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The biological, serological, and genomic characterization of a paramyxovirus recently isolated from rockhopper penguins (Eudyptes chrysocome) suggested that this virus represented a new avian paramyxovirus (APMV) group, APMV10. This penguin virus resembled other APMVs by electron microscopy; however, its viral hemagglutination (HA) activity was not inhibited by antisera against any of the nine defined APMV serotypes. In addition, antiserum generated against this penguin virus did not inhibit the HA of representative viruses of the other APMV serotypes. Sequence data produced using random priming methods revealed a genomic structure typical of APMV. Phylogenetic evaluation of coding regions revealed that amino acid sequences of all six proteins were most closely related to APMV2 and APMV8. The calculation of evolutionary distances among proteins and distances at the nucleotide level confirmed that APMV2, APMV8, and the penguin virus all were sufficiently divergent from each other to be considered different serotypes. We propose that this isolate, named APMV10/penguin/Falkland Islands/324/2007, be the prototype virus for APMV10. Because of the known problems associated with serology, such as antiserum cross-reactivity and one-way immunogenicity, in addition to the reliance on the immune response to a single protein, the hemagglutinin-neuraminidase, as the sole base for viral classification, we suggest the need for new classification guidelines that incorporate genome sequence comparisons.Viruses from the Paramyxoviridae family have caused disease in humans and animals for centuries. Over the last 40 years, many paramyxoviruses isolated from animals and people have been newly described (16, 17, 22, 29, 31, 32, 36, 42, 44, 46, 49, 58, 59, 62-64). Viruses from this family are pleomorphic, enveloped, single-stranded, nonsegmented, negative-sense RNA viruses that demonstrate serological cross-reactivity with other paramyxoviruses related to them (30, 46). The subfamily Paramyxovirinae is divided into five genera: Respirovirus, Morbillivirus, Rubulavirus, Henipavirus, and Avulavirus (30). The Avulavirus genus contains nine distinct avian paramyxovirus (APMV) serotypes (Table (Table1),1), and information on the discovery of each has been reported elsewhere (4, 6, 7, 9, 12, 34, 41, 50, 51, 60, 68).

TABLE 1.

Characteristics of prototype viruses APMV1 to APMV9 and the penguin virus
StrainHostDiseaseDistributionFusion cleavagecGI accession no.
APMV1/Newcastle disease virus>250 speciesHigh mortalityWorldwideGRRQKRF45511218
InapparentWorldwideGGRQGRLa11545722
APMV2/Chicken/CA/Yucaipa/1956Turkey, chickens, psittacines, rails, passerinesDecrease in egg production and respiratory diseaseWorldwideDKPASRF169144527
APMV3/Turkey/WI/1968TurkeyMild respiratory disease and moderate egg decreaseWorldwidePRPSGRLa209484147
APMV3/Parakeet/Netherlands/449/1975Psittacines, passerines, flamingosNeurological, enteric, and respiratory diseaseWorldwideARPRGRLa171472314
APMV4/Duck/Hong Kong/D3/1975Duck, geese, chickensNone knownWorldwideVDIQPRF210076708
APMV5/Budgerigar/Japan/Kunitachi/1974Budgerigars, lorikeetsHigh mortality, enteric diseaseJapan, United Kingdom, AustraliaGKRKKRFa290563909
APMV6/Duck/Hong Kong/199/1977Ducks, geese, turkeysMild respiratory disease and increased mortality in turkeysWorldwidePAPEPRLb15081567
APMV7/Dove/TN/4/1975Pigeons, doves, turkeysMild respiratory disease in turkeysUnited States, England, JapanTLPSSRF224979458
APMV8/Goose/DE/1053/1976Ducks, geeseNone knownUnited States, JapanTYPQTRLa226343050
APMV9/Duck/NY/22/1978DucksNone knownWorldwideRIREGRIa217068693
APMV10/Penguin/Falkland Islands/324/2007Rockhopper penguinsNone KnownFalkland IslandsDKPSQRIa300432141
Open in a separate windowaRequires the addition of an exogenous protease.bProtease requirement depends on the isolate examined.cPutative.Six of these serotypes were classified in the latter half of the 1970s, when the most reliable assay available to classify paramyxoviruses was the hemagglutination inhibition (HI) assay (61). However, there are multiple problems associated with the use of serology, including the inability to classify some APMVs by comparing them to the sera of the nine defined APMVs alone (2, 8). In addition, one-way antigenicity and cross-reactivity between different serotypes have been documented for many years (4, 5, 14, 25, 29, 33, 34, 41, 51, 52, 60). The ability of APMVs, like other viruses, to show antigenic drift as it evolves over time (37, 43, 54) and the wide use and availability of precise molecular methods, such as PCR and genome sequencing, demonstrate the need for a more practical classification system.The genetic diversity of APMVs is still largely unexplored, as hundreds of avian species have never been surveyed for the presence of viruses that do not cause significant signs of disease or are not economically important. The emergence of H5N1 highly pathogenic avian influenza (HPAI) virus as the cause of the largest outbreak of a virulent virus in poultry in the past 100 years has spurred the development of surveillance programs to better understand the ecology of avian influenza (AI) viruses in aquatic birds around the globe, and in some instances it has provided opportunities for observing other viruses in wild bird populations (15, 53). In 2007, as part of a seabird health surveillance program in the Falkland Islands (Islas Malvinas), oral and cloacal swabs and serum were collected from rockhopper penguins (Eudyptes chrysocome) and environmental/fecal swab pools were collected from other seabirds.While AI virus has not yet been isolated from penguins in the sub-Antarctic and Antarctic areas, there have been two reports of serum antibodies positive to H7 and H10 from the Adélie species (11, 40). Rare isolations of APMV1, both virulent (45) and of low virulence (8), have been reported from Antarctic penguins. Sera positive for APMV1 and AMPV2 have also been reported (21, 24, 38, 40, 53). Since 1981, paramyxoviruses have been isolated from king penguins (Aptenodytes patagonicus), royal penguins (Eudyptes schlegeli), and Adélie penguins (Pygoscelis adeliae) from Antarctica and little blue penguins (Eudyptula minor) from Australia that cannot be identified as belonging to APMV1 to -9 and have not yet been classified (8, 11, 38-40). The morphology, biological and genomic characteristics, and antigenic relatedness of an APMV recently isolated from multiple penguin colonies on the Falkland Islands are reported here. Evidence that the virus belongs to a new serotype (APMV10) and a demonstration of the advantages of a whole genome system of analysis based on random sequencing followed by comparison of genetic distances are presented. Only after all APMVs are reported and classified will epidemiological information be known as to how the viruses are moving and spreading as the birds travel and interact with other avian species.  相似文献   

3.
Predator-prey relationships among prokaryotes have received little attention but are likely to be important determinants of the composition, structure, and dynamics of microbial communities. Many species of the soil-dwelling myxobacteria are predators of other microbes, but their predation range is poorly characterized. To better understand the predatory capabilities of myxobacteria in nature, we analyzed the predation performance of numerous Myxococcus isolates across 12 diverse species of bacteria. All predator isolates could utilize most potential prey species to effectively fuel colony expansion, although one species hindered predator swarming relative to a control treatment with no growth substrate. Predator strains varied significantly in their relative performance across prey types, but most variation in predatory performance was determined by prey type, with Gram-negative prey species supporting more Myxococcus growth than Gram-positive species. There was evidence for specialized predator performance in some predator-prey combinations. Such specialization may reduce resource competition among sympatric strains in natural habitats. The broad prey range of the Myxococcus genus coupled with its ubiquity in the soil suggests that myxobacteria are likely to have very important ecological and evolutionary effects on many species of soil prokaryotes.Predation plays a major role in shaping both the ecology and evolution of biological communities. The population and evolutionary dynamics of predators and their prey are often tightly coupled and can greatly influence the dynamics of other organisms as well (1). Predation has been invoked as a major cause of diversity in ecosystems (11, 12). For example, predators may mediate coexistence between superior and inferior competitors (2, 13), and differential trajectories of predator-prey coevolution can lead to divergence between separate populations (70).Predation has been investigated extensively in higher organisms but relatively little among prokaryotes. Predation between prokaryotes is one of the most ancient forms of predation (27), and it has been proposed that this process may have been the origin of eukaryotic cells (16). Prokaryotes are key players in primary biomass production (44) and global nutrient cycling (22), and predation of some prokaryotes by others is likely to significantly affect these processes. Most studies of predatory prokaryotes have focused on Bdellovibrionaceae species (e.g., see references 51, 55, and 67). These small deltaproteobacteria prey on other Gram-negative cells, using flagella to swim rapidly until they collide with a prey cell. After collision, the predator cells then enter the periplasmic space of the prey cell, consume the host cell from within, elongate, and divide into new cells that are released upon host cell lysis (41). Although often described as predatory, the Bdellovibrionaceae may also be considered to be parasitic, as they typically depend (apart from host-independent strains that have been observed [60]) on the infection and death of their host for their reproduction (47).In this study, we examined predation among the myxobacteria, which are also deltaproteobacteria but constitute a monophyletic clade divergent from the Bdellovibrionaceae (17). Myxobacteria are found in most terrestrial soils and in many aquatic environments as well (17, 53, 74). Many myxobacteria, including the model species Myxococcus xanthus, exhibit several complex social traits, including fruiting body formation and spore formation (14, 18, 34, 62, 71), cooperative swarming with two motility systems (64, 87), and group (or “wolf pack”) predation on both bacteria and fungi (4, 5, 8, 9, 15, 50). Using representatives of the genus Myxococcus, we tested for both intra- and interspecific variation in myxobacterial predatory performance across a broad range of prey types. Moreover, we examined whether prey vary substantially in the degree to which they support predatory growth by the myxobacteria and whether patterns of variation in predator performance are constant or variable across prey environments. The latter outcome may reflect adaptive specialization and help to maintain diversity in natural populations (57, 59).Although closely related to the Bdellovibrionaceae (both are deltaproteobacteria), myxobacteria employ a highly divergent mode of predation. Myxobacteria use gliding motility (64) to search the soil matrix for prey and produce a wide range of antibiotics and lytic compounds that kill and decompose prey cells and break down complex polymers, thereby releasing substrates for growth (66). Myxobacterial predation is cooperative both in its “searching” component (6, 31, 82; for details on cooperative swarming, see reference 64) and in its “handling” component (10, 29, 31, 32), in which secreted enzymes turn prey cells into consumable growth substrates (56, 83). There is evidence that M. xanthus employs chemotaxis-like genes in its attack on prey cells (5) and that predation is stimulated by close contact with prey cells (48).Recent studies have revealed great genetic and phenotypic diversity within natural populations of M. xanthus, on both global (79) and local (down to centimeter) scales (78). Phenotypic diversity includes variation in social compatibility (24, 81), the density and nutrient thresholds triggering development (33, 38), developmental timing (38), motility rates and patterns (80), and secondary metabolite production (40). Although natural populations are spatially structured and both genetic diversity and population differentiation decrease with spatial scale (79), substantial genetic diversity is present even among centimeter-scale isolates (78). No study has yet systematically investigated quantitative natural variation in myxobacterial predation phenotypes across a large number of predator genotypes.Given the previous discovery of large variation in all examined phenotypes, even among genetically extremely similar strains, we anticipated extensive predatory variation as well. Using a phylogenetically broad range of prey, we compared and contrasted the predatory performance of 16 natural M. xanthus isolates, sampled from global to local scales, as well as the commonly studied laboratory reference strain DK1622 and representatives of three additional Myxococcus species: M. flavescens (86), M. macrosporus (42), and M. virescens (63) (Table (Table1).1). In particular, we measured myxobacterial swarm expansion rates on prey lawns spread on buffered agar (31, 50) and on control plates with no nutrients or with prehydrolyzed growth substrate.

TABLE 1.

List of myxobacteria used, with geographical origin
Organism abbreviation used in textSpeciesStrainGeographic originReference(s)
A9Myxococcus xanthusA9Tübingen, Germany78
A23Myxococcus xanthusA23Tübingen, Germany78
A30Myxococcus xanthusA30Tübingen, Germany78
A41Myxococcus xanthusA41Tübingen, Germany78
A46Myxococcus xanthusA46Tübingen, Germany78
A47Myxococcus xanthusA47Tübingen, Germany78
A75Myxococcus xanthusA75Tübingen, Germany78
A85Myxococcus xanthusA85Tübingen, Germany78
TVMyxococcus xanthusTvärminneTvärminne, Finland79
PAKMyxococcus xanthusPaklenicaPaklenica, Croatia79
MADMyxococcus xanthusMadeira 1Madeira, Portugal79
WARMyxococcus xanthusWarwick 1Warwick, UK79
TORMyxococcus xanthusToronto 1Toronto, Ontario, Canada79
SUL2Myxococcus xanthusSulawesi 2Sulawesi, Indonesia79
KALMyxococcus xanthusKalalauKalalau, HI79
DAVMyxococcus xanthusDavis 1ADavis, CA79
GJV1Myxococcus xanthusGJV 1Unknown35, 72
MXFL1Myxococcus flavescensMx fl1Unknown65
MXV2Myxococcus virescensMx v2Unknown65
CCM8Myxococcus macrosporusCc m8Unknown65
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4.
5.
A 30-probe assay was developed for simultaneous classification of Listeria monocytogenes isolates by lineage (I to IV), major serogroup (4b, 1/2b, 1/2a, and 1/2c), and epidemic clone (EC) type (ECI, ECIa, ECII, and ECIII). The assay was designed to facilitate rapid strain characterization and the integration of subtype data into risk-based inspection programs.Listeria monocytogenes is a facultative intracellular pathogen that can cause serious invasive illness (listeriosis) in humans and other animals. L. monocytogenes is responsible for over 25% of food-borne-disease-related deaths attributable to known pathogens and is a leading cause of food recalls due to microbial adulteration (12, 21). However, not all L. monocytogenes subtypes contribute equally to human illness, and substantial differences in the ecologies and virulence attributes of different L. monocytogenes subtypes have been identified (9, 13, 14, 23, 24, 33, 35, 36). Among the four major evolutionary lineages of L. monocytogenes, only lineages I and II are commonly isolated from contaminated food and human listeriosis patients (19, 27, 29, 33). Lineage I strains are overrepresented among human listeriosis isolates, particularly those associated with epidemic outbreaks, whereas lineage II strains are overrepresented in foods and the environment (13, 14, 24). Lineage III strains account for approximately 1% of human listeriosis cases but are common among animal listeriosis isolates and appear to be a host-adapted group that is poorly adapted to food-processing environments (6, 34-36). The ecological and virulence attributes of lineage IV are poorly understood, as this lineage is rare and was only recently described based on a small number of strains (19, 26, 29, 33).L. monocytogenes is differentiated into 13 serotypes; however, four major serogroups (4b, 1/2b, 1/2a, and 1/2c) from within lineages I and II account for more than 98% of human and food isolates (16, 31). Serogroups refer to evolutionary complexes typified by a predominant serotype but which include very rare serotypes that represent minor evolutionary variants (7, 9, 33). Phylogenetic analyses have indicated that rare serotypes may have evolved recently, or even multiple times, from one of the major serotypes (9), and numerous molecular methods fail to discriminate minor serotypes as independent groups (1, 4, 7, 9, 18, 22, 33, 38, 39). Serotyping is one of the most common methods for L. monocytogenes subtyping, and serogroup classifications are a useful component of strain characterization because ecotype divisions appear largely congruent with serogroup distinctions (16, 34). Serogroup 4b strains are of particular public health concern because contamination with these strains appears to increase the probability that a ready-to-eat (RTE) food will be implicated in listeriosis (16, 28). Serogroup 4b strains account for approximately 40% of sporadic listeriosis and also are responsible for the majority of listeriosis outbreaks despite being relatively rare contaminants of food products (9, 13, 17, 30, 34). In addition, serogroup 4b strains are associated with more severe clinical presentations and higher mortality rates than other serogroups (11, 16, 20, 31, 34). Serogroups 1/2a and 1/2b are overrepresented among food isolates but also contribute significantly to human listeriosis, whereas serogroup 1/2c rarely causes human illness and may pose a lower risk of listeriosis for humans (16). Serogroup-specific differences in association with human listeriosis are consistent with the prevalence of virulence-attenuating mutations in inlA within these serogroups (32, 34); however, a number of additional factors likely contribute to these differences.Four previously described epidemic clones (ECs; ECI, ECIa, ECII, and ECIII) of L. monocytogenes have been implicated in numerous listeriosis outbreaks and have contributed significantly to sporadic illness (15, 34). ECI, ECIa, and ECII are distinct groups within serogroup 4b that were each responsible for repeated outbreaks of listeriosis in the United States and Europe. ECIII is a lineage II clone of serotype 1/2a that persisted in the same processing facility for more than a decade prior to causing a multistate outbreak linked to contaminated turkey (15, 25). While there has been speculation that epidemic clones possess unique adaptations that explain their frequent involvement in listeriosis outbreaks (9, 34, 37), it is not clear that epidemic clones are more virulent than other strains with the same serotype. However, contamination of RTE food with EC strains would be cause for increased concern due to the previous involvement of these clones in major outbreaks of listeriosis (16).As a result of the L. monocytogenes subtype-specific differences in ecology, virulence, and association with human illness, molecular subtyping technologies have the potential to inform assessments of relative risk and to improve risk-based inspection programs. The objective of the present study was to develop a single assay for rapid and accurate classification of L. monocytogenes isolates by lineage, major serogroup, and epidemic clone in order to facilitate strain characterization and the integration of subtype data into inspection programs that are based on assessment of relative risk.A database of more than 5.3 Mb of comparative DNA sequences from 238 L. monocytogenes isolates (9, 33-35) was scanned for single nucleotide polymorphisms that could be used to differentiate lineages, major serogroups, and epidemic clones via a targeted multilocus genotyping (TMLGT) approach. The acronym TMLGT is used to distinguish this approach from previously published multilocus genotyping (MLGT) assays that were lineage specific and designed for haplotype discrimination (9, 33). To provide for simultaneous interrogation of the selected polymorphisms via TMLGT, six genomic regions (Table (Table1)1) were coamplified in a multiplex PCR. While the previous MLGT assays were based on three lineage-specific multiplexes and required prior identification of lineage identity, TMLGT was designed to target variation across all of the lineages simultaneously and is based on a unique set of amplicons. PCR was performed in 50-μl volumes with 1× High Fidelity PCR buffer (Invitrogen Life Technologies), 2 mM MgSO4, 100 μM deoxynucleoside triphosphate (dNTP), 300 nM primer, 1.5 U Platinum Taq high-fidelity DNA polymerase (Invitrogen Life Technologies), and 100 ng of genomic DNA. PCR consisted of an initial denaturation of 90 s at 96°C, followed by 40 cycles of 30 s at 94°C, 30 s at 50°C, and 90 s at 68°C. Amplification products were purified using Montage PCR cleanup filter plates (Millipore) and served as a template for allele-specific primer extension (ASPE) reactions utilizing subtype-specific probes.

TABLE 1.

Primers used in multiplex amplification for the TMLGT assay
AmpliconPositionaGene(s)PrimerSequence (5′-3′)b
INLa455381-456505inlAinl2-a1GTCCTTGATAGTCTACTG
inl2-a2ACCAAATTAGTAATCTAGCAC
INLb457726-458752inlBinl-f1dGAATTRTTTAGYCAAGAATGT
inlb-rCTACCGGRACTTTATAGTAYG
LMO325116-326096lmo0298-lmo0300lmo-a1AAGGCTTACAAGATGGCT
lmo1a-1rAAATAATAYGTGATACCGAC
VGCa205366-206622plcA, hlyplca-fCTCATCGTATCRTGTGTACC
hly-rTCTGGAAGGTCKTGTAGGTTC
VGCb208447-209465mplra_mpl-fGTGGAYAGAACTCATAAAGG
ra_mpl-rACTCCCTCCTYGTGATASGCT
VGCc209728-211239actAvgc1a-2fTTCMATRCCAGCAGAACG
vgc1a-2rGCAGACCTAATAGCAATGTTG
Open in a separate windowaCorresponding nucleotide positions in the complete genome sequence of L. monocytogenes strain EGD-e (GenBank accession number NC_003210).bSee IUPAC codes for definition of degenerate bases.ASPE was performed in multiplex reactions including 30 probes, with each lineage (I to IV), major serogroup (4b, 1/2b, 1/2a, and 1/2c), and epidemic clone (ECI, ECIa, ECII, and ECIII) targeted by two different probes (Table (Table2).2). In addition, positive-control probes were included to confirm the presence of each amplicon in the multiplex PCR. As serogroups and epidemic clones are nested within a particular lineage, probes for these groups were designed to be specific within the appropriate lineage and values for these probes were evaluated only for isolates of the appropriate lineage. For example, serogroup 1/2a probes were evaluated only for isolates that were positive for lineage II probes. ASPE probes were designed with a unique 5′ sequence tag specific to individual sets of xMAP fluorescent polystyrene microspheres (Luminex Corporation) used to sort extension products. Extension and hybridization reactions were performed as described previously (9) except microspheres were twice pelleted by centrifugation (4 min at 2,250 × g) and resuspended in 75 μl 1× TM buffer prior to being pelleted and resuspended in 100 μl 1× TM buffer containing 2 μg/ml streptavidin-R-phycoerythrin (Invitrogen Life Technologies). Samples were incubated for 15 min at 37°C prior to detecting the microsphere complexes with a Luminex 100 flow cytometer (Luminex Corporation). The median fluorescence intensity (MFI) from biotinylated extension products attached to 100 microspheres was measured for each probe. The average MFI from three template-free control samples was also determined and subtracted from the raw MFI of each sample to account for background fluorescence. Probe performance was initially evaluated via the index of discrimination (ID) as described by Ducey et al. (9), and probes with ID values less than 2.0 were redesigned.

TABLE 2.

TMLGT probes and probe performance data
ProbebTarget (n)cProbe sequencedIDeSensitivity (%)Specificity (%)
VGCb-21Lineage I (506)AATCCTTTCTTTAATCTCAAATCAgcggaagcttgggaagcggtc7.3100100
VGCa-94Lineage ICTTTCTATCTTTCTACTCAATAATcaacccgatgttcttcctgtc51.7100100
VGCc-8Lineage II (340)AATCCTTTTACATTCATTACTTACattagctgattcgctttcct14.1100100
INLb-51Lineage IITCATTTCAATCAATCATCAACAATagcgccaataaagctggc21.9100100
VGCb-19Lineage III (50)TCAATCAATTACTTACTCAAATACccgctattaaaatgtactcca31.0100100
VGCb-29Lineage IIIAATCTTACTACAAATCCTTTCTTTggtataccgctattaaaatgt45.1100100
LMO-17Lineage IV (10)CTTTAATCCTTTATCACTTTATCAgaaccaaacaatgttattggt11.8100100
VGCa-27Lineage IVCTTTTCAAATCAATACTCAACTTTttaacgacggtaacgtgccac58.3100100
INLb-84Serogroup 4b (213)TCAACTAACTAATCATCTATCAATggtaaaaatatgcgaatattg9.7100100
INLb-85Serogroup 4bATACTACATCATAATCAAACATCActcgtgaacaagctttcc5.5100100
INLb-16Serogroup 1/2b (293)AATCAATCTTCATTCAAATCATCAggtaaaaatatgcgtatctta11.7100100
INLb-100Serogroup 1/2bCTATCTTTAAACTACAAATCTAACgtgaataagctatcggtctat13.0100100
LMO-42Serogroup 1/2a (268)CTATCTTCATATTTCACTATAAACtggcgttgctgrctaagtttg6.6100100
VGCb-40Serogroup 1/2aCTTTCTACATTATTCACAACATTAaatcaagcsgctcatatgaag10.410098.6
LMO-9Serogroup 1/2c (72)TAATCTTCTATATCAACATCTTACtttactggtgaaatggcg13.5100100
VGCb-5Serogroup 1/2cCAATTCAAATCACAATAATCAATCaagattacgaatcgcttccac20.898.6100
LMO-10ECI (111)ATCATACATACATACAAATCTACAatgattaaaagtcagggaaag19.0100100
LMO-28ECICTACAAACAAACAAACATTATCAAaatcgaggcttacgaacgt23.7100100
VGCc-80ECIa (44)CTAACTAACAATAATCTAACTAACactacaacgaaaacagcgc10.7100100
VGCa-35ECIaCAATTTCATCATTCATTCATTTCAgttacttttatgtcgagt9.2100100
LMO-12ECII (35)TACACTTTCTTTCTTTCTTTCTTTataccgattatttggacggtt3.8100100
LMO-30ECIITTACCTTTATACCTTTCTTTTTACgacttgtagcagttgatttcaa7.5100100
VGCc-45ECIII (10)TCATTTCACAATTCAATTACTCAActcttatttgcttttgttggtc21.110099.4
INLa-3ECIIITACACTTTATCAAATCTTACAATCgagcttaatgaaaatcagcta17.010099.4
INLa-1INLa controlCTTTAATCTCAATCAATACAAATCagaagtggaagctgggaaNAaNANA
INLb-13INLb controlCAATAAACTATACTTCTTCACTAAtgcacctaaacctccgacNANANA
LMO-88LMO controlTTACTTCACTTTCTATTTACAATCccgtttccttatgccacaNANANA
VGCa-23VGCa controlTTCAATCATTCAAATCTCAACTTTcaagycctaagacgccaatcgNANANA
VGCb-25VGCb controlCTTTTCAATTACTTCAAATCTTCAgcatgcgttagttcatgrccaNANANA
VGCc-82VGCc controlTACATACACTAATAACATACTCATgactgcatgctagaatctaagNANANA
Open in a separate windowaNA, not applicable for positive amplicon control probes.bLuminex microsphere sets (Luminex Corporation) used for hybridization reactions are indicated following the hyphen.cn, number of isolates representing the target subtype among the 906 tested isolates.dThe 5′ sequence tag portions of extension probes are capitalized. See IUPAC codes for definitions of degenerate bases.eID, index of discrimination.Validation of the TMLGT assay was performed using 906 L. monocytogenes isolates for which the lineage, major serogroup, and epidemic clone type had been determined independently (see Table S1 in the supplemental material). A subset of 92 isolates, including at least five isolates from each lineage, serogroup, and epidemic clone type, was used to evaluate the discriminatory power of subtype-specific probes and the repeatability of the assay (see Table S1). Two independent runs of the 30-probe TMLGT assay produced identical results for these 92 isolates. In addition, genotypes matched expectations for all isolate/probe combinations, and the fluorescence intensities for positive genotypes (those targeted by a particular probe) were 3.8 to 58.3 (mean, 18.5) times as high as background values for isolates with negative genotypes (those not targeted by a particular probe) (Table (Table2).2). The performances of individual probes also were assessed in terms of sensitivity and specificity, where sensitivity is defined as the percentage of positive samples that produced positive results and specificity indicates the percentage of negative samples that produce negative results (5). Based on results from all 906 isolates analyzed by TMLGT, probe sensitivity was at least 98.6% and 23 of the 24 subtype-specific probes exhibited 100% sensitivity (Table (Table2).2). The specificities for all probes were also greater than 98.6%, and 21 of the 24 subtype-specific probes exhibited 100% specificity (Table (Table22).All but three of the 906 isolates in the validation panel were fully and accurately typed relative to lineage, serogroup, and epidemic clone by using the TMLGT assay (typeability, 99.9%; accuracy of isolate assignment, 99.8%). One of the lineage II isolates, NRRL B-33880, could not be assigned to a serogroup based on the TMLGT results because this isolate was positive for one of the serogroup 1/2a probes (VGCb-40) and one of the serogroup 1/2c probes (LMO-9). This isolate was previously identified as a member of serogroup 1/2c based on mapping lineage-specific MLGT data onto a multilocus phylogeny (34) but produced a serogroup 1/2a-specific banding pattern (data not shown) with the multiplex PCR assay described by Doumith et al. (7). Similar strains, including the common laboratory strain EGD-e, were found to have genomes that are more similar to serogroup 1/2c strains than to strains from the 1/2a serogroup (8, 33) and likely represent intermediates in the evolution of the 1/2c clade from 1/2a ancestors. There is a poor correlation between genomic and antigenic variation for such isolates (34), consistent with the ambiguous results produced by application of the TMLGT assay to NRRL B-33880. The two other problematic isolates, NRRL B-33555 and NRRL B-33559, were accurately identified based on TMLGT data as lineage II isolates from the 1/2a serogroup. However, these two isolates were positive for both ECIII-specific probes in the TMLGT assay but have lineage-specific MLGT haplotypes (Lm2.46), indicating that they are representatives of a sister group closely related to ECIII (33).In 2005, the Food Safety and Inspection Service (FSIS) implemented an approach to inspection that includes consideration of relative risk in order to determine L. monocytogenes sampling frequency among establishments that produce certain RTE products. This approach incorporates information on production volume, outgrowth potential in the product, steps taken to prevent postlethality contamination, and FSIS sampling history. However, L. monocytogenes subtype-specific variation in ecology and virulence indicates that information on the lineage, major serogroup, and epidemic clone identities of isolates could be used to inform assessments of relative risk and to improve inspection programs that are based on consideration of risk. Several PCR-based methods have been described for differentiation of various combinations of these subgroups (1-3, 5, 7, 10, 35, 37); however, these approaches have focused on a single subgroup or a smaller set of subgroups than is differentiated by TMLGT analysis. Although we previously developed a set of three MLGT assays that can be used to differentiate all of the major serogroups and epidemic clones of L. monocytogenes (9, 33, 34), those assays did not include probes for lineage discrimination and require identification of the lineage prior to application of one of three unique sets of probes. In addition, the MLGT assays were designed to maximize strain discrimination, as opposed to subgroup identification, and require the use of at least twice as many probes as is needed for TMLGT analysis. MLGT data analysis is also more complicated than analysis of TMLGT data, and serogroup or epidemic clone type identification via MLGT requires phylogenetic analyses to place novel haplotypes within an established phylogenetic framework.In the present study, we developed the first assay for simultaneous discrimination of the four lineages, the four major serogroups, and the four previously described epidemic clones of L. monocytogenes. The assay includes multiple markers for each of these subtype probes as well as control probes to ensure that negative probe data were not the result of amplification failure, providing a high degree of internal validation required for use in inspection programs that consider risk in making sampling decisions. In addition, the utility of the assay has been validated with a large and diverse panel of 906 isolates, including 567 isolates from FSIS surveillance of RTE products and processing facilities (see Table S1 in the supplemental material). Data produced by the TMLGT assay are amenable to high-throughput analysis, and a simple spreadsheet utility has been developed to semiautomate subtype identifications and to alert investigators to potentially conflicting probe data (available upon request). In addition to having a potential application in inspection programs, the TMLGT assay provides a rapid and accurate means of characterizing L. monocytogenes isolates from different environments, which would facilitate pathogen tracking and improve understanding of L. monocytogenes ecology.   相似文献   

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Angiostrongylus cantonensis is the most common cause of human eosinophilic meningitis. Humans become infected by ingesting food items contaminated with third-stage larvae that develop in mollusks. We report the development of a real-time PCR assay for the species-specific identification of A. cantonensis in mollusk tissue.Angiostrongylus cantonensis is the most common agent associated with eosinophilic meningitis in humans. Young adult worms develop in the brains of rodents and are carried to pulmonary arteries to reach sexual maturity. Eggs are laid in lung tissues, and first-stage (L1) larvae break into air spaces, migrate to the trachea, are swallowed, and are passed with rodent feces. The L1 larvae must infect mollusks to develop into third-stage (L3) larvae; L3 is the infective stage for rodents and other mammals. Humans become infected by ingesting raw produce contaminated with L3 larvae or infected raw or undercooked mollusks or paratenic hosts. The immature worms remain in the human brain, creating tissue damage and inflammation (2, 19, 21).A. cantonensis is endemic in Southeast Asia, parts of the Caribbean, and the Pacific Islands, including Hawaii (7, 12, 15-17). The worm has been detected in host animals in Louisiana (5, 14) and in one human patient from New Orleans (18), but it is currently unclear to what extent the nematode has spread into other U.S. states (8, 9). Ascertaining the geographic presence of the parasite is important to manage and prevent new cases of eosinophilic meningitis associated with ingestion of infective larvae (12, 18).Detection of A. cantonensis in mollusks can be performed by releasing the larvae from the tissue with pepsin digestion (11). However, that procedure requires access to living mollusks, which complicates analysis of large numbers of samples. After a recent outbreak of angiostrongyliasis in Hawaii (12), we developed a conventional PCR assay and applied it to survey the Hawaiian mollusk population using frozen tissue (20). That PCR assay, as well as morphological identification using pepsin digestion, can only identify the larvae on the superfamily level, so additional molecular work is required for species-specific classification. Here we describe a new real-time PCR assay that allows for a direct detection of A. cantonensis at the species level.The 18S rRNA gene is too conserved among nematode species to allow species-specific detection. The first and second internal transcribed spacers (ITS1 and ITS2) are comparatively more variable than the rRNA coding regions and have thus been used for differentiation of closely related species (1, 4, 6, 10, 22, 23). We PCR amplified and sequenced ITS1 from A. costaricensis (two laboratory strains from Costa Rica and Brazil), A. vasorum (from naturally infected hosts in United Kingdom), and A. cantonensis from three geographical regions (one laboratory strain from Japan plus nine environmental isolates from Hawaii and New Orleans, LA) to assess the variability of this potential PCR target. The oligonucleotide primers used were AngioF1674 (5′-GTCGTAACAAGGTATCTGTAGGTG-3′) and 58SR4 (5′-TAGCTGCGTTTTTCATCGATA-3′). The reaction mixtures contained 0.4 μM each primer and AmpliTaq Gold PCR master mix (Applied Biosystems, Foster City, CA) and were cycled 45 times at 94°C for 30 s, 65°C for 30 s, and 72°C for 1 min. PCR products were cloned into pCR2.1 vectors using the TOPO cloning technique (Invitrogen, Carlsbad, CA) and sequenced on both strands as described elsewhere (20).The sequence analysis revealed high interspecific and low intraspecific variability. A TaqMan assay targeting ITS1 was then designed using Primer Express version 2.3 (Applied Biosystems, Foster City, CA). The real-time PCR assay was performed in a 20-μl total volume containing Platinum qPCR Supermix (Invitrogen, Carlsbad, CA), 0.2 μM (each) primers AcanITS1F1 (5′-TTCATGGATGGCGAACTGATAG-3′) and AcanITS1R1 (5′-GCGCCCATTGAAACATTATACTT-3′), and 0.05 μM the TaqMan probe AcanITS1P1 (5′-6-carboxyfluorescein-ATCGCATATCTACTATACGCATGTGACACCTG-BHQ-3′). The standard cycling conditions for TaqMan assays were used (i.e., 40 cycles of 95°C for 15 s and 60°C for 1 min).We evaluated the real-time PCR assay with a set of 26 Parmarion martensi slugs from Hawaii. Seventeen slugs were positive for L3 larvae as determined by pepsin digestion, and nine slugs were negative. DNA was extracted from approximately 25 mg of tissue of each slug using the DNeasy tissue and blood DNA extraction kit (Qiagen, Inc., Valencia, CA). The real-time PCR performed on this set of samples returned an identical result to the morphological analysis. The real-time PCR amplified only DNA from A. cantonensis and did not react with DNA from other nematode species (Table (Table1).1). The detection limit of the assay was determined by serially diluting a recombinant plasmid containing the ITS1 sequence to less than 1 copy per μl of sample. The real-time PCR reliably detected down to 10 plasmid copies in the reaction.

TABLE 1.

Comparison of conventional and real-time PCR for detection of Angiostrongylus cantonensis in mollusks and nematode samples
Biological origin of DNA sampleGeographic originNo. of samples testedNo. of samples positive by:
18S rRNA-based conventional PCRITS1-based TaqMan PCR
Parmarion martensiHawaii1127583
Veronicella cubensisHawaii5023a22
Laevicaulis alteHawaii534
Achatina fulicaHawaii645
Other/unidentified mollusksHawaii1645
FlatwormsHawaii222
Slime from infected slugsHawaii1311
Pomacea insularumLouisiana3155
A. costaricensisBrazil, Costa Rica22b0
A. vasorumUnited Kingdom22b0
Other nematodescCDC collection1400
Total253121127
Open in a separate windowaThis number includes three samples positive by PCR but later identified as non-Angiostrongylus nematodes by DNA sequencing analysis of the amplicons (20). These three samples were negative in the real-time PCR assay.bThe conventional PCR detects other Angiostrongylus species besides A. cantonensis.cTwo stool samples containing Strongyloides worms, eight environmental samples containing unclassified free-living nematodes and one of each of the following parasitic nematodes: Dipetalonema sp., Toxocara cati, Dracunculus medinensis, and Ascaris lumbricoides.The real-time PCR assay was then used to analyze a larger set of naturally infected host animals from Hawaii, partly described elsewhere (13, 20), and Island Apple snails (Pomacea insularum) from New Orleans, LA. All samples had previously been characterized by the conventional PCR followed by DNA sequencing analysis (20).Table Table11 summarizes the PCR findings and highlights the enhanced performance of the real-time PCR in comparison to the conventional PCR. In addition, the real-time PCR assay was more practical to use since it did not require DNA sequence confirmation to rule out false positives.The findings from Island Apple snails from New Orleans infected with A. cantonensis concur with previous reports about the potential for angiostrongyliasis transmission in this area (5, 14). Another interesting finding was the positive PCR results in two samples of flatworms from Hawaii. Predatory flatworms that ingest infected mollusks are known to be paratenic hosts of A. cantonensis and have been suspected to be an important source of infection for humans in Japan because they hide in leafy vegetables (3).In conclusion, this real-time PCR assay can be a useful tool for environmental surveys of local wildlife to determine the geographic distribution of this reemerging human parasite.  相似文献   

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Genetic markers previously reported to occur at significantly different frequencies in isolates of Escherichia coli O157:H7 obtained from cattle and from clinically affected humans concordantly delineate at least five genetic groups. Isolates in three of these groups consistently carry one or more markers rarely found among clinical isolates.Escherichia coli serotype O157:H7 is an important zoonotic pathogen that may cause diarrhea, bloody diarrhea, and hemolytic-uremic syndrome (3, 10, 14). E. coli O157 is transmitted to humans by direct contact with the animal reservoir (which includes cattle and other ruminant animals) or indirectly by ingestion of contaminated food or water (3, 10). Genetic analyses of bovine isolates of E. coli O157 from diverse geographic origins have provided evidence for the global dissemination of genotypes and also for significant regional differences in the relative prevalence of some genotypes (5, 8, 18, 19).Several research groups have identified genetic markers that occur at different relative frequencies among E. coli O157 isolates from human clinical cases and from cattle. One group initially used octamer-based genomic scanning to identify two lineages of U.S. origin E. coli O157 (7), of which lineage I was composed mostly (36/44) of clinical isolates and lineage II was composed mostly (25/32) of cattle isolates. Subsequently, a simpler multiplex PCR-based assay (Table (Table1),1), the lineage-specific polymorphism assay (LSPA), was developed to indentify these lineages (19). Six LSPA loci with alleles characteristic of lineage I, lineage II, or neither lineage I nor lineage II are, respectively, classified with the digit 1, 2, or 3, and these digits are concatenated to an LSPA code: 111111 indicates lineage I, and 211111 indicates a genetically intermediate group termed lineage I/II (20), whereas all other genotype variations are considered to belong to lineage II. More recently, a typing assay based on Shiga toxin-encoding bacteriophage insertion (SBI) sites grouped 91 of 92 clinical E. coli O157 isolates from the northwestern United States into three clusters, of which clusters 1 and 3 predominated (>90%) (16). SBI consists of six PCRs (Table (Table1)1) that amplify the Stx toxin genes and the insertion site junctions of the Stx1- and Stx2-encoding bacteriophages of E. coli O157. In a subsequent study, the predominance (92.6%) of clusters 1 and 3 was confirmed in 190 additional human (clinical) isolates (1). In contrast, many (48.8%) E. coli O157 isolates from cattle in the northwestern United States and western Canada demonstrated SBI patterns rarely found among human (clinical) isolates (1).

TABLE 1.

Oligonucleotides used in this study
TestTargetTypeSequence (5′→3′)aReference(s)
LSPAFold-sfmAPrimerVIC-TACGTAGGTCGAAGGG18, 20
Z5935PrimerCCAGATTTACAACGCC
yhcGPrimerFAM-GTGTTCCCGGTATTTG
rbsBPrimerCTCACTGGCGTAACCT
rtcBPrimerVIC-CTCTGCAAAAAACTTACGCC
arp-iclRPrimerCAGGTGGTTGATCAGCG
PrimerFAM-AGTTTAATGTTCTTGCCAGCC
PrimerATTCACCGCTTTTTCGCC
PrimerVIC-GCGCCAGATCGATAAAGTAAG
PrimerGCCGTTGTAAACGTGATAAAG
PrimerFAM-GCTCAATCTCATAATGCAGCC
PrimerCACGTATTACCGATGACCG
SBIstx1PrimerCGCTTTGCTGATTTTTCACA16
PrimerGTAACATCGCTCTTGCCACA
stx2PrimerGTTCCGGAATGCAAATCAGT
PrimerCGGCGTCATCGTATACACAG
L yehV-phagePrimerCACCGGAAGGACAATTCATC
R yehV-phagePrimerAACAGATGTGTGGTGAGTGTCTG
L wrbA-phagePrimerAAGTGGCGTTGCTTTGTGAT
R wrbA-phagePrimerGATGCACAATAGGCACTACGC
PrimerCCGACCTTTGTACGGATGTAA
PrimerCGAATCGCTACGGAATAGAGA
PrimerAGGAAGGTACGCATTTGACC
PrimerATCGTTCGCAAGAATCACAA
Q933Q933FPrimerCGGAGGGGATTGTTGAAGGC9
Q21stx2aRPrimerCCGAAGAAAAACCCAGTAACAG
Q21FPrimerGAAATCCTCAATGCCTCGTTG
stx2aRPrimerCCGAAGAAAAACCCAGTAACAG
TirtirFPrimerTGGCGGCGTCTGAGATAAC2
tirRPrimerGAGTATCGAGCGGACCATGATC
tirAProbeVIC-ACTGAATGATGGATTTG-MGBNFQ
tirTProbeFAM-CTGAATGAAGGATTTG-MGBNFQ
Open in a separate windowaVIC, proprietary reporter dye, Applied Biosystems; FAM, 6-carboxyfluorescein; MGBNFQ, molecular-groove-binding nonfluorescent quencher.Additional individual markers reported to occur at differing frequencies among clinical and reservoir isolates include the presence or absence of stx2-Q junction alleles (e.g., Q933 and Q21 alleles in 90% and 15.2% of 66 human isolates versus 44% and 64.8% of 91 bovine isolates, respectively) (9) and the nonsynonymous single nucleotide polymorphism (SNP) 255T→A in tir, a key virulence gene of E. coli O157:H7 (<1% of 108 human isolates versus 44% of 77 bovine isolates had the A allele) (2).The goal of this study was to evaluate the concordance of these various markers reported to occur at different frequencies among isolates from asymptomatic cattle and from human patients. A convenience set of 145 E. coli O157 isolates obtained from cattle, aggregated from two isolate sets chosen to maximize the diversity of geographic and temporal origins within our isolate bank and whose provenance and SBI types were described previously, was used for this study (1, 5, 18). Briefly, these isolates were non-sorbitol-fermenting, beta-glucuronidase-negative E. coli O157 isolates from cattle on 130 different premises in five countries and 14 U.S. states, isolated in 12 different years ranging from 1991 through 2004. The isolates from outside North America included isolates from Australia (n = 7, obtained in 1993 to 2003), Japan (n = 17, obtained in 1996 to 1997), and Scotland (n = 11, obtained in 1999). LSPA was applied to this set by using previously described primer sequences (19), although capillary rather than gel electrophoresis was used (Table (Table1;1; DNA analyzer 3730, LIZ 600 size standard; Applied Biosystems, Foster City, CA). Data were analyzed with GeneMarker software (SoftGenetics, LLC, State College, PA). Q-stx2 alleles Q933 and Q21 were detected by PCR, and the tir polymorphism was detected by real-time PCR as described previously (2, 9).Comparison of typing results produced by the LSPA, SBI, Q-stx2, and tir methods showed considerable overall agreement. Cross-classification of the LSPA and SBI results (Table (Table2)2) showed particularly strong agreement in assignment to the two human disease-associated genotypes (LSPA 111111 and 211111; SBI 1 and 3; chi square = 268, 20 df, P < 0.001; Cramer''s V statistic = 0.681). Q-stx2 typing identified the Q933 allele in 117 isolates, including 59 of 60 LSPA/SBI human disease-associated genotypes. The Q21 allele was detected in 67 isolates but was not strongly associated with either human disease or cattle-associated genotypes overall (data not shown). The tir nucleotide 255A allele was detected in 39 isolates, only 1 of which had an LSPA/SBI human disease-associated genotype.

TABLE 2.

Cross-tabulation of genotypes identified by the SBI and LSPA methods among 145 isolates of bovine E. coli O157:H7 isolates of diverse temporal and geographic origins
LSPA typeNo. of isolates typed by SBI as:
Total
1356Othera
111111044021056
21111116021827
21311100113216
222213105107
222222002507
221213204006
Otherb21202126
Total2145342421145
Open in a separate windowaSBI also identified five isolates of genotype 7; four isolates of genotype 10; three isolates of genotype 16; two isolates each of genotypes 11, 14, and 15; and single isolates of genotypes 4, 12, and 13 (1).bLSPA also identified three isolates each of genotypes 222113 and 222313; two isolates each of genotypes 212111, 222212, 223213, 231111, 231233, and 232233; and single isolates of genotypes 111211, 212113, 222223, 223212, 223313, and 232233 (20).While these cross-comparisons supported a significant degree of concordance between the results of the various typing systems, the data analysis was complicated by the differing numbers of genotypes determined by the different systems, and in particular by the classification by LSPA and SBI of numerous isolates into a number of sparsely populated genotypes (Table (Table2).2). More generally, it seemed likely that the best classification of the isolates would result from a consideration of all of the data generated. Therefore, we used Markov chain Monte Carlo (MCMC) model-based clustering, implemented in the structure software package, version 2.2 (6), to investigate the population structure using as input data the 15 locus-specific test results (i.e., the six loci each from the LSPA and SBI genotyping panels together with the Q933, Q21, and tir loci) (see Table S1 in the supplemental material). The model assumes K populations, each of which is characterized by allele frequencies at multiple unlinked or weakly linked loci. Within each population, the loci are assumed to be at linkage equilibrium. It was not possible to test the validity of these assumptions for the isolate set modeled here, and it is likely that at least some degree of linkage disequilibrium is present within E. coli O157:H7 populations (12). We utilized this model both to determine the most likely number of populations (K) within the isolate set and to assign individual isolates to the best-fitting population(s). K = 1 would imply a lack of genetic substructure within the isolate set, while any K of >1 would assume the presence of the corresponding number of subgroups with distinct sets of allele frequencies. Initial assignments of group membership for each isolate were based on the location (North America, Scotland, Japan, or Australia) of the cattle from which the E. coli O157 isolates were obtained, due to the potential for genetic divergence of geographically separated populations.K values of 1 to 10 were initially evaluated with 10 model runs each, with each run consisting of a 20,000-step burn-in followed by a 50,000-step parameter estimation. Comparison of the estimated logarithmic posterior probabilities [ln P(X|K), where X is the data] of these runs revealed that K values of <4 or >7 were highly unlikely. Additional runs (25 runs, each consisting of 100,000 steps for burn-in, followed by 100,000 steps for parameter estimation), were then performed in order to model each K value from 4 through 7. The results of these models demonstrated nearly equal maximum relative posterior probabilities for K = 5 and K = 6.We selected K = 5 models for assigning isolates to specific clusters, based on (i) the parsimony principle (K = 5 being a less complex population structure than K = 6), (ii) the precision of the posterior probabilities (K = 5 models had consistently lower variances than K = 6 models), (iii) the lack of sensitivity of the model-derived posterior probabilities to the prior population assignments used to initialize the model (posterior probabilities of models initialized or not initialized with each isolate''s country of origin increasingly diverged in values as K increased from 6), and (iv) the admixture determinations for individual isolates (as K increased from 6, an increasing proportion of the study isolates shared characteristics of two or more clusters). Cluster assignments from six independent, randomly selected K = 5 model runs were compared for concordance: using a criterion of a 0.5 or higher probability to assign isolates to their best-fit clusters, all cluster assignments from the six selected runs were perfectly concordant, with 140 to 142 isolates assigned to specific clusters, leaving only 3 to 5 isolates (depending on the run) with no cluster assignable at a 0.5 or higher probability (see Fig. S1 in the supplemental material). However, it is possible that the uncertainty of these ancestry assignments was underestimated or that the assignments were biased as a result of possible violation of the assumptions of linkage equilibrium within populations (6).The concordant assignments of 142 isolates to five genetic clusters (designated A to E) were then used as the basis for individual evaluation of the different genetic typing systems by comparing each genotyping test or system for agreement with the model-derived cluster assignments. These comparisons revealed associations between genetic markers typical of human infection (for example, SBI type 1 and LSPA type 211111 in cluster A and SBI type 3 and LSPA type 111111 in cluster B), whereas isolates in clusters C to E each contained one or more markers rarely found in clinical isolates (Fig. (Fig.1).1). All markers/marker systems were strongly nonindependently distributed among the model-derived clusters (χ2 = 84 to 338; 4 to 16 df, Cramer''s V = 0.662 to 0.937; P < 0.001 for each system). Not surprisingly, some isolates were assigned to clusters C to E by the model based on the complete data set despite carrying one or more markers typical of clinical isolates. For example, LSPA type 211111 was frequent among isolates assigned to both clusters A (17 of 19) and D (9 of 15), suggesting that this LSPA genotype may be polyphyletic. Clusters A and B cumulatively contained 73 of the 142 classified isolates (51%). We previously reported that the proportions of isolates with SBI genotypes typical of clinical isolates in different countries was weakly correlated to the respective national incidences of E. coli O157:H7-associated hemolytic-uremic syndrome (18). The structure version 2.2-derived cluster assignments reported here also differed by isolate provenance (Fig. (Fig.2;2; χ2 = 30.0, 4 df, P < 0.001; Cramer''s V = 0.262). While the number of international source isolates examined here is clearly insufficient to support strong inferences, the data indicate the possibilities of (i) the unique occurrence of cluster C in North America, (ii) a relatively high frequency of cluster A and a low frequency of cluster E in Scotland, and (iii) a relatively low frequency of cluster A in Japan and Australia. As the genetic markers of cluster A have been associated with increased virulence (11), further research on the association of the distribution of E. coli O157:H7 genotypes and the national incidence and severity of E. coli O157:H7-associated disease may be merited.Open in a separate windowFIG. 1.MCMC model-based genetic cluster assignments (A to E) and their association with (a) SBI typing, (b) LSPA typing, (c) Q933 typing, (d) Q21 typing, and (e) tir typing. Clusters A to E included 19, 54, 14, 15, and 40 isolates, respectively.Open in a separate windowFIG. 2.MCMC model-based genetic cluster assignments and their association with regions of origin. Each isolate is depicted as a single vertical bar colored to represent its genetic cluster admixture (orange, cluster A; blue, cluster B; yellow, cluster C; green, cluster D; pink, cluster E). The isolates are sorted by cluster and location of isolation. The locations are North America (n = 108), Scotland (Sc; n = 10), Japan (Jp; n = 17), and Australia (Au; n = 7). This image was generated by using the DISTRUCT 1.1 software (15).Multiple-correspondence analysis (MCA) and hierarchical clustering were used in a second approach to explore the relationships between the isolates defined by the same set of genetic markers by using the methods of Murtagh (13). The application of MCA provided an opportunity to test whether the clusters identified by the MCMC models were supported by this very different analytical method. MCA identifies a lower-dimensional subspace that approximately represents the diversity within a multivariate data set. In initial MCA models using the full data set, uncommon LSPA and SBI types (specifically, those each comprising less than 5% of the isolate set) exhibited a strong tendency to cocluster, and therefore these unusual types were pooled to produce four LSPA categories. MCA of this reduced data set (SBI [1, 3, 5, 6, or other], LSPA [111111, 211111, 213111, or other], Q933 [positive or negative], Q21 [positive or negative], and Tir [255T or 255A]) identified four dimensions (factors) that cumulatively accounted for >80% of the variation within the data set and retained 69 to 93% of the quality of representation of each marker (13) (see Table S2 in the supplemental material). The coordinates of the projections of each marker onto these four dimensions were extracted from the model and hierarchically clustered by using minimum-variance methods, weighting each marker by its mass (marginal total) (13) (Fig. (Fig.3),3), resulting in five clusters very similar (and named accordingly) to those produced by the MCMC model illustrated in Fig. Fig.11.Open in a separate windowFIG. 3.MCA with hierarchical clustering of E. coli O157:H7 genotyping data, weighted to reflect the contributions of individual factors to the total inertia (n = 138 isolates with no missing data, Ward''s minimum-variance method).In summary, these results clearly demonstrate that the several individual genetic tests or multiple test marker systems previously reported to occur at different frequencies among isolates from cattle and humans identify largely concordant genotypes of E. coli O157. The distribution of these markers among this international collection of isolates strongly indicated the existence of five (or more) genetic groups of E. coli O157, only two of which (clusters A and B) predominantly carry markers previously associated with clinical isolates. It is nearly certain that additional genetic groups or subgroups of E. coli O157 exist in nature, since delineation of these five groups is based on the sampling of only a tiny proportion of the genome: For example, in a recent study, 96 SNPs differentiated clinical E. coli O157 isolates into nine discrete clades (11). The Stx content and the relative frequencies of the two numerically predominant clades of clinical isolates identified in reference 11, clades 2 and 8, are consistent with those of clusters B and A, respectively, as described here.The concordance of the multiple genetic markers, each with alleles differentially associated with human disease, supports the hypothesis of the existence of discrete genotypes of E. coli O157 that differ in their virulence for humans. This diversity is consistent with a source-sink ecological model characterized by broad genetic diversity in the reservoir (source) bovine populations that includes at least five genetic clusters, of which only two carry genetic markers typical of clinical isolates (17). In this ecological model, human infections represent a “sink” characterized by relatively short-duration infections unlikely to be persistently transmitted (R0 < 1.0). The source-sink model implies that various E. coli O157 genotypes diverged in the bovine reservoir through genetic drift and/or through bovine fitness-based selection, during which some genotypes evolved into accidental human pathogens. Based on this model, we predict that the genomic DNA sequences of E. coli O157 genotypes largely restricted to the bovine reservoir will reveal more genetic diversity than is apparent from the clinical isolate sequences now available, and SNP data supporting this prediction have already appeared (4). Investigation of the presence and expression of virulence factors by diverse bovine E. coli O157 genotypes may be required to reveal the mechanism(s) underlying their differential association with human disease.   相似文献   

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We determined the prevalence of Escherichia coli O157:H7 in organically and naturally raised beef cattle at slaughter and compared antibiotic susceptibility profiles of the isolates to those of isolates from conventionally raised beef cattle. The prevalences of E. coli O157:H7 were 14.8 and 14.2% for organically and naturally raised cattle, respectively. No major difference in antibiotic susceptibility patterns among the isolates was observed.Many cattle producers have adopted production methods termed niche marketing to meet consumer demand for safe and healthy beef. The two main niches for beef cattle producers are organic and natural production (3). Organic beef cattle production, regulated by the U.S. Department of Agriculture, requires feeding with certified organic feed (16) and raising cattle without the use of antibiotics, hormones, and other veterinary products (3). Guidelines for producers to label the product as “natural” differ among natural beef programs, and such programs are administered and regulated by the company or organization that owns the brand name rather than the U.S. Department of Agriculture (11). Natural production guidelines often include a complete restriction on the use of antibiotics and growth-promoting hormones, but unlike guidelines for organic production, they allow feed from nonorganic sources (11). Escherichia coli O157:H7 is a major food-borne pathogen that causes outbreaks of hemorrhagic enteritis, which often leads to hemolytic uremic syndrome in children and the elderly (10). Cattle are major reservoirs of E. coli O157:H7, which colonizes the hindgut, specifically the rectoanal mucosal region. Cattle feces are the major source of food and water contamination (10). The impact of organic production methods on the prevalence of food-borne pathogens, including E. coli O157:H7 and Campylobacter spp. in dairy cattle (7, 14) and Campylobacter and Salmonella spp. in chickens (6, 19), has been studied previously. However, there is no published study on the prevalence of E. coli O157:H7 in organically and naturally raised beef cattle. Additionally, nothing is known regarding the effects of organic and natural production methods on the antibiotic susceptibilities of E. coli O157:H7 in beef cattle. Our objectives were to determine the prevalence of E. coli O157:H7 in the feces of organically and naturally raised beef cattle at slaughter and compare the antibiotic susceptibilities of isolates from organically, naturally, and conventionally raised beef cattle.Cattle included in this study were from three types of production systems, organic, natural, and conventional. Organically raised beef cattle were from farms that were certified by the National Organic Program (17). The naturally raised beef cattle were from farms that were certified by the All Natural Source Verified Beef Program (17). The collection of samples from these cattle occurred in an abattoir. Samples from conventionally raised cattle from two feedlots were collected in a different abattoir so that the antibiotic susceptibilities of their isolates could be compared with those of isolates from organically and naturally raised cattle. Fecal samples were obtained by cutting open the rectum and spooning out the contents. The mucosa of the rectum was then rinsed with water until free of visible fecal material and swabbed with a sterile foam-tipped applicator (4). The isolation and identification of E. coli O157 and PCR detection of major virulence genes (eae, stx1, stx2, hlyA, and fliC) were carried out as described by Reinstein et al. (13). A subset of 60 isolates, 20 (10 from fecal samples and 10 from rectoanal mucosal swabs [RAMS]) from each production system, was randomly chosen to determine the antibiotic susceptibility patterns by the broth microdilution method (9). The antibiotics (all from Sigma-Aldrich) tested were amikacin, amoxicillin (amoxicilline), ampicillin, apramycin, bacitracin, cefoxitin, ceftazidime, ceftriaxone, cephalothin (cefalotin), chloramphenicol, chlortetracycline, ciprofloxacin, enrofloxacin, erythromycin, florfenicol, gentamicin, kanamycin, lincomycin, monensin, nalidixic acid, neomycin, norfloxacin, novobiocin, oxytetracycline, penicillin, rifampin (rifampicin), spectinomycin, streptomycin, tetracycline, tilmicosin, trimethoprim, tylosin, and vancomycin. The MIC was defined as the lowest concentration of an antibiotic that prevented visible growth of the organism. Each concentration of the antibiotic compound was duplicated in the microtiter plate, and the MIC determination was repeated with a different inoculum preparation. Logistic regression was performed using the PROC GENMOD procedure in the SAS system (SAS Institute, Cary, NC) to compare the prevalences of E. coli O157:H7 (with binomial distribution of outcomes) in fecal samples, RAMS samples, and fecal or RAMS samples (overall animal level prevalence). The MICs of antibiotics for E. coli O157:H7 isolates were analyzed using a nonparametric survival test in the PROC LIFETEST program of SAS to determine the effects of the production system (natural, organic, or conventional). Data were right censored when necessary (when the organism was resistant to the highest concentration evaluated). The Wilcoxon test was utilized to determine the effect of the production system on MICs.Samples from a total of 553, 506, and 322 organically, naturally, and conventionally raised cattle, respectively, were collected. In organically raised cattle, the prevalence of E. coli O157:H7 in fecal samples ranged from 0 to 24.4% across sampling days, with an average of 9.3%, and the prevalence in RAMS ranged from 0 to 30.9%, with an average of 8.7% (Fig. (Fig.1).1). In naturally raised cattle, the prevalence of E. coli O157:H7 in fecal samples ranged from 0 to 20.3%, with an average of 7.2%, and the prevalence in RAMS ranged from 0 to 23.8%, with an average of 8.9% (Fig. (Fig.1).1). In both organically and naturally raised cattle, the prevalence (total) detected by both sampling methods together was greater (P < 0.05) than the prevalence detected by either method alone (Fig. (Fig.1).1). Samples (either feces or RAMS) from 36 (11.2%) of 322 conventionally raised feedlot cattle were culture positive for E. coli O157:H7. The fecal prevalence of E. coli O157:H7 was 6.5%, and the prevalence determined by the RAMS sampling method was 7.1%. Most isolates (66.7% from organically raised beef cattle and 77.8% from naturally raised beef cattle) were positive for eae, stx2, hlyA, and fliC but negative for stx1. The stx2 gene was present in 100 and 95% of isolates from organically and naturally raised cattle, respectively. The prevalences of E. coli O157:H7 that we observed in organically and naturally raised beef cattle were similar to the previously reported prevalence in conventionally raised cattle (1). Our study did not include a statistical comparison of the prevalence data because of a number of differences, particularly in diet, among the organic, natural, and conventional production systems. Organically and naturally raised cattle are either required to graze a pasture or fed a forage-based diet. Although conflicting data exist (1), studies have shown that cattle fed a forage diet have both higher levels and longer durations of fecal shedding of E. coli O157:H7 than cattle fed a grain diet (18).Open in a separate windowFIG. 1.Prevalences of E. coli O157:H7 in organically and naturally raised beef cattle at slaughter. For each production system, bars not labeled with the same letter represent significantly different levels at P of <0.05.None of the tested isolates from the three production systems were susceptible to bacitracin, lincomycin, monensin, novobiocin, tilmicosin, tylosin, and vancomycin (MICs > 50 μg/ml). The MICs of 12 antibiotics (amikacin, apramycin, cefoxitin, ceftriaxone, gentamicin, kanamycin, nalidixic acid, neomycin, penicillin, rifampin, streptomycin, and tetracycline) for isolates collected from different production systems were significantly different (P < 0.05). MICs of gentamicin and neomycin for E. coli O157:H7 isolates from conventionally raised cattle were higher (P < 0.05) than those for isolates from naturally and/or organically raised cattle (Table (Table1).1). However, MICs of amikacin, apramycin, cefoxitin, ceftriaxone, kanamycin, nalidixic acid, penicillin, rifampin, and tetracycline for isolates from conventionally fed cattle were lower (P < 0.05) than those for isolates from naturally and/or organically raised cattle (Table (Table1).1). Among the 60 isolates tested for antibiotic susceptibilities, 6 isolates (10%) were susceptible to all antibiotics included in the study, excluding the seven antibiotics to which all isolates were resistant. Forty-two isolates (70%) were resistant to one antibiotic (MIC, >50 μg or >50 IU/ml), nine isolates (15%) were resistant to two antibiotics, and two isolates (3%) were resistant to five antibiotics. One isolate from the organically raised cattle group was resistant to 10 (amoxicillin, ampicillin, cefoxitin, cephalothin, chloramphenicol, florfenicol, oxytetracycline, penicillin, streptomycin, and tetracycline) of the 26 antibiotics that were inhibitory to other isolates. We have presented the data as the median MICs for each production system. In some instances, the median values were the same but the actual MIC data differed between production systems. This effect occurred because the data were right censored if isolates were not susceptible at 50 μg or 50 IU/ml. If more isolates from a particular production system than from another are censored, it may lead to statistical differences. This pattern justifies the use of survival analysis for this type of data. There were differences between MICs of many antibiotics (cefoxitin, ceftriaxone, gentamicin, nalidixic acid, neomycin, penicillin, rifampin, and tetracycline) for isolates from organically raised cattle and conventionally raised cattle. Similarly, there were differences between MICs of many antibiotics (amikacin, apramycin, ceftriaxone, kanamycin, nalidixic acid, and rifampin) for isolates from naturally raised cattle and conventionally raised cattle. For many of these antibiotics, MICs for isolates from organically or naturally raised cattle were greater than those for isolates from conventionally raised cattle. Resistance genes can be transferred among the enteric pathogen populations in food animals and humans (8), and it is possible that resistance genes from other bacteria in the gastrointestinal system of cattle may be acquired by E. coli O157:H7. For cattle, heavy metals like copper and zinc, which are also antimicrobial, are included in diets at concentrations in excess of the nutritional requirements, often replacing conventional antibiotics, to achieve growth promotion (5). Feeding with metals also results in the emergence of bacterial populations resistant to metals (5), which in some instances may lead to resistance to antibiotics. Mechanisms of resistance to copper at concentrations above those usually tolerated by normal cellular processes have been found on plasmids linked to resistance to antibiotics in some bacteria (5). Therefore, it is possible that isolates from organically or naturally raised cattle that are not exposed to antibiotics still may become resistant to antibiotics.

TABLE 1.

MICs of antimicrobials for E. coli O157:H7 isolates from conventionally, naturally, and organically raised beef cattle
Antibiotic agentMedian MICa (95% confidence interval) for isolates from:
P value (Wilcoxon test)
Conventionally raised cattle (n = 20)Naturally raised cattle (n = 20)Organically raised cattle (n = 20)
Amikacin2.5 (2.3-3.1)*3.9 (3.1-4.7)†2.7 (2.3-3.1)*<0.01
Apramycin9.4 (8.6-9.4)*12.5 (9.4-15.6)†6.3 (6.3-9.4)*<0.01
Cefoxitin7.8 (6.3-7.8)*7.8 (6.3-9.4)*†8.2 (7.8-10.9)†0.08
Ceftriaxone0.04 (0.04-0.05)*0.05 (NE)†0.05 (NE)†0.02
Gentamicin0.6 (0.4-0.6)†0.6 (0.5-0.8)†0.4 (0.3-0.5)*<0.01
Kanamycin3.0 (2.3-3.1)*3.9 (2.7-4.7)†2.3 (2.0-3.1)*<0.01
Nalidixic acid3.1 (3.1-3.9)*4.7 (3.9-6.3)†4.7 (3.1-6.3)†<0.01
Neomycin1.6 (1.2-1.6)†1.6 (1.2-2.3)†1.0 (0.8-1.2)*<0.01
Penicillin50.0 (NE)*50.0 (NE)*†50.0 (NE)†0.02
Rifampin6.3 (5.5-6.3)*6.3 (NE)†6.3 (6.3-12.5)†<0.01
Streptomycin9.4 (9.4-12.5)*†9.4 (9.4-12.5)†7.8 (6.3-9.4)*0.04
Tetracycline3.1 (NE)*3.1 (3.1-4.7)*†4.7 (3.1-4.7)†0.02
Open in a separate windowaMICs of all antibiotics are expressed as micrograms per milliliter, except those of penicillin, which are in international units per milliliter. For each row, values not labeled with the same symbol (* or †) are significantly different (P < 0.05) as determined by survival analysis (Wilcoxon test). NE, not estimable.Information on the prevalence and antibiotic susceptibilities of food-borne pathogens in organic or natural livestock production systems is limited and variable. In a study of organic and conventional dairy cattle farms, conventional farms were found to be more likely than organic farms to have at least one Salmonella isolate resistant to antibiotics (12). Kuhnert et al. (7) observed no difference between the prevalences of E. coli O157:H7 in samples from organic and conventional dairy farms. Sato et al. reported that E. coli isolates from conventional dairies had significantly higher rates of resistance to certain antibiotics than isolates from organic dairies (15). Cho et al. (2) compared the antibiotic susceptibilities of Shiga toxin-producing O157 and non-O157 isolates from organic and conventional dairy farms and concluded that there was no overall significant difference in resistance between isolates from the two production systems.Although organic and natural beef production systems are becoming popular, little is known about the effects of these production systems on food-borne pathogens. Because the safety of the food supply is crucial, further investigation into these production systems and their potential for altering the risk of human illness is warranted. Our study found similar prevalences of E. coli O157:H7 in the feces of organically and naturally raised beef cattle, and our prevalence estimates for cattle in these types of production systems are similar to those reported previously for conventionally raised feedlot cattle.  相似文献   

18.
Feeding high levels of zinc oxide to piglets significantly increased the relative abundance of ileal Weissella spp., Leuconostoc spp., and Streptococcus spp., reduced the occurrence of Sarcina spp. and Neisseria spp., and led to numerical increases of all Gram-negative facultative anaerobic genera. High dietary zinc oxide intake has a major impact on the porcine ileal bacterial composition.Zinc oxide (ZnO) is used as a feed additive for diarrhea prophylaxis in piglets (23). However, the mode of action of ZnO is not fully understood. Besides its effects on the host (10, 30, 31), high dietary zinc levels may affect the diversity of intestinal microbial communities (2, 11, 20). The prevention of postweaning diarrhea in piglets due to high dietary ZnO intake may not be directly related to a reduction of pathogenic E. coli (8) but, rather, to the diversity of the coliform community (15). Studies on the impact of high ZnO levels on the porcine ileal bacterial community are scarce but nevertheless important, as bacterial diarrhea is initiated in the small intestine (9, 17). The small intestine is a very complex habitat with many different factors shaping the bacterial community. Studies on the ecophysiology (22) and maturation of the porcine ileal microbiota (13, 27) indicate a drastic impact directly after weaning and a gradual decline of modifications during the following 2 weeks. Thus, the time point for analysis chosen in this study (14 days postweaning) does reflect a more stable period of the ileal porcine microbiota. In this study, we used bar-coded pyrosequencing of 16S rRNA genes to gain further insight into the mode of action of pharmacological levels of ZnO in the gastrointestinal tract of young pigs.Total DNA was extracted from the ileal digesta of 40- to 42-day-old piglets using a commercial kit (Qiagen stool kit; Qiagen, Hilden, Germany) and PCR amplified with unique bar-coded primer sets targeting the V1-to-V3 and the V6-to-V8 hypervariable regions (see the supplemental material for detailed methods). The rationale behind this approach was derived from the fact that no single “universal” primer pair can completely cover a complex bacterial habitat (4, 24, 32, 33). Furthermore, these studies also show that in silico information on the coverage of selected primer sets diverges from empirical results, and hence, two hypervariable regions were chosen in this study to maximize the detection of phylogenetically diverse bacterial groups.Equimolar dilutions of all samples were combined into one master sample. Pyrosequencing was performed by Agowa (Berlin, Germany) on a Roche genome sequencer FLX system using a Titanium series PicoTiterPlate. The resulting data files were uploaded to the MG-RAST server (http://metagenomics.nmpdr.org/) (19) and processed with its SEED software tool using the RDP database (5) as the reference database. After automated sequence analysis, all sequences with less than five identical reads per sample were deleted in order to increase the confidence of sequence reads and reduce bias from possible sequencing errors (12, 16). Thus, 0.43% of all sequences were not considered (1,882 of 433,302 sequences). These sequences were assigned to a total of 238 genera, of which most only occurred in a few samples (see the supplemental material). Furthermore, all unclassified sequences were removed (8.7%; 41,467 of 474,769 sequences). Due to the use of the RDP reference database, the SEED software incorrectly assigned the majority of unclassified sequences as unclassified Deferribacterales (83%; 34,393 sequences), which were actually identified as 16S soybean or wheat chloroplasts by BLAST or as cyanobacterial chloroplasts by the RDP II seqmatch tool.The pyrosequencing results for the two primer combinations were merged by taking only sequences from the primer combination that yielded the higher number of reads for a specific sequence assignment in a sample. The remaining reads were used to calculate the relative contribution of assigned sequences to total sequence reads in a sample.The Firmicutes phylum dominated the small intestinal bacterial communities in both the control group and the group with high dietary ZnO intake, with 98.3% and 97.0% of total sequence reads, respectively. No significant influence of high dietary ZnO intake was found for the main phyla Proteobacteria (0.92% versus 1.84%), Actinobacteria (0.61% versus 0.75%), Bacteroidetes (0.15% versus 0.17%), and Fusobacteria (0.09% versus 0.12%).On the order level, a total of 20 bacterial orders were detected (data not shown). Lactobacillales dominated bacterial communities in the control and high-dietary-ZnO-intake groups, with 83.37% and 93.24% of total reads. Lactic acid bacteria are well known to dominate the bacterial community in the ileum of piglets (11, 22). No significant difference between the control group and the group with high dietary ZnO intake was observed on the order level, although high dietary ZnO intake led to a strong numerical decrease for Clostridiales (14.4 ± 24.0% [mean ± standard deviation] versus 2.8 ± 1.7%), as well as to numerical increases for Pseudomonadales (0.3 ± 0.3% versus 0.6 ± 0.6%) and Enterobacteriales (0.2 ± 0.2% versus 0.5 ± 0.6%).On the genus level, a total of 103 genera were detected. Table Table11 summarizes the main 31 genera which exceeded 0.05% of total reads (see the supplemental material for a complete list). Lactobacilli clearly dominated the bacterial communities in both trial groups, but they also were numerically lower due to high dietary ZnO intake.

TABLE 1.

Bacterial genera in the ileum of piglets fed diets supplemented with 200 or 3,000 ppm ZnO
GenusProportion (% ± SD) of ileal microbiota in groupa receiving:
200 ppm ZnO3,000 ppm ZnO
Lactobacillus59.3 ± 30.640.7 ± 19.1
Weissella11.6 ± 7.8 A24.1 ± 8.3 B
Sarcina11.4 ± 20.5 A0.84 ± 1.2 B
Leuconostoc4.7 ± 3.2 A9.4 ± 3.1 B
Streptococcus1.8 ± 1.6 A5.7 ± 5.1 B
Lactococcus1.6 ± 1.52.6 ± 3.1
Veillonella0.57 ± 0.630.34 ± 0.30
Gemella0.34 ± 0.67 A0.45 ± 0.25 B
Acinetobacter0.25 ± 0.210.44 ± 0.50
Clostridium0.25 ± 0.400.22 ± 0.21
Enterococcus0.19 ± 0.150.26 ± 0.24
Acidovorax0.14 ± 0.040.16 ± 0.19
Arcobacter0.14 ± 0.150.16 ± 0.17
Neisseria0.14b0.03 ± 0.01
Enterobacter0.13 ± 0.090.29 ± 0.34
Lachnospira0.12 ± 0.130.13 ± 0.03
Peptostreptococcus0.11 ± 0.100.07 ± 0.09
Chryseobacterium0.10 ± 0.070.15 ± 0.16
Actinomyces0.09 ± 0.040.15 ± 0.16
Anaerobacter0.07 ± 0.080.02 ± 0.01
Aerococcus0.07 ± 0.040.07 ± 0.04
Dorea0.07b0.05 ± 0.05
Fusobacterium0.06 ± 0.090.08 ± 0.11
Microbacterium0.06 ± 0.010.07 ± 0.04
Carnobacterium0.06 ± 0.020.08 ± 0.13
Granulicatella0.06 ± 0.020.09 ± 0.10
Staphylococcus0.06 ± 0.040.05 ± 0.02
Facklamia0.05 ± 0.060.03 ± 0.01
Comamonas0.05 ± 0.030.04 ± 0.02
Citrobacter0.05 ± 0.020.07 ± 0.08
Erysipelothrix0.05 ± 0.010.22 ± 0.40
Open in a separate windowan = 6 piglets per trial group. A,B, results are significantly different by Kruskal-Wallis test.bSingle sample.Significant changes due to high dietary ZnO intake were observed for other lactic acid bacteria, including Weissella spp., Leuconostoc spp., and Streptococcus spp. A significant and strong decrease was observed for Sarcina spp., which is a genus of acid-tolerant strictly anaerobic species found in the intestinal tract of piglets and other mammals (6, 28, 29). This genus thus appeared to be very sensitive to modifications induced by high dietary ZnO intake.An interesting result was observed for Gram-negative Proteobacteria, (i.e., enterobacteria and relatives). Although not statistically significant, virtually all detected proteobacteria increased numerically due to high dietary ZnO intake (Enterobacter spp., Microbacterium spp., Citrobacter spp., Neisseria spp., and Acinetobacter spp.). Apparently, enterobacteria gained colonization potential by high dietary ZnO intake. This is in good agreement with the results of studies by Hojberg et al. (11), Amezcua et al. (1), and Castillo et al. (3). Therefore, the frequently observed diarrhea-reducing effect of zinc oxide may not be directly related to a reduction of pathogenic E. coli strains. Considering a possible antagonistic activity of lactobacilli against enterobacteria (25), it can be speculated that a numerical decrease of dominant lactobacilli may lead to increased colonization with Gram-negative enterobacteria. On the other hand, specific plasmid-borne genes for resistance against heavy metals have been reported for both Gram-positive and Gram-negative bacteria present in the intestine (21, 26), and an increased resistance against Zn ions may exist for Gram-negative enterobacteria. Zinc oxide is an amphoteric molecule and shows a high solubility at acid pH. The low pH in the stomach of piglets (pH 3.5 to 4.5) transforms a considerable amount of insoluble ZnO into zinc ions (54 to 84% free Zn2+ at 150 ppm and 24 ppm ZnO, respectively) (7), and thus, high concentrations of toxic zinc ions exist in the stomach. The stomach of piglets harbors large numbers of lactic acid bacteria, especially lactobacilli. Zn ions may thus lead to a modification of the lactic acid bacterial community in the stomach, and the changes observed in the ileum could have been created in the stomach. A reduction of dominant lactobacilli may thus point to an increased adaptation potential of Gram-negative facultative anaerobes and a generally increased bacterial diversity.Additionally, the direct effects of dietary ZnO on intestinal tissues include altered expression of genes responsible for glutathione metabolism and apoptosis (30), enhanced gastric ghrelin secretion, which increases feed intake (31), and increased production of digestive enzymes (10). An analysis of the intestinal morphology was beyond the scope of this study, but although ZnO concentrations are markedly increased in intestinal tissue, the influence of ZnO on morphology is apparently not always observed (10, 14, 18). Consequently, any changes in epithelial cell turnover, feed intake, or digestive capacity may influence the composition of bacterial communities in the small intestine.In conclusion, this study has shown that high dietary zinc oxide has a major impact on ileal bacterial communities in piglets. Future studies on the impact of zinc oxide in pigs should include a detailed analysis of host responses in order to identify the cause for the observed modifications of intestinal bacterial communities.  相似文献   

19.
Halogenated organic compounds serve as terminal electron acceptors for anaerobic respiration in a diverse range of microorganisms. Here, we report on the widespread distribution and diversity of reductive dehalogenase homologous (rdhA) genes in marine subsurface sediments. A total of 32 putative rdhA phylotypes were detected in sediments from the southeast Pacific off Peru, the eastern equatorial Pacific, the Juan de Fuca Ridge flank off Oregon, and the northwest Pacific off Japan, collected at a maximum depth of 358 m below the seafloor. In addition, significant dehalogenation activity involving 2,4,6-tribromophenol and trichloroethene was observed in sediment slurry from the Nankai Trough Forearc Basin. These results suggest that dehalorespiration is an important energy-yielding pathway in the subseafloor microbial ecosystem.Scientific ocean drilling explorations have revealed that marine subsurface sediments harbor remarkable numbers of microbial cells that account for approximately 1/10 to 1/3 of all living biota on Earth (20, 25, 33). Thermodynamic calculations of pore-water chemistry suggest that subseafloor microbial activities are generally supported by nutrient and energy supplies from the seawater and/or underlying basaltic aquifers (6, 7). Although sulfate, nitrate, Fe(III), Mn(IV), and bicarbonate are known to be potential electron acceptors for anaerobic microbial respiration in marine subsurface sediments (5), the incidence of both the dissimilatory dehalorespiration pathway and microbial activity in halogenated organic substrates remains largely unknown.Previous molecular ecological studies using 16S rRNA gene sequences demonstrated that Chloroflexi is one of the most frequently detected phyla in subseafloor sediments of the Pacific Ocean margins (12-14). Some of the sequences within the Chloroflexi are closely related to sequences in the genus Dehalococcoides, which contains obligatory dehalorespiring bacteria that employ halogenated organic compounds as terminal electron acceptors (21, 29). The frequent detection of Dehalococcoides-related 16S rRNA genes from these environments implies the occurrence of dissimilatory dehalorespiration in marine subsurface sediments.In this study, we detected and phylogenetically analyzed the reductive dehalogenase homologous (rdhA) genes, key functional genes for dehalorespiration pathways, from frozen sediment core samples obtained by Ocean Drilling Program (ODP) Leg 201 (Peru margin and eastern equatorial Pacific) (7, 14); Integrated Ocean Drilling Program (IODP) Expedition 301 (Juan de Fuca Ridge flank) (8, 24); Chikyu Shakedown Expedition CK06-06 (Northwest Pacific off Japan) (20, 23); and IODP Expedition 315 (Nankai Trough Forearc Basin off Japan) (Table (Table1).1). DNA was extracted using an ISOIL bead-beating kit (Nippon Gene, Japan) and purified using a MagExtractor DNA fragment purification kit (Toyobo, Japan) according to the manufacturer''s instructions. To increase concentration, DNA was amplified by multiple displacement amplification using the phi29 polymerase supplied with a GenomiPhi kit (GE Healthcare, United Kingdom) (20). Putative rdhA genes were amplified by PCR using Ex Taq polymerase (TaKaRa, Japan) with degenerate primers RRF2 and B1R (17), dehaloF3, dehaloF4, dehaloF5, dehaloR2, dehaloR3, and dehaloR4 (32), and ceRD2S, ceRD2L, and RD7 (26) and the PCR conditions described in those studies. Amplicons of the approximate target size were gel purified and cloned into the pCR2.1 vector (Invitrogen, Japan). Sequence similarity was analyzed using FastGroupII web-based software (34), and sequences with a 95% identity were tentatively assigned to the same phylotype. Amino acid sequences were aligned by ClustalW (31), including known and putative reductive dehalogenase sequences in the genome of Dehalococcoides ethenogenes strain 195 (28), as well as several functionally characterized reductive dehalogenases from other species.

TABLE 1.

Sample locations and results of PCR amplification of rdhA
Sampling site (expedition name)LocationWater depth (m)Core sectionSediment depth (mbsf)rdh amplification resulta
1226 (ODP Leg 201)Eastern equatorial Pacific3,2971-33.2++
6-346.7++
1227 (ODP Leg 201)Southeast Pacific off Peru4271-10.3+
3-216.6+
5D-542.0
9-375.1+
1230 (ODP Leg 201)Southeast Pacific off Peru5,0861-10.3++
10-373.8
27-3209.3
1301 (IODP Expedition 301)Northeast Pacific Juan de Fuca Ridge flank off Oregon2,6561-22.5+
6-651.2
11-190.8
1D-2132.5
C9001 (JAMSTEC Chikyu Shakedown Expedition CK06-06)Northwest Pacific off Japan1,1801-11.0++
2-513.5++
9-478.5+
21-4191.5+
24-4216.8++
25-6228.9
38-7346.3
40-3358.6+
C0002 (IODP Expedition 315)Nankai Trough Forearc Basin off Japan1,9371-31.9+
1-64.7
2-49.2+
2-813.4
3-520.2+
4-530.0
8-366.6+
16-4155.4
Open in a separate windowa−, PCR product of expected size not amplified; +, PCR product of expected size weakly amplified; ++, PCR product of expected size amplified and confirmed by sequencing analysis.Putative rdhA genes were successfully detected by primer set RRF2-B1R in samples from the eastern equatorial Pacific (ODP site 1226, 3.2 and 46.7 m below the seafloor [mbsf]), the Peru margin (ODP site 1227, 0.3, 16.6, and 75.1 mbsf, and ODP site 1230, 0.3 mbsf), the Juan de Fuca Ridge flank (IODP site 1301, 2.5 mbsf), offshore from the Shimokita Peninsula of Japan (CK06-06 site C9001, 1.0, 13.5, 78.5, 191.5, 216.8, and 358.6 mbsf), and the Nankai Trough Forearc Basin off the Kii Peninsula of Japan (IODP site C0002, 1.9, 9.2, 20.2, and 66.6 mbsf) (Table (Table1).1). No amplification was observed in samples from several deep horizons at sites 1227, 1230, 1301, C9001, and C0002 (Table (Table1).1). A total of 92 clones of subseafloor putative rdhA genes were sequenced and classified into 32 phylotypes (Fig. (Fig.1).1). Phylogenetic analysis revealed that all of the detected putative rdhA sequences were related to those of Dehalococcoides.Open in a separate windowFIG. 1.Phylogenetic tree based on the deduced amino acid sequences of rdhA genes, including sequences from marine subsurface sediments. Putative rdhA sequences from marine subsurface sediments (rdhA clones 1 to 32) are marked in red, while those of the Dehalococcoides genome are marked in blue. Clonal frequencies and sequence accession numbers are indicated in parentheses. Bootstrap values from 50% to 84% and 85% to 100% are indicated by open and solid circles at the branches, respectively. Asterisks indicate the following functionally characterized rdhA genes: pceA and prdA, tetrachloroethene reductive dehalogenase; tceA, trichloroethene reductive dehalogenase; vcrA and bvcA, vinyl chloride reductive dehalogenase; dcaA, 1,2-dichloroethane reductive dehalogenase; cprA, chlorophenol reductive dehalogenase; and cbrA, chlorobenzene reductive dehalogenase. The tree was constructed by a neighbor-joining (NJ) method based on an alignment of almost-complete rdhA amino acid sequences with pairwise gap deletion on MEGA version 4.0 software (30). The resulting tree was displayed using Interactive Tree Of Life (19). The scale bar represents 0.1 substitutions per amino acid position.In the alignment of the subseafloor rdhA sequences, we observed two Fe-S cluster-binding motifs as a conserved structure of previously reported reductive dehalogenases (29). The sequences were amplified with primer RRF2 containing the N-terminal twin arginine translocation (Tat) signal sequence and primer B1R containing the rdhB genes encoding a putative dehalogenase membrane anchor protein (17). Thus, the dehalogenases of subseafloor bacteria have a structural framework similar to that of known dehalogenases from terrestrial Dehalococcoides species. However, BLASTP analysis showed that similarities among subseafloor rdhA sequences and previously reported dehalogenase sequences were generally low, ranging from 33.06% to 64.27%. Some sequences were affiliated, with relatively high bootstrap values, with subseafloor rdhA clusters I and II, which are clearly distinct from the rdhA sequences of Dehalococcoides and other known species (Fig. (Fig.1).1). In addition, we were unable to detect subseafloor rdhA genes using other primer sets targeting cprA- and pceA-like genes (26, 32). These results indicate that most subseafloor rdhA genes are distinct from those reported from terrestrial environments, a trend that corroborates the results of a metagenomic survey of subseafloor microbial communities at the Peruvian site (3). However, it is worth noting that the RRF2 and B1R primers used in this study are based on the rdhA sequences present in Dehalococcoides (17) and that sequence retrieval is probably biased by primer mismatch. It is thus likely that there are still unexplored functional genes related to the dehalorespiration pathways in marine subsurface sediments.An interesting finding of the functional gene survey is that the subseafloor rdhA homologues are preferentially detected in shallow sediments. At site C9001 off Japan, the sedimentation ratio is considerably higher than at other sites (54 to 95 cm per 1,000 years) (unpublished data), and rdhA genes were successfully detected in horizons as deep as 358 mbsf (Table (Table1).1). The rdhA genes were also detected in sediments from the open ocean at site 1226, which contained very low concentrations (<0.2%) of organic matter (7). This may be because halogenated compounds are derived not only from terrestrial environments but also from the seawater overlying the sediments. In addition, a diverse range of marine organisms, such as phytoplankton, mollusks, algae, polychaetes, jellyfish, and sponges, are known to produce halogenated organic compounds (11). For example, the amount of brominated organic compounds in the ocean has been estimated at 1 to 2 million tons per year (10). Since these halogenated compounds are generally recalcitrant or not metabolizable by aerobic microorganisms in the seawater column (15), they are effectively buried in marine subsurface sediments. In fact, debromination of brominated phenols in marine, estuarine, or intertidal strait sediments has been reported (4, 9, 16, 22), and a brominated phenol-dehalogenating microbial community has been observed in the marine sponge Aplysina aerophoba, which produces bromophenolic metabolites (1).We also observed reductive dehalogenation activity in subseafloor sediment slurry from site C0002 in the Nankai Trough (Fig. (Fig.2;2; also see the supplemental material). The slurry sample was prepared by mixing sediment samples from 1.9, 4.7, 9.2, 13.4, 20.2, 30.0, 66.6, and 155.4 mbsf. During the initial incubation with 2,4,6-tribromophenol (2,4,6-TBP) for 179 days, 2,4,6-TBP was completely converted to phenol. We then supplemented the same incubation slurry with 2,4,6-TBP and once again observed dehalogenation activity (Fig. (Fig.2A).2A). During the incubation, 2,4-dibromophenol and 4-bromophenol were produced as intermediates (Fig. (Fig.2C),2C), suggesting that ortho debromination occurred in preference to para debromination, as observed previously in marine sponge habitats (1). The maximum phenol production rate during the second incubation was calculated to be 0.094 μM per 1 cm3 of sediment per day (Fig. (Fig.2A2A).Open in a separate windowFIG. 2.Dehalogenation activities of subseafloor microbes. (A) Debromination of 2,4,6-TBP in a subseafloor sediment slurry from site C0002 in the Nankai Trough Forearc Basin. Arrow indicates the timing of 2,4,6-TBP supplementation. (B) Dechlorination of TCE in the same slurry sample. Sterilized control sediment slurries did not exhibit phenol and/or cis-DCE production (data not shown). (C) Potential debromination pathway of 2,4,6-TBP (solid arrows) and (D) potential dechlorination pathway of TCE (solid arrows) observed. The pathways indicated by dashed arrows were not observed in this experiment.Using the same sediment slurry sample, we also observed dehalogenation activity of trichloroethene (TCE), a substantial pollutant in the natural environment. During an incubation lasting more than 200 days, TCE was almost entirely converted to cis-dichloroethene (cis-DCE) (Fig. (Fig.2B).2B). The subsequent dechlorination step of cis-DCE, which is presumably from cis-DCE to monochloroethene, was not observed during the incubation. The rate of cis-DCE production was calculated as 0.045 μM per 1 cm3 of sediment per day.In conclusion, the observed molecular and activity data suggest that metabolically active dehalorespiring microbes are well represented in marine subsurface sediments and that these microbes may be widely distributed in Pacific Ocean margin sediments. Given the relatively high in vitro activity rates, we expect that subseafloor dehalorespiring microbes play important ecological roles in the biogeochemical cycles of chlorine, iodine, and bromine, as well as in halogenated carbon substrates. The distribution of in situ activity rates, chemical and geophysical constraints, metabolic characteristics of the individual dehalorespiring phylotypes, and genetic and enzymatic mechanisms of the microbes remain to be clarified. Nevertheless, the findings of this study provide new evidence of microbial functioning in the subseafloor ecosystem.  相似文献   

20.
A total of 905 enterohemorrhagic Escherichia coli (EHEC) O157:H7 isolates that were recovered from experimentally infected cattle, in addition to the inoculated strain, were analyzed by pulsed-field gel electrophoresis (PFGE). Twelve PFGE profiles other than that of the inoculated strain were observed. We successfully identified five distinct chromosomal deletions that affected the PFGE profiles using whole-genome PCR scanning and DNA sequencing analysis. The changes in PFGE profiles of EHEC O157:H7 isolates after passage through the intestinal tract of cattle were partially generated by deletion of chromosomal regions.Enterohemorrhagic Escherichia coli (EHEC) O157:H7 causes hemorrhagic colitis and hemolytic-uremic syndrome in humans worldwide (18). Cattle are considered the primary reservoir for this pathogen and play a central role in transmission to humans (6). Healthy cattle transiently carry EHEC O157:H7 and shed the bacteria in their feces (5, 7). Human infections have been associated with the consumption of contaminated meat and milk, direct contact with cattle, and the consumption of vegetables, fruits, and water contaminated with cattle manure (6).Because of its high discriminatory power, pulsed-field gel electrophoresis (PFGE) has been widely employed as a molecular typing method in many epidemiological investigations to identify various outbreaks and routes of transmission of EHEC O157:H7 (1, 12, 15, 17). Simpson''s index of diversity (9) was reported to be >0.985 in previous studies (1, 15), supporting the identification of richness (the number of types among isolates) and evenness (the relative distribution of individual strains among the different types) of molecular typing using PFGE.Instability of the PFGE patterns of EHEC O157:H7 isolates has been reported. Changes in PFGE patterns were observed among strains after repeated subculturing and prolonged storage at room temperature (11). Loss of Shiga toxin genes and a large-scale inversion within the genome were identified as genetic events generating changes in PFGE patterns in vitro (10, 13). Shifts in the genotypes of EHEC O157:H7 clinical isolates from patients and cattle have been reported (3, 14). This phenomenon was also observed in EHEC O157:H7 experimental infections of cattle. Spontaneous curing of a 90-kb plasmid resulted in the loss of two restricted fragments from the PFGE profiles of EHEC O157:H7 isolates obtained from experimentally infected cattle (2). The purpose of the present study was to identify the genetic events affecting the PFGE patterns of EHEC O157:H7 after passage through the intestinal tract of cattle, especially for restriction fragments that are >90 kb long.Four 5-month-old Holstein steers were housed individually in climate-controlled biosafety level 2 containment barns in accordance with the guidelines for animal experimentation defined by the National Institute of Animal Health of Japan. The pens had individual floor drains and were cleaned twice daily with water and disinfectant. All animals were healthy and culture negative for EHEC O157:H7 strains, as determined by a previously described technique (2), prior to inoculation.EHEC O157:H7 strain Sakai-215 (12, 23), which was isolated from an outbreak in Sakai, Osaka Prefecture, in 1996 was used for inoculation. This strain harbors the genes encoding Stx1 and Stx2. A spontaneous resistant strain was selected with nalidixic acid in order to facilitate the recovery of this strain from fecal samples. All calves were inoculated using a stomach tube with an exponential-phase culture (109 CFU) of the nalidixic acid-resistant Sakai-215 strain. Fecal samples were collected from the four calves daily for 45 days. Fecal culturing was performed as described previously (2). Eight non-sorbitol-fermenting colonies were selected daily from each animal and identified as EHEC O157:H7 colonies by routine diagnostic methods (25).All animals were clinically normal throughout the experimental period. The EHEC O157:H7-inoculated calves (calves 1 to 4) were culture positive for the organism 24 h after inoculation. Intermittent fecal shedding by the calves was observed until 27, 32, 26, and 39 days postinoculation for calves 1, 2, 3, and 4, respectively (Fig. (Fig.1).1). The numbers of EHEC O157:H7 isolates recovered from calves 1, 2, 3, and 4 were 200, 224, 200, and 281, respectively.Open in a separate windowFIG. 1.Changes in PFGE profiles of EHEC O157:H7 isolates recovered from calves 1 (A), 2 (B), 3 (C), and 4 (D). The absence of a bar indicates that no EHEC O157:H7 was detected. The open horizontal bars under the vertical bars indicate that the eight isolates obtained on a day were obtained from the enrichment culture.A total of 905 recovered isolates in addition to the inoculated strain were used for PFGE analysis. Genomic DNA from each EHEC O157:H7 isolate was prepared using the method of Persing (Mayo Clinic, Rochester, MN) described by Rice et al. (20). Agarose-embedded chromosomal DNA was cleaved with XbaI by following the manufacturer''s instructions. PFGE was performed in a 0.85% megabase agarose gel, using a CHEF DR III apparatus (Bio-Rad Laboratories). The pulse time was increased from 12 to 35 s for 18 h. The PFGE profiles of all of the EHEC O157:H7 isolates recovered from the four calves were compared with that of the inoculated strain. The number of band differences was determined by enumerating the loss and addition of fragments (22).Two hundred eighty-nine isolates had PFGE profiles different from that of the inoculated strain, and 12 distinct PFGE profiles were identified for these isolates (Table (Table1).1). The fact that only one to three band differences were observed for the 12 profiles suggested that these isolates were closely related (22) and were variants of the inoculated strain. In addition, the pens had individual floor drains and were cleaned twice daily with water and disinfectant, which reduced the likelihood of introduction of novel EHEC O157:H7 strains. We designated the PFGE profiles A to L. PFGE profiles A, C, and H were obtained for all four calves and accounted for 30.4% of the 905 isolates recovered. Different PFGE profiles were obtained for all animals at least 2 days postinoculation (Fig. (Fig.1).1). All eight isolates from calf 2 collected on day 15 postinoculation and from calf 3 collected on days 22 and 23 postinoculation had PFGE profiles different from that of the original isolate (Fig. (Fig.1).1). The isolates that had the same PFGE profile as the inoculated strain were detected again later.

TABLE 1.

Temporal distribution of PFGE profiles of EHEC O157:H7 isolates recovered from experimentally infected cattle
PFGE profileNo. of isolates recovered at different times postinoculation from:
Total no. of isolates (%)
Calf 1
Calf 2
Calf 3
Calf 4
1 to 10 days11 to 20 days21 to 27 days1 to 10 days11 to 20 days21 to 30 days31 to 32 days1 to 10 days11 to 20 days21 to 36 days1 to 10 days11 to 20 days21 to 30 days31 to 39 days
Ina63562562465066046559454746616 (68.1)
A1119101372121324410191612181 (20.0)
B11 (0.1)
C132471362776572 (8.0)
D11 (0.1)
E11 (0.1)
F123 (0.3)
G11 (0.1)
H1314133221122 (2.4)
I1113 (0.3)
J11 (0.1)
K112 (0.2)
L11 (0.1)
Total no. of variants (%)b17 (21.3)24 (30.0)15 (37.5)18 (22.5)18 (25.0)22 (30.6)2 (25.0)20 (25.0)34 (42.5)35 (87.5)21 (26.3)27 (37.5)17 (26.6)19 (29.2)289 (31.9)
Open in a separate windowaPFGE profile of inoculated strain Sakai-215.bTotal numbers of isolates having PFGE profiles A to L.Kudva et al. (16) demonstrated that the difference in PFGE profiles between EHEC O157:H7 strains was due to distinct insertions or deletions that contained XbaI sites rather than to single-nucleotide polymorphisms in the XbaI sites themselves. To identify the locations of insertions or deletions in the genome of the EHEC O157:H7 isolates recovered from experimentally infected cattle, whole-genome PCR scanning (WGP scanning) was performed as described previously (19). Briefly, 549 pairs of PCR primers were used to amplify 549 segments covering the whole chromosome of EHEC O157:H7 strain RIMD 0509952, with overlaps of a certain length at every segment end. The inoculated strain (strain Sakai-215) and the strain whose genome was sequenced (RIMD 0509952) were isolated from the same outbreak in Japan in 1996 (23) and had same PFGE profile after XbaI digestion. All primer sequences are available at http://genome.gen-info.osaka-u.ac.jp/bacteria/o157/pcrscan.html. PCR were performed using genomic DNA as the template and long accurate PCR (LA-PCR) kits. The cycling conditions for the LA-PCR included an initial incubation at 96°C for 100 s, followed by 30 cycles of 96°C for 20 s and 69°C for 10 min.Prior to the WGP scanning of the isolates, we scanned an approximately 1.2-Mb region covered by 116 segments (71/72 to 146/147) of the EHEC O157:H7 genome using 24 strains, including inoculated strain Sakai-215, 4 isolates with the same PFGE profile as the inoculated strain, 3 isolates with PFGE profile A, 3 isolates with PFGE profile C, 2 isolates with PFGE profile F, 2 isolates with PFGE profile H, 2 isolates with PFGE profile I, and one isolate each with PFGE profiles B, D, E, G, J, K, and L. The main purpose of this preliminary scanning was to determine the extent of variation in the data for isolates having the same PFGE profiles.As shown in Fig. Fig.2,2, we successfully amplified products that were the expected sizes for 103 of the 116 segments for the 24 strains tested. No amplification in a segment was observed for the 24 strains. Polymorphism (expected amplification was observed in some but not all strains) was observed in 12 segments. Eleven of the 12 polymorphic segments consisted of two different sequentially unamplified regions. An IS629 insertion was also observed in a polymorphic segment in one strain. In other words, variation in the data for isolates with the same PFGE profile was not observed except for the isolates having the same PFGE profile as the inoculated strain. Hence, we performed WGP scanning using one isolate with each of the selected PFGE profiles.Open in a separate windowFIG. 2.Summary of the results of PCR scanning analysis of part of the EHEC O157:H7 genome using 24 strains recovered from experimentally infected cattle. The line at the top indicates data for the inoculated strain. The positions of Sp5 and Sp6 are indicated above the data lines. Segments showing polymorphism (expected amplification was observed in some strains but not in all strains) are indicated below the data lines. In, inoculated strain.The results of WGP scanning of the seven isolates with different PFGE profiles in addition to inoculated strain Sakai-215 are summarized in Fig. Fig.3.3. We successfully amplified products of the expected sizes for 530 of 549 segments for the eight strains tested. No amplification was observed for any of the eight strains for three segments (133.2/133.3, 164.4/164.5, and 164.5/164.6). Polymorphism was observed in 16 segments. Fourteen of the 16 polymorphic segments were located in four different regions.Open in a separate windowFIG. 3.Summary of the results of WGP scanning analysis of the EHEC O157:H7 isolates recovered from experimentally infected cattle and the inoculated strain. The positions of Sp5 and Sp13 are indicated above the data lines. Segments showing polymorphism (expected amplification was observed in some strains but not in all strains) are indicated below the data lines. In, inoculated strain.The 110/110.1-to-110.5/111 region in PFGE profile I, the 122/122.1-to-122.4/123 region in PFGE profile K, and the 199/199.1-to-199.2/200 region in PFGE profiles B, C, and G corresponded to prophages Sp5, Sp6, and Sp13, respectively. The 283/284-to-285/286 region in PFGE profile E and the 448/448.1-to-448.1/448.2 region in PFGE profile B corresponded to nonprophage regions on the chromosome. The sizes of the deletion sites of nonprophage regions 283/284 to 285/286 and 448/448.1 to 448.1/448.2 were 17 kb and 9.5 kb, respectively. We synthesized new primer pairs upstream and downstream of these five regions and performed LA-PCR (data not shown). The results of the sequencing analysis of the products indicated that the three prophage genomes were cured at their integration sites (Fig. 4A to C). It is not clear from this study whether deletion of the three prophages represented phage excisions or simple deletions. We identified short direct CCGCCA and GC repeats at both ends of the 17-kb and 9.5-kb deletion sites, respectively, compared with the sequence data for the Sakai-215 strain, although the deleted regions included one side of the direct repeats (Fig. 5D and E).Open in a separate windowFIG. 4.Schematic diagrams showing the relationships between deletions of chromosomal regions and changes in the sizes of restricted fragments. (A) The 467-kb restricted fragment of PFGE profile I was generated by deletion of prophage Sp5 located in the 530-kb fragments of the inoculated strain. (B) The 759-kb restricted fragment of PFGE profile K was generated by deletion of Sp6 located in the adjacent 530-kb and 278-kb fragments of the inoculated strain. (C) The 291-kb restricted fragment of PFGE profiles C, G, and H was generated by deletion of prophage Sp13 located in the adjacent 255-kb and 55-kb fragments of the inoculated strain. (D) The 188-kb restricted fragment of PFGE profile E was generated by deletion of the 17-kb chromosomal region in the 205-kb fragment of the inoculated strain. (E) The 334-kb restricted fragment of PFGE profile B was generated by deletion of the 9.5-kb region located in the adjacent 343-kb and 6.2-kb fragments of the inoculated strain.Open in a separate windowFIG. 5.Comparison of the PFGE profiles of the EHEC O157:H7 isolates recovered from experimentally infected cattle and the inoculated strain. Lane M, λ ladder used as a size marker; lane 1, inoculated strain; lanes 2 to 13, isolates with PFGE profiles A to L, respectively.The deleted 17-kb region contains 16 open reading frames, including formate hydrogenase-related genes (4), mutS (21), and rpoS (8), suggesting that the strain with PFGE profile E is more susceptible to environmental stresses than the inoculated strain. In fact, the isolate with PFGE profile E was more susceptible to low-pH, high-temperature, and high-osmolarity conditions or to the presence of deoxycholate in vitro than the other isolates obtained in this study (data not shown). The fact that this isolate was obtained 4 days after inoculation from calf 1 and could not be detected after that time suggested that the isolate with PFGE profile E could not survive in the intestine of the calf due to the loss of genes related to stress resistance. The deleted 9.5-kb region contains nine open reading frames whose functions are unknown. The strain with this deletion was isolated 1 day after inoculation from calf 3 and could not be detected after that time.Sp5 is one of the prophages in EHEC O157:H7 RIMD 0509952 carrying the stx2 gene. Deletion of this prophage affected the PFGE profile of inoculated strain Sakai-215. The loss of a 530-kb fragment and the gain of a 467-kb fragment due to deletion of the 63-kb prophage Sp5 were identified in PFGE profile I (Fig. (Fig.4A4A and and55).Sp6 is one of the lambda-like phages and has a single XbaI site in its genome. The loss of 530-kb and 278-kb fragments and the gain of a 759-kb fragment due to deletion of this phage were identified in PFGE profile K (Fig. (Fig.4B4B and and55).Sp13 is one of the P2-like phages that have a single XbaI site in the genome. The loss of 255-kb and 55-kb fragments and the gain of a 291-kb fragment due to deletion of this prophage were identified in PFGE profiles C, G, and H (Fig. (Fig.4C4C and and5).5). The same changes in PFGE profile B were not observed, although we found a sequentially unamplified region in which Sp13 was located in the genomes of isolates with PFGE profile B (Fig. (Fig.3).3). We detected part of the Sp13 sequence by Southern blot analysis; however, this part of the sequence was not detected in isolates with PFGE profiles C, G, and H (data not shown). One possible explanation for this phenomenon is that deletion of part of the Sp13 sequence included deletion of primer annealing sites. However, the details of mutation in this region for the isolates with PFGE profile B are not clear.Deletion of the two nonprophage regions also affected the PFGE profiles. The loss of a 205-kb fragment and the gain of a 188-kb fragment due to deletion of a 17-kb region were identified in PFGE profile E (Fig. (Fig.4D4D and and5).5). The loss of a 343-kb fragment and the gain of a 334-kb fragment due to deletion of a 9.5-kb region were identified in PFGE profile B (Fig. (Fig.4E4E and and55).Two single unamplified segments were both observed in the strain with PFGE profile F (106.3/106.4 and 204.2/204.3). We could not amplify these regions using additional primer pairs (data not shown). Insertion of DNA or large-scale inversion might have occurred in these regions. The other unamplified segments all corresponded to deletion of chromosomal regions. Recombination successfully occurred and cured three prophages and two other chromosomal regions. These data suggest that the changes in PFGE profiles after passage through the intestinal tract of cattle are generated in part by deletion of chromosomal regions. Obviously, deletion of five chromosomal regions does not explain the other changes in the PFGE profiles, including profiles A, D, F, J, and L. The genetic events behind such changes are not clear.Prior to drawing a conclusion, we need to consider the use of nalidixic acid, a potent inducer of bacteriophage induction (24), for selection of the isolates. In addition, most of the EHEC O157:H7 isolates obtained on day 8 postinoculation and later were isolated from enrichment cultures (Fig. (Fig.1).1). The possibility that the culturing process itself affected the deletion events affecting the PFGE profiles cannot be ruled out. Taken together, the results suggest that deletions can cause a single strain to mutate into several variants while it is passing through the gastrointestinal tract of a host, provided that the culture technique used does not contribute to this process. Hence, this study may explain why EHEC O157:H7 isolates with various PFGE profiles can be isolated from a single animal. What causes the deletion mutations and why the PFGE profiles show such patterns after passage through cattle are subjects for future studies.  相似文献   

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