Cell signaling dynamics and transcriptional regulatory activities are variable within specific cell types responding to an identical stimulus. In addition to studying the network interactions, there is much interest in utilizing single cell scale data to elucidate the non-random aspects of the variability involved in cellular decision making. Previous studies have considered the information transfer between the signaling and transcriptional domains based on an instantaneous relationship between the molecular activities. These studies predict a limited binary on/off encoding mechanism which underestimates the complexity of biological information processing, and hence the utility of single cell resolution data. Here we pursue a novel strategy that reformulates the information transfer problem as involving dynamic features of signaling rather than molecular abundances. We pursue a computational approach to test if and how the transcriptional regulatory activity patterns can be informative of the temporal history of signaling. Our analysis reveals (1) the dynamic features of signaling that significantly alter transcriptional regulatory patterns (encoding), and (2) the temporal history of signaling that can be inferred from single cell scale snapshots of transcriptional activity (decoding). Immediate early gene expression patterns were informative of signaling peak retention kinetics, whereas transcription factor activity patterns were informative of activation and deactivation kinetics of signaling. Moreover, the information processing aspects varied across the network, with each component encoding a selective subset of the dynamic signaling features. We developed novel sensitivity and information transfer maps to unravel the dynamic multiplexing of signaling features at each of these network components. Unsupervised clustering of the maps revealed two groups that aligned with network motifs distinguished by transcriptional feedforward vs feedback interactions. Our new computational methodology impacts the single cell scale experiments by identifying downstream snapshot measures required for inferring specific dynamical features of upstream signals involved in the regulation of cellular responses. Single cell studies have shown that differential patterns in the dynamics of signaling proteins, transcription factor activity, gene expression, etc. produce distinct downstream outcomes. The opposite also holds true where particular cellular outcomes have been found to be associated with the dynamical pattern of one or more signaling molecules. Signaling pathways, therefore, serve as signal processing units to inform specific downstream regulation. However, the functional capabilities of the dynamic aspects of signaling are not well understood. To address this issue, we developed a new approach that evaluates information processing between dynamic features in signaling patterns and transcriptional regulatory activity. Our work demonstrates that the information transfer occur through decoding of temporal history of signals rather than only through instantaneous correlations. Moreover, our results identify regulatory network motifs as the critical components in the information processing and filtering of variability in signaling dynamics to produce distinct patterns of downstream transcriptional responses. Our methodology can be broadly applied to single cell scale data on experimentally accessible downstream measures to infer dynamic aspects of upstream signaling. Cells continuously sense a variety of physical and chemical signals and respond suitably to changes in their environment –3. DepenMounting evidence from single cell microscopy , flow cyWe test our hypothesis through analysis of an integrated model of signaling dynamics and gene regulatory networks. Both gene regulatory networks and communication networks have a similar underlying operating principle , 35. As The goal of this manuscript is to assess how variation in dynamic signaling pattern influences downstream regulation at the single cell level. Identifying the mechanism of how a specific downstream phenotype could be triggered by different combinations of kinetic modulation in an upstream signaling pattern is critical to our understanding of the cellular decision making process. Here we employ a mechanistic ODE based model , that isOur results revealed that while signaling dynamics have many potential functional information encoding features, transcriptional regulatory network motifs play a significant role in limiting which features are significant at different phases of the stimulation. Moreover, estimating the information transferred by each signaling feature showed that in the majority of cases a single feature is insufficient to lead to a binary transcriptional decision . In this light, decision tree analysis demonstrated that a combination of signaling features constitutes a feature based modulation code involving crosstalk in order to drive the cellular decision making. Given extensive single cell variability, our methodology is of practical utility to experimentalists who use single cell resolution data and are typically confronted with the variable aspects of the cellular activities , 45. By An integrated signaling dynamics and gene regulatory network model  was emplA phenomenological model for dynamics of a transient signaling kinase was developed to characterize independent time-invariant signaling features based on dynamical phases of delay, activation, peak retention and deactivation kinetics  section.A combination of three distinct dynamical profiles for each signaling kinase ERK, FRK and JNK, respectively, was selected at random and simulations were performed for each of these combinations. A total of 100,000 simulations were conducted. The response of immediate early genes (IEGs) and transcription factors (TFs) resulting from these simulations revealed that variability in dynamical features propagates downstream as signal progresses in the network (gray regions in Downstream response to receptor stimulation was divided into three phases: (1) Early phase where cells predominantly utilized the steady state TF proteins as well as triggered new IEG expression, (2) Intermediate phase where cells utilized new proteins transferred from IEG expression to modulate AP-1 TF composition and levels, and when the heterodimer ppc-FOS:ppc-JUN was the dominant contributor to AP-1 aggregate , 48, andα1J), delay in ERK activation (α1E), duration and time-constant of FRK activation  (α1J). The only common feature that influenced Total AP-1 as well as the hetero/homodimers was α1J. This is consistent with the expectation based on network topology as c-JUN production is necessary to produce both the homodimer as well as the heterodimer. However, the remainder of the key features controlling Total AP-1 were also found to be key in controlling of the heterodimer response pattern. α1E showed a brief impact on heterodimer and AP-1 at around 10 minutes following the initiation of signaling dynamics  . Heteroddynamics . Howeverα2F during intermediate phase, but then became increasingly sensitive to τ1J during the late phase following the receptor stimulation . JNK features (α1J and τ1J), which significantly influenced homodimer activity patterns, were also reflective in the critical features affecting the Total AP-1 response pattern during the intermediate phase. Importantly, the intermediate phase (20–40 minutes) revealed that the cellular information processing at the level of TFs involve signaling history, as indicated by the sensitivity of Total AP-1 as well as of the heterodimer to the dynamic signaling feature α2F. α2F represents activation phase of FRK from 0–18 minutes, which precedes intermediate response phase. Similar behavior was also observed in additional critical features to homodimer . In contrast, the most critical feature influencing heterodimer during the late phase showed that the kinetics of FRK deactivation dynamics (τ3F) were the key feature controlling the late heterodimer response pattern, thus revealing instantaneous information transfer between the signal and downstream response.Total AP-1 was most sensitive to τ1J) that preceded not just the previous phase, but two phases earlier to that of the late phase (i.e. early phase), revealing information transfer via history of signaling activity.In the late response phase, Total AP-1 and the homodimer were sensitive to the same set of dynamic signaling features. This was expected, considering the dominance of homodimer in the composition of AP-1 during the late phase. However, the dynamical feature with the highest influence over the late phase of AP-1 dynamics was not instantaneously linked, but was a feature (α1J) sensitivity pattern showed biphasic behavior, being critical in shaping the AP-1 during the very early phase, and then during the late phase (we explain this phenomena as the property of the regulatory network motifs in the next section). Interestingly, the first order sensitive pattern of α1J and τ1J that influenced homodimer activity pattern crossed each other during the intermediate phase, as homodimer took over the heterodimer as the major contributor to Total AP-1.The dynamics of several critical features to Total AP-1, shown in Transcription regulatory pathways contain a small set of recurring regulation patterns, called network motifs that are critical in processing of signaling information and in shaping of the response pattern. Our network contains two such recurring network motifs : (1) FRKα2F, τ2F, α3F and τ3F in these phases). Since FRK was deactivating during the late phase, the kinetics of FRK deactivation (τ3F) was likely to be decoded at the level of the heterodimer. We test this possibility via estimation of mutual information as described below and illustrated in The ERK/FRK module was connected to activation of downstream TF ppc-FOS:ppc-JUN (heterodimer). The module contained a coherent feedforward loop  in whichτ1J, as the most dominant feature during the late phase of the homodimer response, suggesting that history of the signaling dynamics was important in regulation of the transcriptional response. It further implicated that the downstream outcome was not only reflective of instantaneous observational change, but can be equally representative of the dynamical history of the upstream signaling activity.In contrast to the feedforward module, the positive feedback module played a complementary role in maintaining the sustained dynamics of the Total AP-1. JNK dynamics activated as well as maintained phosphorylated c-JUN levels hence exhibiting a dual role in the regulation of the homodimer. As mentioned earlier, the slow rate of c-Jun expression resulted in the low rate of homodimer production during the early phase, but the dual effect of JNK activation generated a monotonic increase by localizing the dimer during the intermediate as well as the late phases, resulting in eventual dominance over the heterodimer activity levels after around 40 minutes. An ultrasensitive effect was observed where a very small amount of c-JUN protein in early phase resulted in a dramatic increase in the activity levels of the homodimer during the late phase. Therefore, the late phase dynamics of the homodimer downstream was determined by early phase of the JNK dynamics. The feature based sensitivity analysis revealed this non-intuitive result by quantitatively identifying kinetics of the JNK activation α2F and τ1F transferred nearly equal amount of information (∼ 0.4 bits) to both heterodimer as well as to AP-1. In contrast, at 40 minutes (intermediate phase) and 60 minutes (late phase) the amount of information transferred by activation time-constant of JNK (τ1J) to AP-1 was the highest . However, τ1J carried ∼ 0.7 and ∼ 0.5 bits of information to homodimer at 40 and 60 minutes, respectively, a complete reversal to the pattern observed in the Total AP-1. Importantly, the time-constant feature of FRK (τ3F) carried ∼ 0.4 bits at 40 minutes and ∼ 1.2 bits at 60 minutes, suggesting that the network has the capacity to decode this feature to make binary decisions at the later phase of the dynamic response. In contrast, the FRK time-constant τ3F carried less than 0.1 bit of information to Total AP-1. The signaling features of ERK had minimal information transfer to the TFs, in consistent with the network topology, as the effect of changes in ERK on the TFs is indirect and conditional on the dynamic features of FRK and JNK signaling.Estimating the lower bound of the Mutual Information (MI) between a signaling feature and downstream TF response revealed that majority of the information carried by individual features to TFs were less than one bit See . In thisWe extended our results on global sensitivity analysis and information transfer to study whether the variable responses at the single cell level could be analyzed for inferring the dynamic features of upstream signaling. We considered a typical case involving snapshot measures from single cells, e.g., gene expression levels, or immunocytochemistry data on activated TF protein levels . Decisioτ1J, τ2J and τ3J) as primarily yielding downstream phenotypes (τ1J < 5.3) and very slow peak deactivation of JNK (τ2J > = 25) were highly likely to drive cells to a High AP-1 phenotype, even in presence of variations in other kinase signaling features. In contrast, slow activation of JNK (τ1J >= 5.3), fast peak-deactivation of JNK (τ2J < 16), and fast deactivation of JNK (τ3J < 22) were very likely to drive cells to Low AP-1 phenotype. We assessed the decision tree analysis results in an alternative visualization layout considering the top two dominant features from the decision trees in a scatter plot, and mapped the rules as well as the phenotype categories to the plot to observe how these features separated cells with a specific phenotype  during the early phase, and time-constant of peak retention of ERK (τ2E) in later phases were the dominant signaling features that shaped c-Fos expression (α1J) during the early phase, and time-constant of JNK activation (τ1J) and JNK peak retention (τ2J) during the intermediate and late phases, respectively, were dominant features in defining the c-Jun expression pattern (α1) of signals appeared to have significant effect during early phases while the time constant of peak retention (τ2) controlled gene expression during the late phase.Recent technologies have enabled gene expression assays at single cell scale with unprecedented resolution, and at a very high throughput with hundreds of samples , 22, 52.pression . In cont pattern . Consideτ2E) was the key distinguishing feature for the c-Fos gene expression phenotypes (τ2J) and duration of JNK activation (α2J) were the key features in distinguishing the c-Jun gene expression phenotypes  high c-Fos, high c-Jun, (2) high c-Fos, low c-Jun, (3) low c-Fos, high c-Jun, and (4) low c-Fos, low c-Jun. We found that the key controlling features established from our earlier analysis (τ2E) and slow rate of JNK peak deactivation (high τ2J) produced high c-Fos and high c-Jun gene expression pattern, (2) slow rate of ERK peak deactivation (high τ2E) but fast rate of JNK peak deactivation (low τ2J) produced high c-Fos and low c-Jun gene expression pattern, (3) fast rate of ERK peak deactivation (low τ2E) but slow rate of JNK peak deactivation (high τ2J) produced low c-Fos and high c-Jun gene expression pattern, and (4) fast rate of ERK peak deactivation (low τ2E) and fast rate of JNK peak deactivation (low τ2J) produced low c-Fos and low c-Jun gene expression pattern. Our results indicate that the variability is the gene expression patterns in single cells is likely to contain information on features of the upstream signaling dynamics.We further analyzed bivariate gene expression patterns of immediate early genes (c-Fos and c-Jun) as observed in single cell experiments , we emplanalysis , separatanalysis . Decisioanalysis  revealedSi > 0.1) for different nodes in the regulatory network and at different phases of the stimulation   . Similarnd late) . Unsupernd late) . Both ofWe present a new paradigm in which dynamic signaling features and not molecular abundances are introduced as the carriers of functional information. We developed a novel empirical model for signaling profiles based on transient phases of activation, peak retention and deactivation kinetics to characterize the dynamical aspects of the signal. We then performed dense Sobol sampling over an uniform high-dimensional space of signaling features to map the functional space of signaling dynamics which spans all possible essential dynamical profiles for three upstream signaling kinases, namely ERK, FRK and JNK respectively. We employed variance based global sensitivity analysis, estimated mutual information, and developed decision trees to identify the key controlling features. The amount of the information carried to various network components were also analyzed, specifically to the IEG and TF responses. Our results showed that the key controlling feature must carry at least one bit of information in order to distinctively separate downstream IEG and TF dynamical response patterns. Our analysis revealed that in the majority of cases an individual signaling feature carried insufficient amount of information (less than one bit) to directly lead a binary transcriptional regulation, and therefore a combination of features influenced downstream regulatory process. Cellular decision, hence, is a consequence of decoding of multiple dynamic signaling features, rather than a direct function of the abundances of functionally active signaling effectors.κB dynamics  for a response output R is the activity level of the downstream IEG or TF under observation. This is a very useful property of HDMR in determining how the uncertainty in downstream response (R) is influenced by the uncertainties in signaling features (X).Xi’s having a uniform distribution) Si represents first order sensitivity indices, and We began this analysis by considering variability in signaling dynamics as a function of variability in signaling features, gonality , 85. TheThe conditional variance in the sensitivity indices were estimated using Saltelli’s approach . While eSi < 0.1), we assumed that the signaling feature was not sensitive enough to lead to significant reduction in the response variation and was not important. This criteria was used throughout the study and in generation of the heatmap in The objective of the global sensitivity analysis is to identify the most important signaling feature affecting a downstream response at a given time. This is determined through identification of a feature which, when varied simultaneously along with all the features, leads to the greatest reduction in the variance of the downstream response. Similarly, second most important feature can be determined which leads to second greatest reduction in the variance of the downstream response, and so on until all features are ranked in the order of importance. Estimation of sensitivity indices captures this reduction in the variance through a continuous metric, bounded between 0 and 1 . A sensiR, when the measurement of a signaling feature, Xi, is known. The quantity is calculated for each feature to any downstream response at a given time point. Feature based mutual information is a universal quantity by definition, and can be applied to any network topology regardless of its underlying physical basis or complexity. In biological networks, the network topology can be defined as a channel  given as X in We applied information theory to quantify the amount of information transduced by each upstream signaling feature to downstream responses . Feature channel  or a blaS = fs, and downstream response R, mutual information I is defined as:H isI ≥ 0), the conditional entropy is always less than individual entropy, H(R∣Xi) ≤ H(R)  = EXi(V[R∣Xi]). From the law of total variance V[R] = V(E[R∣Xi]) + E(V[R∣Xi]), mutual information is estimated as:H) of any Gaussian distribution pZ(z), is R and Xi in For a input signal ) ≤ H(R) . The res in bits . However2πeV[Z]) , 87. The5 sets of simultaneous variation in the signaling features through application of Sobol sequence ‘MatousekAffineOwen’ algorithm to scramble a total of 18-dimensional pseudo-random numbers generated using Sobol’s method in MATLAB computational environment [We employed decision tree analysis by generating 10ironment . The resironment . The CARironment  using thironment , 92.S1 TableThe citation in the table refers to .(PDF)Click here for additional data file.S2 TableThe citation in the table refers to .(PDF)Click here for additional data file.S1 Figu(t) represents a input stimulus, which is assumed to be a heavyside step function, fs(t) is the resulting transient signal of the phosphorylated (activated) kinase, z1(t), z2(t) and z3(t) are the activation, peak and deactivation processes respectively, and αi are the delays (See (A) Control block diagram of signaling kinase dynamics, modeled as an outcome of enacting activation, peak and deactivation processes in parallel. Symbols used in the diagram are the same as in the Methods section of the main text. (PDF)Click here for additional data file.S2 FigAP-1 regulatory network has two distinct motifs: feedforward motif activating heterodimer ppc-FOS:ppc-JUN (left) and positive feedback loop activating homodimer ppc-JUN:ppc-JUN (right).(PDF)Click here for additional data file.S3 Fig(A) Plots show total sensitivity ((PDF)Click here for additional data file.S4 FigSi) and higher order sensitivity indices Click here for additional data file.S5 FigPlots similar to (PDF)Click here for additional data file.S6 FigPlots similar to (PDF)Click here for additional data file.


N-person game theoretic model. Parameter settings allow for loners and cheaters, and for costly or costless signals. We find a rich dynamics, showing how natural selection, operating on a population of individuals endowed with the simplest strategies, is able to evolve a costly signaling system that allows individuals to respond appropriately to different states of Nature. Signaling robustly promotes cooperative collective action, in particular when coordinated action is most needed and difficult to achieve. Two different signaling systems may emerge depending on Nature’s most prevalent states.Collective signaling for a quorum is found in a wide range of organisms that face collective action problems whose successful solution requires the participation of some quorum of the individuals present. These range from humans, to social insects, to bacteria. The mechanisms involved, the quorum required, and the size of the group may vary. Here we address the general question of the evolution of collective signaling at a high level of abstraction. We investigate the evolutionary dynamics of a population engaging in a signaling  signaling systems evolve when collective action entails a public good, and how meanings co-evolve with individual choices, given Nature’s most prevalent states. We find a rich scenario, showing how natural selection is able to evolve a costly quorum signaling system that allows individuals to coordinate their action so as to provide the appropriate response to different states of Nature. We show that signaling robustly and selectively promotes cooperative collective action when coordinated action is most needed. In light of our results, and despite the complexity that collective action relying on quorum signaling may entail, it is not so surprising how signaling is a ubiquitous property of the living world.From humans to social insects and bacteria, decision-making is often influenced by some form of collective signaling, be it quorum, information exchange, pledges or announcements. Here we investigate how such  This ca(i)T+(i) ,48,52,53,U = 1/Z ,48. Consd before ,51. It ist (ERS) ,41, if t(ERS) [Z ,48. As s(ERS) [Z ,50,54–56of Refs. ,46. The an ρA,B . This saS1 Text(PDF)Click here for additional data file.


Adult T-cell leukemia (ATL) develops in individuals infected with human T-cell lymphotropic virus-1 (HTLV-1). Presently there is no curative therapy for ATL. The HTLV-1-encoded protein Tax up-regulates Bcl-xL expression and constitutively activates interleukin-2 (IL-2), IL-9, and IL-15 autocrine/paracrine systems resulting in amplified JAK/STAT signaling. Consequently, inhibition of JAK signaling reduces ex vivo proliferation of PBMCs from ATL patients in smoldering and chronic stages. Currently, two JAK inhibitors are approved for human use. In this study, we examined activity of multiple JAK inhibitors in IL-2-dependent and IL-2-independent ATL cell lines. The highly selective JAK inhibitor ruxolitinib was examined in a high-throughput matrix screen and the Bcl-2/Bcl-xL inhibitor navitoclax was identified as a strong candidate for multicomponent therapy. An examination of the mechanistic underpinnings of this combination highlighted a stimulation of Bim and PUMA expression and reduced phosphorylation of BAD upon cellular exposure to ruxolitinib. The combination was noted to strongly activate BAX, effect mitochondrial depolarization and increase caspase 3/7 activity that leads to PARP and Mcl-1 cleavage. Ruxolitinib and navitoclax independently demonstrated modest antitumor efficacy while the combination dramatically lowered tumor burden and prolonged survival in an aggressive ATL murine model. Critically, this combination strongly blocked ex vivo proliferation of five ATL patients’ PBMCs. These studies provide support for a therapeutic trial in patients with smoldering and chronic ATL using a drug combination that inhibits JAK signaling and anti-apoptotic protein Bcl-xL.


Using immunohistochemistry (IHC), we localized TSLP in esophageal biopsies of active EoE (≥15 eos/hpf), inactive EoE (<15 eos/hpf) and non-EoE control subjects, and found that TSLP expression was restricted to the differentiated suprabasal layer of the epithelium in actively inflamed EoE biopsies. Consistent with these results in vivo, inducible TSLP protein secretion was higher in CaCl2 differentiated telomerase-immortalized esophageal epithelial cells (EPC2-hTERT) compared to undifferentiated cells of the basal phenotype, following stimulation with the TLR3 ligand poly(I:C). To determine whether food antigens could directly induce epithelial TSLP secretion, differentiated and undifferentiated primary esophageal epithelial cells from EoE and non-EoE subjects were challenged with food antigens clinically relevant to EoE: Chicken egg ovalbumin (OVA), wheat, and milk proteins beta-lactoglobulin (blg) and beta-casein. Food antigens failed to induce TSLP secretion by undifferentiated cells; in contrast, only OVA induced TSLP secretion in differentiated epithelial cells from both EoE and control cell lines, an effect abolished by budesonide and NF-κb inhibition. Together, our study shows that specific food antigens can trigger innate immune mediated esophageal TSLP secretion, suggesting that esophageal epithelial cells at the barrier surface may play a significant role in the pathogenesis of EoE by regulating TSLP expression.Eosinophilic esophagitis (EoE) is a chronic Th2 and food antigen-mediated disease characterized by esophageal eosinophilic infiltration. Thymic stromal lymphopoetin (TSLP), an epithelial derived cytokine which bridges innate and Th2-type adaptive immune responses in other allergic conditions, is overexpressed in esophageal biopsies of EoE subjects. However, the triggers of TSLP expression in the esophageal epithelium are unknown. The objective of the current study was to characterize TSLP expression in human esophageal epithelium in EoE  Eosinophilic esophagitis (EoE) is a chronic allergic disease characterized by the infiltration of eosinophils into the esophageal epithelium. Though the etiology of EoE is incompletely understood, both genetic predisposition and environmental triggers are known to play a role in EoE pathogenesis. A unique challenge in EoE is understanding why the inflammatory infiltrate is restricted to the esophageal epithelium, sparing the distal gastrointestinal tract.Thymic stromal lymphopoietin (TSLP) is an IL-7-like cytokine expressed by multiple cell types, which promotes Th2-type immune responses by influencing dendritic cells (DCs) –3. TSLP in vitro. Establishment of such a model system, including an understanding of the factors that drive esophageal epithelial TSLP protein secretion, would be a powerful tool to study EoE pathobiology in vitro.Similar to its inducible expression in the airway, TSLP mRNin vivo are unknown. Other models suggest that TSLP expression may be specific to disease microenvironments. For example, in atopic dermatitis (AD), epithelial damage promotes TSLP expression in the skin. , chicken egg OVA, and wheat, the most commonly implicated food antigens in the SFED. While BLG induced a modest yet significant induction in the total TSLP transcript, none of the food antigens induced the long TSLP isoform in undifferentiated epithelial cells . More importantly, food antigens failed to induce TSLP secretion in undifferentiated epithelial cells. . However, both OVA and BLG induced expression of the long TSLP transcript in differentiated EPC2-hTERT cells . Surprisingly, OVA, but not BLG, induced TSLP protein secretion in differentiated EPC2-hTERT cells . We again verified these findings using and evaluated the difference between the short and long isoforms in differentiated cells stimulated with food antigens (S3 Appendix).The six-food elimination diet (SFED) is effective in EoE and is based upon the prevalence of cow-milk protein, wheat, egg, peanut, seafood, and soy as causative antigens in EoE.. Based uFig 4D and Fig 4E). Differences between overall OVA-induced responses between EoE and non-EoE cohorts were not statistically significant. Comparisons of OVA-induced TSLP responses between cell lines obtained at the time of active EoE inflammation were similar to epithelial responses from cell lines from inactive EoE subjects . Patient characteristics and in vitro responses are summarized in Table 1.We next determined whether food antigens might induce epithelial TSLP secretion in primary esophageal epithelial cells (EPCs) using primary human EPCs cultivated from seven control (non-EoE) subjects and 20 EoE subjects. Similar to EPC2-hTERT cells, OVA stimulation of undifferentiated primary cell lines did not lead to detectable TSLP secretion (data not shown). In contrast, nearly all differentiated primary cell lines from EoE (18/20) non-EoE (6/7) cell lines secreted detectable TSLP in response to OVA stimulation , an irreversible inhibitor of NF-kB signaling. In the presence of Bay-11, OVA mediated induction of TSLP secretion was significantly suppressed .TSLP expression is regulated in part by the transcription factor NF-kb Fig 5B).In addition to food elimination diets, swallowed steroids, including viscous solutions of budesonide, are effective treatments for EoE ,29,42. SWe have shown that TSLP expression during active EoE is restricted to the suprabasal layer of the esophageal epithelium. We further show that differentiated esophageal epithelial cells, phenotypically similar to cells at the esophageal barrier surface, have altered innate immune responses compared to undifferentiated epithelial cells. Our study is the first to show differential responses to food antigen stimulation by human esophageal epithelial cells, and proposes a potential mechanism by which specific food antigens may incite host immune responses in EoE at the epithelial barrier.in vivo and determine its functional role in response to allergic stimulation.TSLP expression is well documented at barrier surfaces where it may aid in host protection against pathogens and foreign antigens. This is evident from the ability of pathogens to induce TSLP expression by epithelial cells. Taylor et al showed constitutive TSLP expression in murine GI tract, and demonstrated that TSLP-TSLPR interactions were critical for host protection against intestinal nematode infection. Additionin vitro differentiation of airway epithelial cells leads to enhanced TLR3 expression and resistance to rhinovirus infection. [Laser capture microdissection (LCM) studies of the human epidermis have identified significant differences in gene expression patterns between basal and suprabasal epithelial cells. ,45 Additfection. ,47 SimilThough clinical practice demonstrates that food antigens drive EoE disease pathogenesis, the mechanisms by which this occurs are unclear. In murine EoE models, esophageal eosinophilia occurs after systemic allergen sensitization, supporting the notion that EoE is a localized response to systemic food allergen sensitivity. In clinical practice, reductions in esophageal eosinophilia can be achieved by administration of elemental formulas via nasogastric or gastrostomy tubes, bypassinin vitro and in vivo. Both acute and chronic sensitization with OVA is known to recapitulate key features of clinical asthma in murine models including eosinophilia and goblet cell hyperplasia [in vitro via unknown mechanisms. We speculate that undifferentiated epithelial cells in the basal compartment may act as non-professional antigen presenting cells as observed by Mulder et al, while the terminally differentiated cells at the barrier layer are involved in TSLP secretion thus essentially bridging the innate-adaptive immune responses. Additionally, contribution of the pro-inflammatory environment on epithelial antigen response cannot be ignored as they have been shown to enhance peptide internalization in other models[OVA is a classic antigen used to study immune tolerance, antigen presentation, and allergic responses erplasia  We now ser modelsThough cow milk proteins are the most common dietary trigger for pediatric EoE, , neitherin vitro. Furthermore, there was no correlation between individual patient allergies and their TSLP response to those specific foods. These findings are in keeping with previous studies in which epithelial cells from EoE, and non-EoE subjects were found to exhibit similar growth and functional characteristics in vitro [in vitro, supporting the concept that downstream systemic immune responses are critical determinants of the host phenotype. It is plausible that innate esophageal epithelial responses to food antigens are down-regulated in the non-EoE host, whereas these responses may be may be amplified within the EoE host. Indeed, oral tolerance is an active immunologic process, rather than a lack of immune response to oral antigens [52 highlights the complexity of the host immune response in EoE, and suggests that both epithelial immune responses and the host systemic response are involved in EoE pathogenesis. However, these in vitro studies, including the current study, are limited by a small sample size. Future larger studies, will be required to determine whether these results may be generalizable findings. Our small sample size also precludes a determination of whether in vitro food antigen stimulation correlates with clinical food antigen sensitivity in EoE, but may be a provocative avenue for future investigation.In this study, we found that the differentiation characteristics and innate immune responses of primary esophageal epithelial cells from EoE subjects was not distinct from that of non-EoE cell lines in vitro ,53. We santigens –56. Our in vitro and in vivo through TLR4 signaling[in vitro. This is consistent with the findings of Lee et al. who reported that induction of TSLP by bronchial epithelial cells is not induced by LPS [Emerging evidence suggests that epithelial TLRs may be involved in environmental allergen mediated inflammation. Li et al showed that rag weed pollen mediated TSLP induction in corneal epithelial cells signaling. As TLR4signaling, TLR4 isd by LPS . While tIn vitro, budesonide suppresses allergen induced cytokine secretion from epithelial cells and fibroblasts [in vitro, thus offering a plausible new mechanism for budesonide mediated anti-inflammatory effect.Our results are in keeping with previous reports that NF-κB signaling regulates poly (I:C) and cytokine-mediated TSLP expression in various epithelial cell types,60. Swalroblasts ,64. We nin vitro in calcium based terminal differentiation model. More importantly, our observations of OVA-mediated TSLP expression from differentiated epithelial cells offer novel evidence in support of the crucial role of esophageal epithelium in the pathogenesis of EoE.In summary, our study shows that TSLP is predominantly expressed in the differentiated compartment of the esophageal epithelium in EoE and this can be recapitulated S1 AppendixDifferentiated EPC2-hTERT cells were stimulated with increasing concentrations of OVA. *, p<0.05.(TIFF)Click here for additional data file.S2 Appendix*,p<0.05.(TIFF)Click here for additional data file.S3 Appendix*, p<0.001.(TIFF)Click here for additional data file.


Drosophila S2 cells. Ter94, a member of the family of AAA ATPases, was identified as a novel mediator of BMP signaling, which is required for the phosphorylation of Mad in Drosophila S2 cells. Moreover, the mammalian orthlog of Ter94 valosin-containing protein (VCP) plays a critical role in the BMP-Smad1/5/8 signaling pathway in mammalian cells. Genetic evidence suggests that Ter94 is involved in the dorsal-ventral patterning of the Drosophila early embryo through regulating decapentaplegic (Dpp)/BMP signals. Taken together, our data suggest that Ter94/VCP appears to be an evolutionarily conserved component that regulates BMP-Smad1/5/8 signaling.Bone morphogenetic proteins (BMPs), a subgroup of the transforming growth factor (TGF)-β family, transduce their signal through multiple components downstream of their receptors. Even though the components involved in the BMP signaling pathway have been intensely studied, many molecules mediating BMP signaling remain to be addressed. To identify novel components that participate in BMP signaling, RNA interference (RNAi)-based screening was established by detecting phosphorylated Mad (pMad) in  Understanding tissue generation during metazoan development requires knowledge of how cell and tissue identity is established. Central to this issue is the characterization of signaling molecules, cell-cell interactions, receptors, and second messenger systems that contribute to the processes of cell differentiation and specification. The transforming growth factor (TGF)-β family represents the largest collection of growth factors identified to date and consists of more than 30 secreted polypeptides. Within this family, the bone morphogenetic proteins (BMPs) constitute the largest subgroup. BMPs regulate biological processes as diverse as cell proliferation, differentiation, cell-fate determination, apoptosis, and morphogenesis TGF-β type ligands activate downstream signaling by binding to their specific membrane receptors. Upon ligand binding, a tetrameric complex of type I and type II serine/threonine kinase receptors forms. The type II receptors then phosphorylate the type I receptors, which subsequently phosphorylate the receptor-regulated Smads (R-Smads): Smad1/5/8 for BMP-type signaling and Smad2/3 for TGF-β/Activin-type signaling. Thereafter, R-Smads form a complex with common Smads (Co-Smads) and translocate into the nucleus, where transcriptional activation or repression of target genes occurs Drosophila, seven TGF-β family ligands have been identified, three of which, decapentaplegic (Dpp), screw (Scw), and glass bottom boat (Gbb), are within the BMP subgroup. Two type I receptors, thickveins (Tkv) and saxophone (Sax), and two type II receptors, put (Punt) and wishful thinking (Wit), have been identified to function as BMP receptors in Drosophila. Moreover, one R-Smad  and one Co-Smad (Medea) were identified as downstream of BMP signaling in Drosophila. In Drosophila, BMP signals have been implicated in regulating a diverse array of developmental events ranging from dorsal-ventral patterning of the early embryo, oogenesis, mid gut development, the patterning and growth of imaginal tissue, wing vein formation, and synapse function at the neuro-muscular junction Drosophila will impact our understanding of TGF-β signaling in mammals, since the fundamental signaling mechanism is highly conserved in both mammals and DrosophilaDrosophila and the easiness of Drosophila genetic analysis make Drosophila an ideal model for identifying key determinants in TGF-β signaling.In Many fundamental problems in signal transduction have been studied in tissue culture systems. The recent development of functional analysis of genes by RNA interference (RNAi) in cell culture energized the field of functional genomics by enabling genome-scale loss-of-function screens in cultured cells. Driven by genome sequence data, RNAi can serve in high-throughput screens Drosophila, we established a screening system of BMP signaling in Drosophila. Genome-wide RNAi screening in Drosophila cells revealed Ter94 as a novel component of BMP signaling during embryogenesis. Our data also reveal that the mammalian ortholog valosin-containing protein (VCP) is required for BMP signaling in mammalian cells. These data suggest that Ter94/VCP is an evolutionarily conserved component of the BMP signaling pathway.In this study, in order to identify novel genes that regulate TGF-β type signaling in dpp-HA, pBRAcAP-scw-HA, and pBRAcPA-Flag-Mad for cell cultures have been described previously Constitutive active promoter pBRAcPA-5′-CTAACTGTAAAGATCATCGTCGCC-3′.Ter94∶5′-CACCATGGCAGATTCCAAGGGTGAAGAT-3′ and 5′-CACCATGGCTTCTGGAGCCGATTCAAAAGGTGA-3′ and 5′-TTAGCCATACAGGTCATCATCATTGTCTTCTGTG-3′.hVCP: TAATACGACTCACTATAGGGAGA) were designed to amplify the DNA sequences of target genes by PCR. PCR products were used as a template to synthesize dsRNA molecules with an in vitro transcription MEGAscript T7 Kit . An RNeasy Mini Kit  was used for the purification of dsRNAs, and NanoDrop (Thermo Scientific) was used for dsRNA quantification. The following primers were used in the production of dsTer94-3′UTR:As for preparation of dsRNA, pairs of primers containing a T7 sequence overhang , mouse anti-Tubulin (Sigma), and rabbit anti-pMad antibodies; secondary antibodies, anti-mouse-680 (LI-COR) and anti-rabbit-800 (LI-COR) antibodies; all were then analyzed with the Odyssey Infrared Imaging System (LI-COR). Anti-Ter94 antibody was obtained from Dennis McKearin A cell-based BMP signaling assay and RNAi were conducted as described previously with modifications Drosophila Genome Collections (DGC) 1, 2 and 3 as templates, was provided by the High Throughput Center, University of Helsinki The dsRNAi library, which was prepared with Flag-Mad, 20 ng of dpp-HA, and 20 ng of scw-HA in a 384-well plate . A high throughput Biomek robot served for transfection. In the first part of the primary screen, transfections were performed in a duplicate.In a primary screen, S2 cells were transfected with 40 ng of two dsRNAs, 20 ng of pHFW-When the dsRNA pairs affected relative intensities of BMP signaling (below or above 80–120% of the control) in the first part of the RNAi screen, 40 ng of individual dsRNA were collected and used in the second part of primary screen. Transit  was used for transfection in the high throughput screen.2, 1 mM CaCl2, pH 7.4) for 10 min at room temperature, permeabilized wtih HBST  for 10 min, and incubated with HBS-B  for 30 min. The cells were then incubated overnight with mouse anti-Flag M2 (1∶500) and rabbit anti-pMad (1∶200) at 4°C as primary antibodies, and with Alexa Fluor 488 goat anti-mouse (1∶500), Alexa Fluor 647 goat anti-rabbit (1∶500) and DAPI  for one hour at room temperature as secondary antibodies.Five days after transfection, the cells were incubated for 45 minutes at 37°C and for another four hours at room temperature. Then all the cells in the 384-well plates were suspended by the Biomek robot and transferred to a 384-well view plate  precoated with 0.05 mg/ml Concanavalin A (Sigma). The cells were then fixed with 3.7% formaldehyde in HBS , Flag-Mad positive cells, and pMad positive cells, as well as to calculate the relative intensities of the BMP signal in each sample.For those genes regarded as hits in the primary screen, the corresponding bacteria strains were collected from the DGC plasmid library and cultured for preparing DNA templates for PCR. PCR was performed with T7 overhang primers, and verified PCR products were stored at 100 ng/µl in a 96-well plate for future usage.Three independent transfections in 96-well plates were performed in a secondary screen with modified protocols of the primary screen.pAFW-Mad and 400 ng of dsRNA in a 96-well plate. Five days after transfection, the cells were incubated with purified Dpp . The numbers of nuclei, Flag-Mad positive cells, and pMad positive cells were measured with modified protocols of the primary screen in three independent experiments.Those genes regarded as hits in the secondary screen were underwent further analysis. S2 cells were transfected with 200 ng of Recombinant BMP6 was kindly provided by Prof. Dr. S. Vukicevic.Mouse pluripotent mesenchymal KS483 cells C2C12 cells were transfected with Mouse ON-TARGET plus siRNAs targeting VCP, Smad1 or control siRNAs (Dharmacon) and stimulated with BMP6; alkaline phosphatase (ALP) activity was determined subsequently essentially as described previously LacZ expression plasmid together with 0.30 µg of a VCP or control expression plasmid per well essentially as described previously KS483 cells were transfected in 12-well plates with 0.15 µg of firefly luciferase reporter construct, 0.05 µg of a H46dpp or hr4dpp served as a null or hypomorphic allele, respectively. k15002, Ter94EY03486, Ter9403486, Ter9426-8, Ter9422-30Ter94 and 8tkv were obtained from Bloomington. 15cSmurf or 12Mad was from Chip Ferguson or Stuart Newfeld, respectively. The in situ hybridization of race to whole-mount embryos was performed with digoxigenin-labeled RNA probes and visualized with alkaline phosphatase precipitates as described previously lacZ transcripts produced from the Cyo, ftz-lacZ balancer chromosome.t-test served for statistical analysis, and P<0.05 was considered statistically significant.All results were expressed as the mean ± s.d. The Student’s Drosophila S2 cells to be suitable for detecting the BMP signal in a ligand dose-dependent manner and for performing a functional analysis of signaling components by RNAi Drosophila BMP-type ligands Dpp and Scw, and Flag-tagged Mad were cotransfected together with dsRNA in S2 cells and incubated for five days, after which images of both Flag-tagged Mad (Mad expression) and pMad staining were captured. To assess the effects of RNAi in the imaging-based assay, dsRNA against gfp served as a control to rule out unspecific effects caused by RNAi transfection, and RNAi against the BMP type II receptor put served to validate that depletion of upstream signaling components inhibits pMad expression. When gfp RNAi was transfected, most of the cells expressing Flag-Mad showed pMad accumulation in the nucleus. In contrast, the pMad was significantly reduced when put was knocked down by RNAi   .Tertiary screening served to further confirm whether the candidate genes are involved in BMP signaling in S2 cells. To characterize the specificities of candidate genes in BMP signaling, both the pMad score and cell numbers were counted. When a hit resulted in a lower or higher pMad score, a normal Mad score, and a normal cell number, it remained on the hit list. When a hit resulted in a lower or higher pMad score and a normal cell number, but a lower Mad score, it also remained on the hit list. When a hit resulted in a lower pMad score, but a lower Mad score and cell number, it was considered as a common factor involved in cell viability and excluded from the hit list. A summary of the candidate genes listed after tertiary screening appears in Ter94 RNAi could be rescued by ectopic expression of Ter94. For this purpose, we performed Western blotting using pMad antibody to evaluate BMP signaling in S2 cells. Exogenously adding Dpp to S2 cells that overexpress Flag-Mad induced a pMad signal  as well as Ter94 in S2 cells , or wild-type VCP R155H, when overexpressed, reduced luciferase activity  are viable, since Smurf mutations cause a spatial increase in BMP signaling     [26]. Byctively) . These drace gene is expressed at the dorsal-most cells regulated by high-level BMP signaling during the blastoderm stage race expression occurs in a Dpp dose-dependent manner   . In cont embryos . The num and dpp . These dMad, Ter94 and dpp, or tkv, Ter94 and dpp by using Mad, Ter94 or tkv, Ter94 double mutants. Flies transheterozygous for 12Mad and hypomorphic allele hr4dpp showed low viability  and nuclear membrane bound protein moleskin (msk) as novel regulators of BMP signaling Drosophila S2 cells. These genes contain essential components of the canonical BMP pathway, including Tkv, Punt, and Mad as well as modulators of BMP signal transduction, including nej/CBP, DLP, sgg/GSK3 and MAN1  Ter94 is considered a functional ortholog of Cdc48 in budding yeast, as is VCP in mammals; both have been thoroughly studied. They are chaperone-like ATPases that control the degree of ubiquitination of bound substrates in vivo was challenging. Indeed, mutant clones of Ter94 in the wing imaginal discs were eliminated during larvae stages and could not be analyzed for BMP signaling (data not shown). This study revealed strong genetic interaction of Ter94 with BMP signaling pathways, including BMP ligand dpp, Mad or BMP type I receptor tkv in sensitized conditions. Moreover, Ter94 is required for maintaining BMP signaling during early embryogenesis. Since the BMP activity gradient needed for dorsal patterning in the Drosophila blastoderm embryo is maintained through the feedback circuit Since Ter94/VCP is involved in various biological processes, investigating the contributions of Ter94/VCP to BMP signaling in vivo functions through which Ter94/VCP modulates BMP signals.In summary, this study shows that Ter94 andVCP play conserved roles in modulating BMP signals. Further studies will address the molecular mechanisms underlying how Ter94 andVCP regulate BMP signaling and the Table S1Candidates that are involved in BMP signaling after primary screen.(PDF)Click here for additional data file.Table S2Candidate genes that are involved in BMP signaling after secondary screen.(PDF)Click here for additional data file.


Brazil has possibly the largest absolute number of people infected with HTLV worldwide. An asymptomatic carrier of HTLV-1/2 who is unaware of their serological status can spread the virus in their family group and to sexual contacts. The aim of this study was to determine patterns of HTLV-1/2 infection and transmission among family groups in the GIPH cohort. The index cases were former female blood donors found positive for HTLV-1/2 after donating blood at Fundação Hemominas in Belo Horizonte. Their relatives were invited to get tested and the possible pathways of HTLV transmission in these groups were analyzed. Pedigrees were prepared for the family groups after the serologic test results for the family members were ready. We selected 275 women, comprising 95 family groups. In these family groups it was possible to infer that in 23 (24.2%) the contamination occurred by the vertical route [95% CI (20.7 to 27.7)], in 58 (61.1%) through sexual intercourse [CI 95% (57.1 to 65.0)] and in 14 (14.7%) both by sexual and vertical routes [CI 95% (11.8 -17.6)]. Accepting an error of 1% and assuming a confidence interval of 95%, it may be stated that the major route of HTLV contamination of blood donors in this population was the sexual route. The importance of knowing how HTLV is spreading in Brazil is to devise appropriate prevention measures. The results we obtained are consistent with those found in some countries with high prevalence of HTLV, such as Japan.


Biological pathways that significantly contribute to sporadic Alzheimer’s disease are largely unknown and cannot be observed directly. Cognitive symptoms appear only decades after the molecular disease onset, further complicating analyses. As a consequence, molecular research is often restricted to late-stage post-mortem studies of brain tissue. However, the disease process is expected to trigger numerous cellular signaling pathways and modulate the local and systemic environment, and resulting changes in secreted signaling molecules carry information about otherwise inaccessible pathological processes.To access this information we probed relative levels of close to 600 secreted signaling proteins from patients’ blood samples using antibody microarrays and mapped disease-specific molecular networks. Using these networks as seeds we then employed independent genome and transcriptome data sets to corroborate potential pathogenic pathways.We identified Growth-Differentiation Factor (GDF) signaling as a novel Alzheimer’s disease-relevant pathway supported by in vivo and in vitro follow-up experiments, demonstrating the existence of a highly informative link between cellular pathology and changes in circulatory signaling proteins.The online version of this article (doi:10.1186/s13024-016-0095-2) contains supplementary material, which is available to authorized users. Plasma proteins provide a sampling of biological processes throughout the organism and have been applied to diagnose or monitor human disease. However, in neurodegenerative disorders it has so far been more difficult to use unbiased large-scale proteomic approaches to discover blood-based biomarkers for diagnostics –3. WhileTo monitor the secreted signaling proteome in plasma, we manufactured glass-based microarrays with commercially available antibodies to measure the relative levels of close to 600 distinct secreted signaling proteins. Using these arrays, we obtained quantifiable results for 582 signaling proteins  as a starting point, we then queried known pathway or physical interaction databases to test the hypothesis that the signaling proteome could be mined to identify pathologically disturbed pathways . This approach greatly reduces the chance of false-positive discoveries in contrast to following individually significant but unconnected leads. Using this methodology we identified highly interconnected clusters of receptors and ligands with growth factor activity  or with activity linked to apoptosis  via signaling proteome analysis . Pathwaape.org, , Reactomape.org, –70, and ape.org, . Physicaape.org, , IREF Inape.org, . We alloOntology , 75, KEGOntology , and PanOntology  databaseOntology  with thep-value (pRho: significance that slope is not 0) and by computing an empirical p-value by permuting protein scores over MMSE scores 1,000-times . Network representation was performed as described above.MMSE scores were recorded at the time of plasma acquisition at the respective clinical centers. Scores were available for 44/47 AD patients and 26/52 Control patients  conducted by the Alzheimer Disease Genetics Consortium (ADGC) . Melt curve cycles immediately followed and were: 5 s at 95 °C, 1 min at 65 °C and then gradual temperature rise to 97 °C at a rate of 0.11 °C/s followed by 30s at 40 °C. GDF3 levels were normalized to MAPK3  as a refPrimer sequences were obtained from the MIT/Harvard PrimerBank.GDF3 fwd: 5’ ATGCAGCCTTATCAACGGCTTGDF3 rev: 5’ AGGCGCTTTCTCTAATCCCAGGDF3 PrimerBankID: 6679979a1MAPK3 fwd: 5’ TCCGCCATGAGAATGTTATAGGCMAPK3 rev: 5’ GGTGGTGTTGATAAGCAGATTGGMAPK3 PrimerBankID: 21489933a1


Objectives. The psychometric properties regarding sex and age for the revised version of the Temperament and Character Inventory (TCI-R) and its derived short version, the Temperament and Character Inventory (TCI-140), were evaluated with a randomized sample from the community.Methods. A randomized sample of 367 normal adult subjects from a Spanish municipality, who were representative of the general population based on sex and age, participated in the current study. Descriptive statistics and internal consistency according to α coefficient were obtained for all of the dimensions and facets. T-tests and univariate analyses of variance, followed by Bonferroni tests, were conducted to compare the distributions of the TCI-R dimension scores by age and sex.Results. On both the TCI-R and TCI-140, women had higher scores for Harm Avoidance, Reward Dependence and Cooperativeness than men, whereas men had higher scores for Persistence. Age correlated negatively with Novelty Seeking, Reward Dependence and Cooperativeness and positively with Harm Avoidance and Self-transcendence. Young subjects between 18 and 35 years had higher scores than older subjects in NS and RD. Subjects between 51 and 77 years scored higher in both HA and ST. The alphas for the dimensions were between 0.74 and 0.87 for the TCI-R and between 0.63 and 0.83 for the TCI-140.Conclusion. Results, which were obtained with a randomized sample, suggest that there are specific distributions of personality traits by sex and age. Overall, both the TCI-R and the abbreviated TCI-140 were reliable in the ‘good-to-excellent’ range. A strength of the current study is the representativeness of the sample. The personality paradigm proposed by Cloninger and colleagues  providesAll personality modules involve person by situation interactions that are regulated by a set of dynamical nonlinear systems, which allows human beings to be purposeful, flexible, and self-aware in their adaptation to life. Human personality is not adequately characterized as a set of linear traits because the components of personality are nonlinear in their functional effects and relationships with one another. As a result, personality is a complex expression of nonlinear interactions among a whole hierarchy of learning systems that have evolved and that develop over time as a complex adaptive process, as described in detail elsewhere .The revised Temperament and Character Inventory  is a selTo date, the TCI-R has been adapted in various languages and cross-cultural contexts with clinical and non-clinical samples, including in Italy , BelgiumThe majority of the validation studies examining normal populations were conducted with volunteers and/or students, which indicates that there is a self-selection bias. This type of sample selection may not represent the general population in terms of demographic and psychological variables given that it does not consider the moderating effects of these variables. Conclusions generated from studies with these samples make it difficult to extrapolate the results to the general population and may influence the psychometric properties of reliability and construct validity regarding this instrument. The personality dimensions may be influenced by demographic factors, such as age, sex and level of education . One stuTo our knowledge, only a few validation studies of the TCI model have been performed with randomly selected populations . This stThe sample was randomly selected from the electoral roll of a municipality in Tarragona province  and was stratified for age and sex in the source population. Letters of invitation to participate in the study were sent to a total of 650 people. Willingness to participate was confirmed by phone call. If someone refused or could not be contacted following three different phone calls (at different times on different days), they were replaced by the next available candidate of the same age and sex on the list. Inclusion criteria were the following: Individuals of Caucasian origin with the capability of understanding the nature of the study and who did not have serious diseases that prevented participation. The answer format was self-administered. When any subject was illiterate, the questionnaire was read out by a member of the research team.t-test, which revealed a small effect size (d = 0.05). The sample was comprised as follows: 70.57% (n = 259) had completed elementary or secondary school, 15.25% (n = 56) had completed high school, 11.71% (n = 43) had completed a first-level or higher degree and 2.45% (n = 9) were illiterate. Regarding civil status, 25.6% (n = 94) were single, 64.9% (n = 238) were married, 2.7% (n = 10) lived with a partner, 2.7% (n = 10) were widowed and 3.6% (n = 13) were separated. There were two cases missing for the civil status results. All procedures were conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board, and all subjects provided written informed consent.There were 367 subjects  who were between 18- and 77-years-old. The average age was 43.09 years (SD = 15.32) for the males and 42.31 years (SD = 15.03) for the females. There were no significant age differences between the males and females according to a definitely false to 5 = definitely true. Regarding the abbreviated TCI-R inventory, the TCI-140 consists of the first 140 items from the TCI-R. Of these 140 items, 136 items relate to the seven temperament and character domains and the remaining four items measure response accuracy/carelessness. An abbreviated form of the TCI-R is required when the application time is limited, for example when it is necessary to measure the personality along with other variables in the context of research or diagnosis.Temperament and Character Inventory—Revised (TCI-R) and TCI-140. The TCI-R is a 240-item self-administered questionnaire designed to measure four dimensions of temperament and three dimensions of character as shown in t-tests, and alpha internal consistencies for all of the subjects were analyzed according to sex. Effect sizes were estimated from the t-tests with Cohen’s d. Univariate analyses of variance followed by Bonferroni tests were used to compare the TCI-R dimension scores according to age group  and sex. Inter-correlations among the seven dimensions of the TCI-R were obtained with Pearson statistical tests. All of the analyses were performed with version 17.0 of the SPSS  statistical software.Descriptive statistics, mean differences with α reliability for the whole sample and according to sex.Administering the TCI-R and TCI-140 to a general sample revealed that kurtosis and skewness were almost zero for the seven principal dimensions with the exception of Cooperativeness (C). The skewness results for C for the TCI-R and TCI-140 were −.84 and −.84, respectively, whereas the kurtosis results were 1.62 and 1.53, respectively. Given the mild non-normality of Cooperativeness, non-parametric equivalents were tested, which did not change the current results (not shown). d = .45), RD (d = .59) and C (d = .26), whereas men scored higher in PS (d = − .23). These differences were also evident for the TCI-140, with higher scores for women in HA (d = .38), RD (d = .62) and C (d = .34), whereas men scored higher in PS (d = − .27). The Cohen ‘d’ values revealed medium effect sizes.On the TCI-R, women scored higher in HA , RD , SD1, SD2 and C (C1 and C2), whereas positive coefficients were obtained for HA (HA2), PS1, ST . On the TCI-140, negative coefficients with age were evident for NS , RD , SD , C (C1 and C2), whereas positive coefficients were evident for RD1, PS1 and ST . The alphas were between 0.74 and 0.87 for the TCI-R and between 0.63 and 0.83 for the TCI-140.r = .19 and TCI-140 r = 0.12) and ST (TCI-140 r = .20) and correlated negatively with SD (TCI-R r = − .15 and TCI-140 r = − .21) and C (TCI-140 r = − .11). HA correlated negatively with NS (TCI-R r = − .33 and TCI-140 r = − .25), RD (TCI-R r = − .15), PS (TCI-R r = − .36 and TCI-140 r = − .28), SD (TCI-R r = − .41 and TCI-140 r = − .38) and C (TCI-R r = − .27 and TCI-140 r = − .15). RD correlated positively with PS (TCI-R r = .14), SD (TCI-R r = .23 and TCI-140 r = .22), C (TCI-R r = .52 and TCI-140 r = .42) and ST (TCI-R r = .11). PS correlated positively with ST (TCI-R r = 0.35 and TCI-140 r = .32), and SD correlated positively with C (TCI-R r = .51 and TCI-140 r = 0.44) and correlated negatively with ST (TCI-R r = − .28 and TCI-140 r = − .38).p < .000) and age group 3 (p < .000). In contrast, subjects between the ages of 51 and 77 years scored significantly higher in HA than those between 18 and 35 years (p = .010). Younger subjects also scored higher in RD than those in group 3 (p < .000). The results for the TCI-140 were similar, with higher scores in NS for young people compared with group 2 (p = .012) and group 3 (p < .000) and the youngest subjects scored higher in RD than those in group 3 (p = .001). The ST character dimensions showed similar increases for both the TCI-R and TCI-140, with differences between groups 1 and 3 and groups 2 and 3 (p < .000). On the TCI-140, SD and C decreased with age, with differences between the youngest and oldest subjects at p = .041 and p = .025, respectively.The mean scores and standard deviations for the groups according to age and sex are presented in Thus, age and sex had independent effects on personality differences, with no significant results with regard to interactions for either variable.r = 0.37) and a negative correlation with Conscientiousness (−0.22)  .In the present study NS scores were lower than in others. In a Belgian version of the Inventory, The reliability of the TCI-R, as measured by the internal consistency between items of the same dimension, showed values of 0.74 (NS in women) to 0.87, which are values considered to be in the ‘good-to-excellent’ range. In general, these reliability values are similar to or a little lower than those obtained in non-patient cohorts  and in pThese differences in reliability between our results with a randomly selected sample and those from previous research with volunteers may be explained by a cognitive-attentional factor associated with the type of subject being examined. For example, university students and volunteers are interested and highly motivated in participating in studies and have better cognitive and attentional capacities that may improve their consistency of responses. The use of volunteer student subjects in highly homogeneous contexts, such as universities, may affect the external validity of a study given that the subjects share attributes, dispositions and environments with regard to their chosen specializations.Several of the facets did not obtain acceptable alphas. This has been reported before for other TCI-R versions and samples . HoweverComparisons according to sex revealed that men had significantly lower scores in HA, RD and C than women, which is consistent with previous research . Men scoThe age of the subjects was negatively associated with NS, RD and C and positively associated with HA and ST. In sum, this study shows that the psychometric properties of the Temperament and Character scales are suitable for the TCI-R but that there is a loss of internal consistency in the NS dimension of the TCI-140. The youngest subjects scored higher in Novelty Seeking and Reward Dependence, whereas the older subjects scored higher in Harm Avoidance and Self-Transcendence. These new normative data replaces the previously published by our group with volunteers .The greatest strength of the current study is the randomness of the sample. However, one possible limitation is the small sample size when the data are analyzed for each sex and age group. Comparisons in the dimensions of the TCI-R by age and sex are made with other studies with volunteers who have a higher socioeconomic status in relation to the subjects of our sample. This demographic difference can limit the comparisons.Tables with the T scores for each raw score of TCI-R and TCI-140 dimensions can be sent by the authors on request.10.7717/peerj.1481/supp-1Supplemental Information 1Click here for additional data file.


Human T-cell leukemia virus type 1 (HTLV-1) is a retrovirus that causes cancer  and a spectrum of inflammatory diseases . Since virions are particularly unstable, HTLV-1 transmission primarily occurs by transfer of a cell carrying an integrated provirus. After transcription, the viral genomic RNA undergoes reverse transcription and integration into the chromosomal DNA of a cell from the newly infected host. The virus then replicates by either one of two modes: (i) an infectious cycle by virus budding and infection of new targets and (ii) mitotic division of cells harboring an integrated provirus. HTLV-1 replication initiates a series of mechanisms in the host including antiviral immunity and checkpoint control of cell proliferation. HTLV-1 has elaborated strategies to counteract these defense mechanisms allowing continuous persistence in humans. HTLV-1 infects approximately 5–10 million people worldwide mainly in subtropical areas . ATL resThe HTLV-1 genome contains essential structural and enzymatic genes  shared by all retroviral family members (reviewed by ). As a din vivo relies more on a cellular intermediate than on the virion itself. Whatever the route of infection used, the initial contact with HTLV-1 mainly occurs via breast feeding, sexual intercourse, and blood transfusion [i.e., the transit of a virion incorporated into a vesicle from the apical to the basal surface of an epithelial cell [HTLV-1 predominantly infects CD4+ T cells but also targets other cell types such as CD8+ T and B lymphocytes, dendritic cells (DCs), monocytes, and macrophages ,13,14. Tnsfusion . Except nsfusion ,26. Viraial cell . Alternaial cell . Finallyial cell . Having crossed the epithelial barrier, HTLV-1 infects mucosal immune cells directly or via APCs such as DCs or macrophages. APCs can either undergo infection or transfer membrane bound extracellular virions to uninfected T-cells (trans-infection) . Cell-toSoon after primary infection, HTLV-1 attempts to expand by colonizing new targets by cell-to-cell transfer, reverse transcription of the viral RNA, integration of the provirus into the chromosome, expression of viral proteins and budding of new virions , the NF-κB pathway is a key player in regulation of immunity and inflammation . HTLV-1 nescence ,93. In Hnescence ,94. Tax promotes cell proliferation and survival through activator protein-1 (AP-1) and the phosphatidylinositol 3-kinase (PI3K)/Akt pathway . InhibitTax activates 5′-LTR-directed transcription by interacting with CREB, modulating its phosphorylation at Ser133 and connecting the histone acetyltransferase CBP (CREB-binding protein/p300) . The abiHBZ represses viral transcription through interaction with the bZIP domain of CREB proteins and prevents their binding to the viral CRE elements ,115. HBZTax interacts with DAPLE (dishevelled-associating protein with a high frequency of leucine residues) to activate the canonical Wnt pathway. HBZ can suppress this activation by inhibiting DNA binding of TCF-1/LEF-1 transcription factors. On the other side, HBZ promotes transcription of WnT 5a, a key protein of the non-canonical Wnt pathway, by enhancing its promoter activity through transforming growth factor-beta (TGF-beta). Knockdown of Wnt5a represses proliferation and migration of ATL cells, pointing out the role of this pathway in HTLV-1 infected cell growth . Tax represses TGF-β 1 signaling (i) by blocking the association of Smad proteins with Smad-binding elements, (ii) through its interaction with CREB-binding protein/p300 and (iii) via c-jun activation ,120,121.Through interaction with cyclins and CDKs, Tax interferes with cell cycle progression by several mechanisms: (i) Tax stabilizes the cyclin D2/CDK4 complex and favors hyperphosphorylation of the retinoblastoma protein (Rb). Phosphorylated Rb frees E2F1 that activates transcription of genes required for G1/S transition; (ii) Tax represses cyclin-dependent kinases inhibitors (CKIs) such as members of INK4 family and KIP1; (iii) Tax interacts with and directs Rb to the proteasome for subsequent degradation; (iv) Tax activates the cyclin D1 transcription by enhancing p300 recruitment to the CRE site of cyclin D1 promoter through interaction with pCREB and TORC2 . As a reIn contrast to Tax, HBZ exerts a dual regulatory role in cell cycle progression. Indeed, HBZ interacts with CREB and inhibits transcription of cyclin D1 . HBZ alsTogether, this series of data on the signaling pathways illustrates the opposite functions of Tax and HBZ in finely tuned regulatory mechanisms of cell proliferation. Cellular checkpoints act as a failsafe barrier against unrestrained cellular proliferation. Tax subverts the G1 restriction and the spindle mitotic checkpoints. In G1, the tumor-suppressor protein p53 is the main factor that controls the checkpoint. Although approximately 50% of cancers harbor a mutation in p53, this mechanism only appears in a small percentage of ATL patients. Instead, p53 is functionally inactivated in leukemic and HTLV-1 transformed cells . It remaBy accelerating the replication-timing program, the Tax protein induces replicative stress and DSBs . Tax expHBZ induces DNA lesions through activation of miR-17 and miR-21 and downregulation of the DNA damage factor OBFC2A . HBZ assBesides modulating the DDR signaling pathway, Tax also directly interferes with the mechanisms of DNA repair. For example, Tax downregulates the expression of β-polymerase  and inhiHTLV-1 persists and replicates by means of viral proteins, such as Tax and HBZ that finely tune cellular signaling pathways. Viral replication through the infectious and the mitotic routes requires viral expression and faces destruction by the host immune response. Expression of viral proteins creates genomic stress responsible for DNA lesions that initiate the DDR response. Imperfect repair of these errors stabilizes mutations that potentially drive oncogenesis.


Experimental validation was conducted and good agreement with predictions was observed. This suggests certain discrepancies in action of these biostimulants on the genomic content of the initial microbial community for the catabolism of petroleum constituents or aromatics pollutants. In both cases, the emerging microbial communities were phylogenetically highly similar and were composed by very same proteobacterial families. However, examination of taxonomic assignments further revealed different catabolic pathway organization at the organismal level, which should be considered for designing oil spill mitigation strategies in the sea.Biostimulation with different nitrogen sources is often regarded as a strategy of choice in combating oil spills in marine environments. Such environments are typically depleted in nitrogen, therefore limiting the balanced microbial utilization of carbon-rich petroleum constituents. It is fundamental, yet only scarcely accounted for, to analyze the catabolic consequences of application of biostimulants. Here, we examined such alterations in enrichment microcosms using sediments from chronically crude oil-contaminated marine sediment at Ancona harbor  amended with natural fertilizer, uric acid (UA), or ammonium (AMM). We applied the web-based AromaDeg resource using as query Illumina HiSeq meta-sequences  to identify potential catabolic differences. A total of 45 (for UA) and 65 (AMM) gene sequences encoding key catabolic enzymes matched AromaDeg, and their participation in aromatic degradation reactions could be unambiguously suggested. Genomic signatures for the degradation of aromatics such as 2-chlorobenzoate, indole-3-acetate, biphenyl, gentisate, quinoline and phenanthrene were common for both microcosms. However, those for the degradation of orcinol, ibuprofen, phenylpropionate, homoprotocatechuate and benzene (in UA) and 4-aminobenzene-sulfonate,  Oil pollution still is a global problem . At presThe sources of nitrogen for the degradation tests – mostly performed at lab-scale and in minor occasions at field-scale – included nitrate, ammonium (AMM), urea, uric acid (UA), amino acids and the hydrophobic substance lecithin . Slow-reIn an early work using the recently developed AromaDeg analysis  and a me4Cl and 0.5 mM of Na2HPO4 resulting in a molar N/P ratio of approximately 10:1. For UA treatment microcosm, 0.21 g (1.25 mmol = 5 mmol N) of UA was provided as nitrogen source while the AMM treatment microcosms were each supplied with 2.5 mL of a 2 M AMM chloride solution . Both treatments also contained 2.5 mL of a 0.2 M disodium hydrogen phosphate solution . Excess amounts of crude oil were added to compensate for the 35% carbon losses due to evaporation of volatile hydrocarbons over the course of the experiment. Including losses due to evaporation, the C/N/P ratio was approximately 400:10:1. Control treatments were set up: (i) a negative control contained only sterile sand and ONR7a; (ii) two further controls contained sand, ONR7a, crude oil and either UA or AMM chloride solution but no sediment sample; and (iii) one control contained oil, sterile sand, ONR7a medium and a sediment sample, but no additional nitrogen source or phosphorus source was provided. No significant growth was detected under tested control conditions. Under the given assay conditions, the utilization of UA as carbon source is minimal, as the amount of carbon introduced by UA into the microcosms was disproportionately low in contrast to the residual carbon in the sediment and the carbon introduced in form of oil. Briefly, we added 300 mmols of carbon in form of oil and only 6.25 mmols of carbon in form of UA. In addition, the molar ratio C/N in the system  implies there was excess of carbon in the medium and thus the growth was limited by N.The starting point of this study were the meta-sequences previously obtained by direct sequencing from two microcosm sets created using sediment samples from the harbor of Ancona , as described previously . Both miThe resulting microbial communities from microcosms were destructively sampled after 21 days of incubation at 20°C, the isolated DNA subjected to the paired-end sequencing (Illumina HiSeq 2000) at Beijing Genomics Institute , and gene calling performed as described . TaxonomMetaGenome Source, followed by a short name indicating the origin of the sample and the nitrogen source, as follows: MGS-ANC(AMM) (the harbor of Ancona and AMM as nitrogen source); MGS-ANC(UA) (the harbor of Ancona and UA as nitrogen source).The meta-sequences are available at the National Center for Biotechnology Information (NCBI) with the IDs PRJNA222664 [for MGS-ANC(UA)] and PRJNA222663 [for MGS-ANC(AMM)]. The Whole Genome Shotgun projects are also available at DDBJ/EMBL/GenBank under the accession numbers AZIH00000000 [for MGS-ANC(UA)] and AZIK00000000 [for MGS-ANC(AMM)]. All original non-chimeric 16S small subunit rRNA hypervariable tag 454 sequences were archived at the EBI European Read Archive under accession number PRJEB5322. Note that the samples were named based on the code ‘MGS’, which refers to The web-based AromaDeg resource  was usedNote that the sequence material used in the present investigation for biodegradation network reconstruction was based upon single biological microcosm replicate to preserve maximum coverage and sequencing depth as well as for other technical reasons, as described previously . For eacp-cumate, orcinol, 2-chlorobenzoate, phthalate and phenylpropionate, all from Fluka-Aldrich-Sigma Chemical Co. , was added at a final concentration of 2 ppm each. These pollutants were selected on the basis of existing analytical methods to quantify their concentrations  were obtained as described above but omitting Arabian light crude oil; instead, a mix of pollutants containing naphthalene, 2,3-dihydroxybiphenyl, benzene, trations . Controlg at room temperature for 10 min. After supernatant separation, bacterial pellet was used for methanol extraction by adding 1.2 mL of cold (-80°C) high-performance liquid chromatography (HPLC)-grade methanol. The samples were then vortex-mixed (for 10 s) and sonicated for 30 s  at 15 W in an ice cooler (-20°C). This protocol was repeated twice more with a 5-min storage at -20°C between each cycle, and the final pellet was removed following centrifugation at 12,000 g for 10 min at 4°C. Methanol solution was stored at -80°C in 20-mL penicillin vials until they were analyzed by mass spectrometry and different and complementary separation techniques, namely liquid chromatography electrospray ionization quadrupole time-of-flight mass spectrometry (LC-ESI-QTOF-MS) in positive and negative mode, and gas chromatography-mass spectrometry (GC-MS), as described previously  were separated by centrifugation at 13,000 eviously . The abuTable 1). However, differences in the abundance of some community members could be observed on the basis of corresponding read frequency. As an example, the percentage of members of the Rhodobacteraceae and Enterobacteriaceae was elevated in microcosms supplied with AMM . Conversely, lower percentages of members of the Alteromonadaceae (9.6%/19.2%), Halomonadaceae (5.6%/7.8%), Moraxellaceae (0.5%/7.9%) and Flavobacteriaceae (1.8%/5.7%) could be detected in the AMM-supplemented microcosm in comparison to UA-based microcosms. At a genus level, 55 out of 57 identified proteobacterial taxa were common in both communities. However, enrichments containing AMM were characterized by higher percentages  of Alphaproteobacteria, such as Roseovarius sp. (1.4% in AMM vs. 0.1% in UA), Ruegeria spp. (1.1%/0.1%) and Sulfitobacter sp. (1.5%/0.1%), and some Gammaproteobacteria such as Vibrio sp. (2.4%/1.1%). In stark contrast to this, the UA-based enrichments showed significantly elevated percentages of members of the Firmicutes (7.4% in UA enrichments/4.9% in AMM enrichments) and Gammaproteobacteria, such as Aeromonas spp. (1.5%/0.5%) and Pseudoalteromonas sp. (1.9%/0.4%). Highly elevated percentages in UA enrichments were observed for the genera Acinetobacter (0.9% in UA enrichments/0.1% in AMM enrichments), Halomonas (6.1%/4.2%), Marinobacter (16.8%/6.3%) and Psychrobacter (6.8%/0.2%). A direct comparison of percentages of potentially oil degrading microbial genera in both microcosms showed a higher percentage of Acinetobacter sp. (0.9%/0.1%), Idiomarina sp. (0.8%/0.3%), Oleiphilus sp. (0.2%/0.03%) and Marinobacter sp. (16.8%/6.3%) but lower percentages of Alcanivorax sp. (3.9%/4.9%) and Thalassolituus sp. (0.04/0.8%) in the UA treatments . The amount of genes encoding Rieske non-heme iron oxygenases and extradiol dioxygenases (EXDO) of the cupin superfamily increased 2- and 4-fold, respectively, and proved more abundant in the AMM microcosm in comparison to those in the UA microcosm .Using as a query the 27,893 (for UA) and 32,180 (for AMM) potential protein-coding genes (for ≥20 amino acids-long polypeptides) , we idenFigure 2). For that we used the AromaDeg web system that allows identifying catabolic genes and appropriated scripts and commands for graphics (for details see Biodegradation Network Reconstruction: Scripts and Commands for Graphics). Unambiguous reaction specificities could be detected for 35 (in UA) and 48 (in AMM) catabolic genes and were considered in the degradation network . However, no clear specificities could be assigned to 4 (in UA) and 11 (in AMM) Rieske oxygenases and 12 (six in UA and six in AMM) EXDO, which subsequently were not considered in the network. As shown in Figure 2, on the basis of the presence of genes encoding catabolic genes involved in particular transformations, the potential degradation of nine intermediates involved in the degradation of six key pollutants  was found to be common for both microcosms. They include the transformation of biphenyl by Bph, 2,3-dihydroxybiphenyl by Dhb, benzoate by Bzt, indole-3-acetate by Ind, catechol by Cat, gentisate by Gen, 2-oxo-1,2-dihydroquinoline by Odm, 1-hydroxy-2-naphthoate by Hna, and 2-chlorobenzoate by 2-chlorobenzoate dioxygenase (2CB). Within them, genes encoding Cat were most abundant in both communities , in agreement with the fact that catechol is the central intermediate for most cyclic aerobic hydrocarbons degradation , independently whether they are enriched with AMM or UA, only the potential degradation of ibuprofen and 4-aminobenzene-sulfonate was not found associated to bacteria from other chronically crude oil-contaminated sites in oil-polluted sites along the coastlines of the Mediterranean Sea  were used. The capacity to degrade other pollutants predicted as potential substrates such as ibuprofen, phenanthrene, dibenzofuran, indole-3-acetic acid, 4-aminobenzene-sulfonate and quinoline, could not be experimentally proved because no analytical procedures could be designed for their analysis in the pollutant mix.Experimental validation assays were conducted to prove the extent of agreement with metagenomic-based predictions. For that, AMM and UA enrichment cultures were set up in triplicates as described in Section “Experimental Validations of Predicted Biodegradation Capacities,” in which instead of Arabian light crude oil as the carbon source , naphthalene, 2,3-dihydroxybiphenyl, benzene, Figure 3). This demonstrates that the naphthalene-to-salicylate-to-gentisate, 2,3-dihydroxybiphenyl-to-benzoate-to-catechol, and 2-chlorobenzoate-to-catechol degradation pathways occurred or were active in both microcosms. Note that the lower abundance level of gentisate in AMM microcosm may correlate with the 10-fold overabundance of genes encoding Gen enzymes in AMM as compared to UA; this may decrease the pool of gentisate in the microcosm when growing in naphthalene. We further found a decreased level of p-cumate only associated to the AMM enrichment. Phthalate degradation mostly associated to the AMM microcosm, as confirmed by the higher extend of phthalate degradation by meaning of its residual percentage at the end of the assay (21 ± 1.8% in AMM vs. 92.2 ± 2.3% in UA) and the 22.2-fold higher abundance of protocatechuate in AMM compared to UA assays. In addition, decreased level of orcinol, benzene and phenylpropionate associated only to UA enrichments . Accordingly, the benzene-to-catechol, orcinol-, and phenylpropionate-degradation pathways occurred or were active in the UA microcosm, while p-cumate degradation mostly occurred in the AMM enrichments.Samples were taken at 21 days of incubation at 20°C. Fingerprinting by LC-ESI-QTOF-MS and GC-MS was used to confirm the degradation of the initial substrates as well as the existence of degradation intermediates in both cultures. A careful inspection of the mass signatures confirmed the lowering in the abundance level of naphthalene, 2,3-dihydroxybiphenyl, and 2-chlorobenzoate, and the presence of catechol, salicylate, gentisate, and benzoate in both microcosms  fully confirmed our sequence-based predictions  for the degradation of all eight pollutants tested in each of the two amendments. This suggests that the differences herein predicted in UA and AMM microcosms  are due to real biological differences and not random. Uncertainty remains only for phthalate degradation in UA microcosm: experimental analysis demonstrated the slight degradation of this chemical , which was not predicted by sequence analysis .The identification of degrading capacities on microcosms depends heavily on enrichment conditions (including cultivation time frame) and bacteria and protein abundance. While these drawbacks are known, the experimental data presented above . Interestingly, Pseudomonadaceae which was the second most abundant microbial clade at the level of 16S rRNA in both microcosms , did not contribute to the degradation network in AMM but it does in the UA microcosm , where it supports the biphenyl-to-benzoate and homoprotocatechuate degradation.As can be seen in Figure 4, among the common degradation capacities, a number of observations can be made. First, the degradation of indole acetate by Ind was supported by members of Alteromonadaceae in AMM and Rhodobacteraceae in UA, which suggests a catabolic replacement. This was also observed for the degradation of biphenyl and benzene (by Bph/Bzn), most likely supported by members of the Pseudomonadaceae in UA but Rhodobacteraceae in AMM. We identified members of five proteobacterial families  and of the Firmicutes phylum as key groups for the degradation of gentisate (by Gen) in AMM. By contrast, only members of Alteromonadaceae were predicted to support gentisate catabolism in UA. In agreement with this it has been found that AMM promotes the growth of such multiple marine bacteria with the ability to utilize naphthalene (the precursor of gentisate) as a sole carbon in enrichment cultures  in both microcosms, the number of bacterial groups involved in its catabolism was also the highest . Therefore, a number of bacterial groups within the microcosms exhibit also partial catabolism redundancy.Multiple bacteria also contributed to the degradation of catechol (by Cat), with members of abolised . MembersHalomonadaceae family contributed also to degradation of aromatics, particularly, 2-chlorobenzoate (through 2CB) and biphenyl (through Dhb) in the UA microcosm . This suggests that halomonads not only participate in the conversion of UA to AMM, which further stimulated growth of hydrocarbonoclastic bacteria . However, according to the microbial biodegradation networks herein reconstructed, we also found that the two different biostimulants investigated, UA and AMM, have also changed substrate utilization capacities and preferences, which must be considered for the design of petroleum bioremediation techniques. This was demonstrated by showing that UA enriched for bacteria with the capability of degrading pollutants otherwise not degraded, or possibly degraded at low level, by those stimulated by the addition of AMM, and vice versa.Here, we report that different biostimulants applied in chronically polluted sediments have caused significant alteration in degradation capacities, while having no major effect on the taxonomic composition of microbial communities at the level of the family or higher. Experimental validation was conducted for at least eight of the predicted catabolic capacities, and good agreement with metagenomics-based predictions was observed. On the other hand, the metagenomics-guided metabolic reconstruction allowed us to refine the assignment of roles of community members in the utilization of multiple substrates and found different pathway organization at organism level. For example, while biphenyl degradation by Bph, DhB, and Bzt enzymes seems to be carried out by bacteria of Figure 2). In other side, UA will promote the growth of bacteria most active against benzene, orcinol-, ibuprofen- and phenyl-propionate . This will provoke a significant increase in multiple aromatics consumption in polluted areas. Having said that, this work seems to introduce a promising way for future oil-based contamination handling techniques. In this context, it would be very interesting to test the overall cleaning capacity (if any) on a real oil-contaminated marine sample. For that, also another point will be to use the combination of the UA and AMM, which was herein not presented in microcosm assays. It would be interesting to see their combinatory effect in the overall degradation capacity and taxonomic distribution of the microbial niche depending also on their ratio, so to find optimal nitrogen-containing formulations in real scenarios.Therefore, the results of this study show that smart formulations based on the application of multiple nitrogen sources, rather than commonly used single sources (mostly AMM), for example, may increase the efficiency of the biological removal of the widest diversity of aromatic pollutants and could be essential to support effective biodegradation strategies in response to an oil spill incident or in response to chronical pollution. Thus, as herein demonstrated, the utilization of both AMM and UA in conjunction will have a double aim. In one side, AMM may most likely enhance the bio-stimulation of bacterial populations capable of degrading heavy oil components such as naphthalene, phenathrene and dibenzofuran, as well as sulfonated-benzenes and substituted benzoate derivatives such as p-cumate (We would like to stress the attention to the fact that similarities regarding microbial community composition in the AMM microcosm from Ancona port with those reported in enrichments from surface water bodies at other Mediterranean sites, were found . HoweverThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.