Abstract
The outer surface protein, OspC, is highly variable in Borrelia burgdorferi sensu stricto, the agent of Lyme disease. We have shown that even within a single population OspC is highly variable. The variation of ospA and ospC in the 40 infected deer ticks collected from a single site on Shelter Island, New York, was determined using PCR-SSCP. There is very strong apparent linkage disequilibrium between ospA and ospC alleles, even though they are located on separate plasmids. Thirteen discernible SSCP mobility classes for ospC were identified and the DNA sequence for each was determined. These sequences, combined with 40 GenBank sequences, allow us to define 19 major ospC groups. Sequences within a major ospC group are, on average, <1% different from each other, while sequences between major ospC groups are, on average, ∼20% different. The tick sample contains 11 major ospC groups, GenBank contains 16 groups, with 8 groups found in both samples. Thus, the ospC variation within a local population is almost as great as the variation of a similar-sized sample of the entire species. The Ewens-Watterson-Slatkin test of allele frequency showed significant deviation from the neutral expectation, indicating balancing selection for these major ospC groups. The variation represented by major ospC groups needs to be considered if the OspC protein is to be used as a serodiagnostic antigen or a vaccine.
LYME disease is the most important tick-borne disease in the United States (Barbour and Fish 1993). The causative agent is Borrelia burgdorferi, a spirochete, first isolated from Shelter Island, New York (Burgdorferet al. 1982). Subsequent studies have subdivided this species into a species complex which includes B. burgdorferi sensu stricto, B. afzelii, B. garinii, B. japonica (Barantonet al. 1992; Kawabataet al. 1993), and several other groups (Posticet al. 1994; Balmelli and Piffaretti 1996; Valsangiacomoet al. 1997). The first three species are pathogenic and have been shown to be associated with different chronic symptoms (Assouset al. 1993; van Damet al. 1993; Anthonissenet al. 1994; Balmelli and Piffaretti 1995). All three pathogenic species are found in Europe. In contrast, only B. burgdorferi sensu stricto is found in the United States.
In the eastern United States, the deer tick (Ixodes scapularis) is the major vector responsible for the transmission of the spirochetes from one animal to another. The tick has four main stages in its 2-yr life cycle: egg, larva, nymph, and adult (Wilson and Spielman 1985; Yuval and Spielman 1990). Typically, eggs are laid during June and July and hatch into larvae in August. The larvae feed on small mammals such as mice, voles, and chipmunks and develop into nymphs during August and September. The nymphs overwinter and feed on these small mammals and some larger mammals during the summer, developing into adults in August. The adults prefer larger mammals such as deer, horses, dogs, and raccoons. In either October or after overwintering, adults mate on a host and only the females feed. For a detailed model of tick population dynamics see Sandberg et al. (1992).
The spirochete population is maintained by an infection cycle from ticks to mammals and then back into younger ticks. Transovarial passage of the spirochete is considered insignificant in the life cycle of the bacteria because <1% of the unfed I. scapularis larvae caught in nature are infected (Piesmanet al. 1986; Magnarelliet al. 1987). All ticks feed at least twice: once as larvae and again as nymphs, while females feed a third time as adults (Bosler 1993). A tick is infected either as a larva or as a nymph and can pass the infection on to another mammal only after molting to the next stage. The infection of young mice and other small mammals in late spring by infected nymphs and the infection of larvae in late summer by these small mammals seems to be the major transmission route of the spirochete (Spielman 1988; Spielmanet al. 1993).
B. burgdorferi infection induces a strong humoral immune response to as many as 11 proteins (Craftet al. 1986). Some of these proteins are within the outer membrane (Luftet al. 1992) and are not exposed to immune selection even though they induce a strong immune response. Other proteins are part of the membrane (Cunninghamet al. 1988) and might be exposed to immune selection. Of these outer surface proteins (Osp's), OspA and OspC, which are encoded by genes located on two separate plasmids (Barbour and Garon 1988; Marconiet al. 1993; Sadzieneet al. 1993), have been the most studied. Experimental vaccines made against either of these Osp's are successful in warding off challenges from the same strain (Fikriget al. 1990; Simonet al. 1991; Gilmoreet al. 1996; Probertet al. 1997), but not necessarily from different strains (Fikriget al. 1992; Goldeet al. 1995; Probertet al. 1997).
The expression pattern of ospA and ospC is suggestive of their roles in the infection cycle of the spirochetes (Schwanet al. 1995; Stevensonet al. 1995). In unfed ticks, spirochetes express OspA but not OspC. However, when the tick starts feeding on mammals, OspC synthesis is induced and OspA repressed. The switch is in part due to the change in temperature; OspC is induced at 32–37°, but not at 24°. Evidence suggests coregulation of these two genes (Jonsson and Bergstrom 1995).
ospC is located on a 27-kb circular plasmid (Marconiet al. 1993; Sadzieneet al. 1993), and encodes a lipoprotein of ∼22 to 23 kD in size (Padulaet al. 1993). The expression level of OspC varies greatly in different strains (Theisenet al. 1993; Hofmeister and Childs 1995; Pickenet al. 1995). It is well known that ospC is highly polymorphic (Theisen et al. 1993, 1995; Wilskeet al. 1993; Liveyet al. 1995). However, most of the information about variation in ospC is from strains arbitrarily collected in different places at different times. Little is known about the diversity in a small area, although there is some evidence suggesting that local populations are variable (Liveyet al. 1995; Masuzawaet al. 1997).
There are practical implications to how diverse ospC is locally. If local diversity is low, serodiagnostic antigens and anti-OspC vaccines would be effective in the local area where they were developed. On the other hand, if local ospC diversity is high, similar to worldwide diversity, serodiagnostic antigens or vaccines might be more generally effective, perhaps worldwide.
To assess the genetic diversity of ospC in a local population, we used PCR-SSCP, a technique that allowed us to survey ospA variation from single ticks (Guttmanet al. 1996). In this study, we show that the genetic diversity of ospC in a single population is nearly as high as that found worldwide. Furthermore, population genetic evidence suggested that there is very little plasmid transfer between spirochetes, again demonstrating the clonality of B. burgdorferi (Dykhuizenet al. 1993). A model of frequency-dependent selection on ospC by the host immune system is proposed to explain the maintenance of ospC variation.
—Schematic drawing of the ospC regions that are subjected to PCR-SSCP analysis. The primer sequences and the primer pairs used for either the first round or the second round of PCR are also shown. Note that the boldface in primer int(–)(b) shows the eight sites where this primer is different from int(–)(a). For cloning of ospC genes, primer ext(+) and either primer ext(–)(a) or ext(–)(b) were used. However, only sequences between primers ext(+) and ext(–)(a) were used for sequence analyses.
MATERIALS AND METHODS
Extraction of DNA: Adult deer ticks (I. scapularis) collected in 1994 on Shelter Island, Long Island, New York, were bisected and DNA was extracted as described before (Guttmanet al. 1996).
PCR amplifications: Two genes, ospA and ospC, both encoding outer surface lipoproteins in B. burgdorferi, were studied.
ospA amplification: Procedure for ospA PCR amplification was the same as described previously (Guttmanet al. 1996). This amplification combined both nested and “touch-down” PCR. The first round of PCR was performed with low stringency and low cycling number using a pair of external primers to generate a heterogeneous population of amplified DNAs. The second round of PCR, using primer pairs internal to the previously amplified DNAs, was started out at very high stringency; as amplification proceeded the stringency was reduced gradually to increase yield.
ospC amplification: On the basis of our previous experience with ospA and preliminary experiments with ospC, the optimal size of the DNA fragment for SSCP analysis is ∼300 bp. A two-step seminested PCR amplification strategy was used for ospC. The first round of PCR used extracted DNA as template and two external primers for extension. The second round of PCR used the PCR products from the first round as template and either of two internal primers with the paired external primer. This divided the ospC into two roughly equal-sized fragments. Figure 1 shows the regions of ospC being amplified and the primer sequences. The two external primers are as follows: 5′-AAA GAA TAC ATT AAG TGC GAT ATT-3′ (+) beginning at base 6; and 5′-GGG CTT GTA AGC TCT TTA ACT G-3′ (–) ending at base 602. The front half of ospC was amplified using the external primer to the (+) strand and the primer 5′-CAA TCC ACT TAA TTT TTG TGT TAT TAG-3′ (–) ending at base 345. The back half of ospC was amplified using the primer 5′-TTG TTA GCA GGA GCT TAT GCA ATA TC-3′ (+) beginning at base 289 and the external primer to the (–) strand. The external primers amplified a 597-bp fragment. Amplification of the front half produced a 340-bp fragment, while amplification of the back half produced a 314-bp piece. All the base numbers and amplified fragment sizes are based on ospC sequence of strain B31 (GenBank accession number U01894), with start codon as base 1.
The first round of PCR amplification used 10 μl of extracted DNA, dNTP at 0.2 mm per nucleotide, 1× PCR buffer [50 mm KCl, 10 mm Tris-HCl (pH 8.3)], 2.5 mm MgCl2, 1 unit of native or ampli-Taq polymerase (Perkin Elmer-Cetus, Norwalk, CT), 0.5 μm of each external primer, and sterile HPLC water to a final volume of 50 μl. The reaction mixture was overlaid with two drops of mineral oil or without mineral oil when the top lid was heated (MJ Research, Watertown, MA). The temperature cycling profile of the first round of PCR was 1 min at 96° for 1 cycle linked to 40 sec at 95°, 35 sec at 54°, and 2 min at 72° for 20 cycles. For the second round of PCR, 0.5 μl of the first round PCR product was used as template and a pair of primers was added to amplify half of the gene. All the other components of reaction mixture were kept the same. The cycling profile was 1 min at 96° for 1 cycle linked to 40 sec at 95°, 35 sec at 54°, and 1 min at 72° for 35 cycles. To ensure that this two-step PCR strategy amplifies only ospC from B. burgdorferi, negative controls at various stages of amplification were performed for each PCR reaction. No nonspecific amplification was found.
Cold SSCP analysis: To survey genetic variation at both ospA and ospC genes, PCR-amplified fragments were subjected to cold SSCP analysis as described by Hongyo et al. (1993) and modified to suit our purpose (Guttmanet al. 1996). The solution containing the PCR product, the amount depending on the DNA yield as estimated on an agarose gel, was reduced to 5 μl in a speed vac. The samples were then mixed with 0.4 μl of 1 m mercury hydroxide (Alfa Aesar, Ward Hill, MA) and 9.6 μl of loading buffer (Hongyoet al. 1993), heated to 95° for 4 min to denature the double-stranded DNA, and plunged into ice to prevent reannealing. The chilled samples were loaded onto a precast 20% TBE polyacrylamide gel (Novex, San Diego) with 1.5× TBE buffer. For both ospA and ospC, running condition and sample preparation were the same as described previously (Guttmanet al. 1996), except that the ospC samples were run at a constant 240 V for 16.5 hr.
A potential major problem with PCR-SSCP for population genetics studies is that certain variants may not be amplified and included in the sample. This is not a problem here for the following reasons: (1) We previously have shown that the expected frequency of ticks with positive amplification of ospA is not significantly different from the frequency of infected ticks by visual observation (Guttmanet al. 1996). (2) The ospA classes as distinguished by SSCP are all different by a single nucleotide from another class, showing there are no missing classes (Guttmanet al. 1996). Thus we are sampling all the major variants of ospA. Any variant that we have not included would have to be rare. In this study, we amplified both ospA and ospC. (3) There were no ticks in which one gene amplified and not the other, giving us confidence that we have amplified all or nearly all the variants. (4) Both the front and back halves of ospC were amplified and analyzed by SSCP and there were no cases of extra or missing classes in this analysis. The analysis of the association of ospA alleles and ospC alleles allows the possibility of an occasional missing class, but these data can also be explained by recombination.
Cloning of ospC PCR fragments: We identified 13 different ospC mobility classes. Where possible, the cloning was done from ticks that were infected with a single clone of B. burgdorferi (see Table 1). The primer pair for cloning was either the same as that used for the first round of PCR amplification for ospC or the same (+) strand primer with a new (–) strand primer 5′-TTA AGG TTT TTT TGG ACT TTC TGC-3′ (–), which ends at bp 633. This later primer pair gives a 627-bp fragment. The PCR products were separated on a 2% agarose gel and eluted from an agarose block using the Prep-A-Gene kit (Bio-Rad, Hercules, CA). Eluted fragments were cloned into pGEM-T cloning vector (Promega, Madison, WI). Each ospC mobility class was cloned from two different infected ticks. Before sequencing, the identity of each cloned gene was confirmed using PCR-SSCP analysis.
DNA sequencing: Plasmids containing cloned PCR fragments were purified using the high pure plasmid isolation kit (Boehringer Mannheim, Indianapolis) following manufacturer's instructions and cycle-sequenced using FS-TaqDyeDeoxy terminator chemistry and commercially available primers flanking the T-vector cloning sites. Samples were precipitated with ethanol and analyzed on an automated sequencer (Perkin Elmer ABI 373S). Data assembly and editing were done using Sequencher software (Gene Codes, Ann Arbor, MI).
Sequence analyses: Clustal W (1.4), running on a Sun Sparc workstation, was used for primary sequence alignment. Minor adjustment of the output alignment was performed by hand. MEGA (Kumaret al. 1993) was then used for further sequence analyses, including estimation of the ratio of synonymous vs. nonsynonymous substitutions per site (dS/dN) and the significance testing of this ratio. When sequences were compared, the sequence of the external primers was removed. Thus all sequence comparisons, including sequences from GenBank, start at base 31 and end at 579 (numbers based on the ospC sequence of strain B31 with the start codon as base 1).
An Ewens-Watterson-Slatkin test on allele frequency distribution was performed by Arlequin (Schneideret al. 1996), a software package for population genetics. There are two proposed tests for neutrality based on Ewens sampling theory (Ewens 1972): one is the homozygosity test by Watterson (1977), and the other is an exact test by Slatkin (1994, 1996). For an excellent description of the Ewens sampling theory and the mechanics of generating sample configurations see Chapter 3 of Hartl and Clark (1989). For large sample sizes, both tests are required to generate a specified number of random configurations. These configurations are contingent upon the total number of sampled genes (n) and the number of alleles (k) in the sample. Each random configuration was then calculated for its homozygosity F (for Watterson test) and for its exact probability (for Slatkin test). Each calculated value was compared with the value from the observed sample configuration. The test statistic is defined as the proportion of the random configurations with values equal to or smaller than the observed values. The null hypothesis (that the observed sample configuration was drawn from a neutral allele frequency distribution) is rejected if the test probability is <s/2, or >1 – s/2, where s is the prespecified significance level (a two-tailed test). When the sample size is small, both tests lead to similar probabilities. However, the differences could be substantial when the sample size is relatively large (Slatkin 1996). We performed both tests.
RESULTS
ospC mobility classes: In a previous study from the same tick population we showed that there were only four ospA mobility classes, which we named MC1 to MC4 (Guttmanet al. 1996). In this study we found only these same four ospA mobility classes (here we rename them OA1 to OA4). Cold SSCP analysis of ospC revealed more genetic variation than in ospA (Figure 2). Various ospC mobility classes were first identified by studying the variation in the front half of the gene. Of the 40 ticks positive for ospA, only 38 were positive for ospC, while all 22 ticks that were negative for ospA were negative for ospC. We were able to amplify the ospC genes from the other two ospA-positive ticks using other primers (see below).
Four ospA and ospC SSCP mobility classes found in each tick
Ticks containing similar mobility classes were run side by side to confirm their identity. Among the ticks containing identical mobility classes, two independent ospC genes were PCR-amplified and cloned to serve as mobility class standards. This process was repeated several times until we obtained all of the ospC mobility classes present in our tick sample. For the two ticks in which the front half could not be amplified, the entire ospC gene was amplified. These amplified genes were cloned. Sequencing showed that the internal primer sequences contained eight mismatches (see Figure 1). Using a redesigned internal primer, two more mobility classes (9 and 11) were uncovered by screening all 40 ospA-positive ticks. In the end, we obtained 13 different ospC mobility classes (OCs) as shown in Figure 2A.
The mobility classes for the back half were also established by the same methodology as the classes for the front half of the gene. There were 13 classes and each class for the back half corresponded to a class for the front half (Figure 2B). The back half of the gene corroborated the results from the front half of the gene. Table 1 shows the results of all the SSCP mobility classes of both ospA and ospC found in each of the 40 positive ticks. The implications of this table are discussed below.
Multiple infections: Out of 40 ospA-positive ticks, 18 (45%) of them were multiply infected with two or more different ospA alleles, and the average number of strains per infected tick is 1.53 (61/40). For ospC, 50% were multiply infected, and the average is 1.88 (76/40) strains per infected tick. Because the number of distinguishable ospC alleles is over three times the number of ospA alleles, the results from the ospC survey provide a more accurate means to determine multiple infection rates and the average number of strains per infected tick. Because both of the average number of strains infecting a tick are relatively low (<2), we can infer the allele frequencies from the frequencies of the SSCP bands (Qiuet al. 1997; W. Qiu, unpublished results).
Allele frequencies: The number of ticks infected with each ospC mobility class and the estimated allele frequencies are given in Table 2. The distribution of allele frequencies is more even than expected for a neutral model, suggesting the action of balancing selection (see Ewens-Watterson-Slatkin test, below).
Linkage: A close inspection of Table 1 shows that the ospA and ospC mobility classes are strongly associated even though they are on different plasmids. This association is functionally like chromosomal linkage, so we refer to this association as linkage. The linkage relationships are summarized in Table 3. For the great majority of infected ticks in our sample, OA1 is always associated with OC1, OC4, and possibly OC11; OA2 with OC3, OC5, OC8, and possibly OC7; OA3 with OC9, OC12, and OC13; and OA4 with OC2, OC6, OC8, OC10, and possibly OC7 and OC11. Since both OC7 and OC10 appeared only once in the sample, their association is uncertain. However, based on surveys from other populations, we are able to assign OC10 to OA4 (I.-N. Wang, unpublished data). OC11 cannot be assigned to a specific OA with certainty because in both instances (tick numbers 9 and 32 in Table 1) it co-occurred with OA1 and OA4. An expanded survey should be able to resolve its linkage status. The appearance of OC8 with both OA2 and OA4 is not an artifact, because the same association pattern was also observed in tick populations from other localities (I.-N. Wang, unpublished data). In addition, OC12 can be assigned to OA3 because strains isolated from other ticks from this population show this linkage relationship.
—PCR-SSCP of 13 cloned ospC mobility classes. (A) The front half of the gene. (B) The back half of the gene. Lanes 1–13 correspond to OC1–OC13 in both A and B. The exact banding pattern on a SSCP gel is unpredictable. The most frequent pattern is two bands, with each band corresponding to a single-strand DNA of the double-stranded PCR product. However, sometimes both bands comigrate to give a single band.
Number of ticks found carrying a specific ospC mobility class
The linkage relationships listed in Table 3 suggest there are a few instances of missing mobility class or violation of these linkage relationships. These are noted by c in Table 1. The presence of missing mobility classes can be explained three ways: (1) Our PCR-SSCP analysis did not uncover all the variation within a single tick because of chance variation in the amplification; (2) the missing mobility classes could not be amplified because of mutational changes, most likely deletions, in the gene; (3) the linkage relationship has changed. The first explanation can be eliminated. Another aliquot was taken from the DNA sample of all infected ticks and the PCR-SSCP analysis was redone. We obtained the same results; no classes appeared or disappeared. We cannot distinguish between the other two explanations from these data. However, tick 30 (Table 1) with the pair of missing classes is likely to be a case in which the linkage relationship changed: OA2 was transferred into a background carrying OC2. Deletions joining ospA and ospB together have been found in nature (Rosaet al. 1992). If this has happened in one or more of the strains in our sample, the ospA allele like this would not be amplified and consequently be counted as missing. In any case, the overall association or linkage between ospA and ospC is surprisingly high, given that these two genes are on different plasmids.
Linkage relationship between ospA and ospC mobility classes
ospC sequences: To confirm that there are 13 different ospC alleles and only 13 in this sample, 22 genes, as well as 2 genes from other cultures with the same ospC mobility class, were cloned and sequenced. For the 11 different ospC mobility classes where 2 different genes of the same class were cloned and sequenced, the sequences were identical (data not shown). Each mobility class has a different sequence. Figure 3 shows the aligned DNA sequences and the deduced amino acid sequences.
Population variation compared to species variation in ospC: While it was known that ospC is very polymorphic (Theisenet al. 1993; Jauris-Heipkeet al. 1995; Liveyet al. 1995), it was not known whether this ospC variation is found within a single population or only across the species. To create a sample of the variability of the entire species to compare to our sample from a local population, we used 9 different sequences of B. burgdorferi sensu stricto that were found using PCR-RFLP analysis (Liveyet al. 1995) and 4 different sequences from GenBank, MIL (accession number U91802), CA-11.2A (accession number L25413) , CA4 (accession number L81131) , and 272 (accession number X84785) to give a sample of 13 different sequences. Of these 13 strains, 3 are from Connecticut, 3 from California, 2 from elsewhere in the United States (probably Minnesota), and 1 strain each from France, Germany, Slovakia, Pennsylvania, and Maryland. Out of 567 nucleotide sites compared, 244 (43%) of them are polymorphic (gaps in the aligned sequences are omitted from all the analyses) for the local sample compared to 259 (46%) for the sample from the entire species. For the local population, the number of amino acid polymorphic sites is 97 out of 189 residues, the same for the species sample. Clearly, most of the diversity found within the species is found within the local population. Masuzawa et al. (1997) reached a similar conclusion with a much smaller sample size.
Distribution of variation: A sliding-window plot of our 13 sequences, as shown in Figure 4, indicates that the polymorphic sites are not evenly distributed along the ospC sequence. The general trend is that the proportion of polymorphic sites increases as we move from the 5′ end to the 3′ end, with most of the variation clustered in the last two-thirds of the protein sequence. We used five relatively low points to divide the gene into six regions. There are two prominent peaks, one each in regions 3 and 5, which might be antigenically important.
Major ospC groups: The distribution of pairwise differences of DNA sequence among our 13 sequences is bimodal. Most of the pairs are quite different, ranging from 9.7% (OC9 vs. OC11) to 22.3% (OC8 vs. OC10), with an average of 17.5%. Several pairs are quite similar with only 0.7% (OC5 vs. OC7) and 1.8% (OC12 vs. OC13) of their nucleotides different.
A similar pattern also emerges from worldwide surveys. As of September 1997, there were 40 ospC sequences from B. burgdorferi sensu stricto in the GenBank database. We have excluded the ospC sequence from strain KIPP (accession number X84782) from this analysis. When KIPP is aligned with the other sequences, its differences are scattered throughout the sequence and frequently at positions that are monomorphic in all other sequences, indicating numerous sequencing errors. The distribution of pairwise percentage differences for the remaining 39 sequences is also bimodal.
The same pattern again emerges when the two sets of sequences are combined. This bimodal pattern is clearly illustrated in Figure 5. From this we can define major ospC groups (Table 4). Members of the same group will have sequence differences <2%, while members of different groups will differ by >8%. The major ospC groups should not be confused with SSCP mobility classes. For example, within our 13 mobility classes, OC5 and OC7 are from the same major ospC group, as are OC12 and OC13. While the RFLP groups as defined by Livey et al. (1995) are very similar to our major ospC groups, they are not identical. The ospC sequence from strain BUR (GenBank accession number X84765) is missing a DraI site found in RFLP group 4, so BUR would not be placed in this RFLP group, even though sequence similarity reveals that it belongs to the same major ospC group as the other RFLP group 4 strains.
The 53 ospC sequences (from GenBank and our current study) fall into 19 major ospC groups (Table 4). Ten of our 13 OCs belong to groups that include strains previously sequenced. The remaining 3 (OC3, OC8, and OC9) are new. Also, there are 8 major ospC groups that we did not find in our sample. The data allow three possibilities: (1) Each local population contains the entire variation found within the species–the remaining 8 major ospC groups would be found with more extensive sampling of the population; (2) each population contains endemics–some groups have a wide range and some groups are found only in one geographic area; (3) each population contains a subset of all groups found in the species–each group would be found across a wide geographical range, but because of the process of extinction and recolonization, only some of the groups are found in any population at any one time. More extensive sampling within the population and across populations could determine which possibility dominates.
—DNA and amino acid sequences of the 13 ospC mobility classes. (A) Nucleotide sequences (including gaps). (B) Deduced amino acid sequences (including gaps) from A. The nucleotide and amino acid numbers are the numbers for strain B31, which is identical to OC1. The GenBank accession numbers for these sequences are given in Table 4.
Within-group pairwise differences: Table 4 also shows the average within-group pairwise percentage differences. Out of the 10 groups with two or more members, 9 have all their pairwise differences at 1% or less. However, for group K, the average difference is 1.35% (ranging from 0 to 2.98%). The strain that is most different from the others is OC13. Omission of this sequence from analysis drops the average within-group difference to 0.83%. Most of the differences between OC13 and the rest are found clustered at the very end of the sequence. The last 18 nucleotides of OC13, which include 6 of the 10 changes where OC13 is different from the other sequences within group K, are identical to the homologous sequence found in group M, suggesting that OC13 was generated from a recombination between group K and group M.
Between-group pairwise differences: There is a tail of low pairwise differences between major ospC groups (Figure 5). The two lowest points (9.0 and 9.8%) are the pairwise comparisons between major ospC groups H and J. For 116 bp between bases 253 and 368, which is one of the most polymorphic regions of the ospC, the sequences are identical, as if a short piece of DNA has been transferred from one to the other. Most of the remaining pairwise differences between groups that are <14.5% are comparisons between major ospC group C and groups I and B. Groups C and I are very similar over the first 255 bases that include the first major peak. Starting at base 263 and going to base 374, Groups C and B are identical except for one base. This covers the variable region between the two peaks (see Figure 4). Thus it looks as if major ospC group C is composed of three pieces, one from group I, another from group B, and a third from a group yet to be described. Major ospC group C, while common in our sampled population, has been found only on Shelter Island. Consequently, this may be a local endemic, created by recombination and likely to become extinct because of immunological overlap with other groups. None of the pairwise comparisons with differences >14.5% show any extended similarities.
Ewens-Watterson-Slatkin test: On the basis of Ewens sampling theory (Ewens 1972), Watterson (1977) proposed a test to see if an observed allele frequency distribution conforms to neutral expectation. The null hypothesis is that the distribution of allele frequencies is compatible with the neutral hypothesis; i.e., mutation and genetic drift alone explain the distribution of allele frequencies when a Wright-Fisher population equilibrium model is used to determine population structure. The test statistic is F, the probability that two genes chosen at random will be the same allele. For the test, F is conditioned on the number of alleles found in the sample. If F is significantly different from the expected value (the null hypothesis), then selection is important or the population structure differs significantly from the Wright-Fisher model. A significant value of F larger than the expected value implies purifying selection. On the other hand, a significantly smaller F implies balancing selection (Hartl and Clark 1989, Chap. 3). Table 5 shows that both for the alleles as the 13 mobility classes or as the 11 major ospC groups, the value of F is significantly lower than expected given neutrality, indicating balancing selection.
—Sliding-window representation of nucleotide polymorphism of the 13 ospC mobility classes. The DNA sequences in Figure 3 are used. A window size is 51 nucleotides (17 amino acids), which is the optimal size as determined by Tajima's method (Tajima 1991). The six regions (1–6) are defined by localizing regions of relatively low variability. The last nucleotide of this 51-nucleotide window is then specified as the border nucleotide. The five border nucleotides that define these regions are 149, 243, 336, 417, and 519, where the numbers are according to the B31 sequence numbering. The locations of two internal primer sequences used in PCR-SSCP analysis, int(–) and int(+), are indicated by broad-headed arrows.
—Distribution of percentage pairwise differences among 52 ospC sequences. Thirty-nine ospC sequences from GenBank together with 13 sequences from this study were used. The class interval is 0.5%.
Slatkin (1994, 1996) proposed an exact test also on the basis of the same sampling theory. The test provides the probability of obtaining a configuration of alleles with the same or smaller probability as the observed configuration using the Ewens sampling distribution of neutral alleles. This is different from the Watterson test because the Slatkin test does not incorporate a model of selection to test deviation from the distribution of alleles expected under the null hypothesis of neutrality. On the basis of Slatkin's test, the frequency of the 11 major ospC groups is significantly different from that of random expectation, but the frequency of the 13 mobility classes is not significantly different from neutrality (Table 5). The 10-fold lower significance levels for the major ospC groups compared to the mobility classes arise because 2 of the rarer groups are divided to generate the mobility classes. We conclude from these tests that the frequencies of the major ospC groups are not compatible with the neutral model and that the deviation indicates balancing selection.
Balancing selection: The results presented so far strongly suggest that the major ospC groups are under strong balancing selection. The ratio of synonymous vs. nonsynonymous substitutions per site (dS/dN) can be used to investigate various modes of selection (Hughes and Nei 1988; Hughes 1991, 1992; Hugheset al. 1993; Riley 1993; Caporale and Kocher 1994; Hughes and Hughes 1995; Seibertet al. 1995; Wagner and Riley 1996). It is constructed such that if there is no selection on the gene, as in a pseudogene, the value of this ratio is expected to be 1. For most functional proteins, the ratio is higher, usually ∼5, because amino acid sequences are constrained by selection (Li and Graur 1991). A ratio <1 implies positive Darwinian selection for diversity at the amino-acid level. As the Ewens-Watterson-Slatkin test strongly indicates that ospC is selected to be diverse by balancing selection, we might expect the dS/dN ratio to be <1. To estimate the ratio, we used the same 13 sequences as we used for the sliding window. As shown in Table 6, the synonymous substitutions per site (dS = 0.268) are slightly more than the nonsynonymous substitutions per site (dN = 0.155), and the ratio (1.73) is significantly >1 (P < 0.001), rather than <1 as expected.
To understand why this result does not contradict the evidence of balancing selection from the Ewens-Watterson-Slatkin test, one has to understand the dS/dN ratio. Synonymous substitutions are assumed to be neutral, and the diversity increases at a rate proportional to time. The nonsynonymous substitutions can be neutral, detrimental, or advantageous. The neutral nonsynonymous substitutions will accumulate at the same rate as the synonymous substitutions, giving a ratio of one. Because of selection, detrimental nonsynonymous substitutions will accumulate at a much slower rate, giving a smaller distance than expected from neutral mutations and thus giving a ratio of much greater than one. And, because of selection, advantageous nonsynonymous substitutions will accumulate at a much faster rate than the synonymous substitutions, giving a larger distance than expected and thus giving a ratio much less than one. The actual ratio will be made up of the combination of these three ratios.
We can use this understanding of the dynamics of the dS/dN ratio to conclude something about the function of the different regions of the ospC gene. The first region (bp 1–123) shows a ratio of 2.66 with a low number of synonymous substitutions, much lower than any of the other regions. These low dS and dN values are consistent with the first region having a recent common ancestor, and the high value for the ratio suggests that functional constraint plays a dominant role. Were functional constraint also responsible for the low values of dS and dN,, then these values from between-species comparisons should also be low. Yet the dS and dN values between the species (B. burgdorferi sensu stricto, B. afzelii, and B. garinii) are almost 10-fold higher than the within-species values.
For the second region (bp 124–213), the ratio is 5.02, which is significantly (P < 0.001) >1, indicating that most nonsynonymous mutations are detrimental and that functional constraint prevents nonsynonymous divergence in this region. Indeed a similar pattern of a high dS/dN ratio in the between-species comparisons confirms this interpretation.
Regions 3–5 (bp 214–480) of ospC are the regions likely to be subject to balancing selection. The dS/dN ratio of these regions is larger than one, although not significantly (P > 0.1). The reduction in the dS/dN ratio is because there are more nonsynonymous changes within this region than in region 2, while the rate of synonymous substitutions is the same. The large synonymous distance suggests a distant common ancestor. Thus, the combined effects of selection for advantageous mutations and selection against detrimental ones give a ratio larger than one, but much smaller than the ratio in regions 1 and 2.
The 19 major ospC allele groups for Borrelia burgdorferi sensu stricto and the within-group percentage differences
Ewens-Watterson-Slatkin test of allele frequency distribution
Region 6 (bp 481–567) is like regions 3–5, but with a ratio significantly (P < 0.05) greater than one. Region 6 shows more constraint than regions 3–5. The dS/dN ratios of regions 3–6 are consistent with a pattern of balancing selection when the entire region is being maintained for diversity.
This result suggests that the immunological differences are multifactorial, and different clones are selected. This is in contrast to immune escape, where a single amino acid change is selected within a clone. We propose that these two types of selection for diversity be given different names, balancing selection for the first and diversifying selection for the second. In balancing selection, alleles will be maintained in the population for a very long time either by frequency or by niche-dependent selection. Balancing selection will be characterized by a very long time to the common ancestor and a dS/dN ratio greater than one and will usually show significant deviation from the Ewens distribution. In diversifying selection, new alleles are constantly being selected and so each of these new alleles is expected to remain in the population a relatively short time. Diversifying selection will be characterized by a dS/dN ratio significantly less than one, a very short time to the common ancestor, and sometimes can show significant deviation from the Ewens distribution.
DISCUSSION
Population genetics survey using PCR-SSCP: We expect PCR-SSCP analysis to be commonly used in population genetics studies where large sample sizes and intensive data collection are necessary. This was noted by Aguade et al. (1994), “SSCP analysis followed by stratified DNA sequencing is clearly efficient for surveying regions of low polymorphism” (p. 4662). But then they go on to say, “But there are questions about the nature and proportion of polymorphisms that can be reliably surveyed” (p. 4662). While they estimate that as much as 50% of the single nucleotide variation might be missed, they conclude that in general the fraction missed will be of the order of 14%. The large gel format (Hydrolink-MDE gel) used by Aguade et al. (1994) has been shown to be less sensitive (79 vs. 95%) in detecting nucleotide substitutions than the small gel format (PhastSystem gel; Vidal-Puig and Moller 1994) we use. We found the small gel format was easy to use and gave clear, reliable results.
Major ospC groups: We identified 11 major ospC groups within a single population. When combined with sequences obtained worldwide, 19 major ospC groups can be distinguished. So far, 4 groups (groups A, B, J, and L) have been found both in Europe and the United States, 4 groups (groups P, Q, R, and S) have been found only in Europe, while the remaining 11 groups have been found only in the United States.
Foretz et al. (1997) hypothesized that B. burgdorferi sensu stricto originated in North America and only recently spread to Europe. Our data support this idea. Sequences within the same ospC group are identical if they are of European origin (seven sequences for group A, five sequences for group B, and two sequences for group R), while in the United States, there is considerable variation within groups. For the major ospC allele group A, the four United States sequences (from California, New York, and Texas), only OC1 and B31, both isolated from Shelter Island, New York, are identical. In group B, OC2 is different from BUR (from New York); in group D, OC4 is different from CA-11.2A (from California); in group E, OC7 is different from the rest (from Pennsylvania and Connecticut); in group F, Son188 (from California) is different from the rest (from California, Connecticut, and New York); in group I, OC10 is different from the rest (from Connecticut); and in group K, only OC12 and 28354 (from Maryland) are alike. In group A, the sequence found on the east coast of the United States, i.e., the sequence in B31 and OC1, is identical to the sequence found in Europe; however, in group B, the sequence in Europe is about equally distant from all the North American sequences. These data suggest that a strain like B31 was transferred recently from the east coast of the United States and has spread throughout Europe. However, it does not preclude continuous transfer of strains from the United States to Europe over a long period of time.
How can one explain the presence of four major groups found only in Europe given the hypothesis of the United States origin of this genospecies? There are three possible explanations: (1) These groups are common in the United States but simply have not yet been discovered here; (2) these groups could represent much older migrations of strains that are now rare or extinct in the United States; (3) these groups could have recombined with the European genospecies and now have sequences similar to those found in another species. To test this last possibility we did a BLAST search for gene regions 3–5 (bp 166–429, see Table 6) of these four groups. We found that the central region of major ospC group P is almost identical (one and three differences over 257 bp) with the central region of B. afzelii strains DK1 (GenBank accession number X73627) and PLud (GenBank accession number X83552), and the central region of major ospC group S is almost identical (246 out of 249 bp) with B. garinii strains TIs1 (GenBank accession number X81525) and H13 (GenBank accession number L42889). The central regions of groups Q and R did not match any known sequences. The N terminus of all four groups showed the correct species-specific signature (Liveyet al. 1995), excluding the possibility that these strains were misidentified. This shows genetic transfer across genospecies is possible and, if the migration of B. burgdorferi sensu stricto is from United States to Europe, that B. burgdorferi sensu stricto has picked up the central region of ospC from the European species.
dS/dN ratios of different ospC gene regions
Species-wide distribution of major ospC groups: One interesting and important finding from this study is that most of the major ospC groups found worldwide (as listed in GenBank) are found within a single population. This pattern indicates that the geographic distribution of major ospC groups is relatively homogeneous. Different parts of the species range have very similar sets of ospC alleles and local diversity is high. Evidence based on very limited microsequencing of OspC proteins purified from B. burgdorferi strains isolated in Illinois suggests high local diversity of ospC (Pickenet al. 1995). Similarly, as reported by Livey et. al. (1995), five out eight ospC RFLP groups for B. afzelii were found near the city of Vienna. Strains isolated from infected humans from a local area also showed high ospC diversity (Masuzawaet al. 1997). Obviously, a more expanded and detailed survey is needed to see if the pattern also holds in other parts of the world and for other genospecies of Borrelia.
Frequency-dependent selection in maintaining major ospC groups: The evidence for balancing selection at the ospC locus is: (1) There exist a large number of alleles within a local population; (2) the allele frequencies are more even than expected by neutrality (Ewens-Watterson-Slatkin test); (3) the alleles differ at many nucleotides, suggesting a common ancestor a long time ago, even before the genospecies split (D. E. Dykhuizen, unpublished data); and (4) almost all the variation in ospC is between major ospC groups.
One of the striking results of this study is the lack of variation within major ospC groups within a population. Nine of the 11 major groups are isosequential. This lack of polymorphism within major groups indicates that the effective population size of each group is very small, which is typical for parasites. Because a successful transmission of spirochetes from one host to another involves many random events at every stage of the infection cycle, the most likely fate of a newly derived mutant is extinction within a short time. Nevertheless, new mutations will be fixed, by chance, within local populations, causing geographical variation. Variation is found within groups across the United States. Together these facts, lack of variation within a population and variation across populations, suggest that Borrelia is broken into many semi-isolated populations.
Dobzhansky (1970) suggested that balancing selection can arise when different environments favor different genotypes. Because adult and nymphal ticks each prefer different kinds of animals and because the frequencies of ospA alleles are different in nymphal and adult ticks, we have concluded that this type of balancing selection is important in B. burgdorferi (Qiuet al. 1997). However, the difference in the ospA allele frequencies between the nymphal and adult ticks is not large, suggesting this type of balancing selection is not sufficient to maintain the number of alleles and the frequency distribution of the major ospC groups as seen here in a single population of adult ticks.
Thus, rather than adaptation to different animal hosts, we propose that frequency-dependent selection, another form of balancing selection, is the major force maintaining the variation in ospC. We propose that this frequency-dependent selection is created by the host immune system. OspC is clearly a protective antigen. It has been demonstrated that OspC is expressed on the surface of spirochetes during tick feeding on its host (Schwanet al. 1995), and the presence of OspC protein elicits the host to mount a strong IgM response at an early phase of infection (Dressleret al. 1993; Funget al. 1994; Engstromet al. 1995). Furthermore, animals immunized with anti-OspC vaccine are protected against subsequent infection by the same strain of B. burgdorferi (Gilmoreet al. 1996) but not by heterologous strains (Probertet al. 1997). Passive immunization with polyclonal and monospecific mouse immune sera to recombinant OspC can cure chronically infected mice (Zhonget al. 1997). Thus anti-OspC antibodies can prevent reinfection with the same strain and clear present infections.
In an endemic area like Long Island and Shelter Island, many ticks are infected with B. burgdorferi, often with multiple clones (Qiuet al. 1997). Moreover, the mice and chipmunks are simultaneously infested with many ticks (Hofmeister and Childs 1995; Qiuet al. 1997). Thus, every host will be infected with a large and heterogeneous population of B. burgdorferi, expressing many different OspC proteins. The most abundant ospC group will, on average, be the group that a host responds to immunologically both first and most strongly. First because it is the OspC protein most often seen with an initial infection and most strongly because there will be the greatest number of repeated infections with this type. Thus a rare type is more likely to establish a persistent infection than a common one and be passed onto the next generation of ticks.
There is no evidence for immune escape and consequently diversifying selection in these Borrelia. When mice are infected in the laboratory, they become chronically infected, but the genotype of the strain does not change. They are stable in their plasmid profile (Persinget al. 1994), and the ospC sequence remains constant during the infection period (Stevensonet al. 1994). As pointed out by Hofmeister and Childs (1995), the above-mentioned studies used a monoculture containing only a single clone. However, the plasmid profiles usually change over time if a large number of clones are used (Bartholdet al. 1993). This change is due to clonal selection among different strains of B. burgdorferi present in the inocula. A similar phenomenon would be important in the field–selection for the rare clones and against the common ones.
Population dynamics of Borrelia: From the data presented, we propose that Borrelia is differentiated into local populations; i.e., the migration rate is not so high as to make the species a panmictic population. Within each local population, the effective population size is quite small, making genetic drift an important force in molding the population structure of Borrelia. If this drift were not countered by strong balancing selection and some migration, local populations would lack diversity. Frequency-dependent selection mediated by the immune system of the mammalian host acts to preserve the diversity between major ospC groups.
Recombination causes divergence of strains within the same major group and homogenizes strains in different major groups. As reported in the results, those within-group pairwise differences that showed diversity >1% showed evidence of this diversity being created by recombination. Likewise, those between-group pairwise differences that showed differences of <14.5% showed evidence of similarities created by recombination. Balancing selection must select against these recombinants, otherwise the tight bunching of the major groups as seen in Figure 5 would be lost. From this, we conclude that the major ospC groups will be stable over time.
It is interesting to note that the five groups (E, G, I, J, and K) that show the lowest frequency in this data show the evidence for migration. Only groups E and K contain two mobility classes. Also, group G is the only group where the ospC allele is linked to two ospA alleles (OC8, Table 3). We do not expect the same ospC allele to be linked to two different ospA alleles within a single population, if the effective population size is small and the balancing selection on major ospC groups is strong. This diversity should have been lost by drift. However, in different populations, there is no reason to expect the linkage relationships to be the same. Thus, we conclude that this diversity in linkage patterns is created by migration. This result suggests that there is a low level of migration, but so low that migrant alleles are detected only when their frequency is increased by selection.
Clinical implications: Since OspC is one of the few spirochetal proteins that elicit an early strong IgM response specific to B. burgdorferi (Dressleret al. 1993; Funget al. 1994; Engstromet al. 1995), it is only natural to use OspC as an early serodiagnostic antigen for Lyme borreliosis. However, many studies on the efficacy of using OspC protein as a serodiagnostic antigen either on human patients (Funget al. 1994; Padulaet al. 1994; Gerberet al. 1995; Mathiesenet al. 1996) or infected dogs and horses (Magnarelliet al. 1997) showed that it gave specific, but not sensitive, results. That is, there were few false-positive identifications, but many falsenegative ones. This result should not come as a total surprise since OspC proteins are extremely variable even in a local area. Because all of these studies used only one type of OspC protein (strain 297, 2591, or MUL) as a diagnostic antigen to test against a bank of collected sera, specific but not sensitive results can be expected. Clearly, to increase sensitivity and the chance of early detection, it is necessary that the array of immunologically distinct OspC proteins be used as serodiagnostic antigens.
OspC and other surface proteins have been the subjects of intensive efforts to develop a protective vaccine against B. burgdorferi infection. Many animal studies indicate the effectiveness of an anti-OspC vaccine (Preac-Mursicet al. 1992; Probert and LeFebvre 1994; Gilmoreet al. 1996). However, all the studies were conducted with only one type of OspC protein. A proper mix of various types of OspCs will be necessary for an anti-OspC vaccine to be effective. The tremendous amount of genetic variation at ospC decreases the suitability of using OspC as a protective antigen unless a way of handling this variation can be found.
Acknowledgments
We thank Ms. Mary Anderson, Dr. Rafael Zardoya, and Lacey Knowles for technical support and helpful discussion. We thank Tony Dean and two reviewers for many useful suggestions to improve the manuscript. This study is supported by a grant from the National Institute of Allergy and Infectious Disease (RO1AI33454) to B.J.L. and by cooperative agreement numbers U50/CCU206608 and U50/CCU210518 from the Centers for Disease Control and Prevention to B.J.L. and E.M.B., respectively. This is contribution 1009 from Graduate Studies in Ecology and Evolution, State University of New York at Stony Brook.
Footnotes
-
Communicating editor: W. F. Eanes
- Received October 21, 1997.
- Accepted September 24, 1998.
- Copyright © 1999 by the Genetics Society of America
