Escherichia coli. We found that lac operon regulation became strikingly variable, including changes in the mode of environmental response , sensitivity to inducer concentration, and maximum expression level. In addition, some classes of regulatory change were enriched in specific selective environments. Sequencing of evolved clones, combined with reconstruction of individual mutations in the ancestral background, identified mutations within the lac operon that recapitulate many of the evolved regulatory changes. These mutations conferred fitness benefits in environments containing lactose, indicating that the regulatory changes are adaptive. The same mutations conferred different fitness effects when present in an evolved clone, indicating that interactions between the lac operon and other evolved mutations also contribute to fitness. Similarly, changes in lac regulation not explained by lac operon mutations also point to important interactions with other evolved mutations. Together these results underline how dynamic regulatory interactions can be, in this case evolving through mutations both within and external to the canonical lactose utilization network.Adaptation to novel environments is often associated with changes in gene regulation. Nevertheless, few studies have been able both to identify the genetic basis of changes in regulation and to demonstrate why these changes are beneficial. To this end, we have focused on understanding both how and why the lactose utilization network has evolved in replicate populations of  lac operon, that control the utilization of lactose sugar, and we examine how their regulation changes during the adaptation of populations of Escherichia coli bacteria to environments that differ only in the presence of lactose. We find that lac operon regulation is altered in almost all populations that evolve in the presence of lactose and identify two classes of mutations that explain a large part of this change and that confer significant fitness benefits. Interestingly, our study indicates that other mutations, lying outside of the commonly recognized control region, cause new regulation of the lac operon. Together these findings reinforce the importance of changes in gene regulation during evolution and suggest that the biological basis of these changes can be complex and involve novel interactions between genes.Differences in gene regulation underlie many important biological processes and are thought to be important for the adaption of organisms to novel environments. Here we focus on the regulation of a group of well-studied genes, the  Changes in gene regulation are an important and common cause of adaptation. Support for this comes from bioinformatic evidence that changes in regulatory elements are associated with presumably adaptive phenotypic changes  used a cost-benefit analysis to predict the optimum expression level of the lac operon in different inducer concentration environments. They found that populations selected in environments containing a high level of gratuitous inducer, but various concentrations of lactose, generally evolved to regulate expression of the lac operon to the predicted level.Several attributes make the lac operon regulation associated with selection in environments differing in the presentation of its natural substrate and inducer, lactose, and repressor, glucose. In addition to quantitatively characterizing the changes that have occurred, we examine the genetic and demographic basis for selection of different modes of lac regulation. We find that regulatory changes in the lac operon evolved in many replicate populations selected in environments containing lactose. Much, but not all, of these changes were due to mutations in the LacI repressor or its major operator binding site within the lac promoter. By themselves, these mutations conferred significant fitness benefits in all of the evolution environments that contained lactose. We also present evidence for interactions between lac mutations and mutations in genes outside of the canonical lac utilization network and show that these interactions impact lac operon regulation and fitness. Finally, operator and repressor mutations fixed at different frequencies in different selective environments, although the selective basis of this is currently not known.In this study, we examine changes in E. coli B REL606 for 2000 generations in one of four environments that differed only in the concentration and/or presentation of glucose and lactose lac operon had changed during the evolution of our experimental populations, we screened ≥1000 clones from each population on LacZ indicator plates  and PlacO(-)-RFP (CRP reporter) constructs. For each strain we used flow cytometry to measure the population distribution of steady-state GFP and RFP expression over a range of thiomethyl-galactoside (TMG) concentrations (TMG is a non-metabolizable inducer of the lac operon). The Plac-GFP reporter captured several key features of lac regulation. The inducer response of the ancestor is ultra-sensitive, showing a sharp transition from low to high expression states as a function of TMG concentration and is bimodal, with populations showing a mix of non-induced and fully induced cells at intermediate levels of TMG lacO(-)-RFP) shows a unimodal distribution with a constant mean over the range of TMG concentrations, confirming that CRP activity of the ancestor is independent of LacI activity and lac operon expression state , the range of TMG concentrations causing a bimodal population response and the fully induced (maximum) steady state level of lac expression. For the ancestor, TMG½ Max is 25 µM, the range of bimodality is between 15–30 µM TMG, and the level of Plac-GFP at full induction (100 µM TMG) is ∼84 RFU , but were not observed in any of the Glu evolved populations. 2) Lower threshold/graded response: increased sensitivity to inducer with TMG½ Max values ranging from 2 µM to 8 µM. In addition, all but one clone with a lower induction threshold (G+L2-1) also evolved a graded response, with a unimodal distribution of Plac-GFP expression that increased continuously as a function of TMG concentration. 3) Bimodal: bimodal induction response differing from the ancestor by generally being less sensitive to the inducer, with TMG½ Max values ranging from 25 µM to 50 µM TMG (P = 0.002). By contrast, populations evolved in the G/L environment were almost exclusively composed of clones with constitutive lac expression and populations evolved in Lac had either bimodal or constitutive regulation. All clones evolved in Glu showed a bimodal response type (Evolved changes in the inducer response profiles were common and fell into three broad classes . 1) Cons0 µM TMG . Higher nse type .lac-GFP expression at maximum induction (100 µM TMG) was higher than the ancestor in 36 of the 46 evolved clones  but not significantly different for clones evolved in the Glu environment (P = 0.242). To verify that changes in the level of our GFP reporter accurately reflected changes in the expression level of the native lac operon, we used a direct assay to measure the lac promoter activity of a focal evolved clone G+L3-1, which shows an approximate 2-fold increase in maximal Plac-GFP expression lac promoter was significantly higher in the G+L3-1 clone than the ancestor and this increase agreed with our estimate based on flow cytometry -RFP) as a function of inducer concentration. All evolved clones showed a unimodal distribution with a mean response that was independent of inducer concentration, suggesting that, similar to the ancestor, evolved clones maintained predominantly non-cooperative interactions between the LacI and CRP regulators and that CRP activity is independent of lac expression level . We found that all but one clone classified as constitutive had either a deletion or insertion of a 4 bp sequence within the lacI gene, which results in a frame shift . We identified three unique lacO1 mutations, two of which occurred twice in independent G+L populations  and used it to generate inducer response profiles in the G+L3-1 clone , demonstrating that the lacO1 mutation is the primary cause of evolved changes in inducer sensitivity.The lacI and lacO1 mutations can explain many, but not all, of the lac regulation changes seen in the evolved strains. Specifically, neither mutation confers the increase in maximal expression that was seen in most evolved clones , none of which are in genes previously characterized as affecting lac operon expression.The d clones . To concgulation . Whole glacO1 and lacI mutations arose independently in replicate populations and affect a trait of relevance in the evolution environments suggests that they confer a selective advantage. Further, the presence of lacO1 mutations exclusively in the G+L evolved populations suggests that they confer a greater advantage in this environment than do lacI mutations. To test these predictions we introduced the lacI and lacO1 mutants individually into the ancestor and measured the fitness of these constructed strains relative to the ancestor in each of the four evolution environments. We found that lacO1 and lacI mutations conferred a fitness benefit in all environments containing lactose . By contrast, both mutations imposed a small fitness cost in the glucose environment, consistent with them not being observed in Glu populations   (That multiple  = 0.16) . Intrigu = 0.05) .lacI and lacO1 mutations, we quantified their effect on population growth dynamics, focusing on the transitions between glucose and lactose utilization that are encountered in the G+L and G/L environments (environments in which lacO1 and lacI mutants predominated). We found that the lacI and lacO1 mutations significantly decreased the lag phase, relative to the corresponding ancestral alleles, following a shift from growth on glucose to growth on lactose (a part of the G/L environment)  for lacI and lacO1 mutants was not significantly different from that of the ancestor, except during growth on lactose in the G+L environment  , Table 2ironment , Table 2 results .lacI mutant, the lacO1 mutant can repress lac expression to some degree  with and without lactose (87.5 µg/mL) supports this conclusion, with repression of lac expression only occurring in the absence of lactose   and there was no significant difference in the fitness conferred by the lacI and lacO1 mutations in the evolved background when they were directly competed against one another . Therefore, epistatic interactions increase the fitness effect of both the lacI and lacO1 mutations in the evolved background, but do not explain the enrichment of lacO1 mutations in populations evolved in the G+L environment.A possible explanation for the success of ironment . If epis = 0.03) . HoweverlacI and lacO1 mutations, what could explain our finding that lacOI mutations only occurred in populations evolved in the G+L environment? If the lacI and lacO1 mutations occurred with equal probability, the distribution of mutation types over selection environments we observed is unlikely to have occurred by chance . In fact, our observations are even more unlikely than this test implies because the lacO1 mutation will almost certainly occur at a much lower rate than the lacI mutation. The frequency of lacI null mutations in the E. coli strain used in this experiment is ∼3×10−6 per generation (Hana Noh and TFC unpub. obs.), which is in good agreement with a previous measurement for E. coli K12 lacO1 mutation frequency to be <3×10−9 per generation .We considered the possibility that cross-contamination could be responsible for the occurrence of identical ment see . FurtherlacO1 mutation and the G+L evolution environment, despite its high significance, is nevertheless spurious. In this case, repeat evolutionary experiments of the ancestor in the same G+L and G/L environments (where selection of lacI mutations was most consistent) would not be expected to lead to significant mutation-environment association. To test this, we began 12 ‘replay’ populations in each of the G+L and G/L environments, founding each population with the same ancestor as used in the original experiment. Every 100 generations, we examined the frequency of lacI and lacO1 mutations in each population using LacZ indicator plates and by sequencing select clones. Although lacO1 mutants were detected in the majority of G+L replay populations, their frequencies never rose above 4% in any one population  and the primary lac operator (lacO1) that correlated with the constitutive and the lower threshold/graded inducer response, respectively. Addition of these mutations to the ancestor demonstrated that they explained many, but not all, of the broad scale changes in regulation we observed and that, by themselves, they can confer fitness benefits in environments containing lactose. These fitness benefits were relatively large, representing 20%, 27% and 28% of the total mean fitness improvement in the Lac (lacI mutation), G+L (lacO1 mutation) and G/L (lacI mutation) evolved populations, respectively lac regulatory elements — and adaptive.We sought to test whether evolution in environments that differed only in the availability and presentation of lactose selected for changes in the regulation of the lac operon regulation offers the opportunity to connect the genetic and phenotypic changes we observed. Twenty-one of the 22 lacI mutants we identified mapped to a mutational hotspot within lacIlacI mutants. Similarly, the three lacO1 mutations we identified in the G+L evolved populations have been reported as reducing the binding affinity of LacI for this binding site lacO1 mutations are able to repress the lac operon, but are induced at much lower TMG concentrations than the ancestor. lacO1 mutations also conferred a graded response to increasing inducer concentration, which contrasted with the canonical bimodal response of the ancestor. The same regulatory outcome was demonstrated by Ozbudak et al. (2004), who found that decreasing the effective concentration of LacI by providing extra copies of lacO1 binding sites resulted in a graded unimodal induction of the lac operon lacO1 affinity, reducing the effective concentration of LacI repressor. More generally, our results support the concept that small changes in the activity of cis-regulators have the potential to transform the output of a regulatory network between binary and graded responses Extensive previous study of lacI and lacO1 mutations were shown to confer significant fitness benefits in the three lactose containing evolution environments . Analysis of the growth dynamics of strains containing only these mutations indicated that a large part of this benefit is due to a reduction in lag phase when switching from glucose to lactose utilization. Interestingly, when added to the ancestor, both lacO1 and lacI mutations abolished the diauxic lag that separates glucose and lactose growth phases during growth in the G+L environment. This phenomenon is well documented for lacI mutants lacO1 mutants. In the case of lacI mutants, constitutive expression of the lac operon primes the cell for utilization of lactose as soon as glucose resources are exhausted. In contrast to lacI mutants, lacO1 mutants are still capable of repressing lac expression in the absence of inducer. However, when grown in media containing both glucose and lactose the lacO1 mutation essentially phenocopies a lacI mutant, causing constitutive lac expression. Evidently glucose-mediated blockage of lactose import through LacY (inducer exclusion) is insufficient to prevent lactose accumulating in cells to a concentration sufficient to allow lac operon induction in lacO1 mutants Both lac repression in lactose (3/6), but not glucose (0/6), evolved populations is consistent with the ‘use it or lose it’ hypothesis lac operon and be selected against. If a repressor is seldom used, as in the lactose evolution environment, loss of function mutations will not be effectively selected against and can fix through genetic drift. However, in its simplest form, this mutation accumulation mechanism does not capture the dynamics of the regulatory changes we see. First, loss of the lacI repressor actually confers a benefit during growth on lactose, so that underlying mutations will increase in frequency faster than expected if they were neutral. Second, repressor mutations also occurred in environments where glucose was just as common as lactose. Analysis of growth curves suggest a mechanism for this; lac repressor mutants were able to quickly begin growth following a switch from glucose to lactose. Fitness measurements indicated that this advantage outweighed the cost of unnecessary lac expression during growth in glucose.The loss of lacI mutations in the Lac environment, it is interesting that only three of the six Lac populations were enriched for lacI mutations. We identify two possible explanations for this observation. First, clonal interference may have resulted in lacI mutations being outcompeted by higher effect beneficial mutations. Second, populations that did not enrich lacI mutations may have fixed alternative mutations that genetically interact with lacI mutations to reduce their fitness benefit. To distinguish between these possibilities, we are continuing the evolution experiment and tracking the frequency of lacI mutations in the Lac populations. In addition, we are examining the fitness benefit conferred by lacI mutations when introduced into clones from Lac evolved populations that did not fix lacI.Given the large benefit conferred by lac mutations occurred only in lactose containing selective environments. A second layer of environment specificity is less clear; why do lacO1 mutations only reach high frequency in the G+L environment? The lacI and lacO1 mutations had no differential effect on fitness in either the ancestor or an evolved background and conferred indistinguishable growth dynamics in all evolution environments. Without a selective advantage over lacI mutations it is difficult to understand how lacO1 mutants were fixed in 4 of 6 G+L populations, especially considering that the rate of lacI mutations is likely on the order of 1000-fold greater than for lacO1 mutations. In the absence of a plausible mechanism to explain enrichment of lacO1 mutations in the G+L environment, we investigated whether environment-specific selection of lacO1 mutants was reproducible. This was not the case, with all 12 of the independent replay populations selected in G+L eventually fixing (>98%) lacI mutations. It remains formally possible that subtle differences in media or experimental conditions during competition assays or the replay evolution experiments could affect the fitness advantage experienced by lacO1 mutants in focal G+L evolved populations. However, taken at face value, the different outcome between replay and primary populations suggests that, notwithstanding mutation rate differences and the strong statistical association between environment and mutation type, the enrichment of lacO1 mutations over lacI mutations in the G+L environment might have occurred by chance.We can explain why lac operon regulation lac operon is evolutionarily plastic, such that interactions can arise or be altered to fine tune regulation and better fit E. coli to its environment a priori assumptions as to the nature of regulatory changes or the mutations causing them, we were able to identify evolved clones with changes in lac regulation that are likely due to novel interactions with mutations in genes outside of the canonical lac operon. Two results support this conclusion. First, we identified numerous clones with maximal steady state expression levels of the lac operon that were higher than the ancestor. Further examination of evolved clone G+L3-1 indicated that the increase in maximal lac expression level could not be explained by the lacO1 mutation present in this clone. Second, the fitness benefit conferred by the lacO1 mutation in this same evolved clone was significantly greater than in the ancestor, indicating that one or more evolved mutations interact with the lacO1 mutation to determine its effect on fitness. Whole genome sequencing of G+L3-1 identified six additional mutations, however, none of these mutations mapped to the lac operon or genes known to directly impact CRP-cAMP activity. It seems likely, therefore, that one or more mutations in the G+L3-1 clone have directly or indirectly led to new regulatory control of the lac operon.Models incorporating interactions between key regulatory elements can successfully predict aspects of lac expression in each of the three lactose environments? Dekkel and Alon (2005) found that, in the presence of a gratuitous inducer, lac operon expression evolved to a level predicted on the basis of a cost-benefit analysis, dependent on the concentration of lactose in the selection environment lac operon and can be tuned to best fit the environment. At this time, however, we do not know the genetic or molecular basis for the widespread increase in maximum lac expression observed in many evolved clones. Possible ‘local’ explanations include: increases in the level of cAMP, mutations in the lacZYA genes that affect mRNA stability, or changes in DNA supercoiling that increase lac operon transcription. It is also possible that changes in lac maximum expression reflect an alteration in some global process. For example, changes in the function or concentration of ribosomes could affect expression of all genes. In future work we aim to identify the evolved mutations that are responsible for changes in maximum lac expression and then construct strains that will allow us to test the adaptive value of different expression levels as well as probe the underlying molecular mechanisms.Is there an optimal level of lac regulation was that evolved clones displaying bimodal inducer responses tended to also have higher induction thresholds (TMG½Max) than the ancestor. This trend was not environment specific, occurring in clones isolated from Glu, Lac and G/L evolved populations. However, the parallel and large-scale increases in induction threshold observed for Glu-evolved clones suggests that this change in lac regulation is a direct or correlated response to adaptation. The mechanistic bases of increases in induction threshold are currently not understood, but could be the result of both direct and indirect mechanisms. For example, reduction in the activity and/or concentration of the permease LacY could increase the concentration of extracellular inducer required to achieve intracellular levels of inducer sufficient to inactivate LacI. In glucose evolved populations, changes in LacY activity may result from mutations in the PTS system that optimize glucose transport but lead to elevated levels of unphosphorylated EIIAglc, which is a known inhibitor of LacY activity lac operon. Further study will be required to discern between these and other hypotheses.An additional widespread change in lac operon regulation that occurred during selection of replicate populations in different lactose containing environments. In our view, the most important aspects of our findings are how common these changes were and that they likely involve mutations both within and outside of the set of genes that are recognized as regulating the lac operon. Identification of these changes will provide a rare insight into how regulatory networks can be rewired in response to an environmental change.In summary, we have identified and characterized widespread changes in E. coli B REL606 (ara−) and an otherwise isogenic ara+ derivative, REL607 8 cfu/mL) The ancestral strains used for experimental evolution studies were lac operon regulation using TGX medium, which consisted of T agar plates supplemented with 0.5% glucose and 30 µg/mL of the colorimetric LacZ substrate 5-bromo-4-chloro-3-indolyl-beta-D-galactopyranoside . On this medium, the degree of blue coloration correlates with the degree of LacZ activity and, therefore, lac operon expression. Clones representing the diversity in LacZ activity and colony morphology within each evolved population were recovered, scored and stored for future analysis. TGX plates enabled us to distinguish between the ancestor and clones with lacO1 and lacI mutations (mutations that we subsequently identified in evolved populations), which appear white, faint blue and dark blue, respectively.Evolved populations were screened for qualitative changes to lac promoter (containing LacI binding sites O1 and O3), coupled with an optimized ribosomal binding site, drives expression of the fast maturing GFP derivative, GFPmut3.1 lac-GFP module was cloned into a mini-Tn7 delivery vector, to make pTn7-Plac-GFP, and integrated into the chromosome in a site-specific manner We developed a dual fluorescent reporter system that enabled us to independently monitor LacI and CRP activity with single cell resolution. In this system the native lac-GFP reporter depends on both LacI and CRP activity. To isolate these effects we developed a second reporter to monitor the contribution of CRP to lac operon expression independent of LacI. To do this, we constructed a version of the lac promoter (PlacO(-)) with defined mutations in the O1 and O3 operators which have been shown to prevent binding of the repressor LacI while maintaining the integrity of the major binding site for CRP lac promoter that has the LacI binding sites deleted, rather than an unrelated reporter subject to regulation by CRP. Third, DsRed express2 shows low cytotoxicity relative to other commonly used RFP's Expression of GFP from the P7-Plac-GFP reporter was integrated into the chromosome of focal evolved clones by tri-parental mating with the MFDpir (pTn7-Plac-GFP) donor strain and the MFDpir (pTSN2) helper strain 7 had inserted into the single characterized chromosomal integration site The Tnlac operon by binding and inactivating the LacI repressor. Unlike the natural inducer, allolactose, TMG is metabolically stable, which is advantageous for quantitative studies because it allows accurate control of TMG concentrations through the course of the experiment. Importantly, import of TMG into the cell is dependent on the lactose permease LacY, which is not true of other commonly used synthetic inducers such as Isopropyl β-D-1-thiogalactopyranoside (IPTG).Strains were inoculated from glycerol stocks into 500 µL LB media in 2 mL deep-well plates (Phenix Research) and grown overnight at 37°C on a microplate shaker at 750 rpm (Heidolph Titramax 1000). After overnight growth, cultures were diluted 1∶1000 into 500 µL DM+0.4% glycerol and incubated for a further 24 hrs at 37°C. To determine the inducer response of each strain, overnight cultures were diluted 1∶1000 into separate wells of a 96-well plate containing DM+0.4% glycerol supplemented with TMG at concentrations ranging from 0 to 100 µM and incubated at 37°C for a further 15–18 hrs. This time period encompassed approximate steady state reporter expression in ancestral and evolved clones . TMG indlac-GFP expression in response to TMG was similar between replicates, indicating that our protocol for measuring inducer response profiles was robust.Flow cytometry was performed with a FACScalibur (BD Biosciences) equipped with a high throughput sampler. PMT voltages for the flow cytometer were set as follows: SSC-H - E02, FSC-H 580 V, FL1-H 800 V and FL2-H 800 V. The threshold was set at 250 on the SSC-H channel. For each expression assay, a total of 25,000 events were captured at a rate of 1000–3000 events/s. Data was acquired in log mode with no hardware compensation. We examined day-to-day reproducibility by measuring ancestral inducer response curves on 5 separate days using independent cultures . The levRoutine analysis of the flow cytometry data and plotting of inducer response profiles was performed in R (version 2.12) using the Bioconductor packages FlowCore and FlowViz For quantitative analysis of inducer responses, flow cytometry data were processed using Matlab . An elliptical gate corresponding to a Mahalanobis distance of 0.5, centered at peak cell density in the FSC-SSC coordinates, was used to minimize the effects of varying cell size and granularity on the resulting fluorescence histograms. A custom bimodality detection algorithm (to be described elsewhere) was applied to determine the region of bimodality for each histogram. Inducer sensitivity was determined as the point where the population-mean of GFP expression was halfway between baseline and saturation.600 with a microplate reader (Tecan). Cells were permeabilized by mixing 20 µL of cell suspension with 80 µL of permeabilization solution , 0.4 mg/mL sodium deoxycholate, 5 µL/mL β-mercaptoethonal). After incubation for 10 minutes at room temperature, measurement of LacZ activity was initiated by adding 150 µL of o-nitrophenyl-β-D-galactoside  and mixing. Yellow color development was stopped by addition of 250 µL 1 M sodium carbonate and the reaction time recorded. Samples were centrifuged to remove cell debris and absorbance at OD420 was measured for 200 µL of the supernatant. LacZ activity (Miller units) was calculated as (1000×OD420)/(volume (mL)×OD600×reaction time (min)).Assays were carried out as described by Zhang and Bremer (1995) with modifications 600 measured at 5 min intervals. The maximal growth rate constant (μMax) of each strain was calculated by linear regression of the plot of ln(OD600) versus time (hrs) using a sliding window of 10 data points. The steepest of these slopes was used to calculate μMax with units hrs−1. Lag time was calculated by extrapolating the μMax regression line to its intersection with OD600 = 0.06. Extrapolating to the initial density of individual growth curves would have been preferable, however, we found that these measurements were quite variable. To account for this we adopted the approach described by Friesen et al. (2004) where a constant reference density is used, assuming that starting biomass is similar for all strains 600 value coinciding with the end of the first growth phase.Strains were inoculated into LB medium from freezer stocks, incubated overnight at 37°C and then diluted 1∶100 into DM medium supplemented with Glu, Lac or G+L at the concentrations used in the original evolution experiment. Strains were grown overnight and diluted 1∶100 into fresh DM media and incubated for a further 24 hrs to allow them to become physiologically adapted to their resource environment. To measure growth dynamics, a 1∶100 dilution of pre-conditioned culture was inoculated into 200 µL of DM supplemented with Glu, Lac or G+L in a clear 96-well plate. Concentration of sugars was either the same as in the evolution environments or to facilitate higher cell densities and correspondingly more precise OD measurements, were as follows: Glu, 0.2 mg/mL; Lac, 1.8 mg/mL; G+L, 0.2 mg/mL & 1.5 mg/mL, respectively. Incubation and optical density measurements were performed with a Bioscreen C plate reader (Oy Growth curves AB Ltd) at 37°C with continuous shaking and OD420 and linear regression used to fit a line of best slope to the plot of OD420 vs time (min). LacZ activity (Miller units) was calculated as (1000×slope)/(assay volume (mL)×OD600). Doubling rate was measured for each strain in the experimental conditions by linear regression of log2(OD600) plotted against time, with the steepest of these slopes designated as the maximum doubling rate (doublings/hr). Promoter activity was calculated as the product of LacZ activity (Miller units) and doubling rate (hrs−1). To confirm that the inducer concentration we used was sufficient to completely inactivate LacI, we also measured expression from a lacI null mutant. This mutation caused a similar expression increase as induction with 1 mM IPTG, indicating that this level of inducer was sufficient to fully induce the lac operon.Assays were performed as described by Kuhlman et al. (2007) lacO1 and araA- mutations were introduced using a suicide plasmid approach that has been described previously R cells  were selected. Resistant clones were streaked onto LB+sucrose agar to select cells that lost the plasmid (which carries the sacB gene conferring susceptibility to killing by sucrose). These cells were then screened for the presence of the evolved alleles by a PCR-RFLP approach using the enzyme HaeIII (araA-) or on LacZ indicator medium (PlacO1). Putative allelic replacements of the evolved ara- and lacO1 alleles were confirmed by sequencing.Constructs and approaches used for the manipulation of each mutation were as follows. The PlacI(-) mutation was obtained by isolating spontaneous mutants of relevant strains that could grow on minimal media supplemented with P-Gal as the only carbon source and confirmed by sequencing lacI(-) mutants that had either insertion or deletion mutations in a previously identified mutational hotspot The lac regulation types, which allowed them to be distinguished on tetrazolium arabinose (TA) T2/NT0)/ln(NR2/NR0), where NT0 and NR0 represent the initial densities of the test and reference strains, respectively, and NT2 and NR2 represent their corresponding densities at the end of the competition with correction for the number of transfer cycles the competition occurred over. Competitions were generally carried out over two transfer cycles. All assays were carried out with at least four-fold replication unless reported otherwise in The fitness of constructed strains was measured relative to the ancestor strain used to found the evolution experiment or directly to each other. Competing strains contained opposite Ara marker or lacO1 mutations, we assume that mutation of the lacO1 region is random and equally likely for each of the 21 bp that define lacO1. Using a mutation rate of 5×10−10 bp/generation lacO1 is ∼1×10−8 per generation. However, only a subset of these mutations will severely compromise LacI binding. A survey of the literature indicates that approximately 16 single base pair substitutions within lacO1 have been reported to reduce the affinity of LacI by >90% and/or reduce the effective repression of LacZ expression >90% −9 per generation. This is likely an overestimate since only three lacO1 alleles were selected during evolution, two of which were selected twice in independent populations, indicating that relatively few of the possible lacO1 mutations may actually confer an advantage in the evolution environments. In addition, the lacO1 alleles selected during evolution have been reported to reduce the affinity for LacI by >98%, further reinforcing the stringency of our operational criteria for estimating the number of possible lacO1 mutations To estimate the per generation frequency of loss of function Figure S1lac expression state in evolved clones. Inducer response histograms of the CRP-only reporter (Plac(O-)-RFP) for all evolved clones and the ancestor. In all cases, Plac(O-)-RFP expression is unimodal and not affected by LacI activity or lac operon expression state.CRP activity is independent of (TIF)Click here for additional data file.Figure S2lacO1 mutation on lac operon expression. A) Inducer response profiles using Plac-GFP and PlacO1-GFP reporters in ancestral and G+L3-1 strains. B) Steady state LacZ activity for strains grown in DM+0.4% glycerol supplemented with a range of TMG concentrations.Effect of a (TIF)Click here for additional data file.Figure S3lacI and lacO1 mutants in evolution environments. A) Growth curves for the ancestor (green), and ancestor with lacO1 (purple) and lacI (blue) mutations. Conditions used were: Lac→Glu, Glu→Lac and G+L→G+L, where the sugars indicate pre-conditioning and measurement environments, respectively. Concentration of sugars for both pre-conditioning and measurement environments matched those used in the evolution experiment. These transitions correspond to those present in the G/L and G+L environments. OD values are plotted on a log10 scaled axis. Growth curves show the same qualitative trends as described for growth analysis in environments with higher concentrations of sugars Click here for additional data file.Figure S4lac expression. Steady state Plac-GFP levels for ancestor, lacI and lacO1 strains grown in DM+Glu (0.02–0.2%) supplemented with varying levels of lactose or the gratuitous inducer IPTG. Steady state conditions were obtained by starting cultures with small numbers of cells and back diluting cultures into pre-warmed fresh media when they reached an OD600 of ∼0.1, thereby keeping cells in exponential growth. After two propagation cycles GFP levels were measured with a flow cytometer. Under these growth conditions cells do not exhaust glucose levels and do not utilize lactose as a carbon source. As can be seen, lacO1 mutants induce their lac operon in the presence of low external concentrations of lactose, even during growth on relatively high concentrations of glucose.Effectiveness of inducer exclusion at repressing (TIF)Click here for additional data file.Figure S5lac-GFP and PlacO(-)-RFP reporters were grown in conditions with fully active LacI (no TMG) and fully inactive LacI (100 µM TMG). Plac-GFP responds to the changes in LacI activity whereas the PlacO(-)-RFP reporter is invariant with respect to inducer concentration. B) Dependence of pRM102-3 reporter on CRP and cAMP. Expression of the PlacO(-)-RFP reporter was measured in a cya, cpdA, lacY mutant over a range of concentrations of exogenous cAMP. This strain cannot make or degrade cAMP, ensuring that the reporter is responding to the external source of cAMP. As can be seen, RFP levels increase as a function of cAMP levels. To verify that the increases in expression were CRP dependent we looked at the response of the PlacO(-)-RFP reporter in a crp null mutant background. No increase in expression above background was seen.Efficacy of CRP-dependent reporter pRM102-3. A) Insensitivity of pRM102-3 to activity of LacI. Strains harboring both P(TIF)Click here for additional data file.Figure S6lac-GFP) for the ancestor measured at three different time points . Inducer response profiles are stable over this time range and for TMG concentrations with bimodal expression profiles the ratio of cells in the low and high expression states remains constant. B) Mean values for the Plac-GFP reporter over time for the ancestor and nine evolved clones grown in DM+Glycerol (0.4%) supplemented with 4 different TMG concentrations , except the ancestor where TMG concentrations are 0,4,10 & 100 µM. Expression levels were relatively stable between 15–18 hours of growth, which we operationally defined as the pseudo-steady state window.Defining a steady state window for flow cytometry analysis. A) Inducer response profiles (P(TIF)Click here for additional data file.Figure S7lac-GFP) run on five independent days. Flow cytometry traces are overlaid at each TMG concentration. Numbers at the top of individual panels indicate the concentration of TMG (µM) present during population growth. B) Mean response of the ancestral inducer response profiles. Standard error is shown (n = 5) and varied from 1–8% of the mean value over the range of TMG concentrations tested, except at 20 µM TMG which showed slightly larger variation in mean RFU due to small differences in the proportion of cells in low and high expression states.Reproducibility of inducer response measurements. A) Inducer response profiles for the ancestor (P(TIF)Click here for additional data file.Table S1lacI, and lacO1) within 12 replicate populations of E. coli B REL606 after propagation for 100, 200, 300, and 400 generations in the G+L environment.Frequency of alleles Click here for additional data file.Table S2lacI, and lacO1) within 12 replicate populations of E. coli B REL606 after propagation for 100, 200, 300, and 400 generations in the G/L environment.Frequency of alleles Click here for additional data file.


Participant retention is a frequent concern in structured weight-management programs. Although research has explored participant characteristics influencing retention, little attention has been given to the influence of program characteristics. The objective of this study was to examine how program characteristics relate to participant retention in the Veterans Health Administration’s weight-management program, MOVE!We conducted semistructured interviews with coordinators of 12 MOVE! programs located throughout the United States, 5 with high participant retention rates and 7 with low rates. We transcribed and descriptively coded interviews and compared responses from high- and low-retention programs.Characteristics related to retention were provider knowledge of and referral to the program, reputation of the program within the medical facility, the MOVE! meeting schedule, inclusion of physical activity in group meetings, and involvement of the MOVE! physician champion. MOVE! introductory sessions, frequency of group meetings, and meeting topics were not related to retention. Coordinators described efforts to improve retention, including participant contracts and team competitions. Coordinators at 5 high-retention facilities and 1 low-retention facility discussed efforts to improve retention.Coordinators identified important program characteristics that could guide improvements to retention in group-based weight-management programs. Training for providers is needed to assist with referral decisions, and program planners should consider incorporating physical activity in group meetings. Weight management is a major health problem in the United States; more than two-thirds of Americans are overweight or obese  and, theOur study examined the Veterans Health Administration (VHA) weight management program, MOVE!, which was developed by the VHA National Center for Health Promotion and Disease Prevention (NCP). Most 98%) of the approximately 150 VHA medical centers across the country offer a MOVE! program 8% of the. MOVE! sPrevious research on MOVE! identified program characteristics  associated with successful program implementation ,11,12 anPatient characteristics, such as age, depression, and dieting history, influence retention in weight-management programs ,14,15. TWe selected participants using dissimilar case sampling , in whicOur study was reviewed and approved by the authors’ local VHA institutional review board. Coordinators were informed that the purpose of the interview was to gather information about their MOVE! program to identify factors related to patient retention, and to discuss any efforts to improve retention at their facility. The first author (S.M.L.), an experienced qualitative researcher, conducted telephone interviews in 2010 using a semistructured interview guide . Three mTwo research team members developed categories of program stages  based on earlier research describing patient assessment, referral, program attendance , and optional follow-up . Additio2 or higher and, if the patient was interested, request a MOVE! consultation. In 5 programs (3 low- and 2 high-retention), patients were also self-referred; self-referral was the primary referral method in 2 programs, both with low retention.Referral method was not related to differences in retention . In 10 pAlthough referral method appeared unrelated to retention rate, provider support for and knowledge about the program differed between high- and low-retention programs. Coordinators at 4 low-retention programs said patients attended orientation without knowing why they were there or were often referred by providers who offered little discussion of MOVE!: “[The patients] don’t know they’ve been referred . . . they don’t know anything about it . . . I . . . think maybe the doctors just say, ‘Do you want to lose weight?’ and the patient says yes. . . . Maybe the doctors just don’t know what MOVE! is” (low-retention program). Only 1 coordinator of a high-retention program noted any such problems: “I would like to see a more consistent understanding among providers of what [MOVE!] is.”Program reputation may also be related to retention. One coordinator from a high-retention program discussed a positive reputation: “Our program has a good reputation here. We . . . always have plenty of recruits, plenty of veterans referred to us.” On the other hand, a coordinator from a low-retention program discussed a negative reputation: “MOVE! has gotten a lot of negative attention in the past. . . . The doctors got bad feedback.”After referral, participants in 10 programs (5 low- and 5-high-retention) attended an orientation session. Five programs held orientation sessions individually (1 low- and 4 high-retention) and 5 as a group (4 low- and 1 high-retention). One facility that allowed walk-ins without referral at their orientation session had low retention. Overall, use of orientation sessions was unrelated to differences in retention.After orientation, patients in all programs could select self-management support by telephone, Internet, or group meetings. Coordinators said veterans were encouraged to enroll in group meetings.Retention was unrelated to whether veterans could join mid-cycle . PatientProgram retention also was unrelated to frequency of meetings; 5 low- and 5 high-retention programs offered classes once per week or every other week. The number of classes offered in a cycle also did not relate to retention; 9 programs (5 low- and 4 high-retention) offered 6 or more sessions in a cycle. On the other hand, retention rate differed by flexible scheduling; 2 high-retention programs offered more than 1 meeting time, to accommodate different schedules, whereas no low-retention programs did so.When planning the program and determining which sessions to hold, all coordinators used handouts and activities from the MOVE! website; only 2 coordinators, both of high-retention programs, used complete lesson plans provided by NCP. Most coordinators instead supplemented MOVE! materials with presentations by guest speakers, the coordinators’ professional knowledge, and materials from other weight-management programs.All programs offered sessions on diet and nutrition and psychology; for 11 programs, diet and nutrition topics made up more than half of program content. Eight programs (6 low- and 2 high-retention) used lecture primarily; 4 programs (1 low- and 3 high-retention) were interactive, using group discussion and activities. Whereas 9 programs discussed physical activity in class, high-retention programs were more likely to incorporate physical activities into class sessions; 2 low- and 4 high-retention programs included physical activities, such as walking, chair exercises, and balance/stretching in class.All coordinators identified retention as a major issue in the group program. Availability of resources, such as space, was a barrier for both high- and low-retention programs: “Our classroom right now, if I would have everyone show up . . . it would be extremely crowded. I kind of bank on some people not showing up” (high-retention program). “[The veterans] don’t like where we are. It’s a small area. . . . There’s nowhere else. . . . They haven’t allotted us space” (low-retention program).Another barrier to improving retention was patient mix. Coordinators of 2 high- and 5 low-retention programs noted poorer attendance and participation among women: “I notice that some women don’t like to be in a room with 12 men. That makes them uncomfortable” (high-retention program). “The women have different issues than the men. . . . They would probably feel more comfortable with a group of their own” (low-retention program).Two programs (1 low- and 1 high-retention) created a separate MOVE! program for women through collaboration with the Women’s Health Service: “We didn’t have a lot of women participating in the group classes, and when we did, their attendance was sporadic. . . . [Participants] really enjoy just having a women’s-only class and not having to deal with any pressures [with discussing certain issues] in front of a man” (low-retention program).Despite resource limitations, coordinators discussed strategies for improving retention. Coordinators from high-retention programs were more likely to discuss active efforts to improve retention (5 high- vs 1 low-retention), including participant contracts addressing active participation and attendance and team competitions and challenges. Coordinators of low-retention programs, on the other hand, often discussed poor retention as inevitable: “I know that my retention rate is going to drop off by about 60% to 80% after the first week.” “I tell [patients] in their first meeting . . . ‘Look around, because half of you won’t make it to class 3.’ They just don’t come.”At the end of the program cycle, participants were presented with follow-up options. One option that all programs allowed is re-attending classes. Re-attendance was subtly discouraged in 3 programs (2 low- and 1 high-retention) because of space constraints or observations of poor engagement by returning participants: “After they’ve been through [MOVE!] a time or 2, it’s like a social event for them. They come and visit and they’re not very active with trying to lose weight” (high-retention program).Ten programs (5 low- and 5 high-retention) had a support group for past participants. Support groups met weekly or monthly; topics were determined by what was timely  or of interest to participants. Four programs (2 low- and 2 high-retention) also used TeleMOVE! for follow-up. TeleMOVE!, a new home-based program, uses a telephone-connected device to collect and transmit information between the participant and their VHA facility : “Some oSome general infrastructure issues  were related to program retention. Eight programs (3 low- and 5 high-retention) were located in the facility’s Nutrition and Food Services; 4  were located in the facility’s Primary Care department. Additionally, 7 programs (5 low- and 2 high-retention) lacked a physician champion or had one in name only. Only 2 low-retention programs had a physician champion who was involved in teaching classes. Although 2 high-retention programs also lacked a physician champion, coordinators at these facilities felt they had strong support from other sources, such as facility leadership: “Our primary care service line manager is very supportive. He’s all gung-ho for [MOVE!]” (high-retention program). “[Leadership has] been supportive of innovation. . . . I’ve had a lot of autonomy to manage the program” (high-retention program).Although the job description specified the skills and attributes of the physician champion, coordinators felt the role of the physician champion was unclear, which was a barrier to greater involvement. Coordinators reported they had sufficient staff members to conduct classes but lacked personnel for other MOVE! activities, such as scheduling patients, informing facility staff about the program, and planning sessions: “I’m a one-man band here. I do all of the scheduling, all the canceling . . . the support groups, and there’s getting to be too many patients” (high-retention program). “[I would like] all those things that [MOVE!] is supposed to have . . . it’s supposed to be a team effort. . . . It just doesn’t happen. . . . I would like it to be the way they advertise it — a team” (low-retention program).When coordinators were asked what patient characteristics they felt influenced retention, they listed age, health status, and personal investment or motivation. Driving distance was identified as a factor by coordinators of 6 programs (2 low- and 4 high-retention). These coordinators dealt with driving distance in 1 of 2 ways. One way, used by 2 high- and 2 low-retention programs, was to enroll patients in TeleMOVE!. Another method, used by 2 high- and 1 low-retention programs, was to offer MOVE! at affiliated community-based outpatient clinics (CBOCs): “[Our medical center is] not very centrally located . . . and so depending on . . . how far they have to travel, that’s probably the biggest [issue]. . . . We have the CBOCs involved, so they can travel to a CBOC and see the same thing” (high-retention program).Factors that may relate to MOVE! participant retention were provider knowledge and referrals, program reputation, inclusion of physical activity, engagement of physician champions, and multiple meeting times. Although our research design precludes us from inferring that these factors caused retention, many of these factors have been identified as important for other MOVE! outcomes ,11-13 suProviders and participants may be more supportive of the program if they had a better understanding of purpose and content. The MOVE! physician champion could take on a more active role to promote change through social influence, knowledge sharing, and interventions . Woolf  recommenBecause physical activity during meetings was related to high retention in this study, program planners should consider adding these activities. More weight loss occurs when weight-management programs include physical activity rather than nutrition only . MOVE! pRegardless of retention, all programs discussed diet and nutrition and health behavior; there were no differences in materials used for planning classes. Ability to self-refer or join the meetings mid-cycle were also unrelated to retention, indicating that improving access to the program may not necessarily increase retention. Finally, although coordinators perceived resource issues, few programs had a dedicated budget, and coordinators of high- and low-retention programs were equally likely to discuss staff and space constraints.Because little is known about weight-management program features that influence retention, the qualitative approach of this study was appropriate. Our findings provide a foundation for improving retention, albeit one that needs to be confirmed by further research. The study has limitations. We did not randomly select VHA facilities; instead, facilities were selected to represent 2 levels of retention, high and low, to highlight differences between programs. We did not interview program staff other than coordinators. Finally, coding procedures and codebook definitions were discussed with the research team, and the framework used to categorize responses was based on prior research. However, we did not conduct a full inspection of coding reliability.Obesity has become a significant public health concern, receiving growing attention from VHA and the medical community. The results of this study can help support the MOVE! program and non-VHA weight-management programs in determining best practices. Though past research has focused largely on participant characteristics related to retention, program characteristics may also influence retention.


The Internet is a promising medium in the field of health promotion for offering tailored and targeted lifestyle interventions applying computer-tailored (CT) techniques to the general public. Actual exposure to CT interventions is not living up to its high expectations, as only a (limited) proportion of the target group is actually using these programs.To investigate exposure to an Internet-delivered, CT lifestyle intervention, targeting physical activity, fruit and vegetable intake, smoking behavior, and alcohol intake, we focused on three processes: first use, prolonged use, and sustained use. The first objectives were to identify user characteristics that predict initiation of an online CT lifestyle program (first use) and completion of this program (prolonged use). Furthermore, we studied the effect of using a proactive strategy, consisting of periodic email prompts, on program revisits (sustained use).The research population for this study consisted of Dutch adults participating in the Adult Health Monitor, offered by the regional public health services. We used a randomized controlled trial design to assess predictors of first use, prolonged use, and sustained use. Demographics and behavioral characteristics, as well as the strategy used for revisiting, were included as predictors in the model.P < .001).A total of 9169 participants indicated their interest in the new program and 5168 actually logged in to the program. Participants significantly more likely to initiate one of the CT modules were male, older, and employed, and had a lower income, higher body mass index, and relatively unhealthy lifestyle. Participants significantly more likely to complete one of the CT modules were older and had a higher income and a relatively healthier lifestyle. Finally, using a proactive strategy influenced sustained use, with people from the prompting condition being more likely to revisit the program  and Medical Ethics Committee of Maastricht University and the University Hospital Maastricht (NL2723506809/MEC0903016); http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=1786 (Archived by WebCite at http://www.webcitation.org/65hBXA6V7) With a substantial number of people accessing the Internet in search of health-related information ,2, the IDespite these promising prospects, actual exposure to CT interventions is not living up to the high expectations -23, as oTo increase adoption rates of online interventions, it is imperative to obtain detailed profiles of those who successfully adopt an intervention ,25. By cBesides focusing on first use of the program, it is also imperative to optimize prolonged use of the program and to study related determinants. Previous studies indicated that tailoring in itself is a strategy to prevent early disengagement from online behavior change programs ,45,46. FOnline interventions are often offered reactively to the public, implying that a passive approach is used, in which users themselves must act in order to repeatedly benefit from the intervention . However In the present study, we aimed at answering three questions. First, which user characteristics predict initiation of an online CT program (first use)? Second, which user characteristics predict completion of the online CT program (prolonged use)? Third, what is the effect of using a proactive strategy, consisting of periodic email prompts, on program revisits (sustained use)? We addressed these questions among participants in an online CT intervention aimed at multiple health behaviors: physical activity, fruit and vegetable intake, smoking cessation, and alcohol consumption.In this randomized controlled trial (RCT) we compared the effect on program revisit of a proactive technique applying a periodic email prompt versus the use of a reactive approach. Levels of use and reuse of the program within a 4-month period were studied and linked to specific user characteristics. The current RCT was part of a larger RCT testing the effect of the CT program compared with a control group . Only pe The research population for this study consisted of Dutch adults of the provinces of Zeeland and North-Brabant, which participated in the Adult Health Monitor 2009 . This MoAfter completing the online version of the Adult Health Monitor, all participants were introduced to the program and were offered the opportunity to receive, free of charge, CT feedback about their current health behaviors, such as physical activity, fruit and vegetable intake, alcohol consumption, and smoking. This program consisted of several modules, one per behavior, which incorporated questionnaire items and provided feedback on several sociocognitive determinants of each health behavior. The content of the program modules was based on programs that have been proven to be effective in RCTs for increasing smoking cessation, promoting the intake of fruit and vegetables, increasing the level of physical activity, and reducing the consumption of alcohol ,51-54.Participants who were interested in the new program were asked to leave their email addresses. They received an email including an invitation to log in to the CT program with a personal log-in code and password, approximately 3 weeks after completion of the Monitor. By logging in to the program, participants received detailed information on the content and purpose of the study. Subsequently, data on demographics and the five health behaviors obtained through the Monitor were transported to the CT program, resulting in a personal overview of individuals’ current health behavior status. If respondents were not adhering to the Dutch public health guidelines set for these behaviors, a module generating CT health advice for changing behavior was available for each health behavior.During a 4-month period after the baseline visit, we monitored use of the intervention. People in the prompting condition were prompted proactively via email 3 months after their baseline visit to revisit the CT program. Revisits to the program were stimulated to provide participants with the opportunity to monitor their own behavior. During a revisit participants could log in to the program and complete the health risk appraisal questionnaire. Based on their answers, a new personal health overview was composed entailing information on their current health behavior status, as well as on their status during all previous visits. Improvements, deteriorations, or stability of health behaviors were graphically presented. People in the no-prompting condition did not receive any additional prompts and were encouraged only at baseline to revisit the program. Reactions to this email prompt were monitored during a 1-month period.All included participants were randomly allocated to a prompting condition  or a no-prompting condition . We used a computer software randomization device to determine random allocations at the respondent level.awareness of their health behavior status, by comparing their status to the Dutch public health guidelines set for these health behaviors . In this health risk appraisal, feedback messages were used to inform people of their status for each health behavior and to provide them with additional information on the content of the separate guidelines. These feedback messages were complemented by using graphic representations of traffic lights [The CT program used a dual approach to guide people toward behavior change. The first part consisted of a health risk appraisal and was aimed at increasing participants’ c lights , with a changing participants’ health behavior by offering five separate CT modules. The content of these modules was based on the Integrated Model for exploring motivational and behavioral change (I-Change Model) [ Second, assistance was provided in e Model) . The mode Model) -59. DemoThe CT modules were embedded in a website (http://www.mijngezondgedrag.nl/) that was especially designed for the current project. This website contained general information considering a healthy lifestyle and the selected health behaviors. Furthermore, the website provided specific information regarding the project, a direct link to the CT program, and information on frequently asked questions. During the study, new information  was structurally added to the website.Participants in the prompting condition received an email 3 months after their baseline visit, prompting them to revisit the program. This email opened with a personalized greeting and reminded people about their first visit to the program. Subsequently, people were invited to revisit the program to obtain information on their health status and to monitor their progress. Participants were also informed of the opportunity to receive additional, iterative health advice on the health behavior(s) selected at baseline or on a new behavior. Finally, to facilitate logging in to the program, the email also contained details on personal log-in information (user name and password). The email concluded with greetings from the research team and contact information.We collected user characteristics to produce a detailed user profile including information on personal characteristics, health behavior status, and intention to change their health behaviors.Questions pertaining to personal characteristics included questions on age, gender, educational level, personal net monthly income, work situation, marital status, and native country .Health behavior status consisted of information regarding the five key behaviors. Physical activity was measured by the Short Questionnaire to Assess Health-Enhancing Physical Activity , and guiData on participants’ height (in centimeters) and weight (in kilograms) were used to calculate their body mass index (BMI). BMI is a heuristic used to estimate the level of body fat, and it is defined by dividing a participant’s body weight by the square of the participant’s height.Intention to change a health behavior was measured by means of 1 item using an algorithm consisting of 10 stages varying from unawareness to action. This variable was recoded in accordance with an adjusted version of the stages of change concept: immotivation (1), precontemplation (2), contemplation (3), preparation (4), and action (5) .After completion of each single module, visitors were asked to evaluate the module by providing an overall grade (1 to 10).To measure first use of the program, we created a dichotomous variable based on program monitoring data, indicating whether participants initiated a module. Initiating a module was labeled as yes when people filled out the first question of this module. To measure prolonged use, we created a new dichotomous variable to indicate whether participants finished a module. Completion of a module was labeled as yes when people also filled out the final question of the module. To establish sustained use of the program, we created another dichotomous variable, indicating whether participants logged in to the program after baseline. Revisiting of the program was measured by comparing the dates of log-in with the baseline date.First, we used general descriptive statistics to describe personal characteristics of the participants, as well as the main findings concerning current health behavior adherence to the public health guidelines. Second, logistic regression analyses were conducted. Initiation of a module and completion of a module (0 = no/1 = yes) were the dependent variables. Demographics , BMI, and health behavior status were included in the model as predictors of initiation. We included the same variables, with addition of intention (measured at the beginning of each CT module), in the model as predictors of completion. Logistic regression analysis was conducted with revisiting of the program (0 = no/1 = yes) as the dependent variable. The same demographics, as well as study condition, program evaluation, and initiation and completion of a module, were included in the model as predictors of revisiting of the program. All statistical analyses were done in SPSS version 7.0 .In total, 3448 people were allocated to the study arm receiving computer tailoring of the larger RCT and were included in the current RCT. Of all participants, with a mean age of 44 (SD 12.60) years, a little more than half were men, and most had a medium to high education level and an average to high monthly income . Three-qRegarding the five health behaviors included in the program, 14.8% (509/3448) did not comply with the Dutch guidelines of at least 30 minutes of moderately intensive physical activity at least 5 days a week. With regard to fruit and vegetable intake, respectively 54.99% (1896/3448) and 67.49% (2327/3448) were not adhering to the Dutch guidelines of at least two pieces of fruit and at least 200 g of vegetables each day. About one in five visitors indicated to smoke 19.1% (658/3448), and a little more than a quarter  did not comply with the Dutch guidelines for alcohol intake.A total of 1338 participants (38.81%) did not participate in the second part of the program, leaving after receiving their personal health overview. The remaining 2110 visitors (61.19%) decided to obtain CT lifestyle advice by initiating one of the five behavioral modules. Of all the visitors who decided to obtain personalized health advice, 13.0% (n = 275) initiated the physical activity module, 35.4% (n = 747) and 29.4% (n = 621) initiated the vegetable and fruit modules, respectively, 9.1% (n = 191) initiated the smoking module, and 13.1% (n = 276) initiated the alcohol module .P < .001).Results of multiple logistic regression analyses indicated that male, older, and employed participants, and those with a lower income and a higher BMI were more likely to initiate one of the CT modules . InitiatP < .05).*Significant association , 62.37% completed a module (n = 1316). A total of 153 of 275 visitors completed the physical activity module (55.6%), 462/747 (61.9%) and 403/621 (64.9%) completed the vegetable and fruit modules, respectively, 130/191 (68.1%) completed the smoking module, and 168/276 (60.9%) completed the alcohol module . All modResults of multiple logistic regression analyses indicated that older participants and participants with a higher income were more likely to complete one of the CT modules . ParticiP < .05).*Significant association . A total of 113 participants (6.3%) reacted to the email prompt after 3 months and revisited the program. This number was significantly higher than in the no-prompting condition that received no additional reminder .Additional emails prompting a revisit to the program were sent to 51.91% of all participants (n = 1790), while 48.09% (n = 1658) did not receive an additional reminder. In total, 3.5% (n  =  120) of all visitors decided to revisit the program within a 4-month period after baseline. Among those in the prompting condition, 0.2% (n = 3) revisited the program within the first 3 months (P < .001). However, after multivariate analyses, including all predictors, this effect became nonsignificant. Since the number of people from the control condition who revisited the program was extremely low (n = 4), we conducted additional analyses on predictors of revisiting solely within the experimental condition. Univariate logistic regression analyses revealed that completion of a behavior change module significantly predicted revisiting the program  and completion (prolonged use) of the CT program and tested a proactive strategy using periodic email prompts to enhance revisits to the program (sustained use).Since people with a lower income and educational level are generally more inclined to have an unhealthy lifestyle ,65, the  However, taking a closer look at CT module initiation reveals that initiators were significantly older, and likelier to be male, employed, and to have a lower income. Furthermore, participants with a higher BMI and an unhealthier lifestyle were more likely to start with one of the CT modules. Whereas the findings concerning age and gender are largely comparable with those of earlier studies ,30,38,40The results imply that the current program succeeded in stimulating those people who are expected to benefit most from lifestyle interventions  to initiate one of the CT modules. Embedding the CT program in an existing context, the Adult Health Monitor, may account for these findings. The main objective of the Monitor is to assess different aspects of general health and health-related topics, such as curative care, environment and everyday surroundings, social environment, social safety and violence and nursing and care. Due to this simple focus on assessment and not on health behavior change, participation may have been appealing to participants who are not primarily interested in behavior change. However, direct transportation of data from the Monitor to the CT program database allowed for the immediate and effortless composition of a personal health overview. Subsequently, this overview may have increased awareness of people’s lifestyle status and might have served as a cue to action to change their lifestyle, explaining the increased interest in the CT modules, among the unhealthy and low-socioeconomic status participants.Even though the CT program succeeded in attracting people with a relatively unhealthy lifestyle and low-socioeconomic status people to initiate one of the CT modules, it fell short in actually engaging these visitors, since module completers predominantly had a higher income, which is in line with previous studies ,30,38,40 Effort should therefore be put into adjusting program components and content to the needs and wishes of this target group and into developing strategies to keep low-socioeconomic status participants engaged in the program.Our results from the current study indicate that prompting visitors to revisit the CT program is an effective strategy to enhance sustained use. However, the effect was limited, with only small percentages of people actually responding to the prompt. Replicating this study is therefore desirable. A possible explanation for these results might be the level of tailoring in the prompt. Although the content of the prompt was tailored to user characteristics, the level of personalization may have been too low, making the prompt less appealing and relevant. Furthermore, since the first prompt was sent after 3 months, people may have forgotten about the program and their participation in it.The added value of the email prompts used in the present study could be further augmented. Several studies have suggested that the effect of email prompts increases if they alert people to new content that is provided on the program website  and if tSince email is a very popular online function used by substantial number of Internet users , it is aThis study focused on exposure to an online CT lifestyle program by mapping characteristics of first-time users, examining predictors of first, prolonged and sustained use and by testing the efficacy of a proactive strategy to increase sustained use of the program. Even though several studies have highlighted the added value of periodic email prompts , this stHowever, this study is liable to several limitations that need to be accounted for when interpreting the results. First, we used one generic email to prompt revisits to the CT program. Future research should thoroughly study the optimal content of prompts, in order to make them stand out amidst advertisement emails and spam, and to make them more personally relevant and persuasive. Principles used in the field of e-marketing and e-commerce might provide essential information on specific strategies to increase the attractiveness of email prompts.Finally, even though this study provides evidence for the effectiveness of prompts in enhancing program revisits, additional research is needed to study their effect on subsequent behavior change because, due to a high dose–response relation, multiple visits to the program are necessary to maximize its effect on health behavior.We found that the CT modules were significantly more often initiated by older, male, employed, low-income participants with a relatively unhealthy lifestyle, which implies that the program succeeded in reaching and engaging those people who benefit most from online lifestyle interventions. However, people with a lower income tended to disengage from the CT program before finishing the modules. This underlines the importance of additional research into specific program adjustment and strategies that can be used to stimulate prolonged program use by low-socioeconomic status visitors. Furthermore, sending periodic email prompts significantly increased revisits to the program. Though promising, this effect was modest and needs to be further examined, in order to maximize the potential of periodic email prompting.


To the Editor: Toscana virus (TOSV) is an arthropod-borne RNA virus (family Bunyaviridae and genus Phlebovirus) transmitted by sandflies in Mediterranean countries. TOSV causes acute meningitis and meningoencephalitis in patients. In France, cases of TOSV infections involving resident populations and cases imported by tourists traveling in TOSV-endemic countries have been reported  developed by using the recombinant nucleocapsid (N) protein of TOSV. This serologic test was validated in a previous study that revealed high specificity and sensitivity (Presence of immunoglobulin (Ig) G and IgM against TOSV was investigated by using a commercial enzyme immunoassay kit  of 729 plasma samples were positive for IgG against TOSV N protein. Twenty-four (3.3%) plasma samples were positive for IgM, and 5 (0.7%) were positive for IgG and IgM .To confirm the ELISA results, IgG-positive samples were further subjected to Western blot (WB) analysis by using TOSV  in the Alpes Maritimes territorial district, which borders Italy. Our results regarding this area appear of the same order of magnitude as those reported in the general Italian population (>20%) , factors that affect vector distribution and abundance  of the population sampled showed an IgG- or IgM-positive response. In the other districts, the IgM seroprevalence did not exceed 4.4%. The vector that transmits TOSV is known to be present in this area (Our results demonstrate that 14.1% (IgG and IgM) of the healthy population (blood donors) in France living on the Mediterranean border have been in contact with TOSV and show asymptomatic or mild, unidentified symptoms, as it is the case for many other arbovirus infections (Further investigation is needed to better assess how widespread TOSV is in populations. For example, a donor–recipient investigation might confirm virus transmission by blood transfusion, and studies related to the behavior of sandfly vectors, virus biology, and mammalian reservoir hosts could help define populations at higher risk for exposure.


TNFSF13 gene was significantly associated with IgM levels . Besides, smoking was identified to be associated with IgM levels in both stages (P<0.05), but there was no significant interaction between smoking and the identified SNP (P>0.05). It is suggested that TNFSF13 may be a susceptibility gene affecting serum IgM levels in Chinese male population.IgM provides a first line of defense during microbial infections. Serum IgM levels are detected routinely in clinical practice. And IgM is a genetically complex trait. We conducted a two-stage genome-wide association study (GWAS) to identify genetic variants affecting serum IgM levels in a Chinese population of 3495, including 1999 unrelated subjects in the first stage and 1496 independent individuals in the second stage. Our data show that a common single nucleotide polymorphism (SNP), rs11552708 located in the  IgM is the first antibody to be produced during an immune response, the first to appear during ontogeny and is also the oldest, being the sole class of antibody to be present in all vertebrate species IgM is produced by two different B-lymphocyte populations. B1-cells synthesize IgM called natural antibodies (NA), which is not connected with immunization Reports of pedigree studies or twin studies have shown that genetic factors are important in determining serum total immunoglobulin and specific antibody levels in human Stage 1 of the GWAS included 1999 unrelated healthy Chinese men age 20–69 years old from the Fangchenggang Area Male Health and Examination Survey (FAMHES). The FAMHES is described elsewhere Stage 2 of the GWAS consisted of 1496 healthy Chinese men age 20–69 years old. They were randomly selected from male participants who participated in physical examinations from September 2009 to September 2010 in the Medical Centre of Fangchenggang First People's Hospital, Guigang People's Hospital and Yulin First People's Hospital. The stage 2 samples from Fangchenggang First People's Hospital were independently recruited from the stage 1 samples. Among these participants, 996 were of Han ethnicity and 500 were of Zhuang ethnicity.The same recruitment strategy was used in stages 1 and 2. Comprehensive health information was collected through clinical examination, and additional demographic information was obtained via a standardized questionnaire. All participants self-reported to be free of diabetes mellitus, coronary heart disease, stroke, hyperthyroidism, rheumatoid arthritis, tumors, Systemic lupus erythematosus (SLE), Celiac disease and impaired hepatic or renal function. We obtained written documentation of informed consent from all study participants, and the research protocol was approved by the local Ethics Committee. Drinking behavior was assessed on the basis of a self-administered life-style questionnaire. Alcohol consumption was classified into two categories: drinkers and non-drinkers. Respondents that reported drinking any beverage more often than ‘less than once a year’ or ‘never’ were coded as drinkers The description of the laboratory test has been previously reported in detail Two different platforms were used for SNP genotyping. The Illumina Omni 1 platform was used for a genome-wide assay of samples in stage 1. The Sequenom iPLEX system  was used in the second stage. Polymerase chain reaction and extension primers were designed using Mass ARRAY Assay Design 3.1 software . Genotyping procedures were performed according to the manufacturer's iPLEX Application Guide (Sequenom Inc.). All genotyping reactions were performed in 384-well plates. Each plate included a duplicate for three or four participants selected at random, as well as six to nine negative controls in which water was substituted for DNA. The average concordance rate was 99.8%.P<0.001 for the Hardy–Weinberg equilibrium test, minor allele frequency <0.01 and genotype call rate <95%. Based on these criteria, 709 211 SNPs were retained. The IMPUTE computer program First-degree cryptic relationships were evaluated via an identity-by-descent (IBD) analysis. Then, quality control (QC) procedures were applied to 1999 unrelated individuals that were genotyped using the Illumina Omni-Express platform. A total of 1999 individuals passed the call rate of 95% and were used in the final statistical analysis. We then applied the following QC criteria to filter SNPs: IgM values showed a markedly skewed distribution, and natural logarithmic (ln) transformations were performed to approximate normality , S2. AnaP<10−7, multivariate linear regression analysis was applied to test the independence of the respective SNPs. Only the SNPs that remained significant at 10−7 in the multivariate analysis were selected. The combined analysis of two-stage data was performed using a linear regression, adjusting for the covariates and stage information. For the first stage, we have 80% power to detect a 0.155 of difference IgM , based on 1,999 samples, assuming MAF 0.2 and P-value of 5E−07. For the second stage, we have 80% power to detect a 0.07 of difference IgM , based on 1,496 samples, assuming MAF 0.2 and P-value of 1.25E−02.For regions with multiple SNPs that were significant at p = 0.54), body mass index (BMI)  and smoking behavior (p = 0.66), excepting for alcohol consumption (p = 0.02) (P = 0.013), whereas no significant difference was detected in the second stage (P = 0.129). Besides, Smokers had significantly lower IgM levels than non-smokers in both stages  . There wh stages .p-values indicates no systematic bias, with an inflation factor of 1.03 (The quantile-quantile (Q-Q) plot of adjusted  of 1.03 . When thP–value of 5.0×10−7. After testing the independence of the associated single nucleotide polymorphisms (SNPs) at each of the four loci using the multiple regression analysis, one SNP per region remained to be followed in the second stage . They are located in PRKCB, SENP3, TNFSF13, and FXR2, respectively.The GWAS results were presented in the manhattan plot . In the P-value cutoff of 1.34×10−3(adjusting for four tests). When the data from the two stages were combined, the SNP rs11552708 reached a genome-wide significant level of 5.0×10−8, with P-value of 4.22×10−9  was confirmed at .22×10−9 . SNP rs1P>0.05).We further investigated potential interactions between environmental and genetic factors. And none of the identified SNPs showed statistically significant interactions with smoking or alcohol consumption . APRIL  is coded by TNFSF13. It is a type II membrane protein of 250 amino acids and its extracellular domain is cleaved at the RKRR motif of amino acid 101–104 by a furin convertase and then secreted-Rs11552708 was located on chromosome 17p13.1 . It is aAPRIL has been previously confirmed to have the ability to affect antibody production APRIL was originally described to stimulate growth of tumor cells in vitro and in vivo TNFSF13 gene on systemic lupus erythematosus (SLE) TNFSF13 rs11552708 polymorphism and IgM level in Chinese male population.Moreover, there are also GWAS reporting the impact of TNFSF13 was related to serum IgM level in a healthy Chinese male population using a two-stage GWAS. However, this study only focused on male participants, and can't offer the data on female participants about the relationship of rs11552708 at TNFSF13 with serum IgM levels.Our study firstly reported that rs11552708 at TNFSF13 rs11552708 polymorphism was significantly associated with serum IgM, which suggested that TNFSF13 may be a susceptibility gene affecting serum IgM levels in Chinese male population. Further studies on the association of TNFSF13 gene with IgM in other populations are warranted.In summary, we performed a two-stage GWAS in a Chinese male population to explore the genetic influence on serum IgM level. Our study observed the Figure S1A histogram of the log transformed IgM for stage one.(DOC)Click here for additional data file.Figure S2A histogram of the log transformed IgM for stage two.(DOC)Click here for additional data file.Figure S3Regional plots for the associations of the SNPs at chr17 with serum IgM levels in the first stage of GWAS. SNPs plotted with their −log10  in the GWAS based on their physical chromosomal positions. Genotyped SNPs are indicated as circles, while imputed SNPs are indicated as quadrangles. The color scheme indicated the linkage disequilibrium displayed as r2 values between all SNPs and the top-ranked SNP (in purple color). The blue lines represent the recombination rates estimated based on HapMap Phase II database. The plots were drawn using Locus Zoom software.(DOC)Click here for additional data file.Table S1The influence of smoking and alcohol drinking on serum IgM level.(DOCX)Click here for additional data file.Table S2Association results of discovery stage for established IgM loci.(DOCX)Click here for additional data file.