8. Nielsen, R., Bustamante, C., Clark, A.G.,Glanowski, S., Sackton, T.B., Hubisz,M.J., Fledel-Alon, A., Tanenbaum, D.M.,Civello, D., White, T.J., et al. (2005). Ascan for positively selected genes in thegenomes of human and chimpanzee.PLoS Biol. 3, e170.

9. King, M.C., and Wilson, A.C. (1975).Evolution at two levels in humans andchimpanzees. Science 188, 107–116.

10. Keightley, P.D., Lercher, M.J., and Eyre-Walker, A. (2005). Evidence forwidespread degradation of gene controlregions in hominid genomes. PLoS Biol.3, e42.

11. Enard, W., Fassbender, A., Model, F.,Adorjan, P., Pääbo, S., and Olek, A.(2004). Differences in DNA methylationpattens between humans andchimpanzees. Curr. Biol. 14, R148–R149.

12. Khaitovich, P., Weiss, G., Lachmann, M.,Hellmann, I., Enard, W., Muetzel, B.,Wirkner, U., Ansorge, W., and Pääbo, S.(2004). A neutral model of transcriptomeevolution. PLoS Biol. 2, e132.

13. Khaitovich, P., Hellmann, I., Enard, W.,Nowick, K., Leinweber, M., Franz, H.,

Weiss, G., Lachmann, M., and Pääbo, S.(2005). Parallel patterns of evolution inthe genomes and transcriptomes ofhumans and chimpanzees. Science 309,1850–1854.

14. Lercher, M.J., Williams, E.J., and Hurst,L.D. (2001). Local similarity inevolutionary rates extends over wholechromosomes in human-rodent andmouse-rat comparisons: implications forunderstanding the mechanistic basis ofthe male mutation bias. Mol. Biol. Evol.18, 2032–2039.

15. Hardison, R.C., Roskin, K.M., Yang, S.,Diekhans, M., Kent, W.J., Weber, R.,Elnitski, L., Li, J., O’Conner, M., Kolbe,D., et al. (2003). Covariation infrequencies of substitution, deletion,transposition, and recombination duringeutherian evolution. Genome Res. 13,13–26.

16. Haldane, J.B.S. (1947). The mutation rateof the gene for hemophilia and itssegregation ratios in males and females.Ann. Eugen. 13, 262–271.

17. Hwang, D.G., and Green, P. (2004).Bayesian Markov chain Monte Carlo

sequence analysis reveals varyingneutral substitution patterns inmammalian evolution. Proc. Natl. Acad.Sci. USA 101, 13994–14001.

18. Lercher, M.J., and Hurst, L.D. (2002).Human SNP variability and mutation rateare higher in regions of highrecombination. Trends Genet. 18,337–340.

19. Meunier, J., and Duret, L. (2004).Recombination drives the evolution ofGC-content in the human genome. Mol.Biol. Evol. 21, 984–990.

20. Smith, N.G., Webster, M.T., and Ellegren,H. (2002). Deterministic mutation ratevariation in the human genome. GenomeRes. 12, 1350–1356.

Department of Evolutionary Biology,Evolutionary Biology Centre, UppsalaUniversity, Norbyvägen 18D, SE-752 36Uppsala, Sweden. E-mail: Hans.Ellegren@ebc.uu.se.

DOI: 10.1016/j.cub.2005.10.060

Current Biology Vol 15 No 22R922

Infectious Disease: Can We Averta Lethal Flu Pandemic?

If avian flu becomes directly transmissible among humans, could weprevent a pandemic by using prophylactic antivirals? Possibly, if thevirus is not too transmissible, and we react fast and efficiently.

Robert M. May

Since 1997, a highly pathogenicavian influenza virus (AIV), of thesubtype H5N1, has emerged inpoultry in Southeast Asia.Concern is rising, for severalreasons. In May 2005, the viruswas found in migratory waterfowlin western China [1] and mostrecently AIV is reported amongbirds in Turkey and Romania. Todate, essentially all the few, butincreasing, human cases appearto have come from human-to-birdcontact, causing around 60deaths from something like 120known infections [2]. Past flupandemics are believed to havearisen when AIV has reassortedwith a human influenza virus,within a dually infected humanpatient, resulting in a new variantcapable of direct transmissionfrom human to human.

So, what is our chance ofcontaining such a H5N1 or similarflu epidemic, if and when itappears? This question isaddressed in two separate andrecent numerical studies [3,4].Both assume that AIV jumps to

humans in Southeast Asia, anduse evidence from experiencewith previous flu epidemics toassess the parameters in complexand detailed models of the spreadof infection originating in Thailand,and the possibilities of averting apandemic.

Central to any such study is theinfection’s basic reproductivenumber, R0, which quantifies itstransmissibility; R0 is defined asthe average number of secondarycases generated by a typicalprimary case in an entirelysusceptible population [5].Epidemics can arise if R0 exceedsone, and not otherwise. Controlstrategies aim to reduce R0 belowone, by effectively removing aproportion 1 – (1/R0) of thesusceptible population.

Longini et al. [3] estimate R0 asaround 1.4 — based on about33% of the population beinginfected in past Asian pandemics— but their simulations exploreR0-values from 1.1 to 2.4.Ferguson et al. [4] estimate R0 at1.8 — based on re-analysis ofearlier data, suggesting theaverage ‘generation interval’

between an individual becominginfected and infecting a contactis around 2.6 days, rather thanthe previously estimated 4 — butthey also explore a range1 < R0 < 2. Longini et al. [3]assume a model population of500,000 people, with age-structure and patterns ofmovement within and between 36geographical regions,characteristic of rural Thailand(as revealed in its 2000 census).Ferguson et al. [4], using massiveparallel computational capacity,work with spatially detailedsimulations of the 85 millionpeople in Thailand and in a100 kilometre-wide zone outsideits borders, explicitly includinghouseholds, schools andworkplaces.

Figure 1 illustrates how anoutbreak of flu might spread inThailand, if there is nointervention, roughly 90 days afterthe first case. It comes fromFerguson et al.’s [4] simulations,seeded with a single ruralindividual. For the first 30 days,the epidemic tends to be spatiallyconfined. As ‘sparks’ areincreasingly shed into otherregions, numbers increaseexponentially and infectiousindividuals spread over largerdistances [6]. Between 60 and 90days the epidemic changes frombeing mainly local to beingcountry-wide. Any controlstrategy needs to be

Dispatch R923

implemented effectively beforethis time; after this, logisticconstraints make successunlikely. If R0 is 1.8, theunchecked epidemic infectsroughly two-thirds of themodelled population of 85 million.

Methods available for stoppinga flu epidemic come under threeheadings. First, ‘targeted antiviralprophylaxis’ (TAP). Antiviraldrugs, of which the most effectivecurrently is oseltamivir (TradeName ‘Tamiflu’, which inhibits theaction of neuraminidase, the N ofH5N1), work by significantlyreducing transmission. Althoughthey can also help an individual tofight off infection, for population-level prophylaxis they need to beadministered in advance ofapparent infection. Ferguson etal. [4] estimate that blanketing anentire country or region withTamiflu should be able toeliminate a pandemic virus withan R0 of even 3.6 or greater. Butsuch action is logistically difficult,if not impossible. Targetedstrategies are therefore needed.

Both Ferguson et al. [4] andLongini et al. [3] look at forms ofsocial targeting as the moststraightforward approach. Thisinvolves prophylaxing individualsin the same household, school orworkplace as newly diagnosedsymptomatic cases. And doing sovery promptly. Indeed, given that‘social targeting’ may be too slowto be effective, both sets ofauthors assume somewhat widertargeting to local neighbourhoods.Longini et al. [3] call thisGeographical targeted antiviralprophylaxis (GTAP). Second,vaccination would be excellent ifwe had a vaccine. Although ahuman influenza H5N1 vaccine iscurrently being tested, and maybe available in time, the problemis — as is the case for normalseasonal flu vaccines — wecannot be certain how effectivethe vaccine under developmentmay be against the strain thateventually appears. Third, wehave quarantine, or other ways ofeffectively reducing contact rateswithin the population (Fergusonet al. [4] call this “social distancemeasures”).

Exploring variouscombinations of these actions,

Longini et al. [3] concluded that aprepared response with GTAPwould have a high probability ofcontaining the epidemic,provided R0 was below 1.6. Suchcontainment is achieved, in theirsimulations, with an antiviralstock pile of around 100,000 to 1million courses of treatment. Ifeffective pre-vaccinationoccurred — assuming only 30%efficiency, and only halvingtransmissibility — then TAPcould be effective against strainswith R0 as high as 2.1. And if onecould use combinations of TAP,pre-vaccination and quarantine,then success could be achievedeven against strains with R0 ashigh as 2.4.

Ferguson et al.’s [4] studies ofcontrol were focussed mainly ontargeted antiviral prophylaxis.This was partly because they sawlogistic difficulties in “socialdistance measures”, and partlybecause they believed some suchactions could have unintendedadverse consequences as a resultof people dispersing. Broadly,their more computationallyambitious model — remember,they included the actual 85 millionpeople in and around Thailand,versus Longini et al.’s [3] 500,000,rather tidily distributed —concluded that a combination ofGTAP and social distancemeasures could contain anoutbreak in Thailand and avoid apandemic, provided the newvirus’ R0 is below 1.8. But theircorresponding calculationsrequired a stockpile of 3 millioncourses of antiviral drugs. And forlarger values of R0, even thiswould seem insufficient.

In the light of theseconclusions, a glance backwardto 1918 is in order. The numberkilled in that pandemic isestimated at 20 to 50 million. Butthe global population then wasless than 2 billion, with only onequarter urban. And the relativelysmaller number of peoplecrossing oceans did so in ships.Today’s threatened pandemiclooms over a more crowded worldof 6.5 billion, half urban,constantly and rapidly movingaround. We know much more, butour circumstances are inherentlymore difficult.

In the UK, the government hasannounced plans to stockpileenough courses of Tamiflu tocover one quarter of the UKpopulation by the end of 2006. Ina world of good surveillance,efficient delivery of TAP, and nopanic, this would be adequateprovided H5N1 waits until the UKis ready. In the real world ofpanicking people, the UK planscould prove more problematic.Meanwhile, the USA iscommitting $25 million toboosting surveillance in Asia. Anofficial at the NationalImmunization Program of the USCenters for Disease Control hasjuxtaposed this sum against the$800 million spent by the USA onanthrax vaccines, “against apathogen that has killed only ahandful of Americans and whosebioterrorist potential isunproven” [7]. We live ininteresting times.

References1. Liu, J., Xiao, H., Lei, F., Zhu, Q., Qin, K.,

Zhang, X.W., Zhang, X.L., Zhao, D.,Wang, G., Feng, Y., et al. (2005). Highlypathogenic H5N1 influenza virus

Figure 1. Simulated state after 90 daysof a flu epidemic in Thailand, seeded bya single rural individual.

Red, density of infected individuals;green, density of people who haverecovered from infection or died; grey,density of uninfected population.(Courtesy of Neil Ferguson; reproducedwith permission from [4]).

Current Biology Vol 15 No 22R924

infection in migratory birds. Science 309,1206.

2. WHO. (2005). Cumulative number ofconfirmed human cases of AvianInfluenza.http://www.who.int/csr/disease/avian_influenza/table_2005_10_10/en/...16/10/2005.

3. Longini, I.M., Jr., Nizam, A., Xu, S.,Ungchusak, K., Hanshaoworakul, W.,Cummings, D.A., and Halloran, M.E.(2005). Containing pandemic influenza atthe source. Science 309, 1083–1087.

Matthew Freeman

There is no place in an animal fora cell with an identity crisis. Aconfused cell is unlikely tofunction properly and may bedangerous if, for example, it startsto proliferate inappropriately. Theconcept that cells need to makestable, all-or-nothing fatedecisions was developed byWaddington in the first half of thelast century [1]. He termed thisprocess ‘canalisation’ andillustrated the idea with artisticdrawings of what he called anepigenetic landscape (Figure 1A).His rather theoretical idea hasproved to be correct andfundamental. It is becomingapparent that a variety ofmechanisms exist to ensure thatdevelopmental decisions arerobust [2–4]. Although moststudies have focussed on the

Eye Development:Decisions in Insec

The retina provides an example of a fdeveloping cells: cell fate decisions aa double negative feedback loop thatthe colour-detecting photoreceptors

Figure 1. Robustness and cell fates in the Dr

(A) Waddington’s epigenetic landscape. Cell fa‘roll down the hill of development’, they are inridges, i.e. their fates become robust. FroDrosophila retina showing the stochastic dis‘pale’ (red) R8 photoreceptor cells. Image cou

4. Ferguson, N.M., Cummings, D.A.,Cauchemez, S., Fraser, C., Riley, S.,Meeyai, A., Iamsirithaworn, S., andBurke, D.S. (2005). Strategies forcontaining an emerging influenzapandemic in Southeast Asia. Nature 437,209–214.

5. Anderson, R.M., and May, R.M. (1991).Infectious Diseases of Humans:Dynamics and Control (Oxford: OxfordUniversity Press).

6. May, R.M., Gupta, S., and McLean, A.R.(2001). Infectious disease dynamics:

ability of chromatin to converttransient transcriptional states,triggered by apparently fleetingdevelopmental signals, into long-term cellular memory, theincreasingly detailed knowledgeof signalling mechanisms has ledto a recognition that thesepathways themselves havesometimes evolved into robustnetworks that generate stabledecisions [5].

An example of apparentlysimilar cells making distinct fatedecisions are the colour-detecting photoreceptors of theretina. To see colours, our braincomputes the outputs ofphotoreceptors with differentspectral sensitivities. The fruitflyDrosophila has a random mosaicof colour-detectingphotoreceptors in its retina(Figure 1B) [6] and a recent paper[7] has described a mechanism

Stable Cell Fatet Colour Vision

undamental property ofre stable. A recent paper reports leads to bistable fate decisions inof Drosophila.

osophila eye.

tes are represented by valleys and, as cellscreasingly unable to cross the intervening

m [13]. (B) Fluorescent micrograph of atribution of ‘yellow’ (labeled in green) andrtesy of Claude Desplan.

what characterizes a successful invader?Phil. Trans. R. Soc. Lond. B Biol. Sci.356, 901–910.

7. Butler, D. (2005). Drugs could head off aflu pandemic – but only if we respondfast enough. Nature 436, 614–615.

Zoology Department, Oxford University,Oxford OX1 3PS, UK.

DOI: 10.1016/j.cub.2005.10.063

that ensures that these cellsmake a robust choice betweenalternative colour-detecting fates.The Drosophila compound eye isformed from about 800 unit eyes(ommatidia), each with sixmonochromatic outerphotoreceptors surrounding twostacked colour detectingphotoreceptors, known as R7and R8. The R7/R8 pair comes intwo randomly distributed forms:70% are ‘yellow’, with R7expressing the rhodopsin Rh4and R8 expressing rhodopsinRh6; the remaining 30% are‘pale’, with R7 expressing Rh3and R8 expressing Rh5 (‘yellow’and ‘pale’ refer to theirappearance under a microscope)[8,9]. Yellow ommatidia detectlonger wavelengths, into thegreen part of the spectrum, whilethe pale ones detect shorterwavelengths, in the blue and UVrange.

The decision between a yellowor pale fate is initiated when theR7 cell in each ommatidiummakes an apparently stochasticchoice of whether to express Rh3or Rh4. This decision is thencommunicated to the adjacent R8cell, forcing it to comply with the‘golden rule’ that R8 must expressRh5 if partnered with an Rh3-expressing R7, or Rh6 if partneredwith a Rh4-expressing R7 [9](Figure 2A). To investigate furtherthe mechanism underlying retinalmosaicism, Mikeladze-Dvali et al.[7] searched for genes expressedin mosaic patterns and found onethat was restricted to yellow R8s.Intriguingly this was the warts(wts) gene (also known as lats),encoding a cytoplasmicserine/threonine kinase alreadyfamous for its role as a tumoursuppressor that regulates cellgrowth and death [10]. Theirattention was then drawn to