Nutrigenomic analyses reveal miRNAs and mRNAs affected by feed restriction in the mammary gland of midlactation dairy cows

Pierre-Alexis Billa; Yannick Faulconnier; Tao Ye; Céline Bourdon; José A. A. Pires; Christine Leroux

doi:10.1371/journal.pone.0248680

Abstract

The objective of this study was to investigate the effects of feed restriction on mammary miRNAs and coding gene expression in midlactation cows. Five Holstein cows and 6 Montbéliarde cows underwent 6 days of feed restriction, during which feed allowance was reduced to meet 50% of their net energy for lactation requirements. Mammary biopsies were performed before and at the end of the restriction period. Mammary miRNA and mRNA analyses were performed using high-throughput sequencing and microarray analyses, respectively. Feed restriction induced a negative energy balance and decreased milk production and fat and protein yields in both breeds. Feed restriction modified the expression of 27 miRNAs and 374 mRNAs in mammary glands from Holstein cows, whereas no significant miRNA change was observed in Montbéliarde cows. Among the 27 differentially expressed miRNAs, 8 miRNAs were associated with dairy QTL. Analysis of target genes indicate that the 8 most abundantly expressed miRNAs control transcripts related to lipid metabolism, mammary remodeling and stress response. A comparison between the mRNAs targeted by the 8 most strongly expressed miRNAs and 374 differentially expressed mRNAs identified 59 mRNAs in common. The bioinformatic analyses of these 59 mRNAs revealed their implication in lipid metabolism and endothelial cell proliferation. These effects of feed restriction on mammary miRNAs and mRNAs observed in Holstein cows suggest a potential role of miRNAs in mammary structure and lipid biosynthesis that could explain changes in milk production and composition.

Citation: Billa P-A, Faulconnier Y, Ye T, Bourdon C, Pires JAA, Leroux C (2021) Nutrigenomic analyses reveal miRNAs and mRNAs affected by feed restriction in the mammary gland of midlactation dairy cows. PLoS ONE 16(4): e0248680. https://doi.org/10.1371/journal.pone.0248680

Editor: Angel Abuelo, Michigan State University, UNITED STATES

Received: June 5, 2020; Accepted: March 3, 2021; Published: April 15, 2021

Copyright: © 2021 Billa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All RNAseq files are available from the GEO database (accession number(s) GSE146762.). Microarray data were deposited in the Gene Expression Omnibus (GEO): GSE159302.

Funding: We thank the Région Auvergne S3 and FEDER for the funding of P-A Billa’s thesis grants (project nr. 23000794/1130 entitled "Innovative biomarkers for the quality products of tomorrow's cattle farming ") awarded to PAB, and ApisGene for the funding of Céline Bourdon’s thesis grants. This research was funded by GISA meta-program of INRAE (Ruminflame and Longhealth projects).

Competing interests: NO authors have competing interests.

Introduction

The mammary gland (MG) is a complex secretory organ with the ability to synthesize and secrete large quantities of milk components, including proteins, lactose and lipids. Genetics, nutritional and physiological studies of MG functioning have shown that many genes regulate the synthesis and secretion of milk components [1]. Forty-eight hours of feed deprivation induced a severe negative energy balance (NEB) and altered the mammary transcriptome, milk production and composition in lactating goats [2]. However, the molecular mechanisms underlying the nutritional regulation of gene expression have not been fully determined.

MicroRNAs (miRNAs), small noncoding RNAs of 18–25 nucleotides, regulate at least 60% of gene expression by base-pairing with mRNAs, inducing their degradation or the inhibition of their translation [3]. Therefore, the RNAs are involved in many different cellular processes [4–6]. MiRNAs were studied in the MG of different species, including ruminants [7–12], and their potential role in mammary development [8] and milk synthesis [13] was demonstrated. Breed effects on miRNA expression were also studied in different tissues, including MG [14–16]. In a previous study, the comparison of mammary miRNomes from Holstein (HOLS) and Montbéliarde (MONT) cows revealed 22 differentially expressed miRNAs (DEMs), suggesting a potential role of miRNAs in MG plasticity and milk component synthesis [15].

In ruminants, few studies have investigated the effect of nutrition on miRNA expression in the MG. Feed deprivation leading to severe NEB modified the expression of 30 miRNAs in MG of lactating goats [10].

We hypothesized that bovine mammary miRNome would be modified by feed restriction. Thus, the objective was to determine the effects of NEB induced by feed restriction on MG miRNome, assess potential differences between midlactation HOLS and MONT cows, and determine the role of miRNAs in the regulation of milk production and milk component synthesis.

Materials and methods

Ethics statement

The experiment was conducted at the INRAE Herbipole experimental farm of Marcenat (https://doi.org/10.15454/1.5572318050509348E12). Animal procedures were performed in compliance with Regional Animal Care Committee guidelines CEMEAA: Auvergne, French Ministry of Agriculture and European Union guidelines for animal research C2EA-02. All mammary biopsies were performed with relevant national legislation and were performed by an accredited person (N° of certification: 63–20). All procedures involving animals were approved by the Ethics Committee of the French Ministry of Higher Education, Research and Innovation (APAFIS # 3737–2015043014541577v2).

Animals and sampling

Twelve multiparous midlactation HOLS (n = 6; 2.5 ± 0.8 lactations and 170 ± 11 days in milk (DIM)) and MONT (n = 6; 3.3±2.0 lactations and 156 ± 26 DIM) cows were used to study the effects of 6 days of feed restriction on milk production and mammary miRNomes. One HOLS cow was excluded from the experiment due to mastitis. During the control period (CONT) before restriction, all cows were allowed ad libitum intake of a total mixed ration. During the restriction period (d1 to 6, REST), feed allowance was reduced to meet 50% of NE_L requirements calculated from body weight, feed intake and milk production and composition recorded before restriction [17]. Cows had free access to water and were housed in a freestall barn equipped with automatic feed bunks that control individual access and weight feed intake (CRFI, Biocontrol, Rakkestad, Norway). Cows had free access to water and were milked twice daily at approximately 6:30 and 16:00. The nutrient composition of the experimental diets was previously described [18].

MG samples were collected by biopsies for miRNome analyses on day 0 and day 6 relative to initiation of feed restriction. Mammary biopsies were performed as previously described [19]. Briefly, the biopsy site was selected at a midpoint on alternate rear quarters of mammary gland. Approximately 600–650 mg of tissue was collected, rinsed in sterile 0.9% saline solution, snap-frozen in liquid nitrogen, and stored at −80°C until RNA extraction.

Milk samples were collected on day -1 and on day 5 relative to initiation of feed restriction. Milk composition was determined by mid-infrared spectroscopy (LIAL, Aurillac, France), and their means were computed according to PM/AM production and composition. Milk production and energy balance data were analyzed using mixed models of SAS (version 9.4; SAS Institute Inc., Cary, NC) that included day, breed, and their interaction as fixed effects, cow as random effect, and Kenward-Roger adjustment for calculation of degrees of freedom.

RNA preparation

Total RNA was extracted from approximately 50 mg of MG biopsies using the miRVana kit (Thermo Fisher Scientific, USA). The concentration and quality of the RNAs were estimated by spectrophotometry Nanodrop TH (ND-1000, NanoDrop Technologies LLC, Wilmington, DE, USA) and using the Bioanalyzer 2100 (Agilent Technologies Inc., Santa Clara, CA, USA), respectively. The means of the obtained RIN were 8.3 ± 0.6 and 8.3 ± 0.9 in HOLS and MONT, respectively.

RNA-seq analysis

RNA-seq analyses were performed on RNAs from MG of five HOLS and six MONT cows before and at the end of the restriction period, by the IGBMC Microarray and Sequencing Platform (Strasbourg, France), as previously described [15]. Briefly, small RNA-seq libraries were generated from 2 μg of total RNA using the TruSeq Small RNA Library Prep Kit (Illumina, San Diego, CA) according to the manufacturer’s instructions. Specific miRNA and small RNA adapters were added to each end of the miRNA followed by reverse transcription and PCR amplification to obtain cDNA. Acrylamide gel purification of small RNAs was performed. The final cDNA libraries were sequenced on HiSeq 4000 (Illumina) as single-end 50 base reads according to the manufacturer’s instructions. Adaptor sequences and reads with undetected bases were removed (FASTX-Toolkit, http://hannonlab.cshl.edu/fastx_toolkit/index.html), and all remaining reads were filtered according to their size. Read quantification and annotation were performed using the ncPRO-seq pipeline [20]. The sequence reads were aligned against the Bos taurus btau5.0.1 genome as miRBase_v22.1 using the miRDeep2 package [21]. Precursors and mature miRNAs were identified using the miRDeep2 core module, miRDeep2.pl. Potential miRNAs were annotated accordingly against orthologous miRNAs in goat, sheep and humans (miRBase release 22.1). We used a miRDeep2 score ≥ 0 as a cut-off threshold. The normalization and differential expression analyses of miRNAs were conducted using the DESeq2 R package v1.18.1. Significance was considered at P_adj ≤ 0.05. The accession number of the RNA-seq data is GSE146762.

Microarray analyses

Due to the miRNA results, mRNA analyses were performed only in HOLS. Fifty nanograms of total RNA from the MG samples (n = 12) already used for miRNA analyses were analyzed using a three bovine 4 × 44K microarray (Agilent Technologies, Inc., Santa Clara, CA, USA) leading to 12 arrays. All procedures were performed according to the manufacturer’s instructions. Briefly, total RNA was linearly amplified and labeled with Cy3 using the one-color Low Input Quick Amp Labeling Kit (Agilent Technologies, Inc. Santa Clara, CA, USA). Cy3-labeled cRNA (1650 ng) was hybridized using the Gene Expression Hyb Kit (Agilent Technologies, Inc., Santa Clara, CA, USA). Hybridization was performed for 17 h at 65°C in a rotating hybridization oven at 10 rpm. Following hybridization, all microarrays were washed and scanned using the Agilent Microarray Scanner G2565A (Agilent Technologies, Inc. Santa Clara, CA, USA). The resulting TIFF images (Tagged Image File Format) were processed using Feature Extraction software Version 11. Microarrays were analyzed to determine differential gene expression (DEG) at the mRNA level using GeneSpring software, and data were normalized with 75^th percentile shift followed by t-tests moderated with the Benjamini-Hochberg (FDR) correction [22], and significance was considered at P_adj < 0.10. Microarray data were deposited in the Gene Expression Omnibus (GEO): GSE159302.

Bioinformatic analyses

The genomic localization of DEMs was compared with the genomic location of milk-associated QTL. All bovine QTL downloaded from CattleQTLdb (AnimalQTLdb release 40, December 29, 2019, https://www.animalgenome.org) were obtained from the "All data by bp (on UMD3.1)" link in bed format. These localizations were used to identify the mammary miRNAs hosted in these regions. The genomic positions of the miRNAs in the UMD3.1 reference genome permitted their screening in the QTL regions using a homemade bioinformatics script in Python language (S1 Fig). The QTL related to milk production and composition were then extracted.

Target genes of DEMs and corresponding putative biological processes (P ≤ 0.05) were investigated using the miRWalk database (version 3.0) and Enrichr platform [23, 24], integrating several tools for pathway visualizations, including KEGG, BioCarta, for more efficient analyses of genes in networks. Modulated biological processes were grouped using the REVIGO platform (http://revigo.irb.hr/) [25]. Metacore^TM software (release 6), which uses text mining algorithms to identify interactions and build pathways/networks, was also used to corroborate bioinformatics investigations. However, these algorithms do not use the exact representativeness corresponding to the background of the used whole genome microarray. A Venn diagram was obtained using Venny 2.1 (http://bioinfogp.cnb.csic.es/tools/venny/) to find common genes between miRNA target genes and DEGs identified by microarray analyses. Biological processes (P ≤ 0.05) of these common genes were investigated using the Enrichr platform for both up and down-regulated genes together.

Results and discussion

Animal responses and milk analyses

Energy balance, milk production and composition were analyzed to characterize cow response to the feed restriction model (Table 1). Feed restriction induced NEB in both HOLS and MONT cows (-35 and -29 ± 4 MJ/day, respectively) due to the experimental design. Milk production, fat and protein yields decreased by 40, 33 and 43% in HOLS and 43, 34 and 49% in MONT, respectively, during REST (Table 1). Production and metabolic responses are detailed elsewhere [18]. The effects of restriction on milk production are in agreement with previous studies in mid- and late-lactation HOLS cows [26, 27].

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Table 1. Effects of feed restriction on energy balance, DMI, milk, milk fat and protein yields in midlactation Holstein (HOLS) and Montbéliarde (MONT) cows during control (CONT) and restriction (REST) periods.

https://doi.org/10.1371/journal.pone.0248680.t001

A significant breed effect was observed for energy balance and production variables. Montbéliarde cows had lower milk, fat and protein yields than HOLS cows, which is in agreement with previous studies [28, 29]. A significant period by breed interaction was observed for energy balance and because energy balance was lower in MONT during the CONT period compared to HOLS cows. This could be due to lower intake in MONT than in HOLS cows during this period [18].

Mammary gland miRNome responses

Summary of miRNA sequencing data.

Ten and twelve libraries were constructed using RNA extracted from the MG of HOLS and MONT cows at CONT and at REST periods. More than 12 million raw reads and more than 8.5 million clean reads were obtained after removing adapters, no-clipped reads and reads that were too short. The percentage of clean reads mapped into the Bos taurus genome was 97.4 on average for all libraries. In HOLS and MONT cows, the log₂ of the normalized counts of the CONT and REST libraries had a strong correlation of R ≥ 0.96. MiRdeep2 software analysis identified 754 miRNAs (737 known and 17 predicted) for HOLS libraries and 759 miRNAs (743 known and 16 predicted) for MONT libraries. These results are in accordance with other studies on bovine MG using RNA-seq technology, reporting 654 [9] and 324 [11] known miRNAs. However, these studies presented a higher number of predicted miRNAs (679 in Le Guillou et al. [9] and 176 in Li et al. [11]) than in our study. The difference could be due to the version of miRBase used (miRBase V20, V21 and V22 in Le Guillou et al. [9], Li et al. [11] and our study, respectively). In the present study, the output ratio of CONT compared to the REST period was closer in HOLST than in MONT libraries (Δ1.6% and Δ6.4%, respectively; Table 2).

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Table 2. Effect of feed restriction on miRNA RNA-seq data in mammary glands from midlactation Holstein (n = 5) and Montbéliarde (n = 6) cows in control (CONT) and restriction (REST) periods.

https://doi.org/10.1371/journal.pone.0248680.t002

Differentially expressed miRNAs between the control and restriction periods.

The expression of 27 miRNAs was significantly modified by feed restriction in HOLS cows (P_adj ≤ 0.05; Table 3), but no significant miRNA change was observed in MONT cows. The absence of DEMs in MONT cows may be due to the lower quality of their libraries compared to those of HOLS cows (with a greater number of short reads and adapters and thus a smaller output ratio in MONT; Table 3). Another hypothesis is that MONT cows presented a lower energy value during the CONT period than HOLS cows. Montbéliarde cows presented a lower variation of energy balance between the CONT and REST periods (26 MJ in CONT and -28.9 MJ in REST: Δ54.6 MJ) compared to HOLS cows (56.5 MJ in CONT and –35.1 MJ in REST: Δ91.6 MJ). In addition, MONT cows had a lower decrease in the milk, fat, and protein yields than HOLS. Moreover, decades of genetic selection in HOLS have reduced genetic variation in this breed, which may have allowed detecting significant differences according to the diet. These hypotheses or a combination of these may partly explain the absence of significant modification of miRNA gene expression. Thus, the following results and discussion are specific to Holstein breed.

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Table 3. Differentially expressed miRNAs in the mammary gland of midlactation Holstein cows during restriction (REST) compared to the control (CONT) period.

https://doi.org/10.1371/journal.pone.0248680.t003

Among the 27 DEMs affected by the restriction in HOLS, 25 are known and 2 predicted with 19 down- and 8 upregulated by feed restriction. Eight DEMs were highly expressed with more than 1,000 reads (Table 3). Thirty DEMs were modulated by 48 h of feed deprivation in the MG of lactating goats [10]. The number of DEMs in the current study is similar to those reported in the study of Mobuchon et al. [10] using similar analyses expected that the cut-off was 0.1 in the study performed by Mobuchon et al. and 0.05 in the present study. Two miRNAs are common to our study: miR-222, which was upregulated with a similar fold change (FC) between studies (FC: 1.47 vs 1.39 in caprine and bovine, respectively), and miR-671, which was downregulated (FC: 0.73 vs 0.56 in caprine and bovine, respectively). However, whereas miR-671 presented a similar level of expression in bovine and caprine MG, miR-222 showed more reads in caprine (812 during control and 1,408 in feed deprived goats) than in bovine (267 in CONT and 371 in REST), suggesting a higher level of miR-222 expression in caprine MG compared to bovine.

Early lactation is known to be a natural period of NEB for dairy ruminants. A comparison of DEMs obtained during an induced NEB in cows (present study) and during early lactation in goats [30] highlighted similarities between these two species. For instance, Ji et al. [30] observed 378 DEMs in goat MG between early and late lactation periods using all mammalian miRNA sequences for mapping. Among them, 4 DEMs were common to the present study: miR-181a and -299, -2483-5P (mapped on ruminant genome) and miR-200c (mapped on Tasmanian devil [30]). Except for miR-200c, all other miRNAs presented a similar manner of regulation as our study. Among the common miRNAs, miR-181a and miR-200c are highly expressed miRNAs in our study. Wang et al. [7] showed that the expression of 13 miRNAs in bovine MG was greater during NEB (i.e., early lactation) than during the positive energy balance period (i.e., dry period). Despite the dissimilarity of physiological states between Wang et al. [7] (early vs non-lactation period) and our study, two differentially expressed miRNAs (miR-155 and -181a) are common and have a similar sense of regulation by energy balance. These miRNAs belong to the highly expressed DEMs in our study.

Differentially expressed miRNAs located in milk QTL.

In cattle, at least 50,178 quantitative trait loci (QTL) out of a total of 130,471 have been associated with milk production traits (https://www.animalgenome.org). Analyses of genome positions of the 25 known DEMs in the current study revealed that 8 DEMs are associated with a milk QTL (Table 4). One (miR-155) was within a QTL associated with milk α-lactalbumin content, and 7 miRNAs were associated with milk fat composition (Table 4). Among these 7 miRNAs, 5 (miR-200c, -2898, -181a, -181b, and -296-5p) were located in QTL associated with saturated fatty acid content, and 4 (miR-26b, -200c, -296-5p, and -669) were located in QTL linked to monounsaturated fatty acid content. The difference in the number of miRNAs observed in QTL of saturated vs. unsaturated fatty acids may be due to the difference in the abundance of QTL associated with two classes of fatty acids [31]. In bovines, the close genome positions of miR-181a and miR-181b (< 10 kb; referred to miRBase criteria) on chromosome 11 suggest that they are in a cluster. This finding is in line with the results in mice reporting that they belong to the same family and are in a cluster [32]. Their cluster is located within the same QTL associated with the regulation of butyric and caproic acid content in milk (Table 4). Moreover, the miR-181a and miR-181b upregulation and their high level of expression during the REST period may be related, at least partly, to the decrease in short chain fatty acid content. Indeed, in our study, ∑C4 to C9 was 5.41 vs 3.43 g/100 g total fatty acids during CONT and REST (S1 Table), respectively (P < 0.001).

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Table 4. Location of nutriregulated miRNAs in QTL associated with milk production and composition.

https://doi.org/10.1371/journal.pone.0248680.t004

Analyses of the 8 highly expressed and differential miRNAs.

We focused on highly expressed miRNAs to decipher the mechanisms underlying the response of MG to restriction. Among the 25 known DEMs in Holstein cows, 8 (miR-143, miR-181a, miR-26b, miR-200c, miR-25, miR-200b, miR-181b, and miR-155) presented more than 1,000 reads, representing 99% of the total reads of the DEMs (Table 3). MiR-143 was the most abundant miRNA in this study, possessing more than 2 million reads, which is in agreement with previous studies in bovine MG [33] and bovine liver [34, 35]. MiR-143, known to regulate lipid metabolism, was downregulated in bovine liver during severe NEB [34]. In the present study, miR-143 was upregulated in MG during the NEB period. This difference in regulation may be due to the studied tissue (liver vs MG). Indeed, the NEB period is characterized by strong adipose tissue mobilization, increased NEFA esterification, triacylglycerol accumulation, and ketone synthesis in the liver, as well as decreased lipid metabolism in the MG [36].

MiR-26b and -181a are, respectively, the second and third most expressed miRNAs in our study, possessing more than 100,000 reads. MiR-26b was identified as playing a role in inflammation [37]. The upregulation of miR-26b in our study might be linked to inflammation due to restriction. Indeed, lipid mobilization during NEB may lead to inflammation [38]. MiR-181a was reported to influence lipid and glucose metabolism in humans, mice and bovine liver [39]. MiR-181a targets ACSL1 mRNA, which is involved in lipid synthesis in bovine mammary epithelial cells [13]. The upregulation of miR-181a in our study may be related to the decrease in milk fat yield observed during the NEB period (Table 1) and the diminution of short- (data not shown) and medium-chain fatty acids [18]. Ma et al. [40] showed that miR-25 was involved in triacylglycerol synthesis and lipid accumulation in goat mammary epithelial cells. This miRNA directly targets PPARG and regulates the expression of SREBP1, FASN, and GPAM involved in lipid synthesis. The observed upregulation of miR-181a and miR-25 may be related to the decrease in fat yield during the feed restriction period. Among the highly expressed DEMs, miR-200b and miR-200c belong to the same family, have the same seed sequence, and regulate epithelial-to-mesenchymal transition in breast cancer [41, 42]. The downregulation of miR-200b and miR-200c in our study may suggest a link with MG structural changes due to feed restriction [43].

MiR-181b and miR-155 were the least expressed of the 8 miRNAs and were upregulated by feed restriction. The modulation of their expression may be linked to the stress induced by feed restriction. Indeed, miR-181b was upregulated in the serum of heat-stressed Frieswal bulls compared with summer and winter periods [44] and after chirurgical stress in Angus cattle muscle [45]. In addition, miR-181b and miR-155 were also reported to play a role in the immune response [46, 47]. LPS stimulation of mouse macrophages in vitro resulted in the upregulation of miR-155, and similar changes also occurred after intraperitoneal LPS injection in C57BL/6 mice [47]. In addition, miR-155 promoted the production of the proinflammatory cytokine TNF-α in human B cells [47]. Thus, the upregulation of miR-181b and miR-155 observed in the present study may be explained by inflammation that can be affected by NEB, as demonstrated in other studies [38].

Differentially expressed mRNAs in Holstein cows

To elucidate the mechanisms of nutritional regulation of MG function, mRNA analysis was performed. Considering the results obtained by miRNome studies, mRNA analysis was achieved only in HOLS cows. Microarray analyses identified 374 differentially expressed genes (DEGs, P_adj ≤0.1) between the CONT and REST periods with 120 upregulated and 254 downregulated genes during the REST period. Ten DEGs presented an FC ≥ 3. In caprine MG, Ollier et al. [2] identified 161 DEGs whose expression was altered by 48 h of feed deprivation using the same bovine microarray. The lower number of DEGs reported by Ollier et al. [2] might be due to the heterogeneous system used (caprine mRNA with a bovine array). Bioinformatic analysis of the 374 DEGs showed 256 altered biological processes (BPs; P ≤ 0.05). The analyses of the first 25 BPs revealed that 12 BPs were linked to lipid metabolism and involved 28 DEGs (23 downregulated and 5 upregulated), and 9 BPs were related to respiration metabolism and involved 33 DEGs (30 downregulated and 3 upregulated; Table 5). In addition, metabolic process (S2 Table) and SCAP/SREBP, cholesterol biosynthesis and regulation of lipid metabolism (S3 Table) were also highlighted using Metacore^TM analysis. Despite the fact that the results were corroborated between different bioinformatic analyses, it should be noted that a limitation of these algorithms is that they did not consider the background corresponding to the exact gene representation of the whole genome microarray used.

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Table 5. Top 25 biological processes potentially regulated by the 374 DEGs in Holstein cows.

https://doi.org/10.1371/journal.pone.0248680.t005

The effects of feed restriction on genes involved in lipid metabolism in the MG of lactating Holstein cows (Fig 1) were in agreement with previous studies. The downregulation of ACACA, FABP3 and LPL in our study was in accordance with the reduction of their expression after 4 days of restriction (cows fed at 60% of their ad libitum DMI; [48]). The downregulation of ACSL1, FABP3, and LPIN1 in mammary tissue of dairy cows is in line with the decrease in their expression during the natural NEB period of early lactation (day 15 of lactation) compared to midlactation (day 60 of lactation), which is associated with a lower fat yield [49]. Taken together, these results indicate that the downregulation of the DEGs (ACACA, FABP3, LPL, LPIN1 and ACSL1) involved in lipid metabolism observed in our study (Fig 1) could be related to the decrease in milk fat yield observed during REST.

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Fig 1. MiRNAs and coding genes involved in the milk fat synthesis pathway.

Genes in the red circle correspond to DEGs identified in our study, and miRNAs in the purple box correspond to DEMs targeting the DEGs (adapted from Bionaz and Loor [52]).

https://doi.org/10.1371/journal.pone.0248680.g001

In the present study, feed restriction downregulated 21 DEGs (e.g., NDUFA3, NDUFB1, and NDUFS2) which are related to the NADH pathway and respiration (Table 5, S2 Table). A downregulation of several NADH dehydrogenase mRNAs was previously observed in bovine liver during heat stress [50], suggesting that feed restriction may affect energy production through mitochondrial respiration, which was already identified among the most inhibited functions in bovine liver [51].

Analysis of mRNAs common between the DEG list and mRNAs targeted by the 8 major DEMs

Using miRWalk analyses, we identified 2934 mRNAs potentially targeted by the 8 most expressed DEMs in Holstein cows. A comparison between those potential targets and the 374 DEGs identified by microarray analysis showed 59 common mRNAs (Fig 2). Among these mRNAs, 37 were downregulated, and 22 were upregulated. Two DEGs (LPIN1 and LPL) had an absolute FC ≥ 3, and both were downregulated. LPIN1 and LPL are involved in lipid metabolism [48, 49]. Analysis of the 59 common mRNAs by the Enrichr platform identified 266 BPs (P ≤ 0.05). The elimination of redundancy using the REVIGO platform highlighted 146 BPs, which were regrouped into 13 categories denoting a semantic similarity between BPs (Figs 2 and 3), and 6 of these categories represented 89% of BPs. Two categories (positive regulation of endothelial cell proliferation and lipid biosynthesis) contained more than 20% BPs each. Positive regulation of endothelial cell proliferation was the first category, with 38 of the 146 BPs being regulated. Among those 38 BP, 19 are predicted to be regulated by PROX1 (Prospero Homeobox 1; S4 Table), which is a downregulated DEG that is targeted by miR-181a, a highly expressed miRNA that was determined to be upregulated by REST in HOLS cows. PROX1 is a transcriptional factor influencing cell fate, gene transcriptional regulation and progenitor cell regulation, playing a role in lymphatic system development. Wilge and Oliver [53] showed that knockouts in PROX1 mouse embryos lacked lymphatic system development. Elsewhere, a decrease in lymphatic vasculature (identified using PROX1 and CD31) in MG was observed across the lactation of mice [54]. In our study, the upregulation of miR-181a and the downregulation of PROX1 by feed restriction suggest a modification of lymphatic vasculature. In addition, PROX1 was hosted in a QTL associated with milk palmitoleic acid content and cheese-making properties [55]. The modulation of miR-181a and PROX1 gene expression is consistent with the milk fatty acid changes observed during feed restriction [18].

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Fig 2. Comparison between mRNA targeted by the 8 most expressed DEMs and DEGs in Holstein cows.

Venn diagram between the potential targets of DEMs (blue circle) and the 374 DEGs (yellow circle). Red arrows indicate the workflow used to analyze biological processes (BPs) common mRNA with the Enrichr and REVIGO platforms.

https://doi.org/10.1371/journal.pone.0248680.g002

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Fig 3. REVIGO categories obtained from the Enrichr biological process list using the 59 common mRNAs (P ≤ 0.05).

In brackets: Numbers of BPs in each category.

https://doi.org/10.1371/journal.pone.0248680.g003

Lipid biosynthesis had 29 of the 146 BPs and was the second category identified by REVIGO analysis. Furthermore, three BPs (lipid biosynthetic process, triglyceride biosynthetic process and fatty acid metabolic process) were also identified among the top 15 BPs identified by Enrichr analyses. In these three BPs, four DEGs (ACACA, CYP51A1, LPL, and LPIN1) were downregulated by restriction. A downregulation of CYP51A1, a gene involved in cholesterol synthesis, was already reported during feed restriction in chicken liver [56] and in adipose tissue of beef cows [57]. ACACA, CYP51A1, LPL, and LPIN1 are predicted to be targeted by 4 DEMs (miR-155, -181a, -181b, and -200b). MiR-181a and miR-155 target LPL, miR-181b target ACACA, and miR-200b target LPIN1 and CYP51A1. The upregulation of miR-181a, -181b, and -155 was in line with the downregulation of their targeted genes. A study demonstrated that the absence of miR-155 led to an increase in LPL expression in the liver of miR-155^-/- mice [58].

Conclusions

Feed restriction modified MG miRNome and transcriptome in Holstein but not in Montbéliarde cows. The differentially expressed miRNAs highlight a potential role of miRNAs in MG remodeling and lipid synthesis. Moreover, these roles were supported by the analyses of common differential mRNAs and predicted targets of miRNAs sharing the same function. In addition, the location of differentially expressed miRNAs in QTL associated with milk fatty acid composition was consistent with the observed reduction in short- and medium-chain milk fatty acid content in milk. The decrease in de novo milk fatty acid synthesis observed during restriction mediated in part by the regulation of the miRNAs affecting mRNAs involved in lipid metabolism and suggest a role of DEMs in this biological process.

Supporting information

S1 Fig. Homemade bioinformatics script in Python language for screening QTL.

https://doi.org/10.1371/journal.pone.0248680.s001

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S1 Table. Effects of feed restriction on milk fatty acid concentrations (g/100 g of FA) in mid-lactation cows.

https://doi.org/10.1371/journal.pone.0248680.s002

(DOCX)

S2 Table. TOP 20 of the biological processes affected by restriction in mammary gland of mid-lactation cows.

Enrichment by GO Processes using Metacoret^TM software using the 374 differentially expressed genes after restriction. FDR: false Discovery Rate.

https://doi.org/10.1371/journal.pone.0248680.s003

(DOCX)

S3 Table. TOP 20 of the pathways affected by restriction in mammary gland of mid-lactation cows.

Enrichment by Pathway maps using Metacoret^TM software using the 374 differentially expressed genes after restriction. FDR: false Discovery Rate.

https://doi.org/10.1371/journal.pone.0248680.s004

(DOCX)

S4 Table. Biological processes predicted to be regulated by Prox1 identified by bioinformatics analyses of the 59 genes differentially expressed and predicted to be targeted by the 8 major differentially expressed miRNAs highlighted the regulation of endothelial cell proliferation containing 38 biological processes among which 19 were predicted to be regulated by Prox1.

https://doi.org/10.1371/journal.pone.0248680.s005

(DOCX)

Acknowledgments

The authors thank the staff at Herbipôle INRA (UE1414, Theix, France), Simon Collange, Julien Mongiat and Denys Durand for performing mammary gland biopsies, Sébastien Bes, Karol Pawlowski, Arnaud Delavaud, and David Chadeyron for technical assistance and for sample collection; Sébastien Bes for RT-qPCR analyses. We thank Céline Boby for her assistance for microarray experimentations and data submission to GEO. Sequencing was performed by the IGBMC Microarray and Sequencing platform, a member of the ‘France Génomique’ consortium (ANR-10-INBS-0009) and microarray analyses by the Transcriptomic platform (INRAE-Theix).

References

1. Anderson SM, Rudolph MC, McManaman JL, Neville MC. Key stages in mammary gland development. Secretory activation in the mammary gland: it’s not just about milk protein synthesis! Breast Cancer Research. 2007;9: 204. pmid:17338830
- View Article
- PubMed/NCBI
- Google Scholar
2. Ollier S, Robert-Granié C, Bernard L, Chilliard Y, Leroux C. Mammary Transcriptome Analysis of Food-Deprived Lactating Goats Highlights Genes Involved in Milk Secretion and Programmed Cell Death. J Nutr. 2007;137: 560–567. pmid:17311940
- View Article
- PubMed/NCBI
- Google Scholar
3. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. cell. 2004;116: 281–297. pmid:14744438
- View Article
- PubMed/NCBI
- Google Scholar
4. Ambros V. The functions of animal microRNAs. Nature. 2004;431: 350–355. pmid:15372042
- View Article
- PubMed/NCBI
- Google Scholar
5. Friedman RC, Farh KK, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009;19. pmid:19887576
- View Article
- PubMed/NCBI
- Google Scholar
6. Ameres SL, Zamore PD. Diversifying microRNA sequence and function. Nature Reviews Molecular Cell Biology. 2013;14: 475–488. pmid:23800994
- View Article
- PubMed/NCBI
- Google Scholar
7. Wang M, Moisá S, Khan MJ, Wang J, Bu D, Loor JJ. MicroRNA expression patterns in the bovine mammary gland are affected by stage of lactation. Journal of Dairy Science. 2012;95: 6529–6535. pmid:22959945
- View Article
- PubMed/NCBI
- Google Scholar
8. Li Z, Lan X, Guo W, Sun J, Huang Y, Wang J, et al. Comparative Transcriptome Profiling of Dairy Goat MicroRNAs from Dry Period and Peak Lactation Mammary Gland Tissues. Qu L-H, editor. PLoS ONE. 2012;7: e52388. pmid:23300659
- View Article
- PubMed/NCBI
- Google Scholar
9. Le Guillou S, Marthey S, Laloë D, Laubier J, Mobuchon L, Leroux C, et al. Characterisation and Comparison of Lactating Mouse and Bovine Mammary Gland miRNomes. Pfeffer S, editor. PLoS ONE. 2014;9: e91938. pmid:24658750
- View Article
- PubMed/NCBI
- Google Scholar
10. Mobuchon L, Marthey S, Le Guillou S, Laloë D, Le Provost F, Leroux C. Food Deprivation Affects the miRNome in the Lactating Goat Mammary Gland. Schönbach C, editor. PLOS ONE. 2015;10: e0140111. pmid:26473604
- View Article
- PubMed/NCBI
- Google Scholar
11. Li R, Beaudoin F, Ammah AA, Bissonnette N, Benchaar C, Zhao X, et al. Deep sequencing shows microRNA involvement in bovine mammary gland adaptation to diets supplemented with linseed oil or safflower oil. BMC Genomics. 2015;16: 884. pmid:26519053
- View Article
- PubMed/NCBI
- Google Scholar
12. Mobuchon L, Guillou SL, Marthey S, Laubier J, Laloë D, Bes S, et al. Sunflower oil supplementation affects the expression of miR-20a-5p and miR-142-5p in the lactating bovine mammary gland. PLOS ONE. 2017;12: e0185511. pmid:29281677
- View Article
- PubMed/NCBI
- Google Scholar
13. Lian S, Guo JR, Nan XM, Ma L, Loor JJ, Bu DP. MicroRNA Bta-miR-181a regulates the biosynthesis of bovine milk fat by targeting ACSL1. Journal of Dairy Science. 2016;99: 3916–3924. pmid:26971144
- View Article
- PubMed/NCBI
- Google Scholar
14. Wicik Z, Gajewska M, Majewska A, Walkiewicz D, Osińska E, Motyl T. Characterization of microRNA profile in mammary tissue of dairy and beef breed heifers. Journal of Animal Breeding and Genetics. 2016;133: 31–42. pmid:26060050
- View Article
- PubMed/NCBI
- Google Scholar
15. Billa PA, Faulconnier Y, Ye T, Chervet M, Le Provost F, Pires JAA, et al. Deep RNA-Seq reveals miRNome differences in mammary tissue of lactating Holstein and Montbéliarde cows. BMC Genomics. 2019;20: 621. pmid:31362707
- View Article
- PubMed/NCBI
- Google Scholar
16. Le Guillou S, Leduc A, Laubier J, Barbey S, Rossignol M-N, Lefebvre R, et al. Characterization of Holstein and Normande whole milk miRNomes highlights breed specificities. Sci Rep. 2019;9: 1–13. pmid:30626917
- View Article
- PubMed/NCBI
- Google Scholar
17. INRA. Alimentation des bovins, ovins et caprins: besoins des animaux, valeurs des aliments. Editions Quae Versailles, France; 2007.
18. Billa PA, Faulconnier Y, Larsen T, Leroux C, Pires J a. A. Milk metabolites as noninvasive indicators of nutritional status of mid-lactation Holstein and Montbéliarde cows. Journal of Dairy Science. 2020;0.
- View Article
- Google Scholar
19. Bernard L, Richard C, Gelin V, Leroux C, Heyman Y. Milk fatty acid composition and mammary lipogenic genes expression in bovine cloned and control cattle. Livestock Science. 2015;176: 188–195.
- View Article
- Google Scholar
20. Chen C-J, Servant N, Toedling J, Sarazin A, Marchais A, Duvernois-Berthet E, et al. ncPRO-seq: a tool for annotation and profiling of ncRNAs in sRNA-seq data. Bioinformatics. 2012;28: 3147–3149. pmid:23044543
- View Article
- PubMed/NCBI
- Google Scholar
21. Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40: 37–52. pmid:21911355
- View Article
- PubMed/NCBI
- Google Scholar
22. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57: 289–300.
- View Article
- Google Scholar
23. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14: 128. pmid:23586463
- View Article
- PubMed/NCBI
- Google Scholar
24. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44: W90–97. pmid:27141961
- View Article
- PubMed/NCBI
- Google Scholar
25. Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms. PLOS ONE. 2011;6: e21800. pmid:21789182
- View Article
- PubMed/NCBI
- Google Scholar
26. Gross J, van Dorland HA, Bruckmaier RM, Schwarz FJ. Performance and metabolic profile of dairy cows during a lactational and deliberately induced negative energy balance with subsequent realimentation. J Dairy Sci. 2011;94: 1820–1830. pmid:21426971
- View Article
- PubMed/NCBI
- Google Scholar
27. Pires JAA, Stumpf LF, Soutullo ID, Pescara JB, Stocks SE, Grummer RR. Effects of abomasal infusion of nicotinic acid on responses to glucose and β-agonist challenges in underfed lactating cows. J Dairy Sci. 2016;99: 2297–2307. pmid:26774717
- View Article
- PubMed/NCBI
- Google Scholar
28. Pomiès D, Martin B, Chilliard Y, Pradel P, Rémond B. Once-a-day milking of Holstein and Montbéliarde cows for 7 weeks in mid-lactation. Animal. 2007;1. pmid:22444203
- View Article
- PubMed/NCBI
- Google Scholar
29. Ferlay A, Martin B, Lerch S, Gobert M, Pradel P, Chilliard Y. Effects of supplementation of maize silage diets with extruded linseed, vitamin E and plant extracts rich in polyphenols, and morning v. evening milking on milk fatty acid profiles in Holstein and Montbéliarde cows. Animal. 2010;4: 627–640. pmid:22444050
- View Article
- PubMed/NCBI
- Google Scholar
30. Ji Z, Liu Z, Chao T, Hou L, Fan R, He R, et al. Screening of miRNA profiles and construction of regulation networks in early and late lactation of dairy goat mammary glands. Sci Rep. 2017;7: 1–13. pmid:28127051
- View Article
- PubMed/NCBI
- Google Scholar
31. Govignon-Gion A, Fritz S, Larroque H, Brochard M, Chantry C, Lahalle F, et al. QTL detection for milk fatty acids in French dairy cattle. Proceedings, 10th World Congress of Genetics Applied to Livestock Production 2014; 10 World Congress of Genetics Applied to Livestock Production, Vancouver, CAN, 2014-08-17-2014-08-22, 1–3. 2014 [cited 18 Jan 2020]. Available: http://agris.fao.org/agris-search/search.do?recordID=LV2016006646
32. Williams A, Henao-Mejia J, Harman CCD, Flavell RA. miR-181 and Metabolic Regulation in the Immune System. Cold Spring Harb Symp Quant Biol. 2013;78: 223–230. pmid:24163395
- View Article
- PubMed/NCBI
- Google Scholar
33. Gu Z, Eleswarapu S, Jiang H. Identification and characterization of microRNAs from the bovine adipose tissue and mammary gland. FEBS Letters. 2007;581: 981–988. pmid:17306260
- View Article
- PubMed/NCBI
- Google Scholar
34. Fatima A, Lynn DJ, O’Boyle P, Seoighe C, Morris D. The miRNAome of the postpartum dairy cow liver in negative energy balance. BMC Genomics. 2014;15: 279. pmid:24725334
- View Article
- PubMed/NCBI
- Google Scholar
35. Al-Husseini W, Chen Y, Gondro C, Herd RM, Gibson JP, Arthur PF. Characterization and Profiling of Liver microRNAs by RNA-sequencing in Cattle Divergently Selected for Residual Feed Intake. Asian-Australas J Anim Sci. 2016;29: 1371–1382. pmid:26954124
- View Article
- PubMed/NCBI
- Google Scholar
36. Bell AW. Regulation of organic nutrient metabolism during transition from late pregnancy to early lactation. Journal of Animal Science. 1995;73: 2804. pmid:8582872
- View Article
- PubMed/NCBI
- Google Scholar
37. Zhang L, Huang C, Guo Y, Gou X, Hinsdale M, Lloyd P, et al. MicroRNA-26b Modulates the NF-κB Pathway in Alveolar Macrophages by Regulating PTEN. The Journal of Immunology. 2015;195: 5404–5414. pmid:26503952
- View Article
- PubMed/NCBI
- Google Scholar
38. Sordillo LM, Mavangira V. The nexus between nutrient metabolism, oxidative stress and inflammation in transition cows. Anim Prod Sci. 2014;54: 1204.
- View Article
- Google Scholar
39. Du X, Yang Y, Xu C, Peng Z, Zhang M, Lei L, et al. Upregulation of miR-181a impairs hepatic glucose and lipid homeostasis. Oncotarget. 2017;8: 91362–91378. pmid:29207650
- View Article
- PubMed/NCBI
- Google Scholar
40. Ma L, Qiu H, Chen Z, Li L, Zeng Y, Luo J, et al. miR-25 modulates triacylglycerol and lipid accumulation in goat mammary epithelial cells by repressing PGC-1beta. Journal of Animal Science and Biotechnology. 2018;9: 48. pmid:29946461
- View Article
- PubMed/NCBI
- Google Scholar
41. Radisky DC. miR-200c at the nexus of epithelial-mesenchymal transition, resistance to apoptosis, and the breast cancer stem cell phenotype. Breast Cancer Research. 2011;13: 110. pmid:21682933
- View Article
- PubMed/NCBI
- Google Scholar
42. Hilmarsdottir B, Briem E, Bergthorsson JT, Magnusson MK, Gudjonsson T. Functional Role of the microRNA-200 Family in Breast Morphogenesis and Neoplasia. Genes. 2014;5: 804–820. pmid:25216122
- View Article
- PubMed/NCBI
- Google Scholar
43. Dessauge F, Lollivier V, Ponchon B, Bruckmaier R, Finot L, Wiart S, et al. Effects of nutrient restriction on mammary cell turnover and mammary gland remodeling in lactating dairy cows. Journal of Dairy Science. 2011;94: 4623–4635. pmid:21854935
- View Article
- PubMed/NCBI
- Google Scholar
44. Sengar GS, Deb R, Singh U, Raja TV, Kant R, Sajjanar B, et al. Differential expression of microRNAs associated with thermal stress in Frieswal (Bos taurus x Bos indicus) crossbred dairy cattle. Cell Stress Chaperones. 2018;23: 155–170. pmid:28776223
- View Article
- PubMed/NCBI
- Google Scholar
45. Zhao C, Tian F, Yu Y, Liu G, Zan L, Updike M, et al. miRNA-dysregulation associated with tenderness variation induced by acute stress in Angus cattle. J Anim Sci Biotechnol. 2012;3: 12. pmid:22958451
- View Article
- PubMed/NCBI
- Google Scholar
46. Zheng Y, Chen K, Zheng X, Li H, Wang G. Identification and bioinformatics analysis of microRNAs associated with stress and immune response in serum of heat-stressed and normal Holstein cows. Cell Stress and Chaperones. 2014;19: 973–981. pmid:24917036
- View Article
- PubMed/NCBI
- Google Scholar
47. Tili E, Michaille J-J, Cimino A, Costinean S, Dumitru CD, Adair B, et al. Modulation of miR-155 and miR-125b Levels following Lipopolysaccharide/TNF-α Stimulation and Their Possible Roles in Regulating the Response to Endotoxin Shock. J Immunol. 2007;179: 5082–5089. pmid:17911593
- View Article
- PubMed/NCBI
- Google Scholar
48. Abdelatty AM, Iwaniuk ME, Garcia M, Moyes KM, Teter BB, Delmonte P, et al. Effect of short-term feed restriction on temporal changes in milk components and mammary lipogenic gene expression in mid-lactation Holstein dairy cows. J Dairy Sci. 2017;100: 4000–4013. pmid:28237595
- View Article
- PubMed/NCBI
- Google Scholar
49. Bionaz M, Loor JJ. ACSL1, AGPAT6, FABP3, LPIN1, and SLC27A6 Are the Most Abundant Isoforms in Bovine Mammary Tissue and Their Expression Is Affected by Stage of Lactation. J Nutr. 2008;138: 1019–1024. pmid:18492828
- View Article
- PubMed/NCBI
- Google Scholar
50. Skibiel AL, Zachut M, do Amaral BC, Levin Y, Dahl GE. Liver proteomic analysis of postpartum Holstein cows exposed to heat stress or cooling conditions during the dry period. Journal of Dairy Science. 2018;101: 705–716. pmid:29128212
- View Article
- PubMed/NCBI
- Google Scholar
51. Akbar H, Bionaz M, Carlson DB, Rodriguez-Zas SL, Everts RE, Lewin HA, et al. Feed restriction, but not l-carnitine infusion, alters the liver transcriptome by inhibiting sterol synthesis and mitochondrial oxidative phosphorylation and increasing gluconeogenesis in mid-lactation dairy cows. Journal of dairy science. 2013;96(4):2201–13. pmid:23462174
- View Article
- PubMed/NCBI
- Google Scholar
52. Bionaz M, Loor JJ. Gene networks driving bovine milk fat synthesis during the lactation cycle. BMC Genomics. 2008;9: 366. pmid:18671863
- View Article
- PubMed/NCBI
- Google Scholar
53. Wigle JT, Oliver G. Prox1 Function Is Required for the Development of the Murine Lymphatic System. Cell. 1999;98: 769–778. pmid:10499794
- View Article
- PubMed/NCBI
- Google Scholar
54. Betterman KL, Paquet-Fifield S, Asselin-Labat M-L, Visvader JE, Butler LM, Stacker SA, et al. Remodeling of the Lymphatic Vasculature during Mouse Mammary Gland Morphogenesis Is Mediated via Epithelial-Derived Lymphangiogenic Stimuli. The American Journal of Pathology. 2012;181: 2225–2238. pmid:23063660
- View Article
- PubMed/NCBI
- Google Scholar
55. Sanchez M-P, Ramayo-Caldas Y, Wolf V, Laithier C, El Jabri M, Michenet A, et al. Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows. Genetics Selection Evolution. 2019;51: 34. pmid:31262251
- View Article
- PubMed/NCBI
- Google Scholar
56. Désert C, Duclos MJ, Blavy P, Lecerf F, Moreews F, Klopp C, et al. Transcriptome profiling of the feeding-to-fasting transition in chicken liver. BMC Genomics. 2008;9: 611. pmid:19091074
- View Article
- PubMed/NCBI
- Google Scholar
57. Cunningham HC, Cammack KM, Hales KE, Freetly HC, Lindholm-Perry AK. Differential transcript abundance in adipose tissue of mature beef cows during feed restriction and realimentation. PLOS ONE. 2018;13: e0194104. pmid:29534082
- View Article
- PubMed/NCBI
- Google Scholar
58. Miller AM, Gilchrist DS, Nijjar J, Araldi E, Ramirez CM, Lavery CA, et al. MiR-155 Has a Protective Role in the Development of Non-Alcoholic Hepatosteatosis in Mice. PLOS ONE. 2013;8: e72324. pmid:23991091
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Anderson SM, Rudolph MC, McManaman JL, Neville MC. Key stages in mammary gland development. Secretory activation in the mammary gland: it’s not just about milk protein synthesis! Breast Cancer Research. 2007;9: 204. pmid:17338830
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Ollier S, Robert-Granié C, Bernard L, Chilliard Y, Leroux C. Mammary Transcriptome Analysis of Food-Deprived Lactating Goats Highlights Genes Involved in Milk Secretion and Programmed Cell Death. J Nutr. 2007;137: 560–567. pmid:17311940
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. cell. 2004;116: 281–297. pmid:14744438
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Ambros V. The functions of animal microRNAs. Nature. 2004;431: 350–355. pmid:15372042
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Friedman RC, Farh KK, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009;19. pmid:19887576
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Ameres SL, Zamore PD. Diversifying microRNA sequence and function. Nature Reviews Molecular Cell Biology. 2013;14: 475–488. pmid:23800994
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Wang M, Moisá S, Khan MJ, Wang J, Bu D, Loor JJ. MicroRNA expression patterns in the bovine mammary gland are affected by stage of lactation. Journal of Dairy Science. 2012;95: 6529–6535. pmid:22959945
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Li Z, Lan X, Guo W, Sun J, Huang Y, Wang J, et al. Comparative Transcriptome Profiling of Dairy Goat MicroRNAs from Dry Period and Peak Lactation Mammary Gland Tissues. Qu L-H, editor. PLoS ONE. 2012;7: e52388. pmid:23300659
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Le Guillou S, Marthey S, Laloë D, Laubier J, Mobuchon L, Leroux C, et al. Characterisation and Comparison of Lactating Mouse and Bovine Mammary Gland miRNomes. Pfeffer S, editor. PLoS ONE. 2014;9: e91938. pmid:24658750
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref10] 10. Mobuchon L, Marthey S, Le Guillou S, Laloë D, Le Provost F, Leroux C. Food Deprivation Affects the miRNome in the Lactating Goat Mammary Gland. Schönbach C, editor. PLOS ONE. 2015;10: e0140111. pmid:26473604
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref11] 11. Li R, Beaudoin F, Ammah AA, Bissonnette N, Benchaar C, Zhao X, et al. Deep sequencing shows microRNA involvement in bovine mammary gland adaptation to diets supplemented with linseed oil or safflower oil. BMC Genomics. 2015;16: 884. pmid:26519053
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref12] 12. Mobuchon L, Guillou SL, Marthey S, Laubier J, Laloë D, Bes S, et al. Sunflower oil supplementation affects the expression of miR-20a-5p and miR-142-5p in the lactating bovine mammary gland. PLOS ONE. 2017;12: e0185511. pmid:29281677
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref13] 13. Lian S, Guo JR, Nan XM, Ma L, Loor JJ, Bu DP. MicroRNA Bta-miR-181a regulates the biosynthesis of bovine milk fat by targeting ACSL1. Journal of Dairy Science. 2016;99: 3916–3924. pmid:26971144
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref14] 14. Wicik Z, Gajewska M, Majewska A, Walkiewicz D, Osińska E, Motyl T. Characterization of microRNA profile in mammary tissue of dairy and beef breed heifers. Journal of Animal Breeding and Genetics. 2016;133: 31–42. pmid:26060050
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref15] 15. Billa PA, Faulconnier Y, Ye T, Chervet M, Le Provost F, Pires JAA, et al. Deep RNA-Seq reveals miRNome differences in mammary tissue of lactating Holstein and Montbéliarde cows. BMC Genomics. 2019;20: 621. pmid:31362707
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref16] 16. Le Guillou S, Leduc A, Laubier J, Barbey S, Rossignol M-N, Lefebvre R, et al. Characterization of Holstein and Normande whole milk miRNomes highlights breed specificities. Sci Rep. 2019;9: 1–13. pmid:30626917
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref17] 17. INRA. Alimentation des bovins, ovins et caprins: besoins des animaux, valeurs des aliments. Editions Quae Versailles, France; 2007.

[ref18] 18. Billa PA, Faulconnier Y, Larsen T, Leroux C, Pires J a. A. Milk metabolites as noninvasive indicators of nutritional status of mid-lactation Holstein and Montbéliarde cows. Journal of Dairy Science. 2020;0.
View Article
Google Scholar

[67] View Article

[68] Google Scholar

[ref19] 19. Bernard L, Richard C, Gelin V, Leroux C, Heyman Y. Milk fatty acid composition and mammary lipogenic genes expression in bovine cloned and control cattle. Livestock Science. 2015;176: 188–195.
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref20] 20. Chen C-J, Servant N, Toedling J, Sarazin A, Marchais A, Duvernois-Berthet E, et al. ncPRO-seq: a tool for annotation and profiling of ncRNAs in sRNA-seq data. Bioinformatics. 2012;28: 3147–3149. pmid:23044543
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref21] 21. Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40: 37–52. pmid:21911355
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref22] 22. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57: 289–300.
View Article
Google Scholar

[81] View Article

[82] Google Scholar

[ref23] 23. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14: 128. pmid:23586463
View Article
PubMed/NCBI
Google Scholar

[84] View Article

[85] PubMed/NCBI

[86] Google Scholar

[ref24] 24. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44: W90–97. pmid:27141961
View Article
PubMed/NCBI
Google Scholar

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref25] 25. Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms. PLOS ONE. 2011;6: e21800. pmid:21789182
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref26] 26. Gross J, van Dorland HA, Bruckmaier RM, Schwarz FJ. Performance and metabolic profile of dairy cows during a lactational and deliberately induced negative energy balance with subsequent realimentation. J Dairy Sci. 2011;94: 1820–1830. pmid:21426971
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref27] 27. Pires JAA, Stumpf LF, Soutullo ID, Pescara JB, Stocks SE, Grummer RR. Effects of abomasal infusion of nicotinic acid on responses to glucose and β-agonist challenges in underfed lactating cows. J Dairy Sci. 2016;99: 2297–2307. pmid:26774717
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref28] 28. Pomiès D, Martin B, Chilliard Y, Pradel P, Rémond B. Once-a-day milking of Holstein and Montbéliarde cows for 7 weeks in mid-lactation. Animal. 2007;1. pmid:22444203
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref29] 29. Ferlay A, Martin B, Lerch S, Gobert M, Pradel P, Chilliard Y. Effects of supplementation of maize silage diets with extruded linseed, vitamin E and plant extracts rich in polyphenols, and morning v. evening milking on milk fatty acid profiles in Holstein and Montbéliarde cows. Animal. 2010;4: 627–640. pmid:22444050
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref30] 30. Ji Z, Liu Z, Chao T, Hou L, Fan R, He R, et al. Screening of miRNA profiles and construction of regulation networks in early and late lactation of dairy goat mammary glands. Sci Rep. 2017;7: 1–13. pmid:28127051
View Article
PubMed/NCBI
Google Scholar

[112] View Article

[113] PubMed/NCBI

[114] Google Scholar

[ref31] 31. Govignon-Gion A, Fritz S, Larroque H, Brochard M, Chantry C, Lahalle F, et al. QTL detection for milk fatty acids in French dairy cattle. Proceedings, 10th World Congress of Genetics Applied to Livestock Production 2014; 10 World Congress of Genetics Applied to Livestock Production, Vancouver, CAN, 2014-08-17-2014-08-22, 1–3. 2014 [cited 18 Jan 2020]. Available: http://agris.fao.org/agris-search/search.do?recordID=LV2016006646

[ref32] 32. Williams A, Henao-Mejia J, Harman CCD, Flavell RA. miR-181 and Metabolic Regulation in the Immune System. Cold Spring Harb Symp Quant Biol. 2013;78: 223–230. pmid:24163395
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref33] 33. Gu Z, Eleswarapu S, Jiang H. Identification and characterization of microRNAs from the bovine adipose tissue and mammary gland. FEBS Letters. 2007;581: 981–988. pmid:17306260
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref34] 34. Fatima A, Lynn DJ, O’Boyle P, Seoighe C, Morris D. The miRNAome of the postpartum dairy cow liver in negative energy balance. BMC Genomics. 2014;15: 279. pmid:24725334
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref35] 35. Al-Husseini W, Chen Y, Gondro C, Herd RM, Gibson JP, Arthur PF. Characterization and Profiling of Liver microRNAs by RNA-sequencing in Cattle Divergently Selected for Residual Feed Intake. Asian-Australas J Anim Sci. 2016;29: 1371–1382. pmid:26954124
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref36] 36. Bell AW. Regulation of organic nutrient metabolism during transition from late pregnancy to early lactation. Journal of Animal Science. 1995;73: 2804. pmid:8582872
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref37] 37. Zhang L, Huang C, Guo Y, Gou X, Hinsdale M, Lloyd P, et al. MicroRNA-26b Modulates the NF-κB Pathway in Alveolar Macrophages by Regulating PTEN. The Journal of Immunology. 2015;195: 5404–5414. pmid:26503952
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref38] 38. Sordillo LM, Mavangira V. The nexus between nutrient metabolism, oxidative stress and inflammation in transition cows. Anim Prod Sci. 2014;54: 1204.
View Article
Google Scholar

[141] View Article

[142] Google Scholar

[ref39] 39. Du X, Yang Y, Xu C, Peng Z, Zhang M, Lei L, et al. Upregulation of miR-181a impairs hepatic glucose and lipid homeostasis. Oncotarget. 2017;8: 91362–91378. pmid:29207650
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref40] 40. Ma L, Qiu H, Chen Z, Li L, Zeng Y, Luo J, et al. miR-25 modulates triacylglycerol and lipid accumulation in goat mammary epithelial cells by repressing PGC-1beta. Journal of Animal Science and Biotechnology. 2018;9: 48. pmid:29946461
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref41] 41. Radisky DC. miR-200c at the nexus of epithelial-mesenchymal transition, resistance to apoptosis, and the breast cancer stem cell phenotype. Breast Cancer Research. 2011;13: 110. pmid:21682933
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref42] 42. Hilmarsdottir B, Briem E, Bergthorsson JT, Magnusson MK, Gudjonsson T. Functional Role of the microRNA-200 Family in Breast Morphogenesis and Neoplasia. Genes. 2014;5: 804–820. pmid:25216122
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

[ref43] 43. Dessauge F, Lollivier V, Ponchon B, Bruckmaier R, Finot L, Wiart S, et al. Effects of nutrient restriction on mammary cell turnover and mammary gland remodeling in lactating dairy cows. Journal of Dairy Science. 2011;94: 4623–4635. pmid:21854935
View Article
PubMed/NCBI
Google Scholar

[160] View Article

[161] PubMed/NCBI

[162] Google Scholar

[ref44] 44. Sengar GS, Deb R, Singh U, Raja TV, Kant R, Sajjanar B, et al. Differential expression of microRNAs associated with thermal stress in Frieswal (Bos taurus x Bos indicus) crossbred dairy cattle. Cell Stress Chaperones. 2018;23: 155–170. pmid:28776223
View Article
PubMed/NCBI
Google Scholar

[164] View Article

[165] PubMed/NCBI

[166] Google Scholar

[ref45] 45. Zhao C, Tian F, Yu Y, Liu G, Zan L, Updike M, et al. miRNA-dysregulation associated with tenderness variation induced by acute stress in Angus cattle. J Anim Sci Biotechnol. 2012;3: 12. pmid:22958451
View Article
PubMed/NCBI
Google Scholar

[168] View Article

[169] PubMed/NCBI

[170] Google Scholar

[ref46] 46. Zheng Y, Chen K, Zheng X, Li H, Wang G. Identification and bioinformatics analysis of microRNAs associated with stress and immune response in serum of heat-stressed and normal Holstein cows. Cell Stress and Chaperones. 2014;19: 973–981. pmid:24917036
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref47] 47. Tili E, Michaille J-J, Cimino A, Costinean S, Dumitru CD, Adair B, et al. Modulation of miR-155 and miR-125b Levels following Lipopolysaccharide/TNF-α Stimulation and Their Possible Roles in Regulating the Response to Endotoxin Shock. J Immunol. 2007;179: 5082–5089. pmid:17911593
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref48] 48. Abdelatty AM, Iwaniuk ME, Garcia M, Moyes KM, Teter BB, Delmonte P, et al. Effect of short-term feed restriction on temporal changes in milk components and mammary lipogenic gene expression in mid-lactation Holstein dairy cows. J Dairy Sci. 2017;100: 4000–4013. pmid:28237595
View Article
PubMed/NCBI
Google Scholar

[180] View Article

[181] PubMed/NCBI

[182] Google Scholar

[ref49] 49. Bionaz M, Loor JJ. ACSL1, AGPAT6, FABP3, LPIN1, and SLC27A6 Are the Most Abundant Isoforms in Bovine Mammary Tissue and Their Expression Is Affected by Stage of Lactation. J Nutr. 2008;138: 1019–1024. pmid:18492828
View Article
PubMed/NCBI
Google Scholar

[184] View Article

[185] PubMed/NCBI

[186] Google Scholar

[ref50] 50. Skibiel AL, Zachut M, do Amaral BC, Levin Y, Dahl GE. Liver proteomic analysis of postpartum Holstein cows exposed to heat stress or cooling conditions during the dry period. Journal of Dairy Science. 2018;101: 705–716. pmid:29128212
View Article
PubMed/NCBI
Google Scholar

[188] View Article

[189] PubMed/NCBI

[190] Google Scholar

[ref51] 51. Akbar H, Bionaz M, Carlson DB, Rodriguez-Zas SL, Everts RE, Lewin HA, et al. Feed restriction, but not l-carnitine infusion, alters the liver transcriptome by inhibiting sterol synthesis and mitochondrial oxidative phosphorylation and increasing gluconeogenesis in mid-lactation dairy cows. Journal of dairy science. 2013;96(4):2201–13. pmid:23462174
View Article
PubMed/NCBI
Google Scholar

[192] View Article

[193] PubMed/NCBI

[194] Google Scholar

[ref52] 52. Bionaz M, Loor JJ. Gene networks driving bovine milk fat synthesis during the lactation cycle. BMC Genomics. 2008;9: 366. pmid:18671863
View Article
PubMed/NCBI
Google Scholar

[196] View Article

[197] PubMed/NCBI

[198] Google Scholar

[ref53] 53. Wigle JT, Oliver G. Prox1 Function Is Required for the Development of the Murine Lymphatic System. Cell. 1999;98: 769–778. pmid:10499794
View Article
PubMed/NCBI
Google Scholar

[200] View Article

[201] PubMed/NCBI

[202] Google Scholar

[ref54] 54. Betterman KL, Paquet-Fifield S, Asselin-Labat M-L, Visvader JE, Butler LM, Stacker SA, et al. Remodeling of the Lymphatic Vasculature during Mouse Mammary Gland Morphogenesis Is Mediated via Epithelial-Derived Lymphangiogenic Stimuli. The American Journal of Pathology. 2012;181: 2225–2238. pmid:23063660
View Article
PubMed/NCBI
Google Scholar

[204] View Article

[205] PubMed/NCBI

[206] Google Scholar

[ref55] 55. Sanchez M-P, Ramayo-Caldas Y, Wolf V, Laithier C, El Jabri M, Michenet A, et al. Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows. Genetics Selection Evolution. 2019;51: 34. pmid:31262251
View Article
PubMed/NCBI
Google Scholar

[208] View Article

[209] PubMed/NCBI

[210] Google Scholar

[ref56] 56. Désert C, Duclos MJ, Blavy P, Lecerf F, Moreews F, Klopp C, et al. Transcriptome profiling of the feeding-to-fasting transition in chicken liver. BMC Genomics. 2008;9: 611. pmid:19091074
View Article
PubMed/NCBI
Google Scholar

[212] View Article

[213] PubMed/NCBI

[214] Google Scholar

[ref57] 57. Cunningham HC, Cammack KM, Hales KE, Freetly HC, Lindholm-Perry AK. Differential transcript abundance in adipose tissue of mature beef cows during feed restriction and realimentation. PLOS ONE. 2018;13: e0194104. pmid:29534082
View Article
PubMed/NCBI
Google Scholar

[216] View Article

[217] PubMed/NCBI

[218] Google Scholar

[ref58] 58. Miller AM, Gilchrist DS, Nijjar J, Araldi E, Ramirez CM, Lavery CA, et al. MiR-155 Has a Protective Role in the Development of Non-Alcoholic Hepatosteatosis in Mice. PLOS ONE. 2013;8: e72324. pmid:23991091
View Article
PubMed/NCBI
Google Scholar

[220] View Article

[221] PubMed/NCBI

[222] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Ethics statement

Animals and sampling

RNA preparation

RNA-seq analysis

Microarray analyses

Bioinformatic analyses

Results and discussion

Animal responses and milk analyses

Mammary gland miRNome responses

Summary of miRNA sequencing data.

Differentially expressed miRNAs between the control and restriction periods.

Differentially expressed miRNAs located in milk QTL.

Analyses of the 8 highly expressed and differential miRNAs.

Differentially expressed mRNAs in Holstein cows

Analysis of mRNAs common between the DEG list and mRNAs targeted by the 8 major DEMs

Conclusions

Supporting information

S1 Fig. Homemade bioinformatics script in Python language for screening QTL.

S1 Table. Effects of feed restriction on milk fatty acid concentrations (g/100 g of FA) in mid-lactation cows.

S2 Table. TOP 20 of the biological processes affected by restriction in mammary gland of mid-lactation cows.

S3 Table. TOP 20 of the pathways affected by restriction in mammary gland of mid-lactation cows.

Acknowledgments

References