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Endocrinology Vol. 146, No. 10 4391-4398
Copyright © 2005 by The Endocrine Society

Deoxyribonucleic Acid Microarray Analysis of Gene Expression Pattern in the Arcuate Nucleus/Ventromedial Nucleus of Hypothalamus during Lactation

Xiao Qiu Xiao1, Kevin L. Grove1, See Yan Lau, Shannon McWeeney and M. Susan Smith

Division of Neuroscience (X.Q.X., K.L.G., M.S.S.), Oregon National Primate Research Center; Biostatistics & Bioinformatics Shared Resource (S.Y.L., S.M.); and Department of Physiology and Pharmacology (M.S.S.), Oregon Health & Science University, Beaverton, Oregon 97006

Address all correspondence and requests for reprints to: Dr. M. Susan Smith, Division of Neuroscience, Oregon National Primate Research Center, Oregon Health & Science University, 505 Northwest 185th Avenue, Beaverton, Oregon 97006. E-mail: smithsu{at}ohsu.edu.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Lactation is characterized by extreme hyperphagia and negative energy balance resulting from a large energy drain due to milk production and by a suppression of cyclic ovarian function. Increases in neuropeptide Y and agouti-related protein and a decrease in proopiomelanocortin expression in the arcuate nucleus of hypothalamus (ARH) may contribute to the hyperphagia to maintain energy balance and to the suppression of LH secretion associated with lactation. However, little is known about the full extent of neuroendocrine changes in the ARH that may contribute to the various adaptations occurring during lactation. To address this issue, we used Affymetrix microarray to acquire a reliable profile of the lactation-induced transcriptional changes in micropunches containing the ARH and a portion of the ventromedial nucleus of the hypothalamus. Using high stringency criteria, 12 genes were identified as being differentially regulated during lactation, and an additional 10 genes and three transcribed sequences were identified using moderate stringency criteria. Changes in neuropeptide Y, enkephalin, tyrosine hydroxylase, and dynorphin, genes previously shown to be differentially regulated during lactation, provide validation for the microarray analysis. New genes identified as being differentially expressed include those related to neurotransmission, growth factors, signal transduction, and structure remodeling. These data identify new genes in ARH/ventromedial nucleus of the hypothalamus that may play an important role in the adaptations of lactation related to hyperphagia, milk production, and the suppression of cyclic reproductive function and may contribute to elucidating a framework for integrating changes in energy intake with the regulation of reproductive function during lactation.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
LACTATION IS A natural physiological state after parturition, in which a variety of alterations in behavior, hormonal levels, and metabolic rate occur in the dams to ensure that proper adaptations are in place to successfully support the young (1, 2). Several hypothalamic neuronal systems are changed during lactation, which may mediate some of the physiological adaptations that occur, such as increased food intake, suppression of ovarian cyclicity, and suckling-induced milk production. The arcuate nucleus of the hypothalamus (ARH) is considered a key feeding center in the control of energy stores and for transmitting the effects of peripheral metabolic signals, such as leptin, insulin, and ghrelin, into the brain (3). Our previous studies showed that, during lactation, neuronal activity of orexigenic peptides, neuropeptide Y (NPY) and agouti-related protein (AGRP), are greatly increased in the ARH, whereas the anorectic peptide, proopiomelanocortin (POMC), is reduced (4, 5, 6). It has been suggested that these changes in neuropeptide activity in the ARH may be important in mediating the sustained hyperphagia and suppression of LH secretion associated with lactation (2). The ARH receives neuronal inputs from several brain stem regions that are activated by the suckling stimulus (7). In addition, the ARH has extensive reciprocal connections with other hypothalamic regions, including the paraventricular nucleus (PVH) (8), lateral hypothalamic areas, and dorsomedial hypothalamic nucleus, that are important in the regulation of food intake. Furthermore, neural projections from the ARH (NPY, POMC, and neurokinin B) make direct contact with GnRH neurons (9, 10, 11). Thus, the ARH contains a complex neurocircuitry that can serve to integrate changes in food intake/energy balance with the regulation of cyclic reproductive function. However, the full extent of alterations in gene expression in ARH that may contribute to adaptations during lactation is still unknown. To address this issue, we used microarray analysis to identify new genes in the ARH whose expression is changed by lactation.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Experimental design
Pregnant and virgin cycling female Sprague Dawley rats were used in these studies (Simonsen Laboratories, Gilroy, CA). The day of delivery was considered d 0 postpartum. Bilateral ovariectomy (OVX) was performed on d 2 postpartum and, at the same time, litters were adjusted to eight pups. Lactating rats were allowed free access to rat chow and water and allowed to suckle their pups undisturbed. On d 9, these animals were subdivided into two groups: Lac+8 (suckled 8 pups until d 11) or Lac+0 (8 pups were removed at 1500 h on d 9). The lactation groups were killed by decapitation after 1500 h on d 11. For the control group (Cont), virgin female rats were OVX and killed 9 d later. All animal procedures were approved by the Oregon National Primate Research Center Institutional Animal Care and Use Committee.

Microdissection of ARH/ventromedial nucleus of the hypothalamus (VMH), RNA isolation and purification
Tissues for microarray or real-time PCR were collected by rapid decapitation. Brains were quickly removed, frozen on dry ice, and stored at –80 C until the microdissection of hypothalamic nuclei using a micropunch technique. Frozen coronal brain sections (300 µm) were cut in a cryostat at –9 C, then thaw-mounted onto glass slides and refrozen. Using a 1-mm-diameter circular punch (Biomedical Research Instruments, Inc., Malden, MA), tissues were obtained from the sections with coordinates relative to bregma between –2.0 and –4.0, according to the rat atlas. The micropunches from these areas include all of the ARH and the ventral part of the VMH. For microarray, four ARH/VMH pools (each pool contained the punched tissues from three animals) were prepared for the Cont and Lac+8 groups. An additional five ARH/VMH pools (each pool contained the punched tissues from two animals) were prepared from Cont, Lac+8, and Lac+0 groups for real-time PCR. The pools were homogenized in TRIzol reagent (Invitrogen, Carlsbad, CA), and total RNA was isolated according to the manufacturer’s specifications, as previously described (12). RNA was further purified using an affinity resin column (RNeasy Mini Kit; QIAGEN, Valencia, CA). The quality and concentration of the RNA were determined by measuring the absorbance at 260 and 280 nm, and RNA integrity was confirmed by an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Palo Alto, CA).

Microarray procedures
Microarray assays are performed in the Affymetrix Microarray Core (Affymetrix, Inc., Santa Clara, CA), a unit of the OHSU Gene Microarray Shared Resource, using procedures adapted from the Affymetrix Gene-Chip Expression Analysis Technical Manual, revision 3 (http://www.affymetrix.com/support/technical/manual/expression_manual.affx). Briefly, mRNA is amplified and labeled from 4 µg total RNA, and mRNA is converted to double-stranded cDNA using Superscript Reverse Transcriptase (Invitrogen) and an oligo-dT primer linked to a T7 RNA polymerase binding site sequence (Integrated DNA Technologies, Inc., Coralville, IA). The amplified, labeled cRNA (the target) is produced in an in vitro transcription reaction using T7 RNA polymerase and biotin-CTP (GeneChip IVT Labeling Kit, Affymetrix). After removal of free nucleotides, target yield is measured by UV260 absorbance. Using microfluidics separation technology, 200 ng of each labeled target is analyzed alongside a control cRNA target on an RNA NanoChip processed on the 2100 Bioanalyzer (Agilent). Target quality is based on cRNA yield and size distribution. Samples that fail yield and size distribution thresholds are discarded or relabeled. Borderline or atypical targets are applied to test arrays for supplemental quality assessment before hybridization to a GeneChip genome array. Labeled target is fragmented at 95 C in the presence of high magnesium concentration. The fragmented material is combined with biotinylated hybridization control oligomer and biotinylated control cRNAs for the bacterial genes BioB, BioC, BioD, and CreX (Affymetrix) in hybridization buffer. Ten micrograms of target are hybridized with the RAE 230 A GeneChip (http://www.affymetrix.com/products/arrays/specific/rat230.affx) overnight, followed by washing, staining with streptavidin-phycoerythrin (Molecular Probes, Inc., Eugene, OR), signal amplification with biotinylated antistreptavidin antibody (Vector Laboratories, Inc., Burlingame, CA), and a final staining step on the Fluidics Station 450 (Affymetrix). The distribution of fluorescent material on the processed array is determined using the GeneArray laser scanner (Affymetrix). Image inspection is performed manually immediately after each scan.

Data measurement
Scans were performed with an Affymetrix GeneChip scanner, and the expression value for each gene was calculated using the Affymetrix Microarray Analysis Suite 5.0 (MAS5.0), computing the expression intensities in signal units defined by the software. After the initial analysis, the absolute analyses were rerun using global scaling to an average target intensity of 300. The scaling allows for the direct comparison of hybridization values from the different targets analyzed. For each analysis, scaled or unscaled, the parameters {alpha}1 and {alpha}2 were set to 0.1 and 0.15, respectively. These parameters set the point at which a probe set is called present, marginal, or undetectable. This call is based on the detection P value of the probe set as determined by the software.

Affymetrix comparison algorithms incorporated in the MAS5.0 software were used to derive metrics that identify differences between two-probe arrays for every probe set. We performed the following procedures to identify genes affected in each condition. First, a pair-wise comparison analysis was performed among the four hybridizations prepared from Lac+8 (used as experimental arrays) and the four Cont (used as baseline arrays). Among the 16 possible comparisons between four Cont pools and four Lac+8 pools, eight comparisons were randomly selected, which provides a very high level of representation and avoids bias. For the high-stringency analysis, all eight of the eight randomly selected comparisons (100%) had to show differential expression during lactation using a signed-rank test by the MAS software (P < 0.045). As an additional stringency filter, genes with expression ratios lower than ±1.3 were eliminated. However, due to the inherent variability of microarray analysis, it was likely that some important genes would not meet these criteria. Therefore, we performed a moderate-stringency analysis in which differential expression had to be observed in six of eight comparisons (75%) between Cont pools and Lac+8 pools. Because this decreased stringency increased the likelihood of identifying false positives, genes with expression ratios lower than ±1.4 were eliminated.

We used NetAffx (http://www.affymetrix.com), an interactive database, to retrieve the probe information, functional annotation, map location, and other related information for each gene. Genes were manually grouped into functional clusters based on their GeneOntology annotations (primarily GO: biological process) and PubMed literature.

Real-time PCR
Methods used for real-time PCR are similar to those previously described (12, 13). RNA samples were prepared for real-time PCR by random-primed reverse transcription reaction using random hexamer primers (Promega Corp., Madison, WI) and 1 µg RNA. The sequences of primers and probes used are summarized in Table 1Go. The reverse transcription reaction was then diluted 1:50 for PCR analysis. The amplification was performed as follows: 2 min at 50 C, 10 min at 95 C, then 40 cycles each at 95 C for 15 sec and 60 C for 60 sec in the ABI/Prism 7900 Sequences Detector System (Applied Biosystems, Foster City, CA). After PCR was completed, baseline and threshold values were set to optimize the amplification plot, and the data were exported to an Excel spreadsheet. Standard curves were drawn on the basis of the log of the input RNA vs. the critical threshold cycle, which is the cycle in which the fluorescence of the sample was greater than the threshold of baseline fluorescence. These standard curves allowed for the critical threshold values to be converted to relative RNA concentrations for each sample. 18S RNA amplifications were conducted with the Pre-Developed TaqMan Assay Reagent (Applied Biosystems). The primers and probe for G protein-coupled receptor 88 (GPR88) were provided by Custom TaqMan Gene Expression from Applied Biosystems (Rn00581712_m1), and other primers and probes were designed using the Primer Express software from Applied Biosystems (Table 1Go).


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  		TABLE 1. Primer and probe sequences used for real-time PCR

 
Statistical analysis
All real-time PCR data are expressed as mean ± SEM. One-way ANOVA, followed by the Newman-Keuls multiple-range test, was used to determine significant differences among groups.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Genes identified as being differentially expressed during lactation: high-stringency analysis
As an initial stringency filter, genes that were significantly variable in expression among replicates for the same treatment (both Cont and Lac+8) were excluded from further analysis. Differential expression during lactation had to be observed in all eight randomly selected comparisons. Genes with expression ratios lower than ±1.3 were eliminated. Based on these criteria, we identified only 12 genes that displayed significant changes (Table 2Go). There was strong agreement when we compared the gene list identified using our stringent criteria in a discovery framework with a more traditional approach that has a higher false nondiscovery rate (e.g. regularized t tests using Bayesian estimates of variance and {alpha} = 0.001), given that eight of the 12 genes were identified by both methods. Expectedly, the majority of affected genes encoded proteins involved in mechanisms of neurotransmission, signal transduction, growth modulation, transporters, and structure remodeling.


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  		TABLE 2. Transcripts altered during lactation: high stringency analysis

 
Genes previously shown to be differentially expressed during lactation
NPY, preproenkephalin, and tyrosine hydroxylase (TH, Table 2Go) have previously been shown to be present in the ARH and differentially expressed during lactation, using classical in situ hybridization or Northern blot techniques (5, 14, 15, 16, 17). These results provide an important validation for our microarray analysis.

New genes identified as being differentially expressed during lactation
Neurotransmission-related genes.
In ARH/VMH punches, oxytocin transcript increased about 103% during lactation (Table 2Go). Oxytocin has been classically identified in magnocellular neurons of the PVH and supraoptic nucleus (SON) (18). Although it is highly unlikely that these micropunches included any portion of the PVH or the main body of the SON, it is possible that the accessory SON was included, thus explaining the presence of oxytocin. The increase in oxytocin fits with all we know about changes in this system during lactation (18). Galanin, an important peptide neuromodulator involved in stimulating appetite and neuroendocrine regulation (19), was reduced by 36% (Table 2Go). Finally, endothelin-converting enzyme-like 1 (ECEL1), an enzyme of the M13 family of endopeptidases and previously known as XCE (20), was decreased by 50% during lactation (Table 2Go).

Growth factor-related genes.
The most surprising finding of this study was the large increase in IGF binding protein 3 (IGFBP3) (Table 2Go) during lactation, which is an important modulator of IGF action (21). VGF, a secreted polypeptide that is synthesized by neurons and is abundant in the hypothalamus (22), was reduced by 70% during lactation (Table 2Go).

Signaling-related genes.
CR16, an SH3 domain binding protein, which is encoded by a gene previously cloned as a glucocorticoid-regulated mRNA from a rat cDNA library (23), was increased by almost 80% during lactation; and similarly, serum/glucocorticoid-induced kinase (SGK) (24) was increased by 60% (Table 2Go).

Other genes.
The glial-specific structural protein, glial fibrillary acidic protein (GFAP), was down-regulated by 40% during lactation (Table 2Go). The metabolic transporter, Slc16a1 (monocarboxylate transporter 1, MCT1), which is mainly involved in the transport of lactate across cell membranes, showed a moderate reduction (35%, Table 2Go).

Additional genes identified as being differentially expressed during lactation: moderate-stringency analysis
Because the high-stringency analysis likely overlooked genes that were truly differentially expressed, the stringency was reduced by requiring significant changes in only six of eight random comparisons between Cont and Lac+8 pools, instead of eight of eight comparisons as described above. This analysis resulted in identifying an additional 10 genes (Table 3Go) and three transcribed sequences (expressed sequence tags). Two of these genes, dynorphin and cocaine- and amphetamine-regulated transcript (CART), are known as satiety agents (25, 26). Dynorphin, one of endogenous opioids expressed in the ARH, was down-regulated by 60%, consistent with previous reports (25), whereas CART was reduced by 45% during lactation. In addition, remarkable reductions in endothelin receptor type B (EDNRB), GHRH, and fatty acid binding protein 7 and increases in sulfotransferase family 1A, connective tissue growth factor (CTGF), and GPR88 were observed during lactation (Table 3Go).


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  		TABLE 3. Transcripts altered during lactation: moderate stringency analysis

 
Verifying microarray results and effects of lactation by quantitative real-time PCR analysis
Real-time PCR was used to quantify the expression of several representative genes, VGF, IGFBP3, SGK, and CR16, which were identified as being differentially expressed by high-stringency analysis (Table 2Go), and GPR88, which was identified as being differentially expressed by moderate-stringency analysis (Table 3Go). In addition, another experimental group was included in the real-time PCR studies to assess the specificity of the effects of lactation on changes in these genes. In this experimental group, the eight-pup litters were removed for 48 h (Lac+0), which has been shown previously to reverse the effects of the hyperphagia, energy drain of milk production and the suppression of gonadotropin secretion associated with lactation (27). Transcriptional levels of IGFBP3, SGK, and CR16 were significantly up-regulated, whereas VGF was significantly down-regulated during lactation, as assessed by real-time PCR analysis (Fig. 1Go, A–D). The changes in gene expression obtained by real-time PCR were similar to those obtained by microarray analysis with high-stringency analysis (Table 2Go), indicating that the expression profiles of these genes are highly reproducible, and the microarray analysis used here constitutes a highly reliable method to identify differentially expressed genes. In addition, real-time PCR confirmed the increase in expression of GPR88 during lactation (Table 3Go), as identified using the moderate-stringency analysis (Fig. 1EGo). Removal of the pups for 48 h (Lac+0) partially or fully reversed the lactation-induced changes in VGF, SGK, and GPR88, indicating their involvement in lactation-specific adaptations (Fig. 1Go). The inability of pup removal to reverse the increase in IGFBP3 and CR16 (Fig. 1Go, B and D) indicates that some aspects of lactation may take longer to restore to the nonlactation state.



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  		FIG. 1. Transcriptional levels of representative genes in ARH/VMH as measured by real-time PCR analysis. The Cont group consisted of virgin female rats OVX 9 d earlier. The lactation groups were: OVX on d 2 postpartum and then decapitated on d 11, no treatment (Lac+8), or pups removed for 48 h (Lac+0). Each ARH/VMH pool contained tissues from two to three animals, and there were five to eight pools per group. Columns with differing superscripts (a, b, or c) indicate values that are significantly different from each other (one-way ANOVA, followed by Newman-Keuls multiple-range test).

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
In this study, we used Affymetrix microarray and real-time PCR analyses to identify new genes that were differentially regulated in ARH/VMH during lactation, a physiological model of hyperphagia, and negative energy balance resulting from the large energy drain due to milk production. In addition, by artificially removing the nursing pups, which reverses many of the adaptations associated with the sucking stimulus (5, 27, 28), we were able to confirm the specificity of many of the changes in gene expression induced by lactation. Based on the changes in mRNA levels, our results support the idea that altered transcripts in ARH/VMH contribute to the metabolic, behavioral, and hormonal adaptations during lactation.

Methodology and validation of microarray analysis
The micropunches of the ARH/VMH used in this study included the whole rostro-caudal extent of the ARH and portions of the VMH, containing a very heterogenous population of neurons. Therefore, regional specific changes in gene expression may not have been detected in these pools. In addition, these studies were conducted in OVX females to minimize the effects of differences in ovarian sex steroid hormone levels on ARH gene expression. For these studies, we also pooled samples from three animals to obtain sufficient mRNA for microarray analysis. Thus, the observed changes are more representative of the general population. In addition, it should be noted that, in the analysis of the microarray data, we used a very conservative approach to detect the significant changes. The filter to exclude genes that were significantly variable among replicates in the same experimental group likely eliminates some genes that are truly differentially expressed, at the same time providing a robust stringency that limits the selection of false positives. For example, both neurokinin B and signal transducer and activator of transcription 3 tended to be down-regulated during lactation. However, they were not selected by either high-stringency or moderate-stringency analysis due to the variability specifically in the lactation group. When analyzed by real-time PCR, neurokinin B was reduced by 50% (n = 5; P < 0.05), whereas signal transducer and activator of transcription 33 remained unaltered during lactation (data not shown). Another limitation of the current study is the selection of the gene chip, which lacked several interesting genes involved in the regulation of energy balance, such as POMC, AGRP, SOCS3 (suppressor of cytokine signaling-3), and MC3 (melanocortin-3) receptor. An important validation of our analysis was the identification of changes in expression of genes that previously had been shown to be altered during lactation. The increase in ARH NPY during lactation has been firmly established (7, 9, 14) and is thought to be an important component in the hyperphagia and suppression of gonadotropin secretion. The increase in ARH enkephalin gene expression during lactation has been proposed to be involved in the hyperprolactinemia and the suppression of gonadotropin secretion (15). TH, the rate-limiting enzyme for dopamine synthesis and a marker of tuberoinfundibular dopaminergic neurons in the ARH, was down-regulated over 100% in lactation. The decrease in dopamine activity has been shown to be associated with suckling-induced prolactin secretion (16, 17). Because dynorphin, along with other opioids in the ARH, induces satiety, its reduction during lactation is consistent with the hyperphagia and negative energy balance (25).

Identification of new genes in the ARH that are differentially expressed and may contribute to the hyperphagia during lactation
During lactation, 2- to 4-fold increases in food and water intake are induced to meet the dramatic increase in the nutrient requirements of the mother for milk production (2). This is likely accomplished through the changing pattern of orexigenic and anorexigenic signals (4, 5, 6, 25, 29). In the current study, we confirm the changes in some of these compounds using microarray, as well as identify several new genes that may be involved in the hyperphagia.

The reduction in CART in the ARH during lactation is consistent with changes observed in response to food deprivation and its role as a potent anorexic agent (26, 30). CART expression in the ARH is limited to POMC neurons; POMC expression is also down-regulated during lactation (4). Decreased CART may promote the chronic increase in food intake needed to counter the vast efflux of nutrients from the mammary gland for milk production during lactation.

The reduction in galanin observed in the ARH during lactation does not fit with its role as a stimulator of food intake. Administration of galanin into the third ventricle induces a moderate increase in food intake (19). However, it is difficult to discern the effects of endogenous galanin expression on feeding behavior, because galanin-overexpressing and knockout mice both have normal body weights and feeding patterns (31). It is likely that the reduced galanin expression in ARH/VMH during lactation is more closely related to alterations in other neuroendocrine functions instead of food intake (32). Galanin is coexpressed in GHRH neurons in the ARH (33), and its decrease during lactation correlates with the decrease in GHRH expression.

The glucocorticoid, corticosterone, is a factor mediating the regulation of food intake and body adiposity. Interestingly, in rodents, most forms of genetic obesity are associated with increased glucocorticoid levels, whereas a lack of glucocorticoids is linked to hypophagia and reduced body weight (34). It has been reported that lactating animals have elevated levels of corticosterone and ACTH (35). In addition to the glucocorticoids and mineralocorticoids themselves, our study shows that some of their responsive elements, SGK (36) and CR16 (23), were also significantly increased in the ARH/VMH, which may contribute to the hyperphagia.

The role of VGF in the regulation of food intake may be complex. In the normal mouse, VGF is coexpressed in POMC neurons, but expression is increased in NPY neurons in response to fasting (22). VGF knockout mice are lean, hypermetabolic, and infertile (37), suggesting a critical role in the regulation of energy balance and an overall involvement in stimulating food intake. This role does not fit with VGF being inhibited in the ARH during lactation. However, the VGF gene is expressed extensively throughout the CNS, so it is difficult to determine where it acts to modulate energy homeostasis.

Monocarboxylates, such as lactate and pyruvate, play a central role in cellular metabolism. Essential to these roles is their rapid transport across the plasma membrane, which is regulated by recently identified MCTs. MCT1 is strongly expressed by neurons and astrocytes, which may have important implications for delivery of fuel to the brain (38). In the present study, we found a moderate inhibition of MCT1 expression in the ARH/VMH during lactation. This may be a very important adaptation to support the hyperphagia associated with lactation. Lactate is a potent inhibitor of food intake (39, 40), and our studies and others have shown that lactate production by skeletal muscle is greatly increased during lactation (12). To counteract the elevated levels of circulating lactate, a decrease in MCT1 expression in the ARH would prevent a lactate-induced hypophagia.

Somatotropic signaling is another critical component of the anorexia system in the hypothalamic regulation of energy homeostasis. Levels of IGFBP3 are important indicators of states of altered GH secretion and IGF action (21, 41). The high level of expression of IGFBP3 in ARH/VMH during lactation, by binding IGF, would result in a sustained low level of IGF. Given that intracerebroventricular injection of IGF results in a decrease in food intake and body weight (41), low levels of IGF would act to sustain the appetite signal. IGF and GH levels have been shown to be low during lactation in some species (42). Our data showed that GHRH gene expression in the ARH was significantly decreased during lactation (Table 3Go). The reduced GHRH is most likely due to the increase in ARH NPY, because increases in NPY have been shown to inhibit GHRH (43).

Taken together, the sustained hyperphagia during lactation could be achieved through increases in the orexigenic peptides, NPY and AGRP, as well as glucocorticoid signals, and the decreases in the anorexigenic signals, CART, dynorphin, and POMC. At the systemic level, the suppression in serum leptin would enhance all of the alterations in the various appetite-regulating neuropeptide systems that are observed in the ARH (44). It is also likely that the changes in ARH/VMH gene expression involved in the sustained hyperphagia are linked to the suppression of GnRH activity and cyclic reproductive function that is characteristic of lactation.

New differentially regulated genes in ARH that may be involved in several adaptations occurring during lactation
In addition to increased appetite and cessation of reproductive cyclicity, there are other important adaptations during lactation that are under the influence of complex neuroendocrine control mechanisms (2, 18, 45, 46, 47). The decrease in the endothelin system (ECEL1 and EDNRB) during lactation is likely associated with a decrease in endothelin production, although the role of endothelin as a neurotransmitter is presently not clearly understood (20). Another interesting differentially regulated gene identified by these studies is GPR88, an orphan G protein-coupled receptor, although the ligand for this receptor is yet to be identified. Furthermore, removal of the pups for 48 h fully reversed the changes in GPR88 occurring during lactation, indicating that this change correlates with the hyperphagia and suppressed reproductive function that occurs during lactation. Interestingly, several structural proteins, GFAP, and CTGF, as well as CR16, were differentially expressed during lactation, indicating that extracellular matrix and synaptic remodeling may be occurring in ARH/VMH (47).

In summary, by using Affymetrix microarray analysis and a stringent method of data analysis, we acquired a reliable profile of the lactation-induced transcriptional changes in the ARH/VMH areas. These differentially regulated gene transcripts during lactation are involved in a variety of functions, such as metabolic homeostasis, milk production, suppression of cyclic reproductive function, maternal behavior, and structure remodeling. Some of the changes include previously unheralded transcription factors. These data identify additional candidate genes that may be involved in ARH-mediated changes in neuroendocrine function during lactation. Additional studies are required to establish the specific cell types within the ARH/VMH that express these genes and to determine their functional significance.


   Acknowledgments
 
MicroArray assays were performed in the Affymetrix Microarray Core.


   Footnotes
 
This work was supported by National Institutes of Health Grants HD14643, HD18185, and RR00163.

First Published Online July 7, 2005

1 X.Q.X. and K.L.G. contributed equally as senior authors. Back

Abbreviations: AGRP, Agouti-related protein; ARH, arcuate nucleus of hypothalamus; CART, cocaine- and amphetamine-regulated transcript; Cont, control group; CTGF, connective tissue growth factor; ECEL1, endothelin-converting enzyme-like 1; EDNRB, endothelin receptor type B; GFAP, glial fibrillary acidic protein; GPR88, G protein-coupled receptor 88; IGFBP3, IGF binding protein 3; Lac+8, suckled until d 11; Lac+0, removed at 1500 h on d 9; MCT, monocarboxylate transporter; NPY, neuropeptide Y; OVX, ovariectomy/ovariectomized; POMC, proopiomelanocortin; PVH, paraventricular nucleus; SGK, serum/glucocorticoid-induced kinase; SON, supraoptic nucleus; TH, tyrosine hydroxylase; VMH, ventromedial nucleus of the hypothalamus.

Received May 9, 2005.

Accepted for publication June 30, 2005.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Russell JA, Douglas AJ, Ingram CD 2001 Brain preparations for maternity—adaptive changes in behavioral and neuroendocrine systems during pregnancy and lactation. An overview. Prog Brain Res. 133:1–38
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R. Arumugam, D. Fleenor, and M. Freemark
Lactogenic and Somatogenic Hormones Regulate the Expression of Neuropeptide Y and Cocaine- and Amphetamine-Regulated Transcript in Rat Insulinoma (INS-1) Cells: Interactions with Glucose and Glucocorticoids
Endocrinology, January 1, 2007; 148(1): 258 - 267.
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