Bordetella bronchiseptica and the gastrointestinal helminth Trichostrongylus retortaeformis. Our goal was to identify representative mediators and functions that could capture the essence of the host immune response as a whole, and to assess how their relative contribution dynamically changed over time and between single and co-infected individuals. Network-based discrete dynamic models of single infections were built using current knowledge of bacterial and helminth immunology; the two single infection models were combined into a co-infection model that was then verified by our empirical findings. Simulations showed that a T helper cell mediated antibody and neutrophil response led to phagocytosis and clearance of B. bronchiseptica from the lungs. This was consistent in single and co-infection with no significant delay induced by the helminth. In contrast, T. retortaeformis intensity decreased faster when co-infected with the bacterium. Simulations suggested that the robust recruitment of neutrophils in the co-infection, added to the activation of IgG and eosinophil driven reduction of larvae, which also played an important role in single infection, contributed to this fast clearance. Perturbation analysis of the models, through the knockout of individual nodes (immune cells), identified the cells critical to parasite persistence and clearance both in single and co-infections. Our integrated approach captured the within-host immuno-dynamics of bacteria-helminth infection and identified key components that can be crucial for explaining individual variability between single and co-infections in natural populations.Co-infections alter the host immune response but how the systemic and local processes at the site of infection interact is still unclear. The majority of studies on co-infections concentrate on one of the infecting species, an immune function or group of cells and often focus on the initial phase of the infection. Here, we used a combination of experiments and mathematical modelling to investigate the network of immune responses against single and co-infections with the respiratory bacterium  Infections with different infecting agents can alter the immune response against any one parasite and the relative abundance and persistence of the infections within the host. This is because the immune system is not compartmentalized but acts as a whole to allow the host to maintain control of the infections as well as repair damaged tissues and avoid immuno-pathology. There is no comprehensive understanding of the immune responses during co-infections and of how systemic and local mechanisms interact. Here we integrated experimental data with mathematical modelling to describe the network of immune responses of single and co-infection by a respiratory bacterium and a gastrointestinal helminth. We were able to identify key cells and functions responsible for clearing or reducing both parasites and showed that some mechanisms differed between type of infection as a result of different signal outputs and cells contributing to the immune processes. This study highlights the importance of understanding the immuno-dynamics of co-infection as a host response, how immune mechanisms differ from single infections and how they may alter parasite persistence, impact and abundance. Hosts that are immunologically challenged by one infection often show increased susceptibility to a second infectious agent, whether a micro- or a macro-parasite. Changes in the immune status and polarization of the response towards one parasite can indeed facilitate the establishment and survival of a second parasitic species Mycobacterium tuberculosis induces more severe disease when concurrent with intestinal helminths, suggesting increased host infectiousness and bacterial transmission compared to single infected individuals Empirical work on bacteria-macroparasite co-infections has often found that the development of a Th2 mediated response towards the helminth leads to a reduction of the protective Th1 cytokine response against the bacteria and a more severe bacteria-induced pathology Understanding how the infection by a second parasite species can influence the network of immune processes and the polarization towards one of the infecting agents requires the quantification of the immune components both at the systemic level and at the local site of infection, and the ability to follow the kinetics of these processes over time. The immunology of co-infection often considers the Th1/Th2 paradigm a tractable simplification of the overall immune response and its main functions. Yet, this approach tells us only half of the story, namely the systemic component. Indeed, organ compartmentalization and tissue specificity create well defined host-parasite environments that contribute to, as well as are modulated by, the immune system as a whole Bordetella bronchiseptica and the gastrointestinal helminth Trichostrongylus retortaeformis, two common infections of the European rabbit (Oryctolagus cuniculus). Both parasites cause persistent infections that occur with high prevalence and intensity in free-living rabbit populations B. bronchiseptica is a gram-negative bacterium that colonizes the respiratory tract through oral-nasal transmission and usually results in asymptomatic infections. B. bronchiseptica has been largely isolated in wildlife, pets and livestock but rarely in humans Bordetella pertussis and Bordetella parapertussis, the etiological agents of whooping cough B. bronchiseptica agree with the general findings of bacterial clearance from the lower respiratory tract but persistence in the nasal cavity To address these questions we used a combination of laboratory experiments and network-based discrete dynamic modelling, and examined changes in the immune response against single and co-infection with the respiratory bacterium T. retortaeformis has a direct life cycle and colonizes the small intestine following ingestion of pasture contaminated with infective third stage larvae (L3). The majority of larvae settle in the duodenum where they develop into adults in about 11 days T. retortaeformis-rabbit interaction suggested that acquired immunity develops proportionally to the accumulated exposure to infection and successfully reduces helminth intensity in older hosts T. retortaeformis where the quick production of antibodies and eosinophils was associated with the consistent reduction but not complete clearance of the helminth by 120 days post challenge The gastrointestinal helminth B. bronchiseptica-T. retortaeformis co-infection the presence of helminths will delay bacterial clearance from the respiratory tract but there will be no change in helminth abundance in the small intestine. We predicted a T. retortaeformis mediated Th2 polarization at the systemic level and a bystander effect in the distal respiratory tract. This will have suppressed IFNγ, resulting in the enhancement of bacterial intensity and deferred clearance in the lower respiratory tract compared to single infection. We also expected the Th2 systemic environment to control helminth abundance but not to change the numbers compared to the single infection. To examine our hypothesis, laboratory data on single infections were used to build discrete dynamic models describing the immune processes generated in response to each infection. The two single infection models were then connected through the cross-modulation of Th cells and the cytokine network at the systemic level, and allowed to reflect changes in these interactions at the local level. The resulting co-infection model and the dynamics of the parasites were finally compared with our laboratory experiment of bacteria-helminth co-infection to confirm the correctness of the model. Lastly, we examined the robustness of the immune networks with respect to the deactivation of single immune nodes by simulated knockout laboratory experiments. In other words, we tested the role of a large number of immune components, how their knockout affected the dynamics of infection and how the system converged into a potentially novel stable state. This allowed us to elucidate the immune key mechanisms and pathways behind the observed dynamics and the relative differences between single and co-infection.Based on previous studies on bacteria-macroparasite co-infections and our recent work on the rabbit system, we hypothesized that during a B. bronchiseptica and T. retortaeformis were assembled in the form of two distinct pathogen-specific networks of immune responses. The network of interactions against B. bronchiseptica was based on the infection in the lungs, the crucial organ for bacterial clearance, and constructed following Thakar et al. B. bronchiseptica infection in mice  . These and the knockout simulations suggested that neutrophils and eosinophils are critically involved in the clearance of T. retortaeformis infection.The stability of the immune pathways and the reliability of our parsimonious model were explored by systematically knocking out network nodes and examining the effects on the activity of the adult helminth node at the 20ilibrium Fig. 4C.B. bronchiseptica and T. retortaeformis the two single immune networks were connected and the co-infection network simulated as a single entity without changing the Boolean rules built for the single networks, except for the adjustments necessary for assembly  of B. bronchiseptica through Treg cells. Also, the early IL4 expression was suppressed by the Th1 mediated IFNγ phenotype activated both by the helminth, during the initial establishment, and the bacterial co-infection. Mucus IgG remained consistently active from time step 3 while mucus IgA was at the highest between 5 and 10 time steps but decreased thereafter  and Th2 mediated IL5 and IL13 are critical in helminth clearance To provide a parsimonious mechanism that could explain the rapid helminth clearance, the immune nodes of the co-infection network were systematically knocked out and the helminth activity examined at the 20ime-step Fig. 4D.T. retortaeformis dynamics. In the single infection, when neutrophils are only transiently activated, the recruited eosinophils were relatively more important to parasite reduction, although they were not sufficient to clear the infection. In the co-infection, the robust and early activation of recruited neutrophils -which decreased following helminth reduction- and the activation of recruited eosinophils -which are important in reducing the number of infecting larvae and are required for neutrophils to successfully reduce the helminths- highlighted the synergistic role of these cells in the observed fast clearance of T. retortaeformis. To explicitly study the bacterial components inducing these two leukocytes, we switched the nodes to ON one at a time and found that dendritic cells and Th1 cells, activated by bacteria, led to a significant increase in neutrophil activity (results not shown). Counter to this, no bacterial nodes significantly contributed to eosinophil production. Switching ON the type III secretion system node transiently increased eosinophil activity, compared to the unperturbed system, as expected from the role of TTSS in the induction of Th2 related cytokines A comparison between single and dual infection offers insights into the contribution and balance of these two leukocytes to B. bronchiseptica-T. retortaeformis co-infection experiment was carried out and the empirical results were used to validate the co-infection dynamic model. A statistical analysis was also performed between the single and co-infection trials to further reinforce our modelling outputs. However, while the statistical findings provide an insight into the relationships among the immune components, no mechanistic understanding or dynamic outcomes can be established between these variables and parasite abundance. The network-based discrete dynamic models allowed us to establish such connections and causal interactions between the various components. Overall, we found that the parsimonious dynamic model correctly predicted the observed dynamics of concurrent B. bronchiseptica and T. retortaeformis co-infection.A B. bronchiseptica abundance in the lungs increased in the first 7 days post challenge and decreased thereafter, as seen in the dynamic model; by 90 days bacteria were completely cleared from the lungs and trachea but persisted in the nasal cavity , and peripheral eosinophils and neutrophils . To compare the immune response between single and co-infected hosts, variables were scaled over the controls. Co-infected rabbits exhibited higher IL4 , serum IgG (0.166±0.043 P<0.001) and neutrophils  but lower eosinophils  compared to single infected individuals. It is important to note that a low or negative cytokine Ct value  identifies high mRNA expression and vice versa, thus in the models low Ct values are translated as high cytokine activity. The remaining variables were not significant, although this should not be interpreted as a complete lack of variability between the two infections. Indeed, as highlighted in the network model these variables play a secondary but still necessary role in generating immune differences between infections.A combination of principal component analysis (PCA) and generalized linear models (GLM) indicated that , T. retortaeformis was positively associated with the first axis (PCA 1), mainly described by the interaction among the three cytokines, and negatively related to the second axis (PCA 2) represented by eosinophils and antibodies (Table S2). Interestingly, cytokines were positively correlated , indicating the co-occurrence of a specific response to the helminth, through IL4, but also a robust inflammatory/anti-inflammatory reaction probably caused by the parasite damaging the mucosal epithelium and resulting in bacterial tissue infiltration during larval establishment Helminth intensity significantly decreased with the progression of the infection and organ location  however, counter to our expectation and consistent with our model simulations, lower establishment and faster clearance were observed in co-infected compared to single infected hosts , Table 2B. bronchiseptica and the gastrointestinal helminth T. retortaeformis. Our aim was to identify the parsimonious processes and key cells driving parasite reduction or clearance and how they changed between single and co-infections.Co-infections affect the immune responses but how the systemic processes interact and influence the kinetics at the local sites of infection is still unclear. The majority of studies on the immunology of co-infection have focused on either one of the infecting species or a restricted class of cells or immune processes, and often concentrated on the early stage of the infection T. retortaeformis in co-infected compared to single infected individuals, which was observed in the model simulations and confirmed in the empirical data. Neither did we expect to find that B. bronchiseptica infection in the lungs was not significantly altered by the concurrent helminth infection, despite the increase in local IL4 expression observed in both the simulations and the experiment. We found a small difference in bacterial clearance between single and co-infection  or phagocytosis (e.g. Ag-Ab complex or macrophages) increased bacterial abundance in single and dual infections, suggesting that these cells are necessary for controlling B. bronchiseptica.We confirmed the initial hypothesis of immune mediated interactions between the two parasites, however, our initial predictions were only partially supported. The most unexpected result was the faster clearance of ection vsFig. 7AT. retortaeformis was less detailed than that for the bacterial network, nevertheless, the model predictions of the activity pattern of the helminth and the immune variables that have been quantified were in agreement with our empirical studies. To our surprise the prediction of no effect of B. bronchiseptica on T. retortaeformis infection was proven wrong. Simulations suggested that the combined effect of neutrophils, eosinophils and antibodies (IgG and IgE) led to helminth expulsion. Neutrophils and eosinophils were activated through antigen-specific Th1 and Th2 responses, respectively. Th2-mediated differentiation of progenitor eosinophils (i.e. resident eosinophils), modulated by IL5 and IL13, also played an important role in helminth reduction in single infection, as indicated by the perturbation results. Previous studies on murine systems have shown that IL13 can complement IL4 or play an alternative or even stronger role in helminth infections The immune network for B. bronchiseptica co-infection, rather than a helminth induced up-regulation of this cytokine to facilitate tissue colonization Schistosoma mansoni or Trichuris muris systems are well described examples The mixed Th1/Th2 response in the duodenum was driven by different processes. The early IFNγ inflammatory signal observed both in single and co-infection was a host response to the mucosa damage by helminth establishment, and probably bacteria and microflora infiltration from the lumen a priori fixed threshold. They also use a parameter-free combinatorial description for the change in status of the nodes, thus avoid the need for parameter estimation while being sufficiently flexible. Indeed, Boolean models have been successfully used in a variety of contexts, from signal transduction The aim of this study was to develop tractable dynamic models that could capture the interactions of multi-organ, multi-species co-infection immune processes as well as single infection dynamics. We found the discrete dynamic Boolean models a feasible and reliable approach for this task since we lacked accurate spatio-temporal details on the majority of the variables and the kinetic parameters required to develop robust quantitative, differential equation-based models Bordetella and gastrointestinal helminths. In cases of uncertainty (e.g. whether two co-regulators were independent or synergistic) we tested a number of different assumptions (i.e. Boolean transfer functions) and selected the function that best described our single infection experiments in terms of the: timing of events, node activities and importantly, parasite steady state . These simulations followed the classical knockout lab experiments where single immune components (nodes) were turned off from the beginning of the simulation and the dynamics of the immune response, as well as parasite clearance, were examined. This approach allowed us to explore the knockout of a large number of immune variables, determine the most important components modulating the immune response and highlight how they differed between single and co-infection. These findings can be tested in the laboratory by performing knockout experiments of the crucial immune variables in different infection settings. For example, we can block neutrophil production or the cytokine IL13 and examine whether helminths persist -as predicted by our knockout simulations- or are slowly cleared in bacteria co-infected rabbits. Similarly, we can test the predicted different response of knocking out IL4 in helminth and bacteria-helminth co-infection, specifically, whether clearance is higher than in un-manipulated individuals in single helminth infection and lower than in un-manipulated co-infected hosts. We should also pay more attention to In conclusion, we showed that network-based discrete dynamic models are a useful approach to describe the immune mediated dynamics of co-infections. These models are robust as well as sufficiently tractable to qualitatively capture the complexity of the immune system and its kinetics over time. Arguably, the main limitation of our modelling approach is that it lacks a fully quantitative component. Yet, this work demonstrated that it is possible to build comprehensive qualitative dynamic models of the local and systemic immune network of single and co-infection that are validated by empirical observations. Importantly, this study is a fundamental starting point towards the future construction of quantitative models based on simplified networks that describe the kinetics and intensities of the causal relationships among key immune components identified in qualitative models. Our approach showed that we can refine the conventional approach of using the Th1/Th2 paradigm, by identifying system-specific functions or cell groups that can capture crucial immune processes during co-infections. While our parsimonious dynamical models were able to capture the patterns of single and co-infection observed in the experiments, we are aware that they are far from complete in describing the immunological complexity of the processes involved and cells activated. Nevertheless, they provide a parsimonious description of the system that can be experimentally tested. Ultimately, we showed that we cannot predict how the immune system reacts to co-infections based on our knowledge of single infection. More needs to be done to clarify the immune mechanisms involved in bacteria-helminth co-infections and how individual hosts balance the immune system as a whole.B. bronchiseptica and T. retortaeformis infections are known or generally addressed in the rabbit infection model, we extended the set of known interactions following general immunological knowledge on bacterial and helminth infections. We constructed three networks: two networks that describe the respective single infections and one that links the first two and represents a co-infection network. A detailed description of each network is given below.Interaction networks were built from the available literature and adapted to our system. Bacteria, helminth and the components of the immune system (i.e. immune cells and cytokines) were represented as network nodes; interactions, regulatory relationships and transformations among components were described as directed edges starting from the source node (regulator) and ending in the target node. We incorporated regulatory relationships that modulate a process (or an unspecified process mediator) as edges directed toward another edge. The regulatory effect of each edge was classified into activation or inhibition, visually represented by an incoming black arrow or an incoming red blunt segment. Since not all processes involved in natural , Text S1). This node includes generic virulence factors of the bacteria such as the lipopolysaccharide chain (LPS) required for tissue adherence following recognition of bacteria by epithelial cells. Other bacterial virulence factors, particularly O-antigen and type III secretion system (TTSS), are explicitly included as separate nodes in the network and are involved in the initial immune recognition of the bacteria node. Upon detection, epithelial cells activate pro-inflammatory cytokines, which in turn activate dendritic cells, often the most important antigen presenting cells. Dendritic cells are also activated by IFNγ. Dendritic cells induce differentiation of naïve T cells (T0) by producing IL4 and IL12. The cytokine profile along with the antigen leads to the activation of T cell subtypes including helper and regulatory T cells. T helper cells are activated in the lymph nodes (Compartment II) and subsequently transported to the site of infection (Compartment I). IL4 is also produced by differentiated Th2 cells; IL4 and IL12 inhibit each other and IL4 also inhibits IFNγ. T regulatory (Treg) cells are stimulated by the type III secretion system of B. bronchiseptica to produce IL10. Th1 cells produce IFNγ which along with pro-inflammatory cytokines activates neutrophils and macrophages. A different subtype of T cells, follicular T helper cells, is known to stimulate B cell activation. To simplify the network we assumed that naïve T cells could play this role. Antigen-specific B cell proliferation leads to the production of antibodies, namely IgG and IgA. IgA production occurs only in the direct presence of antigen unlike IgG that persists after bacterial clearance Infection of the lungs starts with the node Bacteria that leads to a cascade of immune interactions . Both parasite stages activate epithelial cells that lead to the production of pro-inflammatory cytokines which then activate dendritic cells and neutrophils, with the latter able to inhibit adult helminths. Infective larvae stimulate IL13 production by resident eosinophils and these recruit additional eosinophils from the progenitor cells in the peripheral blood B. bronchiseptica, dendritic cells interact with naïve T cells (T0) leading to the activation of T cell subtypes Th1 and Th2 through the production of IL12 and IL4. IL4 is also produced by Th2 cells and IL4 and IL12 inhibit each other. Consistent with the bacteria network, the activation of T helper cells occurs in the lymph nodes (Compartment II) and subsequently transported to the site of infection (Compartment I). In compartment I, IFNγ is produced by Th1 cells and dendritic cells. IL4 and IL10, produced by Th2 cells, have anti-inflammatory properties and inhibit pro-inflammatory cytokines and neutrophils. Naive T cells stimulate clonal expansion of B cells and these lead to the production of antibodies such as IgG. While B cells can secrete IgG much longer after antigen removal, IgA production is assumed to be in response to larval establishment and development. The IgE isotype is produced upon signalling from either IL4 or IL13. Among these antibodies IgG inhibits adult helminths while IgE and IgA are involved in activating eosinophils and inhibiting pro-inflammatory cytokines respectively.The network starts with infective larvae that develop into adults with no delay in the larval-adult development, adults appear 2 time steps post infection . This network is characterized by three compartments, representing the lungs, the small intestine (duodenum) and the systemic compartment (e.g. the lymphatic system). The connection of the networks and the immune mediated interactions between parasites were represented through the cytokines produced as a single pool. Local cells activated by bacteria and helminths can contribute to cytokine production, which are then transported through the blood and disseminate to other organs B. bronchiseptica- or T. retortaeformis-specific T subtypes or dendritic cells. For the co-infection network, Tregs are induced by bacteria which produce IL10 that can ultimately affect the helminth, since IL10 is not an antigen-specific node. Moreover, there is only a single pool of naïve T cells that induces T cell subtypes against either the bacteria or the helminths, depending on the antigen-specific dendritic cells.The co-infection immune network was developed by combining the two single infection networks together (Text S1) T. retortaeformis infection model with the experimental observations. The second transfer function did not reproduce the observed low activity of IL4 -compared to the other cytokines- in the duodenum at day 14 post infection and it also led to higher neutrophil activity, compared to the other leukocytes, than the empirical data. Since the first transfer function did not lead to such deficiencies, we chose the first over the second rule. The transfer functions used in the co-infection model were the same as, or the relevant composites of, transfer functions used in each individual infection. Thus, the Boolean transfer functions applied in our model provide a mechanistic understanding of the interactions leading to bacterial or helminth clearance.The immune-parasite interaction networks were developed into discrete dynamical models by characterizing each node with a variable that can take the ON state, when the concentration or activity is above the threshold level necessary to activate downstream immune processes, or the OFF state when activity is below this threshold. The evolution of the state of each node was described by a Boolean transfer function  of our model approximately corresponds to nine days. The randomized asynchronicity of the model does not alter the steady states of the dynamical system but causes stochasticity in the trajectory between the initial conditions and the equilibria (attractors) The status of the system across time was simulated by repeatedly applying the Boolean rules for each node until a stationary state (e.g. clearance of the parasite) was found. Since the kinetics and timescales of the individual processes represented as edges are not known, a random order asynchronous update was selected wherein the timescales of each regulatory process were randomly chosen in such a way that the node states were updated in a randomly selected order during each time-step B. bronchiseptica-T. retortaeformis co-infection were not used as inputs to the co-infection model but only to validate the simulated course and intensity of immune responses during co-infection.Our approach of using discrete dynamic modelling allowed us to sample the timescales of interactions and perform replicate simulations as well as provide continuously varying activities of the network nodes over time, which ranged between the lower limit of 0  and upper limit of 1 . However, notice the exception for IFNγ expression higher than one in the helminth infections. While these activities cannot be directly compared to quantitative concentrations, we could compare the qualitative features of the time courses and ask: are they saturating? Do they show single or multiple peaks? We could also compare the relative trends of similar variables. It is important to stress that the empirical data on B. bronchiseptica strain RB50 and T. retortaeformis have been described in detail in Pathak et al. B. bronchiseptica RB50 and T. retortaeformis followed similar procedures. Here, we report a concise description of the experimental design, quantification of the immune variables and parasite intensities.The primary single infections of naïve rabbits with All listed animal procedures were pre-approved by the Institutional Animal Care and Use Committee of The Pennsylvania State University.4B. bronchiseptica RB50 and simultaneously orally challenged with a 5 ml mineral water solution of 5,500 infective third stage T. retortaeformis larvae (L3). Control individuals were treated with 1 ml of PBS or 5 ml of water, respectively. Groups of 6 individuals (4 infected and 2 controls) were euthanized at days 3, 7, 14, 30, 60, 90, 120 post challenge and both the respiratory tract and small intestine were removed to quantify: parasite abundance, cytokine expression in the lungs and small intestine (duodenum) and mucus-specific anti-helminth antibody levels (IgA and IgG) from the duodenum to the ileum (Section SI-1 to SI-4). Blood samples were collected weekly and used for serum-specific antibody quantification against both parasites and leukocyte cells count Out-bred 60 days old New Zealand White male rabbits were intra-nasally inoculated with 1 ml of PBS solution containing 2.5×10A fixed amount of lungs (15 ml), trachea (5 ml) and nasal cavity (15 ml), homogenized in PBS, was serial diluted onto BG blood agar plates supplemented with streptomycin and incubated at 37°C for 48 hours for bacteria quantification  The expression of IFNγ, IL-4 and IL-10 in the lung and duodenum was determined using Taqman qRT- PCR. RNA isolation, reverse transcription and qRT-PCR quantification were performed following protocols we have developed Antibody detection: Antibody IgA and IgG against B. bronchiseptica and adult T. retortaeformis were quantified using Enzyme-Linked Immunosorbance Assay (ELISA) B. bronchiseptica serum dilutions were: 1∶10 for IgA and 1∶10,000 for IgG, secondary detection antibody: IgA 1∶5,000 and IgG 1∶10,000. For T. retortaeformis mucus dilution was: 1∶10 both for IgA and IgG and 1∶5,000 for the secondary antibody. We found cross-reactivity at the antibody level between the somatic third stage infective larvae (L3) and the adults both in the serum and the mucus Blood in anti-coagulated EDTA tubes was processed using the Hemavet 3 haematology system  and the general haematological profile quantified Xij* = Xij-Xc, where Xij is an immune variable for individual i at time j and Xc is the total average of the controls across the infection for that variable.Linear mixed effect models (LME-REML) were applied to identify changes in the immune variables during the course of the co-infection and between single and co-infection. The individual identification code (ID) was included as a random effect and an autoregressive function of order 1 (AR-1) was integrated to take into account the non-independent sampling of the same individual through time or the monitoring of different parts of the same organ from the same individual. To identify the combination of immunological variables that mainly affected parasite abundance a principal component analysis  was used Table S1B. bronchiseptica abundance (CFU/g) and immune variables from the co-infection experiment. A- Summary of the Principal Component Analysis (PCA) based on the most representative immune variables; only the first two PCA axes are reported. Note that the cytokine Ct values are inversely related to the level of expression. B- Summary of the generalized linear model (GLM) between bacteria abundance and PCA axis 1 and axis 2.Relationship between (DOC)Click here for additional data file.Table S2T. retortaeformis abundance (worm/duodenum length) and immune variables from the co-infection experiment. A- Summary of the Principal Component Analysis (PCA) based on the most representative immune variables; only the first two PCA axes are reported. Note that the cytokine Ct values are inversely related to the level of expression. B- Summary of the generalized linear model (GLM) between helminth abundance and PCA axis 1 and axis 2.Relationship between (DOC)Click here for additional data file.Text S1A- Single B. bronchiseptica infection; B- single T. retortaeformis infection; C-B. bronchiseptica-T. retortaeformis co-infection. In the functions we depict the nodes in the intestine with the suffix ‘t’ and the nodes in the lungs with the suffix ‘b’. Abbreviations: Oag: O-antigen; IL4II: Interleukin 4 in systemic compartment; DNE: Dead neutrophils; NE: Recruited neutrophils; IL12I: Interleukin 12 in lungs/intestine; IgA: Antibody A; C: Complement; TrII: T regulatory cells in systemic compartment; IL4I: Interleukin 4 in lungs/small intestine; Th2II: Th2 cells in systemic compartment; TrI: T regulatory cells in lungs/small intestine; Th2I: Th2 cells in lungs/small intestine; IL10II: Interleukin 10 in systemic compartment; TTSSII: Type three secretion system in systemic compartment; TTSSI: Type three secretion system in lungs; IgG: Antibody G; IgE: Antibody E; IL10I: Interleukin 10 in lungs/small intestine; IFNγII: Interferon gamma in systemic compartment; IFNγI: Interferon gamma in lungs/small intestine; IL12II: Interleukin 12 in systemic compartment; BC: B cells; DCII: Dendritic cells in systemic compartment; DCI: Dendritic cells in lungs/small intestine; Th1I: T helper cells subtype I in lungs/small intestine; PIC: Pro-inflammatory cytokines; Th1II: T helper cells subtype I in systemic compartment EC: Epithelial cells lungs/intestine; AP: Activated phagocytes; T0: Naïve T cells; AgAb: Antigen-antibody complexes; MP: Macrophages in lungs; EL2: recruited eosinophils; EL: resident eosinophils; IL13: Interleukin 13; IL5: Interleukin 5; TEL: total eosinophils; TNE: total neutrophils; TR: T. retortaeformis, Bb: B. bronchisepticaDNE: dead neutrophils; IS: T. retortaeformis Larvae; AD: T. retortaeformis Adults; PH: Phagocytosis.Transfer functions for every node of each network: (DOC)Click here for additional data file.


Mother-to-child transmission (MTCT) is responsible for most pediatric HIV-1 infections worldwide. It can occur during pregnancy, labor, or breastfeeding. Numerous studies have used coalescent and molecular clock methods to understand the epidemic history of HIV-1, but the timing of vertical transmission has not been studied using these methods. Taking advantage of the constant accumulation of HIV genetic variation over time and using longitudinally sampled viral sequences, we used a coalescent approach to investigate the timing of MTCT.env sequences from the RNA and DNA viral population were longitudinally sampled from nine HIV-1 infected mother-and-child pairs [range: 277–1034 days]. For each transmission pair, timing of MTCT was determined using a coalescent-based model within a Bayesian statistical framework. Results were compared with available estimates of MTCT timing obtained with the classic biomedical approach based on serial HIV DNA detection by PCR assays.Six-hundred and twenty-two clonal Four children were infected during pregnancy, whereas the remaining five children were infected at time of delivery. For eight out of nine pairs, results were consistent with the transmission periods assessed by standard PCR-based assay. The discordance in the remaining case was likely confused by co-infection, with simultaneous introduction of multiple maternal viral variants at the time of delivery.The study provided the opportunity to validate the Bayesian coalescent approach that determines the timing of MTCT of HIV-1. It illustrates the power of population genetics approaches to reliably estimate the timing of transmission events and deepens our knowledge about the dynamics of viral evolution in HIV-infected children, accounting for the complexity of multiple transmission events. HIV-1 still infects about two million children worldwide, with 330,000 new infections reported in children in 2011 . Without preventive intervention, probability of MTCT is 15–30% in high-income countries and up to 25–40% in sub-Saharan Africa, mainly due to differences in infant feeding practices Application of various phylogenetic approaches to HIV-1 has led to reconstruction of HIV-1 evolutionary history within and between hosts with good precision in utero transmission events env sequences from nine mother-child pairs, we compared these quantitative results with the classic biomedical definition based on serial PCR that more qualitatively discriminates between transmission periods (i.e. in utero vs. intrapartum).Aside from the descriptions of rare early The initial protocol and its amendments were approved by the ethics committees of the Thai Ministry of Public Health, Chiang Mai University, and the Harvard School of Public Health. All study sites complied with regulations of the Department of Health and Human Services for the protection of research subjects. Women were enrolled at 28 weeks' gestation if they provided written informed consent.in utero if the first test (using blood collected within 48 hours of birth) was positive and intrapartum if the first test result was negative but the following test results were positive env gene sequences were obtained from the mothers at delivery and from sequential samples collected at different times in their babies starting from the first positive PCR result up to several months or several years after birth (env genes were done as previously described env gp120 gene (from upstream V1 to downstream V5) was amplified by nested polymerase chain reaction (PCR) or PCR following reverse transcription (RT-PCR) using subtype-specific primers Nine mother-infant pairs, enrolled in the Perinatal HIV Prevention Trial-1 (PHPT-1) in Thailand were studied er birth . ExtractSequences were aligned with the MAFFT v7.13 software Finally, to assess the impact of putative recombination on our phylogenetic analyses, we applied the method of Genetic Algorithm implemented in GARD burn in period). Subsequently, the chain was regularly sampled to get an average estimate of parameter values and associated uncertainty [95% highest posterior density (HPD)]. Maximum clade credibility tree was selected with the software TREEANNOTATOR v1.7.4, after a 10% burn in. Trees were visualized in FIGTREE v.1.3.1.We used the BEAST package v1.7 in utero transmission), whereas five infants were infected at delivery or perinataly   .range = 2–8) with a mean follow-up period of 664 days  and a mean of 69 clones for each mother-infant pair (range: 29–101). Among them, 61.4% were issued from cellular DNA, and 38.6% were issued from plasma viral RNA  mother-child pair, (ii) mother vs infant within each pair, (iii) the temporal stratification of the data, and (iv) the DNA or RNA origin of the sequences . The genetic variability between viral sequences (diversity) was explained by order of importance by:  a poor phylogenetic resolution or (ii) transmission of several maternal variants. According to this second hypothesis, the TMRCA estimate would trace back to some point during the course of the maternal viral evolution. This kind of pattern would then provide support for multiple transmission variants that may have occurred during a single event (co-infection), or as several successive events (super-infections). Biological data obtained from the reference diagnostic PCR-based procedure may help to distinguish between these possibilities. Infection of infant 0939, which occurred perinatally, might be related to a single transmission event. Indeed, transmission of multiple variants during this time-limited period is more likely to have occurred during a single event (co-infection) rather than successive events (superinfections).As expected, the phylogeny of the entire data set  showed win utero, might have resulted from either a co-infection by several variants during a simple event or super-infections due to several sequential events  . This fiHIV-1 pediatric infection is a challenging health issue worldwide, and a primary goal of HIV-1 prevention programs is to limit acquisition of HIV-1 during mother-to-child transmission The maternal and infant viral populations within each pair were significantly differentiated, thus reflecting a drastic bottleneck associated with transmission in utero and four intrapartum infected infants. These results validate the time-scaled Bayesian approach to date MTCT of HIV-1. The single discordant case concerned a pair for which TMRCA was not consistent with the biomedical diagnosis that argued for intrapartum transmission. Transmission of multiple maternal variants was strongly suggested by the phylogenetic analysis of this mother-infant pair. The introduction of several variants in a single transmission event or closely related events could lead to large uncertainty in the estimated timing of transmission, leading the ancestry among viral lineages to trace further back in time in the mother's population. However, it remains difficult to distinguish between this hypothesis supported by the PCR results and a possible misclassification by the standard diagnostic assay.Transmission time estimates using a time-scaled Bayesian phylogenetic approach were shown to be reliable. Despite the short time scales involved (from 277 to 1034 days depending of the mother-infant pair), they were qualitatively highly consistent with the reference diagnostic procedures based on timing of HIV-1 proviral DNA detection. In eight of nine cases, our inferences of the timing of transmission corroborated the PCR results, including four intrapartum infected children. Interestingly, a wide confidence interval was observed for two infants , considered to have been infected in utero according to the PCR results. This uncertainty might be explained by both the transmission of several variants for the two cases and a low number of available sequences for pair 1110.The observed TMRCA variance largely varied among the pairs and could be partially linked to the time of transmission; the more recent the transmission, the more precise the estimate. Hence, TMRCA estimates were consistent (corresponding to a greater coefficient of variance) for five pairs, particularly for the in utero) or during pregnancy and then at delivery (in utero and intrapartum). Discrimination between these two possibilities remains a challenging objective. Unlike phylogenetic methods that follow a lineage backwards to coalescence, probabilistic models were implemented to detect if transmission occurred because of one or several variants We must consider that the transmission of several variants might be attributed either to introduction of several maternal variants in a single transmission event (co-infection) or to several successive introductions of maternal variants (super-infection) that could occur during pregnancy  did not play a major role on the observed molecular variance. Additionally, the use of a relaxed clock model partially accommodates for the potential different evolution rate of sequences present in cellular DNA and plasma viral RNA. The reappearance of archived viral population must be considered This study has benefited from the temporal anchoring of HIV-1 sequences to infer transmission timing and evolutionary rates in mother-infant pairs. Thanks to the validation based on the determination of the transmission period through the reference diagnostic procedure, our study offered a unique opportunity to validate the Bayesian coalescent framework on documented cases. It confirms that time series of viral sequences can provide powerful insights to draw inference about transmission timing. Such theoretical approaches could also provide insight into multiple infections processes (i.e. coinfection or superinfection) not readily detectable with the current diagnostic assays.Table S1Bayes factor of the strict molecular clock (M1) and the uncorrelated lognormal clock (M0) models for each pair. Bayesian Analysis were conducted under the best-fitting set of molecular, coalescent and demographic models as indicated by a Bayes Factor (01B)>20(DOCX)Click here for additional data file.


Two isoforms of the enzyme adenosine kinase (AdK), which differ at their N-terminal ends, are found in mammalian cells. However, there is no information available regarding the unique functional aspects or regulation of these isoforms.2+ and phosphate ions is essential for AdK function. Another mutant, FomR-4, exhibiting increased resistance to only C-adenosine analogs and whose resistance was expressed dominantly in cell-hybrids contained a single mutation leading to Ser191Phe alteration in AdK. We demonstrate that this mutation in AdK is sufficient to confer the novel genetic and biochemical characteristics of this mutant. The unusual genetic and biochemical characteristics of the FomR-4 mutant suggest that AdK in this mutant might be complexed with the enzyme AMP-kinase. Several other AdK mutants were altered in surface residues that likely affect its binding to the adenosine analogs and its interaction with other cellular proteins.We show that the two AdK isoforms differ only in their first exons and the promoter regions; hence they arise via differential splicing of their first exons with the other exons common to both isoforms. The expression of these isoforms also varied greatly in different rat tissues and cell lines with some tissues expressing both isoforms and others expressing only one of the isoforms. To gain insights into cellular functions of these isoforms, mutants resistant to toxic adenosine analogs formycin A and tubercidin were selected from Chinese hamster (CH) cell lines expressing either one or both isoforms. The AdK activity in most of these mutants was reduced to <5% of wild-type cells and they also showed large differences in the expression of the two isoforms. Thus, the genetic alterations in these mutants likely affected both regulatory and structural regions of AdK. We have characterized the molecular alterations in a number of these mutants. One of these mutants lacking AdK activity was affected in the conserved NxxE motif thereby providing evidence that this motif involved in the binding of MgThese AdK mutants provide important insights as well as novel tools for understanding the cellular functions of the two isoforms and their regulation in mammalian cells. Further, as reported in our earlier work -adenosine to [3H]-AMP [3H]-Adenosine (40 Ci/mmol) was purchased from American Radiolabeled Chemicals Inc. For Western blot analysis, 40 μg of cell extracts from different cell lines were electrophoresed on 12% sodium dodecyl sulfate polyacrylamide gels (SDS-PAGE). After transfer to nitrocellulose and blocking, the blot was reacted with 1:1000 dilution of a rabbit polyclonal antibody to human recombinant AdK raised in our lab -AMP ,45. [2,8 our lab . After wsequence . The amp191→F mutation in the CH (or human) cDNA was made using the 'Quickchange' site-directed mutagenesis kit (Stratagene) as described in earlier work [R-4 CHO cells with these plasmid DNAs was carried out using the Lipofectamin-2000 reagent (Invitrogen) as described in earlier work [The full-length AdK cDNA (long isoform) from CH was cloned in the mammalian cell expression vector pcDNA3.1 . The S19ier work ,45. The ier work . Stable XAC and TA carried out selection of various mutants of V79 cells and cross-resistance studies, enzyme activity assay and immunoblot analyses on them. XAC and BS were responsible for examining the tissue distribution of AdK and for molecular characterization of the mutants. BS was responsible for identifying the promoter region for the AdK-S isoform, for creation of site-directed mutants and for transformation studies on them. RSG was responsible for conceiving and directing this study and for writing the manuscript, which has been seen and approved by all authors.


The optimal suction pressure during endobronchial ultrasound guided transbronchial needle aspiration (EBUS-TBNA) remains to be determined. The aim of this study was to compare suction pressures for performance in collecting sufficient tissue specimens from mediastinal and hilar lymph nodes during EBUS-TBNA.Retrospective analysis of consecutive patients with mediastinal and hilar lymphadenopathy who underwent EBUS-TBNA over a 3-year period. Results from patients who underwent EBUS-TBNA using a dedicated 20-mL VacLoc  syringe  were compared with results from patients in whom a disposable 30-mL syringe  was used. The yield for sufficient histologic specimen retrieval and amount of tissue obtained were compared between the 2 groups.p = 0.04). The H group revealed a tissue area approximately twice that of the C group (p = 0.003). There were no major procedure-related complications in either group.Of 178 patients who underwent EBUS-TBNA, 131 had lung cancer confirmed by EBUS-TBNA: 35 in group C and 96 in group H. There were 7 patients in group C and 6 in group H who received final diagnoses by cytology alone. There were 28 in group C and 90 in group H who were diagnosed by both cytology and histology. There was a statistically significant difference between the groups in terms of the rate of sufficient sampling for histological specimens (Higher suction pressures with larger syringe volumes during EBUS-TBNA may be useful for safely collecting sufficient tissue specimens. Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a safe, minimally invasive diagnostic modality with a high yield for the diagnosis of mediastinal and hilar lymphadenopathy. Cytological and histological samples can be obtained by EBUS-TBNA, therefore facilitating comprehensive evaluations such as immunohistochemistry and mutation analysis. EBUS-TBNA can provide the quality and quantity of aspirates important for genetic analysis such as epidermal growth factor receptor (EGFR) mutation or anaplastic lymphoma kinase (ALK) translocation in lung cancer patients –3. The aWe retrospectively examined 178 consecutive patients who underwent EBUS-TBNA for diagnosis of mediastinal and hilar lymphadenopathy at our institute from April 2009 to March 2012. The study was approved by the institutional review board at the Osaka Prefectural Medical Center for Respiratory and Allergic Diseases. Requirement for informed consent was waived by the committee for this retrospective analysis of clinical data and the data were analyzed anonymously. Data were obtained from paper and electronic medical records, and patients were divided into 2 groups according to the volume of suction pressure used for specimen collection during EBUS-TBNA. Patients who underwent EBUS-TBNA using the dedicated 20-mL VacLok syringe were in group C , and those in whom a disposable 30-mL syringe was used were in group H (high pressure). The rates of sufficient histologic specimen retrieval and diagnostic yields were compared between the 2 groups.EBUS-TBNA was performed with a convex probe EBUS  using dedicated 22-gauge needles  in all cases; a bronchoscopist who had been previously trained in EBUS-TBNA techniques on training mannequins and animal models performed the procedure under the supervision of experts. All patients were under moderate sedation with intravenous midazolam during the procedures.Two passes per lymph node were routinely performed in all cases. If these passes did not yield adequate material, only 1 more pass inside the same lymph node was additionally performed. If no histological core was macroscopically available, different lymph node aspiration was performed until up to 3 passes per lymph node. After lymph node puncture, the internal stylet was removed and suction pressure was applied with the syringe. Syringe pressure was applied during each aspiration to a volume of 20 mL in group C  or to approximately 30 mL in group H  . In eachFinally, the needle was retrieved and the internal sheath replaced to push the histologic core specimen from the needle. After completing the puncture, the aspirated material could be smeared onto glass slides. Smears were air-dried and fixed in 95% alcohol for cytology. In cases where core tissues were obtained for histology, the specimens were pushed out of the needle onto a filter paper to absorb excess blood and eventually fixed in formalin for histological analysis. The aspirate remaining in the needle was rinsed into a tube containing normal saline and available for molecular testing if malignancies were confirmed. In our study, histologic specimen included only the tissue core and not the cell block. Rapid, on-site cytological examination (ROSE) was performed in all cases. Histologic cores were stained with hematoxylin and eosin. Immunohistochemistry was also performed if necessary.To assess the usefulness of high suction pressure aspiration during EBUS-TBNA, we distinguished between cases diagnosed only by cytological findings and those diagnosed by both cytological and histological findings with respect to lung cancer. The EBUS-TBNA was regarded as failed in collecting biopsy specimens if nothing was obtained by EBUS-TBNA or if pathologists found submitted specimens insufficient due to the lack of quality and quantity. To compare the amount of histologic tissue between the groups, we scanned the maximum surface area of the histologic samples using hematoxylin and eosin stained slides, counted the number of tissue fragments, and measured the total tissue area obtained with each technique. The measurements were performed using ImageJ software (version 1.47). If EBUS-TBNA did not provide a diagnosis, surgical biopsy or transbronchial biopsy followed. In this study, diagnoses of benign diseases were based on results of clinical follow up of at least 6 months demonstrating reduction in tumor size or a lack of apparent radiologic disease progression.U test and categorical variables were analyzed using Fisher’s exact test. All p values were based on a two-sided hypothesis, and a p value of <0.05 was considered statistically significant.The sensitivity, specificity, and rates of diagnostic accuracy were calculated by standard definitions. All statistical analyses were performed using software R (version 3.0.1). Continuous variables were analyzed using Mann–Whitney There were 178 patients who underwent EBUS-TBNA between April 2009 and March 2012. EBUS-TBNA detected malignancy in 134 patients and benign diseases in 13. The remaining 31 patients had no evidence of disease in specimens and therefore underwent surgical biopsy or transbronchial biopsy after EBUS-TBNA. Final diagnoses were malignant diseases in 143 cases and benign diseases in 31. Four patients were lost to follow up . Lung cap = 1.0, Fisher’s exact test).Lung cancer was detected in 131 of 138 patients by EBUS-TBNA. Seven patients had non-diagnostic EBUS-TBNA specimens, and their lung cancer diagnoses were finally confirmed by surgery or transbronchial lung biopsy. Among the 131 patients with lung cancer diagnosed by EBUS-TBNA, 35 were in group C and 96 were in group H. The patient characteristics are summarized in p = 0.04, Fisher’s exact test). As for the amount of the tissue, there was no significant difference in the number of fragments between the two groups , however, the total tissue area was significantly greater in group H (high pressure group) than in group C . There were no major complications, including infection or severe bleeding, related to the procedures in either group. There were 7 patients in group C  and 6 in group H  who were diagnosed by cytology alone. There were 28 in group C  and 90 in group H  who were given diagnoses by both cytology and histology . There wHere we have compared different volumes of suction pressure during EBUS-TBNA biopsy in terms of sufficient tissue collection, particularly from mediastinal and hilar lymph nodes. EBUS-TBNA has been found to be an accurate and safe diagnostic technique for mediastinal and/or hilar lymphadenopathy and for staging of lung cancer. EBUS-TBNA is a real-time procedure that allows multiple biopsies with high-quality histologic cores –3. It haIn past studies, the sensitivity and rates of diagnostic accuracy of EBUS-TBNA for differentiating malignant and benign disease has been reported from 85 to 93% and 88 to 91%, respectively –7. In thIn this study, the main focus was the rate of sufficient core tissue sampling and not the diagnosis rate. We found that the rate of sufficient histological specimen sampling in group H  was superior to that in group C , and these results suggest that higher aspiration pressures during EBUS-TBNA may be useful for obtaining sufficient histological specimens and assisting with accurate diagnosis, including subclassification of lung cancers, as well as optimal treatment of patients with advanced and recurrent lung cancer. We emphasize that it is very important to obtain sufficient specimens when EBUS-TBNA is performed, not only for diagnosis but for additional studies including immunohistochemistry and genetic analysis, such as epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK). The proper EBUS-TBNA method permits sampling of histologic cores. The quantity of the acquired tumor cell is possibly one of the reasons for the higher rate of pathologic diagnoses in group H; however, further investigation is necessary to explore the most important factor contributing to higher rate of tissue core sampling.Several recent studies comparing EGFR mutation status in primary tumor and local lymph node metastases have suggested a possibility of significant discrepancies between the sites because of tumor heterogeneity, which may be a cause of resistance to treatment with EGFR tyrosine kinase inhibitors –14. TherThere are several limitations of this study. First, it is a retrospective analysis in a single institute and it is not randomized. Second, the EBUS-TBNA procedures were not performed by the same bronchoscopist. In addition, patients in group C were biased around the introduction of EBUS-TBNA at our institute. Prior to the introduction of EBUS-TBNA, we trained bronchoscopists with a demonstration; however, the learning curve of the technique may affect the TBNA sampling rate. Third, the number of patients in this study was too small to definitively evaluate the optimal suction pressures for safe sampling during EBUS-TBNA. In order to do this, prospective trials will be required in the future.


Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcriptional networks composed of transcription factors (TFs) and target genes (TGs). The hormone gastrin activates and stimulates signaling pathways leading to various cellular states through transcriptional programs. Dysregulation of gastrin can result in cancerous tumors, for example. However, the regulatory networks involving gastrin are highly complex, and the roles of most of the components of these networks are unknown. We used time series microarray data of AR42J adenocarcinoma cells treated with gastrin combined with static TF-TG relationships integrated from different sources, and we reconstructed the dynamic activities of TFs using network component analysis (NCA). Based on the peak expression of TGs and activity of TFs, we created active sub-networks at four time ranges after gastrin treatment, namely immediate-early (IE), mid-early (ME), mid-late (ML) and very late (VL). Network analysis revealed that the active sub-networks were topologically different at the early and late time ranges. Gene ontology analysis unveiled that each active sub-network was highly enriched in a particular biological process. Interestingly, network motif patterns were also distinct between the sub-networks. This analysis can be applied to other time series microarray datasets, focusing on smaller sub-networks that are activated in a cascade, allowing better overview of the mechanisms involved at each time range. Helicobacter pylori infection Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms resulting in different cellular states in response to growth factors Gene regulatory networks are highly complex and dynamic, especially the coordinated regulation between transcription factors (TFs) and their target genes (TGs) Understanding cellular functionality by studying its key components, interactions and network topological measures is a common approach in systems biology Saccharomyces cerevisiae and Escherichia coli, a 3-node motif called a feed forward-loop and a four-node motif called a bi-fan are the most common It is well known that transcription networks often contain a small set of recurring regulatory patterns called network motifs. These small networks are frequently found in quantities that are significantly larger than would be expected for random networks In the current study, we combined time series gene expression data and static TF-TG interaction data to study the dynamic features of gastrin-regulated transcriptional networks. We reconstructed the dynamic activities of key TFs using network component analysis (NCA) The gene expression data used in this study were obtained by measuring the response of AR42J adenocarcinoma cells treated with gastrin hormone at 11 time points over a period of 14 hours. We downloaded the data from GEO database (Array Express accession number: GSE32869) We collected the experimentally verified TF-TG regulations from TFacts Active sub-networks for four time ranges were excerpted from static network by incorporating gene expression data and predicted TF activities from NCA. For each time range, active TFs and TGs (which displays higher expression/activity than a threshold) were identified and combined it with static network to define an active sub-network.E] represents the expression values of genes at various time points, the matrix [C] is the control strength of each TF on a target gene (TG), and the matrix [T] represents the activities of all of the TFs. The dimensions of [E], [C] and [T] are N X M , N X L (L is the number of TFs) and L X M, respectively.Network component analysis (NCA) is a computational method for reconstructing hidden regulatory signals (TFs activity) from gene expression data with known connectivity information in terms of matrix decomposition E] into [C] and [T] can be achieved by minimizing the following objective function:Z0 is the initial connectivity pattern. [C] and [T] are estimated using a two-step least-squares algorithm and are normalized through a nonsingular matrix [S] according toBased on above formulation, the decomposition of [To guarantee the uniqueness of the solution for The connectivity matrix [C] must have full-column rankWhen a node in the regulatory layer is removed along with all of the output nodes connected to it, the resulting network must be characterized by a connectivity matrix that still has full-column rankThe [T] matrix must have full row rankUsing NCA as the reconstruction method, we predicted significant TFs and their temporal activity profiles.n, k(n), is the number of edges connected to it. The in-degree and out-degree of a node n is the number of edges with n as their terminal and initial nodes, respectively. The clustering coefficient, nC of a node n with k neighbors is the ratio of the actual number of edges nE between the neighbors to all the possible edges.The degree of a node In the case of directed networks,b(n)C of a node n is defined as follows.The betweenness centrality s and t are nodes in a network different from n, σst denotes the number of shortest paths from s to t, and σst(n) is the number of shortest paths from s to t that n presents in between. The closeness centrality c(n)C of a node n is defined as the reciprocal of the average shortest path length.L is the length of the shortest path between two nodes n and m. The mean values of all the topological metrics of all nodes in a network was computed to create a global view of the network. All computations are performed using the Cytoscape software tool In the case of directed networks,Network motifs are small networks that are present in large complex networks at higher frequencies than in random networks. To understand the regulation patterns, we used the FANMOD tool Gene ontology (GO) analysis aims to capture increasing knowledge of gene functions in a collective manner. We used the ClueGO tool The workflow of our data pre-processing and subsequent analysis is presented in The changes in the expression of differentially expressed genes (DEGs) over the 14-hour period after gastrin stimulation are shown in We used network component analysis (NCA) To understand gastrin regulation in adenocarcinoma cells in terms of quantitative measurements, we computed the number of differentially expressed genes (DEGs) and TFs (computed from NCA) activated at each time point Figure 3File S1).To study the dynamic features of regulatory networks, a static integrated regulatory network was constructed by combining various data resources including Chip-X studies, experiments and predicted data bases. The regulatory TF-TG relationships were extracted from the Transcriptome Browser File S2).The active sub-networks were built based on differential expression of TGs and peak TF activity during a specific time range. We defined four active sub-networks, namely immediate-early (IE), mid-early (ML), mid-late (ML) and very late (VL), as shown in We then examined the structural and modular architecture of the four constructed active sub-networks. This analysis clearly distinguished each active sub-network from the others. The size and topological metrics of the static regulatory network and the four active sub-networks are presented in To corroborate the significance of these topological divergences, we performed the same computations on a set of random networks created from a static network with the same number of nodes as the actual active sub-networks. We generated 100 random networks, and the structural properties were averaged over these 100 networks. The mean and standard deviations are provided in the last row of Transcriptional regulatory networks are made up of small recurring patterns called network motifs. To understand the dynamic functional characteristics of the gastrin network, we performed a network motif analysis for each active sub-network. We identified network motifs with 3-, 4- and 5-nodes in four active sub-networks using the FANMOD tool S10 for complete results). We found four variants of 3-node motifs that were overrepresented in the four active sub-networks. Cliques , cross-regulating TFs co-targeting a TG (type ‘b’), SIMs , and FFLs  are the various types of significantly enriched 3-node motifs. Cliques were most frequently used motif pattern in all active sub-networks, suggesting that formations of three interacting TFs regulatory complexes are involved during the whole response. Network motif types ‘b’ and ‘c’ were significantly missing in the IE network, suggesting that they are not main cause for the early activation of large number of TGs. Cliques and cross-regulating TFs co-targeting a TG (type ‘b’) were previously found in the integrated regulatory networks of Saccharomyces cerevisiaeThe significantly enriched network motifs and their Z-scores in the four active sub-networks are presented in Five types of 5-node motifs were enriched in the constructed networks. MIMs  were the most common regulatory pattern among 5-node motifs. Of these, type ‘b’ was significantly enriched in the VL sub-network and type ‘c’ in the ML network. MIMs are also known to regulate large-scale gene activation  and To identify which and how various biological processes are affected by differentially expressed TGs in each active sub-network, we conducted functional annotations in ClueGO tool in Cytoscape The IE active sub-network is highly enriched in functions involved in cellular differentiation. Many previous studies have confirmed the role of gastrin in differentiation processes The ME active sub-network is enriched in a large number of significant categories involving several metabolic and several types of cancer associated pathways. This sub-network is enriched in energy-related metabolic processes such as carbohydrate, polysaccharide, lipid, organic acid, cellular aromatic compound metabolic processes and generation precursor metabolites and energy processes. This network is overrepresented by cancer pathways such as small cell lung cancer, thyroid cancer, prostate cancer, endometrial cancer and chronic myeloid leukemia. In addition, this network is enriched with response to endoplasmic reticulum (ER) stress, cellular response to stimulus, B cell activation and hemostasis. One of the recent studies confirms the involvement of gastrin in regulating the genes resulting in ER stress The ML network is highly enriched in biological processes related to morphogenesis such as the cell morphogenesis, morphogenesis of an epithelium, embryonic epithelial tube formation, heart morphogenesis. In addition, this network is enriched with regulation of cell migration and cell growth, angiogenesis and activation of Wnt and calcium signaling pathways. Previous studies of gastrin-CCK2R (cholecystokinin-2 receptor) signaling have confirmed some of the processes predicted in this study. Gastrin-CCK2R involvement in the morphogenesis of epithelium cells was found in previous studies. Pagliocca et al. found that stimulation with gastrin promotes branching morphogenesis (process of tubule formation) in gastric AGS cells through the activation of protein kinase C (PKC) The VL sub-network is mainly involved in metabolic processes such as DNA metabolic process, phosphorous metabolic process, cell communication, apoptosis and tube morphogenesis. Gastrin has induced apoptosis in many cells in previous studies Thus, our functional annotation of genes in four active sub-networks revealed several known and new functions of gastrin. In addition, this analysis contributed to identifying the gastrin response from a dynamic perspective.The primary objective of this study was to analyze the time series gene expression data generated by external stimuli to understand the transcriptional regulatory network from a dynamic perspective. To achieve this goal, we integrated information from a static TF-TG network with gene expression data to identify key TFs temporal dynamics. The gene expression and TF activities showed early-, mid-, and late-phase action in response to gastrin. This indicates that gastrin regulates genes over a period of 14 hours, although the majority of the genes were active at 1 and 2 hours and TFs were active 1.5 and 6 hours after gastrin treatment.To more comprehensively understand the mechanisms of transcriptional regulation, we built four active sub-networks at four different time ranges. The active sub-networks defined in this study showed structural differences in their network organization. The ME and VL sub-networks were more strongly interconnected than the others. In addition, we identified key regulatory patterns, called network motifs, in all sub-networks. This analysis showed that distinct network motifs were significantly enriched in each active sub-network. The GO ontology and pathway analysis of active TGs and TFs in each active sub-network revealed interesting facts. Each active sub-network was enriched in unique GO terms/pathways. This shows that gastrin triggers different cellular states through diverse and complex transcription regulation patterns depending on the time of activation. We demonstrated that analyzing time series microarray data through partitioning to smaller temporal sub-networks reveals network properties that are unique for each time range, yet may otherwise be hidden when the whole time range is combined.The development of high-throughput technologies such as microarrays results in large amounts of biological data and demands the rapid development of computational methods and strategies to analyze the data and thus extract biological knowledge. Our current study provides one such strategy for using these data and integrating known biological information to decipher the mechanisms of signaling and transcriptional programs of the biological system.File S1Integrated static network. The integrated static network from different sources used in the analysis of this study is provided as a Cytoscape network file.(ZIP)Click here for additional data file.File S2Active sub-networks. The four active sub-networks extracted at different time ranges in gastrin signaling are provided as a Cytoscape network file.(ZIP)Click here for additional data file.File S3Network motif analysis. The complete results of the 3-node network motif analysis for the IE active sub-network are provided as text file.(OUT)Click here for additional data file.File S4Network motif analysis. The complete results of the 3-node network motif analysis for the ME active sub-network are provided as text file.(OUT)Click here for additional data file.File S5Network motif analysis. The complete results of the 3-node network motif analysis for the ML active sub-network are provided as text file.(OUT)Click here for additional data file.File S6Network motif analysis. The complete results of the 3-node network motif analysis for the VL active sub-network are provided as text file.(OUT)Click here for additional data file.File S7Network motif analysis. The complete results of the 4-node network motif analysis for the IE active sub-network are provided as text file.(OUT)Click here for additional data file.File S8Network motif analysis. The complete results of the 4-node network motif analysis for the ME active sub-network are provided as text file.(OUT)Click here for additional data file.File S9Network motif analysis. The complete results of the 4-node network motif analysis for the ML active sub-network are provided as text file.(OUT)Click here for additional data file.File S10Network motif analysis. The complete results of the 4-node network motif analysis for the VL active sub-network are provided as text file.(OUT)Click here for additional data file.


S/GSK1349572 is a new integrase inhibitor (INI) with in-vitro activity against INI-resistant HIV. Before dosing a large number of subjects in pivotal clinical trials, in-vitro findings were validated in a pilot clinical study (ING112961). At the outset of the study, the degree of susceptibility at which the drug no longer provides benefit was unknown. Planned enrollment for this study was 30 raltegravir (RAL)-resistant patients, incorporating a broad range of in-vitro susceptibility to S/GSK1349572. The clinical team developed novel stopping guidelines to minimise risk to study participants."Unacceptable" response rates were elicited for key numbers of recruited subjects . These rates were translated statistically into a level of evidence for unacceptable efficacy, measured as a likelihood ratio (LR). The LR defined a stopping boundary. This boundary was defined for every assessment of response and was tested after each patient's results were observed.LR stopping guidelines are shown in Table At Day 11, 21/27 (78%) of subjects with resistance to raltegravir and elvitegravir showed a virologic response. However, five of the six failures were from the nine subjects enrolled with decreased susceptibility to GSK1349572. The failure rate in this group (5/9) met the definition of "strong evidence of non-response" according to the LR thresholds and so enrollment into this group was halted early.Monitoring response in a patient enables best treatment for that individual. In trials of new treatments, determining the best option for the next patient requires interpretation of accumulating data in population-level monitoring. This challenging task requires a combination of clinical insight and formal quantification of evidence. This case study shows that likelihood ratio thresholds define "strong evidence" of non-response and are suitable for constant monitoring. This approach enables additional safety checks for the best treatment for subjects when a new treatment has accumulated only limited data.


Taken together, these findings indicate that the HCMV pentamer complex is a major target of the antibody-mediated maternal immunity.Primary human cytomegalovirus (HCMV) infections during pregnancy are associated with a high risk of virus transmission to the fetus. To identify correlates of intrauterine HCMV transmission, serial serum samples from HCMV transmitter and non-transmitter pregnant women with primary HCMV infection were analyzed for the presence of neutralizing antibodies against different glycoproteins and glycoprotein complexes, which are known to mediate entry into distinct types of host cells. Neutralizing activity was detected in the sera early after primary infection; absorption with a soluble pentameric complex formed by gH/gL/pUL128-131, but not with gH/gL dimer or with gB, abolished the capacity of sera to neutralize infection of epithelial cells. Importantly, an early, high antibody response to pentamer antigenic sites was associated with a significantly reduced risk of HCMV transmission to the fetus. This association is consistent with the high  Pregnant women with primary infection were identified among women referred to our Institution for suspected HCMV infection in pregnancy by using a battery of tests, some of which were commercially available and some developed in the laboratory. In detail, HCMV-specific IgG and IgM were determined by ETI-CYTOK-G and ETI-CYTOK-M , respectively. Presence of virus-specific IgM was confirmed by an in-house developed confirmatory ELA assay Primary HCMV infection was diagnosed based on the presence of at least two of the following four criteria: HCMV-specific IgG seroconversion, presence of virus-specificc IgM antibody, low IgG avidity index (AI), and DNAemia HCMV transmission was diagnosed either antenatally by detection of viral DNA in and virus isolation from amniotic fluid samples or by virus isolation from urine samples collected within the first 2 weeks of life.Of the 43 pregnant women, 19 transmitted and 22 did not transmit the infection to the fetus. Regarding the two remaining women, for one, transmission was unknown due to pregnancy termination, while the second had a delayed transmission to the fetus (amniotic fluid was negative but the newborn was infected at birth). Among the 43 pregnant women, 23 were selected  for the study of serum antibody titers against HCMV glycoprotein complexes and site-specific reactivity, based on the availability of multiple sequential serum samples, with the first available sample drawn within 60 days after onset of infection and available data sufficient to estimate the date of infection onset with a high probability. Timing of infection onset was achieved in 21/23 of these women based on the presence of clinical signs/symptoms associated to serologic data. In the remaining 2 women, in whom signs/symptoms were not observed, onset of infection was approximately assigned to a time point in the middle of the 1-month interval between the first positive and the last negative HCMV-specific IgG serum sample.Pfu turbo on cDNA of VR1814-infected MRC-9 cells, using primers introducing the desired restriction sites As previously reported, intronless, full-length UL128, UL130, UL131, gH, gL, gO, and gB were cloned into pcDNA3 vectors (Invitrogen) by PCR with Constructs were used to transfect HEK293F cells (Invitrogen) with DNA and polyethyleneimine MAX (Polysciences) premixed in Opti-PRO SFM medium (Invitrogen). After 10 days culture the supernatant was harvested, and the presence of the proper HCMV glycoprotein complex was verified by ELISA using human mAbs specific for the different neutralization sites of the complex Half-area 96-well polystyrene plates (Corning) were coated overnight with an in-house developed murine anti-gH mAb (mH1P73), or an anti-gB mAb  and blocked with 5% skimmed milk in PBS, as recently reported In order to equalize the amount of pentamer and gH/gL dimer bound to the solid phase and assuming that the different components of the pentamer were stoichiometrically represented at a ratio of 1∶1:1∶1∶1, the two preparations were tested in a capture ELISA system and their dilutions were normalized in order to obtain equal OD values when tested with an anti-gH primary mAb. Subsequently, 23 HCMV-seronegative and 10 HCMV-seropositive healthy blood donors were tested in a capture ELISA system to establish a cut-off indicating the reactivity of human sera with the pentamer or the dimer gH/gL. The mean value +2SD gave a cut-off of 0.10 (OD) for both the pentamer and gH/gL and a cut-off of 0.20 for gB antibodies.50 titer, a dose-response curve plotting log10 serum dilution vs % inhibition was constructed, from which the serum dilution corresponding to the IMAB50 titer was interpolated. By using this curve, it was possible to interpolate positivities between 1∶1 (undiluted serum) and 1∶5 (first tested serum dilution).The study of the reactivity of sequential human sera (from 11 non-transmitter and 12 transmitter pregnant women with primary HCMV infection) with previously identified neutralization sites of glycoproteins forming the pentamer complex was performed by using a competitive binding assay g for 30 min. After 48 h incubation, cells were fixed and stained for HCMV p72 using a pool of murine mAbs Serial dilutions of heat-inactivated human sera were incubated in duplicate for 60 min at 37°C with an equal virus volume containing 100 infectious units of VR1814 Using serial dilutions of human mAbs or human sera from patients with primary HCMV infection, PFI in ARPE-19 cells as well as LTI from HCMV-infected HUVEC was investigated, as previously reported g at RT, 96-well microplate cell cultures were washed and supplemented with medium containing serial dilutions of mAbs or serum for 120 h. Cells were then fixed and stained by using a p72-specific mAb pool PFI was investigated as follows. Following virus absorption (50 pfu) by centrifugation for 30 min at 700×LTI experiments were performed in 24-well microplates by incubating human mAbs for 2 h at 37°C with monolayers of VR1814-infected HUVEC (96 h pi) prior to and overnight during co-cultivation with leukocytes 50 and LTI50 titers, a dose-response curve plotting % inhibition vs mAb concentration (or serum dilution) was constructed, from which the antibody dilution corresponding to PFI50 or LTI50 titer was interpolated.To calculate PFI50 titers). The curves were compared by the extra-sum-of square F test (GraphPad Prism 5.0 software). The relative time to appearance of sera reactivity to the different neutralization sites was analyzed by the Kaplan-Meyer method and compared by the log-rank test. IMAB50 titers to the different neutralization sites, numbers of pentamer epitopes recognized and HCMV DNA load were compared by the Mann-Whitney U-test, whereas the proportion of patients with HCMV DNA in blood was compared by the Fisher’s exact test. Linear regression was used to calculate the correlation between log10 neutralizing and ELISA IgG antibody titers.Non-linear regression models were used to express the kinetics of different serological parameters . As shown in 10 . As tota10 .We then investigated the existence of a correlation between anti-pentamer ELISA-IgG antibodies and titers of serum antibodies able to neutralize epithelial cell infection. As shown in To investigate the relative contribution of antibodies with different specificities to viral neutralization, we absorbed pentamer-, gH/gL- or gB-specific antibodies from the sera of 3 patients by adding the relevant purified antigens or influenza hemagglutin (HA) as a control. Depletion was specific and efficient (greater than 90%) as shown by specific ELISA . Strikinvs undetectable) and second (median 42 DNA copies/ml blood vs undetectable) time interval (≤30 days and 31–60 days after onset), whereas no difference was observed between the two groups in the >60 day interval (We next compared the kinetics of serum antibodies in 11 women who did not transmit the virus to the fetus (non-transmitter) and 12 women who transmitted the virus (transmitter), as reported in interval .Taken together, these results indicate that non-transmitter mothers are characterized by a more rapid IgG antibody response to the pentameric complex and the gH/gL dimer and that this early antibody response correlates with more rapid control of viral infection.We have previously isolated from human memory B cells a panel of neutralizing monoclonal antibodies that target distinct sites on the pentameric complex The IMAB assay was performed on serum samples from 23 pregnant women . When the cumulative kinetics of the antibody response to all neutralization sites was examined, it was found that the antibody response to pUL128 (site 1) appeared significantly earlier (P<0.01) than those to the other pentamer antigenic sites, whereas antibodies to site 4 (anti-pUL130-131), and sites 9 and 10 (anti-gH) were detected significantly later (P<0.05) as compared to antibodies to sites 2, 3, and 5 to 8 . These f50 titers for the 10 antigenic sites in the two groups of transmitter and non-transmitter women examined according to the three time intervals  the IMAB50 titer was significantly lower in the group of transmitters as compared to the group of non-transmitters for most of the sites investigated . In addition, IMAB50 titers to site 1 were significantly higher in non-transmitters in the ≤30 days time interval (50 titers to the other sites remained significantly lower in sera from T women (data not shown).We next compared the IMABinterval , and antinterval  remainedvs 4/8 among non-transmitter women, P = 0.07) and second  month after onset  was observed between the two groups in the >60 day interval  of HCMV in ARPE-19 cell monolayers.Lastly, we tested mAbs of defined specificity and serum samples from transmitter and non-transmitter women for their capacity to inhibit cell-to-cell spread (PFIin vitro the inhibitory effect of human mAbs on the transfer of virus from HUVEC to leukocytes (LTI50) 50 was significantly higher in non-transmitter women during the first month after onset and close to significance (P = 0.06) in the second month after onset  during the first two months after onset of infection there was a significant correlation between the titers of serum IgG antibodies to the pentamer and neutralizing titers determined in epithelial cells. The reduced correlation observed in the subsequent period may be due to the appearance of pentamer-specific antibodies without neutralizing activity, as well as to the appearance of neutralizing antibodies with different specificity. On the other hand, the lack of correlation between gB-specific antibodies and control of HCMV transmission to the fetus may be due to the fact that the great majority of antibodies elicited by gB do not neutralize HCMV infection The important role of antibodies directed against UL128-131 locus gene products is also supported by the following findings: i) the absorption of sera with recombinant soluble pentamer strongly reduced serum neutralizing activity, while absorption with gH/gL or gB had minimal effects, consistent with data obtained using hyper-immune globulin preparations Conventional ELISA measurement of sera reactivity with crude viral antigens does not discriminate between antibodies playing a primary role in antiviral immunity  and antibodies reactive with non-functional sites. In this study, we used the IMAB assay that allowed us to study the kinetics of antibodies directed against 10 neutralization sites on the pentameric complex that were previously identified by a panel of human mAbs in vivo is associated with viral spread by infected polymorphonuclear leukocytes and monocytes in vivo protection was studied by investigating the LTI50 activity  of human mAbs. In this case, all mAbs directed to the pentamer complex were able to block transfer from HUVEC to leukocytes. Thus, the pUL128-131 gene products appear to be involved in the mechanism of virus transfer to leukocytes and virus dissemination Neutralizing antibodies are thought to act primarily by preventing cell-free virus from infecting susceptible cells. However, the main route of HCMV dissemination EMRA phenotype have been identified as two other risk factors predictive of virus transmission from mothers to fetus during pregnancy (7–10). During primary HCMV infection, the appearance in peripheral blood of T cells able to proliferate in vitro in response to a recall HCMV stimulus, as well as the development of TEMRA cells were found to correlate with clearance of virus from blood vice versa.The delayed development in transmitter women of HCMV-specific T-cells with proliferative potential and the TAdministration of human hyperimmune globulin (Ig) in pregnant women with primary HCMV infection has been claimed as useful for both prevention and therapy of congenital HCMV infection In conclusion, the finding that an earlier antibody response to the neutralizing sites of the pentamer in non-transmitter mothers was associated with a reduced rate of HCMV vertical transmission indicates that human anti-pentamer neutralizing mAbs could be used for passive immunization of pregnant HCMV-seronegative women and that the HCMV pentamer complex is a favourable candidate for the development of an effective vaccine against HCMV.Figure S1Kinetics of IgG antibodies to glycoprotein complexes. (A-C). Kinetics of appearance of anti-gB (A), anti-gH/gL (B) and anti-pentamer (C) IgG antibodies in 11 transmitter  and 12 non-transmitter  mothers. Individual values and non-linear regression curves in the two groups of patients are shown. P-value was calculated using the extra-sum-of square F test.(TIF)Click here for additional data file.Figure S250 titer on sequential sera from primary HCMV infection.Determination of IMAB Inhibition of a human mAb anti-pUL128-131  binding by three sequential human sera collected from a pregnant woman with primary HCMV infection at different time points post-infection.(TIF)Click here for additional data file.