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1.
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).

Table 1

Characteristics of the study population
MetS presentaMetS absentaMetS presentbMetS absentb
n = 84n = 265n = 121n = 228P valueaP valueb
Demographics
 Age, years63.8 (± 8)63.1 (± 7)64.3 (± 8)62.7 (± 7)0.460.045
 Female, percentage766374620.0220.028
RA-related characteristics
 DAS284.2 (± 1.3)3.9 (± 1.4)4.1 (± 1.3)3.8 (± 1.4)0.210.062
 ESR, mm/hour22 (10-35)16 (9-30)20 (10-34)17 (9-31)0.0590.33
 CRP, mg/L11 (4-21)6 (3-16)8 (3-18)6 (3-19)0.0210.46
 RA duration, years7 (4-10)7 (4-10)7 (4-10)7 (5-10)0.830.19
 Erosion, percentage778379830.200.36
 Number of DMARDs1 (1-2)1 (1-1)1 (1-2)1 (1-1)0.260.43
 MTX current, percentage626063590.710.46
 MTX only, percentage393941380.950.67
 SSZ only, percentage8139140.230.22
 HCQ only, percentage14340.310.55
 Combination of DMARDs, percentage312529250.240.38
 TNF-blocking agent, percentage1191190.730.65
 Prednisolone only, percentage12311.000.42
Cardiovascular risk factors
 Current smoker, percentage263125320.420.15
 Pack-years, years17 (0-34)19 (2-38)19 (0-35)18 (2-38)0.230.75
 BMI, kg/m230 (± 4)26 (± 5)29 (± 4)25 (± 5)< 0.001< 0.001
 Creatinine, μmol/L89 (± 21)89 (± 16)91 (± 22)87 (± 14)0.990.070
 Renal clearance, mL/minute81 (± 24)72 (± 19)77 (± 23)73 (± 19)0.0030.062
 Pulse, beats per minute76 (± 11)73 (± 9)75 (± 11)73 (± 9)0.0050.015
 Diabetes mellitus, percentage143123< 0.0010.001
 Hypothyroidism, percentage122920.0010.003
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.

Table 2

Variables associated with metabolic syndrome
UnivariateMultivariatea


OR95% CIP valueOR95% CIP value
Body mass index1.21.1-1.3< 0.0011.21.1-1.3< 0.001
Pulse1.031.01-1.060.0111.031.00-1.060.020
Creatinine1.011.00-1.020.0801.021.00-1.030.017
Hypothyroidism4.51.5-13.20.0074.71.5-15.00.009
Diabetes mellitus4.81.8-12.90.0024.51.4-15.20.014
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   

2.
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).

TABLE 1.

Primers targeting mesotoga M1 bacteria constructed and used in this study
PrimerSequence (5′ to 3′)Position in mesotoga 16S rRNA geneNo. of heterogeneity hot spotsaPotential primer match in other Thermotogales lineages
Primer set A1 (helix 17)
    NMes16S.286FCGGCCACAAGGAYACTGAGA286Perfect match in Kosmotoga olearia. The last 7 or 8 nucleotides at the 3′ end are conserved in other Thermotogales lineages.
    NMes16S.786RTGAACATCGTTTAGGGCCAG786One 5′ mismatch in Kosmotoga olearia and Petrotoga mobilis; 2-4 internal and 5′ mismatches in other lineages
Primer set BNone
    BaltD.42FATCACTGGGCGTAAAGGGAG540Perfect match in Kosmotoga olearia; one or two 3′ mismatches in most other Thermotogales lineages
    BaltD.494RGTGGTCGTTCCTCTTTCAAT992No 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 C9 (all 9 regions)
    TSSU-3FTATGGAGGGTTTGATCCTGG3Perfect 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.RACCAACTCGGGTGGCTTGAC1390One 5′ mismatch in Kosmotoga olearia; 1-3 internal or 5′ mismatches in other Thermotogales lineages
Open in a separate windowaHeterogeneity hot spots identified in reference 1.

TABLE 2.

Mesotoga clade sequences detected in environmental samples and enrichment cultures screened in this studya
Site (abbreviation)Temp in situ(°C)WaterfloodedEnvironmental 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)LineageNo. of OTUs (no. of clones)LineageNo. of OTUs (no. of clones)LineageABC
Sidney Tar Ponds sediment (TAR)TemperateNA1 (5)M11M1+++M1, M2, M5
Oil sands settling basin tailings (05mlsb)∼12dNA1 (6)M1+M1
Grosmont A produced water (GrosA)20No1 (15)M11 (22)M12 (14)M1+++M1
Foster Creek produced water (FC)14No1 (21)M11 (23)M11 (1)M1+NDM1
Oil field D wellhead water (DWH)e,f52-53gYes1 (14)Kosmotogai1 (6)M1i1 (1)KosmotogaiNANANANA
Oil field D FWKO water (DF)f,h20-30Yes1 (45)Kosmotogai1 (17)M1i++M1, Kosmotoga, Petrotoga
Oil field H FWKO water (HF)j30-32Yes7 (59)M1, M2, M3, M4, Kosmotoga1 (29)M1++M1, Petrotoga
Oil field H satellite water (HSAT)e,j41 and 50gYes1 (8)M12 (16)Kosmotoga, ThermotogaNANANANA
Oil field H wellhead water (HWH)e,j41 and 50gYesNANANANA+++M1, Petrotoga
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   

3.
4.
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).

TABLE 1

Frequencies of genotypes and fitness parameterizations in model 1
Gametes/haploidsFrequency before selectionFitness
ZygoteMaleFemale
(A)AApfpm1 − α1 − δ
(A)a1/2 A 1/2 apf(1 − pm)11
(a)A1/2 a 1/2 A(1 − pf)pm1 − α1 − δ
(a)aA(1 − pf)(1 − pm)11
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.

TABLE 2

Offspring genotypic proportions from different mating types, sorted among four phenotypic groups/combinations of maternal and paternal effects: model 2
Offspring genotypes/phenotypes
Parental genotypes
Paternal (φ = 1)
Joint (φ = 4)
MaleFemaleABAbaBAbABAbaBab
ABAB1
Ab
aB
ab(1−r)/2r/2r/2(1−r)/2
AbAB
Ab1
aBr/2(1−r)/2(1−r)/2r/2
ab
Offspring genotypes/phenotypes
Parental genotypes
Maternal (φ = 2)
None (φ = 3)
MaleFemaleABAbaBAbABAbaBab
aBAB
Abr/2(1 − r)/2(1 − r)/2r/2
aB1
ab
abAB(1 − r)/2(1 − r)/2
Ab
aB
ab1
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   

5.
6.
7.
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) (
AgeSexLocationHystotypeMax.DimDepthUlcEssInfp53Bcl-2Ki67AML
161MExtrKeratotic10×81No+++URD+++++-
261MFaceAdenoid10×94No+URD+++---
364MExtrSup mult11×130.8No+DRD+---
473MFaceNodular10×82Yes+DRD+++++++++
584MFaceNodular9×122Yes+DRD----
684MFaceAdenoid50.8No+URD+++---
784MExtrNodular13×103No+DRD+++++-
852FFaceNodular40.8No+URD+++-
976FFaceAdenoid10×44No+DRD+++-++-
1077FFaceMorph8×61Yes+++DRD+++---
1186MFaceMorph81Yes+DRD+++-++
1263FFaceAdenoid41No+URD+++++
1376FFaceNodular71.5No+DRD++++++-
1484MFaceNodular114Yes+++DRD+--+
1563FFaceKeratotic10×61.8No++DRD-+++-
1668FTrunkSup mult10×60.7No++URD++--
1767MFaceSup mult12×60.4No+URD+-+-
1867MExtrSup mult4×30.3No+URD+++++-
1932FExtrSup mult1×30.4No+URD+++-
2045MTrunkNodular7×52Yes+++URD+++-
2162MTrunkSup mult11×70.9No++URD-++-++
2265MTrunkAdenoid7×61.5No+URD+++++-
2372MTrunkNodular12×61No+URD+++-++
2486FFaceKeratotic20×113.1No++DRD+++-
2585MFaceNodular0.51.3No++DRD++++-
2674FExtrNodular4×40.9No+URD--+-
2771MFaceNodular6×121.7No+DRD--+-
2864FTrunkSup mult1.3×1.50.4No++URD+++---
2978FFaceNodular4×31.5No++DRD+++-+++
3080MFaceKeratotic4×41.6Yes+DRD--++++
Open in a separate window Our data show that p53 (75%), Bcl2 (50%) and Ki67 (63%) positivity was generally diffuse in the majority of cases. On the contrary, cytoplasmatic α-SMA expression was present only in 8 out of 31 cases (25,8%). All these 8 α-SMA positive BCCs, prevalently found in the mideface (6 out of 8), were characterized by an initial invasion beyond the dermis. Among these 6 face-localized α-SMA positive BCCs, 1 showed a sclerosing aggressive histotype, 1 a keratotic type and 4 a nodular histotype.These 8 little α-SMA-positive BCCs, compared to the others 23 α-SMA negative samples, all showed a major aggressiveness features: facial location, ulceration, morpheiform histotype and deeper infiltration into the dermis (Location
Histotype
Local aggressiveness
Immunohistochemistry
FaceKeratoticMorpheiformDepht of invasion Mean value(mm)UlcerationInfiltration of the dermisP53Bcl-2Ki678 α-SMA Positive cases75%12%12%1.650%63%75%50%63%23 α-SMA Negative cases56%13%4%1.413%48%78%43%65%
Open in a separate windowGiven the absence of a specific difference between α-SMA positive cases and α-SMA negative cases in the expression of aggressive immunohistochemical markers, except for a light reduction of bcl-2 in the α-SMA positive group (and2).2). By the analysis of the data, we selected the combination that could better define an aggressive behaviour even for little BCC: α-SMA, p53, Ki67 positivity and bcl-2 negativity. We considered p53 and ki67 markers of proliferation and cell-cycle alteration, combined with a loss of apoptotic activity expressed by Bcl-2 negativity, quite characteristic of aggressiveness; moreover α-SMA positivity probably reflects invasive potential and acquired mobility by neoplastic cells.This immunohistochemical profile (α-SMA, p53, Ki67 positivity and bcl-2 negativity) in our cases of BCC is present in two of them; one is a morpheiform BCC, that is an aggressive variant, while the other one is a nodular subtype (less aggressive).Therefore, our preliminary data suggest that only α-SMA positivity should be considered as an early diagnostic marker of potential aggressiveness in little BCC: all α-SMA positive little BCC in fact showed clinical and histological features of aggressiveness. Invasive potential is probably acquired by some BCCs not only when they reach large size, but it is probably present also when they have still little size, and can be revealed by α-SMA positivity in the neoplastic cells. Open in a separate windowFigure 1BCC, nodular type, HE, 10×. Open in a separate windowFigure 2BCC, nodular type, α-SMA positivity, 10×.  相似文献   

8.
Escherichia coli O157:H7 and Other E. coli Strains Share Physiological Properties Associated with Intestinal Colonization     
Lisa Jacobsen  Lisa Durso  Tyrell Conway  Kenneth W. Nickerson 《Applied and environmental microbiology》2009,75(13):4633-4635
Escherichia coli isolates (72 commensal and 10 O157:H7 isolates) were compared with regard to physiological and growth parameters related to their ability to survive and persist in the gastrointestinal tract and found to be similar. We propose that nonhuman hosts in E. coli O157:H7 strains function similarly to other E. coli strains in regard to attributes relevant to gastrointestinal colonization.Escherichia coli is well known for its ecological versatility (15). A life cycle which includes both gastrointestinal and environmental stages has been stressed by both Savageau (15) and Adamowicz et al. (1). The gastrointestinal stage would be subjected to acid and detergent stress. The environmental stage is implicit in E. coli having transport systems for fungal siderophores (4) as well as pyrroloquinoline quinone-dependent periplasmic glucose utilization (1) because their presence indicates evolution in a location containing fungal siderophores and pyrroloquinoline quinone (1).Since its recognition as a food-borne pathogen, there have been numerous outbreaks of food-borne infection due to E. coli O157:H7, in both ground beef and vegetable crops (6, 13). Cattle are widely considered to be the primary reservoir of E. coli O157:H7 (14), but E. coli O157:H7 does not appear to cause disease in cattle. To what extent is E. coli O157:H7 physiologically unique compared to the other naturally occurring E. coli strains? We feel that the uniqueness of E. coli O157:H7 should be evaluated against a backdrop of other wild-type E. coli strains, and in this regard, we chose the 72-strain ECOR reference collection originally described by Ochman and Selander (10). These strains were chosen from a collection of 2,600 E. coli isolates to provide diversity with regard to host species, geographical distribution, and electromorph profiles at 11 enzyme loci (10).In our study we compared the 72 strains of the ECOR collection against 10 strains of E. coli O157:H7 and six strains of E. coli which had been in laboratory use for many years (Table (Table1).1). The in vitro comparisons were made with regard to factors potentially relevant to the bacteria''s ability to colonize animal guts, i.e., acid tolerance, detergent tolerance, and the presence of the Entner-Doudoroff (ED) pathway (Table (Table2).2). Our longstanding interest in the ED pathway (11) derives in part from work by Paul Cohen''s group (16, 17) showing that the ED pathway is important for E. coli colonization of the mouse large intestine. Growth was assessed by replica plating 88 strains of E. coli under 40 conditions (Table (Table2).2). These included two LB controls (aerobic and anaerobic), 14 for detergent stress (sodium dodecyl sulfate [SDS], hexadecyltrimethylammonium bromide [CTAB], and benzalkonium chloride, both aerobic and anaerobic), 16 for acid stress (pH 6.5, 6.0, 5.0, 4.6, 4.3, 4.2, 4.1, and 4.0), four for the ability to grow in a defined minimal medium (M63 glucose salts with and without thiamine), and four for the presence or absence of a functional ED pathway (M63 with gluconate or glucuronate). All tests were done with duplicate plates in two or three separate trials. The data are available in Tables S1 to S14 in the supplemental material, and they are summarized in Table Table22.

TABLE 1.

E. coli strains used in this study
E. coli strain (n)Source
ECOR strains (72)Thomas Whittman
Laboratory adapted (6)
    K-12 DavisPaul Blum
    CG5C 4401Paul Blum
    K-12 StanfordPaul Blum
    W3110Paul Blum
    BTyler Kokjohn
    AB 1157Tyler Kokjohn
O157:H7 (10)
    FRIK 528Andrew Benson
    ATCC 43895Andrew Benson
    MC 1061Andrew Benson
    C536Tim Cebula
    C503Tim Cebula
    C535Tim Cebula
    ATCC 43889William Cray, Jr.
    ATCC 43890William Cray, Jr.
    ATCC 43888Willaim Cray, Jr.
    ATCC 43894William Cray, Jr.
Open in a separate window

TABLE 2.

Physiological comparison of 88 strains of Escherichia coli
Growth medium or conditionOxygencNo. of strains with type of growthb
ECOR strains (n = 72)
Laboratory strains (n = 6)
O157:H7 strains (n = 10)
GoodPoorNoneVariableGoodPoorNoneVariableGoodPoorNoneVariable
LB controlaBoth72000600010000
1% SDSAerobic6930060008002
5% SDSAerobic6840060008200
1% SDSAnaerobic53154023101702
5% SDSAnaerobic0684004200704
CTABd (all)Both00720006000100
0.05% BACAerobic31158202220091
0.2% BACAerobic01710105000100
0.05% BACAnaerobic2367001500091
0.2% BACAnaerobic00720006000100
pH 6.5Both72000600010000
pH 6Both72000600010000
pH 5Both7020060009001
pH 4.6Both70200600010000
pH 4.3Aerobic14015731203205
pH 4.3Anaerobic6930031201100
pH 4.1 or 4.2Aerobic00720NDgND
pH 4.0Both0072000600091
M63 with supplemente
    GlucoseAerobicf6912050109010
    GlucoseAnaerobicf7002050109010
    GluconateBoth6912050109010
    GlucuronateAerobic6822050109010
    GlucuronateAnaerobic6912050109010
Open in a separate windowaEight LB controls were run, two for each set of LB experiments: SDS, CTAB, benzalkonium chloride (BAC), and pH stress.bGrowth was measured as either +++, +, or 0 (good, poor, and none, respectively), with +++ being the growth achieved on the LB control plates. “Variable” means that two or three replicates did not agree. All experiments were done at 37°C.c“Anaerobic” refers to use of an Oxoid anaerobic chamber. Aerobic and anaerobic growth data are presented together when the results were identical and separately when the results were not the same or the anaerobic set had not been done. LB plates were measured after 1 (aerobic) or 2 (anaerobic) days, and the M63 plates were measured after 2 or 3 days.dCTAB used at 0.05, 0.2%, and 0.4%.eM63 defined medium (3) was supplemented with glucose, gluconate, or glucuronate, all at 0.2%.fIdentical results were obtained with and without 0.0001% thiamine.gND, not determined.  相似文献   

9.
Correlation of Fragile Histidine Triad (Fhit) Protein Structural Features with Effector Interactions and Biological Functions     
Flavia Pichiorri  Hiroshi Okumura  Tatsuya Nakamura  Preston N. Garrison  Pierluigi Gasparini  Sung-Suk Suh  Teresa Druck  Kelly A. McCorkell  Larry D. Barnes  Carlo M. Croce    Kay Huebner 《The Journal of biological chemistry》2009,284(2):1040-1049
We have previously shown that Fhit tumor suppressor protein interacts with Hsp60 chaperone machinery and ferredoxin reductase (Fdxr) protein. Fhit-effector interactions are associated with a Fhit-dependent increase in Fdxr stability, followed by generation of reactive oxygen species and apoptosis induction under conditions of oxidative stress. To define Fhit structural features that affect interactions, downstream signaling, and biological outcomes, we used cancer cells expressing Fhit mutants with amino acid substitutions that alter enzymatic activity, enzyme substrate binding, or phosphorylation at tyrosine 114. Gastric cancer cell clones stably expressing mutants that do not bind substrate or cannot be phosphorylated showed decreased binding to Hsp60 and Fdxr and reduced mitochondrial localization. Expression of Fhit or mutants that bind interactor proteins results in oxidative damage and accumulation of cells in G2/M or sub-G1 fractions after peroxide treatment; noninteracting mutants are defective in these biological effects. Gastric cancer clones expressing noncomplexing Fhit mutants show reduction of Fhit tumor suppressor activity, confirming that substrate binding, interaction with heat shock proteins, mitochondrial localization, and interaction with Fdxr are important for Fhit tumor suppressor function.Fhit protein is a powerful tumor suppressor that is frequently lost or reduced in cancer cells because of rearrangement of the exquisitely DNA damage-sensitive fragile FHIT gene. Restoration of Fhit expression suppresses tumorigenicity of cancer cells of various types, and the ability to induce apoptosis in cancer cells in vitro is reduced by specific Fhit mutations (1, 2).Through studies of signal pathways affected by Fhit expression, by searches for Fhit protein effectors, and by in vitro analyses of Fhit activity, we and others have defined Fhit enzymatic activity in vitro (3), apoptotic activity in cells and tumors (46), and most recently identification of a Fhit protein complex that affects Fhit stability, mitochondrial localization, and interaction with ferredoxin reductase (Fdxr)5 (7). The complex includes Hsp60 and Hsp10 that mediate Fhit stability and may affect import into mitochondria, where Fhit interacts with Fdxr, which is responsible for transferring electrons from NADPH to cytochrome P450 via ferredoxin. Virally mediated Fhit restoration in Fhit-deficient cancer cells increases production of intracellular reactive oxygen species (ROS), followed by increased apoptosis of cancer cells under oxidative stress conditions; conversely, Fhit-negative cells escape apoptosis, likely carrying oxidative DNA damage that contributes to accumulation of mutations.The Fhit protein sequence, showing high homology to the histidine triad (HIT) family of proteins, suggested that the protein product would hydrolyze diadenosine tetraphosphate or diadenosine triphosphate (Ap3A) (8), and in vitro studies showed that Ap3A was cleaved into ADP and AMP by Fhit. The catalytic histidine triad within Fhit was essential for catalytic activity (3), and a Fhit mutant that substituted Asn for His at the central histidine (H96N mutant) was catalytically inactive, although it bound substrate well (3). Early tumor suppression studies showed that cancer cells stably transfected with wild type (WT) or H96N mutant Fhit were suppressed for tumor growth in nude mice. This suggested the hypothesis that the Fhit-substrate complex sends the tumor suppression signal (9, 10). To test this hypothesis, a series of FHIT alleles was designed to reduce substrate-binding and/or hydrolytic rates and was characterized by quantitative cell-death assays on cancer cells virally infected with each allele. The allele series covered defects as great as 100,000-fold in kcat and increases as large as 30-fold in Km. Mutants with 2–7-fold increases in Km had significantly reduced apoptotic indices and the mutant with a 30-fold increase in Km retained little apoptotic function. Thus, the proapoptotic function of Fhit, which is likely associated with tumor suppressor function, is limited by substrate binding and is unrelated to substrate hydrolysis (11).Fhit, a homodimeric protein of 147 amino acids, is a target of tyrosine phosphorylation by the Src family protein kinases, which can phosphorylate Tyr-114 of Fhit in vitro and in vivo (12). After co-expression of Fhit with the Elk tyrosine kinase in Escherichia coli to generate phosphorylated forms of Fhit, unphosphorylated, mono-, and diphosphorylated Fhit were purified, and enzyme kinetics studies showed that monophosphorylated Fhit exhibited monophasic kinetics with Km and kcat values ∼2- and ∼7-fold lower, respectively, than for unphosphorylated Fhit. Diphosphorylated Fhit exhibited biphasic kinetics; one site had Km and kcat values ∼2- and ∼140-fold lower, respectively, than for unphosphorylated Fhit; the second site had a Km ∼60-fold higher and a kcat ∼6-fold lower than for unphosphorylated Fhit (13). Thus, it was possible that the alterations in Km and kcat values for phosphorylated forms of Fhit might favor formation and lifetime of the Fhit-Ap3A complex and enhance tumor suppressor activity (see Fhit forms
Kinetic parameters
% Sub-G1
Direct binding
Subcellular location
Co-IP in vivo
8-OHdG
Apoptosis
Tumor suppressor
Km (mm)kcat (s–1)A549MKN74Hsp60FdxrHsp60Fdxr Fhit WT 1.6 +/– 0.19 2.7 +/– 0.95 43 24 Yes Yes Cyt & mito Yes Yes Yes Yes Yes Catalyt mutants    H96D Up 2-fold Down >2 × 104 29 NT NT NT Cyt & mito Yes Yes NT Yes NT    H96N Up 2-fold Down >5 × 105 31 14.4 NT NT Cyt & mito Yes Yes Yes Yes Yes Loop mutants    Y114A Up 23-fold Down 2-fold 3.7 NT NT NT Cyt +/– +/– +/– No No    Y114D NT NT 2.9 6 NT NT Cyt +/– +/– – No –/+    Y114E NT NT NT NT NT NT Cyt & mito –/+ –/+ – No NT    Y114F Up 5-fold Up 1.1-fold 11.5 3 NT NT Cyt & mito –/+ –/+ – No No    Y114W Up 5-fold Up 1.4-fold NT NT NT NT Cyt & mito –/+ – – NT NT    del113–117 Up 10-fold Down 38-fold 5 NT NT NT NT NT NT – No NT Other mutants    L25W Up 7-fold Down 4-fold 15 NT NT NT Cyt – – – NT –/+    I10W,L25W Up 32-fold Down 6-fold 11 NT NT NT NT NT NT NT NT NT    F5W Up 3.3 fold NT NT 5 NT NT NT NT NT +/– No NT Purified pFhit    pFhit Down 0.4-fold Down 7-fold NA NA –/+ Yes NA NA NA NA NA NA    ppFhit Down 0.4-fold Down > 100-fold NA NA –/+ Yes NA NA NA NA NA NA Up 60-fold Down 6-fold
Open in a separate windowTo explore the in vivo importance of the Tyr-114 phosphorylation site and define Fhit-mediated signaling events, Semba et al. (14) compared the differential biological effects of Ad-FHIT WT and Ad-FHIT Tyr-114 mutant expression in human lung cancer cells. Caspase-dependent apoptosis was effectively induced only by WT Fhit protein. However, the biological significance of phosphorylation at Tyr-114 has been difficult to study because the endogenous phosphorylated forms have very short half-lives; activation of epidermal growth facto receptor family members induces Fhit phosphorylation by Src and proteasome degradation of phosphorylated Fhit (15).Although there are possible connections among the various pathways known to be altered in Fhit-deficient cells, apoptosis, DNA damage-response checkpoint activation, ROS production, and related biological effects of Fhit loss or overexpression, details of the pathway(s) leading from Fhit overexpression to cell death and tumor suppression have not been delineated. Now that a Fhit signaling complex has been identified, we set out to examine which structural features of Fhit protein might participate in individual steps of the pathway leading from Fhit overexpression through complex formation, subcellular localization, interaction with mitochondrial Fdxr, DNA damage induction, cell cycle changes, apoptosis, and ultimately tumor suppression. The underlying hypotheses were as follows: substrate-binding mutants would behave similarly to WT; nonsubstrate-binding mutants would be defective in some step of the pathway, perhaps complexing with heat shock proteins or Fdxr or perhaps induction of DNA damage; and Tyr-114 mutants, which also affect formation or stability of the enzyme-substrate complex, would also be defective in executing some step of the Fhit overexpression pathway to cell death. One goal was to identify specific mutants that exhibited deficiency in specific steps of the pathway, so that such mutants could be used to dissect each step in more detail. Using in vitro Fhit and Fhit-effector protein interactions, we aimed to determine the following: 1) which proteins of the complex interact directly with Fhit, and 2) the biological role of these interactions in vivo. Using cancer cells expressing exogenous WT and mutant Fhit proteins, we were able to examine the structural features of Fhit that affect the direct interaction with its effectors, participate in ROS production, and are necessary for tumor suppression activity.  相似文献   

10.
Physiological Versatility of the Extremely Thermoacidophilic Archaeon Metallosphaera sedula Supported by Transcriptomic Analysis of Heterotrophic,Autotrophic, and Mixotrophic Growth     
Kathryne S. Auernik  Robert M. Kelly 《Applied and environmental microbiology》2010,76(3):931-935
  相似文献   

11.
Spontaneous Quinolone Resistance in the Zoonotic Serovar of Vibrio vulnificus     
Francisco J. Roig  A. Llorens  B. Fouz  C. Amaro 《Applied and environmental microbiology》2009,75(8):2577-2580
This work demonstrates that Vibrio vulnificus biotype 2, serovar E, an eel pathogen able to infect humans, can become resistant to quinolone by specific mutations in gyrA (substitution of isoleucine for serine at position 83) and to some fluoroquinolones by additional mutations in parC (substitution of lysine for serine at position 85). Thus, to avoid the selection of resistant strains that are potentially pathogenic for humans, antibiotics other than quinolones must be used to treat vibriosis on farms.Vibrio vulnificus is an aquatic bacterium from warm and tropical ecosystems that causes vibriosis in humans and fish (http://www.cdc.gov/nczved/dfbmd/disease_listing/vibriov_gi.html) (33). The species is heterogeneous and has been subdivided into three biotypes and more than eight serovars (6, 15, 33; our unpublished results). While biotypes 1 and 3 are innocuous for fish, biotype 2 can infect nonimmune fish, mainly eels, by colonizing the gills, invading the bloodstream, and causing death by septicemia (23). The disease is rapidly transmitted through water and can result in significant economic losses to fish farmers. Surviving eels are immune to the disease and can act as carriers, transmitting vibriosis between farms. Interestingly, biotype 2 isolates belonging to serovar E have been isolated from human infections, suggesting that serovar E is zoonotic (2). This serovar is also the most virulent for fish and has been responsible for the closure of several farms due to massive losses of fish. A vaccine, named Vulnivaccine, has been developed from serovar E isolates and has been successfully tested in the field (14). Although the vaccine provides fish with long-term protection from vibriosis, at present its use is restricted to Spain. For this reason, in many fish farms around the world, vibriosis is treated with antibiotics, which are usually added to the food or water.Quinolones are considered the most effective antibiotics against human and fish vibriosis (19, 21, 31). These antibiotics can persist for a long time in the environment (20), which could favor the emergence of resistant strains under selective pressure. In fact, spontaneous resistances to quinolones by chromosomal mutations have been described for some gram-negative bacteria (10, 11, 17, 24, 25, 26). Therefore, improper antibiotic treatment of eel vibriosis or inadequate residue elimination at farms could favor the emergence of human-pathogenic serovar E strains resistant to quinolones by spontaneous mutations. Thus, the main objective of the present work was to find out if the zoonotic serovar of biotype 2 can become quinolone resistant under selective pressure and determine the molecular basis of this resistance.Very few reports on resistance to antibiotics in V. vulnificus have been published; most of them have been performed with biotype 1 isolates. For this reason, the first task of this study was to determine the antibiotic resistance patterns in a wide collection of V. vulnificus strains belonging to the three biotypes that had been isolated worldwide from different sources (see Table S1 in the supplemental material). Isolates were screened for antimicrobial susceptibility to the antibiotics listed in Table S1 in the supplemental material by the agar diffusion disk procedure of Bauer et al. (5), according to the standard guideline (9). The resistance pattern found for each isolate is shown in Table S1 in the supplemental material. Less than 14% of isolates were sensitive to all the antibiotics tested, and more than 65% were resistant to more than one antibiotic, irrespective of their biotypes or serovars. The most frequent resistances were to ampicillin-sulbactam (SAM; 65.6% of the strains) and nitrofurantoin (F; 60.8% of the strains), and the least frequent were to tetracycline (12%) and oxytetracycline (8%). In addition, 15% of the strains were resistant to nalidixic acid (NAL) and oxolinic acid (OA), and 75% of these strains came from fish farms (see Table S1 in the supplemental material). Thus, high percentages of strains of the three biotypes were shown to be resistant to one or more antibiotics, with percentages similar to those found in nonbiotyped environmental V. vulnificus isolates from Asia and North America (4, 27, 34). In those studies, resistance to antibiotics could not be related to human contamination. However, the percentage of quinolone-resistant strains found in our study is higher than that reported in other ones, probably due to the inclusion of fish farm isolates, where the majority of quinolone-resistant strains were concentrated. This fact suggests that quinolone resistance could be related to human contamination due to the improper use of these drugs in therapy against fish diseases, as has been previously suggested (18, 20). Although no specific resistance pattern was associated with particular biotypes or serovars, we found certain differences in resistance distribution, as shown in Table Table1.1. In this respect, biotype 3 displayed the narrowest spectrum of resistances and biotype 1 the widest. The latter biotype encompassed the highest number of strains with multiresistance (see Table S1 in the supplemental material). Within biotype 2, there were differences among serovars, with quinolone resistance being restricted to the zoonotic serovar (Table (Table11).

TABLE 1.

Percentage of resistant strains distributed by biotypes and serovars
V. vulnificusNo. of isolatesResistance distribution (%) for indicated antibiotica
SAMCTXENALFOTOASXT-TMPTE
Biotype 14975.524.514.330.683.78.230.628.68.2
Biotype 2 (whole)7258.313.912.54.247.29.74.24.213.9
Biotype 2
    Serovar E3630.312.139.127.315.29.1321.2
    Serovar A231009.118.2077.3009.14.6
    Nontypeable82914.325057.114.30014.3
    Serovar I5100202002020000
Biotype 3510002008000020
Open in a separate windowaCTX, cefotaxime; E, erythromycin; OT, oxytetracycline; SXT-TMP, sulfamethoxazole-trimethoprim; TE, tetracycline.The origin of resistance to quinolones in the zoonotic serovar was further investigated. To this end, spontaneous mutants of sensitive strains were selected from colonies growing within the inhibition halo around OA or NAL disks. Two strains (strain CG100 of biotype 1 and strain CECT 4604 of biotype 2, serovar E) developed isolated colonies within the inhibition zone. These colonies were purified, and maintenance of resistance was confirmed by serial incubations on medium without antibiotics. Using the disk diffusion method, CG100 was shown to be resistant to SAM and F and CECT 4604 to F (see Table S1 in the supplemental material). The MICs for OA, NAL, flumequine (UB), and ciprofloxacin (CIP) were determined by using the microplate assay according to the recommendations of the Clinical and Laboratory Standards Institute and the European Committee for Antimicrobial Susceptibility Testing of the European Society of Clinical Microbiology and Infectious Diseases (8, 12) and interpreted according to the European Committee for Antimicrobial Susceptibility Testing of the European Society of Clinical Microbiology and Infectious Diseases (13). The MICs for OA and NAL and for the fluoroquinolones UB and CIP exhibited by the mutants and their counterparts are shown in Table Table2.2. The inhibition zone diameters correlated well with MICs (data not shown). Mutants FR1, FR2, FR3, and FR4 were resistant to NAL and sensitive to the remaining quinolones, although they showed higher resistances than their parental strains (Table (Table2).2). Thus, these four mutants showed increases of 32- to 128-fold for NAL MICs, 4- to 8-fold for UB MICs, and 16-fold for CIP MICs (Table (Table2).2). The fifth mutant, FR5, was resistant to the two tested quinolones and to UB, a narrow-spectrum fluoroquinolone. This mutant, although sensitive to CIP, multiplied its MIC for this drug by 128 with respect to the parental strain (Table (Table22).

TABLE 2.

MICs for quinolones and fluoroquinolones and mutations in gyrA, gyrB, and parC detected in naturally and artificially induced resistant strains
Strain(s)MIC (μg ml−1) for indicated antibioticb
Gene mutationa
gyrA
gyrB
parC
Position
Codon changeaa changePosition
Codon changeaa changePosition
Codon changeaa change
NALOAUBCIPntaantaantaa
CG1000.5 (S)0.125 (S)0.0625 (S)0.0078 (S)
FR116 (R)1 (S)0.25 (S)0.125 (S)24883AGT→ATTS→INCNCNCNCNCNCNCNC
FR216 (R)1 (S)0.25 (S)0.125 (S)24883AGT→ATTS→INCNCNCNCNCNCNCNC
CECT 46040.25 (S)0.0625 (S)0.0625 (S)0.0078 (S)
FR332 (R)2 (S)0.5 (S)0.125 (S)24883AGT→ATTS→INCNCNCNCNCNCNCNC
FR432 (R)2 (S)0.5 (S)0.125 (S)24883AGT→ATTS→INCNCNCNCNCNCNCNC
FR5256 (R)16 (R)16 (R)1 (S)24883AGT→ATTS→I1156386GCA→ACAA→T25485TCA→TTAS→L
1236412CAG→CACQ→H
CECT 4602128 (R)8 (R)64 (R)1 (S)24883AGT→ATTS→INCNCNCNC25485TCA→TTAS→L
CECT 4603, CECT 4606, CECT 4608, PD-5, PD-12, JE32 (R)2 (S)<1 (S)<1 (S)24883AGT→ATTS→INCNCNCNCNCNCNCNC
CECT 486264 (R)2 (S)2 (S)<1 (S)24983AGT→AGAS→RNCNCNCNCNCNCNCNC
A2, A4, A5, A6, A7, PD-1, PD-364-128 (R)2 (S)4 (S)<1 (S)24883AGT→ATTS→INCNCNCNC338113GCA→GTAA→V
V1128 (R)4 (S)4 (S)<1 (S)24883AGT→ATTS→I1274425GAG→GGGE→GNCNCNCNC
1314438AAC→AAAN→K
Open in a separate windowaMutations in a nucleotide (nt) that gave rise to a codon change and to a change in amino acids (aa) are indicated. NC, no change detected.bThe resistance (R) or sensitivity (S) against the antibiotic determined according to the Clinical and Laboratory Standards Institute and the European Committee for Antimicrobial Susceptibility Testing of the European Society of Clinical Microbiology and Infectious Diseases (9, 13) is indicated in parentheses.For other gram-negative pathogens, quinolone resistance relies on spontaneous mutations in the gyrA, gyrB, parC, and parE genes that occur in a specific region of the protein known as the quinolone resistance-determining region (QRDR) (1, 11, 17, 24, 25, 26, 28). To test the hypothesis that mutations in these genes could also produce quinolone resistance in V. vulnificus, the QRDRs of these genes were sequenced in the naturally resistant strains and in the two sensitive strains that had developed resistances by selective pressure in vitro. The genomic DNA was extracted (3), and the QRDRs of gyrA, gyrB, parE, and parC were amplified using the primers shown in Table Table3,3, which were designed from the published genomes of biotype 1 strains YJ016 and CMCP6 (7, 22). PCR products of the predicted size were sequenced in an ABI 3730 sequencer (Applied Biosystems). Analysis of the QRDR sequences for gyrA, gyrB, parC, and parE of the mutants and the naturally resistant strains revealed that all naturally resistant strains, except one, shared a specific mutation at nucleotide position 248 with the laboratory-induced mutants (Table (Table2).2). This mutation gave rise to a change from serine to isoleucine at amino acid position 83. The exception was a mutation in the adjacent nucleotide that gave rise to a substitution of arginine for serine at the same amino acid position (Table (Table2).2). All the isolates that were resistant to the quinolone NAL had a unique mutation in the gyrA gene, irrespective of whether resistance was acquired naturally or in the laboratory (Table (Table2).2). This result strongly suggests that a point mutation in gyrA that gives rise to a change in nucleotide position 83 can confer resistance to NAL in V. vulnificus biotypes 1 and 2 and that this mutation could be produced by selective pressure under natural conditions. gyrA mutations consisting of a change from serine 83 to isoleucine have also been described in isolates of Aeromonas from water (17) and in diseased fish isolates of Vibrio anguillarum (26). Similarly, replacement of serine by arginine at amino acid position 83 in diseased fish isolates of Yersinia ruckeri (16) suggests that this mechanism of quinolone resistance is widespread among gram-negative pathogens. In all cases, these single mutations were also related to increased resistance to other quinolones (OA) and fluoroquinolones (UB and CIP) (Table (Table2),2), although the mutants remained sensitive according to the standards of the Clinical and Laboratory Standards Institute and the European Committee for Antimicrobial Susceptibility Testing of the European Society of Clinical Microbiology and Infectious Diseases (9, 13). A total of 50% of the naturally resistant strains, all of them of biotype 1, showed additional mutations that affected parC (a change in amino acid position 113) or gyrB (changes in amino acids at positions 425 and 438) (Table (Table2).2). These strains exhibited higher MICs for OA and fluoroquinolones (Table (Table2),2), although they were still sensitive to these drugs (9, 13). Finally, one isolate of biotype 2, serovar E, which was naturally resistant to quinolones and UB, showed a mutation in parC that gave rise to a substitution of leucine for serine at amino acid position 85 (Table (Table2).2). This mutation was shared only with the laboratory-induced mutant, also a biotype 2, serovar E mutant, which was resistant to the fluoroquinolone UB. The same mutation in parC had been previously described in diseased fish isolates of V. anguillarum that were highly resistant to quinolones (28), but this had not been related to fluoroquinolone resistance in Vibrio spp. nor in other gram-negative bacteria. These results strongly suggest that resistance to fluoroquinolones in V. vulnificus is related to specific mutations in gyrA and parC and that mutations in different positions for parC or in gyrB could contribute to increased resistance to quinolones and fluoroquinolones. Our results also agree with previous studies confirming that the acquisition of higher quinolone resistance is more probable when arising from a gyrA parC double mutation than from a gyrA gyrB double mutation (29).

TABLE 3.

Oligonucleotides used in this study
PrimerSequenceAnnealing temp (°C)Size (bp)
GyrAFGGCAACGACTGGAATAAACC55.8416
GyrARCAGCCATCAATCACTTCCGTC
ParCFCGCAAGTTCACCGAAGATGC56.6411
ParCRGGCATCCGCAACTTCACG
GyrBFCGACTTCTGGTGACGATGCG57.4642
GyrBRGACCGATACCACAACCTAGTG
ParEFGCCAGGTAAGTTGACCGATTG56.8512
ParERCACCCAGACCTTTGAATCGTTG
Open in a separate windowFinally, the evolutionary history for each protein was inferred from previously published DNA sequences of the whole genes from different Vibrio species after multiple sequence alignment with MEGA4 software (32) by applying the neighbor-joining method (30) with the Poisson correction (35). The distance tree for each whole protein showed a topology similar to the phylogenetic tree based on 16S rRNA analysis, with the two isolates of V. vulnificus forming a single group, closely related to Vibrio parahaemolyticus, Vibrio cholerae, V. anguillarum, and Vibrio harveyi (see Fig. S1A in the supplemental material). A second analysis was performed with the QRDR sequences of the different mutants and isolates of V. vulnificus (GenBank accession numbers FJ379836 to FJ379927) to infer the intraspecies relationships (see Fig. S1B in the supplemental material). This analysis showed that QRDRs of gyrA, gyrB, parC, and parE were highly homogeneous within V. vulnificus.In summary, the zoonotic serovar of V. vulnificus can mutate spontaneously to gain quinolone resistance, under selective pressure in vitro, due to specific mutations in gyrA that involve a substitution of isoleucine for serine at amino acid position 83. This mutation appears in biotype 2, serovar E diseased-fish isolates and biotype 1 strains, mostly recovered from fish farms. An additional mutation in parC, resulting in a substitution of lysine for serine at amino acid position 85, seems to endow partial fluoroquinolone resistance on biotype 2, serovar E strains. This kind of double mutation is present in diseased-fish isolates of the zoonotic serovar but not in resistant biotype 1 isolates, which show different mutations in gyrB or in parC that increase their resistance levels but do not make the strains resistant to fluoroquinolones. Thus, antibiotics other than quinolones should be used at fish farms to prevent the emergence and spread of quinolone resistances, especially to CIP, a drug widely recommended for human vibriosis treatment.  相似文献   

12.
Perioperative Opioid Counseling Reduces Opioid Use Following Primary Total Joint Arthroplasty     
Christopher N. Carender  Christopher A. Anthony  Edward O. Rojas  Nicolas O. Noiseux  Nicholas A. Bedard  Timothy S. Brown 《The Iowa orthopaedic journal》2022,42(1):169
BackgroundPreoperative counseling may reduce postoperative opioid requirements; however, there is a paucity of randomized controlled trials (RCTs) demonstrating efficacy. The purpose of this study was to perform an interventional, telehealth-based RCT evaluating the effect of peri-operative counseling on quantity and duration of opioid consumption following primary total joint arthroplasty (TJA).MethodsParticipants were randomized into three groups: 1. Control group, no perioperative counseling; 2. Intervention group, preoperative educational video; 3. Intervention group, preoperative educational video and postoperative acceptance and commitment therapy (ACT). Opioid consumption was evaluated daily for 14 days and at 6 weeks postoperatively. Best-case and worse-case intention to treat analyses were performed to account for non-responses. Bonferroni corrections were applied.Results183 participants were analyzed (63 in Group 1, 55 in Group 2, and 65 in Group 3). At 2 weeks postoperatively, there was no difference in opioid consumption between Groups 1, 2, and 3 (p>0.05 for all). At 6 weeks postoperatively, Groups 2 and 3 had consumed significantly less opioids than Group 1 (p=0.04, p<0.001) (VariableGroupp-value1. Control2. Video OnlyVideo + ACTSex (n, % female)39 (62%)32 (58%)40 (62%)0.90Surgery (n, % THA)26 (41%)21 (38%)31 (47%)0.56Age (mean ± SD; years)59 ± 1159 ± 1158 ± 9Overall: 0.83
1v2: 0.98
2v3: 0.65
2v3: 0.56Prolonged Opioid Use > 60 mo. (n, %)000-Opioid Use Within 3 mo. of Index Surgery (n, %)0 (14%)4 (7%)5 (8%)0.34
Open in a separate windowSD – standard deviation.Table 2.Quantity of Opioid Consumption at 2 Weeks Postoperatively, Best-Case Scenario
ValueGroupp-valuep-value (corrected)
1. Control2. Video OnlyVideo + ACT
Median192113901v2: 0.281v2: 0.56
IQR60-3088-30815-2481v3: 0.04*1v3: 0.15
Min0002v3: 0.472v3: 0.56
Max690623694
Open in a separate windowMedian, interquartile range (IQR), minimum (min), and maximum (max) values are reported in morphine milliequivalents (MME). * denotes statistical significance.ConclusionPerioperative opioid counseling significantly decreases the quantity and duration of opioid consumption at 6 weeks following primary TJA. Level of Evidence: I  相似文献   

13.
RNA-Based Investigation of Ammonia-Oxidizing Archaea in Hot Springs of Yunnan Province,China     
Hongchen Jiang  Qiuyuan Huang  Hailiang Dong  Peng Wang  Fengping Wang  Wenjun Li  Chuanlun Zhang 《Applied and environmental microbiology》2010,76(13):4538-4541
  相似文献   

14.
Evolutionary Strata in a Small Mating-Type-Specific Region of the Smut Fungus Microbotryum violaceum          下载免费PDF全文
Antonina A Votintseva  Dmitry A. Filatov 《Genetics》2009,182(4):1391-1396
DNA sequence analysis and genetic mapping of loci from mating-type-specific chromosomes of the smut fungus Microbotryum violaceum demonstrated that the nonrecombining mating-type-specific region in this species comprises ∼25% (∼1 Mb) of the chromosome length. Divergence between homologous mating-type-linked genes in this region varies between 0 and 8.6%, resembling the evolutionary strata of vertebrate and plant sex chromosomes.EVOLUTION of mating types or sex-determining systems often involves the suppression of recombination around the primary sex-determining or mating-type-determining locus. In animals and plants, it is often an entire or almost entire chromosome (Y or W in male or female heterogametic species, respectively) that ceases to recombine with its homologous (X or Z) chromosome (Charlesworth and Charlesworth 2000; Charlesworth 2008). Self-incompatibility loci in plants are also thought to be located in regions of suppressed recombination (Charlesworth et al. 2005; Kamau and Charlesworth 2005; Kamau et al. 2007; Li et al. 2007; Yang et al. 2007). Regardless of the phylogenetic position of a species, such nonrecombining regions are known to follow similar evolutionary trajectories. The nonrecombining region on the sex-specific chromosome expands in several steps, forming evolutionary strata—regions of different X/Y (or Z/W) divergence (Lahn and Page 1999; Handley et al. 2004; Sandstedt and Tucker 2004; Nicolas et al. 2005)—and genes in the nonrecombining regions gradually accumulate deleterious mutations that eventually render them dysfunctional (Charlesworth and Charlesworth 2005; Charlesworth 2008).Fungal mating-type systems are very diverse, with the number of mating types varying from two to several hundred (Casselton 2002). Like sex chromosomes in several animals and plants, suppressed recombination has evolved in regions near fungal mating-type loci, including in Ustilago hordei (Lee et al. 1999), Cryptococcus neoformans (Lengeler et al. 2002), and Neurospora tetrasperma (Menkis et al. 2008). These species have two mating types, but no morphologically distinct sexes. The mating-type locus (the region of suppressed recombination) of C. neoformans is small (∼100 kb) compared with known sex chromosomes and contains only ∼20 genes that, unlike many sex chromosomes (Y or W chromosomes), show no obvious signs of genetic degeneration (Lengeler et al. 2002; Fraser et al. 2004). Judging from the divergence between the homologous genes on the two mating-type-specific chromosomes, C. neoformans started to evolve sex chromosomes a long time ago because silent divergence between the two mating types in the most ancient region exceeds 100% (Fraser et al. 2004). Genes in the younger mating-type-specific region are much less diverged between the two sex chromosomes, suggesting that the evolution of the sex locus in C. neoformans might have proceeded through several steps. The nonrecombining region around the mating-type locus of N. tetrasperma is much larger than in C. neoformans (at least 6.6 Mb), and silent divergence between homologous genes on the mating-type-specific chromosomes ranges from zero to 9%, demonstrating that these mating-type-specific chromosomes evolved recently (Menkis et al. 2008).M. violaceum, which causes anther smut disease in Silene latifolia and other species in the family Caryophyllaceae, has two mating types, A1 and A2 (reviewed by Giraud et al. 2008), which are determined by the presence of mating-type-specific chromosomes (hereafter A1 and A2 chromosomes, or sex chromosomes) in the haploid stage of the life cycle (Hood 2002; Hood et al. 2004). The A1 and A2 chromosomes are distinguishable by size in pulsed-field electrophoresis, and it is possible to isolate individual chromosomes electrophoretically (Hood et al. 2004). Random fragments of A1 and A2 chromosomes have previously been isolated from mating-type-specific bands of pulsed-field separated chromosomes of M. violaceum (Hood et al. 2004). These fragments were assumed to be linked to mating type. The same method was used to isolate fragments of non-mating-type-specific chromosomes. On the basis of the analysis of their sequences, (Hood et al. 2004) proposed that mating-type-specific chromosomes in M. violaceum might be degenerate because they contained a lower proportion of protein-coding genes than other chromosomes. However, it was not determined whether the sequences isolated from the mating-type chromosomes originated from the mating-type-specific or from the recombining regions (Hood et al. 2004), and the relative sizes of these regions are not known for these M. violaceum chromosomes. We tested the mating-type specificity of 86 of these fragments and demonstrate that fewer than a quarter of these loci are located in the mating-type-specific region, suggesting that the nonrecombining region on the A1 and A2 chromosomes is quite small, while the rest of the chromosome probably recombines (like pseudoautosomal regions of sex chromosomes) and is therefore not expected to undergo genetic degeneration. Genetic mapping confirms the presence of two pseudoautosomal regions in the M. violaceum mating-type-specific chromosomes.As these chromosomes are mating type specific in the haploid stage of M. violaceum, mating-type-specific loci (or DNA fragments) can be identified by testing whether they are present exclusively in A1 or A2 haploid strains. We therefore prepared haploid A1 and A2 M. violaceum cultures from S. latifolia plants from two geographically remote locations (accessions Sl405 from Sweden and Sl127 from the French Pyrenees). Haploid sporidial cultures were isolated by a standard dilution method (Kaltz and Shykoff 1997; Oudemans and Alexander 1998). Mating types were determined by PCR amplification of each culture with primers designed for A1 and A2 pheromone receptor genes linked to A1 and A2 mating types (Yockteng et al. 2007). The primers were as follows: 5′-TGGCATCCCTCAATGTTTCC-3′ and 5′-CACCTTTTGATGAGAGGCCG-3′ for the A1 pheromone receptor (GenBank accession no. EF584742) and 5′-TGACGAGAGCATTCCTACCG-3′ and 5′-GAAGCGGAACTTGCCTTTCT-3′ for the A2 pheromone receptor (GenBank accession no. EF584741). Cultures with PCR product amplified only from an A1 or A2 pheromone receptor gene were selected for further use. The mating types of the cultures were verified by conjugating them in all combinations.The GenBank nucleotide database was searched using BLAST for sequences similar to those isolated by Hood et al. (2004). Sequences with similarity to transposable elements (TE) and other repeats were excluded. The resulting set of nonredundant sequences was used to design PCR primers for 98 fragments. Half of these were originally isolated from the A1 and half from the A2 chromosomes and are hereafter called A1-NNN or A2-NNN (where NNN is the locus number; supporting information, Table S1), which does not imply that these loci are A1 or A2 specific, but merely indicates that they were originally isolated from the A1 or A2 chromosomes. Amplification of these regions from new A1 and A2 M. violaceum cultures, independently isolated by ourselves, revealed that only 5 of the 49 loci isolated from the A1 chromosome are indeed A1 specific and only 6 of 49 isolated from the A2 chromosome are A2 specific. All other loci amplified from both A1 and A2 cultures. Figure 1 illustrates some of these results from the Swedish sample (Sl405).Open in a separate windowFigure 1.—Testing of mating-type specificity for loci isolated from A1 and A2 chromosomes. (a) PCR amplifications from haploid cultures from Sl405 using primers designed from six A1-originated loci. Loci in which a PCR product could be amplified only from A1 cultures (boxed) were classified as specific to mating type A1. (b) PCR tests of six A2-originated loci on the same set of haploids as in a. Loci in which a PCR product amplified only from A2 cultures (boxed) were classified as specific to mating type A2. Loci amplified from both A1 and A2 cultures are not mating type specific.The fragments that amplified from both A1 and A2 mating types may be in recombining regions, or they could be present in mating-type-specific regions on both A1 and A2 chromosomes. If they are in recombining regions, the A1- and A2-linked homologs should not be diverged from each other, but if they are in nonrecombining, mating-type-specific regions, the divergence of the A1- and A2-linked homologs should be roughly proportional to the time since recombination stopped in the region. We therefore sequenced and compared PCR fragments amplified from the two mating types of Sl405 or Sl127 cultures (GenBank accession nos. FI855822FI856001). Sequencing of PCR products showed that 12 (4 A1 and 8 A2) loci have more than one copy, and they were excluded from further analysis. Sequences of 61 loci were identical between the A1 and A2 strains, and four loci demonstrated low total divergence (0.24–0.61%) between the two mating types (otintseva and D. Filatov, unpublished results). Thus, these loci might be located in the recombining part of the mating-type-specific chromosomes. Ten of 75 loci that amplified in both mating types demonstrated multiple polymorphisms fixed between the mating types rather than between the locations. Given that the strains that we used in the analysis originated from two geographically distant locations, it is highly unlikely that multiple polymorphisms distinguishing the A1 and A2 sequences arose purely by chance; thus, these loci are probably located in the nonrecombining mating-type-specific region of the M. violaceum A1 and A2 chromosomes.

TABLE 1

Loci from mating-type-specific chromosomes of M. violaceum used for PCR analysis and genetic map construction
With nonzero A1/A2 divergenceb
LociMating type specific<1%>1%With zero A1/A2 divergencebTotal
A1a52 (1)3 (3)35 (3)45 (7)
A2a62 (0)7 (7)26 (3)41 (10)
Subtotal4 (1)10 (10)
Total1114 (11)61 (6)86 (17)
Open in a separate windowaA1, loci originated from the A1 sex chromosome; A2, loci originated from the A2 sex chromosome.bThe number of loci used for genetic map construction is in parentheses.To confirm the mating-type-specific or pseudoautosomal locations of the loci with and without A1/A2 divergence, we conducted genetic mapping in a family of 99 individuals, 50 of which were of mating type A1 and 49 of mating type A2. The family was generated by a cross between A1 and A2 M. violaceum strains from S. latifolia accessions Sl405 (Sweden) and Sl127 (France), respectively. The choice of strains from geographically distant locations was motivated by the hope of maximizing the number of DNA sequence differences between them that can be used as molecular genetic markers in segregation analysis. We inoculated S. latifolia seedlings with sporidial cultures of both mating types. For inoculation, petri dishes with 12-day-old seedlings of S. latifolia were flooded with 2.5 ml of inoculum suspension. Inoculum suspension consisted of equal volumes of the A1 and A2 sporidial cultures that were mixed and conjugated overnight at 14° under rotation (Biere and Honders 1996; Van Putten et al. 2003). Seedlings were potted 3 days after inoculation. Two months later, teliospores were collected from the flowers of the infected plant and grown in petri dishes on 3.6% potato dextrose agar medium. Haploid sporidia formed after meiosis were isolated and grown as separate cultures for DNA extraction. The mating types of single sporidia cultures were identified as described above. The loci analyzed in the segregation analysis were sequenced in the two parental haploid strains and in 99 (50 A1 and 49 A2) haploid strains that were generated in the cross. Single nucleotide differences between the parental strains were used as molecular genetic markers for segregation analysis in the progeny. The genetic map was constructed using MAPMAKER/EXP v3.0 (Lincoln et al. 1992) and MapDisto v1.7 (http://mapdisto.free.fr/).The resulting genetic map is shown in Figure 2. As expected, no recombination was observed between the 10 loci with diverged A1- and A2-linked copies. In addition, one marker with no A1/A2 divergence, A2-397, was also completely linked to the loci with significant A1/A2 divergence. This locus either may be very tightly linked to the nonrecombining mating-type-specific region or may have been added to that region more recently than the loci that had already accumulated some divergence between the alleles in the two mating types. The mating-type-specific pheromone receptor locus (Devier et al. 2009) and 11 mating-type-specific loci are also located in this nonrecombining region (Figure 2). Interestingly, the cluster of nonrecombining markers is flanked on both sides with markers that recombine in meiosis, demonstrating that there are pseudoautosomal regions on both ends of the mating-type-specific chromosomes.Open in a separate windowFigure 2.—Genetic map of the mating-type-determining chromosome in M. violaceum. Genetic distance (in centimorgans) and the relative positions of the markers are shown to the left and the right of the chromosome, respectively. The position of the nonrecombining region corresponds to the cluster of linked markers shown on the right of the figure. Total A1/A2 divergence is shown in parentheses. Eleven mating-type-specific markers (for which sequences are available from only one mating type), located in the nonrecombining mating-type-specific region, are not shown.Our results demonstrate that although the loci reported by Hood et al. (2004) were isolated from the A1 and A2 chromosomes, most of these loci are not located in the nonrecombining mating-type-specific regions. In fact, the nonrecombining region might be relatively small: of 86 tested fragments, only 21 appeared to be either mating type specific or linked to the mating-type locus. Assuming that these loci represent a random set of DNA fragments isolated from the A1 and A2 chromosomes, it is possible to estimate the size of the nonrecombining region using the binomial distribution: the nonrecombining region is expected to be 24.4% (95% CI: 16.7–33.6%) of the chromosome length. As the sizes of the A1 and A2 chromosomes are ∼3.4 and 4.2 Mb long (Hood 2002; Hood et al. 2004), the nonrecombining region might be ∼1 Mb long.Interestingly, total A1/A2 divergence for the 11 loci with A1- and A2-linked copies mapped to the nonrecombining region varied from 0% to 8.6% (Figure 2). In addition, 11 loci amplified from only one mating type. These genes could represent degenerated genes, some of which degenerated in A1 strains, and some in A2 strains. Alternatively, they might be highly diverged genes, such that the PCR primers amplify only one allele, and not the other. Variation in divergence may be the result of the stepwise cessation of recombination between the A1 and A2 chromosomes in M. violaceum, resembling the evolutionary strata reported for human, chicken, and white campion sex chromosomes (Lahn and Page 1999; Handley et al. 2004; Bergero et al. 2007). However, only the differences between the most and the least diverged loci are statistically significant (Devier et al. 2009), the M. violaceum mating-type region has at least three strata: one oldest stratum, including the pheromone receptor locus; a younger stratum with ∼5–9% A1/A2 divergence; and the youngest stratum with 1–4% divergence between the two mating types. There may also be an additional very recently evolved stratum containing the locus named A2-397, which is also present in all A1 strains tested, with no fixed differences between the A1 and A2 strains (No. of sites analyzedWithin A1
Within A2
Fixed differences between A1 and A2A1/A2 divergence (%)LociaSb totalSπ (%)cSπ (%)cA1/A2 divergence <1%A1-23645630020.4410.44A1-0456544000040.61A2-568413220.4820.4800.24A2-411480210.210010.31A1/A2 divergence >1%A1-2176679000091.35A1-12856990010.1881.49A1-199618130010.16122.02A2-4223449000092.62A2-516470140000142.98A2-404508200030.59173.64A2-4355062220.3920.39183.95A2-4734572310.2210.22214.81A2-4573031710.3300165.54A2-5755034750.9930.59398.55
Open in a separate windowaA1, loci originated from the A1 sex chromosome; A2, loci originated from the A2 sex chromosome.bS, number of polymorphic sites.cπ (%), average number of differences per 100 nucleotides.

TABLE 3

P-values for the 2 × 2 G-tests for significance of differences in A1/A2 divergence between the loci in the nonrecombining region
LaSbLocusA2-397A1-217A1-128A1-199A2-422A2-516A2-404A2-435A2-473A2-457
5190A2-397
6679A1-2170.006
5698A1-1280.0060.93
61812A1-1990.00070.410.48
3449A2-4220.00030.170.210.51
47014A2-5160.000030.060.0860.280.76
50817A2-40400.0250.0380.150.550.75
50618A2-43500.0150.0240.1040.450.620.86
45721A2-47300.0010.0030.01630.150.210.340.43
30316A2-45700.00090.00170.00970.090.130.2030.260.69
50339A2-5750000.000010.0020.0020.0030.0060.0550.199
Open in a separate windowP-values <0.05 are in boldface type.aL, the length of the region compared.bS, the number of nucleotide differences observed.As most of the loci isolated from the A1 and A2 chromosomes recombine in meiosis, they are not expected to degenerate. Thus, the observation of a higher proportion of TEs in these loci, compared to other chromosomes (Hood et al. 2004), is unlikely to reflect genetic degeneration attributable to a lack of recombination in these loci. A higher abundance of TEs in the sequences isolated from the A1 and A2 chromosomes, as reported by Hood et al. (2004), may simply reflect variation in the TE density across the genome. Thus, it remains to be seen whether M. violaceum mating-type-specific regions degenerate, similar to vertebrate Y (or W) chromosomes, or remain largely intact, as in C. neoformans (Lengeler et al. 2002). If the latter were the case, it may suggest that nonrecombining regions in fungi do not necessarily follow the same degenerative path as animal Y and W chromosomes. The analysis of sequences from the M. violaceum genome (and perhaps other fungal genomes) will hopefully provide the answer to this question.The lack of degeneration of mating-type-specific regions in C. neoformans may be due to the relatively small size of the nonrecombining regions. The 20 genes present in this region may not be sufficient for the operation of such detrimental population genetic processes as background selection or Muller''s ratchet because the speed of these processes depends critically on the number of active genes linked together (Charlesworth 2008). Larger mating-type-specific regions in M. violaceum might contain more genes; thus, more active genetic degeneration may be expected in this species. Indeed, many strains of M. violaceum show haplolethality linked to one of the mating types (Hood and Antonivics 2000; Thomas et al. 2003; Tellier et al. 2005), which may reflect the accumulation of deleterious mutations in the nonrecombining regions around the mating-type loci. Mating-type specificity of the markers that amplified in only A1 or A2 strains in this study may also reflect genetic degeneration.Another factor that may potentially prevent degeneration of genes linked to mating-type loci in fungi is the haploid expression of genes in these regions. In animals, many Y-linked genes have functional homologs on the X chromosome, and loss of the Y-linked gene may be compensated for by expression of the X-linked homologs. The haploid stage in an animal''s life cycle is very short, and very few genes are actively expressed in animal gametes (Schultz et al. 2003). In plants, on the other hand, a significant proportion of the genome is expressed in pollen (da Costa-Nunes and Grossniklaus 2003), and so the loss of Y-linked genes expressed in gametes may be more detrimental than in animals. Indeed, most genes isolated from the white campion X chromosome have intact Y-linked copies (Filatov 2005; Bergero et al. 2007), but due to the small number of genes available, it is still unclear whether genetic degeneration of Y-linked genes is indeed slower in this species (and in plants generally) compared to animal Y chromosomes. Haploid expression could be an even more powerful force in fungi and other organisms with haploid sexes, such as bryophytes, as most genes are expressed in the haploid stage. Further analysis of genetic degeneration in nonrecombining sex- or mating-type-specific regions in fungi and bryophytes will help to shed light on this question.  相似文献   

15.
Distribution of Sulfonamide Resistance Genes in Escherichia coli and Salmonella Isolates from Swine and Chickens at Abattoirs in Ontario and Québec,Canada     
Gosia K. Kozak  David L. Pearl  Julia Parkman  Richard J. Reid-Smith  Anne Deckert  Patrick Boerlin 《Applied and environmental microbiology》2009,75(18):5999-6001
Sulfonamide-resistant Escherichia coli and Salmonella isolates from pigs and chickens in Ontario and Québec were screened for sul1, sul2, and sul3 by PCR. Each sul gene was distributed differently across populations, with a significant difference between distribution in commensal E. coli and Salmonella isolates and sul3 restricted mainly to porcine E. coli isolates.Resistance to sulfonamides is frequent in bacteria from farm animals (7, 8, 9, 10) and is usually caused by the acquisition of the genes sul1, sul2, and sul3 (20, 22). The objectives of this study were (i) to assess the distribution of these genes in Escherichia coli and Salmonella enterica isolates in swine and chickens from two major provinces in Canada, (ii) to assess whether differences occur in the distribution of these genes among bacterial species found within two different animal host species, and (iii) to assess whether significant differences in the distribution of these genes are present between the commensal E. coli strains used as indicators for surveillance of antimicrobial resistance and the zoonotic Salmonella pathogens found in the same ecological niche. In contrast to previous studies, a multivariable logistic regression model was used to analyze the data, control for confounding factors, and assess the interaction effect between animal and bacterial species in terms of the probability of an isolate carrying a specific sul gene. The distribution of sulfonamide resistance genes among sulfonamide-resistant E. coli (393 isolates from chickens and 311 from swine) and Salmonella (13 isolates from chickens and 221 from swine) isolates was assessed. These isolates were collected by the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) between 2003 and 2005 from ceca of apparently healthy animals at abattoirs in Ontario (n = 435) and Québec (n = 503). The methods used by CIPARS are presented in detail elsewhere (8-10). The isolates were screened with a previously published multiplex PCR for sul1, sul2, and sul3 (16). The sul1 and sul2 genes were found in E. coli and Salmonella isolates from both animal species. The sul3 gene was detected in both E. coli and Salmonella isolates from swine but only in E. coli isolates from chickens (Table (Table1).1). Three percent of the isolates had no detectable sul gene, 12.5% possessed two genes, and two isolates carried three genes (Table (Table1).1). Similar (2, 3, 14, 19) or higher (4, 11, 17) values for multiple genes have been reported by others. The overall higher prevalence of sul1 in Salmonella isolates and of sul2 and sul3 in E. coli isolates was in agreement with the results of previous studies (2-4, 11, 12, 14, 21).

TABLE 1.

Frequency of the three resistance genes sul1, sul2, and sul3 in sulfonamide-resistant E. coli and Salmonella isolates from chickens and swine in Ontario and Québec between 2003 and 2005
Bacterial speciesSource (no. of isolates)No. (%) of isolates with indicated gene(s)
sul1sul2sul3sul1 + sul2sul1+ sul3sul2 + sul3sul1 + sul2 + sul3None
E. coliSwine (311)61 (19.6)66 (21.2)132 (42.4)11 (3.5)9 (2.9)11 (3.5)2 (0.6)19 (6.1)
Chickens (393)103 (26.2)211 (53.7)9 (2.3)64 (16.3)0 (0.0)0 (0.0)0 (0.0)6 (1.5)
Total (704)164 (23.3)277 (39.3)141 (20.0)75 (10.7)9 (1.3)11 (1.6)2 (0.3)25 (3.6)
SalmonellaSwine (221)173 (78.3)20 (9.0)5 (2.3)7 (3.2)0 (0.0)14 (6.3)0 (0.0)2 (0.9)
Chickens (13)7 (53.8)5 (38.5)0 (0.0)0 (0.0)0 (0.0)0 (0.0)0 (0.0)1 (7.7)
Total (234)180 (76.9)25 (10.7)5 (2.1)7 (3.0)0 (0.0)14 (6.0)0 (0.0)3 (1.3)
Open in a separate windowThree logistic regression models (Table (Table2)2) for associations between the presence of each sul gene and the bacterial species, animal species, province of origin of the animals, and year of isolation were built using Stata 9 (StataCorp, College Station, TX). Statistical interactions between bacterial and animal species were assessed in the sul1 and sul2 models but not in the sul3 model (the sample size was insufficient). Tests were two tailed, with significance at a P value of ≤0.05. A significant interaction between bacterial and animal species was observed for sul1 (Table (Table2).2). The presence of statistical interactions indicates that the effect of each variable depends on the state of the other variable. Thus, the effects of bacterial and animal species on the distribution of sul1 cannot be interpreted independently. For instance, sul1 was more frequent in Salmonella isolates from swine than from chickens, but the reverse was true for E. coli isolates (Table (Table3).3). Genomic islands containing sul1 are present in some Salmonella serovars, including S. enterica serovar Typhimurium and S. Derby (1, 6, 18), which were the most frequent in the swine samples of this study (Table (Table4).4). This contrasts with E. coli strains, in which sul1 is usually associated with transposons and large transferable plasmids (22). Thus, the presence of significant statistical interactions in the sul1 model could be an indicator of the differential importance of horizontal gene transfer in E. coli strains versus clonal expansion of specific Salmonella serovars in the animal species investigated. No significant interaction was detected for sul2 (P = 0.66) (Table (Table2).2). This could be the consequence of the relatively small number of sul2-positive Salmonella isolates and the resultant lack of statistical power; however, it is also possible that sul2 has a different epidemiology than sul1. The sul2 gene has not been shown to be located on genomic islands and is usually plasmid borne (22). It may therefore be transferred more frequently than sul1 between E. coli and Salmonella and between bacteria from swine and chickens, leading to the absence of a significant interaction in the sul2 model. Serotyping, molecular typing, and assessment of gene transferability would be required to test these hypotheses.

TABLE 2.

Multivariable models for the distribution of the sul1, sul2, and sul3 sulfonamide resistance genes from swine and chickens at slaughter in Ontario and Québec between 2003 and 2005
Explanatory variable (referent group) or interactionOdds ratio (95% confidence interval)b
sul1sul2sul3
Salmonella (E. coli)1.54 (0.50-4.74)0.33 (0.21-0.51)0.10 (0.06-0.16)
Swine (chickens)0.49 (0.36-0.68)c0.16 (0.12-0.23)c39.57 (20.21-77.48)c
Québec (Ontario)0.80 (0.60-1.07)2.20 (1.61-2.99)c0.83 (0.56-1.23)
2004 (2003)0.77 (0.56-1.06)0.99 (0.71-1.40)1.84 (1.18-2.86)
2005 (2003)0.68 (0.45-1.04)1.36 (0.88-2.09)1.66 (0.99-2.79)
2005 (2004)0.88 (0.58-1.36)1.36 (0.88-2.11)0.90 (0.53-1.53)
Bacterial species × animal speciesa12.84 (3.79-43.46)cNINI
Open in a separate windowaStatistical interaction between data for bacterial species and animal species. The terms for this interaction are described in Table Table33.bExample of odds ratio interpretation: the odds of a porcine isolate carrying sul3 were 39.57 times higher than for an isolate obtained from chickens. NI, not included in the model. The sul2 model was not included because the P value for this interaction was equal to 0.66, and the sul3 model was not included because it did not converge when including this interaction.cP < 0.001.

TABLE 3.

Interaction terms for the association between bacterial and animal species for the presence of sul1
Contrast variablesOdds ratioa95% Confidence intervalP value
Salmonella in pigs vs Salmonella in chickens6.301.95-20.390.002
E. coli in pigs vs E. coli in chickens0.490.36-0.67<0.001
Salmonella in chickens vs E. coli in chickens1.540.50-4.740.451
Salmonella in pigs vs E. coli in pigs19.7912.28-31.87<0.001
Open in a separate windowaExample of interpretation: the interaction effect suggests that sul1-mediated sulfonamide resistance is 19.79 times more likely to occur in Salmonella in pigs than in E. coli in pigs.

TABLE 4.

Salmonella serovars and frequency of resistance genes detected in each serovar
Salmonella serovarNo. of isolatesaNo. of isolates with indicated genea
sul1sul2sul3
Agona14/014/00/00/0
Berta3/00/03/00/0
Bovismorbificans1/00/00/00/0
Brandenburg3/01/01/00/0
Derby63/059/04/02/0
Give O:15+1/01/00/00/0
Heidelberg2/40/30/02/0
4,12:−:−3/03/00/00/0
4,12:i:−2/02/00/00/0
4,5,12:−:−1/01/00/01/0
Rough-O:fg:−1/01/00/00/0
Rough-O:l,v:enz151/01/01/00/0
Infantis3/01/02/00/0
Johannesburg1/01/00/00/0
London2/01/00/01/0
Manhattan2/00/02/00/0
Mbandaka6/06/01/00/0
Ohio O:14+1/00/01/00/0
Putten1/01/00/00/0
Schwarzengrund0/60/10/50/0
Typhimurium110/3101/312/013/0
Total221/13194/727/519/0
Open in a separate windowaThe first and second numbers in each column represent swine and chicken isolates, respectively; the total number of tabulated occurrences of sulfonamide genes is larger than the number of resistant isolates investigated because some isolates carried several genes simultaneously.A significant increase in the prevalence of sul3 was detected between 2003 and 2004 (P = 0.007) but not between 2004 and 2005 (P = 0.055) nor at any time for sul1 and sul2 (Table (Table2).2). The sul3 gene has emerged recently (5), and its prevalence was probably still increasing in 2003. The odds of finding sul3 in Salmonella isolates were 10 times lower than for E. coli isolates and 40 times higher in swine than in chickens (Table (Table2).2). Other studies have also found high frequencies of sul3 in porcine E. coli isolates (5, 12, 20) and much lower frequencies in other sources (4, 11, 12, 14). Although these isolates were not typed, previous results have shown that sul3 is present in both pathogenic and commensal porcine E. coli isolates (5) of at least 13 different serotypes (P. Boerlin and R. M. Travis, unpublished data). The sul3 gene has spread extensively across porcine E. coli populations in North America and Europe but remains uncommon in other major farm animal species and in Salmonella populations (Table (Table1)1) (2, 13). It may require more time to spread to other populations. There may be biological and ecological barriers slowing its spread to bacteria of other animal species or coselection factors that favor its presence in porcine E. coli populations.Using the example of sulfonamides as a model for the application of multivariable statistical approaches to the study of antimicrobial resistance epidemiology, this study indicates that the relative frequencies of genes encoding resistance to the same antimicrobial either present in bacterial populations for decades or recently emerged can vary significantly between animal hosts and phylogenetically related bacterial species sharing the same ecological niche. Differences in the distribution of resistance determinants may remain hidden when assessing resistance phenotypes. Similar antimicrobial susceptibility results do not necessarily imply similar resistance genes. These findings highlight the need to further explore the interactions between commensals and pathogens and the ways in which commensal bacteria are interpreted as indicators of antimicrobial resistance in pathogens.  相似文献   

16.
Distribution of Shiga-Toxigenic Escherichia coli O157 in the Gastrointestinal Tract of Naturally O157-Shedding Cattle at Necropsy     
James E. Keen  William W. Laegreid  Carol G. Chitko-McKown  Lisa M. Durso  James L. Bono 《Applied and environmental microbiology》2010,76(15):5278-5281
  相似文献   

17.
Impact of Viral Dose and Major Histocompatibility Complex Class IB Haplotype on Viral Outcome in Mauritian Cynomolgus Monkeys Vaccinated with Tat upon Challenge with Simian/Human Immunodeficiency Virus SHIV89.6P     
Aurelio Cafaro  Stefania Bellino  Fausto Titti  Maria Teresa Maggiorella  Leonardo Sernicola  Roger W. Wiseman  David Venzon  Julie A. Karl  David O'Connor  Paolo Monini  Marjorie Robert-Guroff  Barbara Ensoli 《Journal of virology》2010,84(17):8953-8958
The effects of the challenge dose and major histocompatibility complex (MHC) class IB alleles were analyzed in 112 Mauritian cynomolgus monkeys vaccinated (n = 67) or not vaccinated (n = 45) with Tat and challenged with simian/human immunodeficiency virus (SHIV) 89.6Pcy243. In the controls, the challenge dose (10 to 20 50% monkey infectious doses [MID50]) or MHC did not affect susceptibility to infection, peak viral load, or acute CD4 T-cell loss, whereas in the chronic phase of infection, the H1 haplotype correlated with a high viral load (P = 0.0280) and CD4 loss (P = 0.0343). Vaccination reduced the rate of infection acquisition at 10 MID50 (P < 0.0001), and contained acute CD4 loss at 15 MID50 (P = 0.0099). Haplotypes H2 and H6 were correlated with increased susceptibility (P = 0.0199) and resistance (P = 0.0087) to infection, respectively. Vaccination also contained CD4 depletion (P = 0.0391) during chronic infection, independently of the challenge dose or haplotype.Advances in typing of the major histocompatibility complex (MHC) of Mauritian cynomolgus macaques (14, 20, 26) have provided the opportunity to address the influence of host factors on vaccine studies (13). Retrospective analysis of 22 macaques vaccinated with Tat or a Tat-expressing adenoviral vector revealed that monkeys with the H6 or H3 MHC class IB haplotype were overrepresented among aviremic or controller animals, whereas macaques with the H2 or H5 haplotype clustered in the noncontrollers (12). More recently, the H6 haplotype was reported to correlate with control of chronic infection with simian immunodeficiency virus (SIV) mac251, regardless of vaccination (18).Here, we performed a retrospective analysis of 112 Mauritian cynomolgus macaques, which included the 22 animals studied previously (12), to evaluate the impact of the challenge dose and class IB haplotype on the acquisition and severity of simian/human immunodeficiency virus (SHIV) 89.6Pcy243 infection in 45 control monkeys and 67 monkeys vaccinated with Tat from different protocols (Table (Table11).

TABLE 1.

Summary of treatment, challenge dose, and outcome of infection in cynomolgus monkeys
Protocol codeNo. of monkeysImmunogen (dose)aAdjuvantbSchedule of immunization (wk)RoutecChallenged (MID50)Virological outcomee
Reference(s) or source
ACV
ISS-ST6Tat (10)Alum or RIBI0, 2, 6, 12, 15, 21, 28, 32, 36s.c., i.m.104114, 17
ISS-ST1Tat (6)None0, 5, 12, 17, 22, 27, 32, 38, 42, 48i.d.101004, 17
ISS-PCV3pCV-tat (1 mg)Bupivacaine + methylparaben0, 2, 6, 11, 15, 21, 28, 32, 36i.m.103006
ISS-ID3Tat (6)none0, 4, 8, 12, 16, 20, 24, 28, 39, 43, 60i.d.10111B. Ensoli, unpublished data
ISS-TR6Tat (10)Alum-Iscom0, 2, 6, 11, 16, 21, 28, 32, 36s.c., i.d., i.m.10420Ensoli, unpublished
ISS-TGf3Tat (10)Alum0, 4, 12, 22s.c.1503Ensoli, unpublished
ISS-TG3Tatcys22 (10)Alum1503Ensoli, unpublished
ISS-TG4Tatcys22 (10) + Gag (60)Alum1504Ensoli, unpublished
ISS-TG4Tat (10) + Gag (60)Alum1504Ensoli, unpublished
ISS-MP3Tat (10)H1D-Alum0, 4, 12, 18, 21, 38s.c., i.m.15021Ensoli, unpublished
ISS-MP3Tat (10)Alums.c.15003Ensoli, unpublished
ISS-GS6Tat (10)H1D-Alum0, 4, 12, 18, 21, 36s.c., i.m.15132Ensoli, unpublished
NCI-Ad-tat/Tat7Ad-tat (5 × 108 PFU), Tat (10)Alum0, 12, 24, 36i.n., i.t., s.c.15232Ensoli, unpublished
NCI-Tat9Tat (6 and 10)Alum/Iscom0, 2, 6, 11, 15, 21, 28, 32, 36s.c., i.d., i.m.1524312
ISS-NPT3pCV-tat (1 mg)Bupivacaine + methylparaben-Iscom0, 2, 8, 13, 17, 22, 28, 46, 71i.m.20003Ensoli, unpublished
ISS-NPT3pCV-tatcys22 (1 mg)Bupivacaine + methylparaben-Iscom0, 2, 8, 13, 17, 22, 28, 46, 71i.m.20111
    Total vaccinated67191731
        Naive11NoneNoneNAgNA10 or 15137
        Control34None, Ad, or pCV-0Alum, RIBI, H1D, Iscom or bupivacaine + methylparaben-Iscoms.c., i.d., i.n., i.t., i.m.10, 15, or 2051316
    Total controls4561623
    Total112253354
Open in a separate windowaAll animals were inoculated with the indicated dose of Tat plasmid DNA (pCV-tat [8], adenovirus-tat [Ad-tat] [27]) or protein, Gag protein, or empty vectors (pCV-0, adenovirus [Ad]) by the indicated route. Doses are in micrograms unless indicated otherwise.bAlum, aluminum phosphate (4); RIBI oil-in-water emulsions containing squalene, bacterial monophosphoryl lipid A, and refined mycobacterial products (4); Iscom, immune-stimulating complex (4); H1D are biocompatible anionic polymeric microparticles used for vaccine delivery (10, 12, 25a).cs.c., subcutaneous; i.m., intramuscular; i.d., intradermal; i.n., intranasal; i.t., intratracheal.dAll animals were inoculated intravenously with the indicated dose of the same SHIV89.6.Pcy243 stock.eAccording to the virological outcome upon challenge, monkeys were grouped as aviremic (A), controllers (C), or viremic (V).fBecause of the short follow-up, controller status could not be determined and all infected monkeys of the ISS-TG protocol were therefore considered viremic.gNA, not applicable.  相似文献   

18.
Quantitative Real-Time PCR Assays for Sensitive Detection of Canada Goose-Specific Fecal Pollution in Water Sources     
B. Fremaux  T. Boa  C. K. Yost 《Applied and environmental microbiology》2010,76(14):4886-4889
Canada geese (Branta canadensis) are prevalent in North America and may contribute to fecal pollution of water systems where they congregate. This work provides two novel real-time PCR assays (CGOF1-Bac and CGOF2-Bac) allowing for the specific and sensitive detection of Bacteroides 16S rRNA gene markers present within Canada goose feces.The Canada goose (Branta canadensis) is a prevalent waterfowl species in North America. The population density of Canada geese has doubled during the past 15 years, and the population was estimated to be close to 3 million in 2007 (4). Canada geese often congregate within urban settings, likely due to available water sources, predator-free grasslands, and readily available food supplied by humans (6). They are suspected to contribute to pollution of aquatic environments due to the large amounts of fecal matter that can be transported into the water. This can create a public health threat if the fecal droppings contain pathogenic microorganisms (6, 7, 9, 10, 12, 13, 19). Therefore, tracking transient fecal pollution of water due to fecal inputs from waterfowl, such as Canada geese, is of importance for protecting public health.PCR detection of host-specific 16S rRNA gene sequences from Bacteroidales of fecal origin has been described as a promising microbial source-tracking (MST) approach due to its rapidity and high specificity (2, 3). Recently, Lu et al. (15) characterized the fecal microbial community from Canada geese by constructing a 16S rRNA gene sequence database using primers designed to amplify all bacterial 16S rRNA gene sequences. The authors reported that the majority of the 16S rRNA gene sequences obtained were related to Clostridia or Bacilli and to a lesser degree Bacteroidetes, which represent possible targets for host-specific source-tracking assays.The main objective of this study was to identify novel Bacteroidales 16S rRNA gene sequences that are specific to Canada goose feces and design primers and TaqMan fluorescent probes for sensitive and specific quantification of Canada goose fecal contamination in water sources.Primers 32F and 708R from Bernhard and Field (2) were used to construct a Bacteroidales-specific 16S rRNA gene clone library from Canada goose fecal samples (n = 15) collected from grass lawns surrounding Wascana Lake (Regina, SK, Canada) in May 2009 (for a detailed protocol, see File S1 in the supplemental material). Two hundred eighty-eight clones were randomly selected and subjected to DNA sequencing (at the Plant Biotechnology Institute DNA Technologies Unit, Saskatoon, SK, Canada). Representative sequences of each operational taxonomic unit (OTU) were recovered using an approach similar to that described by Mieszkin et al. (16). Sequences that were less than 93% similar to 16S rRNA gene sequences from nontarget host species in GenBank were used in multiple alignments to identify regions of DNA sequence that were putatively goose specific. Subsequently, two TaqMan fluorescent probe sets (targeting markers designated CGOF1-Bac and CGOF2-Bac) were designed using the RealTimeDesign software provided by Biosearch Technologies (http://www.biosearchtech.com/). The newly designed primer and probe set for the CGOF1-Bac assay included CG1F (5′-GTAGGCCGTGTTTTAAGTCAGC-3′) and CG1R (5′-AGTTCCGCCTGCCTTGTCTA-3′) and a TaqMan probe (5′-6-carboxyfluorescein [FAM]-CCGTGCCGTTATACTGAGACACTTGAG-Black Hole Quencher 1 [BHQ-1]-3′), and the CGOF2-Bac assay had primers CG2F (5′-ACTCAGGGATAGCCTTTCGA-3′) and CG2R (5′-ACCGATGAATCTTTCTTTGTCTCC-3′) and a TaqMan probe (5′-FAM-AATACCTGATGCCTTTGTTTCCCTGCA-BHQ-1-3′). Oligonucleotide specificities for the Canada goose-associated Bacteroides 16S rRNA primers were verified through in silico analysis using BLASTN (1) and the probe match program of the Ribosomal Database Project (release 10) (5). Host specificity was further confirmed using DNA extracts from 6 raw human sewage samples from various geographical locations in Saskatchewan and 386 fecal samples originating from 17 different animal species in Saskatchewan, including samples from Canada geese (n = 101) (Table (Table1).1). An existing nested PCR assay for detecting Canada goose feces (15) (targeting genetic marker CG-Prev f5) (see Table S1 in the supplemental material) was also tested for specificity using the individual fecal and raw sewage samples (Table (Table1).1). All fecal DNA extracts were obtained from 0.25 g of fecal material by using the PowerSoil DNA extraction kit (Mo Bio Inc., Carlsbad, CA) (File S1 in the supplemental material provides details on the sample collection).

TABLE 1.

Specificities of the CGOF1-Bac, CGOF2-Bac, and CG-Prev f5 PCR assays for different species present in Saskatchewan, Canada
Host group or sample typeNo. of samplesNo. positive for Bacteroidales marker:
CGOF1-BacCGOF2-BacCG-Prev f5All-Bac
Individual human feces2500125
Raw human sewage60006
Cows4100041
Pigs4800148
Chickens3400834
Geese10158515995a
Gulls1600614
Pigeons2510222
Ducks1000010
Swans10001
Moose1000010
Deer
    White tailed1000010
    Mule1000010
    Fallow1000010
Caribou1000010
Bison1000010
Goats1000010
Horses1500015
Total392595177381
Open in a separate windowaThe 6 goose samples that tested negative for the All-Bac marker also tested negative for the three goose markers.The majority of the Canada goose feces analyzed in this study (94%; 95 of 101) carried the Bacteroidales order-specific genetic marker designated All-Bac, with a relatively high median concentration of 8.2 log10 copies g1 wet feces (Table (Table11 and Fig. Fig.1).1). The high prevalence and abundance of Bacteroidales in Canada goose feces suggested that detecting members of this order could be useful in identifying fecal contamination associated with Canada goose populations.Open in a separate windowFIG. 1.Concentrations of the Bacteroidales (All-Bac, CGOF1-Bac, and CGOF2-Bac) genetic markers in feces from various individual Canada geese.The composition of the Bacteroidales community in Canada goose feces (n = 15) was found to be relatively diverse since 52 OTUs (with a cutoff of 98% similarity) were identified among 211 nonchimeric 16S rRNA gene sequences. Phylogenetic analysis of the 52 OTUs (labeled CGOF1 to CGOF52) revealed that 43 (representing 84% of the 16S rRNA gene sequences) were Bacteroides like and that 9 (representing 16% of the 16S rRNA gene sequences) were likely to be members of the Prevotella-specific cluster (see Fig. S2 in the supplemental material). Similarly, Jeter et al. (11) reported that 75.7% of the Bacteroidales 16S rRNA clone library sequences generated from goose fecal samples were Bacteroides like. The majority of the Bacteroides- and Prevotella-like OTUs were dispersed among a wide range of previously characterized sequences from various hosts and did not occur in distinct clusters suitable for the design of Canada goose-associated real-time quantitative PCR (qPCR) assays (see Fig. S2 in the supplemental material). However, two single Bacteroides-like OTU sequences (CGOF1 and CGOF2) contained putative goose-specific DNA regions that were identified by in silico analysis (using BLASTN, the probe match program of the Ribosomal Database Project, and multiple alignment). The primers and probe for the CGOF1-Bac and CGOF2-Bac assays were designed with no mismatches to the clones CGOF1 and CGOF2, respectively.The CGOF2-Bac assay demonstrated no cross-amplification with fecal DNA from other host groups, while cross-amplification for the CGOF1-Bac assay was limited to one pigeon fecal sample (1 of 25, i.e., 4% of the samples) (Table (Table1).1). Since the abundance in the pigeon sample was low (3.3 log10 marker copies g1 feces) and detection occurred late in the qPCR (with a threshold cycle [CT] value of 37.1), it is unlikely that this false amplification would negatively impact the use of the assay as a tool for detection of Canada goose-specific fecal pollution in environmental samples. In comparison, the nested PCR CG-Prev f5 assay described by Lu and colleagues (15) demonstrated non-host-specific DNA amplification with fecal DNA samples from several animals, including samples from humans, pigeons, gulls, and agriculturally relevant pigs and chickens (Table (Table11).Both CGOF1-Bac and CGOF2-Bac assays showed limits of quantification (less than 10 copies of target DNA per reaction) similar to those of other host-specific Bacteroidales real-time qPCR assays (14, 16, 18). The sensitivities of the CGOF1-Bac and CGOF2-Bac assays were 57% (with 58 of 101 samples testing positive) and 50% (with 51 of 101 samples testing positive) for Canada goose feces, respectively (Table (Table1).1). A similar sensitivity of 58% (with 59 of 101 samples testing positive) was obtained using the CG-Prev f5 PCR assay. The combined use of the three assays increased the detection level to 72% (73 of 101) (Fig. (Fig.2).2). Importantly, all markers were detected within groups of Canada goose feces collected each month from May to September, indicating relative temporal stability of the markers. The CG-Prev f5 PCR assay is an end point assay, and therefore the abundance of the gene marker in Canada goose fecal samples could not be determined. However, development of the CGOF1-Bac and CGOF2-Bac qPCR approach allowed for the quantification of the host-specific CGOF1-Bac and CGOF2-Bac markers. In the feces of some individual Canada geese, the concentrations of CGOF1-Bac and CGOF2-Bac were high, reaching levels up to 8.8 and 7.9 log10 copies g1, respectively (Fig. (Fig.11).Open in a separate windowFIG. 2.Venn diagram for Canada goose fecal samples testing positive with the CGOF1-Bac, CGOF2-Bac, and/or CG-Prev f5 PCR assay. The number outside the circles indicates the number of Canada goose fecal samples for which none of the markers were detected.The potential of the Canada goose-specific Bacteroides qPCR assays to detect Canada goose fecal pollution in an environmental context was tested using water samples collected weekly during September to November 2009 from 8 shoreline sampling sites at Wascana Lake (see File S1 and Fig. S1 in the supplemental material). Wascana Lake is an urban lake, located in the center of Regina, that is routinely frequented by Canada geese. In brief, a single water sample of approximately 1 liter was taken from the surface water at each sampling site. Each water sample was analyzed for Escherichia coli enumeration using the Colilert-18/Quanti-Tray detection system (IDEXX Laboratories, Westbrook, ME) (8) and subjected to DNA extraction (with a PowerSoil DNA extraction kit [Mo Bio Inc., Carlsbad, CA]) for the detection of Bacteroidales 16S rRNA genetic markers using the Bacteroidales order-specific (All-Bac) qPCR assay (14), the two Canada goose-specific (CGOF1-Bac and CGOF2-Bac) qPCR assays developed in this study, and the human-specific (BacH) qPCR assay (17). All real-time and conventional PCR procedures as well as subsequent data analysis are described in the supplemental material and methods. The E. coli and All-Bac quantification data demonstrated that Wascana Lake was regularly subjected to some form of fecal pollution (Table (Table2).2). The All-Bac genetic marker was consistently detected in high concentrations (6 to 7 log10 copies 100 ml1) in all the water samples, while E. coli concentrations fluctuated according to the sampling dates and sites, ranging from 0 to a most probable number (MPN) of more than 2,000 100 ml1. High concentrations of E. coli were consistently observed when near-shore water experienced strong wave action under windy conditions or when dense communities of birds were present at a given site and time point.

TABLE 2.

Levels of E. coli and incidences of the Canada goose-specific (CGOF1-Bac and CGOF2-Bac), human-specific (BacH), and generic (All-Bac) Bacteroidales 16S rRNA markers at the different Wascana Lake sites sampled weeklya
SiteE. coli
All-Bac
CGOF1-Bac
CGOF2-Bac
BacH
No. of positive water samples/total no. of samples analyzed (%)Min level-max level (MPN 100 ml−1)Mean level (MPN 100 ml−1)No. of positive water samples/total no. of samples analyzed (%)Min level-max level (log copies 100 ml−1)Mean level (log copies 100 ml−1)No. of positive water samples/total no. of samples analyzed (%)Min level-max level (log copies 100 ml−1)Mean level (log copies 100 ml−1)No. of positive water samples/total no. of samples analyzed (%)Min level-max level (log copies 100 ml−1)Mean level (log copies 100 ml−1)No. of positive water samples/total no. of samples analyzedMin level-max level (log copies 100 ml−1)Mean level (log copies 100 ml−1)
W18/8 (100)6-19671.18/8 (100)6.2-8.16.96/8 (75)0-4.72.44/8 (50)0-41.72/80-3.71.7
W29/10 (90)0-1,12019410/10 (100)5.8-6.86.49/10 (90)0-3.72.68/10 (80)0-3.32.20/1000
W310/10 (100)6-1,55053410/10 (100)6-7.8710/10 (100)2.9-4.83.810/10 (100)2-4.53.40/1000
W410/10 (100)16-1,73252910/10 (100)6.4-7.6710/10 (100)3.2-4.63.910/10 (100)2.8-4.33.40/1000
W510/10 (100)2-2,42068710/10 (100)5.5-6.96.37/10 (70)0-3.21.75/10 (50)0-3.11.20/1000
W610/10 (100)3-1,99038910/10 (100)5.5-76.39/10 (90)0-4.32.86/10 (60)0-5.121/100-3.41.3
W77/7 (100)5-2,4204457/7 (100)5.7-7.876/7 (86)0-3.82.65/7 (71)0-4.42.42/70-5.12.8
W810/10 (100)17-98016010/10 (100)6.3-8.67.18/10 (80)0-4.62.87/10 (70)0-4.42.30/1000
Open in a separate windowaMin, minimum; max, maximum.The frequent detection of the genetic markers CGOF1-Bac (in 65 of 75 water samples [87%]), CGOF2-Bac (in 55 of 75 samples [73%]), and CG-Prev f5 (in 60 of 75 samples [79%]) and the infrequent detection of the human-specific Bacteroidales 16S rRNA gene marker BacH (17) (in 5 of 75 water samples [7%[) confirmed that Canada geese significantly contributed to the fecal pollution in Wascana Lake during the sampling period. Highest mean concentrations of both CGOF1-Bac and CGOF2-Bac markers were obtained at the sampling sites W3 (3.8 and 3.9 log10 copies 100 ml1) and W4 (3.4 log10 copies 100 ml1 for both), which are heavily frequented by Canada geese (Table (Table2),2), further confirming their significant contribution to fecal pollution at these particular sites. It is worth noting that concentrations of the CGOF1-Bac and CGOF2-Bac markers in water samples displayed a significant positive relationship with each other (correlation coefficient = 0.87; P < 0.0001), supporting the accuracy of both assays for identifying Canada goose-associated fecal pollution in freshwater.In conclusion, the CGOF1-Bac and CGOF2-Bac qPCR assays developed in this study are efficient tools for estimating freshwater fecal inputs from Canada goose populations. Preliminary results obtained during the course of the present study also confirmed that Canada geese can serve as reservoirs of Salmonella and Campylobacter species (see Fig. S3 in the supplemental material). Therefore, future work will investigate the cooccurence of these enteric pathogens with the Canada goose fecal markers in the environment.  相似文献   

19.
MitoMiner, an Integrated Database for the Storage and Analysis of Mitochondrial Proteomics Data     
Anthony C. Smith  Alan J. Robinson 《Molecular & cellular proteomics : MCP》2009,8(6):1324-1337
  相似文献   

20.
Molecular Determinants of Adaptation of Highly Pathogenic Avian Influenza H7N7 Viruses to Efficient Replication in the Human Host     
Emmie de Wit  Vincent J. Munster  Debby van Riel  Walter E. P. Beyer  Guus F. Rimmelzwaan  Thijs Kuiken  Albert D. M. E. Osterhaus  Ron A. M. Fouchier 《Journal of virology》2010,84(3):1597-1606
  相似文献   

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