Listeria monocytogenes propulsion that explicitly simulates a large number of monomer-scale biochemical and mechanical interactions. The literature on actin networks and L. monocytogenes motility provides the foundation for a realistic mathematical/computer simulation, because most of the key rate constants governing actin network dynamics have been measured. We use a cluster of 80 Linux processors and our own suite of simulation and analysis software to characterize salient features of bacterial motion. Our “in silico reconstitution” produces qualitatively realistic bacterial motion with regard to speed and persistence of motion and actin tail morphology. The model also produces smaller scale emergent behavior; we demonstrate how the observed nano-saltatory motion of L. monocytogenes, in which runs punctuate pauses, can emerge from a cooperative binding and breaking of attachments between actin filaments and the bacterium. We describe our modeling methodology in detail, as it is likely to be useful for understanding any subcellular system in which the dynamics of many simple interactions lead to complex emergent behavior, e.g., lamellipodia and filopodia extension, cellular organization, and cytokinesis.To understand how the actin-polymerization-mediated movements in cells emerge from myriad individual protein–protein interactions, we developed a computational model of  A detailed computer simulation explicitly simulates monomer- scale biochemical and mechanical interactions to characterize bacterial motion These interactions can be both biochemical, as in the activation of one protein by another, and mechanical, as in the application of force between bodies. Even when each individual interaction is simple and understood in detail, neither intuition nor qualitative description can forecast the emergent behavior of the whole system. We describe a methodology to characterize such emergent behavior using a detailed computer simulation of both biochemical kinetics and mechanical dynamics. In this paper, we apply the technique to the motility of the bacteria Listeria monocytogenes, a well-studied system in which actin network growth produces a force that moves the bacterium inside of cells. We discuss the model design, compare behaviors of the computational and biological systems, use the model to explain observed features of the bacterial motion, and identify observable experimental correlates of our hypotheses through which our interpretations may be confirmed or rejected.Cellular processes generally involve interactions among 10L. monocytogenes is a pathogenic rod-shaped bacterium that invades cells, reproduces, and spreads to neighboring cells, never exposing itself to the extracellular environment, thus avoiding a humoral immune response  affect this larger scale behavior. The exploration of putative mechanisms is also straightforward, as it is easy to add, remove, or modify each individual interaction.With our approach, we formalize experimentally based models of specific protein–protein interactions and biochemical kinetics in a direct and flexible way, but there are drawbacks. The theoretical approaches used to analyze the Brownian ratchet model and its refinements , 2003 faThe biochemical and mechanical interactions near the bacterial surface are stochastic processes involving hundreds of filaments. We model dynamic processes on a per filament basis, rather than through bulk network properties and average filament growth. The growth of any particular filament depends upon that filament's precise location, orientation, and biochemical state, all of which change through time. There is no better way to simulate such a system than with a model that tracks each of these variables for each individual filament. In the future, this type of detail will be essential to capture (and thus explain) many observed biological phenomena.L. monocytogenes motility display repeated runs and pauses that closely resemble the actual nanoscale measurements of bacterial motion  exert equal and opposite forces that hold those objects together; these links break under sufficient strain. Forces of random orientation act on every explicit player to simulate Brownian motion . This system never approaches an equilibrium; Brownian motion and biochemical events ceaselessly create collisions and perturb protein–protein links. Thus, we must compute new forces, exchanged between new neighbors, in each time-step. At the heart of this simulation is the dendritic nucleation model of actin dynamics . AsynchrL. monocytogenes associating with their actin tails; to match the biological reality, we need to modify our simulation of Brownian motion, since we cannot yet use much smaller time-steps. We compensate for this technical problem by carrying out simulations both for the two extreme cases  and for an intermediate degree of Brownian motion. Advances in computer processing speeds will, most likely, make such attenuation unnecessary in the near future. We will henceforth use the term “Brownian simulation force” to refer to the forces and torques that we apply to the bacterium to simulate its Brownian motion.The simulation time-step has a subtle effect on the simulation of Brownian motion for constrained objects . Applying the same forces and torques that are appropriate for free objects exaggerates the simulated Brownian motion of constrained objects since the motion restriction that results from those constraints can only be resolved over several time-steps, and those time-steps are large relative to the intrinsic time-scale of the constraints. Experimental measurements  show verD = 0.6 μM) does not sufficiently characterize that interaction since the interaction rates may be limited by the flux of Arp2/3 or G-actin onto the surface. We have calculated the flux of Arp2/3 and G-actin onto the bacterium's surface to determine the expected equilibrium number of ActA–Arp2/3 and ActA–actin complexes there, as explained in For our model, we use typical physiological concentrations for each of the proteins involved; these are listed in The ActA protein is distributed asymmetrically on the bacterial surface, with more ActA near the rearward tail-forming end. The unipolar shape of our distribution is based on measurements of the fluorescence signal from RFP-labeled ActA along the bacterial length for newly divided bacteria . New filaments are produced by two pathways. By activating Arp2/3, ActA is thought to catalyze the creation of new actin filaments and side-branches. We simulate the co-binding of ActA, Arp2/3, and an existing actin filament, allowing binding in any order. This complex leads to a new side-branch on the existing filament. Binding of ActA to the actin filament can occur only at specific ATP or ADP-Pi actin sites and is binding site limited, meaning that each bound ActA occludes a linear region of five monomers on the filament from further binding. Creation of a new filament de novo in our model involves the co-binding by ActA of G-actin and Arp2/3, in any order .In conjunction with other proteins , ActA may also regulate actin dynamics in other important ways . In thisL. monocytogenes. Average speeds of motion varied from ten to hundreds of nanometers per second, as do real observed bacterial speeds in different experiments (40 nm/s in purified proteins in www.celldynamics.org). The gross behavior of our simulated bacterium is life-like; model bacteria move in a qualitatively similar way to wild-type L. monocytogenes motility is multiphasic; motion of the bacterium that appears smooth on the micrometer length-scale actually consists of pauses that last many milliseconds, discrete nanometer-scale steps, as well as uninterrupted runs. No current model of this bacterial motility has fully explained these discrete steps, though numerical simulations with the tethered ratchet :A particularly large Brownian simulation force  in the forward path direction causes an unusually rapid but small forward displacement of the bacterium (region A).The steady-state rate of filament link turnover increases slightly as highly strained links break and are replaced by an ensemble of new links that all form nearly simultaneously in an unstrained state, thus creating a larger than steady-state population of coordinately unstrained links.A particularly large Brownian simulation force, or correlated sequence of forces, opposite to the path direction forces the bacterium backward against the population of linked barbed end actin filaments; filament collision force increases, filament link force falls, and actin polymerization near the surface decreases. A pause ensues.During the pause new filaments form and existing but distant barbed ends “catch up” with the bacterium, thus increasing the filament collision force forward which the links restrain. The pause terminates when a particularly large Brownian simulation force  in the forward path direction is sufficient to break a few of the strain-synchronized filament links. As these links break, the force stretching each remaining link increases, setting in motion an avalanche of cooperative link breakage and initiating a run.We are justified in interpreting these correlations of Brownian simulation forces as causal because those forces are generated in our simulations so as to be random in direction and magnitude (representing a Gaussian distribution). Nothing in our model can cause such Brownian simulation force “accidents.” Their correlation with pause initiation or termination must therefore be causal.Absent Brownian simulation forces on the bacterium, the system response throughout the course of a pause is very different. In this case, a pause occurs only when a population of synchronized filament links is able to balance the filament collision forces, which on average increase only slightly during a pause, until a cascade of breaking links allows the bacterium to run again. Judging from the shape of the step-size histograms in The small amplitude of experimentally measured fluctuations of bacteria in vivo  suggest L. monocytogenes motility is mediated by actin-mediated forces. Building a simulation from basic, well-understood structures and interactions, we have reconstituted bacterial motility in silico. Appropriate speeds and persistence of motion emerge, reproducing experimentally observed values. Additionally, our simulation yields as an emergent behavior the nanometer-scale saltatory motion reported by experimentalists. We can analyze details of the simulated bacterial trajectories to investigate characteristics of this saltation: what is the mechanism behind the stepping, and is there a favored step-size?We have used our model to ask how Our computational experiments lead us to conclude that the tethered-ratchet model is an inherent “pauser” with several important attributes. First, there is no characteristic step-size or pause length; shorter steps and pauses are more frequent than longer ones. Second, the intensity of Brownian agitation of the bacterium influences average pause duration and frequency, but this agitation is not necessary for persistent saltatory motion. Third, pauses start when a population of filament links happen to form nearly simultaneously with low strain to balance filament collision forces. Pauses end when those links catastrophically break.To produce nanometer-scale pauses and runs, no special function need be attributed to the bacterial bound ActA protein, beyond an elastic linkage to actin filaments and some mechanism that prevents barbed end filament capping. Specifically, the ActA protein does not need a motor-protein-like stepping ability, nor need it act as a clamped-filament elongation motor . Given tThe speed of motion during a run is variable, but it is mostly confined to a narrow range of speeds that depends on the parameter set see C. PausesLastly, we have explored the variation of key biochemical events and mechanical interactions during a typical nanometer-scale saltation, looking at both individual pause events and averages of many such events, in an effort to uncover the causal factors. We conclude that the tethered-Brownian ratchet model is an inherent pauser; forward motion is temporarily halted whenever a population of synchronously strained filament links balances filament collision forces. Different mechanisms cause pause initiation/termination with and without simulated Brownian motion of the bacterium. With simulated Brownian motion of the bacterium, we find that pauses events are largely driven by coordinated Brownian simulation forces: a series of forces in the forward direction helps establish a set of coordinately-strained links, forces in the backward direction can help maintain a pause, and lastly forces in the forward direction help break links to terminate a pause. Without simulated Brownian motion of the bacterium, we find that a coordinately strained set of filament links balances the filament collision forces and that a pause will ensue until those links break en masse. Formation of such a set of filament links is an accidental, but frequent, occurrence, explaining the shape of the step-size and pause duration histograms see .That we find no characteristic step-size in our simulated nanoscale stepping constrasts with experimental results . RestricL. monocytogenes motility described here represents a new tool that should be useful for understanding many complex subcellular systems. The construction of this computational model requires experimental measurements of the biological details in L. monocytogenes propulsion and actin dynamics in general. Only in the last several years have crucial biological details come to light, e.g., the role of Arp2/3 in filament branching, or the binding sites and functionality of the ActA protein. Additionally, the implementation of the model in silico requires significant computational power, now affordable in the form of clusters of “off-the-shelf” machines . Powerful object-oriented languages are also recently mature (we use Java™), making it possible to write computer code to implement such models. We believe that the confluence of detailed biological information and computational power/software heralds a new approach for understanding subcellular systems in which many thousands of simple biochemical and mechanical interactions lead to complex emergent behavior.The computational analysis of The biological systems in which this approach will be useful are, by definition, rich in detail. This complexity favors collaborations between modelers and the experimentalists who discover and quantify the molecular details without which this study would be specious. Creating a simulation environment that makes intuitive sense to experimentalists, i.e., one in which there is clear correspondence between biological entities and their modeled counterparts, greatly facilitates communication between modelers and biologists, and it ensures appropriate refinement of the model as new biological facts are uncovered.There are many future refinements and research directions for this model. We can incorporate a more sophisticated representation of the actin hydrolysis cycle  and inclA large set of differential equations determine how our state variables change with time. We solve these equations numerically, but not in a standard way because discontinuities in time occur frequently as objects collide suddenly and as objects suddenly spring into existence or disappear (due to new filament nucleation and depolymerization). To solve these thousands of differential equations, we divide time into discrete steps  balancing the necessities of capturing the system dynamics and accomplishing the simulation in reasonable human time . At the beginning of each time-step, the biochemical events and forces experienced during the last time-step will have changed the state of the system. New collisions and link forces may have arisen, as well as new objects. Existing links may break if they experience excessive strain for several consecutive time-steps. Each explicit player thus experiences a net force vector; in the next step, we move each explicit player in this vector direction so as to reduce or eliminate the strain energy associated with its collisions and links. To accomplish this practically, we calculate the forces required to resolve each individual collision (or strained linkage) in a single time-step. Each individual computer run simulates bacterial motion for a period of up to many minutes. We run hundreds of such simulations and then statistically analyze the ensemble of runs. Fitting straight line segments to each trajectory and filtering those segments by slope (speed of motion) reveals that each simulated bacterial trajectory is composed of a sequence of pauses (of varying duration) separated by near-constant speed runs between pause locations. After we identify all the pauses, we measure the distances between adjacent pauses; these are the putative step-sizes. Histograms of pause duration and step-size, distilled from multiple simulations, then allow comparison with experimental observations and reveal whether there exists a preferred step-size or pause duration. We average many thousands of pause events into portraits characterizing system behavior preceding, during, and following the typical pause. To do this, we align pauses, time and space shifting short sections of the path projected trajectories that span a single pause event so as to superimpose their starting or stopping points . These pAny trend remaining after the averaging of many thousands of events will reveal significant system behavior near the alignment point. Any individual event, however, might not exhibit all the trends revealed in such an average, so that the interpretation of these average profiles should be tempered accordingly.Not all of the capabilities of our model have been enabled in the simulations contributing to this study. Our calculations show that the local depletion of the implicit players, due to their incorporation into a larger assembly, is not significant for the concentrations, rate constants, and geometries of this system (data not shown). Thus, we do not simulate the diffusion of any of the implicit players (proteins), but rather assume that each exists at a constant concentration see . With thIn addition, F-actin interacts with cellular components in vivo that are not explicitly represented in either the dendritic nucleation model or our simulations . Some of these interactions have the effect of locking down actin tail in cellular space. We approximate their effect with a time- and actin length-dependent application of adhesions that eventually fix F-actin and the actin tail in our simulation space.Dataset S1(29 KB DOC).Click here for additional data file.Dataset S2(52 KB DOC).Click here for additional data file.Video S1A close-up look at the interaction of a single polymerizing filament with the bacterium. This filament has an artificially durable link with an ActA protein on the bacterium's surface; these links are typically very transient. The tip-clearance (drawn with a cyan line), the polymerization probability, the capping probability, and the Arp2/3 binding probability (set to zero for this demonstration) are reported at each simulation time-step.(1 MB MOV).Click here for additional data file.Video S2L. monocytogenes motility. Microscale hops, as opposed to the nanoscale steps we investigate in this paper, are apparent at this scale view. The bacterium induces an actin tail of variable density and demonstrates persistent motion.An animation rendered from the output of one simulation of (9.8 MB MOV).Click here for additional data file.


Between November 2 and 10, 2002 several patients with psoriasis and personnel staying in the health centre in Gran Canaria, Spain fell ill with diarrhoea, vomiting or both. Patient original came from Norway, Sweden and Finland. The patient group was scheduled to stay until 8 November. A new group of patients were due to arrive from 7 November.A retrospective cohort study was conducted to assess the extent of the outbreak, to identify the source and mode of transmission and to prevent similar problems in the following group.Altogether 41% (48/116) of persons staying at the centre fell ill. Norovirus infection was suspected based on clinical presentations and the fact that no bacteria were identified. Kaplan criteria were met. Five persons in this outbreak were hospitalised and the mean duration of diarrhoea was 3 days. The consequences of the illness were more severe compared to many other norovirus outbreaks, possibly because many of the cases suffered from chronic diseases and were treated with drugs reported to affect the immunity (methotrexate or steroids).During the two first days of the outbreak, the attack rate was higher in residents who had consumed dried fruit  and strawberry jam  than those who did not. In the following days, no association was found. The investigation suggests two modes of transmission: a common source for those who fell ill during the two first days of the outbreak and thereafter mainly person to person transmission. This is supported by a lower risk associated with the two food items at the end of the outbreak.We believe that the food items were contaminated by foodhandlers who reported sick before the outbreak started. Control measures were successfully implemented; food buffets were banned, strict hygiene measures were implemented and sick personnel stayed at home >48 hours after last symptoms. On 5 November 2002, the Department of Infectious Disease Epidemiology at the Norwegian Institute of Public Health (NIPH) was contacted by a dermatologist at Rikshospitalet, a tertiary care university hospital in Oslo, Norway. He reported several cases of gastro-enteritis in a medical care centre in Argúinegúin, Gran Canaria, Spain where Nordic patients with psoriasis undergo climate therapy. The symptoms (diarrhoea and vomiting) were reported to be of short duration (1–2 days). The patient group was scheduled to stay until 8 November. A new group of patients were due to arrive from 7 November. An outbreak management team was created in order to investigate and control the current outbreak, and to prevent similar problems in the following group. The outbreak management team consisted of investigators working in public health institutes in the countries involved . The investigation was organised and co-ordinated from the NIPH in Oslo, Norway.The objectives of this investigation were to assess the extent of the outbreak, identify the mode of transmission, the vehicle and the causative pathogen and recommend appropriate control measures.The health centre is a private institution owned by the Norwegian Asthma and Allergy Association. Every three weeks the health centre receives a group of 100–110 Nordic patients suffering from skin diseases (mainly psoriasis). Rikshospitalet, Oslo, Norway administers the project. Patients are residents of Norway, Sweden and Finland. On site, Nordic medical staff  provides health services for the patients. The centre also hosts private individuals and employees of the Asthma and Allergy Association.We conducted a retrospective cohort study among all Nordic patients attending dermatological care and employees staying at the health centre between 2 and 10 November. The persons from the Asthma and Allergy Association, and employees eating and staying outside the centre in the same time period were not included since there were no reported cases among this group and they did not eat or use the same facilities as the dermatological patients.A case was defined as a person who 1) attended dermatological care or worked at the Health Centre in Gran Canaria, 2) took meals at the Centre and 3) fell ill between the 2 and 10 November 2002, with symptoms of diarrhoea (3 or more loose stools in 24 hours), vomiting or both.Health personnel at the health centre provided a guest list with name, address, age, sex and bungalow number. For cases they also recorded date of onset of symptoms and stool sampling and results of microbiological analysis.A standard questionnaire was developed at the Norwegian Institute of Public Health, translated in each national centre and mailed to the entire cohort, with instructions to fill it in and send it back to the public health institute in their country of origin.The local health authorities in Gran Canaria enquired about gastro-intestinal illness in the community. They contacted health centres, churches and schools in the area.An exposure was defined as a food item eaten or an environmental factor present within the 2 days before onset of illness . Possible exposures included consumption of all food items served in the health centre's restaurant, consumption of food served outside the centre, brushing teeth in tap water, drinking tap water, consumption of ice cubes, swimming in one of the two pools, swimming in the ocean, having had contact with an ill person, hand washing habits before meals, and the sharing of bungalow with symptomatic persons. We recorded the number of times food items were eaten to enable the study of potential dose-response relationships.Day by day, we compared food-specific attack rates (AR) for each item for the exposed and the non-exposed. Suspecting the etiological agent to be norovirus and knowing it has a short incubation period (24–48 hours)  only fooWe used EpiData software  for data collection and analysed them with SPSS version 10.0 . Attack rates, relative risk and 95% confidence intervals were calculated for each of the food items and other exposures. Only variables with RR greater than 2.0 are presented in this report. A multivariable analysis was run to assess potential confounding, including age, gender and the pooled variables with RR greater than 2.0 in the univariable analysis.All cases in the cohort presenting gastro-enteric symptoms were encouraged to deliver a stool sample. The samples were sent to a local private laboratory in Gran Canaria. Both virological and bacterial tests were requested.Food sampling of some of the served items was performed on 5, 21 and 22 November by the local health authorities in Gran Canaria. The health authorities also took samples on 12 November from water taps and from the pools. Both stool and environmental samples were to be sent to Madrid for virology testing. Due to misunderstandings the samples were never forwarded to Madrid. Virological analyses were therefore not performed.We mailed 110 questionnaires to the patients who stayed at the Centre between 17 October and 8 November 2002, and employees (n = 6) eating at the centre. Ninety-one questionnaires (response rate = 78%) were returned .Among the 91 respondents there were 47 men and 44 women. The median age was 48 years (range 18–80).Forty-eight persons fulfilled the case definition (attack rate (AR) = 53%). The AR was not significantly different by genders or nationalities. The AR was higher among those above 70 years of age (80%).The outbreak peaked on November 4, with 16 cases , AR for those sharing room with one person 38% (3/8) and AR among those 9 living in a single room was 44%. Two cases were employees living private or in a separate section. Six cases fell ill subsequent (more than 10 hours later and within two days) to their roommate.There were no gastro-enteritis cases reported in the community concurrently to the outbreak in the health centre.From the daily analysis of food consumption, strawberry jam, dried fruit (eaten on both 1 and 2 November) and butter eaten on 31 October) were associated with risk of illness (RR> 2.0) for those who became ill on 2 and 3 November  and strawberry jam  were independently associated with disease  were reported negative for bacteria at the private laboratory in Gran Canaria. Virological analyses were not performed.Six food items and water from the two pools were analysed for bacteria: chicken croquettes, frozen chicken breast and leg, sausage, cheese and salmon. Results were negative.We initiated an epidemiological investigation of an outbreak of gastro-enteritis in a health centre in Gran Canaria, Spain. Based on Kaplan criteria, our findings suggest that the aetiological agent was norovirus. Results of the cohort study suggests that the outbreak was initiated by ingestion of dried fruits, strawberry jam or both, followed by person-to-person transmission. There is no supporting microbiological or environmental evidence.Norovirus outbreaks can often be diagnosed presumptively on clinical grounds from their characteristic epidemiological features . Kaplan A high proportion of persons in this outbreak reported that they received IV-treatment , were hospitalised n = 5) and the mean duration of diarrhoea was 3.7 days. The reported consequences, especially the duration of the illness was severe compared to many other norovirus outbreaks  and the  and in hWe believe there were two modes of transmission: a common source for those who fell ill on 2 and 3 November and thereafter mainly person to person transmission. Low RR for all food items at the end of the outbreak and the short incubation period for noroviruses, together with the fact that norovirus outbreaks usually have high rates of person-to-person transmission , supportWe did however not find a higher attack rate among those who shared room or bungalow. Only nine persons had a single room.There were no cases among those persons who stayed at the health centre, but did not eat in the health centre restaurant or in the community during the outbreak. This suggests a foodborne outbreak with its origin at the centre's restaurant.Dried fruits, strawberry jam or both were the probable vehicles of contamination. Strawberry jam and dried fruits were handled and kept at the kitchen in the Health Centre and not supplied by the catering service. They were served in buffet style on plates. Both food items are biologically plausible vehicles. Contaminated hands or silverware could explain contamination of these food items, but these modes of transmission were not verified. One hypothesis was that the food items were contaminated by the foodhandlers who reported being ill before the outbreak started. Both foodhandlers were involved in preparing the food for the buffet. Very few organisms of norovirus are needed to transmit the disease . The jamDose-response analyses give further support for contamination of the strawberry jam. Those who ate jam twice doubled their risk of developing gastro-enteritis.Suspecting norovirus, with a short incubation period, as the causal agent , we treaWhen looking at the whole period as a cohort and thus looking at all persons falling ill between 2 and 10 November, then, none of the exposures seemed to increase the risk of disease. The association between food items and disease was probably masked by a high number of cases infected by person-to-person transmission.Eight cases had not eaten dried fruit during the 2 days before falling ill. The concept of dried fruit is different in Spain from Scandinavia. In Spain, dried fruit means raisins, which were served in the restaurant. In the Nordic countries, dried fruit is in general understood to be a mixture of different types of dried fruits. This difference in concepts may have introduced an information bias.We asked for food history for 5 days. The food was always served buffet-style. The menus included more than 300 different food items. To reduce potential recall bias, we pooled the food items consumed on either of the 2 days prior to onset. The fact that strawberry jam and dried fruit remained associated for those with onset 2 and 3 November , we recommended that this group should travel as planned.Salmonella was not found in any of the 22 stool-samples from our cohort. This, together with the reported symptoms, suggests that there may have been two different outbreaks at the centre during this period.Among persons arriving 7 November (n = 100), 18 became ill with gastro-enteritis. There were no cases after 14 November. Four of seven stool samples were positive for Salmonella. This finding did not change the belief that norovirus caused the gastro-enteritis among persons in the outbreak described in this article. Between 2 and 7 November, 66 persons fell ill with diarrhoea, vomiting or both in a health centre for Nordic patients with skin diseases at Gran Canaria. Our data suggest that norovirus caused the outbreak.Our findings suggest that individuals who consumed dried fruit  or strawberry jam  were more likely to contract the disease. One hypothesis is that the food items were contaminated by foodhandlers that had had gastro-enteritis shortly before the outbreak started.Improved hygiene measures and individually served food were successfully implemented.Norwegian Institute of Public Health; NIPHAttack rate; ARRelative risk RRThe author(s) declare that they have no competing interests.HME: In charge of the investigation, data handeling and writing of the articlePJG: In the outbreak management team, contributed in writing and distribution of the questionaire, and review and comment on the different versions of the article.KN: In the outbreak management team, contributed in writing and distribution of the questionaire, and review and comment on the different versions of the article.MH: In the outbreak management team, contributed in writing and distribution of the questionaire, and review and comment on the different versions of the article.BdJ: In the outbreak management team, contributed in writing and distribution of the questionaire, and review and comment on the different versions of the article.AMCR: In the outbreak management team, contributed in writing and distribution of the questionaire, and review and comment on the different versions of the article.MK: In the outbreak management team, contributed in writing and distribution of the questionaire, and review and comment on the different versions of the article.UD: In the outbreak management team, contributed in writing and distribution of the questionaire, and review and comment on the different versions of the article.AGR: Working locally with the outbreak, contributed in writing and distribution of the questionaire, and review and comment on the different versions of the article.CM: In the outbreak management team, contributed in writing and distribution of the questionaire, and review and comment on the different versions of the article.PA: In the outbreak management team, contributed in writing and distribution of the questionaire, and review and comment on the different versions of the article.The pre-publication history for this paper can be accessed here:


One of the major players in prostate cancer is a nuclear signaling protein called the androgen receptor. Prostate growth and development is regulated by androgen hormones (like testosterone) that activate the androgen receptor. When an androgen binds the receptor, the receptor binds other proteins, called coactivators, to activate genes controlling cell growth, survival, and differentiation. Unlike other receptors that function in the nucleus, the androgen receptor normally shuns coactivators with a leucine-rich binding domain in favor of those with “aromatic” domains. (Aromatic amino acids are defined by their ring structure.) But during prostate cancer, the receptor interacts with both coactivator types, to promote disease progression.The secret to a protein's binding preference rests in the underlying sequence of its amino acids, which determines the protein's structure and ultimate behavior. Robert Fletterick and colleagues set out to identify the “full repertoire” of amino acid sequences that might conceivably consort with the androgen receptor. Their findings help explain the unusual behavior of the androgen receptor during prostate cancer progression—a first step toward developing new anticancer therapies.Treatment for hormone-dependent prostate cancers focuses primarily on reducing androgen levels by using chemicals that compete for androgen receptor docking rights in the hormone-binding pocket of the ligand-binding domain, or LBD.  But cancer cells eventually circumvent these chemical assaults through increased levels of either androgen receptors or their coactivators, or through mutations that make androgen receptors immune to chemotherapy. That's why Fletterick and colleagues turned their attentions to the receptor's consorts. Since targeting the hormone-binding pocket of the receptor offers limited benefits, a better strategy might involve disrupting associations with the receptor's coactivators.Dozens of proteins interact with different regions of the androgen receptor, but the details of these interactions were not known. When a hormone binds to the LBD of other nuclear receptors, it triggers a conformational change that creates a binding surface called AF-2 for the leucine-rich domains of the coactivator proteins. It was not clear, however, how the AF-2 region of the androgen receptor distinguishes between aromatic and leucine-rich domains. To characterize the receptor's binding selectivity, Fletterick and colleagues tested 20 billion peptides, or protein fragments, to see whether they interacted with the LBD region of a hormone-bound androgen receptor.As expected, most of the peptides that associated with the LBD domain were aromatic. And they interacted with the same region that naturally occurring coactivators bind to. Next, Fletterick and colleagues determined the three-dimensional structure of both the receptor bound to just the androgen hormone and the androgen–receptor pair bound to a subset of seven peptides. The different structures showed that the androgen receptor uses a single surface to bind both leucine-rich and aromatic peptides; when the aromatic peptides have bulky appendages, the receptor's AF-2 domain reorganizes to accommodate them.The various structures and binding affinities for the different receptor–peptide complexes described here show how the receptor can interact with a diverse array of proteins. The androgen receptor, unlike other nuclear receptors, has specific amino acid sequences that better support aromatic peptide binding. Interestingly, mutations in one of these amino acid sequences have been found in prostate cancer. Altogether, the authors conclude, the unique properties of the receptor's AF-2 surface make it “an attractive target for pharmaceutical design.” Drugs that directly interfere with coactivator binding, they explain, are likely to inhibit androgen receptor activity. Here, the authors recommend novel sites on the receptor as promising targets for androgen-receptor-specific inhibitors.


While prevailing methods for modeling and parsing genes using GHMMs have been described in the literature, little attention has been paid as of yet to their proper training. The few hints available in the literature together with anecdotal observations suggest that most practitioners perform maximum likelihood parameter estimation only at the local submodel level, and then attend to the optimization of global parameter structure using some form of ad hoc manual tuning of individual parameters.Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a We decided to investigate the utility of applying a more systematic optimization approach to the tuning of global parameter structure by implementing a global discriminative training procedure for our GHMM-based gene finder. Our results show that significant improvement in prediction accuracy can be achieved by this method.We conclude that training of GHMM-based gene finders is best performed using some form of discriminative training rather than simple maximum likelihood estimation at the submodel level, and that generalized gradient ascent methods are suitable for this task. We also conclude that partitioning of training data for the twin purposes of maximum likelihood initialization and gradient ascent optimization appears to be unnecessary, but that strict segregation of test data must be enforced during final gene finder evaluation to avoid artificially inflated accuracy measurements. The number of generalized hidden Markov model (GHMM) gene finders reported in the literature has increased fairly dramatically of late -8, and tWhile the decoding problem for GHMM gene finders is arguably well understood, being a relatively straightforward extension of the same problem for traditional HMMs and amenable to a Viterbi-like solution , methods for optimally training a GHMM gene finder have received scant attention in the gene-finding literature to date. What information is available e.g., ,4) seems seems4])maximum likelihood estimation (MLE). Baum-Welch training [expectation maximization (EM) procedure, is itself a form of MLE [The training of HMMs and GHMMs has traditionally been carried out using some form of training , which im of MLE . In the q and for Si a feature of length di from the state-q-specific training set T. The submodels are then merged into a composite model  by observing transition probabilities between features in the training data corresponding to each of the GHMM states.for state n-gram statistics  from known exon sequences and normalizing these into transition probabilities for an (n-1)th-order Markov chain [For example, an exon state in a GHMM can be trained by collecting ov chain . SimilarSuch an approach does not, however, attend to the global optimality of the GHMM as a whole. Ideally, one would like to maximize the expected accuracy of the gene finder on unseen data. A reasonable approximation to this ideal would be to maximize the average probability of the gene parses in the training set:S, φ) of the training set T comprise pairs of sequences S and their known parses φ. This argmax gives us the parameterization  under which the full gene parses (rather than the sequences) in the training set will be maximally likely (on average). Decomposing each parse φ into a series of  pairs, for state qi and state duration  di, we get:where the collection of model parameters making up the GHMM is denoted θ and the elements  whereby the individual submodels "compete" for nucleotides . Our hope is that by addressing the issue of submodel competition explicitly during parameter estimation, we will thereby empower the gene finder to better discriminate at a global sequence level between the features modeled by individual submodels in the GHMM, thereby producing more accurate gene predictions.where et al. in 1986 [global discriminative training [conditional maximum likelihood [Maximum discrimination HMMs have already been applied successfully to problems in the realm of biological sequence analysis [A similar optimization problem occurs in the field of speech recognition, in which systems of interacting acoustic models and language models are employed to optimally parse an audio stream into a series of discrete words. Interestingly, the trend in that field, starting with Bahl  in 1986 , has inctraining -19 or cokelihood . The prokelihood . Maximumanalysis , though analysis , a gene ∈T P term in Equation 2.In light of these considerations, it is worth contemplating the possible gains in gene finder accuracy that might be obtained through the use of some form of discriminative training applied to a GHMM – that is, training aimed more directly at optimizing the ability of the gene finder to discriminate between exons and non-exons, thereby improving the expected accuracy of the gene finder's predictions. Anecdotal evidence already suggests that investigation of such methods may indeed be fruitful, as the process of manual tuning of GHMM parameters  after MLE training is commonly acknowledged by those with experience training GHMM-based gene finders (including our own systems). The practice of performing such tuning on the training set, especially when done iteratively, can be viewed as a manual form of gradient ascent optimization using the percentages of correctly predicted nucleotides, exons, and whole genes as surrogates for the ΣWe therefore decided to investigate the use of a simple form of global discriminative training for gene-finding. We did this by building a rudimentary gradient ascent optimizer and applying it to a subset of the model parameters for our GHMM-based gene finder, TigrScan, as described in the Methods.Arabidopsis thaliana are shown in Table Aspergillus fumigatus are shown in Table Results for train column indicates whether training  was performed on the entire training set (T) or on separate 800-gene cross-validation partitions (CV). The test column indicates whether accuracy was measured on the full training set (T), on one-fifth of the training set (CV), or on the unseen data (H). We will consider the evaluation on H to be the most reliable measure of gene finder accuracy. For any row containing a CV, we report the average of five runs, where each run used a different 800-gene subset of the training data for parameter estimation.The Arabidopsis, gradient ascent applied to the full training set improved over the MLE method from 71% to 81% at the level of exons and 33% to 48% at the level of whole genes. In Aspergillus the improvement was even more dramatic: 30% to 51% at the exon level and 18% to 31% for whole genes. A gain of 4% nucleotide accuracy was measured for both organisms.Both tables give compelling evidence for the value of gradient ascent training, as shown in Figure Arabidopsis, gradient ascent training produced greater gains in accuracy when performed on the entire training set rather than using the cross-validation structure, while on Aspergillus the improvement due to using a cross-validation structure was either small (nucleotide level: 1%), zero (exon level), or negative (gene level: -2%). Thus, the recommended training protocol would be to apply MLE to the entire training set followed by gradient ascent on the full training set as well.A tangible improvement was still seen when a cross-validation design was used to split the training set so as to separate the data used for maximum likelihood estimation (800 genes) and subsequent gradient ascent 200 genes). However, results from both organisms suggest that this separation did not improve the accuracy of the gene finder, as shown in Figure 00 genes.Although use of a cross-validation structure to split the training set for the twin purposes of maximum likelihood estimation of ~90,000 parameters and gradient ascent refinement of 29 parameters is therefore not justified (according to the above results), cross-validation does seem to have some value in terms of predicting how well the gene finder will perform on unseen data, as suggested by Figure On both genomes and at all levels , accuracy measurements obtained through cross-validation were closer to the accuracy measured on unseen data than were the measurements taken from the full training set, as we expected. This was true both with and without gradient ascent, though when gradient ascent was applied, even the cross-validation results were slightly inflated. The latter observation is presumably attributable to the "peeking" that was permitted (see Methods), whereby the gradient ascent procedure received feedback from the 200 evaluation genes held out from the training set, T. This suggests that estimating even small numbers of parameters (in this case 29) from the test set can artificially inflate accuracy measurements on that set.Figure The results presented above provide a clear demonstration that independent maximum likelihood estimation of submodel parameters is sufficiently neglectful of global GHMM behavior as to compromise gene finder accuracy. Even such a crude method as our 29-parameter gradient ascent procedure proved to be effective at significantly improving accuracy over that achievable by simple MLE training. The potential for more sophisticated global discriminative training methods to produce even greater improvements is surely worthy of investigation.It is interesting to observe that the natural language processing and speech recognition communities, from whom HMM-based methods were originally borrowed for use in bioinformatics, have been moving toward global discriminative training methods for some time. The two most popular forms of discriminative training for speech recognition are Maximum Mutual Information (MMI) and Minimum Classification Error (MCE). Both methods can be implemented using an iterative gradient ascent/descent algorithm. Our approach is most similar in spirit to that of MCE.axis-oblique  manner; i.e., multiple parameters can be adjusted simultaneously, so that the optimizer is less constrained in following the direction of steepest gradient in parameter space. This may reduce the number of steps required for convergence. Indeed, more rapid convergence  has been cited as a concrete advantage of these EM-style formulations over more generalized gradient ascent methods [In the case of "pure"  HMMs, expectation-maximization (EM) update formulas have been derived for both MMI and MCE. These formulas allow model parameters to be updated in an  methods . However methods ,24,18,26 methods .∈TP. Although P can be more directly computed using a modified Forward algorithm [In the case of GHMM-based gene finders, the advantages of EM over a generalized gradient ascent procedure may indeed be rather slim. The very flexibility which we find attractive in GHMMs can be expected to complicate the derivation of such EM-like update formulas for arbitrary GHMM-based gene finders, likely requiring additional assumptions and approximations that would further compromise the optimality of the EM procedure. It was for this reason that we decided to employ a more generalized gradient ascent method for the present study. A rudimentary gradient ascent optimizer is simple to implement, and the use of prediction accuracy as an objective function affords great convenience in approximating Σlgorithm , to do sThere are a number of other variations and enhancements which we are at present contemplating for our discriminative trainer. One of these involves the joint training of pairs of submodels in the GHMM using a maximum discrimination criterion rather than the usual one based on maximum likelihood. Although such an approach would not in itself directly attend to the global optimality of the GHMM , it would at least seem to offer a promising direction for improving our existing optimizer and may be feasible without increasing the computational cost beyond what is practical.For the present, we feel confident in making the recommendation that others tasked with the training of GHMM gene finders consider applying an automated gradient ascent procedure like that described here as a more systematic alternative to manual tuning of parameters following maximum likelihood training of individual submodels. Beyond the obvious advantage of likely improving gene finder accuracy, such an automated method may offer some degree of reproducibility  and uniformity for the purposes of comparing gene finders and gene finding algorithms. In addition, we urge those practicing manual tuning on their final "test" set to consider that their reported accuracy results may well be inflated as a result of "peeking" at the test set before the final evaluation – a practice that has been criticized in the field of machine learning . That sFinally, we would like to make note of an unfortunate consequence of discriminative training of HMMs for biological sequence analysis, namely, that while the resulting models may possess improved ability for discrimination and therefore greater utility for specific tasks such as gene prediction, their suitability as representative models of biological knowledge  may well be reduced relative to models induced with simple MLE techniques. Indeed, some authors in the field of speech recognition  have noWe have shown that discriminative training for GHMM-based gene finders is feasible using a rudimentary gradient ascent approach, and have briefly explored the relation between this method and the EM-like techniques which have been proposed in the field of speech recognition. Our experiments show that the gradient ascent method can result in a gene finder with substantially greater prediction accuracy. It is our hope that even greater gains in accuracy will result from extension and refinement of discriminative training techniques applied to GHMM-based gene finders.Sn (Sn = TP/(TP+FN), TP = true positive count, FN = false negative count) on a training set of true and "decoy" signals. "Boosting" of signal sensors was performed by iteratively retraining each signal sensor on sets of training features in which the lowest scoring features were duplicated so as to focus the training procedure on the most difficult examples. Boosting has been found to improve signal detection in other application areas [The gene finder TigrScan  is a GHMon areas . Most tron areas ) which hThe total number of parameters which need to be estimated when training TigrScan is roughly 90,000; the large bulk of these are the n-gram statistics comprising the IMMs used for the content sensors. As an initial attempt at applying discriminative training to TigrScan, we selected 29 of these ~90,000 parameters to subject to gradient ascent optimization. Although this is a miniscule proportion of the available parameters, our previous experiences with hand-tuning our GHMM on other data sets suggested that these 29 parameters exert a disproportionately large influence on the accuracy of the gene predictions. By limiting the number of parameters to be optimized we hoped to both accelerate the training procedure and also reduce the risk of overtraining. The selected parameters were:• mean intron, intergenic, and UTR lengths (3)• transition probabilities (4)• exon optimism (1)• WAM size and relative positioning (8)• WAM order (4)• signal sensitivity (1)• number of signal boosting iterations (8)• skew and kurtosis of exon length distributionsModifications to skew and kurtosis of exon length distributions were found during early exploration to produce no improvements; these parameters were therefore left unchanged in all further experiments. All remaining parameters were estimated using standard MLE techniques.For those runs in which gradient ascent was disabled (see below), the following methods were used to estimate the above 29 parameters: mean intron and UTR lengths as well as transition probabilities were estimated using MLE from training data; mean intergenic length was set to a fixed value based on the known intergenic lengths in the test set; exon optimism was set to zero; remaining parameters were selected so as to minimize the misclassification rate on a set of true and "decoy" signals selected from the training set.objective function for use by the gradient ascent procedure, we decided to measure the accuracy of the current parameterization by running the gene finder on a subset of the training genes. Our hope was that this accuracy measure would provide a reasonable approximation of Σ∈T P by indicating roughly how often the current model θ would cause the correct parse φ to be predicted for training sequence S. We defined the nucleotide accuracy Anuc as the percentage of nucleotides correctly classified as coding vs. noncoding; Aexon was defined as an average of exon sensitivity and specificity (where a predicted exon is considered correct only if both boundary coordinates were predicted correctly); and Agene was defined as the percentage of training genes which were predicted exactly correctly. These were all rounded to integral percentages between 0 and 100%. The objective function was then defined as:As an f(θ) = 100Anuc+Aexon+Agene.    (4)Anuc and Aexon terms were included in an effort to smooth the function, which would otherwise have been insensitive to changes not reflected in the number of genes predicted exactly correctly – i.e., a step function. Though the Anuc term was given much greater weight for this study, additional work needs to be undertaken to determine the most suitable set of weights for our objective function.The Parameters were optimized using an iterative gradient ascent procedure operating in the selected 29-dimensional parameter space, as illustrated schematically in Figure f(θ) on a held-out subset of the training set. The training set was limited to 1000 genes, and all experiments were repeated separately on two highly divergent species, the model plant Arabidopsis thaliana and the pathogenic fungus Aspergillus fumigatus. Five-fold cross-validation was employed, so that the entire optimization procedure was carried out five times on four-fifths of the data (800 genes) and each time evaluated on the remaining one-fifth (200 genes); accuracy results reported here were obtained by averaging the five sets of accuracy numbers obtained from the cross-validation.The quality of a given parameterization θ was measured by evaluating the objective function not made available to the optimizer, but are instead reported here as a more objective assessment of final model accuracy. We will refer to the training set as T and the additional 1000 genes for testing as H. BLAST [The held-out one-fifth was also used by the gradient ascent procedure to tune the selected 29 parameters. The practice of using a held-out set for smoothing or to estimate a small number of additional parameters is common in the natural language processing field , where iH. BLAST  was usedSeveral variations of this experiment were also performed. To evaluate the utility of splitting the training set and performing MLE and gradient ascent parameter estimation on separate subsets (as described above), we also performed MLE followed by gradient ascent training on the full training set T and again evaluated the induced models on H. To assess whether gradient ascent provided any improvement in accuracy we also trained a model on T using only MLE and evaluated that model on H. Although the virtues of cross-validation have been well explored in the context of many other applications, we decided to use the above experimental design as a convenient opportunity to verify our expectation that it would also prove useful for objective analysis of gene finder accuracy.Software implementation and computational experiments were performed by WHM. The manuscript was written by WHM with assistance from SLS.


Stem cells resident in adult tissues are principally quiescent, yet harbor enormous capacity for proliferation to achieve self renewal and to replenish their tissue constituents. Although a single hematopoietic stem cell (HSC) can generate sufficient primitive progeny to repopulate many recipients, little is known about the molecular mechanisms that maintain their potency or regulate their self renewal. Here we have examined the gene expression changes that occur over a time course when HSCs are induced to proliferate and return to quiescence in vivo. These data were compared to data representing differences between naturally proliferating fetal HSCs and their quiescent adult counterparts. Bioinformatic strategies were used to group time-ordered gene expression profiles generated from microarrays into signatures of quiescent and dividing stem cells. A novel method for calculating statistically significant enrichments in Gene Ontology groupings for our gene lists revealed elemental subgroups within the signatures that underlie HSC behavior, and allowed us to build a molecular model of the HSC activation cycle. Initially, quiescent HSCs evince a state of readiness. The proliferative signal induces a preparative state, which is followed by active proliferation divisible into early and late phases. Re-induction of quiescence involves changes in migratory molecule expression, prior to reestablishment of homeostasis. We also identified two genes that increase in both gene and protein expression during activation, and potentially represent new markers for proliferating stem cells. These data will be of use in attempts to recapitulate the HSC self renewal process for therapeutic expansion of stem cells, and our model may correlate with acquisition of self renewal characteristics by cancer stem cells. This comprehensive study of gene expression in hematopoietic stem cells reveals some key cellular changes that occur when the stem cells transition from quiescence to proliferation and back again Hematopoietic stem cells (HSCs) are the best-described adult stem cell population at a phenotypic and functional level. Recent attempts have been made to characterize their molecular regulation by comparing their gene expression profiles with those of other stem cell populations . These aIn a normal adult, HSCs reside in the bone marrow, where they are relatively inactive. Long-term HSCs divide infrequently to produce more proliferative short-term HSCs, which in turn generate the lineage-committed progenitors that manufacture the billions of differentiated hematopoietic cells that daily enter the peripheral blood. One hallmark of HSCs is their ability to rapidly proliferate in response to stressors such as myelosuppressive chemotherapy or bone marrow transplantation in order to quickly generate work-horse progenitors as well as additional stem cells, which then return to quiescence . While tOur approach was to examine the transcriptional profiles of purified adult HSCs throughout a time course of induced proliferation, and compare the gene expression in these cells to that of naturally dividing fetal liver HSCs (FL-HSCs). Normal adult HSCs are largely nondividing, with around 1%–3% in cycle and approximately 90% in G0 . A singlDuring the latter part of mammalian embryonic development, HSCs reside in the fetal liver, where they undergo a massive expansion prior to entering the bone marrow. Approximately 30% of murine FL-HSCs are in cycle , and simOur overall approach was to isolate highly purified HSCs in the states described above, obtain their gene expression profiles using Affymetrix microarrays, and apply statistical and bioinformatics methods to facilitate comparisons among the samples. To construct a profile of the time-dependent induction of HSC proliferation, 5FU-HSCs were isolated at days 0, 1, 2, 3, 6, 10, and 30 after treatment.+ characteristics . FL-HSCs were isolated by FACS for AA4.1+, c-Kit+, Sca1+, and Lineage− characteristics from embryos 13.5–14.5 d postcoitus (http://www.bioconductor.org). Further statistical analysis was performed in R (http://www.r-project.org). Quality control was performed both pre- and postnormalization. Briefly, chips were inspected for spatial defects, intensity outliers, and amplification bias. After screening, the two chips representing biological replicates with the highest correlation  in each group or time point were selected for further analysis. Raw data and normalized expression data are available for download from Gene Expression Omnibus (http://ncbi.nlm.nih.gov/geo) or http://franklin.imgen.bcm.tmc.edu/SCGAP/downloads/SPTimecourse. Normalized expression data along with all filtering criteria used to obtain our gene lists are available in http://franklin.imgen.bcm.tmc.edu/PLoS.Adult quiescent HSCs and 5FU-HSCs were isolated according to Hoechst 33342 efflux, termed the side population (SP) and Sca1eristics  . Principle component analysis revealed that the time course data consisted of two major groups: genes that up-regulated and genes that down-regulated over the time course (data not shown). We further explored the expression data with unsupervised (k-means) clustering and observed that when the number of predefined groups was low (2–3), only the overall pattern of up- or down-regulation was discernable; however, as we increased the number of groups (4–8), more complex patterns with peaks early or late in the time course were visible (data not shown). Since the 5FU treatment consisted of a single dose administered at time zero, we speculated that the downstream effects of 5FU treatment would be represented by groups of genes whose gene expression profiles showed time-ordered peaks propagating through the time course. The expression profile of groups created by k-means clustering supported this hypothesis. Therefore, to more directly delineate these peaking subsets, we sorted the genes into groups by their time of maximum expression (TOM). Strikingly, these groups had two predominant patterns over the time course: one group up-regulated with 5FU treatment with a TOM at day 2, 3, or 6, and one group down-regulated, exhibiting TOM at day 0, 1, 10, or 30  the “common quiescence signature” (cQ-sig) and “common proliferation signature” (cP-sig) F and 1G.http://www.geneontology.org) to analyze the content of gene lists. The GO is a controlled vocabulary that describes gene functions in their cellular context and is arranged in a quasi-hierarchical structure from more general to more specific. Since the vocabulary of annotations is fixed, it allows for functional comparisons of mutually exclusive gene lists, such at the TOM groups. We began by mapping each gene in the lists being analyzed to the GO tree structure. This allowed us to count the number of times each gene hit at or below any particular node in the GO structure. Once the lists were mapped, we were able (a) to calculate a measure of similarity (distance) between the lists using the distributions of each list across the various levels of the GO tree and (b) to calculate the enrichment of the various GO categories in each list  to be 2.1-fold increased in the Q-sig over the total array  onto the GO structure and then calculating fold enrichments for each GO category in our lists relative to the microarray. We expected to find differences between the Q-sig and P-sig in the frequencies of antiproliferative and proproliferative genes, and verification of this served as proof-of-principle for our experimental design. Indeed, we found the GO category “regulation of cell cycle” , Btg3/APRO4 (B-cell translocation gene 3), cyclin G1, GADD45β, and melanoma antigen, family D, 1. Prior experiments have shown a decrease in the number of HSCs in cycle during the first day after 5FU treatment as compared to untreated HSCs . Many genes in these groups are negative regulators of cell cycle, such as HSCs see . We therHSCs see A and 2C.In the P-sig, TOM 3 and TOM 6 showed astonishingly different GO contents despite their similar expression patterns see A. Genes SOCS3 , whose product suppresses responses to growth factors in part by inhibiting JAK/STAT signaling, was most highly expressed at day 10, along with STAT3 and STAT6. JAK/STAT signaling has been implicated in regulation of proliferation and differentiation of various hematopoietic cell types.As discussed above, by day 10 after 5FU treatment the number of HSCs in cycle is reduced to near pretreatment levels see A. Althoup21, a prototypic member of the Q-sig, was specifically found within a QTL on Chromosome 17 associated with regulating HSC frequency. This region is syntenic with human Chromosome 6p21, a known hot spot for translocations linked to leukemias and lymphomas . Furthermore, since the GO vocabulary is not specific to any species, this method allows for cross-species and cross-platform comparisons of gene lists. Re-analysis of data from previous studies may reveal a functional stem cell signature in GO space that was not evident at the gene level .Applying GO analysis to the TOM groupings revealed elemental subgroups within the signature lists that allowed us to construct a molecular model of the HSC activation cycle. The majority of unperturbed HSCs reside in a quiescence niche and express receptors, for example the metabolism- and ageing-associated receptor IGF1R and the receptor tyrosine kinase Tie1, that allow them to respond to multiple mitogenic signals A. They aTob1, p21, and Btg3 , HSCs enter a superquiescent “pause.” This state, found at TOM 1 and also observed by cell cycle analysis , is mediand Btg3 B. Intere renewal . We obse renewal . At leasα4-integrin at day 3 followed by a dramatic decrease at day 6 post 5FU treatment. Experiments that block α4-integrin function by blocking antibodies or via knockout technology have previously shown that down-regulation induces increased mobilization and delays recovery after 5FU treatment . Cells were stained with a biotinylated Sca1 antibody  and visualized with strepavidin-PE . Sca1-enriched WBM was sorted for the SP profile and Sca1 positivity on a MoFlo . Representative flow diagrams of cell sorting can be found in For quiescent adult HSCs and 5FU-HSCs, whole bone marrow (WBM) was collected from the femurs and tibias of ten to fifteen 8- to12-wk-old normal or 5FU-treated C57Bl/6 mice. For 5FU treatment, mice were injected intravenously with a single dose of 5FU  and killed at day 0, 1, 2, 3, 6, 10, or 30 after injection. Day 0 mice were untreated, and day 1 WBM was isolated 17–19 h after injection; all subsequent days were in 24-h increments. WBM was stained with Hoechst 33342 to identify the SP cells  and then+ cells using c-Kit-biotin  and visualized with strepavidin-APC (Molecular Probes). The c-Kit-enriched cells were stained with a lineage cocktail consisting of cychrome-conjugated CD4 (L3T4), CD8 (53–6.7), B220 (RA3–6B2), GR1 (RB6–8C5), and Ter119 (Ter119) as well as Sca1-PE, and AA4.1-FITC . FL-HSCs were identified as negative for the lineage markers and positive for Sca1, c-Kit, and AA4.1  by flow cytometry.Total RNA was isolated from approximately 35,000–70,000 sorted HSCs using the RNaqeuous kit . All samples were then digested with DNaseI to eliminate residual genomic DNA, and extracted with phenol:chloroform. Total RNA was then subjected to two rounds of linear amplification using T7-based in vitro transcription (IVT) . Briefly, total RNA was reverse transcribed with an oligo-dT primer containing a T7 promoter sequence at the 5′ end (oligo-dT-T7 primer). To prime second-strand synthesis, RNA–cDNA hybrids were digested with RNaseH, producing patches of single-stranded cDNA. The second strand was filled in by DNA polymerase. The double-stranded cDNA served as a template for T7 RNA polymerase-driven IVT, which yielded up to 100× the starting mRNA pool. RNA probes were labeled in the second round of IVT with biotinylated nucleotides . The second round of amplification was performed using random primers for first-strand synthesis and the oligo-dT-T7 primer to prime second-strand synthesis. Overall amplification was estimated to be 10,000-fold or greater .http://www.affymetrix.com).Affymetrix  MG-U74Av2 chips were hybridized with fragmented, biotinylated aRNA according to standard protocols. Chips were then washed and counterstained using PE-conjugated strepavidin. Signal was amplified using the Affymetrix protocol for antibody amplification. The raw image (.DAT) and intensity (.CEL) files were generated using MAS 5.0 software . Twenty-one chips were hybridized and analyzed, but only 16 (approximately 75%) passed our quality control standards . Normalization and model-based expression values were calculated using the GeneChip Robust Multichip Analysis method . The smooth curve fitting assumed that the expression trajectory for each gene followed a continuous time pattern. The class of fifth-degree polynomials was chosen for the fits, because it was the highest degree polynomial that did not interpolate the time point means. Analysis was performed in R 1.7.1 (http://www.r-project.org) using the Bioconductor suite of R packages. Source code for the analysis, including the curve fitting procedure, is available in Time-dependent expression profiles for each gene were analyzed by regressing the normalized expression values using polynomial least squares regression. ANOVA was performed on the coefficients of regression to identify genes with significant time patterns (http://www.geneontology.org) and the GO annotations for each probe set on the MGU74Av2 chip, provided by Affymetrix . The GO vocabulary structure was then instantiated as a directed acyclic graph and traversed to obtain hit counts for the genes in our lists that mapped at or below each node in the GO structure. To assess the significance of gene counts at each term, the annotations for the entire array were mapped to the GO structure, and counts for the whole array were obtained at each GO term. The significance of counts in particular categories was obtained via a sampling-without-replacement statistical model for the gene counts in each GO category.GO analysis was performed using the 1 October 2003 build of the gene ontologies  is the binomial coefficient for x choose y. The value C is the total number of genes annotated to the GO node under consideration for the entire gene set. The value of L is the number of genes annotated to all nodes at the same level of the GO hierarchy, again considering the entire arrayed gene set. The value n is the number of genes annotated to terms at the same GO level for the gene list under consideration. The p value (one sided) for the node under consideration is obtained by summing probabilities as determined by the formula for all values of X from k to n. A web-based tool to perform this analysis on any gene list is available at http://franklin.imgen.bcm.tmc.edu/OntologyTraverser.In the formula, The list distance metric was determined from the estimated joint distribution of probe counts across the GO structure for each gene list. This joint distribution was estimated by obtaining the counts at each GO node at each level. Only those nodes with non-zero counts in at least one list were included in the calculations. Relative frequencies at each GO node at each GO level were obtained by normalizing to the total counts at each level for each list. Once the frequency distribution at each level was determined, a Kullback–Leibler-like distance metric was constructed. Briefly, the distance metric is a weighted average of Kullback–Leibler distances at each level of the GO. The formula for computing distance between a pair of lists isk were normalized to sum to one and were drawn from the Poisson mass function with a mean of four. Since the GO levels are ordered in terms of increasing specificity, the contribution of each level was weighted differently: positive weight was applied to the middle of the GO hierarchy (levels 3–8), and weights for levels lower than 3 and higher than 8 were set to 0. The indices i and j in the formula indicate the lists being compared. The index k indicates the level of the GO under consideration, and the index γ considers each GO node at the level k.The weights αTo compute the significance of our list dendrogram we determined the probability that we could arrive at the grouping pattern by chance. We determined the number of dendrograms with the “two group” pattern divided by the total number of labeled dendrograms. For our case, our “two group” dendrogram consisted of two subtrees with three and five arms, respectively. The total number of labeled dendrograms was the product of the number of labeled three-leaf dendrograms (three) and the number of labeled five-leaf dendrograms (105), which is 415. We divided this number by the total number of eight-leaf dendrograms  to attain the 0.003 probability. An R function for making this calculation is contained in the R script provided in X − i,nP where X equals the number of genes in list on chromosome i, n equals the total number of genes in list, and iP equals the frequency of chromosome i hits on total chip .Gene hits per chromosome were counted for Q-sig and P-sig as well as the total MGU74Av2 chip. Number of hits in our signatures was centered to the expected frequency of the number of hits on the total chip using the following equation. The number of hits above/below expected equals Z-score with the following equation.To determine the significance of enrichments and depletions of gene hits on each chromosome, we calculated a Z-score of Q-sig and P-sig was significant to 0.02 < α < 0.05.Chromosome enrichment or depletion between signatures was considered significant if the additive Figure S1Cell cycle analysis of bone marrow SP cells before (left) and 6 d post (right) 5FU treatment. Before treatment, approximately 2% of adult quiescent HSCs are in cycle; 6 d after 5FU treatment, approximately 22% of HSCs are in cycle.(2.0 MB EPS).Click here for additional data file.Figure S2+ cells at each time point. The indicated regions contain the SP cells. The table shows prevalence and purity from several isolations.(A) Representative flow cytometry plots of bone marrow enriched for Sca1+ cells.(B) Representative flow cytometry analysis of fetal liver enriched for c-Kit(2.9 MB EPS).Click here for additional data file.Figure S3+ and Lineage− expression.SP profile of adult HSCs and 5FU-HSCs 6 d post 5FU treatment. Arrows point to analysis in SP cells of Sca1 and lineage marker expression showing greater than 97% homogeneity for Sca1For analysis of adult HSCs on day 0, the lineage markers used were Mac1, CD4, CD8, B220, GR1, and Ter119. For analysis of 5FU-HSCs on day 6, all of the above markers were used except for Mac1, because of its low level expression on HSCs after 5FU treatment.(2.2 MB EPS).Click here for additional data file.Protocol S1(8 KB TXT).Click here for additional data file.Table S1(800 KB HTML).Click here for additional data file.Table S2(666 KB HTML).Click here for additional data file.Table S3(1.4 MB HTML).Click here for additional data file.Table S4(790 KB HTML).Click here for additional data file.Table S5(933 KB HTML).Click here for additional data file.Table S6(336 KB HTML).Click here for additional data file.Table S7(296 KB HTML).Click here for additional data file.Table S8(164 KB HTML).Click here for additional data file.Table S9(77 KB HTML).Click here for additional data file.Table S10(122 KB HTML).Click here for additional data file.Table S11(59 KB HTML).Click here for additional data file.Table S12(62 KB HTML).Click here for additional data file.Table S13(70 KB HTML).Click here for additional data file.Table S14(169 KB HTML).Click here for additional data file.Table S15(45 KB HTML).Click here for additional data file.Table S16(52 KB HTML).Click here for additional data file.Table S17(18 KB HTML).Click here for additional data file.Table S18(280 KB HTML).Click here for additional data file.Table S19(2 KB HTML).Click here for additional data file.Table S20(7 KB HTML).Click here for additional data file.Table S21(3 KB HTML).Click here for additional data file.Table S22(6 KB HTML).Click here for additional data file.Table S23(8 KB HTML).Click here for additional data file.Table S24(6 KB HTML).Click here for additional data file.Table S25(7 KB HTML).Click here for additional data file.Table S26(7 KB HTML).Click here for additional data file.Table S27(9 KB HTML).Click here for additional data file.Table S28(7 KB HTML).Click here for additional data file.Table S29(6 KB HTML).Click here for additional data file.Table S30(92 KB HTML).Click here for additional data file.Table S31(31 KB HTML).Click here for additional data file.Table S32(136 KB HTML).Click here for additional data file.Table S33(45 KB HTML).Click here for additional data file.Table S34(244 KB HTML).Click here for additional data file.Table S35(14 KB HTML).Click here for additional data file.Table S36(52 KB HTML).Click here for additional data file.Table S37(8 KB HTML).Click here for additional data file.Table S38(61 KB HTML).Click here for additional data file.Table S39(14 KB HTML).Click here for additional data file.Table S40(65 KB HTML).Click here for additional data file.Table S41(32 KB HTML).Click here for additional data file.Table S42(215 KB HTML).Click here for additional data file.Table S43(199 KB HTML).Click here for additional data file.Table S44(244 KB HTML).Click here for additional data file.Table S45(14 KB HTML).Click here for additional data file.Table S46(9.7 MB XLS).Click here for additional data file.Table S47This table gives the 0.25, 0.5, and 0.75 quartile of the gene correlations (Pearson's) to their TOM group mean shown in (27 KB DOC).Click here for additional data file.http://www.ncbi.nlm.nih.gov/LocusLink/) accession numbers for the genes and gene products discussed in this paper are BTG1 (Locuslink 12226), Btg3/APRO4 (Locuslink 12228), CD48 (Locuslink 12506), c-fos (Locuslink 14281), c-maf (Locuslink 17134), cyclin G1 (Locuslink 12450), Endoglin (Locuslink 13805), GADD45β (Locuslink 17873), GATA-2 (Locuslink 14461), histone deacetylase 5 (Locuslink 15184), IGF1R (Locuslink 16001), melanoma antigen, family D, 1 (Locuslink 94275), p21 (Locuslink12575), receptor tyrosine kinase Tie1 (Locuslink 21846), serine proteinase inhibitor A-3 g (Locuslink 20715), SOCS3 (Locuslink 12702), STAT3 (Locuslink 20848), STAT6 (Locuslink 20852), Suppressor of cytokine signaling 3 (Locuslink 12702), TIMP3 (Locuslink 21859), Tob 1/APRO6 (Locuslink 22057), and α4-integrin (Locuslink 16401). The GEO (www.ncbi.nlm.nih.gov/geo) accession numbers for microarrays discussed in this paper are GSM26734-GSM26749.The LocusLink (