Malaria is one of the oldest and deadliest infectious diseases in humans. Many mathematical models of malaria have been developed during the past century, and applied to potential interventions. However, malaria remains uncontrolled and is increasing in many areas, as are vector and parasite resistance to insecticides and drugs.Anopheles population dynamics and their relations to the environment. One of its main strengths is that it is based on both biological and environmental variables.This study presents a simulation model of African malaria vectors. This individual-based model incorporates current knowledge of the mechanisms underlying Anopheles biology about which knowledge is lacking. One simulation showed several patterns similar to those seen in the field, and made it possible to examine different analyses and hypotheses for these patterns; sensitivity analyses on temperature, moisture, predation and preliminary investigations of nutrient competition were also conducted.The model made it possible to structure existing knowledge, assembled in a comprehensive review of the literature, and also pointed out important aspects of basic Anopheles population dynamics in general and also a better understanding of the dynamics in specific local geographic environments. It points out many important areas for new investigations that will be critical to effective, efficient, sustainable interventions.Although based on some mathematical formulae and parameters, this new tool has been developed in order to be as explicit as possible, transparent in use, close to reality and amenable to direct use by field workers. It allows a better understanding of the mechanisms underlying  Plasmodium, disables and kills more people than any other infectious disease." [Not so long ago, in 1998, Sherman declared: "Of all the human afflictions, the greatest toll has been exacted by malaria. Even today, malaria, which is caused by protozoan parasites of the genus In line with the pioneering models of Ross (1911) and Macdonald (1957), malaria interventions such as breeding-site reduction and insecticide use have been considered the most effective and practical ones for reducing malaria transmission. Bednets and house screening serve as personal protection, and bednet-associated effects on malaria prevalence appear to be greater than can be accounted for by personal protection . These iThe study also aims to achieve a broader analysis and deeper understanding of the complex mechanisms involved in malaria transmission, in order to aid intervention programs. The idea of controlling malaria through the introduction of genetically modified mosquitoes is gaining increasing attention, for instance, but will first need to be tested critically, in trials that will necessarily involve models.Thus the work presented below represents only a beginning, and it has two major aims. First, it introduces an approach to help researchers account for ecological variables that are key determinants of malaria vector population dynamics. When fully calibrated, this approach will provide an integrated platform for hypothesis testing with complex temporal and spatial data; ultimately, it should help by providing forecasting capabilities.Of perhaps even greater importance, this first model provides a vehicle for assembling and structuring existing knowledge, thereby pointing out critical areas in which knowledge is lacking and very much needed. Thus it is a means of identifying and organizing important research priorities and indicating their epidemiological implications.One of the most important strengths of this model is to combine biological and environmental variables. As stated by , the comAnopheles have been implicated in malaria transmission worldwide. In Africa the major vectors are Anopheles gambiae sensu lato, which is considered the most important in most regions, Anopheles arabiensis, which is part of the preceding complex but with distinct characteristics, and Anopheles funestus, which is often reported as the second most important species in terms of malaria transmission and, more particularly, is considered the end-of-rainy-season vector that sustains the parasite. This work focuses on the major vector in sub-Saharan Africa An. gambiae, but much of what follows may be applicable to An. arabiensis, and even to An. funestus separately and all together, with inter-as well as intra-species competition.Approximately 70 species of This paper describes the first model of malaria vector population dynamics integrating both biological and environmental factors.Anopheles development on an individual basis and, using local environmental data as input, allows the simulation of the aggregate dynamics of Anopheles populations. The life cycle of each individual proceeds through four stages: three immature stages, which occur in a water body – egg, larva, pupa – and then the mature stage, a flying adult. An adult female disperses from the natal water body and begins a cycle which is maintained throughout the rest of life-alternating between obtaining a bloodmeal and ovipositing in a water body.The model incorporates basic biological requirements for Anopheles population dynamics, by means of mechanisms detailed below ·Δtk.     (1) is the mean temperature (°K) over the time interval k and r the developmental rate per hour at temperature T(°K), given by the following equation:ρ25°C is the development rate per hour at 25°C, under the assumption that there is no temperature inactivation of the critical enzyme;  is the enthalpy of activation of the reaction catalyzed by the enzyme ; ΔHL is the enthalpy change associated with low temperature inactivation of the enzyme ;  is the temperature (°K) where 50% of the enzyme is inactivated by low temperature; ΔHH is the enthalpy change associated with high temperature inactivation of the enzyme ;  is the temperature (°K) where 50% of the enzyme is inactivated by high temperature; and R is the universal gas constant .where tn, of each of the three stages (egg, larvae, pupae) and the length of the adult gonotrophic cycle is defined as:The cumulative development, depending only on temperature at each time step dk defined above in equation 1.with As detailed below, other factors are also considered, including a particular case for the larval stage that takes food requirements into account.CD(tn), with a default value of 10% and a stage is considered completed, such that the next stage begins when:Variability is allowed for in the cumulative development time, CD(t) >CDf = 1 + G(0,0.l)     (4)G is a normal random variable.where Anopheles, even for the most important African malaria vectors. The deficit is striking for all of the three major malaria vector species in Africa. We have fit the curve defined by equation 3 to all of the relevant published data. Those data are compiled in tables An. gambiae sensus lato.A survey of the literature reveals how very little developmental-rate data is available for An. gambiae development time from egg to adult [One reference provided only the total to adult , we haveAn. arabiensis, part of the An. gambiae complex.The only gonotrophic cycle data available in relation to temperature was for An. gambiae data in tables All three curves shown in figure An. gambiae females are one-day old when they take their first blood meal, according to [UFirstGon which represents the time lag before the first blood meal expressed as a percentage of the gonotrophic cycle length. Therefore, the first gonotrophic cycle is considered completed if:rding to . This grrding to 10] by d by dAn. CD(t) >CDf = 1 + UFirstGon + G(0,0.1)     (5)UFirstGon has been set to 0.5 for An. gambiae. All subsequent gonotrophic cycles follow equation 4.An. funestus [An. gambiae is set to 40°C.Although the range of variation of water temperature is very wide, it is rarely taken into account in the literature. Some authors have recorded temperatures close to 40°C in small pools ,11,12. Sfunestus ,12; thisfunestus ,12, a dafunestus  the therAnopheles usually develop in natural water bodies, such as puddles, pools or streams [ streams -14. The RH was made. A clear sky, without clouds (0), for relative humidity below 50%, linearly increases to completely cloudy (1) for relative humidity above 95%, as follows:Cloud coverage is likely to be relatively important because of its impact on the water temperature, but this variable is rarely available in climate data. However, it is known that a relative humidity of 100% is usually associated with complete cloud coverage and rain and a relative humidity less than 50% with dryness and almost no clouds. Hence an estimate of cloud coverage as a function of relative humidity USunExpo that describes the water body's sun exposure. This user-defined coefficient represents the coverage or shaded percentage of the particular water body, ranging from 0 for complete shade to 1 for complete sunlight exposure. By default it is set to 1.The maximum water temperature of a water body depends on the cloud coverage and a user-defined coefficient TM, it is estimated that the maximum water temperature  in accord with the water volume x (in liters) is , where:If the maximum air temperature in degrees Celsius is CSE = USunExpo·CloudCover(RH). The minimum water temperature is taken as the minimum air temperature.with WH in a water body:The following formula estimates the daily dynamics of water height UIF is the fixed daily water intake in mm·day-1 (e.g. from a stream, pipeline, human activity, etc.); its default value is 0. UIV, the variable daily water intake in mm·day-1is set in accord with the precipitation and the surrounding area's topology. Its default value is 1, which would apply to a water body in a flat area, such that only direct rainfall fills the water body. The user can set a particular value: for a water body on a slope, this coefficient should reflect the volume of water intake given 1 ml of precipitation in the area. P is the precipitation in mm per day, and RH is the relative humidity. UO, in mm·day-1, is the daily loss of water due to soil infiltration and evapotranspiration. By default, this parameter is set to a mean value of 3 mm·day-1.where The water bodies are approximated by means of simple geometric objects, such as cubes and cylinders. The default geometric object is a box; its dimensions (length, width, depth) can be entered by the user. Therefore, the volume of water available in the water body is calculated from the particular shape of the water body and the water height calculated above (equation 6).Aedes aegypti, which, it has been shown, can survive in dry soil for more than two months [Anopheles eggs cannot survive more than 15 days on dry soil. Thus, since some African regions with endemic malaria experience drought periods longer than two months, the only plausible alternative seems to be adult aestivation. This is another aspect of Anopheles biology in which much more data is needed. The different survival probability during aestivation has been arbitrarily set as shown in table Unlike the eggs of o months , recent o months  indicateAestivation or diapause is triggered by the non-availability of water (when water bodies are completely dry) for all stages. For the adult stage, aestivation is also triggered by a relative humidity arbitrarily chosen here at less than 40%, though even this may prove to be high in some area.Some combination of regulatory mechanisms limits the size of any population of any species. The most important, for many species, can be described as density-dependent regulation, or competition for space and/or food, which is assumed to summarize or integrate complex, difficult-to-measure mechanisms, such as food mass conversion. For the sake of simplicity and practicality, the basic ecological concept of carrying capacity  has beeni a carrying capacity K(i) (in mg) has been defined as:For each water body K(i) = LMax·S(i)·UCarrying     (7)LMax is the maximum larval biomass density, defined for all species j by:where Nj is the larval population size per surface unit (m2) for species j, and Wj is the approximate mean weight of species , with  and  being the maximum and minimum possible weight in species j, respectively), divided by 2 in equation 8 to correct for the greater size of the low-weight larval population. LMax = 300 mg·m-2 has been arbitrarily set for larvae. S(i) is the available water surface in water body i, and UCarrying is a positive user-defined coefficient for each water body, to correct for particular water-body characteristics; by default it is set to 1. Thus, for each water body at peak season periods, the maximum larval biomass density LMax is estimated by measuring the larval population size at its maximum.where j [N,  can be defined by:Resource competition is considered as a cause of mosquito mortality only for the larval stage. For species j  the natup is the proportion of larvae that is newly-hatched eggs, estimated by:where ΔNe(t) is the number of individual eggs entering the larval stage.where K(i) of a particular water body i is defined above (Equation 7). In general, the larval population increase is given by:The carrying capacity W(t) is the current larval biomass overall .where m for all species can be approximated by:The larval per capita density-dependent mortality rate As noted above, the larval stage is the only immature stage with food intake and, therefore, with weight changes. Thus, this stage is the key determinant of the final adult weight.where  is a coefficient that describes food availability for an individual i of species j,  is the maximum possible weight for species j, W(t) is the current larval biomass, K is the carrying capacity of the water body, and Wi, j(t) the weight of individual i of species j at time t. For each time step k, for species j, the weight of individual i increases linearly as , where dk is the thermal development in time period k (equation 2). The weight in the larval stage is then calculated as:and  when the larval biomass W << K. At the other extreme the weight increase will be almost zero if W ≈ K. Note that this formula allows both intra-and inter-species competition for food.This formula allows the individual larva to have a maximum weight in accord with its species From ,17-19 thFor the purpose of stochastic simulation variability has been allowed, again with a default value of 10%, as follows:Wi, j = Wi, j + G(0, 0.1)     (16)G is a normal random variable. The larval stage is regarded as completed, such that the pupa stage begins, when the thermal development CD is completed (Eq. 4) and Weight >WeightMin.where The relative weight of an individual within its species is used as an important factor in subsequent subsections on fecundity and number of blood meals, in which the following coefficient is used:Predators and pathogens are an important regulating factor and are sometimes reported to be the major cause of mortality .An. gambiae egg mortality, from predation or any other cause, beyond an observation  that up to 83% of eggs hatch after one day of drying on sandy loam soil. Without more information, the total egg mortality for each species was arbitrarily set at 5% as a fixed pre-development mortality for the overall batch and a daily survivorship of 0.99.Little has been reported about An. gambiae population sizes rise to a peak just after a drought period and then decrease to a roughly stationary level. Life cycles of predators on immature An. gambiae are generally longer than those of their prey, and during the latter phases predators are found in non-predatory stages (i.e. not preying on immature An. gambiae) [Service  points ogambiae) . Intensigambiae) .An. gambiae sensu lato mortality from predation in two experiments, one in which predator density was high and another in which spraying had reduced predator density. His results are summarized in table An. gambiae were infected.Service  evaluatel, if t = 0 is the start of this period, a curve should show a gradual increase in predation.Active predation exhibits a lag time around the mean life-cycle length of the prey . During The conditions leading to a new predator lag period could occur, for instance, when a dry water body gains water or after a control intervention killing the predators. If fig. : and p = 0.001, then the total larval and pupal mortality due to predators and pathogens for species j, can be expressed as:with Δmj(t) differs from m(t) in equation 12, which represents density-dependent mortality. For all species j the following were arbitrarily set:  = 25% for larvae and  = 10% for pupae.  = 25% is converted to a daily mortality rate as:Note that T is the individual's developmental time. Thus at t = 0, the beginning of the lag period, Δmj(t) ≈ 0, and at t ≥ l,  for species j.where mj the mortality due to predation and pathogens Δmj(t), for each species j, we obtain a new equilibrium Kp <K, given K in equation 11, whereOn adding to the density-dependent mortality Nj is the larval population size for species j (N(t) = .where An. gambiae and An. funestus. The causal mechanisms are not clear, but some authors report adult predators preying on adult mosquitoes at oviposition sites [There are several published studies of adult mortality rates ,21 for Aon sites . It is aCLag(t) for An. gambiae sensus lato.Oviposition typically occurs every two to three days (see above). Accounting for the low predation during the previously-defined predator lag time, the daily adult survival probability is taken to be 0.911 for a non-ovipositing day and 0.911 - 0.1·Anopheles, but these cues, along with distance, must be key factors in dispersal.The mechanisms governing mosquito dispersal in general remain unknown. Wind strength and direction are likely to be important factors, for instance, but relevant data are rarely reported. Very little is known about the relative attractiveness of individual humans and individual water bodies to Anopheles species overall; the default value of this parameter is 1. Exophily is expressed as the proportion of fed mosquitoes that leave the house during the first half of the gonotrophic cycle. For An. gambiae the default value of this parameter is 75%.In most tropical regions, bloodmeals are taken at night, between 6:00 pm and 6:00 am. As the mosquitoes are active during the night, for simplicity bites were modelled only in houses. Bloodmeal source selection is modelled by a two-step process, first a choice of house and second a choice of individual human within the house. Anthropophily, the proportion of bites taken on humans, can be set for each The model explicitly, dynamically represents individual locations in space, but at this stage the adult female alternately chooses at random among some number of water bodies for an oviposition site, and at random among some number of houses and individuals within the chosen house, for a bloodmeal. That is, the choices do not reflect relative distance, attractiveness, wind or other features the model is designed to address in future phases of development.An. gambiae and 63% of female An. funestus require a second bloodmeal one day after the first one. Here the probability of having a second bloodmeal within the first gonotrophic cycle is related to the weight of the individuals: there is a second bloodmeal when the coefficient Cweight is less than 0.4 for An. gambiae.In addition to the greater length of the first gonotrophic cycle (Equation 5), Brengues  has showCweight is less than 0.1.For multiparous females, there is a second bloodmeal when An. funestus and 19% of female An. gambiae that had just fed had taken only a partial bloodmeal. These figures are used to represent the proportion of females that take a subsequent bite within what is considered the same bloodmeal.According to , 14% of m = 100, with a standard deviation s = 50. In the absence of more precise information these values are assumed. The number of eggs oviposited is simulated as:The number of eggs oviposited by individuals shows a wide range of variation, both within and between experiments ,18,23,24N = G(m, s)·UEgg     (21)UEgg is a positive user-defined coefficient set to fit local observations, by default set to 1, and G is a normal random variable. Because fecundity is closely tied to body size, a variability of 50% of the number of eggs is allowed as a function of the individual's weight, as follows (see [where ows (see 23]):):UEgg isN' = N·(0.5 + 0.5·Cweight)     (22)The male-female ratio at emergence from the pupa stage is assumed to be 1:1.A simple example is used to show how the model can help to achieve a better understanding of vector population dynamics and determine key underlying factors. In particular, the influence of temperature, moisture, predation and nutrient competition on adult abundance is investigated. The example is taken as a small cluster of six houses, each with five residents, and a total of three oviposition sites  will remain constant.Here Although the focus is the first major peak in adult abundance, the analysis could be transposed to any period. Interventions that take effect in two periods are compared, the first beginning on May 6, 2000, at the beginning of the first major peak, and the second beginning 15 days later, on May 21, 2000. A successful one-time larval control intervention is simulated by imposing 80% mortality on all larvae present during 10 consecutive days. An adult control intervention that consists of spraying surfaces inside houses with residual insecticide is simulated by imposing 75% mortality on blood feeding adults during a 25-day period.Figure Similarly, for an adult-control effort figure , the latIn this analysis the same conditions are considered as the preceding section but the potential impact of the control interventions on predators is also taken into account. In the case of the larval control intervention, 80% mortality in the predator population is assumed, as was observed by . The preAnopheles within houses, but spiders in particular are thought to be very efficient in preying on mosquitoes. Here the impact of the destruction of these predators is investigated under an assumption that they represent an adult mosquito mortality of 5%. It is also assumed that the predator-pressure returns to its normal level after a time lag of 21 days.To the best of our knowledge, no study has focused on predators on adult Figures Figure The lack of predator pressure allows a much quicker reconstruction of the larval population.For the adult control intervention, the curves in figure Figure Anopheles population dynamics in an explicit, transparent way. It focuses on five basic factors, two of them abiotic – temperature and moisture – and three biotic – nutrient competition, predation or death by disease, and dispersal.This model integrates important mechanisms underlying Little of the published literature takes into account the effects of temperature on vector populations. It may be that temperature shows little fluctuation compared to countries with marked seasonality, but most African regions like Kenya exhibit temperature fluctuations ranging from 16°C to 35°C, which can be critical. Futhermore, temperature range is a key determinant for species dispersal and is, therefore, of high epidemiological importance: the species have different vectorial capacities and require different control programs., ΔHH and ΔHL, should reflect the sensitivity of each species to temperature changes in temperate, high and low temperature areas respectively, and thus could be highly informative. Many studies focus on vector breeding site characteristics, which the model addresses simply in terms of moisture. As yet no particular variables have been found to be crucial determinants of breeding site selection or success, but when these are determined, the model can implement them relatively easily. The transient patterns of breeding sites are taken into account as key determinants of predator and vector disease dynamics, however.Each parameter in equation 2 is individually related to the slopes of the curves for each stage of insect development , and thAnopheles dispersal, though this is clearly a critical factor. Here simple random dispersal has been used, but it may be possible to implement a more sophisticated dispersal algorithm soon.Nutrient competition is considered one of the major regulators of vector populations. Here the carrying capacity concept is used to allow both intra-and inter-species competition. Very few studies of vector predators and pathogens have been undertaken to date, but some literature suggests that this may also be an important determinant, so it has been incorporated accordingly. Little is known about Anopheles biology is needed: if nothing else, the model provides an organized view of the huge gaps in the existing information. A framework has been developed by exploiting what is available, but, at this point, far too many parameters and mechanisms involve arbitrary values or estimates.Thus, a basic tool has been developed for use by field workers and will be vastly improved by their efforts. First, more complete and precise data on Nonetheless, as an example, a vector population was simulated for a 20-month period, from May 1, 2000 to December 31, 2001, with meteorological data from Kilifi in Kenya and it was possible to roughly assess the sensitivity of vector population dynamics to four of the five basic factors – temperature, moisture, competition, and predation. The focus was on adult abundance curves.Temperature is very important to the adult abundance curve and, particularly, to the occurrence of the initial peak after a drought period; this may be critical for control purposes. Moisture is a key determinant of particular high peaks that occur not only after a drought period but throughout the year for temporary breeding sites. These peaks were attributed to the lower larval mortality proceeding from lower predation and disease pressure.These peaks may be of great epidemiological importance, in that they could bring malaria prevalence in humans above a threshold at which relatively high transmission could occur despite a low vector density. One concern with such large fluctuations is that the proportion of people susceptible may be very high at the beginning of the peak period. Furthermore, the earliest emergent adult mosquitoes may have a higher vectorial capacity; with almost no food competition, their weight is greater, which implies a longer life . With diAnopheles, the resulting de-regulation may backfire, producing a vicious cycle that leads to ever-increasing insecticide use. This further supports the argument that great improvements in our understanding of Anopheles ecology and population dynamics are needed.Preliminary results on species competition suggest the existence of competitive exclusion, i.e. the survival of only one species in a given habitat, which highlights the necessity of niche differentiation for species coexistence. The example also suggests that if insecticides impact populations of predators on The model is based on the data and knowledge currently available, and it can reproduce some broad, diverse patterns found in the field; its mechanisms and rules are explicit, and they allow us to provide detailed analyses and explanations of vector population dynamics. However, it requires considerable, continued application in the field to improve the data and our understanding of the underlying mechanisms. This is exactly the plan for subsequent research, to contribute to improved control of the scourge of malaria.Table 1. A description of the geographical area with the pools and houses.2. Climate information (temperature, precipitation, relative humidity) for the period considered.Anopheles vector population dynamics, and highlight crucial elements that are missing.This model made it possible to structure existing knowledge of The data and other information currently available made it possible to build a model that can reproduce diverse patterns found in the field. It incorporates explicit mechanisms and rules that can provide detailed analyses and explanations, and thus is a tool to help the malaria research and intervention community gain a better understanding of vector dynamics.The model should be greatly improved as more precise data and hypotheses become available and as it is applied in the field.• JMD contributed conceptualisation and design of the model, main literature review and authorship of the paper.• CM contributed conceptual and data input, review and comments.• GK, BK, JB and JC contributed conceptual input, review and comments.• JD, PB, HM, JG and AT contributed review and comments.• FEM contributed the initial concept and general supervision.All authors read and approved the manuscript.


Viruses, bacteria, and other pathogens betray their presence in the body through exterior proteins, distinct to each strain. To prepare for the multitude of potential infectious agents, developing B-cells shuffle their genes to produce as many as a billion different antibodies, one to match almost any foreign protein. Upon infection, a limited subset of these antibodies will recognize a particular pathogen and mobilize a larger, targeted immune response. B-cells producing the “recognizing” antibody refine and test genetic modifications, adjusting the antibody's fit to the foreign entity. B-cells compete for the best match, or highest affinity; the winners survive to produce more cells and more antibodies against the invader.B-cells require an enzyme called activation-induced cytidine deaminase (AID) to develop the most effective antibody. AID generates mutations in the highly variable target-recognition region of an antibody. Removing the AID gene prevents antibody refinement in mature human and mouse B-cells—which use a process called somatic hypermutation to alter single nucleotides in the antibody gene—as well as chicken cells that use a different process called gene conversion to produce variation. Unlike the single nucleotide changes caused by hypermutation, gene conversion modifies an antibody by swapping part of its antigen-binding region for a replacement gene segment. Preference for hypermutation versus gene conversion varies across species, and can even vary within a species. B-cells in chickens use gene conversion through adolescence, when the cells move from a hindgut organ called the bursa into the spleen, where hypermutation takes over.It is unclear precisely how AID induces either somatic hypermutation or gene conversion, and how it chooses one over the other. Several recent studies suggest that AID's effectiveness may depend on damage to a single DNA base—specifically, changing a cytidine to uracil, which AID can do in either DNA or RNA.To test whether AID causes hypermutation and gene conversion through a common pathway, Jean-Marie Buerstedde and colleagues at the National Research Center for Environment and Health in Munich, Germany, deleted the donor genes that supply replacement segments for gene conversion in chicken bursa cells. The cells not only stopped performing gene conversion; they revved up single nucleotide mutations in a pattern that looked suspiciously like somatic hypermutation. The mutations targeted hotspots for gene conversion, suggesting that hypermutation and gene conversion share common starting points along antibody genes. This paper adds evidence that AID functions by swapping a single DNA base to induce multiple modes of gene shuffling and refinement in B-cells.


Olfactory receptor (OR) genes constitute the molecular basis for the sense of smell and are encoded by the largest gene family in mammalian genomes. Previous studies suggested that the proportion of pseudogenes in the OR gene family is significantly larger in humans than in other apes and significantly larger in apes than in the mouse. To investigate the process of degeneration of the olfactory repertoire in primates, we estimated the proportion of OR pseudogenes in 19 primate species by surveying randomly chosen subsets of 100 OR genes from each species. We find that apes, Old World monkeys and one New World monkey, the howler monkey, have a significantly higher proportion of OR pseudogenes than do other New World monkeys or the lemur (a prosimian). Strikingly, the howler monkey is also the only New World monkey to possess full trichromatic vision, along with Old World monkeys and apes. Our findings suggest that the deterioration of the olfactory repertoire occurred concomitant with the acquisition of full trichromatic color vision in primates. Examination of olfactory receptor genes in 19 primate species suggests that the olfactory repertoire lost complexity as our ancestors acquired full-color vision Olfactory receptor (OR) genes provide the basis for the sense of smell  and, witInterestingly, approximately 60% of human OR genes carry one or more coding region disruptions and are therefore considered pseudogenes . In nonhAlthough the causes are unclear, it seems reasonable to speculate that the high fraction of OR pseudogenes in apes reflects a decreased reliance on the sense of smell in species for whom auditory and visual cues may be more important e.g., . We wereOwing to the high levels of DNA sequence divergence among the primate species in our sample, orthologous OR genes could not be amplified by primers designed based on human sequences . InsteadWe therefore proceeded to sequence 100 genes from 18 nonhuman primates using these primer pairs. Since the genome sequence is not available for these species, we were not able to compare the familial composition of our samples of OR genes to that of the full OR repertoires. However, with the exception of OR families 3, 11, 12, and 55 (whose absence in a sample of 100 genes is not unlikely, as they represent less than 1.8% of human OR genes), we identified OR genes from all families in all species . MoreoveWe then tabulated the proportion of OR pseudogenes in each species . Consistp < 0.02 for the difference between the howler monkey and the NWM with the second highest proportion of pseudogenes, the Wooly monkey, as assessed by a Fisher's exact test [FET]). Thus, it appears that a deterioration of the olfactory repertoire occurred in all apes and OWMs as well as, independently, in the howler monkey lineage.We further found that the proportion of OR pseudogenes in OWMs (29.3% ± 2.4%) is very similar to that of nonhuman apes (33.0% ± 0.8%), but notably higher than that of NWMs (18.4% ± 5.6%). One NWM species, the howler monkey, was a conspicuous exception, with an elevated proportion of OR pseudogenes, similar to that of OWMs and apes (31.0%)  and signStrikingly, a second phenotype is shared only by the howler monkey, OWMs, and apes: full (or “routine”) trichromatic color vision. In primates, trichromatic color vision is accomplished by three opsin genes whose products are pigments sensitive to short, medium, or long wavelength ranges of visible light . In OWMsp < 10−4, or, excluding humans from the full trichromatic group, p < 10−3, as assessed by a Mann–Whitney U test). This p value is only indicative since the species lineages are not all independent. However, if significance is instead assessed by a FET for all pairwise comparisons of species with full trichromatic color vision and without, the difference is again striking: 94 out of 96 comparisons are significant at the 5% level. Thus, the evolution of full trichromatic vision coincided with an increase in the fraction of OR pseudogenes, indicative of a deterioration of the sense of smell.While OWMs, apes, and the howler monkey carry 32.5% ± 6.3% OR pseudogenes in their OR gene repertoire, species without full trichromatic vision have 16.7% ± 1.0%, significantly fewer . As a consequence of these biases, estimates of the proportion of pseudogenes in human and mouse obtained with these primers (p < 0.01, assessed by FET).We first tested an existing set of primers, used by  primers  differ sWe proceeded by designing new pairs of degenerate primers for the OR gene family by using the program HYDEN . The fir2, 50 mM KCl, 10 mM Tris (pH 8.3), 2 U of Taq DNA polymerase, and 50 ng of genomic DNA. Conditions for the PCR amplification from all species were as follows: 35 cycles of denaturation at 94°C, annealing at a gradient temperature of 48°C to 60°C, and extension at 72°C, each step for 1 min. The first step of denaturation and the last step of extension were 3 min each. The PCR products were separated and visualized in a 1% agarose gel. From each amplification set (a given primer pair in a given species), all successful products were mixed and subjected to cloning using a TA cloning kit (Boehringer, Mannheim, Germany). Cloning was followed by a touchdown PCR using the vector primers for amplifications from isolated bacterial colonies. Products were purified using the High Pure PCR Product Purification Kit (Boehringer). Sequencing reactions were performed in both directions on PCR products, using the vector primers and the dye-terminator cycle sequencing kit (Perkin Elmer, Wellesley, Massachusetts, United States) on an ABI 3700 automated sequencer (Perkin Elmer).Each primer pair was used to amplify a set of eight reactions in each species using a temperature-gradient PCR. The use of several annealing temperatures for each species yielded a greater diversity of amplified OR genes. PCR was performed in a total volume of 25 μl, containing 0.2 μM of each deoxynucleotide (Promega, Madison, Wisconsin, United States), 50 pmol of each primer, 1.5 mM MgClhttp://genome.ucsc.edu/). In every case, the best hit was a human OR gene. This analysis was also used to insure that none of the genes were an artifact of (“jumping”) PCR fusion. Finally, each consensus sequence was searched for an uninterrupted open reading frame (ORF) in all six possible frames. If an uninterrupted ORF was found, the gene was annotated as intact. If no ORF was identified, the gene was annotated as a pseudogene. This approach probably results in an underestimate of the proportion of pseudogenes, as not all OR genes with an intact coding region are functional. Mutations in promoter or control regions of OR genes may lead to reduced or no expression. Similarly, radical missense mutations in highly conserved positions of the OR protein may result in dysfunction , the data were edited and assembled using the Sequencher program, version 4.0 (GeneCodes Corporation, Ann Arbor, Michigan, United States). Assembly of the clones was done using a similarity cutoff of 98%. This cutoff ensures that Taq-generated mutations that may have been sequenced in individual clones are not counted as independent genes. Clones that were collapsed to the same contig by the assembly process were counted as one gene. Once 25 and 75 genes (independent contigs) were identified from PC1 and PC2 primer pairs, respectively, a majority consensus was generated for each gene. In order to confirm that only OR genes were amplified from all the species, we used the consensus sequences of all genes from all species as queries in a BLAT search against the human genome sequence (function . Althoughttp://www.ncbi.nlm.nih.gov/Genbank/) as accession numbers AY448037–AY449380 and AY454789–AY455274.Sequences for all OR genes from all primate species were deposited to GenBank (


While significant strides have been made in health research, the incorporation of research evidence into healthcare decision-making has been marginal. The purpose of this paper is to provide an overview of how the utility of health services research can be improved through the use of theory. Integrating theory into health services research can improve research methodology and encourage stronger collaboration with decision-makers.Recognizing the importance of theory calls for new expectations in the practice of health services research. These include: the formation of interdisciplinary research teams; broadening the training for those who will practice health services research; and supportive organizational conditions that promote collaboration between researchers and decision makers. Further, funding bodies can provide a significant role in guiding and supporting the use of theory in the practice of health services research.Institutions and researchers should incorporate the use of theory if health services research is to fulfill its potential for improving the delivery of health care. While significant strides have been made in medical research over the past several decades, many research results considered important by researchers and expert committees are not being used by health care practitioners. While the value of health services research must be judged by its validity, its utility cannot be taken for granted. There has been an assumption that when research information is available it will be accessed, appraised and then applied . HoweverThe gap between research evidence and its' incorporation into practice has led to an increase in research in how to bring new knowledge to bear on everyday health care. Factors influencing the adoption of research evidence have been studied extensively -5. PersoIn recent years a number of researchers have advocated a greater role for the use of theory in strengthening the practice of research -12. HoweTheory provides a systematic view of a phenomena by specifying the relations among variables and propositions with the purpose to explain or predict phenomena that occurs in the world ,17. In hTheory offers many advantages to the health services researcher. Theory helps to identify the appropriate study question and target group; clarify methods and measurement issues; provide more detailed and informative descriptions on characteristics of the intervention and supportive implementation conditions; uncover unintended effects; assist in analysis and interpretation of results; and, the successful application of an intervention to different settings ,12.Theory-driven studies are addressing the challenge of both decision-makers and funding agencies to move beyond simplistic explanations of significance in health services research. Decision-makers are seeking explanations about how an intervention works and whether it will work in a fashion similar to the intervention that was evaluated when applied to a different environment ,12,20.Despite these potential benefits, there are a number of reasons offered as to why there has been a failure to integrate theory into research. Ironically, clinical randomized control trials have discouraged the use of theory in health services research. Given the genesis of clinical trial methodology, this may derive, in part, from the very origins of epidemiology, whereby John Snow allegedly ended an epidemic of cholera by removing the handle from the Broad Street water pump, even though he had no concept of what actually caused cholera. By ignoring the need for theory, Snow was able to overcome the fact that the theories he would have needed had not yet been elucidated. Similarly, we know that lung cancer incidence can be reduced by elimination of cigarette smoking, even though we do not know exactly how cigarette smoke causes lung cancer. Experimental trials often determine intervention effects without considering how the component features of an intervention work together to bring about study outcomes ,15,21. TAdopting a theory-driven approach in health services research is not without its challenges. Given the typical training of researchers and the uni-disciplinary nature of the practice the first challenge is the capacity of researchers to engage in theory driven research. Second, a theory driven approach requires organizational conditions that support researchers and decision makers collaborating in the development and testing of theory. Finally, theory development and testing is cumulative in nature, encouraging researchers to pursue a programmatic approach in research. This approach has implications on how funding agencies support health services research. Despite the potential challenges, a theory based approach offers promise for a greater understanding on what happens when interventions work to address social/health problems.Collaborative research partnerships between academic researchers and decision-makers describe a relationship and process between individuals from different backgrounds, who together, develop an integrative cooperative approach to resolve a research problem . It has Collaborative practice has also been identified as a key strategy in facilitating a theory driven approach. Weiss  recommenViewing program stakeholders as a key source in developing theory in health services research demands stronger collaboration between researchers and program decision makers . In thisThe role and impact of the researcher and the research process in practice settings have received greater attention in other fields such as program evaluation, nursing, anthropology and community psychology. For example, core principles of community psychology practice include: a) consistency of goals and values between the researcher and the setting, and b) the notion that interventions should have the potential for being "institutionalized" or systematically established within the setting in such a way that strengthens the natural resources of the setting -27. RathRecognizing the importance of theory calls for new expectations in the practice of health services research. There are a number of challenges that must be met in order for these perspectives to gain acceptance in the health services research community.Evolving perspectives on the practice of health services research require recognition that few disciplines are able to span the breadth of responsibilities associated with the research process. To date there has been a tendency for health services research to be practiced as a uni-discipline where clinical disciplines tend to practice separately from the social science disciplines. A priority is to encourage the formation of research teams that are inter-disciplinary. Pursuing this agenda will promote the formation of research teams that may include: business, anthropology, sociology, psychology, education, engineering, nursing and medicine. Combined disciplinary skills would, in a complementary fashion, address the breadth of skills required in a more complex research environment that includes the development and testing of theory.A second point concerns broadening the training for those who will practice health services research. By and large, academic training has focused on methodological issues. While a focus on research methods has made an important contribution to the practice of health services research, relying on research methods as a core curriculum has led to limitations in the training of health services researchers such as inadequate attention to the value of theory driven research. As health services research expands its methodological repertoire beyond the classical randomized control trial, researchers face increased ambiguity in attributing the source of intervention impact. It is in this circumstance that theory can guide health services researchers in understanding the causal linkages within an intervention. Further, students are educated in separate departments with little planned, formal activity across disciplines, which discourages co-operative approaches to research and service -35. EducCollaboration between researchers and decision-makers are contingent upon supportive organizational conditions for both partners. Researchers have, and most likely will continue to operate from university-based settings where incentives for promotion and tenure can act as barriers to changes in the practice of health services research ,36. MostFunding bodies have the potential to play a significant role in guiding and integrating these considerations into health services research. Research sponsors can develop evaluation criteria that encourage the application of theory. As an example, the Agency for Healthcare Research and Quality (AHRQ) in the USA funded an initiative (Translating Research into Practice) to identify sustainable and reproducible strategies that will: 1) accelerate the impact of health services research on direct patient care; and 2) improve the outcomes, quality, effectiveness, efficiency, and/or cost effectiveness of care through partnerships between health care organizations and researchers . FurtherThis paper has examined the importance of theory in health services research. We have argued that by strengthening the role of theory encourages collaborative practice between researchers and decision-makers. It has been noted that a theory driven approach in health services research is not without its challenges. However, given the modest advances towards incorporating research evidence into healthcare decisions, a theory driven approach is well worth the effort. The implication of this approach for health services research is that it has impact on the training and practice of health services research. Institutions and researchers should consider this emerging model of practice if health services research is to fulfill its potential for improving the delivery of care.The authors declare that there are no competing interests. Disclaimer: The opinions expressed are the authors' and do not necessarily represent official policy of AHRQ or the Department of Health and Human ServicesKB drafted the manuscript, edited and revised the contents, EO edited and revised the manuscript, KB, EO, MC, RS, DS all contributed to the conceptual development, editing and review of the manuscript.The pre-publication history for this paper can be accessed here:


Bayesian phylogenetic inference holds promise as an alternative to maximum likelihood, particularly for large molecular-sequence data sets. We have investigated the performance of Bayesian inference with empirical and simulated protein-sequence data under conditions of relative branch-length differences and model violation.With empirical protein-sequence data, Bayesian posterior probabilities provide more-generous estimates of subtree reliability than does the nonparametric bootstrap combined with maximum likelihood inference, reaching 100% posterior probability at bootstrap proportions around 80%. With simulated 7-taxon protein-sequence datasets, Bayesian posterior probabilities are somewhat more generous than bootstrap proportions, but do not saturate. Compared with likelihood, Bayesian phylogenetic inference can be as or more robust to relative branch-length differences for datasets of this size, particularly when among-sites rate variation is modeled using a gamma distribution. When the (known) correct model was used to infer trees, Bayesian inference recovered the (known) correct tree in 100% of instances in which one or two branches were up to 20-fold longer than the others. At ratios more extreme than 20-fold, topological accuracy of reconstruction degraded only slowly when only one branch was of relatively greater length, but more rapidly when there were two such branches. Under an incorrect model of sequence change, inaccurate trees were sometimes observed at less extreme branch-length ratios, and (particularly for trees with single long branches) such trees tended to be more inaccurate. The effect of model violation on accuracy of reconstruction for trees with two long branches was more variable, but gamma-corrected Bayesian inference nonetheless yielded more-accurate trees than did either maximum likelihood or uncorrected Bayesian inference across the range of conditions we examined. Assuming an exponential Bayesian prior on branch lengths did not improve, and under certain extreme conditions significantly diminished, performance. The two topology-comparison metrics we employed, edit distance and Robinson-Foulds symmetric distance, yielded different but highly complementary measures of performance.Our results demonstrate that Bayesian inference can be relatively robust against biologically reasonable levels of relative branch-length differences and model violation, and thus may provide a promising alternative to maximum likelihood for inference of phylogenetic trees from protein-sequence data. The inference of phylogenies from molecular sequence data, like most other quantitative problems in science, is most powerful within a model-based statistical framework. Sophisticated models are available to describe how sequences change along branches of a tree, and how the rate of sequence change varies among sites. Statistical measures describe both the quality of inferred trees, and the confidence that can be assigned to the existence and position of subtrees. Likelihood-based approaches have proven especially powerful for inferring phylogenetic trees ,2 but are.g. the relative contributions of vertical and lateral gene transfer to genomic diversity [At the same time, the ongoing success of genomic sequencing – new microbial genome sequences are now appearing at the rate of at least one per week – is yielding a wealth of ever-larger gene and protein datasets suitable for large-scale analysis of deep issues in comparative and evolutionary genomics, iversity ,8. Howeviversity  we have i.e. a tree) is associated with its probability of being correct, given the prior probability, model and data [Among the most interesting of these is Bayesian inference, in which the posterior probability of a hypothesis  that do not actually exist [Bayesian phylogenetic inference has been applied to simulated -18 as wely exist . Certaina priori reason that protein-sequence data should be more or less problematic than nucleotide data for Bayesian phylogenetics, gene and protein sequences have distinct statistical properties, and are subject to different selective constraints; so it is not inconceivable that, in practice, the corresponding models of sequence change might tend to fail in different ways, or to different extents. Bayesian inference has been applied to inference of phylogenetic trees for cytochrome b [et al. [Much less is known about the behaviour of Bayesian inference with protein-sequence data. While there is no chrome b , elongatchrome b , hydropechrome b , 3-hydrochrome b , membranchrome b , and conchrome b  and largchrome b  datasets [et al.  report aTo better characterize the behavior of Bayesian phylogenetic inference with protein-sequence data, we have applied MrBayes ,32 to boe.g. examining solution space, estimating support, and assessing stability to stochastic error. Only a subset of these broad bodies of theory and practice has so far been applied to phylogenetic inference, and even less implemented in platform-independent software. If we apply BIC to alternative trees and assume equal prior probabilities, it becomes possible to estimate Bayesian posteriors from their likelihood differences, linking the two approaches at this level [In this work we compare and contrast results obtained using two popular software programs, PROML  and MrBais level ,38. Stocis level  and the is level , can be is level ,18. The is level -43, the is level , and a mis level  lie fartEmpirical data under Methods) yielded the same topology. Interestingly, for these, the bootstrap consensus ML trees were topologically identical to the ML and Bayesian trees, indicating that the sequences in these datasets show a high degree of internal consistency across positions (i.e. bear few homoplasies). For another 10 datasets, one or more of these four approaches yielded a tree that differs slightly (edit distance ≤ 2) from the others. No pattern was obvious among these disagreements: the differences do not, for example, systematically separate ML from Bayesian trees. For these 10 datasets, the differences are simple edits, e.g. -(A(BC)) to -(B(AC)), or -((AB)(CD)) to -(A(B(CD))). For the remaining 4 datasets, one or more of the four approaches yielded a tree that differed more substantially (edit distance ≥ 3). Over these examples, the datasets that yield more-conflicted trees are slightly larger (mean, 12.25 sequences each) than those yielding slightly conflicted (mean, 11.50 sequences each) or identical trees (mean, 10.86 sequences each), although the numbers of datasets involved are too few for this observation to be generalized.We inferred maximum likelihood (ML) and Bayesian (B) trees for the 21 empirical protein-sequence datasets. For 7 of these datasets, every combination of approach and model that we investigated (ML-JTT-HMM, ML-JTT-gamma, B-JTT, B-EQ: see We compared PROML bootstrap proportions (BPs) with Bayesian posterior probabilities (PPs) separately for all subtrees among the three groups of trees inferred from these 21 empirical datasets: the 7 trees for which all four sets of approaches and models (ML-JTT-HMM, ML-JTT-gamma, B-JTT, B-EQ) yielded the same topology, the 10 for which one or more approach yielded a slightly different tree, and the 4 for which one or more tree differed more substantially. In Figure e.g. involving minority subtrees (those not in the extended 50% majority-rule consensus) or higher-order  fit curves.From our data, it is not possible to reject the hypothesis that the relationship between BP and PP has the same slope whether the Bayesian inference is conducted using JTT, or EQ, as the model of sequence change. Analysis of covariation (ANCOVA) yields probabilities 0.579 (Panel A), 0.235 (Panel D) and 0.195 (Panel E) that the lines described in Table Because for these trees the true molecular phylogeny is unknown, these results do not speak to the accuracy of the inferred topologies. For this, it is necessary to examine inferences based data simulated on trees of known topology.We first examine cases where tree inference was carried out under the same model (JTT) as that used to generate the data, and where a single branch was progressively extended in length (see Methods). When trees were inferred using gamma-corrected ML, the correct tree was recovered in 100% (50/50) of the cases in which the relative branch-length difference was 5-, 10- or 20-fold . The similarities we observe in both magnitude and trend for the two approaches demonstrate that the comparison we are making between ML and Bayesian inference does not, in these cases at least, depend on whether or not the performance of ML is assessed using an approach that involves the nonparametric bootstrap.We investigated two ways of assessing the performance of ML inference. In panel A of Figures With Bayesian inference, inaccurately reconstructed trees were also first seen at the 30-fold branch-length ratio Figure , panel Di.e. the long branches were topologically adjacent in the reconstructed tree.In Figures i.e. identical with the known topology). By structuring the comparison in this way, we avoid cases where the ML consensus might be topologically different from the best component tree, and avoid dealing with the plethora of cases and sub-cases that arise in comparing topologically non-congruent trees.In Figure i.e. shows little or no "saturation". For both the single- and two-long-branches cases, the PP is greatest, compared to BP, for Bayesian trees inferred without correction for ASRV, and least generous for gamma-corrected trees where the prior distribution was assumed to be uniform. Unsurprisingly, the lower values of subtree support, as measured both by BP and by PP, arise from the trees with the most extreme relative branch length differences.For all three combinations of ASRV correction and prior , but inferring trees under the JTT model (see Methods). Performance of each of the four sets of approaches and methods was assessed by comparing four measures: the branch-length ratio at which inaccurate trees were first observed; the total number of steps (summed over the eight ratios) by which the 400 trees differ from the true topology; the weighted sum ("burden") of these steps; and the mean number of steps by which each inaccurate tree differs from the known tree. The latter two measures were each calculated using both Robinson-Foulds symmetric distance, and edit distance, yielding six comparisons in all. A more-complete description is provided at footnote 2 of Table For datasets in which a single branch was of relatively greater length, violating the model of sequence change degraded performance of the four approaches Table . In eachFor datasets containing two long branches, model violation affected performance of ML and Bayesian inference differently. With ML, inference under the wrong model produced a somewhat lower frequency of topologically inaccurate trees, although each inaccurate tree was more inaccurate as judged by symmetric distance Table . With BaUnlike the situation with established approaches based on pairwise distances, parsimony or maximum likelihood, relatively little experience has accumulated so far on the application of Bayesian approaches to phylogenetic inference, especially for protein-sequence datasets. In this work we (a) extend the comparison of Bayesian posterior probabilities with nonparametric bootstrap proportions as measures of confidence in subtrees, (b) systematically investigate the robustness of ML and Bayesian inference to branch-length differences, and (c) compare the behavior of these two approaches to one specific violation of the model of sequence change. We used two measures to compare topologies (Robinson-Foulds symmetric distance, and edit distance), and it is clear that they captured different facets of topological incongruence.Model violation, below). As it is unlikely that any existing model – certainly any that fails to account for lineage-specific processes and temporal variations along these lineages – fully represents the historical complexity of molecular evolution, the same criticism could be levelled, albeit perhaps in lesser degree, against all current applications of statistically based phylogenetic inference to empirical datasets.Using 21 empirical protein-sequence datasets, we compared Bayesian posterior probabilities with bootstrap proportions based on ML as measures of support for subtrees. To make this comparison as fair as possible, we restricted our analysis to a model of sequence change (JTT) and a correction for ASRV (discrete approximation to the gamma distribution) available in both PROML and MrBayes. We did not optimize models separately for each approach or for each dataset, as JTT+gamma represents the most-parameterized combination that these two programs support in common. It is therefore possible that some of the difference observed between the two measures results from differential sensitivity of ML and Bayesian inference, as implemented in these programs, to deviation of JTT and the discrete gamma distribution from an optimal description of the processes of sequence change that actually gave rise to these sequences , with our simulated data the relationship between BP and PP resembles a smooth curve reaching 100% PP only at BP greater than 99%. Further studies will be required to disentangle why little or no saturation was observed; possibilities include the structure of our simulated trees , the way that data were evolved on these trees (e.g. assuming strict independence among sites, or rigorous adherence to the JTT model), and/or the way we summarize the support data for ML (via extended majority-rule consensus trees).For sets of consensus trees inferred from simulated protein-sequence data Figure , Bayesiae.g. in selection of outgroups and resolution of topologically problematic regions. Parsimony analysis is particularly susceptible to "long branch attraction" (LBA) artefacts, in which two or more branches are resolved adjacent in a tree solely because they are highly divergent from the others [Dissimilar sequences (represented in phylogenetic trees as long branches) create difficulties in phylogenetic analysis. The issue has been most extensively explored in parsimony analysis, where branch length can be an important consideration, e others . ML infeOur results Figures , 3, 4, 5Both the mammalian mitochondrial (mtmam) and JTT models embody empirical probabilistic models of amino acid substitution. Codon usage is highly skewed in mitochondrial genomes compared with the cognate nucleocytoplasmic components , and theParticularly in simulations where a single branch was differentially lengthened Table , using tSupport for subtrees) that the relative performance of these approaches as reported herein might, in part, reflect their differential sensitivity to sub-optimality in the models used.The degree of insensitivity to model violation we observe for gamma-corrected ML and Bayesian inference goes some way toward mitigating possible concern  from each other. Our results also illustrate the difference in dynamic range offered by these metrics, while simulation studies [e.g. in distinguishing transformations that affect large numbers of termini from those that affect small numbers of termini, in robustness against displacement of particular termini, or in application to very large trees [Our results Figures  illustra studies ,55,56 inge trees -59.e.g. 100 or 1000 replicate runs required to estimate bootstrap proportions, ASRV-corrected Bayesian inference must be seen as an important alternative for statistically based phylogenetic analysis of protein-sequence data when computational resources are limiting. It appears that the interpretation of bootstrap proportions and posterior probabilities being developed for nucleotide sequences will apply as well to protein sequences.Bayesian inference can be as robust as ML against relative branch-length differences of 20-fold or greater in inference of correct topologies from protein-sequence data, although details depend on the number of relatively long branches, the presence or absence of an effective correction for ASRV, and (presumably) other factors. One might doubt that sequences so dissimilar as to produce a 20-fold (or more) difference in branch lengths could be believably recognised as homologous, or reliably aligned. Bayesian inference can also be as robust as ML to violation of the model of amino acid transition probability. For empirical protein-sequence data that might reasonably be encountered in biological research, then, both gamma-corrected ML and gamma-corrected Bayesian inference perform well in recovering the correct topology. As Bayesian inference is typically very much faster than even a single ML run, not to mention than Our interest in lateral genetic transfer (LGT) -9 led usSimulated data were evolved using the "evolver" program within PAML version 3.13a ,63. FirsProtein data sets were then generated on each of the 16 trees with differentially lengthened branches, using the "evolver" program in PAML. On each tree we evolved 100 replicate protein datasets under the JTT model of sequence change , with amvs simuated data. Here we present methods for the simulated data; methods specific to the empirical data are given below.All maximum-likelihood (ML) trees were inferred using PROML version 3.6a3 in Felsenstein's PHYLIP package  implemenN = 10) each of the 50 replicate datasets, as described in the preceding paragraph.For simulated data, we report results from both (1) single ML inference runs based on each of the 50 replicate protein datasets at each branch-length ratio increment, and (2) bootstrapping  among these sampled trees.Bayesian inference (B) was carried out using MRBAYES version 2.01  implemeni.e. did not merely serve to initialize estimation by MRBAYES). In runs where an exponential prior was used, the value of the exponent was estimated from the simulated data, and differed according to sequence-change model: under JTT, 0.10 for datasets with both one and two long branches, and under mtmam, 1.04 for one long branch, and 0.60 for two long branches.For simulated data, we examined three models of different complexities: (1) a uniform prior distribution over branch lengths, and a single rate category; (2) a uniform prior, and an 8-category gamma model of ASRV; and (3) an exponential prior, and an 8-category gamma. The gamma shape parameter was, as above, estimated using Tree-Puzzle, and was fixed  the Robinson-Foulds symmetric distance [For trees inferred from the 16 sets of simulated data (1 or 2 long branches, 8 ratios of branch-length difference), topologies were compared against that of the (known) tree on which the data had been evolved. For this we employed two metrics: (1) the minimum number of break-and-reanneal edits required to convert one tree into the other. This metric goes under various names, including distance ; we refedistance  as impledistance . The valdistance ) becauseMethods and procedures followed those for simulated data (above), except as described subsequently here.Aligned protein sequence datasets see  were obtFor empirical data, we inferred ML trees in two ways: (1) using a user-defined hidden Markov model (HMM) with 8 categories, each set to 12.5% of sites, and with rates in each category estimated using Tree-Puzzle version 5.0 ; and (2)N = 100 for the 21 empirical data sets, N = 10 for the 1600 simulated datasets) with preservation of rate-class information as described in the SEQBOOT documentation. Nonparametric bootstrap proportions (BPs) were computed under extended majority rule consensus using CONSENSE. Both SEQBOOT and CONSENSE are in PHYLIP [ML analyses were bootstrapped  were therefore compared amongst themselves, using edit distance  as the comparison metric. Topologies of the bootstrap ML consensus trees were compared as well, although as consensus trees they do not necessarily reflect most-likely topologies.The 21 empirical protein-sequence datasets from Dr Nick Goldman, and our simulated datasets with one or two long branches, are available for download at .JCM was responsible for data simulation and analysis. TJH was responsible for high-performance computing, and generated the figures. MAR initiated and supervised the project, and wrote the manuscript.Description of 21 empirical datasets This PDF file contains information on each of the 21 empirical datasets provided by Dr Nick Goldman, including: number of sequences, GenBank ID (gi number) of first sequence in dataset, key words from description line of first sequence, PAUP* parsimony score of dataset, number of internal nodes, and number of zero-length internal edges observed with PROML to have support in three non-overlapping intervals: P < 0.01, P < 0.05 but not P < 0.01, and worse than P < 0.05.Click here for file


Cancer specialists often talk about cancer as an umbrella term for over 200 different diseases, each having unique characteristics. But even these categories are too broad, as the same type of cancer can take very different paths in different people. It's not uncommon, for example, for a tumor to grow aggressively in one patient and stabilize or regress in another, even though their tumors are indistinguishable and are treated in the same way. Researchers have traditionally diagnosed and treated cancer based on microscopic analysis of cell size and shape, a method that's especially difficult for very closely related cancers, such as non-Hodgkin's lymphoma, which has 20 subtypes. As scientists learn more about the molecular alterations in cancer, they're beginning to establish cancer subtypes based on the underlying molecular footprint of a tumor. Four years ago, DNA microarray analysis revealed that the most common subtype of non-Hodgkin's lymphoma is in fact two separate diseases. Though the tumor cells of both cancers appear large and diffusely dispersed in a tissue sample under a microscope, each has a distinct genetic profile, possibly explaining why only 40% of patients with this subtype respond to the standard chemotherapy treatment.Such molecular pathology has led to the discovery of subtypes of several different tumor types and has successfully identified patients with different survival times. But such correlations work best when cancer subtypes based on genetic profiles are already known. If you know that different subtypes exist and which patients belong to which subtype, then you can build a statistical model to diagnose such cancers in future patients. But in most situations, clinicians don't know either of these variables—or even whether such a subtype exists—information that is crucial to developing effective diagnostic and treatment protocols. Statistical methods to identify such subtypes exist, but they can generate classifications that lack clinical relevance. Now Eric Bair and Robert Tibshirani describe a procedure that combines both gene expression data and the patients' clinical history to identify biologically significant cancer subtypes and show that this method is a powerful predictor of patient survival.Their approach uses clinical data to identify a list of genes that correspond to a particular clinical factor—such as survival time, tumor stage, or metastasis—in tandem with statistical analysis to look for additional patterns in the data to identify clinically relevant subsets of genes. In many retrospective studies, patient survival time is known, even though tumor subtypes are not; Bair and Tibshirani used that survival data to guide their analysis of the microarray data. They calculated the correlation of each gene in the microarray data with patient survival to generate a list of “significant” genes and then used these genes to identify tumor subtypes. Creating a list of candidate genes based on clinical data, the authors explain, reduces the chances of including genes unrelated to survival, increasing the probability of identifying gene clusters with clinical and thus predictive significance. Such “indicator gene lists” could identify subgroups of patients with similar gene expression profiles. The lists of subgroups, based on gene expression profiles and clinical outcomes of previous patients, could be used to assign future patients to the appropriate subgroup.An important goal of microarray research is to identify genetic profiles that can predict the risk of tumor metastasis. Being able to distinguish the subtle differences in cancer subtype will help doctors assess a patient's risk profile and to prescribe a course of treatment tailored to that profile. A patient with a particularly aggressive tumor, for example, would be a candidate for aggressive treatment, while a patient whose cancer seems unlikely to metastasize could be spared the debilitating side effects of aggressive anticancer therapies. By providing a method to cull the thousands of genes generated by a microarray to those most likely to have clinical relevance, Bair and Tibshirani have created a powerful tool to identify new cancer subtypes, predict expected patient survival, and, in some cases, help suggest the most appropriate course of treatment.


With the recent progress made in large-scale genome sequencing projects a vast amount of novel data is becoming available. A comparative sequence analysis, exploiting sequence information from various resources, can be used to uncover hidden information, such as genetic variation. Although there are enormous amounts of SNPs for a wide variety of organisms submitted to NCBI dbSNP and annotated in most genome assembly viewers like Ensembl and the UCSC Genome Browser, these platforms do not easily allow for extensive annotation and incorporation of experimental data supporting the polymorphism. However, such information is very important for selecting the most promising and useful candidate polymorphisms for use in experimental setups.in silico mining of high-throughput sequencing data. Currently, the database provides collections of laboratory rat (Rattus norvegicus) and zebrafish (Danio rerio) candidate SNPs. The database stores detailed information about raw data supporting the candidate, extensive annotation and links to external databases (e.g. GenBank, Ensembl, UniGene, and LocusLink), verification information, and predictions of a potential effect for non-synonymous polymorphisms in coding regions. The CASCAD website allows search based on an arbitrary combination of 27 different parameters related to characteristics like candidate SNP quality, genomic localization, and sequence data source or strain. In addition, the database can be queried with any custom nucleotide sequences of interest. The interface is crosslinked to other public databases and tightly coupled with primer design and local genome assembly interfaces in order to facilitate experimental verification of candidates.The CASCAD database is designed for presentation and query of candidate SNPs that are retrieved by  allows universal access to the database content and allows various queries supporting many types of research utilizing single nucleotide polymorphisms.The CASCAD database discloses detailed information on rat and zebrafish candidate SNPs, including the raw data underlying its discovery. An advanced web-based search interface  Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation within species. As a result, SNPs are now becoming the most popular type of marker in genetic association and mapping studies. SNPs are also most likely to be the molecular basis for the majority of phenotypic variation in (outbred) populations. In particular, SNPs in regulatory and protein-coding regions can have an effect on gene expression levels and protein activity, respectively. The phenotypic differences observed between selected (sub) strains in model organisms may be the result of specific (combinations of) natural occurring polymorphisms. Hence, a comprehensive inventory of SNPs, including extensive annotation will be extremely valuable in the search for functional polymorphisms.in silico candidate SNP mining pipeline that uses all publicly available sequence data for a specific organism, and designed a database, CASCAD (CAscad SNP CAndidates Database), that allows storage of a wide variety of primary source data, cross-annotation to other databases, and analysis parameters for SNPs associated with expressed sequences.There is often a vast unexplored potential in large sequence datasets that have been collected for other purposes, for example, EST and whole genome sequencing (WGS) projects. In an effort to address these two issues, we have developed an We applied the SNP discovery pipeline to both rat  and zebrWe designed a web-based interface with Perl scripts communicating to a MySQL database, and displaying HTML pages through Apache server running on SuSE Linux. The interface provides simple, advanced Figure , and seqIn addition to primary sequence data analysis, the effect of all SNPs on protein coding capacity was evaluated and non-synonymous SNPs were categorized in classes reflecting the severity of the polymorphism using a BLOSUM-based score. The predicted missense SNPs were analyzed by SIFT  and PolyQuery results are summarized on the SNP details page Figure , listingin silico discovery pipelines [For many applications, it is important to be able to distinguish between SNP candidates by their characteristics, as they may be predictive for verification success rate or carry biologically relevant information. Non-confirmed candidate polymorphisms may represent variants uncommon for a given population, but also sequencing errors , RNA editing events and reverse transcriptase errors (EST reads). In order to minimize the contribution of false positives, one can exclude polymorphisms based on a single read for either allele, as is common for many ipelines . AlthougWe have developed our database to fulfill the needs of any particular SNP application by providing control over every parameter we used in the polymorphism discovery step.Applications of the CASCAD database include queries for potentially deleterious SNPs in a specific genomic region of interest, for example a QTL interval, design of SNP-based mapping panels using either RFLP or any other technology, and identification of informative SNPs for fine-mapping. Custom sequences can be provided to search for known SNPs in any sequence of interest. In addition, the CASCAD pipeline  can be uThe main purpose of CASCAD database is to provide flexible access to candidate single nucleotide polymorphisms, which were predicted using a computational approach from publicly available sequence data of the rat and zebrafish. The resulting database is crosslinked to most common public databases and can be queried for SNPs using accession numbers, sequence context, SNP characteristics, but also using parameters specific to the SNP discovery process, allowing stringent or relaxed conditions suitable for different types of applications.. Programs, scripts, MySQL database dumps, and instructions for setting up a species-specific SNP database can be obtained from the authors upon request.The database is freely accessible through the website VG designed and implemented the CASCAD database. EB tested database and interface. EC provided supervision and guidance for the project.


The circulating estrogen concentration elevated gradually along with time after ovariectomy in rats. To explore the source of the increased circulation estrogen, the extragonadal aromatization as well as the synthesis of androgen in the adrenal cortex of the ovariectomized rats was evaluated.Female rats were divided into twelve groups: 1 month after ovariectomy (OVX1M), OVX2M, OVX3M, OVX4M, OVX5M, OVX6M; intact 1 month (INT1M), INT2M, INT3M, INT4M, INT5M, INT6M. The blood concentration of testosterone (T) was measured by radioimmunoassay. The mRNA expressions of P450 aromatase in the liver and subcutaneous abdominal (SA) adipose as well as the adrenal cytochrome P450 17 alpha hydroxylase/lyase (P450c17) were semiquantified by RT-PCR. The P450 aromatase protein expressions in the liver and SA adipose were detected by Western blot.The blood E2 concentrations increased gradually along with time after ovariectomy in the rats. The 58-kDa aromatase protein and mRNA expressions normalized to β-actin in the OVX6M rats' SA adipose tissues showed higher levels than those from corresponding tissues in the INT6M (p < 0.05). And the ratios of aromatase mRNA and protein to β-actin in the OVX6M rats' liver tissues increased significantly compared with those in the OVX1M rats (p < 0.05). The ratio of adrenal P450c17 to beta-actin in the OVX6M increased markedly, and was higher than OVX1M (p < 0.05), though the blood concentration of T decreased significantly in all the ovariectomized rats (p < 0.05).Both the subcutaneous abdominal adipose tissues and the liver tissues contributed to the extragonadal aromatisation to promote the circulating E2 levels in the rats along with time after ovariectomy; the adrenal compensation might also be activated naturally. Ovaries are the primary source of estrogen. In ovariectomized rats, the production of estrogen is shifted from ovary to a number of extragonadal sites ,2. SimpsEstrogen production in extragonadal sites is dependent on an external source of C19 androgenic precursors . Circula2) and T as well as the aromatase expressions in adipose and liver tissues in the ovariectomized rats.We have observed that the release of corticotrophin-releasing hormone (CRH) in the hypothalamic paraventricular nucleus of the ovariectomized rats increased significantly compared with the intact rats . And morFemale Sprague-Dawley rats (180–200 g), with regular 4-day estrus cycles were purchased from Medical Experimental Animals Centre of Fudan University (Shanghai, China). The animals were housed under laminar flow in an isolated room with controlled temperature and at a 12 /12 (light /dark) schedule. Forty-eight of them underwent ovariectomy with ether anaesthesia, which were then divided randomly into six groups: 1 month after ovariectomy (OVX1M), OVX2M, OVX3M, OVX4M, OVX5M, OVX6M. The corresponding control groups were: intact 1 month (INT1M), INT2M, INT3M, INT4M, INT5M, INT6M. All experimental procedures involving the use of animals were conducted in accordance with NIH Guidelines and were reviewed and approved by the Animal Use and Care Committee for the Fudan University.At the time of sacrifice, the vaginal cytology of each rat was first examined. The tissues of the rats were collected respectively, and those of the intact control animals, during the period of diestrous. All the operations were carried out at 4°C. The liver tissues, subcutaneous abdominal (SA) adipose tissues and the adrenals were excised, and then snap-frozen in liquid nitrogen, and stored at -80°C. The preparation of the microsomal pellet was accordance with the report by Hiroshi . Total tTo compare the level of adrenal cytochrome P450 17α hydroxylase/lyase (P450c17), liver and SA adipose P450arom expressions after different treatments, PCR methologies were adapted to provide a semiquantitative measure of mRNA levels. Primers were synthesized based on published reports . Table 1A 50 μg sample of the microsomal protein was loaded into each lane along with a prestained protein size marker (Bio-Rad Laboratories, Inc., Hercules, CA), electrophoresed on a 10% SDS-polyacrylamide gel at 18 V/cm, and electroblotted onto a polyvinylidene difluoride membrane (Micron Separations, Westboro, MA) using a wet electroblotter. After blocking in fat-free milk, incubation was conducted with the antiaromatase antibody  and β-actin antibody (1:3000) at room temperature in 18°C for 4 h in TBS-T solution (20 mM Tris, 137 mM NaCl, and 0.1% Tween-20, pH 7.6). After extensive washing, blots were incubated with AP-labelled goat antirabbit antiserum (Sino-American Biotechnology Co., China) for 60 min at room temperature in 18°C and developed using NBT/BCIP detection system (Amersham Pharmacia Biotech). The intensities of the bands were evaluated using the Image Master Software (SYDR-1990, SYNGENE, U.S.A.), and values were normalized to β-actin immunoreactivity in each sample and expressed as percent of the control. Specificity of the aromatase immuno-staining was determined by preincubation of antiserum for 24 h at 4°C with varying concentrations of aromatase, with the primary antibody omitted to identify non-specific staining as well.At the time of sacrifice, the blood samples (0.8 ml) of the rats were collected from tail veins respectively, and the corresponding intact controlling animals, during the period of diestrous. The plasma was separated by centrifugation and stored at -70°C until assayed. Concentration of blood hormones were determined by double-antibody RIA kits purchased from the National Atomic Energy Research Institute (Beijing, China.). The samples were assayed in duplicate, and all the subjects' samples were assayed together. The sensitivity of the kit was 0.8 pg/ml (testosterone) and 1.4 pg/ml (estrogen), the intra- and interassay coefficients of variation, 3.7–8.0% and 4.74–7.7%.All data are presented as means ± S.E.M. Statistical analysis was performed on raw data using two-way analysis of variance (ANOVA), with the significance set at p < 0.05 and p < 0.01 in two-tailed testing chosen.The epithelial cells were stained by haematoxylin-eosin (HE). The intact rats (INT1M, 2M, 3M, 4M, 5M, 6M) showed regular 4-day estrus cycle change. The cyclic change disappeared in the ovariectomized (OVX1M, 2M, 3M, 4M, 5M, 6M) rats. A few of mature vaginal epithelia were observed in the smears of the OVX5M and OVX6M rats, and the percent of mature epithelia increased significantly in the OVX6M rats (p < 0.05) (Table 2 concentrations decreased significantly in the OVX1M, 2M and 3M (p < 0.01) compared with those in the INT1M, 2M and 3M. The concentrations in OVX4M, 5M and 6M groups increased significantly (p < 0.05) compared with OVX1M, though still lower than INT4M, 5M and 6M. There were no disparities between the INT1M, 2M, 3M, 4M, 5M and 6M groups . Densitometric analysis of the mRNA concentration using target product/β-actin was expressed as the mean with SEM. The ratios of liver P450arom to β-actin in the OVX1M, 2M, 3M, 4M and 5M groups were lower than the corresponding intact controls (p < 0.05). The ratio increased significantly in the OVX6M compared with OVX1M (p < 0.05), and no difference was detected between OVX6M and INT6M . But the ratio increased significantly in the OVX6M compared with OVX1M (p < 0.05)  . Its actThough it has been reported that the splanchnic tissue is a minor site for extragonadal aromatization of androgens , there i2 levels in the rats along with time after ovariectomy; the adrenal compensation might also be activated naturally.Both the subcutaneous abdominal adipose tissues and the liver tissues contributed to the extragonadal aromatization to promote the circulating EHong Zhao and Zhanzhuang Tian designed the study, performed the studies and the statistical analysis, and drafted the manuscript. Junwei Hao performed the animal experiment. Boying Chen conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.