Direct comparisons between our study and previous ones are limited by different methodologies, timeframe and product categorisation.

However, our country-level results are consistent with the recent trends and degree of nutrient variability reported by previous peer-reviewed studies using similar methodological approaches and timeframe.

For sugars, the authors of [19] used an annual cross-sectional study using nutrient composition data for products available online to assess changes in sales of soft drinks in the UK from 2015 to 2018.

They estimated a 30% decline in the per capita volume of sugars sold from soft drinks in the UK between 2015 and 2018 (equivalent to a reduction of 4.6 g per capita per day), which were translated into a statistically significant reduction of 1.5 g/100 mL of the sales-weighted mean sugars content of soft drinks.


Using the same methodology, the same authors estimated a 13.3% decrease (95% confidence intervals (CI): −19.2% and −7.4%) in the sales-weighted mean sugar content for breakfast cereals in the same period [20], close to our point-estimate of −9.0%.

Similar analyses were conducted by [25], and they found that energy drinks presented a statistically significant reduction of 10% in their total sugars content (from 10.6 (±3.2 g) to 9.5 (±3.3 g) g/100 mL) between 2015 and 2017 in the UK.

Manufacturers of the sugar-reduced products had either only reduced sugars or had alternatively reduced sugars, replaced with non-caloric sweeteners without changing the product name, for example, by calling the product 'light' and so on.

Despite the observed reduction, the sugars content of energy drinks was still at concerning levels by the end of the study period.


Particularly for Soft Drinks in the UK, our estimates point to a 5% reduction (−0.3 g/100 mL) in the market-share weighted sugars mean and a 5% decrease (−0.3 g/pc/day) in the daily per capita sales between 2015 and 2018.


Focusing on yogurts, the authors of [26] detected a highly significant reduction of 14% in the median total sugars contents in 2019 compared to those in 2016 (from 11.9 (CI: 8.8, 13.6) to 10.4 (CI: 6.6, 13.0) g/100 g) based on online data-nutrient information, serving size, size of pack, claims on pack and ingredients-collected from major UK supermarkets.

When scrutinising paired products, i.e., products that were present with the same brand name both in 2016 and 2019, only 1/3 of the 539 products surveyed had reduced sugars contents, with a smaller mean difference of −0.65 g/100 g (CI: −0.78, −0.52).

This suggests that the overall median had dropped due to the discontinuity of products with relatively higher sugars content and/or the introduction of new products with relatively lower sugars content.

These reported trends are aligned with the results presented in this study and others [20].

Taking Dairy as a reference category, we calculated decreases of −0.7 g/100 g (or −6%) in the market-share weighted sugars mean for the UK between 2015 and 2018.


For salt, using Dutch food composition data with monitoring reports and relevant labelling information collected from the largest supermarket chain, the authors of [30] found no statistically significant change in sodium levels of processed meat products between 2011 and 2015 in the Netherlands.

However, according to the authors, a reversal in this trend was expected to occur in the upcoming years as per the 2014 agreement between the Dutch government and the private sector, in which a 10% mandatory reduction target for meat products to be reached before the end of 2020 was established.

The authors of [30] also estimated a significant reduction in the saturated fat levels for only one subcategory of meat products (heated meat products): from approximately 10 g/100 g (±3.0 g) in 2014 to 8 g/100 g (±3.0 g) in 2015.

Notably, a 5% target for saturated fat was also established by the Government and the private sector for these products.

For the 2015-2018 period, we calculated decreases of 16% (or −0.3 g/100 g) and 19% (or −1.1 g/100 g) in the market- share weighted salt and saturated fat mean for Processed M & S in the Netherlands, which suggests that the establishment of mandatory targets might have been effective in reducing salt and saturated fat levels in meat products in the country.


The grey literature also supports and complements our findings and the ones from peer-reviewed studies.

A monitoring report from Public Health England (PHE) highlighted that there has been progress in some, but not all, food categories between 2015 and 2019.

Sustained progress in sales-weighted average sugars was observed for breakfast cereals (−13.3% or −2.2 g/100 g) and yogurt and fromage frais (−12.9% or −1.6 g/100 g); remark- ably, sales-weighted average sugars in soft drinks subject to the levy decreased by 44% (or −1.7 g/100 mL), even though overall sales volume increased by 14%.

In contrast, sugars levels in chocolate and sweet confectionery remained relatively unchanged, with variations of −0.4% (−0.2 g/100 g) and −0.1% (−0.04 g/100 g).

Overall, the average sugars reduction across all monitored food categories (including the ones previously mentioned) stands at 3% from 2015 to 2019, falling extremely short of the targeted 20% reduction by 2020 [31].


PHE also reported a slow progress in the salt reduction programme, in which just over half (52%) of the salt reduction targets for product averages set in 2014 were met by 2017.

Retailers' own brands made more progress than manufacturers towards achieving these targets, meeting 73% of them compared with manufacturers, who met 37%.

However, where targets on maximum salt contents were set, 83% of in-home products met these targets vis-à-vis 71% for out-of-home products [32].


Another study for the Netherlands, released by the Ministry of Health, Welfare and Sports, found that the salt content in bread in 2016 was, on average, 19% lower than in 2011.

Certain savoury sauces, soups, tinned vegetables and pulses, and chips, had between 12% and 25% less salt for the same period.

However, despite the improvements in salt content, the average saturated fat and sugars content in most food categories remained at the same levels over the past years [33].


The results of this study are relevant and serve to document and provide evidence to policymakers, industry and citizens on the evolution of sugars, saturated fat, salt and fibre content of packaged foods and soft drinks sold in Europe.

The strength of the market-share weighted average approach lies in its ability to capture the true market influence of products that could otherwise be interpreted as outliers, such as those with a low nutrient content but high market share or, alternatively, those with a low market share but high nutrient content.

It also gives greater impetus to the reformulation of leading market products and limits the impact of new and/or reformulated products with improved nutrient contents but low market share.

It should also be noted that this approach does not only capture producers' changes in the food offer, it also reflects the market success of products with better nutritional quality with consumers, which also depends on consumer attitude, preference and motivation (e.g., shift towards existing low-sugar or sugar-free products).


However, despite being an interesting and effective data source to monitor the progress of the food quality across different countries, there are limitations on the use of commercial data and on the results generated by them [24].

Given that Euromonitor's methodological approach heavily relies on sampling only the most representative variants of a prod- uct/brand within each country, our results should be interpreted as reflections of the behaviour and efforts of the market-leading brands and products rather than the per- formance of the overall food supply.

Furthermore, Euromonitor has innovated its data collection methodology using automatic scraping of product information from online stores, affecting the data in the nutrition module from 2019 onwards.

Whilst this comes with advantages, such as nutrition information at the stock keeping unit (or SKU) level rather than most representative variant of brands, this break in methodology does not easily allow for assessing trends in daily per capita nutrient sales figures and alike from before 2019 and 2019 onwards.

We are also constrained by the accuracy of the information presented on the label, as it is assumed that manufacturers provide accurate and up-to-date information, in line with EU regulations.

Additionally, in the absence of market share in value terms in the Euromonitor database, the assumption made in this paper to use value shares as the equivalent to volume share can overestimate the market share of products that are more expensive per unit volume, as well as the point-estimates of the weighted average nutrient content.

Lastly, as data on package size is not available in Euromonitor's Nutrition database, it was not possible to separate the effects of reformulation and changes in package size on the market-share weighted nutrient mean estimates.


In this sense, the development of a standardised system to track wide reformulation initiatives across the EU Member States appears to be very much needed [14], and the outcomes of the joint endeavour of EU countries within the Joint Action Best ReMaP [17], which builds on previous pan-European efforts, is expected to reduce this gap.

This system could ensure sustainability on the monitoring and surveillance of nutrition policies at the country and the European level, as well as to help in identifying best practices at a bigger scale, and to establish unbiased targets for reduction of sugars, salt and saturated fat, as well as increments in fibre among countries.

Improvements in the overall quality of the food offer would also contribute to enhancing diets (a priority in several EU Strategies, such as the Farm to Fork Strategy or Europe's Beating Cancer Plan), which could be then translated into significant benefits for the population by promoting health and reducing the health and economic burden of NCDs.


Future research is needed to better understand the supply and demand drivers be- hind changes in nutrition content, as well as the impact of integrated strategies on sales, purchases and ultimately diets and health.

By combining food composition and purchas- ing data, the methodology developed in [34] and applied to the French context in [22] allows the decomposition of the overall change in the nutrient content into what can be attributable to: (i) consumers switching from some products to others, (ii) firms reformu- lating food products and (iii) net product introduction (the combination of new products and product withdrawals).

Additional studies could also shed light on the evolution of portion sizes across different food categories and countries, as well as its impact on the overall market-share weighted mean of selected nutrients.


Using commercially available third-party data, this analysis shows some progress, albeit modest, in Europe towards the direction desired by public health proponents, namely the reductions in people's salt, sugars and saturated fat intakes.

We also demonstrated that there is little evidence of increases in fibre content from the packaged food offers covered in our study.

Beyond providing a better understanding of recent trends of packaged food's nutrient content to governments, industry sector and the research community, our results allow the identification of areas of the food supply most urgently requiring action, as well as highlight those that are making advances.

In addition, our analyses indicate that for food reformulation strategies to be effective, products with better nutrient profiles need to compete well on the market.

However, these findings are limited by the characteristics of the third-party data used in this study, as well as the assumptions related to the market shares.

We suggest to further research the aspect of (lack of) market success, including consumer demand, and assess the effectiveness of demand-side policies, in particular in parallel with reformulation strategies, on sales or purchases and finally, population diet and health.


The European soft drinks industry fully supports efforts to monitor nutrition composition of food products.

We were therefore interested to read the European Commission’s Joint Research Centre (JRC) publication by Moz-Christofoletti and Wollgast, which attempted to assess progress on processed foods versus existing commitments across European countries between 2015 and 2018 [1].


The rationale for the study was based firstly on a recognition that monitoring activities are limited by lack of a harmonised system, with studies employing different methodologies, time frames and product categorisations, and secondly that the Europe-wide Joint Action Best-ReMaP project [2], which is developing a European standardised monitoring system to try and address these limitations, will take time to produce results.

Given the JRC’s remit to provide independent scientific advice to support EU policy, it is vital that progress which has been achieved is not undermined by analyses being subject to some of the aforementioned limitations, in this case by only including retail sales and using product category definitions which are not in line with those of the EU.


Although the paper provides definitions for the sub-categories of products included in each of the food and beverage categories in an Appendix [1], it may not be sufficiently clear to the reader that their “Soft Drinks” category included 100% juice—the latter is a separate category under EU food law which cannot legally be allowed to be reformulated and still sold as juice.

While we understand that inclusion of 100% juice has been driven by the product categories of the commercial provider of the data, this inclusion has severely diluted the relative progress which has been achieved in sugar reduction using the EU definition of soft drinks.

In addition, the maximum levels of total sugars (g/100 mL) for soft drinks in Table 2 [1], appear to include concentrates (liquids/powders) which, without application of a dilution factor that is always applied when consumed, result in exceedingly high sugar levels (>90 g/100 mL).


The authors briefly discussed various limitations of their approach but given the significance of their suggestion that we are falling short in our progress against commitments, we feel they should have more clearly identified and discussed such limitations.

UNESDA’s own data from a similar commercial provider estimated that the European soft drink industry has achieved a 14.6% reduction in added sugars from 2015 to 2019 [3], exceeding the pledge to the EU Annex on Added Sugars [4].

While we appreciate that all data, from whatever source, has limitations, we would kindly request that the limitations in this case are clearly understood.


We acknowledge the points raised by Hodac and Wittekind [1] and would like to address the concerns raised by the authors.

We thank the authors for the opportunity to discuss our findings in more detail.


Figure 1 presents the 2015–2018 estimated changes in average sugars (g/100 mL), sales-weighted mean sugars (g/100 mL) and total sugars sold to consumers through retailers (g/per capita/day) when removing (i) 100% juice products (light blue bar) and (ii) 100% juices and concentrates (dark blue bar).

It shows that the inclusion of 100% juice and concentrates (liquids/powders) within the soft drinks category as in Moz-Christofoletti and Wollgast [2]—driven by the categorisation used by Euromonitor—has not severely diluted the relative progress estimated for this product group.

Compared to our published estimates (white bar), a dilution effect of approximately 2.3 percentage points (p.p.) is observed for average sugars, suggesting that products that can be reformulated have decreased their sugar content.


However, (i) the sales-weighted mean sugars estimates are considerably less affected (0.4 p.p.) and (ii) the per capita sales of total sugars have not changed after excluding 100% juices and concentrates.

This is particularly important since these two metrics are used to assess progress against the ambition for sugar reduction from a public health perspective [3,4].

As in [2], the results from Figure 1 confirm that greater sugar reduction is observed in drinks with relatively lower market volume, while products with a higher market share have reduced their sugar content to a lesser extent.


A more in-depth comparison between our estimates and the one provided by [1] is limited by the total absence of information on methodology and data sources used.

Beyond methodological aspects, possible discrepancies might arise from different nutrient coverage (total vs. added sugars), distribution channels considered (only retail vs. retail and food service), country and category coverages, as well as timeframe.

More transparency on data and methodology would greatly help researchers, consumers, stakeholders and the public at large to obtain more reliable and precise information about the industry’s reformulation efforts and its achievements and credibility.


MGEs such as plasmids or phages are defined by their ability to undergo horizontal gene trans- fer (HGT) between bacterial hosts [1], and are widespread in nature.

Genes present on MGEs often affect their hosts' fitness in a specific environment [2].

Particularly, many mobile genes increase virulence or antibiotic resistance and thus have harmful consequences on human health.

Antibiotic resistance genes are enriched on plasmids [3], leading to their fast spread among bacterial species via horizontal transfer [4].

Genes coding for secreted proteins, often involved in virulence, are also enriched on MGEs promoting cooperative secretion [5,6].


In order to better combat the medical issues arising from horizontal transfer, we must understand the selective pressures acting on gene mobility.

The population dynamics and evolution of transfer have mostly been studied by focusing on MGEs themselves [7]; however, transfer is influenced not only by MGE genes, but also by genes of the bacterial host chromosome.

Both donor [8] and recipient cells [9,10] can regulate transfer, with different donor and recipient genetic backgrounds resulting in as much as eight orders of magnitude variance in the transfer rates for the same plasmid [11,12].

Thus, to fully understand the evolution of horizontal gene spread and the natural variation in transfer rates among hosts, we must consider the selective pressures acting on hosts.


On one side, horizontal transfer confers varied and often extreme costs onto the bacterial host.

Phage mobility usually requires host cell lysis that leads to death, while plasmid transfer through conjugation renders host cells sensitive to male-specific phages [13] and decreases the host's growth rate and fitness [14,15].

Because of these costs, horizontal transfer has classically been considered as a selfish trait of parasitic MGEs, selected as it favours their spread [7].

Direct support for transfer being a purely costly trait to the host came from studies of plasmid- host coevolution, where host genes that decrease transfer were selected [16].


On the other side, it has also been suggested that HGT could benefit the host because of the transfer of accessory genes not directly involved in MGE maintenance and transfer.

Indeed, MGEs are often thought to constitute a communal pool of genes [2], a flexible genome [17] that can be quickly shuffled by HGT in response to environmental changes, making host populations more robust [18].

In this view, HGT is beneficial to the host population because it allows cells to share beneficial traits and provides diversity at the population level.


However, it is not clear that these proposed benefits are sufficient for HGT to be favoured by the host.

Traits advantageous at the group level-here the maintenance of a communal pool of genes-are not necessarily selected for at the individual level, especially when individuals can benefit from others that invest in the trait while not paying the cost of investing themselves [19].

Indirect, population-wide benefits alone are not necessarily sufficient to explain the selection of host genes promoting costly transfer [20].

The ability to receive genes can clearly be directly selected for when these genes enhance individual fitness: for instance, CRISPR immunity against antibiotic resistance plasmids, a form of HGT resistance, was rapidly lost in the presence of antibiotics when receiving plasmids was beneficial to the host [21].


On the contrary, the ability to donate genes need not be selected, as the donor cell does not directly benefit from transferring genes to neighbouring recipients.


To quantitatively understand HGT, the selection acting on donor ability must be analysed in a social context, taking into account both the costs and benefits transfer bestows onto donor and recipient hosts.

Here, we theoretically and experimentally analyse the evolution of host genes controlling plasmid transfer.

We show that from the host side, transfer represents a form of altruism: actors pay a cost of investing in transmission and deliver a benefit to recipients of beneficial mobile elements.

Altruistic donation of MGEs can be maintained when transfer is sufficiently biased towards cells sharing donation alleles, increasing the donor allele inclusive fitness.

This bias can arise in structured populations or by an association between transfer and discrimination alleles.

Fitness gains due to the transfer of mutualistic plasmids further select for genotypes where donor ability alleles, discrimination alleles, and mutualistic plasmids are associated.


We first perform a qualitative analysis to identify if and in which conditions a strain with high donor ability can be selected.

We model the fitness of nonmobile host genes controlling donor ability for a given plasmid using a neighbour-modulated fitness approach that partitions fitness into the effects of an individual's own genotype and those of social neighbours [22,23].

We con- sider a population of bacteria structured in an infinite number of patches [24] and model a simplified life cycle with nonoverlapping patch generations, in which the following processes occur successively [25,26]: founding, reproduction, transfer, selection, and dispersal (see S1 Text for details).


A cell i in patch j is characterized by three traits: plasmid carriage p ij (p ij = 1 for plasmid- bearing cells and 0 for plasmid-free cells), donor ability q ij and recipient ability s ij .

Successful transfer is controlled by three factors: the probability of contact between plasmid-bearing and plasmid-free cells, the donor ability of plasmid-bearing cells, and the recipient ability of plas- mid-free cells.

We assume that plasmid and host traits are distributed independently in the starting population so that the cell's donor ability q ij is independent from its initial plasmid content p ij .

Initially uninfected cells become infected with a probability proportional to the patch level frequency of plasmid-bearing cells modulated by their own recipient ability and by the average patch donor ability.

A cell i in patch j will thus be modified by transfer with the probability (1 − p ij ) p j q j s ij .


Plasmid presence has an effect e p on the host cell, and we can express the plasmid effect on host fitness as e p p ij .

The cost of donor ability is c q leading to an effect of transfer on host fitness that is proportional to donor ability, experienced only by cells bearing plasmids before transfer, and equal to −p ij c q q ij .

Donor ability is costly independently of actual transfer efficiency, Bacterial Altruism Promotes Horizontal Gene Transfer PLOS Biology | DOI:10.1371/journal.pbio.1002478 June 7, 2016 3 / 28 modelling the effect of expressing the transfer machinery (which happens even in the absence of successful transfer).


The fitness of an individual founding cell i in patch j, measured over the patch life cycle, is noted by W ij .

With W 0 being the basal host fitness, we obtain (see S1 Text):


To understand selection acting on donor ability q, we apply the Price equation [27,28] to Eq (1).

We obtain the regression coefficient between fitness and donor ability, β(W ij ,q ij ), that describes the effect of donor ability on fitness (Eq 2).

We provide the derivation of Eq (2) and a detailed analysis in S1 Text.


The E j [p j (1 − p j )] term describes the effect of patch composition on the efficiency of plasmid transfer: transfer events are more likely when both plasmid-bearing and plasmid-free cells are abundant within each patch.

β(q j s ij ,q ij ) is a regression coefficient between individual donor abil- ity q ij and the product of individual recipient ability with patch-level donor ability.

It corre- sponds to the relatedness between plasmid donor and recipient cells, noted by R q , at the locus determining donor ability (see S1 Text for a detailed analysis): R q is higher when donor cells preferentially encounter recipients that share their donation allele and when transfer is more successful towards those cells.

R q thus determines how much a donor cell transfers plasmids to individuals bearing the same donation allele because of population structure and specificity in transfer.

Finally, p c q is the average cost of transfer for the donor genotype: high donor ability is costly to the proportion of cells that bear plasmids and express their transfer machinery.


An increase in donor ability is selected for when it is correlated with increase in fitness, namely when β(W ij ,q ij ) > 0, which combined with Eq (2) leads to the following condition:


Eq 3 is a form of Hamilton's rule [29], which postulates that a cooperative allele is selected for when its indirect benefits, weighted by relatedness among actors and recipients, outweigh its direct cost, maximizing its inclusive fitness (fitness inclusive of alleles present in other indi- viduals).

Applied here to donor ability, the indirect benefits are the benefits of plasmids to the recipient cells after transfer e p E j [p j (1 − p j )], and the direct cost is the cost of donor ability for cells bearing plasmids p c q .


R q is the relatedness at the donor ability locus among donor and recipient cells of plasmid transfer.

High, positive R q implies that most of transfer events from cells with high donor ability will be directed towards recipients sharing their donation allele.

On the contrary, negative R q means that transfer will be biased towards cells with a different allele than the one carried by the donor.

Thus, a high donor ability allele can be selected even when individually costly, when transfer maximizes its inclusive fitness through plasmid effects on recipient cells.


We note that relatedness in bacteria can vary across loci [30], as it can be modified in a locus-specific way by mutation [31] or HGT [5,6].

Thus, unlike relatedness aris- ing from genealogical kinship in sexually reproducing organisms, it will not necessarily tend to be the same across the genome.

To underline this and avoid any potential semantic confusion, we follow nomenclature defined already in [30] and consider that cells that specifically share alleles at the locus of interest (plasmid donation) are cells of the same kind but not necessarily kin.


R q will be positive when donors preferentially transfer plasmids to recipients of their kind.

Positive relatedness generally arises through the combination of two processes: limited dis- persal and discrimination mechanisms [29,32].

Here, limited dispersal is due to patch Bacterial Altruism Promotes Horizontal Gene Transfer PLOS Biology | DOI:10.1371/journal.pbio.1002478 June 7, 2016 4 / 28 structure: the correlation between q j and q ij is governed by the initial repartition of genotypes among patches, with no migration before transfer occurs.

Positive relatedness can arise from strong population bottlenecks leading to stochastic variations in founding cell frequencies among patches, followed by clonal reproduction [33].

Effective discrimination in transfer also leading to positive relatedness arises if s ij and q ij are positively correlated, with genotypes with high donor ability having higher recipient ability than average, or if donors have a way to direct transfer specifically to their kind (see S1 Text for discussion).

Alternatively, negative related- ness can arise if s ij and q ij are negatively correlated, leading to preferential transfer to cells bear- ing a different donation allele.


We can distinguish two scenarios for the effect and selection of transfer depending on the plasmid effects on the host cell.

In the first case, the transferred plasmid is mutualistic with its host (e p > 0), for instance conferring antibiotic resistance: transfer is therefore an altruistic behaviour [29] with a direct cost of performing transfer and indirect benefits through the plas- mid benefits in recipient cells.

Transfer is selected if R q is positive and sufficiently high: R q > p c q / (e p E j [p j (1 − p j )]).

In the second case, the transferred plasmid is parasitic (e p < 0): donor ability for parasitic plasmids, decreasing the fitness of recipient cells, is selected if R q is negative and sufficiently low: R q < p c q / (e p E j [p j (1 − p j )]).

This would be a case of spiteful behaviour [32,34].

Specific population structure or discrimination processes are required to produce neg- ative relatedness, and spite is thus thought to be less common than altruism [34].


We focus here on the transfer of mutualistic MGEs and more specifically on antibiotic resis- tance plasmids that allow their hosts to grow when antibiotics are present.

The main prediction arising from our model is that donor ability for these mutualistic plasmids is an altruistic trait, counterselected if transfer occurs indiscriminately towards any cell, but selected for when plas- mid donors and recipients share donation alleles.

We present the model graphically in Fig 1, focusing on the three relevant scenarios affecting relatedness: random interactions between individuals (Fig 1A), discrimination in transfer (Fig 1B), and structured populations (Fig 1C).

We next test the model's predictions with both simulations and experiments, performing com- petition assays between strains with varying donor ability in order to investigate quantitatively if and how much selection favours donor ability in biologically realistic settings.


Discrimination in transfer occurs if during the encounters between a donor and potential recipients the plasmids are transferred to cells of the donor's kind more often than would be expected based on its frequency in the population.

Discrimination of plasmid recipients could be based on differences in the initial recognition between cells or differences in plasmid estab- lishment in recipient cells.


To search for evidence of discrimination, we analyse two available datasets [11,12] that quantify plasmid conjugation rates among different pairs of natural iso- lates.

Both studies measured conjugation rates for the multiresistant R1 plasmid, among 10 strains from the ECOR collection [11] or 9 other natural Escherichia coli strains [12].

In each dataset, we compute normalized donor ability for each pair of donor and recipient strains (see Materials and Methods), which corrects for basal differences in donor ability between strains.

We find that transfer to self occurs at rates higher than average in 18 out of 19 cases (Fig 2A).

Additionally, in 8 out of 19 cases, the highest rate of transfer is from a strain to itself.

Overall, transfer to self is 7.3 times higher than average donor ability over all tested isolates (two tailed t test for difference from 0 for normalized donor ability to self, p = 0.0003).

In a mixed popula- tion with many different strains, the high rates of transfer to self we describe here would trans- late into a biased transfer between cells sharing donation alleles.

This apparent discrimination does not imply that the same locus is responsible for high donor ability and for discrimination, Bacterial Altruism Promotes Horizontal Gene Transfer PLOS Biology | DOI:10.1371/journal.pbio.1002478 June 7, 2016 5 / 28 as multiple genes could be involved in discrimination.

However, the signal we observe in Fig 2A suggests that alleles for high donor ability and for discrimination in transfer are linked in natural isolates sufficiently to lead to an effective discrimination at the donor ability locus.


We next experimentally investigate if discrimination may allow for the selection of host transfer genes.

We use two widely studied E. coli strains, the K12 strain MG1655 [35] and the B strain REL606 [36], and the multiresistant R1-19 plasmid [37].

K12 and B strains bear different restriction-modification systems [9], potentially leading to discrimination in plasmid transfer [38,39].

We first measure the conjugation rate in a well-mixed environment between all four combinations of K12 and B as donor and recipient strains (Fig 2B, red bars).

We find that K12 is generally a better donor, but also that K12 transfers R1-19 plasmid to itself at a 5-times higher rate than to B (Mann-Whitney Wilcoxon test, p = 0.003).

Overall, the K12 strain is an example of good donor strain displaying discrimination for transfer, in comparison to the lower donor B strain.

Moreover, R1-19 carriage leads to a 54% reduction in exponential growth rate for K12 strain, compared to a 1.6% reduction only for B strain (S1 Fig).

To test if part of the costly effect R1-19 has on K12 is due to donor ability, we use a K12-derived strain with a deletion in the arcA gene, a gene known to affect transfer [40], and the repressed R1 plasmid, which transfers approximately 1,000-fold less than R1-19 [37,15].

The K12ΔarcA mutant transfers R1-19 plasmid at a strongly reduced rate to both itself and B (Fig 2B, grey bars), and R1-19 cost is reduced as well (6.4%, S1 Fig).

Similarly, R1 plasmid imposes almost no cost to K12 growth (1.8%, S1 Fig).

Both results suggest that most of R1-19 cost to K12 is due to its high transfer rate.


We then test whether K12 discrimination in transfer can lead to biased transfer towards other K12 cells in a well-mixed population and subsequent selection of the better donor strain.

We compete the K12 and B strains by mixing them equally in a well-mixed population, with a common proportion of cells from each strain initially bearing R1-19 plas- mid.

In the absence of antibiotic selection, the better donor K12 decreases in frequency (Fig 2C, dark blue), showing a lower basal fitness than B in those culture conditions.

When antibi- otic selection is applied at the end of the competition by plating the population on kanamycin (Kn)-containing medium, only Kn-resistant, plasmid-bearing cells grow.

When all cells ini- tially bear plasmids, selection does not favour the K12 strain (Fig 2C orange, 19% decrease in K12 frequency, two sided t test for difference from 0, p = 0.003).

However, when only a fraction (2.5%) of both K12 and B cells initially bear R1-19 plasmid, providing opportunity for plasmid transfer, K12 is selected (19% increase in K12 frequency, two sided t test for difference from 0, p = 0.009).

Finally, to confirm that this specific selection of donors is due to R1-19 transfer to K12 cells, we analyse the outcome of competition when the arcA gene is deleted from K12 and transfer is impaired.

In the absence of antibiotic selection (Fig 2C, light blue), the arcA deletion does not affect K12 fitness when plasmids are absent or rare and increases K12 fitness when all cells bear plasmids (11% decrease for K12ΔarcA versus 25% decrease for K12, two-sided t test, p = 0.043), possibly because of the reduced plasmid cost for K12ΔarcA. With antibiotic selec- tion, the specific selection of K12 when a fraction of cells bear plasmids disappears for K12ΔarcA (Fig 2C middle, yellow bars, two-sided t test, p = 2.10 −5 ), demonstrating that K12 selection was due to plasmid transfer.

Discrimination effectively biases antibiotic resistance transfer strongly enough so that the better donor K12 strain is selected for in the presence of antibiotics.

Thus, when transferred plasmids are needed for growth, discrimination in transfer towards kind, at naturally appearing levels, can be sufficient to select for the better donor.


A second possible reason for transfer bias is bacterial growth in structured populations, where donors interact preferentially with their kind.


Next, we examine whether, in the absence of dis- crimination, structured populations can provide a sufficient bias in transfer to select for good donors.


To analyse the effect of biased transfer in structured populations, we use a synthetic system with fluorescently tagged plasmids in which we can identify plasmid transfer between two strains.


We adapted the system from the one we designed for an earlier study on interac- tion between conjugation and cooperation [6].


A helper plasmid F HR , that is nonmobile and thus behaves like a chromosomal allele, governs the host cell donor ability for a mobile plasmid C, which confers chloramphenicol (Cm) resistance.


We compete (Fig 3A) two strains differing in their donor ability: the good donor D + strain bears F HR that transfers C plasmids, and the nondonor D − strain does not (S2A Fig).


After a transfer phase (t 0 to t 1 ), populations are grown with or without Cm during the selection phase (t 1 to t 2 ).


We compare a single, well-mixed pop- ulation (m), where D + and D − are mixed in equal proportions, to a structured metapopulation (s), consisting of two subpopulations that grow separately during the transfer phase, s 1 and s 2 , founded respectively with a 10% and 90% proportion of D + (leading to equal proportions of D + Bacterial Altruism Promotes Horizontal Gene Transfer PLOS Biology | DOI:10.1371/journal.pbio.1002478 June 7, 2016 8 / 28 and D − at the metapopulation level).


In this setup, the changes in the good donor frequency can be followed both within and among populations to evaluate the effect of population struc- ture on donor selection.


D + strain frequency does not change significantly in m or s populations during the transfer phase (Fig 3B left).

D + frequency then increases at t 2 only for the structured s population grown in the presence of Cm (Fig 3B right, 26% increase from t 0 , Mann-Whitney Wilcoxon test, p = 0.004).

It decreases for m population with Cm (19% decrease, two-sided t test for dif- ference from 0, p = 3.10 −9 ) and stays constant in the absence of Cm.

The dynamics generally follows our predictions: D + selection requires both population structure and plasmid selection.

However, the expected cost of D + during the transfer phase is not present at the population level.

We next investigate in more detail both this cost and the selection of the good donor strain.


By looking at the dynamics of individual subpopulations during the transfer phase, we observed that D + increases in frequency when prevalent (S3A Fig).

We confirmed with an inde- pendent experiment that D + fitness in competition with D − linearly increases with D + fre- quency (S3B Fig).

This positive frequency-dependence for donor fitness could be due to lethal zygosis, a phenomenon known to damage recipients at high donor cell frequencies [41], which could be aggravated by the absence of entry exclusion in our strains [42].

In natural systems, entry exclusion may protect new transconjugants but would also make initial plasmid-bearing donors immune to lethal zygosis, probably leading to a similar frequency-dependence of fitness when most donor cells initially bear plasmids.

In our system, frequency-dependence leads to no observable cost for D + at the metapopulation level.

At low frequencies, donor ability still has a cost, which is also observed as a decrease in the strain's growth rate when growing in iso- lation (D + versus D − , S2C Fig).

Interestingly, donor cells grow significantly more slowly when they bear C plasmids, which is not the case for nondonor cells (D + C versus D − C , S2C Fig), sug- gesting that donor ability cost is enhanced by the presence of transferable plasmids in the cell.


During the selection phase, good donors are selected only in the structured s population and only in the presence of Cm, meaning that donor selection requires both population structure during the transfer phase and subsequent antibiotic selection.

We see that, as predicted by our model, biased transfer due to population structure promotes indirect selection of the donor strain.

To better understand the factors affecting D + selection, we proceed to analyse the dynamics of C plasmids (Fig 3C).

During the transfer phase, plasmid frequency changes depend on the proportion of cells able to transfer.


In the s 1 population where D + cells are few, plasmid frequency declines slightly.

It increases mostly in the s 2 population enriched in D + strain.

Increases are due to transfer, as the increase in plasmids present in D − strain is due to plasmids that originate from D + (as identified by fluorescence markers, see Materials and Methods and S4 Fig).

We then follow the proportion of C plasmids that are present in D + cells, as plasmid localization controls survival in the presence of antibiotics.


During the transfer phase, the proportion of C plasmids present in D + cells compared to D − cells decreases in the well-mixed m population (13% decrease, Mann-Whitney Wilcoxon test, p = 0.004) but increases in the structured s population (28% increase, Mann-Whitney Wilcoxon test, p = 0.004) (Fig 3D).

In the well-mixed population, the decrease is probably due to the strong fitness cost D + cells incur specifically when they bear C plasmids (S2 Fig).

The same cost also explains the subsequent decrease in D + strain frequency under Cm selection in both popula- tions.


The enrichment of C plasmids in D + cells depends on the population structure of the s population: total plasmid transfer is more prevalent in the s 2 subpopulation, effectively biasing transfer towards D + at the metapopulation level.

These results experimentally confirm our models prediction: in the absence of discrimination mechanisms, donor ability for antibiotic resistance plasmids can be selected when population structure ensures preferential transfer to cells sharing donation alleles.


So far, we have shown that both discrimination and population structuring can select for donor ability.

However, we have always assumed and ensured that discrimination and popula- tion structure are present in the system.

Here, we study how both phenomena can themselves emerge.


When analysing effects of population structure, we imposed starting proportions of both the strains and the plasmids in order to dissect the dynamics of transfer.

We continue by study- ing the outcome of competitions arising in more natural population structures using simula- tions.

In the absence of a mechanism for discrimination of recipients, our model suggests that population structure may influence the selection of donor ability in two opposing ways (Eq 3).

Efficient transfer is favoured by the coexistence of plasmid-bearing and plasmid-free cells within patches, while biased transfer towards kind is favoured by high relatedness at the dona- tion locus.

The selection of transfer thus requires preferential interactions between cells sharing donor ability alleles but also sufficient cell mixing that would favour the encounter between Bacterial Altruism Promotes Horizontal Gene Transfer PLOS Biology | DOI:10.1371/journal.pbio.1002478 June 7, 2016 10 / 28 plasmid-bearing and plasmid-free cells.


To study the effects of a natural population structure and the possibility that both conditions are met simultaneously, we simulate strong population dilution, which leads to stochastic founder cell numbers and genotype frequencies [6,33].

We follow the frequency of the donor D + strain in a simulated metapopulation initiated from a strongly diluted mix of equal proportions of D + and D − .

With growth parameters based on our experimental results, we vary both the dilution factor applied to founding populations and the proportion of plasmid-bearing cells (Fig 4).

The results exhibit a clear pattern: the donor strain is selected under the combination of strong initial dilution and low initial plasmid frequency.


D + selection in the presence of antibiotics is controlled primarily by the enrichment of plas- mids in D + at t 1 (S5A Fig), which occurs when there is biased transfer towards D + during the transfer phase.

As predicted by Eq 3, biased transfer requires high relatedness at the donation locus but also effective transfer, which will be affected by dilution and initial plasmid preva- lence.

First, diluting down to low founding cell numbers provides sufficient variation in D + fre- quencies among populations to ensure high relatedness (S5B Fig).

Second, the contribution of transfer to plasmid abundance declines with increasing dilution and increasing plasmid initial abundance (S5C Fig).


Transfer promotes plasmid invasion primarily when plasmid-bearing cells are initially scarce, because in those conditions, the majority of plasmid-bearing cells actu- ally arise from transfer.

Increasing dilution leads to the frequent absence of one of the cell types from each population, which in turn decreases the number of possible encounters between plasmid-bearing and plasmid-free cells.

Selection for donor ability is strongest at high dilution because of high relatedness; however, dilution simultaneously limits transfer, which decreases the strength of selection compared to the optimal population structure studied in Fig 3.

Overall, our simulations suggest that the conditions for selection of donor ability can be met in natural environments through limited dispersal alone, despite the trade-off between relatedness and transfer efficiency that arises from population structure.


We now focus on the association between discrimination mechanisms and plasmid transfer, which was apparent in natural isolates data (Fig 2A) and ask how this association itself could emerge.

The effect on donor ability (Fig 2B and Fig 2C) suggests that a genotype bearing both discrimination and high donor ability alleles could be favoured by selection.

We consider as an example the case of a strain inactivated at a chromosomal restriction-modification locus: such a mutant appearing in a wild type population will transfer plasmids preferentially to clone- mates bearing the same allele, as unmodified plasmids transferred from this mutant will be degraded in a wild-type recipient cell [38,39].

The mutant allele with no modification is denoted by M − , and the wild type allele by M + , D + , and D − stand for high and low donor ability, as before.

We use simulations to follow the dynamics of modification and donation alleles, in populations of cells with no initial association between M − and D + .


In a well-mixed population (analogous to the one studied in Fig 2C), the M − allele is selected for, but the D + is not (Fig 5A, left).

Discrimination in transfer by M − cells leads to a reduced total transfer to M + cells, and direct selection of M − with comparatively higher recipient ability.

D + cells are outcompeted, as they do not receive more plasmids than D − cells in the absence of association to M − alleles.

On the contrary, in a population where D + cell frequency differs sufficiently among subpopula- tions, both M − and D + alleles are selected for (Fig 5A, right): similarly to the dynamics pre- sented in Fig 3, population structure biases transfer towards D + cells, allowing for D + selection in the presence of antibiotics.


Linkage between M − and D + alleles is also controlled by popula- tion structure (Fig 5B): with antibiotic selection, positive linkage appears when D + cell fre- quency varies among subpopulations.

This does not occur in the absence of antibiotics, suggesting that linkage is due to the specific selection of plasmid-bearing cells.

With increasing D + population structure, most plasmids end up in D + M − cells (Fig 5C) due to the combined effect of higher recipient ability and biased transfer by D + cells.

We conclude that the Bacterial Altruism Promotes Horizontal Gene Transfer PLOS Biology | DOI:10.1371/journal.pbio.1002478 June 7, 2016 11 / 28 association between discrimination and transfer alleles can emerge simply through selection of plasmid-bearing cells, when population structure ensures that cells with high donor ability are favoured.


Selective pressures acting on donor ability depend on the fitness effects of genes carried by the transmitted plasmids.

Here, we focus on mutualistic antibiotic resistance plasmids, but para- sitic and mutualistic plasmids can coexist in host populations, and hosts may not be able to evolve differential control of transfer based only on the accessory genes plasmids carry.

Next, we consider the presence of parasitic plasmids N, which are similar to mutualistic plasmids C but do not confer benefits during the selection phase (see S2B Fig and Materials and Methods).

In simulations, increase in the initial proportion of parasitic plasmids lead to a decrease in final frequency of the good donor strain (S6 Fig).

We can conclude that the strength of selection for donor ability decreases when donors encounter a mix of mutualistic and parasitic plasmids, as they cannot distinguish between the two plasmid types.

However, the benefits conferred by mutualistic plasmids could indirectly favour their association to the host.


To study the association between plasmids and hosts, we measure experimentally and with simulations the linkage disequilibrium between plasmids and the good donor allele in a meta- population similar to the previous one (Fig 3) but now with a mix of C and N plasmids in equal proportions instead of only C plasmids, and with no initial association between plasmids and a specific strain (each plasmid is equally present in D + and D - strains).

After the transfer phase, both C and N plasmids become significantly linked to the good donor D + strain in the struc- tured population, but not in the well-mixed population (Fig 6A, left).

Moreover, after Cm selec- tion, linkage to D + slightly increases for C plasmids but decreases to zero for N plasmids in the structured population and remains at zero in the well-mixed population (Fig 6A, right, plain lines).

Finally, this pattern relies on the specific selection of C-bearing cells: when selecting with Kn (an antibiotic to which both plasmids confer resistance) instead of Cm, linkage decreases to zero for both plasmids (dashed lines).

Our experiments show that linkage between plasmids and good donor cells arises only when two specific conditions are met: (1) the popula- tion is structured, and (2) plasmids are beneficial.


To better understand the factors controlling the association, we independently vary D + donor ability and C plasmid beneficial effect on the host strain fitness in our simulations.

The linkage that appears at t 1 between both plasmids and D + strain increases with D + donor ability, independently of subsequent plasmid benefits (S7A Fig).

This linkage arises because transfer is biased towards D + cells at the metapopulation level (as seen in Fig 3).

In our experiments, the observed linkage is stronger for N plasmids because they are preferentially transferred.


At t 2 , however, C plasmid benefits modify the linkage patterns.

With increasing C benefits, C linkage to D + increases (Fig 6B), and N linkage to D + decreases (S7B Fig), when donor ability is suffi- ciently high.

The specific association between C plasmids and the donor strain thus arises from the benefits provided by C plasmids to the host: antibiotics promote the selection of cells bear- ing C plasmids, which are mainly good donor cells because of previous transfer.

Overall, this mechanism selects for cells simultaneously bearing both D + and C alleles.

Linkage between the good donor strain and beneficial plasmids arises without directly enforcing any association between the two, due to the combination of two effects: population structure biasing transfer towards good donor cells and the plasmids benefiting the host.


Our work investigates the evolution of host genes controlling the transfer of mutualistic MGEs such as those conferring antibiotic resistance.

We focus on genes modulating plasmid dona- tion, a property that, unlike plasmid reception, does not directly benefit the host.

Earlier inter- pretations have described MGEs as a communal pool of genes conferring benefits at the population level [2,18].

We demonstrate here that donor ability, when it is costly to the host, is not selected directly.

However, we do not need to invoke population-level benefits to explain why the host may promote MGE transfer.

Instead, we show that host donor ability alleles can be selected indirectly when transfer increases their inclusive fitness (Fig 1).

We then investigate further this qualitative result by measuring selection direction and strength in simulations as well as experiments using both natural and synthetic microbes, in situations close to ones that could be observed in nature.


In the absence of discrimination, population structure is a simple mechanism ensuring that cells encounter preferentially neighbours of the same kind.

Here, we demonstrate that in a syn- thetic biological system devoid of any mechanism for discrimination in transfer, population structure enables the selection of donor ability, biasing plasmid transfer prior to the selection of plasmid-bearing cells (Fig 3).

Donor ability is not selected within well-mixed populations where donors do not interact preferentially with their kind, and good donors decline in fre- quency due to donor ability costs.

However, donor ability is selected at a metapopulation scale, where population structure provides sufficient relatedness at the locus controlling donor abil- ity.

With simulations, we then show that populations with sufficient relatedness can arise sim- ply through strong population dilution, despite the reduction in transfer due to fewer interactions between plasmid-bearing and plasmid-free cells (Fig 4).


Further, we experimentally demonstrate that differences in transfer rates between isolates, leading to effective discrimination in transfer, can also be sufficiently high to favour a strain with high donor ability (Fig 2).

In natural isolates, we observe discrimination for the transfer of an antibiotic resistance plasmid.


These results motivate future studies that would quantify the generality of discrimination by examining other plasmids and strains, as well as determine the underlying mechanisms.

Discrimination can result from specific recognition during cell-cell contact [30] or even direct spread through the cytoplasm of clonemates in the case of bacterial chains [43].


Alternatively, discrimination can arise during the establishment of plasmids in recipient cells.

In particular, plasmid transfer rate is greatly diminished when restriction-modi- fication systems present in recipients differ from those in donor cells [9,38,39].

At a larger phy- logenetic scale, a plasmid host range can be limited by its mechanisms of replication or transfer [44].


Even when plasmids are successfully transferred, they need not confer any fitness benefit, because genes beneficial in the initial donor may be suboptimal in a novel, unfamiliar host [45], favouring a donor strain over distant competitors in which the transferred accessory genes are not fully beneficial.


Finally, discrimination may rely on quorum-sensing mechanisms regulating transfer [46], which can provide an indication of the local abundance of related cells.


Any of these mechanisms could lead to discrimination among transfer recipients, but they may not all be controlled by the same locus as donor ability.

Discrimination by plasmid donors towards their kind necessitates genetic linkage between donation and discrimination alleles.

The pattern observed for natural isolates (Fig 2A) suggests that a sufficient level of association does exist in nature, at least for the R1 plasmid.

Moreover, this observation may be explained and maintained by the dynamics we describe in Fig 5, where linkage between discrimination and donor alleles emerges from their coselection in structured populations.


Biased transfer to kind can thus happen in host cells that differ from others at a single locus modulating donor ability in structured populations; the benefits of transfer then promote the emergence of discriminating genotypes through linkage with a second locus determining the specificity of transfer.

Population structure plays a central role, allowing both the spread of donor alleles in the absence of discrimination mechanisms and the emergence of discrimination.


As the selective pressures we describe here are indirect, they may be too weak to have a signifi- cant effect on the evolution of transfer rates.

To examine this, we calculate selection coefficients acting on the donor allele in our experiments and simulations.

The strength of selection observed for the discriminating strain K12 in competition with B is high (s = 0.35, S8 Fig).

As the degree of discrimination displayed by K12 is close to the average one measured across Bacterial Altruism Promotes Horizontal Gene Transfer PLOS Biology | DOI:10.1371/journal.pbio.1002478 June 7, 2016 15 / 28 natural isolates (Fig 2A), this result suggests that selection of donor strains that transfer prefer- entially to their kind may occur widely in nature, even in unstructured populations.


In the structured populations we studied, the strength of selection depends on the details of popula- tion structure: when relatedness at the donor ability locus is high but plasmids are present in each strain in equal abundance, donors are again efficiently selected for in our experiments (s = 0.10, S9 Fig).

When both parameters are controlled purely by initial dilution, they behave in opposing ways and selection is lower (S10 Fig, s = 0.0025 in the optimal case).

In natural populations, selection arising through population structure might thus be weaker than the one due to discrimination in transfer and vary depending on the details of host and plasmid popu- lation dynamics.


Still, bacteria are characterized by large population sizes, leading to estimates of effective population sizes around 10 7 [47,48], which implies that mutations with selection coefficients larger than 10 −7 can be selected for [49].

Thus, even in the presence of a trade-off between relatedness and transfer efficiency, selection acting on hosts can result in biologically significant changes in transfer rates.


In the long-term, continued selection for transfer requires that plasmids do not spread to fixa- tion.

As plasmid transfer itself increases plasmid prevalence in host populations, selection for donors will be progressively decreased with plasmid spread.

However, many factors may con- tribute to maintaining plasmids at intermediate frequencies in bacterial populations.


Accessory genes on plasmids are often beneficial in transient or local conditions [50,51] and could be repeatedly lost when they are not selected for.

Plasmid-free segregants occur regularly and will rapidly invade populations when plasmids are costly.

Other factors like the presence of bacteri- ophages can also lead to unstable dynamics, increasing plasmid loss [52].

Moreover, transfer is strongly regulated as a function of environmental conditions [8] and could be induced specifi- cally in the conditions where plasmid-bearing cells are favoured.


A striking case of such a sce- nario are mobile elements providing tetracycline resistance, whose transfer is induced by subinhibitory concentrations of tetracycline [53]: transfer occurs in conditions where mobile elements are likely to increase host fitness in the near future, as indicated by antibiotic gradi- ents.

Regulation of plasmid transfer will also modify the cost of transfer to the host.

In our model, we assumed that plasmid-bearing cells experience a constitutive cost proportional to donor ability, leading to a higher cost to donor genotypes when plasmids are abundant.

Trans- fer can be repressed when plasmid-free cells are likely to be rare [46], leading to the expression of transfer genes only when transfer efficiency is maximised.


Finally, on a wider scale, cell migration between populations that experience different selection pressures for plasmid traits strongly increases the potential for horizontal transfer, as the immigration of plasmid-free cells in populations where plasmid traits are beneficial prevents plasmid fixation and allows sus- tained transfer [54].


Transferring plasmids increases the donor allele inclusive fitness because it enriches cells of the same kind with beneficial alleles.

This phenomenon can be compared to the evolution of teaching in animals: teaching of adaptive information can be selected when teachers and pupils are related [55].

The difference between genetic information transfer in bacteria and cultural transmission is that beneficial genes are by default also transmitted vertically (together with the donor allele), making transfer ineffective if they are already prevalent in the population.

Thus in order to be selected, horizontal transfer needs to improve transmission to kind compared to vertical transmission.

Indeed, horizontal transfer is selected mostly when initially only few cells bear plasmids (Fig 4 and S5C Fig), as in these conditions it allows a more rapid and efficient spread than vertical transfer.

Interestingly, the phenomenon of lethal zygosis suggested by the positive frequency-depen- dence observed in our synthetic system (S3 Fig) [41,42] could act on the selection of donor abil- ity in a complementary way, by selecting for donor genotypes when plasmids are prevalent.

Transfer would then be a spiteful behaviour, in this case, not because of the indirect effects of transferred genes but due to the direct damage to recipient cells.


Bacteria frequently encounter parasitic MGE decreasing fitness.

Eq 3 suggests that the transfer of parasitic elements could be selected if it can be preferentially directed towards cells of another kind.

The spread of parasites has been suggested to be a typical case of spiteful behav- iour, since the donors may be immune to the negative effects of their parasites [56].

Bacterio- phages are a well-known example, where phage lysogeny ensures that most cells of the initial strain are protected from lysis and phages preferentially lyse the cells of a competing strain, at the same time ensuring phage spread [57].

Similar mechanisms are not yet known for plasmids.

However, transfer to unrelated cells is well described in the case of the Ti plasmid of Agrobac- terium tumefasciens, where the T-DNA is transferred to plant cells [58], and specific transfer and gene expression ensure that another species produces resources.

Even with no specific tar- geting, suboptimal effects of transferred genes could render the plasmid harmful, damaging specifically unrelated recipients and effectively leading to spite.


We conclude that the inclusive fitness benefits conferred by transferred plasmids can lead to indirect selection for host donor ability.

Plasmid transfer rates thus can be shaped not only by their direct effects on plasmid fitness [7] but also by their indirect effects on host fitness.

The direction and strength of selection acting on donor ability will depend on the potential for plas- mid transfer, its bias towards kind, the fitness effects of plasmids present in the host popula- tion, and the costs of transfer.

Thus, all these factors might, at least in part, determine both the strikingly large variability of transfer rates observed among bacterial isolates [11,12] and the existence of high donor ability strains.


Our findings have consequences in the context of the fight against the spread of antibiotic resistance, as the indirect selection of donor strains could promote widespread dissemination of antibiotic resistance.

Treatments that decrease the spread of MGEs have already been con- sidered, like male-specific phages that inhibit plasmid transfer but also kill preferentially the cells that actively transfer plasmids [59].

Our work suggests that, the same as for other coopera- tive behaviours [60], bacteria resistant to such treatments may evolve, but relatively slowly [61], which should be taken into account when aiming to diminish horizontal transfer [62].


More generally, our results underline the active role hosts may play in the evolution of transfer rates and the necessity to take bacterial social interactions into account when studying plasmid transfer.

Plasmids themselves often bear public good genes involved in host sociality and interaction with neighbouring cells [5,20], and plasmid transfer promotes host public good production by modifying relatedness in structured populations [6].

The indirect benefits of added public good production may in turn further favour the hosts that are investing in transfer.


Finally, we show that biased transfer in structured populations combined with selection of plasmid-bearing cells promotes association between hosts with high donor ability, discrimina- tion mechanisms (Fig 5), and beneficial plasmids (Fig 6).

Donor ability can be selected in the absence of initial linkage with discrimination alleles or mutualistic plasmids, but selection itself creates linkage at the population level.

This dynamic will alleviate the cost of parasitic plasmids Bacterial Altruism Promotes Horizontal Gene Transfer PLOS Biology | DOI:10.1371/journal.pbio.1002478 June 7, 2016 17 / 28 and lead to a prevalence of donor strains associated with mobile, transiently beneficial plas- mids.

In the long term, the phenomenon could promote mutualistic coevolution between bene- ficial plasmids and strains that transmit them at high rates to their kind, in a way analogous to the evolution of mutualism between species.

The benefits generated by mutualism can create an association between mutualistic partners [63], while the association itself favours further mutualism [64,65].

Plasmids would be a special case of mutualism, with a complex and impor- tant role of horizontal transmission, a mechanism that is generally expected to inhibit mutual- ism [66] but here actually benefits both partners.

Social selection promotes host investment in plasmid transfer, increasing plasmid fitness but simultaneously promoting host association to mutualist plasmids.

This will likely lead to complex social selective pressures acting on plas- mids themselves and shape the mobile gene pool.


To test for discrimination, the better plasmid donor was the Escherichia coli K12 strain MG1655 red , which is MG1655 [35] marked with the td−Cherry gene.

The worse donor was the E. coli B strain REL606 [36].

To measure conjugation rates, two spontaneous mutants resistant to rifampicin (Rif R ) for each strain MG1655 and REL606 were used as recipients.

The plasmid used was the multiresistant R1-19 plasmid (that provides resistance to Cm, sulfonamides, ampicillin, Kn, streptomycin, and spectinomycin) [37].

The K12ΔarcA strain was MG1655 red transduced with the Keio collection arcA deletion mutant [67].


To test for transfer selection in structured populations, we used two synthetic strains, D − and D + , and two associated plasmids, C and N. D − strain is E. coli K12 MG1655.

D + strain is a derivative of MG1655 marked with the td−Cherry gene and bearing the helper plasmid F HR .

F HR is a variant of the F plasmid with low self-transfer and entry exclusion [6], which provides efficient mobilization of plasmids carrying F oriT sequence.

N plasmids bear F oriT and an aph gene providing Kn resistance, while C plasmids additionally carry a cat gene providing Cm resistance.

N and C plasmids express either YFP or GFP under control of the strong promoter P R .

For selection experiments (Fig 3), D − strain initially bears C-GFP plasmid, and D + strain initially bears C-YFP plasmid, in order to identify the origin of C plasmid (see S2A Fig).

For linkage experiments (Fig 6), C-YFP and N-GFP plasmids were used respectively as C and N plasmids (see S2B Fig).


F HR and N-GFP plasmids and their construction are described in detail in [6] (where N-GFP was called the T + P − plasmid).

N-YFP was constructed by amplification of YFP sequence with primers AGCGACTCGAGGATAAATATCTAACACCGTGCGTGTTGAC and AGCACAAG CTTTTCCCGGGTCATTATTTGTATAG, then ligation of N-GFP plasmid and the PCR prod- uct after digestion with XhoI and HindIII.

To construct C plasmids, the cat gene was amplified from pKD3 plasmid [68] with primers TACTAAGACGTCAGGAACTTCATTTAAATGGCG and TACTAGCTCGAGAAGAGGTTCCAACTTTCACC.

The PCR product was ligated into the corresponding (GFP or YFP) N plasmid after digestion with AatII and XhoI.


The D + and MG1655 red strains were constructed by integration of the pRNA1-tdCherry gene construction on pNDL32 plasmid obtained from Nathan Lord (Paulsson laboratory, Har- vard Medical School).

pNDL32 was transformed into MG1655 with selection on 100 μg/mL ampicillin, then streaked twice at 30°C on LB-agar (Luria-Bertani, BD Difco).

Colonies were streaked overnight on LB-agar at 42°C, and plasmid loss was confirmed by checking that clones were ampicillin-sensitive.

F HR was finally added to D + strain by conjugation.

To construct Bacterial Altruism Promotes Horizontal Gene Transfer PLOS Biology | DOI:10.1371/journal.pbio.1002478 June 7, 2016 18 / 28 K12ΔarcA, the Keio collection arcA deletion mutant was used for P1 transduction of MG1655 red, then the kan resistance cassette was removed with pCP20 plasmid [68].


Spontaneous Rif R mutants of MG1655 red and REL606 were obtained by plating overnight cultures on LB-agar with Rif (Sigma-Aldrich) at 100 μg/mL.


Experiments were conducted under well-mixed conditions with 5 mL medium in 50 mL tubes (Sarstedt).

Exponential growth rates (S1 Fig and S2C Fig) were measured in a Tecan Infinite M200 reader on 100 μL cultures with 50 μL mineral oil (Sigma) in 96-well plates, after 100-fold dilution from stationary phase cultures.


For the experiments focusing on discrimination (Fig 2B and Fig 2C), cells were grown at 37°C in M9 minimal medium (BD Difco), with 0.4% glucose and appropriate antibiotics for precultures (Rif, 100 μg/mL for Rif R mutants; Kn, 50 μg/mL for R1-19 plasmid-bearing cells).


To measure conjugation rates (Fig 2B), stationary phase cultures of donor (plasmid-bear- ing) and recipient (Rif R ) strains were washed to remove antibiotics.

They were then diluted 1/ 50 into fresh M9 medium and grown separately at 37°C until the cultures reached an OD (600 nm) of 0.2.

500 μL aliquots of donor and recipient cultures were then mixed and incubated at 37°C for 15 min, limiting secondary transfer.

When ΔarcA mutants were used as donors, the incubation time was extended to one hour to allow detection of rare transconjugants.

Finally, mixes were plated at appropriate dilutions on LB-agar containing Rif (measuring recipients and transconjugants), Kn (measuring donors and transconjugants) and Rif + Kn (measuring transconjugants only).

Conjugation rates were measured as g ¼ T DRt (mL.cell -1 .h -1 ) where T, D, and R respectively indicate the density of transconjugants, donors, and recipients, and t indi- cates the incubation time.


For competition experiments (Fig 2C), stationary phase cultures of both strains were washed then mixed at a 50/50 ratio into a well-mixed population, with a varying proportion of plasmid-bearing cells for each strain, and grown from a 1/10 dilution (t 0 ).

Mixes were then fur- ther diluted 1/10 at 37°C into medium lacking antibiotics when they reached the OD (600 nm) of 1.2, and grown to stationary phase.

Finally, they were plated at appropriate dilutions on LB- agar with or without Kn selection.

Strain and plasmid frequencies were followed by identifying MG1655 red mCherry fluorescence from colonies with an Illumatool (Lightools) with 540 nm excitation and 590 nm emission filters.


For the experiments focusing on structured popula- tions (Fig 3 and Fig 6), cells were grown in Luria-Bertani (BD Difco) medium.

Antibiotics used were Kn (Sigma-Aldrich) at 50 μg/mL for selection experiments and 10 μg/mL for linkage experiments; Cm (Sigma-Aldrich) at 30 μg/mL for selection experiments and 6.25 μg/mL for linkage experiments.

Antibiotic concentration was lowered in linkage experiments in order for all cell types to grow to some extent, allowing more precise linkage measurements.


Stationary phase cultures of D + and D − strains were first washed and mixed at a 50/50 ratio (well-mixed population m), 10/90 ratio (s 1 subpopulation), and 90/10 ratio (s 2 subpopulations) of D + versus D − .

A 2.5% proportion of each strain was bearing C plasmids for selection experi- ments (Fig 3) and C or N plasmids for linkage experiments (1.25% cells bearing C and 1.25% cells bearing N, Fig 6).

Cultures were grown from a 1/10 dilution (t 0 ), then further diluted 1/10 at 37°C into medium lacking antibiotics when reaching the OD (600 nm) of 3.

Cultures were then diluted 1/10 at 30°C, and grown until stationary phase (t 1 ).

Finally, subpopulations s 1 and s 2 from the structured population (s) were pooled, and all cultures were diluted 1/100 at 30°C with or without antibiotics for the selection phase of the experiments, until stationary phase (t 2 ).

Cultures were analysed for strain and plasmid proportions by flow cytometry after fixation, as described in [6].

Data acquisition was performed on the Cochin Cytometry and Immuno- biology Facility.

For each sample, 100,000 cells were analysed using a BD LSR Fortessa cell ana- lyzer (BD Biosciences).

D + strain was identified with RFP, plasmids with YFP and GFP fluorescence (S2 Fig).


Discrimination in transfer in natural isolates.

The two datasets we analysed were Table 1 from [11] and Table 2 from [12].

In both datasets for each pair of donor and recipient strains, we computed a measure of donor ability normalized over all recipient strains tested in order to detect differences in conjugation rate to different recipients.

Let i indicate the donor strain and j the recipient strain, we compute the mean donor ability per strain, γ i , and the stan- dard deviation in the said donor ability, σ i , using logarithmic values for conjugation rates.

The normalized donor ability for a given pair is D ij ¼ g ij Àg i s i Measures of selection.

Fitness of a strain i relative to a strain j between times 0 and 1 was measured as W ij = ln(N i,1 /N i,0 )/ln(N j,1 /N j,0 ), where N represents cell densities [36].

When the cell densities were not known, the increase in number for strain i, N i,1 /N i,0 , was approximated as f i,1 /f i,0 Ã x, where f is strain i frequency in the population and x is the population increase in number.

Selection coefficients were then calculated as s = W − 1 [36].


Relatedness.

Relatedness at a locus is calculated as the linear regression coefficient con- necting an individual's allele g with the alleles of its interactants at the same locus, G [69].

For the locus controlling donor ability, we consider that g = 1 if individual is D + and 0 if it is D − , and G is the proportion of D + in the subpopulation of a focal individual.

Let d i and n i be respec- tively the number of D + and number of bacteria within subpopulation i, and d tot and n tot be respectively the number of D + and number of bacteria in the metapopulation.

Then, when sub- populations are of the same size, the relatedness of D + cells is calculated as follows:


Linkage disequilibrium quantifies the deviation from random association between alleles at different loci.

Here, we apply the concept to the association between the discrimination allele M − and the D + allele (Fig 5) and to the association between a plasmid and the D + allele (Fig 6).

For two alleles X and Y, LD X;Y ¼ F XY ÀF X F Y ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi F X ð1ÀF X ÞF Y ð1ÀF Y Þ p , where Fxy is the frequency of individual cells bearing both X and Y alleles, and Fx and Fy are respectively the frequencies of X- and Y-bearing cells in the metapopulation.

Positive linkage indicates that X and Y are associated with each other more than what would be expected from their individ- ual frequencies.


Our simulations mimic the experimental conditions of strain growth and plasmid transfer in the same way as described in our previous work [6].

Plasmid transfer follows a mass-action law: the number of transfer events is proportional to both donor and recipient cell densities in the local population.

The probability coefficient is the transfer rate constant γ (mL.cell -1 .h -1 ).

Strains are characterized by their donor ability q that modulates effective transfer and leads to a proportional cost of donor ability for the donor cell c q .

Similarly to our experiments, we model two steps: a transfer phase (from t 0 to t 1 ), then a selection phase, in conditions where the Bacterial Altruism Promotes Horizontal Gene Transfer PLOS Biology | DOI:10.1371/journal.pbio.1002478 June 7, 2016 20 / 28 plasmid genes affect growth (from t 1 to t 2 ).

The length of the transfer phase is set to 12 h after 100-fold initial dilution from carrying capacity, and growth for the selection phase is allowed for 36 h after a second 100-fold dilution.

Equations governing changes in cell densities, pre- sented below, are common to the two steps.

N tot is the total cell density.


Donor ability with no discrimination.

In these simulations (Fig 4 and Fig 6), two incom- patible plasmids can be present.

C plasmid (encoding Cm resistance) promotes growth during the selection phase; N plasmid (parasitic plasmid, not encoding Cm resistance) does not.

D + cells have donor ability q, D − cells have no donor ability.

C plasmid-bearing cells are noted by D C , N plasmid-bearing cells are noted by D N , and plasmid-free cells are noted by D ; .


Donor ability and discrimination in transfer.

In these simulations (Fig 5), only the mutualistic C plasmid is present.

In addition to the D locus controlling donor ability, the cells are characterized by the M locus controlling restriction-modification.

M + cells do transfer indiscriminately towards M − and M + , whereas transfer from M − towards M + cells is reduced by a factor m. We assume that there is no direct cost of the M − allele to the cells.

To follow the experimental design used in Fig 2, the selection phase is not explicitly modelled here, but we Bacterial Altruism Promotes Horizontal Gene Transfer PLOS Biology | DOI:10.1371/journal.pbio.1002478 June 7, 2016 21 / 28 consider a selection at the end of the transfer phase where only plasmid-bearing cells survive.


The default parameter values used in simulations are shown in Table 1 and were estimated from our experimental data (except for the cost of donor ability in Fig 4, see below):


selection phase to prevent excessive fitness loss for donor cells, assuming a regulation prevent- ing the expression of transfer genes when densities are low and transfer unlikely.

For each parameter combination, the results were averaged over 1,000 replicate simulations.

The high number of simulations was necessary because Poisson distribution produces high variation between replicates, especially with low founding cell numbers and small initial proportion of plasmid-bearing cells.


To study linkage between donor ability and discrimination (Fig 5), competitors and initial conditions were the same as the ones for Fig 3 experiments, with each cell type ðD þ ; ; D þ p ; D À ; ; D À p Þ having an equal proportion of cells bearing M + and M − alleles.

Additionally, population structur- ing concerning the D + allele was varied by founding subpopulations s 1 and s 2 with varying D + to D − proportion (maintaining a 0.5 total proportion in s population).

With d 1 and d 2 being respec- tively D + proportion in s 1 and s 2 , we quantify the strength of population structuring by d 2 − d 1 .


In their 2016 paper, Dimitriu and colleagues [1] make use of both experimental and analytical techniques to study horizontal gene transfer and the conditions under which indirect fitness effects select for cells with high donor ability.

They report both empirically and theoretically that population bottlenecks can select for immobile genes that facilitate the spread of mobile gene elements.

Unfortunately, while Dimitriu and colleagues’ in vitro and in silico work provides solid evidence of donor selection, the mathematical model used to explain these results does not agree with the conceptual model described in the surrounding paragraphs and displays nonphysical behavior.

Here, we present a correction to the algebra, bringing it in line with the conceptual model presented; we find that selection for donor ability is no longer supported.

We then discuss the key differences between Dimitriu and colleagues’ simulation (which select for plasmid donation) and their conceptual patch model (which does not), highlighting the subtle modeling decisions that lead the 2 models to diverge.

Our sole focus in what follows is the patch model described in Dimitriu and colleagues’ paper (S1 Text [1]).

We make no attempt to discuss other possible circumstances, such as preferential sharing, as explored elsewhere in their paper.


In their patch model, Dimitriu and colleagues consider a population of bacteria with plasmid transfer rate q and plasmid susceptibility s. Each bacteria either contains some focal plasmid (p = 1) or doesn’t (p = 0).

At the start of each cycle, bacteria are split into an infinite number of patches, each with a small number of “founders” sampled from the global population.

In each patch, bacteria multiply to large numbers, and then plasmid transfer takes place.

The plasmid carriage, transfer rate, and plasmid susceptibility of founder i from patch j are denoted pi,j, qi,j, si,j, respectively.


All founding strains are assumed to grow to equal numbers during the growth stage.

Fitness effects apply during the colonization stage: Individuals have a base fitness W0, plasmid bearing individuals have an increased fitness W0 + ep, and conjugating individuals pay a cost cqqijpij.

This cost is proportional to transfer rate and is paid only by those individuals carrying plasmids before the transmission stage.

This cost is paid regardless of the presence of recipients.


Dimitriu and colleagues then assert that the fitness of individual i from patch j is the following:


Wij = W0 + ep[pij + (1 - pij)pjqjsij] - pijcqqij;


While intuitively plausible, this pj, qj term assertion runs into difficulties whenever p and q are correlated or when population numbers are low enough to create incidental correlations, as is the case during population bottlenecks—the particular case Dimitriu and colleagues wish to study.

As a concrete example of this problem, consider a patch with 2 founding cells.

Cell A contains a plasmid, and cell B contains the machinery necessary to transport it.


Naturally, nothing is going to happen, as the plasmid is stuck in a cell with no transfer apparatus.

Nonetheless, pj = 1/2 = qj and hence pjqj = 1/4, indicating a positive transfer rate in Eq 1.

Eq 1 is thus inconsistent with the physical model described.


To determine plasmid availability in a population, it is not enough to determine the average occurrence of plasmids and transfer apparatus, but instead their average co-occurrence.

Plasmid transfer takes place at a rate of (1 − pij)sij[pq]j, where [pq]j = (Σpijqij)/N, the mean of the product, as opposed to the product of the means.

Under these assumptions, we have the following:


Wi,j = W0 + ep pij + (sij/N)(1 - pij)pijqij + (sij/N)(1 - pij)Σi≠k pkjqkj - pijcqqij;


Here, we deliberately separate sij/N(1 - pij)pijqij (the “self-transfer” term) from the rest of the sum. Because (1 − pij)pij = 0, we see that, inevitably, sij/N(1 - pij)pijqij = 0.

No cell can transfer plasmids to itself, nor to any “sister cells” that originate from the same founder.

This is the critical difference brought about by [pq]j: While population bottlenecks increase relatedness in terms of q (making cooperation easier), they simultaneously increase relatedness for p, making plasmid transfer ineffective.


Assuming founding bacteria are sampled independently (as is done in Dimitriu and colleagues’ simulations), pkjqkj will be independent of qij, and we can calculate the regression coefficient β(Wij, qij) as follows:


β(Wij, qij) = Cov(Wij, qij)/Var(qij) = β(sij(1 - pij), qij)E[(N - 1)/N qkjpkj] + β(eppij, qij) - β(cqpijqij, qij);


If we also assume that qij is independent of pij and sij (a case Dimitriu and colleagues consider), this simplifies to:


β(Wij, qij) = -E(pij)cq;


The only possible effect from conjugation is negative, corresponding to the cost of conjugation.

This contrasts with the corresponding result from Dimitriu and colleagues:


β(Wij, qij) = epEj[(1 - pj)pj]β(qjsij, qij) - pijcq;


Dimitriu’s result suggests that q is favored if transfer is sufficiently efficient (Ej[(1 - pj)pj] large); however, this possibility is an artifact of the pjqj term.

When considering the more appropriate [pq]j, the only transfer term that qij is multiplied by is the self-transfer term sij/N(1 - pij)pijqij, which is, by necessity, 0.


If we drop the independent genes assumption, Eq 3 indicates that q is selected for when either β(eppij, qij) or β(sij (1 - pij), qij) compensate for the cost of conjugation; q must be sufficiently correlated with the beneficial p or s genes.

As currently presented, the patch model does not provide any mechanism for producing this correlation.

While it is possible to drop the assumption that founders are sampled independently, doing so would appear to be at odds with both the patch dispersal model described in Dimitriu and colleagues’ Supporting information (S1 Text [1]) and their simulation code (S1 Code).


Surprisingly, while Dimitriu and colleagues’ simulations do account for correlations between p and q, as described above, they nonetheless observe weak positive selection for q, contrary to Eq 4.

This apparent contradiction can be resolved by examining the differences between the 2 models in more detail: The analytic model allows only a single “conjugation event” per transfer cycle, while Dimitriu and colleagues’ simulations allow conjugation across a 36-hour time window.

In the simulations, a bacteria can both receive plasmids from an unrelated cell and subsequently donate plasmid to sister cell within a single transfer cycle.

This is not possible in the analytic model, as all conjugation takes place “simultaneously.

” Hence, we see that donor selection in spatial models is a second-order effect: Fitness benefits rely on the occurrence of multiple consecutive conjugation events.

This is suggestive of the importance of the “transcriptional burst after plasmid conjugation” [2], which has been observed experimentally, and is consistent with Dimitriu and colleagues’ observation in simulations that effect size was very small.


As shown by Dimitriu and colleagues, bacterial cooperation and spatial segregation can contribute to the evolution of plasmid transfer; however, as we show here, such results are sensitive to modeling details in particularly subtle ways.

This leads to interesting questions about how robust such effects are in real life: Are population bottlenecks and spatial structures a significant selective force in the selection and maintenance of plasmid conjugation? Or does the sensitivity of the models reflect fragility in the real-world phenomena?

These questions we leave to future authors.


In our original work [1], we demonstrated experimentally that selection can favor chromosomal alleles that confer higher plasmid donation rates, given the plasmid is beneficial and the recipient has an elevated chance of carrying the donor allele (i.e., preferential donation to kin).

Our experiments demonstrated this effect via 2 mechanisms of preferential donation: biased conjugation rates and structured populations.

We interpreted these results through the lens of kin selection theory (benefits via horizontal gene transfer to kin), supported by simulations and an analytical fitness function model.

These results hold importance by outlining that the evolution of plasmid transfer rates (a key aspect of the antibiotic resistance crisis) is not necessarily the sole product of selection on the plasmid itself and forms part of a broader series of papers from our labs investigating the sociomicrobiology of plasmids [2][3][4].


A new commentary raises concerns over our fitness function model, flagging issues with both the structure of the model and assumptions made in our analysis [5].

We stand by the general conclusions of our work but accept that our fitness function and stated analysis assumptions could be better formulated.

Our initial fitness function is heuristic in the sense it was designed to capture general processes acting on the fitness of individuals, dependent on the plasmid and donor allele status—without explicitly modeling the myriad demographic events of dispersal, reproduction, conjugation, and death that result in selective shifts across a metapopulation of cells.

Specifically, we captured the “force of infection” faced by an uninfected cell as the product of average plasmid prevalence and average donor allele prevalence in the local patch (pjqj; see commentary for notation details).

We agree with the authors that this force of infection is better phrased as the average of the product ((1/N)∑pij qij), in part because this avoids the potential pathology under limit conditions described by the authors, but also because this approach better highlights that the particular social trait in question is an “other only” cooperative trait [6], illustrated by commentary equation [2], where transmission to self and transmission to others are separated.

This separation has the important consequence of highlighting that unlike many microbial social traits where benefits accrue to a group (including self), a cooperative plasmid donor trait can only benefit other cells that lack the plasmid.

Given established costs of donation (e.g., see figure S2 in our original article), this defines our “donor” behavior as an altruistic trait, which can, therefore, only be favored by selection given nonrandom interactions among individuals (e.g., [7]).


Our experimental results outline 2 mechanisms of nonrandom interactions: preferential donation to kin and population structure.

Each of these mechanisms will generate positive covariances between focal individual qij and non-self-recipient qj donor allele states (cov(qj, qij) > 0).

The pathway via preferential donation to kin (order-of-magnitude differences according to our analyses and more recent measurements among lineages coexisting within natural populations [8]) will also likely generate positive covariances between donor and recipient abilities (cov(sij, qij) > 0).

In contrast, to arrive at the result that selection always works against plasmid donor alleles (equation [4]), the commentary makes the assumption that both of the above covariances are zero.

We suggest that the additional analyses begun by the authors are an exciting starting point to better map selection on donor alleles, under a broader array of defined assumptions on cell–cell and gene–gene structure, ideally informed by data on structures found in natural bacterial populations.


Self-monitoring of blood pressure (BP) by patients at home (HBP) is being widely used in several countries and endorsed by hypertension guidelines in Europe, US and elsewhere.

1-4 HBP monitoring shares most of the advantages of ambulatory BP monitoring (ABP), despite the fact that the two methods are applied in different daily life con- ditions.

The main clinical needs for applying these methods are the white coat and masked hypertension (MH) phenomena, which are common in both untreated and treated subjects and are missed when evaluation is exclusively based on clinic BP measurements.

1-4 Early research on ABP monitoring demonstrated its diagnostic and prognostic ability and established its role in the management of hypertension.

1 Indeed, the existence and clinical relevance of the white coat and the MH phenomena has been defined using ABP monitoring.

1,5 Despite the wide use of HBP monitoring in the community, only recently has evidence accumulated on the diagnostic 6 and prognostic 7 usefulness of the method.


On the basis of this evidence, recent European and American guidelines on HBP monitoring adopted an entirely different position and recommended for the first time that HBP should be offered to almost all subjects with elevated BP.

2,3 This recommendation appears to be stronger for the long-term follow-up of treated hypertensives, 8 in whom there is additional benefit due to the improvement of hypertension control rates when applying HBP monitoring.

9 On the other hand, the evidence on the usefulness of HBP in the initial evaluation and diagnosis is less clear, and it has been argued that ABP is more appropriate for this indication.

2 Several studies have assessed the diagnostic ability of HBP by taking ABP as reference.

6 However, they had relatively small sample size, did not always apply HBP monitoring according to guidelines and did not examine all the potential diagnoses together (sustained, white coat and MH) and in both untreated and treated subjects.

6 The aim of this study was to investigate the ability of HBP to diagnose sustained, white coat and MH by taking ABP as the reference in a large sample of untreated and treated subjects referred to a BP clinic and using a standardized protocol for all the measurement methods according to the current guidelines.


A data set of subjects assessed with clinic BP, ABP and HBP measurements was constructed using data from previous prospective studies conducted in a university hospital BP clinic from 1994 to 2007.

Participants were consecutive adults referred for elevated BP, untreated or on stable antihypertensive treat- ment for X4 weeks.

They were invited to participate in trials involving measurements of clinic BP, HBP and ABP using a standard protocol as described below.

Exclusion criteria were severe renal, cardiac or other systemic diseases, sustained arrhythmia and evidence of secondary hypertension.

The protocol of the present study was approved by the hospital scientific committee, which did not require an informed consent for this analysis of the patients' BP data and the retrospective collection of other data from patients' records.


The average number of valid clinic BP readings included in the analysis was 5.0±1.2 (range 2-6), HBP 21.3±4.2 (range 8-24) and awake ABP 42.3 ± 6.7 (range 21-60).

In the total population, clinic BP was higher than HBP (mean difference, 3.4 ± 12.9/4.6 ± 7.6 mm Hg, systolic/diastolic, Po0.001 for both) and awake ABP (mean differ- ence, 3.1 ± 14/2.8 ± 8.4 mm Hg, systolic/diastolic, for both Po0.001), whereas there is no difference between systolic HBP and awake ABP, yet diastolic HBP was lower by 1.8±7.1 mm Hg (Po0.001) (Table 2).

In treated subjects, systolic HBP was slightly higher than awake ABP, whereas the reverse was true in the untreated subjects (Table 2).


Significant associations were found between HBP and awake ABP (r¼0.63/0.76, systolic/diastolic, Po0.001 for both), clinic BP and HBP (r¼0.62/0.73, Po0.001 for both) and clinic BP and awake ABP (r¼0.53/0.69, Po0.001 for both).


The BP threshold for hypertension diagnostic was 140 mm Hg and/or 90 mm Hg (systolic/diastolic) for clinic BP and 135 mm Hg and/or 85 mm Hg for HBP and awake ABP.

1 Sustained hypertension was defined as elevated clinic and out-of-clinic BP (home or awake ambulatory).

White coat phenomenon (WCP) was defined as elevated clinic and low out-of-clinic BP in both treated and untreated subjects, and MH the reverse (low clinic and elevated out-of-clinic BP).


Participants who provided fewer than eight valid HBP readings were excluded.

HBP readings that were 450% higher than the next highest HBP reading of the individual subject were considered erroneous and were discarded, as were measurements with systolic BP o60 mm Hg or 4250 mm Hg and those with diastolic BPo30 mm Hg or 4150 mm Hg.

All HBP readings were averaged to give a single number per individual.


ABP data and additional recorded information from the report files generated by the ambulatory monitors were batch imported and organized in a relational database (Microsoft Access Office Premium 2000, Ireland) using a Visual Basic program (Microsoft Visual Basic 6 Professional Edition, Ireland 2000).

This program designed by LGR (author) for statistical analysis of ABP- derived data reads the ASCII text files generated by the ABP monitor and performs multiple data procedures and analyses, including flagging erroneous readings, valid readings and duplicate readings (repeats), and calculates the average awake and asleep ABP, according to each individual's report for in-bed and out-of-bed period.

ABP recordings with o20 valid awake readings were excluded.

Early readings taken o20 min after the monitor had been attached to subjects were also excluded because these had been taken in the clinic.

The average of the second and third clinic BP reading of the three study visits (six readings) was calculated to give clinic BP per individual.


Student's paired t-test was used for the comparison of BP measurements in the same subjects with Bonferroni's correction for multiple comparisons applied where appropriate.

We estimated that using a 5-mm Hg cutoff for clinical significant difference between systolic awake ABP and HBP, the power of a 613 subjects study sample (retrospective data set analysis) was up to 100% (a¼0.05, two-tailed), indicating an oversized study.

Student's unpaired t-test was used to compare values among subgroup of participants.

The diagnostic ability of HBP (sensitivity, specificity, positive and negative predictive value) to detect sustained, WCP and MH in the total population and separately in treated and untreated subgroups was investigated.