Luca Cantarini
email: cantariniluca@hotmail.com

Francesca Fanti

MD Mauro Galeazzi

MD Silvana Martino

MD Clelia Miracco

MD Maurizio Biagioli

MD Norberto Dal Canto

MD Rolando Cimaz

EFFICACY OF CYCLOSPORINE A TREATMENT IN RELAPSING FEBRILE LOBULAR PANNICULITIS ASSOCIATED WITH SMALL VESSEL VASCULITIS

Keywords: Weber-Christian Disease, relapsing febrile lobular non-suppurative panniculiits, small vessels vasculitis, cyclosporin A

Weber-Christian Disease (WCD), also known as relapsing febrile lobular non-suppurative panniculiits, is a rare condition characterized by recurrent subcutaneous inflammatory nodules in the adipose tissue in addition to fever, malaise and other systemic manifestations such as polyarthralgia and polymyalgia. The association with small vessel vasculitis has been rarely reported. We report here an unusual case of WCD associated with small vessels vasculitis also describing the efficacy of Cyclosporin A treatment.

INTRODUCTION

Weber-Christian Disease (WCD), also known as relapsing febrile lobular non-suppurative panniculiits, is a rare condition characterized by recurrent subcutaneous inflammatory nodules in the adipose tissue in addition to fever, malaise and other systemic manifestations such as polyarthralgia and polymyalgia; the subcutaneous nodules are painful and mainly observed in the extremities and in the trunk.

Lobular panniculitis is the essential feature in biopsy specimens from the nodules [START_REF] Panush | Weber-Christian disease. Analysis of 15 cases and review of the literature[END_REF][START_REF] White | Weber-Christian panniculitis: a review of 30 cases with this diagnosis[END_REF] and the association with small vessel vasculitis has been reported only once [START_REF] Lamprecht | Small vessel vasculitis and relapsing panniculitis in tumour necrosis factor receptor associated periodic syndrome (TRAPS)[END_REF],in association with an autoinflammatory gene mutation in TNFRSF1A encodying for the p55 Tumor Necrosis Factor-α receptor.

WCD may present as idiopathic or secondary to a variety of other conditions, such as connective tissue diseases, lymphomas, leukaemia, sarcoidosis and fungal and bacterial infections.The cause of this disease is still unclear, and an immune mechanism has been implicated in the pathogenesis [START_REF] Jimenez-Mazuecos | Weber-Christian disease associated with lupus anticoagulant and anticardiolipin antibodies[END_REF].

We report here a rare case of WCD in a child, associated with small vessel vasculitis; we also describe the good response to Cyclosporine A (Cy A) treatment.

CASE REPORT

An 8 year-old boy was first seen in our Unit with an 8-months history of recurrent febrile attacks with painful, palpable, subcutaneous, erithematous nodules localized on the distal extremity of both lower limbs (Fig. 1A).

Test for antinuclear and anti-DNA antibodies were negative and extensive microbiological and serological investigations were negative for bacteria, fungi, parasites, including Borrelia burgdorferi and Brucella; no significant titers of antiviral antibodies were detected, including antibodies to Cytomegalovirus and Epstein-Barr virus. Erytrocyte sedimentation rate and C-reactive protein were not increased; renal and liver function was normal. Alpha-1-antitrypsin serum levels were within Linking powered by eXtyles normal limts, and a chest X-ray did not show any abnormality. A skin biopsy was performed and documented a mild lobular panniculitis, with foamy histiocytes and lymphocytes forming perivascular cuffing; isolated multinucleated giant cells were observable. The patient was hence diagnosed with WCD and small vessel vasculitis (Fig. 2B-C-D) and started treatment with prednisone 1 mg/kg/die, gradually tapered and eventually discontinued after 5 months. Corticosteroids determined a reduction in pain and inflammatory features of the skin lesions but did not alter the progression of panniculitis.

The patient therefore started treatment with Cy A at 5 mg/kg/day, andAlready one month later the cutaneous lesions were stable, not painful nor erythematous, and the patient did not have further febrile episodes.. The improvement was confirmed at a 6-months follow-up (Fig. 1B), although obviously the depressed lesions from previous inflammation were still evident.

DISCUSSION

Panniculitis in association with small vessel vasculitis is rare, and to the best of our knowledge has been reported only twice, in association with TNFRSF1A gene mutation [START_REF] Lamprecht | Small vessel vasculitis and relapsing panniculitis in tumour necrosis factor receptor associated periodic syndrome (TRAPS)[END_REF] and as the initial manifestation of brucellosis [START_REF] Tanyel | Panniculitis as the initial manifestation of brucellosis: a case report[END_REF].

Our patient underwent genetic testing for mutations in TNFRSF1A gene and other autoinflammatory genes such as MEFV and CIAS1. No mutations were found.

WCD is a primary panniculitis, which is a histiocytic disorder described as a form of lobular panniculitis with infiltration of haemophagocytic benign histiocytes [START_REF] Panush | Weber-Christian disease. Analysis of 15 cases and review of the literature[END_REF]. No specific treatment is available for WCD. Corticosteroids have been used without satisfactory results, although they are often effective to treat acute attacks of the disease.

Two patients successfully treated with thalidomide have been reported, although the question of a possible spontaneous remission remains unanswered [START_REF] Barthel | Successful treatment of idiopathic febrile panniculitis (Weber-Christian disease) with thalidomide in a patient having failed multiple other medical therapies[END_REF][START_REF] Eravelly | Thalidomide in Weber-Christian disease[END_REF]. Linking powered by eXtyles Successful treatment of WCD with Cy A was first described in 1987 [START_REF] Entzian | Treatment of Weber-Christian panniculitis with cyclosporine A[END_REF] and several additional reports described a similar response [START_REF] Hinata | Successful treatment of steroid-resistant Weber-Christian disease with biliary ductopenia using cyclosporin A[END_REF][START_REF] Iwasaki | Successful treatment of a patient with febrile, lobular panniculitis (Weber-Christian disease) with oral cyclosporin A: implications for pathogenesis and therapy[END_REF].

Cy A has potent immunosuppressive properties that result from selective inhibition of Tlymphocyte activation [START_REF] Sigal | Cyclosporin A, FK-506, and rapamycin: pharmacologic probes of lymphocyte signal transduction[END_REF]. The patient reported by Lamprecht et al. with panniculitis and vascultis in the context of TRAPS was successfully treated with the human soluble p75 TNF-α-receptor fusion protein etanercept.

Our report describes an uncommon association of WCD and small vessel vasculitis, previously described only twice, and confirms the efficacy of Cy A in the treatment of WCD. The T-cell immune response may be implicated in the pathogenesis of WCD [12], thus explaining the efficacy of Cy A in the treatment of this disease.

12. Hojo N, Hasegawa H, Iwamasa K, et al (2004) A case of Weber-Christian disease associated with myelodysplastic syndrome. Mod Rheumatol 14:73-76 Medline. doi:10.1007/s10165-003-0270-5

Figure 1 .Figure 2 .

 12 Figure 1. Cutaneous lesions before treatment (A) and at six months follow up (B)


MD Markéta Fojtíková

Pavlína Čejková

Radim Bečvář

Jiří Vencovský

Jana Tomasová Studýnková

Marie Černá

Fojtikova Marketa¹

Cejkova Pavlina²

Becvar Radim¹

Vencovsky Jiri¹

Studynkova Tomasova

Cerna Jana¹

Marie²

Polymorphism of the extrapituitary prolactin promoter and systemic sclerosis

Keywords: Prolactin, polymorphism, promoter, systemic sclerosis, polymyositis, dermatomyositis

association between TT genotype in SSc patients with disease onset above 45 years compared to individuals with SSc and diseases onset prior 45 years (P corrected = 0.02; OR = 0.13; CI (95%): 0.02 -0.69) and healthy subjects (P corrected=0.04; OR 0.2; CI (95%) 0.04-0.82). TT genotype of the -1149G/T SNP of the extrapituitary PRL promoter decreased a risk for SSc development in older age. Further investigation of the PRL role in SSc pathogenesis is necessary.

Sirs,

Increased serum prolactin (PRL) levels have been demonstrated in several autoimmune diseases including polymyositis (PM) and systemic sclerosis (SSc) [START_REF] Orbach | Novel biomarkers in autoimmune diseasesprolactin, ferritin, vitamin D, and TPA levels in autoimmune diseases[END_REF][START_REF] Montagna | Bioactivity of prolactin in systemic sclerosis[END_REF][START_REF] Mirone | Androgen and prolactin (Prl) levels in systemic sclerosis (SSc) Relationship to disease Severiny[END_REF]. PRL is produced by the lactotrophs of the pituitary gland, but the immune cells can produce PRL as well, and PRL acts as a cytokine with role in B, T and dendritic cells maturation [START_REF] Orbach | Novel biomarkers in autoimmune diseasesprolactin, ferritin, vitamin D, and TPA levels in autoimmune diseases[END_REF]. Czuwara-Ladykowska et al. demonstrated higher PRL mRNA synthesis in lymphocytes from patients suffering from SSc than in healthy individuals (4). Multiple promoters regulate PRL gene transcription. Unlike the pituitary production controlled by the pituitary promoter under the influence of transcriptional factor Pit-1, the extrapituitary (including that by immune cells) PRL secretion is regulated by the Pit-1 independent alternative promoter located 5.840 bp distal to the start of the PRL transcriptional site [START_REF] Gellersen | Nonpituitary human prolactin gene transcription is independent of Pit-1 and differentially controlled in lymphocytes and in endometrial stroma[END_REF]. This extrapituitary promoter contains functional single nucleotide polymorphism (SNP) -1149 G/T (rs1341239) in the GATA sequence; G allele leads to higher PRL mRNA in lymphocytes synthesis [START_REF] Stevens | Polymorphisms of the human prolactin gene -implications for production of lymphocyte prolactin and systemic lupus erythematosus[END_REF]. GG genotype has been associated with systemic lupus erythematosus (SLE) (6,7), while TT genotype was identified as protective for non-Hodgkin and follicular lymphoma and rheumatoid arthritis development [START_REF] Skibola | Polymorphisms and haplotypes in the cytochrome P450 17A1, prolactin, and catechol-O-methyltransferase genes and non-Hodgkin lymphoma risk[END_REF][START_REF] Lee | The PRL -1149 G/T polymorphism and rheumatoid arthritis susceptibility[END_REF].

In this study we looked for distribution of allele and genotype of the -1149 G/T SNP of the extrapituitary PRL promoter in 75 patients with SSc (average age 61.4), in 47 subjects with PM (average age 57.8) and in 68 DM patients (average age 58.3), and in 123 healthy Czech individuals (blood donors and medical students, average age 39.7). SSc and PM/DM patients met classification criteria for these illnesses [START_REF] Rheumatism | Preliminary criteria for the classification of systemic sclerosis (scleroderma)[END_REF][START_REF] Bohan | Polymyositis and dermatomyositis (First of two parts)[END_REF]. PCR-RFLP methodology was used for -1149G/T SNP detection [START_REF] Fojtikova | Extrapituitary prolactin promoter polymorphism in Czech patients with systemic lupus erythematosus and rheumatoid arthritis[END_REF]. Briefly, we amplified 137 bp region of the PRL extrapituitary The genotype and allele distribution did not show any significant differences in groups of PM, DM and SSc patients compared to healthy individuals (Table I) and we detected neither gender distribution differences (data not shown) nor correlation with specific clinical or serological manifestation of these diseases (lung fibrosis, type of scleroderma, antiScl-70 and antiJo-1 positivity) (Table I). However, we found a significant inverse association between TT genotype in SSc patients with disease onset above 45 years compared to individuals with SSc and diseases onset prior 45 years (2/47 = 4.1% versus 6/18 = 25%, respectively; P corrected = 0.02; OR = 0.13; CI (95%): 0.02 -0.69). Similar results were obtained when compared TT genotype in patients with SSc onset after the age of 45 and healthy subjects (P corrected=0.04; OR 0.2; CI (95%) 0.04-0.82), see Table I. Thus, genotype -1149 TT of the extrapituitary PRL promoter SNP (via reduced PRL promoter activity and consequently lower PRL levels in lymphocytes) seems to be associated with decreased risk to SSc development in older age. On the other hand, any genotype or allele differences in patients with polymyositis and dermatomyositis according to the age of manifestation onset were found (data not shown). We can assume that this polymorphism might be somewhat specific for SSc and therefore warrants further investigation of prolactin genetics and its levels during the disease course. 15.

Genotype and allele distribution of the prolactin (PRL) extrapituitary promoter -1149 G/T SNP in

  promoter and for restriction used ApoI endonuclease. We determined genotypes: the homozygote TT characterized by 120 bp + 17 bp, the homozygote GG by 85 bp + 35 bp + 17 bp, and the heterozygote GT by 120 bp + 85 bp + 35 bp + 17 bp DNA fragments. Results were evaluated by χ² test with Bonferroni correction. Control group was in Hardy-Weinberg equilibrium.

the autoimmune diseases: Polymyositis (PM), Dermatomyositis (DM), Systemic Sclerosis (SSc) and healthy individuals (Co ntrols)

			PRL extrapituitary promoter -1149 G/T SNP
	Characterization of tested groups			Genotype		Allele
			GG	GT	TT	G	T
		n=47	20	21
	PM	(% )	(42.5)	(44.7)	6 (12.8)	61 (64.9) 33 (35.1)
		P 1	ns	ns	ns	ns	ns
			37.5	56.3
	Jo-1 positive (34.0%) % (P 2 )	(ns)	(ns)	6.2 (ns)	65.6 (ns)	34.4 (ns)
			50.0	50.0
	CPM (4.3%)	% (P 2 )	(ns)	(ns)	0 (ns)	75 (ns)	25 (ns)
		n=68	19	35
	DM	(% )	(27.9)	(51.5)	14 (20.6)	73 (53.7) 63 (46.3)
		P 1	ns	ns	ns	ns	ns
	Jo-1 positive (19.1%) % (P 2 )

  Abbreviations: n: nu mber of cases, P1: P value was determined by the χ² test comparing each group (PM,DM,SSc) and controls. P2: P value was determined by the χ² test comparing positive/negative feature and comparing each feature and controls; Pc.: P value corrected fo r mu ltip le co mparisons (3 detected genotypes, 2 alleles), ns -not significant, ANA: Antinuclear antibodies, Jo-1: antibodies against histidyl-tRNA synthethase , Scl-70 antibodies against DNA topoisomerase I, lSSc-limited cutaneous SSc, dSSc-d iffuse cutaneus SSc, CPM : cancer associated Polymyositis, CDM : cancer associated Dermatomyositis

	Pc=0.04; OR 0.2; CI(95%) 0.04-0.82
							61.5
						.03 a )	(0.03 b )
			22.2	55.6
	CDM (13.2%)	% (P 2 )	(ns)	(ns)	22.2 (ns)	50 (ns)	50 (ns)
		n=75	31	36
	SSc	(% )	(41.3)	(48.0)	8 (10.7)	98 (65.3) 52 (34.7)
		P 1	ns	ns	ns	ns	ns
	Scl-70 positive (45.2%)	% (P 2 )	51.5 (ns)	42.4 (ns)	6.1 (ns)	72.7 (ns)	27.3 (ns)
	Lung Fibrosis		41.4	51.7
	(40.3%)	% (P 2 )	(ns)	(ns)	6.9 (ns)	67.2 (ns)	32.8 (ns)
			46.3	48.8
	lSSc (54.6%)	% (P 2 )	(ns)	(ns)	4.9 (0.03)	70.7 (ns)	29.3 (ns)
			34.5	48.3
	dSSc (38.6%)	% (P 2 )	(ns)	(ns)	17.2 (ns)	58.7 (ns)	41.3(ns)
	Onset after 45 years (65.3%)	% (P 2 )	40.8 (ns)	55.1 (ns)	4.1 (0.007 d ;0.01 e )	68.3 (ns)	31.3 (ns)
	Controls	n= 123 (% )	49 (39.8)	51 (41.5)	23 (18.7)	149 (60.6) 97 (39.4)
	a) P value is defined when compare Jo-1positive DM and healthy controls. Pc.=0.06, ns
	b) P value is defined when compare Jo-1positive DM and healthy controls. Pc.=0.06, ns

c) P value is defined when compare SSc patients with limited disease compare to controls, Pc=0.09,ns d) P value is defined when compare SSc patients with disease onset before and after 45 years. Pc=0.02; OR 0.13; CI(95%) 0.02-0.69 e) P value is defined when compare SSc patients with disease onset after 45 years and controls.

Acknowledgement:

This study was supported by the grant of the Czech Ministry of Health: NR/9292 -3


Dr Elena Cichero
email: cichero@unige.it

Sara Cesarini

Luisa Mosti

Paola Fossa

Keywords: cannabinoid receptor, CB2 agonist, 3D-QSAR, CoMFA, CoMSIA Cannabinoid receptor  CB2 agonist  3D-QSAR  CoMFA 

Novel classes of cannabinoid 2 receptor (CB2) agonists based on 1,2,3,4tetrahydropyrrolo [3,4-b]indole and benzimidazole scaffolds have shown high binding affinity toward CB2 receptor and good selectivity over cannabinoid 1 receptor (CB1). A computational study of Comparative Molecular Fields Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) was performed, initially on each series of agonists, and subsequently on all compounds together, in order to identify the key structural features impacting their binding affinity. The final CoMSIA model resulted to be the more predictive, showing cross-validated r2 (rcv2) = 0.680, non cross-validated r2 (rncv2) = 0.97 and test set r2 (r2pred) = 0.93. The study provides useful suggestions for the design of new analogues with improved affinity.

Response to Reviewers: In this manuscript, CoMFA and CoMSIA analyses were performed as suggested by reviewer#1. Thus, to derive the final CoMFA and CoMSIA models, 44 compounds were assigned to the training set, for model generation, and 19 ligands to the test set, for model validation. The test set molecules represent the 30% of the training set and cover 4 log orders of magnitude.

Introduction

Cannabinoid receptors interact with cannabinoid drugs including the classical cannabinoids, such as  9 -tetrahydrocannabinol ( 9 -THC), their synthetic analogs and the endogenous cannabinoids [START_REF] Palmer | Cannabinergic ligands[END_REF][START_REF] Howlett | International Union of Pharmacology. XXVII. Classification of cannabinoid receptors[END_REF][START_REF] Pertwee | Cannabinoid receptors and pain[END_REF][START_REF] Goya | Cannabinoids and neuropathic pain[END_REF]. The pharmacological effects of cannabinoids are mediated throught at least two receptors, the cannabinoid 1 receptor (CB1) and the cannabinoid 2 receptor (CB2), even if at present there is some experimental evidence that supports the existence of additional types of cannabinoid receptors [START_REF] Calignano | Antinociceptive activity of the endogenous fatty acid amide, palmitylethanolamide[END_REF][START_REF] Marzo | Levels, metabolism, and pharmacological activity of anandamide in CB(1) cannabinoid receptor knockout mice: evidence for non-CB(1), non-CB(2) receptor-mediated actions of anandamide in mouse brain[END_REF][START_REF] Hájos | Novel cannabinoid-sensitive receptor mediates inhibition of glutamatergic synaptic transmission in the hippocampus[END_REF]. While CB1 is located principally in the central nervous system, CB2 is found in peripheral tissues, such as the spleen, tonsils and thymus. This subtype is of particular interest, since it has been identified as a potential target for therapeutic immune treatment, due to its involvement in signal transduction processes in the immune system. Furthermore, CB2 selective compounds were active in different neuropathic and inflammatory pain models [START_REF] Whiteside | The role of the cannabinoid CB2 receptor in pain transmission and therapeutic potential of small molecule CB2 receptor agonists[END_REF][START_REF] Ashton | Cannabinoids for the treatment of inflammation[END_REF][START_REF] Giblin | Discovery of 2-[(2,4-dichlorophenyl)amino]-N-[(tetrahydro-2H-pyran-4-yl)methyl]-4-(trifluoromethyl)-5-pyrimidinecarboxamide, a selective CB2 receptor agonist for the treatment of inflammatory pain[END_REF][START_REF] Guindon | Cannabinoid CB2 receptors:a therapeutic target for the treatment of inflammatory and neuropathic pain[END_REF][START_REF] Khanolkar | Cannabilactones:a novel class of CB2 selective agonists with peripheral analgesic activity[END_REF]. Some neuroprotective roles have also been associated with CB2 agents, that could lead to the prevention of some neurodegenerative disorders, such as Huntington and Alzheimer's diseases [13][START_REF] Maccarrone | The endocannabinoid pathway in Huntington's disease: a comparison with other neurodegenerative diseases[END_REF][START_REF] Centonze | The endocannabinoid system in targeting inflammatory neurodegenerative diseases[END_REF]. Other studies have also highlighted potentials roles for CB2 in cancer [START_REF] Mckallip | Targeting CB2 cannabinoid receptors as a novel therapy to treat malignant lymphoblastic disease[END_REF][START_REF] Velasco | Hypothesis: cannabinoid therapy for the treatment of gliomas?[END_REF], multiple sclerosis [START_REF] Pertwee | Cannabinoids and multiple sclerosis[END_REF] and bone regeneration [START_REF] Ofek | Peripheral cannabinoid receptor, CB2, regulates bone mass[END_REF][START_REF] Idris | Regulation of bone mass, bone loss and osteoclast activity by cannabinoid receptors[END_REF]. Since majority of CB2 receptors are distributed in peripheral tissues, with only low levels in neurons of central nervous system, centrally mediated side-effects would be greatly diminished with CB2 selective agents.

Both CB1 and CB2 belong to the large family of G-protein coupled receptors (GPCRs) [START_REF] Lu | Seven-transmembrane receptors: crystals clarify[END_REF] controlling a wide variety of signal transduction. Since GPCR are membrane proteins, their expression, purification, crystallization and structure determination present major challenges to the discovery of new drugs. In the absence of experimental data about human cannabinoid receptor 3D structures, computer-aided GPCR-targeted drug design can be performed on the basis of ligand-based modeling techniques, such as pharmacophore model generation or 3D-QSAR analysis.

Recently, novel classes of CB2 agonists based on 1,2,3,4-tetrahydropyrrolo [3,4-b]indole and benzimidazole scaffolds [START_REF] Pagé | New 1,2,3,4-tetrahydropyrrolo[3,4-b]indole derivatives as selective CB2 receptor agonists[END_REF][START_REF] Pagé | Novel benzimidazole derivatives as selective CB2 agonists[END_REF] (in Fig. 1, representative compounds 8 and 31 are depicted) have shown high binding affinity toward CB2 receptor and good selectivity over CB1 receptor.

In order to identify the key structural features impacting the binding affinity, and with the aim at providing useful suggestions for the synthesis of new selective analogues with improved affinity, a computational study of Comparative Molecular Fields Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) was performed. Initially, the two classes of selective CB2 agonists were studied separately: CoMFA and CoMSIA analyses were performed on both 1,2,3,4-tetrahydropyrrolo [3,4-b]indole derivatives and benzimidazole compounds, and one model for each series was derived (models A and B, respectively). Subsequently, all compounds together (in the same minimized conformation used for the generation of models A and B) were aligned on the basis of the common 1,5-disubstitutedindole or 1,5-disubstituted-benzimidazole scaffold (common scaffold depicted in blue in Fig. 1), and the final CoMFA and CoMSIA model C was calculated. The superimposition of the two series of agonists was allowed not only by the common structural features of the two scaffolds, but also by the spatial correspondence of the molecular field maps previously elaborated for each series (models A and B). The generation of a single model (model C) for the two series of compounds offers the possibility to obtain additional information and suggestions for the design of new potent and selective analogues.

Materials and methods

Data set

A dataset of twenty-seven 1,2,3,4-tetrahydropyrrolo [3,4-b]indole derivatives (Table 1, compounds 1-27) and thirty-six benzimidazole derivatives (Table 2, compounds 28-63), screened according to the same pharmacological protocol, were selected from literature [START_REF] Pagé | New 1,2,3,4-tetrahydropyrrolo[3,4-b]indole derivatives as selective CB2 receptor agonists[END_REF][START_REF] Pagé | Novel benzimidazole derivatives as selective CB2 agonists[END_REF]. All the compounds have been built, parameterized (Gasteiger-Hückel method) and energy minimized within MOE using MMFF94 forcefield [24].

3D-QSAR analysis

In the absence of crystallographic data about human CB2 3D structure, the ligand-based approach of CoMFA and CoMSIA analyses [START_REF] Cramer | Recent advances in comparative molecular field analysis (CoMFA)[END_REF][START_REF] Klebe | Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity[END_REF], performed using Sybyl7.0 software [27], could provide a complementary tool for drug design.

CoMFA and CoMSIA interaction energies

CoMFA method [START_REF] Pagé | Novel benzimidazole derivatives as selective CB2 agonists[END_REF] is a widely used 3D-QSAR technique to relate the biological activity of a series of molecules to their steric and electrostatic fields, which are calculated placing the aligned molecules, one by one, into a 3D cubic lattice with a 2 Å grid spacing. The van der Waals potential and Coulombic terms, which represent steric and electrostatic fields, respectively, were calculated using the standard Tripos force field method. The columnfiltering threshold value was set to 2.0 kcal mol -1 to improve the signal-noise ratio. A methyl probe with a +1 charge was used to calculate the CoMFA steric and electrostatic fields. A 30 kcal mol -1 energy cut-off was applied to avoid infinity of energy values inside the molecule.

The CoMSIA method [24] calculates five descriptors, namely steric, electrostatic and hydrophobic parameters, and the H-bond donor and H-bond acceptor properties. The similarity index descriptors were calculated using the same lattice box employed for the CoMFA calculations and a sp 3 carbon as probe atom with a +1 charge, +1 hydrophobicity and +1 H-bond donor and +1 H-bond acceptor properties.

Partial least square (PLS) analysis and models validation

The partial least-squares (PLS) approach, an extension of the multiple regression analysis, was used to derive the 3D-QSAR models. CoMFA and CoMSIA descriptors were used as independent variables and pEC 50 values were used as dependent variables. Prior to the PLS analysis, CoMFA and CoMSIA columns with a variance of less than 2.0 kcal mol -1 were filtered by using column filtering to improve the signal-to-noise ratio.

The leave one out (LOO) cross-validation method was used to check the predictivity of the derived model and to identify the optimal number of components (ONC) leading to the highest cross-validated r 2 (r 2 cv ). In the LOO methodology, one molecule is omitted from the dataset and a model is derived involving the rest of the compounds. Employing this model, the activity of the omitted molecule is then predicted.

The ONC obtained from cross-validation methodology was used in the subsequent regression model. Final CoMFA and CoMSIA models were generated using non-cross-validated PLS analysis. To further assess the statistical confidence and robustness of the derived models, a 100-cycle bootstrap analysis was performed. This is a procedure in which n random selections out of the original set of n objects are performed several times (100-times were required to obtain a good statistical information). In each run, some objects may not be included in the PLS analysis, whereas some others might be included more than once. The mean correlation coefficient is represented as bootstrap r 2 (r 2 boot ).

Predictive correlation coefficient (r 2 pred )

To further validate the CoMFA and CoMSIA derived model, the predictive ability for the test set of compounds (expressed as r 2 pred ) was determined by using the following equation:

r 2 pred = (SD -PRESS)/SD (1)
SD is the sum of the squared deviations between the biological activities of the test set molecules and the mean activity of the training set compounds and PRESS is the sum of the squared deviation between the observed and the predicted activities of the test set compounds.

All calculations were carried out using a PC running the Windows XP operating system and an SGI O2 Silicon Graphics.

Results and discussion

CoMFA and CoMSIA analyses

Model A

To develop the 3D-QSAR analyses, 1-27 (Table 1) were manually aligned on the basis of the tetrahydropyrrolo [3,4- Fig. 4a illustrates that H-bond acceptor groups are predicted to be beneficial (magenta areas) in proximity of the oxygen atoms of R sulfonyl group, and detrimental (green polyhedra) near the position 4 of R2 cyclopentyl and in the region surrounding the position 4 of R1 piperidine.

Moreover, H-bond donor functions (Fig. 4b) would result to be unfavorable (cyan polyhedra) around the sulfonyl group. Accordingly, compounds 6-9 (pKi = 8.03-8.38) show higher pKi values than 3-5 (pKi = 6.97-7.63), and 7 (pKi = 8.15) and 11 (pKi = 7.76) display higher binding affinity than 12-14 (pKi = 6.51-7.56). Besides, the pKi values of 7 (pKi = 8.15) and 3 (pKi = 7.07) are higher than those of 17 (pKi = 7.56) and 2 (pKi = 5.33), respectively.

Model B

Compounds 28-63 (Table 2 8.

As shown in Fig. 5a (for simplicity, only the structure of compound 31, displaying the highest pKi value in the benzimidazole series, is depicted as representative), the steric contour map predicts favorable interaction polyhedra (green) around one of the two ethyl groups of substituent R1, and near the methylene linked to the oxygen atom in substituent R3.

Unfavorable polyhedra (yellow) surround the methylene of the other ethyl group of R1, and are located near the positions 3 and 5 of the 4-ethoxyphenyl moiety. Moreover, bulkier groups than cyclopentyl result to be detrimental on substituent R. These results are in agreement with the higher pKi values of 59 (pKi = 8.40) and 62 (pKi = 8.23) compared to those of 57 (pKi = 5.87) and 61 (pKi = 7.39), respectively, and by the following affinity trends: 52 (pKi = 8.55) > 29 (pKi = 8.39) > 51 (pKi = 8.12) > 49 (pKi = 7.20) > 58 (pKi = 5.36) > 50 (pKi = 5.30), and 41 (pKi = 8.35) > 40 (pKi = 7.77) > 39 (pKi = 6.10). In addition, 31 (pKi = 9.00) shows higher affinity than 32 (pKi = 8.43) and 33 (pKi = 7.92).

According to the electrostatic fields contour map of the CoMFA analysis plotted in Fig. 5b, less positive moieties would be favored (red polyhedra) in proximity of the amide (R1) and ethoxy (R3) oxygen atoms. On the other hand, more electropositive substituent are predicted to be beneficial (blue area) near the methylene of the ethoxy group (R3). The CoMSIA steric and electrostatic regions are in agreement with the CoMFA steric and electrostatic areas.

The calculated CoMSIA hydrophobic contours (Fig. 6) display favorable hydrophobic substituents (yellow polyhedra) in proximity of the R1 ethyl groups and of the cyclopentyl Fig. 7a shows that the introduction of groups bearing H-bond acceptor functions onto the cyclopentyl position 2 (R) or onto the methylene of one of the R1 ethyl groups would be beneficial (magenta polyhedra). H-bond acceptor functions are predicted to be disfavored (green areas) in the region occupied by the methyl of the same R1 ethyl group and in proximity of the cyclopentyl position 5 (R). Accordingly, H-bond donors (Fig. 7b) are predicted to be favorable (purple region) near the above-mentioned methyl and unfavorable (cyan areas) around most of the R1 ethyl groups. In addition, cyan polyhedra are positioned near the R3 methylene and methyl, and near the cyclopentyl position 2 (R). These results are supported by the evidence of the high affinity levels of compounds 34 (pKi = 7.41), 36 (pKi = 7.96) and 60-63 (pKi = 7.39-8.30).

Model C

To develop a single model for all compounds, 1-63 were aligned (rigid alignment of the same minimized conformation used for the generation of models A and B) by overlapping the common 1,5-disubstituted-indole or 1,5-disubstituted-benzimidazole moiety (Fig. 8). The superimposition was allowed not only by the common structural features of the two scaffolds, but also by the spatial correspondence of the molecular field maps previously elaborated for each series (models A and B). The generation of a single model (model C) for the two series of compounds offers the possibility to obtain additional information and suggestions for the design of new potent and selective analogues. The CoMFA steric contour map (Fig. 10) displays that the green area (favorable interactions) in proximity of the position 4 of R1 piperidine in 1,2,3,4-tetrahydropyrrolo [3,4-b]indoles (Fig. 10a) and near one of the R1 ethyl groups in benzimidazoles (Fig. 10b) is wider that in models A (Fig. 2a) and B (Fig. 5a), respectively, thus suggesting the possible further occupation of that space. With regard to substituent R, the isopropyl (Fig. 10a) and the cyclopentyl (Fig. 10b) are enclosed in green polyhedra adjacent to a yellow area (unfavorable steric interactions), thus partially limiting the possibility to introduce bulkier groups. These green polyhedra were much wider in model A (Fig. 2a) and absent in model B (Fig. 5a). The large yellow area surrounding the cyclopentyl ring (Fig. 10a) appears much larger than in model A (Fig. 2a) and is located in a region not evaluated by model B (Fig. 5a). On one hand, this indicates that the introduction of much bulkier substituents R2 than cyclopentyl on the 1,2,3,4-tetrahydropyrrolo [3,4-b]indole derivatives (Fig. 10a) is not beneficial, and, on the other hand, it suggests the possibility to exploit that region for benzoimidazoles by introducing a substituent onto the benzylic methylene (Fig. 10b). The green contour below these yellow polyhedra (Fig. 10a) is located in a region not taken into consideration by model A (Fig. 2a), thus suggesting the possibility to introduce a substituent onto the methylene between the pyrrole nitrogen and the carbon atom of indole position 2, in order to increase the binding affinity of 1,2,3,4-tetrahydropyrrolo [3,4-b]indoles. The same green region surrounds the methylene of the R3 ethyl (Fig. 16b); it is larger than in model B (Fig. 5a) and it indicates that slightly bulkier groups adjacent to the oxygen atom could be effective.

According to the electrostatic fields contour map of CoMFA analysis plotted in Fig. 11, less positive moieties would be favored (red polyhedra) in proximity of one oxygen of the R sulfonyl group (Fig. 11a), as predicted also by model A (Fig. 2b), and in the vicinity the methylene attached to the benzoimidazole N1 (Fig. 11b), while model B (Fig. 5b) had not furnished information about substituent R. In addition, in the vicinity of the red area, more electropositive substituents are predicted to be beneficial (blue polyhedra), information missing also in model A (Fig. 2b). Concerning substituent R1, a red area is localized in the position adjacent to the amidic (CON) function (Fig. 11a and11b), whereas it was larger in model A (Fig. 2b) and it was positioned in proximity of the oxygen atom in model B (Fig. 5b). In addition, an extended blue area, not appeared in models A and B, is positioned across the group link to the amidic function. Differing from model A (Fig. 2b), the region of the R2 cyclopentyl presents only a red area around position 2 (Fig. 11a). The same red area indicates that the introduction of substituents, bearing less positive moieties, onto the benzylic methylene (Fig. 11b) would be beneficial, providing further information about a region unexplored by model B (Fig. 5b).

The CoMSIA steric and electrostatic contour maps are in agreement with those obtained by the CoMFA model.

The CoMSIA hydrophobic counter map (Fig. 12) displays two wide yellow areas (favorable hydrophobic interactions): the first one surrounding the alkyl portion of the R1 amidic function (Fig. 12a and12b) [a larger area than that present in models A (Fig. 3) and B (Fig. 6)], and the second one located in the vicinity of the tetrahydropyrrole ring and enclosing part of the R2 cyclopentyl ring (Fig. 12a). At a further distance, there are white polyhedra (hydrophobic unfavorable or hydrophilic favorable) This is in agreement with model A (Fig. 3) and it provides suggestions (information missing in model B, Fig. 6) about the nature of the substituent that could be introduced onto the benzylic methylene of the benzoimidazole derivatives (Fig. 12b) in order to improve the binding affinity. Concerning substituent R (for which no indications were given by model A, Fig. 3), white polyhedra surround the sulfonyl group (Fig. 12a) and the methylene attached to the benzoimidazole N1 (Fig. 12b), while a yellow area is positioned at a short distance from one of the two methyl of the isopropyl group (Fig. 12a) and around the cyclopentyl position 3 (Fig. 12b).

The hydrogen bond acceptor contour map of the CoMSIA model (Fig. 13) shows a magenta area (favorable) near the oxygen atoms of the R sulfonyl group (Fig. 13a) and in proximity of the R cyclopentyl positions 1 and 2 (Fig. 13b), approximately in accordance with models A (Fig. 4a) and B (Fig. 7a), respectively. Concerning substituent R1, the cyan region (unfavorable), surrounding the R1 piperidine positions 3 and 4 (Fig. 13a) and one of the two R1 ethyl groups (Fig. 13b), is larger than that present in models A (Fig. 4a) and B (Fig. 7a). In addition, a magenta area (absent in model A) is located in the vicinity of the R1 amidic function. Additional interesting information deriving from model C concerns the wide magenta area in the vicinity of the nitrogen tetrahydropyrrole (Fig. 13a) and the benzylic methylene (Fig. 13b). This highlights the possibility to introduce an hydrogen bond acceptor group onto that nitrogen (Fig. 13a) or that methylene (Fig. 13b) in order to enhance the potency of 1,2,3,4-tetrahydropyrrolo [3,4-b]indoles and benzoimidazoles, respectively.

The CoMSIA hydrogen bond donor contour map (Fig. 14) shows that a cyan area (unfavorable) is located in proximity of substituent R, in correspondence of the magenta area in the hydrogen bond acceptor contour map (Fig. 13). Regarding substituent R1, the map of model C (Fig. 14b) resemble the map of model B (Fig. 7b), but with the difference that the purple area (favorable) enclosed in the cyan polyhedra is absent. It should be noted that model A (Fig. 4b) had not given information about substituents R1 and R2. Finally, according to model C, a cyan region is positioned in proximity of the R2 cyclopentyl position 2 (Fig. 14a) and the oxygen atom linked to the substituent R3 (Fig. 14b).

Taking into account simultaneously all the information deriving from the model C 3D-QSAR contour maps, we have identified some structural modifications (Fig. 15 and Fig. 16) potentially useful to improve CB2 affinity of the two series of selective CB2 agonists. In detail, in the 1,2,3,4-tetrahydropyrrolo [3,4-b]indole series (Fig. 15), an alkyl-(ethyl-, isopropyl-or cyclopropyl-) -carbonyl or -sulfonyl group would seem particularly favorable as substituent R2, while a methylene linked to the position 2 of a 1,4-dimethylpyrrolidine or 4methylpyrane ring would result to be effective as substituents R. The amidic or ureic R1 groups depicted in Fig. 15 would seem to be optimal. In addition, the introduction of an alkyl or benzyl group onto the methylene between the pyrrole nitrogen and the carbon atom of indole position 2 could further enhance the binding affinity.

In the benzimidazole series (Fig. 16), an alkyl-(ethyl-, isopropyl-or cyclopropyl-) -carbonyl or -sulfonyl group, or a methylene linked to the position 2 of a 1,4-dimethylpyrrolidine or 4methylpyrane ring, would seem particularly effective as substituent R. Regarding substituent R3, the ethyl group could be more efficiently replaced by a cyclopropyl ring. The amidic or ureic R1 groups depicted in Fig. 16 would result to be particularly beneficial. Finally, the introduction of an ethoxy, isopropoxy or cyclopropoxy group onto the benzylic methylene could further increase the binding affinity.

Conclusions

The 3D-QSAR studies here presented highlight the key structural features impacting the binding affinity of 1,2,3,4-tetrahydropyrrolo [3,4-b]indole and benzimidazole derivatives as selective CB2 agonists. Moreover, they provide useful suggestions for the design of new selective analogues with improved affinity. The models elaborated could be exploited to design new ligands and predict their binding affinity prior to synthesis.

Table 3 Summary of Model A CoMFA results

No. compounds 19

Optimal number of components (ONC) 3

Leave one out r 2 (r

  Initially, the two classes of selective CB2 agonists were studied separately: CoMFA and CoMSIA analyses were performed on compounds 1-27 (aligned on the common 1,2,3,4tetrahydropyrrolo[3,4-b]indole moiety) and on compounds 28-63 (aligned on the benzimidazole moiety), and one model for each series was derived (models A and B, respectively). Subsequently, all compounds together were aligned on the basis of the common 1,5-disubstituted-indole or 1,5-disubstitued-benzimidazole scaffold (rigid alignment of the same minimized conformations used to build models A and B) and the final CoMFA and CoMSIA model C was calculated. Training set and test set 1,2,3,4-Tetrahydropyrrolo[3,4-b]indole derivatives (Model A) All the compounds were grouped into a training set, for model generation, and a test set, for model validation, containing 19 and 8 compounds respectively. Both the training and the test set were divided manually according to a representative range of biological activities and structural variations. For QSAR analysis, Ki values have been transformed into pKi values and then used as response variables. Compound binding affinity covered 3 log orders of magnitude. Benzimidazole derivatives (Model B) All the compounds were grouped into a training set, for model generation, and a test set, for model validation, containing 25 and 11 compounds respectively. Both the training and the test set were divided manually according to a representative range of biological activities and structural variations. For QSAR analysis, Ki values have been transformed into pKi values and then used as response variables. Compound binding affinity covered 4 log orders of magnitude. 1,2,3,4-Tetrahydropyrrolo[3,4-b]indole and Benzimidazole derivatives (Model C) To derive the final CoMFA and CoMSIA models, 44 compounds were assigned to the training set, for model generation, and 19 ligands to the test set, for model validation. The test set molecules, which are the same ligands belonging to the test sets of the previously described statistical studies, represent the 30% (considered an appropriate percentage to validate a molecular model) of the training set. For QSAR analysis, Ki values have been transformed into pKi values and then used as response variables. Compound binding affinity covered 4 log orders of magnitude.

  model was generated employing non-cross-validated PLS analysis with the optimum number of components (ONC = 3) to give a non-cross validated r 2 (r 2 ncv ) = 0.942, Standard Error of Estimate (SEE) = 0.254, steric contribution = 0.432 and electrostatic contribution = 0.568. The model reliability thus generated was supported by bootstrapping results. All statistical parameters supporting CoMFA model are reported inTable 3. A CoMSIA model consisting of steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor fields with a r 2 ncv = 0.970, SEE = 0.232, steric contribution = 0.176, electrostatic contribution = 0.253, hydrophobic contribution = 0.232, H-bond acceptor contribution = 0.194 and H-bond donor contribution = 0.144 was derived. All statistical parameters supporting CoMSIA model are reported in Table 4. Experimental and predicted binding affinities values for the training set and test set are reported in Table 5.As shown in Fig. 2a (for simplicity, only the structure of compound 8, displaying the highest pKi value in the tetrahydropyrrolo[3,4-b]indole series, is depicted as representative), the steric contour map predicts favorable interaction polyhedra (green) around the position 4 of piperidine in substituent R1 and around group R, and unfavorable polyhedra (yellow) in proximity of the positions 3 of the cyclopentyl ring in substituent R2. The reliability of the steric map calculations is verified by the higher affinity of 7 (pKi = 8.15) compared to that of 11 (pKi = 7.76) and 12 (pKi = 7.35), and by the following affinity trends: 8 (pKi = 8.38) > 7 (pKi = 8.15) > 6 (pKi = 8.10), and 24 (pKi = 8.20) ≈ 7 (pKi = 8.15) > 25 (pKi = 6.03) > 26 (pKi = 5.70). According to the electrostatic field contour map of the CoMFA analysis plotted in Fig. 2b, less positive moieties are predicted to be favored (red areas) in proximity of one oxygen of the R sulfonyl group, near one of the two positions 3 of R1 piperidine, and around position 2 and between positions 4 and 5 of R2 cyclopentyl. On the other hand, more electropositive substituents are predicted to be beneficial (blue area) around position 3 of the cyclopentyl. These results are in agreement with the pKi value trend 19 (pKi = 7.75) > 17 (pKi = 7.56) > 18 (pKi = 6.67), and with the higher affinity of 6-9 (pKi = 8.03-8.38) in comparison with that of 3-5 (pKi = 6.97-7.63). The CoMSIA steric and electrostatic regions are in agreement with the CoMFA steric and electrostatic areas. The calculated CoMSIA hydrophobic contours (Fig. 3) display favorable hydrophobic substituents (yellow polyhedra) in proximity of the position 4 of R1 piperidine and around the positions 3 of R2 cyclopentyl; at a higher distance from the cyclopentyl, hydrophobic substituents become disfavored (white areas). The reliability of the hydrophobic map calculation is verified by the following affinity trends: 7 (pKi = 8.15) > 14 (pKi = 7.56) > 13 (pKi = 6.51), and 24 (pKi = 8.20) ≈ 7 (pKi = 8.15) > 25 (pKi = 6.03) > 26 (pKi = 5.70).

  ring (R), and around the positions 2 and 3 of the 4-ethoxyphenyl moiety. Unfavorable areas (white) are located around the methylene between the nitrogen atom and the cyclopentyl in substituent R, and in proximity of the R3 ethyl group. The reliability of the hydrophobic map calculation is verified by the high pKi values of 28-33 (pKi = 7.92-9.00), and by the higher affinity of 29 (pKi = 8.39) and 51-56 (pKi = 7.85-8.55) in comparison with that of 49 (pKi = 7.20), 50 (pKi = 5.30) and 58 (pKi = 5.36). Moreover, the results are in agreement with the slightly higher pKi of 40 (pKi = 7.77) compared to that of 45 (pKi = 7.60), and with the following affinity trend: 41 (pKi = 8.35) > 47 (pKi = 7.28) > 43 (pKi = 7.37) ≈ 42 (pKi = 7.36) > 46 (pKi = 7.24) > 44 (pKi = 6.28).

  CoMFA analysis was performed dividing compounds 1-63 into a training set (1-3, 5-12, 16, 18-20, 22, 24, 25, 27-29, 31, 32, 34-42, 45, 48-52, 55, 58-61, 63 ) for model generation and into a test set (4, 13-15, 17, 21, 23, 26, 30, 33, 43, 44, 46, 47, 53, 54, 56, 57, 62) for model validation. CoMFA and CoMSIA studies were developed using, respectively, CoMFA steric and electrostatic fields and CoMSIA steric, electrostatic, hydrophobic, H-bond donor and Hbond acceptor properties, as independent variables, and the ligand pKi as dependent variable. The final CoMFA model was generated employing non-cross-validated PLS analysis with the optimum number of components (ONC = 5) to give a non-cross validated r 2 (r 2 ncv ) = 0.90, Standard Error of Estimate (SEE) = 0.332, steric contribution = 0.600 and electrostatic contribution = 0.400. The model reliability thus generated was supported by bootstrapping results. All statistical parameters supporting CoMFA model are reported in Table 9. A CoMSIA model consisting of steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor fields with a r 2 ncv = 0.97, SEE = 0.267, steric contribution= 0.201, electrostatic contribution = 0.157, hydrophobic contribution = 0.240, H-bond acceptor contribution = 0.140 and H-bond donor contribution = 0.262 was derived. All statistical parameters supporting CoMSIA model are reported inTable 10. Experimental and predicted binding affinities values for the training set and test set are reported in Table 11, while distribution of experimental and predicted pKi values for training set and test set according to CoMFA and CoMSIA models are represented in Fig. 9.

Fig. 10 -

 10 Fig. 10-14 illustrate the model-C 3D-QSAR contour maps in the presence of the structures of compound 8 (left side, a), as representative of the 1,2,3,4-tetrahydropyrrolo[3,4-b]indole derivatives, and compound 31 (right side, b), as representative of the benzimidazole derivatives.

Figure captions Fig. 1 Fig. 2 8 .Fig. 3 Fig. 4 Fig. 5 Fig. 6

 captions1283456 Figure captions

Fig. 16

 16 Fig.[START_REF] Mckallip | Targeting CB2 cannabinoid receptors as a novel therapy to treat malignant lymphoblastic disease[END_REF] Structural modifications potentially useful to increase the binding affinity of the selective CB2-agonist benzimidazole derivatives

  Figure 1 Click here to download high resolution image

-12, 16, 18-20, 22, 24, 25, 27) for

  model generation and into a test set(4,

	b]indole common moiety.
	CoMFA analysis was performed dividing compounds 1-27 into a training set (1-3, 5

13-15, 17, 21, 23, 26) for

	model validation. CoMFA and CoMSIA studies were developed using, respectively, CoMFA
	steric and electrostatic fields and CoMSIA steric, electrostatic, hydrophobic, H-bond donor
	and H-bond acceptor properties, as independent variables, and the ligand pKi as dependent

, 31, 32, 34-42, 45, 48-52, 55, 58-61, 63) for

  ) were manually aligned on the basis of the benzimidazole common moiety. CoMFA analysis was performed dividing compounds 28-63 into a training set (28, 29

	model generation and into a test set
	(30, 33

, 43, 44, 46, 47, 53, 54, 56, 57, 62) for

  model validation. CoMFA and CoMSIA studies were developed using, respectively, CoMFA steric and electrostatic fields and CoMSIA steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor properties, as independent variables, and the ligand pKi as dependent variable. The final CoMFA model was generated employing non-cross-validated PLS analysis with the optimum number of components (ONC = 5) to give a non-cross validated r 2 (r 2 ncv ) = 0.96, Standard Error of Estimate (SEE) = 0.298, steric contribution = 0.593 and electrostatic contribution = 0.407. The model reliability thus generated was supported by bootstrapping results. All statistical parameters supporting CoMFA model are reported in Table6.

	A CoMSIA model consisting of steric, electrostatic, hydrophobic, H-bond donor and H-bond
	acceptor fields with a r 2	ncv = 0.91, SEE = 0.383, steric contribution = 0.164, electrostatic
	contribution = 0.108, hydrophobic contribution = 0.198, H-bond acceptor contribution =
	0.400 and H-bond donor contribution = 0.130 was derived. All statistical parameters
	supporting CoMSIA model are reported in

Table 7 .

 7 Experimental and predicted binding affinities values for the training set and test set are reported in Table

Table 4

 4 Summary of Model A CoMSIA results

			2	loo )	0.515
	Cross-validated r 2 (r 2	cv )	0.610
	Std. error of estimate (SEE)	0.254
	Non cross-validated r 2 (r 2	ncv )	0.942
	F value				98.827
	Steric contribution		0.432
	Electrostatic contribution	0.568
	Bootstrap r 2 (r 2	boot )		0.96
	Standard error of estimate r 2	boot (SEE r 2	boot )	0.242
	Test set r 2 (r 2	pred )		0.85

Table 5

 5 Experimental and predicted pKi values of compounds 1-27 according to model A

			CoMFA Model	CoMSIA Model
	Compound	Exp. pKi	Pred. pKi	Residual	Pred. pKi	Residual
	1	7.75	7.75	0.00	7.88	-0.13
	2	5.33	6.01	-0.68	5.34	-0.01
	3	7.07	6.73	0.34	7.02	0.05
	4 a	7.63	7.37	0.26	7.89	-0.26
	5	6.97	7.11	-0.14	6.80	0.17
	6	8.10	8.04	0.06	7.96	0.14
	7	8.15	7.90	0.25	7.96	0.19
	8	8.38	8.15	0.24	8.09	0.29
	9	8.03	8.18	-0.15	8.05	-0.02
	10	7.65	7.96	-0.31	7.98	-0.33
	11	7.76	7.75	0.01	7.78	-0.02
	12	7.35	7.19	0.16	7.19	0.16
	13 a	6.51	6.27	0.24	6.25	0.26
	14 a	7.56	7.33	0.23	7.20	0.36
	15 a	6.97	7.12	-0.15	7.38	-0.41
	16	7.65	7.33	0.32	7.41	0.25
	17 a	7.56	7.04	0.52	7.22	0.34
	18	6.67	6.52	0.15	6.59	0.08
	19	7.75	7.74	0.01	7.85	-0.09
	20	8.19	8.43	-0.24	8.33	-0.14
	21 a	8.05	7.77	0.28	8.14	-0.09
	22	7.95	7.84	0.11	7.95	0.00
	23 a	7.77	7.50	0.27	7.67	0.10
	24	8.20	8.01	0.19	7.62	0.58
	25	6.03	6.01	0.02	6.43	-0.40
	26 a	5.70	6.02	-0.32	5.82	-0.12
	27	7.43	7.48	-0.05	7.34	0.09
	a Test set compounds

Table 6

 6 Summary of Model B CoMFA results

	No. compounds		25
	Optimal number of components (ONC)	5
	Leave one out r 2 (r 2	loo )	0.510
	Cross-validated r 2 (r 2	cv )	0.543
	Std. error of estimate (SEE)	0.298
	Non cross-validated r 2 (r 2	ncv )	0.96
	F value				108.104
	Steric contribution		0.593
	Electrostatic contribution	0.407
	Bootstrap r 2 (r 2	boot )		0.95
	Standard error of estimate r 2	boot (SEE r 2	boot )	0.210
	Test set r 2 (r 2	pred )		0.93

Table 7

 7 Summary of Model B CoMSIA results

	No. compounds		25
	Optimal number of components (ONC)	4
	Leave one out r 2 (r 2	loo )	0.496
	Cross-validated r 2 (r 2	cv )	0.512
	Std. error of estimate (SEE)	0.383
	Non cross-validated r 2 (r 2	ncv )	0.91
	F value				60.667
	Steric contribution		0.164
	Electrostatic contribution	0.108
	Hydrophobic contribution	0.198
	H-bond acceptor contribution	0.400
	H-bond donor contribution	0.130
	Bootstrap r 2 (r 2	boot )		0.91
	Standard error of estimate r 2	boot (SEE r 2	boot )	0.223
	Test set r 2 (r 2	pred )		0.76

Table 8

 8 Experimental and predicted pKi values of compounds 28-63 according to model B

			CoMFA Model	CoMSIA Model
	Compound	Exp. pKi	Pred. pKi	Residual	Pred. pKi	Residual
	28	8.35	8.48	-0.13	8.38	-0.03
	29	8.39	8.42	-0.03	8.52	-0.13
	30 a	8.80	8.62	0.18	8.62	0.18
	31	9.00	8.80	0.20	8.79	0.21
	32	8.43	8.67	-0.24	8.69	-0.26
	33 a	7.92	7.63	0.30	7.65	0.27
	34	7.41	7.30	0.12	7.54	-0.13
	35	7.80	7.91	-0.11	8.00	-0.20
	36	7.96	7.70	0.26	7.84	0.13
	37	8.41	8.02	0.39	8.53	-0.12
	38	5.53	6.20	-0.67	5.63	-0.10
	39	6.10	6.29	-0.19	6.67	-0.57
	40	7.77	7.33	0.44	7.85	-0.08
	41	8.35	8.37	-0.02	8.22	0.13
	42	7.36	7.47	-0.11	8.26	-0.90
	43 a	7.37	7.66	-0.29	7.92	-0.55
	44 a	6.28	6.40	-0.12	6.91	-0.63
	45	7.60	6.39	-0.11	6.71	-0.43
	46 a	7.24	7.51	0.09	7.82	-0.22
	47 a	7.28	7.09	0.15	7.32	-0.08
	48	8.31	7.09	0.15	7.62	-0.38
	49	7.20	7.62	-0.34	8.01	-0.73
	50	5.30	8.59	-0.28	8.27	0.04
	51	8.12	7.38	-0.18	7.19	0.01
	52	8.55	5.34	-0.04	5.79	-0.49
	53 a	7.85	7.85	0.27	7.93	0.19
	54 a	8.16	8.64	-0.09	8.12	0.43
	55	7.96	7.68	0.17	7.59	0.26
	56 a	8.25	8.03	-0.18	7.59	0.26
	57 a	5.87	8.01	0.15	7.82	0.34
	58	5.36	7.85	0.11	8.21	-0.25
	59	8.40	8.10	0.15	7.73	0.52
	60	8.30	5.95	-0.08	6.02	-0.15
	61	7.39	6.12	-0.25	6.10	-0.23

Table 9

 9 Summary of Model C CoMFA results

	No. compounds		44
	Optimal number of components (ONC)	5
	Leave one out r 2 (r 2	loo )	0.562
	Cross-validated r 2 (r 2	cv )	0.640
	Std. error of estimate (SEE)	0.332
	Non cross-validated r 2 (r 2	ncv )	0.90
	F value				81.402
	Steric contribution		0.600
	Electrostatic contribution	0.400
	Bootstrap r 2 (r 2	boot )		0.95
	Standard error of estimate r 2	boot (SEE r 2	boot )	0.241
	Test set r 2 (r 2	pred )		0.89

Table 11

 11 Experimental and predicted pKi values of compounds 1-63, according to model C

			CoMFA Model	CoMSIA Model
	Compound	Exp. pKi	Pred. pKi	Residual	Pred. pKi	Residual
	1	7.75	7.67	0.08	7.86	-0.11
	2	5.33	5.41	-0.08	4.99	0.34
	3	7.07	6.82	0.25	7.22	-0.15
	4 a	7.63	7.56	0.07	7.52	0.12
	5	6.97	6.68	0.29	6.88	0.09
	6	8.10	7.95	0.15	7.99	0.11
	7	8.15	7.92	0.23	7.88	0.27
	8	8.38	8.47	-0.09	8.17	0.21
	9	8.03	7.95	0.08	8.00	0.03
	10	7.65	7.65	0.00	8.01	-0.36
	11	7.76	7.64	0.12	7.64	0.12
	12	7.35	7.11	0.24	7.24	0.11
	13 a	6.51	6.78	-0.27	6.63	-0.12
	14 a	7.56	7.25	0.31	7.29	0.27
	15 a	6.97	6.69	0.28	7.24	-0.27
	16	7.65	7.54	0.11	7.53	0.12
	17 a	7.56	7.42	0.14	7.22	0.34
	18	6.67	6.82	-0.15	6.75	-0.08
	19	7.75	7.62	0.13	7.79	-0.04
	20	8.19	8.37	-0.18	8.21	-0.02
	21 a	8.05	8.22	-0.16	7.82	0.23
	22	7.95	8.31	-0.36	7.93	0.02
	23 a	7.77	7.45	0.32	7.84	-0.07
	24	8.20	8.37	-0.17	7.91	0.29
	25	6.03	6.02	0.01	6.21	-0.18
	26 a	5.70	5.91	-0.21	5.89	-0.19
	27	7.43	7.52	-0.09	7.23	0.20
	28	8.35	8.38	-0.03	8.23	0.12
	29	8.39	8.15	0.24	8.47	-0.08
	30 a	8.80	8.42	0.39	8.62	0.18
	31	9.00	8.64	0.36	8.82	0.18

a Test set compounds

Table 11 continued

 11

			CoMFA Model	CoMSIA Model
	Compound	Exp. pKi	Pred. pKi	Residual	Pred. pKi	Residual
	32	8.43	8.54	-0.11	8.62	-0.19
	33 a	7.92	7.67	0.25	7.71	0.21
	34	7.41	7.62	-0.21	7.52	-0.11
	35	7.80	7.68	0.12	7.97	-0.17
	36	7.96	7.87	0.09	7.81	0.15
	37	8.41	7.87	0.54	8.50	-0.09
	38	5.53	5.78	-0.25	5.69	-0.16
	39	6.10	6.72	-0.62	5.86	0.24
	40	7.77	7.48	0.29	7.50	0.27
	41	8.35	8.03	0.32	8.69	-0.34
	42	7.36	7.73	-0.37	7.63	-0.27
	43 a	7.37	7.68	-0.31	7.56	-0.19
	44 a	6.28	6.41	-0.13	6.26	0.02
	45	7.60	7.34	0.26	7.48	0.12
	46 a	7.24	7.18	0.06	7.35	-0.11
	47 a	7.28	7.76	-0.48	7.54	-0.26
	48	8.31	8.34	-0.03	7.99	0.32
	49	7.20	6.78	0.42	6.98	0.22
	50	5.30	5.32	-0.02	5.32	-0.02
	51	8.12	8.09	0.03	8.20	-0.08
	52	8.55	8.84	-0.29	8.59	-0.04
	53 a	7.85	7.54	0.31	7.95	-0.10
	54 a	8.16	7.73	0.44	8.45	-0.29
	55	7.96	8.32	-0.36	8.02	-0.06
	56 a	8.25	8.21	0.04	7.99	0.26
	57 a	5.87	6.22	-0.35	6.11	-0.24
	58	5.36	5.30	0.06	5.60	-0.24
	59	8.40	8.48	-0.08	8.53	-0.13
	60	8.30	7.89	0.41	8.18	0.12
	61	7.39	7.50	-0.11	7.64	-0.25
	62 a	8.23	8.14	0.09	7.90	0.33
	63	8.24	8.37	-0.13	7.98	0.26

a Test set compounds

Acknowledgments

This work was supported by University of Genova. Progetto Ateneo 2007. Fondazione Carige is gratefully acknowledged for financially supporting E.C. and S.C.

Tables

 Table 1 Molecular


Grzegorz Orłowski

Zbigniew Kasprzykowski

Wojciech Dobicki

Przemysław Pokorny

Ryszard Polechoński

Geographical and habitat differences in concentrations of copper, zinc and arsenic in eggshells of the Rook Corvus frugilegus in Poland

Keywords: agricultural contaminants, soil contamination, biomonitoring, trace elements, pesticides, arsenic, bird eggs, rookeries

Levels of copper, zinc and arsenic were analyzed in rook Corvus frugilegus eggshells from 43 breeding colonies located in different parts of Poland. The average (95% confidence limit) level of copper was found to be 8. 13 (0.64-15.62) ppm, of zinc 13.81 (7.99-19.62) ppm and of arsenic 32.57 (25.60-39.54) ppm. The metal concentration levels varied widely in the surveyed shells. In case of copper the difference between extreme values was 90-fold, for zinc 57-fold and for arsenic 36-fold. General linear model (GLM) analysis revealed that copper accumulation in eggshells from urban rookeries was significantly higher than in those from villages (average (with 95% c.l.), 19.31 ppm (4.57-43.20) and 2.74 ppm (2.36-3.12), respectively). In the eggshells of rooks nesting in cities the concentration of zinc was only close to being significantly higher (P = 0.07). The arsenic levels in rural and urban colonies were similar. No significant differences were proved for any of the metals between the rookeries located in the areas of intensive (western Poland) and extensive (eastern Poland) agriculture. Compared to other data on the arsenic contamination affecting different biota, including tissues and bird eggs of terrestrial and aquatic animals, the level of this metal in rook eggshells should be regarded as extremely high. It is probably due to the widespread use of pesticides containing arsenic.

Introduction

The rook Corvus frugilegus represents a group of birds strongly associated with agroecosystems, especially in central and eastern Europe where the considerable majority of rooks nest within farmed landscapes. Their diet is mainly based on Coleoptera and cereal grains which are found in the arable fields (Jabłoński 1979, reviews in Cramp 1998[START_REF] Kasprzykowski | Habitat preferences of foraging Rooks Corvus frugilegus during the breeding period in the agricultural landscape of eastern Poland[END_REF][START_REF] Orłowski | Stomach content and grit ingestion by Rook Corvus frugilegus nestlings[END_REF]. The strong link between the rook and the arable areas may make the species prone to the adverse or even lethal effects of agricultural-related contaminants, especially pesticides and heavy metals [START_REF] Malmberg | Pesticides and the rook Corvus frugilegus in Scania, Sweden between 1955 and 1970[END_REF]. In spite of the fact that the rook is a common species in central and western Europe [START_REF] Brenchley | Rook Corvus frugilegus[END_REF], the ecotoxicological studies were conducted only in the distant past and concerned merely single or at most several rookeries (reviewed in [START_REF] Beyerbach | Chlorierte Kohlenwasserstoffe in Eiern und Lebern von Saatkrähen (Corvus frugilegus) aus niedersächsichen Brutkolonien[END_REF]).

Moreover it is worth underlining that there is a shortage of studies investigating the level of contamination among different populations within the same species in the terms of ecotoxicological research. Revolutionary changes in the intensification of agricultural productivity including introduction of new fertilizing methods and the application of technologically advanced pesticides imply an acute need to determine the level of contamination in the tissues of species strongly linked with agricultural areas. It is also of crucial importance to compare birds` contamination with that of the environment.

The main source of contamination of agricultural areas with heavy metals, including copper (Cu) and zinc (Zn) are commonly used manure and slurry [START_REF] Dach | Heavy metals balance in Polish and Dutch agronomy: Actual state and previsions for the future[END_REF].

An increase in livestock numbers and faulty utilization of huge amounts of animal faeces containing chemical remnants of food additives (mainly copper and zinc) are regarded as the main threats connected with the intensification of agricultural productivity in Europe (de Vries et al. 2002, Dach and[START_REF] Dach | Heavy metals balance in Polish and Dutch agronomy: Actual state and previsions for the future[END_REF]. The contamination of agricultural areas with arsenic (As) is linked to the massive use of pesticides containing this metal. It is believed that introducing of first arsenical pesticides began in the end of the 19th century [START_REF] Murphy | An assessment of the amount of arsenical pesticide used historically in a geographic area[END_REF][START_REF] Peryea | Historical use of lead arsenate insecticides, resulting soil contamination and implications for soil remediation[END_REF], review in Mandal and Suzuki 2002). Copper (as copper hydroxide and copper sulfate) is similarly applied in many different kinds of pesticides [START_REF] Epstein | Pesticide applications of copper on perennial crops in California, 1993-1998[END_REF]) while arsenic appears nowadays mainly in the form of monosodium methanearsonate (MSMA), monomethylarsocic acid (MMAV) and dimethylarsinic acid (DMAV) from which herbicides, including defoliants, are manufactured. Two other arsenic compounds (lead arsenate and copper acetoarsenite), in use for a considerably longer time, are the base for insecticide production. Arsenic and its derivatives affect biota and human health and their biogeochemical cycles are currently being studied in many countries of the world [START_REF] Mandal | Arsenic round the world: a review[END_REF]Suzuki 2002, Sierra-Alvarez et al. 2006).

The metals can permeate the birds` organisms through ingestion [START_REF] Best | Granular insecticides and birds: factors to be considered in understanding exposure and reducing risk[END_REF]Fisher 1982, Schafer et al. 1983). In Coleoptera, constituting the main rook's prey (Orlowski et al. 2009), after pesticide application the level of arsenic may be very high [START_REF] Morrissey | Arsenic accumulation in bark beetles and forest birds occupying mountain pine beetle infested stands treated with monosodium methanearsonate[END_REF], Orlowski et al. 2009). Females of birds can get rid of the surplus of the heavy metals by eliminating it through the egg shells (Burger 1994[START_REF] Dauwe | Heavy metal concentrations in female laying great tits (Parus major) and their clutches[END_REF]. Some pesticides contribute to the accumulation of the heavy metals in bird tissues, e.g. addition of DDE to the diet of black duck Anas rubripes resulted in an increased copper content in the eggshells [START_REF] Longcore | Changes in mineral composition of eggshells from black ducks and mallards fed DDE in the diet[END_REF]. According to Dauwe et al. (1999) the eggshell is a suitable indicator for heavy metal contamination, especially in case of arsenic, the level of which was higher in eggshells from the contaminated breeding sites. However, the content of metals in eggs varies from species to species and contamination level is different in the shell and in the content of the egg itself (Burger 1994, Morera et al., 1997[START_REF] Dauve | Are eggshells and egg contents of great and blue tits suitable as indicators of heavy metal pollution?[END_REF][START_REF] Mora | Heavy metals and metalloids in egg contents and eggshells of passerine birds from Arizona[END_REF]. [START_REF] Mora | Heavy metals and metalloids in egg contents and eggshells of passerine birds from Arizona[END_REF] proved a higher arsenic and copper concentration in the eggshells of small Northern American passerines than in the egg contents while in the case of zinc it was lower. Morera et al. (1997) found lower concentration of zinc and copper in Audouin's gull Larus audouinii egg content.

However, [START_REF] Swaileh | Monitoring urban heavy metal pollution using the House Sparrow (Passer domesticus)[END_REF] showed that the concentration of copper and zinc in the eggshell and the egg content of house sparrow Passer domesticus were highly correlated.

The aim of this study was to define the level of the three metals (arsenic, copper and zinc) in the eggshells of rooks nesting in Poland. The study draws a comparison in the metal concentration depending on the geographical position (western vs. eastern Poland) and rookery location (rural vs. urban habitat). Current growth in intensification of agricultural production and the differences between western and eastern Poland (i.e. intensive vs. extensive agriculture) were expected to cause different concentration of agriculture-related contaminants, e.g. the level of arsenic seemed likely to be higher in colonies of the western part of the country. Due to the high production of manure in rural areas we expected the concentration of zinc and copper there to be higher than in urban rookeries.

Material and methods

The rook eggshells were collected in spring 2005 in 43 rookeries (number of nests in particular colonies ranged from 5 to 480) located in Poland. The shells were collected from the ground under nests. The material was then deprived of membrane remnants and visible external dirt and stored in glass containers so that the analysis of the metal concentration levels in samples from different geographical locations, with varying production intensity and habitat type, could be made.

During the analysis of the results the division between the colonies located in the west (intensive agriculture) and east (extensive agriculture) Poland as well as between urban and rural sites was applied. The river Vistula played the main role in the geographical division into east and west as it flows through the middle of Poland (Fig. 1). This division was made in relation to a set of eight agricultural statistics i.e. the number of farm workers per km, milking capacity and livestock number, mineral fertilization intensity, the number of available tractors and harvesters, the cereal crops area, and the farm size (acc. to [START_REF] Chylarecki | Common Breeding Bird Monitoring in Poland: Annual report 2003-2004[END_REF]). The basis for the division into colonies located in villages (n = 29) and cities (n = 14) was the number of inhabitants in each locality. The number of 50 000 people was taken as the threshold for defining a city (after [START_REF] Orłowski | Different extinction risks of the breeding colonies of Rook Corvus frugilegus in rural and urban areas of SW Poland[END_REF].

Due to a rather small amount of shells successfully collected in some rookeries, the metal concentration for each colony was determined using just two different shells or their fragments. In practice, the big pieces of shells were used, to ensure that they came from different eggs. Prior to the chemical analysis all eggshells were rinsed twice with water with detergent and air dried. They were then mineralized in the mixture of nitric and hypochloric acid in the high-pressure microwave MARS-5 digestion system (CEM, USA). The metal content was determined by means of atomic-absorption spectroscopy in Varian SpectrAA FS220. The measurement process was validated with the aid of reference material, DOLT-2 (fish liver) and DORM-2 (fish muscles), provided by the National Research Council of Canada Institute for National Measurement. The precision of method, understood as the degree of conformity between the results of multiple analyses performed on the same sample, corresponded to 5% (relative standard deviation, RSD). Reference values amounted to 25.8 ± 1.1, 85.8 ± 2.5 and 16.6 ± 1.1 ppm for copper, zinc and arsenic respectively; whilst the average determined values (6 measurements in 0.9 g samples) were 25.24 ± 0.25, 87.20 ± 0.35 and 17.6 ± 1.0 ppm respectively. All concentrations of metals were expressed in milligrams per kilogram (mg kg -1 or parts per million; ppm) of dry mass (d.w.) with the accuracy of two decimal points.

Statistical analysis of the collected material was conducted with the help of Statistica 7.0 (StatSoft 2006) and Excel software. The statistical significance level was 0.05. The differences between heavy metal concentrations of specified data sets were tested by general linear model (GLM) analysis. In GLM analysis two values of metal concentration from each rookery were used. Combined effect between the levels of metals, taking into consideration an interaction between geographic and habitat divisions, was analyzed in GLM (StatSoft, 2006) resulting in Wilks λ. Spearman rank correlation coefficient (r s ) was used to define relationships between the analysed metals. Data are presented as means with 95% confidence limit (c.l.).

Results

Average (95% c.l.) concentration of copper amounted to 8.13 (0.64-15.62) ppm, zinc 13.81 (7.99-19.62) ppm and arsenic 32.57 (25.60-39.54) ppm. The levels of metal concentration in the shells collected in the surveyed colonies varied enormously. The difference between extreme values was 90-fold for copper, 57-fold for zinc and 36-fold for arsenic (Appendix 1). Extremely high concentrations of particular metals were obtained only in a few colonies. In case of copper such extremely high levels (>8 ppm) were measured in three rookeries located in two cities (633 000 and 71 000 inhabitants) in western Poland (Wrocław -two rookeries and Głogów; Appendix 1). In the other 40 (93%) rookeries the concentration of copper did not exceed 7.60 ppm. Similar results were obtained for zinc, the concentration of which in 40 rookeries did not exceed 20.38 ppm and extreme values were measured only in three rookeries in western Poland (Appendix 1). Arsenic concentration was more stable, and despite the lack of extremely high values, in 30 (70%) rookeries a very high concentration of over 20 ppm was recorded.

Statistically significant correlations among metal concentrations were recorded only in the case of zinc and copper (Spearman rank correlation coefficient, r s = 0.22, P = 0.04). For the other pairs of metals these relationships were insignificant (Cu and As, r s = -0.16, P = 0.14; As and Zn, r s = 0.10, P = 0.37).

The copper accumulation in eggshells of town-nesting rooks was significantly (7-fold) higher than in villages (Table 1). The concentration of zinc was also 2-fold higher there than in villages, however this difference was only close to being statistically significant (P = 0.07). Arsenic levels were similar in both rural and urban colonies. No significant differences were proved for any of the metals between the colonies located in the areas of intensive (western Poland) and extensive (eastern Poland) agriculture (Table 1).

An analysis of combined effect among the levels of metals in the surveyed rookeries, after taking into consideration their geographical and habitat division, showed statistically significant interaction only in case of copper (GLM: Wilks λ = 0.89, df = 83, P = 0.01). For the rest of metals the combined effects were not statistically significant (GLM for Zn: Wilks λ = 0.95, df = 83, P = 0.16; GLM for As: Wilks λ = 0.97, df = 83, P = 0.26).

Discussion

The arsenic concentration in the eggshells of the rook in Poland exceeded 20 ppm d.w.

(see Appendix 1) in 30 out of 43 surveyed rookeries. Compared to the data on the level of contamination with arsenic for biota, including tissues of terrestrial and aquatic animals and bird eggs (Dauwe et al. 1999[START_REF] Kubota | Maternal transfer of arsenic to eggs of black-tailed gull (Larus crassirostris) from Rishiri Island, Japan[END_REF][START_REF] Mandal | Arsenic round the world: a review[END_REF][START_REF] Mora | Heavy metals and metalloids in egg contents and eggshells of passerine birds from Arizona[END_REF][START_REF] Dauwe | Heavy metal concentrations in female laying great tits (Parus major) and their clutches[END_REF], the level of this metal should be regarded as extremely high. High level of arsenic in the rook eggshells should be linked with its long-term accumulation in soil as a result of using pesticides containing this metal [START_REF] Murphy | An assessment of the amount of arsenical pesticide used historically in a geographic area[END_REF]Aucott 1998, Peryea 1998). The high usage of pesticides in Poland (total weight of insecticides, fungicides, rodenticides and others) has appeared to be stable since the beginning of 1960s (in 1960, 45 013 -3 kg; in 2005, 44 130 -3 kg; GUS 1970-2007). However, it is difficult to relate pesticide usage to the absolute amount of active ingredients (currently used determiner), as the total usage of pesticides was defined by using of different determiner in the past. This is the reason why data on the combined weight of the pesticides used in 1960 in Poland is incomparably higher than the statistics from western Europe, where an almost 10-fold increase of pesticide use was recorded between 1957 and 1995. It was measured there in relation to the amount of active ingredients (review in [START_REF] Sotherton | Indirect Effects of Pesticides on Farmland Wildlife[END_REF]. [START_REF] Mora | Heavy metals and metalloids in egg contents and eggshells of passerine birds from Arizona[END_REF] established an average arsenic concentration (± SD) in the shells of two species of North American woodland passerines at 2.1 (± 0.4) and 1.3 (± 0.2) ppm d.w. The level of arsenic in the shells of great tit Parus major and blue tit P. caeruleus living in contaminated areas in Belgium did not exceed 4.2 ppm (Dauwe et al. 1999), while arsenic concentration in tissues and egg contents in black-tailed gull Larus crassirostris from Japan did not exceed 2.91 ppm d.w. [START_REF] Kubota | Maternal transfer of arsenic to eggs of black-tailed gull (Larus crassirostris) from Rishiri Island, Japan[END_REF]. According to data of Evry et al. (1999) the arsenic concentration in birds' of prey organs originating from Britain did not exceed 0.346 ppm d.w. while the highest concentration described in literature (56 ppm d.w and 40.6 ppm d.w.) was found in the liver of a dead osprey Pandion haliaetus (Wiemeyer et al. 1980) and brown-headed cowbird Molothrus ater (NAS 1977). According to [START_REF] Mandal | Arsenic round the world: a review[END_REF] an average level of arsenic measured in freshwater fish amounts to 0.54 ppm in wet weight, although some values reach as much as 77 ppm. Equally high levels of arsenic in natural environment were recorded only in soil, especially in areas of arsenic geological origin or in regions heavily contaminated by industry or agriculture [START_REF] Mandal | Arsenic round the world: a review[END_REF]Suzuki 2002, Hossain 2006). The arsenic level in unpolluted soils from various countries ranged from 0.1 to 40 ppm; in some areas contaminated by pesticides or mining activity rising to between 57 and 2470 ppm (review in [START_REF] Hossain | Arsenic contamination in Bangladesh -An overview[END_REF]) and in south-western Poland it reached up to 800 ppm (WIOŚ 2008).

Contrary to arsenic, concentrations of zinc and copper in rook eggshells in Poland (excluding three extreme values for both metals; see Appendix 1) were not considerably different from the results obtained by other scientists studying the level of these metals in eggshells. The average level of copper and zinc in eggshells of two small woodland passerines from Arizona amounted to 3.0 and 6.2 ppm (Cu) and 9.5 and 46.2 ppm (Zn) [START_REF] Mora | Heavy metals and metalloids in egg contents and eggshells of passerine birds from Arizona[END_REF]. The average concentration of copper in eggshells of house sparrow originating from Middle East amounted to 1.0 ppm (range: 0.9-1.2 ppm) and zinc -20.0 ppm (range: 14.3-25.5 ppm) [START_REF] Swaileh | Monitoring urban heavy metal pollution using the House Sparrow (Passer domesticus)[END_REF]. The average level of copper and zinc in shells of great and blue tit living in contaminated area in Belgium was respectively 3.2 ppm and 32 ppm (Dauwe et al. 1999). The mean level of copper in eggshells of grey heron Ardea cinerea, night heron Nycticorax nycticorax and Audouin`s gull nesting in Turkey amounted to 6.75 and 1.37 ppm and in case of the last mentioned species (data for two breeding colonies), 1.85 and 10.20 ppm [START_REF] Ayas | Trace element residues in eggshells of grey heron (Ardea cinerea) and blackcrowned night heron (Nycticorax nycticorax) from Nallihan Bird Paradise, Ankara-Turkey[END_REF][START_REF] Ayas | Lead (Pb) and Copper (Cu) Concentration in the Eggshells of Audouin's Gulls (Larus audouinii) in Turkey[END_REF]. However, the eggshells of grey heron nesting in three different colonies in Poland in unpolluted areas had an average level of copper ranging between 4.13 and 5.27 ppm, and zinc from 1.62 to 2.24 ppm (Dmowski 1999). In two oceanic species such as short-tailed albatross Phoebastria albatrus and black-footed albatross, Ph. nigripes, found in Japan, the average copper concentration in eggshells was 0.77-0.78 ppm and zinc 3.39-5.61 ppm [START_REF] Ikemoto | Non-destructive monitoring of trace element levels in short-tailed albatrosses (Phoebastria albatrus) and black-footed albatrosses (Phoebastria nigripes) from Torishima Island, Japan, using eggs and blood[END_REF].

The significantly higher level of copper in eggshells of the rook from the colonies located in towns results mainly from its extremely high concentration in three rookeries in south-western Poland (see Appendix 1). After excluding these values the copper levels in both habitats are very similar (villages, 2.74 ppm; towns, 3.08 ppm). An extremely high copper concentration in three rookeries in south-western Poland was probably related to the geology of the area where some intensively exploited copper deposits are located. The average level of copper in rook eggshells in Poland (with the exception of three extreme values) was found to be 2.83 (min-max = 1.67-7.21) ppm (see Appendix 1). It was considerably lower than the minimal value established as the lowest indicator (indices on five class scale from 0 to 5 -≥100 ppm) of soil contamination in Poland [START_REF] Ioś | The State of Environment in Poland 1996-2001[END_REF]. A similar picture emerges for zinc, as its level in eggshells after excluding three extreme values was found to be 9.41 ppm (min-max = 1. 98-20.38). Such concentration is also considerably lower than the minimal value (50 ppm) reported as the lowest indicator of soil contamination in Poland [START_REF] Ioś | The State of Environment in Poland 1996-2001[END_REF]. The three extremely high values of zinc concentration were noted in the heavily industrialized cities in western Poland. It proves that the contamination does not originate from agriculture, especially if we note that the level of zinc in the soil exceeded the highest contamination class (≥500 ppm;acc. data presented by WIOŚ 2008).

The extremely high level of arsenic in rook eggshells in Poland indicates that this metal can be regarded as a potential threat to the proper growth and development of birds` organisms. The lack of confirmation of preliminary expectations about the higher metal concentration in the eggshells of birds nesting in western Poland may result from the species` biology and it may relate to the fact of wintering away from their breeding areas. Rooks originating from Poland winter mainly in western Europe where they spend several months [START_REF] Gromadzki | Migrations and wintering of the Rook Corvus frugilegus in Polandanalysis based on recoveries[END_REF]. Hence the contamination level in eggshells of rook and other migratory bird species may be linked to a lesser degree with the contamination around nesting sites than in resident species (Dmowski 1999). However, studies conducted on domesticated hens indicated that bioaccumulation and excretion of arsenic is fastbirds which were being fed on food with high content of arsenic (up to 100 ppm) reached maximum concentration in the sixth week, while the excretion from the organism was completed up to two weeks after the experiment [START_REF] Daghir | Determination of total As residues in chicken eggs[END_REF]. The two main arsenic derivatives which have been applied as pesticides (arsenobetaine and dimethylarsinic acid) can permeate to the inside of the bird eggs and the first mentioned compound may account for as much as 95% of the total arsenic content [START_REF] Kubota | Maternal transfer of arsenic to eggs of black-tailed gull (Larus crassirostris) from Rishiri Island, Japan[END_REF]. Arsenic accumulation in nestlings` tissues may lead to weight reduction; however no influence was reported on hatching success or on teratogenic activities [START_REF] Stanley | Main and interactive effects of arsenic and selenium on mallard reproduction and duckling growth and survival[END_REF]. According to [START_REF] Camardese | Effects of arsenate on growth and physiology in mallard ducklings[END_REF] an extremely high arsenic concentration (up to 430 ppm d.w.) in the vegetable food of mallard Anas platyrhynchos nestlings increased nesting time and decreased the overall nestlings` growth. It must be emphasized that despite the methodical inconveniences with assessment of local contamination levels due to the rook migration cycle, the species' eggshells should be used as a tool in monitoring of agricultural contamination, especially pesticides and their remnants.

Constant threat of heavy agricultural contamination implies an acute need for further studies on defining relations between its level and amount in different tissues and the nestlings` condition.

Zusammenfassung

  Geographische und Habitatunterschiede in denKonzentrationen von Kupfer, Zink und Arsen in Eierschalen der Saatkrähe Corvus frugilegus in Polen Der Gehalt von Kupfer, Zink und Arsen wurde in Eierschalen der Saatkrähe Corvus frugilegus aus 43 in verschiedenen Teilen Polens gelegenen Brutkolonien analysiert. Der durchschnittliche (95%-Konfidenzintervall) Gehalt von Kupfer betrug 8,13 (0,64-15,62) ppm, der von Zink 13,81 (7,99-19,62) ppm und der von Arsen 32,57 (25,60-39,54) ppm. Die Metallkonzentrationen in den untersuchten Eierschalen schwankten stark. Im Falle von Kupfer bestand ein 90-facher Unterschied zwischen den Extremwerten, für Zink war er 57fach und für Arsen 36-fach. Eine allgemeine lineare Modellanalyse zeigte, dass die Anreicherung von Kupfer in Eierschalen aus Krähenhorsten in Städten signifikant höher war als aus Horsten in Dörfern (Mittelwerte (95%-Konfidenzintervall) 19,31 ppm (4,57-43,20) bzw. 2,74 ppm (2,36-3,12)). In den Eierschalen von in Städten nistenden Saatkrähen war die Zinkkonzentration gerade eben nicht signifikant höher (P = 0,07). Die Arsenkonzentrationen in Kolonien auf dem Land und in der Stadt waren ähnlich. Für keines der Metalle bestanden signifikante Konzentrationsunterschiede zwischen Horsten in Regionen intensiven (Westpolen) und extensiven (Ostpolen) Ackerbaus. Im Vergleich mit anderen Daten zur Arsenbelastung verschiedener Biota, einschließlich der Gewebe und Eier landlebender und aquatischer Vögel, sollte der Gehalt dieses Metalls in den Eierschalen von Saatkrähen als extrem hoch angesehen werden. Dies ist wahrscheinlich auf den weitverbreiteten Gebrauch von arsenhaltigen Pestiziden zurückzuführen. StatSoft (2006) Statistica© (data analysis software system), version 7.1.

Fig. 1 .

 1 Fig. 1. Distribution of breeding colonies of the rook Corvus frugilegus in Poland, where the levels of copper, zinc and arsenic in the eggshells were surveyed. Rookeries numbered as in Appendix 1. The river of Wisła (Vistula) is the border between western and eastern Poland (= intensive vs extensive agriculture; see chapter Material and methods).

Table 1 .

 1 Tulsa, USA. Results of GLM on concentrations (ppm) of copper, zinc and arsenic in eggshells of rook Corvus frugilegus from breeding colonies located in villages (n = 29) and towns (n = 14) and in areas of intensive (West; n = 28) and extensive agriculture (East; n = 15) in Poland. Concentration (ppm) of copper, zinc and arsenic in eggshells of rook Corvus frugilegus from the surveyed breeding colonies in Poland; metal levels from two analyzed shells per colony are shown, respectively. 1 Rookery numbers as in Fig.1. 2 Region (voivodship): D -dolnośląskie, L -lubuskie, Ś -śląskie, Wwielkopolskie, Mmazowieckie, Ppodlaskie. Three highest concentrations are marked in bold.

	(www.statsoft.com)
	WIOŚ (2008)

Acknowledgements

We are grateful to A. Wuczyński, P.T. Dolata, K. Kujawa, R. Kruszyk, J. Pietrowiak, M. Rachel, T. Mazgajski, R. Piekarski, S. Kuźniak, S. Jędrzejewski, A. Sokołowska, T. Wilżak,

J. Ratajczak, P. Tryjanowski, T. Stawarczyk, M. Słupek, M and U. Martini and Management of Landscape Parks in Sieradz for participating in the ecotoxicological study of the rook in Poland. We would also like to thank M. Sęk for checking the English version of the text.