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1.
Hennie G Raterman Alexandre E Voskuyl Ben AC Dijkmans Michael T Nurmohamed 《Arthritis research & therapy》2009,11(5):413-2
With great interest, we read the article by Toms and colleagues [1] in the previous issue of Arthritis Research & Therapy, in which they assessed prevalences of metabolic syndrome (MetS) in rheumatoid arthritis (RA) patients. Moreover, they identified demographic and clinical factors that may be associated with MetS. Toms and colleagues found prevalences of up to 45% of MetS and demonstrated older age and health status (health assessment questionnaire) to be associated with MetS irrespectively of the definition used. Of most interest, an association between methotrexate (MTX) use and decreased presence of MetS was observed in patients more than 60 years of age. The investigators hypothesized that this may be attributed to a drug-specific effect (and not to an anti-inflammatory effect) either by changing levels of adenosine, which is known to interact with glucose and lipid metabolism, or by an indirect effect mediated through concomitant folic acid administration, thereby decreasing homocysteine levels.Recently, we also examined the prevalence of MetS in (a subgroup of) RA patients in the CARRÉ investigation, a prospective cohort study on prevalent and incident cardiovascular disease and its underlying cardiovascular risk factors [2]. The findings of Toms and colleagues stimulated us to perform additional analyses in our total study population (n = 353).The prevalences of MetS were 35% and 25% (Table (Table1)1) according to criteria of National Cholesterol Education Program (NCEP) 2004 and NCEP 2001, respectively. In multivariate backward regression analyses, we found significant associations between body mass index, pulse rate, creatinine levels, hypothyroidism and diabetes mellitus and the presence of MetS independently of the criteria used (Table (Table2).2). However, an independent association between single use of MTX or use of MTX in combination with other disease-modifying antirheumatic drugs, on the one hand, and a decreased prevalence of MetS, on the other hand, could not be demonstrated (even in the subgroup of patients over the age of 60).
Open in a separate windowaMetabolic syndrome (MetS) according to National Cholesterol Education Program (NCEP) 2001; bMetS according to NCEP 2004. Continuous variables are presented as means (± standard deviations) in cases of normal distribution or as medians (interquartile ranges) in cases of non-normal distribution. BMI, body mass index; CRP, C-reactive protein; DAS28, disease activity score using 28 joint counts; DMARD, disease-modifying antirheumatic drug; ESR, erythrocyte sedimentation rate; HCQ, hydroxychloroquine; MTX, methotrexate; RA, rheumatoid arthritis; SSZ, sulfasalazine; TNF, tumour necrosis factor.
Open in a separate windowaIn multivariate analyses, the following variables were used: gender, age, prednisolone only, methotrexate only, sulfasalazine only, hydroxychloroquine only, tumour necrosis factor-blocking agents, combination of disease-modifying antirheumatic drugs, pack-years, smoking, erosions, DAS28 (disease activity score using 28 joint counts), body mass index, pulse rate, creatinine levels, renal clearance, hypothyroidism and diabetes mellitus. CI, confidence interval; OR, odds ratio.Therefore, to get more support for a drug-specific effect, it is of interest to know whether or not in the study of Toms and colleagues the MTX effect was present only in the group of RA patients with single use of MTX or in the group of MTX-treated patients with other antirheumatic drugs. As patients with MetS were significantly older, it would give further information whether age was an independent risk factor for MetS in regression analyses. Moreover, as readers, we are not informed about comorbidities like diabetes and clinical hypothyroidism, which are notorious cardiometabolic risk factors. On the whole, we could not confirm a plausible protective role for the use of MTX and presence of MetS, and hence further investigation is required to explain the discrepancy between our findings and those of Toms and colleagues. 相似文献
Table 1
Characteristics of the study populationMetS presenta | MetS absenta | MetS presentb | MetS absentb | |||
---|---|---|---|---|---|---|
n = 84 | n = 265 | n = 121 | n = 228 | P valuea | P valueb | |
Demographics | ||||||
Age, years | 63.8 (± 8) | 63.1 (± 7) | 64.3 (± 8) | 62.7 (± 7) | 0.46 | 0.045 |
Female, percentage | 76 | 63 | 74 | 62 | 0.022 | 0.028 |
RA-related characteristics | ||||||
DAS28 | 4.2 (± 1.3) | 3.9 (± 1.4) | 4.1 (± 1.3) | 3.8 (± 1.4) | 0.21 | 0.062 |
ESR, mm/hour | 22 (10-35) | 16 (9-30) | 20 (10-34) | 17 (9-31) | 0.059 | 0.33 |
CRP, mg/L | 11 (4-21) | 6 (3-16) | 8 (3-18) | 6 (3-19) | 0.021 | 0.46 |
RA duration, years | 7 (4-10) | 7 (4-10) | 7 (4-10) | 7 (5-10) | 0.83 | 0.19 |
Erosion, percentage | 77 | 83 | 79 | 83 | 0.20 | 0.36 |
Number of DMARDs | 1 (1-2) | 1 (1-1) | 1 (1-2) | 1 (1-1) | 0.26 | 0.43 |
MTX current, percentage | 62 | 60 | 63 | 59 | 0.71 | 0.46 |
MTX only, percentage | 39 | 39 | 41 | 38 | 0.95 | 0.67 |
SSZ only, percentage | 8 | 13 | 9 | 14 | 0.23 | 0.22 |
HCQ only, percentage | 1 | 4 | 3 | 4 | 0.31 | 0.55 |
Combination of DMARDs, percentage | 31 | 25 | 29 | 25 | 0.24 | 0.38 |
TNF-blocking agent, percentage | 11 | 9 | 11 | 9 | 0.73 | 0.65 |
Prednisolone only, percentage | 1 | 2 | 3 | 1 | 1.00 | 0.42 |
Cardiovascular risk factors | ||||||
Current smoker, percentage | 26 | 31 | 25 | 32 | 0.42 | 0.15 |
Pack-years, years | 17 (0-34) | 19 (2-38) | 19 (0-35) | 18 (2-38) | 0.23 | 0.75 |
BMI, kg/m2 | 30 (± 4) | 26 (± 5) | 29 (± 4) | 25 (± 5) | < 0.001 | < 0.001 |
Creatinine, μmol/L | 89 (± 21) | 89 (± 16) | 91 (± 22) | 87 (± 14) | 0.99 | 0.070 |
Renal clearance, mL/minute | 81 (± 24) | 72 (± 19) | 77 (± 23) | 73 (± 19) | 0.003 | 0.062 |
Pulse, beats per minute | 76 (± 11) | 73 (± 9) | 75 (± 11) | 73 (± 9) | 0.005 | 0.015 |
Diabetes mellitus, percentage | 14 | 3 | 12 | 3 | < 0.001 | 0.001 |
Hypothyroidism, percentage | 12 | 2 | 9 | 2 | 0.001 | 0.003 |
Table 2
Variables associated with metabolic syndromeUnivariate | Multivariatea | |||||
---|---|---|---|---|---|---|
OR | 95% CI | P value | OR | 95% CI | P value | |
Body mass index | 1.2 | 1.1-1.3 | < 0.001 | 1.2 | 1.1-1.3 | < 0.001 |
Pulse | 1.03 | 1.01-1.06 | 0.011 | 1.03 | 1.00-1.06 | 0.020 |
Creatinine | 1.01 | 1.00-1.02 | 0.080 | 1.02 | 1.00-1.03 | 0.017 |
Hypothyroidism | 4.5 | 1.5-13.2 | 0.007 | 4.7 | 1.5-15.0 | 0.009 |
Diabetes mellitus | 4.8 | 1.8-12.9 | 0.002 | 4.5 | 1.4-15.2 | 0.014 |
2.
Camilla L. Nesb? Rajkumari Kumaraswamy Marlena Dlutek W. Ford Doolittle Julia Foght 《Applied and environmental microbiology》2010,76(14):4896-4900
All cultivated Thermotogales are thermophiles or hyperthermophiles. However, optimized 16S rRNA primers successfully amplified Thermotogales sequences from temperate hydrocarbon-impacted sites, mesothermic oil reservoirs, and enrichment cultures incubated at <46°C. We conclude that distinct Thermotogales lineages commonly inhabit low-temperature environments but may be underreported, likely due to “universal” 16S rRNA gene primer bias.Thermotogales, a bacterial group in which all cultivated members are anaerobic thermophiles or hyperthermophiles (5), are rarely detected in anoxic mesothermic environments, yet their presence in corresponding enrichment cultures, bioreactors, and fermentors has been observed using metagenomic methods and 16S rRNA gene amplification (6) (see Table S1 in the supplemental material). The most commonly detected lineage is informally designated here “mesotoga M1” (see Table S1 in the supplemental material). PCR experiments indicated that mesotoga M1 sequences amplified inconsistently using “universal” 16S rRNA gene primers, perhaps explaining their poor detection in DNA isolated from environmental samples (see text and Table S2 in the supplemental material). We therefore designed three 16S rRNA PCR primer sets (Table (Table1)1) targeting mesotoga M1 bacteria and their closest cultivated relative, Kosmotoga olearia. Primer set A was the most successful set, detecting a wider diversity of Thermotogales sequences than set B and being more Thermotogales-specific than primer set C (Table (Table22).
Open in a separate windowaHeterogeneity hot spots identified in reference 1.
Open in a separate windowaSee the supplemental material for site and methodological details. NA, not applicable; ND, not determined.bThe number of OTUs observed at a 0.01 distance cutoff is given for each primer set. The numbers of clones with Thermotogales sequences are in parentheses. —, PCR was attempted but no Thermotogales sequences were obtained or the PCR consistently failed.c+, sequence(s) detected; −, not detected. For more information on the enrichments, see the text and Table S3 in the supplemental material.dFrom April to May 2004, the temperature at the depth where the sample was taken was 12°C (7).eThere were no water samples from DWH and HSAT available for enrichment cultures, and no DNA was available from HWH.fThis reservoir has been treated with biocides; moreover, at this site, the water is filtered before being reinjected into the reservoir.gTemperatures of the oil pool where the water sample was obtained. The HSAT facility receives water from two oil pools, one at 41°C and one at 50°C.hWe screened DNA from samples taken in 2006 and 2008 but detected the same sequences in both, so sequences from the two samples were pooled.iThe mesotoga M1 and Kosmotoga sequences from DWH and DF were >99% similar and were assembled into one sequence in Fig. Fig.11.jThis reservoir has been injected with water from a neighboring oil reservoir.Since the putative mesophilic Thermotogales have been overwhelmingly associated with polluted and hydrocarbon-impacted environments and mesothermic oil reservoirs are the only natural environments where mesotoga M1 sequences previously were detected (see Table S1 in the supplemental material), we selected four oil reservoirs with in situ temperatures of 14°C to 53°C and two temperate, chronically hydrocarbon-impacted sites for analysis (Table (Table2).2). Total community DNA was extracted, the 16S rRNA genes were amplified, cloned, and sequenced as described in the supplemental material. 相似文献
TABLE 1.
Primers targeting mesotoga M1 bacteria constructed and used in this studyPrimer | Sequence (5′ to 3′) | Position in mesotoga 16S rRNA gene | No. of heterogeneity hot spotsa | Potential primer match in other Thermotogales lineages |
---|---|---|---|---|
Primer set A | 1 (helix 17) | |||
NMes16S.286F | CGGCCACAAGGAYACTGAGA | 286 | Perfect match in Kosmotoga olearia. The last 7 or 8 nucleotides at the 3′ end are conserved in other Thermotogales lineages. | |
NMes16S.786R | TGAACATCGTTTAGGGCCAG | 786 | One 5′ mismatch in Kosmotoga olearia and Petrotoga mobilis; 2-4 internal and 5′ mismatches in other lineages | |
Primer set B | None | |||
BaltD.42F | ATCACTGGGCGTAAAGGGAG | 540 | Perfect match in Kosmotoga olearia; one or two 3′ mismatches in most other Thermotogales lineages | |
BaltD.494R | GTGGTCGTTCCTCTTTCAAT | 992 | No match in other Thermotogaleslineages. The primer is located in heterogeneity hot spot helices 33 and 34. This primer also fails to amplify some mesotoga M1 sequences. | |
Primer set C | 9 (all 9 regions) | |||
TSSU-3F | TATGGAGGGTTTGATCCTGG | 3 | Perfect match in Thermotoga spp., Kosmotoga olearia, and Petrotoga mobilis; two or three 5′ mismatches in other Thermotogales lineages; one 5′ mismatch to mesotoga M1 16S rRNA genes | |
Mes16S.R | ACCAACTCGGGTGGCTTGAC | 1390 | One 5′ mismatch in Kosmotoga olearia; 1-3 internal or 5′ mismatches in other Thermotogales lineages |
TABLE 2.
Mesotoga clade sequences detected in environmental samples and enrichment cultures screened in this studyaSite (abbreviation) | Temp in situ(°C) | Waterflooded | Environmental samplesb | Enrichment cultures | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Primer set A | Primer set B | Primer set C | Thermotogalesdetected by primer setc: | Lineage(s) detected | ||||||||
No. of OTUs (no. of clones) | Lineage | No. of OTUs (no. of clones) | Lineage | No. of OTUs (no. of clones) | Lineage | A | B | C | ||||
Sidney Tar Ponds sediment (TAR) | Temperate | NA | 1 (5) | M1 | 1 | M1 | — | — | + | + | + | M1, M2, M5 |
Oil sands settling basin tailings (05mlsb) | ∼12d | NA | — | — | 1 (6) | M1 | — | — | − | + | − | M1 |
Grosmont A produced water (GrosA) | 20 | No | 1 (15) | M1 | 1 (22) | M1 | 2 (14) | M1 | + | + | + | M1 |
Foster Creek produced water (FC) | 14 | No | 1 (21) | M1 | 1 (23) | M1 | 1 (1) | M1 | + | ND | − | M1 |
Oil field D wellhead water (DWH)e,f | 52-53g | Yes | 1 (14) | Kosmotogai | 1 (6) | M1i | 1 (1) | Kosmotogai | NA | NA | NA | NA |
Oil field D FWKO water (DF)f,h | 20-30 | Yes | 1 (45) | Kosmotogai | 1 (17) | M1i | — | — | + | + | − | M1, Kosmotoga, Petrotoga |
Oil field H FWKO water (HF)j | 30-32 | Yes | 7 (59) | M1, M2, M3, M4, Kosmotoga | 1 (29) | M1 | — | — | + | + | − | M1, Petrotoga |
Oil field H satellite water (HSAT)e,j | 41 and 50g | Yes | 1 (8) | M1 | — | — | 2 (16) | Kosmotoga, Thermotoga | NA | NA | NA | NA |
Oil field H wellhead water (HWH)e,j | 41 and 50g | Yes | NA | — | — | NA | NA | NA | + | + | + | M1, Petrotoga |
3.
4.
One- and Two-Locus Population Models With Differential Viability Between Sexes: Parallels Between Haploid Parental Selection and Genomic Imprinting
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Alexey Yanchukov 《Genetics》2009,182(4):1117-1127
A model of genomic imprinting with complete inactivation of the imprinted allele is shown to be formally equivalent to the haploid model of parental selection. When single-locus dynamics are considered, an internal equilibrium is possible only if selection acts in the opposite directions in males and females. I study a two-locus version of the latter model, in which maternal and paternal effects are attributed to the single alleles at two different loci. A necessary condition for the allele frequency equilibria to remain on the linkage equilibrium surface is the multiplicative interaction between maternal and paternal fitness parameters. In this case the equilibrium dynamics are independent at both loci and results from the single-locus model apply. When fitness parameters are additive, analytic treatment was not possible but numerical simulations revealed that stable polymorphism characterized by association between loci is possible only in several special cases in which maternal and paternal fitness contributions are precisely balanced. As in the single-locus case, antagonistic selection in males and females is a necessary condition for the maintenance of polymorphism. I also show that the above two-locus results of the parental selection model are very sensitive to the inclusion of weak directional selection on the individual''s own genotypes.PARENTAL genetic effects refer to the influence of the mother''s and father''s genotypes on the phenotypes of their offspring, not attributable just to the transfer of genes. Examples have been documented across a wide range of areas of organism biology; see, for example, Wade (1998) and and22 in Rasanen and Kruuk (2007). Parental selection is a more formal concept used in theoretical modeling and concerns situations where the fitness of the offspring depends, besides other factors, on the genotypes of its parent(s) (generalizing from Kirkpatrick and Lande 1989).
Open in a separate windowParentheses in the first column indicate maternal genotype (parental selection model) or inactivation of the maternally derived allele (imprinting model). Whether selection occurs at the diploid (first column) or subsequent haploid (second column) stage does not change the resulting allele frequencies.
Open in a separate windowAnother well-known parent-of-origin phenomenon is genomic imprinting. Here, the level of expression of one of the alleles depends on which parent it is inherited from. Often it is difficult to tell apart the phenotypic patterns due to parental effects and genomic imprinting, and thus a problem arises in the process of identifying the candidate genes for such effects (Hager et al. 2008). Analytic methods (Weinberg et al. 1998; Santure and Spencer 2006; Hager et al. 2008) have been developed to quantify subtle differences between the two. In this article, I point out that a simple mathematical model, first suggested for genomic imprinting at a diploid locus, can be interpreted, without any formal changes, to describe parental selection on haploids.While there has been much progress in understanding the evolution of genomic imprinting (Hunter 2007), including advances in modeling (Spencer 2000, 2008), the population genetics theory of parental effects received less attention. Existing major-locus effect models of parental selection are single-locus, two-allele, and mostly concern uniparental (maternal) selection (Wright 1969; Spencer 2003; Gavrilets and Rice 2006; Santure and Spencer 2006), with only one specific case where the fitness effects of both parents interact studied by Gavrilets and Rice (2006). No attempt to extend this theory into multilocus systems has yet been made. Considering a two-locus model with both parents playing a role in selection on the offspring is called for by the observation that many maternal and paternal effects aim at the different traits or different life stages of their progeny. Among birds, for example, body condition soon after hatching is largely determined by the mother, while paternally transmitted sexual display traits develop much later in life (Price 1998). Such effects are therefore unlikely to be regulated within a single locus. Sometimes the effects are on the same trait, but still attributed to different loci: expression of gene Avy that causes the “agouti” phenotype (yellow fur coat and obesity) in mice is enhanced by maternal epigenetic modification (Rakyan et al. 2003), while paternal mutations at the other locus, MommeD4, contribute to a reverse phenotypic pattern in the offspring (Rakyan et al. 2003). The epigenetic state of the murine AxinFu allele is both maternally and paternally inherited (Rakyan et al. 2003).Focusing selection on haploids reduces the number of genotypes that need to be taken into account, while preserving the main properties of the multilocus system. Genes with haploid expression and a potential of parental effects can be found in two major taxonomic kingdoms. A notable candidate is Spam1 in mice, which is expressed during spermogenesis and encodes a factor that enables sperm to penetrate the egg cumulus (Zheng et al. 2001). This gene remains a target for effectively haploid selection, because its product is not shared via cytoplasm bridges between developing spermatides. Mutations at Spam1 alter performance of the male gametes that carry it and might indirectly, perhaps by altering the timing of fertilization, affect the fitness of the zygote. The highest estimated number of mouse genes expressed in the male gametes is currently 2375 (Joseph and Kirkpatrick 2004), and one might expect some of them to have similar paternal effects. Plants go through a profound haploid stage in their life cycles, and genes involved at this stage have an inevitable effect on the fitness of the future generations. In angiosperms, seed development is known to be controlled by both maternal (Chaudhury and Berger 2001; Yadegari and Drews 2004) and paternal (Nowack et al. 2006) effect genes, expressed, respectively, in female and male gametophytes.Under haploid selection, there can be no overdominance, and thus polymorphism is much more difficult to maintain than in diploid selection models (summarized in Feldman 1971). Nevertheless, differential or antagonistic selection between sexes can lead to a new class of stable internal equilibria in the diploid systems (Owen 1953; Bodmer 1965; Mandel 1971; Kidwell et al. 1977; Reed 2007), and I make use of this property in the haploid models developed below. In the experiment by Chippindale and colleagues (Chippindale et al. 2001), ∼75% of the total fitness variation in the adult stage of Drosophila melanogaster was negatively correlated between males and females, which suggests that a substantial portion of the fruit fly expressed genome is under sexually antagonistic selection. I assume that the effect of either parent on the fitness of the individual depends on the sex of the latter, which in respect to modeling is equivalent to the assumption of differential viability between the sexes in the progeny of the same parent(s). Biological systems that satisfy the latter assumptions can be found among colonial green algae: many members of the order Volvocales are haploid except for the short zygotic stage, and during sexual reproduction, they are also dioecious and anisogametic. I return to this example in the discussion. The possibility that genes expressed in animal gametes may be under antagonistic selection between sexes has been discussed (Bernasconi et al. 2004). For example, a (hypothetical) mutation increasing the ATP production in mitochondria would be beneficial in sperm, because of the increased mobility of the latter, but neutral or detrimental in the egg, due to a higher level of oxidative damage to DNA (Zeh and Zeh 2007).My main purpose was to derive conditions for existence and stability of the internal equilibria of the model(s). I begin with a simple one-locus case, which can be analyzed explicitly, and show how these one-locus results can be extended to the case of two recombining loci with multiplicative fitness. Then, I assume an additive relation between the maternal and paternal effect parameters and study the special cases where parental effects are symmetric. 相似文献
TABLE 1
Frequencies of genotypes and fitness parameterizations in model 1Gametes/haploids | Frequency before selection | Fitness
| ||
---|---|---|---|---|
Zygote | Male | Female | ||
(A)A | A | pfpm | 1 − α | 1 − δ |
(A)a | 1/2 A 1/2 a | pf(1 − pm) | 1 | 1 |
(a)A | 1/2 a 1/2 A | (1 − pf)pm | 1 − α | 1 − δ |
(a)a | A | (1 − pf)(1 − pm) | 1 | 1 |
TABLE 2
Offspring genotypic proportions from different mating types, sorted among four phenotypic groups/combinations of maternal and paternal effects: model 2Offspring genotypes/phenotypes
| |||||||||
---|---|---|---|---|---|---|---|---|---|
Parental genotypes
| Paternal (φ = 1)
| Joint (φ = 4)
| |||||||
Male | Female | AB | Ab | aB | Ab | AB | Ab | aB | ab |
AB | AB | 1 | |||||||
Ab | |||||||||
aB | |||||||||
ab | (1−r)/2 | r/2 | r/2 | (1−r)/2 | |||||
Ab | AB | ||||||||
Ab | 1 | ||||||||
aB | r/2 | (1−r)/2 | (1−r)/2 | r/2 | |||||
ab | |||||||||
Offspring genotypes/phenotypes
| |||||||||
Parental genotypes
| Maternal (φ = 2)
| None (φ = 3)
| |||||||
Male | Female | AB | Ab | aB | Ab | AB | Ab | aB | ab |
aB | AB | ||||||||
Ab | r/2 | (1 − r)/2 | (1 − r)/2 | r/2 | |||||
aB | 1 | ||||||||
ab | |||||||||
ab | AB | (1 − r)/2 | (1 − r)/2 | ||||||
Ab | |||||||||
aB | |||||||||
ab | 1 |
5.
6.
7.
L. Pilloni P. Bianco C. Manieli G. Senes P. Coni L. Atzori N. Aste G. Faa 《European journal of histochemistry : EJH》2009,53(2)
Basal cell carcinoma (BCC) is a very common malignant skin tumor that rarely metastatizes, but is often locally aggressive. Several factors, like large size (more than 3 cm), exposure to ultraviolet rays, histological variants, level of infiltration and perineural or perivascular invasion, are associated with a more aggressive clinical course. These morphological features seem to be more determinant in mideface localized BCC, which frequently show a significantly higher recurrence rate. An immunohistochemical profile, characterized by reactivity of tumor cells for p53, Ki67 and alpha-SMA has been associated with a more aggressive behaviour in large BCCs. The aim of this study was to verify if also little (<3 cm) basal cell carcinomas can express immunohistochemical markers typical for an aggressive behaviour.Basal cell carcinoma (BCC) is a very common malignant skin tumor that rarely metastatizes, even If Is often locally aggressive. Several factors, like large size (more than 3 cm), face localization, exposure to ultraviolet rays, histological variants, infiltration level and perineural or perivascular invasion, are associated with a more aggressive clinical course. In particular, the incidence of metastasis and/or death correlates with tumors greater than 3 cm in diameter in which setting patients are said to have 1–2 % risk of metastases that increases to 20–25% in lesions greater than 5 cm and to 50% in lesions greater than 10 cm in diameter (Snow et al., 1994). Histologically morpheiform, keratotic types and infiltrative growth of BCC are also considered features of the most aggressive course (Crowson, 2006). This can be explained by the fact that both the superficial and nodular variants of BCC are surrounded by a continuous basement membrane zone comprising collagens type IV and V admixed with laminin, while the aggressive growth variants (i.e. morpheiform, metatypical, and infiltrative growth subtypes) manifest the absence of basement membrane (Barsky et al., 1987).The molecular markers which characterize aggressive BCC include: increased expression of stromolysin (MMP-3) and collagenase-1 (MMP-1) (Cribier et al., 2001), decreased expression of syndecan-1 proteoglycan (Bayer-Garner et al., 2000) and of anti-apoptotic protein bcl-2 (Ramdial et al., 2000; Staibano et al., 2001).C-ras , c-fos (Urabe et al., 1994; Van der Schroeff et al., 1990) and p53 tumor supressor gene mutations (Auepemikiate et al., 2002) are indicative of an aggressive course.Focusing upon bcl-2 and p53 expression in BCC, there have been numerous studies documenting the utility of bcl-2 as a marker of favourable clinical behaviour while p53 expression may be a feature of a more aggressive outcome (Ramdial et al., 2000; Staibano et al., 2001; Bozdogan et al., 2002).An increased expression of cytoskeletal microfilaments like α–smooth muscle actin, frequently found in invasive BCC subtypes (Jones JCR et al., 1989), may explain an enhanced tumor mobility and deep tissue invasion through the stroma. (Cristian et al., 2001; Law et al., 2003). The aim of this preliminary study was to verify if also little (<3 cm) basal cell carcinomas may express aggressive immunohistochemical markers like p53, Ki67 and alpha-SMA. We used 31 excisional BCCs with tumor size less than 2 cm (ranging from 2 up to 20 mm) and with different skin localization (19 in the face, 6 in the trunk and 6 in the body extremities). All cases were immunostained for p53, BCL2, Ki67 and alpha-smooth muscle actin (α-SMA) (Age Sex Location Hystotype Max.Dim Depth Ulc Ess Inf p53 Bcl-2 Ki67 AML 1 61 M Extr Keratotic 10×8 1 No +++ URD +++ + + - 2 61 M Face Adenoid 10×9 4 No + URD +++ - - - 3 64 M Extr Sup mult 11×13 0.8 No + DRD + - - - 4 73 M Face Nodular 10×8 2 Yes + DRD +++ + ++ +++ 5 84 M Face Nodular 9×12 2 Yes + DRD - - - - 6 84 M Face Adenoid 5 0.8 No + URD +++ - - - 7 84 M Extr Nodular 13×10 3 No + DRD +++ + + - 8 52 F Face Nodular 4 0.8 No + URD + + + - 9 76 F Face Adenoid 10×4 4 No + DRD +++ - ++ - 10 77 F Face Morph 8×6 1 Yes +++ DRD +++ - - - 11 86 M Face Morph 8 1 Yes + DRD +++ - + + 12 63 F Face Adenoid 4 1 No + URD ++ + + + 13 76 F Face Nodular 7 1.5 No + DRD +++ + ++ - 14 84 M Face Nodular 11 4 Yes +++ DRD + - - + 15 63 F Face Keratotic 10×6 1.8 No ++ DRD - + ++ - 16 68 F Trunk Sup mult 10×6 0.7 No ++ URD + + - - 17 67 M Face Sup mult 12×6 0.4 No + URD + - + - 18 67 M Extr Sup mult 4×3 0.3 No + URD + +++ + - 19 32 F Extr Sup mult 1×3 0.4 No + URD + + + - 20 45 M Trunk Nodular 7×5 2 Yes +++ URD + + + - 21 62 M Trunk Sup mult 11×7 0.9 No ++ URD - ++ - ++ 22 65 M Trunk Adenoid 7×6 1.5 No + URD +++ + + - 23 72 M Trunk Nodular 12×6 1 No + URD +++ - + + 24 86 F Face Keratotic 20×11 3.1 No ++ DRD + + + - 25 85 M Face Nodular 0.5 1.3 No ++ DRD ++ + + - 26 74 F Extr Nodular 4×4 0.9 No + URD - - + - 27 71 M Face Nodular 6×12 1.7 No + DRD - - + - 28 64 F Trunk Sup mult 1.3×1.5 0.4 No ++ URD +++ - - - 29 78 F Face Nodular 4×3 1.5 No ++ DRD ++ + - +++ 30 80 M Face Keratotic 4×4 1.6 Yes + DRD - - + +++