Management strategy evaluation [MSE, also known as the management procedure (MP) approach] was developed to address problems traditional management approaches (e.g. Kirkwood, 1992, 1996), initially with respect to the management of commercial whaling by the International Whaling Commission (IWC).

Critical appraisals of MSE are opportune, particularly given the slow but steady moves towards increased application of the approach at both national and international (e.g. Regional Fisheries Management Organization, RFMO) levels [1].

As developers of and hence advocates for MSE, because of the great improvements the approach can bring to ﬁsheries management [2][3][4][5][6][7][8], we welcome pertinent warnings offered by Rochet and Rice (2009; hereinafter referred to as R&R) concerning some aspects of the approach that are potentially subject to poor implementation.

Their criticisms centre on some of the ways in which uncertainty is represented and risk levels are estimated in MSE, although they do note that “these tools improve practice compared with ignoring uncertainty and applying ad hoc decision-making”.

However, in welcoming R&R’s critique, we also consider that it is important to draw attention to some important misunderstandings evident in their paper that bring some of their conclusions under question.

Further, we are concerned that some of the issues raised by R&R may be misconstrued as basic ﬂaws of MSE, rather than concerns about some technical aspects of its implementation.

The most important misunderstanding seems to be what precisely is meant by the term “management strategy evaluation”.

It is essential to be clear about the concept and implementation of MSE, which has come to have a speciﬁc meaning in a ﬁsheries context (see the Glossary in Rademeyer et al., 2007).

MSE is a process for the evaluation of decision rules for providing scientiﬁc recommendations for management measures (such as total allowable catch, TAC, or total allowable effort) for a marine renewable resource, or a set of linked species.

This evaluation is made against user- and resource-orientated objectives set by managers that should take into account the various stakeholders’ views and needs.

Importantly, the data inputs to such rules are prespeciﬁed, and in line with the precautionary approach [9][10], the rules are checked using simulation to ensure that they are likely to provide reasonably robust performance given the plausible range of scientiﬁc and operational uncertainties considered to apply to the resource and associated ﬁshery, group of ﬁsheries, or even ecosystem [11][12].

It is especially this simulation testing, which takes proper account of feedback effects, which differentiates MSE from the more traditional “best assessment” approaches to providing ﬁshery management advice [13].

MSE can be applied at three levels: 1.

development of the speciﬁc and operational MP for a particular ﬁshery, e.g. those for the major South African ﬁsheries summarized in Plaga´nyi et al. ( 2007), or the strike limit algorithms developed for subsistence ﬁsheries by the IWC [14][15]; 2.

the evaluation of generic MPs, such as the IWC’s revised MP (RMP) for baleen whales on their feeding grounds [16], which can be applied to a number of stocks [note that speciﬁc implementations of the generic RMP are also evaluated using the simulation approach, e.g. IWC (2008)]; 3. more broadly the evaluation of management strategies in the form, for example, of general formulations for harvest control rules where, as R&R state, the process can inter alia be used to identify some formulations that will not work and can hence be eliminated.

For brevity of expression in what follows, we will use the acronym “MP” to refer to the outcome from MSE at all three of these levels.

Nevertheless we note that MSE does not extend to broader concepts still, such as those underlying, for example, the European Common Fisheries Policy, which R&R also term a “management strategy”, or national “harvest strategy standards” such as the US National Standard Guidelines that expand upon the Magnusson –Stevens Act [17].

However, MSE could, and we would suggest should, be used to evaluate how well a speciﬁc MP, with its embedded monitoring, assessment method, and decision rule, could be expected to achieve these broader goals [18].

In what follows, we comment ﬁrst on some positive and then on some questionable viewpoints advanced by R&R and argue that most of the problems raised concerning MSE are addressed through its proper interpretation and implementation.


Some of the criticisms made by R&R do not apply to MSE per se.

Rather they reﬂect instances of poor implementation of MSE, as elaborated below.

If MSE is to address the requirements of the precautionary approach so as to identify MPs that are robust to scientiﬁc uncertainties, which was the key factor behind the development of that approach, those uncertainties have to be realistically reﬂected in the simulation “trials” on which the testing of proposed MPs is based.

Among other things, care needs to be taken not to input probability distributions, e.g. Bayesian priors, that are unrealistically narrow, or misleadingly low estimates of risk would result.

Equally, problems can arise if questionably high levels of uncertainty are input to the process—see the debate in Kolody et al. ( 2008) and Butterworth (2008a), as well as in Butterworth et al. ( 1996).

The proponents of MSE have never suggested that it can compensate for poor data inputs or inappropriate assumptions, nor has it been suggested that it can calculate very precisely the differences in outcomes expected under marginally different strategy selections, any more than can other quantitative analyses.

In essence, these, and a number of other reservations raised in the Discussion section of R&R, are drawing attention to the GIGO (garbage in garbage out) principle, with which we naturally concur.

Similarly, we agree with R&R that the complete MSE process must be thoroughly documented for each application, particularly the details of the simulation trials considered and their motivations, so that the reliability of eventual outputs from the process is susceptible to thorough independent review.

The documentation that accompanies IWC reports of the implementation reviews of their MPs for both commercial and aboriginal whaling [19][15] provides useful benchmarks for best practice for such documentation.


The whole exercise involves an assessment of risk in some way, and this necessarily requires inferences about the lower portions of probability distributions (typically 5 or 10%).

It would be a poor analyst who showed no cognizance of the difﬁculties of estimating tail probabilities, and the sensitivity of such estimates to the probability distribution shapes assumed, particularly for low percentiles.

The above alone should be sufﬁcient to conﬁrm that MSE, provided properly applied to avoid poor practices such as those discussed above, does not remove “intelligence” from the ﬁshery management process, a concern raised by R&R in their Discussion section.

However, there are different places where “intelligence” can be applied to modify intermediate outcomes in the multistep process of MSE, and here we have some reservations concerning R&R’s suggestions about when such interventions should occur, for reasons on which we elaborate in the penultimate section of this manuscript.


R&R assert that stochastic modelling of uncertainty, which applies to Bayesian assessments of stock status as well as to MSE, is paradoxical because “it implies knowing more than deterministic approaches: to know the distribution of a quantity requires more information than only estimating its expected value”.

However, use only of a deterministic value, such as an expectation, ignores the facts that that value had to be estimated in some way and that estimation would not have been exact; there may be associated bias and certainly lack of precision.

Surely, analyses that assume uncertainty does not exist cannot be argued to be preferable in a management context to others that attempt to take some account of such uncertainty, though imperfectly?

Certainly, the former would be totally at variance with the precautionary approach, and ignoring uncertainty has been a major cause of the failure of some natural resource management attempts, e.g. not accounting for uncertainty about the value of natural mortality M for orange roughy (Hoplostethus atlanticus) in New Zealand [20], and about the productivity of groundﬁsh species off the west coast of North America [21][22].

The argued paradox seems to us a consequence rather of a mis-focus away from the basic question: which of a range of possible MPs or strategies best meets management objectives? Or conversely, what is the basis for considering that a particular MP is likely to achieve its intended outcome in the context of the information and uncertainties in the observation, assessment, and management processes?


R&R also claim that non-parametric bootstrap re-sampling, a method which provides probability distributions without the need to assume parametric forms and which has been widely applied in statistics since being introduced by Efron [23][24], “underestimates the probabilities of low values”, and hence also of the risks of undesirable outcomes.

However, their justiﬁcation for this conclusion is ﬂawed.

( Furthermore, the results shown in Figure 3 of R&R demonstrate an overestimation of risk, not an underestimation, as they state.) The sparser distribution of observations farther from the centre of their distribution will certainly lead to a lower proportion of resamples drawn from those outer compared with central regions, but that is exactly in line with the relatively lower level of the distribution in such outer regions.

Therefore, this in no way implies a necessary bias, as argued by R&R. It simply means that the bootstrap process estimates probabilities near the centre of distributions more precisely than near the tails, a problem that may be severe when the sample size is small, as in the examples provided by R&R. Bootstrap percentiles can be biased because the bootstrap distribution is centred on the estimated rather than the true value of the parameter in question, and methods exist to correct for this [25], but the issue raised by R&R relates to variance, not bias.

Although R&R’s critique that the tails of distributions are often poorly estimated may be true in absolute terms, MSE is almost always concerned with relative performance among alternative MPs (and the trade-offs involved across multiple objectives), rather than in absolute levels of performance for a particular MP [3].

MSE does not purport to know more about issues that are uncertain, but instead addresses uncertainty by concentrating on evaluating the robustness of MP performance to that uncertainty.


R&R state that “Robustness may be tested by combining the full suite of related simulations probabilistically into a combined probability density function of possible outcomes, and evaluating the risk of an undesirable outcome from each management option”.

Of course this could be done, but it is certainly not, in our experience, a standard practice in MSE implementations.

Most MSE studies integrate across uncertainty in some parameters/variables, such as variation in future recruitment and future observation error, and some go an extra step to integrate over a plausibility-weighted reference set of operating models (OMs) describing a range of highly plausible scenarios [26].

However, when it comes to evaluating robustness, one wants to demonstrate this robustness in anticipated performance under simulation across different OMs, not to consider only some tail-probability risk criterion being met for a single probability distribution generated from a weighted set of all OMs.

The relative plausibility of different scenarios (OMs) has to be taken into account to some extent, because clearly no MP can be robust to every conceivable situation [12].

However, although there are standard methods for calculating relative probabilities associated with parameter estimation uncertainty within a single model structure, assigning plausibility weightings across different model structures, e.g. how many stocks of the species are present in the area under management, is recognized as a far more difﬁcult and certainly as yet not fully solved problem.

Probably the most advanced approach to date is that developed in the IWC, which accords scenarios corresponding in particular to different model structures a high, medium, or low plausibility on qualitative grounds (“intelligence”).

It then disregards the low ones and requires the performance of candidate MPs to satisfy less stringent conservation-related criteria for trials with medium plausibility weighting compared with the criteria that apply for those given high weighting [27].

The focus is therefore on consistency across stocks and over time in evaluating risk, rather than claiming a single integrative measure of risk as a probability calculated in absolute terms.


If indeed MSEs are not “powerful at discriminating the best approach” (R&R), is that of great consequence?

As indicated above, the priority in selecting management approaches is robustness to unavoidable uncertainty rather than seeking an optimality that is in any case difﬁcult to deﬁne given the inevitable trade-offs in performance between catch on average, catch variability, and the risk of depleting the resource to too low a level.

We are therefore somewhat bemused by R&R’s concern that MSE is being used “as if small differences in analytical results are meaningful biologically and in management”.

We equally would be concerned if this was common practice in MSE, but we are not aware of any instances where the outputs from an MSE process have been used in that manner.

If such instances do exist, they are the exception rather than the rule.

Indeed, in our experience, the far greater problem in current stock assessments is for small differences in estimated values, e.g. the estimated current ﬁshing mortality F compared with a target F each year, to be treated as meaningful despite the stock assessment model not including important sources of uncertainty.

The resulting scientiﬁc overconﬁdence and lack of robustness have arguably contributed to overexploitation of several ﬁsheries [20][21][22].


Finally, R&R’s argument that “implementation uncertainty is very likely to greatly exceed any nuanced quantitative differences amongst simulation results” is hardly an argument against MSE per se.

If implementation issues are among the major of the set of uncertainties that apply in a particular situation, the trials used for testing associated candidate MPs should certainly be attempting the not always straightforward task of making appropriate allowance for implementation error.

For example, Dichmont et al. ( 2006) characterized implementation uncertainty in Australia’s northern prawn ﬁshery (NPF) based on historical decision-making, and incorporated that source of uncertainty in the scenarios considered in the simulation trials used to compare MPs for the two commercially important tiger prawn species (Penaeus esculentus and P. semisulcatus) in the NPF.

The implementation of MSE for the management of a tropical trawl ﬁshery [28] explicitly included a high level of implementation uncertainty.

The evaluation of alternative composite ﬁshery management scenarios by Fulton et al. ( 2008) goes to considerable lengths to represent both implementation uncertainty and the response of ﬁshers to changes in management arrangements.

Open source software available to apply MSE to ﬁsheries explicitly includes implementation uncertainty in several of the management processes [29].

In any case though, the feedback nature of MPs will, at least partially, adjust for implementation error such as failure to take account of unreported catches [30].


R&R admit that they can offer no cure to a number of problems they raise in relation to MSE.

They suggest that MSE be augmented by alternative methods to evaluate risks, including qualitative methods and post hoc analyses.

However, much of this is already accepted and implemented best practice where MSE is used.

The Multi-Criteria-Decision-Making context of the interpretation of outputs from the simulation-testing process, which incorporates more than one measure of risk and emphasizes the key role of trade-offs among conﬂicting objectives, is widely recognized.

Regular reviews are frequently mandated, e.g. ﬁve-yearly in the IWC [16] and four-yearly in South Africa [31], and these allow further knowledge and experience gained in the interim to be taken into account.

We agree that qualitative methods [32] have a useful supplementary role to play alongside more quantitative approaches, from several perspectives, including facilitating stakeholder engagement, and the speed and cost with which analyses can be completed.

However, qualitative methods tend to be less adept at incorporating uncertainty and can be poor at capturing the often unexpected effects of feedback, including future learning [33].

Post hoc analyses can be even more problematic, because the limited number of real world “experiments” to hand, combined with severe problems of attribution of cause, make learning from such analyses difﬁcult.

Ongoing contention over explanations for the failure of NW Atlantic cod (Gadus morhua) to recover [34], and for the decline of Steller sea lions (Eumetopias jubatus) in the North Paciﬁc [35], are cases in point.

Moreover, analyses that provide only qualitative or directional management insight are necessarily incomplete; the ﬁrst question that will be asked by a manager when advised that the TAC needs to be reduced is going to be: “By how much?”.

More to the point, we believe, are the advantages that MSE brings over previous approaches, an aspect that R&R do acknowledge partially.

These are elaborated in more detail in Punt (2006), Butterworth (2007), and Punt and Donovan (2007), but it is worth re-emphasizing some of the more important ones, such as a structured approach to taking uncertainty into account, being able to make allowance for the feedback (learning) contribution forthcoming from future data as the MP is put into practice, and providing a basis to consider trade-offs between interannual catch variability and resource risk, or between short-term pain and achieving resource rebuilding targets, so that the aim of socio-economic stability can be factored in more objectively.


R&R quote the Sparholt et al. ( 2007) observation that despite the implementation of the precautionary approach by ICES for providing scientiﬁc advice, demersal stocks have still declined.

However, Cadrin and Pastoors (2008) showed that of 137 management units for which advice is provided by ICES, only 17% actually have the necessary estimates to implement a precautionary control rule, and 61% have no estimates of reference points at all.

The failure has been related to not implementing scientiﬁc advice in an effective management context, and to not evaluating the extent to which management strategies are able to meet the objectives set out for them, given uncertainties.

MSE has helped address these failings by developing management advice for stocks for which detailed assessments have yet to be developed [18][36].

We certainly agree with R&R that MSEs provide a mechanism for a desirable strengthening of “the use of intelligence in developing approaches to ﬁsheries management”.

As discussed above, the properly conducted process of setting up the simulation trials required for MSE and the interpretation of the results already requires full exercise of that attribute.

R&R subsequently comment, however, that few OMs “can simulate the acts of scientists, managers, and ﬁshers making choices in each year of an ongoing cyclic process of ﬁshery-assessment-advice-management plan-ﬁshery” and that “this is the wrong time to remove intelligence from the process.

If all their quantitative details are taken as reliable, then simulation-based MSEs can only evaluate impoverished procedures, those without the need for human intervention.

This is not the type of management procedure we want”. What R&R apparently advocate is, we believe, unwise and indeed quite the reverse of the philosophy underlying levels 1 and 2 of MSE (see above), which insists on the MPs adopted being exactly of the form that has been simulation-tested, and to be implemented in the “auto-pilot” mode, i.e. the management measures they output to be applied unchanged unless there is compelling evidence for the need for some amendment (what are termed exceptional circumstances).

Jurisdictions such as Australia that use the MSE at level 3 to inform both policy and choice of ﬁshery-speciﬁc harvest strategies without fully adopting an MP do provide more room for human intervention in annual decision-making, but even this is formulated as the application of meta-rules that constrain the freedom of decision-makers to simply override application of the strategy without sound justiﬁcation [37].

R&R motivate their position on Punt (1997) having used Laurec–Shepherd tuning rather than ADAPT to simulation-test VPA-based management “because the latter requires making educated choices that cannot be simulated in an operating model”.

If a procedure cannot be simulated, how can one have conﬁdence that it will perform better than one which has been? A counter-example is provided by Punt’s (1993) simulation-based comparison of the age-aggregated production model and the VPA-based management.

The educated choice would surely be for VPA, because this can make use of additional information (catch-at-age data).

Yet as the case investigated showed, compared with the production model, VPA added nothing in terms of greater catches on average, or less risk to the resource, but led to far greater interannual variability in the catches, i.e. the addition of “intelligence” led to a deterioration in performance in meeting one management objective without any compensating gains for others.

It is surely far better to develop an MP that can be simulated, and thus that can be shown to be likely to meet the user- and resource-based objectives identiﬁed.

The philosophy apparently espoused by R&R of, even after conducting an MSE, returning to the annual cycle of scientiﬁcally debating the management action to follow an assessment process which itself may be contested, reintroduces many of the problems that levels 1 and 2 of MSE are intended to avoid, by providing a default decision rule which has been tested and proved to be robust.

On what bases could one claim that “intelligence” routinely entered at the stage of developing management advice leads to improved performance in the longer term? Or does it instead only “tinker”, by providing management recommendations that follow noise rather than signal in the data, and reopen the door to ad hoc decisions that lack a clear and consistent basis?

A particular problem with this last approach is that it admits a role for strengthening advocacy positions over time as resource indices deteriorate, resulting in the necessary management response of adequate reductions in catch and/or effort being enacted too little too late.

Avoiding such problems underlay the IWC’s (and subsequently a number of other organizations’) decision to opt instead for the MP auto-pilot route of levels 1 and 2 of MSE.

Even when advocacy is not a problem, frequent changes in models or data are often introduced in an effort to provide the best stock assessment every year.

Such changes may lead to unnecessary disruptions in the management advice, when a simpler, consistent decision rule applied every year might perform better in the long term [4].

Relatively simple estimation models are often both sufﬁcient and more robust than their “more realistic” counterparts as the basis for MPs, because of the high noise-to-signal ratio typical of ﬁsheries data.

For example, in the development of the IWC’s RMP, the relatively simple Cooke population estimation model that was eventually adopted performed at least as well in simulations as other MPs based on more complex models that attempted to match features of the underlying OMs of whale population dynamics, such as time-lags, more closely [30].

This is not to deny any role for such more realistic models, but this is generally at the OM level in providing necessary realism for the possible underlying dynamics of the system in the simulation trials used for testing purposes, rather than in calculating management measures to be applied in practice.

Therefore, for example, a recent FAO workshop on modelling ecosystem interactions for informing an ecosystem approach to ﬁsheries concluded that although such ecosystem models were not yet at the stage where they might be used directly to provide management recommendations, they did have a role to play as OMs in evaluating the performance of simpler models used as the basis for MPs [38].

There is a role for “intelligence” at the stage of implementing an MP for either of levels 1 or 2, but it is rather in the oversight role of a pilot regularly checking that his autopilot continues to achieve the results planned.

This includes checking that exceptional circumstances have not arisen in the form of compelling scientiﬁc evidence that the resource has moved outside the range of circumstances (including implementation error) spanned by the simulation testing, which necessitates that the advice provided by the MP be overruled (for level 3 of MSE as applied in Australia, the constraints on human intervention provided by the meta-rules play a similar role).

This should be a rare occurrence—it is not a mechanism to allow annual “tinkering” with the scientiﬁc recommendations output by an MP.

Best practice for MSE implementation includes documentation of an agreed process for such an eventuality, as well as a regular MP review process during which “intelligence” can be brought to bear in considering whether amendments to an MP are required [39]; and see speciﬁc examples in CCSBT, 2005b, and Appendix 2 of Rademeyer et al., 2008; the 5-year regular IWC MP review process [16] includes the option to call for an “exceptional” review under certain circumstances.


R&R admonish a frequent absence in MSE publications of details of how stakeholders were involved in the MP development process, given that the opportunity that MSE provides for such involvement, and hence for more likely acceptance of the product, is often advanced as one of its major beneﬁts.

Indeed, an interesting feature of the growing application of MSE and MPs is increasing acceptance by industry of the process, because it provides them with greater planning security by removing the uncertainty associated with the outcome of the annual assessment/TAC scientiﬁc debate.

Failures to report details of these interactive processes may reﬂect the fact that in many fora, these processes settled down some time ago and are now fairly routine.

At an international level, in the IWC, there has been a concerted effort to ensure stakeholder participation.

This is normally carried out via national delegations, which often include representatives of environment and ﬁsheries ministries, industry, and NGOs.

With respect to the development of the MPs for subsistence whaling, the chair of the scientiﬁc group working on development talked directly with representatives of the subsistence communities throughout the development process.

The primary interests of stakeholders have been: determination of the user- and resource-orientated objectives and the balance between these; performance measures of proposed procedures against those objectives; and data and analysis requirements and availability.

Technical details of approaches have rarely been the focus of concern of the stakeholders.

The Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) has so far adopted the approach of a broad interchange between scientists and managers (Commissioners) at its annual meetings in its work towards development of an MP to govern spatial allocation of the krill (Euphausia superba) catches allowed in the Scotia Sea [40].

Moreover, stakeholders such as the ﬁshing industry and NGOs have been party to relevant discussions by virtue of the fact that they are either involved as members of national delegations or serve as observers at the Commission or Scientiﬁc Committee level.

In the Commission for the Conservation of Southern Blueﬁn Tuna (CCSBT), where adoption of an MP is nearing ﬁnality (though after some hiccoughs—see Kolody et al., 2008), there have been several special meetings held to allow for interaction between scientists, managers, and industry representatives on the details of the MP as it is developed [41], and an initiation of this process at NAFO for Greenland halibut (Reinhardtius hippoglossoides) took a similar route [42].

In Europe, there is increased emphasis on stakeholder participation and the ecosystem-based approach to ﬁsheries management in the reform of the CFP [43].

The development of MSE is therefore an important tool because it allows a broad array of European, national, and local stakeholders to be involved.

For example, MSE is being used in a participatory modelling process in the JAKFISH project (http://www.imares.wur.nl/UK/research/marineﬁsheries/projects/jakﬁsh/), so that scientists and stakeholders can jointly develop ﬂexible and transparent models to evaluate alternative management strategies before implementation.

This will allow stakeholders to decide upon the best assessment, monitoring, and MPs, based upon incomplete knowledge of stock structure and subpopulation catches for the management of the herring (Clupea harengus) meta-population [44], and to help perform bioeconomic evaluation of different management measures [45].

Nationally, in South Africa, the primary discussions take place in species-speciﬁc scientiﬁc working groups on which industry and managers are present as observers, but in practice can and often do participate fully in discussions [39].

These discussions have focused particularly on the trade-off arising from restrictions on interannual TAC variability that industries seek for reasons of stability, and the associated lesser average ﬁshing mortality levels and hence lower catches on average over time that such restrictions require.

Involvement of stakeholders, including environmental NGOs as well as the ﬁshing industry, is a basic and widespread feature of the Australian system of ﬁsheries governance.

This stakeholder participation has been an important aspect of developing and applying the MSE approach in Australia [3].

Cox and Kronlund (2008) used MSE to develop practical stakeholder-driven harvest policies for groundﬁsh ﬁsheries in British Columbia, Canada.

They found that MSE helped in developing co-management, because in traditional stock assessment there is only a scientiﬁc choice of what constitutes the best assessment method, and the long-term policy consequences of particular assessment model choices are rarely evaluated.

Therefore, MSE offered a potential vehicle for addressing both policy and process conﬂicts in ﬁshery co-management.

In New Zealand, stakeholder (commercial, recreational, customary Maori, and environmental) involvement in scientiﬁc and management meetings is the norm for all ﬁsheries.

In addition, all decision-making, including the setting of TACs, is subject to statutory public consultation.

Although most stocks are managed using the traditional assessment approach, MPs have been developed to guide TAC-setting for some rock lobster stocks since the mid-1990s [46].

MP development and recommendations and consequent TAC advice have been provided by a national multistakeholder group with responsibility for providing advice directly to the Minister of Fisheries in place of the usual government-led processes.

The MP development processes have been highly collaborative involving stakeholders, scientists, and government ofﬁcials.

Based on advice from the multistakeholder group [47] reﬂecting changing stakeholder goals, successive ministers have decided upon a sequence of 5-year MPs since 1996 and have also implemented TAC cuts and increases as indicated by the adopted MPs and advised on annually by the multistakeholder group.


Simulation-based management strategy evaluation is a valuable tool, when appropriately implemented.

Implementation, however, may not always have been appropriate, and some reasons are provided why perhaps there is incomplete faith in certain of its technical aspects, such as knowing the distribution of the parameters of population processes from the information in limited datasets.

A management strategy that has been evaluated by simulation should not be used as an “autopilot”, because even the most competent of experts can develop autopilots with imperfect and incomplete knowledge of reality, and all information should be incorporated when decisions have to be made.

The manuscript by Butterworth et al. ( 2010) is a response to a paper published in the ICES Journal of Marine Science by Rochet and Rice (2009), referred to hereafter as R&R. The authors make ﬁve substantive criticisms of R&R.


They argue that most concerns regarding management strategy evaluation (MSE) raised in the R&R paper are addressed when the method is properly implemented. They also argue that the paradox identiﬁed by R&R, namely that modelling a distribution implies knowing more than only estimating its expected value, is a misunderstanding; instead, they assume that modelling uncertainty is always preferable to ignoring it.

They challenge R&R’s interpretation that their simulations showed that re-sampling methods may underestimate the probabilities of low values.

They further argue that R&R’s criticism of implementation uncertainty can in fact be addressed by simulations as part of an MSE.

They suggest that the alternative approaches proposed by R&R for the evaluation of management options are not appropriate either for incorporating uncertainty or the effects of feedback.


Several points are discussed without direct criticism of R&R, particularly the robustness of MSE conclusions to various operating models and whether simulation results can be used to identify the best MSE.

R&R did not assert that there are basic ﬂaws in MSE, a point conceded by Butterworth et al. ( 2010) notwithstanding their title, and we agree that if simulation-based methods were always appropriately implemented, there would have been no need to write the R&R paper.

Doug Butterworth and his colleagues are among the leaders in MSE development, and implementations they have led are generally not implicated in the issues raised by R&R. However, although analytical stock assessments carried out carefully by assessment scientists meet high standards, there is no guarantee that all stock assessments are analytically without ﬂaws, and the fact that good MSEs can be produced by expert scientists does not ensure that all MSEs will avoid the types of ﬂaw discussed in R&R.


Although R&R did not go to the extreme criticism of “garbage in –garbage out”, as used by Butterworth et al. ( 2010), we have seen the types of ﬂaw discussed in R&R in work presented in various fora.

As we all agree they should be avoided, if this exchange leads to increased vigilance by practitioners and reviewers, the discipline and the resources will be much better off.

With agreement on many core points between the Rochet and Rice (2009) and Butterworth et al. ( 2010) papers, it is difﬁcult to identify the source of the “misinterpretation” referred to in the title of the latter paper.

The most important misunderstanding of deﬁnition could be that the deﬁnition in Butterworth et al. ( 2010) is not consistent with R&R’s usage.


However, Butterworth et al. ( 2010) might misunderstand or misrepresent some key parts of R&R’s thesis.

Some are details, such as their criticism of R&R’s interpretation of the use of re-sampling methods in MSEs, where we believe that Figure 3 of R&R does demonstrate the possibility (but not the certainty) of underestimating the likelihood of rare events.

Some are more conceptual, but more serious.

Butterworth et al. ( 2010) represent R&R’s cautions regarding MSE stock assessment as a desire to restrict practice to only “traditional assessment”.

Rather, unlike Butterworth et al. ( 2010), who suggest that MSE is the only alternative to traditional stock assessment, the main point by R&R was that there are different complementary approaches.

Each, including MSE, will have strengths and drawbacks that need to be considered on a case-by-case basis, and no one should be considered the universal solution to ﬁsheries assessment and advice.


For example, post hoc analyses suffer problems of attribution of cause, as outlined by Butterworth et al. ( 2010).

However, such analyses are required to form the knowledge base underlying operating models and MSEs: if causes are not understood in real cases, there is little hope that they can be simulated adequately in models.

Likewise, when R&R stated that, paradoxically, modelling a distribution implies knowing more than just taking its expected value, R&R did not argue or imply that it was preferable to ignore uncertainty.

Rather they cautioned about the risks of assuming that uncertainty could be captured fully and accurately by using distributions for key parameters, especially if the distributions were not securely supported by adequate data.


R&R were not speciﬁc about what alternative was a better way to deal with uncertainty, again because they felt that it was unlikely that there was some single best way.

For example, in their commentary on R&R’s reference to implementation uncertainty, Butterworth et al. ( 2010) argue for considering implementation within the MSE and provide examples where it has been done.

We do not share the belief that we should be conﬁdent of modelling accurately the complex human processes underlying implementation uncertainty.

Rather, we encourage efforts aimed at using MSE to establish likely implementation errors that would cause speciﬁc management strategies to fail, a point made in R&R.


We advise caution in believing that human behaviour and economic externalities can be modelled well enough to develop conﬁdence that a management strategy will be found that is reliable enough to use as an “autopilot”. It is perhaps this search for an autopilot that best differentiates Butterworth et al. ( 2010) from R&R. The latter argue for maximizing intelligence in science support for ﬁsheries decision-making.

Contrary to the discussion of this point by the former, this is not arguing for the annual debate of assessment results that opens the door to partisanship—be it power-brokering by industry lobbies or advocacy science by special interest groups.

Intelligence is getting as much information as possible into public dialogue and decision-making, ensuring that all of it is understood by all parties, and designing decision-making processes that are inclusive and equitable.


We think that this should not be done once, during an MSE—even one that, as Butterworth et al. ( 2010) say, can engage stakeholders—but each time a decision has to be made. We are well aware that Sparholt et al. ( 2007) found that implementation of the precautionary approach at ICES did not prevent demersal stocks from declining, but Gerjan Piet and some Dutch colleagues show, in a manuscript currently under preparation (“The necessity of response indicators as part of an ecosystem approach to ﬁsheries management”), but Piet et al. ( 2010) show that for 121 stocks for which ICES provided advice over the period 1987–2006, the ofﬁcial TAC was on average 30% higher than the scientiﬁc advice, suggesting that quantitative advice is used qualitatively anyway.


We agree that such a system needs to be improved.

However, we believe that improvement can be made in many ways and certainly not by adopting a system built on an autopilot.

Even with stakeholders involved in the process, the details and parametrization of the operating model that determine the ﬁnal answer are proposed usually by scientiﬁc and technical experts whom stakeholders trust and feel they are not competent to contest, or in some instances, do not fully understand.

As a consequence, dictatorship of technical expertise is difﬁcult to avoid, even in the best of faith.


The innate immune system uses pattern recognition to sense infection, and it initiates an immune response upon pathogen detection (Janeway, 1989).

Recent ad- vances have defined two broad categories of microbial stimuli.

The first class includes products unique to mi- crobes, such as lipopolysaccharide and lipotechoic acid.

The second class encompasses nucleic acids de- rived from pathogens, particularly viruses.

Receptors that detect nucleic acids generally activate type I inter- ferons (IFN), which establish a cell-autonomous antiviral state, alert neighboring cells to the presence of infec- tion, and enhance cytotoxic lymphocyte responses to infected cells (Levy et al., 2003).

Importantly, self/non- self discrimination is straightforward for receptors that detect unique microbial products but more complex for nucleic acid sensors because their ligands are not unique to pathogens.


Two types of receptors detect and respond to nucleic acids.

The first type includes several Toll-like receptors (TLRs) and samples material captured in endosomes (for review, see Kawai and Akira, 2005).

TLR3 is acti- vated by double-stranded RNA (dsRNA), while TLRs 7 and 8 recognize single-stranded RNA.

TLR9, the only known primary sensor of foreign DNA, recognizes un- methylated CpG motifs.

TLRs are important for detec- tion of foreign nucleic acids, but they are only expressed in a subset of cells, while almost all nucleated cells can activate type I IFN in response to viral infection.


Introduction into the cytosol of double-stranded RNA or its synthetic analog Poly I:C activates potent type I IFN production.


The RNA helicases RIG-I and MDA5 were recently found to be essential for this response (Yoneyama et al., 2004, 2005).

Both are activated by dsRNA, and RIG-I is essential for the type I IFN response to diverse RNA viruses (Kato et al., 2005; Sumpter et al., 2005).


RIG-I and MDA5 signal via a pathway that includes FADD, the RIP-1 kinase, Tank binding kinase 1 (TBK-1), and the adaptor protein MAVS/IPS-1/VISA/CARDIF (Balachandran et al., 2004; Kawai et al., 2005; Meylan et al., 2005; Seth et al., 2005; Xu et al., 2005).


These recent findings are a major advance in our un- derstanding of cytosolic RNA recognition and innate an- tiviral responses.

In contrast, TLR9 remains the only known sensor of foreign DNA.

Whether a pathway anal- ogous to RIG-I/MDA5 exists to signal an antiviral re- sponse to DNA is currently unknown.

Such a sensor would be consistent with recent observations that HSV activates type I interferons by TLR9-dependent and -independent pathways depending on the cell type infected (Hochrein et al., 2004; Malmgaard et al., 2004).


Listeria monocytogenes is a gram-positive bacterium that grows in the cytosol of infected host cells (Portnoy, 2005).

Upon entry of Listeria into cells by endocytosis, the pore-forming protein Listeriolysin O (LLO) ruptures the nascent phagosome, allowing escape into the cyto- sol.

Invasive versus noninvasive Listeria activate distinct signaling pathways (Hauf et al., 1997), and cytosolic bacteria trigger a unique cytokine response that in- cludes production of type I IFN and monocyte chemo- tactic protein (MCP-1) (O'Riordan et al., 2002; Serbina et al., 2003).

While MCP-1 is essential for in vivo immu- nity to Listeria infection, type I IFN appear to be delete- rious to the host (Serbina et al., 2003; Auerbuch et al., 2004; Carrero et al., 2004; O'Connell et al., 2004).

Recent reports have shown that type I IFN activation by Listeria requires IRF3 and TBK-1 and is independent of TLRs and NOD proteins (O'Connell et al., 2005; Stockinger et al., 2004), but the ligand(s) responsible remain un- known.


Several lines of evidence suggest that the IFN-induc- ing ligand(s) detected upon cytosolic invasion are not unique to Listeria.

First, the invasive enterobacterium Shigella flexneri activates potent IFN production in fibro- blasts, but a noninvasive variant does not (Hess et al., 1987).

Second, noninvasive bacteria engineered to ex- press LLO, including Escherichia coli and Bacillus subti- lis, activate an identical type I IFN response (O'Riordan et al., 2002).

Third, Trypanosoma cruzi, a protozoan par- asite, activates IFN production with similar kinetics to its escape from the vacuole (Vaena de Avalos et al., 2002).

These studies suggest that a common feature might unite the IFN response to these pathogens and that products unique to bacteria are unlikely to be responsi- ble for activating type I IFNs.


Here we show that apoptotic cells loaded with LLO re- capitulate the IFN response activated by Listeria and that DNA is a major IFN-inducing ligand in Listeria extracts.

We demonstrate that Legionella pneumophila, a vacuolar pathogen, activates an identical response through its type IV secretion system.

By using purified DNA, we define the key features of a novel cytosolic in- nate immune recognition system and compare this path- way to other known nucleic acid sensors.

Remarkably, intracellular DNA triggers a unique antiviral response without detectable activation of NF-kB or MAP kinases, suggesting that the signaling pathways activated by cytosolic DNA and RNA are distinct.


Listeria monocytogenes contains multiple ligands for TLRs and possibly NOD proteins (Portnoy, 2005).

To ex- amine cytokine responses to Listeria infection in the ab- sence of these pathways, we used MyD88/Trif double knockout (DKO) macrophages, which lack all TLR sig- naling (Yamamoto et al., 2003), and MyD88/RIP2 DKO macrophages, which lack TLR- and NOD1/NOD2- mediated signaling (Chin et al., 2002; Kobayashi et al., 2002).


In agreement with previous studies, we found that macrophages infected with L. monocytogenes acti- vated a potent IFN-b response that was intact in cells doubly deficient for MyD88/Trif or MyD88/RIP2 (Fig- ure 1A).

Robust IL-6 production was also MyD88/Trif/ RIP2 independent (Figure 1A).


Interestingly, induction of KC (CXCL1), a chemokine homologous to human IL-8, was intact in MyD88/Trif DKO macrophages but se- verely impaired in MyD88/RIP2 DKO cells (Figure 1A).

We confirmed these observations in vivo and found that IFN-b and IL-6 induction in spleens 24 hr after intra- venous Listeria infection remained intact in MyD88/Trif DKO and MyD88/RIP2 DKO mice (Figure 1B).


However, KC production by splenocytes appeared to be MyD88 Immunity dependent at this early time point, perhaps reflecting a contribution from other cell types (data not shown).

To- gether with previous studies, these data demonstrate that live Listeria activate at least three signaling path- ways in macrophages: one that requires TLR/MyD88 sig- naling (McCaffrey et al., 2004), one that requires RIP2, and one that is independent of both TLRs and RIP2.


We next tested whether material from mammalian cells would initiate a similar response if endowed with the ability to enter the cytosol.

We prepared dexameth- asone-treated apoptotic thymocytes, loaded them with recombinant LLO, and fed them to peritoneal macro- phages.

Both the thymocytes and macrophages were derived from MyD88/Trif DKO mice to eliminate any TLR signaling activated by LLO (Park et al., 2004).

We observed an induction of IFN-b transcription when mac- rophages were fed LLO-loaded apoptotic cells, but neg- ligible induction with apoptotic cells alone or a similar amount of recombinant LLO alone (Figure 1C).

There- fore, ligand(s) that activate IFN-b upon cytosolic entry are also present in mammalian cells.


We tested whether nucleic acids could account for the potent IFN-b response to Listeria infection.

Extracts pre- pared from sonicated Listeria recapitulated the MyD88/ Trif/RIP2-independent induction of both IFN-b and IL-6 when transfected into macrophages (Figure 1E).

With extracts alone, we were unable to consistently repro- duce the RIP2-dependent KC induction observed during live Listeria infection, possibly because the ligand(s) that activate RIP2-mediated signaling were lost during ex- tract preparation or are not transfectable (data not shown).

Pretreatment of Listeria extracts with DNase im- paired their ability to activate both IFN-b and IL-6 by more than 98%, while RNase pretreatment had no effect (Figures 1D and 1E).

Identical results were obtained with E. coli extracts (data not shown).

These data demon- strate that DNA in Listeria extracts is a major activating ligand of IFN-b and IL-6 and that a cytosolic sensor of DNA may account for the potent TLR- and RIP2-inde- pendent IFN response to Listeria.


LLO-mediated delivery of bacteria or apoptotic cells into the cytosol activates the response described above.

Whether a vacuole-confined pathogen can trig- ger a similar pathway has not been fully explored.


We examined the innate immune response to Legionella pneumophila, a pathogen of phagocytic protozoa and the causative agent of Legionnaire's disease (McDade et al., 1977).

L. pneumophila injects proteins into the host cell cytosol that disrupt phagosome-lysosome transport to establish a replicative organelle.

These ef- fectors are transferred via the Dot/Icm type IV secretion apparatus, which is ancestrally related to bacterial con- jugation systems (Vogel and Isberg, 1999).

Importantly, the Dot/Icm system retains the ability to transfer DNA (Segal et al., 1998; Vogel et al., 1998).


We infected MyD88/Trif DKO macrophages with sev- eral doses of opsonized L. pneumophila and observed potent IFN-b and IL-6 transcription that was detectable 3 hr after infection and dramatically increased by 6 hr (Figure 2A).

Mutant bacteria lacking the dotA gene, which encodes a polytopic inner membrane protein es- sential for translocation of all Dot/Icm substrates (Roy and Isberg, 1997), failed to activate either cytokine (Fig- ure 2A).

Identical results were obtained with MyD88/ RIP2 DKO cells (Figure 2B).

L. pneumophila deficient for the icmW gene, which retain type IV secretion but are unable to evade transport to lysosomes due to a de- fect in transfer of a subset of Dot/Icm protein substrates (Zuckman et al., 1999), activated a potent cytokine re- sponse comparable to wild-type bacteria (Figure 2B).

Macrophages infected in the presence of chloramphen- icol, which inhibits bacterial protein synthesis but not type IV secretion, activated similarly robust IFN-b and IL-6 transcription, suggesting that neither newly formed proteins nor other indirect consequences of bacterial growth can account for activation of these cytokines (Figure 2C).


L. pneumophila therefore triggers, via Dot/ Icm-dependent transfer of a preformed substrate into the host cell cytosol, a TLR- and RIP2-independent re- sponse identical to that activated by Listeria.

Induction of these same cytokines by Listeria extracts is sensitive to DNase pretreatment (Figure 1E).

Together with the well-established capability of type IV secretion systems to transfer DNA (Christie et al., 2005), these data lead us to suggest that cytosolic DNA might also activate the in- terferon response to Legionella and that this innate im- mune recognition pathway may detect a broader range of pathogens than just cytosol-invasive bacteria.


Because the TLR9-mediated response to DNA is strictly CpG sequence specific (Hemmi et al., 2000; Krieg et al., 1995), we reasoned that DNA devoid of these motifs would allow us to explore TLR9-independent responses to DNA.

Double-stranded 45 base pair oligonucleotides lacking contiguous CpG sequences failed to activate a detectable cytokine response when added to the cell- culture medium at concentrations of up to 100 mg/ml (data not shown).


However, we observed a potent induc- tion of type I IFN when the DNA was transfected into sev- eral different cell types, including plasmacytoid den- dritic cells (Flt3 ligand DCs, Figure 3A), ''conventional'' dendritic cells (GM-CSF DCs, Figure 3B), macrophages (Figure 3C), and embryonic fibroblasts (MEFs, Fig- ure 3D), as measured by both ELISA for IFN-a (Figures 3A and 3B) and quantitative RT-PCR for IFN-b (Figures 3C and 3D).

We refer hereafter to these DNA oligos as in- terferon stimulatory DNA (ISD) for simplicity.


Type I IFN Immunity induction by ISD was intact in cells lacking MyD88, Trif, or RIP2 (Figures 3A-3D).

However, IFN-b activation in IRF3-deficient MEFs after ISD treatment was reduced by more than 99% compared to wild-type controls (Fig- ure 3D), consistent with the key role for IRF3 in virus- induced type I IFN production (Sato et al., 2000).

The IFN-a/b response to ISD was intact in TLR9-deficient cells (Figure 3A), and ISD did not activate murine TLR9 expressed in HEK-293T cells (Figure 3E).

In contrast, CpG oligonucleotides induced type I IFN production in a strictly TLR9/MyD88-dependent manner only in pDCs (Figures 3A-3D).


The potency of ISD was sequence in- dependent, because ISD with a reversed nucleotide se- quence and an unrelated 132 bp DNA fragment had identical activity (Figures 3A-3D).

However, DNA oligo- nucleotides smaller than 25 bp failed to activate type I IFN production, suggesting a minimum size required for their recognition (data not shown).

ISD failed to acti- vate an IFN-b reporter plasmid in 293T cells expressing RIG-I or MDA5 (data not shown).


These data show that purified DNA recapitulates the IFN-inducing activity of Listeria and Legionella and that a broad range of primary cell types mounts an IRF3-dependent innate immune re- sponse to intracellular DNA.


We considered whether components of the DNA dam- age repair pathway might be involved in the detection of ISD, especially since the DNA-dependent protein kinase (DNA-PK) can directly phosphorylate IRF3 (Karpova et al., 2002).

However, cells lacking Ku70, the catalytic subunit of DNA-PK (DNA-PKcs), p53, or the Ataxia- Telangiectasia mutated kinase (ATM) mounted an iden- tical IFN-b response when compared to their wild-type controls (Figure 3F).

Moreover, IFN-b activation by ISD was normal in cells treated with the PI-3 kinase family in- hibitors wortmannin (20 mM) or caffeine (5 mM), suggest- ing that DNA-PK, ATM, the ATR kinase, and PI-3K itself do not participate in ISD-activated signaling (data not shown).


Although the response to phosphodiester ISD trans- fection was sequence independent, CpG oligonucleo- tides synthesized with nuclease-resistant phosphoro- thioate bonds failed to activate the same pathway (Figure 3).

We found that while phosphodiester ISD po- tently activated type I IFN as measured by quantitative RT-PCR and intracellular cytokine staining, transfected p-thio oligonucleotides of identical sequence stimulated negligible IFN-b transcription and no production of type I IFN proteins (Figures 4A and 4B), suggesting that a na- tive sugar-phosphate DNA backbone is essential for ISD activity.


TLR9 activation by CpG DNA occurs in endosomes and can be inhibited by preventing endosomal acidifi- cation with the buffering agent chloroquine (Hacker et al., 1998).

As expected, chloroquine pretreatment completely inhibited the response to CpG DNA; how- ever, ISD activity was unimpaired (Figure 4C).

ISD acti- vated robust IFN-b and IL-6 transcription but not KC (Figure 4C), which is identical to the TLR- and RIP2-inde- pendent pathway activated by live Listeria and Legion- ella (Figures 1A and 2A).

We next transfected ISD or CpG DNA into chloroquine-pretreated macrophages by calcium phosphate precipitation.

ISD activated ro- bust IFN-b and IL-6 transcription while CpG DNA was in- active when transfected by this method (Figure 4D).

To- gether with the data from Figures 1 and 2, these findings indicate that ISD recognition occurs in the cytosol.


We examined several well-characterized signaling re- sponses to inflammatory stimuli and nucleic acid recog- nition, particularly those activated by TLRs and RIG-I. We first compared NF-kB activation in macrophages transfected with ISD or treated with LPS, which acti- vates TLR4/Trif-dependent IFN-b production (Yama- moto et al., 2003).

Both treatments activated robust IFN-b transcription, with ISD-stimulated IFN-b mRNA in- duction delayed by 30 min compared to LPS (Figure 5A, top).

LPS stimulation led to rapid and sustained nuclear NF-kB binding activity assessed by EMSA, while ISD treatment failed to stimulate detectable NF-kB binding (Figure 5A, bottom).

We next examined NF-kB activity in RAW 264.7 murine macrophages stably expressing a luciferase reporter driven by two consensus NF-kB binding sites.

ISD transfection of RAW-kB cells failed to activate the NF-kB-luciferase reporter but stimulated transcription of the endogenous IFN-b gene (Figure 5B).

In contrast, CpG stimulation of these cells activated the NF-kB reporter but not IFN-b (Figure 5B).

Taken to- gether, these data suggest that ISD potently activate type I IFN without NF-kB.


We next compared MAP kinase activation in response to ISD transfection versus CpG DNA treatment.

As ex- pected, treatment of wild-type macrophages with CpG DNA induced a rapid and robust activation of ERK, JNK, and p38 (Figure 5C).

In contrast, ISD treatment did not activate any of these MAP kinases (Figure 5C).

Identical results were observed in cDCs and MEFs (data not shown).


Finally, we used MyD88/Trif DKO macrophages to di- rectly compare the cytosolic response to transfected DNA and dsRNA.

The use of cells deficient in all TLR sig- naling allowed us to examine MAP kinase activation by ISD, genomic DNA isolated from calf thymus (which is contaminated with TLR ligands), and Poly I:C (which ac- tivates RIG-I and MDA5 upon transfection but also acti- vates TLR3).

Quantitative RT-PCR analysis of RNA from cells processed in parallel revealed that all three stimuli activated similarly robust transcription of the endoge- nous IFN-b gene that was detectable by 60 min Immunity posttransfection (Figure 5D, top).

In contrast, only Poly I:C transfection activated JNK and p38 with similar ki- netics to IFN-b induction (Figure 5D, bottom), suggest- ing that distinct signaling pathways are activated by cy- tosolic DNA versus dsRNA.


We performed a microarray analysis of cDCs stimulated for 4 hr with ISD or CpG DNA to identify the gene-expres- sion programs activated by each response.

Both ISD and CpG DNA matured DCs, as evidenced by an in- crease in surface expression of MHC Class II, CD86, and CD40 (Figure 6A).

A two-dimensional plot of the n- fold change in expression of each gene revealed an overlapping response with evidence for unique genes activated by both ISD and CpG (Figure 6B).

Importantly, several type I IFNs were specifically induced by ISD but not CpG (Figure 6C), confirming the ability of ISDs to ac- tivate a potent antiviral response.

As seen in Figure 4C, CpG DNA treatment but not ISD stimulation induced ro- bust transcription of KC (Figure 6D).

CpG DNA activated expression of both subunits of IL-12, the chemokine Dcip-1, and IL-1 far more potently than ISD (Figure 6D).

Both ISD and CpG DNA activated expression of IL-6, IP-10 (CXCL10), CD40, lymphotoxin-a, and tumor necro- sis factor, among others (Figure 6E).


To examine primary response genes in the ISD path- way versus those that required type I IFN receptor signal- ing for expression, we compared the ISD response in wild-type versus IFN-a receptor (IFNaR1)-deficient mac- rophages.

IFN-b induction in IFNaR1 2/2 cells was mildly reduced but still robust (Figure 6F).

In contrast, all other type I IFNs strictly required IFNaR1 signaling for expres- sion (Figure 6F).

This observation is consistent with the recently defined role for IRF7 in IFNaR1-dependent tran- scriptional activation of all type I IFNs (Honda et al., 2005).


Remarkably, IL-6 induction by ISD was impaired by more than 95% in IFNaR1 2/2 cells, while TLR9-medi- ated IL-6 activation was normal in these cells (Figure 6F).

While IL-6 is an NF-kB-dependent gene in response to IL-1 receptor and TLR signaling (Yamamoto et al., 2004), these data suggest that IL-6 is an IFN-dependent gene in the ISD pathway.

These findings detail a tran- scriptional profile of the acute response to ISD and dem- onstrate distinct gene expression programs activated by detection of endosomal versus cytosolic DNA.


We next compared gene expression in MyD88/Trif DKO macrophages after ISD or Poly I:C transfection.

De- spite the differences in ISD- and Poly I:C-activated sig- naling pathways (Figure 5), microarray analysis revealed a remarkably similar gene expression pattern 3 hr after transfection (Figure 7A).

Type I IFNs, IL-6, and key IFN- inducible genes were comparably activated by ISD and Poly I:C (Figure 7B).

Among the relatively few tran- scripts differentially expressed at this early time point, we observed several interesting genes that may account for unique responses to DNA and dsRNA (Figure 7C).

Consistent with our observations that the RIG-I/MDA5 pathway activates MAP kinases but the ISD response does not (Figure 5D), the MAP kinase-inducible tran- scription factor Early Growth Response 1 (Egr1) was strongly induced only by Poly I:C (Figure 7C).

Tran- scripts for Mafb were dramatically reduced after ISD but not Poly I:C treatment.

The lectin Ocil/Clrb, the apo- ptosis-associated gene Tdag, and the NF-kB-activating adaptor TANK were selectively activated by Poly I:C treatment.

Transcripts for the protein phosphatase PtprJ were robustly increased only by Poly I:C, while the phosphatases Dusp6 and Ptpn16 were selectively downregulated by ISD treatment.

Among the genes with expression specifically increased after ISD treat- ment, we found the GTPase RhoE, the cytokine-induc- ible SH2 protein Cish, Fas, and Lck (Figure 7C).

Further work will be required to establish the functional rele- vance of these genes and whether these pathways diverge more dramatically at later time points.

These data provide a direct comparison of genes activated by intracellular DNA and dsRNA and suggest that over- lapping but unique responses are initiated depending on the type of nucleic acid detected.


We provide evidence for a novel cytosolic innate im- mune response to DNA and suggest that recognition of bacterial DNA in the cytosol may be responsible for the potent induction of type I IFN upon Listeria monocy- togenes and Legionella pneumophila infection.

Removal of DNA from Listeria extracts abolishes their IFN-induc- ing activity, and purified DNA recapitulates all aspects of the TLR- and RIP2-independent cytokine response acti- vated by live bacteria.

We directly compare the signaling and gene expression programs activated by ISD, TLR9, and RIG-I/MDA5.

These findings define a unique innate immune pathway and implicate a novel mechanism for nucleic acid recognition.


Our data implicating DNA in Listeria-activated IFN production may explain why diverse bacteria, eukary- otic parasites, and even apoptotic cells trigger an iden- tical response upon entry into the cytosol.

DNA may be released during Listeria infection as a passive conse- quence of LLO-mediated disruption of the vacuole.

The majority of Listeria are killed shortly after infection of macrophages (Portnoy et al., 1989), which could pro- vide a source of DNA for cytosolic recognition.

More- over, LLO-expressing E. coli are lysed upon entry yet still dissolve the vacuole and expose its contents to the cy- tosol, including soluble proteins and DNA once con- tained within the bacteria (Higgins et al., 1999).


Alterna- tively, live Listeria may actively secrete DNA, which is a fundamental process in all prokaryotes.

L. monocyto- genes can conjugate with both gram-negative and gram-positive bacteria, and conjugal transfer of DNA into eukaryotic cells is known to occur (Charpentier et al., 1999; Waters, 2001).

Legionella pneumophila, which remains confined within a host cell vacuole, trig- gers an identical response through its Dot/Icm type IV secretion system.

We have not formally demonstrated that DNA is the activating ligand for this TLR- and RIP2-independent response.

However, L. pneumophila injects effector proteins into recipient bacteria during Dot/Icm-dependent conjugal DNA transfer (Luo and Isberg, 2004), suggesting that the nature of the target cell does not influence substrate selection and that DNA might be translocated along with effector proteins into eukaryotic cells.


The elegant original demonstration of the cytosolic surveillance pathway activated by Listeria showed that IFN-b induction requires p38 and is associated with NF-kB activation (O'Riordan et al., 2002), while purified DNA activates neither of these (Figure 5).

We found that p38 inhibitors delayed IFN-b induction and reduced it by approximately 70%, but significantly fewer bacteria entered inhibitor-treated cells, perhaps because p38 is important for phagocytosis of bacteria (D.B.S., unpub- lished data; Blander and Medzhitov, 2004).

We also found that live Listeria activate a RIP2-dependent path- way in macrophages that is separable from the IFN-b and IL-6 response to bacterial DNA.

Further character- ization of the multiple signaling pathways activated by Listeria will be required to establish the relative contri- butions of these individual components.


A native phosphodiester sugar-phosphate backbone is required for activation of the antiviral response by cy- tosolic DNA.

Several studies have shown sequence- independent immunostimulatory activity specifically as- sociated with transfected DNA (Ishii et al., 2001; Suzuki et al., 1999), and one recent report found TLR9-indepen- dent activity of phosphodiester DNA (Yasuda et al., 2005).

While we used this feature to our advantage to compare the cytosolic DNA response to TLR9-depen- dent recognition, it may offer important clues to the nature of the sensor.


Perhaps the sensor has intrinsic nuclease activity that is essential for DNA recognition.

However, chimeric oligonucleotides with p-thio linkages at the ends and phosphodiester bonds in the center are fully active (D.B.S. and R.M., unpublished data).

Alterna- tively, the phosphodiester backbone may provide a sequence-independent binding site for the sensor and allow it to distinguish between DNA and RNA.


We found that intracellular DNA potently activates the IFN-b gene without detectable activation of NF-kB and MAP kinases.

Classical studies of this promoter estab- lished that robust transcriptional activation requires the coordinated assembly of several transcription fac- tors termed the enhanceosome (Maniatis et al., 1998).

These factors include IRF3/7, NF-kB p50/p65, and the MAP kinase-activated AP-1 (Atf2/c-Jun).

The vast ma- jority of studies defining the assembly of the enhanceo- some were done with RNA viruses, and the recent char- acterization of the RIG-I-dependent response to RNA viruses validates the classical model (Kato et al., 2005).

We suggest that ISD recognition might activate IFN-b transcription by using some but not all components of the enhanceosome.

Numerous studies have shown that overexpression of IRF3 alone robustly activates the IFN-b promoter, presumably without any direct con- tribution of NF-kB or MAP kinases, and multimerization of IRF3 binding sites renders an ectopic promoter virus inducible (Fujita et al., 1987).

We emphasize that live DNA viruses may activate these pathways via other li- gands or effects on cells, such as recognition of viral RNA transcripts or a global stress response to viral rep- lication.

Identification of the sensor and unique elements of the ISD signaling pathway will allow dissection of the components of the response to DNA viruses and intra- cellular bacteria.


We used microarray analysis to directly compare the gene expression patterns activated by ISD versus TLR9 and RIG-I/MDA5 and identified a unique but over- lapping response to intracellular DNA.

Together, RIG-I, MDA5, and the ISD pathway may be sufficient to recog- nize a majority of viruses based on the type and structure of nucleic acids exposed inside the cell.

Importantly, just as RNA viruses encode proteins that antagonize the RIG-I and MDA5 pathways (Andrejeva et al., 2004; Foy et al., 2005; Meylan et al., 2005), it is almost cer- tain that DNA viruses encode proteins that interfere with the ISD response.

Examination of these proteins will illuminate the specific mechanisms of DNA virus detection.


Finally, the existence of this pathway raises an impor- tant question of self/nonself discrimination: how does a sequence-independent sensor avoid recognition of the more than 10 9 base pairs of genomic DNA in all nu- cleated cells?

Exclusion from the nucleus is an attractive explanation, but the breakdown of the nuclear envelope during cell division suggests that other mechanisms might prevent autoreactivity.

Perhaps genomic DNA, with its myriad modifications and association with chro- matin components, is specifically marked as ''self.

'' This would suggest that a failure to mark the genome as ''self'' would result in activation of the sensor.

Such self-recognition could be beneficial in sensing genomic instability in severely damaged or neoplastic cells and may contribute to the important role for type I IFNs in tumor immunosurveillance (Dunn et al., 2005).

Alterna- tively, a loss of cell-autonomous tolerance to genomic DNA would result in IFN-dependent autoimmunity.


While these proposed functions are entirely speculative, we suggest that this pathway is responsible for the TLR- independent, type I IFN-mediated pathology observed in DNase II-deficient embryos, where liver macrophages become engorged with undigestable nuclei from eryth- rocyte precursors (Okabe et al., 2005).

Further charac- terization of this pathway will provide valuable insights into the mechanisms of nucleic acid recognition and host resistance to pathogens.


Mice, Cells, and Antibodies C57Bl/6 mice and IFNaR1 2/2 mice were purchased from Jackson Laboratories (Bar Harbor, ME).

MyD88 2/2 , Trif 2/2 , and TLR9 2/2 mice were kindly provided by Dr. Shizuo Akira.

RIP2 2/2 mice and MEFs have been described (Kobayashi et al., 2002).

Ku70 2/2 and DNA-PK 2/2 bone marrow and Atm 2/2 MEFs were generously pro- vided by Dr. Frederick Alt.

IRF3 2/2 MEFs were kindly provided by Dr. Tadatsugu Taniguchi.

All animals were maintained in accordance with guidelines of the Yale Institutional Animal Use and Care Com- mittee.

RAW264.7 cells stably expressing an NF-kB-luciferase re- porter were generated by standard procedures.


Antibodies to MHC Class II (I-A b ), CD86, CD40, CD11c, and p38a were from BD Biosciences (San Diego, CA).

Antibodies to SAPK/JNK and p44/42 MAPK (Erk1/2) were from Cell Signaling Technologies (Beverly, MA).

Antibodies to IFN-a were from HyCult Biotechnology (the Neth- erlands) and PBL laboratories (Piscataway, NJ), and monoclonal an- tibody to murine IFN-b was from Seikagaku America (East Falmouth, MA).


Calf thymus genomic DNA was from Sigma (St. Louis, MO).

Poly I:C was from Amersham (Piscataway, NJ).

All oligonucleotides were synthesized at the Yale W.M. Keck facility, with p-thio linkages rep- resented as lowercase: ISD (sense), TACAGATCTACTAGTGATC TATGACTGATCTGTACATGATCTACA; CpG, tccatgacgttcctgacgtt.


Equimolar amounts of ISD and their antisense oligos were an- nealed in PBS at 75ºC for 30 min before cooling to room tempera- ture.

ISD-reverse was the exact reverse sequence of that listed above.


Macrophages were infected with Listeria monocytogenes strain 10403S at a moi of 2 bacteria per cell.

Gentamycin (50 mg/ml) was added to cells 1.5 hr postinfection to kill extracellular bacteria.

For in vivo infections, mice were injected intravenously with 5000 L. monocytogenes.

Apoptotic thymocytes from MyD88/Trif DKO mice were prepared by a 4.5 hr treatment with 10 mM dexametha- sone.

Washed cells were loaded with recombinant LLO (100 ng per 10 6 cells) for 15 min in ice-cold PBS, washed, and added to MyD88/Trif DKO peritoneal-derived macrophages at a ratio of 5:1.


Listeria extracts were prepared from a pelleted 5 ml culture freeze- thawed in liquid nitrogen, resuspended in 400 ml PBS containing 1 mg/ml lysozyme (Roche), and sonicated for 2 min using a VirSonic 101 Digital 600 Cell Disrupter (VirTis, Gardiner, NY).

Remaining debris was removed by centrifugation, and cleared extracts were adjusted to contain 2 mM MgCl 2 , 50 mM KCl, and 20 mM Tris-Cl (pH 8). 100 ml aliquots were treated separately with DNase I (Invitrogen, Carlsbad, CA; 100 units/ml) or RNase (Qiagen, Valencia, CA; 100 mg/ml) for 45 min at 37ºC. EDTA was added to 2.5 mM and samples were heated for 10 min at 70ºC. Extracts were again cleared by a 5 min centrifugation, and 5-10 ml were complexed with Lipofectamine 2000 (Invitrogen) and transfected as described below.


Legionella pneumophila strains and opsonizing antibody were generously pro- vided by Dr. Craig Roy.

ISD were complexed with Lipofectamine 2000 (Invitrogen) at a ratio of 1 ml lipofectamine to 1 mg DNA.

Cells in 12-well plates were stimulated with 5 mg ISD.

Calcium phosphate transfections were done in the presence of 50 mM chloroquine by standard techniques.

CpG was added directly to the media at 3 mM. E. coli LPS (Sigma) was used at 100 ng/ml.


For analysis of IFN-a production, supernatants were harvested 18 hr poststimula- tion and used for a sandwich ELISA.

For quantitative RT-PCR, cells were harvested into RNA-Bee (Teltest, Friendswood, TX).

RNA was reverse transcribed with Superscript II (Invitrogen), and cDNAs were used for PCR with Quantitect SYBR Green reagents (Qiagen) on a Stratagene MX3000 bioanalyzer (La Jolla, CA).

The abundance of each cytokine mRNA was normalized to HPRT expression and com- pared to untreated cells to calculate the relative induction.


cDCs or macrophages were stimulated for 4 hr and harvested into RNA Bee.

Sample preparation and hybridization to Affymetrix Mouse genome 430 2.0 arrays were performed at the Yale W.M. Keck facility.

The complete data set has been submitted to the GEO database (http://www.ncbi.nlm.nih.gov/geo; submission #GSE2197).


Whole-cell lysates from macrophages stimulated for the indicated times were separated by SDS-PAGE, transferred to PVDF mem- branes, and probed with antibodies specific for the indicated pro- teins.

Gel shift analysis was performed with standard techniques.


Induction of type I interferons (IFNs) by viruses and other pathogens is crucial for innate immunity, and it is mediated by the activation of pattern-recognition receptors, such as Toll-like receptors and cytosolic receptors such as RIG-I and MDA5.

The type I IFN induction is primarily controlled at the gene transcriptional level, wherein a family of transcription factors, interferon regulatory factors (IRFs), plays central roles.

Here, we summarize the recent studies on IRFs, providing a paradigm of how genes are ingeniously regulated during immune responses.

We also consider some evolutional aspects on the IFN-IRF system.


Type I interferons (IFNs) are a family of cytokines specialized to coordinate immunity to viruses and other intracellular infections.

In the past several years, many of the receptors and signaling pathways that link pathogen detection to induction of type I IFNs have been identified and characterized.

An integrated picture has emerged in which type I IFNs have essential functions in several seemingly disparate processes: they restrict viral spread by engaging machinery that ultimately cripples and kills infected cells, yet they are also positively linked to the activation and expansion of lymphocytes that are important for control of intracellular infections.

These advances highlight the context-specific actions of type I IFNs and clarify the multiple points at which they are integrated into both innate and adaptive immunity.


Viruses are the most abundant and diverse pathogens on earth.

One look at the genome of any organism confirms that viruses have been around for a very long time.

It is estimated that up to 40% of the human genome is derived from fragments of ancestral viral sequence, and approximately 1% of the genome consists of intact endogenous retroviruses [1].

The impact of viruses on our genes cannot be overstated: viruses are likely responsible for generating much of the variation that fueled selection of new traits and the evolution of our species, including exon shuffling, gene duplication, and diversification of noncoding regulatory elements.


Viruses can also be formidable pathogens and pose a unique challenge for the host’s detection systems.

It is now well established that immune recognition focuses on highly conserved, invariant structures of microbes that are distinct from the host [2].

Unlike bacteria and fungi, which contain easily distinguishable microbe-specific structures, viruses are made entirely of host-derived components.

With no microbe-specific “pattern” to latch onto, the receptors that detect viruses have instead evolved to recognize the one feature that is shared without exception among all viruses: a genome composed of nucleic acids.

Importantly, nucleic acid recognition is in principle an imperfect way for cells to distinguish self from non-self for obvious reasons, yet most (if not all) ways for a cell to signal the presence of viral infection are based on it.

The receptors that accomplish this are generally very successful at identifying viral nucleic acids in the midst of an abundance of host-derived RNA and DNA.

Because there is little structural basis for discrimination between viral and host nucleic acids, it appears that pattern recognition in this case is aided by the compartmentalization of antiviral sensors and by activities of host nucleases that help eliminate self nucleic acids from the extracellular fluids and from lysosomes [3][4][5].

Additional mechanisms that help such discrimination undoubtedly exist and await elucidation; insight into this fundamental question is attainable now that we know the identity of many of the receptors involved, as discussed below.


All organisms are susceptible to viral infections and all have a dedicated system to detect and prevent nucleic acid parasitism.

These systems range from restriction enzymes and antiphage defense in prokaryotes to the multilayered and complex networks of antiviral responses in higher metazoans.

Each of these systems directs responses that vary according to the organism’s physiology and needs.

Jawed vertebrates, which evolved an adaptive immune system of T and B cells, also developed the type I interferon (IFN) cytokine family that is dedicated to signaling the presence of intracellular infection and facilitating communication among the cells that provide defense against viruses and intracellular bacteria.

All vertebrates examined to date have a gene that encodes IFN-b and at least two that encode IFN-a, together with natural killer (NK) cells, T cells, and B cells; there is no known organism that has one of these components without all of the others.

The preservation of type I IFNs throughout the vertebrate lineage suggests an intimate functional linkage to adaptive immunity that has become increasingly apparent with recent experimental insights.


In this review, we will discuss the major receptor systems that detect viral infection and activate type I IFNs in vertebrates, with an emphasis on how these systems complement each other in host defense.

We will attempt to integrate important recent developments into the well-established framework of IFN function that is discussed in the accompanying reviews [6][7][8] and other papers [9][10][11][12].

These exciting advances clarify the mechanisms by which IFNs are activated, provide insight into their multiple and context-specific roles in antiviral immunity, and may suggest new directions for therapeutics and vaccine development.


Two Pathways for Type I IFN Induction: Toll-like Receptors Prior to the year 2000, no mammalian receptors that linked viral recognition to activation of type I IFNs had been identified.

After several years of remarkable progress, it is now clear that two complementary receptor systems account for most virus detection.

A general principle that has emerged is that all of these receptors detect nucleic acids.

Furthermore, these receptors fall into two categories: one class of receptors (exemplified by RIG-I and MDA5 proteins) is expressed ubiquitously and is localized to the cell’s cytosol where it detects viral nucleic acids produced upon infection.


The second class of receptors (members of the Toll-like receptor [TLR] family) detects viral nucleic acids in endosomes and only in specialized cell types.

This latter mode of recognition does not require that the IFN-producing cells are infected themselves and hence need not be ubiquitous.

The Toll-like receptors (TLRs) have been the subject of intense investigation as prototypical signaling receptors that activate immunity in response to microbial stimuli [13].

Several of the mammalian TLRs recognize different types of nucleic acids, and together they provide enough coverage to detect most types of viruses.


TLR3 recognizes double-stranded RNA, a common feature of both DNA and RNA viruses that until recently was thought to be the principal (or even only) form of viral nucleic acid recognized by the immune system [14].

TLR7 and TLR8 detect single-stranded RNA (ssRNA) and viruses that contain ssRNA genomes [15][16][17].

TLR9 detects unmethylated CpG motifs in DNA that are common in DNA viruses and also bacteria [18][19][20].


The only known exception to the rule that all receptors that activate type I IFNs detect nucleic acids is TLR4, which detects lipopolysaccharides (LPS) derived from gram-negative bacteria and activates type I IFNs in conventional dendritic cells (DCs) and macrophages through a signaling pathway that utilizes the adaptor protein Trif [21][22].

It is completely unclear at present why the TLR4-Trif pathway links recognition of bacterial cell walls to cytokines that generally signal the presence of a viral infection.

By comparison, TLR2 also recognizes cell wall components (peptidoglycans) but does not activate IFNs [23][24].

It is likely that the TLR4-Trif axis arose to harness some of the functions of IFNs in enhancing T cell differentiation and NK cell function, as discussed below.


TLRs are primarily expressed on key sentinel cells of the innate immune system: macrophages and dendritic cells (DCs).

TLR3 is expressed by conventional DCs, whereas TLRs 7 and 9 are expressed by both conventional and plasmacytoid dendritic cells (pDCs).

pDCs have a specialized signaling pathway that links activation of TLRs 7 and 9 to the production of copious amounts of type I IFNs, and they are primarily responsible for the systemic concentrations of IFNs detected during many experimental viral infections [25][26].

One feature shared by the four TLRs that sense nucleic acids is their intracellular localization: unlike all other TLRs, which are located at the cell surface, TLRs 3, 7, and 9 signal from within an acidified endosomal compartment.

Consequently, inhibitors of endosomal acidification block the function of these three TLRs but none of the others [27][28][29].

This intracellular localization is important for the ability of these TLRs to discriminate between self and non-self nucleic acids: relocating TLR9 to the cell surface abolishes its ability to respond to virus-encapsulated DNA but enables recognition of self-derived genomic DNA in the extracellular milieu [3].


A key feature of TLR-dependent nucleic acid recognition is directly tied to their intracellular location: TLRs sample material entering cells from the outside and thus do not detect the presence of infection from within.

This material often consists of viral particles that become uncoated or degraded upon endosomal acidification, thus exposing their nucleic acids for recognition.

Recent evidence indicates that TLR3 also samples apoptotic cells for viral dsRNA as they are engulfed and degraded in the phagosome [30].

Considering the strong link between viral infection and apoptosis in vertebrates and the role of TLRs in antigen presentation, this mechanism would provide an efficient means for viral antigens within dead cells derived from any tissue to be presented to T cells by uninfected DCs, a process known as crosspresentation.

The separation of viral infection from viral recognition in DCs has another important consequence: most viruses encode proteins that potently antagonize type I IFN expression and signaling within infected cells, but uninfected DCs would be refractory to such inhibition.


Two Pathways for Type I IFN Induction: Cytosolic Receptors
Although some TLRs can activate IFNs in a subset of specialized cells, almost all nucleated cells respond to viral infection by producing type I IFNs.

Fujita and colleagues advanced our understanding of this more broadly expressed virus-detection system by identifying the caspase recruitment domain (CARD)-containing RNA helicases retinoic acid inducible gene-I (RIG-I) and melanoma differentiation antigen 5 (MDA5) as sensors that link detection of dsRNA to activation of type I IFNs [31].

Both RIG-I and MDA5 use the CARD-containing adaptor protein MAVS (also known as IPS-1, VISA, or CARDIF) to activate the IFN-b gene [32][33][34][35].

The details of the RNA-activated signaling pathways have been reviewed elsewhere [36][37], and recent developments will also be discussed in the accompanying review [6].


Knockout mice lacking RIG-I and MDA5 have dramatic phenotypes when challenged with RNA viruses in vitro and in vivo.

RIG-I-deficient cells fail to activate type I IFN production when challenged with several classes of viruses, including vesicular stomatitis virus (VSV), Newcastle disease virus, Sendai virus, influenza virus, and Japanese encephalitis virus [38][39].

Interestingly, pDC-derived type I IFN production in response to these viruses requires MyD88-dependent TLR signaling but not RIG-I, highlighting the fact that TLRs and cytosolic sensors can independently activate type I IFNs in vivo [38].

MDA5 is the primary cytosolic receptor for the RNA polymer poly I:C and is required for detection of the picornavirus encephalomyocarditis virus (EMCV) [40][39].

Together, these studies establish that RIG-I and MDA5 detect different viral RNA structures, although the precise nature of these structures and the biochemical basis for this differential recognition remains unknown.


The generation of MAVS-deficient mice demonstrated that both RIG-I and MDA5 use this adaptor protein to activate type I IFNs [41][42].

Consequently, the phenotype of MAVS-deficient mice resembles the sum of RIG-I and MDA5 deficiency when challenged with RNA viruses that are detected by these helicases.

Importantly, MAVS-deficient mice are highly susceptible to infection with these viruses despite an intact TLR-dependent recognition system.

This interesting observation formally demonstrates that although the two receptor systems can independently activate the type I IFN response, they are not functionally redundant.

As discussed below, this also implies that type I IFNs are not sufficient for antiviral defense and that these cytosolic receptors may also control essential antiviral functions in an IFN-independent manner.


In addition to intracellular recognition of viral dsRNA, which has been known to exist for many years, foreign DNA is also recognized by an intracellular sensor that triggers type I IFN production [43][44][45].

This DNA-recognition system presumably evolved to detect DNA derived from DNA viruses during infection.

Thus, similar to RIG-I and MDA5, DNA recognition is a cell-autonomous and, presumably ubiquitous virus-recognition system.

The sensor for intracellular viral DNA is currently unknown.

Interestingly, however, the signaling pathway induced by this sensor is distinct from RIG-I and MDA5 signaling in that it does not result in activation of NF-kB transcription factor or MAP kinases, but does require IRF3 for the induction of interferon genes [45].

Consistent with the different downstream signaling pathway, DNA-induced interferon production is MAVS independent [41][42].

The reason why dsRNA and DNA trigger different signaling pathways presumably has to do with the induction of distinct effector responses to RNA and DNA viruses.

These two types of viruses differ dramatically with regards to their size and infection strategies, and so it is not surprising that different antiviral responses need to be induced to interfere with stages of the infection cycle unique to RNA or DNA viruses.


Interestingly, a potent type I interferon response is also induced by several intracellular bacterial infections.

This has been most clearly demonstrated in the case of Listeria monocytogenes [46], although other intracellular bacteria like Shigella flexneri and Legionella pneumophila induce interferon responses as well [47][45].

It appears that the induction of IFN-b in Listeria-infected macrophages may be mediated by the DNA-recognition pathway [45].

Consistent with this possibility, IFN-b induction by Listeria is TLR and nucleotide binding oligomerization domain (NOD) protein independent, but requires invasion of the host cell’s cytosol [48][46][49].

In the case of Listeria, IFN-b production and type I IFN-activated signaling in lymphocytes makes the host more susceptible to infection [50][51][48].

It is important to note that although type I IFNs appear to be detrimental to the host during Listeria infection, they are likely only one facet of the response activated by the sensor(s) that detect cytosolic Listeria; this pathway probably induces other cytokines and effector mechanisms, some of which might be beneficial to the host.

Nonetheless, this finding is a dramatic example of context-dependent effects of type I IFNs in the course of infection, which appears to be a common theme in IFN biology, as discussed below.

In the case of other bacteria that invade the host cytosol, it remains to be seen whether IFNs have a protective or detrimental effect at different stages of infection.


In summary, two distinct IFN-inducing systems are now known to exist: one is restricted to endosomes of specialized sentinel cells and mediated by TLRs, and the other is a ubiquitous cytosolic viral detection system mediated by RIG-I and MDA5 and a DNA sensor(s).

In principle, any type of virus would be vulnerable to detection by both a TLR and a cytosolic sensor; this breadth of coverage is remarkable considering the staggering diversity of viruses and the comparably tiny number of receptors charged with sensing them.

It would be interesting to determine whether retroviruses, which generate both RNA and DNA during their infectious cycle, are detected by sensors of both of these nucleic acids or whether one type of receptor predominates.

Based on the recent insights into the receptor systems that sense nucleic acids and the generation of knockout mice lacking specific components of each pathway, we are now able to evaluate the relative contributions of each of these systems to antiviral immunity.

What is beginning to emerge from these studies is a more integrated picture of the different ways in which type I IFNs facilitate antiviral defense through both cell-intrinsic and non-cell-autonomous mechanisms.


Cell-Intrinsic Antiviral Defense: The “Antiviral State”
The activity of type I IFNs was originally described almost 50 years ago as a soluble factor produced by cells treated with inactivated, nonreplicating viruses that blocked subsequent infection with live virus [52][53][54].

Interestingly, one group used the RNA influenza virus whereas the other used the DNA vaccinia virus, implying the existence of cellular sensors of both types of viral nucleic acids even in the first reports describing type I IFNs.

As indicated in these initial studies, type I IFNs produced by infected cells act in an autocrine and paracrine manner to signal the presence of viral infection.

By signaling through the type I IFN receptor, IFNs activate the inducible expression of hundreds of genes that together establish the “antiviral state” [7][12].

Antiviral state is a generic term and perhaps belies the fact that we still have only an elementary understanding of what exactly it is.

Indeed, there are entire families of IFN-inducible genes with completely unknown function, including several large GTPases and a series of proteins with tetratricopeptide repeat domains (IFITs) [55].

Although the function of each of these and other IFN-inducible genes awaits elucidation, it is safe to say that they act to cripple every stage of the viral life cycle.


Some IFN-inducible proteins have broad antiviral effects.

For example, 2'-5' oligoadenylate synthases (OAS) are activated by viral dsRNA and produce 2'-5' oligoadenylates, which in turn activate the latent nuclease, RNase L, resulting in the degradation of viral RNA transcripts as well as host RNAs [12].

Another well-studied IFN-induced antiviral effector is PKR, a member of the eukaryotic initiation factor 2a (eIF2a) kinase family.

Activation of PKR by dsRNA leads to eIF2a phosphorylation with a consequent blockade of translation of most cellular and viral mRNAs.

Interestingly, translation of mRNAs containing upstream open reading frames in their 5' UTRs is enhanced upon eIF2a phosphorylation.

Most known examples of proteins encoded by such mRNAs are restricted to the unfolded protein response and nutrient deprivation or stress conditions that activate other members of the eIF2a kinase family [56].

Conceivably, these proteins may indirectly contribute to the antiviral state.

However, one would expect that some of the bona fide antiviral effectors, including perhaps type I IFNs themselves, may also be translated preferentially when PKR is activated and eIF2a phosphorylated.


In addition to the relatively nonspecific antiviral activities of PKR and OAS-RNaseL systems, certain IFN-inducible genes may also exhibit specificity for distinct classes of viruses as implied by the existence of distinct sensors and signaling pathways that detect DNA or RNA within infected cells.

Recent studies demonstrate that there are indeed IFN-inducible genes that are able to intercept specific types of viruses.

The MX and guanylate binding proteins are large, IFN-inducible GTPases of the dynamin family.

The MX proteins have been shown to have a protective effect against two RNA viruses, influenza and VSV [57][58].

Although detailed mechanisms of MX and GBP functions await elucidation, their structural similarity with dynamin suggests that these proteins may interfere with viral assembly and trafficking in the cell.


Perhaps the best example of virus-specific functions of IFN-inducible genes and the most promising recent development in the search for novel HIV therapeutics is the recently discovered APOBEC3 family of cytidine deaminases.

It was established several years ago that HIV deficient in the virion infectivity factor (vif) is only able to form infectious viral particles in certain cell lines (termed permissive) and not in human T cells and several other cell lines (termed nonpermissive) [59][60].

The phenotype of the nonpermissive cells was interesting: they supported a single round of virus production, but the newly formed virions were unable to infect even permissive cells.

Fusion of permissive and nonpermissive cells established that the nonpermissive phenotype was dominant and suggested that a cellular factor expressed only in nonpermissive cells could suppress infectivity of vif-deficient HIV [61].

Malim and colleagues used subtractive cDNA analysis to identify CEM15 (also known as APOBEC3G) as this factor and showed that it is incorporated into vif-deficient, but not wild-type, virions [62].

Virion-incorporated APOBEC3G deaminates cytosine to uracil in reverse-transcribed retroviral DNA, generating catastrophic mutations and rendering viral DNA susceptible to cleavage by uracil DNA glycosylase and apurinic-apyrimidinic (AP) endonuclease enzymes [63][64].

Interestingly, the deaminase activity of APOBEC3G is not required for its antiretroviral function [65], and cellular (non-virus-incorporated) APOBEC3G potently blocks vif+ HIV infection in resting CD4+ T cells [66], suggesting that the regulation of APOBEC3G function may be more complex than initially suggested.

Nonetheless, these findings provide a remarkable example of IFN-inducible genes targeting a specific class of viruses.


Although the effector proteins induced by type I IFNs are clearly an essential component of the antiviral state, mounting evidence suggests that these cannot mediate antiviral immunity on their own and instead cooperate with numerous other signaling pathways to activate the full complement of antiviral functions.

For example, MAVS-deficient mice infected with VSV suffer severe mortality despite a strong systemic type I IFN response provided by TLR-dependent recognition in pDCs [42].

Moreover, fibroblasts lacking Fas-associated death domain protein (FADD), an important signaling molecule in the RIG-I and MDA5 pathways, are unable to curtail VSV replication in vitro, even when pretreated with recombinant type I IFNs [67].

Thus, although the paracrine function of type I IFNs is a very efficient way for infected cells to disseminate an alarm signal, these findings suggest that IFN-inducible effectors on their own are not sufficient to confer immunity and highlight the other key roles that type I IFNs play in coordinating the antiviral response.


Cell-Intrinsic Antiviral Defense: Type I IFNs and Apoptosis
It is well known that treatment of cells with type I IFNs sensitizes them to apoptosis upon subsequent viral infection [12].

The rationale for this response is fairly simple: because viruses require host cell machinery to replicate, elimination of an infected cell would shut down this machinery and thus prevent viral spread.

Many viruses have evolved mechanisms to interfere with host cell apoptosis, reflecting the importance of this process in antiviral defense [68].