Mammary gene expression profiles during an intramammary challenge reveal potential mechanisms linking negative energy balance with impaired immune response

Abstract

Our objective was to compare mammary tissue gene expression profiles during a Streptococcus uberis (S. uberis) mastitis challenge between lactating cows subjected to dietary-induced negative energy balance (NEB; n = 5) and cows fed ad libitum to maintain positive energy balance (PEB; n = 5) to better understand the mechanisms associated with NEB and risk of mastitis during the transition period. The NEB cows were feed-restricted to 60% of calculated net energy for lactation requirements for 7 days, and cows assigned to PEB were fed the same diet for ad libitum intake. Five days after feed restriction, one rear mammary quarter of each cow was inoculated with 5,000 cfu of S. uberis (O140J). At 20 h postinoculation, S. uberis-infected mammary quarters from all cows were biopsied for RNA extraction. Negative energy balance resulted in 287 differentially expressed genes (DEG; false discovery rate ≤ 0.05), with 86 DEG upregulated and 201 DEG downregulated in NEB vs. PEB. Canonical pathways most affected by NEB were IL-8 signaling (10 genes), glucocorticoid receptor signaling (13), and NRF2-mediated oxidative stress response (10). Among the genes differentially expressed by NEB, cell growth and proliferation (48) and cellular development (36) were the most enriched functions. Regarding immune response, HLA-A was upregulated due to NEB, whereas the majority of genes involved in immune response were downregulated (e.g., AKT1, IRAK1, MAPK9, and TRAF6). This study provided new avenues for investigation into the mechanisms relating NEB and susceptibility to mastitis in lactating dairy cows.

during the transition period and through early lactation there is a lag for most cows in the increase in dry matter intake (DMI) relative to the increase in energy demands for milk production, resulting in a period of negative energy balance (NEB) (29). During this energy deficit period, extensive mobilization of body tissue energy reserves results in increased circulating concentrations of nonesterified fatty acids (NEFA) and ketone bodies such as β-hydroxybutyric acid (BHBA) (15). In addition, the risk of developing infectious diseases and metabolic disorders such as milk fever, mastitis, ketosis, and retained placenta is increased during this time relative to later in lactation, especially for high-producing dairy cows (17, 22).

During the transition period, the immune system function is impaired at least in part by increased circulating glucocorticoids around parturition, which may partly explain the increased susceptibility to infections (7). However, studies have indicated that the metabolic components of NEB (e.g., increased NEFA and BHBA, decreased glucose) were associated with a deficiency in the functions of the immune system in vitro (34, 61, 63). In addition, epidemiological studies reported that the severity of NEB was associated with an increased incidence of mastitis during early lactation (22, 44, 49). This relationship has not been specifically established in vivo, and diet-induced NEB in midlactating dairy cattle has proven to be a useful model to examine the effects of NEB on the immune system function while negating the immunosuppression normally observed in cows during the transition period (53, 54).

Mastitis is one of the most costly of all metabolic diseases and disorders in the dairy industry (5, 23) and occurs most frequently during early lactation. Streptococcus uberis (S. uberis) is recognized as one of the major mastitis-causing pathogens and as such is a good candidate to examine the effect of energy balance on the innate immune response during an intramammary infection (IMI) (31). The innate immune response, primarily consisting of milk macrophages and neutrophils (PMN), is the first line of defense against invading pathogens. However, mammary epithelial cells (MEC) also have immunological functions that contribute to the initial response to an IMI (51). Researchers have used MEC lines or tissue biopsies to study the immunological role of epithelial cells through response to in vitro challenges with both Gram-positive and Gram-negative bacteria (6, 62, 66).

Little is known about the effect of NEB on mammary gene expression in response to IMI. Microarrays as well as quantitative reverse-transcription PCR (qPCR) technology could provide useful information on additional signals produced by the mammary gland during an IMI. A better understanding of the effect of NEB on the mammary gland immune response may provide information on the mechanistic link between severity of postpartal NEB and risk of mastitis, which in turn may help to improve overall animal health, help to identify markers for genetic selection, and decrease economic losses associated with IMI to dairy farmers. The objective of this study was to determine the effect of NEB on immunity and metabolic gene expression profiles in mammary tissue during IMI with S. uberis.

MATERIALS AND METHODS

All procedures involving animals received approval from the University of Illinois Institutional Animal Care and Use Committee (protocol 05179).

Animals and diets.

A detailed description of experimental design and data collection was described in the companion paper (43). Ten multiparous Holstein cows past peak lactation (>60 days postpartum) were used. To be eligible, each cow must have exhibited positive energy balance (PEB) for >2 consecutive weeks and must not have been treated for clinical signs of any metabolic disorder or disease prior to the start of the study. Cows were enrolled if quarter milk somatic cell count (SCC) <200,000 cells/ml and aseptically collected quarter milk samples were bacteriologically negative prior to the start of feed restriction as well as prior to intramammary challenge. Once eligible, cows were paired based on parity, day in milk, and milk yield. Cows were housed and fed in individual tie-stalls, had free access to water, and were milked twice daily at 0500 and 1700. Cows averaged 39.2 ± 7.4 kg milk/day and were 77 ± 12 days in milk at the start of the trial.

Throughout the study period, all cows were fed a total mixed ration (TMR) balanced to meet National Research Council (NRC) (47) requirements for cows producing ∼40 kg milk/day. The TMR was mixed once daily; approximately half of the TMR was fed in the morning (1100), and the remainder was fed in the afternoon (1700). Cows within a pair were randomly assigned to receive either PEB (n = 5) or NEB treatments (n = 5). The cows assigned to PEB were fed for ad libitum intake, and the cows assigned to NEB were restricted to 60% of calculated net energy for lactation (NE_L) requirements of the cow based on body weight and milk production. Cows received the dietary treatments for 7 days (168 h). After 7 days of feed restriction, NEB cows were returned to full feed and antibiotic therapy was administered.

Calculation of energy balance.

To calculate energy balance, feed intake was recorded daily and milk production was recorded twice daily at each milking. In addition, daily morning and evening composite milk samples were collected, pooled, and analyzed for fat, protein, and lactose content as previously described by Moyes et al. (43). For each cow, daily DMI and calculated dietary NE_L density of the TMR were used to determine calculated daily NE_L intake (Mcal/day). Energy requirements for maintenance were calculated (47), where requirement = [body wt (kg)^0.75] × [0.08 Mcal/kg]. Energy requirements for milk production (47) were calculated, where NE_L (Mcal/kg) = [(0.0929 × % fat) + (0.0547× % protein) + (0.0395 × % lactose)]. Calculated energy balance (Mcal/day) = [NE_L intake − (maintenance energy + milk energy output)].

Intramammary challenge with S. uberis (O140J).

S. uberis strain O140J (gift of Dr. J. Hogan, The Ohio State University, Wooster, OH), was stored at −80°C until use. Description of the preparation of the S. uberis inoculum is described by Moyes et al. (43). Briefly, a 10 μl loopful of S. uberis colonies was incubated in 100 ml of Todd-Hewitt broth for 6 h at 37°C. Following incubation, the broth culture was diluted in sterile mammalian Ringer's solution (Electron Microscopy Sciences, Hatfield, PA) to yield ∼5,000 colony-forming units (cfu) in a 2 ml volume (i.e., 2,500 cfu/ml). Following the afternoon milking on day 5 (132 h; h = 0 of infection) of feed restriction, 2 ml of inoculum containing S. uberis was infused into one rear quarter of each cow via a sterile disposable syringe fitted with a sterile teat cannula by the full insertion infusion method. Prior to inoculation, challenged teats were rigorously cleaned with cotton balls containing 70% isopropyl alcohol. Immediately following inoculation, all teats were immersed in a postmilking teat disinfectant containing 1% iodine with lanolin. Systemic and local inflammatory indicators were used to monitor the clinical response to intramammary S. uberis challenge as described by Moyes et al. (43). At 20 h postinoculation, and before peak clinical signs, both the S. uberis infected (i.e., YES) and noninfected (i.e., NO) rear quarters were biopsied for RNA extraction and microarray analysis.

Indicators of clinical disease.

Systemic and local inflammatory indicators were used to monitor the clinical response to intramammary S. uberis challenge and detailed results can be found in Moyes et al. (43). Rectal temperature, heart rate, respiration rate, and fecal score measurements were recorded at 0, 3, 6, 12, 14, 16, 18, 20, 24, 30, 36, 42, and 48 h postchallenge. Based on previous experience, peak clinical signs of S. uberis inoculation was expected at 18 to 24 h postinoculation (41). Duplicate samples of quarter foremilk were aseptically collected for bacteriological examination and SCC before feed restriction and immediately before challenge. In addition, samples were collected at 12, 20, 24, 30, and 36 h postchallenge to confirm infection by quantifying bacterial and SCC concentrations. The SCC was determined using infrared procedures (FOSS 4000; Dairy Lab Services, Dubuque, IA). Foremilk samples for culture were collected aseptically according to National Mastitis Council recommendations (46).

Blood and milk collection and analysis.

For details on methodology, assay descriptions, and results of immune system and energy metabolite analyses see Moyes et al. (43). In brief, throughout the study period, jugular blood samples were collected by venipuncture daily before the morning feeding and every 6 h postchallenge for 36 h. Serum or plasma was analyzed for NEFA, BHBA, glucose, cholesterol, triglycerides, insulin, cortisol, SAA, haptoglobin (Hp), and albumin. Whey was isolated from the infected quarter at 0, 12 and 20 h postinoculation (prior to biopsy) and analyzed for TNF-α, IL-1β, IL-10, SOD, glutathione peroxidase (GPX), SAA, and Hp. Results are reported elsewhere (43).

Microarray and qPCR.

Detailed description of mammary biopsy, RNA isolation, microarray procedure, primer design and qPCR analysis are found in Supplemental Materials 1.¹

Statistical analysis.

For microarrays, oligonucleotides that were flagged with “−100” by GenePix were removed from the analysis, and the remaining data were normalized to control for dye effects using the median of control elements on the microarray. In a subsequent normalization step, the log2 normalized ratio of mammary versus reference (i.e., RNA mixture of different tissues including mammary) signal intensities were adjusted for global dye and microarray effects and normalized by Lowess. A mixed-effects model was then fitted to the adjusted ratios (mammary/reference) using Proc MIXED (60). The model consisted of treatment (TRT: NEB and PEB), infection (INF: YES and NO), and the TRT × INF interaction. YES identifies mammary quarters inoculated with 5,000 cfu of S. uberis, and NO identifies contralateral rear control quarters (i.e., noninoculated). The fixed effect was dye with cow and microarray as random effects. Statistical significance probability values for TRT, INF, and TRT × INF effects were adjusted for the number of comparisons using Benjamini and Hochberg's false discovery rate (FDR). For this paper, we dealt exclusively with gene expression differences due to the interaction of TRT (NEB vs. PEB) within S. uberis infected (i.e., YES) quarters. Moyes et al. (42) reported the effect of INF (YES vs. NO) on mammary tissue transcriptomic profiles. Other TRT × INF effects are not reported here. Differentially expressed genes were based on FDR P value ≤0.06, which corresponded to an unadjusted P ≤ 0.01. Fold change was presented as the back-transformed LSMeans (i.e., adjusted log2 normalized ratios of mammary vs. reference) of the NEB vs. PEB infected quarters.

For qPCR, after normalization with internal control genes (ICG, see Supplemental Materials 1), data were analyzed using the MIXED procedure of SAS with a random effect of pair within block (day of inoculation). Class variables included cow, TRT, INF, pair, and block. The model included TRT, INF, and the TRT × INF. Again for this paper, we dealt exclusively with gene expression differences due to TRT (NEB vs. PEB) within S. uberis infected (i.e., YES) quarters. Statistical differences were declared as significant and highly significant at P ≤ 0.05 and P ≤ 0.01. Trends toward significance are discussed at P < 0.10. Relative expression values are presented as LSMeans. The fold change was presented as the LSMeans of the NEB vs. PEB infected quarters.

Data mining.

Data were mined by means of Ingenuity Pathways Analysis (IPA, Ingenuity Systems, http://www.ingenuity.com). Annotated differentially expressed genes (DEG) with FDR ≤ 0.06 and unadjusted P ≤ 0.01 were uploaded into IPA with associated annotation (when present) and least squares means (after back-transformation). Data from qPCR analysis were used in verified genes instead of microarray data. Because we uploaded only part of the oligos present in the microarray (with FDR ≤ 0.06) the Ingenuity Pathways Knowledge Base was used as a reference set for statistical analysis of enriched function/pathways and networks. This approach can suffer for the biases toward overrepresented functions in the bovine oligoarray platform. Each annotated gene was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. Criteria for IPA analysis were set at an FDR ≤ 0.06 and a fold-change in expression ≥ 1.2 (i.e., NEB vs. PEB) either up or down for the purpose of identifying highly affected functions (Supplemental Materials 3).

Microarray data accession number.

The arrays discussed in this publication have been deposited in the National Center for Biotechnology Information's Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO series accession number GSE15344.

RESULTS

Indicators of energy balance.

For details on results of blood metabolites, milk composition, and cow-level immune response relative to IMI challenge with S. uberis see Moyes et al. (43). Briefly, cows subjected to dietary-induced NEB had increased NEFA and BHBA, and lower calculated energy balance than PEB cows throughout the study period, and responses were similar to those of cows experiencing postpartal NEB (56). After IMI challenge, overall serum glucose concentrations tended to be lower in NEB cows (P = 0.07) compared with PEB cows, but no differences (P > 0.10) between NEB and PEB groups were observed at any given time point during IMI challenge. Overall serum insulin concentrations were lower (P < 0.01) in NEB than PEB cows during IMI challenge. During the feed restriction period only (i.e., days 1–5), serum cortisol concentrations tended (P = 0.06) to be higher in NEB cows than in PEB cows (490 ± 175 and 336 ± 177 ηmol/l, respectively).

Indicators of clinical response to IMI challenge.

Regardless of TRT, all cows developed both local and systemic responses to IMI challenge. Details on local and systemic responses to IMI challenge with S. uberis are described in Moyes et al. (43). In response to IMI challenge, heart rate and body temperature were significantly elevated, and there was a trend (P = 0.058) toward increased respiration rate. All cows developed mastitis after IMI challenge with S. uberis. Clinical signs, such as flakes, watery, or yellowish-colored milk, were observed after inoculation. Milk SCC from challenged quarters were increased (5.41 ± 0.17 log₁₀ cells/ml) by 20 h compared with 0 and 12 h postinoculation. An overall increase (P < 0.001) in growth of S. uberis was observed in inoculated quarters, which was similar to results of others (4, 26). By 12 h postinoculation, S. uberis was recovered from all challenged quarters and shedding continued through 36 h postinoculation similar to results of others (4, 26). Details on individual quarter SCC and shedding of S. uberis are reported elsewhere (42). Regardless of energy balance status (i.e., NEB or PEB), S. uberis IMI elicited a strong transcriptomic response, leading to an overall up-regulation of genes (1, 082) primarily involved with immune response such as IL-10 signaling and IL-6 signaling (42).

Based on previous work in our laboratory (41), as well as challenging four “test” cows prior to our experiment, peak clinical signs based on heart rate, respiration rate, milk secretion, shedding of S. uberis and, most importantly, increased SCC occurred between 24 and 36 h postchallenge. Therefore, biopsies were taken prior to peak clinical signs to be more confident that the majority of gene expression data was attributed to MEC and not infiltrating neutrophils. Additionally, after biopsy (for details see Supplemental Materials 1), tissue (≥0.5 g) was blotted with sterile gauze to remove any visible milk secretions, and visible connective tissue was cut off and removed. The infiltration of immune cells was assessed via specific macrophage and PMN gene markers present on the bovine microarray, and results are reported elsewhere (42). The data indicated a very slight increase in infiltration due to IMI by 20 h postinoculation. Therefore, most of the responses in the present analysis must be attributed to MEC; however, resident macrophages constitute ∼5% or more of the parenchyma tissue (64), and increased activity of those cells could be detectable via gene expression, particularly for genes with low inherent expression in MEC.

Differential expression of genes.

A total of 287 annotated genes were differentially expressed (DEG) expressed between NEB cows and PEB cows during IMI challenge with S. uberis (FDR P ≤ 0.06; unadjusted P ≤ 0.01) (Supplemental Table S5 in Supplemental Materials 2). Of these, 86 genes were upregulated and 201 genes were downregulated. When a 1.2-fold change cut-off was applied in IPA (Ingenuity Systems), a total of 243 genes were mapped or recognized based on annotation to a human or mouse ortholog within the IPA Knowledge base. Of these, 178 genes were eligible for generating networks, and 158 genes were mapped to known functions and/or pathways based on published data across several species, including human, rat, and mouse (Supplemental Materials 3).

qPCR.

Table 1 lists genes selected for qPCR. A total of 51 genes were analyzed: 9 were differentially expressed with microarrays, 6 genes were not present on the microarray platform, and 35 genes were not significant at an FDR ≤ 0.06. The latter genes were selected based on their involvement with immune response and metabolism (1, 37). Among DEG present on the microarray platform, 77% (7 out of 9 genes) correspond to results of microarrays. Considering all the genes tested with qPCR we observed that ACADS, JAK1, SOCS2, and SPARC had responses opposite to microarray results, ADFP, C3, CSN2, FOS, HLA-A, HLA-DRA, JAK1, MAPK9, and SPARC, which were not significantly affected with microarrays (FDR > 0.06) were found to be affected significantly by qPCR (P ≤ 0.05). In addition, ANXA1, HMOX1, INSR, SOCS2, and SREBF1 were significant with microarrays (FDR ≤ 0.05) but not with qPCR (P ≤ 0.05). Six genes (BNBD5, CASP8, COX1, INSIG1, IRAK1, and TRAF6) measured via qPCR were not present on the microarray platform but have been shown to be involved in immune response or metabolic pathways in mammary tissue (1, 37). Of these, COX1, INSIG1, IRAK1, and TRAF6 were downregulated by NEB. Quantitative PCR is a more sensitive method of for gene expression analysis, thus, the qPCR data instead of the microarray data were used for IPA analysis in all cases as we have done in previous work (36).

Table 1.

qPCR and microarray gene expression analyses of mammary tissue collected from negative (NEB) vs. positive energy balance (PEB) cows 20 h after an intramammary mastitis challenge with Streptococcus uberis

		Fold NEB vs. PEB
Gene	Gene Name	Microarray1	qPCR
ACADS‡	acyl-coenzyme A dehydrogenase, C-2 to C-3 short chain	1.66§	−1.48§
ACP2	acid phosphatase 2, lysosomal	−1.49§	−1.16§
ADFP	adipose differentiation-related protein	−1.04	−1.31§
AKT1	v-akt murine thymoma viral oncogene homolog 1	−1.21§	−1.70§
ANXA1	annexin A1	1.52§	1.14
BAX	BCL2-associated X protein	−1.61	1.12
BNBD5†	neutrophil β-defensin 5	NA	1.01
C1QC	complement component 1, q subcomponent, C chain	1.21	−1.26
C3	complement component 3	1.22	−1.42§
CASP8†	caspase 8, apoptosis-related cysteine peptidase	NA	−1.04
CCR1	chemokine (C-C motif) receptor 1	1.31	1.58
CD14	CD14 molecule	−1.14	−1.27
COX1†	cyclooxygenase-1	NA	−1.54§
CSN2	casein β	−1.12	−1.50§
FOS	v-fos FBJ murine osteosarcoma viral oncogene homolog	1.32	2.31§
HLA-A	major histocompatibility complex, class I, A	1.83	9.39§
HLA-DRA	major histocompatibility complex, class II, DR α	−1.05	−1.30§
HMOX1	heme oxygenase (decycling) 1	1.56§	1.83*
IL1B	interleukin 1, β	−1.12	−1.18
IL1R2	interleukin 1 receptor, type II	−1.05	1.36
IL1RN	interleukin 1 receptor antagonist	1.2	−1.17
IL6	interleukin 6	−1.29	1.03
IL8	interleukin 8	1.08	1.08
IL10	interleukin 10	−1.34	1.68
IL10RB	interleukin 10 receptor, β	−1.04	1.38
IL15	interleukin 15	−1.06	−1.05
IL18	interleukin 18	1.05	1.28
INSIG1†	Insulin-induced gene 1, transcript variant 2	NA	−1.67§
INSR	insulin receptor	1.24§	1.08
IRAK1†	interleukin-1 receptor-associated kinase 1	NA	−1.32§
JAK1‡	Janus kinase 1 (a protein tyrosine kinase)	1.12	−1.70§
LTF	lactotransferrin	−1.01	−1.30*
LYZ	lysozyme (renal amyloidosis)	1.25	−1.05
MAPK9	mitogen-activated protein kinase 9	−1.04	−1.28§
NFKB1A	nuclear factor of κ light polypeptide gene enhancer in B-cells inhibitor, α	1.38	−1.22
NR3C1	nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor)	−1.08	−1.2
SAA3	serum amyloid A3	−1.05	1.64
SDHD	succinate dehydrogenase complex, subunit D, integral membrane protein	−1.55§	−1.32§
SELL	selectin L (lymphocyte adhesion molecule 1)	1.02	1.56
SELP	selectin P (granule membrane protein 140 kDa, antigen CD62)	−1.2	−1.32
SOCS2‡	suppressor of cytokine signaling 2	−1.45§	1.2
SOD1	superoxide dismutase 1, soluble [amyotrophic lateral sclerosis 1 (adult)]	−1.19	−1.2
SOD2	superoxide dismutase 2, mitochondrial	−1.11	1.42
SPARC‡	secreted protein, acidic, cysteine-rich (osteonectin)	1.25*	−1.90§
SREBF1	sterol regulatory element binding transcription factor 1	−1.51§	−1.3
STAT3	signal transducer and activator of transcription 3 (acute-phase response factor)	1.04	−1.12
TLR2	Toll-like receptor 2	1.07	−1.28
TLR4	Toll-like receptor 4	−1.07	1.09
TNF	tumor necrosis factor (TNF superfamily, member 2)	−1.17	1.16
TRAF6†	TNF receptor-associated factor 6	NA	−1.29§

¹Differentially expressed genes on microarray were based on the adjusted P value false discovery rate (FDR) ≤0.06.

^*P ≤ 0.10;

^§P < 0.05.

^†Gene not present on microarray platform.

^‡Quantitative RT-PCR (qPCR) did not correspond to results obtained from differentially expressed genes by microarray. NEB, negative energy balance; PEB, positive energy balance; NA, not available.

Most affected DEG.

Table 2 lists the DEG most affected by NEB during IMI with S. uberis. HLA-A was the most upregulated gene with a 9.39-fold in expression compared with PEB cows. Upregulated genes included HSPA2 (1.64-fold) that encodes heat shock 70 kDa protein 3 as well as ACADS (1.66-fold) and GPR56 (1.45-fold) that are associated with lipid and carbohydrate metabolism. HMOX1 and ANXA1 were also upregulated by 1.56- and 1.52-fold, respectively, in expression in NEB vs. PEB cows during IMI. Genes most negatively affected by NEB during IMI were involved in cell shape (SPARC, −1.90-fold), cell survival (AKT1, −1.70-fold), immunity (JAK1, −1.70-fold), cholesterol metabolism (INSIG1, −1.67-fold), and energy metabolism (SDHD, −1.55-fold).

Table 2.

List of top 5 up- and downregulated genes most affected by energy balance (negative vs. positive) in mammary quarters during intramammary challenge with S. uberis using IPA

Gene	Gene Name	Primary Functions	Fold Change
HLA-A	major histocompatibility complex, class I, A	antigen presentation complex that binds to CD8 receptor on cytotoxic T cells	9.39
ACADS	acyl-coenzyme a dehydrogenase, C-2 to C-3 short chain	catalyzes the initial step of the mitochondrial fatty acid β-oxidation pathway	1.66
HSPA2	heat shock 70 kDa protein 3	plays a role in anti-inflammatory response mechanisms; elicits apoptosis	1.64
HMOX1	heme oxygenase (decycling) 1	cleaves heme to form biliverdin; involved in iron ion binding; antiapoptotic; promotes cell survival	1.56
ANXA1	annexin A1/lipocortin1	phospholipase A2 inhibitory activity; potential anti-inflammatory activity; promotes lipolysis	1.52
GPR56	G protein-coupled receptor 56	G protein-coupled receptor activity; cell adhesion; metabolic process	1.45
SPARC	secreted protein, acidic, cysteine-rich (osteonectin)	elicits changes in cell shape, inhibits cell-cycle progression, and influences synthesis of extracellular matrix	−1.90
AKT1	v-akt murine thymoma viral oncogene homolog 1	mediator of growth factor-induced neuronal survival; antiapoptotic properties	−1.70
JAK1	Janus kinase 1 (a protein tyrosine kinase)	involved in the interferon-α/β and -γ signal transduction pathways; involved in stat binding	−1.70
INSIG1	insulin induced gene 1, transcript variant 2	regulates cholesterol concentrations in cells; binds sterol-sensing domains of SREBP cleavage-activating protein (SCAP) and HMG CoA reductase	−1.67
SDHD	succinate dehydrogenase complex, subunit D, integral membrane protein/succinate-Coq reductase	complex II of the respiratory chain, involved in the oxidation of succinate, carries electrons from FADH to CoQ; in citric acid cycle, oxidizes succinate to fumerate	−1.55

IPA, Ingenuity Pathway Analysis.

Top canonical pathways.

The top canonical pathways affected by NEB during IMI (Table 3) were IL-8 signaling, glucocorticoid receptor signaling, and NRF2-mediated oxidative stress response. Ten DEG (2 upregulated; 8 downregulated) were associated with IL-8 signaling. GNG12 (1.35-fold) and HMOX1 were genes up-regulated within IL-8 signaling. Genes downregulated that were unique to IL-8 signaling included CCND1 (−1.33-fold), IRAK1 (−1.32-fold), RHOU (−1.56-fold), and SRC (−1.22-fold).

Table 3.

Canonical pathways of genes most affected by energy balance (negative vs. positive) in mammary quarters during intramammary challenge with S. uberis using IPA

	#Genes/Total§		Genes
Canonical Pathway	Upregulated	Downregulated	Upregulated	Downregulated
IL-8 signaling	2/183	8/183	GNG12, HMOX1*§	AKT1*, CCND1, GNG3, IRAK1*, MAPK9*, RHOU, SRC, TRAF6*
Glucocorticoid receptor signaling	3/275	10/275	ANXA1*, HSPA2, DUSP1	AKT1*, CCL5, CSN2*, GTF2F2, JAK1*, MAPK9*, POLR2E, TAF6L, TGFBR2, TRAF6*
NRF2-mediated oxidative stress response	4/181	6/181	ACTG1, DNAJB2, HMOX1*, JUND	AKT1*, CDC34, GSTA4, GSTM3, MAPK9*

^§# Genes/Total, number of differentially expressed genes from microarray (FDR P ≤ 0.06; P < 0.01) and qPCR analysis (P ≤ 0.05) out of total #of genes associated with the canonical pathway according to IPA.

^*qPCR expression results for verification of genes differentially expressed on microarray as well as genes selected for qPCR analysis not present on microarray.

A total of 13 DEG (3 upregulated, 10 downregulated) were associated with glucocorticoid receptor signaling. ANXA1 and HSPA2 (1.64-fold) were among the DEG upregulated; whereas CSN2 (−1.50-fold), JAK1, and genes involved in immune response (JAK1 and MAPK9) and transcription (GTF2F2, POLR2E, and TGFBR2) were all downregulated within glucocorticoid receptor signaling.

For NRF2-mediated oxidative stress response, four genes were upregulated, including ACTG1 (1.38-fold), which is involved in cell movement, and HMOX1. Six genes associated with NRF2-mediated oxidative stress response were downregulated, including GSTA4 (−1.53-fold) and GSTM3 (−1.36-fold). These genes encode glutathione S-transferase A4 and M3, respectively, which are involved in cell protection from reactive oxygen metabolites (ROM) generated during respiratory burst, similar to GPX. Several DEG overlap into one or more of the three pathways, including MAPK9 and AKT1, which were associated with all three canonical pathways. TRAF6 was involved in both IL-8 and glucocorticoid receptor signaling, and HMOX1 plays a role in IL-8 signaling as well as NRF2-mediated oxidative stress response.

Top molecular functions among DEG.

Use of IPA Knowledge base allowed grouping of DEG into their respective molecular functions, thus helping identify those that were most affected by NEB during IMI with S. uberis (Table 4). These analyses were performed separately for both up- and downregulated genes. Cell growth and proliferation and cellular development were the most enriched molecular functions within both up- and downregulated DEG. A subset of genes shown to be directly or indirectly associated with these functions is shown in Table 4. Forty-eight DEG were associated with Cell Growth and Proliferation. Genes that are involved with decreasing cell proliferation include ANXA1, EPHA2, HMOX1, NOTCH1, SPARC, and TGFBR2, and genes that are involved with increasing cell proliferation include APP, CCNA2, CCND1, FSCN1, HK1, and SOCS2. Several DEG involved with cell proliferation were also involved with cell growth. Genes such as ACTN4, CCND1, EPHA2, HMOX1, INSR, NOTCH1, and SREBF1 were associated with decreasing cell growth, whereas CCNA2, CCND1, FXYD1, HSPB2, and UBE2B were involved in increased cellular growth.

Table 4.

Functions of genes most affected by energy balance (negative vs. positive) in mammary quarters during intramammary challenge with S. uberis using IPA (FDR P ≤ 0.06; P ≤ 0.01)

	Genes
Function§	Upregulated	Downregulated
Cell Growth and Proliferation (48)
Growth of cells	ACTG1, ACTN4, ANXA1*§, ACPDD1, ARHGEF1, DNAJB2, ELGN2, HMOX1*, HSPB2, INSR*, LEF1, USP21	ADFP, ADRA1A, AKT1*, CCNA2, CCND1, COX17, DAP, ELF1, EPHA2, FXYD1, HK1, MAPK9*, MMP15, NOTCH1, PLEC1, SOCS2, SPARC*, SPINT2, SRC, SREBF1, TGFBR2, UBA1, UBE2B, USP9X
Proliferation of cell lines	ANXA1*, FSCN1, HMOX1*, INSR*, LEF1	ADRA1A, AKT1*, APP, CCNA2, CCND1, EPHA2, HK1, JAK1*, MAPK9*, NOTCH1, PPAP2C, PTPRF, SOCS2*, SPARC*, SRC, STARD13, TGFBR2
Cellular Development (36)
Development of cells	ARHGEF1, CTNNA1, GNG12, INSR*, LEF1, RSP19	AKT1*, ANPEP, CCL5, CCND1, EPHA2, HSF1, IRAK1* MAPK9*, MYF5, NFYA, NOTCH1, RAPGEF1, RHOU, SPARC*, SRC, TGFBR2, TRAF6*, UBE2B
Development of breast cell lines		AKT1,CCND1,EPHA2,NOTCH1,SRC, UBE2B

^§Within Cell Growth and Proliferation, a total of 14 genes were upregulated and 24 genes were downregulated. Within Cellular Development, a total of 9 genes were upregulated and 27 genes were downregulated.

^*qPCR expression results for verification of genes differentially expressed on microarray as well as genes selected for qPCR analysis not present on microarray.

Another major function affected by NEB during IMI challenge with S. uberis was cellular development (n = 36) and several of these genes were also involved with cell growth and proliferation (Table 4). The majority of DEG associated with cell development were downregulated (n = 28) and were primarily involved with increasing cell development, including AKT1, ANPEP, CCL5, CCND1, EPHA2, IRAK1, NFYA, and SRC. Other downregulated genes involved with cell development were associated with increasing the development of mammary cell lines and include CCND1, EPHA2, NOTCH1, SRC, and UBE2B.

Other genes of interest.

Other genes downregulated due to NEB in infected mammary quarters have been shown to play major roles in immune response and the transcription and secretion of proinflammatory cytokines, including C3, IRAK1, TRAF6, and CCL5, which are involved in chemotactic response of immune cells. SOCS2, a suppressor of cytokine signaling, was also downregulated. Other genes downregulated are involved in metabolism, such as glycolysis (HK1) and de novo lipid synthesis (PPAP2C).

Other DEG that were not incorporated into pathways and functions via IPA Knowledge base include upregulated genes such as C1QTNF5 (1.30-fold), which IPA indicated is involved in complement activation, cell adhesion and immune response. Genes involved in catabolism of lipids and carbohydrates, including COX10 (1.31-fold) and SUCLG2 (1.36-fold) were also upregulated by NEB in mammary tissue after IMI challenge with S. uberis. Downregulated genes included CRHR1 (−1.48-fold), which encodes the corticotropin-releasing hormone receptor that binds to corticotropin-releasing hormone, a potent mediator of endocrine, autonomic, behavioral, and immune responses to stress (12, 16, 50). In addition, BTN1A1 (−1.57-fold) and CSN2 (−1.50-fold), which are involved in metabolism, DAP (−1.35-fold) and COX1 (−1.54-fold; see Supplemental Table S5 in Supplemental Materials 2), which are involved in the synthesis of prostaglandins, were also downregulated by NEB during IMI.

DISCUSSION

Cellular functions.

Whether up- or downregulated, the large majority of DEG due to NEB were not associated with immune system function but were primarily involved with altering cell development, proliferation, and growth (Table 4). The majority of genes involved in cell growth, proliferation, and development were downregulated in S. uberis-infected mammary tissue from NEB cows. For example, activation of the Akt pathway has been shown to enhance abnormal cell growth, proliferation, and survival in human mammary epithelial cells (13); and AKT1 was downregulated by NEB in mammary tissue after IMI challenge. In addition, NOTCH1, which plays a major role in cell fate determination and has been implicated in mammary gland development (28), was downregulated in mammary tissue from NEB cows, and the repression of the CCND1 gene (downregulated by NEB) has been shown to inhibit mammary tumor cell growth (32). Among the genes upregulated in mammary tissue from NEB cows during IMI was HMOX1. Through its inhibitory effects on cell proliferation and cell cycle and its ability to enhance apoptosis, this protein has recently been considered an antitumor associated gene in both rat and human mammary tissue (25).

Overall, microarray expression profiles in mammary tissue from NEB cows compared with PEB cows during IMI challenge with S. uberis indicated an inhibition of genes involved in enhancing cell proliferation, growth, and development. In support of these findings, caloric restriction resulted in decreased mammary cell proliferation (35) and inhibition of tumor cell growth compared with ad libitum-fed control subjects (21, 52). In ruminants, Nørgaard et al. (48) observed lower MEC proliferation from cows fed a low-energy density diet compared with cows fed a high-energy density diet during the first 8 wk of lactation. Similar to our results, milk yield was lower in the cows fed the low- compared with high-energy density diet. Cell turnover, as a balance between cell proliferation and apoptosis, determines the number of MEC and milk yield influences the activity of MEC (33). Our results are similar to those reporting reduced cell proliferation, growth, and development when nutrient availability is low (21, 48, 52). To our knowledge, there is no previous research investigating the effect of NEB on mammary tissue gene expression during an IMI challenge. Our results suggest that in cows challenged with an S. uberis infection, constraints on nutrient availability are an influential mediator of mammary cell proliferation, growth and development.

Glucocorticoid receptor signaling.

Glucocorticoids, a class of steroid hormones, exert dramatic effects on metabolism, have anti-inflammatory effects on the immune response, and may play a role in milk synthesis and secretion (3, 8, 27). NR3C1, which encodes the glucocorticoid receptor, was not differentially expressed between NEB and PEB cows during IMI with S. uberis (Supplemental Table S5 in Supplemental Materials 2). When glucocorticoid binds to the glucocorticoid receptor there is an inhibition of the transcription of genes involved in the NF-κB proinflammatory response (18) and activation of genes involved in the anti-inflammatory response (e.g., ANXA1) (11). Despite the fact that NR3C1 was not differentially expressed in NEB during infection, we observed an upregulation of the anti-inflammatory gene ANXA1, a downregulation of genes involved in NF-κB signaling (e.g., AKT1, JAK1, and TRAF6) (3), and a downregulation of other immunorelevant response elements (e.g., CCL5) (51). In addition, HSPA2, a gene upregulated in NEB cows during IMI (Table 3; Supplemental Table S5 in Supplemental Materials 2), encodes the heat shock protein Hsp70 that has been shown to play a role in the anti-inflammatory response mechanisms and apoptosis elicited via the glucocorticoid receptor signaling pathway (3, 40, 55). Therefore, the gene expression profiles observed relative to glucocorticoid receptor signaling are suggestive of an upregulation of anti-inflammatory mechanisms in mammary tissue of NEB cows during IMI challenge compared with PEB cows.

The overall decrease in expression of DEG involved with glucocorticoid receptor signaling may also be explained by the potential role glucocorticoids play in mammary secretory activation and milk synthesis (8, 10, 19). The mechanisms involved in milk synthesis are complex and the relationship between glucocorticoids and mammary activation is not well understood. The majority of the research examining the role of glucocorticoids in mammary gland activation in milk synthesis stem primarily from human, rodent, and sheep models. Results suggest that glucocorticoids are involved in mammary gland synthesis of milk protein in mice (58), lactose in sheep (24), and fat in rats (19). With regard to lipogenesis, unphosphorylated GSK3 (i.e., activated) has been shown to inhibit the expression of lipogenic enzymes via the phosphorylation of SREBP-1 in mice (59). In our study, expression of SREBF1 and AKT1 were downregulated in S. uberis-infected mammary tissue of NEB vs. PEB cows. Unfortunately, milk composition was not measured after IMI challenge with S. uberis. The potential role of glucocorticoids in milk synthesis during an IMI in cows experiencing NEB warrants further investigation.

NRF2-mediated oxidative stress response.

NRF2 localizes to the cytoplasm where it interacts with ACTG1 and regulates mechanisms of cellular oxidative stress via increased transcription of HMOX1 (2) and genes involved in the production of GSTA4 and GSTM3 (9). HMOX1 catalyzes the rate-limiting reaction in heme catabolism that yields biliverdin, which is converted to bilirubin, a potent antioxidant. Glutathione S-transferases are a group of enzymes that conjugate damaging ROM with glutathione, thereby reducing tissue damage caused during respiratory burst of phagocytes during an innate immune response. Within the NRF2-mediated oxidative stress response, ACTG1 and HMOX1 were upregulated and genes GSTA4 and GSTM3 were downregulated by NEB compared with PEB cows during IMI.

Because our results indicate that genes involved in regulating cellular oxidative stress were both up- and downregulated coupled with the lack of effects observed in the expression of SOD1 and SOD2 (Table 1), it is unlikely that a true change in oxidative stress response occurred in mammary tissue of NEB cows compared with PEB cows during IMI. In further support of this, we observed that milk activity of enzymes involved with reducing ROM produced from respiratory burst, e.g., SOD and GPX, was not different between NEB and PEB cows after IMI challenge with S. uberis (43). Total ROM was not analyzed in milk samples, but the results provide evidence that the transcriptomic-level differences in oxidative burst between NEB and PEB cows were minimal, and, thus, the degree of energy balance likely did not affect overall host protection during respiratory burst.

Immune system response.

An important objective of this experiment was to evaluate the effect of NEB on the transcriptomic-level immune response in mammary tissue during IMI challenge with S. uberis. One of the top canonical pathways affected by NEB during IMI (Table 3) was IL-8 signaling. IL-8 is a cytokine that primarily stimulates PMN recruitment and enhances PMN function via its effects on PMN degranulation and respiratory burst activity (45). The gene encoding IL-8 (IL8) is expressed in many tissues types including MEC during IMI challenge (51). At the protein level, Bannerman et al. (4) evaluated cytokine secretions in whey collected from mammary quarters challenged with S. uberis and observed elevated milk concentrations of IL-1β, IL-8, IL-10, and TNF-α compared with healthy quarters by ∼30 h postchallenge. Moyes et al. (42) reported that at 20 h postchallenge, IL8 expression was the most highly upregulated DEG (verified via qPCR) in mammary quarters during IMI challenge with S. uberis (1,054-fold). However, no differences in IL8 expression were observed between NEB and PEB cows during IMI challenge with S. uberis (see Supplemental Table S5 in Supplemental Materials 2).

Within IL-8 signaling, 80% of the genes were downregulated by NEB compared with PEB during IMI (Table 3). These genes are associated with the downstream effects of IL-8 after its binding to the IL-8 receptor (e.g., CXCR1/R2), but not with the expression and secretion of IL-8 which may support the lack of effects of NEB on IL8 expression observed in our study. The binding of IL-8 to CXCR1/R2 increases expression of AKT1 (38), which leads to an increase FRAP1 expression (57) that in turn leads to the downstream activation of CCND1 expression. This response is positively associated with cell proliferation, as noted in prostate cancer cells (38). The binding of IL-8 to the CXCR1/R2 also results in 1) increased expression of IRAK1, which stimulates expression of TRAF6; 2) increased expression of MAPK9 (SAPK/JNK signaling); and 3) increased expression of RHOU, all of which have been shown to activate the proinflammatory NF-κB signaling pathway (39). Because these genes were downregulated in mammary tissue of NEB cows during IMI, it is possible, at least judging by human/rodent data within the IPA knowledge base, that there was suppression of the proinflammatory response via the NF-κB signaling pathway along with inhibition of cell proliferation through the downregulation of CCND1 and AKT1.

HLA-A is the only gene upregulated in NEB cows that has been previously observed as having direct relationships with the immune system response (14, 65). The majority of genes having direct associations with the immune system during IMI in the mammary gland were downregulated in NEB vs. PEB cows, providing additional evidence that cows in NEB are immunosuppressed during an IMI compared with cows in PEB. Among those downregulated was HLA-DRA, which encodes MHC II that is involved in antigen presentation via CD4 receptor on T-helper cells and has been shown to have specificity toward Gram-positive bacteriological infections (20). Other genes downregulated were associated with activation of proinflammatory signaling via NF-κB (e.g., AKT1, IRAK1, MAPK9, and TRAF6) and chemokine signaling (CCL5 and SOCS2). Our results seem to indicate an inhibitory effect of NEB on gene expression profiles involved in the immune response during IMI challenge. A shortfall of the required nutrients to mammary cells might partly explain these results. At the transcriptomic level, our results provide potential mechanisms linking NEB and impaired immune response during the postpartal period (30, 44, 49).

At the quarter level, we observed that milk SCC prior to biopsy and growth of S. uberis throughout the infection period were not significantly different between NEB and PEB cows (43). In addition, no differences were observed between NEB and PEB cows for milk cytokine concentrations (TNF-α, IL-1β, and IL-10), enzymes involved in respiratory burst (SOD and GPX), and acute phase proteins (SAA and Hp) at the time of biopsy (i.e., 20 h postinoculation). In support, microarray and qPCR results indicated no significant differences in mammary tissue gene expression of IL1B, IL10, TNF, SOD1, SOD2, and mammary-specific SAA3 between NEB and PEB cows 20 h after S. uberis intramammary challenge. Studies comparable to ours previously reported minimal changes in immune response parameters during diet-induced NEB (e.g., feeding at 80% of metabolizable energy requirements) coupled with an endotoxin challenge (100 μg) in midlactation nonpregnant dairy cows (53). Modest responses also were observed due to feeding yearling Holstein steers at 60% of metabolizable energy requirements (54).

Conclusions

Cows subjected to diet-induced NEB and IMI challenge with S. uberis had lower expression of genes involved in enhancing cell development, proliferation, and growth. Of the DEG associated with immune response in this study (e.g., AKT1, CCL5, HLA-A, HLA-DRA, IRAK1, MAPK9, SOCS2, and TRAF6), the majority of were downregulated and were primarily involved in enhancing the proinflammatory response, specifically the NF-κB signaling pathway. These results, at least judging by human/rodent data within the IPA knowledge base, indicated that the primary mechanisms affected by NEB during an IMI challenge are inhibition of the proinflammatory response, cell growth, and cellular development. This study provided new avenues for investigation into the mechanisms relating NEB and susceptibility to mastitis in lactating dairy cows.

GRANTS

Financial support for the animal study, microarray analysis, and qPCR analysis was provided in part by the US Department of Agriculture (USDA), Cooperative State Research, Education and Extension Service, Washington, DC, Section 1433 Animal Health and Disease Funds appropriated to the Illinois Agricultural Experiment Station, Urbana, IL, under project no. ILLU-538-981 (J. J. Loor), and also by National Research Initiative Competitive Grant 2007-35204-17758 (J. J. Loor). Partial support for qPCR analysis also was provided by the USDA Cooperative State Research, Education and Extension Service and the Illinois Agricultural Experiment Station, Urbana, IL, through multistate project number W-1181 (J. K. Drackley). The Mitchell Fellowship in Animal Nutrition program, University of Illinois, provided financial support to K. M. Moyes during 2007 and 2008.

DISCLOSURES

No conflicts of interest are declared by the authors.