Temporal gene expression profiling of liver from periparturient dairy cows reveals complex adaptive mechanisms in hepatic function

Juan J. Loor, Heather M. Dann, Robin E. Everts, Rosane Oliveira, Cheryl A. Green, Nicole A. Janovick Guretzky, Sandra L. Rodriguez-Zas, Harris A. Lewin and James K. Drackley

Department of Animal Sciences, University of Illinois, Urbana, Illinois

ABSTRACT

Long-term molecular adaptations in liver from high-producing dairy cows are virtually unknown. Liver from five Holstein cows was biopsied at –65, –30, –14, +1, +14, +28, and +49 days relative to parturition for transcript profiling using a microarray consisting of 7,872 annotated cattle cDNA inserts. More than 5,000 cDNA elements represented on the microarray were expressed in liver. From this set we identified 62 differentially expressed genes related to physiological state, with a false discovery rate threshold of P = 0.20. The dominant expression pattern consisted of upregulation from day –30 through day +1, followed by downregulation through day +28. There was a threefold decrease from day –65 through day +14 in expression of IGFBP3, GSTM5, and PDPK1. These genes mediate IGF-I transport, oxidative stress, and glucose homeostasis, respectively. IGFBP3, EIF4B, and GSTM5 mRNA levels were positively correlated with blood serum total protein. Correlation analysis showed positive associations between serum nonesterified fatty acids and mRNA expression for SAA1, CPT1A, ACADVL, and TFAP2A. Transcript levels of ACSL1, PPARA, and TFAP2A were positively correlated with serum ß-hydroxybutyrate. Expression patterns for certain genes (e.g., IGFBP3, HNF4A, GPAM) revealed adaptations commencing well ahead of parturition, suggesting they are regulated by factors other than periparturient hormonal environment. Results provide evidence that hepatic inflammatory responses occurring near parturition initiate or augment adipose catabolism. In this context, cytokines, acute-phase proteins, and serum nonesterified fatty acids are key players in periparturient cow metabolism. We propose a model for integrating gene expression, metabolite, and liver composition data to explain physiological events in placenta, adipose, and liver during the periparturient period.

microarray; lactation; inflammation

THE PERIPARTURIENT, or "transition period," may be the most critical phase of the lactation cycle for dairy cows because the incidence of metabolic and infectious diseases centers disproportionately on this time (11). Despite initial efforts to quantify aspects of periparturient liver metabolism (38), knowledge on hepatic metabolite [e.g., ammonia, nonesterified fatty acids (NEFA)] fluxes remains limited. Genomic technologies provide a complementary approach to the measurement of metabolic fluxes in liver. Expression of genes that play important roles in liver development can be measured en masse using microarrays (26).

The liver performs essential functions in the body through the expression of genes encoding plasma proteins, clotting factors, and enzymes involved in detoxification, gluconeogenesis, glycogen synthesis, and metabolism of glucose, lipid, and cholesterol (22). Adult liver is a quiescent organ exhibiting minimal proliferative capacity, in which mitosis is observed in ~1 of every 20,000 hepatocytes (7). Hepatocytes, however, begin to proliferate in a synchronized fashion in response to chemical, nutritional, vascular, and/or pathogen-induced stimuli (i.e., compensatory regeneration or hyperplasia). The coordinated expression of a large number of genes is required for hepatocyte differentiation and function of the adult liver (8). Hepatic gene expression is primarily controlled through transcription factors that respond to environmental, autocrine, or paracrine signals (8). Complex interactions of hormones, growth factors, and signal transduction pathways ultimately lead to expression of developmentally regulated genes.

The genomics approach may help identify regulatory mechanisms in liver during the dry period and early stages of lactation. A previous study examined gene expression in bovine liver at various stages of pregnancy using microarray technology; however, there were few biological replicates, and a relatively small microarray consisting of 2,675 genes was employed (21). We report here temporal expression profiling of >6,300 unique genes in liver of five dairy cows fed to current National Research Council (33) recommendations throughout the dry period and early lactation using a cattle-specific, high-density cDNA microarray (15). Transcriptional changes observed indicate a complex and previously unrecognized adaptive program in bovine liver.

MATERIALS AND METHODS

Animals and management.
Five multiparous Holstein cows were randomly selected from a group of twelve control cows enrolled in a large experiment designed to assess the effects of plane of nutrition during the "far-off" and "close-up" dry periods on prepartum metabolism and postpartum metabolism and performance. Cows were housed in individual tie stalls throughout the experiment and were allowed to exercise daily in an outside lot from 0700 to 1000. All cows underwent normal parturition and were free from health disorders during the study. All procedures were conducted under protocols approved by the University of Illinois Institutional Animal Care and Use Committee.

Liver tissue collection and RNA extraction.
Liver was sampled via puncture biopsy (12) from cows under local anesthesia at 0700 on days –65 (dry-off), –30, –14, +1, +14, +28, and +49 relative to parturition. Tissue (1.0–1.5 g) was weighed postbiopsy, placed in 10–15 ml of ice-cold TRIzol reagent (Invitrogen, Carlsbad, CA), homogenized, and RNA extracted according to the manufacturer's protocol. RNA was resuspended in RNA storage buffer (Ambion, Austin, TX) and stored at –80°C until use.

Liver tissue composition, blood serum metabolites, body condition score, and energy balance.
A portion of liver tissue collected for RNA extraction was stored in liquid N for analysis of total lipid, triacylglycerol, and glycogen. Blood serum was assayed for concentrations of NEFA, total protein, urea nitrogen, ß-hydroxybutyrate (BHBA), glucose, and insulin as described previously (10). Body weight and body condition score were determined for each cow weekly from day –65 to day +56 relative to parturition. Three persons assigned body condition scores independently at each time of scoring throughout the experiment. Energy balance was calculated (33) individually for each cow (see Supplemental Material; available at the Physiological Genomics web site).1 .

Microarrays.
A 7,872-element cDNA microarray spotted in duplicate on amino silane-coated glass slides (15) was used for transcript profiling. Annotation was based on similarity searches (January 2005) using sequential Basic Local Alignment Search Tool (BLAST)N and TBLASTX against human and mouse UniGene databases and the human genome, as previously described (15). The 7,872-element array represents >6,300 unique genes. Gene Ontology (GO) terms were parsed from LocusLink to functionally annotate cDNA sequences. RNA (20 µg) from liver tissue and a reference standard derived from a mixture of tissues not including liver (15) were used to make aminoallyl-labeled cDNA followed by incorporation of Cy3-ester and Cy5-ester (Amersham, Piscataway, NJ) (44). Each experimental sample was cohybridized with the reference standard, which allowed the treatment of fluorescence ratios as measurements of relative expression across all samples and time points. Probes were hybridized to the array for 2 days at 42°C.

All samples were hybridized to duplicate slides for a total of four spots per cDNA element. Slides were scanned for both dye channels with a Scanarray 4000 (GSI-Lumonics, Billerica, MA) dual-laser confocal scanner, and images were processed using GenePix 4.0 (Axon Instruments). Microarray data are deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (accession no. GSE2692).

Quantitative real-time RT-PCR.
Total RNA was purified with the RNeasy Mini Kit and residual DNA removed using the RNase-Free DNase Set (Qiagen, Valencia, CA). RT-PCR reactions were carried out in triplicate according to the manufacturer's instructions (Invitrogen). Primers were designed to specifically amplify 16 target cDNAs and an endogenous control gene, bovine ß-actin (ACTB) (Primer Express Software v2.0; Applied Biosystems, Foster City, CA). For relative quantification of target cDNA, samples were run in triplicate in a 384-well plate using an ABI Prism 7900 HT SDS instrument (Applied Biosystems). To control reproducibility of the essay, each primer set was run in triplicate with cDNAs originated from three separate RT-PCR reactions. Details regarding reaction conditions, primer design, and relative quantification of target cDNAs are presented in the Supplemental Material (Supplemental "Materials and Methods" and Supplemental Table S1).

Data analyses.
During an initial screening, microarrays that did not contain at least 10,000 spots with median background-subtracted signal intensities >3 SD above background in both Cy3 and Cy5 channels were repeated. Data from a total of 68 microarrays were normalized and used for statistical analysis. Median background-subtracted signal intensities for each spot were computed before normalization. Three criteria were then implemented to filter unreliable intensity values: 1) if foreground was less than background, then signal intensity was set to 1.0; 2) only those spots that had signal intensities >3 SD above background for both Cy3 and Cy5 channels were kept for further analysis; and 3) sequences with less than 8 observations per time point (out of a potential total of 20) were removed. Data were normalized for dye and array effects (i.e., Loess normalization and array centering). A mixed-effects model with repeated measures was then fitted to the adjusted ratios (liver/reference standard) using Proc MIXED (SAS; SAS Institute, Cary, NC). The model consisted of day as a fixed effect and cow as a random variable. Probability values for the effect of day were adjusted for the number of comparisons using Benjamini and Hochberg's false discovery rate (FDR) (37). Estimates for the available genes at each time point were then computed. Differences in relative expression were considered significant at an FDR-adjusted P of 0.20, corresponding to raw P values ≤ 10–3. Data from quantitative real-time RT-PCR (qPCR) (relative mRNA levels calculated with a standard curve) were analyzed as described elsewhere (3), using the same statistical model described above; differences were considered significant at P ≤ 0.05. Expression patterns for the 62 differentially expressed genes identified by microarray were analyzed using k-means clustering (13) (GeneSpring 6.1; Silicon Genetics, Redwood City, CA). Normalized, log2-transformed ratios computed by GeneSpring were used for k-means clustering. These genes were classified according to their GO terms, "molecular function" and "biological process," to facilitate data mining (Supplemental Table S2). The Proc CORR procedure of SAS was used to analyze correlations among expression profiles over time for each differentially expressed gene, blood and liver metabolites, and energy balance (Supplemental Table S3).

RESULTS

Energy balance, body weight and body condition score, metabolic indicators in blood, and liver composition.
Cows were in positive energy balance for nearly the entire dry period (Fig. 1A). Energy balance was negative during the first week postpartum but became positive at week 2 and remained so thereafter. Body weight, body condition score, and energy intake increased from day –65 to day –14 (Supplemental Fig. S1). Subsequently, there was a decrease in body weight and body condition score from day –14 through day +21 relative to parturition. Serum NEFA concentration increased steadily from 1 wk before parturition through the end of week 1 postpartum (Fig. 1B). Overall, energy balance reflected the amount of energy consumed from the diet (Supplemental Fig. S1). Temporal concentrations for serum BHBA, total protein, urea nitrogen, insulin, and glucose are presented in Supplemental Fig. S2. Total lipid, triacylglycerol, and glycogen content in liver did not change appreciably on days –65, –30, and –14 relative to parturition (Fig. 1C). Liver total lipid and triacylglycerol content was markedly greater on days +1 and +14 postpartum relative to day –14 and then declined to prepartum values by day +49. In contrast, glycogen content decreased substantially between day –14 and day +1 and then increased gradually through day +49 (Fig. 1C). Changes in blood metabolites and liver composition were typical of those previously reported for periparturient dairy cows (11, 19).



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Fig. 1. Energy balance (A), serum nonesterified fatty acid (NEFA) concentration (B), and liver tissue total lipid, glycogen, and triacylglycerol concentration (C) during the dry period through early lactation.

 
Differential expression of genes in liver tissue.
There were 5,369 cDNA elements representing 4,636 unique genes (4,357 genes with unique UniGene identification and 279 nonredundant novels) on the microarray ({approx}68%) that were found to be expressed in liver. When applying repeated-measures ANOVA and a cutoff FDR P value of 0.20, we found a total of 62 differentially expressed genes (Table 1). A total of 32 of 66 differentially expressed genes, including the 4 genes not present on the array that were assessed by qPCR, have a GO functional annotation (Supplemental Table S2). Quantitative PCR detected differential expression of hepatocyte nuclear factor-4A (HNF4A), peroxisome proliferator activated receptor-{alpha} (PPARA), tumor necrosis factor-{alpha} (TNFA), and glycerol-3-phosphate acyltransferase, mitochondrial (GPAM) at specific times during the dry period through peak lactation (Fig. 2). Expression of HNF4A and GPAM decreased linearly from day –65 through day +1, whereas there was a gradual increase in expression of TNFA in the same time frame. Relative to days –14 and +1, there was a marked increase in expression by day +14 for PPARA and GPAM, respectively, which closely mirrored concentrations of serum NEFA, BHBA, and liver triacylglycerol (Fig. 1, Supplemental Fig. S2). Expression of transcription factor HNF4A mRNA increased gradually from day –14 through day +28, at which point it was {approx}1.5-fold higher (P < 0.05).


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Table 1. Complete list of differentially expressed genes obtained using microarray and/or qPCR analysis

 


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Fig. 2. Fold change in expression patterns of tumor necrosis factor-{alpha} (TNFA), peroxisome proliferator activated receptor-{alpha} (PPARA), glycerol-3-phosphate acyltransferase, mitochondrial (GPAM), and hepatocyte nuclear factor-4A (HNF4A) using quantitative real-time RT-PCR (qPCR). Day relative to parturition resulted in a significant (P < 0.05) effect in the relative abundance of these transcripts (Supplemental Fig. S4). Asterisks denote differences between time points, as follows. TNFA: *day 1 > days –65, –30, and –14; **day 49 > all other time points. PPARA: *days 1, 14, 28, and 49 > days –65 and –14; **day 49 > day –30. GPAM: *days –65, 14, 28, and 49 > day 1. HNF4A: *day –65 > all other time points; **day 28 > day –14.

 
Gene expression analysis using k-means clustering.
Eight clusters accounted for the dominant expression patterns in the data (Fig. 3). Twenty-eight differentially expressed genes were characterized by gradual increases in expression between late prepartum and day +1, followed by a gradual decline in expression through day +28 (Fig. 3, clusters 1, 3, 6, and 8). Genes within these clusters included interleukin enhancer binding factor 3 (ILF3), cyclin L2 (CCNL2), calcium/calmodulin-dependent serine protein kinase membrane-associated guanylate kinase (CASK), serum amyloid A1 (SAA1), glutamine-fructose-6-phosphate transaminase 1 (GFPT1), carnitine palmitoyltransferase 1A (liver) (CPT1A), acyl-CoA synthetase long-chain family member 1 (ACSL1), and hypothetical protein FLJ11011 (FLJ11011). Another distinct pattern among a cluster of six differentially expressed genes (cluster 4) was a linear decrease in expression from day –65 through day +14. This represented an {approx}2.5-fold decrease in expression between day –65 and day +14. Genes with this pattern of expression included insulin-like growth factor binding protein 3 (IGFBP3), glutathione S-transferase M5 (GSTM5), lectin, mannose-binding 2 (LMAN2), and 3-phosphoinositide dependent protein kinase-1 (PDPK1). A total of 10 genes (cluster 5) had expression patterns that increased from day –65 through day –14, decreased on day +1, and increased again to peak expression by day +14. Genes with this pattern of expression were peptidase D (PEPD), neuroblastoma, suppression of tumorigenicity 1 (NBL1), adaptor-related protein complex 2, ß1 subunit (AP2B1), mastermind-like 1 (MAML1), CDK2-associated protein 1 (CDK2AP1), transcription factor AP-2{alpha} (TFAP2A), and adaptor-related protein complex AP-1, {sigma}3 (AP1S3). The periparturient period was characterized by downregulation of 12 differentially expressed genes (cluster 2), as evidenced by expression patterns on day –14 through day +14. These genes included interleukin 27 receptor, {alpha} (IL27RA), cysteine-rich protein 1 (intestinal) (CRIP1), eukaryotic translation initiation factor 4B (EIF4B), proteasome (prosome, macropain) 26S subunit, ATPase 2 (PSMC2), and aldolase A (ALDOA). Another cluster formed by four unannotated genes (cluster 7) was characterized by upregulation on days –14 through +1 and +28.



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Fig. 3. Mean expression patterns for 62 differentially expressed genes obtained using k-means cluster analysis. See Table 1 for listing of genes within clusters.

 
Differential gene expression detected using qPCR and microarray analysis.
Four of the sixteen genes assayed by qPCR (HNF4A, PPARA, TNFA, and GPAM) were not present on the array but are key components of lipid metabolism and inflammation-related pathways in rodent liver (6, 28). The remaining 12 genes [SAA1, EIF4B, CPT1A, ACSL1, acyl-CoA oxidase 1, palmitoyl (ACOX1), GSTM5, IGFBP3, ALDOA, sterol regulatory element binding transcription factor 1 (SREBF1), FLJ11011, acyl-CoA dehydrogenase, very long chain (ACADVL), and IL27RA] were a subset of differentially expressed genes with crucial functions in hepatic fatty acid oxidation, glycolysis/gluconeogenesis, lipid synthesis, protein synthesis, acute-phase response, immune response, and oxidative stress (Supplemental Table S2). Expression patterns for 11 of 12 differentially expressed genes detected by microarray analysis were similar when assessed by qPCR (Supplemental Figs. S3–S7). There were minor discrepancies in expression patterns for the 12 genes measured using both techniques, but this was apparent at only 1 or 2 time points in a very large data set. Representative results for two key genes involved in fatty acid oxidation and one gene in metabolic stress responses are shown in Fig. 4. SREBF1 expression assessed by qPCR showed a gradual decrease between day –30 and day +1, followed by a gradual increase through day +28, which was different to what was found with microarrays. It appeared the main discrepancy between microarray and qPCR results with this gene occurred on day +1. We also observed that expression of SAA1 assessed by qPCR on day –14 was {approx}3-fold higher than on day –65, as opposed to the lack of change detected using microarrays. Relative fold changes across most time points were higher when assessed using qPCR only for ACADVL, CPT1A, ALDOA, and SAA1.



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Fig. 4. Comparison of expression patterns (fold change relative to day –65) observed with microarrays vs. qPCR for 2 genes [acyl-CoA dehydrogenase, very long chain (ACADVL); acyl-CoA synthetase long-chain family member 1 (ACSL1)] involved in hepatic fatty acid oxidation (A and B) and 1 gene [glutathione S-transferase M5 (GSTM5)] involved in hepatic metabolic stress responses (C). See Supplemental Figs. S6 and S7 for remaining comparisons of expression patterns between microarrays and qPCR.

 
Correlations among hepatic gene expression patterns, metabolites, and energy balance.
We associated hepatic gene function with measurements of liver and whole animal physiology (Supplemental Table S3). For example, expression patterns for IGFBP3, EIF4B, GSTM5, and PDPK1 were positively correlated (P ≤ 0.05) with both serum urea nitrogen and total protein. These genes are associated with several aspects of cell growth and maintenance (Supplemental Table S2). Liver glycogen was positively correlated with expression patterns of IGFBP3, GSTM5, LMAN2, MAML1, and two unannotated genes. The expression pattern of GFPT1, which has been implicated as a mediator of nutrient sensing in liver (29), was negatively correlated (P ≤ 0.05) with serum glucose, serum insulin, and liver glycogen, but positively correlated with serum BHBA. Among genes with known functions in lipid metabolism, ACADVL, ACSL1, PPARA, CPT1A, and ACOX1 were positively correlated with liver triacylglycerol concentration and ACADVL, ACSL1, and CPT1A with serum BHBA and NEFA. Expression patterns for SAA1, FLJ11011, TNFA, and TFAP2A, all with no known function in lipid metabolism, were positively correlated (P ≤ 0.05) with serum BHBA, NEFA, or liver triacylglycerol concentration. Twenty unannotated genes showed correlations with at least one of the various metabolic parameters (Supplemental Table S3).

DISCUSSION

We have conducted the first experiment to our knowledge linking the bovine liver transcriptome to metabolic indicators during the dry period and early lactation. Although transcription profiling cannot by itself explain complex physiological processes occurring in liver prepartum and during lactation, the significant correlation between transcript and protein levels (30), the wealth of information on hepatic metabolism in periparturient dairy cows (11, 19, 38), and recent studies in rodents (6, 28) allowed us to integrate our data into a general model to explain the physiological events in placenta, adipose, and liver (see Fig. 6). The model takes into account gene expression, blood metabolites, liver composition, and energy balance data. The following discussion of the significant effects of physiological state on hepatic gene expression is organized around physiological and metabolic parameters of lactation biology. While many elements of the model are only suggestive, we have used the extensive gene annotation system for our microarray and other powerful data mining tools to help understand the complex biology of periparturient dairy cows.

Periparturient liver mass and gene expression.
The increase in mRNA expression level between days +14 and +28 postpartum (Fig. 3, clusters 2 and 4) for genes associated with translation initiation and protein biosynthesis (EIF4B), intracellular protein degradation (PSMC2), protein localization and transport (LMAN2), insulin signaling and glucose homeostasis (PDPK1), cellular proliferation (CRIP1), regulation of various aspects of cell growth and maintenance (IGFBP3), and regulation of transcription (HNF4A, Fig. 2) coincided not only with an increase in serum total protein (Supplemental Fig. S2) but also with greater liver mass observed by others around this time (39). Four unannotated transcripts were upregulated from day +14 through day +28 (Fig. 3, cluster 7), suggesting they may play unidentified roles in bovine liver biology. Correlations among serum total protein and expression patterns for IGFBP3, HNF4A, EIF4B, PDPK1, and CRIP1 were positive (P ≤ 0.05; Supplemental Table S3).

Environmental stresses and nutrition can reduce protein biosynthesis in mammalian cells, which are closely correlated with the concentration and activity of EIF4F and EIF4B (16). Nutrient availability (e.g., amino acids) is the primary signal that induces activation of EIF4B (16). CRIP1, found in many tissues including immune, intestinal, and liver, is a newly recognized transcription factor that has a role in cell growth, differentiation, and turnover (23). LMAN2 is a protein synthesized by liver that responds as an acute-phase reactant, activates the complement cascade, and serves a crucial role in intracellular protein transport (20). There was a gradual decline in energy balance, feed intake, serum total protein, and insulin from the last week prepartum through the end of the first week postpartum. We found the opposite response for NEFA, and others have shown marked muscle protein degradation (2) indicating a general state of catabolism in the cow. Taken together, downregulation of EIF4B, HNF4A, PSMC2, IGFBP3, LMAN2, and CRIP1 in liver around parturition (Figs. 2 and 5A) partly explains these physiological measurements (Fig. 6).



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Fig. 5. Overlay of expression patterns for a subset of genes significantly correlated (P ≤ 0.05) with serum concentration of total protein (A) and NEFA (B and C). Best-fit equation lines for the concentrations of total protein and NEFA are shown in gray. Values on the y-axis are fold changes relative to day –65, with expression on day –65 set to 1.0. IGFBP3, insulin-like growth factor binding protein 3; EIF4B, eukaryotic translation initiation factor 4B; TFAP2A, transcription factor AP-2{alpha}; ACOX1, acyl-CoA oxidase 1, palmitoyl; SAA1, serum amyloid A1.

 


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Fig. 6. Schematic representation of the interactions among gene expression patterns in liver and physiological events in placenta, adipose, and liver during the periparturient period. We propose that IL-8 and IL-1ß secreted from placenta directly upregulate expression of SAA1 and TNFA in liver and also have negative effects on neurophysiological mechanisms regulating feed intake. Depressed feed intake results in negative energy balance and hypoinsulinemia, increasing adipose tissue lipolysis, and limiting availability of nutrients to liver. Cytokines from placenta and/or liver may further stimulate lipolysis and markedly increase blood NEFA and ß-hydroxybutyrate (BHBA). Circulating NEFA likely are endogenous ligands for PPARA and HNF4A resulting in their upregulation and downstream activation of genes with key functions in fatty acid oxidation [ACSL1, ACOX1, carnitine palmitoyltransferase 1A (CPT1A), ACADVL], ketogenesis, and gluconeogenesis (PCK1). The end result of metabolic events initiated, in part, by PPARA and HNF4A upregulation is net hepatic glucose synthesis and sparing of glucose and amino acids for milk synthesis. Activation of PPARA can downregulate sterol regulatory element binding transcription factor 1 (SREBF1) expression, a gene closely associated with lipid synthesis via its direct upregulation of GPAM, fatty acid synthase (FASN), ATP-citrate lyase (ACLY), and Spot 14 (S14). Upregulation of SREBF1, via cytokines or fatty acids, and GPAM is associated with greater concentrations of liver triacylglycerol. Limitations in insulin and amino acids to liver may downregulate IGFBP3, EIF4B, 3-phosphoinositide dependent protein kinase-1 (PDPK1), and/or proteasome (prosome, macropain) 26S subunit, ATPase 2 (PSMC2) resulting in decreased hepatic protein synthesis, circulating blood IGF-I, and liver glycogen. Downregulation of GSTM5 expression may increase lipid peroxidation in liver. Both reduced capacity to detoxify lipid peroxidation products and greater triacylglycerol accumulation in liver increase the risk for hepatic periparturient health disorders.

 
Cytokines, inflammation, and periparturient hepatic gene expression.
One of the most important cytokines involved in initiating and developing the acute-phase response is TNF-{alpha} (32). TNF-{alpha} is essential for normal liver regeneration and augments liver DNA synthesis via NF-{kappa}B activation. Hepatic acute-phase responsiveness is preserved during chronic malnutrition in rat liver (27). The temporal expression of TNFA (Fig. 2) followed a similar pattern to that of SAA1 (Fig. 3, cluster 8, and Fig. 5C) during days –14 through +14. Although the observed increase in expression of TNFA between day –14 and day +1 (Fig. 2) was minor, it may have been the result of a systemic inflammatory response initiated within the uterus as parturition approached, which has been observed in humans (i.e., increased IL-1ß and IL-8 synthesis; Ref. 14). Decreased energy balance (Fig. 1A) driven by the reduction in energy intake placed additional metabolic stress on the cows. It is well established in humans that IL-1ß has appetite-depressing effects, whereas plasma concentrations of TNF-{alpha} are associated with increased energy expenditure (41). Recombinant bovine TNF-{alpha} administration to lactating cows increased serum haptoglobin, NEFA, cortisol, growth hormone, and nitric oxide and decreased feed intake (25). Some of these conditions closely resemble responses that we and others have observed during the periparturient period (17), as well as in response to endotoxin administration (46).

TNF-{alpha} and IL-1ß stimulate the synthesis of positive acute-phase proteins such as SAA1 (36), partly explaining the nearly sixfold increase in expression of SAA1 (Fig. 3, cluster 8, and Fig. 5C) observed on day +1 relative to day –14 or +14. Importantly, qPCR analysis of SAA1 expression showed marked upregulation ({approx}3-fold) by day –14 (Supplemental Figs. S3 and S6). SAA1 synthesis in liver is upregulated by as much as 1,000-fold during the acute-phase response (36). Its promoter is occupied by HNF4{alpha} in human hepatocytes (34), but expression of HNF4A (Fig. 2) did not quite parallel that of SAA1, indicating that only small changes in abundance of this transcription factor may be necessary to elicit drastic changes in expression for some of the genes under its control. TNFA upregulation in liver macrophages may thus act in a paracrine fashion and elicit potent upregulation of SAA1 in hepatocytes, and it has been shown that proinflammatory and signaling genes are upregulated in liver from mice induced to develop fatty liver and insulin resistance by high-fat diets (6). Our data provide evidence for a role of bovine hepatic macrophages or Kupffer cells in the etiology of periparturient fatty liver (Fig. 6).

Periparturient hepatic lipid metabolism.
Upregulation of SREBF1 in mice resulted in fatty liver (45). Deletion of SREBF1 in Lepob/Lepob mice lowered hepatic expression for fatty acid synthase (Fasn), Gpam, ATP-citrate lyase (Acly), and Spot 14 (S14), which in turn correlated with reduced liver triacylglycerol concentration (47). Our data on plasma NEFA and liver composition verified typical responses occurring in bovine liver during the periparturient period (11, 19). More importantly, we show that increased hepatic expression of mRNA encoding proinflammatory cytokines and acute-phase proteins (TNFA, SAA1) is positively correlated with lipid mobilization from adipose tissue (i.e., increased plasma NEFA, Fig. 1B) and fatty acid oxidation (i.e., increased plasma BHBA, Supplemental Fig. S2). Relative to day +1, the marked increase in expression of GPAM ({approx}2-fold) and SREBF1 ({approx}3-fold) assessed by qPCR on day +14 through day +49 resembled the pattern of serum NEFA (Fig. 1B) and liver triacylglycerol concentration (Fig. 1C). These data offer support for a role of both genes in the development of periparturient fatty liver (Fig. 6). We present strong evidence that periparturient serum NEFA concentrations and thus hepatic NEFA uptake (38) are not only a primary mediator of GPAM and SREBF1 expression but also of HNF4A and PPARA (Fig. 2 and Fig. 6). Despite marked upregulation of PPARA, upregulation of SREBF1 and subsequent upregulation of GPAM are necessary responses in mice (24, 42) to accommodate the greater influx of NEFA into liver, i.e., there is an imbalance between fatty acid oxidation and lipid synthesis. The relative importance of GPAM and SREBF1 in the etiology of periparturient fatty liver remains to be determined.

PPAR-{alpha} is a nuclear protein that mediates effects of NEFA on peroxisomal and mitochondrial fatty acid oxidation and upregulates genes associated with ketogenesis and ureagenesis (28). Our data (e.g., Figs. 1 and 2) strongly support the concept that PPAR-{alpha} plays a central role in these metabolic events in periparturient bovine liver (Fig. 6). PPARA upregulation also exerts anti-inflammatory responses by inhibiting expression of proinflammatory cytokines and acute-phase proteins, along with reducing hepatic amino acid catabolism (28). The marked upregulation of PPARA on day +1 along with increased serum NEFA and BHBA, and the expression patterns of CPT1A, ACOX1, ACSL1, and ACADVL (Supplemental Fig. S3), show for the first time that PPARA expression in transition dairy cows is highly associated with expression of these genes, as proposed earlier (11) (Fig. 6). The expression pattern of HNF4A is also suggestive of an important role for this gene in fatty acid oxidation and gluconeogenesis through binding (34) of the promoter region of ACADVL and ACOX1 (ß-oxidation) and PCK1 (gluconeogenesis). The gradual increase in periparturient liver triacylglycerol despite PPARA activation may be an example of an anti-inflammatory mechanism failing upon greater proinflammatory pressure. Similar to mice (42), an imbalance between PPAR-{alpha}-mediated anti-inflammatory and NF-{kappa}B-mediated proinflammatory signals in transition dairy cows may contribute to increased inflammation during fatty liver development.

Metabolic stress, insulin signaling, growth hormone/IGF-I axis, and hepatic gene expression.
Expression of GSTM5 decreased nearly 3-fold from day –30 through day +14 (Fig. 3, cluster 4) during the time when liver triacylglycerol accumulated (Fig. 1C) and hepatic oxidative activity increased {approx}2-fold (38). Our result agrees with observations in fatty liver from mice fed high-fat diets short-term (i.e., 11 days; Ref. 24) but contrasts with the marked upregulation (>3-fold) of GST mRNAs in fatty liver from mice fed high-fat diets long-term (i.e., 12 wk) (18). Those studies and ours indicate that, in liver, there is short- and long-term transcriptional regulation of biological stress responses to oxidative stress elicited by nutrition. The decline in GSTM5 around parturition could be a factor in the higher risk for hepatic health disorders observed in dairy cows (11) (Fig. 6), because GST expression and activity has profound effects on sensitivity to physiological insults (4). Recent studies in mice support a role for growth hormone/IGF-I signaling in the regulation of antioxidant defense (4), i.e., increased systemic concentration of growth hormone results in lower liver S-adenosylmethionine, S-adenosylhomocysteine, methionine adenosyltransferase activity, and liver GST activity. These observations provide a link between elevated serum growth hormone observed by others (35) and reduced hepatic GSTM5 expression during the periparturient period.

Upregulation of cytosolic PCK1 mRNA abundance in liver (1) increases hepatic glucose output (38) needed to meet mammary demands for milk synthesis after parturition (2). Greater mammary uptake of glucose soon after parturition maintains low serum glucose concentration despite greater than twofold increase in hepatic glucose output (38). Insulin inhibits gluconeogenesis in mice through insulin receptor-mediated PDPK1 activation (31). Our data show that PDPK1 expression (Fig. 3, cluster 4) decreased {approx}3-fold between day –65 and day +14. More importantly, PDPK1 downregulation during days –14 through +14 corresponded with liver glycogen depletion (Fig. 1) indicating, as in liver-PDPK1–/– mice (31), that the PDPK1 signaling pathway in periparturient dairy cows plays an important role in regulating glucose homeostasis and controlling expression of insulin-regulated genes (43) (Fig. 6).

Hepatic abundance of IGFBP3 has profound implications for liver and peripheral tissues because of the longer half-life of the IGFBP3/IGF-I ternary complex (9). Periparturient dairy cows have higher plasma growth hormone, the primary regulator of IGF-I synthesis and secretion in hepatocytes, but have lower plasma IGF-I due to reduced hepatic mRNA abundance of growth hormone receptor 1A (35). Recently, the possibility has been raised that insulin regulates the efficiency of growth hormone signaling in periparturient liver (5, 40). Infusion of insulin under euglycemia increased plasma IGF-I and hepatic levels of IGF-I mRNA and growth hormone receptor protein, along with increased plasma IGFBP3 (5, 40). We observed an {approx}3-fold gradual decrease in expression of IGFBP3 from dry-off through day +14 (Fig. 3, cluster 4), similar to the pattern of serum insulin concentration (Supplemental Fig. S2) and hepatic mRNA and plasma IGF-I observed by others around this time (35, 40). The parallel expression pattern for PDPK1 and IGFBP3 suggests that IGFBP3 actions in bovine liver occur via PDPK1 signaling pathways. We show that differential liver IGFBP3 expression indeed represents one plausible mechanism whereby insulin regulates plasma IGF-I concentrations (5, 40) (Fig. 6).

In conclusion, hepatic adaptations to the onset of parturition are characterized by gene expression changes commencing well ahead of parturition, suggesting they are regulated by other factors (e.g., nutrient availability) besides the hormonal environment characteristic of the periparturient period. We propose a model (Fig. 6) with our gene expression, metabolite, hormone, and energy balance data, in addition to data from the literature, to account for observed periparturient behavioral and hepatic adaptations. The dynamics of hepatic energy metabolism during the transition period likely vary depending on the nature and intensity of the inflammatory response, which, on the basis of our results and those of others (e.g., Ref. 25), seems to be a key signal initiating adipose and muscle tissue catabolism. Cytokines, acute-phase proteins, and the increasing NEFA pool are key players affecting biological functions in peripheral tissues and brain in transition dairy cows.

GRANTS

Gene expression profiling was supported by award no. 2001-35206-10946 from National Research Initiative Competitive Grants Program/Cooperative State Research, Education, and Extension Service/United States Department of Agriculture to J. K. Drackley, H. A. Lewin, and S. L. Rodriguez-Zas. Support for the animal work was provided by funds from the State of Illinois through the Illinois Council on Food and Agricultural Research (C-FAR).

ACKNOWLEDGMENTS

We gratefully acknowledge the help from the staff of the Univ. of Illinois Dairy Research and Teaching Unit for Animal Care. The advice of Dr. Mark R. Band (Director, Functional Genomics Unit, The W. M. Keck Center for Comparative and Functional Genomics, Univ. of Illinois) during portions of the microarray work also is greatly appreciated.

FOOTNOTES

Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: J. J. Loor, Dept. of Animal Sciences, Univ. of Illinois, 1207 West Gregory Dr., 498 Animal Sciences Laboratory, Urbana, IL 61801 (e-mail: jloor{at}uiuc.edu).

10.1152/physiolgenomics.00132.2005.

1 The Supplemental Material for this article is available online at http://physiolgenomics.physiology.org/cgi/content/full/00132.2005/DC1 Back

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