E Delande

⋆ E Duflos

⋆ P Vanheeghe

⋆ D Heurguier

Sea

MULTI-SENSOR PHD: CONSTRUCTION AND IMPLEMENTATION BY SPACE PARTITIONING

Keywords: Probability Hypothesis Density, Multi-sensor system, Multi-target tracking

The Probability Hypothesis Density (PHD) is a well-known method for single-sensor multi-target tracking problems in a Bayesian framework, but the extension to the multi-sensor case seems to remain a challenge. In this paper, an extension of Mahler's work to the multi-sensor case provides an expression of the true PHD multi-sensor data update equation. Then, based on the configuration of the sensors' fields of view (FOVs), a joint partitioning of both the sensors and the state space provides an equivalent yet more practical expression of the data update equation, allowing a more effective implementation in specific FOV configurations.

INTRODUCTION

In the general multi-sensor multi-target Bayesian framework, an unknown (and possibly varying) number of targets whose states x1, ...xn 1 are observed by several sensors which produce a collection of measurements z1, ..., zm at every time step k. Well-known models to this problem are track-based models such as the joint probability data association (JPDA) [START_REF] Helmick | Multitarget-Multisensor Tracking: Applications and Advances[END_REF] or joint multi-target probabilities such as the joint multi-target probability density (JMPD) [START_REF] Kreucher | Multitarget tracking using the joint multitarget probability density[END_REF]. This paper is based on Mahler's work on finite sets statistics (FISST) [START_REF] Mahler | Handbook of Multisensor Data Fusion[END_REF] which provides a mathematical framework to build multi-object densities and derive the Bayesian rules. Randomness on object number and their states are encapsulated into random finite sets (RFS), namely multi-target (state) sets X = {x1, ..., xn} and multi-sensor (measurement) set Z k = {z1, ..., zm}. The objective is then to propagate the multi-target probability density f k|k (X|Z (k) ) using the Bayesian set equations at every time step k:

f k+1|k (X|Z (k) ) = f k+1|k (X|W )f k|k (W |Z (k) )δW (1)
f k+1|k+1 (X|Z (k+1) ) = f k+1 (Z k+1 |X)f k+1|k (X|Z (k) ) f k+1 (Z k+1 |W )f k+1|k (W |Z (k) )δW (2)
where Z (k) = t k Zt is the collection of measurements up to time k, f k|k (W |Z (k) ) is the current multi-target posterior density in set W , f k+1|k (X|W ) is the current multi-target Markov transition

This work was supported by the Direction Générale de l'Armement (DGA) and the Centre National de la Recherche Scientifique (CNRS) 1 The target state x i ∈ X is usually composed of a position, a velocity, etc. density, from set W to set X, f k+1 (Z|X) is the current multi-sensor multi-target likelihood function.

Although the FISST framework provides set calculus rules, set integrals as well as set probability densities are widely impractical and equations ( 1), ( 2) are generally untractable. Mahler then introduced the PHD ( [START_REF] Mahler | Multitarget bayes filtering via first-order multitarget moments[END_REF]), defined on single-target state space X as follows:

Definition 1.1. The PHD D k|k (x|Z (k) ) is the quantity whose in- tegral S D k|k (x|Z (k) )dx on any region S ⊂ X is the expected number N k|k (S) = |X ∩ S|f k|k (X|Z (k) )δX of targets inside S.
Mahler proved ( [START_REF] Mahler | Multitarget bayes filtering via first-order multitarget moments[END_REF]) that the PHD D k|k (x|Z (k) ) is the first-moment density of the multi-target probability density f k|k (X|Z (k) ). Although defined on single-state space X the PHD encapsulates information on both target number and states. Assuming that propagating the PHD is sufficient enough for an accurate estimation of target number and target states, the challenge is to find the PHD-equivalent of Bayesian set equations (1) and (2) in order to propagate singletarget moments D k+1|k (x|Z (k) ) and D k+1|k+1 (x|Z (k+1) ) rather than multi-target probability densities.

Under certain assumptions on the target motion model, Mahler provided ( [START_REF] Mahler | Multitarget bayes filtering via first-order multitarget moments[END_REF]) a tractable PHD-equivalent of the time update set equation [START_REF] Delande | Multitarget phd filtering: proposition of extensions to the multi-sensor case[END_REF]. Likewise, under certain assumptions on the observation model which will be detailed later (section 2.1), the PHD-equivalent of the data update equation ( 2) is given in [START_REF] Mahler | Multitarget bayes filtering via first-order multitarget moments[END_REF] for the single-sensor case, in [START_REF] Mahler | The multisensor phd filter, i: General solution via multitarget calculus[END_REF], [START_REF] Mahler | The multisensor phd filter, ii: Erroneous solution via 'Poisson magic[END_REF] for the two-sensor case. In this paper, the authors first propose an expression of the data update equation in the multi-sensor case and then a partition method in order to lighten the computational cost of the data update step, depending upon the configuration of the sensors' FOVs. The results are more detailed in [START_REF] Delande | Multitarget phd filtering: proposition of extensions to the multi-sensor case[END_REF]. A full understanding of this work requires some knowledge about FISST theory and calculus rules which may found in [START_REF] Mahler | Handbook of Multisensor Data Fusion[END_REF].

MULTI-SENSOR DATA UPDATE EQUATION

Following the time update step and with the same assumption than exposed by Mahler ([5]), the updated distribution f k+1|k (X|Z (k) ) is assumed Poisson with parameter µ and intensity µs(x)2 . Since f k+1|k (X|Z (k) ) is Poisson, its intensity µs(x) equals the time updated PHD D k+1|k (x|Z (k) ) ( [START_REF] Mahler | Multitarget bayes filtering via first-order multitarget moments[END_REF]). Note that the following notations were chosen as close as possible to Mahler's work for clarity's sake.

Observation model

Assume that, following target transition between time steps k and k + 1, each sensor j ∈ [1 N ] produces measurements independently of the others according to the observation model described as follows:

• Target i is detected with probability p

[j] d (x i k+1 ) 3 ; • If detected, target i produces a single measurement z ∈ Z [j]
with probability distribution f

O,[j] k+1 (z|x i k+1 ) = L [j]
z (x i k+1 ); • False alarms are Poisson with parameter λ [j] and intensity λ [j] c [j] (z); • Observation processes on each target are independent conditionally on the multi-target set X k+1 .

Cross-terms

Generalizing the single-sensor case led the authors to the introduction and the definition of the cross-terms which played an important role in the construction of the multi-sensor data update equation:

Definition 2.1. For each sensor j ∈ [1 N ], let g [j]
be a realvalued function on observation space Z [j] such that ∀z ∈ Z [j] , 0 g [j] (z) 1. Let h be a real-valued function on state space X such that ∀x ∈ X , 0 h(x) 1. The cross-term β[g [1] , ..., g [N ] , h] is the functional defined by: β[g [1] , ..., g [N ] , h]

= N j=1 (λ [j] c [j] [g [j] ] -λ [j] ) + µs h N j=1 1 -p [j] d + p [j] d p O,[j] g [j]
µ (3)

where p O,[j] g [j] (x) = g [j] (z)f O,[j]
k+1 (z|x)dz, c [j] [g [j] ] = g [j] (z)c [j] (z)dz and s[h] = h(x)s(x)dx.

Using FISST calculus rules ( [START_REF] Mahler | Multitarget bayes filtering via first-order multitarget moments[END_REF]), the cross-term β can be differentiated on a single-target space point x ∈ X and/or a combination of various single-sensor observation points z [j] ∈ Z [j] ( [START_REF] Delande | Multitarget phd filtering: proposition of extensions to the multi-sensor case[END_REF]). The analytical expressions of the differentiated cross-terms allows an intuitive interpretation as "likelihoods" 4 for example, with N = 3:

• δβ[g [1] ,g [2] ,g [3] ,h] δx g [i] =0,h=1

= µs(x)

3 j=1 1 -p [j] d (x) : a
target is in state x and was not detected by any sensor;

• δ 3 β[g [1]
,g [2] ,g [3] ,h] δxδz [1] δz [2] g

[i] =0,h=1 = µs(x) 2 j=1 p [j] d (x)L [j] z [j] (x) 1 -p [3]
d (x) : a target is in state x, generated measurements z [1] and z [2] and was not detected by sensor 3;

• δ 2 β[g [1]
,g [2] ,g [3] ,h] δz [1] δz [2] g

[i] =0,h=1 = µs 2 j=1 p [j] d L [j] z [j] 1 -p [3] d
: a single target generated measurements z [1] and z [2] and was not detected by sensor 3. Each cross-term above denotes a "likelihood" of a "link" between points in the single-state space X and/or the observation spaces Z [j] , hence their name.

3 p

[j] d (.) = p [j]
d,k+1 (.), time subscripts are omitted for simplicity's sake 4 "Likelihood" should not be interpreted in its classical Bayesian sense

Data update equation

The authors then constructed the data update equation using the differentiated cross-terms (see [START_REF] Delande | Multitarget phd filtering: proposition of extensions to the multi-sensor case[END_REF] for demonstration):

Proposition 2.1. For each sensor j ∈ [1 N ], let Z [j] k+1 = {z [j] 1 , ..., z [j] m[j]
} be the set of current measurements produced by sensor j. The data update equation is given by:

D k+1|k+1 (x|Z (k+1) ) = δ δx δ m[1]+...+m[N ] δz [1] 1 ...δz [N ] m[N ] e β[g [1] ,...,g [N ] ,h] g [i] =0,h=1 δ m[1]+...+m[N ] δz [1] 1 ...δz [N ] m[N ] e β[g [1] ,...,g [N ] ,h] g [i] =0,h=1 (4)
The multi-sensor data update equation ( 4) is generally untractable because the number of differentiated cross-terms increases dramatically with the number of sensors and/or measurements. A notable exception occurs in the single-sensor case (N = 1), where (4) reto the well-known equation developed by Mahler ([5]):

D k+1|k+1 (x|Z (k+1) ) =   1 -p d (x) + z∈Z k+1 p d (x)Lz(x) λc(z) + D k+1|k [p d Lz]   D k+1|k (x|Z (k) ) (5)
When expanded in the two-sensor case, (4) also matches Mahler's results ( [START_REF] Mahler | The multisensor phd filter, i: General solution via multitarget calculus[END_REF], [START_REF] Mahler | The multisensor phd filter, ii: Erroneous solution via 'Poisson magic[END_REF]).

Simplification by state and sensor partitioning

Should the sensors' FOVs be such that they do not all overlap with each other, a significant waste in computational resources would occur in the implementation of (4) because many cross-terms are likely to vanish ( [START_REF] Delande | Multitarget phd filtering: proposition of extensions to the multi-sensor case[END_REF]). Hence the proposed partitions of the sensors and the state space: Definition 2.2. For any sensor j ∈

[1 N ], let F [j]
k+1 ⊂ X denote its field of view at time k + 1 defined as:

∀x ∈ X , x ∈ F [j] k+1 ⇔ p [j] d,k+1 (x) = 0 (6)
Define the equivalence relation "cross" (↔) between sensors as:

∀i, j ∈ [1 N ], (i ↔ j) ⇔ (F [i] k+1 ∩ F [j] k+1 = ∅) (7)
Let {PS(p)} P p=1 be the sensor partition of [1 N ] formed by the equivalence classes of the transitive closure of the "cross" relation. Let {PT (p)} P p=0 5 be the space partition of the state space X defined by: Note that any sensor whose current FOV is void must be discarded when constructing PS; likewise, PT (0) must be discarded if empty.

PT (p) =            N j=1 F [j] k+1 (p = 0) j∈P S (p) F [j] k+1 (p = 0) (8)
Then one can show ( [START_REF] Delande | Multitarget phd filtering: proposition of extensions to the multi-sensor case[END_REF]) that equation ( 4) can be simplified and is equivalent to:

D k+1|k+1 (x|Z (k+1) ) =                                  D k+1|k (x|Z (k) ) (x ∈ PT (0))   δ δx   δ m[p 1 ]+...+m[pn p ] δz [p 1 ] 1 ...δz [pn p ] m[pn p ] e βp[g [p 1 ] ,...,g [pn p ] ,h]     g [p i ] =0,h=1   δ m[p 1 ]+...+m[pn p ] δz [p 1 ] 1 ...δz [pn p ] m[pn p ] e βp[g [p 1 ] ,...,g [pn p ] ,h]   g [p i ] =0,h=1 (x ∈ PT (p), p = 0) ( 9
)
where βp is the cross-term restricted to sensors j ∈ PS(p) ⊂ [1 N ] and to the subregion PT (p) ⊂ X . Since the set derivatives in ( 9) involve less measurements and a "smaller" cross-term βp for each partition element p than the "Brute Force" data update (4), provided that the partitioning itself is not too costly, implementing the Partition method is likely to yield the true data updated density (since ( 4) and ( 9) are equivalent) with a reduced computational cost.

SIMULATION

Scenario description

A target state x ∈ R 4 is composed of position (x, y) and velocity ( ẋ, ẏ) variables. Targets evolve according to a nearly constant velocity (NCV) model. The birth Process is Poisson with a constant rate, new targets are spread uniformly in the state space. Targets die whenever the edges of the 2-D position subspace. The test scenario lasts 400 time steps and involves 12 targets, the targets' positions are depicted in figures 1 and 2. The PHD multi-target tracker was implemented with a particle filter ( [START_REF] Vo | Sequential monte carlo methods for multi-target filtering with random finite sets[END_REF]), in this particular case the "cross" relation ( 7) is restricted as follows: two sensors j1, j2 cross each other if and only if at least one particle xi belongs to both FOVs. Note (fig. 3) that sensors 4 and 5 have isolated FOVs; therefore, each one is a singleton in the sensor partition regardless of the current spread of the particles in the state space. Sensors 1, 2 and 3, on the other hand, may be in the same partition element or split in several elements at each time step.

Results

The following results were averaged over a batch of 10 runs from the scenario described above. At every time step, the PDH D k+1|k was data updated with the Brute Force method (4) and the Partition method [START_REF] Vo | Sequential monte carlo methods for multi-target filtering with random finite sets[END_REF]. As expected, the data updated densities with the two methods were identical; the computed Kullback-Leibler distance between the two udpated PHDs remainded around 10 -16 during the scenario.

Figure 4 shows that the computational cost of the data update is likely to increase around time steps 200 and 280. Indeed, the estimated target number is high and several targets, evolving in the subregion covered by sensors 1, 2 and 3, are likely to generate several measurements each. Figure 6 shows that the Partition method was most effective around time step 200 where, according to the target positions (fig. 1 and2), targets were spread all over the state space. It is likely that the particules were also spread all over the state space, therefore increasing the impact of the partioning. Figure 6 also shows that the cost of the partitioning itself may be greater than the cost reduction of the data update, in which case the Partition method was less efficient than the Brute Force method; however the cost incurred by the Partition remained low enough (fig. 5). In overall, the computational gain of the Partition method on a run of the test scenario was around 35 seconds.

CONCLUSION AND FURTHER WORK

In this paper a generalization of the single-sensor PHD data update equation to the multi-sensor case is proposed. Then, a joint sensors and state space partitioning is proposed in order to lighten the computational cost of its implementation. Essentialy, the Partition method shows that the data update can be processed independently in each partition element of the state space. This allows the design of PHD multi-sensor multi-target tracker which provides the true data updated density with a lighter computational cost, depending on the configuration of the sensors' FOVs.

Partition method may have other practical applications that could be explored in further works. The first lead would be to apply either a true data update or a iterated-corrector approximation ( [START_REF] Mahler | The multisensor phd filter, ii: Erroneous solution via 'Poisson magic[END_REF]) independently on each partitioned element PT (p) of the target space, depending on the number of sensors and their related measurements in the corresponding sensor partition element PS(p). This method could lead to an interesting compromise between the cost-effective iterated-corrector approximation and the untractable true multi-sensor update. Another lead would be to apply a similar partitioning technique in order to obtain a tractable PENT-based ( [START_REF] Mahler | Sensor management with non-ideal sensor dynamics[END_REF]) multi-sensor manager.

Finally, for any

  element PS(p) ot the sensor partition, let np = |PS(p)| denotes the number of sensors in PS(p), and let p1, ..., pn p denote the increasing indexes in [1 N ] of sensors belonging to PS(p).

5

  {P S (p)} P p=1 = {P S,k+1 (p)} P k+1 p=1 and {P T (p)} P p=0 = {P T,k+1 (p)} P k+1p=0 , time subscripts are omitted for simplicity's sake.

1

 1

Fig. 1 :Fig. 2 :Fig. 3 :

 123 Fig. 1: Targets' positions along x-axis

Fig. 4 :Fig. 5 :

 45 Fig. 4: Target number (real and estimated)

Fig. 6 :

 6 Fig. 6: Computing time ratio (Partition over Brute Force)

µs(.) = µ k+1|k s k+1|k (.), time subscripts are omitted for simplicity's sake


Barbara Toffoli

Stella Bernardi

Riccardo Candido

Nicoletta Sabato

Renzo Carretta

Federica Corallini

Paola Secchiero

Giorgio Zauli

Bruno Fabris
email: b.fabris@fmc.units.it

Prof Bruno Fabris

OSTEOPROTEGERIN INJECTION INDUCES MORPHOLOGICAL AND FUNCTIONAL ALTERATIONS IN MOUSE PANCREATIC ISLETS

Keywords: Osteoprotegerin, diabetes, renin-angiotensin system, angiotensin converting enzyme inhibitor

Although serum osteoprotegerin (OPG) is significantly increased in diabetic subjects, its potential role in beta cell dysfunction has not been investigated. This study aimed to assess the effect of full length OPG administered in vivo in mice on pancreatic islet structure and function and its interaction with the renin-angiotensin system (RAS).

OPG-treated mice showed increased islet monocyte/macrophage infiltration, fibrosis and apoptosis with reduction of islet function. The remodeling of islet architecture was associated with increased pancreatic expression of components of the RAS, growth factor genes (transforming growth factor  and connective tissue growth factor) and inflammatory molecules (monocyte chemotactic protein-1 and vascular adhesion molecule type 1). Prevention of these changes with improvement of insulin secretion was observed in ramipril treated animals.

Our data suggest that OPG might play an important role in promoting beta cell dysfunction and that the upregulation of the local RAS represents one possible mechanism responsible for the OPGinduced beta cell dysfunction.

Introduction

Diabetes mellitus occurs under different clinical forms, however the loss of beta cell mass is recognised as a causal and committed stage in the development of both type 1 and type 2 diabetes [START_REF] Donath | Decreased beta-cell mass in diabetes: significance, mechanisms and therapeutic implications[END_REF]. Osteoprotegerin (OPG) is a soluble glycoprotein of the tumor necrosis factor (TNF) receptor superfamily, which was initially identified as a key regulator in bone turnover [START_REF] Simonet | Osteoprotegerin: a novel secreted protein involved in the regulation of bone density[END_REF]. It acts as a decoy receptor for the receptor activator of nuclear factor kB ligand (RANKL), a cytokine with strong osteoclast-inducing activity [START_REF] Boyle | Osteoclast differentiation and activation[END_REF].

Moreover, there is additional evidence that OPG can promote cell survival by binding to the TNFrelated apoptosis-inducing ligand (TRAIL) [START_REF] Emery | Osteoprotegerin is a receptor for the cytotoxic ligand TRAIL[END_REF]. OPG is mainly secreted by bone but it is also produced by a wide range of tissues including endothelial and smooth muscle cells [START_REF] Malyankar | Osteoprotegerin is an alpha vbeta 3-induced, NF-kappa B-dependent survival factor for endothelial cells[END_REF][START_REF] Zhang | PDGF induces osteoprotegerin expression in vascular smooth muscle cells by multiple signal pathways[END_REF]. In the general population, OPG is an independent risk factor for the progression of atherosclerosis and onset of cardiovascular disease [START_REF] Kiechl | Osteoprotegerin is a risk factor for progressive atherosclerosis and cardiovascular disease[END_REF][START_REF] Ueland | Dysregulated osteoprotegerin/RANK ligand/RANK axis in clinical and experimental heart failure[END_REF]. Moreover, in patients with coronary artery disease OPG is associated with the severity of coronary atherosclerosis and mortality [START_REF] Jono | Serum osteoprotegerin levels are associated with the presence and severity of coronary artery disease[END_REF][START_REF] Lieb | Biomarkers of the Osteoprotegerin Pathway. Clinical Correlates, Subclinical Disease, Incident Cardiovascular Disease, and Mortality[END_REF][START_REF] Schoppet | Increased osteoprotegerin serum levels in men with coronary artery disease[END_REF][START_REF] Ueland | Prognostic value of osteoprotegerin in heart failure after acute myocardial infarction[END_REF]. Interestingly, different groups of investigators have reported that serum OPG levels are significantly increased in both type 1 and type 2 diabetic patients [START_REF] Browner | Associations of serum osteoprotegerin levels with diabetes, stroke, bone density, fractures, and mortality in elderly women[END_REF][START_REF] Galluzzi | Osteoprotegerin serum levels in children with type 1 diabetes: a potential modulating role in bone status[END_REF][START_REF] Knudsen | Increased plasma concentrations of osteoprotegerin in type 2 diabetic patients with microvascular complications[END_REF][START_REF] Rasmussen | Plasma osteoprotegerin levels are associated with glycaemic status, systolic blood pressure, kidney function and cardiovascular morbidity in type 1 diabetic patients[END_REF][START_REF] Secchiero | An increased osteoprotegerin serum release characterizes the early onset of diabetes mellitus and may contribute to endothelial cell dysfunction[END_REF] and in more recent studies performed in diabetic subjects a strong association between plasma levels of OPG and micro-and macroangiopathy was observed [START_REF] Avignon | Osteoprotegerin is associated with silent coronary artery disease in high-risk but asymptomatic type 2 diabetic patients[END_REF][START_REF] Grauslund | Does osteoprotegerin relate to micro-and macrovascular complications in long-term type 1 diabetes?[END_REF][START_REF] Knudsen | Increased plasma concentrations of osteoprotegerin in type 2 diabetic patients with microvascular complications[END_REF][START_REF] Xiang | Association between plasma osteoprotegerin concentrations and urinary albumin excretion in Type 2 diabetes[END_REF].

However, in spite of the reported findings, the physiopathological role of elevated levels of OPG in pancreatic islet function is not known. The recent demonstration that OPG is expressed in the pancreas and that it can act as a cytokine induced gene in beta cells [START_REF] Schrader | Cytokine-induced osteoprotegerin expression protects pancreatic beta cells through p38 mitogen-activated protein kinase signalling against cell death[END_REF] suggests a potential role for OPG in endocrine pancreatic function. Therefore, the aim of this tudy was to investigate whether OPG is involved in pathogenetic aspects of diabetes mellitus. For this purpose, we studied the effect of full-length OPG administered in vivo in mice on the structure and A c c e p t e d M a n u s c r i p t 5 function of pancreatic beta cells. In addition, there is evidence for an interplay between OPG and the renin-angiotensin system (RAS) pathways in human aortas, where OPG has been shown to modulate angiotensin II type 1 (AT1) receptor gene expression [START_REF] Moran | Interaction between angiotensin II, osteoprotegerin, and peroxisome proliferator-activated receptor-gamma in abdominal aortic aneurysm[END_REF] and, in turn, angiotensin II promotes OPG production [START_REF] Zhang | PDGF induces osteoprotegerin expression in vascular smooth muscle cells by multiple signal pathways[END_REF]. Since in our preliminary results the expression of angiotensin converting enzyme (ACE) in the pancreas was markedly increased after chronic OPG administration, we specifically sought to determine whether long term ACE-inhibitor treatment could hinder the effect of exogenous OPG on beta cells remodeling and function.

A c c e p t e d M a n u s c r i p t 6

Material and methods

Animals and experimental protocol

Animal care and treatments were conducted in conformity with institutional guidelines in compliance with national and international laws and policies for the care and use of laboratory animals. 40 male mice C57BL/6J, aged 10 weeks, were randomized into 4 groups and studied for 12 weeks. Group 1 received every 3 weeks an i.p. injection of vehicle and served as a control.

Group 2 received every 3 weeks an i.p. injection of human recombinant OPG (R&D Systems, Minneapolis, MN), (1g/mouse in a total of 200 l HEPES-buffered saline) [START_REF] Candido | Human full-length osteoprotegerin induces the proliferation of rodent vascular smooth muscle cells both in vitro and in vivo[END_REF].

Group 3 received the ACE inhibitor ramipril (Sigma Aldrich, St. Louis, MO, USA) at the dose of 10 mg/kg/die in drinking water in co-treatment with i.p. injections of vehicle. Group 4 received ramipril in co-treatment with i.p. injections of OPG. The animals were maintained on regular mouse chow. Ramipril was added to the drinking water, stored at 4°C and replaced twice a week. Previous studies had demonstrated that ramipril remains stable in water for more than four days at room temperature and that the dosage used in mice is well below the toxic range and equivalent to the maximum therapeutic dose of 10 mg per day in humans [START_REF] Gross | Preemptive ramipril therapy delays renal failure and reduces renal fibrosis in COL4A3-knockout mice with Alport syndrome[END_REF]. Adjustment of drug concentration according to the fluid intake ensured that the daily dose was applied during the whole experiment. In addition, we have previously shown the effectiveness of an oral chronic delivery of ACE inhibitor [START_REF] Candido | Prevention of accelerated atherosclerosis by angiotensin-converting enzyme inhibition in diabetic apolipoprotein E-deficient mice[END_REF].

Measurement of physiological and biochemical parameters

At 4-week intervals, the following parameters were measured in all groups: body weight; systolic blood pressure measured by tail-cuff plethysmography in conscious, prewarmed mice; fasting glucose levels using a glucose oxidase method (Menarini, Florence, Italy); and fasting insulin serum levels. After 3 months, animals were anesthetized by an i.p. injection of pentobarbital sodium (60 mg/kg body weight). Blood was collected from the left ventricle and centrifuged and plasma was stored at -20° C for subsequent analysis. The mouse pancreas was rapidly dissected out A c c e p t e d M a n u s c r i p t 7 and bisected longitudinally, with one half snap frozen in liquid nitrogen and stored at -80° C for subsequent quantitative RT-PCR measurements, and the other half fixed in 4% paraformaldehyde and embedded in paraffin for immunohistochemical studies.

Oral glucose tolerance test

An oral glucose tolerance test was performed at the end of the experimental study. For this purpose, conscious mice, overnight fasted, received an oral glucose bolus (1g/Kg body weight) by gavage and blood was collected from the tail vein at 0, 30, 90 and 120 minutes [START_REF] Chu | Angiotensin II type 1 receptor blockade improves beta-cell function and glucose tolerance in a mouse model of type 2 diabetes[END_REF].

Serum insulin levels were determined by using the Mouse Insulin ultrasensitive ELISA kit (DRG International, Inc., USA).

Picrosirius red staining

Sirius red polarization microscopy was used to detect interstitial collagen. Four-micron paraffin sections were prepared from 4% paraformaldeyde-fixed, paraffin-embedded mice pancreas.

Sections were stained with 0.1% Sirius red (Direct red 80; Fluka Chemika, St Louis, MO, USA) in satured picric acid (picrosirius red) for 90 minutes and mounted.

Immunohistochemistry

The expression of the beta cell marker insulin, ACE and AT1 receptor were examined by immunohistochemistry on four-micron paraffin pancreas sections. The primary antibodies used were a polyclonal guinea pig anti-swine insulin antibody (1:100; DAKO, Glostrup, Denmark), a monoclonal mouse anti-human ACE antibody (1:100; Chemicon, Temecula, CA, USA), and a polyclonal rabbit anti-human AT1 receptor antibody (1:200; Santa Cruz Biotechnology, Inc, Santa Cruz, California). After neutralization of endogenous peroxidase, the sections were incubated with the primary antibodies for 1 hour at room temperature for ACE and AT1 receptor or overnight at 4°C for insulin. After washing, biotinylated secondary antibodies (all from Vector Laboratories, A c c e p t e d M a n u s c r i p t 8 Burlingame, CA) were applied for 60 minutes for ACE and AT1 receptor or 30 minutes for insulin. Specific immunohistochemical staining was detected using the standard avidin-biotin complex (Vector Laboratories) method. After an extensive washing, the final detection step was carried out using 3,3'-diaminobenzidine tetrahydrochloride (Sigma Chemical, St Louis, MO, USA) as the chromogen.

Detection of cells of the monocytic/macrophagic lineage was performed by using the primary antibody for F4/80 (1:200; AbD Serotec, Oxford, UK), followed by secondary anti-rat immunoglobulins (1:200; Vector Laboratories) and the CSA mouse amplification kit (DAKO, Glostrup, Denmark), following manufacturer's instructions.

After counterstaining with hematoxylin, all the sections were examined by light microscopy (Olympus BX50WI, Hamburg, Germany) and digitized with a high resolution camera (Q-Imaging Fast 1394, Surrey, BC, Canada). Semiquantitative assessment of islet proteins was performed by determining the percentage proportion of area or number of cells per islet section occupied by the brown (DAB) staining within each islet (20X objective) using an image analysis system (Image Pro Plus 6.3 Software, Media-Cybernetics, Silver Spring, MD, USA). A total of 40-50 islets per mouse pancreas (n=10 mice/group) were analyzed.

Pancreatic beta cell mass was estimated by multiplying the mean density of staining for insulin in the islet section by the mean islet area per area of pancreas. This was expressed in arbitrary units adjusted for the pancreatic wet weight for individual animals [START_REF] Tikellis | Improved islet morphology after blockade of the renin-angiotensin system in the ZDF rat[END_REF].

In situ detection of apoptosis

The localization of beta cell apoptosis was identified using dual immunofluorescence for Terminal Deoxynucleotidyl Transferase-Mediated dUTP Nick End Labeling (TUNEL) and insulin staining. After digestion with Proteinase K (20 g/ml; Sigma Chemical), four micron paraffin sections were labeled with polyclonal guinea pig anti swine insulin (1:100; DAKO), followed by a Texas Red dye-conjugated secondary antibody (1:100; Vector Laboratories). Slides were A c c e p t e d M a n u s c r i p t 9 subsequently labeled for TUNEL-positive cells and visualized using fluorescein dye according to the manufacturer's instructions (Roche Diagnostic, Indianapolis, IN, USA). TUNEL-positive beta cells were identified by the presence of green nuclei and red cytoplasm.

Real-Time quantitative PCR

Total RNA was isolated from snap-frozen pancreatic tissue after homogenisation in Trizol (Invitrogen, Milan, Italy). A reverse transcriptase reaction was performed on three micrograms of RNA using random hexamers, dNTPs and M-MLV reverse transcriptase (Invitrogen).

Angiotensin converting enzyme (ACE), angiotensin II type 1 (AT1) receptor, monocyte chemotactic protein (MCP-1), vascular adhesion molecule type 1 (VCAM-1), transforming growth factor  (TGF-) and connective tissue growth factor (CTGF) gene expression were analyzed by real-time quantitative RT-PCR using the TaqMan system based on real time detection of accumulated fluorescence (ABI Prism 7900HT, Foster City, CA, USA). To control for variation in the amount of cDNA available for PCR in the different samples, gene expression of the target sequence was normalized in relation to the expression of an endogenous control, 18S ribosomal RNA (rRNA) (18S rRNA TaqMan Control Reagent kit; Applied Biosystem). Specific primers and Taqman probes for ACE, AT1 receptor, MCP-1, VCAM-1, TGF- and CTGF (Table 1) were constructed with the help of Primer Express (PE Applied Biosystem). Each sample was tested in triplicate. Results were expressed relative to control pancreas values, which were arbitrarily assigned a value of 1.

Statistical analysis

Analysis of variance (ANOVA), calculated using Statview 512 software for Apple Macintosh computer (Brainpower, Calabasas, California, USA), was used to determine the presence of significant difference between groups. Comparisons of group means were performed by Fisher's

Body weight and systolic blood pressure

No difference between body weight and baseline systolic blood pressure was determined in animals prior to randomization. The body weight and systolic blood pressure were not significantly different in animals injected or not with human recombinant OPG (Table 2). Treatment with ACE inhibitor ramipril did not influence body weight. A significant reduction in systolic blood pressure was observed in animals treated with ramipril. Moreover no gross skeletal alterations at necroscopic examination in OPG-treated animals with respect to vehicle-or ramipril treated mice was observed.

Glicemic control and insulinemia

Fasting blood glucose levels were measured every 4 weeks for 3 months using a glucose oxidase method. Mice injected with recombinant human OPG had significantly higher blood glucose levels after 2 and 3 months of treatment compared to control mice (P<0.05). Treatment with ramipril completely prevented the glicemic increase induced by OPG administration (P<0.05) (Figure 1, panel A). Consistently with the situation of fasting hyperglycemia, OPG-treated mice exhibited a reduction in serum insulin with respect to the CONT animals, that was statistically significant after 3 months of treatment (P<0.05). In the same way, co-treatment with the ACE inhibitor significantly improved insulin secretion in OPG injected mice (P<0.05) (Figure 1, panel B). Similarly the area under the insulin curve (AUC), evaluated between 0 and 120 minutes after the glucose challenge, was significantly reduced in OPG-treated mice compared to control animals (P<0.01). The co-treatment with ramipril induced a statistically significant improve in the AUC of the OPG injected mice (P<0.01). Treatment with ramipril alone didn't alter the AUC with respect to untreated mice (Table 2). Immunohistochemical staining for beta cell marker insulin was strong and intense in control mice islets. In contrast, mice injected with recombinant human OPG presented islets that showed an attenuated and variable pattern of staining suggestive of progressive beta cell loss (Figure 2).

Insulin and beta cell mass

Consistent with these evidences, the mean percentage of proportional area staining positively for insulin in the OPG treated mice was significantly reduced with respect to CONT mice (CONT 47.1  1.3; OPG 39.6  1.4; P<0.0001). Treatment with ramipril significantly increased the staining density for insulin (OPG-R 50.7  1.5; vs OPG P<0.0001). No difference in insulin pattern was observed in mice treated with ramipril alone compared to control mice islets (CONT-R 49.6  1.3).

Pancreatic beta cell mass was estimated by multiplying the mean islet density of staining for insulin by the mean islet area per area of pancreas. This was expressed in arbitrary units adjusted for the pancreatic wet weight for individual animals. OPG treated mice had a marked reduction in beta cell mass with respect to the mice injected with vehicle (CONT 14.8  1.6; OPG 9.56  0.9; P<0.05). Blockade of the RAS further increased total pancreas insulin content (OPG-R 14.2  1.6 vs OPG P<0.05). Treatment with ramipril alone had no effect on the total beta cell mass (CONT-R 13.9  1.5).

Islet architecture and fibrosis

Islets in OPG treated animals were significantly enlarged with disarray of islet architecture, and irregular islet boundaries. Treatment with ramipril largely attenuated these changes (Figure 2).

The in vivo administration of human OPG increased significantly Picrosirius staining both within and at the boundary of the islets (P<0.05) and this increase was completely prevented by ramipril treatment. Ramipril alone didn't alter the total collagen content of the islets compared to the control animals (Table 3).

Islet cell apoptosis

A c c e p t e d M a n u s c r i p t 13 TUNEL staining was used to identify apoptotic cells within islet boundaries. Intraislet cell death was significantly greater in OPG injected mice (7-fold) compared with infrequent apoptotic cells seen in control islets (P<0.01). Blockade of the RAS with ramipril reduced TUNEL staining within islet cells to control levels (Figure 3).

Infiltration of cells of the monocytic/macrophagic lineage

To investigate whether OPG treatment affected the degree of infiltration of cells of the monocytic/macrophagic lineage, serial sections of paraffin-embedded pancreas were stained for F4/80 antigen (Figure 2). Peri-insular and intra-insular infiltrations were significantly (P<0.05) increased in OPG-injected mice (2-and 3.7 fold, respectively) when compared with control animals injected with vehicle. Co-treatment with lifelong ramipril significantly reduced the expression of F4/80 protein in islets (Table 3).

MCP-1, VCAM-1, TGF- and CTGF expression

To quantify mRNA levels of the inflammatory and prosclerotic cytokines reverse transcription polymerase chain reaction was performed in whole pancreas extracts. The injections with recombinant human OPG promoted a significant increase in MCP-1, VCAM-1, TGF- and CTGF gene expression (Figure 4). These increases were completely prevented by the treatment with the ACE inhibitor ramipril (Figure 4).

ACE and AT1 receptor gene and protein expression

OPG-treated mice showed an activation of the local RAS as assessed by a significant increase in ACE and AT1 receptor gene expression (by 8.7-and 2.2 fold, respectively) in the pancreas when compared with control animals (Table 4). Densitometric analysis of pancreatic ACE and AT1 receptor demonstrated the same pattern as seen with respect to ACE and AT1 gene expression. Ramipril treatment prevented the up-regulation of ACE and AT1 receptor compared to A c c e p t e d M a n u s c r i p t 14 treated OPG animals with mRNA and proteins levels similar to that observed in control animals (Table 4).

A c c e p t e d M a n u s c r i p t 15

Discussion

In the present study we have demonstrated for the first time that the systemic administration of recombinant OPG promotes significant histopathological changes in pancreatic islets including selective loss of beta cells by apoptosis, increased infiltration of monocytes/macrophages, increased expression of inflammatory molecules and fibrosis. In addition, exogenous administration of OPG also induced a significant increase of gene and protein expression of the ACE and AT1 receptor and we have also observed the attenuation of these changes after chronic blockade of the RAS, in keeping with previous strong evidence that most of the components of functioning RAS are present locally in the endocrine pancreas and that islet RAS has a role in regulating pancreatic islet function [START_REF] Leung | Tissue renin-angiotensin system: its expression, localization, regulation and potential role in the pancreas[END_REF][START_REF] Tikellis | Role of the renin-angiotensin system in the endocrine pancreas: implications for the development of diabetes[END_REF]. These findings are particularly relevant since it has been shown that angiotensin II plays an important role in regulating insulin islet secretion [START_REF] Carlsson | Angiotensin II and the endocrine pancreas: effects on islet blood flow and insulin secretion in rats[END_REF][START_REF] Lau | Evidence for a local angiotensin-generating system and dose-dependent inhibition of glucose-stimulated insulin release by angiotensin II in isolated pancreatic islets[END_REF] and the process that leads to beta cell loss and dysfunction in type 1 and type 2 diabetes is significantly influenced by local activation of the RAS [START_REF] Tikellis | Role of the renin-angiotensin system in the endocrine pancreas: implications for the development of diabetes[END_REF].

The molecular mechanism by which OPG controls ACE and AT1 gene expression is not known. The expression of ACE and AT1 receptor have been shown to be associated with activation of the mitogen-activated protein kinase (MAPK) and nuclear factor-kB transduction pathways [START_REF] Cowling | Transcription factor NF-kappa B is necessary for up-regulation of type 1 angiotensin II receptor mRNA in rat cardiac fibroblasts treated with tumor necrosis factor-alpha or interleukin-1 beta[END_REF][START_REF] Koka | Angiotensin II upregulates angiotensin I-converting enzyme (ACE), but down-regulates ACE2 via the AT1-ERK/p38 MAP kinase pathway[END_REF][START_REF] Martin | TGF-beta1 stimulates human AT1 receptor expression in lung fibroblasts by cross talk between the Smad, p38 MAPK, JNK, and PI3K signaling pathways[END_REF]. In this respect, it should be emphasized that OPG is able to stimulate MAPK signaling [START_REF] Theoleyre | Cellular activity and signaling induced by osteoprotegerin in osteoclasts: involvement of receptor activator of nuclear factor kappaB ligand and MAPK[END_REF]. Taking these data together with the present study it prompts us to hypothesize that OPG might control transcription mechanism governing ACE and AT1 gene expression. In this respect, it should also be taken into account that pancreatic inflammation may also result in the upregulation of local RAS as demonstrated in animal models of pancreatitis [START_REF] Kuno | Angiotensin-converting enzyme inhibitor attenuates pancreatic inflammation and fibrosis in male Wistar Bonn/Kobori rats[END_REF]. A possible pathogenetic link between elevated levels of OPG and inflammation has been suggested by recent in vitro studies by our group [START_REF] Zauli | Osteoprotegerin increases leukocyte adhesion to endothelial cells both in vitro and in vivo[END_REF] and that of [START_REF] Mangan | Osteoprotegerin upregulates endothelial cell adhesion molecule response to tumor necrosis factor-alpha associated with induction of angiopoietin-2[END_REF] demonstrating that exposure to recombinant OPG promotes leukocyte adhesion to endothelial cells. In our experimental setting, an A c c e p t e d M a n u s c r i p t 16 islet accumulation of cells of the monocytic/macrophagic lineage was noted after OPG administration. This was associated with an increase expression of MCP-1, a proinflammatory chemoattractant whose expression correlates with the progression of insulitis and beta cell destruction in NOD mice [START_REF] Chen | Monocyte chemoattractant protein-1 is expressed in pancreatic islets from prediabetic NOD mice and in interleukin-1 beta-exposed human and rat islet cells[END_REF]. Recently, an increase mRNA for MCP-1 was found in islet from GK rat, a spontaneous model of type II diabetes [START_REF] Ehses | IL-1 antagonism reduces hyperglycemia and tissue inflammation in the type 2 diabetic GK rat[END_REF]. In addition, the endothelial adhesion molecule VCAM-1, which is important for the adhesion of macrophages to the vascular wall, as demonstrated in experimental diabetes [START_REF] Candido | Irbesartan but not amlodipine suppresses diabetes-associated atherosclerosis[END_REF], was also significantly increased in OPG-treated animals. Previous studies have demonstrated that the proinflammatory effect of OPG is mediated by a direct interactivity with cell surface heparan solfate [START_REF] Mosheimer | Syndecan-1 is involved in osteoprotegerin-induced A c c e p t e d M a n u s c r i p t 22 chemotaxis in human peripheral blood monocytes[END_REF] or alternatively by preventing the antiinflammatory activity of TRAIL [START_REF] Zauli | Role of full-length osteoprotegerin in tumor cell biology[END_REF]. In this respect, it is particularly noteworthy that the TRAIL death pathway is present in islet beta cells [START_REF] Ou | TNF-related apoptosisinducing ligand death pathway-mediated human beta-cell destruction[END_REF] and we have recently demonstrated that recombinant TRAIL ameliorates the severity of streptozotocin-induced type 1 diabetes [START_REF] Zauli | Treatment with recombinant tumor necrosis factor-related apoptosis-inducing ligand alleviates the severity of streptozotocin-induced diabetes[END_REF].

Therefore, a possible mechanism by which OPG might contribute to beta cell dysfunction is by inhibiting the antiinflammatory activity of circulating TRAIL. Notwithstanding these multiple potential pathways mediating the inflammatory response to OPG, all the inflammatory effects observed in our study after administration of OPG may be explained by the local RAS upregulation and its downstream sequelae. In accordance with this hypothesis, ACE inhibitor treatment prevented all the OPG-induced inflammatory changes.

In native tissues, the interaction between pancreatic beta cells and elements of their local micro-environment including extracellular matrix is essential to activate intracellular signaling pathways that regulate cell proliferation, survival and function. Loss of cell-to-cell communications associated with increased intraislet fibrosis may reduce the secretory efficiency of the islet and promote islet cell apoptosis [START_REF] Tikellis | Improved islet morphology after blockade of the renin-angiotensin system in the ZDF rat[END_REF]. We report here that chronic OPG treatment increases peri-and intrainsula fibrosis and promotes the expression of TGF- mRNA and its downstream effector CTGF in the pancreas. Both TGF- and CTGF have been associated with A c c e p t e d M a n u s c r i p t 17 fibrosis in diabetic nephropathy [START_REF] Murphy | Suppression subtractive hybridization identifies high glucose levels as a stimulus for expression of connective tissue growth factor and other genes in human mesangial cells[END_REF] and pancreatitis [START_REF] Di Mola | Connective tissue growth factor is a regulator for fibrosis in human chronic pancreatitis[END_REF][START_REF] Kuno | Angiotensin-converting enzyme inhibitor attenuates pancreatic inflammation and fibrosis in male Wistar Bonn/Kobori rats[END_REF]. Furthermore, Tikellis and coll. have previously reported that diabetes mellitus is associated with increased expression of TGF- and CTGF in the islet of ZDF rats [START_REF] Tikellis | Improved islet morphology after blockade of the renin-angiotensin system in the ZDF rat[END_REF]. Also the RAS has been linked to increased fibrosis in a variety of tissues including the heart [START_REF] Seccia | Cardiac fibrosis occurs early and involves endothelin and AT-1 receptors in hypertension due to endogenous angiotensin II[END_REF], kidney [START_REF] Satoh | Renal interstitial fibrosis is reduced in angiotensin II type 1a receptor-deficient mice[END_REF] liver [START_REF] Yoshiji | Angiotensin-II type 1 receptor interaction is a major regulator for liver fibrosis development in rats[END_REF]. In fact, locally produced angiotensin II is thought to upregulate, via AT1 receptor, the expression of fibrogenic cytokines and growth factors including TGF- and CTGF [START_REF] Ruperez | Angiotensin II increases connective tissue growth factor in the kidney[END_REF]. Moreover, the use of an ACE inhibitor or AT1 receptor antagonist have been shown to result in reduced fibrosis from inhibtion of TGF- and CTGF in animal models of diabetes mellitus [START_REF] Tikellis | Improved islet morphology after blockade of the renin-angiotensin system in the ZDF rat[END_REF] and pancreatitis [START_REF] Kuno | Angiotensin-converting enzyme inhibitor attenuates pancreatic inflammation and fibrosis in male Wistar Bonn/Kobori rats[END_REF]. It is conceivable that the reduction in islet fibrosis observed in this study after treatment with ramipril may be mediated through a similar pathway.

Pancreatic beta cell death by apoptosis is believed to be the primary mechanism for the reduction of beta cell mass and volume in both type 1 and type 2 diabetes [START_REF] Thomas | Beta cell apoptosis in diabetes[END_REF].

Apoptosis in beta cell is a highly complex process and angiotensin II has been shown to play a relevant role in the control of beta cell life and death [START_REF] Yuan | Effects of renin-angiotensin system blockade on islet function in diabetic rats[END_REF]. In the present study the OPG induction of islet apoptosis is closely related to upregulation of pancreatic RAS and these changes were prevented after treatment with ramipril.

A limitation of the present study relates to the use of whole pancreas extract for gene studies while it is well known that measurement of mRNA levels in isolated islets would be more informative and accurate. Nevertheless this limitation does not influence our basic finding of a strict association of the OPG-induced beta cell dysfunction with increased pancreatic expression of component of the RAS.

The maintenance of beta cell area/density is important for maintaining a correct insulin secretion. Consistently with a protective role of ramipril the levels of insulinemia were significantly higher in mice treated with OPG + ramipril, and OPG + ramipril treated animals showed reduced level of hyperglycemia throughout the whole period of the study with respect to OPG-injected A c c e p t e d M a n u s c r i p t 18 animals. Moreover ramipril co-treatment significantly ameliorated insulin response after the glucose challenge, suggesting an improvement in beta cell function. Because disruption of contacts between beta cells and beta cell loss reduces the secretory efficiency of islets, results illustrated in our study suggest a potential mechanism to explain why pathologically elevated serum OPG levels, frequently occurring in both type 1 and type 2 diabetes [START_REF] Browner | Associations of serum osteoprotegerin levels with diabetes, stroke, bone density, fractures, and mortality in elderly women[END_REF][START_REF] Galluzzi | Osteoprotegerin serum levels in children with type 1 diabetes: a potential modulating role in bone status[END_REF][START_REF] Knudsen | Increased plasma concentrations of osteoprotegerin in type 2 diabetic patients with microvascular complications[END_REF][START_REF] Rasmussen | Plasma osteoprotegerin levels are associated with glycaemic status, systolic blood pressure, kidney function and cardiovascular morbidity in type 1 diabetic patients[END_REF][START_REF] Secchiero | An increased osteoprotegerin serum release characterizes the early onset of diabetes mellitus and may contribute to endothelial cell dysfunction[END_REF], are linked to the development and/or maintenance of diabetes mellitus.

Previous studies have demonstrated a pro-atherosclerotic role for OPG in the vasculature which may be particularly prominent in diabetes mellitus [START_REF] Candido | Human full-length osteoprotegerin induces the proliferation of rodent vascular smooth muscle cells both in vitro and in vivo[END_REF][START_REF] Knudsen | Increased plasma concentrations of osteoprotegerin in type 2 diabetic patients with microvascular complications[END_REF][START_REF] Rasmussen | Plasma osteoprotegerin levels are associated with glycaemic status, systolic blood pressure, kidney function and cardiovascular morbidity in type 1 diabetic patients[END_REF]. Moreover it has been shown that OPG is an NF-kB-inducible gene, whose expression and release in culture of vascular cells is significantly increased by inflammatory cytokines [START_REF] Collin-Osdoby | Receptor activator of NF-kappa B and osteoprotegerin expression by human microvascular endothelial cells, regulation by inflammatory cytokines, and role in human osteoclastogenesis[END_REF]. Although it cannot be ruled out that high plasma OPG levels are an epiphenomenon of inflammatory processes harbored in vascular lesions, our present data suggest rather that elevated level of OPG in diabetes may have an active role in pancreatic islet pathophysiology.

  A c c e p t e d M a n u s c r i p t 10 least significant difference (LSD) method unless otherwise specified. Results are expressed as mean  SEM. A P value of less than 0.05 was considered statistically significant.

  The present study indicates in the overactivity of the local RAS one of the possible mechanisms responsible for the OPG-induced islet beta cell dysfunction, and this may explain why the use of an ACE inhibitor may be suitable to target OPG pancreatic effect. Further studies are necessary to fully elucidate the potential relevance of OPG in the development of beta cell failure and provide the rationale to consider this cytokine as a target for new approaches to retard the development of diabetes.

Fig. 1 .

 1 Figure legends

Fig. 2 .

 2 Fig. 2. Effect of rOPG injections on pancreas islet morphology. Representative histological

Fig. 3 .Fig. 4 .

 34 Fig. 3. Effect of in vivo injections of rOPG on beta cell apoptosis. Beta cell apoptosis is

  TaqMan Primers and Probe Sequences used for real-time RT-PCR in pancreas

  Figure 1

  Figure 3

Figure 4

 4 Figure 4

Table 3 .

 3 Intra-and peri-islet fibrosis and macrophages infiltrationData for picrosirius are expressed as proportional area (%) of each islet (intra-) or of islet boundaries (peri-) occupied by positive staining, shown as mean SEM. Data for F4/80 are expressed as average of the number of cells staining positive in insular-or peri-insular level SEM.

		Picrosirius		F4/80
		Intra-islet	Peri-islet	Intra-islet	Peri-islet
	CONT	3.7 0.3	12.4 0.9	0.019 0.005	0.163 0.036
	OPG	6.3 0.7 a	16.6 1.2 a	0.098 0.01 a	0.406 0.079 a
	CONT-R	3.0 0.4	11.3 0.3	0.013 0.009	0.197 0.086
	OPG-R	3.9 0.1	13.5 0.8	0.017 0.007	0.132 0.038

a P<0.05 vs all groups.

Table 4 .

 4 Expression of components of the RAS as quantified by immunohistochemistry and real-Data are proportional area (%) of each islet occupied by positive staining for specific islet proteins, shown as mean SEM (for immunohistochemistry); and data are results from gene expression study presented as a ratio compared with CONT, shown as mean SEM (for real-time RT-PCR).

	time RT-PCR
		ACE	AT1 receptor
	Immunohistochemistry
	CONT	8.0 1.7	2.7 1.0
	OPG	16.1 3.2 a	5.5 0.6 a
	CONT-T	6.3 2.2	2.9 0.8
	OPG-R	8.7 1.1	3.1 0.5
	Real-time RT-PCR
	CONT	1.0 0.6	1.0 0.2
	OPG	8.7 2.8 b	2.2 0.4 a
	CONT-R	0.6 0.1	1.0 0.2
	OPG-R	0.7 0.3	1.1 0.3

a P<0.05 vs other groups; b P<0.0001 vs other groups.