Lichen planus remission is associated with a decrease of human herpes virus type 7 protein expression in plasmacytoid dendritic cells

The cause of lichen planus is still unknown. Previously we showed human herpes virus 7 (HHV-7) DNA and proteins in lesional lichen planus skin, and significantly less in non-lesional lichen planus, psoriasis or healthy skin. Remarkably, lesional lichen planus skin was infiltrated with plasmacytoid dendritic cells. If HHV-7 is associated with lichen planus, then HHV-7 replication would reduce upon lichen planus remission. HHV-7 DNA detection was performed by nested PCR and HHV-7 protein by immunohistochemistry on lesional skin biopsies from lichen planus patients before treatment and after remission. Biopsies were obtained from lichen planus lesions before treatment (n = 18 patients) and after remission (n = 13). Before treatment 61% biopsies contained HHV-7 DNA versus 8% after remission (P = 0.01). HHV-7-protein positive cell numbers diminished significantly after remission in both dermis and epidermis. Expression of HHV-7 was mainly detected in BDCA-2 positive plasmacytoid dendritic cells rather than CD-3 positive lymphocytes. HHV-7 replicates in plasmacytoid dendritic cells in lesional lichen planus skin and diminishes after remission. This study further supports our hypothesis that HHV-7 is associated with lichen planus pathogenesis.
Introduction
Lichen planus is clinically characterized by flat topped polygonal, itchy erythematous papules with Wickams’ striae, nail deformation, and white mucosal patches [4]. The histopathological substrate comprises a dermal band like (lichenoid) lymphocytic infiltrate directed towards the basal epidermal layer with destruction of the basal membrane and apoptosis of epidermal cells (colloid bodies). A viral cause infecting skin cells has been proposed. Hepatitis C virus was epidemiologically linked to lichen planus [14], but this finding could not be confirmed in later studies [13].
We recently described an association of human herpes virus type 7 (HHV-7) DNA and large infiltrates of plasmacytoid dendritic cells (associated with virally induced inflammatory processes) in lesional skin of lichen planus patients but not in non-lesional skin of the same patients [5]. Moreover, skin of healthy volunteers and of patients with another inflammatory skin condition like psoriasis showed significantly less HHV-7 activity. Others have described plasmacytoid dendritic cell recruitment with interferon-alpha and antiviral protein MxA production, in oral and cutaneuous lichen planus lesions [16, 18]. The presence of HHV-7 in lesional skin could play a role in the pathogenesis of lichen planus. HHV-7 is an ubiquitous organism affecting more than 90% of the adult population worldwide, and primary infection occurs mainly during early childhood [1]. Like Epstein Barr virus reactivation is associated with Burkitt lymphoma, reactivation of HHV-7 during adulthood could be associated with lichen planus. To support this hypothesis, we quantified and compared the presence of HHV-7 DNA and protein in lesional lichen planus skin before, and after treatment-induced remission.
Materials and methods
The hospital medical ethics review board approved this study. Before entering the study, patients gave their written informed consent to participate. Skin biopsies were collected in a follow up study from the same patient group with lichen planus as described before [5]. In short, before treatment was initiated, two 4 mm full thickness skin biopsies were obtained from lesional skin. Patients were treated with 40 mg prednisone per os daily, diminished with 5 mg every other day until a daily dose of 20 mg was reached. From that point on, the prednisone dosage was diminished with 5 mg per week. If new lichen planus lesions recurred, the previous dose was administered and diminishment halted for 3 weeks. In case prednisone treatment was contra-indicated, other treatment options like topical corticosteroids, oral acitretin (0.5 mg/kg body weight) and psoralen ultraviolet A combined photo therapy were considered, depending on the extent of lesions and underlying medical conditions.
Treatment was terminated after complete clinical remission was achieved and 2 weeks later, provided no disease activity had recurred, another two biopsies were collected from recovered lesional skin adjacent to the pretreatment biopsy sites. Both time points biopsies were taken, one biopsy was processed for routine histopathology and examined by both a pathologist and a dermatologist separately to confirm the lichen planus diagnosis or signs of ongoing inflammation. The final diagnosis was made on mutual agreement and based on typical lichen planus associated dermatopathological signs like parakeratosis, wedge shaped hypergranulosis, saw-tooth configured rete ridges, vacuolar degeneration of the basal layer, colloid bodies, a band-like dermal inflammatory infiltration and pigment incontinence [17].
The second biopsy of each time point was snap frozen in liquid nitrogen, stored at minus 80°C, and thawed later for molecular biological analysis (HHV-7 DNA). DNA isolation and the nested PCR techniques have been previously described [5, 12]. The amplified fragment (296 bp) was identified by gel electrophoresis. The lower limit of detection of the PCR is 1–5 copies of HHV-7 DNA.
We performed immunohistochemistry as described before [5, 11]. The antibodies used in this study were directed against: HHV-7, clone 5E1 and clone KR4, both resulting in a similar specific staining pattern (data not shown; Advanced Biotechnologies Inc., Columbia, MD), CD3 (pan T cell marker; clone SK7, FITC-conjugated; BD Biosciences, San Jose, CA), and BDCA-2 (plasmacytoid dendritic cell marker; clone AC144, FITC conjugated; Miltenyi Biotec, Bergisch Gladbach, Germany). In the single-staining experiments we used clone 5E1 (directed against HHV-7 tegument protein pp85), but unfortunately this clone was no longer available at the time we performed the double staining. For this reason we used in the double staining experiments clone KR4 (directed against an unknown HHV-7 epitope). In brief the following sequence of steps was applied for single staining: blocking endogenous peroxidase activity; anti-HHV-7 (clone 5E1); biotin-conjugated goat anti-mouse antibody (Dako); horseradish peroxidase-conjugated streptavidin; and for clone KR4 a TSA amplification step (Perkin Elmer) was applied. In four patients specimens obtained before and after remission were double stained for HHV-7 and CD3 or for HHV-7 and BDCA-2. In this case the following incubation steps were performed: blocking endogenous peroxidase activity; anti-HHV-7 (clone KR4); biotin-conjugated goat anti-mouse antibody (Dako); horseradish peroxidase-conjugated streptavidin; TSA amplification according to the manufacturer’s protocol (Perkin Elmer); an AEC kit to visualize the peroxidase activity (Vector); blocking potential available biotin, anti-mouse immunoglobulin, and peroxidase activity; FITC-BDCA-2 or FITC-CD3 antibody; rabbit anti-FITC (Dako); peroxidase-conjugated goat anti-rabbit (Dako) for BDCA-2 and phosphatase-conjugated goat anti-rabbit (Dako) for CD3; for BDCA-2 a TSA amplification step followed by phosphatase-conjugated streptavidin; and finally an AP kit (Vector) to visualize the phosphatase activity. All single-stained sections were counterstained with Mayer’s hematoxylin (Fluka). The expression of single and double stained cells was counted in each entire section at 200× magnification using a standard binocular light microscope (Olympus, Tokyo, Japan) equipped with a 0.5 × 0.5 mm ocular grid. The number of cells in the epidermis was expressed per mm and the number in the dermis per mm2.
During a follow up period of 1 year, visits were scheduled every 6 months. In case a patient experienced a relapse in between follow up visits, or after the initial 1 year follow up period, extra visits were performed at the patient’s initiative for additional monitoring and treatment. Patients without a relapse on record were interviewed by telephone to exclude disease reoccurrence. The Wilcoxon rank sum test was used for statistical analysis of cell counts between samples before and after remission and the McNemar test for binominal data; P ≤ 0.05 was considered significant.
Results
Eighteen patients entered this study with a mean age of 43.9 ± 11.5 years at the time of inclusion (mean age of 12 males was 41.7 ± 10.9 and for 6 females 48.4 ± 12.3 years). The mean disease duration was 4.7 ± 3.3 months (Table 1). Lesional biopsies were taken from the lower leg in 14 cases, 3 times from the trunk and in 1 patient from the wrist. Fourteen patients were treated with oral prednisone, two patients received local corticosteroid treatment, one psoralen and ultraviolet A photo therapy and one acitretin. Three patients were lost to follow up, two patients refused biopsies after remission but their relapse data could be recorded. From the remaining 13 patients, biopsies could be collected after remission. The mean treatment duration until remission was 136 ± 60 days (in the prednisone treated subgroup 134 ± 60 days, in the subgroup who received other treatment modalities 148 ± 74 days). In 4 out of 13 biopsies a minimal remnant inflammatory infiltrate around the basal layer was still present, in spite of clinical remission.
Table 1Treatment characteristics and HHV-7 data before treatment and after remissionPatientDisease duration (months)Treatment modalityTreatment duration (days)Biopsy siteRelapse interval (months)Before treatmentAfter remissionHHV-7 positive epidermal cellsHHV-7 positive dermal cells BDCA2 positive cellsHHV-7/BDCA2 double positive cellsHHV-7 PCRHHV-7 positive epidermal cellsHHV-7 positive dermal cellsBDCA2 positive cellsHHV-7/BDCA2 double positive cellsHHV7 PCRPathology112Prednisone66Lower leg55,124,7Positive8,88,8NegativeNormal22Prednisone169Lower leg135,789,2Positive6,211,1NegativeNormal312Prednisone139Lower legNone4,711,7118,738,7Positive3,345,36511NegativeNormal45Prednisone72Lower leg16,753PositiveNANANANA56Prednisone64Foot62,429,4Negative4,720,5NegativeInflam61TopicalNAFootNA53,882,92813,3PositiveNANANANANANA76PUVA96Lower legNone22,339Negative2,35,9NegativeInflam81Prednisone75Foot4316,967Positive1,85,3NegativeNormal94Topical200Lower legNone9,62369,735,3Positive2,963812,3NegativeNormal102Prednisone205Wrist716115,9Negative3,226,9NegativeInflam112PrednisoneNALower legNA1234,9PositiveNANANANA122AcitretinNAFlankNone7,438,6PositiveNANANANA137Prednisone95Back513,466,118748Positive417,7426,7NegativeNormal147Prednisone247Lower leg44942,8Negative2,415,5NegativeNormal156Prednisone131Foot14,1979,5Negative24,7PositiveNormal162PrednisoneNABackNone7,435,6NegativeNANANANA173Prednisone154FootNone19,3119,3Positive0,82,5NegativeInflam184Prednisone187FootNone13,1106,832,75,7Negative0,83,2224,3NegativeNormalThe relapse interval was counted from discontinuation of treatment until the first relapse. HHV-7 immunohistochemical positive dermal and epidermal cells and BDCA2 positive cells were counted per mm2. Some biopsies after remission showed minimal basal layer inflammatory activity (inflam)NA not available
During follow up (with a minimum period of 30 months) 9 out of 16 patients experienced a relapse of lichen planus symptoms, 6 patients within 1 year, 3 within 4 years. All relapses occurred in patients treated with oral prednisone. In 2 patients with relapses, remnant inflammation after remission had been noticed.
In 11/18 (61%) patients lichen planus affected skin contained HHV-7 specific DNA before treatment compared to 1/13 (8%) patients after remission (P = 0.01, McNemar test). In one patient (no. 15) HHV-7 DNA was detected after remission whereas before treatment no HHV-7 DNA was found.
Immunohistochemical single staining for the presence of HHV-7 tegument protein revealed that before treatment the mean number of HHV-7 positive cells was 58.9 ± 33.4 per mm2 in the dermis and 12.7 ± 11.7 per mm in the epidermis (Fig. 1). After remission the number of positive cells had dropped to 13.3 ± 12.2 and 3.3 ± 2.2, respectively (P < 0.05 with the paired samples t test).
Fig. 1Mean cell count (in mm for the epidermis and mm2 for the dermis) of HHV-7 tegument pp85 positive cells with immunohistochemical staining in lichen planus affected epidermis and dermis, before treatment (white bars) and after remission (black bars). * and # P < 0.05 with the Wilcoxon rank sum test
Immunohistochemical double staining was performed on skin specimens of four patients to investigate whether HHV-7 protein expression may be associated with T cells or plasmacytoid dendritic cells, which both represent major constituents of the inflammatory infiltrate. We found a considerable number of HHV-7/BDCA-2 double positive cells in lesional lichen planus skin before treatment but not after treatment (Fig. 2). The number of single stained BDCA2 positive cells decreased approximately twofold “before treatment” compared to “after remission” (Table 1). With HHV-7/CD3 double staining, we predominantly observed single stained HHV-7 or CD3 cells whereas only an occasional double positive cell could be found (Fig. 3).
Fig. 2Double positive stained plasmacytoid dendritic cells reflects intracellular HHV-7 protein. BDCA-2 positive plasmacytoid dendritic cells (blue stain) containing HHV-7 protein (red stain) in lesional LP skin before (a) and after (b) treatment (overview 200× and inlay 400× magnification)Fig. 3HHV-7 replication does not coincide with lymphocytes. HHV-7 positive cells (red stain) and CD3 positive lymphocytes (blue stain) cells in lesional lichen planus skin are in majority mutually exclusive (400× magnification)
Discussion
Lichen planus is characterized by a band-like lymphocytic infiltrate, which seems to be involved in an attack of the basal epidermal layer. It is suggested that these T lymphocytes are directed towards infected skin cells expressing viral antigens. Molecular mimicry is a second conceivable inflammatory mechanism initiated by microbial antigens or microbial-reactive T lymphocytes that cross react with self antigens sharing homology (i.e., basal layer structures).
In our previous study we localized HHV-7 pp85 antigen containing cells in lesional lichen planus tissue around de dermo-epidermal junction, coinciding with the location of the inflammatory infiltrate [5]. In addition, we detected high numbers of plasmacytoid dendritic cells (cells that are associated with the clearance of virally induced diseases) within the infiltrate. The presence of HHV-7 DNA and HHV-7 antigen did not coincide with non-lesional lichen planus skin, psoriatic skin and normal skin from healthy donors. Since HHV-7 was not found in psoriatic skin (which is also characterized by a lymphocytic infiltrate) it is unlikely that HHV-7 in lichen planus is a reflection of an innocent bystander in a dense influx of lymphocytes. Moreover, in the current study we could localize HHV-7 protein, mainly in BDCA-2 positive plasmacytoid dendritic cells and sporadic in CD3 positive lymphocytes. This is remarkable since HHV-7 has been characterized as a T-lymphotropic virus [10]. Our results suggest that plasmacytoid dendritic cells can also host HHV-7. This is conceivable since plasmacytoid dendritic cells express CD4 [15], which is the main receptor used by HHV-7 to enter target cells [21].
Here we investigated the effect of therapy-induced remission of lichen planus on HHV-7 replication. Both the number of HHV-7 DNA containing skin samples and the absolute number of HHV-7 antigen containing skin cells (in dermis and epidermis) diminished significantly after treatment-induced remission. Since we included a 2-week washout period between treatment termination and obtaining the post remission biopsies, a direct medication effect on viral replication is unlikely. In contrast to a fourfold diminution in HHV-7 positive cells after treatment, there was only a twofold reduction in the number of BDCA2 positive plasmacytoid dendritic cells. This makes it unlikely that the diminished HHV-7 expression only depends on a reduced number of plasmacytoid dendritic cells after treatment. Whether plasmacytoid dendritic cells are carriers of HHV-7 driving the disease activity or that these cells are infected as innocent bystanders, or actively induce the inflammatory process is not yet clear and needs further study.
HHV-7, a member of the beta herpes virus subfamily was first discovered by Frenkel et al. [10] in CD4 positive T lymphocytes of healthy individuals. It is an ubiquitous virus with a worldwide prevalence exceeding 90%. Most of the primo infections occur during infancy [1]. Since the discovery of HHV-8 as the causative agent for Kaposi sarcoma [3], all three human herpes viruses (HHV-6, -7 and -8) discovered in the last few decades have been subjected to studies on their role in skin diseases of unknown origin like pitryasis rosea [7] and exanthema subitum [2, 19].
A shared characteristic of all herpes viruses is their unique ability to persist in the host after primary infection, and become reactivated later in life (sometimes after many years of latency). Symptomatic reactivation of herpes viruses is a well-known phenomenon in the daily dermatological practice. For example, shingles afflicting patients later in life after acquisition of the causative varicella zoster virus during childhood, and the recurrence of labial and genital herpes simplex virus lesions after reactivation of viral DNA. Human herpes virus 6 reactivation is believed to cause the drug related exanthema and systemic side effect syndrome (DRESS) [6].
It is unlikely that HHV-7 primary infections cause lichen planus, since first transmission mostly occurs during childhood, whereas lichen planus affects mainly patients during midlife. Yet, it is conceivable that HHV-7 reactivation triggers lichen planus, in the same fashion as Epstein-Barr virus reactivation is associated with Burkitt lymphoma.
The apparent paradoxical efficacy of steroid therapy in lichen planus and a considered viral cause can be explained by a “hit and run” model where the actual viral reactivation would be short lived but subsequently responsible for an independently propagated inflammatory skin reaction. This could also explain why not in all patients HHV-7 was detected at the time of inclusion. Moreover, in one patient (no. 15) HHV-7 DNA was detected after remission. It is this patient that experienced a relapse 1 month later and returned to the clinic for additional treatment. Possibly HHV-7 can trigger lichen planus episodes, including relapses, but resolve before clinical lichen planus symptoms appear. The other patient (no. 4) with a relapse within 1 month unfortunately refused us a post remission skin sample. On the other hand, the absence of HHV-7 replication in some of our patients could also be explained assuming a multi causal pathogenesis for lichen planus.
Further evidence for the causal association of HHV-7 and lichen planus could come from clinical studies on the efficacy of anti-herpetic medication. Which drug would be the best option is not yet clear. Guanoside analogons with little side effects like acyclovir show little activity against HHV-7 [8]. HHV-7 is more susceptible to cidofovir and foscarnet but these drugs are known for serious side effects [20].
Fredericks and Relman proposed a reconsideration of Koch’s postulates for the identification of microbial antigens as causative agents in diseases [9]. With data presented here and in our previous study [5] we were able to meet with three out of their seven postulates for the establishment of a causal relationship between HHV-7 and lichen planus. (1) Microbial nucleic acid sequences were present in most cases in diseased sites and not in sites that lack pathology. (2) Fewer nucleic acid sequences were found in hosts without the disease. (3) Nucleic acid sequences correlates were present at a cellular level. The fourth postulate, a decrease of the number of nucleic acid sequences during resolution of the disease and increase with relapse, was met in this study as far as disease resolution is concerned. Unfortunately we did not collect lesional biopsies in those patients that suffered from relapses. HHV-7 reactivation during relapses will have to be investigated in future studies. (5) The number of nucleic acid sequences should correlate with disease severity and (6) reproducibility of the evidence will have to be met in future longitudinal studies. The seventh postulate, clinical features and pathology should be consistent with known biological characteristics of the microbe, will also have to be addressed in future studies since still little is known about the effects of HHV-7 in vivo yet. The hypothesis of HHV-7 as the causative agent for lichen planus deserves further research in larger studies.

lichen planus
plasmacytoid dendritic cells
human herpes virus 7
skin disease
herpes virus replication


Pyrazolo-triazolo-pyrimidines as adenosine receptor antagonists: Effect of the N-5 bond type on the affinity and selectivity at the four adenosine receptor subtypes

In the last few years, many efforts have been made to search for potent and selective human A3 adenosine antagonists. In particular, one of the most promising human A3 adenosine receptor antagonists is represented by the pyrazolo-triazolo-pyrimidine family. This class of compounds has been strongly investigated from the point of view of structure-activity relationships. In particular, it has been observed that fundamental requisites for having both potency and selectivity at the human A3 adenosine receptors are the presence of a small substituent at the N8 position and an unsubstitued phenyl carbamoyl moiety at the N5 position. In this study, we report the role of the N5-bond type on the affinity and selectivity at the four adenosine receptor subtypes. The observed structure-activity relationships of this class of antagonists are also exhaustively rationalized using the recently published ligand-based homology modeling approach.
Introduction
Adenosine is an ubiquitous modulator, which exerts its functions through interaction with four G-protein-coupled receptors classified as A1, A2A, A2B and A3 [1]. In recent decades, intensive and successful efforts have been made by medicinal chemists to discover potent and selective ligands (both agonists and antagonists) for almost all adenosine receptor subtypes [2, 3]. Only the A2B subtype is still without a very potent and selective agonist ligand [4–6]. At the same time, great progress has been made in the field of human A3 adenosine receptor antagonists. This receptor seems to be associated with cerebroprotection [7, 8] and cardioprotection [9] and effects on the immune and inflammatory systems [10, 11]. The A3 adenosine receptor subtype, recently cloned from different species (e.g., rat, human, dog, sheep) [12, 13], is coupled to the modulation of at least two second-messenger systems: inhibition of adenylate cyclase and stimulation of phospholipase C and D [12, 13]. In humans, A3 receptors have been found in several organs, such as lung, liver, kidney, heart, and with a lower density, in the brain [9, 12, 13]. This receptor subtype is under examination in relation to its potential therapeutic applications. In particular, antagonists for A3 receptors seem to be useful for the treatment of inflammation or glaucoma [14, 15].
In the last 5 years, many efforts have been made in the context of searching for potent and selective human A3 adenosine antagonists. In this field, several different classes of compounds have been proposed, possessing good affinity (nM range) and with a broad range of selectivity [16].
In particular one of the most attractive human A3 adenosine receptor antagonists is represented by the pyrazolo-triazolo-pyrimidine family, reported by our group [17–23]. In Fig. 1, the most interesting members of this class of compounds (1–3), which display high affinity and selectivity for this receptor subtype, are depicted [17–23].
Fig. 1Structures and binding profiles of some representative pyrazolo-triazolo-pyrimidines as human A3 adenosine receptor antagonists
This class of compounds has been strongly investigated from the point of view of structure-activity relationships. In particular, it has been observed that fundamental requisites for having both potency and selectivity at the human A3 adenosine receptors are a small substituent at the N8 position and an unsubstituted phenyl carbamoyl moiety at the N5 position [17, 21]. In contrast, when the phenyl carbamoyl moiety (2) was replaced with a phenylacetyl group (3), a significant decrease in affinity at the hA3 adenosine receptors with a simultaneous retention or a slight increase in affinity at the hA2B adenosine receptor subtype was observed [24].
These data seem to suggest that bond type at the N5 position plays a fundamental role in the receptor recognition. For this reason, we decided to further investigate the effect of the bond nature at this position on affinity and selectivity, synthesizing two new derivatives bearing the benzoyl- (4) and benzensulfonamido (5) moieties at the N5 position.
Chemistry
The designed compounds (4, 5) were easily prepared by treating the well-known N8 methyl derivative (6) [18] with benzoyl (7) or benzensulfonyl (8) chlorides in dioxane in the presence of pyridine at reflux for 16 h (Scheme 1).
Scheme 1Preparation of designed compounds
Results and discussion
Newly synthesized (4, 5) and reference compounds (2, 3) were tested at the human A1, A2A and A3 receptors expressed in CHO cells; [3H]CCPA (A1) and [3H]NECA (A2A, A3) were used as radioligands in binding assays [25]. Radioligand binding at A2B adenosine receptors is problematic as no high-affinity ligand is readily available for this subtype. Therefore, inhibition of NECA-stimulated adenylyl cyclase activity was determined as a measurement of affinity of compounds (Table 1).
Table 1Biological profile of synthesized (4, 5) and reference (2, 3) compoundsCompoundRhA1a (Ki nM)hA2Ab (Ki nM)hA2Bc (IC50 nM)hA3d (Ki nM)hA1/hA3hA2A/hA32CONHPh310 (295–327)27.7 (13.3–57.8)3,440 (2,880–4,110)1.80 (0.88–3.68)172153COCH2Ph1,040 (864–1,260)282 (201–375)12,620 (9,730–16,400)0.92 (0.80–1.06)1,1303604COPh2,030 (1,710–2,400)879 (643–1,200)>30,00015.7 (7.85–31.5)129565SO2Ph20,700 (16,700–25,700)6,060 (5,170–7,110)>30,000744 (534–1,040)288Data are expressed as geometric means, with 95% confidence limitsaDisplacement of specific [3H]-CCPA binding at human A1 receptors expressed in CHO cells, (n = 3–6)bDisplacement of specific [3H]-NECA binding at human A2A receptors expressed in CHO cellscIC50 values of the inhibition of NECA-stimulated adenylyl cyclase activity in CHO cells expressing hA2B receptorsdDisplacement of specific [3H]-NECA binding at human A3 receptors expressed in CHO cells
A careful examination of the data clearly revealed significant differences in the binding profile of the reference compounds (2, 3) compared with the literature data (see Fig. 1). In fact, in these binding studies, compound 2 was found to be ninefold less potent at the hA3 receptors with a consequent reduction in selectivity versus the other receptor subtypes, in particular versus hA2A (hA2A/hA3 = 15). In contrast, derivative 3 retained good affinity at the hA3 adenosine receptor and a quite good range of selectivity versus the other receptors in comparison with literature data. Also if these discrepancies in binding profile could be attributed to a different biological method utilized (e.g., radioligand), the data seem to indicate that the phenylacetyl moiety retains or enhances both potency and selectivity compared with the well-known phenylcarbamoyl chain. A significantly different binding profile was instead observed when the chain at the N5 position was alternatively modified. Introduction of a shorter amide (4) with respect phenylacetyl chain (3) led to a loss of potency at all four adenosine receptor subtypes, but a quite good retention of selectivity was observed. In contrast, the presence of a benzensulfonamido moiety at the N5 position induced a substantial loss of both potency and selectivity at the four adenosine receptor subtypes.
Molecular modeling studies were performed on the pyrazolo-triazolo-pyrimidine derivatives 2–5 in order to identify the hypothetical binding motif of these N5 analogues and to rationalize their structure-activity relationship.
Following our previously reported modeling studies [17–23], we built up a refined model of human A3 receptor by using a rhodopsin-based homology modeling (RBHM) approach [26–29]. Moreover, our recently described ligand-based homology modeling methodology (LBHM) has been used to simulate the conformational changes induced by ligand binding (methodological details are summarized in the Experimental Section) [29, 30].
Using this methodology, we found an “expanded” conformational model of the human A3 receptor reverse agonist-like state, in which both shape and chemical complementarities have been specifically optimized around each ligand. Considering these new N5 analogues, the molecular volume of transmembrane (TM) binding cavity has been changed from 660 (A3 model obtained by the conventional rhodopsin-based homology modeling) to 840 Å3 (expanded A3 model obtained by ligand-based homology modeling) without altering the conventional rhodopsin-like receptor topology. The binding cavity reorganization induced by ligand binding is due to the conformational change in several amino acid side chains, such as Leu90 (3.32), Leu91 (3.33), Thr94 (3.36), His95 (3.37), Ile98 (3.40), Gln167 (EL2), Phe168 (EL2), Phe182 (5.43), Ile186 (5.47), Leu190 (5.51), Phe239 (6.44), Trp243 (6.48), Leu244 (6.49), Leu264 (7.35), and Ile268 (7.39).
Interestingly, none of the new pyrazoloquinoline antagonists found an energetically stable docking pose in the conventional RBHM-driven A3 model. This is mainly due to the unfavorable topological complementarity among these antagonists and corresponding RBHM-driven TM binding cavity. In particular, highly destabilizing van der Waals interactions (steric conflicts) seem to be the reason for absent topological complementarities. These steric conflicts are drastically reduced or completely eliminated after the application of the LBHM approach.
Molecular docking studies were carried out for the pyrazolo-triazolo-pyrimidine antagonists 2–4, using the “expanded” conformational state of the receptor. As shown in Fig. 2, we found a similar binding motif indicating that a common receptor-driven pharmacophore model can be depicted. This finding is in agreement with our previously reported studies [17–23].
Fig. 2Hypothetical binding motif of the newly synthesized pyrazolo-triazolo-pyrimidine antagonists 2–4. The most energetically favorable docked conformation of each derivative is viewed from the membrane side facing TM helices 4 and 5. To clarify the TM cavity, the view of TM4 was omitted. Side chains of some amino acids important for ligand recognition are highlighted. Hydrogen atoms are not displayed
Indeed, ligand recognition occurs in the upper region of the TM bundle, and the pyrazolo-triazolo-pyrimidine moiety is surrounded by TMs 3, 5, 6, 7 with the substituent in the N5 position oriented toward the intracellular environment. As shown in Fig. 2, the furan ring at the 2-position is close to TMs 3 and 7. Interestingly, an important hydrogen bonding network can be observed in all energetically stable docked conformations of pyrazolo-triazolo-pyrimidine antagonists. In particular His95 (3.37) and Ser247 (6.52) are able to interact through hydrogen bonding with the N5-carbonyl oxygen of compounds 2–4 (2C=O·His95 ca. 3.0 Å; 3C=O·Ser247 ca. 2.8 Å; 4C=O·His95 ca. 2.9 Å.)
These polar amino acids seem to be critical for the recognition of all antagonist structures and for receptor selectivity. In particular, Ser247 (6.52) of human A3 receptor subtype is not present in the corresponding position of A1 and A2 receptors, where the residue is replaced by a histidine (His251 in human A1, His250 in human A2A and His251 in human A2B). Histidine side chain is bulkier than serine, and possibly for this reason, large substituents at the N5 position of pyrazolo-triazolo-pyrimidine framework are not well tolerated by A1 and A2 receptor subtypes. In contrast, the hydroxyl group of Ser247 (6.52) of human A3 receptor is appropriately positioned to form a hydrogen-bonding interaction with the carbonyl oxygen of the N5-amide/ureide group of compounds 2–4. These observations support the importance of an N5-acyl/carbamoyl group in modulating receptor selectivity. The hydrophobic environment of the five nonpolar amino acids Ile98 (3.40), Ile186 (5.47), Leu190 (5.51), Phe239 (6.44), and Leu244 (6.49) can comfortably accommodate the phenyl ring of all N5-acyl/carbamoyl derivatives.
In contrast, the introduction of the N5-sulfonamido moiety, as present in derivative 5, drastically reduces the affinity at the human A3 receptor. Interestingly, in this specific case molecular docking is not able to find an antagonist pose comparable to those described for the other N5-acyl/carbamoyl derivatives. As shown in Fig. 3, the rigid tetrahedral configuration associated with the N5-sulfonamido moiety avoids the sampling of energetically favorable antagonist poses in which the phenyl ring is linked to the N5 position in the hydrophobic pocket delimited by Ile98 (3.40), Ile186 (5.47), Leu190 (5.51), Phe239 (6.44), and Leu244 (6.49).
Fig. 3Hypothetical binding motif of the newly synthesized N5-sulfonamido pyrazolo-triazolo-pyrimidine antagonist 5. The most energetically favorable docked conformation of each derivative is viewed from the membrane side facing TM helices 4 and 5. To clarify the TM cavity, the view of TM4 was omitted. Hydrogen atoms are not displayed
The most stable docking pose of 5 presents the N5-sulfonamido moiety close to TM3 and TM7, and the phenyl ring linked to N5 position is surrounded by a hydrophobic pocket delimited by Leu90 (3.32) and Ile268 (7.39). This antagonist pose is energetically less stable (ca. 15 kcal/mol) with respect to those found for derivatives 2–4, due to the absence of the stabilizing interactions among the polar residues Thr94 (3.36), His95 (3.37), and Ser247 (6.52) and the N5-sulfonamido moiety. Structure superimposition of compounds 4 and 5 is shown in Fig. 4.
Fig. 4Structure superimposition of compounds 4 (in magenta) and 5 (in green) inside the receptor binding site
This severe steric constriction might explain the drastic reduction in affinity of derivative 5 at the human A3 receptor.
Conclusions
The present study has led to potent and selective human A3 adenosine receptor antagonists belonging to the class of the pyrazolo-triazolo-pyrimidine. In this study, we clarified the role of the N5-bond type on the affinity and selectivity at the four adenosine receptor subtypes. In particular, we confirmed the critical function played by the N5-acyl/carbamoyl moiety in receptor recognition. The observed structure-activity relationship was also rationalized by the recently published ligand-based homology modeling approach, indicating that chemical and topological properties of the different ligands might induce important conformational changes in the antagonist-driven binding site of the human A3 adenosine receptor.
Experimental section
Chemistry
General Reactions were routinely monitored by thin-layer chromatography (TLC) ion silica gel (precoated F254 Merck plates) and products visualized with iodine or potassium permanganate solution. Infrared spectra (IR) were measured on a Perkin Elmer 257 instrument. 1H NMR was determined in CDCl3 or DMSO-d6 solutions with a Bruker AC 200 spectrometer; peak positions are given in parts per million (6) downfield from tetramethylsilane as internal standard, and J values are given in Hz. Light petroleum ether refers to the fractions boiling at 40–60°C. Melting points were determined on a Buchi-Tottoli instrument and are uncorrected. Chromatographies were performed using Merck 60–200 mesh silica gel. All products reported showed IR and 1H NMR spectra in agreement with the assigned structures. Organic solutions were dried over anhydrous magnesium sulphate. Elemental analyses were performed by the microanalytical laboratory of Dipartimento di Chimica, University of Trieste.
General procedures for the preparation of 5-(benzoyl)amino-8-methyl-2-(2-furyl)-pyrazolo[4,3 ]-1,2,4-triazolo[1,5-c]pyrimidine (4) and 5-benzensulfonamido-8-methyl-2-(2-furyl)-pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidine (5)
Amino compound (6) (10 mmol) was dissolved in freshly distilled dioxane (15 mL), and benzoylchloride or benzesulfonyl chloride (13 mmol) and pyridine (20 mmol) were added. The mixture was refluxed under argon for 16 h. Then the solvent was removed under reduced pressure and the residue was dissolved in EtOAc (30 ml) and washed twice with water (15 mL). The organic phase was dried on Na2SO4 and concentrated under reduced pressure. The residue was purified by flash chromatography (EtOAc-light petroleum 8:2) to afford the desired compounds (4, 5) as solids.
5-(Benzoyl)amino-8-methyl-2-(2-furyl)-pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidine (4)
Yield 78%, pale yellow solid, mp (EtOAc-light petroleum) 240°C; IR (KBr): 3,425-2,965, 1,675, 1,645, 1,620, 1,545, 1,450 cm−1; 1 H NMR (DMSO d6) δ: 4.42 (s, 3H); 6.65 (dd, 1H, J = 2, J = 4); 7.24 (d, 1H, J = 4); 7.41–7.62 (m, 4H); 7.85–7.98 (m, 2H); 8.14 (s, 1H); 9.87 (bs, 1H). Anal. for C18H13N7O2 (MW 359.34). Calculated: C 50.16; H 3.69; N 27.29. Found: C 50.01; H 3.61; N 27.35.
5-Benzensulfonamido-8-methyl-2-(2-furyl)-pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidine (5)
Yield 62%, white solid, mp (EtOAc-light petroleum) > 300°C; IR (KBr): 3,375-2,985, 1,640, 1,600, 1,540, 1,355, 1,150 cm−1; 1H NMR (DMSO d6) δ: 4.39 (s, 3H); 6.68 (dd, 1H, J = 2, J = 4); 7.22 (d, 1H, J = 4); 7.38–7.59 (m, 4H); 7.88-8.02 (m, 2H); 8.11 (s, 1H); 11.05 (bs, 1H). Anal. for C17H13N7O3S (MW 395.40). Calculated: C 51.64; H 3.31; N 24.80; S 8.11. Found: C 51.87; H 3.35; N 24.97; S 8.07.
Biology
All pharmacological methods followed the procedures as described earlier [25]. In brief, membranes for radioligand binding were prepared from CHO cells stably transfected with human adenosine receptor subtypes in a two-step procedure. In a first low-speed step (1,000 × g), cell fragments and nuclei were removed. The crude membrane fraction was sedimented from the supernatant at 100,000 × g. The membrane pellet was resuspended in the buffer used for the respective binding experiments, frozen in liquid nitrogen and stored at −80°C. For the measurement of adenylyl cyclase activity only one high-speed centrifugation of the homogenate was used. The resulting crude membrane pellet was resuspended in 50 mM Tris/HCl, pH 7.4 and immediately used for the cyclase assay.
For radioligand binding at A1 adenosine receptors, 1 nM [3H]CCPA was used, whereas 30 and 10 nM [3H]NECA were used for A2A and A3 receptors, respectively. Nonspecific binding of [3H]CCPA was determined in the presence of 1 mM theophylline, and in the case of [3H]NECA, 100 pM R-PIA was used. Ki values from competition experiments were calculated with the program SCTFIT [31].
Radioligand binding at A2B adenosine receptors is problematic as no high-affinity ligand is available for this subtype. Therefore, inhibition of NECA-stimulated adenylyl cyclase activity was determined as a measurement of affinity of compounds. IC50 values from these experiments were converted to Ki values with the Cheng and Prusoff equation [32].
Computational methodologies
All modeling studies were carried out on a 10 CPU (PIV-3.0GHZ and AMD64) Linux cluster running under openMosix architecture [33].
Homology modeling, energy calculation, and docking studies were performed using the Molecular Operating Environment (MOE, version 2006.08) suite [34].
All docked structures were fully optimized without geometry constraints using RHF/AM1 semiempirical calculations. Vibrational frequency analysis was used to characterize the minima stationary points (zero imaginary frequencies). The software package MOPAC (ver. 7) [35], implemented in MOE suite, was utilized for all quantum mechanical calculations.
Homology model of the human A3 AR Based on the assumption that GPCRs share similar TM boundaries and overall topology, a homology model of the hA3 receptor was constructed. First, the amino acid sequences of TM helices of the A3 receptor were aligned with those of bovine rhodopsin, guided by the highly conserved amino acid residues, including the DRY motif (D3.49, R3.50, and Y3.51) and three proline residues (P4.60, P6.50, and P7.50) in the TM segments of GPCRs. The same boundaries were applied for the TM helices of the A3 receptor as they were identified from the X-ray crystal structure for the corresponding sequences of bovine rhodopsin [36], the CR coordinates of which were used to construct the seven TM helices for the hA3 receptor.The loop domains of the hA3 receptor were constructed by the loop search method implemented in MOE. In particular, loops are modeled first in random order. For each loop, a contact energy function analyzes the list of candidates collected in the segment searching stage, taking into account all atoms already modeled and any atoms specified by the user as belonging to the model environment. These energies are then used to make a Boltzmann-weighted choice from the candidates, the coordinates of which are then copied to the model. Any missing side-chain atoms are modeled using the same procedure. Side chains belonging to residues whose backbone coordinates were copied from a template are modeled first, followed by side chains of modeled loops. Outgaps and their side chains are modeled last.Special caution has to be given to the second extracellular (EL2) loop, which has been described in bovine rhodopsin as folding back over transmembrane helices [36] and, therefore, limiting the size of the active site. Hence, amino acids of this loop could be involved in direct interactions with the ligands. A driving force for this peculiar fold of the EL2 loop might be the presence of a disulfide bridge between cysteines in TM3 and EL2. Since this covalent link is conserved in all receptors modeled in the current study, the EL2 loop was modeled using a rhodopsin-like constrained geometry around the EL2-TM3 disulfide bridge. After the heavy atoms were modeled, all hydrogen atoms were added, and the protein coordinates were then minimized with MOE using the AMBER94 force field [37]. The minimizations were carried out by the 1,000 steps of steepest descent followed by conjugate gradient minimization until the rms gradient of the potential energy was less than 0.1 kcal mol−1 Å−1. Protein stereochemistry evaluation was performed by several tools (Ramachandran and Chi plots measure phi/psi and chi1/chi2 angles, clash contacts reports) implemented in MOE suite [34].
Ligand-based homology modeling We have recently revisited the rhodopsin-based model of the human A3 receptor in its resting state (antagonist-like state), taking into account a novel strategy to simulate the possible receptor reorganization induced by the antagonist-binding [28]. We called this new strategy ligand-based homology modeling. Briefly, ligand-based homology modeling technique is an evolution of a conventional homology modeling algorithm based on a Boltzmann-weighted randomized modeling procedure adapted from Levitt [38] with specialized logic for the proper handling of insertions and deletions so that any selected atoms will be included in the energy tests and minimization stages of the modeling procedure. Ligand-based option is very useful when one wishes to build a homology model in the presence of a ligand docked to the primary template, or other proteins known to be complexed with the sequence to be modeled [34]. In this specific case, both model building and refinement take into account the presence of the ligand in terms of specific steric and chemical features. In order to generate an initial ensemble of ligand poses, a conventional docking procedure (see next section for details) with reduced van der Waals radii (equal to 75%) and an increased Coulomb-vdW cutoff (cutoff on 10 Å; cutoff on 12 Å) was performed. For each pose, an homology model is then generated to accommodate the ligand by reorienting nearby side chains. These residues and the ligand are then locally minimized. Finally, each ligand is re-docked into its corresponding low energy protein structures and the resulting complexes are ranked according to MOEScore [34].
Different quantitative measures of molecular volume of the receptor binding cavities have been carried out by using MOE suite [34]. Prediction of antagonist-receptor complex stability (in terms of corresponding pKi value) and the quantitative analysis for non-bonded intermolecular interactions (H-bonds, transition metal, water bridges, hydrophobic) were calculated and visualized using several tools implemented into MOE suite [34].
Molecular docking of the hA3 AR antagonists All antagonist structures were docked into the hypothetical TM binding site by using the MOE-dock tool, part of the MOE suite. Searching is conducted within a user-specified 3D docking box (the standard protocol selects all atoms inside 12 Å from the center of mass of the binding cavity), using the Tabu Search [39] protocol (standard parameters are 1,000 steps/run, 10 attempts/step, and 10 Tabu list length), and the MMFF94 force field [40]. MOE-dock performs a user-specified number of independent docking runs (50 in our specific case) and writes the resulting conformations and their energies in a molecular database file. The resulting docked complexes were subjected to MMFF94 energy minimization until the rms of conjugate gradient was <0.1 kcal mol−1 Å−1. Charges for the ligands were imported from the MOPAC output files. To better refine all antagonist-receptor complexes, a rotamer exploration of all side chains involved in the antagonist-binding was carried out. Rotamer exploration methodology was implemented in MOE suite [34].

adenosine receptors
ligand-based homology modeling
molecular modeling
antagonist binding


Case-control study of environmental and social factors influencing cryptosporidiosis

We report on the first case-control study to investigate the role of wider environmental and socioeconomic factors upon human cryptosporidiosis. Using GIS the detailed locations of 3368 laboratory-confirmed cases were compared to the locations of an equal number of controls. All cases were genotyped enabling Cryptosporidium hominis and Cryptosporidium parvum to be examined separately. When all cryptosporidiosis cases were analyzed, several location variables were strongly associated with illness: areas with many higher socioeconomic status individuals, many individuals aged less than 4 years, areas with a high estimate of Cryptosporidium applied to land from manure, and areas with poorer water treatment. For C. hominis cases, the strongly significant risk factors were areas with many higher socioeconomic status individuals, areas with many young children and urban areas. Socioeconomic status and areas with many individuals aged less then 4 years had a greater impact for infection with C. hominis than for C. parvum. Policy implications are discussed.
Introduction
Cryptosporidiosis is a common gastrointestinal disease, and is widespread in many developed and developing countries [1]. Our understandings of the causes of cryptosporidiosis are based upon a large number of outbreak studies and a smaller number of case-control studies. These have highlighted risks from drinking water from poorly treated public and private supplies, swimming in swimming pools, contact with farm animals and spread within institutions such as day care centres. Contact with young children is also known to be a risk factor [2]. There are 16 species of Cryptosporidium but C. hominis and C. parvum are the most important pathogens for humans [3]. In England and Wales there is a large environmental reservoir of C. parvum in livestock and transmission is related to direct or indirect contact (e.g. through faecally contaminated drinking water) with these animals. The only major environmental reservoir for C. hominis is humans, and so infection is acquired through direct or indirect contact with other infected or colonised humans particularly when associated with foreign travel [2, 4]. For both species once a primary infection has occurred, secondary infections may result from person-to-person transmission.
Epidemiological studies into cryptosporidiosis have used questionnaires to identify proximal risk factors (patient activity) for cryptosporidiosis such as drinking water consumption or farm visits [5]. The objective of this study was to investigate the role of wider environmental and socioeconomic factors (e.g. water supply, socioeconomic status, land use, livestock densities and healthcare accessibility) upon human cryptosporidiosis.
This was achieved by linking small area data with postcoded cryptosporidiosis cases. All cases were genotyped to the species level enabling us to explore the aetiologies of the two main species C. hominis and C. parvum separately. We used a Geographical Information System (GIS) to derive detailed and aetiologically relevant indicators of environmental sources and points of contamination. We generated detailed measures of drinking water risks by tracing each individual’s mains water supply back to the water treatment works and into the source catchment.
Background
The incidence of cryptosporidiosis varies geographically and studies have indicated that C. hominis is more common in urban areas and C. parvum in rural areas [6, 7]. However, few studies have examined the reasons behind this. The C. parvum excess in rural areas may be related to higher probabilities of animal contact [6], a risk factor identified in a case-control study in England and Wales [2]. Higher population densities in urban areas may lead to greater person–to-person transmission for C. hominis. Socioeconomic status varies between geographical areas and this may lead to differences in reported incidence of disease. Higher social classes are more likely to undertake foreign travel [8] and use swimming pools [9]; known risk factors for cryptosporidiosis, especially C. hominis. Conversely, individuals of higher social class are more likely to consume fresh fruit and vegetables [10], known to be negatively associated with cryptosporidiosis [2]. Agricultural workers are more likely to be exposed to Cryptosporidium [11] and their distribution varies spatially. Individuals with poor access to healthcare are less likely to seek treatment [12] and this may vary geographically or socially. Water supplies are a well-known risk factor for cryptosporidiosis [13] and the raw water quality and the efficiency and efficacy of treatment systems vary. Biofilms, organic coatings in water distribution systems, may play a role in prolonging the presence of Cryptosporidium in water distribution systems [14]. The probability of biofilm build-up is likely to be higher in long water distribution systems.
Methods
Species specific cryptosporidiosis incidence data were obtained from the UK Cryptosporidium reference unit for the 4 year period from January 2000, collected as part of the national collection of Cryptosporidium oocysts [15]. These data represent just under half of all the cases reported to national surveillance in England and Wales over that period (Chalmers et al. in preparation). From the original dataset of 8075 cases, 3343 were analysed once a number of records were removed from the analysis. All cases where the patient had reported recent foreign travel (22%) were removed from the sample as the infection may have been acquired overseas. These data were based upon information collected by laboratories from the faecal sample request form that was filled in by the patient or physician. It is likely, based on information from previous work, that travel information is under-reported. Further cases were removed due to gaps in the geographical coverage of the independent variables (5%) and the inability to identify the species using PCR-based methods (2%).
However, the main reason for the removal of cases were records which, had incomplete postcodes (59%) meaning that it was impossible to identify an accurate residential location. It is important to consider whether this omission may be correlated with any of our independent variables and hence introduce bias into the results. One source of missing postcodes is transcription omissions at individual laboratories but because these are likely to affect samples in a random manner they are unlikely to be correlated with any independent variables. The second major source is omissions by general practitioners due to them having inaccurate patient records or failing to include postcodes when submitting samples to laboratories for analysis. There is no evidence as to whether this varies systematically in a manner, which would bias the results of this research.
For each case one comparator control postcode was randomly selected from within the service area of the laboratory to which the case stool sample was sent. The process was weighted to account for differences in population between postcodes. Control postcodes were selected from within the same laboratory service area as the case postcode because different laboratories have different selection criteria and screening protocols for cryptosporidiosis [16] and so differences in incidence between laboratory service areas may be artefactual. Service areas were generated by assuming that each sample was sent to its nearest laboratory (based on shortest road distance). This resulted in 135 service areas across England and Wales and the method was validated successfully by comparing the postcodes of the cases, to the laboratory to which their sample was sent. This indicated that 87% of cases were assigned to the correct laboratory. These service areas varied in size from 106 km2 to 3,770 km2 with an average size of 1,142 km2. They were consequently large enough to contain a variety of different drinking water sources and encompass a range of agricultural landscapes and socioeconomic characteristics.
Our control postcodes represent areas where no cryptosporidiosis was reported. However, due to issues of under reporting [17] and asymptomatic infection [18, 19] some of the control postcodes may have contained affected individuals. However, the reported rate of cryptosporidiosis in England and Wales is only 9 per 100,000 per year and so even taking into account under reporting and asymptomatic infection, the probability of a control postcode containing cases remains extremely low. Furthermore, such a bias would only tend our results to the null hypothesis.
For each case postcode and control postcode explanatory variables were derived using GIS (ArcGIS9.1). A measure of the degree of rurality was obtained using the Rural and Urban Area Classification [20]. This groups census areas into one of eight categories ranging from “urban areas” to “hamlets and isolated dwellings in sparse surroundings”. A buffer zone of 2.5 km was created around the centre of each postcode and within these areas the GIS was used to extract estimates of the total amount of Cryptosporidium applied to land through animal manures. These were based upon a 1 km2 map of manure applications developed by the Agricultural Land Advisory Service [21]. This was created by combining information from the agricultural census with land use data, information from animal excreta and manure management surveys and estimates of oocyst concentrations in manure [22]. The estimates will consist of C. parvum and other species/genotypes common in animals, which are unlikely to include C. hominis.
A range of socioeconomic variables were obtained by identifying the 2001 Output Area within which each postcode was located. Output areas are the smallest area for which census data are released and contain approximately 125 individuals. Within each Output Area the percentage of people in each of the eight socio-economic status bands were identified. These ranged from higher managerial and professional occupations to those in routine occupations. Additionally, the proportion of the population aged 0–4 years and the percentage employed in agriculture was obtained. Finally a measure of health care accessibility was derived by calculating the travel time from each postcode to the nearest GP.
Individuals receive their water from one or more water treatment works, which abstract their water from one or more sources and are subject to different forms of water treatment. Consequently, a number of volume-weighted measures were derived to describe the water supply of each case and control postcode. Information on the proportion of water supplied from different sources (surface vs. groundwater) and subject to different treatments (e.g. membrane filtration, simple disinfection) was obtained from the England and Wales Drinking Water Inspectorate. These data included any changes in treatment that occurred during the 2000–2003 period. Information could only be obtained for public water supplies. For each surface or groundwater abstraction, catchments were calculated using the GIS and the density of Cryptosporidium applications to land, sewage discharges and sewage overflows in each were calculated. The probability of biofilm build-up was simulated by calculating the volume weighted, average straight line distance between each postcode and the water treatment works supplying their water.
In total over 50 explanatory variables were produced. Multivariable models were then constructed using conditional logistic regression for all Cryptosporidium infections followed by separate models for C. hominis and C. parvum. A forward regression technique was performed by adding the most significant variable in turn. Collinearity was avoided by ensuring that the addition of each variable did not lead to significant changes in the coefficients or significance of any other variables in the model. Standardised forms of the independent variables were entered into the models. Results are presented in terms of Odds Ratio (OR) estimates and the 95% Confidence Interval (CI). The statistical analysis was undertaken using STATA/SE 8.2.
Much of our understanding of the aetiology of cryptosporidiosis is obtained from an analysis of reported outbreaks. However, these only represent about 8% of all cases reported to national surveillance. In order to examine whether outbreak cases were significantly influencing the results presented in this paper all our models were fitted with and without outbreak cases. Cases associated with a particular outbreak are identified within the national collection of Cryptosporidium oocysts used for this study from records held by local Health Protection Teams or Units. The omission of outbreak cases had a minimal impact upon the results implying that outbreak cases are not significantly influencing the results presented. Consequently, the models presented in this paper are inclusive of all cases.
Results
The results for all Cryptosporidium infections are shown in Table 1. These indicate that living in an area with higher amounts of Cryptosporidium applied to land in a 2.5 km buffer around each postcode (OR 1.084 P = 0.022), larger proportions of individuals in the 0–4 years age group (OR 1.145 P < 0.001) and more individuals in the highest socioeconomic status groups (OR 1.203 P < 0.001) were all positively associated with risk. Drinking water subject to superior water treatment (OR 0.770 P < 0.001) and groundwater sourced drinking water (OR 0.821 P = 0.001) were negatively associated with risk. Once these two drinking water variables were included in the model the proportion of the water supply which was from non superiorly treated surface water sources was negatively associated with risk (OR 0.869 P = 0.019).
Table 1Multivariate model for all Cryptosporidium infectionVariableOR95% CIP valueStandardised quantity of Cryptosporidium applied to land in a 2.5 km buffer1.0841.012–1.1630.022Standardised proportion of population in ages 0–41.1451.090–1.203<0.001Standardised proportion of population in highest socioeconomic status groups (1–4)a1.2031.140–1.270<0.001Standardised proportion of water supply that is subject to superior treatmentb0.7700.679–0.874<0.001 Standardised proportion of water supply that is groundwater0.8210.729–0.9250.001Standardised proportion of water supply from non superiorly treated surface water0.8690.772–0.9770.019 R2 = 2.23%, estimated from 3368 case and 3368 control postcodesaWorking in managerial and professional occupations, intermediate occupations and small employers and own account workersbDefined as water treated with coagulation and flocculation, slow sand filtration, cartridge or membrane filtration
The dataset was then split into C. parvum and C. hominis. The results for C. parvum are shown in Table 2 and indicate that living in an urban area was negatively associated with risk (OR 0.852 P < 0.001). Living in an area with higher amounts of Cryptosporidium applied to land in a 2.5 km buffer around each postcode (OR 1.167 P < 0.005), larger proportions of individuals in the 0–4 age group (OR 1.094 P = 0.018) and more individuals in the highest socioeconomic status groups (OR 1.109 P = 0.010) were all positively associated with risk. Drinking water subject to superior water treatment (OR 0.738 P < 0.001) and groundwater sourced drinking water (OR 0.679 P < 0.001) were negatively associated with risk. Once these two drinking water variables were controlled for two further variables became significant. An interaction between the proportion of groundwater supply and the amount of Cryptosporidium applied to land in the catchment (OR 1.289 P < 0.001) was positively associated with risk. The proportion of the water supply which was not superiorly treated, from a surface water source with a high amount of Cryptosporidium applied to land in the catchment was negatively associated with risk (OR 0.846 P = 0.009).
Table 2Multivariate model for Cryptosporidium parvum infectionVariableOR95% CIP valueStandardised urban area0.8520.779–0.932<0.001Standardised quantity of Cryptosporidium applied to land in a 2.5 km buffer1.1671.047–1.2300.005Standardised proportion of population in ages 0–41.094 1.015–1.1790.018Standardised proportion of population in highest socioeconomic status groups (1–4)a1.109 1.025–1.2000.010 Standardised proportion of water supply that is subject to superior treatmentb0.738 0.646–0.842<0.001 Standardised proportion of water supply that is groundwater0.679 0.554–0.833<0.001Standardised interaction between the groundwater proportion and the C. parvum density in catchment1.289 1.088–1.527<0.001Standardised proportion of water supply from non superiorly treated surface water with high C. parvum density in catchment0.846 0.746–0.9590.009 R2 = 4.24%, estimated from 1,623 case and 1,623 control postcodesaWorking in managerial and professional occupations, intermediate occupations and small employers and own account workersbDefined as water treated with coagulation and flocculation, slow sand filtration, cartridge or membrane filtration
The results for C. hominis are presented in Table 3 and indicate positive associations with living in an urban area (OR 1.261 P < 0.001), an area with a high proportion of individuals from social classes 1–4 (OR 1.297 P < 0.001) and many individuals in the 0–4 age group (OR 1.189 P < 0.001).
Table 3Multivariate model for Cryptosporidiumhominis infectionVariablesOR95% CIP valueStandardised urban area1.2611.154–1.378<0.001Standardised proportion of population in highest socioeconomic status groups (1–4)a1.2971.201–1.401<0.001Standardised proportion of population in ages 0–41.1901.112–1.274<0.001R2 = 3.57, estimated from 1,720 case and 1,720 control postcodesaWorking in managerial and professional occupations, intermediate occupations and small employers and own account workers
Discussion
In the full model all Cryptosporidium illness was positively associated with the quantity of Cryptosporidium applied to land in a 2.5 km buffer around each postcode. This may be due to the increased probability of direct contact with the environmental reservoir of Cryptosporidium in animal manures. When the analysis was split into C. hominis and C. parvum, surrounding Cryptosporidium applications to land was only a significant risk factor for C. parvum. This is unsurprising as animal manures are unlikely to include C. hominis.
There were positive associations between cryptosporidiosis and higher social classes for all Cryptosporidium illness but when the individual species were examined the social class gradient was significantly stronger for C. hominis in comparison to C. parvum (OR 1.297 vs. 1.109). There are a number of explanations for this. If we consider C. parvum, then there is a large environmental reservoir and individuals of higher social classes are more likely to undertake recreational activities such as walking in the countryside [9] which may increase the chance of animal-to-human transmission. Additionally individuals of higher social classes are more likely to undertake foreign travel [8] a known risk factor for cryptosporidiosis but especially for C. hominis [4]. Foreign travel cases were removed from our analysis, though it is likely that some travel related cases remained within the dataset. Furthermore, the association with social class may indicate community transmission from travel cases to other individuals in the same social group with whom they are most likely to interact. This may be direct person-to-person transmission [2] or indirect transmission such as through swimming pools. It is known that recreational swimming is positively associated with higher social groups [9]. It is also worth highlighting that these positive associations with higher social classes are overriding other factors. Individuals of a higher social class are more likely to consume fresh fruit and vegetables [10], known to be negatively associated with cryptosporidiosis [2].
Living in an area with a higher proportion of individuals aged 0–4 years is a risk factor for all illness with Cryptosporidium and for C. parvum and C. hominis individually. It is known that cryptosporidiosis incidence is highest in this age group. However, the observation that the magnitude of effect is greater for C. hominis than for C. parvum (OR 1.190 vs. 1.094) is surprising as previous studies have shown a greater proportion of C. parvum (33%) occur in children in the 0–4 age group compared to C. hominis (20%) [2]. Toileting contact with a child under 5 years, even in the absence of symptoms, has been shown to be a risk factor for C. hominis but not C. parvum (2). The authors of this study suggested that asymptomatic carriage in young children may be one of the main reservoirs of C. hominis.
The association with children in the 0–4 year age group was examined further by subdividing the C. parvum and C. hominis models into those cases where the affected individual was 4 years or younger and those where the individual was 5 years and older. To ensure comparability, identical explanatory variables were fitted to each sub model as in the original models. The resulting models were then analysed for major shifts in the significance of the explanatory variables. The only major difference for C. parvum was that the percentage of individuals aged 0–4 in the area was not significant (P = 0.9) in the model for those 5 years and older. This suggests that young children are not a risk factor for C. parvum illness in older individuals. In contrast the percentage aged 0–4 was a significant risk factor in both young and old for C. hominis, highlighting the importance of person-to-person transmission between young and older individuals. In the 4 years and younger model, living in an urban area was an insignificant risk factor (P = 0.7) for C. hominis.
All illness with Cryptosporidium showed negative associations with areas having superior water treatment, and negative associations with areas supplied by large amounts of groundwater. When the models were split by species, this effect was observed for only C. parvum. Although there have been outbreaks of C. hominis associated with sewage contamination of drinking water, C. parvum dominates in the environment and so the lack of association between C. hominis and drinking water is unsurprising.
Once water source and treatment were included in the model, further variables became significant in the overall model and in the C. parvum model. These need to be interpreted with caution as they will be greatly impacted by any measurement error in our source and treatment drinking water variables. In the overall model the proportion of poorly treated water supplied from surface water sources was negatively associated with risk. In the C. parvum model a similar variable became significant namely the proportion of poorly treated water supplied from surface water sources from catchments with high amounts of Cryptosporidium applications to land. Both these associations indicate reductions in risk from the drinking water supplies most at risk of Cryptosporidium contamination. This may highlight immunity in a population periodically exposed to Cryptosporidium [19]. Alternatively new Cryptosporidium regulations to improve water supplies have led to improved water treatment at sites most at risk of Cryptosporidium contamination [23] and these improvements have been incorporated into our water supply database. Consequently the negative association with the most at risk sites may be evidence of the new regulations leading to the improved monitoring and attention being paid to these most at risk plants [23].
In the C. parvum model a positive association was observed in the interaction variable between the proportion of groundwater sourced drinking water and the quantity of Cryptosporidium applied to land in the groundwater catchment. This highlights that, although generally of lower risk, if there is a high density of Cryptosporidium in the groundwater catchment then these can be a risk factor for illness with C. parvum.
The links between cryptosporidiosis and drinking water are noteworthy as this period covers the new drinking water regulations to control Cryptosporidium implemented in 2000 [23]. Since this time a reduction in cryptosporidiosis has been reported, especially the reduction in the size of the spring peak nationally [24]. Consequently, although the regulations appear to have had success in reducing illness, drinking water remains a significant risk factor for cryptosporidiosis in England and Wales.
The urban–rural gradient was not a significant variable in the full model. However, in the C. parvum model negative associations were found with urban areas. It is important to note that these associations were independent of any impacts relating to water supply and Cryptosporidium applications to land, which we would expect to be correlated with urban areas. It may be that other factors associated with living in urban areas are important. It may indicate that these individuals are less likely to interact with their surroundings reducing the probability of C. parvum infection. An alternative explanation is that our water treatment variables reflect the supply pattern in 2003. In earlier years it may be that the association with land use is accounting for the greater uncertainty in the water treatment variables. This is plausible as rural water supplies are likely to be at greater risk of C. parvum contamination from agricultural and possibly wildlife sources. This argument is strengthened by the observation that when the data were stratified into 2000–2001 and 2002–2003 the land use variable was only significant in the earlier period.
For C. hominis a positive association with urban areas was observed. This may be due to increased opportunities for person-to-person transmission in these areas due to factors such as more nursing homes, day care centres and enhanced availability of swimming pools. The association between increased risk of a person-to person-transmitted disease and increased population density is in line with that predicted by epidemiological theory [25]. In the model of all illness with Cryptosporidium the urban–rural density is not a significant variable implying that the different relationships observed for C. parvum and C. hominis are cancelling each other out in the full model. The observation of different associations with urban areas for C. parvum and C. hominis is also noteworthy because of our earlier concern about omissions of postcodes by general practitioner and whether this could bias the study results. It could be suggested that postcode completeness varies between general practitioners in urban and non-urban areas. However, the models for C. parvum and C. hominis show different signs for the urban area variable. This implies that any bias due to differential general practitioner completeness of postcode information between urban and non-urban areas is likely to be minimal.
In this study no associations were found between cryptosporidiosis and the proxy variable for biofilm build-up, accessibility to healthcare or the proportion of the population employed in agriculture. This suggests that these variables are not of major importance to cryptosporidiosis aetiology or that our methodology was unable to create effective measures of them.
In conclusion, this is the first study to examine the influence of the wider environment upon illness with Cryptosporidium and of the two species C. hominis and C. parvum separately. It has shown separate aetiologies for each species highlighting the importance of separating C. hominis and C. parvum in epidemiological studies. For the first time it has also demonstrated all illness with Cryptosporidium to be higher in areas populated by higher social classes and this effect is greater for illness with C. hominis. It also supports studies indicating the importance of young children to illness with both Cryptosporidium species. This is the first study to highlight the importance of the agriculture surrounding place of residence as a risk factor for C. parvum illness. Elevated incidence of C. parvum illness was found in rural areas and elevated levels of C. hominis illness in urban areas, effects that were independent of water supply and agricultural factors. The analysis highlighted that, in spite of new regulations to reduce Cryptosporidium concentrations in drinking water, water supply remained a risk factor for illness with C. parvum. Risk was lowered in areas with superior water treatment and in areas supplied by groundwater. There was also evidence that, in certain circumstances, groundwater could be a risk factor for C. parvum illness. Finally there were indications of a lowering of risk of C. parvum illness in the most at risk water supplies, an impact that was independent of superior treatment. This may be evidence of population immunity or effective regulation governing water treatment and monitoring.

cryptosporidiosis
cryptosporidium
water
social class
agriculture
environment and public health