*Department of Zoology, Brigham Young University;
and
Department of Pediatrics, The Johns Hopkins University School of Medicine
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Abstract |
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Introduction |
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Protein 1 (PI), encoded by the porB locus, is the major outer membrane protein of Neisseria. It functions as an anion-selective porin allowing the passage of small molecules through the outer membrane. The general structure of these porins consists of nine internal conserved regions separated by eight surface-exposed regions that are highly variable in both amino acid sequence and length (Carbonetti and Sparling 1987
; Carbonetti et al. 1988
; van der Ley et al. 1991
; Feavers et al. 1992
; Mee et al. 1993
) (see fig 1
). Protein 1 is constitutively expressed at high levels in all gonococci, is surface-exposed, and elicits a strong immune reaction during infection (Ison 1988
). This indicates that PI may play a crucial role in gonococcal interaction with host cells and, in general, with the immune system (Butt, Lambden, and Heckels 1990
; Smith, Maynard Smith, and Spratt 1995
), affecting the transmission probability and the length of the infectious state and consequently influencing the growth rate. It is not surprising, then, that PI is considered a potential vaccine target (Elkins et al. 1992
; Heckels, Virji, and Tinsley 1990
).
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The characterization of the molecular evolution of the PIA and PIB groups can offer great insights into the evolutionary processes acting in N. gonorhoeae and into their interconnections with epidemiology. Knowledge of the population genetic structure is important for an understanding of the responses of pathogen populations to selective pressures imposed by host immunity and by antimicrobial drug therapy (Levin, Lipsitch, and Bonhoeffer 1999
). Smith, Maynard Smith, and Spratt (1995)
previously investigated the action of positive Darwinian selection on the evolution of the PIA and PIB alleles. They found, using only two sequences from PIA and two from PIB, that these homology groups showed different levels of selection, with the PIA sequences having an increased number of nonsynonymous substitutions compared with the PIB sequences. They also found these changes to be localized on the surface-exposed loops of the outer membrane of the protein. However, their study was based on a very limited number of sequences (four), and their estimate of nonsynonymous-to-synonymous substitution was based on a biased estimator (Crandall et al. 1999
; Yang and Nielsen 2000
). Finally, their study, due to the small sample sizes, did not include any estimates of important population genetic parameters such as nucleotide diversity and recombination rate, thus limiting their ability to explain the differences between the PIA and PIB homology groups. We are interested in simultaneously estimating several distinct population parameters, such as genetic diversity, gene flow, population structure, recombination rate, and growth rate, to describe the evolution of the porB gene from a multivariable perspective. Addressing the questions of whether there are differences in the molecular evolution of the PIA and PIB homology groups and whether epidemiological differences in PIA and PIB are associated with changes in different population genetic parameters is the main goal of this work.
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Materials and Methods |
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Sequencing of the porB Gene of N. gonorrhoeae
We established a polymerase chain reaction (PCR)-based method for sequencing a large fragment of the porB gene of N. gonorrhoeae starting with genomic DNA extracted from urine specimens or bacterial isolates.
For recovery of genomic DNA from urine, an aliquot of 1.8 ml of urine was centrifuged at 10,000 x g for 10 min in an Eppendorf microfuge, and the pellet was resuspended in 600 µl of Tris-EDTA buffer (pH 7.4). For extraction of genomic DNA from bacterial isolates, gonococcal colonies were scraped off an agar plate of a primary culture and resuspended in 600 µl of Tris-EDTA buffer (pH 7.4). After addition of sodium dodecyl sulfate (SDS) and proteinase K to final volumes of 0.5% and 100 µg/ml, respectively, the suspension was mixed by inverting repeatedly and incubated for 1 h at 37°C. The lysate was adjusted to 0.7 M NaCl and 1% cetyltrimethylammonium bromide (CTAB), mixed thoroughly, and incubated for 10 min at 65°C in order to precipitate cell wall debris, denatured proteins, and polysaccharides. The sample was extracted once with chloroform/isoamyl alcohol (24:1), once with phenol/chloroform/isoamyl alcohol (25:24:1), and then a second time with chloroform/isoamyl alcohol. DNA was precipitated with isopropanol, and the pellet was washed once with 70% ethanol and resuspended in 15 µl distilled water.
A seminested PCR was used to amplify two overlapping fragments of the porB gene. In the first round, a 2-µl aliquot of the crude bacterial DNA was amplified in a 50-µl reaction volume containing 200 µM dNTPs, 0.5 µM primers (POR-01, 5'-CTGACTTTG GCAGCCCTTCCTGTTG-3', nt 179203 [MS11 strain, accession number M21289) and POR-14, 5'-CAGATTAGAATTTGTGGC GC-3', nt 12141195), 2.1 U Expand High Fidelity (Boehringer Manheim, Indianapolis, Ind.), a mix of Taq and pwo DNA polymerases, and the manufacturer's recommended buffer with 1.5 mM MgCl2. A hot-start PCR protocol was performed using wax beads from PE Applied Biosystems, Inc. (Foster City, Calif.). Cycle conditions were 94°C for 2 min to melt the beads, then 30 cycles of 94°C for 40 s, 65°C for 40 s, and 72°C for 1 min, then a final extension reaction for 10 min at 72°C. First- and second-round reactions were performed in a 9700 thermal cycler (PE Applied Biosystems). The PCR products were diluted 1:100 in distilled water, and two nested reactions were performed using primers with 5' extensions encoding the M13 forward or M13 reverse sequencing primers. A 2-µl aliquot was amplified in a 100-µl reaction volume containing 200 µM of dNTPs, 0.5 µM primers (M13F-POR-01: 5'-GTCACGACGTTGTAAAACGACGGCCAGTCTGACTTTGGCAGCCCTT- 3' [M13F sequence is underlined) and M13R-POR-08: 5'-CACACAGGAAACAGCTATGACCGT ATTGTGCGAAGAAGC-3', nt 742726 [M13R sequence is underlined), or M13F-POR-11: 5'-GTCACGACGTTGTAAAACGACGGCC AGTCTGTCCGTACGCTACG-3', nt 602617, and M13R-POR14: 5'-CACACAGGAAACAGCTATGACCAGATTAGAATTTGTGGC GC-3'), 1.4 U Expand High Fidelity enzyme mix, and the manufacturer's recommended buffer with 1.5 mM MgCl2. A hot-start PCR protocol was used with cycle conditions of 94°C for 2 min, followed by 35 cycles of 94°C for 40 s, 60°C for 20 s, and 68°C for 40 s, and a final extension reaction for 10 min at 72°C.
The PCR products were passed through a GeneClean spin column (Bio 101, Inc., Vista, Calif.) and eluted with 40 µl dH2O, and the recovered DNA was measured by UV spectrometry. For dideoxy sequencing reactions, 6080 ng of PCR product was added to a final reaction volume of 5 µl containing 2 µl of Big Dye Terminator RR mix (PE Applied Biosystems) and 1 µM primer (M13 forward or M13 reverse sequencing primer). Cycle conditions were 95°C for 15 s, 50°C for 15 s, and 60°C for 4 min. After 25 cycles, reaction products were denatured by heating to 95°C for 30 s. The reaction volume was diluted with 15 µl of distilled water and passed over a minicolum (Spin-50, BioMax, Inc., Odenton, Mo.) equilibrated with distilled water. The labeled nucleic acids were dried in a Speed Vac concentrator (Savant, Farmingdale, N.Y.), resuspended in 7 µl of loading buffer, and loaded into lanes of an ABI 377 automated DNA sequencer (Synthesis and Sequencing Facility, Department of Biological Chemistry, Johns Hopkins University School of Medicine). Trace data were edited and nucleotide sequences assembled with the SeqMan software program (DNASTAR, Inc., Madison, Wis.). The edited sequences typically spanned the region corresponding to amino acids 18346 of the MS11 strain.
Analysis of N. gonorrhoeae porB Gene Sequences
Sequences were aligned using CLUSTAL X (Thompson et al. 1997
). Alignments were then adjusted by eye as needed (the PIA sequences had no indels, and the PIB sequences had only a single region of 18 nt of questionable alignment that was removed from the analysis). The best-fit model of DNA substitution and the parameter estimates used for the tree reconstruction were chosen by performing hierarchical likelihood ratio tests (see Huelsenbeck and Crandall 1997
) using PAUP* beta 1 (Swofford 1998) and Modeltest 1.05 (Posada and Crandall 1998
). A neighbor-joining tree (Saitou and Nei 1987
) was estimated for each data set using PAUP* beta 1 incorporating the best-fit maximum-likelihood model of evolution. Confidence in the tree relationships was assessed using 1,000 replicates of the bootstrap procedure (Felsenstein 1985
).
Since recombination can affect the phylogenetic estimate of relationships among the porB sequences, we tested each data set for evidence of recombination using the likelihood approach of Grassly and Holmes (1997)
. Several genetic population parameters were also estimated. The recombination parameter C (=2Neic, where Nei is the inbreeding effective population size and c is the recombination rate per site per generation) (Hey and Wakeley 1997
; Hudson 1987
) was estimated using a coalescent approach implemented in SITES (Hey and Wakeley 1997
). Genetic diversity,
(=2Neiµ, where µ is the mutation rate per site per generation), was estimated using the program FLUCTUATE (Kuhner, Yamato, and Felsenstein 1998
). This coalescent-based method uses genealogical information and allows for variable population sizes when estimating genetic diversity (Kuhner, Yamato, and Felsenstein 1998
).
We tested for genetic differentiation of populations using a categorical approach and a quantitative approach. The categorical analysis consisted of a 2 test of sequence absolute frequencies at each location (Hudson, Boos, and Kaplan 1992
). Due to bias with low expected values, the P value was obtained by simulating the null distribution of no geographic subdivision (10,000 permutations) using the algorithm of Roff and Bentzen (1989)
implemented in the program CHIPERM (D. Posada, available at http://bioag.byu.edu/zoology/crandall_lab/programs.htm). The quantitative analysis consisted of a molecular analysis of variance (AMOVA) (Excoffier, Smouse, and Quattro 1992
) performed using ARLEQUIN (Schneider et al. 1997
). Gene flow can be easily estimated for recombining sequences by measuring FST (Hudson, Slatkin, and Maddison 1992
) and using the standard relationship FST = 1/(1 + 2Neim) (Wright 1951
) to obtain Neim, where m is the migration rate per generation. F statistics and Neim values were estimated using the program ARLEQUIN.
Finally, to infer the extent of selection in the PIA and PIB homology groups, we estimated the changes in nonsynonymous substitution rates (those resulting in an amino acid replacement) and synonymous substitution rates (those causing no change in the amino acid). Since the majority of nonsynonymous substitutions are eliminated by purifying selection, neutral evolution predicts a predominance of synonymous substitutions (Miyata and Yasunaga 1980
). When positive Darwinian selection occurs, the nonsynonymous rate of substitution accelerates (Hughes and Nei 1988
; Messier and Stewart 1997
). Therefore, the relative rates of nonsynonymous to synonymous substitutions can be good indicators of the amount and types of selection affecting a gene (Sharp 1997
). We estimated the rates of synonymous substitutions (Ks) and nonsynonymous substitutions (Ka) using the maximum-likelihood approach of Muse and Gaut (1994
; Muse 1996
). The maximum-likelihood estimates avoid many of the problems associated with estimates based on pairwise comparisons and allow the incorporation of more complex and realistic models of evolution (Nielsen and Yang 1998
; Crandall et al. 1999
).
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Results |
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The best-fit model of evolution for the PIA homology group was the Kimura two-parameter model (K80 or K2P) (Kimura 1980
), with a transition/transversion (ti/tv) ratio of 2.1451, a significant proportion of invariable sites (I = 0.8061), and rate heterogeneity among sites (G = 0.8619). For the PIB group, the best-fit model was HKY (Hasegawa, Kishino, and Yano 1985
), with a ti/tv ratio of 2.2891, a significant proportion of invariable sites (I = 0.8313), and rate heterogeneity among sites (G = 1.0973). Thus, the optimization of a model of evolution for the two homology groups resulted in different models for each group. The major difference in models between the PIA and PIB groups was the incorporation of nucleotide frequency differences for the PIB sequences. We failed to reject the null hypothesis of equal base frequencies for the PIA sequences (table 1
), whereas the PIB sequences have significantly different base frequencies (A = 0.27, C = 0.28, G = 0.24, and T = 0.21). Also, the molecular-clock hypothesis was rejected for both data sets (table 1
). The neighbor-joining trees estimated using these models are shown in figures 2 and 3 . For both PIA (fig. 2
) and PIB (fig. 3
) phylogenies, the sequences did not cluster according to sampling time or locality. Bootstrap support was very low across the trees.
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Discussion |
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Researchers of infectious diseases typically use a Kimura two-parameter model (K80 or K2P) (Kimura 1980
) to model molecular changes. The K80 model only accounts for differences between transitions and transversions. However, there are other parameters worth considering, e.g., nucleotide frequency differences, rate heterogeneity, etc. Therefore, an alternative model of evolution which takes into account more parameters may be more appropriate for a given data set. Indeed, the resulting topology and conclusions based on that topology can be greatly influenced by the choice of model of evolution (Kelsey, Crandall, and Voevodin 1999
). Thus, it is important to optimize models of evolution for particular data sets to infer phylogenies and then to use these phylogenies to test for recombination. As far as the detection of recombination depends on the estimated topologies, the use of a correct model of evolution will make its inference more reliable.
We have developed a hypothesis-testing framework to justify the choice of a model of evolution (Huelsenbeck and Crandall 1997
). We have also developed software (Modeltest, freely distributed at our web site: http://bioag.byu.edu/zoology/crandall_lab/programs.htm) that uses likelihood ratio tests to determine the model that best fits the data at hand (Posada and Crandall 1998
). Once a model of evolution is chosen, phylogenetic relationships among sequences can be estimated using either the neighbor-joining algorithm (Saitou and Nei 1987
) or the maximum-likelihood criterion (Felsenstein 1981
). Our data sets were too large for maximum-likelihood analyses because of the computational expense of this method with large numbers of sequences. Therefore, we used the neighbor-joining method to estimate phylogenetic relationships. The PIA and PIB sequences show little phylogenetic structure due to geography or date of isolation, in concordance with previous results (Smith, Maynard Smith, and Spratt 1995
). This lack of phylogenetic structure is likely to be the consequence of extensive recombination in the porB gene, since we have reduced the possibility of error due to inappropriate models of evolution. Even after removing recombinant fragments that were detected by the method of Grassly and Holmes (1997)
, we did not observe geographic or temporal phylogenetic structure. This is not surprising, because most of the recombination is probably still undetected by their method. Further progress in phylogenetic analysis of N. gonorrhoeae will require the development of improved methods to detect recombination or to deal with recombination in phylogenetic reconstructions. These are both areas of research that our group is actively pursuing.
The results of our population genetic analysis clearly reinforce the idea that recombination is extensive in N. gonorrhoeae. We also show that recombination appears to be similar in both the PIA and the PIB homology groups. However, these groups differ greatly in their levels of genetic diversity. The diversity generated by point mutation is reflected by the estimates of the parameter (2Neiµ), while the estimates of the parameter C (2Neic) indicate the diversity generated by recombination. Hence, if we divide one by the other, we will have an estimate of the ratio c/µ, which can be interpreted as the relative chance of recombination per site versus the chance of point mutation per site. The high ratios of the recombination rate to the mutation rate (c/µ) for all of the populations (table 2
) indicate that recombination is a major force generating diversity in N. gonorrhoeae. Again, there is a distinction between the PIA and the PIB groups in that the PIA group has a much higher c/µ ratio than does the PIB group, suggesting differences in the roles of recombination and mutation in generating diversity in these two homology groups. This interaction among recombination and mutation and its contribution to the evolution of natural populations has been also described for plant viruses (Aranda et al. 1997
).
The Neim estimates for the PIA sequences did not make geographic sense, in that those locations in closer geographic proximity showed lower levels of gene flow, most likely because of the small sample sizes of sequences from North Carolina (two) and Washington, D.C. (three). However, for the PIB sequences, closer locations showed higher levels of gene flow, suggesting that geographic distance may be a factor in the spread of genetic diversity among isolates of N. gonorrhoeae. This is contradictory to the interpretation by Smith, Maynard Smith, and Spratt (1995)
that N. gonorrhoeae is a panmictic species. This is also reflected in the moderately high FST estimate and in the categorical analysis of geographic association, suggesting that there is geographic subdivision for the PIB sequences, although we probably do not have enough power (i.e., a large enough sample size) to reject geographic homogeneity for the PIA sequences. This subdivision is at the level of the population. As one of the main forces generating diversity is recombination, most of the variation is located within populations. As compared with the phylogenetic analysis, which failed to show structure due to geography, these population genetic analyses suggest that there is population substructure based on geographic locality.
Directional positive selection appears to be operating to a significant extent in these sequences. This is not a surprise given the intensive selection pressures put on these bacteria by the immune system and antibiotic treatment (Smith, Maynard Smith, and Spratt 1995
). The selective distribution of the replacements in the surface-exposed loops supports this idea. However, selection intensities as a whole appear to differ between the two homology groups. The PIA sequences are clearly evolving as a whole under positive selection, especially in the exposed regions. This supports the earlier conclusions of Smith, Maynard Smith, and Spratt (1995)
based on a very limited data set. Given the reduced genetic variation associated with the PIA sequences, this suggests a role for selective sweeps in reducing the amount of genetic variation within a population. Although uncomplicated gonorrhea is caused by isolates of either PIA or PIB homology groups, there are epidemiological differences among these groups. Blood isolates during disseminated gonococcal infection belong almost invariably to the PIA group (Sandstrom et al. 1984
), whereas isolates from mucosal surfaces more often belong to the PIB group (Morse et al. 1982
). These epidemiological correlates may provide an explanation for the apparent difference in selection pressures acting on the PIA and PIB sequences. Invasive disease would be expected to subject N. gonorrhoeae to more intense selection pressure from the host immune system than would occur during infections confined to mucosal surfaces. The PIB sequences, although they accumulate many replacements on the exposed regions, appear to be subject also to some extent of purifying selection. However, recombination is so high in these sequences that they maintain a great amount of diversity.
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Conclusions |
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Acknowledgements |
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Footnotes |
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1 Keywords: Neisseria gonorrhoeae, porB,
recombination
selection
genetic diversity
population genetics
2 Address for correspondence and reprints: Keith A. Crandall, Department of Zoology, 574 Widtsoe Building, Brigham Young University, Provo, Utah 84602-5255. E-mail: keith_crandall{at}byu.edu
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