulated genes identified in our study, 37 were not shown to be differentially regulated (Rieck et al. 2009). A few of the notable exceptions were suppressor of cytokine signaling 2, Ngn3 and ZnT8, genes in which a pregnancy-induced change in expression was verified in our study. These differences may be explained, in part, by differences in the gene expression platforms used and the day of gestation studied, E13.5 vs E14.5.

To date, there are three genes that have been specifically identified as playing a role in gestational diabetes using mouse models. The PRLR, as discussed below, has a clearly established role. The other two genes suggested to be involved in gestational diabetes are FoxM1 and menin. The level of FoxM1 mRNA was shown by Zhang et al. (2010) to be increased on E14.5. Our data show that FoxM1 mRNA levels were increased on E13.5; however, this increase was not statistically significant. Zhang et al. (2010) also developed mice with pancreatic specific null mutations for FoxM1 and found that these mice developed gestational diabetes. The other gene identified as having an important role in gestational diabetes was menin (Karnik et al. 2007). Karnik et al. demonstrated decreased menin mRNA levels in islets during pregnancy, while overexpression of menin led to hyperglycemia during pregnancy. Finally, expression of menin was shown to be regulated by prolactin (Karnik et al. 2007). In our study, a pregnancy-induced change in menin mRNA levels was not demonstrated, although only a single time point in pregnancy was examined.

Increased PRLR expression in islets during pregnancy was demonstrated over 10 years ago (Sorenson & Brelje 2009). In this report, we have confirmed those findings using the global RNA expression analysis and follow-up confirmation by real-time RT-PCR. Furthermore, a transgenic mouse model has confirmed the role of the PRLR in glucose homeostasis during pregnancy (Huang et al. 2009). Although prolactin signaling undoubtedly contributes to pregnancy-induced changes in β cell mass (Sorenson & Brelje 2009), other signaling pathways and downstream molecules are likely important in this process. Together, with the recently published study by Rieck et al. (2009), our study demonstrates multiple novel proteins that warrant further investigation for their role in pregnancy-induced changes in β cells. Many of these genes were not previously known to be regulated by pregnancy, and, in some cases, expression of the gene in islets had not been previously demonstrated. These data should lay the foundation upon which select genes can be more fully examined for their role in pregnancy-induced changes in pancreatic β cell function.

Supplementary data

This is linked to the online version of the paper at http://dx.doi.org/10.1677/JOE-10-0298.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

This work was supported by the Endocrine Fellows Foundation Grant (to BTL) and the Northwestern Memorial Foundation MD-Scientist Fellowship (to BTL).

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    Global gene expression analysis of pancreatic islets at day 13.5 of pregnancy. (A) Heat map of differentially regulated pancreatic islet genes in pooled islets from either pregnant mice on day 13.5 (E1–E3) or control mice. E1, E2, C1, and C2 represent independent samples prepared from separate pools of pregnant (E1 and 2) and control (C1 and 2) islets. E3 and C3 are data obtained using the same RNA as E1/C1 respectively but prepared for microarray analysis independently. Scale bar: relative RNA expression (mean centered, standardized). (B) Graphical representation of the 110 differentially regulated genes from islets of pregnant mice compared with those of control mice using a functional classification scheme from ingenuity. Genes could be included in more than one category. The number of genes in each category is shown. P values for each category were determined by a category overrepresentation P test to examine the relative proportion of the genes in each category among the differentially regulated genes compared with the chip as a whole. The P values were as follows: cellular movement (P=2.9×10−3), molecular transport (P=4.5×10−4), cell-to-cell signaling and interaction (P=4.5×10−4), cell death (P=9.3×10−4), cellular growth and proliferation (P=4.5×10−4), inflammation (P=1.5×10−2), and metabolism (P=3.4×10−3).

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    Pregnancy induced changes in expression of select genes by real-time RT-PCR and western blot analysis. (A and B) Fold change in the level of select mRNAs in islets isolated from pregnant mice at day E13.5 compared with that in age- and sex-matched control mice. (A) Shows the results for genes with increased mRNA levels and (B) shows the results for genes with deceased mRNA levels in pregnant compared with the control islets. The change in the level of each of the transcripts in both (A) and (B) was statistically significant at P<0.05 (indicated by *), except for GRP (P=0.13) and VIP (P=0.13). Values are the mean fold change±s.e.m. from three independently prepared samples (each individual sample was a pooled sample from three mice). Fold change was calculated by a comparative cycle time (Ct) approach (see Materials and Methods) where the housekeeping genes (HPRT, TBP, and PPIA) were determined by geNORM (Vandesompele et al. 2002) using an approach defined in section ‘Materials and Methods’. The housekeeping Cts were used to derive the geometric means for the comparative Ct analyses. (C) Changes in the level of proteins encoded by three genes that exhibited pregnancy-induced changes in expression. Proteins were extracted from isolated islets from pregnant mice at day E13.5 and age- and sex-matched control mice. Representative immunoblots for prolactin receptor (PRLR), FFAR2, and TPH1 are shown. Shown are the results of a single experiment. Three independent experiments were performed. Each experiment was performed using islets prepared from a different control and pregnant mouse. After hybridization, the blots were stripped and re-probed with antibody against GAPDH.

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    Surfactant protein D (SP-D) expression in mouse islets. (A) Expression of SP-D in the lungs and islets. Proteins were extracted from mouse lung and islets. Samples from the brain (lane 1), lung (lane 2), mouse lung – not reduced (lane 3), islets (lane 4), islets – not reduced (lane 5), lung (lane 6), pancreas (lane 7), spleen (lane 8), and islets (lane 9) were either reduced or not reduced prior to separation by PAGE. The results are representative of the results of two to three independent experiments. (B) SP-D expression in pregnant islets. Protein extracts were prepared from islets isolated from pregnant mice at day E13.5 and age- and sex-matched control mice. Western blot analysis was performed as described in section ‘Materials and Methods.’ C, control; P, pregnant. After hybridization, the blots were stripped and re-probed with antibody against GAPDH. The findings are representative of the results of three independent experiments. (C) Effect of cytokines on SP-D mRNA levels in murine islets. RNA was prepared from murine islets treated for 48 h with 5 ng/ml IL1β or 10 ng/ml TNF-α. The fold change in SP-D mRNA was determined by real-time RT-PCR, using the comparative method and the housekeeping gene, 18S. Values represent the mean±s.e.m. (three independent samples, n=3) and are the relative level of SP-D mRNA compared with the level in control islets not treated with cytokines. *P<0.05 compared with control mice.

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    Real-time RT-PCR analysis of GPCR mRNAs regulated by pregnancy in mouse islets at day 13.5 of pregnancy. The fold change in the level of mRNA encoding the following GPCRs, CCKAR, FFAR2, and GAL3R, in islets isolated from pregnant mice (day E13.5) compared with that in age- and sex-matched control mice was determined by real-time RT-PCR. Values are the mean±s.e.m. from three independent experiments. The change in the level of each transcript was statistically significant at P<0.05 (indicated by *), except for GAL3R (P=0.19).