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1.
We analyzed the temporal and spatial diversity of the microbiota in a low-usage and a high-usage hospital tap. We identified a tap-specific colonization pattern, with potential human pathogens being overrepresented in the low-usage tap. We propose that founder effects and local adaptation caused the tap-specific colonization patterns. Our conclusion is that tap-specific colonization represents a potential challenge for water safety.Humans are exposed to and consume large amounts of tap water in their everyday life, with the tap water microbiota representing a potent reservoir for pathogens (8). Despite the potential impact, our knowledge about the ecological diversification processes of the tap water microbiota is limited (4, 11).The aim of the present work was to determine the temporal and spatial distribution patterns of the planktonic tap water microbiota. We compared the summer and winter microbiota from two hospital taps supplied from the same water source. We analyzed 16S rRNA gene clone libraries by using a novel alignment-independent approach for operational taxonomic unit (OTU) designation (6), while established OTU diversity and richness estimators were used for the ecological interpretations.Tap water samples (1 liter) from a high-usage kitchen and a low-usage toilet cold-water tap in Akershus University Hospital, Lørenskog, Norway, were collected in January and July 2006. The total DNA was isolated and the 16S rRNA gene PCR amplified and sequenced. Based on the sequences, we estimated the species richness and diversity, we calculated the distances between the communities, and trees were constructed to reflect the relatedness of the microbiota in the samples analyzed. Details about these analytical approaches are given in the materials and methods section in the supplemental material.Our initial analysis of species composition was done using the RDPII hierarchical classifier. We found that the majority of pathogen-related bacteria in our data set belonged to the class Gammaproteobacteria. The genera encompassed Legionella, Pseudomonas, and Vibrio (Table (Table1).1). We found a significant overrepresentation of pathogen-related bacteria in the toilet tap (P = 0.04), while there were no significant differences between summer and winter samples. Legionella showed the highest relative abundance for the pathogen-related bacteria. With respect to the total diversity, we found that Proteobacteria dominated the tap water microbiota (representing 86% of the taxa) (see Table S1 in the supplemental material). There was, however, a large portion (56%) of the taxa that could not be assigned to the genus level using this classifier.
TABLE 1.
Cloned sequences related to human pathogensaOpen in a separate windowaThe relatedness between the cloned sequences and potential pathogens was determined by BLAST searches of the NCBI database, carried out using default settings.To obtain a better resolution of the uncharacterized microbiota, we analyzed the data using a clustering approach that is not dependent on a predefined bacterial group (see the materials and methods section in the supplemental material for details). These analyses showed that there were three relatively tightly clustered groups in our data set (Fig. (Fig.1A).1A). The largest group (n = 590) was only distantly related to characterized betaproteobacteria within the order Rhodocyclales. We also identified another large betaproteocaterial group (n = 320) related to Polynucleobacter. Finally, a tight group (n = 145) related to the alphaproteobacterium Sphingomonas was identified.Open in a separate windowFIG. 1.Tap water microbiota diversity, determined by use of a principal component analysis coordinate system. (A) Each bacterium is classified by coordinates, with the following color code: brown squares, kitchen summer; red diamonds, toilet summer; green triangles, kitchen winter; and green circles, toilet winter. (B and C) Each square represents a 1 × 1 (B) or 5 × 5 (C) OTU. PC1, first principal component; PC2, second principal component.The tap-specific distributions of the bacterial groups were investigated using density distribution analyses. A dominant population related to Polynucleobacter was identified for the toilet summer samples, while for the winter samples there was a dominance of the Rhodocyclales-related bacteria. The kitchen summer samples revealed a dominance of Sphingomonas. The corresponding winter samples did not reveal distinct high-density bacterial populations (see Table S2 in the supplemental material).Hierarchical clustering for the 1 × 1 OTU density distribution confirmed the relatively low overlap for the microbiota in the samples analyzed (Fig. (Fig.2).2). We found that the microbiota clustered according to tap and not season.Open in a separate windowFIG. 2.Hierarchical clustering for the density distribution of the tap water microbiota. The density of 1 × 1 OTUs was used as a pseudospecies for hierarchical clustering. The tree for the Cord distance matrix is presented, while the distances calculated using the three distance matrices Cord, Brad Curtis, and Sneath Sokal, respectively, are shown for each branch.We have described the species diversity and richness of the microbiota in Table S3 in the supplemental material. For the low taxonomic level, these analyses showed that the diversity and species richness were greater for the winter samples than for the summer samples. Comparing the two taps, the diversity and richness were greater in the kitchen tap than in the toilet tap. In particular, the winter sample from the kitchen showed great richness and diversity. The high taxonomic level, however, did not reveal the same clear differences as did the low level, and the distributions were more even. Rarefaction analyses for the low taxonomic level confirmed the richness and diversity estimates (see Fig. S1 in the supplemental material).Our final analyses sought to fit the species rank distributions to common rank abundance curves. Generally, the rank abundance curves were best fitted to log series or truncated log normal distributions (see Table S4 in the supplemental material). The log series distribution could be fit to all of the samples except the kitchen summer samples at the low taxonomic level, while the truncated log normal distribution could not be fit to the kitchen samples at the high taxonomic level. Interestingly, however, the kitchen winter sample was best fit to a geometric curve at both the high and the low taxonomic level.Diversifying, adaptive biofilm barriers have been documented for tap water bacteria (7), and it is known that planktonic bacteria can interact with biofilms in an adaptive manner (3). On the other hand, tap usage leads to water flowthrough and replacement of the global with the local water population by stochastic founder effects (1).Therefore, we propose that parts of the local diversity observed can be explained by local adaptation (10) and parts by founder effects (9).Most prokaryote diversity measures assume log normal or log series OTU dominance density distributions (5). The kitchen winter sample, however, showed deviations from these patterns by being correlated to geometric distributions (in addition to the log series and truncated log normal distributions for the high taxonomic level). This sample also showed a much greater species richness than the other samples. A possible explanation is that the species richness of the tap water microbiota can be linked to usage and that the kitchen tap is driven toward a founder microbiota by high usage.Since our work indicates an overrepresentation of Legionella in the low-usage tap, it would be of high interest to determine whether the processes for local Legionella colonization can be related to tap usage. Understanding the ecological forces affecting Legionella and other pathogens are of great importance for human health. At the Akerhus University Hospital, this was exemplified by a Pseudomonas aeruginosa outbreak in an intensive care unit, where the outbreak could be traced back to a single tap (2). 相似文献2.
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Paul W. J. J. van der Wielen Gertjan Medema 《Applied and environmental microbiology》2010,76(14):4876-4881
Bacteroidales species were detected in (tap) water samples from treatment plants with three different PCR assays. 16S rRNA gene sequence analysis indicated that the sequences had an environmental rather than fecal origin. We conclude that assays for Bacteroidales 16S rRNA genes are not specific enough to discern fecal contamination of drinking water in the Netherlands.Drinking water in many countries is routinely monitored for recent fecal contamination by testing for fecal indicator organisms Escherichia coli, thermotolerant coliforms, and/or intestinal enterococci to demonstrate microbial safety (13, 21, 42). Although these indicator organisms have been used for many decades, they have some limitations: the number of E. coli/coliform/enterococcus bacteria in feces is relatively low (18, 38), and they sometimes might be able to grow in the environment (10, 11, 14, 27). Consequently, scientists have been searching for alternative indicator organisms to determine fecal contamination of water. In 1967, bacteria belonging to the genus Bacteroides were suggested as alternative indicator organisms (26). Bacteroides spp. might have some advantages over the traditional indicator organisms. The numbers of Bacteroides spp. in the intestinal tract of humans and animals are 10 to 100 times higher than the numbers of E. coli or intestinal enterococci (1, 2, 12, 26). However, the use of Bacteroides spp. as indicator organisms was hampered by the complex cultivation conditions required (1, 2). The introduction of molecular methods made it possible to detect bacterial species that belong to the order Bacteroidales, an order that includes the genus Bacteroides, without cultivation. As a result, real-time PCR methods were developed for the quantitative detection of Bacteroidales in surface and recreation water and the potential of Bacteroidales species as an indication of fecal contamination of recreational waters was demonstrated (6, 12, 16, 19, 20, 29). Bacteroidales species might be useful indicator organisms for fecal contamination of drinking water as well. However, methods to detect fecal contamination in drinking water should be more sensitive, because people ingest more drinking water and the quality assessments and standards for fecal contamination are stricter than for bathing water. Studies exploring real-time PCR for the detection of Bacteroidales genes in drinking water have not been published to our knowledge. The objective of our study was, therefore, to determine if assays for the detection of Bacteroidales 16S rRNA genes can be used to detect fecal contamination in drinking water.Unchlorinated tap water samples were obtained in November 2007 and February 2010 from one or more locations in the distribution systems of nine different drinking water treatment plants (plants A to I; Table Table1)1) that produced unchlorinated drinking water from confined (plants B, C, E, F, and G) and unconfined (plants A, D, H, and I) groundwater. The treatment plants are located in the central part of the Netherlands within 90 km of each other. In addition, untreated groundwater from extraction wells and/or untreated raw groundwater (mixture of groundwater from different extraction wells) was sampled in March 2008 (Table (Table1).1). Water samples (100 ml) were filtered over a 25-mm polycarbonate filter (0.22-μm pore size, type GTTP; Millipore, Netherlands) and a DNA fragment was added as internal control to determine the recovery efficiency of DNA isolation and PCR analysis (2a, 40). DNA was isolated using a FastDNA spin kit for soil (Qbiogene, United States) according to the supplier''s protocol. Primer sets AllBac 296f and AllBac 412r, resulting in a PCR product of 108 bp, were used in combination with TaqMan probe AllBac375Bhqr to quantitatively determine the number of Bacteroidales 16S rRNA gene copies in the water samples using a real-time PCR instrument (20). The PCR cycle after which the fluorescence signal of the amplified DNA was detected (threshold cycle [CT]) was used to quantify the concentration of 16S rRNA gene copies. Quantification was based on comparison of the sample CT value with the CT values of a calibration curve graphed using known copy numbers of the Bacteroidales 16S rRNA gene, as previously described (12, 20). The correlation coefficient of the calibration curve was 0.99, and the efficiency of the PCR 95 to 105%. Finally, the Bacteroidales cell number was calculated by using the recovery rate of the internal standard and assuming five 16S rRNA gene copy numbers per cell (5). The detection limit of this gene assay was 50 Bacteroidales cells 100 ml−1 (corresponding to 10 16S rRNA gene copies per reaction mixture). Furthermore, the 16S rRNA genes that were obtained from several water samples from treatment plant C with the AllBac and TotBac (12) primer sets were sequenced, and the nearest relatives were obtained from the GenBank database using BLAST searches.
Open in a separate windowaValues are the average results and standard deviations from replicate PCRs on the same water sample using the AllBac primer set (20). In November 2007, the distribution systems (tap water) of plants A, B, and G were sampled at three different locations, whereas for the other plants, one location in the distribution system was sampled. In March 2008, raw water of plants A to G was sampled, as well as one (plant E) or three (plant C) different extraction wells. Finally, in February 2010, the distribution systems of plants A, B, C, D, E, and G were sampled again.bMore than one tap water sample from a treatment plant means that samples were taken at different locations in the distribution system.The Bacteroidales 16S rRNA gene, quantified with the AllBac primer set, was detected in all tap water samples in November 2007 and February 2010. The number of cells varied between 154 and 7,862 Bacteroidales cells 100 ml−1, and the numbers in tap water of each plant were similar in 2007 and 2010 (Table (Table1).1). The Bacteroidales counts were high compared to the number of E. coli that are occasionally observed in fecally contaminated drinking water (17a) but low compared to numbers observed in surface water (4, 20, 22). Water from the extraction wells and raw water used for unchlorinated drinking water production were analyzed, and Bacteroidales species were detected in 10 out of 15 samples (Table (Table1).1). These results would imply that the extracted groundwater, raw water, and tap water were fecally contaminated. According to the Dutch drinking water decree (2b), both raw and tap water from the nine different treatment plants are regularly analyzed for fecal contamination by monitoring for E. coli, F-specific RNA phages, and somatic coliphages. For at least the last 10 years, these indicator organisms have not been detected in these waters.Additional qualitative PCR analyses using TotBac and BacUni primer sets (12, 19) targeting other parts of the Bacteroidales 16S rRNA gene were performed to confirm the presence of Bacteroidales species in the water samples of November 2007 and March 2008. Nine or 10 of the 11 samples that were positive with the AllBac primer set were also positive with the TotBac and BacUni primer sets (data not shown). The BacUni primer set has a higher detection limit (30 gene copies per PCR; 19), which could explain the difference from the results with the AllBac primer set. The TotBac primer set has the same detection limit as the AllBac primer set (12), but small differences in PCR efficiencies might have resulted in different results, since some water samples showed Bacteroidales 16S rRNA gene copy numbers around the detection limit (Table (Table1).1). Nevertheless, the additional PCR analyses demonstrated that the detection of Bacteroidales species in tap, raw, and extracted well water with the AllBac primer set was not an artifact. The primer sets used were developed in three different studies (12, 19, 20) but have been applied in a number of recent studies to detect fecal contamination of surface water (3, 4, 16, 22, 33, 34). The results from most of these studies showed that 16S rRNA genes of Bacteroidales were present in all surface water samples tested. Only Sinigalliano et al. (34) observed that 2 out of 4 water samples were negative with the TotBac primer set. However, the detection limit of the assay was not specified in that study.The nine different treatment plants tested in our study produce unchlorinated drinking water from groundwater, which is considered to be of high hygienic quality. In addition, the extraction wells are protected from fecal contamination by a protection zone where no activities related to human waste or animal manure are allowed. In the Netherlands, this protection zone is based on a 60-day residence time of the water. Previous studies have demonstrated that a residence time of 60 days is highly effective in removing fecal bacteria and viruses (30, 31, 39). Moreover, the Bacteroidales numbers in tap water in November 2007 were significantly higher than the numbers in raw groundwater in March 2008 (Mann-Whitney U test; P < 0.01). Because the recovery efficiency of the internal control was the same between raw water and tap water samples, this result demonstrates that Bacteroidales cell numbers increased during treatment and/or drinking water distribution. This result could suggest that the water was fecally contaminated during drinking water treatment and/or distribution. However, it is unlikely that the integrity of nine different treatment trains and/or supply systems was affected in the sampling period. The statutory monitoring did not show the presence of E. coli at these sites. Another hypothesis is that the increase of Bacteroidales cell numbers in tap water was caused by the growth of Bacteroidales species in (drinking) water systems. In summary, it is unexpected that the majority of the tap water, raw water, and extracted groundwater samples were fecally contaminated. These unexpected observations raise the question of whether the PCR methods detect only fecal Bacteroidales species and, thus, if the gene assays are suitable to discern fecal contamination in drinking water in the Netherlands.Sequence analyses of the Bacteroidales 16S rRNA genes were performed to determine the relatedness of sequences from the different sampling sites to sequences from the nearest relatives in the GenBank database. All sequences contained the primer regions, indicating that nonspecific amplification had not occurred in the PCRs. Because the PCR product from the AllBac primer set was small (108 bp), many 16S rRNA gene sequences (100 to 5,000) in the GenBank database were identical to the Bacteroidales 16S rRNA gene sequences obtained from groundwater and unchlorinated tap water samples from plant C. These identical 16S rRNA gene sequences were in general obtained from fecal sources, but some of them came from environmental rather than fecal sources (Table (Table2).2). The AllBac 16S rRNA gene sequences from tap water and groundwater had relative high similarities (96.3 to 100%) to sequences from bacterial species of the genera Bacteroides, Prevotella, and Tannerella (Table (Table2),2), which all belong to the order Bacteroidales.
TABLE 1.
Numbers of Bacteroidales cells in extraction wells, raw groundwater, and unchlorinated tap water of nine different groundwater plants in the NetherlandsaPlant | Source of sample | No. (100 ml−1) of Bacteroidales cells in: | ||
---|---|---|---|---|
2007 | 2008 | 2010 | ||
A | Tap water 1b | 5,948 ± 950 | ||
Tap water 2 | 2,682 ± 1,459 | 1,254 ± 216 | ||
Tap water 3 | 4,362 ± 947 | 439 ± 136 | ||
Raw water | 96 ± 15 | |||
B | Tap water 1 | 3,553 ± 981 | 5,302 ± 2,952 | |
Tap water 2 | 4,487 ± 391 | 2,119 ± 1,367 | ||
Tap water 3 | 7,862 ± 4,588 | 3,896 ± 3,003 | ||
Raw water | 3,209 ± 833 | |||
C | Tap water 1 | 661 ± 75 | 386 ± 199 | |
Tap water 2 | 1,051 ± 626 | |||
Tap water 3 | 831 ± 584 | |||
Tap water 4 | 1,254 ± 216 | |||
Extraction well 1 | 1,126 ± 262 | |||
Extraction well 2 | 2,666 ± 51 | |||
Extraction well 3 | <50 | |||
Raw water | 90 ± 44 | |||
D | Tap water | 1,103 ± 29 | 1,254 ± 216 | |
Raw water | 48 ± 16 | |||
E | Tap water | 1,302 ± 222 | 1,254 ± 216 | |
Extraction well 1 | 671 ± 97 | |||
F | Tap water | 1,317 ± 198 | ||
Raw water | <50 | |||
G | Tap water 1 | 675 ± 92 | 439 ± 300 | |
Tap water 2 | 216 ± 65 | 249 ± 98 | ||
Tap water 3 | 154 ± 6 | 322 ± 137 | ||
Raw water | <50 | |||
H | Tap water | 7,073 ± 845 | ||
Raw water | 511 ± 254 | |||
I | Tap water | 1,577 ± 176 | ||
Raw water | 420 ± 66 |
TABLE 2.
Nearest relatives in GenBank to the Bacteroidales 16S rRNA gene sequences obtained from groundwater and unchlorinated tap water from plant C using different primer setsaOpen in a separate windowaPrimer sets AllBac (20) and TotBac (12) were used in PCRs of samples, and GenBank was searched for relatives using BLAST.bOTUs are indicated by the values in parentheses (number of sequences belonging to the OTU/total number of sequences analyzed).cNumber of base pairs identical in both sequences/total number of base pairs in sequences.16S rRNA gene sequences obtained with the TotBac primer set were longer (∼370 bp) and did not show 100% similarity with the nearest relatives in the GenBank database (Table (Table2).2). Sequences from the GenBank database that showed the highest similarity (91.6% to 99.7%) with the 16S rRNA gene sequences from tap water and groundwater from plant C were in general isolated from environmental sources (Table (Table2).2). The 16S rRNA gene sequences from cultivated bacterial species that showed the highest similarity to the 16S rRNA gene sequences obtained in our study belonged to different genera (Table (Table2).2). Some of these genera (Salinimicrobium, Xanthobacillum, and Psychroserpens) did not belong to the order Bacteroidales. However, the 16S rRNA gene sequences from bacterial species of these genera showed low similarities with the sequences obtained in this study (83.2% to 90.1%) and six mismatches to the TotBac primers. Thus, it is unlikely that DNA from bacterial species belonging to Salinimicrobium, Xanthobacillum, and Psychroserpens was amplified in the gene assay. More importantly, the majority of the nearest environmental clone sequences retrieved from the GenBank database showed no or a single mismatch with the AllBac and TotBac primer and probe sequences. Thus, these primer sets are capable of amplifying 16S rRNA genes from bacteria that have been observed in ecosystems outside the intestinal tract of humans and animals.16S rRNA gene sequences related to Prevotella species were commonly observed in extracted groundwater, raw water, and tap water (Table (Table2).2). The isolation of Prevotella paludivivens from rice roots in a rice field soil (35) demonstrated the environmental nature of some Prevotella species. In addition, primer sequences developed for the detection of fecal Bacteroidales species (8, 12, 19, 20, 25, 29) showed no or a single mismatch with 16S rRNA gene sequences from P. paludivivens, Xylanibacterium oryzae, Paludibacter propionicigenes, Proteiniphilum acetatigenes, and Petrimonas sulfuriphila that are present in the GenBank database. These five Bacteroidales species have all been isolated from ecosystems other than the gastrointestinal tract. Consequently, primer sets for 16S rRNA genes of Bacteroidales species cannot always be used to discern fecal contamination in water.A number of 16S rRNA gene sequences observed in groundwater and tap water fell in the genus Bacteroides. The presence of Bacteroides 16S rRNA gene sequences in groundwater and tap water might also suggest that some Bacteroides species are capable of growth in the environment. However, until now, type strains of Bacteroides species growing outside the animal intestinal tract have not been published. Another possible explanation is that the observed 16S rRNA gene sequences originate from Bacteroides species that inhabit the anoxic intestinal tract of insects. Previous studies have shown that bacterial species belonging to the genus Bacteroides are common inhabitants of the hindguts of insects (15, 23, 24, 28, 32). Some of the 16S rRNA gene sequences obtained with the AllBac primer set in our study showed 100% similarity to 16S rRNA gene sequences from the hindgut of insects. Moreover, a number of 16S rRNA gene sequences isolated from the hindguts of insects (15, 23, 24, 32) showed no or a single mismatch with the TotBac and AllBac primer and probe sequences. In conclusion, these primer sets are capable of detecting Bacteroides species from the hindgut of insects as well. Water insects are normal inhabitants of groundwater and drinking water distribution systems (7, 41) and might be a source of Bacteroides species in water. Bacteroides species from insect feces do not indicate fecal pollution by warm-blooded animals, and insects do not normally shed human fecal pathogenic microorganisms. Bacteroides species from insect feces, therefore, can hamper Bacteroides gene assays developed for the detection of water fecally contaminated by warm-blooded animals. Additional cultivation techniques in combination with molecular tools are required to demonstrate the persistence or growth of Bacteroides bacteria in groundwater and drinking water or whether Bacteroides bacteria are present in water insects. However, these experiments were beyond the scope of our study.The three extraction wells of plant C are located close to each other and extract water from the same aquifer. Subsequently, extracted water from the three wells is mixed and enters the treatment plant as raw water. We hypothesize that if a fecal source in the vicinity of the extraction field of plant C contaminated the groundwater, water from the extraction wells and raw water should (partly) have the same Bacteroidales species. Although a relatively limited amount of clones was sequenced per sample (16), the diversity of Bacteroidales operational taxonomic units (OTU) within a sample was low (Table (Table2).2). In contrast, unique 16S rRNA gene sequences were observed between the different water types (e.g., extracted groundwater, raw water, and tap water) and sequence overlap between water types was low. These results demonstrate that the Bacteroidales 16S rRNA gene sequences at the sampling locations were not from the same fecal source and imply once again that Bacteroidales species were environmental rather than fecal.Finally, we hypothesized that if the Bacteroidales species observed in tap water were of nonfecal origin, human- and/or bovine-specific Bacteroidales strains should not be present in tap water. We tested for the presence of human- or bovine-specific Bacteroidales strains by using source-specific 16S rRNA gene assays (5) on tap water samples from February 2010. The results showed that human- and bovine-specific Bacteroidales 16S rRNA genes could not be detected in tap water, whereas a PCR product was always detected with the positive control. Again, these results indicate that the Bacteroidales species observed in tap water were of nonfecal origin.Overall, the results from our study indicate that gene assays for Bacteroidales detected environmental rather than fecal Bacteroidales species in groundwater and tap water from treatment plants in the Netherlands. First, Bacteroidales 16S rRNA gene sequences obtained from water samples taken at plant C showed (high) similarity to clone sequences that were isolated from environmental sources. The majority of these clone sequences and several Bacteroides clone sequences from the hindguts of insects showed no or a single mismatch with AllBac, TotBac, and BacUni primer and probe sequences. Second, the primer and probe sequences used for the gene assays have no or a single mismatch with 16S rRNA gene sequences of environmental Bacteroidales species P. paludivivens, X. oryzae, P. propionicigenes, P. acetatigenes, and/or P. sulfuriphila (9, 17, 35-37). Third, Bacteroidales 16S rRNA gene sequences from raw water and water from extraction wells were unique, and sequence overlap between water types was low. It is expected that in the case of fecal contamination of groundwater, different water types from the same groundwater area have similar Bacteroidales species. Fourth, the quantitative assays for Bacteroidales 16S rRNA genes commonly used to detect fecal contamination (3, 4, 12, 16, 19, 20, 22, 33, 34) detected Bacteroidales species in deep groundwater and tap water that have no history of fecal contamination. Fifth, Bacteroidales gene copy numbers were significantly higher in tap water than in raw groundwater, demonstrating an increase or growth of Bacteroidales species during the treatment and/or distribution of drinking water. Finally, human- and bovine-specific Bacteroidales strains were not detected in tap water. Consequently, (quantitative) assays for general Bacteroidales 16S rRNA genes are not suitable to discern fecal contamination in groundwater and unchlorinated drinking water in the Netherlands.Nucleotide sequence accession numbers.The 16S rRNA gene sequences obtained in this study were deposited in the GenBank database under accession numbers . GQ169588 to GQ169609相似文献4.
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Banumathi Sankaran Shilah A. Bonnett Kinjal Shah Scott Gabriel Robert Reddy Paul Schimmel Dmitry A. Rodionov Valérie de Crécy-Lagard John D. Helmann Dirk Iwata-Reuyl Manal A. Swairjo 《Journal of bacteriology》2009,191(22):6936-6949
GTP cyclohydrolase I (GCYH-I) is an essential Zn2+-dependent enzyme that catalyzes the first step of the de novo folate biosynthetic pathway in bacteria and plants, the 7-deazapurine biosynthetic pathway in Bacteria and Archaea, and the biopterin pathway in mammals. We recently reported the discovery of a new prokaryotic-specific GCYH-I (GCYH-IB) that displays no sequence identity to the canonical enzyme and is present in ∼25% of bacteria, the majority of which lack the canonical GCYH-I (renamed GCYH-IA). Genomic and genetic analyses indicate that in those organisms possessing both enzymes, e.g., Bacillus subtilis, GCYH-IA and -IB are functionally redundant, but differentially expressed. Whereas GCYH-IA is constitutively expressed, GCYH-IB is expressed only under Zn2+-limiting conditions. These observations are consistent with the hypothesis that GCYH-IB functions to allow folate biosynthesis during Zn2+ starvation. Here, we present biochemical and structural data showing that bacterial GCYH-IB, like GCYH-IA, belongs to the tunneling-fold (T-fold) superfamily. However, the GCYH-IA and -IB enzymes exhibit significant differences in global structure and active-site architecture. While GCYH-IA is a unimodular, homodecameric, Zn2+-dependent enzyme, GCYH-IB is a bimodular, homotetrameric enzyme activated by a variety of divalent cations. The structure of GCYH-IB and the broad metal dependence exhibited by this enzyme further underscore the mechanistic plasticity that is emerging for the T-fold superfamily. Notably, while humans possess the canonical GCYH-IA enzyme, many clinically important human pathogens possess only the GCYH-IB enzyme, suggesting that this enzyme is a potential new molecular target for antibacterial development.The Zn2+-dependent enzyme GTP cyclohydrolase I (GCYH-I; EC 3.5.4.16) is the first enzyme of the de novo tetrahydrofolate (THF) biosynthesis pathway (Fig. (Fig.1)1) (38). THF is an essential cofactor in one-carbon transfer reactions in the synthesis of purines, thymidylate, pantothenate, glycine, serine, and methionine in all kingdoms of life (38), and formylmethionyl-tRNA in bacteria (7). Recently, it has also been shown that GCYH-I is required for the biosynthesis of the 7-deazaguanosine-modified tRNA nucleosides queuosine and archaeosine produced in Bacteria and Archaea (44), respectively, as well as the 7-deazaadenosine metabolites produced in some Streptomyces species (33). GCYH-I is encoded in Escherichia coli by the folE gene (28) and catalyzes the conversion of GTP to 7,8-dihydroneopterin triphosphate (55), a complex reaction that begins with hydrolytic opening of the purine ring at C-8 of GTP to generate an N-formyl intermediate, followed by deformylation and subsequent rearrangement and cyclization of the ribosyl moiety to generate the pterin ring in THF (Fig. (Fig.1).1). Notably, the enzyme is dependent on an essential active-site Zn2+ that serves to activate a water molecule for nucleophilic attack at C-8 in the first step of the reaction (2).Open in a separate windowFIG. 1.Reaction catalyzed by GCYH-I, and metabolic fate of 7,8-dihydroneopterin triphosphate.A homologous GCYH-I is found in mammals and other higher eukaryotes, where it catalyzes the first step of the biopterin (BH4) pathway (Fig. (Fig.1),1), an essential cofactor in the biosynthesis of tyrosine and neurotransmitters, such as serotonin and l-3,4-dihydroxyphenylalanine (3, 52). Recently, a distinct class of GCYH-I enzymes, GCYH-IB (encoded by the folE2 gene), was discovered in microbes (26% of sequenced Bacteria and most Archaea) (12), including several clinically important human pathogens, e.g., Neisseria and Staphylococcus species. Notably, GCYH-IB is absent in eukaryotes.The distribution of folE (gene product renamed GCYH-IA) and folE2 (GCYH-IB) in bacteria is diverse (12). The majority of organisms possess either a folE (65%; e.g., Escherichia coli) or a folE2 (14%; e.g., Neisseria gonorrhoeae) gene. A significant number (12%; e.g., B. subtilis) possess both genes (a subset of 50 bacterial species is shown in Table Table1),1), and 9% lack both genes, although members of the latter group are mainly intracellular or symbiotic bacteria that rely on external sources of folate. The majority of Archaea possess only a folE2 gene, and the encoded GCYH-IB appears to be necessary only for the biosynthesis of the modified tRNA nucleoside archaeosine (44) except in the few halophilic Archaea that are known to synthesize folates, such as Haloferax volcanii, where GCYH-IB is involved in both archaeosine and folate formation (13, 44).
TABLE 1.
Distribution and candidate Zur-dependent regulation of alternative GCYH-I genes in bacteriaaOpen in a separate windowaGenes that are preceded by candidate Zur binding sites.bZur-regulated cluster is on the virulence plasmid pLVPK.cExamples of organisms with no folE genes are in boldface type.dZn-dependent regulation of B. subtilis folE2 by Zur was experimentally verified (17).Expression of the Bacillus subtilis folE2 gene, yciA, is controlled by the Zn2+-dependent Zur repressor and is upregulated under Zn2+-limiting conditions (17). This led us to propose that the GCYH-IB family utilizes a metal other than Zn2+ to allow growth in Zn2+-limiting environments, a hypothesis strengthened by the observation that an archaeal ortholog from Methanocaldococcus jannaschii has recently been shown to be Fe2+ dependent (22). To test this hypothesis, we investigated the physiological role of GCYH-IB in B. subtilis, an organism that contains both isozymes, as well as the metal dependence of B. subtilis GCYH-IB in vitro. To gain a structural understanding of the metal dependence of GCYH-IB, we determined high-resolution crystal structures of Zn2+- and Mn2+-bound forms of the N. gonorrhoeae ortholog. Notably, although the GCYH-IA and -IB enzymes belong to the tunneling-fold (T-fold) superfamily, there are significant differences in their global and active-site architecture. These studies shed light on the physiological significance of the alternative folate biosynthesis isozymes in bacteria exposed to various metal environments, and offer a structural understanding of the differential metal dependence of GCYH-IA and -IB. 相似文献7.
Riboflavin significantly enhanced the efficacy of simulated solar disinfection (SODIS) at 150 watts per square meter (W m−2) against a variety of microorganisms, including Escherichia coli, Fusarium solani, Candida albicans, and Acanthamoeba polyphaga trophozoites (>3 to 4 log10 after 2 to 6 h; P < 0.001). With A. polyphaga cysts, the kill (3.5 log10 after 6 h) was obtained only in the presence of riboflavin and 250 W m−2 irradiance.Solar disinfection (SODIS) is an established and proven technique for the generation of safer drinking water (11). Water is collected into transparent plastic polyethylene terephthalate (PET) bottles and placed in direct sunlight for 6 to 8 h prior to consumption (14). The application of SODIS has been shown to be a simple and cost-effective method for reducing the incidence of gastrointestinal infection in communities where potable water is not available (2-4). Under laboratory conditions using simulated sunlight, SODIS has been shown to inactivate pathogenic bacteria, fungi, viruses, and protozoa (6, 12, 15). Although SODIS is not fully understood, it is believed to achieve microbial killing through a combination of DNA-damaging effects of ultraviolet (UV) radiation and thermal inactivation from solar heating (21).The combination of UVA radiation and riboflavin (vitamin B2) has recently been reported to have therapeutic application in the treatment of bacterial and fungal ocular pathogens (13, 17) and has also been proposed as a method for decontaminating donor blood products prior to transfusion (1). In the present study, we report that the addition of riboflavin significantly enhances the disinfectant efficacy of simulated SODIS against bacterial, fungal, and protozoan pathogens.Chemicals and media were obtained from Sigma (Dorset, United Kingdom), Oxoid (Basingstoke, United Kingdom), and BD (Oxford, United Kingdom). Pseudomonas aeruginosa (ATCC 9027), Staphylococcus aureus (ATCC 6538), Bacillus subtilis (ATCC 6633), Candida albicans (ATCC 10231), and Fusarium solani (ATCC 36031) were obtained from ATCC (through LGC Standards, United Kingdom). Escherichia coli (JM101) was obtained in house, and the Legionella pneumophila strain used was a recent environmental isolate.B. subtilis spores were produced from culture on a previously published defined sporulation medium (19). L. pneumophila was grown on buffered charcoal-yeast extract agar (5). All other bacteria were cultured on tryptone soy agar, and C. albicans was cultured on Sabouraud dextrose agar as described previously (9). Fusarium solani was cultured on potato dextrose agar, and conidia were prepared as reported previously (7). Acanthamoeba polyphaga (Ros) was isolated from an unpublished keratitis case at Moorfields Eye Hospital, London, United Kingdom, in 1991. Trophozoites were maintained and cysts prepared as described previously (8, 18).Assays were conducted in transparent 12-well tissue culture microtiter plates with UV-transparent lids (Helena Biosciences, United Kingdom). Test organisms (1 × 106/ml) were suspended in 3 ml of one-quarter-strength Ringer''s solution or natural freshwater (as pretreated water from a reservoir in United Kingdom) with or without riboflavin (250 μM). The plates were exposed to simulated sunlight at an optical output irradiance of 150 watts per square meter (W m−2) delivered from an HPR125 W quartz mercury arc lamp (Philips, Guildford, United Kingdom). Optical irradiances were measured using a calibrated broadband optical power meter (Melles Griot, Netherlands). Test plates were maintained at 30°C by partial submersion in a water bath.At timed intervals for bacteria and fungi, the aliquots were plated out by using a WASP spiral plater and colonies subsequently counted by using a ProtoCOL automated colony counter (Don Whitley, West Yorkshire, United Kingdom). Acanthamoeba trophozoite and cyst viabilities were determined as described previously (6). Statistical analysis was performed using a one-way analysis of variance (ANOVA) of data from triplicate experiments via the InStat statistical software package (GraphPad, La Jolla, CA).The efficacies of simulated sunlight at an optical output irradiance of 150 W m−2 alone (SODIS) and in the presence of 250 μM riboflavin (SODIS-R) against the test organisms are shown in Table Table1.1. With the exception of B. subtilis spores and A. polyphaga cysts, SODIS-R resulted in a significant increase in microbial killing compared to SODIS alone (P < 0.001). In most instances, SODIS-R achieved total inactivation by 2 h, compared to 6 h for SODIS alone (Table (Table1).1). For F. solani, C. albicans, ands A. polyphaga trophozoites, only SODIS-R achieved a complete organism kill after 4 to 6 h (P < 0.001). All control experiments in which the experiments were protected from the light source showed no reduction in organism viability over the time course (results not shown).
Open in a separate windowaConditions are at an intensity of 150 W m−2 unless otherwise indicated.bThe values reported are means ± standard errors of the means from triplicate experiments.cAdditional experiments for this condition were performed using natural freshwater.The highly resistant A. polyphaga cysts and B. subtilis spores were unaffected by SODIS or SODIS-R at an optical irradiance of 150 W m−2. However, a significant reduction in cyst viability was observed at 6 h when the optical irradiance was increased to 250 W m−2 for SODIS-R only (P < 0.001; Table Table1).1). For spores, a kill was obtained only at 320 W m−2 after 6-h exposure, and no difference between SODIS and SODIS-R was observed (Table (Table1).1). Previously, we reported a >2-log kill at 6 h for Acanthamoeba cysts by using SODIS at the higher optical irradiance of 850 W m−2, compared to the 0.1-log10 kill observed here using the lower intensity of 250 W m−2 or the 3.5-log10 kill with SODIS-R.Inactivation experiments performed with Acanthamoeba cysts and trophozoites suspended in natural freshwater gave results comparable to those obtained with Ringer''s solution (P > 0.05; Table Table1).1). However, it is acknowledged that the findings of this study are based on laboratory-grade water and freshwater and that differences in water quality through changes in turbidity, pH, and mineral composition may significantly affect the performance of SODIS (20). Accordingly, further studies are indicated to evaluate the enhanced efficacy of SODIS-R by using natural waters of varying composition in the areas where SODIS is to be employed.Previous studies with SODIS under laboratory conditions have employed lamps delivering an optical irradiance of 850 W m−2 to reflect typical natural sunlight conditions (6, 11, 12, 15, 16). Here, we used an optical irradiance of 150 to 320 W m−2 to obtain slower organism inactivation and, hence, determine the potential enhancing effect of riboflavin on SODIS.In conclusion, this study has shown that the addition of riboflavin significantly enhances the efficacy of simulated SODIS against a range of microorganisms. The precise mechanism by which photoactivated riboflavin enhances antimicrobial activity is unknown, but studies have indicated that the process may be due, in part, to the generation of singlet oxygen, H2O2, superoxide, and hydroxyl free radicals (10). Further studies are warranted to assess the potential benefits from riboflavin-enhanced SODIS in reducing the incidence of gastrointestinal infection in communities where potable water is not available. 相似文献
TABLE 1.
Efficacies of simulated SODIS for 6 h alone and with 250 μM riboflavin (SODIS-R)Organism | Conditiona | Log10 reduction in viability at indicated h of exposureb | |||
---|---|---|---|---|---|
1 | 2 | 4 | 6 | ||
E. coli | SODIS | 0.0 ± 0.0 | 0.2 ± 0.1 | 5.7 ± 0.0 | 5.7 ± 0.0 |
SODIS-R | 1.1 ± 0.0 | 5.7 ± 0.0 | 5.7 ± 0.0 | 5.7 ± 0.0 | |
L. pneumophila | SODIS | 0.7 ± 0.2 | 1.3 ± 0.3 | 4.8 ± 0.2 | 4.8 ± 0.2 |
SODIS-R | 4.4 ± 0.0 | 4.4 ± 0.0 | 4.4 ± 0.0 | 4.4 ± 0.0 | |
P. aeruginosa | SODIS | 0.7 ± 0.0 | 1.8 ± 0.0 | 4.9 ± 0.0 | 4.9 ± 0.0 |
SODIS-R | 5.0 ± 0.0 | 5.0 ± 0.0 | 5.0 ± 0.0 | 5.0 ± 0.0 | |
S. aureus | SODIS | 0.0 ± 0.0 | 0.0 ± 0.0 | 6.2 ± 0.0 | 6.2 ± 0.0 |
SODIS-R | 0.2 ± 0.1 | 6.3 ± 0.0 | 6.3 ± 0.0 | 6.3 ± 0.0 | |
C. albicans | SODIS | 0.2 ± 0.0 | 0.4 ± 0.1 | 0.5 ± 0.1 | 1.0 ± 0.1 |
SODIS-R | 0.1 ± 0.0 | 0.7 ± 0.1 | 5.3 ± 0.0 | 5.3 ± 0.0 | |
F. solani conidia | SODIS | 0.2 ± 0.1 | 0.3 ± 0.0 | 0.2 ± 0.0 | 0.7 ± 0.1 |
SODIS-R | 0.3 ± 0.1 | 0.8 ± 0.1 | 1.3 ± 0.1 | 4.4 ± 0.0 | |
B. subtilis spores | SODIS | 0.3 ± 0.0 | 0.2 ± 0.0 | 0.0 ± 0.0 | 0.1 ± 0.0 |
SODIS-R | 0.1 ± 0.1 | 0.2 ± 0.1 | 0.3 ± 0.3 | 0.1 ± 0.0 | |
SODIS (250 W m−2) | 0.1 ± 0.0 | 0.1 ± 0.1 | 0.1 ± 0.1 | 0.0 ± 0.0 | |
SODIS-R (250 W m−2) | 0.0 ± 0.0 | 0.0 ± 0.0 | 0.2 ± 0.0 | 0.4 ± 0.0 | |
SODIS (320 W m−2) | 0.1 ± 0.1 | 0.1 ± 0.0 | 0.0 ± 0.1 | 4.3 ± 0.0 | |
SODIS-R (320 W m−2) | 0.1 ± 0.0 | 0.1 ± 0.1 | 0.9 ± 0.0 | 4.3 ± 0.0 | |
A. polyphaga trophozoites | SODIS | 0.4 ± 0.2 | 0.6 ± 0.1 | 0.6 ± 0.2 | 0.4 ± 0.1 |
SODIS-R | 0.3 ± 0.1 | 1.3 ± 0.1 | 2.3 ± 0.4 | 3.1 ± 0.2 | |
SODIS, naturalc | 0.3 ± 0.1 | 0.4 ± 0.1 | 0.5 ± 0.2 | 0.3 ± 0.2 | |
SODIS-R, naturalc | 0.2 ± 0.1 | 1.0 ± 0.2 | 2.2 ± 0.3 | 2.9 ± 0.3 | |
A. polyphaga cysts | SODIS | 0.4 ± 0.1 | 0.1 ± 0.3 | 0.3 ± 0.1 | 0.4 ± 0.2 |
SODIS-R | 0.4 ± 0.2 | 0.3 ± 0.2 | 0.5 ± 0.1 | 0.8 ± 0.3 | |
SODIS (250 W m−2) | 0.0 ± 0.1 | 0.2 ± 0.3 | 0.2 ± 0.1 | 0.1 ± 0.2 | |
SODIS-R (250 W m−2) | 0.4 ± 0.2 | 0.3 ± 0.2 | 0.8 ± 0.1 | 3.5 ± 0.3 | |
SODIS (250 W m−2), naturalc | 0.0 ± 0.3 | 0.2 ± 0.1 | 0.1 ± 0.1 | 0.2 ± 0.1 | |
SODIS-R (250 W m−2), naturalc | 0.1 ± 0.1 | 0.2 ± 0.2 | 0.6 ± 0.1 | 3.4 ± 0.2 |
8.
9.
F. Joseph Pollock Pamela J. Morris Bette L. Willis David G. Bourne 《Applied and environmental microbiology》2010,76(15):5282-5286
A real-time quantitative PCR-based detection assay targeting the dnaJ gene (encoding heat shock protein 40) of the coral pathogen Vibrio coralliilyticus was developed. The assay is sensitive, detecting as little as 1 CFU per ml in seawater and 104 CFU per cm2 of coral tissue. Moreover, inhibition by DNA and cells derived from bacteria other than V. coralliilyticus was minimal. This assay represents a novel approach to coral disease diagnosis that will advance the field of coral disease research.Vibrio coralliilyticus has recently emerged as a coral pathogen of concern on reefs throughout the Indo-Pacific. It was first implicated as the etiological agent responsible for bleaching and tissue lysis of the coral Pocillopora damicornis on Zanzibar reefs (2). More recently, V. coralliilyticus has been identified as the causative agent of white syndrome (WS) outbreaks on several Pacific reefs (14). WS is a collective term describing coral diseases characterized by a spreading band of tissue loss exposing white skeleton on Indo-Pacific scleractinian corals (16). V. coralliilyticus is an emerging model pathogen for understanding the mechanisms linking bacterial infection and coral disease (13) and therefore provides an ideal model for the development of diagnostic assays to detect coral disease. Current coral disease diagnostic methods, which are based primarily upon field-based observations of macroscopic disease signs, often detect disease only at the latest stages of infection, when control measures are least effective. The development of diagnostic tools targeting pathogens underlying coral disease pathologies may provide early indications of infection, aid the identification of disease vectors and reservoirs, and assist managers in developing strategies to prevent the spread of coral disease outbreaks. In this paper, we describe the development and validation of a TaqMan-based real-time quantitative PCR (qPCR) assay that targets a segment of the V. coralliilyticus heat shock protein 40-encoding gene (dnaJ).Nucleotide sequences of the dnaJ gene were retrieved from relevant Vibrio species, including V. coralliilyticus (LMG 20984), using the National Center for Biotechnology Information''s (NCBI) Entrez Nucleotide Database search tool (http://www.ncbi.nlm.nih.gov/). Gene sequences of strains not available in public databases (V. coralliilyticus strains LMG 21348, LMG 21349, LMG 21350, LMG 10953, LMG 20538, LMG 23696, LMG 23691, LMG 23693, LMG 23692, and LMG 23694) were obtained through extraction of total DNA using a Promega Wizard Prep DNA Purification Kit (Promega, Sydney, Australia), PCR amplification, and sequencing using primers and thermal cycling parameters described by Nhung et al. (8). A 128-bp region (nucleotides 363 to 490) containing high concentrations of single nucleotide polymorphisms (SNPs), which were conserved within V. coralliilyticus strains but differed from non-V. coralliilyticus strains, was identified, and oligonucleotide primers Vc_dnaJ_F1 (5′-CGG TTC GYG GTG TTT CAA AA-3′) and Vc_dnaJ_R1 (5′-AAC CTG ACC ATG ACC GTG ACA-3′) and a TaqMan probe, Vc_dnaJ_TMP (5′-6-FAM-CAG TGG CGC GAA G-MGBNFQ-3′; 6-FAM is 6-carboxyfluorescein and MGBNFQ is molecular groove binding nonfluorescent quencher), were designed to target this region. The qPCR assay was optimized and validated using DNA extracted from V. coralliilyticus isolates, nontarget Vibrio species, and other bacterial species grown in marine broth (MB) (Table (Table1),1), under the following optimal conditions: 1× TaqMan buffer A, 0.5 U of AmpliTaq Gold DNA polymerase, 200 μM deoxynucleotide triphosphates (with 400 μM dUTP replacing deoxythymidine triphosphate), 0.2 U of AmpErase uracil N-glycosylase (UNG), 3 mM MgCl2, 0.6 μM each primer, 0.2 μM fluorophore-labeled TaqMan, 1 μl of template, and sterile MilliQ water for a total reaction volume to 20 μl. All assays were conducted on a RotoGene 300 (Corbett Research, Sydney, Australia) real-time analyzer with the following cycling parameters: 50°C for 120 s (UNG activation) and 95°C for 10 min (AmpliTaq Gold DNA polymerase activation), followed by 40 cycles of 95°C for 15 s (denaturation) and 60°C for 60 s (annealing/extension). During the annealing/extension phase of each thermal cycle, fluorescence was measured in the FAM channel (470-nm excitation and 510-nm detection).
Open in a separate windowaOrigin, host organism, and dnaJ gene sequence accession numbers are shown for V. coralliilyticus strains.bStrain designations beginning with LMG were derived from the Belgian Coordinated Collections of Microorganisms, ATCC strains are from the American Type Culture Collection, DSM strains are from the Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH culture collection, AIMS strains are from the Australian Institute of Marine Science culture collection, and C1 and C2 were provided by Pamela Morris.c†, amplification in one of three reactions; ††, amplification in two of three reactions; NA, no amplification.dIsolated from seawater above coral.The qPCR assay specifically detected 12 out of 13 isolated V. coralliilyticus strains tested in this study (Table (Table1).1). The exception was one Caribbean strain (C2), which failed to give specific amplification despite repeated attempts. Positive detection of the target gene segment was determined by the increase in fluorescent signal beyond the fluorescence threshold value (normalized fluorescence, 0.010) at a specific cycle, referred to as the threshold cycle (CT). Specific detection was further confirmed by gel electrophoresis, which revealed a PCR product of the correct theoretical size (128 bp) (data not shown), and DNA sequencing, which confirmed the target amplified product to be a segment of the dnaJ gene. No amplification with the assay was detected for 13 other closely related Vibrio strains, including the closely related Vibrio neptunius and two non-Vibrio species (Table (Table1).1). A total of five other Vibrio strains and one non-Vibrio strain (Shewanella sp.) exhibited CT values less than the cutoff of 32 cycles. However, CT values for these strains (mean ± standard error of the mean [SEM], 27.96 ± 2.40) were all much higher than those for V. coralliilyticus strains (12.30 ± 1.52), and no amplicons were evident in post-qPCR gel electrophoresis (data not shown).The detection limit for purified V. coralliilyticus genomic DNA was 0.1 pg of DNA, determined by performing 10-fold serial dilutions (100 ng to 0.01 pg per reaction), followed by qPCR amplification. Similarly, qPCR assays of serial dilutions of V. coralliilyticus (LMG 23696) cells cultured overnight in MB (108 CFU ml−1 to extinction) were able to detect as few as 104 CFU (Fig. (Fig.1).1). Standard curves revealed a strong linear negative correlation between CT values and both DNA and cell concentrations of V. coralliilyticus over several orders of magnitude, with r2 values of 0.998 and 0.953 for DNA and cells, respectively (Fig. (Fig.11).Open in a separate windowFIG. 1.Standard curves delineating threshold (CT) values of fluorescence for indicators of pathogen presence: (A) concentration of V. coralliilyticus DNA and (B) number of V. coralliilyticus cells in pure culture. Error bars indicate standard error of the mean for three replicate qPCRs.Little interference of the qPCR assay was observed when purified V. coralliilyticus (LMG 23696) DNA (10 ng) was combined with 10-fold serial dilutions (0.01 to 100 ng per reaction) of non-V. coralliilyticus DNA (i.e., Vibrio campbellii [ATCC 25920T]). Over the entire range of nontarget DNA concentrations tested, the resulting CT values (mean ± SEM, 17.76 ± 0.53) were not significantly different from those of a control treatment containing 10 ng of V. coralliilyticus DNA and no nonspecific DNA (16.75 ± 0.18; analysis of variance [ANOVA], P = 0.51) (Table (Table2).2). Detection of V. coralliilyticus (LMG 23696) bacterial cells (104, 105, 106, 107, or 108 CFU per ml) in a background of non-V. coralliilyticus cells (i.e., V. campbellii [ATCC 25920T] at 0, 10, 104, or 107 CFU per ml) showed little reduction in assay sensitivity (see Fig. S1 in the supplemental material). For example, when V. coralliilyticus was seeded at 107 cells with similarly high concentrations of nontarget cells, little inhibition of the assay was observed.
Open in a separate windowaV. coralliilyticus (LMG 23696) DNA (10 ng) free of nontarget DNA and cells served as positive controls.bA qPCR mixture containing no bacterial DNA served as a no-template, or negative, control (NTC).The assay''s detection limit in seawater was tested by inoculating 10-fold serial dilutions of V. coralliilyticus (LMG 23696) cultures (grown overnight in MB medium, pelleted at 14,000 rpm for 10 min, and washed twice with sterile phosphate-buffered saline [PBS]) into 1 liter of seawater (equivalent final concentrations were 106 to 1 CFU ml−1). The entire volume of V. coralliilyticus-seeded seawater was filtered through a Sterivex-GP filter (Millipore), and DNA was extracted using the method described by Schauer et al. (11). The lowest detection limit for V. coralliilyticus cells seeded into seawater was 1 CFU ml−1 (Fig. (Fig.2),2), with no detection in a 1-liter volume of an unseeded seawater negative control. Standard curves revealed a strong correlation between CT values and the concentrations of V. coralliilyticus bacteria seeded into the seawater over several orders of magnitude (r2 of 0.968) (Fig. (Fig.22).Open in a separate windowFIG. 2.Standard curves showing CT values of the fluorescent signal versus the number of V. coralliilyticus cells per ml seawater (▿), and cells per cm2 of M. aequituberculata tissue, with (○) or without (·) enrichment. Each dot represents an independent experiment. Error bars indicate standard error of the mean for three replicate qPCR runs.The detection limit in seeded coral tissue homogenate was determined by seeding 10-fold dilutions (1010 to 103 CFU ml−1) of pelleted, PBS-washed and resuspended (in 10 ml of sterile PBS) V. coralliilyticus cells onto healthy fragments (∼10 cm2) of the coral Montipora aequituberculata collected from Nelly Bay (Magnetic Island, Australia). Corals were collected in March 2009 and maintained in holding tanks supplied with flowthrough ambient seawater. Resuspended cells were inoculated onto M. aequituberculata fragments, each contained in an individual 3.8-liter plastic bag, allowed to sit at room temperature for 30 min, and then air brushed with compressed air until only white skeleton remained. One-milliliter aliquots of the resulting slurry (PBS, bacteria, and coral tissue) was vortexed for 10 min at 14,000 rpm, and DNA was extracted using a PowerPlant DNA Isolation Kit (Mo Bio, Carlsbad, CA). The lowest detection limits for V. coralliilyticus cells seeded onto coral fragments was 104 CFU per cm2 of coral tissue (Fig. (Fig.2).2). Again, standard curves revealed a strong correlation between CT values and the concentrations of seeded bacteria over several orders of magnitude (r2 of 0.981) (Fig. (Fig.2).2). When a 1-ml aliquot of the slurry was also inoculated into 25 ml of MB and enriched for 6 h at 28°C (with shaking at 170 rpm), the detection limit increased by 1 order of magnitude, to 103 CFU of V. coralliilyticus per cm2 of coral tissue (Fig. (Fig.2).2). The slope of the standard curve reveals some inhibition, particularly at the highest V. coralliilyticus concentrations, which could result from lower replication rates in the cultures with the highest bacterial densities (i.e., 109 CFU). However, since this effect is most pronounced only at the highest bacterial concentrations, the detection limit is still valid. In all trials, unseeded coral fragments and enrichment cultures derived from uninoculated coral fragments served as negative controls.The current study describes the first assay developed to detect and quantify a coral pathogen using a real-time quantitative PCR (qPCR) approach. While previous studies have utilized antibodies or fluorescent in situ hybridization (FISH) to detect coral pathogens (1, 6), the combination of high sensitivity and specificity, low contamination risk, and ease and speed of performance (5) make qPCR technology an ideal choice for rapid pathogen detection in complex hosts, such as corals. The assay developed is highly sensitive for V. coralliilyticus, detecting as few as 1 CFU ml−1 of seawater and 104 CFU cm−2 of coral tissue (103 CFU cm−2 of coral tissue with a 6-h enrichment). These detection limits are likely to be within biologically relevant pathogen concentrations. For example, antibodies for specific detection of the coral bleaching pathogen Vibrio shiloi showed that bacterial densities reached 8.4 × 108 cells cm−3 1 month prior to maximum visual bleaching signs on the coral Oculina patagonica (6). Each seeded seawater and coral (enriched and nonenriched) dilution assay was performed in triplicate. The linearity of the resulting standard curves indicates consistent extraction efficiencies over V. coralliilyticus concentrations spanning 6 orders of magnitude (Fig. (Fig.2)2) and provides strong support for the robustness of the assay. In addition, the presence of competing, non-V. coralliilyticus bacterial cells and DNA had a minimal impact on the detection of V. coralliilyticus. This is an important consideration for accurate detection within the complex coral holobiont, where the target organism is present within a matrix of other microbial and host cells.V. coralliilyticus, like V. shiloi (10), is becoming a model pathogen for the study of coral disease. Recent research efforts have characterized the organism''s genome (W. R. Johnson et al., submitted for publication), proteome (N. E. Kimes et al., submitted for publication), resistome (15), and metabolome (4) and enhanced our understanding of the genetic (7, 9) and physiological (7, 13) basis of its virulence. Before effective management response plans can be formulated, however, continuing research on the genetic and cellular aspects of V. coralliilyticus must be complemented with knowledge of the epidemiology of this pathogen, including information on its distribution, incidence of infection, and rates of transmission throughout populations. The V. coralliilyticus-specific qPCR assay developed in this study will provide important insights into the dynamics of pathogen invasion and spread within populations (6) while also aiding in the identification of disease vectors and reservoirs (12). These capabilities will play an important role in advancing the field of coral disease research and effective management of coral reefs worldwide. 相似文献
TABLE 1.
Species, strain, and threshold cycle for all bacterial strains testedaSpecies | Strainb | Origin | Host organism | CT ± SEMc | dnaJ gene sequence accession no. | Reference |
---|---|---|---|---|---|---|
Vibrio coralliilyticus | LMG 23696 | Nelly Bay, Magnetic Island, Australia | Montipora aequituberculata | 12.43 ± 0.20 | HM215570 | 14 |
LMG 23691 | Majuro Atoll, Republic of Marshall Islands | Acropora cytherea | 14.07 ± 1.33 | HM215571 | 14 | |
LMG 23693 | Nikko Bay, Palau | Pachyseris speciosa | 10.83 ± 2.76 | HM215572 | 14 | |
LMG 23692 | Nikko Bay, Palau | Pachyseris speciosa | 9.40 ± 0.36 | HM215573 | 14 | |
LMG 23694 | Nikko Bay, Palau | Pachyseris speciosad | 12.54 ± 0.24 | HM215574 | 14 | |
LMG 20984T | Indian Ocean, Zanzibar, Tanzania | Pocillopora damicornis | 12.80 ± 0.71 | HM215575 | 2 | |
LMG 21348 | Red Sea, Eilat, Israel | Pocillopora damicornis | 13.81 ± 0.49 | HM215576 | 3 | |
LMG 21349 | Red Sea, Eilat, | Pocillopora damicornis | 12.98 ± 0.94 | HM215577 | 3 | |
LMG 21350 | Red Sea, Eilat, | Pocillopora damicornis | 11.49 ± 0.19 | HM215578 | 3 | |
LMG 10953 | Kent, United Kingdom | Crassostrea gigas (oyster) larvae | 10.53 ± 0.40 | HM215579 | 3 | |
LMG 20538 | Atlantic Ocean, Florianópolis, Brazil | Nodipecten nodosus (bivalve) larvae | 12.13 ± 0.50 | HM215580 | 3 | |
C1 | Caribbean Sea, La Parguera, Puerto Rico | Pseudopterogorgia americana | 14.53 ± 0.28 | HM215568 | 15 | |
C2 | Caribbean Sea, La Parguera, Puerto Rico | Pseudopterogorgia americana | NA | HM215569 | 15 | |
Vibrio alginolyticus | ATCC 17749 | 33.74 ± 0.33 | ||||
Vibio brasiliensis | DSM 17184 | 37.84† | ||||
Vibrio calviensis | DSM 14347 | 27.06 ± 0.52 | ||||
Vibrio campbellii | ATCC 25920T | 39.10† | ||||
Enterovibrio campbellii | LMG 21363 | 37.33 ± 2.41 | ||||
Alliivibrio fischeri | DSM 507 | 31.36 ± 1.42 | ||||
Vibrio fortis | DSM 19133 | NA | ||||
Vibrio furnissii | DSM 19622 | NA | ||||
Vibrio harveyi | DSM 19623 | NA | ||||
Vibrio natriegens | ATCC 14048 | 28.56 ± 0.60 | ||||
Vibrio neptunius | LMG 20536 | NA | ||||
Vibrio ordalii | ATCC 33509 | 25.56 ± 0.41 | ||||
Vibrio parahaemolyticus | ATCC 17802 | NA | ||||
Vibrio proteolyticus | ATCC 15338 | 30.00 ± 0.89†† | ||||
Vibrio rotiferianus | LMG 21460 | NA | ||||
Vibrio splendidus | ATCC 33125 | 32.31 ± 0.82 | ||||
Vibrio tubiashii | ATCC 19109 | NA | ||||
Vibrio xuii | LMG 21346 | NA | ||||
Escherichia coli | ATCC 25922 | NA | ||||
Psychrobacter sp. | AIMS 1618 | NA | ||||
Shewanella sp. | AIMS C041 | 25.34 ± 0.45 |
TABLE 2.
Effect of nontarget bacterial DNA on the detection of 10 ng of purified V. coralliilyticus DNAAmt of nontarget DNA (ng) | CT (mean ± SEM) |
---|---|
100 | 16.97 ± 0.33 |
10 | 16.9 ± 0.08 |
1 | 16.74 ± 0.10 |
0.1 | 17 ± 0.09 |
0.01 | 16.37 ± 0.43 |
0a | 16.75 ± 0.18 |
NTCb | 35.04 ± 0.02 |
10.
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12.
Yongchao Li Diana C. Irwin David B. Wilson 《Applied and environmental microbiology》2010,76(8):2582-2588
Amino acid modifications of the Thermobifida fusca Cel9A-68 catalytic domain or carbohydrate binding module 3c (CBM3c) were combined to create enzymes with changed amino acids in both domains. Bacterial crystalline cellulose (BC) and swollen cellulose (SWC) assays of the expressed and purified enzymes showed that three combinations resulted in 150% and 200% increased activity, respectively, and also increased synergistic activity with other cellulases. Several other combinations resulted in drastically lowered activity, giving insight into the need for a balance between the binding in the catalytic cleft on either side of the cleavage site, as well as coordination between binding affinity for the catalytic domain and CBM3c. The same combinations of amino acid variants in the whole enzyme, Cel9A-90, did not increase BC or SWC activity but did have higher filter paper (FP) activity at 12% digestion.Cellulases catalyze the breakdown of cellulose into simple sugars that can be fermented to ethanol. The large amount of natural cellulose available is an exciting potential source of fuels and chemicals. However, the detailed molecular mechanisms of crystalline cellulose degradation by glycoside hydrolases are still not well understood and their low efficiency is a major barrier to cellulosic ethanol production.Thermobifida fusca is a filamentous soil bacterium that grows at 50°C in defined medium and can utilize cellulose as its sole carbon source. It is a major degrader of plant cell walls in heated organic materials such as compost piles and rotting hay and produces a set of enzymes that includes six different cellulases, three xylanases, a xyloglucanase, and two CBM33 binding proteins (12). Among them are three endocellulases, Cel9B, Cel6A, and Cel5A (7, 8), two exocellulases, Cel48A and Cel6B (6, 19), and a processive endocellulase, Cel9A (5, 7).T. fusca Cel9A-90 (Uniprot and P26221) is a multidomain enzyme consisting of a family 9 catalytic domain (CD) rigidly attached by a short linker to a family 3c cellulose binding module (CBM3c), followed by a fibronectin III-like domain and a family 2 CBM (CBM2). Cel9A-68 consists of the family 9 CD and CBM3c. The crystal structure of this species (Fig. YP_290232(Fig.1)1) was determined by X-ray crystallography at 1.9 Å resolution (Protein Data Bank [PDB] code 4tf4) (15). Previous work has shown that E424 is the catalytic acid and D58 is the catalytic base (11, 20). H125 and Y206 were shown to play an important role in activity by forming a hydrogen bonding network with D58, an important supporting residue, D55, and Glc(−1)O1. Several enzymes with amino acid changes in subsites Glc(−1) to Glc(−4) had less than 20% activity on bacterial cellulose (BC) and markedly reduced processivity. It was proposed that these modifications disturb the coordination between product release and the subsequent binding of a cellulose chain into subsites Glc(−1) to Glc(−4) (11). Another variant enzyme with a deletion of a group of amino acids forming a block at the end of the catalytic cleft, Cel9A-68 Δ(T245-L251)R252K (DEL), showed slightly improved filter paper (FP) activity and binding to BC (20).Open in a separate windowFIG. 1.Crystal structure of Cel9A-68 (PDB code 4tf4) showing the locations of the variant residues, catalytic acid E424, catalytic base D58, hydrogen bonding network residues D55, H125, and Y206, and six glucose residues, Glc(−4) to Glc(+2). Part of the linker is visible in dark blue.The CBM3c domain is critical for hydrolysis and processivity. Cel9A-51, an enzyme with the family 9 CD and the linker but without CBM3c, had low activity on carboxymethyl cellulose (CMC), BC, and swollen cellulose (SWC) and showed no processivity (4). The role of CBM3c was investigated by mutagenesis, and one modified enzyme, R557A/E559A, had impaired activity on all of these substrates but normal binding and processivity (11). Variants with changes at five other CBM3c residues were found to slightly lower the activity of the modified enzymes, while Cel9A-68 enzymes containing either F476A, D513A, or I514H were found to have slightly increased binding and processivity (11) (see Table Table1).1). In the present work, CBM3c has been investigated more extensively to identify residues involved in substrate binding and processivity, understand the role of CBM3c more clearly, and study the coordination between the CD and CBM3c. An additional goal was to combine amino acid variants showing increased crystalline cellulose activity to see if this further increased activity. Finally, we have investigated whether the changes that improved the activity of Cel9A-68 also enhanced the activity of intact Cel9A-90.
Open in a separate windowaThe target percent digestion could not be reached; activity was calculated using 1.5 μM enzyme.bDEL refers to deletion of T245 to L251 and R252K. 相似文献
TABLE 1.
Activities of Cel9A-68 CBM3c variant enzymes and CD variant enzymes used to create the double variantsEnzyme | Activity (% of wild type) on: | % Processivity | % BC binding | Reference | |||
---|---|---|---|---|---|---|---|
CMC | SWC | BC | FPa | ||||
Wild type | 100 | 100 | 100 | 100 | 100 | 15 | This work |
R378K | 98 | 91 | 103 | 93 | 139 | 20 | 11 |
DELb | 98 | 101 | 101 | 128 | 96 | 20 | |
F476A | 97 | 105 | 79 | 100 | 145 | 21 | 11 |
D513A | 100 | 115 | 121 | 107 | 119 | 20 | 11 |
I514H | 104 | 91 | 112 | 104 | 110 | 23 | 11 |
Y520A | 108 | 78 | 33a | 79 | 87 | 14 | 11 |
R557A | 103 | 98 | 60a | 93 | 90 | This work | |
E559A | 86 | 90 | 30a | 70 | 94 | This work | |
R557A+E559A | 90 | 75 | 15a | 75 | 106 | 15 | 11 |
Q561A | 103 | 56 | 51a | 78 | 74 | This work | |
R563A | 97 | 70 | 52a | 93 | 129 | 20 | 11 |
13.
14.
Andrea Jesenská Marta Monincová Táňa Koudeláková Khomaini Hasan Radka Chaloupková Zbyněk Prokop Arie Geerlof Ji?í Damborsky 《Applied and environmental microbiology》2009,75(15):5157-5160
This study focuses on two representatives of experimentally uncharacterized haloalkane dehalogenases from the subfamily HLD-III. We report biochemical characterization of the expression products of haloalkane dehalogenase genes drbA from Rhodopirellula baltica SH1 and dmbC from Mycobacterium bovis 5033/66. The DrbA and DmbC enzymes show highly oligomeric structures and very low activities with typical substrates of haloalkane dehalogenases.Haloalkane dehalogenases (EC 3.8.1.5.) acting on halogenated aliphatic hydrocarbons catalyze carbon-halogen bond cleavage, leading to an alcohol, a halide ion, and a proton as the reaction products (7). Haloalkane dehalogenases originating from various bacterial strains have potential for application in bioremediation technologies (4, 6, 22), construction of biosensors (2), decontamination of warfare agents (17), and synthesis of optically pure compounds (19). Recent evolutionary study of haloalkane dehalogenase sequences revealed the existence of three subfamilies, denoted HLD-I, HLD-II, and HLD-III (3). In contrast to subfamilies HLD-I and HLD-II, the subfamily HLD-III is currently lacking experimentally characterized proteins. We have therefore focused on the isolation and study of two selected representatives of the HLD-III subfamily, DrbA and DmbC.The drbA gene was amplified by PCR using the cosmid pircos.a3g10 originating from marine bacterium Rhodopirellula baltica SH1, and the dmbC gene was amplified from DNA originating from obligatory pathogen Mycobacterium bovis 5033/66. Six-histidine tails were added to the C termini of DrbA and DmbC in a cloning step, enabling single-step purification using Ni-nitrilotriacetic acid resin. Haloalkane dehalogenase DrbA was expressed under the T7 promoter and purified, with a resulting yield of 0.1 mg of protein per gram of cell mass. Haloalkane dehalogenase DmbC was obtained by expression in Mycobacterium smegmatis, with a yield of 0.07 mg of purified protein per gram of cell mass.The correct folding and secondary structures of the newly prepared enzymes were verified by circular dichroism (CD) spectroscopy. Far-UV CD spectra were recorded for DrbA and DmbC enzymes and other, related haloalkane dehalogenases. All enzymes tested exhibited CD spectra with two negative features at 208 and 222 nm and one positive peak at 195 nm, which are characteristic of α-helical content (Fig. (Fig.1).1). This suggested that both new enzymes, DrbA and DmbC, were folded correctly. However, DmbC exhibited more intense negative maxima which differed from other haloalkane dehalogenases in the θ222/θ208 ratio. This finding indicated a slight variation in the arrangement of secondary structure elements of the DmbC enzyme. Thermally induced denaturations of DrbA and DmbC were tested in parallel. Both enzymes showed changes in ellipticity during increasing temperature. The melting temperatures calculated from these curves were 45.8 ± 0.4°C for DmbC and 39.4 ± 0.1°C for DrbA. The thermostability results obtained for DrbA and DmbC were in good agreement with the range of melting temperatures determined for other, related haloalkane dehalogenases.Open in a separate windowFIG. 1.Far-UV CD spectra of DrbA, DmbC, and seven different biochemically characterized haloalkane dehalogenases. Protein concentration used for far-UV CD spectrum measurement was 0.2 mg/ml.The sizes of the purified proteins were estimated by electrophoresis under native conditions conducted using a 10% polyacrylamide gel (Fig. (Fig.2).2). More precise determination of the sizes of DrbA and DmbC was achieved by gel filtration chromatography performed on Sephacryl S-500 HR (GE Healthcare, Uppsala, Sweden), calibrated with blue dextran 2000, thyroglobulin (669 kDa), ferritin (440 kDa), catalase (240 kDa), conalbumin (75 kDa), and ovalbumin (43 kDa) (Fig. (Fig.3A).3A). Both DrbA and DmbC were eluted from the column in the fraction prior to blue dextran, indicating that both enzymes form oligomeric complexes of a size larger than 2,000 kDa (Fig. 3B and C). The haloalkane dehalogenases which have been biochemically characterized so far form monomers, except for DbjA isolated from Bradyrhizobium japonicum USDA110 (21), which shows monomeric, dimeric, and tetrameric forms according to the pH of the buffer (R. Chaloupkova, submitted for publication).Open in a separate windowFIG. 2.Native protein electrophoresis of DrbA and DmbC. Lane 1, carbonic anhydrase (29 kDa); lane 2, ovalbumin (43 kDa); lane 3, bovine albumin (67 kDa); lane 4, conalbumin (75 kDa); lane 5, catalase (240 kDa); lane 6, ferritin (440 kDa); lane 7, DrbA; lane 8, DmbC.Open in a separate windowFIG. 3.Gel filtration chromatogram of DrbA and DmbC. (A) The following calibration kit samples (0.5 ml of a concentration of 2 mg/ml protein loaded) were analyzed using 50 mM Tris-HCl with 150 mM NaCl, pH 7.5, as elution buffer: blue dextran (line 1, 9.6-ml fraction), thyroglobulin (line 2, 15.95-ml fraction), ferritin (line 3, 16.78-ml fraction), ovalbumin (line 4, 18.55-ml fraction), and RNase A (line 5, 20.08-ml fraction). (B and C) Haloalkane dehalogenase DrbA eluted in the 9.03-ml fraction (B), and haloalkane dehalogenase DmbC in the 9.31-ml fraction (C).The substrate specificities of DrbA and DmbC were investigated with a set of 30 selected chlorinated, brominated, and iodinated hydrocarbons. Standardized specific activities related to 1-chlorobutane (summarized in Table Table1)1) were compared with the activity profiles of other haloalkane dehalogenases (Fig. (Fig.4).4). DrbA and DmbC displayed similar activity patterns, with catalytic activities approximately two orders of magnitude lower than those of other known haloalkane dehalogenases (1, 5, 8-11, 13-16, 18, 20, 23). HLD-III subfamily enzymes showed a restricted specificity range and a preference for iodinated short-chain hydrocarbons. Both phenomena may be related to the composition of the catalytic pentad Asp-His-Asp+Asn-Trp, which is unique to the members of the HLD-III subfamily (3). The preference for substrates carrying an iodine substituent can be related to a pair of halide-binding residues and their spatial arrangement with the catalytic triad. These residues make up the catalytic pentad, playing a critical role in substrate binding, formation of the transition states, and the reaction intermediates of the dehalogenation reaction (12).Open in a separate windowFIG. 4.Substrate specificity profiles of DrbA, DmbC, and seven different biochemically characterized haloalkane dehalogenases. Activities were determined using a consistent set of 30 halogenated substrates (see Table Table1).1). Data were standardized by dividing each value by the sum of all activities determined for individual enzymes in order to mask the differences in absolute activities. Specific activities (in μmol·s−1·mg−1) with 1-chlorobutane are 0.0003 (DrbA), 0.0001 (DmbC), 0.0003 (DatA), 0.0133 (DbjA), 0.0010 (DbeA), 0.0128 (DhaA), 0.0231 (LinB), 0.0171 (DmbA), and 0.0117 (DhlA).
Open in a separate windowaNA, no activity detected.Substrates 1-iodobutane and 1,3-diiodopropane, identified as the best substrates for haloalkane dehalogenases DrbA and DmbC, were used for measuring the dependency of enzyme activity on temperature and for determination of the pH optima. DrbA exhibited the highest activity with 1-iodobutane at 50°C, although above this temperature, the enzyme rapidly became inactivated. DmbC showed the highest activity toward 1,3-diiodopropane at 40°C, which is similar to the temperature determined with the haloalkane dehalogenases DmbA and DmbB (45°C), isolated from the same species (10). Irrespective of the reaction temperature, DrbA showed the maximum activity at pH 9.15. DrbA kept 80% of its activity throughout a relatively wide range of pH values (pH 7.00 and 9.91) compared to DmbC, which showed a sharp maximum at pH 8.30. The Michaelis-Menten kinetics of DrbA and DmbC determined by isothermal titration microcalorimetry were investigated with 1-iodobutane, which is an iodinated analogue of 1-chlorobutane routinely used for characterization of haloalkane dehalogenases. The low magnitudes of the Michaelis constants (Km = 0.063 ± 0.003 mM for DrbA and 0.018 ± 0.001 mM for DmbC) suggest a high affinity of both enzymes for 1-iodobutane. The catalytic constants determined with 1-iodobutane (kcat = 0.128 ± 0.002 s−1 for DrbA and 0.0715 ± 0.0004 s−1 for DmbC) suggest that the low specific activities observed during substrate screening are not due to poor affinity but are instead due to a low conversion rate.The biochemical characteristics of purified DrbA and DmbC suggest that these proteins represent novel enzymes differing from previously characterized haloalkane dehalogenases by (i) their unique ability to form oligomers and (ii) low levels of dehalogenating activity with typical substrates of haloalkane dehalogenases. This study further illustrates how genome sequencing projects and phylogenetic analyses contribute to the identification of novel enzymes. Characterization of DrbA and DmbC, belonging to the subfamily HLD-III, partially filled a gap in the knowledge of the haloalkane dehalogenase family and provided an additional insight into evolutionary relationships among its members. 相似文献
TABLE 1.
Specific activities of haloalkane dehalogenases DrbA and DmbC toward a set of 30 halogenated hydrocarbonsaSubstrate | DrbA
| DmbC
| ||
---|---|---|---|---|
Sp act (nmol product·s−1· mg−1 protein) | Relative activity (%) | Sp act (nmol product·s−1· mg−1 protein) | Relative activity (%) | |
1-Chlorobutane | 0.291 | 100 | 0.122 | 100 |
1-Chlorohexane | 0.129 | 44 | 0.122 | 100 |
1-Bromobutane | 0.081 | 28 | 1.221 | 1,000 |
1-Bromohexane | 0.181 | 62 | 0.977 | 800 |
1-Iodopropane | 0.143 | 49 | 2.198 | 1,800 |
1-Iodobutane | 0.506 | 174 | 2.564 | 2,100 |
1-Iodohexane | 0.095 | 33 | 0.244 | 200 |
1,2-Dichloroethane | NA | NA | NA | NA |
1,3-Dichloropropane | NA | NA | 0.012 | 10 |
1,5-Dichloropentane | NA | NA | 0.061 | 50 |
1,2-Dibromoethane | 0.098 | 34 | 0.855 | 700 |
1,3-Dibromopropane | NA | NA | 5.007 | 4,100 |
1-Bromo-3-chloropropane | 0.001 | 0 | 1.465 | 1,200 |
1,3-Diiodopropane | 0.358 | 123 | 6.716 | 5,500 |
2-Iodobutane | 0.028 | 9 | NA | NA |
1,2-Dichloropropane | NA | NA | NA | NA |
1,2-Dibromopropane | 0.148 | 51 | 0.244 | 200 |
2-Bromo-1-chloropropane | 0.091 | 31 | 0.488 | 400 |
1,2,3-Trichloropropane | NA | NA | NA | NA |
Bis-(2-chloroethyl) ether | NA | NA | NA | NA |
Chlorocyclohexane | NA | NA | NA | NA |
Bromocyclohexane | 0.026 | 9 | NA | NA |
(1-Bromomethyl)-cyclohexane | NA | NA | 0.089 | 73 |
1-Bromo-2-chloroethane | 0.167 | 57 | 0.111 | 91 |
Chlorocyclopentane | NA | NA | NA | NA |
4-Bromobutyronitrile | 0.200 | 69 | 0.444 | 364 |
1,2,3-Tribromopropane | NA | NA | 0.222 | 182 |
3-Chloro-2-methyl propene | NA | NA | NA | NA |
2,3-Dichloropropene | 0.276 | 95 | NA | NA |
1,2-Dibromo-3-chloropropane | 0.010 | 3 | 0.044 | 36 |
15.
16.
Evolutionary Strata in a Small Mating-Type-Specific Region of the Smut Fungus Microbotryum violaceum
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DNA sequence analysis and genetic mapping of loci from mating-type-specific chromosomes of the smut fungus Microbotryum violaceum demonstrated that the nonrecombining mating-type-specific region in this species comprises ∼25% (∼1 Mb) of the chromosome length. Divergence between homologous mating-type-linked genes in this region varies between 0 and 8.6%, resembling the evolutionary strata of vertebrate and plant sex chromosomes.EVOLUTION of mating types or sex-determining systems often involves the suppression of recombination around the primary sex-determining or mating-type-determining locus. In animals and plants, it is often an entire or almost entire chromosome (Y or W in male or female heterogametic species, respectively) that ceases to recombine with its homologous (X or Z) chromosome (Charlesworth and Charlesworth 2000; Charlesworth 2008). Self-incompatibility loci in plants are also thought to be located in regions of suppressed recombination (Charlesworth et al. 2005; Kamau and Charlesworth 2005; Kamau et al. 2007; Li et al. 2007; Yang et al. 2007). Regardless of the phylogenetic position of a species, such nonrecombining regions are known to follow similar evolutionary trajectories. The nonrecombining region on the sex-specific chromosome expands in several steps, forming evolutionary strata—regions of different X/Y (or Z/W) divergence (Lahn and Page 1999; Handley et al. 2004; Sandstedt and Tucker 2004; Nicolas et al. 2005)—and genes in the nonrecombining regions gradually accumulate deleterious mutations that eventually render them dysfunctional (Charlesworth and Charlesworth 2005; Charlesworth 2008).Fungal mating-type systems are very diverse, with the number of mating types varying from two to several hundred (Casselton 2002). Like sex chromosomes in several animals and plants, suppressed recombination has evolved in regions near fungal mating-type loci, including in Ustilago hordei (Lee et al. 1999), Cryptococcus neoformans (Lengeler et al. 2002), and Neurospora tetrasperma (Menkis et al. 2008). These species have two mating types, but no morphologically distinct sexes. The mating-type locus (the region of suppressed recombination) of C. neoformans is small (∼100 kb) compared with known sex chromosomes and contains only ∼20 genes that, unlike many sex chromosomes (Y or W chromosomes), show no obvious signs of genetic degeneration (Lengeler et al. 2002; Fraser et al. 2004). Judging from the divergence between the homologous genes on the two mating-type-specific chromosomes, C. neoformans started to evolve sex chromosomes a long time ago because silent divergence between the two mating types in the most ancient region exceeds 100% (Fraser et al. 2004). Genes in the younger mating-type-specific region are much less diverged between the two sex chromosomes, suggesting that the evolution of the sex locus in C. neoformans might have proceeded through several steps. The nonrecombining region around the mating-type locus of N. tetrasperma is much larger than in C. neoformans (at least 6.6 Mb), and silent divergence between homologous genes on the mating-type-specific chromosomes ranges from zero to 9%, demonstrating that these mating-type-specific chromosomes evolved recently (Menkis et al. 2008).M. violaceum, which causes anther smut disease in Silene latifolia and other species in the family Caryophyllaceae, has two mating types, A1 and A2 (reviewed by Giraud et al. 2008), which are determined by the presence of mating-type-specific chromosomes (hereafter A1 and A2 chromosomes, or sex chromosomes) in the haploid stage of the life cycle (Hood 2002; Hood et al. 2004). The A1 and A2 chromosomes are distinguishable by size in pulsed-field electrophoresis, and it is possible to isolate individual chromosomes electrophoretically (Hood et al. 2004). Random fragments of A1 and A2 chromosomes have previously been isolated from mating-type-specific bands of pulsed-field separated chromosomes of M. violaceum (Hood et al. 2004). These fragments were assumed to be linked to mating type. The same method was used to isolate fragments of non-mating-type-specific chromosomes. On the basis of the analysis of their sequences, (Hood et al. 2004) proposed that mating-type-specific chromosomes in M. violaceum might be degenerate because they contained a lower proportion of protein-coding genes than other chromosomes. However, it was not determined whether the sequences isolated from the mating-type chromosomes originated from the mating-type-specific or from the recombining regions (Hood et al. 2004), and the relative sizes of these regions are not known for these M. violaceum chromosomes. We tested the mating-type specificity of 86 of these fragments and demonstrate that fewer than a quarter of these loci are located in the mating-type-specific region, suggesting that the nonrecombining region on the A1 and A2 chromosomes is quite small, while the rest of the chromosome probably recombines (like pseudoautosomal regions of sex chromosomes) and is therefore not expected to undergo genetic degeneration. Genetic mapping confirms the presence of two pseudoautosomal regions in the M. violaceum mating-type-specific chromosomes.As these chromosomes are mating type specific in the haploid stage of M. violaceum, mating-type-specific loci (or DNA fragments) can be identified by testing whether they are present exclusively in A1 or A2 haploid strains. We therefore prepared haploid A1 and A2 M. violaceum cultures from S. latifolia plants from two geographically remote locations (accessions Sl405 from Sweden and Sl127 from the French Pyrenees). Haploid sporidial cultures were isolated by a standard dilution method (Kaltz and Shykoff 1997; Oudemans and Alexander 1998). Mating types were determined by PCR amplification of each culture with primers designed for A1 and A2 pheromone receptor genes linked to A1 and A2 mating types (Yockteng et al. 2007). The primers were as follows: 5′-TGGCATCCCTCAATGTTTCC-3′ and 5′-CACCTTTTGATGAGAGGCCG-3′ for the A1 pheromone receptor (GenBank accession no. ) and 5′-TGACGAGAGCATTCCTACCG-3′ and 5′-GAAGCGGAACTTGCCTTTCT-3′ for the A2 pheromone receptor (GenBank accession no. EF584742). Cultures with PCR product amplified only from an A1 or A2 pheromone receptor gene were selected for further use. The mating types of the cultures were verified by conjugating them in all combinations.The GenBank nucleotide database was searched using BLAST for sequences similar to those isolated by EF584741Hood et al. (2004). Sequences with similarity to transposable elements (TE) and other repeats were excluded. The resulting set of nonredundant sequences was used to design PCR primers for 98 fragments. Half of these were originally isolated from the A1 and half from the A2 chromosomes and are hereafter called A1-NNN or A2-NNN (where NNN is the locus number; supporting information, Table S1), which does not imply that these loci are A1 or A2 specific, but merely indicates that they were originally isolated from the A1 or A2 chromosomes. Amplification of these regions from new A1 and A2 M. violaceum cultures, independently isolated by ourselves, revealed that only 5 of the 49 loci isolated from the A1 chromosome are indeed A1 specific and only 6 of 49 isolated from the A2 chromosome are A2 specific. All other loci amplified from both A1 and A2 cultures. Figure 1 illustrates some of these results from the Swedish sample (Sl405).Open in a separate windowFigure 1.—Testing of mating-type specificity for loci isolated from A1 and A2 chromosomes. (a) PCR amplifications from haploid cultures from Sl405 using primers designed from six A1-originated loci. Loci in which a PCR product could be amplified only from A1 cultures (boxed) were classified as specific to mating type A1. (b) PCR tests of six A2-originated loci on the same set of haploids as in a. Loci in which a PCR product amplified only from A2 cultures (boxed) were classified as specific to mating type A2. Loci amplified from both A1 and A2 cultures are not mating type specific.The fragments that amplified from both A1 and A2 mating types may be in recombining regions, or they could be present in mating-type-specific regions on both A1 and A2 chromosomes. If they are in recombining regions, the A1- and A2-linked homologs should not be diverged from each other, but if they are in nonrecombining, mating-type-specific regions, the divergence of the A1- and A2-linked homologs should be roughly proportional to the time since recombination stopped in the region. We therefore sequenced and compared PCR fragments amplified from the two mating types of Sl405 or Sl127 cultures (GenBank accession nos. – FI855822). Sequencing of PCR products showed that 12 (4 A1 and 8 A2) loci have more than one copy, and they were excluded from further analysis. Sequences of 61 loci were identical between the A1 and A2 strains, and four loci demonstrated low total divergence (0.24–0.61%) between the two mating types ( FI856001otintseva and D. Filatov, unpublished results). Thus, these loci might be located in the recombining part of the mating-type-specific chromosomes. Ten of 75 loci that amplified in both mating types demonstrated multiple polymorphisms fixed between the mating types rather than between the locations. Given that the strains that we used in the analysis originated from two geographically distant locations, it is highly unlikely that multiple polymorphisms distinguishing the A1 and A2 sequences arose purely by chance; thus, these loci are probably located in the nonrecombining mating-type-specific region of the M. violaceum A1 and A2 chromosomes.
Open in a separate windowaA1, loci originated from the A1 sex chromosome; A2, loci originated from the A2 sex chromosome.bThe number of loci used for genetic map construction is in parentheses.To confirm the mating-type-specific or pseudoautosomal locations of the loci with and without A1/A2 divergence, we conducted genetic mapping in a family of 99 individuals, 50 of which were of mating type A1 and 49 of mating type A2. The family was generated by a cross between A1 and A2 M. violaceum strains from S. latifolia accessions Sl405 (Sweden) and Sl127 (France), respectively. The choice of strains from geographically distant locations was motivated by the hope of maximizing the number of DNA sequence differences between them that can be used as molecular genetic markers in segregation analysis. We inoculated S. latifolia seedlings with sporidial cultures of both mating types. For inoculation, petri dishes with 12-day-old seedlings of S. latifolia were flooded with 2.5 ml of inoculum suspension. Inoculum suspension consisted of equal volumes of the A1 and A2 sporidial cultures that were mixed and conjugated overnight at 14° under rotation (Biere and Honders 1996; Van Putten et al. 2003). Seedlings were potted 3 days after inoculation. Two months later, teliospores were collected from the flowers of the infected plant and grown in petri dishes on 3.6% potato dextrose agar medium. Haploid sporidia formed after meiosis were isolated and grown as separate cultures for DNA extraction. The mating types of single sporidia cultures were identified as described above. The loci analyzed in the segregation analysis were sequenced in the two parental haploid strains and in 99 (50 A1 and 49 A2) haploid strains that were generated in the cross. Single nucleotide differences between the parental strains were used as molecular genetic markers for segregation analysis in the progeny. The genetic map was constructed using MAPMAKER/EXP v3.0 (Lincoln et al. 1992) and MapDisto v1.7 (http://mapdisto.free.fr/).The resulting genetic map is shown in Figure 2. As expected, no recombination was observed between the 10 loci with diverged A1- and A2-linked copies. In addition, one marker with no A1/A2 divergence, A2-397, was also completely linked to the loci with significant A1/A2 divergence. This locus either may be very tightly linked to the nonrecombining mating-type-specific region or may have been added to that region more recently than the loci that had already accumulated some divergence between the alleles in the two mating types. The mating-type-specific pheromone receptor locus (Devier et al. 2009) and 11 mating-type-specific loci are also located in this nonrecombining region (Figure 2). Interestingly, the cluster of nonrecombining markers is flanked on both sides with markers that recombine in meiosis, demonstrating that there are pseudoautosomal regions on both ends of the mating-type-specific chromosomes.Open in a separate windowFigure 2.—Genetic map of the mating-type-determining chromosome in M. violaceum. Genetic distance (in centimorgans) and the relative positions of the markers are shown to the left and the right of the chromosome, respectively. The position of the nonrecombining region corresponds to the cluster of linked markers shown on the right of the figure. Total A1/A2 divergence is shown in parentheses. Eleven mating-type-specific markers (for which sequences are available from only one mating type), located in the nonrecombining mating-type-specific region, are not shown.Our results demonstrate that although the loci reported by Hood et al. (2004) were isolated from the A1 and A2 chromosomes, most of these loci are not located in the nonrecombining mating-type-specific regions. In fact, the nonrecombining region might be relatively small: of 86 tested fragments, only 21 appeared to be either mating type specific or linked to the mating-type locus. Assuming that these loci represent a random set of DNA fragments isolated from the A1 and A2 chromosomes, it is possible to estimate the size of the nonrecombining region using the binomial distribution: the nonrecombining region is expected to be 24.4% (95% CI: 16.7–33.6%) of the chromosome length. As the sizes of the A1 and A2 chromosomes are ∼3.4 and 4.2 Mb long (Hood 2002; Hood et al. 2004), the nonrecombining region might be ∼1 Mb long.Interestingly, total A1/A2 divergence for the 11 loci with A1- and A2-linked copies mapped to the nonrecombining region varied from 0% to 8.6% (Figure 2). In addition, 11 loci amplified from only one mating type. These genes could represent degenerated genes, some of which degenerated in A1 strains, and some in A2 strains. Alternatively, they might be highly diverged genes, such that the PCR primers amplify only one allele, and not the other. Variation in divergence may be the result of the stepwise cessation of recombination between the A1 and A2 chromosomes in M. violaceum, resembling the evolutionary strata reported for human, chicken, and white campion sex chromosomes (Lahn and Page 1999; Handley et al. 2004; Bergero et al. 2007). However, only the differences between the most and the least diverged loci are statistically significant (Devier et al. 2009), the M. violaceum mating-type region has at least three strata: one oldest stratum, including the pheromone receptor locus; a younger stratum with ∼5–9% A1/A2 divergence; and the youngest stratum with 1–4% divergence between the two mating types. There may also be an additional very recently evolved stratum containing the locus named A2-397, which is also present in all A1 strains tested, with no fixed differences between the A1 and A2 strains (No. of sites analyzed Within A1
Within A2
Fixed differences between A1 and A2 A1/A2 divergence (%) Locia Sb total S π (%)c S π (%)c A1/A2 divergence <1% A1-236 456 3 0 0 2 0.44 1 0.44 A1-045 654 4 0 0 0 0 4 0.61 A2-568 413 2 2 0.48 2 0.48 0 0.24 A2-411 480 2 1 0.21 0 0 1 0.31 A1/A2 divergence >1% A1-217 667 9 0 0 0 0 9 1.35 A1-128 569 9 0 0 1 0.18 8 1.49 A1-199 618 13 0 0 1 0.16 12 2.02 A2-422 344 9 0 0 0 0 9 2.62 A2-516 470 14 0 0 0 0 14 2.98 A2-404 508 20 0 0 3 0.59 17 3.64 A2-435 506 22 2 0.39 2 0.39 18 3.95 A2-473 457 23 1 0.22 1 0.22 21 4.81 A2-457 303 17 1 0.33 0 0 16 5.54 A2-575 503 47 5 0.99 3 0.59 39 8.55
TABLE 1
Loci from mating-type-specific chromosomes of M. violaceum used for PCR analysis and genetic map constructionWith nonzero A1/A2 divergenceb
| |||||
---|---|---|---|---|---|
Loci | Mating type specific | <1% | >1% | With zero A1/A2 divergenceb | Total |
A1a | 5 | 2 (1) | 3 (3) | 35 (3) | 45 (7) |
A2a | 6 | 2 (0) | 7 (7) | 26 (3) | 41 (10) |
Subtotal | 4 (1) | 10 (10) | |||
Total | 11 | 14 (11) | 61 (6) | 86 (17) |