Journal of Nutrition Animal Diets/Enrichment Products...

Home Help [Feedback] [For Subscribers] [Archive] [Search] [Contents]
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Online Supporting Material
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Ollier, S.
Right arrow Articles by Leroux, C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ollier, S.
Right arrow Articles by Leroux, C.
© 2007 The American Society for Nutrition J. Nutr. 137:560-567, March 2007

Biochemical, Molecular, and Genetic Mechanisms

Mammary Transcriptome Analysis of Food-Deprived Lactating Goats Highlights Genes Involved in Milk Secretion and Programmed Cell Death1,2

Séverine Ollier3, Christèle Robert-Granié4, Laurence Bernard3, Yves Chilliard3 and Christine Leroux3,*

3 Unité de Recherches sur les Herbivores, Institut National de la Recherche Agronomique, Theix, 63122 Saint Genès-Champanelle, France and 4 Station d'Amélioration Génétique des Animaux, Institut National de la Recherche Agronomique, Chemin de Borde-Rouge-Auzeville, BP 52627, 31326 Castanet-Tolosan cedex, France

* To whom correspondence should be addressed. E-mail: cleroux{at}clermont.inra.fr.


   ABSTRACT
TOP
 ABSTRACT
Introduction
 Materials and Methods
 Results
 Discussion
 LITERATURE CITED
 
Animal nutrition considerably affects milk composition that influences its nutritional quality. Milk component synthesis and secretion by the mammary gland involve expression of a large number of genes whose nutritional regulation remains poorly defined. In this study, we examined the effect of food deprivation (FD) on the expression of 8379 genes in caprine mammary gland using a bovine oligonucleotide microarray. Twelve lactating goats were assigned to 2 groups based on their feeding level (control diet ad libitum vs. 48-h FD). We identified 161 genes whose expression was altered by FD. Most of these genes (88%) were downregulated, suggesting a stress response by the mammary gland. In particular, the decrease in expression of genes involved in milk protein, lactose, and lipid metabolism could contribute together with the shortage of nutrients to the drop in milk protein, lactose, and fat secretion. In addition, this study highlights modification of the expression of at least 14 genes that could be responsible for a slowdown in mammary cell proliferation and differentiation and/or an increase in programmed cell death in response to 48-h FD in goats.


   Introduction
TOP
 ABSTRACT
 Introduction
Materials and Methods
 Results
 Discussion
 LITERATURE CITED
 
The basic function of the lactating mammary gland is to produce milk, providing nutrients for growth and development of the mammalian neonate. After parturition, the proliferation and differentiation of mammary secretory cells lead to an increase in milk secretion, whereas after lactation peak, milk production declines largely because of apoptotic mammary cell death, which exceeds cell proliferation (1). During lactation, mammary epithelial cells synthesize and secrete large quantities of specific milk components, including proteins, lactose, and lipids, which have a considerable effect on the nutritional, technological, and sensorial properties of milk products. Milk production and composition are affected by genetic, nutritional, physiological, and environmental factors. Nutrition in particular has a considerable effect on the composition of milk lipids (2), whereas composition of the protein fraction generally is only marginally affected by this factor (3).

Milk component synthesis and secretion by the mammary gland involve expression of a large number of genes. However, until now, studies were focused on the effect of nutritional factors on expression of a few genes encoding the key lipogenic enzymes in the mammary gland. A significant reduction in mRNA abundance for genes involved in the de novo fatty acid (FA)5 synthesis pathway and triacylglycerol synthesis pathway induced by a milk fat-depressing diet has been observed in lactating dairy cows (4,5). Similarly, Bernard et al. (6) demonstrated in lactating goats that the mechanisms by which lipid supplementation modulates milk FA composition are partly related to the level of mammary expression of genes encoding enzymes involved in FA uptake and FA desaturation. These nutritional studies can be extended to several thousands of genes thanks to the recent development of functional genomic tools.

A first generation of ruminant macroarray gathering 400 gene probes corresponding to bovine and caprine cDNA was used to study the impact of genotype on the mammary transcriptome of lactating goats (7). This first generation of arrays was very limited due to the small number of genes represented. Now, the identification of a larger number of bovine sequences (8) has allowed the development of more extensive oligonucleotide microarrays in that species. By contrast, in caprine species, the absence of large sequencing programs means less knowledge of gene sequences and the absence of a caprine microarray. However, the cross-species hybridization approach for transcriptome analyses has already been reported. Human oligonucleotide microarrays were used, for example, by Anderson et al. (9) to study woodchuck carcinoma and by Hernandez et al. (10) to analyze bovine immune system response. Elsewhere, structural genomic studies of domestic animals have shown that goats are closely related to bovine species (11). For all these reasons, bovine tools may be used in transcriptomic analyses in goats.

To better understand the mammary mechanisms underlying milk secretion and composition in response to dietary factors, the objective of this study was to identify genes whose expression was regulated by feeding level. So mammary gene expression profiles of lactating goats either consuming a control diet ad libitum or after 48-h food deprivation (FD), a treatment known to have a large effect on milk production and composition (12), were compared using a bovine oligonucleotide microarray representing 8379 genes.


   Materials and Methods
TOP
 ABSTRACT
 Introduction
 Materials and Methods
Results
 Discussion
 LITERATURE CITED
 
    Animals and diets. Twelve peak-lactating Alpine goats (48 ± 2 d postpartum at the beginning of the experiment) from the Lusignan experimental station (France) were chosen based on the homogeneity of their milk yield, number of lactations, and their genotype at the {alpha}S1-casein (CSN1S1) locus, which has an impact on milk protein content and indirectly on milk fat content (13). The goats were fed an orchard grass hay-based diet with a 35:65 forage to concentrate ratio during a 2-wk preexperimental period. For 48 h before slaughtering, 6 goats consumed this diet ad libitum (control goats) and the 6 others were food deprived (FDd). Goats were milked at 0800 and 1600. Goats were housed in individual stalls, had free access to water, and were fed twice daily (except during the 48-h FD) just after milking. The goats were cared for and handled in compliance with the INRA Animal Care Committee guidelines.

    Sampling and analysis. Blood samples were collected 1 h before slaughter from the jugular vein in tubes containing EDTA (Venoject, C.M.L.) and centrifuged. Plasma was frozen at –20°C until determination of insulin and metabolite concentrations. Milk yield was recorded. One milk sample (30 mL) was collected before slaughter to analyze milk fat, protein, and lactose contents (14) (C.I.L.A.L.) and 1 milk sample (3 mL) was stored at –20°C until determination of FA composition.

At the end of the experiment, all goats were slaughtered just after milking to eliminate most of the milk contained in the glands. Immediately after death, ~50 g of mammary tissue was collected under sterile conditions in the secretory area containing lobulo-alveolar structures (acini). Samples were immediately frozen in liquid nitrogen and stored at –80°C until RNA extraction.

    Analysis of milk FA. FA from lyophilized milk were extracted, methylated, and samples injected into a Trace-GC 2000 Series gas chromatograph with a 100-m x 0.25-mm i.d. fused silica capillary column (CP-Sil 88, Chrompack), as described by Chilliard et al. (15).

    Plasma measurements. Plasma concentration of insulin was assayed using a commercially available porcine RIA kit (Insulin-CT, Cis Bio International) validated for caprine plasma. Plasma samples were analyzed in duplicate following the manufacturer's instructions and the intra-assay CV was 8.2%. Plasma glucose, nonesterified FA (NEFA), and 3-hydroxybutyrate (BHBA) concentrations were determined spectrophotometrically by the glucose dehydrogenase method (Glucose RTU kit, BioMérieux), the acyl-CoA synthetase method (Wako-Unipath NEFA-C kit, Oxoid), and the BHBA dehydrogenase method (16), respectively.

    RNA extraction. Total RNA was prepared from ~150 mg mammary tissue using TRIZOL Reagent (Invitrogen Life Technologies) and further purified with the SV Total RNA Isolation system (Promega) to eliminate contaminating genomic DNA. RNA concentration and purity were determined by spectrophotometry at 260, 280, and 320 nm (Lambda 25 UV/VIS spectrometer, Perkin Elmer Instruments) and RNA integrity was verified using a Bioanalyser 2100 (Agilent Technologies).

    Microarray procedures. Transcriptomic analyses were performed using the Bovine Genome Oligo Set V1.1 obtained from Operon Biotechnologies, which contains 8329 long oligonucleotides (70-mer) representing 8329 unique genes from the Bos taurus genome, completed with 50 genes chosen for their implication in mammary metabolism. Gene sequences used for probe design are obtained from TIGR Cattle Gene Index Release 11 and GenBank. These 8379 oligonucleotides were spotted in duplicate on UltraGAPS coated slides (Corning B.V. Life Sciences). This platform has been deposited in NCBI's Gene Expression Omnibus (17) and is accessible through GEO Platform accession number GPL4594.

Purified total RNA (5 µg) from each mammary tissue sample was reverse transcribed and fluorescently labeled in the presence of either Cy3- or Cy5-dCTP (Amersham Biosciences Europe) using the Pronto Plus Direct system (Promega and Corning B.V. Life Sciences) according to the manufacturer's instructions. Quantity and labeling efficiency of labeled single-strand cDNA were determined by quantitating the absorbance at 260, 550, and 650 nm using a Lambda 25 UV/VIS spectrometer. Each microarray was cohybridized at 42°C for 16 h with 43 pmol of incorporated Cy3 from mammary mRNA from each control goat and 43 pmol of incorporated Cy5 from mammary mRNA from 1 FDd goat carrying the same CSN1S1 genotype. The hybridization and washes were performed using the Pronto Plus Direct system according to the manufacturer's instructions. Each hybridization was repeated in a dye-swap manner for reducing technical bias for a total of 4 replicated spots per oligonucleotide (2 intra- and 2 inter-slides). The 12 slides performed for a total of 6 independent comparisons were scanned for both dye channels at 532 and 635 nm with an Affymetrix 428 Array Scanner (MWG Biotech) at 10-µm resolution. The photomultiplier tube voltage was adjusted to obtain an equal global intensity in both channels.

    Microarray image and data analyses. The signal and background intensity values for the Cy3 and Cy5 channels from each spot were obtained using ImaGene 6.0 software (BioDiscovery). Data have been deposited in NCBI's Gene Expression Omnibus (17) and are accessible through GEO Series accession number GSE6380. Data were filtered using the ImaGene "empty spots" option that automatically flags low-expressed and missing spots (spots with a background mean-subtracted signal mean <2 background SD) to remove genes from the analysis that were too weakly expressed. Thus, the remaining genes were considered as expressed in the caprine lactating mammary tissues studied. After base-2 logarithm transformation, data were corrected for systematic dye bias by Lowess normalization using GeneSight 4.1 software (BioDiscovery) and controlled by M A-plot representation (18). For each comparison, data from the 4 replicated spots per oligonucleotide were averaged to eliminate technical variations. Statistical analyses were performed using the free R 2.1 software (19); after controlling the variance of each gene, log ratios between 48-h FD and control (n = 6) were analyzed with an ANOVA model and we used a standard Student's t test to detect differentially expressed genes between the 2 nutrition levels. P values were adjusted using the Bonferroni correction for multiple testing to eliminate false positives. Differences were considered significant at adjusted P < 0.0001. The identity of all differentially expressed genes was validated by sequence comparison of spotted oligonucleotides with the NCBI nonredundant database using BLASTN algorithm (20). Gene symbols were obtained from the HUGO Gene Nomenclature Committee database (21). These genes were classified according to their biological process ontology determined from UniProt database (22) and the QuickGO Gene Ontology browser (23).

    Quantitative real-time RT-PCR. The RT was carried out on 4 µg of purified total RNA with 10 pmol of oligo(dT) and 200 U of Superscript II RNase H– reverse transcriptase (Invitrogen Life Technologies) in a final volume of 20 µL. The reaction mix was then diluted with 30 µL of sterile water. Specific primers (Supplemental Table 1) were designed in an area close to that of the microarray probe either from caprine sequences, when available, or from bovine sequences using PrimerExpress software (Applied Biosystems) and obtained from Eurogentec. PCR was carried out with 4 µL of 50-fold diluted single-strand cDNA on the LightCycler system (Roche Molecular Biochemicals) using the LightCycler FastStart DNA Master SYBR Green I kit (Roche Applied Science) according to the manufacturer's instructions, with 10 pmol of specific primers. After an initial denaturing step (95°C for 15 min), the PCR mixture was subjected to the following 3-step cycle repeated 40 times: denaturing for 15 s at 94°C, annealing for 20 s at the temperature indicated in Supplemental Table 1, and extension for 16 s at 72°C. Melting curves and gel electrophoresis were used to characterize the final products. Calibration curves for each gene were generated from 5-point serial dilutions of a standard goat mammary cDNA. The resulting calibration curves demonstrated good efficiencies: from 1.78 to 1.99 according to the calculation E = 10(–1/slope). A nontemplate negative control was incorporated into all PCR runs. These validations are essential, because we used bovine sequences to study gene expression in goats. In particular, a mismatch between cDNA target and short primer (18–28-mer) sequences used for RT-PCR could have a greater impact than with the long oligonucleotides (70-mer) used in our microarray analyses. Elsewhere, amplification of {gamma}-actin mRNA was performed using the LightCycler FastStart DNA Master HybProbe kit (Roche Applied Science), as described by Bernard et al. (6), with 10 pmol of each primer and Taqman probe (Applied Biosystems; Supplemental Table 1) under the following conditions: 10 s at 95°C and 20 s at 58°C, repeated 40 times. Due to the lack of response of goat mammary gland cyclophilin mRNA to nutritional factors (24), cyclophilin mRNA was used as a housekeeping gene, as in other studies on mammary gland (25,26), to take analytical errors and cDNA synthesis efficiency into account. It was quantified on the same 50-fold diluted single-strand cDNA using primers and Taqman probe, as described by Bonnet et al. (27). Results are expressed as the mRNA copy number of each gene of interest relative to the cyclophilin mRNA copy number.

    Statistical analyses. Data are expressed as means ± SEM (n = 6). Statistical analyses were performed using R 2.1 software. Homogeneity of variances was evaluated using Levene's test. The significance of differences between the 2 feeding levels was analyzed using Student's t test when variances were homogeneous or using the nonparametric Mann-Whitney U test when variances were unequal. Differences with P < 0.05 were considered significant.


   Results
TOP
 ABSTRACT
 Introduction
 Materials and Methods
 Results
Discussion
 LITERATURE CITED
 
    Milk and plasma analyses. After 48 h of treatment, milk yield and milk lactose, protein, and fat yields were significantly lower in FDd than in control goats (Table 1). Secretion of short-chain (C4:0, C6:0, and C8:0), medium-chain (C10:0, C12:0, and C14:0), and C16:0 saturated FA was significantly lower in FDd than in control goats, as well as secretion of cis-9, cis-12 C18:2, cis-9, trans-11 C18:2, and cis-9, cis-12, cis-15 C18:3 FA. However, secretion of C18:0 and cis-9 C18:1 FA did not differ between the 2 groups.


View this table:
[in this window]
[in a new window]

  		TABLE 1 Milk production and components of fed (control) and 48-h FDd goats1

 
Plasma insulin and glucose concentrations were significantly lower in FDd than in control goats, whereas plasma BHBA and NEFA concentrations were significantly higher (Table 2).


View this table:
[in this window]
[in a new window]

  		TABLE 2 Plasma insulin and metabolite concentrations of fed (control) and 48-h FDd goats1

 
    Mammary transcriptome analysis. Hybridization of the RNA studied on the bovine oligochip showed that 7140 genes of the total 8379 different probes spotted were expressed in caprine lactating mammary tissue. Student's t tests and correction for multiple testing using Bonferroni procedure at 0.01% level allowed 161 differentially expressed genes in mammary tissue to be identified between the 2 feeding levels studied. Data are provided in the online supporting material (Supplemental Tables 2 and 3). Most of these genes (141) were downregulated in FDd compared with control goats, whereas only 20 genes were upregulated. These genes were classified into 11 functional categories according to Gene ontology annotation (Table 3).


View this table:
[in this window]
[in a new window]

  		TABLE 3 Distribution into functional categories of genes altered by FD in caprine mammary tissue1

 
    Genes affected by FD in mammary tissue. Genes encoding {alpha}-lactalbumin (LALBA), CSN1S1, {alpha}S2-casein (CSN1S2), ß-casein (CSN2), and ß-lactoglobulin (LGB), which were proteins abundantly secreted in milk were downregulated in FDd compared with control goats (Table 4).


View this table:
[in this window]
[in a new window]

  		TABLE 4 Selected differentially expressed genes in mammary tissue of FDd compared with control goats1

 
Among the 141 downregulated genes in FDd compared with control animals, 6 were involved in lipid metabolism and transport (Table 3), such as FASN and ACSBG1 involved in FA metabolism, and AZGP1 (Table 4).

Note that in the regulation of transcription and RNA metabolism class, most of the downregulated genes in FDd goats were involved in transcription, such as POU1F1 and ESR1, whereas the 2 upregulated genes, PCBP2 and PABPC1, were involved in mRNA metabolism (Tables 3 and 4).

Other modifications in FDd compared with control goats concerned the downregulation of genes involved in cell cycle and cell proliferation (RAD9A and PA2G4), cell death (CASP8 and BECN1), and signal transduction (LEP and ERBB3), and the upregulation of 2 genes (KRT19 and ACTG1) encoding structural constituents of cytoskeleton (Table 4). Moreover, among the 18 genes in the cellular protein metabolism and transport class, 7 were involved in protein folding, such as DNAJB11 and ST13, which were downregulated, and HSPA8, which was upregulated (Tables 3 and 4).

    Validation of the microarray results. Changes in expression of 12 genes revealed by microarray analysis and representing all functional categories (Table 4) were verified by real-time RT-PCR. Profiles obtained using both techniques (Fig. 1) confirmed the down- or upregulation of 10 genes, whereas for the 2 others, PPAP2A and SLC2A4, the profiles were not in agreement. For 7 of the 10 validated genes, ratios between 48-h FD and control mRNA abundance obtained by real-time RT-PCR were greater than those obtained using microarray.


Figure 1
View larger version (21K):
[in this window]
[in a new window]

  		Figure 1  Validation of differentially expressed genes identified by microarray using real-time RT-PCR mRNA quantifications in mammary tissue of FDd compared with control goats. Mean (n = 6) log ratio of 48-h FD mRNA abundance relative to control mRNA abundance for 12 genes was compared between the 2 techniques. A positive value means a greater mRNA abundance in 48-h FDd than in the control group, whereas a negative value means a lower mRNA abundance in 48-h FDd than in the control group. *Significant changes in gene expression between the 2 feeding levels using real-time RT-PCR, P < 0.05.

 

   Discussion
TOP
 ABSTRACT
 Introduction
 Materials and Methods
 Results
 Discussion
LITERATURE CITED
 
To better understand the mammary mechanisms underlying milk secretion and composition in response to dietary factors, we compared mammary transcriptome profiles as well as milk production and composition of lactating goats consuming a control diet ad libitum or after 48-h FD.

Several parameters were analyzed to confirm the impact of nutritional restriction on goats in our experiment. The lower milk yield in 48-h FDd compared with control goats (Table 1) was consistent with previous studies of the FD effect in lactating cows (28) and goats (12). This was partly due to a shortage of nutrients for milk synthesis, leading in particular to a decrease in lactose synthesis, which was responsible for milk osmotic pressure and was in accordance with the observed drop in milk lactose secretion. We also report decreases in plasma glucose and insulin concentrations after 48-h FD (Table 2), as typically observed during undernutrition in ruminants (28,29). Moreover, plasma NEFA concentration was higher in FDd than in control goats due to fat mobilization from adipose tissue, which resulted in NEFA release in plasma during undernutrition (29). During FD, whole body ketogenesis also increased through partial oxidation of NEFA in the liver (29), resulting in an increase in plasma BHBA concentration, as observed in this study.

Until now, in ruminants, the effect of nutritional factors on mammary gene expression was studied only with respect to the key lipogenic enzymes (4–6). The transcriptomic analyses developed and described here provided the means for simultaneous investigation of expression profiles of 7140 genes expressed in the mammary tissue. Among these genes, we identified a set of 161 genes whose expression was altered by 48-h FD. Differentially expressed genes were identified using the adjusted P-values (P < 0.0001) for a multiple testing procedure, called Bonferroni, which provides a strict check on family-wise Type I error rate. This procedure is more stringent than a less conservative one that checks the false discovery rate. The procedures based on false discovery rate control, which minimize the number of false negatives but tolerate some false positives, are used within the framework of an exploratory analysis, whereas the procedures based on family-wise Type I error rate control are used within the framework of decisional analysis, for which the cost of Type I errors (or false positives) is high (30). Therefore, the Bonferroni procedure was used in this study to obtain a high confidence in the genes detected as responding to FD and thus to identify potential new candidate genes to study the impact of nutrition on mammary gland of lactating ruminants. Moreover, real-time RT-PCR analyses were performed to validate microarray data (Fig. 1). PCR primers were designed near the microarray probe and outside the sequences involved in known alternative splicing. Thus, the alternative use of polyadenylation sites described for ADFP in bovines (31) and the CSN1S2 alternative splicing detected in ruminants (32) were taken into account in the design of primers. Other unknown mRNA variants cannot be excluded, however. Among the 12 analyzed genes, only PPAP2A and SLC2A4 showed different expression patterns between the 2 techniques. These discrepancies could be due to the fact they belong to multi-gene families or to unknown alternative splicing. Furthermore, it is worth noting that for most genes, the microarray method underestimated the change in transcript abundance compared with real-time RT-PCR, as classically observed. Both the stringent statistical procedure used for microarray data analyses and the validations by RT-PCR provide confidence in the differentially expressed genes identified by our transcriptomic analysis.

Interestingly, PCBP2 and PABPC1 were identified among the few upregulated genes (Table 4); they encode, respectively, poly(rC)-binding protein 2 and cytoplasmic poly(A)-binding protein 1, 2 proteins known to increase mRNA stabilization (33,34). This result is in agreement with the stabilization of labile mRNAs described during stress (35,36). Moreover, the fact that more genes were downregulated (141 genes) than upregulated (20 genes) by FD was also previously observed during responses to the stress caused by nutritional restriction in bovine muscle tissue (37) and to thermal stress in bovine mammary epithelial cells (38). This result suggests a slowdown in the mammary transcriptional machinery in FDd goats, which, along with the shortage of nutrients, could contribute to the drop in milk production and milk component secretion. Thus, the decrease due to FD in expression of LALBA, CSN1S1, CSN1S2, CSN2, and LGB genes (Table 4), encoding 5 of the 6 main ruminant milk proteins (39), is associated with a sharp fall in milk protein yield (Table 1). Moreover, the downregulation of LALBA expression, involved in lactose synthesis, is in agreement with the drop in milk lactose yield. Furthermore, FD led to an expected decrease in milk fat secretion [(12); Table 1], as well as an alteration in expression of 7 genes involved in lipid metabolism (Table 3). Thus, mammary mRNA abundance of FASN, encoding FA synthase, was lower in FDd than in control goats. This result is in accordance with the sharp decrease in milk secretion of FA synthesized de novo (short- and medium-chain saturated FA; Table 1), suggesting regulation of this gene at a transcriptional level by nutritional status. Regarding long-chain FA, which are imported from the plasma, secretions of C18:0 and cis-9 C18:1 FA were not modified by FD (Table 1). This could be partly attributed to the mobilization of adipose deposits that in the goat are rich in C18:0 and cis-9 C18:1 FA (40). Conversely, FD reduced milk secretion of C18:2 and C18:3 FA (Table 1), which mainly have a dietary origin and are taken up by the mammary gland from the plasma triacylglycerol fraction. In other respects, FD reduced mRNA abundance of ACSBG1, encoding lipidosin that has acyl-CoA synthetase activity when expressed in COS-1 cells (41), and AZGP1, encoding zinc-{alpha}2-glycoprotein, a lipid mobilizing factor in adipocytes (42). Nevertheless, the function of these 2 last genes has not yet been described in the mammary gland. ACSBG1 and AZGP1 genes could become new candidates to study the mammary mechanisms underlying milk fat composition in response to dietary factors and need to be studied thoroughly.

Regarding the other mammary pathways modified by FD, for several genes downregulated in our study (Table 4), it was shown elsewhere that their expression is associated with an activation of cell differentiation or proliferation during mammary tumorigenesis: ERBB3, ESR1, FASN, LEP, PA2G4, POU1F1, and RAD9A (43–49). Collectively, the downregulation of these genes is consistent with a slowdown in mammary cell proliferation and differentiation in 48-h FDd compared with control goats. Moreover, it was shown in human endometrial adenocarcinoma cells that expression of ESR1 was downregulated by inhibition of FASN expression, which stimulated apoptosis of these cells (50), called type I programmed cell death (PCD). Furthermore, it was previously shown in rat that energy restriction inhibited cell proliferation and induced apoptosis in premalignant and malignant mammary gland lesions (51). In our study, the expression of other genes involved in PCD was also altered by FD. First, we observed a decrease in expression of CASP8, encoding caspase 8, and it was reported in the mouse L929 cell line that an inhibition of CASP8 expression induced autophagy of these cells (52), called type II PCD. Second, heat shock cognate (Hsc)70, encoded by the HSPA8 gene, whose expression increased in mammary gland of FDd goats, was identified as a chaperone protein involved in chaperone-mediated autophagy, a mechanism activated in rodents by physiological stress such as prolonged starvation (53,54). Third, autophagic cell death requires an intact cytoskeleton (55), which could be related to the observed increase in expression of KRT19 and ACTG1 genes, encoding, respectively, cytokeratin 19 and {gamma}-actin. Altogether, these elements suggest an orientation of mammary cells toward autophagic cell death in FDd goats. Conversely, decreases in expression of 2 other genes involved in the chaperone-mediated autophagy complex [ST13 and DNAJB11, encoding, respectively, Hip and Hsp40 (54)] and a key autophagy gene necessary for a nonapoptotic death pathway [BECN1, encoding beclin-1 (52)], suggest that autophagy was not the only PCD pathway that may occur in the mammary gland of FDd goats. Altogether, these observations indicate that autophagy and apoptosis could occur in the same tissue as previously described in bovine mammary epithelial cells (56), pointing out a complex regulation of these pathways. Furthermore, the orientation of cells toward PCD reflects the first step of mammary gland involution, a remodeling program that results in changes in mammary cellular composition (57). In mice, where the mammary organogenesis is particularly well studied, the mammary involution can be divided into several phases (58–60). In the initial stage, which encompasses the first 48 h after weaning, mammary epithelial cells lose their differentiated function. However, major structural changes do not occur in the gland during this reversible phase. The second stage is characterized by a destruction of the mammary lobulo-alveolar architecture, followed by a regrowth of the stromal adipose tissue. Due to the differences in the length of pregnancy and lactation between ruminants and rodents, and thus in the timing in which involution takes place, 48-h FD in lactating goats is likely to mimic in part the first step of this process. The milk synthesis is indeed slowed down, but the secretion of milk components is not completely stopped. Thus, refeeding the goats after a 48-h FD led to a recovery of milk yield and fat yield within 3 and 6 d, respectively, to levels similar to those observed before FD (61). Furthermore, a sharp rebound of milk lipoprotein lipase activity occurred after a 1-d refeeding, followed after 6 d by a return to a level similar to that observed before FD (40). It can therefore be suggested that 48-h FD did not lead to a great and irreversible change in mammary cellular composition but probably did lead to the first stage of the involution process. However, the pathways involving the types I and II PCD need further investigation and could bring greater insight into the mechanisms involved in the initiation of mammary gland involution in ruminants.

In conclusion, to our knowledge, this is the first transcriptomic analysis studying the impact of nutrition on ruminant mammary gene expression, which demonstrates that FD alters mammary transcriptome simultaneously to milk production and composition. As observed during stress responses, most genes altered by FD are downregulated. Among them, we identified genes involved in the drop in milk production and milk component secretion. In addition, our results highlight genes that could be responsible for a slowdown in mammary cell proliferation and differentiation in response to FD as well as an orientation of mammary cells toward PCD that could correspond to an early step in mammary gland involution.


   ACKNOWLEDGMENTS
 
We thank specially M. Goutte and S. Bes for their technical assistance for real-time RT-PCR. We thank J. Rouel and P. Capitan, C. Delavaud and M. Tourret, Y. Faulconnier and S. Chauvet for their assistance in milk composition analysis, plasma measurements, and statistical analyses, respectively. We also thank A. Combeau and his team for the management of the animals and R. Jailler and his team for slaughtering the animals.


   FOOTNOTES
 
1 Supported by the LIPGENE EU-FP6 project (contract FOOD-CT-2003-505944). S. Ollier is funded by INRA and "Auvergne region" grants. Back

2 Supplemental Tables 1–3 are available with the online posting of this paper at jn.nutrition.org. Back

5 Abbreviations used: BHBA, 3-hydroxybutyrate; CSN1S1, {alpha}S1-casein; CSN1S2, {alpha}S2-casein; CSN2, ß-casein; FA, fatty acid; FD, food deprivation; FDd, food deprived; Hsc, heat shock cognate; LALBA, {alpha}-lactalbumin; LGB, ß-lactoglobulin; NEFA, nonesterified fatty acid; PCD, programmed cell death. Back

Manuscript received 28 July 2006. Initial review completed 12 September 2006. Revision accepted 12 December 2006.


   LITERATURE CITED
TOP
 ABSTRACT
 Introduction
 Materials and Methods
 Results
 Discussion
 LITERATURE CITED
 

1. Capuco AV, Wood DL, Baldwin R, McLeod K, Paape MJ. Mammary cell number, proliferation, and apoptosis during a bovine lactation: relation to milk production and effect of bST. J Dairy Sci. 2001;84:2177–87.[Abstract]

2. Chilliard Y, Ferlay A. Dietary lipids and forages interactions on cow and goat milk fatty acid composition and sensory properties. Reprod Nutr Dev. 2004;44:467–92.[Medline]

3. Coulon JB, Dupont D, Pochet S, Pradel P, Duployer H. Effect of genetic potential and level of feeding on milk protein composition. J Dairy Res. 2001;68:569–77.[Medline]

4. Piperova LS, Teter BB, Bruckental I, Sampugna J, Mills SE, Yurawecz MP, Fritsche J, Ku K, Erdman RA. Mammary lipogenic enzyme activity, trans fatty acids and conjugated linoleic acids are altered in lactating dairy cows fed a milk fat-depressing diet. J Nutr. 2000;130:2568–74.[Abstract/Free Full Text]

5. Peterson DG, Matitashvili EA, Bauman DE. Diet-induced milk fat depression in dairy cows results in increased trans-10, cis-12 CLA in milk fat and coordinate suppression of mRNA abundance for mammary enzymes involved in milk fat synthesis. J Nutr. 2003;133:3098–102.[Abstract/Free Full Text]

6. Bernard L, Rouel J, Leroux C, Ferlay A, Faulconnier Y, Legrand P, Chilliard Y. Mammary lipid metabolism and milk fatty acid secretion in alpine goats fed vegetable lipids. J Dairy Sci. 2005;88:1478–89.[Abstract/Free Full Text]

7. Leroux C, Le Provost F, Petit E, Bernard L, Chilliard Y, Martin P. Real-time RT-PCR and cDNA macroarray to study the impact of the genetic polymorphism at the alphas1-casein locus on the expression of genes in the goat mammary gland during lactation. Reprod Nutr Dev. 2003;43:459–69.[Medline]

8. Womack JE. Advances in livestock genomics: opening the barn door. Genome Res. 2005;15:1699–705.[Abstract/Free Full Text]

9. Anderson PW, Tennant BC, Lee Z. Cross-species hybridization of woodchuck hepatitis virus-induced hepatocellular carcinoma using human oligonucleotide microarrays. World J Gastroenterol. 2006;12:4646–51.[Medline]

10. Hernandez A, Karrow N, Mallard BA. Evaluation of immune responses of cattle as a means to identify high or low responders and use of a human microarray to differentiate gene expression. Genet Sel Evol. 2003;35 Suppl 1:S67–81.

11. Schibler L, Vaiman D, Oustry A, Giraud-Delville C, Cribiu EP. Comparative gene mapping: a fine-scale survey of chromosome rearrangements between ruminants and humans. Genome Res. 1998;8:901–15.[Abstract/Free Full Text]

12. Massart-Leen AM, Peeters G. Changes in the fatty acid composition of goat milk fat after a 48-hour fast. Reprod Nutr Dev. 1985;25:873–81.

13. Grosclaude F, Ricordeau G, Martin P, Remeuf F, Vassal L, Bouillon J. Du gène au fromage: le polymorphisme de la caséine alpha s1 caprine, ses effets, son évolution. INRA Prod Anim. 1994;7:3–19.

14. AOAC. Official methods of analysis. 16th ed. Gaithersburg (MD): Association of Official Analytical Chemists; 1997.

15. Chilliard Y, Rouel J, Leroux C. Goat's alpha-s1 casein genotype influences its milk fatty acid composition and delta-9 desaturation ratios. Anim Feed Sci Technol. 2006;131:474–87.

16. Brashear A, Cook GA. A spectrophotometric, enzymatic assay for D-3-hydroxybutyrate that is not dependent on hydrazine. Anal Biochem. 1983;131:478–82.[Medline]

17. GEO, the Gene Expression Omnibus [database on the Internet]. Bethesda (MD): National Center for Biotechnology Information, U.S. National Library of Medicine. [cited 2006 Nov 29]. Available from: http://www.ncbi.nlm.nih.gov/geo/

18. Dudoit S, Yang YH, Callow MJ, Speed TP. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sinica. 2002;12:111–39.

19. The R Project for Statistical Computing [homepage on the Internet]. Vienna: Department of Statistics and Mathematics of the Wirtschaftsuniversität Wien; [updated 2006 Oct 3; cited 2006 Oct 19]. Available from: http://www.r-project.org/

20. National Center for Biotechnology Information. BLASTN 2.2.14. [database on the Internet]. Bethesda (MD): U.S. National Library of Medicine. [updated 2006 May 7; cited 2006 Sep 25]. Available from: http://www.ncbi.nlm.nih.gov/blast/

21. Searchgenes [database on the Internet]. London: HUGO Gene Nomenclature Committee. [updated 2006 Oct 18; cited 2006 Oct 19]. Available from: http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/searchgenes.pl

22. UniProt Knowledgebase [database on the Internet]. UniProt Consortium. c2006 - [cited 2006 Oct 19]. Available from: http://www.pir.uniprot.org/database/knowledgebase.shtml

23. QuickGO GO Browser [database on the Internet]. Cambridge: European Bioinformatics Institute. [cited 2006 Oct 19]. Available from: http://www.ebi.ac.uk/ego/

24. Bernard L, Leroux C, Bonnet M, Rouel J, Martin P, Chilliard Y. Expression and nutritional regulation of lipogenic genes in mammary gland and adipose tissues of lactating goats. J Dairy Res. 2005;72:250–5.[Medline]

25. Boutinaud M, Shand JH, Park MA, Phillips K, Beattie J, Flint DJ, Allan GJ. A quantitative RT-PCR study of the mRNA expression profile of the IGF axis during mammary gland development. J Mol Endocrinol. 2004;33:195–207.[Abstract]

26. Flint DJ, Boutinaud M, Whitelaw CB, Allan GJ, Kolb AF. Prolactin inhibits cell loss and decreases matrix metalloproteinase expression in the involuting mouse mammary gland but fails to prevent cell loss in the mammary glands of mice expressing IGFBP-5 as a mammary transgene. J Mol Endocrinol. 2006;36:435–48.[Abstract/Free Full Text]

27. Bonnet M, Leroux C, Faulconnier Y, Hocquette JF, Bocquier F, Martin P, Chilliard Y. Lipoprotein lipase activity and mRNA are up-regulated by refeeding in adipose tissue and cardiac muscle of sheep. J Nutr. 2000;130:749–56.[Abstract/Free Full Text]

28. Agenäs S, Dahlborn K, Holtenius K. Changes in metabolism and milk production during and after feed deprivation in primiparous cows selected for different milk fat content. Livest Prod Sci. 2003;83:153–64.

29. Chilliard Y, Bocquier F, Doreau M. Digestive and metabolic adaptations of ruminants to undernutrition, and consequences on reproduction. Reprod Nutr Dev. 1998;38:131–52.[Medline]

30. Dudoit S, Shaffer JP, Boldrick JC. Multiple hypothesis testing in microarray experiments. Stat Sci. 2003;18:71–103.

31. Nielsen RL, Andersen MH, Mabhout P, Berglund L, Petersen TE, Rasmussen JT. Isolation of adipophilin and butyrophilin from bovine milk and characterization of a cDNA encoding adipophilin. J Dairy Sci. 1999;82:2543–9.[Abstract]

32. Boisnard M, Hue D, Bouniol C, Mercier JC, Gaye P. Multiple mRNA species code for two non-allelic forms of ovine alpha s2-casein. Eur J Biochem. 1991;201:633–41.[Medline]

33. Makeyev AV, Liebhaber SA. The poly(C)-binding proteins: a multiplicity of functions and a search for mechanisms. RNA. 2002;8:265–78.[Abstract]

34. Mangus DA, Evans MC, Jacobson A. Poly(A)-binding proteins: multifunctional scaffolds for the post-transcriptional control of gene expression. Genome Biol. 2003;4:223.[Medline]

35. Laroia G, Cuesta R, Brewer G, Schneider RJ. Control of mRNA decay by heat shock-ubiquitin-proteasome pathway. Science. 1999;284:499–502.[Abstract/Free Full Text]

36. Bollig F, Winzen R, Kracht M, Ghebremedhin B, Ritter B, Wilhelm A, Resch K, Holtmann H. Evidence for general stabilization of mRNAs in response to UV light. Eur J Biochem. 2002;269:5830–9.[Medline]

37. Byrne KA, Wang YH, Lehnert SA, Harper GS, McWilliam SM, Bruce HL, Reverter A. Gene expression profiling of muscle tissue in Brahman steers during nutritional restriction. J Anim Sci. 2005;83:1–12.[Abstract/Free Full Text]

38. Collier RJ, Stiening CM, Pollard BC, VanBaale MJ, Baumgard LH, Gentry PC, Coussens PM. Use of gene expression microarrays for evaluating environmental stress tolerance at the cellular level in cattle. J Anim Sci. 2006;84 Suppl:E1–13.[Abstract/Free Full Text]

39. Martin P, Szymanowska M, Zwierzchowski L, Leroux C. The impact of genetic polymorphisms on the protein composition of ruminant milks. Reprod Nutr Dev. 2002;42:433–59.[Medline]

40. Chilliard Y, Ferlay A, Rouel J, Lamberet G. A review of nutritional and physiological factors affecting goat milk lipid synthesis and lipolysis. J Dairy Sci. 2003;86:1751–70.[Abstract/Free Full Text]

41. Steinberg SJ, Morgenthaler J, Heinzer AK, Smith KD, Watkins PA. Very long-chain acyl-CoA synthetases. Human "bubblegum" represents a new family of proteins capable of activating very long-chain fatty acids. J Biol Chem. 2000;275:35162–9.[Abstract/Free Full Text]

42. Bao Y, Bing C, Hunter L, Jenkins JR, Wabitsch M, Trayhurn P. Zinc-alpha2-glycoprotein, a lipid mobilizing factor, is expressed and secreted by human (SGBS) adipocytes. FEBS Lett. 2005;579:41–7.[Medline]

43. Xue C, Liang F, Mahmood R, Vuolo M, Wyckoff J, Qian H, Tsai KL, Kim M, Locker J, et al. ErbB3-dependent motility and intravasation in breast cancer metastasis. Cancer Res. 2006;66:1418–26.[Abstract/Free Full Text]

44. Lopez-Otin C, Diamandis EP. Breast and prostate cancer: an analysis of common epidemiological, genetic, and biochemical features. Endocr Rev. 1998;19:365–96.[Abstract/Free Full Text]

45. Oskouian B. Overexpression of fatty acid synthase in SKBR3 breast cancer cell line is mediated via a transcriptional mechanism. Cancer Lett. 2000;149:43–51.[Medline]

46. Dieudonne MN, Machinal-Quelin F, Serazin-Leroy V, Leneveu MC, Pecquery R, Giudicelli Y. Leptin mediates a proliferative response in human MCF7 breast cancer cells. Biochem Biophys Res Commun. 2002;293:622–8.[Medline]

47. Lessor TJ, Yoo JY, Xia X, Woodford N, Hamburger AW. Ectopic expression of the ErbB-3 binding protein ebp1 inhibits growth and induces differentiation of human breast cancer cell lines. J Cell Physiol. 2000;183:321–9.[Medline]

48. Gil-Puig C, Seoane S, Blanco M, Macia M, Garcia-Caballero T, Segura C, Perez-Fernandez R. Pit-1 is expressed in normal and tumorous human breast and regulates GH secretion and cell proliferation. Eur J Endocrinol. 2005;153:335–44.[Abstract/Free Full Text]

49. Cheng CK, Chow LW, Loo WT, Chan TK, Chan V. The cell cycle checkpoint gene Rad9 is a novel oncogene activated by 11q13 amplification and DNA methylation in breast cancer. Cancer Res. 2005;65:8646–54.[Abstract/Free Full Text]

50. Menendez JA, Oza BP, Atlas E, Verma VA, Mehmi I, Lupu R. Inhibition of tumor-associated fatty acid synthase activity antagonizes estradiol- and tamoxifen-induced agonist transactivation of estrogen receptor (ER) in human endometrial adenocarcinoma cells. Oncogene. 2004;23:4945–58.[Medline]

51. Zhu Z, Jiang W, Thompson HJ. Effect of energy restriction on tissue size regulation during chemically induced mammary carcinogenesis. Carcinogenesis. 1999;20:1721–6.[Abstract/Free Full Text]

52. Yu L, Alva A, Su H, Dutt P, Freundt E, Welsh S, Baehrecke EH, Lenardo MJ. Regulation of an ATG7-beclin 1 program of autophagic cell death by caspase-8. Science. 2004;304:1500–2.[Abstract/Free Full Text]

53. Cuervo AM, Knecht E, Terlecky SR, Dice JF. Activation of a selective pathway of lysosomal proteolysis in rat liver by prolonged starvation. Am J Physiol. 1995;269:C1200–8.

54. Majeski AE, Dice JF. Mechanisms of chaperone-mediated autophagy. Int J Biochem Cell Biol. 2004;36:2435–44.[Medline]

55. Bursch W, Hochegger K, Torok L, Marian B, Ellinger A, Hermann RS. Autophagic and apoptotic types of programmed cell death exhibit different fates of cytoskeletal filaments. J Cell Sci. 2000;113:1189–98.[Abstract]

56. Gajewska M, Gajkowska B, Motyl T. Apoptosis and autophagy induced by TGF-B1 in bovine mammary epithelial BME-UV1 cells. J Physiol Pharmacol. 2005;56 Suppl 3:143–57.

57. Hennighausen L, Robinson GW. Information networks in the mammary gland. Nat Rev Mol Cell Biol. 2005;6:715–25.[Medline]

58. Lund LR, Romer J, Thomasset N, Solberg H, Pyke C, Bissell MJ, Dano K, Werb Z. Two distinct phases of apoptosis in mammary gland involution: proteinase-independent and -dependent pathways. Development. 1996;122:181–93.[Abstract]

59. Furth PA, Bar-Peled U, Li M. Apoptosis and mammary gland involution: reviewing the process. Apoptosis. 1997;2:19–24.[Medline]

60. Monks J, Geske FJ, Lehman L, Fadok VA. Do inflammatory cells participate in mammary gland involution? J Mammary Gland Biol Neoplasia. 2002;7:163–76.[Medline]

61. Chilliard Y. Métabolisme du tissu adipeux, lipogénèse mammaire et activités lipoprotéine lipasiques chez la chèvre au cours du cycle gestation lactation [Thèse Doctorat Etat ès-Science]: Université Paris; 1985.




This article has been cited by other articles:


Home page
 J ANIM SCIHome page
J. D. Lippolis
Immunological signaling networks: Integrating the body's immune response
J Anim Sci, April 1, 2008; 86(14_suppl): E53 - E63.
[Abstract] [Full Text] [PDF]

Home page
 J DAIRY SCIHome page
M. Boutinaud, M. H. Ben Chedly, E. Delamaire, and J. Guinard-Flament
Milking and Feed Restriction Regulate Transcripts of Mammary Epithelial Cells Purified from Milk
J Dairy Sci, March 1, 2008; 91(3): 988 - 998.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Online Supporting Material
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Ollier, S.
Right arrow Articles by Leroux, C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ollier, S.
Right arrow Articles by Leroux, C.

Home Help [Feedback] [For Subscribers] [Archive] [Search] [Contents]