PhD Lauriane Cornuault

MD François-Xavier Hérion

MS Célia Bourguignon

MS Paul Rouault

MD Ninon Foussard

BS Philippe Alzieu

PharmD Candice Chapouly

PhD Alain-Pierre Gadeau

MD, PhD Thierry Couffinhal

PhD Marie-Ange Renault
email: marie-ange.renault@inserm.fr

Partial mural cell ablation disrupts coronary vasculature integrity and induces systolic dysfunction

Keywords: Pericytes, heart, coronary micro-vasculature, cardiac function

Background: While the critical role of pericytes in maintaining vascular integrity has been extensively demonstrated in the brain and the retina, little is known about their role in the heart. We aim to investigate structural and functional consequences of partial pericyte depletion (about 60%) in the heart of adult mice. Methods and Results: To deplete pericyte in adult mice, we used Pdgfrb-Cre/ERT2; Rosa DTA mice and compared their phenotype to the one of control mice (Rosa DTA ) chosen among their littermates. Cardiac function was assessed via echocardiography and left ventricle (LV) catheterization one month after the first tamoxifen injection. Mice depleted with pericytes displayed increased coronary endothelium leakage and activation, which was associated with increased CD45 + cell infiltration in the heart. Pericyte depletion also modified the phenotype of cardiomyocytes with an increased expression of Myosin Heavy Chain 7 and decreased expression of ATPase Sarcoplasmic/Endoplasmic Reticulum Ca2+ Transporting 2 and Connexin-43. As a consequence, mice depleted with pericytes had a reduced LVEF and an increased end-diastolic pressure, demonstrating both systolic and diastolic dysfunction. Consistently, mice depleted with pericytes presented a decreased LV contractility and an increased LV relaxation time (dP/dt min ). Besides, this study reveals that cardiac pericytes may undergo strong remodeling upon injury. Conclusions: Cardiac pericyte depletion induces both systolic and diastolic dysfunction, suggesting that pericyte dysfunction may contribute to the occurrence of cardiac diseases.

Clinical Perspective

What Is New?

 The present study demonstrates cardiac pericytes are critical to maintain proper cardiac function.

 Cardiac pericytes modulate the properties of cardiomyocytes directly, notably CX43 expression level.

 Cardiac pericytes prevent cardiac inflammation by downregulating Il-6 in both ECs and cardiomyocytes.

What Are the Clinical Implications?

 Cardiac pericytes should be taken into account when investigating the pathophysiology of cardiac disease.

Research Perspective

What New Question Does This Study Raise?

 Do cardiac pericyte participate in the pathophysiology of cardiac diseases notably heart failure What Question Should be Addressed Next?

 How is the phenotype of cardiac pericyte modified by cardiovascular risk factor?

Non-standard Abbreviations and Acronyms

Introduction

Pericytes are cells that reside in the microvasculature and share a basement membrane with the underlying endothelial cells (ECs). Pericyte density varies between different organs. In the brain, the organ with the highest density of pericytes; the EC-pericyte ratio varies between 1:1 and 3:1 [START_REF] Armulik | Endothelial/pericyte interactions[END_REF] . Accordingly, the brain is also the organ in which the role of pericyte has been the most investigated. Notably, in this organ, pericytes are thought to play a role in regulating permeability across the blood-brain barrier [START_REF] Armulik | Pericytes regulate the blood-brain barrier[END_REF][START_REF] Daneman | Pericytes are required for blood-brain barrier integrity during embryogenesis[END_REF] since mice deficient in pericytes or exhibiting decreased pericyte function have increased vascular permeability [START_REF] Armulik | Pericytes regulate the blood-brain barrier[END_REF][START_REF] Daneman | Pericytes are required for blood-brain barrier integrity during embryogenesis[END_REF] . Notably, pericyte function and maintenance depends on Plateletderived growth factor submit B (PDGFB) expression by quiescent adult microvascular brain endothelium [START_REF] Vazquez-Liebanas | Adult-induced genetic ablation distinguishes PDGFB roles in blood-brain barrier maintenance and development[END_REF] .

The pericyte content of the cardiac micro-vasculature is thought to be closer to the one of the cerebral vasculature, with an endothelial-pericyte ratio of 2:1-3:1 [START_REF] Su | Emerging Role of Pericytes and Their Secretome in the Heart[END_REF] . It is also likely that pericytes contribute to the maintenance of microvascular function in the heart given that the loss of pericytes has been associated with increased vascular permeability 6 7 , endothelial activation [START_REF] Siao | ProNGF, a cytokine induced after myocardial infarction in humans, targets pericytes to promote microvascular damage and activation[END_REF] and altered vasomotricity [START_REF] Chintalgattu | Coronary microvascular pericytes are the cellular target of sunitinib malateinduced cardiotoxicity[END_REF] . Notably, in mice treated with PDGF receptor inhibitors (sunitinib and CP-673451), decreased pericyte coverage of cardiac capillaries was associated with increased permeability, impaired endothelium-dependent vasodilation and decreased coronary flow reserve [START_REF] Chintalgattu | Coronary microvascular pericytes are the cellular target of sunitinib malateinduced cardiotoxicity[END_REF] . Besides, the expression of a mutant form of pro-Nerve growth factor (ProNGF), unable to be matured and which may target pericytes, was shown to induce endothelial activation and vascular leakage [START_REF] Siao | ProNGF, a cytokine induced after myocardial infarction in humans, targets pericytes to promote microvascular damage and activation[END_REF] . Finally, pericyte loss observed in both Sirtuin 3 [START_REF] He | Ablation of SIRT3 Causes Coronary Microvascular Dysfunction and Impairs Cardiac Recovery Post Myocardial Ischemia[END_REF] and Notch Receptor 3 (Notch3) [START_REF] Tao | Notch3 deficiency impairs coronary microvascular maturation and reduces cardiac recovery after myocardial ischemia[END_REF] deficient mice was associated with decreased coronary flow reserve. Importantly, in each of these studies, impaired microvascular integrity and function has been also associated with cardiac dysfunction. Notably, administration of the PDGF receptor-specific inhibitor CP-673451 was shown to decrease left ventricular ejection fraction (LVEF) [START_REF] Chintalgattu | Coronary microvascular pericytes are the cellular target of sunitinib malateinduced cardiotoxicity[END_REF] , expression of the mutant form of ProNGF induced dilated cardiomyopathy, cardiac fibrosis, and contractile dysfunction [START_REF] Siao | ProNGF, a cytokine induced after myocardial infarction in humans, targets pericytes to promote microvascular damage and activation[END_REF] , Sirtuin 3 deficient mice showed decreased EF and fractional shortening after myocardial infarction 8 while Notch3 deficient mice were shown to exhibit cardiac hypertrophy [START_REF] Tao | Notch3 deficiency impairs coronary microvascular maturation and reduces cardiac recovery after myocardial ischemia[END_REF] . Altogether these studies suggest that pericytes may be beneficial to the coronary vasculature which may have a direct or indirect impact on cardiac function. However, in the heart, pericytes were also proposed to have deleterious effects. Notably, pericytes may become myofibroblasts and contribute to fibrosis. [START_REF] Greenhalgh | Origins of fibrosis: pericytes take centre stage[END_REF] . Also, capillary pericytes are suggested to be responsible for coronary no-reflow after myocardial ischemia by inducing capillary constriction [START_REF] O'farrell | Capillary pericytes mediate coronary no-reflow after myocardial ischaemia[END_REF] via a mechanism involving G Protein-Coupled Receptor 39 [START_REF] Methner | Mechanism and potential treatment of the "no reflow" phenomenon after acute myocardial infarction: role of pericytes and GPR39[END_REF] .

In pathophysiological conditions, cardiac pericytes have been shown to either decrease or increase in number. Loss of pericytes has been shown to constitute a remarkable feature of aging in several organs including the heart in both mice and humans [START_REF] Chen | High-resolution 3D imaging uncovers organ-specific vascular control of tissue aging[END_REF] . Also, high-fat diet was shown to induce pericyte loss in the heart of mice [START_REF] Zeng | High-fat diet induces cardiac remodelling and dysfunction: assessment of the role played by SIRT3 loss[END_REF] . On the contrary, cardiac pericytes were shown to be disorganized and more numerous in Zucker fatty/spontaneously hypertensive heart failure F1 hybrid (ZSF1) rats [START_REF] Van Dijk | Distinct Endothelial Cell Responses in the Heart and Kidney Microvasculature Characterize the Progression of Heart Failure With Preserved Ejection Fraction in the Obese ZSF1 Rat With Cardiorenal Metabolic Syndrome[END_REF] . In other conditions, such as Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the phenotype of pericytes is proposed to be modified by the acquisition of a more contractile phenotype [START_REF] Robinson | Role of angiotensin-converting enzyme 2 and pericytes in cardiac complications of COVID-19 infection[END_REF] . Finally, pericyte dedifferentiation leading to increased endothelial permeability is proposed to be induced by the downregulation of Hypoxia-Induced Endoplasmic Reticulum Stress Regulating lncRNA in human heart failure [START_REF] Bischoff | Identification and Functional Characterization of Hypoxia-Induced Endoplasmic Reticulum Stress Regulating lncRNA (HypERlnc) in Pericytes[END_REF] .

In conclusion, cardiac pericytes may participate in the pathophysiology of cardiac diseases through a wide range of mechanisms. However, studies performed so far are mainly descriptive and their conclusions are based on associations. The purpose of the present study is to investigate the specific consequences of pericyte depletion on cardiac structure and function.

Methods

The authors declare that all supporting data are available within the article and its online supplementary files. Also, we used the ARRIVE reporting guidelines [START_REF] Du Sert | The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research[END_REF] .

Mice

B6.Cg-Tg(Pdgfrb-cre/ERT2)6096Rha/J (strain# 029684), Gt(ROSA)26Sortm4(ACTB-tdTomato,-EGFP)Luo/J (Strain # 007576) (named Rosa mTmG in the manuscript) and B6.129P2-Gt(ROSA)26Sortm1(DTA)Lky/J (Strain#009669) (naimed Rosa DTA in the manuscript) were obtained from the Jackson laboratory.

Pdgfrb-cre/ERT2 mice were crossed with Rosa DTA/DTA mice to obtain Pdgfrb-CreERT2; Rosa DTA /+ (Pdgfrb-DTA) mice and control Rosa DTA /+ mice.

Pdgfrb-cre/ERT2 mice were crossed with Rosa mTmG/mTmG mice to obtain Pdgfrb-CreERT2; Rosa mTmG /+ mice.

The Cre recombinase in Pdgfrb-Cre/ERT2 mice was activated by intraperitoneal injection of 50 mg/kg/day tamoxifen (Sigma-Aldrich) for 5 consecutive days at 8 weeks of age and eventually repeated 2 weeks later at 10 weeks of age.

Animal were house in a conventional animal facility. Animal experiments were performed in accordance with the guidelines from Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes and approved by the local Animal Care and Use Committee of Bordeaux University. The appearance, weight, clinical signs and the behavior of the mice were monitored every day in order to prevent animal pain and distress. Both males and females were used. The number of animals per group necessary to have a 90% chance to find a significant difference between 2 groups was calculated using G power 3.1 software. The calculation was made for the measurement of cardiac function (notably LVEF). Mice were either sacrificed by cervical dislocation or exsanguination under deep anesthesia (ketamine 100 mg/kg and xylazine 20 mg/kg, IP). A total of 60 mice were used in the study. No animal were excluded from the study.

Echocardiography

Left-ventricular (LV) EF and LV dimension were measured on a high-resolution echocardiographic system equipped with a 30-MHz mechanical transducer (VEVO 2100, VisualSonics Inc.) as previously described [START_REF] Renault | The Hedgehog transcription factor Gli3 modulates angiogenesis[END_REF][START_REF] Roncalli | Sonic hedgehog-induced functional recovery after myocardial infarction is enhanced by AMD3100-mediated progenitor-cell mobilization[END_REF] . Mice were anchored to a warming platform in a supine position, limbs were taped to the echocardiograph electrodes, and chests were shaved and cleaned with a chemical hair remover to minimize ultrasound attenuation. UNI'GEL ECG (Asept Inmed), from which all air bubbles had been expelled, was applied to the thorax to optimize the visibility of the cardiac chambers. LVEF were evaluated by planimetry as recommended [START_REF] Schiller | Recommendations for quantitation of the left ventricle by twodimensional echocardiography. American Society of Echocardiography Committee on Standards, Subcommittee on Quantitation of Two-Dimensional Echocardiograms[END_REF] . Two-dimensional, parasternal long-axis and short-axis views were acquired, and the endocardial area of each frame was calculated by tracing the endocardial limits in the long-axis view, then the minimal and maximal areas were used to determine the left-ventricular end-systolic (ESV) and end-diastolic (EDV) volumes, respectively. The system software uses a formula based on a cylindrical-hemiellipsoid model (volume=8.area²/3π/ length) [START_REF] Mohan | Cross sectional echocardiographic left ventricular ejection fraction: method based variability[END_REF] .

The LVEF was derived from the following formula: (EDV-ESV)/EDV*100. The cardiac wall thickness, left ventricular posterior wall (LVPW), inter-ventricular septum (IVS) and left ventricular internal diameter (LVID) were calculated by tracing wall limits in both the long and short axis views.

LV pressure /systolic blood pressure measurement LV diastolic pressure measurement was assessed via an invasive catheterization technique. Mice were anesthetized with Isoflurane. A Scisense pressure catheter (Transonic) was inserted into the LV through the common carotid artery. Pressure was recorded using LabChart software. End diastolic pressure, dP/dt minimum and maximum, Tau and heart rate were automatically calculated by a curve fit through end-systolic and end-diastolic points on the pressure plot.

Blood sampling for biochemical marker analysis/NFS

Blood samples were collected by the heparin retroorbital bleeding method at sacrifice. Blood cell counts were determined using an automated counter (scil Vet abc Plus+). Plasma was separated by a 10-min centrifugation at 2500 g and then stored at -80°C. Concentrations of the following biomarkers were measured using an Architect CI8200 analyzer (Abbott Diagnostics, North Chicago, Illinois, USA): triglycerides, using the lipoprotein-lipase/glycerol kinase/oxidase/peroxidase method; total cholesterol, using the esterase/oxidase/peroxidase method; and high density lipoproteins (HDL) cholesterol, using the accelerator/selective detergent/esterase/oxidase/peroxidase method. Low density lipoproteins (LDL) cholesterol was then estimated using the Friedewald formula

(LDL cholesterol [mmol/L] = total cholesterol -HDL cholesterol -[triglycerides/2,2], or LDL cholesterol [mg/dL] = total cholesterol -HDL cholesterol -[triglycerides/5]).

Tissue staining/Immunostaining

ECs were identified using anti-CD31 or anti-Podocalyxin (PODXL) antibodies or Biotin-conjugated Isolectin B4 (Vector). Muscularized vessels were identified using anti-alpha-smooth muscle actin (SMA) antibodies. Pericyte were identified using anti-Chondroitin Sulfate Proteoglycan 4 (CSPG4 also known as NG2) or anti-Platelet Derived Growth Factor Receptor Beta (PDGFRB) antibodies.

Leucocyte, macrophage, T-cell and B-cell infiltrations were quantified using anti-CD45, anti-CD68, anti-CD3 and anti-B220 antibodies, respectively. Endothelial adherens junction was characterized using anti-Cadherin-5 (CDH5) antibodies. Edema was measured after Fibrinogen B chain (FGB) staining of muscle sections and quantified as the mean FGB-positive areas. (See Supplemental table 1 for antibody references).

Cardiomyocyte mean surface area was measured using ImageJ software after membrane staining with Wheat Germ Agglutinin (WGA), Alexa Fluor™ 488 Conjugate (Invitrogen). Fibrosis was assessed after sirius red staining of heart sections by quantifying the percentage of red stained area.

Quantifications were done on images were acquired by using an Axiozoom V16 or axioscope A1 (Zeiss) under 200x magnification. They were conducted on 10 randomly selected images were performed using ImageJ/Fiji v2.0.0-rc-59 software (National Institute of Health, USA) by an investigator blinded to genotype. More precisely, each sample received a unique number. At the end of the experiment, the genotype/treatment for each sample was revealed to enable data analysis.

For immunohistochemical analyses, primary antibodies were sequentially coupled with biotinconjugated secondary antibodies and streptavidin-horseradish peroxidase (HRP) complex (Amersham). Staining was then revealed with a diaminobenzidine (DAB) substrate kit (Vector Laboratories) and tissue sections were counterstained with hematoxylin (see Supplemental table 2 for secondary antibody references).

For immunofluorescence analyzes, primary antibodies were resolved with Alexa-Fluor-conjugated secondary polyclonal antibodies (see Supplemental table 2). Nuclei were counterstained with DAPI (4′,6-diamidino-2-phenylindole).

For both immunohistochemical and immunofluorescence analyses, negative control experiments to check for antibody specificity were performed using secondary antibodies.

Cell culture

Human cardiac microvascular endothelial cells (HMVEC-C) (Lonza) were cultured in endothelial basal medium-2 (EBM-2) supplemented with endothelial growth medium (EGM™)-2 BulletKits™ (Lonza). Cells at passage 3 were used.

Mouse pericytes were isolated from 10 day-old pups. Briefly, hearts were dissociated using 2 mg/mL type IV collagenase (Gibco™, ThermoFisher) for 1 hour at 37°C and the resulting dissociated cells were filtrated on a 30 µm strainer. Pericytes were labelled with rat anti-mouse CD146 microbeads (Miltenyi Biotec). Labelled cells were then isolated magnetically, plated and cultured in pericyte Growth Medium 2 (PromoCell). Cells at passage 1 and 2 were used.

HMVEC-C and mouse pericytes were co-cultured in 0.4 µm-pored Transwells in 6-well plates (Corning). HMVEC-C were cultured together or not with pericytes for 24 hours.

Mouse adult cardiomyocytes were isolated: hearts were dissociated using 300 U/mL Collagenase type 2 (Worthington) via aortic catheterization and a 5-minute perfusion at 37°C. The resulting dissociated cells were then filtrated on a 400 µm mesh and centrifuged at 500 rpm for Mouse cardiac pericytes were added or not to the cardiomyocytes at the same time as the change of the adhesion medium into culture medium. Cardiomyocytes were cultured together or not with pericytes for 24 hours.

Real-time quantitative polymerase chain reaction (RT-qPCR)

RNA was isolated using Tri Reagent® (Molecular Research Center Inc) as instructed by the manufacturer, from heart tissue that had been snap-frozen in liquid nitrogen and homogenized. For quantitative RT-PCR analyses, total RNA was reverse transcribed with M-MLV reverse transcriptase (Promega) and amplification was performed on an AriaMx Real Time PCR system (Agilent Technologies) using GoTaq® qPCR master mix (Promega). Primer sequences are reported in Supplementary table 3.

The relative expression of each mRNA was calculated by the comparative threshold cycle method and normalized to 18S rRNA or Actb mRNA expression.

Western blot analysis

Intercellular Adhesion Molecule 1 (ICAM1) and Vascular Cell Adhesion Molecule 1 (VCAM1) protein levels were evaluated by SDS polyacrylamide gel electrophoresis (PAGE) using goat anti-ICAM1 (R&D systems Cat# AF-796) and rabbit anti-VCAM1 (abcam, Cat# ab134047) antibodies respectively. ATPase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 2 (ATP2A2) protein level was evaluated by SDS PAGE using rabbit anti-ATP2A2 antibodies (Badrilla, Cat# A010-80). Phospholamban (PLN) phosphorylation at Serine (Ser) 16 and Threonin (Thr)17 was evaluated by SDS PAGE using rabbit anti-phospho-PLN Ser16 (Badrilla, Cat# A010-12), rabbit anti-phospho-PLN Thr17 (Badrilla, Cat# A010-13) and mouse anti-total PLN (Badrilla, Cat# A010-14). Ryanodine Receptor 2 (RYR2) phosphorylation was evaluated by SDS PAGE using rabbit anti-phosphoRYR2 antibodies (Badrilla, Cat# A010-31).

Human CDH5 and ICAM1 protein levels were evaluated by SDS PAGE using rabbit anti-CDH5 (Cell signaling technology Cat# 2500) and mouse anti-ICAM1 (Santa Cruz biotechnology, Cat# sc-8439) antibodies respectively.

Protein loading quantity was controlled using mouse monoclonal anti-α-tubulin (TUB1A1) antibodies (Sigma, Cat# T5168).

Statistics

Results are reported as mean ± SEM. Comparisons between groups were analyzed for significance with the non-parametric Mann-Whitney test using GraphPad Prism v8.0.2 (GraphPad Inc, San Diego, Calif). The normality and variance were not tested. Differences between groups were considered significant when p≤0.05.

Results

Cardiac capillaries are covered by smMHC-, NG2+ pericytes

In the heart, less than 2% of vessels, essentially arterioles, are covered by smooth muscle myosin heavy chain (smMHC) positive smooth muscle cells. Indeed, the vast majorities of capillaries, i.e. 95% of them, are covered by NG2 positive, SMA negative pericytes (Figure 1A-C). Cardiac pericytes are stellate shaped cells with cytoplasmic processes contacting several capillaries (Figure 1D-F). Importantly, contrary to the brain which contains a significant amount of ensheathing and mesh pericytes [START_REF] Abdelazim | Pericyte heterogeneity identified by 3D ultrastructural analysis of the microvessel wall[END_REF] , more than 95% of cardiac pericytes are thin-stranded pericytes (Figure S1) that do not express smMHC (Figure S2).

Pdgfrb-DTA mice display a 60% pericyte depletion in the heart

To investigate the role of cardiac pericytes, we have chosen to measure the pathophysiological consequences of partial pericyte depletion on cardiac microvasculature, cardiomyocytes, and heart function. To do so, we specifically induced diphtheria toxin A (DTA) expression under the Pdgfrb promoter in adult mice. At first, we verified that the Pdgfrb promoter was active in cardiac pericytes in adult mice, by crossing Pdgfrb-Cre/ERT2 mice with Rosa mTmG mice. As shown in Figure S3A-B, GFP expression, in the heart was detected in most NG2+ pericytes but also, as expected, by SMA+ smooth muscle cells.

In the meantime, we have examined GFP expression in several other organs, notably the aorta, in which GFP expression was detected in a few smooth muscle cells of the media, in the brain, in which GFP was detected in perivascular cells but also unexpectedly in glial cells, in the kidney in which GFP expression was detected in some SMA+ cells of arterioles but not in NG2 + cells within the glomerulus. Also, GFP expression was detected in capillaries within the lacteals but not in SMCs surrounding the intestine. In the lung GFP expression was detected in both arterial and capillary mural cells, however, mural cells covering lung capillaries did not express NG2. Finally in the skeletal muscle, similarly to what we found in the heart, GFP expression was detected in every pericyte and a few SMCs (Figure S4).

Pericyte depletion was then induced by tamoxifen injections in 8 week-old Pdgfrb-Cre/ERT2; Rosa-DTA mice. Notably, to maintain constant pericyte depletion in the heart over time, we had to repeat tamoxifen administrations every 2 weeks. Indeed, in mice administered with tamoxifen once during 5 consecutive days, while the percentage of NG2 positive vessels was about 20% 7 days after the first tamoxifen injection, it was back to 90% after 28 days (Figure S5A-B) suggesting cardiac pericytes can be renewed.

After 2 series of tamoxifen injections, i.e. one month after the first one, the percentage of cardiac capillaries covered by pericytes was reduced by about 60% in Cre-positive mice compared to Crenegative mice (Figure 1G-H), and the number of arterioles covered by SMCs reduced by about 50% (Figure 1I-J), SMC coverage of coronary arteries was sparser (Figure 1I).

In parallel, we have examined the effectiveness of mural cell depletion in other organs. The most severe phenotypes were observed in the aorta, in which the media was strongly decellularized, in the intestine lacteals, and in the skeletal muscle in which NG2+ pericytes have been fully depleted (Figure S6). These "side" depletions induced significant weight loss (Figure S7A-B) associated with circulating markers of malnutrition including decreased hemoglobin levels, triglyceride, cholesterol, and proteins levels together with decreased Aspartate aminotransferase and Alkaline phosphatase levels (Figure S7C-K). As expected, the SMC loss in the aorta decreased diastolic blood pressure. Systolic blood pressure and heart rate were not modified (Figure S7L-O). Finally, consistent with pericyte loss in the skeletal muscle, mice were severely intolerant to exercise (Figure S7P).

Altogether, these results demonstrate that Pdgfrb-DTA mice display successful pericyte-depletion in the heart. However, considering the absence of pericyte specific marker and/or the absence of cardiac mural cell-specific markers allowing pericyte-depletion in the heart only, the interpretation of the data presented in this paper may have some limitations.

Cardiac Pericyte are renewed

While performing a thorough histological analysis of the heart of Pdgfrb-DTA mice, we were surprised to see that the number of small vessels (mainly capillaries with a diameter <10 µm) covered by SMA+ cells was significantly increased in the heart of Pdgfrb-DTA mice compared to control mice (Figure 2A -B). These SMA+ cells were actually NG2 positive, suggesting that the remaining pericytes in these mice display an activated phenotype characterized by SMA expression (Figure S8A). Besides, the heart of Pdgfrb-DTA mice displayed an increased number of Vimentin (VIM) positive mesenchymal cells (Figure S8B-C). Notably, among VIM-positive cells, the number of PDGFRB negative cells (fibroblasts) did not increase (Figure S8D-E) while the number of PDGFRB positive cells significantly increased (Figure S8F). These results suggest that (1) the number of cardiac fibroblasts did not change and (2) that some pericytes expressed PDGFRB but not NG2, which we verified by doing a PDGFRB, NG2 double immunostaining (Figure 2C). Indeed, we found that, while all NG2+ pericytes expressed PDGFRB in both Pdgfrb-DTA and control mice (Figure 2D), all PDPGFRB+ pericytes did not express NG2, especially in Pdgfrb-DTA mice (Figure 2E). Considering that we found that cardiac pericytes are renewed (Figure S5), and that mice were sacrificed 10 to 13 days after the last tamoxifen injection, we believe that PDGFRB+NG2-cells are pericytes being renewed.

Consistent with the fact that the number of cardiac fibroblasts did not change, interstitial fibrosis was not significantly modulated in Pdgfrb-DTA mice; only perivascular fibrosis was (Figure S9A-C). Moreover, the expression of several markers of fibrosis including Collagen Type I Alpha 1 Chain (Col1a1), Collagen Type III Alpha 1 Chain (Col3a1), Transforming Growth Factor Beta 1 (Tgfb1), and Connective Tissue Growth Factor (Ctgf) mRNA was not significantly different between Pdgfrb-DTA mice and control mice (Figure S9D-G).

Altogether, these results suggest that partial pericyte depletion induces activation and renewal of the remaining pericytes. Pericyte undergoing remodeling may at some points express SMA and/or PDGFRB, but not NG2.

Pericyte depletion modifies the phenotype of cardiac capillary

Then we measured the consequences of mural cell depletion on the cardiac microvasculature. As mentioned above, this was done one month after the induction of pericyte depletion. As shown in figure 3A-B, pericyte depletion did not induce microvessel rarefaction, since capillary density was equivalent in both Pdgfrb-DTA and control mice. However, pericyte depletion led to an increased capillary diameter (Figure 3C), possibly reflecting capillary disorganization, and endothelial activation characterized by a significant increase in ICAM1 and VCAM1 expression. This phenotype was attested via immunostaining (Figure 3D-E) and confirmed by western blot analyses of total heart extracts (Figure 3F-H). Also pericyte depletion led to altered endothelial intercellular junctions since we observed that CDH5 staining was discontinuous in pericyte-depleted mice (Figure 3I-J). We confirmed that endothelial intercellular junction integrity was altered in Pdgfrb-DTA mice since Fibrinogen (FGB) extravasation was significantly increased in Pdgfrb-DTA mice compared to control mice (Figure 3K-L) revealing that capillary permeability is abnormally increased in pericyte depleted mice.

To make sure these phenotypes were not due to the "unwanted" features of Pdgfrb-DTA mice such as malnutrition or decreased diastolic pressure, we confirmed the critical role of cardiac pericytes in maintaining endothelial integrity and immune quiescence in vitro. To do so, we co-cultured mouse cardiac pericytes with human microvascular cardiac ECs (HMVEC-C) (Figure S10A). HMVECs cocultured with pericytes expressed significantly higher CDH5 protein levels, while Cadherin-2 (CDH2) mRNA was downregulated (Figure S10B-D) signing increased endothelial differentiation. Consistent with in vivo data, pericytes prevented HMVEC-C activation by decreasing ICAM1, VCAM1, and Interleukin 6 (IL-6 levels) (Figure S10E-J).

Overall, these results confirm association studies and demonstrate that pericytes are necessary for cardiac capillary integrity and immune quiescence, just as they are in the brain.

Notably, modification of the coronary vessel phenotype did not lead to cardiac hypoxia, since Hypoxia Inducible Factor 1 Subunit Alpha (HIF1A) levels were not different in the heart of Pdgfrb-DTA and control mice (Figure S11A). However, consistent with endothelial activation, Pdgfrb-DTA mice display cardiac inflammation with significantly increased Il-6 mRNA level (Figure S11B) and increased CD45+ leukocyte infiltration (Figure S11C-D) in the heart. Notably, neither CD68+ macrophage nor CD3+ T-cell infiltration was increased (Figure S11E-H), while B220+ B-cell infiltration was significantly increased (Figure S11I-J).

Pericyte depletion modifies the phenotype of cardiomyocytes

To assess the phenotype of cardiomyocytes, we first performed WGA staining on heart cross-sections and found that cardiomyocytes did not show hypertrophy (Figure 4A-B). On the contrary, Pdgfrb-DTA mice even show slight cardiac atrophy. Indeed, the heart weight over tibia length ratio tended to decrease (Figure 4C) which was confirmed by measuring the LV mass via echocardiography (Figure 4D). Consistently, cardiac wall thickness (i.e. IVS and LVPW) measured via echocardiography was thinner in Pdgfrb-DTA mice (Figure 4E-F). The LVID in diastole was not modified (Figure 4G).

Notably, WGA staining revealed that Pdgfrb-DTA had expanded cardiac interstitial space (Figure 4A), which is consistent with the increased vascular leakage.

Importantly, cardiomyocytes showed a modified phenotype with increased Myosin Heavy Chain 7 (Myh7) mRNA expression (Figure 4H) and decreased Connexin 43 (CX43) levels (Figure 4I-K). To test whether cardiomyocyte contractility could be affected by pericyte depletion, we measured expression and/or phosphorylation of proteins regulating calcium homeostasis. We first measured ATPase Sarcoplasmic/Endoplasmic Reticulum Ca2+ Transporting 2 (ATP2A2) expression and found it significantly downregulated at both mRNA and protein levels in Pdgfrb-DTA mice compared to control mice (Figure 5A-C). This was associated with diminished Phospholamban (PLN) phosphorylation at both Serine 16 and Threonine 17. (Figure 5D-F). On the contrary, Ryanodine Receptor 2 (RYR2) phosphorylation at serine 2814 was not significantly different in both groups (Figure 5G-H). To assess cardiomyocyte stiffness, we quantified the mRNA expression of each Titin (Ttn) isoform. Expression of stiff Ttn isoform N2B was significantly diminished while expression of Ttn N2BA isoform tended to increase. Accordingly, the N2BA/N2B ratio was significantly increased (Figure 5I-K).

Altogether these results demonstrate that pericyte depletion modifies the phenotype of cardiomyocytes which displays a Titin isoform ratio consistent with heart failure in humans [START_REF] Borbély | Hypophosphorylation of the Stiff N2B titin isoform raises cardiomyocyte resting tension in failing human myocardium[END_REF] .

Once again, in order to make sure these changes in the phenotype of cardiomyocytes were not due to the "unwanted" features of Pdgfrb-DTA mice such as malnutrition or decreased diastolic pressure, we performed co-culture assays. More specifically, mouse adult cardiomyocytes were co-cultured or not with mouse cardiac pericytes (Figure S12A). While the presence of pericytes did not modify Atp2a2 or Myh7 mRNA expression in cardiomyocytes (Figure S12B-C), it did significantly increase Cx43 mRNA expression (Figure S12D) demonstrating for the first time that cardiac pericytes may modify the phenotype of cardiomyocytes directly. Besides, we found that pericytes have an antiinflammatory effect on cardiomyocytes like they have on ECs by downregulating Il-6 mRNA expression (Figure S12E).

In order to test whether cardiac inflammation (i.e. Il-6 overexpression) may indirectly also affect cardiomyocytes, mouse adult cardiomyocytes were treated or not with recombinant IL-6. We found that IL-6 did not modulate Cx43 or Myh7 mRNA expression. However, it significantly downregulated Atp2a2 mRNA expression.

Altogether these results demonstrate that cardiac pericytes may affect the phenotype of cardiomyocytes directly, notably by upregulating CX43 and downregulating IL-6. The increased cardiac inflammation secondary to pericyte depletion is likely responsible for the decreased Atp2a2 mRNA expression observed in the heart of Pdgfrb-DTA mice. However, the mechanism leading to the increased Myh7 mRNA expression remains to be identified.

Pericyte depletion impairs both systolic and diastolic function

Finally, to test whether pericyte depletion and the associated cardiac remodeling result in cardiac dysfunction, we performed an echocardiography and left ventricular catheterization. As shown in figure 6, LVEF was significantly reduced in Pdgfrb-DTA mice indicating systolic dysfunction (Figure 6A). LVEF was positively correlated with pericyte coverage (Figure 6B). Consistent with systolic dysfunction, cardiac contractility (attested by dP/dt max and contractility index) significantly decreased (Figure 6C-E) while LV end-systolic volume significantly increased (Figure 6F-G) in pericyte depleted mice compared to control mice.

Mice depleted from cardiac pericytes also displayed diastolic dysfunction attested by increased end diastolic pressure (EDP) (Figure 6H,I) and decreased dP/dt min (Figure 6J-K). Relaxation constant Tau tended to increase in Pdgfrb-DTA mice but the difference did not reach significance (Figure 6L).

In conclusion, this study demonstrates for the first time that pericytes are necessary for proper cardiac function possibly by affecting the phenotype of cardiomyocytes directly.

Discussion

The present study demonstrates for the first time that, just like in the brain, pericytes are necessary for endothelium integrity and immune quiescence in the heart. Importantly, in this organ, pericytes are not only necessary for coronary vasculature integrity but also for proper cardiac function suggesting that modifications of pericyte properties may participate in the pathophysiology of cardiac diseases.

Besides, this study reveals that cardiac pericytes may undergo strong remodeling upon injury (e.i. renewal, expression of SMA). This is consistent with what has been observed in obese and hypertensive ZSF1rats 15 in which pericytes were shown to be disorganized and more numerous. This piece of data actually suggests that, even though pericytes may induce similar effects in the brain and in the heart by maintaining microvasculature integrity, pericyte turnover and biology in these organs may be very different. Indeed, in the central nervous system, pericyte impairment seems to be essentially characterized by pericyte loss. Notably, one of the earliest hallmarks of diabetic retinopathy is the loss of pericytes. The diabetic microenvironment is suggested to be particularly detrimental to pericyte survival in the retina since during the early stages of retinopathy, the pericyte to EC ratio decreases from 1:1 to 1:4 [START_REF] Robison | Early retinal microangiopathy: prevention with aldose reductase inhibitors[END_REF] . In the brain, pericyte number and coverage in the cortex and hippocampus of subjects with Alzheimer's disease compared with neurologically intact controls were shown to be reduced by 59% and 60% [START_REF] Sengillo | Deficiency in mural vascular cells coincides with blood-brain barrier disruption in Alzheimer's disease[END_REF] . Similarly, in the spinal cord, immunostaining for PDGFRB, has indicated a 54 % (p < 0.01) reduction in the number of pericytes in amyotrophic lateral sclerosis patients compared to control patients [START_REF] Winkler | Blood-spinal cord barrier breakdown and pericyte reductions in amyotrophic lateral sclerosis[END_REF] . However, in the heart, pericyte impairment is more likely characterized by phenotypic changes with the acquisition of an 'activated' phenotype. Notably, the number of pericytes was shown to increase in the heart of mice infused with angiotensin-2. Moreover, these pericytes express higher levels of SMA [START_REF] Su | Sirtuin 3 is essential for hypertension-induced cardiac fibrosis via mediating pericyte transition[END_REF] [START_REF] Øie | Activation of Notch signaling in cardiomyocytes during post-infarction remodeling[END_REF] .

Importantly, this study suggests that pericytes may modify the phenotype of cardiomyocytes directly, notably by upregulating Cx43 and downregulating Il-6. Another study has also suggested that pericytes may dialogue with cardiomyocytes directly via MiR-132 transfer [START_REF] Katare | Transplantation of Human Pericyte Progenitor Cells Improves the Repair of Infarcted Heart Through Activation of an Angiogenic Program Involving Micro-RNA-132[END_REF] .

One of the main limitations of this study is technical because a mouse model only targeting cardiac pericytes still needs to be generated to obtain robust and specific characterization of the role of cardiac pericytes. Indeed, the Pdgfrb promoter does not only target pericytes but also SMCs and the Pdgfrb promoter does not only target cardiac mural cells but mural cells of the entire body. However, several mouse models targeting pericytes have been used especially in the brain and most of these models are based on PDGFB-PDGFRB signaling which is crucial for pericyte recruitment and survival 31 . At first, developmental studies were performed on Pdgfb [START_REF] Levéen | Mice deficient for PDGF B show renal, cardiovascular, and hematological abnormalities[END_REF] and Pdgfrb 33 deficient embryos, which are not viable. Then mice harboring mutations within the Pdgfrb gene (the Pdgfrb redeye/redeye mutant [START_REF] Jadeja | A CNS-Specific Hypomorphic Pdgfr-Beta Mutant Model of Diabetic Retinopathy[END_REF] and the Pdgfrb F7/F7 mutant 35 ) were used. These mutants display progressive pericyte loss, especially in the central nervous system (brain and retina). Mouse models with inducible pericyte depletion have been developed. These models are tamoxifen-inducible, and use either Diphtheria toxin A (DTA) or its receptor Diphtheria toxin receptor (DTR) to induce cell ablation [START_REF] Eilken | Pericytes regulate VEGF-induced endothelial sprouting through VEGFR1[END_REF] . These last models allow the exploration of the role of pericytes in adults excluding the potential consequences of developmental defects. The Pdgfrb-Cre/ERT2; Rosa iDTR model is proposed to induce a less severe pericyte depletion than the Pdgfrb-Cre/ERT2; Rosa iDTA model however, it requires administration of both tamoxifen and Diphtheria toxin [START_REF] Eilken | Pericytes regulate VEGF-induced endothelial sprouting through VEGFR1[END_REF] . Alternatively to the Pdgfrb promoter, the Cspg4 (also known as NG2) promoter has been proposed to induce pericyte depletion. However, its induction efficiency is lower than the one of Pdgfrb, and just like the Pdgfrb promoter, it induces recombination in SMCs [START_REF] Mayr | Characterization of the Two Inducible Cre Recombinase-Based Mouse Models NG2-CreER TM and PDGFRb-P2A-CreERT2 for Pericyte Labeling in the Retina[END_REF] . Finally, a strategy utilizing a double-promoter approach with Pdgfrb and the Cspg4 has been used [START_REF] Nikolakopoulou | Pericyte loss leads to circulatory failure and pleiotrophin depletion causing neuron loss[END_REF] to target pericytes more specifically, but both promoters also target SMCs as described above. Cell culture assays, have also limitations, however, they may help demonstrating pericyte specific effects.

In conclusion, the present study highlights the critical role of pericytes in the heart. Just like in the brain, cardiac pericytes are necessary to maintain coronary vasculature integrity and immune quiescence. Importantly, cardiac pericytes may protect from cardiac inflammation, not only by acting on ECs, but also by downregulating Il-6 mRNA expression in cardiomyocytes. Finally, cardiac pericytes increase Cx43 mRNA expression in cardiomyocytes suggesting they may directly participate in the regulation of cardiac function. Altogether, these results demonstrate that the role of cardiac pericytes should be considered when investigating the pathophysiology of cardiac diseases. The percentage of smMHC+ vessels over total PODXL+ vessels was calculated. (B) Heart crosssections were co-immunostained with anti-NG2 and anti-PODXL antibodies to identify pericytes and ECs respectively. The percentage of NG2+ vessels over total PODXL+ vessels was calculated. (C) Heart cross-section were co-immunostained with anti-SMA (in red), anti-NG2 (in green), and anti-PODXL (in white) antibodies to identify SMCs, pericytes and ECs respectively. (D) Heart thick sections were coimmunostained with anti-NG2 (in red) and anti-PODXL (in green) antibodies to identify pericytes, and ECs respectively. (E) Heart thick sections were co-immunostained with anti-NG2 (in green) and anti-CD31 (in red) antibodies to identify pericytes and ECs respectively. (F) Heart thick sections were coimmunostained with anti-NG2 (in red), anti-PODXL (in green), and anti-ACTN2 (in white) antibodies to identify pericytes, ECs, and cardiomyocytes respectively. (G-J) Pdgfrb-Cre/ERT2; Rosa DTA (Pdgfrb-DTA) and Rosa DTA (Control) mice were administered with tamoxifen. Mice were sacrificed 28 days after the first injection. (G) Heart cross-sections were co-immunostained with anti-NG2 (in red) and anti-PODXL (in green) antibodies to identify pericytes and ECs respectively. (H) The percentage of NG2+ vessels over total PODXL+ vessels was calculated (n=8 and 9 mice/group). (I) Heart crosssections were co-immunostained with anti-SMA (in red) and anti-PODXL (in green) antibodies to identify SMCs and ECs respectively. (J) The number of SMA+ vessels per mm² was counted (n=9 and 11 mice per group). Mann Whitney tests (E) Heart cross sections were co-immunostained with anti-VCAM-1 (in green) and anti-PODXL (in red) antibodies. (F) ICAM1 and VCAM1 protein expression was evaluated in total heart extract by western blot analyses. ICAM1 (G) and VCAM1 (H) protein was quantified using image J software and normalized to α-tubulin (n=8 mice per group). (I-J) Heart cross sections were immunostained with anti-CDH5 antibodies to identify adherens junction. (K) Heart cross sections were co-immunostained with anti-FGB and anti-PODXL antibodies. (L) FGB+ surface area was measured using Image J software (n=10 and 8 mice per group). Mann Whitney tests

Figures and

PDGFRB:CDH5: Cadherin- 5 FGB 2 PAGE

 52 Platelet Derived Growth Factor Receptor Beta DTA: diphtheria toxin A PDGFB: Platelet-derived growth factor submit B LVEF: left ventricular ejection fraction ProNGF: pro-Nerve growth factor Notch3: Notch Receptor 3 ZSF1: Zucker fatty/spontaneously hypertensive heart failure F1 hybrid SARS-CoV-2: Severe acute respiratory syndrome coronavirus 2 lncRNA: Long Non-coding LV: Left-ventricular ESV: end-systolic volume EDV: end-diastolic volume LVPW: left ventricular posterior wall IVS: inter-ventricular septum LVID: Left ventricular internal diameter HDL: high density lipoproteins LDL: Low density lipoproteins PODXL: Podocalyxin SMA: anti-alpha-smooth muscle actin (also known as ACTA2) NG2: Chondroitin Sulfate Proteoglycan 4 (also known as CSPG4) DAPI: (4′,6-diamidino-2-phenylindole) EBM-2: Endothelial basal medium-2 HMVEC-C: Human cardiac microvascular endothelial cells EGM: Endothelial growth medium MEM: Minimum Essential Medium ATP: Adenosine Triphosphate RT-qPCR: Real-time quantitative polymerase chain reaction ICAM1: Intercellular Adhesion Molecule 1 VCAM1: Vascular Cell Adhesion Molecule 1 ATP2A2: ATPase sarcoplasmic/endoplasmic reticulum Ca2+ transporting Heavy Chain 7 DTR: diphtheria toxin receptor HIF1A: Hypoxia Inducible Factor 1 Subunit Alpha

Figure 1 :

 1 Figure1: 95 % of the cardiac vasculature is covered by pericytes. (A) Heart cross-sections were coimmunostained with anti-smMHC and anti-PODXL antibodies to identify SMCs and ECs respectively. The percentage of smMHC+ vessels over total PODXL+ vessels was calculated. (B) Heart crosssections were co-immunostained with anti-NG2 and anti-PODXL antibodies to identify pericytes and ECs respectively. The percentage of NG2+ vessels over total PODXL+ vessels was calculated. (C) Heart cross-section were co-immunostained with anti-SMA (in red), anti-NG2 (in green), and anti-PODXL (in white) antibodies to identify SMCs, pericytes and ECs respectively. (D) Heart thick sections were coimmunostained with anti-NG2 (in red) and anti-PODXL (in green) antibodies to identify pericytes, and ECs respectively. (E) Heart thick sections were co-immunostained with anti-NG2 (in green) and anti-CD31 (in red) antibodies to identify pericytes and ECs respectively. (F) Heart thick sections were coimmunostained with anti-NG2 (in red), anti-PODXL (in green), and anti-ACTN2 (in white) antibodies to identify pericytes, ECs, and cardiomyocytes respectively. (G-J) Pdgfrb-Cre/ERT2; Rosa DTA (Pdgfrb-DTA) and Rosa DTA (Control) mice were administered with tamoxifen. Mice were sacrificed 28 days after the first injection. (G) Heart cross-sections were co-immunostained with anti-NG2 (in red) and anti-PODXL (in green) antibodies to identify pericytes and ECs respectively. (H) The percentage of NG2+ vessels over total PODXL+ vessels was calculated (n=8 and 9 mice/group). (I) Heart crosssections were co-immunostained with anti-SMA (in red) and anti-PODXL (in green) antibodies to identify SMCs and ECs respectively. (J) The number of SMA+ vessels per mm² was counted (n=9 and 11 mice per group). Mann Whitney tests

Figure 2 :Figure 3 :

 23 Figure 2: Cardiac pericytes may undergo strong remodeling. Pdgfrb-Cre/ERT2; Rosa DTA (Pdgfrb-DTA) and Rosa DTA (Control) mice were administered with tamoxifen. Mice were sacrificed 28 days after the first injection. (A) Heart cross sections were co-immunostained with anti-SMA (in red) and anti-PODXL (in green) antibodies to identify SMCs and ECs respectively. (B) The number of SMA+ vessels with a diameter ≤25 µm per mm² was counted (n=9 and 11 mice per group). (C) Heart cross sections were co-immunostained with anti-PDGFRB (in green), anti-NG2 (in red) antibodies. (D) The percentage of PDGFRB+ cells among NG2+ cells was calculated (n=7 and 10 mice per group). (E) The percentage of NG2+ cells among PDGFRB+ cells was calculated (n=7 and 10 mice per group). Mann Whitney tests

Figure 4 :

 4 Figure 4: Pericyte depletion induces cardiomyocyte dedifferentiation but not cardiomyocyte hypertrophy. Pdgfrb-Cre/ERT2; Rosa DTA (Pdgfrb-DTA) and Rosa DTA (Control) mice were administered with tamoxifen. Mice were sacrificed 28 days after the first injection. (A) Heart cross sections were co-stained with WGA (in green) and anti-ACTN2 (in red) antibodies. (B) Cardiomyocyte cross section area was measured using Image J software (n=11 and 9 mice per group). (C) The heart weight over tibia length was measured (n=10 and 8 mice per group). (D) The LV mass corrected was measured via echocardiography. IVS thickness (E), LVPW thickness (F) and left ventricular internal diameter (G) were measured by echocardiography in diastole. (H) Myh7 mRNA expression was quantified by RT-qPCR in total heart extract and normalized to 18S rRNA (n= 9 and 10 mice per group). (I) Heart cross sections were co-immunostained with anti-CX43 (in green) and anti-ACTN2 (in red) antibodies. (J) CX43 protein expression was analyzed by western blot and (K) quantified using Image J software (n=7 and 8 mice per group). Mann Whitney tests

Figure 5 :Figure 6 :

 56 Figure 5: Pericyte depletion modifies ATP2A2 level and Ttn splicing in cardiomyocytes. Pdgfrb-Cre/ERT2; Rosa DTA (Pdgfrb-DTA) and Rosa DTA (Control) mice were administered with tamoxifen. Mice were sacrificed 28 days after the first injection. (A) Atp2a2 mRNA expression was quantified by RT-qPCR in total heart extract and normalized to 18S rRNA (n=10 mice in each group). (B) ATP2A2 protein expression was analysed by western blot in total heart extract and (C) quantified using Image J software (n=8 mice in each group). (D) PLN phosphorylation was assessed by western blot analyses and (E,F) quantified using Image J software (n=5 to 8 mice per group). (G) RYR2 phosphorylation was assessed by western blot analyses and (H) quantified using Image J software (n=8 mice in each

  5 minutes to separate cardiomyocytes. Centrifuged cardiomyocytes were then incubated in increasing CaCl 2 concentrations (from 12,5 µmol/L to 900 µmol/L in the presence of 2 mmol/L Adenosine Triphosphate (ATP) (Sigma). Cells were centrifuged again at 500 rpm for 5 minutes, re-suspended in Minimum Essential Medium (MEM) (Gibco) containing 5% FBS, 1.26 mM CaCl 2 , 10 mmol/L 2,3-Butanedione monoxime (Sigma) and 2 mmol/L ATP and seeded in cell culture wells coated with 10 µg/mL Laminine (Sigma).

	After a 2-hour incubation at 37°C, culture medium was replaced by MEM
	containing 0,2% bovine serum albumin (Sigma), 1.26 mM CaCl 2, 1X Insulin/Transferin/Selenium
	(Gibco) and 25 µM blebbistatin (Sigma).

  . As mentioned above, cardiac pericytes were shown to be disorganized and more numerous in obese and hypertensive ZSF1 rats (van Dijk et al. 2016). In our model, pericyte depletion induced pericyte activation and renewal. Pericyte loss has been previously associated with reduced LVEF (Chintalgattu et al. 2013) (He, Zeng, and Chen 2016), dilated cardiomyopathy, cardiac fibrosis, contractile dysfunction (Siao et al. 2012), and cardiac hypertrophy (Tao et al. 2017). Our study confirms that pericytes are necessary for proper cardiac contractility and systolic function, while pericyte loss does not seem to promote cardiac fibrosis, hypertrophy, or dilation. In Siao et al. study, cardiac fibrosis, and dilation may then be due to the effect of the mutant form of ProNGF on other cell types, notably cardiomyocytes and/or fibroblasts (Siao et al. 2012), while in Tao et al. study (Tao et al. 2017), cardiac hypertrophy may be due to Notch3 deletion in cardiomyocytes

Acknowledgments

We thank Annabel Reynaud, Sylvain Grolleau, and Maxime David for their technical help.

Sources of funding

This study was supported by grants from the Fondation pour la Recherche Médicale (équipe FRM), and the Agence Nationale pour la Recherche (Appel à Projet Générique). Additionally, this study was co-funded by the "Institut National de la Santé et de la Recherche Médicale" (Inserm), and by the University of Bordeaux. Lauriane Cornuault received a fellowship from the Fondation pour la Recherche Médicale. François-Xavier Hérion received a fellowship from Bordeaux university hopital.

Conflict of Interest Disclosures


On the Origin of Preferences

Marek Jenöffy-Lochau

The Signal-Preference Model shows how the information an individual receives influences the individual's preferences. A person's receipt of a signal can shift the indifference curve. The model reflects the credibility of the signal.

Preferences: Concept and Interpretation

A main concept of economic theory is the differentiation between preferences and restrictions. Kirchgässner (2008, p.12) describes this concept.

"In a given situation the restrictions limit the individual's leeway of action.…Within this leeway, there are various alternatives of acting which are available and from which the individual can choose.…The preferences are derived from the intentions of the individual, they reflect the individual's ideas of value as they have been developed during the process of socialization, and they are principally independent of the actual possibilities of action. According to these preferences, the individual assesses the various alternatives at his disposal, he weighs up the pros and cons, the costs and benefits of the alternatives against each other and finally chooses that (those) alternative(s) which come(s) closest to his preferences or which promise(s) to bring about the maximum net benefit."

The importance of the differentiation between preferences and restrictions is emphasized hereby. However, the origin of the preferences is unclear. Kirchgässner (2008, p.24) is aware of this. Does it make sense to assume each child is born with an unchangeable grid of preferences that cannot be changed afterwards? Kirchgässner (2008, p.12, p.24, p.38) remarks that preferences accrue "over the process of socialization." This thought raises two questions: How do preferences accrue during the socialization and when does the socialization end? The origin of preferences is of great importance in economic analysis because preferences are a basic element to explain human behavior.

Utility Function

Microeconomics uses a utility function as a common concept to display preferences. The utility of an individual is normally a product of the amount of goods that the individual consumes. Often, there are two goods x 1 and x 2 . Good x 2 can be all goods other than x 1 expressed in money. The Cobb Douglas utility function is a commonly used model to depict the preferences of an individual. Varian (1992) shows a simple Cobb Douglas utility function originally based on the work of [START_REF] Cobb | A Theory of Production[END_REF]. Displaying preferences like in Figure 1 often reflects reality very well. The performance of the model decreases when new, unexpected events occur in the life of an individual. After the fall of the wall, East Germans were suddenly allowed to go to the West. One of their most prominent destinations was the famous department store KaDeWe ("Kaufhaus des Westens") in West Berlin. There, an East German could see, for instance, a guava for the first time in his or her life. This case cannot be explained by the existing utility function (Equation 1). The case that an individual receives new information during the process of consuming is not part of the model. Instead of using the existing utility function, the new good has to be included in Equation 1, thereby delivering a new utility function, Equation 1a. So, under specific circumstances it makes sense to search for a different approach to display preferences.

Brief Philosophical Excursus

Economists know that there are inconsistencies regarding preferences. Other social sciences aim to help economics solve these issues. Hausman (2012, p.44) outlines: "If economists want to say more about choice among some set of alternative actions than people choose what they prefer, they need to say something substantial about what determines what people prefer." He adds (2012, p.37): "If economists truly have nothing to say about what shapes final preferences….their predictions and explanation of choices would be trivial." The relationship of other social sciences to economics regarding preferences is so complicated that it can be described as a Gordian Knot.

Mainstream microeconomic theory works fine in so many cases. I think economics is strong enough to address its challenges itself. This, however, requires openness to new ideas and approaches. If new ideas are marginalized or shunned, there would be only conformity but no further development. The aim of this paper is to make microeconomic theory stronger and simpler, arguing from a centrist perspective. Although the ideas might appear new at first glance, I am convinced, based on careful analysis, that they fit well into mainstream economics. Working on unraveling a Gordian Knot requires one to examine things from a different angle.

Characteristics and Commodities

I turn back to the case of the guava. Going back to [START_REF] Lancaster | Consumer Demand: A New Approach[END_REF], multi-attribute utility theory opined that individuals have preferences not for goods but for characteristics of goods. For preference theory, this approach adds a lot of complexity. What are the characteristics of goods that individuals prefer? If the characteristic is the color "yellow," what about "dark yellow" and "light yellow?" How many shades of yellow does it make sense to differentiate? However, Lancaster's approach will be helpful in a later part of this paper. Stigler and Becker (1977) probably had in mind cases like the guava when they developed their theory of "commodities." They assume that preferences do not exist for specific consumer goods. Instead, they argue that preferences exist for specific psychological states that they call "commodities." The commodity Z i is among other things a function of market goods. Stigler and Becker assume the following equation for commodity Z i (Equation 2).

Z i =f i (X 1i ,…X ki ,t 1i ,…t li , S 1 …S l , Y i ) (2)
Commodity i is a function of the market goods 1 to k (X i ), the time input of person 1 to l (t i ), the human capital of person 1 to l (S) and other inputs (Y).

The case of the guava can be explained using Stigler and Becker's model. The object of the consuming is not the guava itself but a commodity I will call the "joy of consuming exotic food."

The guava helps the individual to "produce" the commodity. The appearance of the new good does not shift the individual's preferences. Rather, a new market good is included in the utility function to produce the commodity. This approach seems to seamlessly solve the problem of a new product with constant preferences.

However, questions remain: What exactly are the commodities? And again, how do preferences regarding commodities accrue? 1.4. Meta-preferences [START_REF] Becker | Habits, Addictions, and Traditions[END_REF] himself suggests modifications to the concept of commodities. He acknowledges that not all preferences of individuals are stable. Based on this realization, he formulates the concept of meta-preferences. He states (Becker 1992, p.340): "Meta-preferences include past choices and choices by others as arguments in a person's current utility function." So, what are meta-preferences? One concept of meta-preferences is linked to Bentham's concept of "pleasures" [START_REF] Bentham | An Introduction to the Principles of Morals and Legislation[END_REF]). Meta-preferences can be understood as highlighted "commodities." This appears similar to the widely published insights of the psychologist [START_REF] Maslow | A Theory of Human Motivation[END_REF], 1987) defines a need hierarchy of an individual: (1) physiological needs, such as food, sleep and healthcare; (2), safety needs; (3) belongingness and love needs; (4) esteem needs; and (5) the self-actualization needs. If the individual has satisfied his basic needs (such as the need for food), he or she will, basically, seek to fulfill needs that are higher in the hierarchy (Maslow 1943, pp.385f). Maslow obviously assumes that some needs of human beings have a higher significance than others. Physiological needs can be regarded as highlighted "commodities."

Sen develops another concept of "moral rankings" which are "rankings of rankings." He remarks (Sen 1977, p.337): "A particular morality can be viewed, not just in terms of the "most moral" ranking of the set of alternative actions, but as a moral ranking of the rankings of actions." Sen (1977, p.339) adds that this "structure demands much more information than is yielded by the observation of people's actual choices."

What about meta-preferences in the following case? According to Thottam (2009), Tamil LTTE rebels in Sri Lanka "...pioneered two of the most brutal tactics of modern guerrilla warfare: child recruitment and suicide bombing. Children as young as 10 were used to kill women and children in remote rural villages, according to a 1996 U.N. report. In combat in the 1990s, between 40% and 60% of the dead Tiger fighters in Sri Lanka were children under the age of 18, according to a 2004 Human Rights Watch report." Thottam (2009) adds about leader Prabhakaran who was killed in 2009: "In 1987, the LTTE established the Black Tigers -suicide cadres, many of them young women, who would be honored with a private meal in the company of Prabhakaran before being sent out on their missions."

This case shows that obviously some "commodities" Maslow puts at the bottom of his need hierarchy, such as physiological needs, lose their importance under certain circumstances. Also, the "moral rankings" in the sense of Sen are obviously different in this case. Provided the Tamil rebels really behaved as described by Thottam, the commodity "physical integrity" is less important than the commodities "joy for the independence of a Tamil state" and "having a meal with Prabhakaran." This appears odd, in particular, because these commodities are without any importance for over 99% of all human beings. So, how do the preferences of the Tamil rebels accrue? 1.5. Information and Behavioral Change I attempt to explain the behavior of the Tamil rebels. Their leader Prabhakaran delivers specific information to the rebels. This information ("signals") leads to a shift in their behavior. The Tamil rebels are now willing to sacrifice everything, including their own lives, for an independent Tamil state. It appears that information is capable of making individuals regard their own lives as less important than an abstract political concept. Can meta-preferences satisfactorily explain this behavior? This is doubtful.

Kirchgässner also refers to "information." Consequently, it makes sense to look at information and preferences in context. [START_REF] Kirchgässner | Homo Oeconomicus[END_REF] uses the term "state of information" in order to express changes in information. He does not posit a change in the preferences if "changes in the restrictions and/or in the information of individuals are sufficient to explain changes in their behavior" (Kirchgässner 2008, p.37). This creates the outward impression that the state of information is not a restriction. The "state of information" that Kirchgässner describes seems to be somewhere between restrictions and preferences. This makes it a hybrid. The appeal of Kirchgässner's Homo Oeconomicus is the strict differentiation between preferences and restrictions. It vanishes when the state of information is introduced as a third hybrid element.

Whenever preferences are obviously "changing," there is new information at the same time. At the stock exchange, it is common knowledge that stock prices are influenced by new information. The U.S. Securities and Exchange Commission requires each publicly traded company to promptly publish new information that could affect its stock price. Laws ban the use of "insider information" to gain profits. Everybody who works in the financial services industry knows how sensitive stock prices are to new information. High-frequency trading at the stock exchange aims to react some milliseconds earlier to new information than other market participants. The success of each talk with customers depends on how effectively information is transmitted. So, it makes sense to have a closer look at the context between information and preferences.

Information and Preferences

The concept of expected utilities links information and preferences. It assumes that the preferences of individuals for alternatives depend on uncertain samples from the future. This context cannot be depicted by a utility function like Equation 1. The concept of expected utility by [START_REF] Savage | The Foundation of Statistics[END_REF]von Neumann andMorgenstern (1947) models this context.

Expected Utilities

I demonstrate the concept of expected utilities. An individual chooses between j=3 alternatives named A, B and C. I assume the outcome of the alternatives A, B, C is the return on investment in three different cities in California. There are N=2 possible states in the future (Z 1 , Z 2 ). Z 1 occurs with a probability of p(Z 1 ) = 60%, Z 2 occurs with a probability of p(Z 2 ) = 40%. Alternative A leads to a financial outcome of $150 (Z 1 ) or $300 (Z 2 ), etc. Table 1 shows the financial outcome of the individual depending on states.

I assume the following function for the expected utility of a risk neutral individual (Equation 3).

= ( ) (3)

I assume that the expected utility EU i of the individual is the expected amount of money he or she receives. So, EU of Alternative A is $210, EU of Alternative B is $190 and EU of Alternative C is $220. The rational individual decides in favor of Alternative C. Given the assumptions above, the individual has preferences for Alternative C.

How could information possibly influence the utility function in this case? A modification demonstrates this. I assume new research (a "signal") shows that there is the probability of an earthquake in city C and that this leads to an outcome of $0. The expected utility of alternatives A and B remains unchanged. The probabilities of Alternative C change. A new state Z 3 occurs with a probability of p(Z 3 ) = 10% . The probability of the states Z 1 and Z 2 is equally reduced. So, p(Z 1 ) goes down from 60% to 54% and p(Z 2 ) is reduced from 40% to 36%. This case shows that new information can cause behavioral change. The preferences of the individual remain unchanged. Only the probabilities of the states change, which causes the individual to make a different decision.

Bayes

In the model above, new information was exogenous. Bayes' Theorem is an endogenous way of depicting new information in an expected utility function. This is how Bayes' Theorem works: Assume that there are two alternatives, Alternative I and Alternative II. These alternatives are linked to two states s 1 and s 2 . The states are defined as followed: s 1 : Alternative I is better than Alternative II. s 2 : Alternative II is better than Alternative I. I assume for simplification that the probabilities of both states are equal.

p(s 1 ) = p(s 2 ) = 0.5 (4)

There are N = 2 signals (π 1 , π 2 ). The signals contain information. Signal π 1 : s 1 is true. Signal π 2 : s 2 is true. Both signals are equally probable. The Expected Utility Theory differs between "prior probabilities" and "posterior probabilities." The "prior probability" is the probability of one or more particular states before the new signal is received. The "posterior probability" is the probability of the same states after receiving the signals. Bayes' Theorem connects both probabilities (Hirshleifer and Riley 1992, pp.170ff). 1 psπ = = s * (5) p sπ = the posterior probability of state s, given signal π. p s = the prior probability of states s. q π = the probability of receiving signal π. j πs = the joint probability of states s and signal π. q πs = the conditional probability of signal π, given state s.

In the example above, both prior probabilities (p s ) are assumed to be 0.5. Also, it is assumed that the probability of both signals is 0.5. Thus, p sπ = q πs . How does a signal influence the behavior of the individual? As long as:

qπ 1 s 1 = qπ 2 s 2 = 0.5 = 1/N (6)
1 The symbols are gradually changed in order to be congruent with the symbols used before.

the signal does not influence the posterior probabilities, the individual regards receipt of the signal as accidental. So, the signal does not influence the preferences of the individual.

If q π1s1 > 0.5, the signal influences the individual's behavior. The individual regards State 1 as being more probable if he or she receives signal π 1. The posterior probability of State 1 exceeds its prior probability. If q π1s1 = 1, the individual is sure that State 1 is true after having received only one signal π 1. The evaluation of the influence of a signal on the preferences of an individual requires exogenous given q πs .

The key element of the model is the definition of "states," which are assigned probabilities. Therefore, it makes sense to have a closer look at the concept of "states." Savage defines the terms shown in Table 3 below (Savage 1972, pp.9f).

Basically, Savage includes "everything" that concerns the "world" in the "state." Therefore, a "state" must consist of a vast amount of elements. In order to reduce the number of elements, Savage differentiates between a "large world" and a "small world." The "smaller" the world is, the fewer elements it contains. This approach suggests that states of various sizes were used. However, this is not the case. Normally, states either contain only one element or some elements are aggregated into one element that is often expressed as a financial outcome.

Complicated States

If a state includes an element that cannot be expressed with a financial figure, the Expected Utility Theory gets in trouble. Referring to [START_REF] Cook | The Demand for Insurance and Protection: The Case of Irreplaceable Commodities[END_REF] and [START_REF] Marshall | Gambles and the Shadow Price of Death[END_REF], [START_REF] Hirshleifer | The Analytics of Uncertainty and Information[END_REF] name some examples for a "state-dependent utility".

"In risk-bearing situations it sometimes appears that the individual's preference scaling function v(c) might itself depend upon the state of the world, contrary to our assumption…above. To take an extreme case, if the states under consideration were "being alive" versus "being dead" a typical individual would likely value rights to income in the former state more heavily! (Yet he might attach some "bequest utility" to income contingent upon his own death.) Similar considerations could apply if states of the world were defined in terms of one's sickness versus health, or life versus death of one's child, or success versus failure at love, or retention versus loss of a unique heirloom." [START_REF] Hirshleifer | The Analytics of Uncertainty and Information[END_REF] show why a "state" may contain an additional element. As an example of such an element, they name "the life of the own child." They define this element being "l"= living or "d"=dead. They put the two cases into the function v(c) that depicts the preferences of an individual. The utility of state "l" lies for all possible financial outcomes above the utility of state "d." v l (c) is always above v d (c).

Hirshleifer and Riley's model brings up additional questions to consider: What if it is necessary to include more elements in a state? What if these elements cannot be measured as a financial outcome? Then, the v(c) function quickly gets very complicated (Binmore 1994, pp. 304ff). If I add more elements to the state, for example, health of one's liver, proximity of one's dwelling to road noise and eyesight the state would become so complex that it does not make sense to use it. However, a "world" that contains only a total of five elements is still "small." So, Bayes' Theorem seems to be capable of depicting preferences in a manageable way, provided the state consists of one element. However, what is the difference between a state in an ultra "small world" and an information signal? What is the use of defining a "state?" Does the additional complexity deliver additional insight? It makes sense to try to explain preferences without "states."

3. The Signal-Preference Model Some economic authors work with signals and "states. [START_REF] Bikhchandani | A Theory of Fads, Fashion, Custom, and Cultural Change as Information Cascades[END_REF] develop a model of "information cascades." Individuals observe the behavior of others about the quality of a good. This is regarded as a "signal." A signal forms the behavior of the next individual. This behavior produces the next signal: an information cascade starts. [START_REF] Banerjee | A Simple Model of Herd Behavior[END_REF] similarly models "herd behavior," when individuals observe the behavior of other individuals and adjust their behavior to that what they observe. Vriend ( 2004) models "information-contagious behavior." Similar ideas are used by marketing strategists developing the tactics of "guerilla marketing" and "viral marketing." Krackhardt (1998) models "adaptive preferences." These preferences depend on past preferences and a weight variable that depicts interaction with other individuals. [START_REF] Eizenberg | Grab Them Before They Go Generic: Habit Formation and the Emerging Middle Class[END_REF] model "habitual behavior." The constant preferences of the individuals contain a persistence or "habit variable." Habit formation is also modeled by Stigler and Becker (1977) and Jenöffy-Lochau (1997, pp.64ff).

Further developing the models of Bikhchandani, Hirshleifer and Welch and Banerjee, I use some ideas taken from Jenöffy-Lochau (1997, pp.96ff) in order to create a general model of the origin of preferences. This model also reflects a huge number of empirical results in the field of communications research discussed by Jenöffy-Lochau (1997, pp.43ff) and fifteen years experience in sales coaching by the author. The Signal-Preference Model aims to portray the dependence between preferences and information. The model uses the idea that information is transmitted by signals. The Signal-Preferences Model makes the following assumptions.

There are alternatives. The signals π i contain information about the alternatives. Each signal π i delivers an information value parameter ω i . The information value parameter influences the preferences of the individual. An individual receives N signals.

A simple form of the model has the following additional assumptions.

There are two alternatives, Alternative I and Alternative II. There are only two signals π 1 , π 2.

Signal π 1 contains the information: "Alternative I is better." Signal π 2 contains the information: "Alternative II is better." Signal π 1 delivers ω 1 = 0 whereas signal π 2 delivers ω 2 = 1.

The preference function of an individual is depicted as shown in Equation 7.

= 1 N N j=1 (7)
Preferences are generated from the information an individual has gathered in the past. Alternative I is preferred to Alternative II if P I < 0.5. Alternative II is preferred to Alternative I, if P I > 0.5. The individual is indifferent between the alternatives if P I = 0.5. Table 4 shows three examples of possible outcomes.

Equation 7 and Table 4 show a direct connection between the information that the individual receives and the preferences of the individual. The model is capable of explaining which information causes a swing in the preferences of the individual and which information does not.

If an individual has already received many similar signals in his or her life, then the preferences are more stable than preferences that are based on fewer signals. The following number of signals π 2 causes a swing in the preferences of the individual from Alternative I to Alternative II.

N π2 > N 1 -N 2 (8)
N 1 and N 2 are defined as the number of signals of π 1 and π 2 , respectively, that the individual has received in the past. The following number of signals of the type π 1 causes a swing from Alternative II to Alternative I.

N π1 > N 2 -N 1 (9)
This model can be applied to all sorts of preferences. The desire for chewing gum can be explained as well as the general desire for a safe environment, etc. The need to differentiate between "preferences," "beliefs," "values," "tastes," "meta-preferences" or "consumer preferences" is superfluous. The preferences differ regarding their strengths. Some preferences are cemented by receipt of the same information every day. These preferences are almost impossible to change. One such unchangeable preference is the wish of an individual to have food every day. If the individual does not have food his or her stomach begins to growl (signal). This starts in a very early phase of the existence of a human being. So, trying to make an individual prefer starvation is almost impossible. 2 The situation of an inexperienced investor regarding an unknown stock is entirely different. Just one signal can cause a complete swing of the preferences. That having been said, the model recognizes that older people generally have stronger preferences than younger ones. This is why advertising generally targets individuals between the age of 14 and 49.

4. The Signal-Preference Model and the Use of a Utility Function This approach to preferences might be long awaited. However, it raises fears because it turns economic theory upside down. These fears are needless. The model does not abolish the mainstream economic theory. Rather, it puts it on its feet and makes it stronger. I demonstrate this with this application of the model.

The preferences of an individual are depicted by a Cobb-Douglas utility function (Equation 1).

U(x 1 ,x 2 ) = α x 1 a x 2 b (1)
The preferences of the individual towards the two goods x 1 , x 2 are influenced by the signals π 1 , π 2 . I assume:

a + b = 1, a = b = 0.5, α = 1. There are two signals π 1 and π 2. Signal π 1 contains the information "x 1 is better." Signal π 2 contains the information: "x 2 is better." Signal π 1 delivers ω 1 = 0 whereas signal π 2 delivers ω 2 = 1. The exponents a and b are based on the reception of one signal π 1 and one signal π 2 in the past.

The preference axioms (Varian 1992, pp.94ff) apply for states of constant information. They do not apply comparing different states of information. The context between signals and preferences modeled in the Signal-Preference Model (Equation 7) is implemented in the utility function of the individual (Equation 1). The new utility function U' that reflects the signals the individual receives is shown in Equation 10. The received signal π 1 provides ω π1 = 0. P I is determined by using the Signal-Preference Model (Equation 7).

" ( , $ ) = % ( &' ( ) $ ' ( (10
= 1 * (7)

Equation 7 provides P I = 1/3. So, a' = 2/3 and b' = 1/3 shifting the old utility function U (Equation 11) to the new utility function U' (Equation 12).

U(x 1 ,x 2 ) = x 1 0.5 x 2 0.5 (11) U'(x 1 ,x 2 ) = x 1 2/3 x 2 1/3 (12)
In the model, the reception of one signal shifted the utility function from U to U'. The preferences of the individual changed. The shift of an individual's indifference curve is shown in Figure 2. The indifference curve rotates clockwise at the point x 1 = x 2 = 10. The consumption of x 1 delivers a higher utility for the individual after the reception of the new signal.

The information the signals provide can also be information about one or more characteristics of a good in the sense of [START_REF] Lancaster | Consumer Demand: A New Approach[END_REF]. The strength of Lancaster's approach lies on the side of the signal. Effects of agenda setting can occur.

The key factor of how intensely the utility function reacts to new information is the amount of information that the individual has received in the past. In the case of the guava, the individual hadn't received any signal prior to his or her first contact with the guava, so there are no received signals in the past. The signal that the individual receives upon tasting the guava for the first time entirely shapes his or her preferences. The preferences of the Tamil rebels towards an independent Tamil state are shaped by a continuous flow of similar signals ("brain washing" and negative experiences). This current flow of signals has most likely created strong preferences.

So, the model also explains why the omission of the origin of preferences from many economic models does not void the models. These models describe ongoing consuming processes. They rest upon a greater number of signals received in the past. Consequently, it makes sense to treat preferences as constant and exogenous in order to reduce the complexity of the models ("complete information"). With the Signal-Preference Model, other models that focus on new information like the Product Life Cycle Theory, originally by [START_REF] Levitt | Exploit the Product Life Cycle[END_REF] and Vernon (1966), can be integrated in microeconomic theory.

The Source of Signals and the Credibility

Individuals generally receive signals from two different sources: Personal experiences and input from others. Personal experience can turn into habitual behavior. "Others" means signals by observing the behavior of others or signals that are transmitted by the media. Orr (1987, p.91) remarks: "Market advertising and production can continue only so long as the advertised good is accepted by purchasers." After a one-time purchase, an individual will purchase the advertised product again as long as the product provides characteristics that are at least as good as the characteristics of a competing good. Signals from advertising (Signal 1) can influence the individual only if the signal is not voided by the personal experiences of the individual (Signal 2, Signal 3, etc.). The individual would normally value his or her own experiences higher than signals from advertisements. Orr (1987, p.91) remarks that this control mechanism only applies to market goods, not to politics: "There is no feet-back mechanism of comparable strength, which involves the entire purchasing public, and which governs the type or level of activity, in political firms."

The intensity of a signal from advertisement is often very weak. Schultz, Tannenbaum and Lauterborn (1994, p.24) remark: "From the myriad of choices, we pick those things we either want to or must process...." If an advertisement is only shown once, only one signal is transmitted. Because of this, it seems highly unlikely that a single advertisement has a significant impact on the preferences of an individual. More likely, it has no appreciable impact. With a higher number of signals, the impact of the signals increases and wins power to influence the preferences of the individual.

5.1.The Signal-Preference Model Including Credibility

Signals that individuals receive beyond their personal experiences differ regarding perceptibility, salience, and credibility. The key elements of information transmission, as described in various empirical studies, are discussed by Jenöffy-Lochau (1997, pp.43ff). These characteristics influence the information value parameter ω.

A signal that is less credible has less impact on the preferences of an individual. A signal that is less perceptible has less impact on the preferences of an individual. The same can be stated about a vague signal with an opaque content. I call this a signal of little salience. The case of a perfectly credible signal complies with the model in the previous chapter. The impact of the signal on the preferences of the individual and his or her indifference curves is depicted in Figure 2.

In the case of a perfectly incredible signal, the model has the following assumptions.

The signal influences the preference function of the individual according to Equation 10. There are two signals π 1 , π 2 . Signal π 1 contains the information "x 1 is better." Signal π 2 contains the information: "x 2 is better." P I bases on the reception of two signals, one signal π 1 and one signal π 2, in the past. These signals were completely credible (γ = 1, γ = -1) . The individual receives one new signal of the type π 1 . The signal is not credible. So, γ = 0.

The three signals, including the new signal, lead to P I = 1/3 * (0.5 * (1-1) + 0.5 *(1-(-1)) + 0.5 *(1-0)) = 1/6*(0+2+1) = 0.5 (Equation 14). So, the new, incredible signal does not affect the preferences of the individual. The indifference curve in Figure 1 remains unchanged. According to this, a signal of no perceptibility as well as a signal of no salience does not influence the preferences of the individual.

The third case is the reception of a signal π 1 with reduced credibility. The credibility of the signal is just in the middle of a completely credible signal and a completely incredible signal: γ = 0.5. Equation 14 leads to P I = 1/3 * (0.5 * (1-1) + 0.5 *(1-(-1)) + 0.5 *(1-0.5)) = 1/6*(0+2+0.5) = 5/12.

This leads to a shift of the indifference curve. However, the shift from U to U'' is less strong than the shift from U to U' caused by the reception of a completely credible signal. Figure 3 shows both shifts.

The Signal-Preference Model and Expected Utility

The Probability Theory did a good job of explaining the preferences of an individual as described in Chapter 2.1. How would the Signal-Preference Model approach this? I model the investment in cities in California according to Table 1. The difference between both approaches is the view of the world. The Expected Utility Model assumes complete information and unchangeable preferences. The Signal-Preference Model does not require these assumptions, and in spite of this, delivers the same results. We lose nothing by applying the Signal-Preference Model. Hopefully, some understanding about the origin of preferences is gained.

New Signals and Existing Preferences

The case of the new information about a possible earthquake in city C (Table 2) that shifted the preferences from Alternative C to Alternative A is modeled as it follows.

There The new signal π 3 influences the preferences as depicted in Equation 16. The model is capable of explaining which preferences of individuals are stable and which preferences can be possibly shifted by information. The model is capable of explaining when the assumption of constant and exogenous preferences in an economic model makes sense. The model also can assess whether the media is capable of influencing the preferences of an individual.

Sen, Amartya K. 1977. Rational Fools: A Critique of the Behavioral Foundations of Economic Theory. Philosophy and Public Affairs 6(4): 317-344.

Stigler, George J. and Gary S. Becker. 1977 The object about which the person is concerned A states (of the world)

A description of the world, leaving no relevant aspect un-described A true state (of the world)

The state that does in fact obtain, i.e. the true description of the world An event

A set of states

U

  situations of equal utilities of an individual. I assume a + b = 1, a = b = 0.5 and α = 1 in Equation 1. The indifference curves for U = 10 and U = 20 are shown in Figure 1.

)

  The reception of signals influences the utility function and the indifference curve of the individual. The individual receives one new signal π 1 . The past signals on which the preferences of the individual were based prior to the receiving of the new signal provide ω π1 = 0 and ω π2 = 1.

I

  integrate now the credibility of a signal in the Signal-Preference Model. I first state the following assumptions of the model. There are two alternatives, Alternative I and Alternative II. The signals transfer information about the alternatives. An individual receives N signals. Each signal has an information value parameter ω. All regarded signals are perfectly perceptible and salient. A perfectly credible signal π 1 delivers ω 1 = 0 whereas a perfectly credible signal π 2 delivers ω 2 = 1. Signals with an imperfect credibility are reduced in its information value ω by the credibility factor γ.Perfectly credible signals of the type π 1 have γ π1 = 1. Perfectly credible signals of the type π 2 have γ π2 = -1. Entirely incredible signals have an information value close to 0.5. For π 1 : 0 ≤ γ π1 ≤ 1. For π 2 : -1 ≤ γ π2 ≤ 0. The less credible the signal is, the closer is γ π to 0. The information value of a signal is defined as (Equation13).ω π (γ π ) = 0.5 * (1γ π )(13)This leads to a function P I adjusted by the parameter for the credibility of the individual.

  There are three alternatives: Alternative A, Alternative B, and Alternative C. There are two signals. Signal 1 delivers ω A = 150, ω B = 250, and ω C = 200. Signal 2 delivers ω A = 300, ω B = 100, and ω C = 250. Signal 1 has a credibility of γ π1 = 0.6. Signal 2 has a credibility of γ π2 = 0.4. The preferences of the individual are determined by Equation 15. For each alternative, the P I is determined delivering P IA = 210, P IB = 190 and P IC = 220. So, the rational individual decides in favor of Alternative C. Nothing has changed regarding the considerations in Chapter 2.1. The results are the same. Equation 3 is a specific form of the Signal-Preference Model having additional assumptions. The credibility of the signals sums up to 1. ∑ 0 = 1 . This implies that there are no contradictory signals. This is a special case. Probabilities of states are special forms of the credibility of signals. credibility of the signals is determined by exogenous situations instead of the possible untruthfulness of the emitter.

  are three alternatives: Alternative A, Alternative B, and Alternative C. Received signals (Signal 1, Signal 2) delivered P IA = 210, P IB = 190 and P IC = 220. There is Signal 3 with information on Alternative C. Signal 3 delivers ω C = 0. Signal 3 has a credibility of γ π3 = 0.1.

  P ICnew = 198. Now, the individual decides for Alternative A.Equation 16 also shows why the assumption of constant preferences is appropriate in many economic models. The reason is the credibility γ π3 of a new signal π 3 . Mainstream economics implicitly assumes γ π3 =0. Under this assumption preferences are constant ( 4567 = 4 ).The relevance of preference forming considerations requires at least one credible a new signal π 3 . However, if a decent number of credible new signals are present, economic theory must evaluate these signals and figure out whether these signals influence the behavior of the individuals or the behavior of economies as a whole.6. ConclusionThe Signal-Preference Model puts into context the information an individual has received in the past ("signals") and the preferences of an individual without the use of "states." New signals directly influence the preferences of the individual. The shifted preferences are integrated in a Cobb-Douglas utility function showing a shifted indifference curve due to the reception of a signal. Received signals differ regarding their credibility. The impact of the credibility of the signal is integrated in the Signal-Preference Model.

Figure 1 :Figure 2 :Figure 3 :

 123 Figure 1: Indifference Curves of an Individual for U=10 and U=20

  Table 2 shows the new probabilities p'(Z) of the states. This leads to a new EU of Alternative C of $198. So, the rational individual now prefers Alternative A. Alternative A still delivers EU A = $210. The individual has preferences for Alternative A.

Table 2 :

 2 . De Gustibus Non Est Disputandum. American Economic Review 67(2): 76-90. Thottam, Jyoti. 2009. Prabhakaran: The Life and Death of a Tiger. Time Magazine. 173(19): 19 May 2009. Varian, Hal R. 1992. Microeconomic Analysis. 3rd edition. New York: W.W.Norton. Vernon, Raymond. 1966. International Investment and International Trade in the Product Cycle. The Quarterly Journal of Economics 80(2): 190-207. von Neumann, John and Oskar Morgenstern. 1947. The Theory of Games and Economic Behavior. 2nd edition. Princeton University Press. Vriend, Nicolaas J. 2004. On Information-Contagious Behavior. In: Economic Complexity: Nonlinear Dynamics, Multi-agents Economies, and Learning. Edited by Barnett, William A., Christophe Deissenberg, and Gustav Feichtinger. Amsterdam: Elsevier: pp.125-157. The Probability of the States Influenced by a Signal

	8. Tables and Figures
	State	p(Z)	A	B	C
	I	0.6	150	250	200
	II	0.4	300	100	250
	Table 1: A Financial Outcome Depending on States
	State	p(Z)	p'(Z)	C
	I	0.6	0.54		200
	II	0.4	0.36		250
	III	0	0.1		0
	Term	Definition
	The world

Table 3

 3 Elements of the Expected Utility Theory of Savage

	Signals π 1	Signals π 2 Preferences P I	Decision
	1	0	0	Alternative I
	3	2	0.4	Alternative I
	2	3	0.6	Alternative II

Table 4 :

 4 Preferences Depending on Received Signals

The case of bulimia shows that even this is not impossible.