Sokrates Stein
email: sokrates.stein@uzh.ch.

Julien Weber

Stefanie Nusser-Stein

Jürgen Pahla

Hui E Zhang

Shafeeq A Mohammed

Sara Oppi

Daniel S Gaul

Francesco Paneni

Anne Tailleux

Bart Staels

Ferdinand Von Meyenn

Frank Ruschitzka

Mark D Gorrell

Thomas F Lüscher

Christian M Matter
email: christian.matter@usz.ch.

Deletion of fibroblast activation protein provides atheroprotection

Keywords:

Aims. Fibroblast activation protein (FAP) is upregulated at sites of tissue remodelling including chronic arthritis, solid tumours, and fibrotic hearts. It has also been associated with human coronary atherosclerotic plaques. Yet, the causal role of FAP in atherosclerosis remains unknown. To investigate the cause-effect relationship of endogenous FAP in atherogenesis, we assessed the effects of constitutive Fap deletion on plaque formation in atherosclerosis-prone apolipoprotein E (Apoe) or lowdensity lipoprotein receptor (Ldlr) knockout mice.

Methods and results.

Using en face analyses of thoraco-abdominal aortae and aortic sinus crosssections, we demonstrate that Fap deficiency decreased plaque formation in two atherosclerotic mouse models (-46% in Apoe and -34 % in Ldlr knockout mice). As a surrogate of plaque vulnerability fibrous cap thickness was used; it was increased in Fap-deficient mice, whereas Sirius red staining demonstrated that total collagen content remained unchanged. Using polarized light, atherosclerotic lesions from Fap-deficient mice displayed increased FAP targets in terms of enhanced collagen birefringence in plaques and increased pre-COL3A1 expression in aortic lysates. Analyses of the Stockholm Atherosclerosis Gene Expression (STAGE) data revealed that FAP expression was increased in human atherosclerotic compared to non-atherosclerotic arteries.

Conclusions.

Our data provide causal evidence that constitutive Fap deletion decreases progression of experimental atherosclerosis and increases features of plaque stability with decreased collagen breakdown. Thus, inhibition of FAP expression or activity may not only represent a promising therapeutic target in atherosclerosis but appears safe at the experimental level for FAP-targeted cancer therapies.

Translational Perspective

Fibroblast activation protein (FAP) is upregulated at sites of chronic tissue remodelling including rheumatoid arthritis and solid tumours. Indeed, depletion of FAP-positive cells inhibits tumour growth by increasing antitumour immunity. FAP has also been correlated with human coronary plaques, whereby its causal role remains unknown. Our data provide causal evidence that constitutive Fap deletion decreases progression of experimental atherosclerosis and increases features of plaque stability. Thus, inhibition of FAP expression or activity may not only represent a novel therapeutic target for atherosclerosis but appears safeat the experimental levelfor FAP-targeted cancer therapies.

Introduction

Vulnerable plaques pose a central problem in human atherosclerosis. Their rupture may cause complete thrombotic arterial lumen obstruction, which may lead to myocardial infarction or stroke. A sufficiently thick fibrous cap ensures plaque stability and a barrier between blood and the prothrombotic necrotic core within the plaque 1,[START_REF] Stary | A definition of initial, fatty streak, and intermediate lesions of atherosclerosis. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association[END_REF] . Collagenases (e.g. matrix metalloproteinases (MMPs) and cathepsins) degrade collagen within the fibrous cap, thereby thinning the cap and making them more prone to rupture [START_REF] Galis | Increased Expression of Matrix Metalloproteinases and Matrix Degrading Activity in Vulnerable Regions of Human Atherosclerotic Plaques[END_REF] . Therefore, extra-cellular matrix-degrading collagenases have emerged as promising therapeutic targets against rupture of vulnerable plaque [START_REF] Libby | Mechanisms of acute coronary syndromes and their implications for therapy[END_REF] . Interestingly, many collagenases involved in tissue remodelling such as rheumatoid arthritis or tumour expansion play similar roles in atherogenesis 3, 5 .

Fibroblast activation protein (FAP) can be membrane-bound or soluble, and is a constitutively active serine protease that exhibits dipeptidyl peptidase IV activity and prolyl endopeptidase activity with a specificity for collagens [START_REF] Park | Fibroblast activation protein, a dual specificity serine protease expressed in reactive human tumor stromal fibroblasts[END_REF][START_REF] Christiansen | Effect of fibroblast activation protein and alpha2-antiplasmin cleaving enzyme on collagen types I, III, and IV[END_REF][START_REF] Zhang | Identification of Novel Natural Substrates of Fibroblast Activation Protein-alpha by Differential Degradomics and Proteomics[END_REF] . Matrix turnover is a central event in the pathogenesis of chronic inflammatory diseases such as rheumatoid arthritis, tumour formation and atherosclerosis. Along these lines, FAP is expressed by activated fibroblasts in epithelial tumour stroma, arthritis and wound healing, but remains virtually undetectable in healthy tissues [START_REF] Park | Fibroblast activation protein, a dual specificity serine protease expressed in reactive human tumor stromal fibroblasts[END_REF][START_REF] Bauer | Fibroblast activation protein is expressed by rheumatoid myofibroblast-like synoviocytes[END_REF][START_REF] Edosada | Selective inhibition of fibroblast activation protein protease based on dipeptide substrate specificity[END_REF][START_REF] Keane | Quantitation of fibroblast activation protein (FAP)-specific protease activity in mouse, baboon and human fluids and organs[END_REF] . In rheumatoid arthritis, FAP is expressed in SMA-positive myofibroblasts and associated with matrix metalloproteinase expression [START_REF] Bauer | Fibroblast activation protein is expressed by rheumatoid myofibroblast-like synoviocytes[END_REF] . Moreover, a recent study showed that FAP was increased in cardiac fibrosis, which in turn promotes the development of various cardiac diseases and heart failure [START_REF] Aghajanian | Targeting cardiac fibrosis with engineered T cells[END_REF] . Interestingly, engineered chimeric (FAP-)antigen receptor (CAR) T cells could be used to target FAP in cardiac fibrosis; this strategy restored cardiac function in a hypertensive heart failure model [START_REF] Aghajanian | Targeting cardiac fibrosis with engineered T cells[END_REF] .

Previously, we have reported that FAP expression is enhanced in human fibroatheromata versus plaque-free aortae, and its expression increases upon plaque progression [START_REF] Brokopp | Fibroblast activation protein is induced by inflammation and degrades type I collagen in thin-cap fibroatheromata[END_REF] . Although expression of FAP protein correlated with the macrophage burden in human aortic plaques, colocalization images revealed that FAP was mainly expressed by smooth muscle cells [START_REF] Brokopp | Fibroblast activation protein is induced by inflammation and degrades type I collagen in thin-cap fibroatheromata[END_REF] .

Many collagenases involved in chronic tissue remodelling processes such as rheumatoid arthritis and tumour progression play similar roles in atherogenesis [START_REF] Galis | Increased Expression of Matrix Metalloproteinases and Matrix Degrading Activity in Vulnerable Regions of Human Atherosclerotic Plaques[END_REF][START_REF] Libby | Inflammation: a common contributor to cancer, aging, and cardiovascular diseases-expanding the concept of cardio-oncology[END_REF] . Yet, the causal role of FAP in atherogenesis remains unknown. Therefore, we assessed the cause-effect relationship between the loss-of-function of FAP and the phenotype of atherosclerosis by crossbreeding Fap knockout mice [START_REF] Tan | Fibroblast activation protein is dispensable in the anti-influenza immune response in mice[END_REF] with 1 atherosclerosis-prone apolipoprotein E (Apoe) or low-density lipoprotein receptor (Ldlr) knockout mice.

Methods

Mice.

Mice with a germline Fap deletion were obtained from the Centenary Institute, Sydney, AU 14, 15 and licensed from Boehringer Ingelheim Pharma GmbH & Co KG. Congenic C57BL6/J Fap -/-and Fap +/+ mice were kept in a temperature-controlled facility with a 12-h light/dark cycle and free access to normal chow and water. To assess atherosclerosis development, Fap -/-mice were crossbred to either Apoe -/-or Ldlr -/-mice. Of those, male mice were fed a high-cholesterol diet (1.25% total cholesterol, Research Diets) for 12 weeks starting at the age of 8 weeks. After being anesthetised with isoflurane using a gas flow vaporizer (4.5 to 5% isoflurane) for 5 minutes, mice were euthanised by cardiac puncture and exsanguination.

Study approval.

All animal experiments were approved by the Veterinary Office of the Canton Zurich (animal licenses: 189/2011 and 5263) and all procedures were in accordance to the guidelines from Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes.

FAP activity assay.

The FAP specific enzyme assay was adapted from published assays [START_REF] Keane | Quantitation of fibroblast activation protein (FAP)-specific protease activity in mouse, baboon and human fluids and organs[END_REF][START_REF] Lee | Antiplasmin-cleaving enzyme is a soluble form of fibroblast activation protein[END_REF] , using 100 uM Z-Gly-Pro-aminomethylcoumarin (AMC) as substrate, and the prolyl endopeptidase selective inhibitor S-17092 (Sigma-Aldrich #SML0181) at 0.1 uM to ensure FAP specificity.

FAP proteomics.

Generation of Fap-deficient mouse embryonic fibroblasts and cultivation of the cells was described previously [START_REF] Zhang | Identification of Novel Natural Substrates of Fibroblast Activation Protein-alpha by Differential Degradomics and Proteomics[END_REF] . Terminal amine isotopic labelling of substrates (TAILS) and tests of data significance were performed previously [START_REF] Zhang | Identification of Novel Natural Substrates of Fibroblast Activation Protein-alpha by Differential Degradomics and Proteomics[END_REF] .The statistically significant cleavages in collagens were arranged by location in primary structure and the cleavage sites that follow each Gly-Pro, the motif typical of FAP mediated cleavage, were identified.

2.5

Western blot analysis. 30 mg aortic lysates were pulverized using a steel tissue grinder, and then extracted in lysis buffer (50 mM Tris HCl pH 8.0, 125 mM NaCl, 0.25% NP-40, 0.5 mM phenylmethylsulfonyl fluoride, and protease/phosphatase inhibitor cocktail) for 10 minutes. The supernatant was transferred, and the protein content measured using Bradford reagent. Before running SDS-PAGE, protein lysates were boiled for 5 min at 95°C. Following proteins were used for Western blotting: anti-COL3A1 (22734-1-AP, Proteintech), anti-COL2A1 (sc-52658, Santa Cruz), anti-COL1A1 (PA1-26204, Invitrogen), anti- tubulin (sc-8035, Santa Cruz), anti-actin (sc-47778, Santa Cruz), and anti-GAPDH (TA802519, Origene).

Plasma lipid analysis.

Total cholesterol (TC) and triglyceride (TG) plasma concentrations were measured using an enzymatic colorimetric assay (Biomerieux). To obtain lipoprotein fractions for fastprotein liquid chromatography (FPLC) analysis, each individual plasma sample was loaded on a filtration chromatography column onto a Superose 6 10/300 GL column (GE Healthcare), which allows separation of the three major lipoprotein classes (VLDLs, LDLs, HDLs and free glycerol) according to their size. Cholesterol and triglyceride concentrations were continuously measured in the effluent using an enzymatic colorimetric assay (Biomerieux) and results are expressed as O.D.

Immunohistochemistry.

The heart-aortic root samples were stored in optimal cutting temperature (OCT) compound at -80°C, and then cut with a cryostat set at -20°C. The samples were then cut from the left ventricular basis towards the aortic valves. Once reaching the cusps, the specimen was reorientated to achieve stringent cross-sectional cutting. 5 m thick serial cross sections were then collected at approximately 100 m intervals on microscopic glass slides until the aortic cusps were no longer visible. These 5 μm-thick serial cryosections were stained with Oil-red O (ORO), rat anti-CD68 (MCA 1957GA, Serotec), rat anti-CD3 (MCA 500GA, Serotec), rat anti-VCAM-1 (MCA 1129, Serotec), and rabbit anti-SMA (D4K9N, Cell Signaling). Means were taken from at least 6 different mice evaluating at least 6 serial cryosections/tissue from each mouse. Thoraco-abdominal aortae were fixed with 4% paraformaldehyde and plaques stained with Oil-red O. Collagen content, fibrous cap thickness, and necrotic core size were analysed by Elastica van Gieson staining. Sirius Red stainings were performed to visualize collagen content under normal light.

2.8 Second harmonics. 5-μm-thick serial cryosections from the aortic sinus were used to perform second-harmonic generation of collagen fibers using two-photon microscopy. Second-harmonic signal was generated by tuning the Mai Tai Ti:sapphire laser to 880 nm and collecting the resultant tissue emission at 440 nm.

2.9 Gene expression analysis. Aortic RNAs were extracted using TRIZOL (Thermo Fisher, no. 15596026). For RT-qPCR, cDNA was generated using the All-in-One cDNA Synthesis kit (Biotool, no. B24403), and analysed by qPCR using a SYBR Green qPCR Master Mix (Biotool, no. B21202) and the primers listed in the online supplement (Supplemental Table S4). Expression data were normalized to housekeeping genes, i.e. Actb, B2m and/or Ppib mRNA levels.

Transcriptomic data analyses.

The raw and/or normalized transcriptomic data from previous studies are publicly available on Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo) under the accession number GSE40231 (STAGE [START_REF] Hagg | Multi-organ expression profiling uncovers a gene module in coronary artery disease involving transendothelial migration of leukocytes and LIM domain binding 2: the Stockholm Atherosclerosis Gene Expression (STAGE) study[END_REF] ) and analysed as described previously [START_REF] Oppi | Macrophage NCOR1 protects from atherosclerosis by repressing a pro-atherogenic PPARgamma signature[END_REF] . The tabula muris data can be accessed and analysed online (https://tabula-muris.ds.czbiohub.org). The single cell-RNA seq data was obtained from Prof. Qinbo Xu from the King's College London British Heart Foundation Centre (United Kingdom). Differences of p < 0.05 were considered statistically significant. Statistical analyses were performed using GraphPad Prism 6.

Results

Deletion of Fap protects against atherosclerosis

To address the causal role of endogenous FAP in atherogenesis, we crossbred Fap-deficient mice [START_REF] Niedermeyer | Targeted disruption of mouse fibroblast activation protein[END_REF] with two different atherosclerosis-prone mouse models, i.e. Ldlr -/-and Apoe -/-mice 20 (Supplemental Figure 1). Deletion of FAP was assessed by genotyping and confirmed by measuring its enzymatic activity: FAP activity was abolished in both mouse models with constitutive Fap deletion (Figure 1A,B). We next placed 8-week old male mice on a high-cholesterol diet for 12 weeks and assessed atherosclerotic plaque development. Deletion of Fap diminished formation of atherosclerotic lesions by approximately one third in Ldlr -/-Fap -/-and by about half in Apoe -/-Fap -/-mice (Figure 1C,D). While Apoe -/-Fap -/-were slightly heavier than control Apoe -/-Fap +/+ mice, no difference in body weight was noted in the Ldlr -/-background (Supplemental Figure 2).

We next examined whether plasma levels of total cholesterol and triglycerides at the time of harvesting correlated with the atherosclerosis phenotype. Total cholesterol content did not differ in any of the two atherosclerotic mouse models, while plasma triglyceride levels were increased in Apoe -/-Fap -/-but not in Ldlr -/-Fap -/-mice (Supplemental Figure 3). Hematological analyses did not reveal differences in the number of leukocytes subpopulations, erythrocytes or platelets between Ldlr -/-Fap -/-and Ldlr -/-Fap +/+ mice (Supplemental Table 1). On the other side, Apoe -/-Fap -/-had a reduced amount of blood neutrophils and platelets, and an increased number of monocytes compared to Apoe -/-Fap +/+ controls. Apoe knockout mice on a high-cholesterol diet display a stronger proinflammatory and atherogenic phenotype compared with Ldlr -/-mice 20 , which could be related to the differences observed in leukocyte subpopulations (Supplemental Table 1).

Fap-deletion increases protective fibrous cap thickness

To better characterize the atherosclerotic lesions, we carried out several immunohistochemical stainings on cross-sections of the aortic sinus of Apoe -/-Fap -/-and Apoe -/-Fap +/+ mice. Along with the reduced number of thoraco-abdominal lesions, Apoe -/-Fap -/-mice displayed reduced content of neutral lipids in the aortic sinus compared to Apoe -/-Fap +/+ mice (Figure 2A and Supplemental Figure 4). Interestingly, we did not observe any difference in the amount of the vascular cell adhesion molecule 1 (VCAM-1), macrophages (CD68 + cells), or T cells (CD3 + cells) between the two genotypes (Figure 2B -D). These findings suggest that the atherosclerotic phenotype is not secondary to a differential accumulation of immune cells within the lesions.

Since FAP is an extra-cellular matrix-degrading collagenase, we performed Elastica van Gieson stainings to measure the content of collagen and to assess advanced plaque parameters in aortic sinus cross-sections. Surprisingly, there was no significant difference in the content of collagen in Apoe -/-Fap - /-compared to Apoe -/-Fap +/+ mice (Figure 3A). However, atherosclerotic lesions from Apoe -/-Fap -/-mice displayed larger fibrous caps, whereas the size of the necrotic cores was comparable to Apoe -/-Fap +/+ mice (Figure 3B-D), suggesting that Apoe -/-Fap -/-mice do not only develop less atherosclerosis, but their lesions are also less prone to rupture. -smooth mucle actin (-SMA) staining showed a trend for increased vascular smooth mucle cell content within the atherosclerotic lesions, suggesting that vascular smooth cells migrated into the plaques to reinforce the fibrous cap (Supplemental Figure 5).

Altered collagen organization in aortic sinus lesions from Fap-deficient mice

We next performed Sirius red stainings as a alternative method to quantify the collagen content. It confirmed that there is no difference in collagen content between the genotypes (Figure 3E).

Previously, Santos et al. showed that tumours from Fap-deficient mice (with a LacZ knockin, Fap lacZ/lacZ ) display increased collagen birefringence under polarised light in comparison to wildtype mice [START_REF] Santos | Targeting fibroblast activation protein inhibits tumor stromagenesis and growth in mice[END_REF] , suggesting a reduced organization of the collagen fibers. To investigate a possible change in collagen organization, we performed second-harmonic generation (SHG) of collagen on aortic sinus lesions using two-photon microscopy laser microscopy [START_REF] Hutcheson | Genesis and growth of extracellular-vesicle-derived microcalcification in atherosclerotic plaques[END_REF][START_REF] Cox | 3-dimensional imaging of collagen using second harmonic generation[END_REF] . These analyses revealed that lesions of Apoe -/- Fap -/-mice displayed increased SHG signals compared to Apoe -/-Fap +/+ mice (Figure 3F), confirming that deletion of FAP indeed augments the deposition of fibrillar collagens in these lesions.

Proteomic analysis demonstrates that FAP cleaves several collagens

To investigate the underlying mechanisms, we first analysed the expression of collagens, extracellular matrix proteases and fibrosis markers in the aortae. Despite a trend for increases fibrosis markers, we observed no significant difference of the transcripts at mRNA level (Supplemental Figure 6). In order to understand how FAP induces the observed changes in collagen structure, we drilled down into our targeted proteomic analyses of cell culture supernatants from Fap -/-mouse embryonic fibroblasts to identify potential collagen targets of FAP. We had found by stable isotope labelling with amino acids in cell culture (SILAC) that the quantities of collagens 3, 5 and 6 differed in the presence of enzymatically active FAP compared to inactive FAP [START_REF] Zhang | Identification of Novel Natural Substrates of Fibroblast Activation Protein-alpha by Differential Degradomics and Proteomics[END_REF] . In addition, terminal amine isotopic labelling of substrates (TAILS) showed that the major collagen species COL1A1, COL1A2, COL5A2 and COL3A1 are proteolytic targets of FAP in murine fibroblast cultures. The multiple cleavage sites in these precollagens are probably generated co-operatively by FAP with other collagenases [START_REF] Zhang | Identification of Novel Natural Substrates of Fibroblast Activation Protein-alpha by Differential Degradomics and Proteomics[END_REF] . However, we found that many of these cleavage sites followed Gly-Pro, which is prototypical of FAP-induced hydrolysis, and that most of the greatest increases in collagen fragmentation were coincident with cleavage after Gly-Pro (Figure 4A-D).

Reduced cleavage of pre-COL3A1 in Fap knockout mice

To validate the SILAC-TAILS data and verify if collagen degradation could also be affected in the Fapdeficient mice, we analysed the protein expression of the three main collagens in aortic lysates of Apoe -/-Fap -/-and Apoe -/-Fap +/+ mice. Interestingly, we observed an increase of the precursor of COL3A1 in Apoe -/-Fap -/-compared to Apoe -/-Fap +/+ aortae (Figure 4E, F, and Supplemental Figure 7A), suggesting that the precursor cannot be properly cleaved in FAP-deficient mice. Conversely, COL1A1 and COL2A1 products were not altered between the two genotypes (Supplemental Figure 7C-F). Noteworthy, there is no known molecular basis for this preference of FAP for COL3 over other collagen subtypes.

Pathway analyses of FAP-expressing cells supports its role in collagen remodelling

Previously studies showed that FAP is mainly expressed in fibroblast and vascular smooth muscle cells.

Analysis of the transcriptome of targeted aortic fluorescent activated cell sorting (FACS)-isolated aortic cell available at Tabula muris [START_REF] Muris | Logistical c, Organ c, processing, Library p, sequencing, Computational data a, Cell type a, Writing g, Supplemental text writing g, Principal i. Singlecell transcriptomics of 20 mouse organs creates a Tabula Muris[END_REF] showed and confirmed that FAP is mainly expressed by fibroblasts (Supplemental Figure 8). Since these were aortae from non-atherosclerotic mice, we further analysed an untargeted single cell RNA-sequencing dataset from aortae of Apoe knockout mice [START_REF] Gu | Adventitial Cell Atlas of wt (Wild Type) and ApoE (Apolipoprotein E)-Deficient Mice Defined by Single-Cell RNA Sequencing[END_REF] . We first selected all cells displaying FAP sequencing counts (>2), and then filtered the top 20 highest expressed transcripts of each of these cells (Supplemental Table S2). Interestingly, pathway analysis shows enrichment of extracellular remodelling, such as collagen and extracellular fibril organisation, extracellular matrix and exosome, and collagen binding (Supplemental Table S3).

To evaluate whether the expression of FAP could be altered in human atherosclerotic plaques compared to healthy arteries, we compared the expression of FAP in atherosclerotic aortic roots to nonatherosclerotic mammary arteries from the Stockholm Atherosclerosis Gene Expression (STAGE) [START_REF] Hagg | Multi-organ expression profiling uncovers a gene module in coronary artery disease involving transendothelial migration of leukocytes and LIM domain binding 2: the Stockholm Atherosclerosis Gene Expression (STAGE) study[END_REF] study. Atherosclerotic aortae revealed an increased expression of FAP as compared to nonatherosclerotic mammary arteries (Figure 4G). Together, these data suggest that FAP expression is increased in human atherosclerotic plaques and could be a promising target for pharmacologic inhibition.

Discussion

In a previous study, we demonstrated that FAP expression is increased in human fibroatheromata versus plaque-free aortae, and that its expression positively correlates with the stages of plaque progression [START_REF] Brokopp | Fibroblast activation protein is induced by inflammation and degrades type I collagen in thin-cap fibroatheromata[END_REF] . Moreover, soluble FAP cleaves 2-antiplasmin, which enhances its activity and incorporation into fibrin clots, thus promoting coagulation [START_REF] De Willige | The level of circulating fibroblast activation protein correlates with incorporation of alpha-2-antiplasmin into the fibrin clot[END_REF] . Surprisingly, the levels of circulating FAP are reduced in patients with coronary artery disease or stroke [START_REF] De Willige | Plasma levels of soluble fibroblast activation protein in arterial thrombosis: determinants and cleavage of its substrate alpha-2-antiplasmin[END_REF][START_REF] Tillmanns | Circulating concentrations of fibroblast activation protein alpha in apparently healthy individuals and patients with acute coronary syndrome as assessed by sandwich ELISA[END_REF][START_REF] Tillmanns | Changes in concentrations of circulating fibroblast activation protein alpha are associated with myocardial damage in patients with acute ST-elevation MI[END_REF][START_REF] Baerts | Acute Ischemic Stroke Severity, Progression, and Outcome Relate to Changes in Dipeptidyl Peptidase IV and Fibroblast Activation Protein Activity[END_REF] , and suggest that tissue resident and circulating FAP are independent markers of cardiovascular diseases [START_REF] Lay | Fibroblast activation protein in liver fibrosis[END_REF] .

The current study provides the following novel findings (Figure 5):

1) Constitutive genetic Fap loss-of-function in atherosclerosis-prone Apoe or Ldlr-deficient mice decreases plaque formation that is independent of plasma lipid levels. Moreover, the content of plaque VCAM-1, macrophages, and T cells was not different between the groups.

2) Fap deletion increases features of plaque stability with increased fibrous cap thickness. Analyses of aortic roots demonstrated that atherosclerotic lesions from Apoe -/-Fap -/-mice harbour thicker fibrous caps.

3) Plaque matrix analyses display increased SHG signals compared to Apoe -/-Fap +/+ mice. The enhanced SHG signal suggests that the deletion of Fap increases deposition of fibrillar collagens in the lesions.

Using a targeted proteomic analyses of cell culture supernatants from Fap -/-mouse embryonic fibroblasts that express enzymatically active FAP or inactive FAP, we showed that FAP cleaves major collagens species at several sites, including COL3A1. When analysing COL3A1 protein expression in aortic lysates, we detected reduced cleavage of pre-COL3A1 in Apoe -/-Fap -/-compared to Apoe -/-Fap +/+ mice. Notably, in a recent study with acromegaly patients, surgical or medical treatment of the pituitary somatotroph tumour markedly reduced the levels of FAP compared to untreated subjects. These effects were closely associated with a reduction in biomarkers of collagen turnover [START_REF] Mc | Fibroblast activation protein is a GH target: A prospective study of patients with acromegaly before and after treatment[END_REF] .

The exact mechanisms by which impaired COL3A1 expression and/or processing affect collagen fibril organization is not yet well known. In the first Col3a1 knockout study, Rudolph Jaenisch's group demonstrated that collagen SHG imaging of fibroblast-derived matrices showed that Col3-deficient fibroblasts produced a more aligned and fibrillar matrix than wild-type cells, and that COL3A1 is crucial for collagen I fibrillogenesis [START_REF] Liu | Type III collagen is crucial for collagen I fibrillogenesis and for normal cardiovascular development[END_REF] . Therefore, changes in COL3A1 processing have an impact on the formation of fibrillar collagens.

Moreover, the vascular (type IV) form of the Ehlers-Danlos Syndrome, which is caused by mutations in the COL3A1 gene, leads to an impaired fibrillar collagen disposition [START_REF] Liu | Type III collagen is crucial for collagen I fibrillogenesis and for normal cardiovascular development[END_REF][START_REF] Hondt | Type III collagen affects dermal and vascular collagen fibrillogenesis and tissue integrity in a mutant Col3a1 transgenic mouse model[END_REF][START_REF] Mao | The Ehlers-Danlos syndrome: on beyond collagens[END_REF][START_REF] Schwarze | Haploinsufficiency for one COL3A1 allele of type III procollagen results in a phenotype similar to the vascular form of Ehlers-Danlos syndrome, Ehlers-Danlos syndrome type IV[END_REF][START_REF] Smith | Haploinsufficiency of the murine Col3a1 locus causes aortic dissection: a novel model of the vascular type of Ehlers-Danlos syndrome[END_REF] . Both exon-skipping or missense mutations in the COL3A1 loci can disrupt the collagen triple helix. These malformed helixes are then degraded or accumulate in intracellular compartments, and hence not secreted into the extracellular matrix. As a consequence, the reduction in COL3A1 caused by the mutations alters the size and structure of collagen fibrils [START_REF] Mao | The Ehlers-Danlos syndrome: on beyond collagens[END_REF] . Nevertheless, the precise mechanisms by which mutant COL3A1 causes dermal and vascular fragility remain largely unexplored.

Taken together, our current mouse and published human data [START_REF] Brokopp | Fibroblast activation protein is induced by inflammation and degrades type I collagen in thin-cap fibroatheromata[END_REF] demonstrate that endogenous prooncogenic FAP promotes atherosclerosis development. Interestingly, depletion of FAP-positive cells inhibits tumour growth by increasing antitumour immunity [START_REF] Santos | Targeting fibroblast activation protein inhibits tumor stromagenesis and growth in mice[END_REF] , and small antagonists and blocking antibodies for FAP are already being applied in oncology [START_REF] Busek | Targeting fibroblast activation protein in cancer -Prospects and caveats[END_REF] . Along these lines, it will not only be exciting to further test the impact of FAP inhibition on atherosclerosis in the experimental and clinical setting, FAP targeting may also comprise a promising cardio-oncological treatment avenue.

Interestingly, a recent study suggest that FAP also plays a central role in cardiac fibrosis, which promotes the development of various cardiac diseases and heart failure [START_REF] Aghajanian | Targeting cardiac fibrosis with engineered T cells[END_REF] . The authors showed that engineered chimeric FAP-antigen receptor T cells can be used to reduce experimental cardiac fibrosis and restore cardiac function using an angiotensin II/phenylephrine-induced hypertensive heart failure mouse model [START_REF] Aghajanian | Targeting cardiac fibrosis with engineered T cells[END_REF] .

Thus, FAP appears an attractive therapeutic target for fibrotic heart failure and atherosclerosis worth being further investigated in the clinical arena.

A B

Apoe -/-Fap +/+ Apoe -/-Fap -/- Ldlr -/-Fap +/+ Ldlr -/-Fap -/- p = 0.0267 p = 0.661 Apoe -/-Fap +/+ Apoe -/-Fap -/- Ldlr -/-Fap +/+ Ldlr -/-Fap -/- Apoe -/-Fap +/+ versus Apoe -/-Fap -/-mice. n = 9 Apoe -/-Fap +/+ , n = 8 Apoe -/-Fap -/-mice. Data are represented in scatter plots with means; unpaired two-tailed Student's t-test. versus Apoe -/-Fap -/-mice. n = 6 per genotype. Data are represented in bar graphs with means and standard errors.

A c t a C o l 1 a 1 C o l 1 a 2 C o l 1 a 3 C t g f C t h r c 1 D e s F n

19

 19

2. 11

 11 Statistics. Data are presented as scatter plots plus mean. Comparison of differences between two groups was assessed using unpaired two-tailed Student's t-tests. A nonparametric Mann-Whitney test was used for the analysis of the SGH data since the data distribution did not pass the D'Agostino & Pearson omnibus normality test. Multiple group comparisons were assessed by ANOVA analysis and Bonferroni post-hoc tests, and multiplicity adjusted P values are displayed for the comparisons.

Figures legendsFigure 1 .

 1 Figures legends

Figure 2 .

 2 Figure 2. Fap deficiency diminishes accumulation of neutral lipids in atherosclerotic plaques of Apoe -/-mice without affecting macrophages and T cells. (A) Quantification of neutral lipids and representative images of aortic sinus cross-sections with Oil-red O. (B-D) Quantification of immunohistochemical stainings and representative images of VCAM-1 (B), CD68 (C), and CD3 (D) in cross-sections from the aortic sinus. n  8 per genotype (4 cross-sections per mouse). Black bars = 400 m, orange bars = 40 m. Black arrows, examples of CD3-positive cells. Scatter blots with means, unpaired two-tailed Student's t-test.

Figure 3 .

 3 Figure 3. Fap deletion alters collagen structure in atherosclerotic lesions. (A-D) Quantification of total collagen content (A), necrotic core size (B), fibrous cap thickness (C), and (D) representative images of aortic sinus cross-sections stained with Elastica van Gieson (EvG). Green line, fibrous cap; dashed green line, necrotic core. n = 7 Apoe -/-Fap +/+ mice, n = 9 Apoe -/-Fap -/-. (E) Quantification of collagen content and representative images of aortic sinus cross-sections stained with Sirius Red and visualized under normal light. n = 7 Apoe -/-Fap +/+ mice, n = 9 Apoe -/-Fap -/-. (F) Representative images of second harmonics generation (SHG) and quantification of fibrillar collagen in aortic sinus crosssections. n = 9 Apoe -/-Fap +/+ mice, n = 6 Apoe -/-Fap -/-. Black and white bars = 400 m, orange bars = 100 m. Scatter blots with means, unpaired two-tailed Student's t-test (A-E); nonparametric Mann-Whitney test (F).

Figure 4 .

 4 Figure 4. Cleavage events in collagens revealed by proteomics in the secretomes of mouse embryonic fibroblasts that express active FAP compared with inactive FAP. (A-D) Log transformed fold-change of neo-N-terminal peptides is shown for individual cleavage sites and arranged by amino acid position for COL1A1 (A), COL1A2 (B), COL3A1 (C), and COL5A2 (D). Neo-N-terminal peptides resulting from cleavage after Gly-Pro are represented by coloured squares, whereas other cleavage sites are plotted as black circles. A fold change ≥ 0.58 represents increased peptide abundance of ≥ 50% in the presence of FAP activity. A fold change ≤ -0.58 represents decreased peptide abundance of ≥ 50% in the presence of FAP activity. Mean and standard deviation of data that reached significance (limma statistic with p < 0.05). (E, F) COL3A1 immunoblots from aortae from Apoe -/-Fap -/-and Apoe -/-Fap +/+ mice. Pre, precursor COL3A1; cleaved, cleaved COL3A1 fibers. n = 4 per genotype. (G) Expression of FAP in biopsies of non-atherosclerotic mammary arteries (Mammary) and atherosclerotic aortic roots (Athero) (raw data, GSE40231). Unpaired two-tailed Student's t-test (F, G).

Figure 5 .

 5 Figure 5. Graphical abstract. Scheme demonstrating how the deletion of FAP protects against atherosclerosis development.

Figure 5

 5 Figure 5

Figure S2 Figure S2 .

 S2S2 Figure S2

Figure S3 .

 S3 Figure S3

  -Fap +/+ Apoe -/-Fap -/- Ldlr -/-Fap +/+ Ldlr -/-Fap -/- dL)Apoe -/-Fap +/+ Apoe -/-Fap -/- p = 0.045Ldlr -/-Fap +/+ Ldlr -/-Fap -/-

Figure S4 Figure S4 .

 S4S4 Figure S4

Figure S5 .Figure S6 .

 S5S6 Figure S5

Figure S7 .

 S7 Figure S7

Figure S8 .

 S8 Figure S8

Figure 4E COL2A1

 4E Figure 4E

  Figure S7AFigureS7C & E

Table S1 .

 S1 Hematological analyses.

		WBC	RBC	PLT	Neutrophils	Lymphocytes	Monocytes	Eosinophils	Basophils
		number 10*9/L	number 10*12/L	number 10 3 /µL	%	number 10 3 /µL	%	number 10e3/µL	%	number 10 3 /µL	%	number 10 3 /µL	%	number 10 3 /µL
	Apoe -/-Fap +/+ (n=10)	6.593 ± 0.5620	9.190 ± 0.1246	1891 ± 164.2	52.20 ± 2.401	3.502 ± 0.4123	39.18 ± 2.422	2.549 ± 0.2428	1.290 ± 0.2442	0.0790 ± 0.01130	2.790 ± 1.143	0.1630 ± 0.05727	0.4000 ± 0.06325	0.0260 ± 0.003712
	Apoe -/-Fap-/-(n=12)	6.234 ± 0.7441	9.234 ± 0.2201	1480 ± 118.2	28.86 ± 1.881	1.694 ± 0.1213	61.47 ± 1.883	3.926 ± 0.6243	2.833 ± 0.2076	0.1767 ± 0.0255	3.700 ± 0.6370	0.2275 ± 0.04432	0.3750 ± 0.02787	0.02333 ± 0.002562
	P value	0.7138	0.8704	0.0511	< 0.0001	0.0002	< 0.0001	0.0707	< 0.0001	0.0038	0.4760	0.3764	0.7051	0.5508
	Ldlr -/-Fap +/+ (n=9)	7.557 ± 0.9580	9.794 ± 0.1407	1208 ± 104.4	17.09 ± 1.656	1.199 ± 0.1184	74.19 ± 1.708	5.714 ± 0.8037	2.711 ± 0.4118	0.1967 ± 0.02867	2.422 ± 0.3800	0.1911 ± 0.03642	0.4667 ± 0.03333	0.03444 ± 0.005031
	Ldlr -/-Fap-/-(n=10)	9.163 ± 0.4785	9.808 ± 0.09269	1414 ± 140.5	13.50 ± 0.7397	1.241 ± 0.09821	79.31 ± 1.316	7.252 ± 0.3676	2.750 ± 0.4342	0.2560 ± 0.04190	1.580 ± 0.4685	0.1442 ± 0.05537	0.4500 ± 0.03727	0.0420 ± 0.005121
	P value	0.14	0.9356	0.2644	0.056	0.786	0.028	0.0894	0.9492	0.2691	0.1866	0.499	0.7452	0.3091

WBC, white blood cell; PLT, platelets; RBC, red blood cells; unpaired two-tailed Student's t-test.

Table S3 .

 S3 Pathway analyses of the selected transcripts from tableS2.Top 10 terms as indicated in the column titles: BP, biological process; CC, cellular component; MF, molecular function; UP, UniProtKB keywords.

	GO:0003735~structural constituent of ribosome	4.48E-20	20.651042	5.82E-18	5.82E-18	5.21E-17
	Top 10 GOTERM_BP Term GO:0044822~poly(A) RNA binding GO:0006412~translation GO:0019843~rRNA binding GO:0032496~response to lipopolysaccharide GO:0003729~mRNA binding GO:0030199~collagen fibril organization GO:0005518~collagen binding GO:0007568~aging GO:0005201~extracellular matrix structural constituent GO:0009612~response to mechanical stimulus GO:0008201~heparin binding GO:0009314~response to radiation GO:0035662~Toll-like receptor 4 binding GO:0042060~wound healing GO:0048306~calcium-dependent protein binding GO:0043206~extracellular fibril organization GO:0050786~RAGE receptor binding	P Value 6.98E-09 3.99E-15 2.67E-05 8.19E-06 1.60E-04 1.28E-05 0.001263 4.33E-05 0.009613 8.70E-05 0.017288 1.84E-04 0.017928 4.09E-04 0.025784 7.28E-04 0.028532	Fold Enrichment 4.898360 12.599311 28.395182 10.798447 11.518046 34.091252 18.480932 10.759436 19.945884 21.104108 7.221026 35.454902 109.037500 14.144243 11.851902 72.521390 68.148438	Bonferroni 9.08E-07 1.90E-12 0.003461 0.003885 0.020602 0.006081 0.151523 0.020352 0.715115 0.040482 0.896388 0.083596 0.904799 0.176457 0.966489 0.292442 0.976789	Benjamini 4.54E-07 1.90E-12 0.001155 0.001944 0.005191 0.002031 0.032328 0.005127 0.188831 0.008231 0.276657 0.014444 0.254701 0.027353 0.314303 0.042320 0.313613	8.13E-06 FDR 0.031050 5.72E-12 0.186307 0.011715 1.461140 0.018358 10.638815 0.061869 18.379381 0.124299 18.996059 0.262405 26.228997 0.582618 1.035793 28.616601
	GO:0001649~osteoblast differentiation GO:0042989~sequestering of actin monomers Top 10 UP_KEYWORDS Term Ribosomal protein	8.19E-04 8.72E-04 P Value 2.15E-23	11.766007 66.477941 Fold Enrichment 31.471588	0.322242 0.339104 Bonferroni 2.50E-21	0.042298 0.040570 Benjamini 2.50E-21	1.163880 FDR 1.238798 2.46E-20
	Top 10 GOTERM_CC Term Ribonucleoprotein GO:0005840~ribosome Secreted GO:0031012~extracellular matrix Extracellular matrix GO:0070062~extracellular exosome Acetylation GO:0022627~cytosolic small ribosomal subunit Disulfide bond GO:0005576~extracellular region Signal GO:0030529~intracellular ribonucleoprotein complex Chemotaxis GO:0005925~focal adhesion Pyrrolidone carboxylic acid GO:0005615~extracellular space Innate immunity	P Value 6.32E-20 2.15E-21 2.12E-13 9.45E-21 4.44E-08 5.39E-16 8.75E-07 2.45E-15 3.23E-06 1.99E-13 9.87E-05 4.76E-12 2.06E-04 6.88E-11 8.09E-04 1.07E-10 8.97E-04	Fold Enrichment 20.742638 28.38738602 5.308146 20.06326531 13.593048 4.09668768 2.759932 58.29703504 2.658563 4.806943199 2.039107 13.16651786 16.991310 10.77566679 21.656720 4.668977964 7.952779	Bonferroni 7.33E-18 2.57E-19 2.46E-11 1.13E-18 5.15E-06 6.66E-14 1.01E-04 2.93E-13 3.74E-04 2.38E-11 0.011380 5.71E-10 0.023592 8.25E-09 0.089582 1.29E-08 0.098838	Benjamini 3.66E-18 2.57E-19 8.21E-12 5.67E-19 1.29E-06 2.22E-14 2.03E-05 7.33E-14 6.24E-05 4.77E-12 0.001634 9.52E-11 0.002980 1.18E-09 0.010374 1.61E-09 0.010353	7.21E-17 FDR 2.42E-10 2.46E-18 5.06E-05 1.08E-17 9.98E-04 6.33E-13 0.003683 2.81E-12 0.112503 2.28E-10 0.234539 5.47E-09 0.918799 7.90E-08 1.23E-07 1.018331
	GO:0022625~cytosolic large ribosomal subunit	3.07E-08	24.69324961	3.69E-06	4.10E-07	3.53E-05
	GO:0005578~proteinaceous extracellular matrix	1.55E-06	8.888788427	1.86E-04	1.86E-05	0.001782
	GO:0015935~small ribosomal subunit	3.09E-06	48.42857143	3.70E-04	3.37E-05	0.003544
	Top 10 GOTERM_MF Term	P Value	Fold Enrichment	Bonferroni	Benjamini	FDR

Table S4 .

 S4 Primers used for qPCR.

	kDa	Transcript	Forward (5' to 3')	Reverse(5' to 3')
		Acta	CCAGAGCAAGAGAGGGATCCT	TGTCGTCCCAGTTGGTGATG
		Actb	TGTTACCAACTGGGACGACA	GGGGTGTTGAAGGTCTCAAA
		B2m	TTCTGGTGCTTGTCTCACTG	TATGTTCGGCTTCCCATTCT
		Col1a1 Col1a2 Col3a1 Ctgf	GCACGAGTCACACCGGAACT CTACTGGTGAAACCTGCATCCA TCCTGAAGATGTCGTTGATGTG CACTCTGCCAGTGGAGTTCA	AAGGGAGCCACATCGATGAT GGGCGCGGCTGTATGAG COL3A1 TTTTTGCAGTGGTATGTAATGTTCTG AAGATGTCATTGTCCCCAGG (N-term, rabbit
		Cthrc1 Des	CGGGATGGATTCAAAGGGGA CTCGGAAGTTGAGAGCAGAGA	CGTGAATGTACACTCCGCAAT GTGAAGATGGCCTTGGATGT Proteintech)
		Fn1	ACTGGATGGGGTGGGAAT	GGAGTGGCACTGTCAACCTC
		Fn1 Gfap	ACTGGATGGGGTGGGAAT CCTTCTGACACGGATTTGGT	GGAGTGGCACTGTCAACCTC ACATCGAGATCGCCACCTAC low contrast
		Htra1	GATCTTCCTGCCCTTGGC	CTACACCAACCTGTGCCAGC
		Mgp	AGGACTCCATGCTTTCGTGA	ACCCGAGACACCATGAAGAG
		Mgp	AGGACTCCATGCTTTCGTGA	ACCCGAGACACCATGAAGAG
		Mmp13	TTGCCCTGGGAAGGAGAGA	AGTCCAGCTCAACAAGAAGAAGGT
		Mmp14	AGTCAGGGTCACCCACAAAGA	TTTGGGCTTATCTGGGACAGA
		Mmp2	ACCACCTTAACTGTTGCTTTTG	AGGAAATGCAGTGGAGTGGAA
		Mmp3	GGAGCTAGCAGGTTATCCTAAAAGC	TAGAAATGGCAGCATCGATCTTC
		Mmp7	GGTGAGGACGCAGGAGTGAA	GAAGAGTGACTCAGACCCAGA
		Mmp8	AAAAGGGAAGCTCAGTCTGTATACTC	AGAGGGCTGCAGAGTTAGTTACCA
		Mmp9	GGACGACGTGGGCTACGT	CACGGTTGAAGCAAAGAAGGA
		Npc2 Npc2 Ppib Reln	TTGTGCAGCTGACAGGGAT TTGTGCAGCTGACAGGGAT CAGGGGAGATGGCACAGGAG ACATGAGAGGCCACCACACT	CCCCTGCACTTCAAGGACT CCCCTGCACTTCAAGGACT COL3A1 CGGCTGTCTGTCTTGGTGCTCTCC CTTCTCAGAGCATTGGAGGC (N-term, rabbit
		Serpine1 Tgfb1	TTGTCCAGCGGGACCTAGAG CAACCCAGGTCCTTCCTAAA	AAGTCCACCTGTTTCACCATAGTCT GGAGAGCCCTGGATACCAAC Proteintech)
		Tgfb1	CAACCCAGGTCCTTCCTAAA	GGAGAGCCCTGGATACCAAC
		Thbs2 Thbs2	AGTGCACAGCTACAGCCTGA AGTGCACAGCTACAGCCTGA	ACAGAGTACTGGCGTCGGTC ACAGAGTACTGGCGTCGGTC high contrast
		Timp1	CCTTCGCATGGACATTATTCTC	TCTCTAGGAGCCCCGATCTG
		Vim	GGATTCCACTTTCCGTTCAA	GAAATTGCAGGAGGAGATGC

Apoe-/-Fap+/+ Apoe-/-Fap-/-

Funding

This work was supported by the Swiss National Science Foundation (PZOOP3_161521 to S.S. and 310030_165990 and 320030_189229 to C.M.M.), the Novartis Foundation for medical-biological Research (#16B103 to S.S.), the Olga-Mayenfisch Foundation (to S.S.), the SwissLife Foundation (to S.S.), and the OPO Foundation (to S.S.), the Swiss Heart Foundation (to S.N.-S., S.S. and C.M.M.), and by Matching Funds UZH, University Research Priority Program Integrative Human Physiology at the University of Zurich (to C.M.M.).

Data Availability Statement

The data underlying this article is either publicly available (transcriptomic data) or will be shared on reasonable request to the corresponding author.

Conflict of Interest

None declared.

Author Contributions

 Christian Matter initiated the study.

Supplemental Information for

Deletion of fibroblast activation protein provides atheroprotection

Contact e-mail: sokrates.stein@uzh.ch and christian.matter@usz.ch


P Bisquert

P Buche

F Fort

R Gohier

V Guillard

A Valle

Wine waste management and opportunities for new valorisation technologies

Keywords: circular economy, wine by-products valorisation, waste management

Circular economy is an economic system that replaces the 'end-of-life' concept with reducing, alternatively reusing, recycling and recovering materials in production/distribution and consumption processes. It operates at different levels, with the aim to accomplish sustainable development, thus simultaneously creating environmental quality, economic prosperity and social equity, to the benefit of current and future generations [START_REF] Kirchherr | Conceptualizing the circular economy: An analysis of 114 definitions[END_REF]. [START_REF] Mousset | Rencontres INRA : Biomasse et économie circulaire : quels enjeux, quelles opportunités et quels risques ?[END_REF] applies the circular economy concept to biomass in order to offer a better understanding of issues and challenges in this sector. In the context of agriculture and agri-food chain, the circular economy aims to reduce the pressure on natural ressources by implementing strategies at three levels: Production (preserve natural lands, eco-conception of new products or agricultural routes, optimize biomass valorisation, prioritize use per sector and find innovative pathway for by-products), Consumption and use (Consumer information, rational choice of a product according to its sustainability, sustainable nutrition, reduce food waste) and Waste management (material recovery, bioenergy production, nutrients return to the soil ground).

This study focuses on the waste management in the second most important French agricultural sector: the viticulture. In the wine production sector, huge amounts of waste and by-products are generated each year. French wineries generate 850 000 tons of grape marc, 1,5 million hectolitres of wine lees and wastewater (Réséda, 2017). Thus, valorisation pathways are necessary to develop and implement in the local vine and wine chain to favour the reuse, recycling and recovering of the waste and by-products yearly produced and ensure optimum use of residue streams. Since almost one century, distilleries industries are located in French vineyard areas. These structures were created to avoid the wine overpressing and to improve the wine quality. The distillery achieves nowadays a performant valorisation pathway for wine waste. By using different treatments and technologies, distillery transforms grape marcs and wine lees into polyphenols, anthocyanins, tannins, tartric acid, must concentrate, brandies and spirits, animal feed, alcohol, bioethanol and fertilizer. However, the current valorisation pathway is not completely circular, generating some ultimate waste that could be still valorised. Therefore, distilleries need to upgrade their technologies to diversify their economy into higher value added activities and definitively enter into the virtuous loop of circular economy. In parallel, since the French decree n° 2014-903 winemakers are authorized to valorise by themselves their by-products through methanization or spreading, making possible to created value added products on site. In practice, these solutions are never chosen by the winemakers because the large majority of them often lack the background knowledge regarding their waste valorization routes that would otherwise enable them to move forward. In order to help the stakeholders of the vine and wine sector to select the most economic and sustainable waste treatment system, a better understanding of waste management constraints and opportunities is necessary to identify the new technologies implementation potential 1 .

In this purpose, two surveys were conducted in south of France (Languedoc Roussillon area). The first one aims at providing a clear picture of the current situation as regard the nature of wastes generating by the vine and wine sector and collecting all relevant information to understand why a waste management system is preferred among all the other existing valorization routes. This survey collected also information to understand how sustainable development is viewed by the stakeholders with its constraints and advantages. The second survey aims to evaluate the new technologies acceptance and potential development.

For the first survey, twelve winemakers (wine productions directors) were selected according to different criteria (cooperative versus independent, organic versus conventional, small, medium and large wine volume production) and interviewed through face-to-face meetings with a semi-directive discussion. The interview transcriptions were analysed with a thematic analysis.

For the second survey, a total of six wine-makers and technical centers, two distilleries and three public decision-makers have been interviewed in the Languedoc-Roussillon area in July and August 2018. Interviews were composed of approximately twenty questions evaluating interviewees' knowledge about the valorization potentialities of their by-products and the new valorization processes, but also about their preferences, expectations, demands and constraints regarding these new valorization possibilities. Each survey took about 45 minutes to be completed through face-to-face meeting. After having interviewed all stakeholders, all data has been stored in the "NoAWVote", a software specifically developed for the NoAW project to aggregate stakeholders' preferences [START_REF] Karanikolas Nikos | A Decision Support Tool for Agricultural Applications Based on Computational Social Choice and Argumentation[END_REF].

The first survey shows that the wine waste management is well structured around the distilleries logistics and their valorisation pathways. However, the management of wastewater and grape-growing waste (vine shoots, vine stalks and vines) is almost non-existent and not yet developed. Distilleries organise the logistics for lee and marc collection at the production place. This first survey permitted to know that, among others, winemakers interviewed are satisfied about the relationship with the distillery. They mentionned different advantages: the compliance with the legal obligation of waste treatment, a good working relationship, the low cost of this valorisation pathway, the collection convenience in periods of high activity and the environmental performance. However, they identified also some disadvantages: producers of small wine quantity have to organize by themeselves the transportation of their waste to the nearest diststillery wich leads to an additonal cost; the bad quality of storage container and the vine marcs separation. In addition, discussions with winemakers revealed that they have a poor knowledge of distilleries activities and are unable to mention products made of wine by-products (except alcohol, grape seed oil and fertilizers). This survey also highlighted that wastewater traitement is an issue for winemakers. Different options are possible (treatment plant on site, convention with municipality to reject in the wastewater network, spreading on fields, management by a subcontractor) but none of these seems to satisfy them because of the cost, labour time and administrative complexity.

The second survey shows that, with regard to preferred valorization products, stakeholders have different visions according to their activities. For instance, if winemakers and distilleries have chosen biofertilizers as their preferred new by-product, winemakers selected them for their easiness and relative cheapness and also because it is a prority for them while distilleries selected them because they are potentially high value added products. But as distilleries do not have agricultural fields to directly spread them, they have to evaluate their cost-effectiveness at the market price. Technical centers and decision-makers chose bioenergy. Indeed, they know that biofuel is made from distilleries alcohol and it is widely spread, they also think biomass for heating could be developed at a local scale. Regarding expected benefits for new valorization routes, low environmental and economic costs are the most supported ones. An ease of implementation and management is also expected by wineries and distilleries (third choice for both of them). Distilleries are very interested in reducing their very high external energy consumption and decision-makers hope to improve soils quality by returning back organic wastes; however they do not expect further benefits, they think the winemaking sector is already efficient in wastes management treatment compared to other sectors. Lastly, when asked about principal obstacles, industries agree in cost as the most important one, partially related to the amount of available waste that not allow economic scale. Decisionmakers think the principal problem is that distilleries are the best possible choice and they are already working and performing well, making difficult to other valorization industries to rise.

This study permits to conclude that if lees and marcs management by distillery seems efficient and convenient for winemakers, while the other by-products (vine shoot, etc.) potential are under-exploited: the priority is get rid of this material, instead of valorise it. We can thus conclude that the implementation of a valorisation technology for wine by-products have to be preferably implanted in distillery because this structured system represents significant advantages for all players and permits a residues volume massification. There is a need for developing innovative valorisation pathways for grape-growing by-products.

However, new valorization routes present logistical and quantitative problems if implemented at an individual scale. Even for a collective implementation, cost-effectiveness (which is one of the most important criteria to encourage industries) has not been proved yet. Stakeholders do not have access to enough information about new valorization routes and they are not willing to try and implement them without guarantees of profitability, despite their interest in reducing final wastes and environmental damages. If any new valorization route is to be developed, it has either to beat the performance and easiness of distilleries or to complement them in reducing winemaking wastes.

Acknowledgement: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 688338.


Loïc Lévi
email: loic.levi@agrocampus-ouest.fr.

Laure Latruffe

Aude Ridier

Lévi Loïc

The role of farm performance on investment decisions: evidence from the French (Brittany) dairy sector

Keywords: farm investment, adjustment cost model, performance, dairy sector, France. JEL classification: D22, D24, D92, Q12

The objective of this paper is to investigate the role of farm performance on investment decisions by estimating an adjustment cost model including explicitly farm performance on a panel sample of specialised dairy farms in Brittany (western France) between 2005 and 2014.Two types of farms are considered: one with high capital intensity and the other with low capital intensity. The results show that spreading investment over time is, on average, an optimal strategy to maintain performance in the presence of adjustment costs. It is the high price perspectives that give incentives to farmers to invest rather than their performance. In addition, the effect of performance on investment behaviour differs between both farm types.

Introduction

Investment helps farms adapt to changing conditions such as higher price volatility and policy changes. In the past decades, trade liberalization and reforms of the Common Agricultural Policy (CAP), particularly the 2003 Luxembourg agreement which has replaced most of the coupled payments by decoupled ones (the Single Farm Payment -SFP), have resulted in higher uncertainties for farmers and in higher prices' volatility, impacting farmers' decisions.

In this context, farmers need to adapt so as to remain competitive. Besides optimizing existing production capacities, investment is key for a competitive and economically sustainable agricultural production. In the particular case of dairy farms, one recent major change is the end of milk quotas: in place in 2015, it had been announced in 2003 and reconfirmed in 2008 with a range of measures aimed at achieving a "soft landing". Dairy farmers may have increased their investments as early as 2003 so as to be ready in 2015.

The economic literature has largely studied the determinants of firms' investment behaviour. The main ones are economic and relate to the output price, the capital price and the output quantity produced. In his literature review, [START_REF] Chirinko | Business fixed investment spending: Modeling strategies, empirical results, and policy implications[END_REF] pointed out that the quantity of output produced influences more firms' investment behaviour than capital price does. A second type of determinants are financial (financial constraint, interest rate). For example, [START_REF] Budina | Liquidity Constraints and Investment in Transition Economies[END_REF] found that Bulgarian firms for the period 1993-1995 faced high liquidity constraints, and that firms' size and financial structure contributed to distinguish between firms that were more or less liquidity constrained. [START_REF] Latruffe | The Impact of Credit Market Imperfections on Farm Investment in Poland[END_REF] confirmed the presence of rural credit market imperfections in Poland during 1996-2000, in the sense that, for some farms, the only source or the least expensive source of funds, were internal funds. O' Toole et al. (2014) suggested that the Irish farm sector encountered difficulties in accessing credit to invest in productivity enhancing technology. A third type of determinants of investment relates to public policy, namely public support, tax system, standards or regulations. [START_REF] Sckokai | Modelling the impact of the CAP Single Farm Payment on farm investment and output[END_REF] found, for a sample of Italian specialised arable crop farms during [1994][1995][1996][1997][1998][1999][2000][2001][2002], that an increase in intervention price would significantly affect farm investment, mainly through reduced price volatility. A fourth type of determinants is structural, relating to the quasi-fixity of assets, irreversibility of investment, sunk costs and adjustment costs. The adjustment cost theory assumes that farms experience adjustment costs when they invest, such as the cost of extra-time or production losses until farmers and herd become more familiar with new machines and technologies. [START_REF] Bokusheva | Exploring Farm Investment Behaviour in Transition: The Case of Russian Agriculture[END_REF] showed that the adjustmentcost model is adequate for evaluating investment behaviour mainly in the farming sector in the short term, but less satisfactory for explaining long-term decisions. A fifth type of investment determinants are sociological and psychological factors. They relate for example with age and education, and with personal attitudes or values.

The sixth determinant type is organisational and relates to managerial performance and labour productivity. However, there is little literature illustrating the role of organisational drivers. In theory, the effect of farm performance on investment is ambiguous. On the one hand, high farm performance can allow farmers to afford investments in the future, in line with the accelerator effect; on the other hand, farmers with a highly performing farm may postpone investments in order to avoid adjustment costs that would decrease their performance in the short term. In addition, when accounting for farm performance in investment decisions, endogeneity has to be controlled for: not only performance influences investment decisions, but also conversely. In the literature, several authors have indeed shown that farm performance is influenced by investment [START_REF] Sauer | Investment, technical change and efficiency: empirical evidence from German dairy production[END_REF][START_REF] Zhengfei | The Source of Productivity Growth in Dutch Agriculture: A Perspective from Finance[END_REF]. [START_REF] Sauer | Investment, technical change and efficiency: empirical evidence from German dairy production[END_REF] showed, for dairy farms in Germany for the period 1995-2010, that investment in innovative technology allows to increase performance (measured by productivity of dairy production). [START_REF] Zhengfei | The Source of Productivity Growth in Dutch Agriculture: A Perspective from Finance[END_REF] showed, for Dutch cash crop farms over the period 1990-1999, that investment, proxied by the level of debt, had no significant effect on global financial performance (measured by the return on equity), whereas it had a positive effect on productivity growth. However, although the endogeneity of performance and investment variables is sometimes recognised and controlled for [START_REF] Zhengfei | The Source of Productivity Growth in Dutch Agriculture: A Perspective from Finance[END_REF]), the explicit investigation of the effect of current performance on future investment decisions, accounting for endogeneity, has never been performed. Moreover, in the context of the end of milk quotas, structural changes could occur. The progressive ending of milk quotas initiated in 2008 could favor investment in some types of farms to the detriment of others. Is the link between performance and investment the same for capital intensive farms and less capital-intensive farms?

In this context, the objective of this paper is to investigate the effect of current performance on future investment decisions for the particular case of the French dairy sector. The adjustment cost model is used with performance being introduced in the modelling strategy, accounting for endogeneity as well as for farm heterogeneity through different capital intensities. We indeed consider two types of farms that may have a different investment strategy: farms that have a high capital intensity, and farms that have a low capital intensity. Investment behaviour of both farm types may differ for several reasons. Farms may differ in their current performance, which would differently affect future investment decisions; the adjustment costs may also have a different impact depending on the initial capital endowment.

Our model is applied to a sample of specialised Brittany dairy farms during the 2005-2014 period.

The article is structured as follows. Section two develops the theoretical framework. Section three describes the database while section four explains the econometric specifications. Section five presents the results and the last section concludes.

Theoretical framework

To study farmers' investment decisions accounting for the link with farm performance, we use an adjustment cost model. Contrary to the ad hoc accelerator model, the adjustment cost model provides a consistent theoretical basis for explaining agricultural investment patterns in the context of dynamically optimizing economic agents. Adjustment cost theory has been the main approach used in the literature on investment to explain why firms partially adapt their capital stock to the optimal level [START_REF] Bond | Dynamic Investment Models and the Firm's Financial Policy[END_REF][START_REF] Hubbard | Internal Net Worth and the Investment Process: An Application to U.S. Agriculture[END_REF][START_REF] Lizal | Investment, Credit Rationing, and the Soft Budget Constraint: Evidence from Czech Panel Data[END_REF][START_REF] Rizov | Firm investment in transition[END_REF]. According to this theory, firms undergo a short-run loss in output or profit when they modify their stocks of quasi-fixed production factors due to adjustment costs. These costs arise from ensuring frictionless flow (maintenance), and may include gradual adjustments (refinements and improvements necessitating training) or more substantial adjustments [START_REF] Caballero | Chapter 12 Aggregate investment[END_REF]. In the profit's maximising framework, the adjustment cost hypothesis is formalized by including investment as an argument in the profit function. The adjustment cost model is particularly relevant for the agricultural sector. Asset fixity is a particular feature of this sector, especially in livestock sector, as [START_REF] Galbraith | The Maintenance of Agricultural Production During Depression: The Explanations Reviewed[END_REF] argued early. Farms' fixed production factors are lumpy, implying that their reduction or reorganization is costly explaining why farms may be unprofitable in the short run.

The theoretical model assumes that dairy farmers are risk neutral and maximize the expected net present value of their profits 𝜋 at time t over an infinite horizon (eq. 1):

∂𝐼 𝑡+1 } = 0 (7)
Following this, the Euler equation defining the optimal investment path can be derived (eq. 8).

We assume here rational expectations [START_REF] Muth | Rational expectations and the theory of price movements[END_REF], implying that the expected value in period t-1 is equal to the value in period t corrected with an error term:

𝐸 𝑡 { 𝜕𝜋 𝑡 𝜕𝐼 𝑡 } -(1 -𝛿) (β t+1 +𝜇 𝑡+1 ) (β t +𝜇 𝑡 ) 𝐸 𝑡 { 𝜕𝜋 𝑡+1 𝜕𝐼 𝑡+1 } + 𝐸 𝑡 { 𝜕𝜋 𝑡 𝜕𝐾 𝑡 } = 𝜀 𝑡+1 (8)
where 𝜀 𝑡+1 is an error term that is assumed to be uncorrelated with explanatory variables.

The profit function at time t is specified, as follows:

𝜋 𝑡 {𝐾 𝑡 , 𝐼 𝑡 , 𝑋 𝑡 } = 𝑝 𝑡 𝑌 𝑡 -𝐶 𝑡 -𝑤 𝑡 𝑋 𝑡 -𝑝 𝑡 𝐼 𝐼 𝑡 (9)
where 𝑝 𝑡 is the output price; 𝑌 𝑡 is the output production function; 𝐶 𝑡 is the adjustment cost function; 𝑤 𝑡 is the variable input price and 𝑝 𝑡 𝐼 is the investment price.

To model the link between performance and investment decisions, we assume that the output depends not only on the production factors (fixed and variable inputs), but also on a performance parameter noted 𝑢 𝑡 (eq. 10), which could be viewed as farmer's managerial ability [START_REF] Galanopoulos | Assessing the effects of managerial and production practices on the efficiency of commercial pig farming[END_REF][START_REF] Ondersteijn | Identification of farmer characteristics and farm strategies explaining changes in environmental management and environmental and economic performance of dairy farms[END_REF][START_REF] Solano | Using farmer decision-making profiles and managerial capacity as predictors of farm management and performance in Costa Rican dairy farms[END_REF])

𝑌 𝑡 = 𝑓(𝐾 𝑡 , 𝑋 𝑡 , 𝑢 𝑡 ) (10)
The production function 𝑓 is assumed to be quadratic and increasing with performance. We further assume that performance depends on capital stock, capturing size effects (eq.11 and ). However, no specific assumption is made on the sign of the first derivative of the performance function 𝑔 with respect to capital, that is to say on the sign of the scalar b in equation ( 12): the derivative may be negative, respectively positive, meaning that farmers on larger farms would have a lower, respectively higher, performance than farmers operating smaller farms. It is assumed that the effect of capital size on performance depends on the level of performance itself (eq. 12), that is to say that the effect is amplified at high levels of performance.

𝑢 𝑡 = 𝑔(𝐾 𝑡 ) (11) 𝜕𝑔(𝐾 𝑡 ) 𝜕𝐾 𝑡 = 𝑏𝑢 𝑡 (12)
The first derivatives of the production function with respect to capital and to performance are as follows (eq. 13 and 14):

𝜕𝑓(𝑌 𝑡 ) 𝜕𝐾 𝑡 = 𝛼 0 + 𝛼 1 𝐾 𝑡 + 𝛼 2 𝑋 𝑡 + 𝛼 3 𝑢 𝑡 > 0 (13) 𝜕𝑓(𝑌 𝑡 ) 𝜕𝑢 𝑡 = 𝑎 > 0 (14)
Equation ( 13) shows that the derivative with respect to capital is assumed to be positive, meaning that output increases when capital increases but no assumption is made on the sign of the parameters 𝛼 0 , 𝛼 1 , 𝛼 2 , , 𝛼 3 . Equation ( 14) represents the intuitive idea that the higher the farmer's performance, the higher the output produced.

As it is standard in the literature, the adjustment costs incurred by farms are assumed to be quadratic and to depend on 𝐾 𝑡 and 𝐼 𝑡 through a function ℎ (eq. 15) whose derivative with respect to investment is increasing with investment (eq. 16) and derivative with respect to capital depends on investment squared (eq. 17):

𝐶 𝑡 = ℎ(𝐾 𝑡 , 𝐼 𝑡 ) (15) 𝜕ℎ(𝐾 𝑡 ,𝐼 𝑡 ) 𝜕𝐼 𝑡 = 𝜃 0 + 𝜃 1 𝐼 𝑡 with 𝜃 1 > 0 (16) 𝜕ℎ(𝐾 𝑡 ,𝐼 𝑡 ) 𝜕𝐾 𝑡 = 𝛾 0 + 𝛾 1 𝐼 𝑡 2 (17)
Using equations ( 9), ( 10) and ( 15), the Euler equation in equation ( 8) can then be rewritten as follows (eq. 18):

𝜕𝜋 𝑡 𝜕𝐶 𝑡 𝜕𝐶 𝑡 𝜕𝐼 𝑡 -𝑝 𝑡 𝐼 -(1 -𝛿) (β t+1 +𝜇 𝑡+1 ) (β t +𝜇 𝑡 ) ( 𝜕𝜋 𝑡+1 𝜕𝐶 𝑡+1 𝜕𝐶 𝑡+1 𝜕𝐼 𝑡+1 -𝑝 𝑡+1 𝐼 ) + 𝜕𝜋 𝑡 𝜕𝑌 𝑡 𝜕𝑌 𝑡 𝜕𝐾 𝑡 - 𝜕𝜋 𝑡 𝜕𝐶 𝑡 𝜕𝐶 𝑡 𝜕𝐾 𝑡 = 𝜀 𝑡+1 (18)
Furthermore, using equations ( 12), ( 13), ( 14), ( 16), ( 17), it can be written as (eq. 19):

-(𝜃 0 + 𝜃 1 𝐼 𝑡 ) -𝑝 𝑡 𝐼 -(1 -𝛿) (β t+1 +𝜇 𝑡+1 ) (β t +𝜇 𝑡 ) (-(𝜃 0 + 𝜃 1 𝐼 𝑡+1 ) -𝑝 𝑡+1 𝐼 ) + 𝑝 𝑡 (𝛼 0 + 𝛼 1 𝐾 𝑡 + 𝛼 2 𝑋 𝑡 + 𝛼 3 𝑢 𝑡 ) -(𝛾 0 + 𝛾 1 𝐼 𝑡 2 ) = 𝜀 𝑡+1 (19)
Assuming that the price of investment (𝑝 𝑡 𝐼 ) is constant across farms and years, the final model is therefore (eq. 20):

𝐼 𝑡+1 = 𝜗 0 + 𝜗 1 𝐼 𝑡 + 𝜗 2 𝐼 𝑡 2 +𝜗 3 𝑢 𝑡 𝑝 𝑡 + 𝜗 4 𝑋 𝑡 𝑝 𝑡 + 𝜗 5 𝐾 𝑡 𝑝 𝑡 + 𝜗 6 𝑝 𝑡 + 𝜀 𝑡+1 (20)
with:

𝜗 1 = (β t +𝜇 𝑡 ) (1-𝛿)(β t+1 +𝜇 𝑡+1 ) (21) 𝜗 2 = 𝛾 1 𝜃 1 (β t +𝜇 𝑡 ) (1-𝛿)(β t+1 +𝜇 𝑡+1 ) (22) 𝜗 3 = - 𝛼 3 𝜃 1 (β t +𝜇 𝑡 ) (1-𝛿)(β t+1 +𝜇 𝑡+1 ) (23) 𝜗 4 = - 𝛼 2 𝜃 1 (β t +𝜇 𝑡 ) (1-𝛿)(β t+1 +𝜇 𝑡+1 ) (24)
𝜗 5 = - 𝛼 1 𝜃 1 (β t +𝜇 𝑡 ) (1-𝛿)(β t+1 +𝜇 𝑡+1 ) (25)
𝜗 6 = - 𝛼 0 𝜃 1 (β t +𝜇 𝑡 ) (1-𝛿)(β t+1 +𝜇 𝑡+1 ) (26)
Equation ( 21) shows that 𝜗 1 is positive, and hence a positive impact of 𝐼 𝑡 on 𝐼 𝑡+1 is expected (eq. 20). As 𝜃 1 is assumed to be positive and

(β t +𝜇 𝑡 )
(1-𝛿)(β t+1 +𝜇 𝑡+1 ) as well, the direction of the impact of 𝐼 𝑡 2 on 𝐼 𝑡+1 (that is to say the sign of 𝜗 2 , eq. 22) can inform on the sign of 𝛾 1 that is to say on the shape of the adjustment cost function (eq. 17). The sign of 𝜗 3 (equation 23), related to the effect of 𝑢 𝑡 𝑝 𝑡 on 𝐼 𝑡+1 , can inform on the sign of 𝛼 3 that is to say the direction of the impact of performance 𝑢 𝑡 on the marginal productivity of 𝐾 𝑡 (eq. 13). The sign of 𝜗 4 (equation 24), related to the effect of 𝑋 𝑡 𝑝 𝑡 on 𝐼 𝑡+1 , can inform on the sign of 𝛼 2 namely the effect of 𝑋 𝑡 on the marginal productivity of 𝐾 𝑡 . The direction of the impact of 𝐾 𝑡 𝑝 𝑡 on 𝐼 𝑡+1 (𝜗 5 , equation 25) can inform on the sign of 𝛼 1 namely on the effect of 𝐾 𝑡 on the marginal productivity of 𝐾 𝑡 .

Data and econometric specification

The data set includes accountancy information for a fully balanced sample of 661 specialized dairy farms of one sub-region of Brittany (namely Ille-et-Vilaine with main town Rennes), provided by a regional private accounting office 1 , covering the period 2005-2014. Hence, the pooled sample for the ten years includes 6,610 observations. Capital (𝐾 𝑡 ) is proxied by the net value of fixed assets, including all tangible assets such as buildings, machinery and equipment, breeding livestock and land. Investment (𝐼 𝑡 ) is net investment computed as the difference, between periods t and t-1, of the net capital 2 . The output price (𝑝 𝑡 ) is the milk sale price 3 in period t, milk being the main output for this farm sample. The variable input (𝑋 𝑡 ) is proxied by operational expenses 4 , i.e. the costs related to the farming operations including: costs for purchased animal feed, produced forage, straw litter, and fuel; veterinary and animal reproduction costs; costs of temporary labour.

1 CER FRANCE Ille-et-Vilaine. This accounting office manages the accounts of the majority of farmers in Brittany.

2 Values of capital and investment in period t were deflated by the price index of the means for agricultural production and more precisely the price index of investment goods with base year 2010.

3 It was deflated by the price index of agricultural products with base year 2010.

4 It was deflated by the price index of the means for agricultural production and more precisely the price index of goods and services consumed in agriculture with base year 2010.

As a proxy of the managerial performance (𝑢 𝑡 ), we use the current value of Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA)5 in period t divided by the number of labour units in the same period. EBITDA is an indicator of the operating profitability of a farm as it measures the potential cash flow obtained from the farming activity and is used to remunerate the farm labour. It can thus be seen as a proxy for the farmer's managerial ability, which influences the farm output generated. Relating the EBITDA to labour controls for size.

A high EBITDA per labour unit at the end of period t reveals that the farmer has been highly performing in this period, and thus is a good proxy for high 𝑢 𝑡 .

As shown in Table 1, during the period considered, on average farms in the sample operated 77 hectares (ha) of utilized agricultural area (UAA), used 2 full-time equivalent labour units, bred 52 dairy cows producing 7,155 litres of milk per cow. Table 1 also shows that farms in our sample are larger on average than those from the exhaustive Agricultural Census population of the same sub-region in terms of UAA and labour use, but similar in terms of number of cows and smaller in terms of capital used [START_REF] Agreste | Recensement agricole d'Ille-et-Vilaine : premières tendances. Agreste[END_REF]. They have a higher milk yield but a lower EBITDA per litre of milk.

Figure 1 displays, for our sample, the evolution of the yearly average level of investment and of milk price over the period considered. Total investment increases up to 2009, and then drops until 2011 where it increases again, continuously until the end of the period considered.

The investment peak in 2007 may be due to the significant milk price increase in 2007-2008, which was followed by a significant decrease in 2010, after the beginning of the economic crisis. In 2009, the dairy sector experienced a deep crisis in the form of a sudden milk price decrease and, at the same time, input prices remained at a high level. Milk price has since then been at its lowest level in historic terms.

where subscript i refers to the i-th farm and subscript t refers to the t-th period; ϑ 0 , ϑ 1 , ϑ 2 , ϑ 3 , ϑ 4 , ϑ 5 and ϑ 6 are parameters to be estimated; 𝜀 𝑖,𝑡+1 = 𝑠 𝑖 + 𝑤 𝑖,𝑡 is the disturbance containing farm-specific effects 𝑠 𝑖 and random noise 𝑤 𝑖,𝑡 .

All variables are divided by the net capital (𝐾 𝑡 ) to control for farm size effect. We employ the generalised method of moments (GMM) [START_REF] Arellano | Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations[END_REF][START_REF] Arellano | Another look at the instrumental variable estimation of errorcomponents models[END_REF] to account for potential endogeneity arising firstly from the correlation between explanatory variables and the error term, that can be due to a simultaneity bias or to unobserved heterogeneity such as soil conditions. Secondly, this enables to account for potential endogeneity arising from the correlation between the performance variable and the investment variable. As instruments we use internal instruments, that is to say the endogenous variables , lagged over two periods [START_REF] Barran | Internal finance and corporate investment: Belgian evidence with panel data[END_REF][START_REF] Bond | Dynamic Investment Models and the Firm's Financial Policy[END_REF][START_REF] Rizov | Firm investment in transition[END_REF]. We estimate the model in first differences [START_REF] Bokusheva | Exploring Farm Investment Behaviour in Transition: The Case of Russian Agriculture[END_REF][START_REF] O'toole | Financing constraints and agricultural investment: effects of the Irish financial crisis[END_REF] to eliminate the farm-specific effect 𝑠 𝑖 from the investment equation. 27) is estimated twice, once on the whole sample and once with interaction effects when each explanatory variable is interacted with a dummy variable taking the value 1 if the farm has a "high capital intensity" and 0 if it has a "low capital intensity". The dummy variable to identify both groups of farms is created as follows. Using Hierarchical Ascendant Classification (HAC) with Ward's method, a cluster analysis is performed in order to identify groups of farms with similar capital intensity. We separate farms into different groups based on the following specific characteristics: the herd size (i.e. number of dairy cows); the share of fodder maize in the farm forage area; the stocking rate (i.e. number of Livestock Units6 per hectare, LU/ha); the cost of work outsourcing per LU; the cost of concentrates per dairy cow; and the capital stock per LU. In order to distinguish farms based on the difference in their average capital intensity as well as on the difference in evolution over time, we use two types of variables: static ones, namely the average value over the whole period for each characteristic listed above; and dynamic ones, namely the growth rate computed between 2005 to 2014. The growth rate of the cost of work outsourcing is not used because it is correlated with other variables.

The HAC performed here allows identifying two clusters of farms in our sample (tables 3 and 4).

Results

Estimation results for the full sample

Table 5 shows the results of the estimation of the investment model presented in equation ( 27). The results for the full sample are presented in the middle column. Results indicate that the model is highly significant, as shown by the Wald test. Also, the Sargan test of overidentifying restrictions does not reject the null hypothesis of the validity of instruments at the 10 percent level of significance.

Three main findings can be noted from Table 5. Firstly, the coefficient for the investment to capital at period t (𝜗 1 ), respectively the coefficient for the square investment to capital at period t (𝜗 2 ), are significant and positive, respectively, negative. This indicates that higher investments in period t increase investments in t+1, consistent with the model and suggesting that farmers smooth their investment over time in order to undergo the lowest adjustment costs. The latter are captured by the negative 𝜗 2 , and hence the negative 𝛾 1 (see equation 22), showing the marginal cost of having a higher level of capital in the profit function. , (𝜗 3 ) is non-significant (Table 5). This suggests that, for a given (positive) price, there is no effect of performance in t on investment in t+1 when the full sample is considered. We will investigate this further in the next sub-section with the separation of farms across the clusters of capital intensity.

Thirdly, as expected, the average effect of price in period t on investment to capital in period t+1 is positive. For each farm i in each period t, the effect of price is obtained by deriving equation ( 27) with respect to 𝑝 𝑖,𝑡 , yielding:

𝜕( 𝐼 𝑖,𝑡+1 𝐾 𝑖,𝑡 ) 𝜕𝑃 𝑡 = 𝜗 3 𝑢 𝑖,𝑡 𝐾 𝑖,𝑡 + 𝜗 4 𝑋 𝑖,𝑡 𝐾 𝑖,𝑡 + 𝜗 5 + 𝜗 6 1 𝐾 𝑖,𝑡 (28)
The individual values are then averaged over the farms and over the year. The result is presented in the first row of Table 6. For the whole sample, the average price effect is 0.46 suggesting that when milk price increases (respectively decreases) by one Euro per 1,000 Litres, the ratio of investment to capital in period t+1 increases (respectively decreases) by 0.46. This positive effect is conform to intuition and reveals that higher sale opportunities give incentives to farmers to expand, and thus to invest. The evolution of the average price effect over time (see figure 2) shows lower effects from 2012 onwards, revealing weaker price incentives in this period.

Estimation results for farm groups based on their capital intensity

As seen above, the estimation on the full sample does not show a significant effect of performance on investment, and this may hide heterogeneous behaviours within the sample as already revealed by the sample's descriptive statics. For this reasons we had separated farms into two groups based on their capital intensity with the help of HAC. To investigate whether the two groups have a different strategy in terms of investment decisions, we estimate our investment model (equation 27) as an interaction model. More precisely, we interacted all explanatory variables with a dummy variable taking the value 1 for farms with high capital intensity and 0 for farms with low capital intensity.

The last column of Table 5 reports the results of the estimation of this interaction investment model where the reference group is low capital intensity farms. The coefficients for this reference group are those for the variables without interaction (i.e. when the dummy equals 0), while a coefficient for the high capital intensity farms is obtained by adding the coefficient for the variable without interaction and the coefficient with interaction. For example, the coefficient for the investment to capital ratio in period t is 0.717 ( 𝜗 1 ) for low capital intensive farms, while the coefficient for high capital intensive farms is obtained by adding 0.717 and -0.944 which gives the value -0.227.

Three main findings can be noted. Firstly, the coefficient for the investment to capital in period t is (significant and) positive for low capital intensive farms but (significant and) negative for high capital intensive farms, while the coefficient for the square investment to capital in period t is (significant and) negative for both sub-samples. While the latter result reveals that both sub-samples undergo adjustment costs, the result regarding the investment to capital confirms these costs (through a behaviour of smoothing investment over time) for low capital intensive farms only. By contrast, high capital intensive farms decrease their investment in period t+1 when they have already implemented high investment in period t.

Secondly, the coefficient for the performance variable 𝑢 𝑖,𝑡 𝑝 𝑖,𝑡 𝐾 𝑖,𝑡 (𝜗 3 ) is (significant and)

negative for the high capital intensity sub-sample and (significant and) positive for the low capital intensity sub-sample. This suggests that high capital intensive farms face the abovementioned trade-off between investing now to increase their size and hence their performance, or postponing investment in order to avoid a decrease in performance in the following year due to adjustment costs. This may also explain the above-mentioned finding that these high capital intensive farms, when having invested in current period, decrease their investment in the next period so as to maintain their performance level.

On the opposite, during the period studied, a high performance in t has encouraged the low capital intensive sub-sample to invest in t+1 (positive coefficient 𝝑 𝟏 ) in order to increase their size and hence their performance. This may be explained by the quotas removal which has progressively been implemented. Between 2008 and 2015, the dairy quota allocation has increased progressively to achieve a "soft landing", creating new opportunities for Brittany dairy sector to take advantage of increasing global demand for dairy products even before the effective removal of quotas.

Thirdly, on average the effect of price in period t on investment to capital in period t+1 is much lower for low capital intensive farms (0.03) than for high capital intensive farms (0.84) (Table 6). This discrepancy might suggest that low capital intensity farms still have a long way to go to adapt to the quota system elimination, more than high capital intensity farms.

Low capital intensity farms need to invest whatever the economic conditions so as to be ready when quotas are effectively removed, and they do so regardless of the price levels.

Conclusion

This article provides a new perspective on investment decisions in the dairy farm sector taking into account i) the link between farm investment and farm performance as well as ii) farmers' different investment strategies depending on the level of their farm capital intensity. For this, the effect of current farm performance on future investment decisions in the dairy sector in a sub-region of Brittany (western France) is investigated during the 2005-2014 period, using an adjustment cost model and including farm performance in the modelling strategy. The model is also estimated with interaction terms that capture both sub-samples identified with HAC: high capital intensity farms and low capital intensity farms.

Firstly, results show that, smoothing farm investment over time is on average an optimal strategy in the presence of adjustment costs, as for example reported by [START_REF] Gardebroek | Farm-specific Adjustment Costs in Dutch Pig Farming[END_REF]. Secondly, on average for the full sample it is rather the high price perspectives that give incentives to farmers to invest than their performance. Thirdly, the influence of performance on farm investment differs between high capital intensive farms and low capital intensive farms. Indeed, on average, the high capital intensity farms may prefer not to invest in order to avoid adjustment costs in the short term, while low capital intensive farms seem to invest all along the period regardless their performance and regardless milk prices. Their investment strategy might be linked to a strategy of increasing milk productivity in the perspective of dairy quota removal. Our finding highlight that farmers' heterogeneity needs to be accounted for in modelling investment behaviour. It allows reveal differentiated strategies and can help design targeted policies aimed at encouraging investment, in particular in the context of quota system elimination. Indeed, the structural changes are taking place in dairy sector, especially in the western part of France. An intensification of more extensive farm (grass-based systems) goes on to increase dairy productivity and, on the other hand, the investment of large structures is rather curbed by the existence of adjustment costs. So, it is possible to observe a standardization trend in terms of technology in this specialized dairy region which is on average more intensive than the others French region.

We should note here some limits to our analysis. Our objective was to investigate how performance was linked to farms' investment decisions, and in order to limit the complexity of the modelling framework and of the econometric estimations, we deliberately made some simplification assumptions. Firstly, we assumed that farmers' were risk neutral, although some literature has shown that some farmers may be risk averse [START_REF] Liu | Time to change what to sow: Risk preferences and technology adoption decisions of cotton farmers in China[END_REF][START_REF] Young | Risk Preferences of Agricultural Producers: Their Use in Extension and Research[END_REF].

Introducing risk in the modelling strategy is hence one avenue for future research. Secondly, we modelled rational expectations but the literature on investment has highlighted that farmers may have other types of expectations [START_REF] Thijssen | Farmers' Investment Behavior: An Empirical Assessment of Two Specifications of Expectations[END_REF][START_REF] Chavas | On the Economic Rationality of Market Participants: The Case of Expectations in the U.S. Pork Market[END_REF]. Finally, we have here proceeded in two steps, separating farms in a first step and investigating their investment decisions in a second step. But a more efficient way may be to estimate simultaneously a latent class model and the investment model. [START_REF] Alvarez | Identifying different technologies using a latent class model: extensive versus intensive dairy farms[END_REF] estimated a latent class model and a stochastic frontier model simultaneously to assess the technical efficiency of more and less intensive farms, and this approach may be developed for future research on investment decisions accounting for farm heterogeneity.

  𝑀𝑎𝑥 𝐸 𝑡 {∑ 𝛽 𝑡 𝜋 𝑡 {𝐾 𝑡 , 𝐼 𝑡 , 𝑋 𝑡 }

		∞ 𝑡=0				}	(1)
	on 𝐾 𝑡 , 𝐼 𝑡 , 𝑋 𝑡
	subject to
	𝐾 𝑡 = (1 -𝛿)𝐾 𝑡-1 + 𝐼 𝑡	(2)
	𝜋 𝑡 {𝐾 𝑡 , 𝐼 𝑡 , 𝑋 𝑡 } ≥ 0			(3)
	where the capital 𝐾 𝑡 is a stock variable and the investment 𝐼 𝑡 is a flow variable; 𝑋 𝑡 is the level
	of variable inputs; 𝛽 is the discount factor; 𝛿 is the depreciation rate; 𝐸 𝑡 is the expectation
	operator conditional on information available at the start of period t, expectations being taken
	over future prices and technologies (Bond and Meghir, 1994). For simplification the farm
	subscript i is dropped from all variables.
	Equation (2) represents capital accumulation, stating that the current capital stock consists of
	last year's capital stock, adjusted for depreciation at rate 𝛿, plus current investment. Equation
	(3) is a non-negativity constraint that ensures that the farm profit is positive at each period.
	The Lagrangian function can be written as follows:
	L=E t {∑ β t π t {K t ,I t , 𝑋 𝑡 } ∞ t=0	}+⋯+𝜆 𝑡 [𝐼 𝑡 -𝐾 𝑡 + (1 -𝛿)𝐾 𝑡-1 ] + 𝜆 𝑡+1 [𝐼 𝑡+1 -𝐾 𝑡+1 +
	(1 -𝛿)𝐾 𝑡 ] + ⋯ + 𝜇 𝑡 [𝜋 𝑡 {K t ,I t , 𝑋 𝑡 }] + 𝜇 𝑡+1 [𝜋 𝑡+1 {K t+1 ,I t+1 , 𝑋 𝑡+1 }]	(4)
	where 𝜆 𝑡 and 𝜇 𝑡 are the Lagrangian multipliers associated with constraints (2) and (3)
	respectively.
	The first order conditions for investment I t and capital 𝐾 𝑡 are respectively as follows:
	𝜕𝐿 𝜕𝐼 𝑡	= 𝐸 𝑡 {(β t + 𝜇 𝑡 )	𝜕π t 𝜕𝐼 𝑡	} + 𝜆 𝑡 = 0	(5)
	∂𝐿 ∂𝐾 𝑡	= 𝐸 𝑡 {(β t + 𝜇 𝑡 )	∂π t ∂𝐾 𝑡	} -𝜆 𝑡 + 𝜆 𝑡+1 (1 -𝛿) = 0	(6)
	Combining first order conditions (5) for investment and (6) for capital gives:
	𝐸 𝑡 {(β t + 𝜇 𝑡 )	∂π t ∂𝐼 𝑡	} + 𝐸 𝑡 {(β t + 𝜇 𝑡 )	∂π t ∂𝐾 𝑡	} -(1 -𝛿)𝐸 𝑡 {(β t+1 + 𝜇 𝑡+1 )	∂π t+1

Table 1 : Descriptive statistics of the sample used and comparison with the Agricultural

 1

	Census
		Sample used	Total farm population
		(Sample's average	in the same sub-
		over 2005-2014)	region as our sample
			(Population's average
			in 2010; Agricultural
			Census)
	Structural variables
	Milk produced (Litres)	376,251	356,110
	UAA (ha)	77	63
	Number of dairy cows	52	52
	Number of labour full-time equivalent units	2.0	1.7
	Milk yield (Litres / cow)	7,155	7,036
	Financial variables
	Capital (€ / 1,000 Litres)	713	953
	Indebtedness (%)	49.5	49.9
	EBITDA (€ / 1,000 Litres)	150	173
	Number of observations	661	3,248

Source: The authors based on CER FRANCE Ille-et-Vilaine and Agreste (2010) Figure 1:

Evolution of the average level of investment and milk price for the sample used between 2005 and 2014

  Based on the theoretical model of farmer's investment decisions defined by equation (20), our baseline empirical specification is as follows (eq. 27):

	𝐼 𝑖,𝑡+1 𝐾 𝑖,𝑡	= 𝜗 0 + 𝜗 1	𝐼 𝑖,𝑡 𝐾 𝑖,𝑡	+ 𝜗 2	𝐼 𝑖,𝑡 𝐾 𝑖,𝑡 2	+𝜗 3	𝑢 𝑖,𝑡 𝑝 𝑖,𝑡 𝐾 𝑖,𝑡	+ 𝜗 4	𝑋 𝑖,𝑡 𝑝 𝑖,𝑡 𝐾 𝑖,𝑡	+ 𝜗 5 𝑝 𝑖,𝑡 + 𝜗 6	𝑝 𝑖,𝑡 𝐾 𝑖,𝑡	+ 𝜀 𝑖,𝑡+1
			400												14,000
	average milk price,€ per 1,000L	300	309		296		294	351		326		297	339	345	336	381	0 2,000 4,000 6,000 8,000 10,000 per 12,000 of the average level of investment	farm, €
	Evolution of the	200	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	-6,000 -4,000 -2,000	Evolution
									Milk price	Total investment

Source: The authors based on CER FRANCE Ille-et-Vilaine

Table 2 : Descriptive statistics of the main variables of interest for the sample used

 2

	Variables	Mean	Std. Dev	Min	Max	Number of observations
	𝐼 𝑡 𝐾 𝑡	-0.0074	0.15	-1.36	0.85	5,949
	2 𝐾 𝑡 𝐼 𝑡	7,097	25,287	0.00	1,107,676	5,949
	𝑢 𝑡 𝑃 𝑡 𝐾 𝑡	62	43	-80	1,223	6,610
	𝑋 𝑡 𝑃 𝑡 𝐾 𝑡	140	86	11	796	6,610
	𝑃 𝑡 𝐾 𝑡	1.72e-03	1.09e-03	1.93e-04	13.4e-03	6,610
	Variables in levels
	Investment (𝐼 𝑡 ) (€)	3,270	55,070	-333,685	1,467,339	5,949
	Output price (𝑃 𝑡 ) (€ / 1,000 Litres) 327	33	252	483	6,610
	Variable inputs (𝑋 𝑡 ) (€)	103,309	84,217	5,352	1,102,166	6,610
	Capital (𝐾 𝑡 ) (€)	256,678	152,203	23,411	1,943,785	6,610
	EBITDA (€)	77,908	46,094	-11,312	539,760	6,610
	EBITDA per labour unit (𝑢 𝑡 ) (€)	39,458	17,458	-11,312	221,829	6,610

Source: The authors based on CER FRANCE Ille-et-Vilaine

  Table2provides summary statistics for the key variables included in the model. The level of investment over the period is € 3,270 per farm on average in our sample. The standard deviation is high pointing out high heterogeneity in investment behaviour across farms and years. Over the period considered, the annual percentage of zero and negative investment values is on average 58%, while it is 42% for positive investment values. The percentage of zero and negative values increases significantly since 2011 (not shown in Table2). Output price is on average 327 € per 1,000 Litres. Variable input cost is on average 103,309 € and the variable input (𝑋 𝑡 ) to output (𝑌 𝑡 ) ratio is about 0.48, with a high standard deviation (0.10) revealing a high heterogeneity in the technology relating to the cost of concentrates and of forage. The performance ratio (𝑢 𝑡 ) is on average 39,458 € per labour unit which is relatively high but hides here also a large heterogeneity across farms shown by the high standard deviation.The heterogeneity revealed by Table2in terms of capital and variable inputs notably, indicates heterogeneous technologies across the sample. Such technological heterogeneity may imply differing adjustment costs, and hence differing impact. For this reason equation (

  Table3reports, for these two clusters, the descriptive statistics of the variables used for the classification, while table 4 presents descriptive statistics of additional characteristics of the clusters. On average, farms in cluster 1 (422 farms) exhibit significantly larger size in terms of number of dairy cows (55 vs. 48), are more intensive with a higher share of fodder

	maize in forage area (9 vs. 4%), stocking rate (1.68 vs 1.62 LU/ha) and concentrates cost per
	dairy cow (402 vs. 224€), and have higher costs of work outsourcing per LU (1.84 vs. 1.52€)
	than farms in cluster 2 (239 farms). Likewise, farms in cluster 1 experienced a higher growth
	of dairy cows (0.35 vs. 0.22) and stocking rate (0.06 vs. 0.01) between 2005 and 2014. This
	suggests that farms in cluster 1 are more capital intensive than farms in cluster 2 on average.
	Thus, in what follows farms in cluster 1 are called farms with "high capital intensity", while
	farms in cluster 2 are called farms with "low capital intensity".

Table 3 : Descriptive statistics of the variables used in the hierarchical ascendant classification analysis for the two clusters identified

 3

	Cluster 1	Cluster 2	t-test (equality
	High	Low capital	of means)
	capital	intensity
	intensity	farms
	farms	(239 farms)
	(422 farms)

Notes: The rate of growth is computed as the difference between the value in 2014 and the value in 2005, divided by the value in 2005. *, **, ***: significance at 10, 5, 1 percent level. Source: The authors based on CER FRANCE Ille-et-Vilaine

Table 4 : Descriptive statistics of additional characteristics of the clusters

 4

Source: The authors based on CER FRANCE Ille-et-Vilaine

  adjustment cost model is the adequate framework for our sample.Secondly, the coefficient for the variable including the performance indicator,

	Cluster 1 High reveals that the 𝑢 𝑖,𝑡 𝑝 𝑖,𝑡 Cluster 2 Low Cluster 1 High Cluster 2 Low 𝐾 𝑖,𝑡
		capital	capital	capital	capital
		intensity	intensity	intensity	intensity
		farms	farms	farms	farms
		Average in 2005	Average in 2014
		(standard deviation)	(standard deviation)
	Technical variables
	Number of full time equivalent	2.1	1.8	2.1	1.7
	labour units	(0.84)	(1.04)	(0.83)	(1.14)
	UAA (ha)	76	61	89	68
		(34)	(31)	(43)	(34)
	Total milk produced (Litres)	355,350	266,916	508,786	346,466
		(137,390)	(127,128)	(197,646)	(167,383)
	Milk yield (Litres / cow)	7,375	6,076	7,962	6,475
		(989)	(1,156)	(1,110)	(1,230)
	Financial variables
	EBITDA (€)	87,022	65,922	90,770	65,112
		(44,120)	(41,644)	(47,573)	(43,527)
	EBITDA (€ / 1,000 Litres)	169	195	124	146
		(103)	(110)	(54)	(69)
	EBITDA / Full time equivalent labour unit (€)	41,994 (16,584)	37,636 (16,625)	43,312 (18,318)	39,227 (21,769)
	Current income (€)	10,913	13,097	17,708	18,344
		(22,613)	(20,683)	(26,716)	(20,991)
	Indebtedness (%)	51.6	46.7	50.8	42.8
		(20)	(22)	(20)	(21)
	Number of observations	422	239	422	239
					All this

Table 5 : Results of the estimation of the investment model for the whole sample and with interaction terms: estimated coefficients

 5

	Dependent variable: investment per capital in t+1
	Whole sample	Whole sample (Interaction terms)

Notes: Robust standard errors in parentheses. *, **, ***: significance at 10, 5, 1 percent level. Source: The authors based on

CER FRANCE Ille-et-Vilaine

Table 6 : Average price (in period t) effect on investment per capital in t+1

 6

		Number of observations	Mean	Standard deviation	Minimum	Maximum
	Whole sample	6,610	0.46	1.61	-1.99	17.68
	High capital intensity farms sub-sample	4,220	0.84	1.45	-1.52	15.05
	Low capital intensity farms sub-sample	2,390	0.03	2.14	-2.79	19.80
	Source: The authors based on CER FRANCE Ille-et-Vilaine
	Figure 2:

Evolution of the average price (in period t) effect on investment per capital in t+1 in the sample used between 2005 and 2014

Source: The authors based on CER FRANCE Ille-et-Vilaine

The EBITDA was deflated by the price index of agricultural products with base year 2010.

Livestock Units (LU) allow aggregating the number of livestock heads from different types of animals, here dairy heifers, calves and dairy cows. Each type of animal is assigned a coefficient depending on its feed consumption.


P Chavatte-Palmer
email: pascale.chavatte-palmer@inra.fr

M A Velazquez

H Jammes

V Duranthon

Review: Epigenetics, developmental programming and nutrition in herbivores

Epidemiological studies in humans and animal models (including ruminants and horses) have highlighted the critical role of nutrition on developmental programming. Indeed, it has been demonstrated that the nutritional environment during the periconceptional period and fetal development can altered the postnatal performance of the resultant offspring. This nutritional programming can be exerted by maternal and paternal lineages and can affect offspring beyond the F1 generation. Alterations in epigenetic mechanisms has been proposed as the causative link behind the programming trajectories observed in the offspring. Although a clear cause-effect relationship between epigenetic modifications during early development and later offspring phenotype has not been demonstrated in livestock species, strong associations have been reported for some epigenetic marks (e.g. miRNA) that are worth exploring as possible predictors of future offspring phenotype. In this review we shortly describe the main epigenetic mechanisms studied so far in mammals (i.e. mainly in the mouse) thought to be associated with developmental programing, and discuss the few studies available in mammalian herbivores (e.g. cattle) showing the effect of nutrition on epigenetic marks and the associated phenotype. Clearly, there is a need to develop research on nutritional strategies capable of modulating the epigenetic machinery with positive influence on the phenotype of livestock herbivores. This type of research is needed to alleviate the challenges currently faced by the livestock industry (e.g. impaired fertility of high-yielding dairy cows). This in turn will have a positive influence on animal welfare and productivity of livestock enterprises.

Epigenetics, developmental programming and nutrition in herbivores

  Pascale Chavatte-Palmer, M.A. Velasquez, Hélène Jammes, Véronique Duranthon To cite this version: Pascale Chavatte-Palmer, M.A. Velasquez, Hélène Jammes, Véronique Duranthon. Epigenetics, developmental programming and nutrition in herbivores. 10th International Symposium on the Nutrition of Herbivores, Sep 2018, Clermont-Ferrand, France. pp.500, 10.1017/S2040470018000146. hal-02735571