text:
- Research
- Open Access
- Published:
The multiplicity of divergence mechanisms in a single evolving population
Genome Biology volume 13, Article number: R41 (2012)
9149 Accesses
45 Citations
2 Altmetric
Evolutionary divergence is common within bacterial species and populations, even during a single bacterial infection. We use large-scale genomic and phenotypic analysis to identify the extent of diversification in controlled experimental populations and apply these data to differentiate between several potential mechanisms of evolutionary divergence.
We defined testable differences between five proposed mechanisms and used experimental evolution studies to follow eight glucose-limited Escherichia coli chemostat populations at two growth rates. Simple phenotypic tests identified 11 phenotype combinations evolving under glucose limitation. Each evolved population exhibited 3 to 5 different combinations of the 11 phenotypic clusters. Genome sequencing of a representative of each phenotypic cluster from each population identified 193 mutations in 48 isolates. Only two of the 48 strains had evolved identically. Convergent paths to the same phenotype occurred, but two pleiotropic mutations were unique to slow-growing bacteria, permitting them greater phenotypic variance. Indeed, greater diversity arose in slower-growing, more stressed cultures. Mutation accumulation, hypermutator presence and fitness mechanisms varied between and within populations, with the evolved fitness considerably more uniform with fast growth cultures. Negative frequency-dependent fitness was shown by a subset of isolates.
Evolutionary diversity is unlikely to be explained by any one of the available mechanisms. For a large population as used in this study, our results suggest that multiple mechanisms contribute to the mix of phenotypes and evolved fitness types in a diversifying population. Another major conclusion is that the capacity of a population to diversify is a function of growth rate.
Heritable variation is common in evolution and certainly within bacterial species and populations [1, 2]. Bacterial divergence is rapid and even evident within the course of a single bacterial infection [3]. One of the most intriguing questions in evolutionary biology is how such variation emerges and persists. It used to be believed that a population evolving in a homogeneous environment should exhibit low sympatric diversity as a result of periodic selection of a fitter isolate and the competitive niche exclusion of weaker isolates [4]. Therefore, environmental heterogeneity was considered to be the main source of diversity [5]. However, several experimental evolution studies suggest that diverse types coexist even in an initially homogeneous culture supported by a single resource [6–8].
Several distinct mechanisms or explanations have been proposed for the maintenance of diversity in evolving populations, including frequency-dependent selection [9, 10], cross-feeding and cheating between population members [11, 12], metabolic and other trade-offs [13, 14], mutation selection balance [15, 16] and regulatory degeneracy leading to convergent evolution [7, 17]. The multiplicity of mechanisms begs the question: what happens in real diversifying populations? Do individual populations predominantly diversify by one of the above mechanisms or is evolved diversity a result of a mixture of mutation-selection balance, frequency-dependent selection, trade-offs with or without mutation rate differences, niche creation or convergent evolution through degenerate pathways? The predictability and a systems-level description of evolution [18] require an answer to this question.
To approach the above questions, the signatures of individual divergence mechanisms need to be searched for in the evolved isolates. It is also necessary to obtain an accurate characterization of the extent of diversity in populations at both phenotypic and genotypic levels. Bacterial cultures, with their large populations, rapid growth and regulated mutation rates should be ideal subjects for the experimental analysis of evolution and diversification [5, 18, 19]. Genomic diversity can be readily identified through genome sequencing, as has been done in several experimental populations [7, 17, 20, 21]. However, owing to the difficulty of identifying phenotypic diversity, most previous experimental studies focused on a few adaptive isolates or mutations and no experimental study has systematically characterized the extent of diversity at both phenotypic and genotypic levels in multiple parallel populations. Also on ecological grounds, studies of diversity in experimental populations have tended to use culture conditions with multiple or alternative niches [22–27]. Even the intensively studied Lenski populations evolved in a selection environment that fluctuated temporally in terms of nutrient availability through multiple cycles and coexisting types have evolved to these fluctuations [28]. Recent genomic analyses of these long-term populations have revealed multiple mutations [20]. The beneficial effects of these mutations are beginning to be revealed [29, 30], but many of the mutations have unidentified benefits or fitness effects. A systematic description of diversity in these populations has not been reported, although metagenomic studies indicate diversity does exist in the Lenski populations. The detection of minor types was limited by the resolution of the metagenomic technique [20]. In contrast, we showed that extensive diversification can be detected when extensive phenotypic screening is applied to populations, and described in detail the complex population structure of a single chemostat population [6].
We report a study that relates the experimental results on eight evolving populations to the above divergence mechanisms. The phenotypic testing permitted the identification of multiple phenotypic groupings, and fitness assays were applied to test for trade-offs and frequency dependence. A detailed analysis of genomic changes revealed whether the number of mutations in each isolate is significantly different. These data are used to discuss whether mutation-selection balance [15, 16] or trade-offs with differences in mutational robustness [14] or regulatory degeneracy leading to convergent evolution [7, 17] are involved in the mechanism of diversification. We therefore compare the number of mutations per isolate in both sympatric and allopatric populations. In addition, we analyzed all 320 isolates compared here for mutator activity, because hypermutation can be selected in evolving populations [31, 32]. We indeed find that two of eight populations contain a subset of isolates with elevated mutation rates. Our strain comparisons also address the proposal that convergent alternative mutational pathways lead to similar levels of fitness in the same culture [17, 33]. We also followed frequency dependence in fitness assays and looked for mutations known to cause altered trade-offs, such as hfq and rpoS, that reset the trade-off between stress protection and nutritional competence (SPaNC) [13, 34]. The availability of 48 genome sequences further allows us to detect patterns of mutations as well as simultaneous analysis of phenotype-genotype relationships in a large number of isolates. Altogether, the numbers of mutations responsible for multiple phenotypes provide for the first time a comprehensive analysis and clues to the kind of mechanisms and properties of isolates relevant to the various evolutionary models.
Evolution of phenotypic diversity in parallel populations
In order to test how diversity emerges and is maintained in bacterial populations, we performed replicate evolution experiments using glucose-limited chemostat cultures of Escherichia coli at two separate growth or dilution rates (D); four at slow with D = 0.1 h-1 (generation time of approximately of 6.9 hours) and four at fast with D = 0.6 h-1 (generation time of 1.15 hours). These two conditions result in a 30-fold difference in basal mutation frequencies per generation [35] and this difference is important for determining the mechanisms of diversification; the two series of experiments test whether the extent of diversity changes under these otherwise near-identical selection conditions. All chemostats were initiated with a common ancestral strain, BW2952 [36], and the population size was maintained at approximately 1.6 × 1010 cells by a constant concentration of glucose in the feed medium throughout the evolution experiment. The cell density did not vary appreciably during the course of the experiments.
To determine the phenotypic diversity in evolving populations, 39 to 41 isolates from each of the 8 independent chemostat cultures were analyzed. Population 1 was used previously and the properties of its 41 isolates reported [6]. The samples were obtained from the last day before a particular chemostat malfunctioned or was terminated, between days 23 and 26, except for population 4, which stopped at day 18. Isolates were randomly chosen and characterized using the triple-phenotypic-test approach (see Materials and methods). This method identifies the six phenotype clusters in our best-analyzed population and detects the major population fluctuations that occur in the first two weeks of culture [6]. Co-existence of four or more combinations of phenotypes was evident after two to three weeks in this earlier study [6] and population diversity did not change markedly between days 15 and 37, as shown in Figure S1 in Additional file 1. So samples from the eight populations at days 21 to 27 provide a good snapshot of population heterogeneity. We may underestimate the extent of diversity in this way, but as shown in Figure 1a, significant heterogeneity was detectable in a single sample from all eight cultures. We found two to six combinations of phenotypic classes (PCs) in each population (Figure 1a). Individual characteristics of all analyzed isolates are shown in Table S1 in Additional file 1 and criteria used for classification of each PC are shown in Table S2 in Additional file 1.
Altogether, 11 different PCs were distinguishable in the eight populations (Figure 1a). The extent of the diversity and the phenotypic relationships of the 11 PCs is shown in Figure 1b. Most, but not all, of the PCs were found in both slow- and fast-dilution rate cultures, although none were universally present. PC1 (ancestor class), PC3 and PC8 were present in five, six and seven cultures, respectively, making them the most widely distributed classes (Figure 1a). Some PCs were present only with a particular dilution rate; for example, PC 5 and PCs 9 to 11 were absent in fast-dilution rate cultures while all PCs except PC6 were found in slow-dilution populations. As shown in Figure 1c, on average, there were 4.75 PCs in 0.1 h-1 cultures compared to 3.0 in 0.6 h-1 cultures, which suggests that the slow-dilution cultures became more diverse than the fast-dilution cultures, despite dividing for one-sixth of the number of bulk generations but with higher mutation rates. Simpson's diversity indices (Di) also indicated that populations from the slow-dilution cultures were more diverse (Di = 0.0282) compared to those from fast-dilution cultures (Di = 0.0134) (Figure 1d; two-tailed t-test P = 0.0329). Hence, both the types of PC and the overall diversity in evolution over the same elapsed time are influenced by small differences in evolutionary selection conditions, in this case residual glucose concentration determining growth rate in the chemostat.
In addition, the proportion of individual PCs varied between slow and fast dilution populations. PCs 5 and 10 are the most abundant classes in the slow-dilution populations while PCs 8 and 6 accounted for more than 50% of the total isolates analyzed from the fast-dilution populations. Some low-abundance types were unique to one population (for example, PC11 in population 1 (Figure 1a)). Some of these differences may be due to a focus on a single time point or limited sampling. However, in the one population for which we analyzed the population structure over a greater period (over 37 days) we found slightly changing proportions beyond day 27 due to continuing population turnover, but no major sweeps (Figure S1 in Additional file 1). Overall, there appears to be no sympatric or allopatric uniformity between the populations when subject to a similar, relatively homogeneous, constrained selection condition. In common with studies on another experimental system [27], unparallel diversification is an important feature of bacterial evolution.
Mutations in evolved genomes
To investigate the genetic basis of phenotypic diversity and which mechanism(s) were responsible for the emergence of diversity, we sequenced the whole genomes of at least one isolate from each PC in all eight populations. Altogether we sequenced 48 isolates, 27 from slow- and 21 from fast-dilution cultures (the strains are listed in Table S3 in Additional file 1). The fully sequenced ancestral genome [36] was used as the template for identifying the mutations in the evolved isolates.
We detected a total of 193 mutational events in the 48 evolved isolates (see Figure 2 for a graphic summary and Table S4 in Additional file 1 for a complete list of all identified mutations). There were a total of 155 SNPs, of which 140 were located in protein coding regions and the remaining 21 in intergenic regions. There were also 15 insertions and 23 deletions (indels). The majority of evolved isolates contained one to five mutations except in four isolates (BW3767, BW4004 and BW4040 from population 1 and 10W34 from population 10) that contained 28 to 46 mutations. This greatly elevated rate of genomic mutation was a result of mutY gene deletions as all four genomes contained a deletion of approximately 28 kb DNA including mutY, consistent with the demonstrated insertion sequence (IS)5-mediated rearrangement involved in mutY deletions [37]. Lack of mutY activity causes C:G > A:T transversions [38] and almost all SNPs in these isolates showed a C:G > A:T signature. To test the overall presence of mutators, we screened all the 321 isolates for the mutY-related mutator phenotype by testing their ability to grow on glycolate (the deleted region in all mutY-mutator isolates contained the glc locus responsible for glycolate utilization [37]) and compared the frequency of spontaneous rifampicin-resistant mutants. We found an additional 20 and 15 isolates with mutY-mutator phenotypes from populations 1 and 10, respectively. No isolates with elevated mutation rates were detected in the other six populations.
Mutational patterns in slow and fast growing cultures
Figure 2 and Additional file 2 indicate that the mutational patterns under the two selection conditions were not identical. In the 44 non-mutator isolates, we found 27 SNPs (22 in coding and 2 in intergenic regions) and 23 SNPs (17 in coding and 6 in intergenic regions) in 0.1 h-1 and 0.6 h-1 populations, respectively (Table 1). All SNPs except one in a fast-growth culture (metB in 11Za13; Table S4 in Additional file 1) were non-synonymous (nsSNPs), affecting polypeptide sequence. We used the SIFT algorithm [39] to evaluate the effect of nsSNPs on protein function. All of them were predicted to be non-conservative and therefore expected to exert some effect on protein function. This result is consistent with the predominance of nsSNPs in the recent studies of the E. coli populations evolving under different experimental settings [21, 40, 41], suggesting that most mutations fixed in the early phase of evolution are adaptive.
About 85% and 70% of all SNPs in the 0.1 h-1 and 0.6 h-1 populations, respectively, were transversions. When analyzing transitions and transversion events, the ratio was found to be different between the 0.1 h-1 and 0.6 h-1 populations (Figure 3a, b). The ratio was 0.6 in the 0.6 h-1 population, which was slightly higher than the cumulative ratio obtained from sequence comparison of bacterial genomes [42]. By contrast, the ratio of 0.17 in slow-growth populations was nearly three-fold less than the expected ratio of 0.5, indicating that transversion mutations are more common under slow-growing environments; the reason for this skewed profile, together with the higher mutation rates in 0.1 h-1 cultures, needs investigating. However, in line with the Ochman's analysis [42], we observed only one C:G→G:C transversion mutation in all of the 48 genomes, confirming the rarity of this mutation.
We also found strikingly different patterns of indel mutations between the 0.1 h-1 and 0.6 h-1 populations. Among the 31 indels in non-mutator isolates, 12 were single-base while the remaining 19 were large indels. Interestingly, 10 out of 12 single-base indels were in the 0.6 h-1 population. The majority of the large indels (> 1 bp) were associated with IS elements, consistent with the extensive movement of a variety of IS elements demonstrated recently [37]. The SNP-to-indel ratio of 2.08 in 0.1 h-1 populations was nearly two-fold higher than that of 1.15 in 0.6 h-1 cultures, which reflects that indels are more common in 0.6 h-1 populations (Table 1). However, in comparison to other bacteria, the observed SNP-to-indel ratio is significantly lower under both selection conditions, suggesting the importance of indels during constant selection under nutrient limitations. The genome-wide SNP-to-indel ratio in bacterial genome comparisons is estimated at around 19.61 [43].
In addition to differences in mutational patterns, mutation accumulation rates (MARs) were also found to be affected by the growth rate. As shown in Figure 3c, there was approximately a four-fold higher MAR in 0.1 h-1 than in 0.6 h-1 populations (two tail t-test P = 0.009). The mean MAR in 0.1 h-1 populations was 1.78 × 10-12 mutations per site per generation compared to 0.40 × 10-12 mutations per generation in 0.6 h-1 populations (Figure 3c). These values cannot be compared to real mutation rates, which are approximately 100-fold lower [41, 44, 45]. Of course, MAR is affected by selection as well as mutation rate as mutation supply is probably not limiting in large populations [46].
Fitness properties of evolving isolates
The nature of the fitness can indicate the means of selection of particular isolates. Thus, identifying the fitness properties of evolved isolates is crucial for understanding the mechanism of adaptation. To estimate the fitness, we measured the selection coefficient of each of the 48 isolates used for genome sequencing by competing head-to-head at a 50:50 initial ratio with the ancestral isolate under the dilution conditions under which they were isolated (see Materials and methods). We showed previously that isolate-to-isolate competition gives a similar rank order of fitness characteristics as isolate-to-ancestor competition [6]. As shown in Figure 4a, b, the majority, but not all, of the isolates from both slow and fast-dilution cultures have a significant competitive advantage against the common ancestor in their respective original selection environments.
Perhaps most interestingly, isolates with significantly reduced fitness compared to the ancestral isolate were present in each of the slow growth populations; these include BW4005 from population 1 and six newly identified isolates, 2W2 from population 2, 3X16 and 3X31 from population 3 and 4R18 and 4R10 from population 4. These isolates may have survived through adaptation to a novel niche created within the original glucose-limited environment and/or negative frequency dependence. In fact, when pair-wise competitions were started at 1% against 99% ancestral isolate, we found that these isolates exhibited a positive fitness against the ancestral clone (Figure 4c). Hence, negative frequency-dependent selection is a feature of several evolving populations, but not all isolates show this property. Interestingly, 2W2, 3X16, 3X31, 4R10, and 4R18 all belonged to PC3 while BW4005 belonged to PC2, indicating that frequency-dependence is not always associated with the same phenotype. There is also diversity in genotypes; as shown in Table S4 in Additional file 1, the frequency-dependent isolates had a variety of mutations. For example, 2W2 contained mutations associated with csgD, 3X16, 3X31, 4R10, and 4R18 contained a SNP in rpoA (RNA polymerase A) and BW4005 contained an in-frame deletion of 3 bp in aphC and lpxM and a deletion of ogrK-to-yegS, suggesting convergence of phenotype through multiple pathways. The molecular mechanism(s) of negative frequency-dependent fitness in these populations has not been identified, but the diverse mutations suggest several alternatives exist.
Fitness and the growth rate under selection
The data in Figure 4a, b also showed that both the magnitude of fitness gain and the extent of fitness distributions within and across the populations were markedly different between 0.1 h-1 and 0.6 h-1 populations. Amongst the 48 isolates, the PC11 isolate (BW4040), which contained 29 mutations, including in rpoS, mglD and malT, exhibited the highest fitness relative to the ancestral isolate. The measured fitness (1.29 S/g) was over two-fold higher than that of the most-fit counterparts in the 0.6 h-1 populations, suggesting the fitness peak heights are very different under the two conditions.
Consistent with the phenotypic diversity, the 0.1 h-1 populations exhibited a greater level of fitness diversity than the 0.6 h-1 populations. The standard deviation of measured fitness amongst the 26 0.1 h-1 isolates (0.53 S/g) was more than two-fold higher than the 0.21 S/g value of the 0.6 h-1 populations. The mean fitness gain of 0.36 S/g in the 0.1 h-1 populations was slightly higher that that of 0.3 S/g in the 0.6 h-1 populations. The difference, however, was not significantly different (two-tailed t-test P = 0.09). Furthermore, the normal or Gaussian distribution of fitness based on the standard deviation and the mean fitness showed that the width of fitness distributions in the 0.1 h-1 populations is broader than in the 0.6 h-1 populations (Figure 4d). These results are notable, particularly in the 0.1 h-1 populations, because the mutation-selection balance proposal (analyzed below in detail) predicts that elimination of less fit isolates through clonal interference depends on the fitness distribution between contending lineages. However, the observed wide distribution of fitness within the 0.1 h-1 populations did not lead to clonal replacement; instead we observed expansion of some less-fit subpopulations in the later samples. For example, we found that isolates from PC9 to PC11 in population 1 have the highest measured fitness but failed to replace members of the PC5 subpopulation (Figure S1 in Additional file 1). In fact, the proportion of PC5 isolates gradually increased from 5% of the total population at day 17 to over 50% at day 37 despite their fitness deficit against the isolates belonging to PC9 to PC11.
These results together with genome and phenotypic data also revealed that, in some cases, fitness convergence was achieved despite having no genotypic or phenotypic similarity. For example, BW4001 and BW4029 in population 1, 2W14 and 2W29 in population 2, 3X10 and 3X17 in population 3, 8W33 and 8W21 in population 8 and 10W21 and 10W34 in population 10 were different at both phenotypic and genotypic levels (Figure 4; Table S4 in Additional file 1) but had similar competitive fitness. These results clearly indicate that it is possible for coexisting subpopulations to achieve the same height of fitness peak through multiple mutational pathways.
Parallel mutations and genetic basis of the biased phenotypic diversity between 0.1 and 0.6 h-1 populations
To gain further insight into the genetic basis of the phenotypic diversity and to understand which divergence mechanism is responsible for the observed diversity, we identified genes that acquired mutations in parallel populations. Genes repeatedly mutated in more than one population are more likely to contribute fitness. The 193 mutations in isolates were distributed in 165 genes or regions, of which 12 genes were found in more than one population (Table 2). Multiple alleles were found in the majority of repeatedly mutated genes and in almost all cases, the evolved alleles were different between populations. Mutations in rpoS and mglD/O were the most common, both appearing in six out of eight populations, three each in the 0.1 and 0.6 h-1 populations. Other genes mutated under both selection conditions include csgD-ig, cobB, inaA_ig, xapA, rpoB and rpoA as well as an approximately 29 kb DNA deletion including mutY (Table 2).
The most striking specific parallel adaptation was a nsSNP in the rpoA gene encoding RNA polymerase subunit A. The same base change at the same position was present in three separate populations. The mutation affects a residue in the α-CTD domain of RpoA that is involved in a multitude of regulatory interactions with DNA and transcription factors [47]. This mutation reduces RpoS levels slightly (Figure S2 in Additional file 1) so may have similar benefits to other mutations, such as in hfq, that reduce RpoS levels [48]. However, the effect of the rpoA mutation, as well as that in rpoB, may be on transcription more generally and needs deeper investigation.
There were also some genes that were selected in only one of the two selection conditions. For example, malT and hfq were selected only in 0.1 h-1 cultures, while the slyD gene encoding an FKBP-type peptidyl prolyl cis-trans isomerase was selected in three out of four 0.6 h-1 cultures but absent in slow-growth populations. The significance of slyD under this selection condition has not been investigated and future studies will need to investigate these and other less understood mutations, as in inaA (which may affect a hypothetical protein BWG-2010) and rpoA.
Mutations in malT cause elevated levels (7- to 15-fold relative to ancestor) of expression of maltose transporter genes malG and lamB [49] and, consistent with a previous study [35], mutations in malT were observed in three out of four populations. We did not find any isolates with high levels of malG-lacZ activity in the 160 isolates from 0.6 h-1 cultures. This finding confirms an earlier observation that a malT-constitutive mutation is deleterious at high dilution rates [35]. Previously, we showed that a mutation in hfq was responsible for the multiple benefits under glucose limitation through changing at least five regulation targets [48]. Mutations in hfq exhibit a characteristic phenotype of increased sensitivity to methyl a-glucoside (α-MG) and a slightly elevated level of malG-lacZ activity [48]. α-MG sensitive isolates were found in six out of the eight surveyed populations, although the abundance of such isolates varied widely between populations (Table S1 in Additional file 1). To test if hfq mutations were responsible for α-MG sensitivity, we sequenced hfq, including its transcription region, in all α-MG sensitive isolates. We found an additional 19 hfq isolates, but none of them were from 0.6 h-1 cultures. The α-MG-sensitive mutants in fast dilution rate populations were not altered in hfq and were probably mlc or ptsG mutations [35]. The mutations in hfq were beneficial at slow growth rates but deleterious at the fast growth rate, explaining why they were not found in 0.6 h-1 cultures [50]. This bias in selection of pleiotropic malT and hfq could be a contributing factor to the observed difference in phenotypic diversity between 0.1 h-1 and 0.6 h-1 populations.
A mechanism of mutational divergence and adaptive convergence
Having determined the diverse phenotype, genotype and fitness properties of isolates, we next investigated whether the underlying mechanism(s) leading to the observed diversity could be identified. A recent mutation-selection balance model [16, 51] suggested that adaptive mutations with small fitness effects are able to fix only in combination with those of larger effects; independent mutations with small effects were thought to be suppressed as a result of clonal interference, particularly in large populations [52]. Applying this multiple mutation model, one would expect that later-arising isolates should contain beneficial mutations on top of previously accumulated beneficial mutations. By contrast, the convergent evolution mechanism proposed that multiple mutational pathways impacting on the same physiology could generate and maintain diversity within the evolving populations [7, 17].
We tested these notions in two ways. Firstly, if mutation-selection balance is important, all the evolved isolates within the same population should have originated from the most-fit initial clone. As found earlier, frequently the most-fit isolates harbored mutations in rpoS, which according to the previous studies using the same population, initially were present in all eight populations [35]. Therefore, we expected all evolved isolates to contain an rpoS mutation. However, contrary to the model, our maximum likelihood phylogenetic analysis showed that multiple lineages emerged independently in all populations and coexisted with the rpoS subpopulations (Figure 5). For example, mutations in rpoA, hfq and rssB occurred independently in the ancestor in population 3, separately from the rpoS isolates. In all eight populations, multiple beneficial mutations routinely accumulated in competing lineages, rarely resulting in the complete clonal replacement of subpopulations. These results suggest clonal interference is not strong enough to prevent eventual accumulation of competitors, as was also found in evolving asexual yeast populations [53].
Secondly, we investigated the function of mutated genes within each population to test whether our results fit with the convergent evolution proposal [7, 17]. As shown in Figure 6, most of the 85 mutations in Table S4 in Additional file 1 (about 55% in 0.1 and 30% in 0.6 h-1 populations) were located in genes with regulatory functions according to COG (Cluster of Orthologous Groups) functional categories. These results confirm the earlier studies by Conrad et al. [21] and Barrick et al. [41], that regulatory mutations provide a main route of mutational adaptation. Mutations in rpoA, rpoB, csgD, rssB and fhlA as well as previously identified mutations in regulatory genes hfq, rpoS, mglD (galS) and malT were the most frequent (Table 2; Table S4 in Additional file 1). In addition to controlling glucose transport, several of these genes are involved in resetting the trade-off between stress protection and nutritional competence (abbreviated as SPaNC [34]). Mutations in csgD, rssB, cyaA, hfq and rpoS, as well as possibly rpoA and rpoB, affect regulation of the general stress response directly or indirectly. Mutations in rpoS were the most common, appearing in six out of eight populations. This gene directly, and the others indirectly, influence the SPaNC balance [13, 34]. Hence, an altered SPaNC trade-off, which can allow co-existence of multiple types [13, 34], is a common event in the eight populations.
Newly found mutations affecting the SPaNC balance include two distinct mutations, a SNP and an IS1 insertion, in the transcribed message of csgD in isolates from three populations. The csgD gene encodes a DNA-binding transcription activator that regulates several genes involved in biofilm formation and also influences σS [54, 55]. Also, in addition to a previously identified mutation in hfq [48], we found two further alleles of hfq at slow growth rates affecting translation of σS . We also found one change in rssB relative to the ancestor; RssB facilitates and regulates degradation of σS by the protease ClpXP under nutrient-sufficient conditions [56]. RssB can also inhibit σS function even in the absence of proteolysis, possibly via stoichiometric binding [56]. An immuno-blot analysis with an anti-σS serum also indicated that these csgD, rssB and hfq isolates indeed contain lowered σS levels (Figure S2 in Additional file 1), suggesting that retuning of the SPaNC trade-off may be the common consequence of the different regulatory mutations. The convergent evolution through diverged mutational pathways has also been identified in E. coli populations during thermal adaptation [57].
A combination of phenotypic and genotypic analysis has revealed the considerable extent of diversity that develops in eight glucose-limited chemostat populations. A simple phenotypic screen revealed 11 PC groups with 10 sharing similar properties across more than one population. The genomic diversity was even greater, because several mutational means of achieving the same phenotype were possible within PC groups. Only 2 out of 48 isolates had the same combination of mutations, revealing the extent of diversity present in glucose-limited E. coli populations. Even strains from the same PC group and the same population generally contained many mutational differences; our limited sampling means we have not covered all possibilities. We have identified many new mutations in addition to those that have been identified in earlier studies of glucose-limited populations (rpoS, ptsG, mglD, malT, hfq) [48, 58]. Especially intriguing are potential global regulatory mutations in rpoA, csgD, and rssB as well as mutations in lpx genes, fimA, frvB and ptsP affecting membranes and surface functions. These mutations will be individually studied for fitness and physiological effects in future studies. The main emphasis in this discussion though is to discuss the impact of these results on the previously identified diversification mechanisms and then the novel differences in evolutionary paths between bacteria growing at two growth rates, especially to explain the greater phenotypic diversity at the slower growth rates.
How the new data fit with mechanisms explaining diversity and evolutionary coexistence
Some mechanisms of diversification involve ecological interactions exhibited as frequency-dependence [11]. This includes niche creation [11, 12] and frequency-dependent coexistence due to cross-feeding, where the cross-feeder organism is maintained in balance with the producer [59], or through cannibalism of one or the other coexisting type [28]. Another frequency-dependent scenario is when two or more resources in a system result in specialization of different bacteria on different resources in the same environment [11, 60]. None of these ecological scenarios are readily extended to the current studies. Cross-feeding and cannibalism do not appear to have evolved in the chemostats, probably because the time-scale is relatively short. None of the sequenced mutations hint at cross-feeding, such as the acs mutation noted in [8]. There is no evidence for multiple resources, multiple niches or mutations enhancing uptake or regulation of alternative carbon sources besides glucose. Nevertheless, isolates that are less fit than the ancestor at a 50:50 population ratio do show negative frequency dependence, such as BW4005. This PC class is a minority in the 0.1 h-1 populations and absent from the 0.6 h-1 populations, so frequency-dependent selection is present. At the moment we have no ecological or mechanistic explanation of the frequency dependence or the mutations in BW4005. Likewise, the mutations in 2W2, 3X16, 3X31, 4R10, and 4R18 do not indicate the frequency-dependence mechanism responsible. Perhaps ecological interactions are not essential for frequency dependence, which may be a manifestation of internal metabolic or regulatory differences. Recent studies suggest the appearance of frequency-dependence can be due to complex fitness-growth rate relationships determined by altered patterns of gene expression [50]. Further investigation is needed to determine whether the mutations in the frequency-dependent isolates show non-linear fitness-growth rate relationships.
The diversity in an evolving population is dependent on the rate of appearance of mutations and the elimination of mutations through selection of the fittest types. Mutation-selection balance is therefore a potential contributor to the coexistence of many types [15, 16, 51]. The genomics indicates that in a large population such as ours with 1010 bacteria, multiple mutational lineages arise directly from the ancestor; hfq, rpoA, rpoS mutations and other co-existing mutations in various populations do not appear on a sequential basis and clonal inference is not sufficient to eliminate the contending lineages. This is in agreement with the model for events in a large population [53, 61]. If mutation-selection balance drives diversity, the mutation rate difference between slow- and fast-growing populations should contribute to greater diversity at 0.1 h-1. A difference in diversity was indeed seen, but the problem is that the fitness properties of the isolates are also different. An even more dramatic case of altered mutation supply is with the mutY mutator sub-populations in two populations, with ten times as many mutations as the rest of the isolates. Contrary to the expectation that these high mutation rate lineages should diversify more readily by mutation selection balance, we do not see this result. Indeed, in time points subsequent to the one studied here, the mutator sub-populations do not diversify or take over populations (Figure S1 in Additional file 1). So overall, firm conclusions on mutation-selection balancing as a factor in chemostat populations are not possible, but it is unlikely to be the dominant contributor to the observed divergence.
Trade-offs in metabolism [33, 62], rate-yield [63, 64], and stress-nutrition [13, 34] have all been postulated to lead to alternative evolutionary solutions, even in uniform environments. More recently, combinations of trade-offs have been modeled [10] to explain the level of diversity seen in chemostat populations. Several trade-offs indeed change in chemostat evolution and result in isolates with altered stress resistance-nutrition properties as well as changes in growth rate and yield [33, 65]. Even more recently, trade-offs with differences in mutational robustness rate have also been postulated to lead to diversity in evolving populations [14]. Since all of the proposed ingredients (trade-offs, mutation rate differences) are present in chemostat populations as demonstrated in this study, our results are not inconsistent with these proposals. A mathematical model of the SPaNC trade-off, which is being altered by mutation in these populations, does suggest that coexistence of types with distinct settings of the SPaNC balance can be demonstrated [13]. The various hfq, rssB, rpoS and possibly rpoA mutants do offer alternative settings of the SPANC balance, so may contribute to heterogeneity by providing alternative SPaNC settings. The characterization of the settings and experimental coexistence studies require further work, but trade-offs do offer possibilities for heterogeneity in the eight populations. This is not a blanket explanation, however, because there are isolates in all populations that do not have changes to RpoS levels and SPaNC balances. Here again, this is a partial explanation of diversity.
Finally, a simple explanation of coexistence is the convergent evolution of genetically distinct mutants that have achieved a comparable level of fitness. A particular level of fitness (as measured against ancestor) could be reached in strains having entirely different genomic changes (Figure 4). Bacteria exhibiting alternative regulatory and metabolic pathways and mutational degeneracy in these pathways can lead to co-existing organisms in nutrient-limited chemostat populations [7, 17, 33, 58]. As already noted previously, mutations in hfq and rpoS can both reset the stress-nutrition trade-off towards better nutrient uptake [17]. Furthermore, in line with a recent study [57], the presence of mutations in rpoA, csgD, rssB and fimA, each in different members of the same PC group, indicates that there are degenerate means of evolving similar properties within a complex regulatory network. These changes can also overlap with the trade-offs discussed above, so alternative SPaNC settings can be arrived at in degenerate ways.
The effects of a difference in growth rate in the selection environment
It is instructive to compare adaptational events at two chemostat dilution rates but with otherwise identical ancestors, conditions and sole resources in the shape of glucose. As evident in PCs and with mutations, an adaptive advantage at one growth rate does not necessarily provide an advantage under both growth conditions. There was already evidence that malT-con mutations are deleterious at faster growth rates [35], and this study confirms this, as no such mutations were selected in any of the fast-growth populations. It is not clear why malT mutations are deleterious at faster growth rates, but the finding of malT-negative mutations in batch culture selection with glucose [30] suggests that there is a negative effect of MalT on fitness when growing fast on glucose.
Another example of a gene with mutations beneficial and selected at 0.1 h-1 but not at 0.6 h-1 is hfq. All three evolved alleles of this gene provided a benefit at 0.1 h-1, which occurs through multiple pleiotropic effects [48]. The mutations were found to be deleterious at 0.6 h-1 [50], explaining their absence from 0.6 h-1 cultures. This fitness difference is likely to be because global gene expression alters with different dilution rates [66] and the role of Hfq changes. We postulate that hfq mutations, through numerous epistatic effects, are beneficial when cells are more highly stressed (at 0.1 h-1) than when growing faster (at 0.6 h-1).
Fitness in evolved isolates is by no means uniform in coexisting types or at different growth rates. The fitness distributions as well as the diversity differ between slow- and fast-growth populations. The high proportion of regulatory mutations observed in the 0.1 h-1 but not the 0.6 h-1 population (Figure 6) is consistent with a different global-regulatory background and demonstrates differences in gene expression between the two conditions [66]. The greater phenotypic diversity in the 0.1 h-1 cultures may well reflect a greater range of regulatory mutations observed at the slower growth rate.
E. coli has at its disposal a redundant set of fitness strategies under glucose limitation involving various combinations of mutations. These permit concurrent evolution of distinct lineages within the same population. The simplest explanation is that fitness arose through multiple lineages of beneficial mutations that did not fully eliminate competitors and invariably led to diversification within the eight clonal cultures. The kind of mutants and level of diversity were influenced by the growth rate of cultures. In total, our results suggest that large bacterial populations are a diverse collection of clonal lineages in most settings, complex or otherwise. Therefore, it is likely that in a natural habitat with a large population size, bacteria develop enormous sympatric variation. This is likely to be of considerable importance in clinical populations of bacteria that exhibit diversification within the lifetime of an infection [3].
Materials and methods
Bacterial strains and growth conditions
For the experimental evolution, the E. coli K-12 strain BW2952, an MC4100 derivative, was propagated at growth rates (dilution rates) of 0.1 h-1 and 0.6 h-1 in a glucose-limited chemostat exactly as described in [35]. Individuals from the culture were isolated and propagated as in [6]. At least 39 randomly picked isolates (colonies) from days specified in the text were obtained by streaking chemostat samples on nutrient agar.
Phenotypic assays, classification of phenotypic properties, cluster analysis
The changes in chemostat isolates resulting in rpoS mutations were detected by iodine staining as previously described [67] and confirmed by growth tests on acetate plates [65]. The method for assaying malG-lacZ fusion b-galactosidase activity for mal regulation was described in [67]. The growth conditions for the sensitivity assay for methyl a-glucoside (α-MG) were as described in [68]. Sensitive isolates had an inhibition zone of over 2.5 cm compared to the resistant ancestral strain BW2952.
We then used these triple-phenotypic test results to classify evolved isolates using the criteria shown in Table S2 in Additional file 1. We related differences in the phenotypic properties of E. coli chemostat isolates by using the neighbor-joining method of tree construction based on distance estimated by using PAST [69].
Genome re-sequencing
Chromosomal DNA was prepared as previously described [36]. Libraries for Illumina paired-end sequencing were prepared using the protocols provided by Illumina. A Solexa Genome Analyzer IIx (Illumina, Little Chesterford, Essex, UK) was used to sequence each isolate to a depth of between 96- and 159-fold coverage. The 101 bp Solexa reads generated, after removal of the duplication, were mapped to the E. coli BW2952 genome [GenBank:NC_012759] to generate the assembly using BWA [70] with the default parameter, which allows 4% mismatches. The Smith-Waterman algorithm was used to rescue the unmapped end. All the reads with extremely large or small insert size (< 20% or > 200% of normal) were mapped again using BLASTn with an e-value of 0.00001 and the -F F flag. Only the read pairs mapped to a proper insert length and with at least one end of the pairs mapped to non-repeat regions were taken into account. For positions in doubt, due to sequence coverage reduction and in repeat regions, Sanger sequencing was used to resolve the sequence. SAMtools [71] was used to calculate the per-position coverage and base calls for each position.
SNPs in the sequenced strain were called where a position was covered by at least ten reads with at least 80% of the covering reads showing the same mismatch. Other suspected positions were sequenced using Sanger methodology.
Small indels were identified from the alignment between reads and the reference genome. Cases where an indel occurred within homopolymer tracts were manually examined, as BWA and BLASTn alignment can place such indels at different positions along the homopolymer. Indels in homopolymers are not subject to elevated error or bias in Solexa sequencing [72]. Therefore, small indels could be reliably resolved by the set of individual Solexa reads spanning the homopolymer region.
Large insertion, duplication or deletion events were also detected from the alignment between reads and the reference genome. Deletions were determined as regions that were not covered by any single read and flanked by read pairs with extremely large insert size. The positions of the large insertions or duplications, such as IS movement or copy number variation in the sequenced genome relative to the reference genome, could be detected as read pairs aligned around the insert positions that have one of their ends unaligned or aligned to other regions that represent the beginning or end of the inserted sequence.
Estimation of diversity index
Simpson's diversity index (D) was determined using the following equation:
where 'n' is the total number of isolates of a particular phenotype and 'N' is the total number of isolates of all phenotypes.
Pair-wise competition experiments and estimation of selection coefficients (S) and estimation of fitness
To estimate the selection coefficient, each evolved isolate was competed with the metC::Tn10 derivative of the ancestral strain (BW2952) in the same conditions in which isolates had evolved. In each case, both evolved isolates and reference strains were grown independently overnight in glucose-limited chemostats in modified Mcbride agar (MMA) medium supplemented with 0.02% (w/v) glucose plus 4 μg/ml methionine to ensure they were in the same physiological conditions as well as reacclimatize the selection condition. The selection coefficient (S) of evolved isolates was estimated using at least three time points with R2 ≥ 0.95 as previously described in [6]. The selection coefficient for evolved isolates was determined from the slope of the linear regression of ln[p(t)/q(t)] against at least three time points [73], where p(t) and q(t) are the proportions of two competing strains at time t hours. Since, competition experiments were performed under two different dilution rates, the fitness of evolved isolates is expressed as the selection per generation, Sg (calculated as (ln 2)S/D, where D is dilution rate) [73].
Cluster analysis
The dendrograms were constructed based on mutations identified by whole genome resequencing using the maximum parsimony method in PAST [69].
Estimation of mutation accumulation rates
MARs were estimated using the following equation as described in [52]:
where k is the average number observed mutations, T is the number of generations, Ne is the effective population size and Sg is the selection coefficient per generation.
Data availability
The sequencing data generated in this study have been deposited at the DNA Data Bank of Japan under accession number DRA000563.
- α-MG:
methyl a-glucoside
- bp:
base pair
Cluster of Orthologous Groups
insertion sequence
mutation accumulation rate
- nsSNP:
non-synonymous SNP
phenotypic class
single nucleotide polymorphism
- SPaNC:
stress protection and nutritional competence.
Feil EJ: Small change: keeping pace with microevolution. Nat Rev Microbiol. 2004, 2: 483-495. 10.1038/nrmicro904.
Wirth T, Falush D, Lan RT, Colles F, Mensa P, Wieler LH, Karch H, Reeves PR, Maiden MCJ, Ochman H, Achtman M: Sex and virulence in Escherichia coli: an evolutionary perspective. Mol Microbiol. 2006, 60: 1136-1151. 10.1111/j.1365-2958.2006.05172.x.
Levert M, Zamfir O, Clermont O, Bouvet O, Lespinats S, Hipeaux M, Branger C, Picard B, Saint-Ruf C, Norel F, Balliau T, Zivy M, Le Nagard H, Cruvellier S, Chane-Woon-Ming B, Nilsson S, Gudelj I, Phan K, Ferenci T, Tenaillon O, Denamur E: Molecular and evolutionary bases of within-patient genotypic and phenotypic diversity in Escherichia coli extraintestinal infections. PLoS Path. 2010, 6: e1001125-10.1371/journal.ppat.1001125.
Hardin G: Competitive exclusion principle. Science. 1960, 131: 1292-1297. 10.1126/science.131.3409.1292.
Kassen R, Rainey PB: The ecology and genetics of microbial diversity. Annu Rev Microbiol. 2004, 58: 207-231. 10.1146/annurev.micro.58.030603.123654.
Maharjan R, Seeto S, Notley-McRobb L, Ferenci T: Clonal adaptive radiation in a constant environment. Science. 2006, 313: 514-517. 10.1126/science.1129865.
Gresham D, Desai MM, Tucker CM, Jenq HT, Pai DA, Ward A, DeSevo CG, Botstein D, Dunham MJ: The repertoire and dynamics of evolutionary adaptations to controlled nutrient-limited environments in yeast. PLoS Genet. 2008, 4: e1000303-10.1371/journal.pgen.1000303.
Kinnersley MA, Holben WE, Rosenzweig F: E Unibus Plurum: genomic analysis of an experimentally evolved polymorphism in Escherichia coli. PLoS Genet. 2009, 5: e1000713-10.1371/journal.pgen.1000713.
Levin BR: Frequency dependent selection in bacterial populations. Phil Trans R Soc B. 1988, 319: 459-472. 10.1098/rstb.1988.0059.
Doebeli M, Ispolatov I: Complexity and diversity. Science. 2010, 328: 494-497. 10.1126/science.1187468.
Friesen ML, Saxer G, Travisano M, Doebeli M: Experimental evidence for sympatric ecological diversification due to frequency-dependent competition in Escherichia coli. Evolution. 2004, 58: 245-260.
Rosenzweig RF, Sharp RR, Treves DS, Adams J: Microbial evolution in a simple unstructured environment: Genetic differentiation in Escherichia coli. Genetics. 1994, 137: 903-917.
Gudelj I, Weitz JS, Ferenci T, Horner-Devine MC, Marx CJ, Meyer JR, Forde SE: An integrative approach to understanding microbial diversity: from intracellular mechanisms to community structure. Ecol Lett. 2010, 13: 1073-1084. 10.1111/j.1461-0248.2010.01507.x.
Beardmore RE, Gudelj I, Lipson DA, Hurst LD: Metabolic trade-offs and the maintenance of the fittest and the flattest. Nature. 2011, 472: 342-346. 10.1038/nature09905.
Zhang X-S, Hill WG: Genetic variability under mutation selection balance. Trends Ecol Evol. 2005, 20: 468-470. 10.1016/j.tree.2005.06.010.
Desai MM, Fisher DS: Beneficial mutation selection balance and the effect of linkage on positive selection. Genetics. 2007, 176: 1759-1798. 10.1534/genetics.106.067678.
Wang L, Spira B, Zhou ZM, Feng L, Maharjan RP, Li XM, Li FF, McKenzie C, Reeves PR, Ferenci T: Divergence involving global regulatory gene mutations in an Escherichia coli population evolving under phosphate limitation. Genome Biol Evol. 2010, 2: 478-487. 10.1093/gbe/evq035.
Conrad TM, Lewis NE, Palsson BO: Microbial laboratory evolution in the era of genome-scale science. Mol Syst Biol. 2011, 7: 509-509.
Elena SF, Lenski RE: Evolution experiments with microorganisms: The dynamics and genetic bases of adaptation. Nat Rev Genet. 2003, 4: 457-469.
Barrick JE, Lenski RE: Genome-wide mutational diversity in an evolving population of Escherichia coli. Cold Spring Harb Symp Quant Biol. 2009, 74: 119-129. 10.1101/sqb.2009.74.018.
Conrad TM, Joyce AR, Applebee MK, Barrett CL, Xie B, Gao Y, Palsson BO: Whole-genome resequencing of Escherichia coli K-12 MG1655 undergoing short-term laboratory evolution in lactate minimal media reveals flexible selection of adaptive mutations. Genome Biol. 2009, 10: R118-10.1186/gb-2009-10-10-r118.
Brockhurst MA, Rainey PB, Buckling A: The effect of spatial heterogeneity and parasites on the evolution of host diversity. Proc R Soc Lond Ser B-Biol Sci. 2004, 271: 107-111. 10.1098/rspb.2003.2556.
Kerr B, Riley MA, Feldman MW, Bohannan BJM: Local dispersal promotes biodiversity in a real-life game of rock-paper-scissors. Nature. 2002, 418: 171-174. 10.1038/nature00823.
Rainey PB, Travisano M: Adaptive radiation in a heterogeneous environment. Nature. 1998, 394: 69-72. 10.1038/27900.
Barrett RDH, MacLean RC, Bell G: Experimental evolution of Pseudomonas fluorescens in simple and complex environments. Am Nat. 2005, 166: 470-480. 10.1086/444440.
Habets M, Rozen DE, Hoekstra RF, de Visser J: The effect of population structure on the adaptive radiation of microbial populations evolving in spatially structured environments. Ecol Lett. 2006, 9: 1041-1048. 10.1111/j.1461-0248.2006.00955.x.
Tyerman J, Havard N, Saxer G, Travisano M, Doebeli M: Unparallel diversification in bacterial microcosms. Proc R Soc B Biol Sci. 2005, 272: 1393-1398. 10.1098/rspb.2005.3068.
Rozen DE, Philippe N, Arjan de Visser J, Lenski RE, Schneider D: Death and cannibalism in a seasonal environment facilitate bacterial coexistence. Ecol Lett. 2009, 12: 34-44. 10.1111/j.1461-0248.2008.01257.x.
Crozat E, Hindre T, Kuhn L, Garin J, Lenski RE, Schneider D: Altered regulation of the OmpF porin by Fis in Escherichia coli during an evolution experiment and between B and K-12 strains. J Bacteriol. 2011, 193: 429-440. 10.1128/JB.01341-10.
Philippe N, Crozat E, Lenski RE, Schneider D: Evolution of global regulatory networks during a long-term experiment with Escherichia coli. Bioessays. 2007, 29: 846-860. 10.1002/bies.20629.
Sniegowski PD, Gerrish PJ, Lenski RE: Evolution of high mutation rates in experimental populations of E. coli. Nature. 1997, 387: 703-705. 10.1038/42701.
Notley-McRobb L, Seeto S, Ferenci T: Enrichment and elimination of mutY mutators in Escherichia coli populations. Genetics. 2002, 162: 1055-1062.
Maharjan R, Seeto S, Ferenci T: Divergence and redundancy of transport and metabolic rate-yield strategies in a single Escherichia coli population. J Bacteriol. 2007, 189: 2350-2358. 10.1128/JB.01414-06.
Ferenci T: Maintaining a healthy SPANC balance through regulatory and mutational adaptation. Mol Microbiol. 2005, 57: 1-8. 10.1111/j.1365-2958.2005.04649.x.
Notley-McRobb L, Seeto S, Ferenci T: The influence of cellular physiology on the initiation of mutational pathways in Escherichia coli populations. Proc R Soc Lond Ser B Biol Sci. 2003, 270: 843-848. 10.1098/rspb.2002.2295.
Ferenci T, Zhou ZM, Betteridge T, Ren Y, Liu Y, Feng L, Reeves PR, Wang L: Genomic sequencing reveals regulatory mutations and recombinational events in the widely used MC4100 lineage of Escherichia coli K-12. J Bacteriol. 2009, 191: 4025-4029. 10.1128/JB.00118-09.
Gaffe J, McKenzie C, Maharjan RP, Coursange E, Ferenci T, Schneider D: Insertion sequence-driven evolution of Escherichia coli in chemostats. J Mol Evol. 2011, 72: 398-412. 10.1007/s00239-011-9439-2.
Miller JH: Spontaneous mutators in bacteria -insights into pathways of mutagenesis and repair. Annu Rev Microbiol. 1996, 50: 625-643. 10.1146/annurev.micro.50.1.625.
Ng PC, Henikoff S: SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003, 31: 3812-3814. 10.1093/nar/gkg509.
Herring CD, Raghunathan A, Honisch C, Patel T, Applebee MK, Joyce AR, Albert TJ, Blattner FR, van den Boom D, Cantor CR, Palsson BO: Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale. Nat Genet. 2006, 38: 1406-1412. 10.1038/ng1906.
Barrick JE, Yu DS, Yoon SH, Jeong H, Oh TK, Schneider D, Lenski RE, Kim JF: Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature. 2009, 461: 1243-U1274. 10.1038/nature08480.
Ochman H: Neutral mutations and neutral substitutions in bacterial genomes. Mol Biol Evol. 2003, 20: 2091-2096. 10.1093/molbev/msg229.
Chen JQ, Wu Y, Yang HW, Bergelson J, Kreitman M, Tian DC: Variation in the ratio of nucleotide substitution and indel rates across genomes in mammals and bacteria. Mol Biol Evol. 2009, 26: 1523-1531. 10.1093/molbev/msp063.
Drake JW, Charlesworth B, Charlesworth D, Crow JF: Rates of spontaneous mutation. Genetics. 1998, 148: 1667-1686.
Rozen DE, de Visser J, Gerrish PJ: Fitness effects of fixed beneficial mutations in microbial populations. Curr Biol. 2002, 12: 1040-1045. 10.1016/S0960-9822(02)00896-5.
de Visser JAGM, Zeyl CW, Gerrish PJ, Blanchard JL, Lenski RE: Diminishing returns from mutation supply rate in asexual populations. Science. 1999, 283: 404-406. 10.1126/science.283.5400.404.
Blatter EE, Ross W, Tang H, Gourse RL, Ebright RH: Domain organization of RNA polymerase alpha subunit: C-terminal 85 amino acids constitute a domain capable of dimerization and DNA binding. Cell. 1994, 78: 889-896. 10.1016/S0092-8674(94)90682-3.
Maharjan R, Zhou ZM, Ren Y, Li Y, Gaffe J, Schneider D, McKenzie C, Reeves PR, Ferenci T, Wang L: Genomic identification of a novel mutation in hfq that provides multiple benefits in evolving glucose-limited populations of Escherichia coli. J Bacteriol. 2010, 192: 4517-4521. 10.1128/JB.00368-10.
Notley-McRobb L, Ferenci T: The generation of multiple coexisting mal-regulatory mutations through polygenic evolution in glucose-limited populations of Escherichia coli. Environ Microbiol. 1999, 1: 45-52. 10.1046/j.1462-2920.1999.00003.x.
Maharjan R, McKenzie C, Yeung A, Ferenci T: The basis of antagonistic pleiotropy in hfqmutations that have opposite effects on fitness at slow and fast growth rates. Heredity. 2012,
Desai MM, Fisher DS, Murray AW: The speed of evolution and maintenance of variation in asexual populations. Curr Biol. 2007, 17: 385-394. 10.1016/j.cub.2007.01.072.
Perfeito L, Fernandes L, Mota C, Gordo I: Adaptive mutations in bacteria: High rate and small effects. Science. 2007, 317: 813-815. 10.1126/science.1142284.
Kao KC, Sherlock G: Molecular characterization of clonal interference during adaptive evolution in asexual populations of Saccharomyces cerevisiae. Nat Genet. 2008, 40: 1499-1504. 10.1038/ng.280.
Gualdi L, Tagliabue L, Landini P: Biofilm formation-gene expression relay system in Escherichia coli: Modulation of sigma(s)-dependent gene expression by the CsgD regulatory protein via sigma(s) protein stabilization. J Bacteriol. 2007, 189: 8034-8043. 10.1128/JB.00900-07.
White AP, Weljie AM, Apel D, Zhang P, Shaykhutdinov R, Vogel HJ, Surette MG: A Global metabolic shift is linked to Salmonella multicellular development. PLoS ONE. 2010, 5: e11814-10.1371/journal.pone.0011814.
Becker G, Klauck E, Hengge-Aronis R: The response regulator RssB, a recognition factor for sigma(s) proteolysis in Escherichia coli, can act like an anti-sigma(s) factor. Mol Microbiol. 2000, 35: 657-666.
Tenaillon O, Rodriguez-Verdugo A, Gaut RL, McDonald P, Bennett AF, Long AD, Gaut BS: The molecular diversity of adaptive convergence. Science. 2012, 335: 457-461. 10.1126/science.1212986.
Ferenci T: Bacterial physiology, regulation and mutational adaptation in a chemostat environment. Adv Microb Physiol. 2007, 53: 169-229.
Doebeli N: A model for the evolutionary dynamics of cross-feeding polymorphisms in microorganisms. Popul Ecol. 2002, 44: 59-70. 10.1007/s101440200008.
Dykhuizen DE, Dean AM: Evolution of specialists in an experimental microcosm. Genetics. 2004, 167: 2015-2026. 10.1534/genetics.103.025205.
Park SC, Krug J: Clonal interference in large populations. Proc Natl Acad Sci USA. 2007, 104: 18135-18140. 10.1073/pnas.0705778104.
Gudelj I, Beardmore RE, Arkin SS, Maclean RC: Constraints on microbial metabolism drive evolutionary diversification in homogeneous environments. J Evol Biol. 2007, 20: 1882-1889. 10.1111/j.1420-9101.2007.01376.x.
Pfeiffer T, Schuster S, Bonhoeffer S: Cooperation and competition in the evolution of ATP-producing pathways. Science. 2001, 292: 504-507. 10.1126/science.1058079.
Novak M, Pfeiffer T, Lenski RE, Sauer U, Bonhoeffer S: Experimental tests for an evolutionary trade-off between growth rate and yield in E. coli. Am Nat. 2006, 168: 242-251. 10.1086/506527.
King T, Ishihama A, Kori A, Ferenci T: A regulatory trade-off as a source of strain variation in the species Escherichia coli. J Bacteriol. 2004, 186: 5614-5620. 10.1128/JB.186.17.5614-5620.2004.
Ferenci T: Regulation by nutrient limitation. Curr Opin Microbiol. 1999, 2: 208-213. 10.1016/S1369-5274(99)80036-8.
Notley L, Ferenci T: Differential expression of mal genes under cAMP and endogenous inducer control in nutrient stressed Escherichia coli. Mol Microbiol. 1995, 16: 121-129. 10.1111/j.1365-2958.1995.tb02397.x.
Notley-McRobb L, Ferenci T: Substrate specificity and signal transduction pathways in the glucose-specific enzyme II (EIIGlc) component of the Escherichia coli phosphotransferase system. J Bacteriol. 2000, 182: 4437-4442. 10.1128/JB.182.16.4437-4442.2000.
Hammer O, Harper DAT, Ryan PD: PAST: Palaeontological Statistics software package for education and data analysis. Palaeontologica Electronica. 2001, 4: Article 4
Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25: 1754-1760. 10.1093/bioinformatics/btp324.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25: 2078-2079. 10.1093/bioinformatics/btp352.
Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, Hall KP, Evers DJ, Barnes CL, Bignell HR, Boutell JM, Bryant J, Carter RJ, Cheetham RK, Cox AJ, Ellis DJ, Flatbush MR, Gormley NA, Humphray SJ, Irving LJ, Karbelashvili MS, Kirk SM, Li H, Liu XH, Maisinger KS, Murray LJ, Obradovic B, Ost T, Parkinson ML, Pratt MR, et al: Accurate whole human genome sequencing using reversible terminator chemistry. Nature. 2008, 456: 53-59. 10.1038/nature07517.
Dykhuizen DE, Hartl DE: Selection in chemostats. Microbiol Rev. 1983, 47: 150-168.
Tatusov RL, Koonin EV, Lipman DJ: A genomic perspective on protein families. Science. 1997, 278: 631-637. 10.1126/science.278.5338.631.
We thank Shona Seeto for the initial characterization of evolved strains. The research was supported by a research grant from the Australian Research Council to TF and PRR. The work was also supported by the Chinese National Science Fund for Distinguished Young Scholars (30788001); the National Natural Science Foundation of China (NSFC) Key Program Grant (31030002); the Fundamental Research Funds for the Central Universities (65020121 and 65020061); the National 973 Program of China Grant (2009CB522603, 2011CB504900, 2012CB721101 and 2012CB721001); and the National Key Programs for Infectious Diseases of China (2009ZX10004-108).
Additional information
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
RM carried out the phenotypic studies, as well as analyses of fitness in chemostats and mutation effects. TF conceived of the study, and participated in its design and coordination and wrote the draft manuscript. PR was involved in data analysis and paper writing. YL and BL carried out sequencing and analysis. LW participated in the design of the study and coordinated the genome sequencing and data analysis. All authors read and approved the final manuscript.
Electronic supplementary material
Additional file 1: Supplementary Tables S1 to S4 and Figures S1 and S2. Table 1: phenotypic characteristics of 320 E. coli isolates evolving under glucose-limited limited. Table 2: groupings of phenotypic properties found in eight glucose-limited populations of E. coli. Table 3: clones chosen for full genome resequencing. Table 4: genomic changes detected in the 44 non-mutator evolved clones. Figure 1: maintenance of phenotypic diversity in an E. coli population evolving in a glucose-limited chemostat. Figure 2: relative level of RpoS protein in glucose-limited chemostats. (DOC 452 KB)
Additional file 2: SNPs of 48 strains compared with BW2952. Sheet 1: SNPs of slow-growth isolates. Sheet 2: SNPs of fast-growth isolates. Sheet 3: the SNPs of isolates with mutY mutator. (XLS 100 KB)
Authors' original submitted files for images
Below are the links to the authors' original submitted files for images.
Rights and permissions
About this article
Cite this article
Maharjan, R.P., Ferenci, T., Reeves, P.R. et al. The multiplicity of divergence mechanisms in a single evolving population. Genome Biol 13, R41 (2012). https://doi.org/10.1186/gb-2012-13-6-r41
DOI: https://doi.org/10.1186/gb-2012-13-6-r41
- Dilution Rate
- Selection Coefficient
- Mutational Robustness
- rpoS Mutation
- Clonal Interference


text:
- Sofas & Sectionals
- American Leather Kendall
American Leather Kendall
Creativity knows no bounds with the Kendall collection, a grouping with enough potential configurations to solve any decorating dilemma. Options include a four seat sofa, a double deep sofa, an armless version and more. Tailored lines foster still more versatility, along with contemporary design details such as high track arms and angled back cushions that rise above the frame. And for a designer look, contrasting seat and back cushions are available.

subset: commoncrawl


text:
The Master Chemical Mechanism
MCMv3.2 Archive site
The Master Chemical Mechanism (MCM) is a near-explicit chemical mechanism which describes the detailed gas-phase chemical processes involved in the tropospheric degradation of a series of primary emitted volatile organic compounds (VOCs). Currently, the degradation of methane and 142 non-methane VOCs is represented.
The MCM was originally developed to provide accurate, robust and up-to-date information concerning the role of specific organic compounds in ground-level ozone formation in relation to air quality policy development in Europe. However, it also provides a research tool for investigating other areas where a detailed representation of the chemistry is required, e.g. the generation of distributions of speciated radical and closed-shell intermediates formed during VOC degradation.
The main intention of this web site is to provide a flexible, easily utilised platform for the MCM that is readily accessed by the research and user communities, and to help promote its development and validation.
Browse the mechanismView the list of primary VOCs from which you can navigate through the mechanism either from reactants to products, or vice-versa. Help
Construction methodologyDetailed information about the MCM and its history. New in MCMv3.3.1
Search the mechanismEnter a SMILES string or MCM name to jump straight to matching species in the mechanism. Help
MCM WorkshopsInformation and presentations from the MCM development and user workshops.
Extract a subsetSelect your own list of primary species and extract a complete mechanism for them. Mechanisms can be viewed as HTML or exported in a range of formats. Help
TutorialsLearn more about how to use the MCM. Tutorials are available on using the MCM with either AtChem Online or FACSIMILE
AtChem Online Tutorial
Facsimile Tutorial
DownloadFACSIMILE input files for box model and trajectory model runs are available for current and previous versions of the MCM.
EUPHORE Chamber SimulationsNotes on simulating EUPHORE chamber experiments.
CitationDetails of the correct citation for MCM
ToolsVarious tools for use with the MCM
LinksUseful tropospheric chemistry links.
CRIAccess to the Common Representative Intermediates mechanism (CRI v2) via a parallel searchable and extractable facility
MCM ArchiveArchived website for MCMv3.2
FeedbackJoin the MCM mailing list or contact the maintainers directly.


text:
Financial Accounting Foundation Appoints Three New Members to Its Board of TrusteesNorwalk, CT, November 16, 2011—The Financial Accounting Foundation (FAF) today announced that Paul G. Camell, W. Daniel Ebersole, and Michelle R. Seitz have been appointed to the FAF Board of Trustees. The FAF is the independent, private-sector organization responsible for the oversight of the Financial Accounting Standards Board (FASB) and the Governmental Accounting Standards Board (GASB).
"The FAF Board of Trustees is proud to welcome its newest Board members Paul, Dan, and Michelle," said FAF Chairman John J. Brennan. "Their wide-ranging experience and extensive financial backgrounds will add significant value to the Trustees as we work to support the FASB and GASB's mission of independent and open standard setting."
Paul G. Camell, MBA, is executive vice president of mergers & acquisitions and chief administrative officer for CDM, a global consulting, engineering, construction, and operations firm. Mr. Camell previously served as the firm's chief financial officer and senior vice president of finance.
Dan Ebersole served as the state treasurer for the state of Georgia from 1997 to 2010, and was responsible for investing more than $14 billion of state and local government funds and administering two local government investment pools. Mr. Ebersole has more than 30 years of experience in both the executive and legislative branches of Georgia state government and served as a member of the Governmental Accounting Standards Advisory Council (GASAC) from 2003 to 2010. He was chairman of the group from 2008 until 2010.
Michelle R. Seitz, CFA, is a member of the Executive Committee of William Blair & Company, L.L.C. Ms. Seitz has more than 24 years of investment experience and leads William Blair Investment Management, consisting of the institutional, mutual fund, and private wealth management businesses.
All three Trustees will serve a five-year term that begins January 1, 2012.
About the Financial Accounting Foundation
The FAF is responsible for the oversight, administration, and finances of both the Financial Accounting Standards Board (FASB) and its counterpart for state and local government, the Governmental Accounting Standards Board (GASB). The Foundation is also responsible for selecting the members of both Boards and their respective Advisory Councils.


text:
DTP PMC Meeting, December 16, 2008
← Back to DTP PMC Meeting Page
- Brian Fitzpatrick
- Linda Chan
- John Graham
- Sheila Sholars
- Brian Payton
- Linda's AC nomination
- EclipseCon schedule
- Webinar in March 2009? 40-45 minute technical presentation and demo, 15 minutes of questions at the end. Lynn was shooting for March 5 at 12:00 pm EST as a starting point. She's looking for one person to present, and another to type up responses to questions as they come up during the presentation.
- What do we do with the New Table Wizard? No response from NEC.
- No meetings for next two weeks, so next meeting on January 6, 2009
- Open discussion
- Discussed possibly doing the Webinar on the SQL Query Builder and having Duncan lead it, with some help from Linda. Showing SQB functionality and how it integrates with BIRT. Will discuss further in January.
- Ask Brian Payton & Duncan to make their SQB plan more public (the spreadsheet) (done here http://wiki.eclipse.org/SQB_Galileo_Work_in_Progress)
- For the New Table wizard... Do the following in January
- 1) Send a final e-mail to NEC explaining the situation and saying we would remove committer rights if we don't hear anything (give a deadline).
- 2) Send a message to the newsgroup/mailing lists explaining the situation, that we are going to archive the wizard unless someone steps up to maintain the code (do bug fixes/enhancements).
- Add Bugzilla Versions for 1.6.2 and 1.7 (done)


text:
MULVANE, Kan. (AP) A parade planned in a southern Kansas town turned instead to a vigil after a 15-year-old girl died before she could return home.
Residents in Mulvane decorated Main Street on Tuesday to welcome home Taylor Heersche, who had battled leukemia. The town rallied for four years to support Taylor and her family.
But Taylor died at Children's Mercy Hospital in Kansas City just hours before she was to go home.
Taylor, a sophomore at Mulvane High School, was diagnosed with the leukemia in May 2008, when she was 10.
Her mother, DeAnne Heersche, told The Wichita Eagle the disease returned last December in a genetic mutation that couldn't be cured.
Services are Sunday at Central Community Church in Wichita, with a private burial Monday.


text:
Research around home destruction vs. home survival in wildfires point to embers and small flames as the main way that the majority of homes ignite in wildfires. Embers are burning pieces of airborne wood and/or vegetation that can be carried more than a mile through the wind can cause spot fires and ignite homes, debris and other objects.
There are methods for homeowners to prepare their homes to withstand ember attacks and minimize the likelihood of flames or surface fire touching the home or any attachments. Experiments, models and post-fire studies have shown homes ignite due to the condition of the home and everything around it, up to 200' from the foundation. This is called the Home Ignition Zone (HIZ).
Learn more about how wildfires spread and ignite home in our online course Understanding the Wildfire Threat to Homes. An overview of fire history, fire basics, and how homes burn.
Get some wildfire risk reduction steps that can make your home safer during a wildfire.
Download the fact sheet.
This fact sheet is also available in Spanish.
The concept of the home ignition zone was developed by retired USDA Forest Service fire scientist Jack Cohen in the late 1990s, following some breakthrough experimental research into how homes ignite due to the effects of radiant heat. The HIZ is divided into three zones.
The home and the area 0-5' from the furthest attached exterior point of the home; defined as a non-combustible area. Science tells us this is the most important zone to take immediate action on as it is the most vulnerable to embers. START WITH THE HOUSE ITSELF then move into the landscaping section of the Immediate Zone.
5-30' from the furthest exterior point of the home. Landscaping/hardscaping- employing careful landscaping or creating breaks that can help influence and decrease fire behavior
30-100 feet, out to 200 feet. Landscaping – the goal here is not to eliminate fire but to interrupt fire's path and keep flames smaller and on the ground.
*The distances listed for crown spacing are suggested based on NFPA 1144. However, the crown spacing needed to reduce/prevent crown fire potential could be significantly greater due to slope, the species of trees involved and other site specific conditions. Check with your local forestry professional to get advice on what is appropriate for your property.


text:
The Associate Reformed Presbyterian Church as we know it today comes to us across a lengthy history of service in two lands, the British Isles and North America. It had its beginnings in the preaching of John Knox in Scotland when the Scottish Church became the official church of Scotland in 1560 A.D. As always the case when the church and state become too closely allied, controversy and bitter strife over control became a way of life for church and state alike.
Things improved somewhat under King William III in 1688 A.D. as he reorganized the Church of Scotland into the Established Presbyterian Church of Scotland. In spite of the improvement, however, a great number of problems still existed, and in 1733 a pastor by the name of Ebenezer Erskine led a group of Christians in forming a separate Associate Presbytery (from thence comes the first part of our name). Ten years later, another group of Christians who for years had suffered problems with the established church organized themselves into the Reformed Presbytery.
Both churches spread to Northern Ireland as the Scots were forced to emigrate and both came to America with those "Scots-Irish" folks. The immigrants came to the Pennsylvania area at first, and it was there that both the Associate and the Reformed Presbyteries of Pennsylvania were organized in the 1750-1770 time period.
It was a heady time in the new world, and all the "old alliances" were being called into question. The new America was emerging and at the same time our forefathers were seeking to create a new church as well. Formal union talks between the "Associates" and the "Reformed" began in 1777 and by 1782 the Associate Reformed Synod came to be in Philadelphia. This Synod, even though all "Associates" and "Reformeds" did not join, included churches in Pennsylvania, New York, Ohio, North and South Carolina and Georgia.
Eight years later, the Associate Reformed Presbytery of the Carolinas and Georgia was formed in Fairfield County, S.C., at the Old Brick Church (pictured above) followed some twenty years later (1803) by the division of the entire church into four Synods and one General Synod. The Synods were those of the Carolinas, Pennsylvania, New York and Scioto with the headquarters of the church in Philadelphia. In 1822 the Synod of the Carolinas was granted separate status, and by the end of the century was the sole remaining body of the Associate Reformed Presbyterian Church as several mergers over the years had absorbed the rest of the denomination into the old United Presbyterian Church. The remaining "A.R.P.s" in the Southeast continued on as the denomination we have today.


text:
Move over Justin Bieber and Suze Orman. The corporate greed-fighting movement, Occupy Wall Street, is now joining in on the prepaid card craze with plans to launch the Occupy Card.
An offshoot of the movement, the Occupy Money Cooperative, is currently raising money to launch the card.
An Occupy Card may seem strange, since Occupy Wall Street has been protesting the financial industry for years now. But the group said the card will "start to fix the inequities in the present financial services system" by providing a low-cost cashless option for people who want to stay outside of the traditional banking system.
That said, the card will still be issued through a bank -- although Occupy hasn't disclosed which one yet. That way, the card can be insured through the Federal Deposit Insurance Corp. so cardholders' money will be safe if the venture falls apart.
Occupy says it will keep costs low by funding its operations through interchange fees, which are charged to merchants each time a consumer makes a purchase using a debit or credit card. And every cardholder will become a member of the cooperative, meaning they will share in any profits and help make strategic decisions about the group.
Related: Occupy offshoot forgives $1 million in random people's debt
"People who are unbanked, underbanked, and even just angry-banked will all benefit from using the Card because it is a better and more affordable product," the Occupy Money Cooperative's website states.
So far, the group has raised just $6,000 of its $900,000 goal through social media. If the full amount isn't raised over the next few months, donors will get their money back -- although the co-op says it may keep a small percentage for the costs it has already incurred.
How the fees stack up
Prepaid debit cards are notoriously laden with fees. And while some of the Occupy Card fees will beat those of the high-cost celebrity prepaid cards out there, it still carries its fair share of charges.
The monthly fee is 99 cents (unless you make five direct deposits per month), and getting cash from an ATM will cost you $1.95 per transaction. You can add money to the account by using MoneyPak or Swipe Reload at 7-Eleven or CVS, but this will cost $4.95 (charged by the stores). Walmart will let you load cash onto the card for $3.74. Speaking to a live agent is another $2, and writing a check is $2.
Related: Why Occupy Wall Street fizzled
Overall, the Occupy Wall Street Card would cost the average cardholder about $250 per year in fees, personal finance site NerdWallet estimates.
That's better than the $300 in fees that the average prepaid card comes with, but it's much higher than the amount you would spend using certain prepaid cards from the major banks -- the institutions Occupy Wall Street opposes most. The American Express BlueBird card, for example, costs an average of only $72 per year in fees.
"It's not the worst card out there, but now that we have banks taking advantage of scale and offering such low-cost cards, it's hard to make a case for the Occupy card," said Anisha Sekar, vice president of credit and debit cards at NerdWallet.
And many times, a traditional checking account is the cheapest option. About 38% of checking accounts at major banks are still free with no requirements like enrolling in direct deposit or maintaining a minimum balance, according to Bankrate.com.
"Overall, The Occupy Prepaid Card will wind up garnering more attention for its ties to the socioeconomic movement, Occupy Wall Street, than anything that it actually offers to consumers," said CardHub CEO Odysseas Papadimitriou.
Related: How income inequality hurts America
Carne Ross, a founding board member of the Occupy cooperative, acknowledged that the fees aren't the lowest out there. But he said the more customers the Occupy Card gets, the lower the fees will become.
"We want our card to be cheapest on market -- and if not cheapest, it will be among the cheapest," he said. "But we're also offering things others don't offer: we're going to be completely transparent and tell people how to avoid fees, and being a customer enables you to join the cooperative so you actually become part of the company."
Yet, even Ross isn't confident the card will be a slam dunk.
"Either we'll do amazingly well or it won't take off," he said. "I don't expect it to be a moderate success."


text:
The Penguins got a home game last night and now everything has changed! After taking care of Detroit in a must-win Game 3, the Stanley Cup Finals have become, say it with me ... a series.
As opposed to those non-series series you're always reading about. Personally, I think it was Pittsburgh's innovative "six men on the ice" strategy that turned the tide. The Penguins many not have scored a goal during the 30 seconds or so that their extra attacker skated penalty-free, but it's that kind of outside-the-box thinking that turns also-rans into champions.
And you know what turns champions into also-rans? Horrific penalty killing, which is exactly what the Red Wings have. They're killing at atrocious rate of 71% in the playoffs (the Thrashers were worst in the NHL this regular season and they were at 74%), but everything will be fine provided they don't commit any penalties ever again.
So now we have Game 4 on Thursday—opposite the NBA Finals; kudos once again to the schedulers—where Pittsburgh will either complete their comeback or sink back into oblivion, meaning it really was one of those fake series and not the actual kind we wanted.
Talbot scores twice, Malkin bags three assists, Gonchar gets winner [Post-Gazette]
Game 3 Highlights [NHL.tv]
Crosby puts intangibles on full display [NHL.com]
No love lost for ex-Pen Marian Hossa in Steel City [Free Press]


text: Let the spellbinding shores of the magnificent Chaleur Bay draw you into a panorama of astonishing beauty, fascinating history and vibrant culture. Explore the timeless wonder that awaits you in northern New-Brunswick! Chaleur Bay has been designated as a select member of the Most Beautiful Bays in the World Club (only 31 bays currently ranked) in recognition of its great natural worth and global importance.


text:
Services on Demand
Related links
- Cited by Google
- Similars in Google
Journal of the South African Veterinary Association
On-line version ISSN 2224-9435
Print version ISSN 1019-9128
J. S. Afr. Vet. Assoc. vol.79 n.3 Pretoria 2008
The use of the milk ring test and rose bengal test in brucellosis control and eradication in Nigeria
S I B CadmusI; H K AdesokanI; J StackII
IDepartment of Veterinary Public Health and Preventive Medicine, University of Ibadan, Ibadan, Nigeria
IIDepartment of Statutory & Exotic Bacteria, Veterinary Laboratories Agency, New Haw, Addlestone, Surrey KT15 3NB, United Kingdom
In this study, milk and blood samples collected simultaneously from 532 trade cows to be slaughtered at Bodija abattoir, Ibadan (southwestern, Nigeria) were examined for antibodies to Brucella using the milk ring test (MRT) and the rose bengal test (RBT). Overall, 18.61 % of the milk samples were positive according to the MRT, while 9.77 % of the serum samples were positive according to the RBT. The difference was highly significant (Chi-square value 16.33; P < 0.05); only 32 (6.02 %) of the samples were positive for both tests. The Red Bororo breed of cattle and the White Fulani had the highest positive rates, namely 20.93 % and 11.69 % for the MRT and RBT respectively. No conclusion can be drawn about sensitivity because we do not know the true status of the animals tested. It is, however, obvious that although the MRT and RBT are 1st-line screening tests for brucellosis in cows in some countries, their lack of specificity is of concern. Therefore, the requirement for other confirmatory tests that are more specific should be considered for control and eradication of the disease, especially in Nigeria.
Key words: brucellosis, cows, diagnosis, serology, zoonosis
"Full text available only in PDF format"
1. Acha P N, Szyfres B 1987 Zoonoses and communicable diseases common to man and animals (2nd edn). Scientific Publication No. 503, Pan American Health Organization/ World Health Organization, Washington DC [ Links ]
2. Alton G G, Jones L M, Angus R D, Verger J M 1988 Techniques for the brucellosis laboratory. Institut National de la Recherche Agronomique, Paris [ Links ]
3. Beh K J 1974 Quantitative distribution of Brucella antibody amongst immunoglobulin classes in vaccinated and infected cattle. Research in Veterinary Science 17: [ Links ]
4. Beh K J 1973 Distribution of Brucella antibody among immunoglobulin classes and a low molecular weight antibody fraction in serum and whey of cattle. Research in Veterinary Science 14: 381-384 [ Links ]
5. Bercovich Z, Moerman A 1979 Non-specific positive milk ring test(s) in tank milk and Estrumater in the treatment of cattle. Tijdschrift voor Diergeneeskunde 104: 713-716 [ Links ]
6. Cadmus S I B, Ijagbone I F, Oputa H E, Adesokan H K, Stack J A 2006 Serological survey of brucellosis in livestock animals and workers in Ibadan, southwestern Nigeria. African Journal of Biomedical Research 9: 163-168 [ Links ]
7. Food and Agriculture Organisatio of the United Nations 2004 Bovine brucellosis. Animal health/disease cards. Online at: http://www.fao.org/ag/againfo/subjects/en/health/diseases-cards/brucellosi-bo.html (accessed on 11 February 2008) [ Links ]
8. Hill W K W 1966 Een onderzoek over de waarde van de abortus bang-ringtest bij melkmonsters uit melktanks. Tijdschrift voor Diergeneeskunde 14: 885-895 [ Links ]
9. Keer W R, Pearson J K L, Rankin J E F 1959 The bovine udder and its agglutinins. British Veterinary Journal 115: 105-109 [ Links ]
10. MacMillan A 1990 Conventional serological tests. Animal brucellosis 206: 153-197 [ Links ]
11. Morgan W J B 1967 The serological diagnosis of bovine brucellosis. Veterinary Record 80: 612-621 [ Links ]
12. Patterson J M, Deyoe B L 1976 Effect of physical properties of milk fat globules on Brucella ring test sensitivity. Journal of Dairy Science 60: 851-856 [ Links ]
13. Tanwani S K, Pathak P N 1971 Studies on abortus bang ring test: factors affecting the nature of reaction in different milk samples. Indian Journal for Animal Science 41: 1037-1040 [ Links ]
Received: March 2008
Accepted: July 2008
* Author for correspondence. E-mail: firstname.lastname@example.com


text:
Sometimes you are hungry
for something that is just not good for you.
And you eat it.
And regret doing so.
Or not!
Wednesday nights I take a class at Manchester Craftsmen's and eat an early dinner on the South Side, driving down the slopes from Arlington. The last two weeks it was Carson Street Deli sandwiches but then I started thinking French fries. Undoing all the work at the gym yesterday.
So I crossed at 16th St and went into Mario's, the only patron at that off hour but not for long. Cory was hosting and tending bar and took my order for an Alpine burger (don't ask) and their house cut fries. A tall ginger ale. I asked if I could photograph the stained glass windows. Designed by Williams Stained Glass of Bethel Park. It was a good time to photograph the architectural details. Woshner's was once "haberdashery" and is now Mario's. I was too early for Karaoke night. Had to get to class.


text:
2125 Market St, Camp Hill, PA 17011
24hr: 717-737-4506 Local: 717-737-4506 Fax: 717-737-7646Email Us
The Most Beautiful GiftB1C
"Elegant, pure and fabulous bouquet of five white roses and green garnish. An ideal gift to be sent not only to lovers of white color but a great gift suitable for any festive occasion. Approx. Size may vary. Your purchase includes a complimentary personalized gift message."
All items featured on this site represent the types of arrangements we offer and may vary depending upon availability in certain regions. Substitution Policy


text:
While President Obama diverts the nation's attention with his other myriad scandals, the Obamacare Express continues to careen off the rails, leaving a wide path of destruction and rendering its supporters defenseless.
The latest outrage, in a yearslong string, is that at least 4 million of the 8 million total Obamacare sign-ups contain flawed applications, according to a report by House Republicans.
But this isn't some arbitrarily divined number chosen by Republicans for political purposes. It is based on a chart prepared by federal Obamacare contractor Serco that shows more than 4 million application discrepancies as of May 27. These include inconsistencies regarding income, citizenship and immigration, residency, incarceration, Indian status, Social Security numbers, and eligibility for another federal health program.
According to The Daily Caller, more than 1.6 million annual and current income inconsistencies were identified, but none of them had been resolved by May 27, and only 62,000 of the 1.4 million anomalies involving citizenship and immigration problems had been resolved. A total of only 97,000 of the 4.1 million discrepancies had been addressed by the end of May.
Is this any way to run even a woefully bloated, incompetent and bureaucrat-infested government?
It's not as if this just happened serendipitously. The Obama White House knew, in its rush to pile up applications no matter how flawed in order to meet its self-imposed deadline, that it was cutting significant corners. This arrogant administration failed to create an income verification system in 2013 before it launched the notorious Obamacare website, HealthCare.gov, and opted to approve Obamacare applications without any verification of the customers' representations of their income.
Can you imagine any business running itself this recklessly? A bank? It's not conceivable. And it won't do for people to excuse this as merely the way government works. If they make that excuse, they are admitting that the government should never be entrusted with one-seventh of the economy, which it shouldn't. This wasn't a case of mere bureaucratic ineptitude; it was a conscious decision at the highest executive level to cut corners for political purposes, and it screams out for accountability.
Of the latest Obamacare nightmare revelations, these application inconsistencies are only the tip of the iceberg. Consider:
–At least 2.9 million Americans who signed up for Medicaid coverage pursuant to Obamacare changes have not had their applications processed, meaning some low-income people will be prevented from accessing benefits they are legally entitled to receive.
–According to the Congressional Budget Office, only a small fraction of the uninsured will end up paying the Obamacare mandate penalty, as 87 percent will claim an exemption. Not only does this make a mockery of the ill-conceived penalty but also it increases the odds of an insurance industry "death spiral" and puts Obamacare even further out of budgetary balance. These consequences will be perfectly copasetic with Obama, who abhors the insurance industry, desires complete government control of health care and relishes further subsidizing of those not paying their own way as another proud notch in his redistribution belt. What a great day for Obama! Everything he promises turns to mush, but that mush just happens to be perfectly consistent with the deplorable direction in which he wants to take the country. Though the overwhelming majority of the uninsured will sidestep the mandated penalty, absent court intervention, there will be no such leniency for families and businesses who
seek to avoid the mandate on moral grounds because it forces them to provide coverage for abortion-inducing drugs and devices.
–The Congressional Budget Office said Obamacare would reduce the deficit by more than $120 billion in a decade, but in April, the CBO quietly conceded that the projection wasn't reliable. Shocker.
–The Washington Times reports that as a direct result of the Obamacare scheme to make health care more affordable, insurance premiums will be increasing in Colorado, Vermont (from 9.8 percent to 18.3 percent), Kentucky (up to a 17 percent increase by 2015) and Virginia (as much as 22 percent). This is an inevitable trend we're seeing all over the country, as everyone who understands socialism anticipated.
–Stephen Parente, a professor of health economics at the University of Minnesota, and Michael Ramlet of American Action Forum project nationwide increases for the Obamacare silver plan of some 25 percent for individuals and families within five years, as well as an increase in the uninsured. Similarly bad results are projected for other categories of plans. This means that despite government spending increases, health care costs will still be higher, which is to say nothing of the reduced health care choices and deteriorating quality of treatment.
–Though Obamacare promised to reduce pressure on emergency room treatment by giving people greater access to primary care, we're witnessing just the opposite result in many hospitals in Kentucky and across the nation, which are experiencing a surge of newly insured Medicaid patients going to emergency rooms.
–A headline reads, "NC Small Business Will Be 'Hit Especially Hard' By ObamaCare, May Drop Insurance For Workers." Another reads, "Obamacare Hits Academia, Hard."
I'm just part way through the list; shall I continue? Sorry. Out of space. But I trust you're getting my drift.


text:
If students send you academic questions via email then you have an opportunity! The current student culture has as a default to ask academic questions via email. However, I think everyone can benefit if Teaching Teams actively try to resist this practice. For every question raised by one student we know that are others that have the same question (especially when the class is large). So I like the practice of reinforcing, whenever possible, that academic questions are to be asked in the discussion forums on the class website (i.e. on the Learning Management System).
In addition to writing it in the syllabus, I have used several tactics over the years, such as:
- If you send out a regular weekly News item to students, mention there that academic questions should be placed in the discussion forums on the class website and openly encourage students to answer questions from their peers.
- I model the behaviour in the discussion forum early and I pose academic questions in the forum myself (usually I try to link it to a current event, an interesting academic resource, or I try to use the opportunity to discuss a particularly difficult question on a recent assignment, etc).
- When I get an academic question from a student via email, I answer the question and then I strongly encourage that the student communicate the question and the answer to the class in the discussion forum (for everyone's benefit)
- I also include in the email reply that in the future, academic questions should go into the discussion forum because peers may be able to provide feedback quicker than I can as the instructor, and that open discussion of these issues can benefit many students.
- I gently praise those students that answer questions posed by their peers in the class discussion forums, and I very gently correct or add to the discussion, if required.
Do you have any tips or tricks on how to keep you student generated email under control? Share it with us in a comment below.