Cross-sectional correlations between two variables have limited implications for causality. We examine here whether it is possible to make causal inferences from steady-state data in a homeostatic system with three or more inter-correlated variables. Every putative pathway between three variables makes a set of differential predictions that can be tested with steady state data. For example, among 3 variables, A, B and C, the coefficient of determination,  In the field of biomedicine, the nature of causality, and the use of correlations as an evidence for causality are much debated –4. ThereMore specifically, here we look at homeostatic systems which are extremely common in fields such as physiology. Homeostatic systems pose a unique problem for causal inferences. Causal inference can be based on time-series analysis with longitudinal data. LongituAlthough the use of correlations to infer causality is doubted, intervention experiments are generally taken as a convincing evidence of causality. However, causality in steady-state can be substantially different than causality in a perturbed-state and inferences from a perturbation experiment may not be applicable to steady-state causality. This necessitates a set of tools to infer causality in a steady state which is independent of perturbing interventions. We argue in this paper that it is possible to infer causal relationships among three or more variables from cross-sectional data in a homeostatic system in which the variables and their relationships are stable in time.Our motivation and the need for this tool came from some debated causal pathways in the pathophysiology of type 2 diabetes (T2D). According to the classical view, obesity-induced insulin resistance is primary, and rise in insulin levels is a compensatory response to insulin resistance, mediated by raised levels of glucose ,20. ThisElucidation of the causal pathway for a pre-diabetic or diabetic state is critical at the clinical level because the current approaches to medication are designed assuming one particular pathway, and have largely failed to cure diabetes. If it is possible to determine causality reliably, it can potentially change diabetes medicine. It has long been recognized that levels of glucose and insulin are under homeostatic control, and that fasting is a steady state –30. WithBeyond the specific problem of causality in pre-diabetes, a set of methods that can help infer causality from steady-state data will find a large number of applications, not only in physiology and disease, but in many other areas of science. Although our investigations began with the pathophysiology of diabetes, the emerging principles are generalizable and valuable for inferential statistics in general.Since the question addressed here and the approach suggested is complex we have organized the paper in the following sections. Section 2 describes the underlying principle and basic assumptions and spells out the nomenclature used in the rest of the paper. Section 3 details the development of the theoretical framework that is the central element of this work. We discuss first, possible alternative causal pathways between three inter-correlated variables, followed by the distinction between causal and regression equations that will underlie the subsequent analysis. In the interest of brevity, in the main text we restrict ourselves to reproducing only the final analytical prediction for each pathway, classified into acyclic and cyclic pathways, adding a few lines where pathway-specific predictions necessitate discussion. Complete proofs and derivations are instead relegated to the supplementary text. Section 4 discusses the testing of pathway predictions against suitable null hypotheses, and Section 5 describes findings regarding the sensitivity and robustness of the analytical predictions derived so far. Section 6 provides a general description of potential applications of the above theoretical framework. Section 7 encompasses a comprehensive demonstration of application of the framework to a real-life question in diabetes research. We first discuss in some detail, the current view on causal factors in diabetic and pre-diabetic states, before moving on to the dual feedback model that forms the main hypothesis to be tested. We analyze the system according to the methods detailed in previous sections, and present several novel observations along with their significance to diabetes research.We show here that when three inter-correlated variables are considered together with two or more causal arrows connecting them to make a causal pathway, each of the possible pathways makes a set of differential predictions by which the pathways can be differentiated from each other. Our approach to develop a method of inferring causality from cross sectional regression correlation parameters comprises following steps:We first list the perceived possible hypothetical causal pathways among three variables.For each pathway, we write a set of causal equations arising out of the hypothetical pathways. Steady-state solutions of these equations lead to a set of four general, and a few pathway-specific predictions. Each pathway therefore has a unique combination of such predictions or a prediction signature by which it can be differentiated from other pathways.Development of a theoretical framework-We test, using simulated data generated from assumed causal pathways, the conditions under which the predictions can be used to accept or reject a pathway reliably.Based on these results, we suggest ways of handling multivariate data and infer causal networks among them.Testing the pathways by simulations-We apply this logic to the specific case of pre-diabetes to examine the pathway classically thought to give rise to this condition.Demonstration of a specific example-We consider three variables labelled A, B and C. Additional variables if needed to describe a pathway will be labelled X, Y and so on. All causal relationships represented by a single arrow are assumed to be linear, and all primary input variables are assumed to be normally distributed. In a given operation, the slopes of causal pathways are assumed to be constant; the errors in causal pathways are assumed to be distributed normally, with means of zero and a constant standard deviations, and no covariance with each other. We assume that the errors are caused by variation in individual responses, and that a given individual’s response is consistent in time sufficiently long to reach a steady state. Thus, the errors are randomized over the population, but for a given individual, they are constant in time. We assume no measurement error in the baseline models. Since all our predictions are related to correlation coefficients and regression slopes, we will ignore the intercepts for the sake of simplicity in deriving many of the predictions.A variety of cyclic and acyclic pathways can exist in three variables. Based on hypothesized pathways, we can write specific causal equations for each. The causal equations are derived from the hypothesized pathway, while the regression equations can be obtained from the given cross-sectional data using regression and correlation analysis. Our causal equations are similar to the structural equations of . HoweverFor ensuring steady-state, we assume that a given variable has a rate of formation/increase and a rate of degradation/decrease. If the rate of degradation is positively dependent, or the rate of formation negatively dependent on the standing level, then the variable invariably reaches a steady state determined by the set of input parameters. Such steady states are characteristic of homeostatic systems, and this principle is central to our methods.A → B → C, B and C will be For example, in a linear pathway m1 and m2. For pathways involving loops and feedback, the relationships between variables are more complex, and therefore necessitate the use of degradation constants in the causal equations for ensuring steady-states.In simple cases, we need not explicitly include the rates of degradation in the equations but directly use parameters, Causal and regression parameters can be analytically related, and these relationships allow the formation of regression-based predictions for each pathway based on the known relationships between the corresponding causal parameters. Here, we put the two together and describe four general predictions across all pathways and formulate a null hypothesis for each. In addition, there are certain pathway specific predictions that will be discussed along with the description of the corresponding pathway. The four general predictions are:Whether Mca can be estimated from the product of the slopes Mba and Mcb.Whether slope B on A (Eba) are correlated with those of C on B (Ecb): The errors or residuals in a regression are assumed to be random independent errors. However, we will show below that if there are loops, convergent or confounding elements in a pathway, Eba and Ecb do not remain independent. Based on the nature of dependence between Eba and Ecb, presence of, and possible nature of the loops and convergence can be inferred.Whether the residuals of the regression of A improves or reduces the correlation of B with C, i.e. whether a. Whether correction for b. Whether the extent to which We will now state how each of the pathways makes specific predictions. For detailed formal proofs and derivations refer to Pathways P1 to P5 represent acyclic pathways and P6 and P7 represents pathways with loops. In pathways with loops, since there is a cyclic dependence between the variables we begin with differential equations with variable-specific constant rates of disintegration that assure steady states. This set of equations is then used to derive equilibrium solutions.The causal equations for a linear pathway are:ea,e1,e2 are not correlated.Where B on A, the slope = cov /var A,Regression parameters can be derived from the causal equations as follows. Since in regression of For linear equations, there is little difference between the causal equations and regression equations . The regrAC − rABrBC = 0  The causal equations for this model would beNote that the relationship between causal parameters and regression parameters is substantially different in this pathway than the linear pathway . For exaHowever, this difference is not detectable from cross-sectional data alone. Therefore, the standard four testable predictions of this pathway remain similar to the linear pathway. We will describe later that differentiating between pathways P1 and P2 is possible using a different strategy.Prediction R1: From Mca can be predicted from the product McbMbaPrediction R2: From Prediction R3: From Prediction R4: As formally shown in ea,e1 and ec are uncorrelated.The causal equations for this model would beRegression parameters derived from the causal equations are given in A and C from this pathway, unless there are additional external pathways linking the two. The other unique feature of this pathway is that both A and C have independent causal influence on B. As a result, the effect of A adds to the error in the correlation between B and C and similarly, the effect of C contributes to the error in the correlation between A and B. As a result, There are two pathway specific predictions for the convergent pathway, shared only by the different cause pathway. Firstly, we expect no correlation between A and C to be independent input variables we assume no correlation between them. However, if they are correlated due to some cause other than this pathway, only then This prediction is so robust that if Prediction R1: Unlike pathways P1 and P2, for the convergent pathway, it can be seen that Mca is zero, and both Mba and Mcb are non-zero, their product is not a predictor of Mca.Prediction R2: Since the expected slope Mcb.Prediction R3: The correlation Prediction R4: It can be shown that (a) Because this expression differs from R4 (b) of the earlier pathways, we can use a more generalized form for R4 (b) as exe1e2e3 are not correlated.he causal equations for this model would be e1e2e3 are important in defining this pathway. If e2 is negligible the pathway approximates to the radiating pathway with B being the mediator between A and C. Similarly, at small e1, A becomes the mediator and at small e3, C becomes the mediator in a radiating pathway. For the way we have defined our predictions, e2 is the most important error term in this pathway  Prediction R4: For this pathway (a) exeae1e2 are not correlated. Regression parameters derived are in The causal equations for this model would beTwo specific predictions of this pathway shared only by the convergent pathway (P3) are that Prediction R1: Mca| < McbMbaPrediction R2: |cb.Prediction R3: The correlation  and further (b Prediction R4: (a) exeye1e2e3 are not correlated. Regression parameters derived are in The causal equations for this model would beTwo specific predictions of this pathway shared only by the convergent pathway (P3) are that Prediction R1: Mca| < McbMbaPrediction R2: |Mcb.Prediction R3: The correlation  and further (b) Prediction R4: (a) It can be seen that all predictions of pathways P5a and P5b are identical and henceforth we will treat both of them in a single group as pathway P5.eae1e2 are not correlated, and d1 and d2 are positive. For a consistent definition of feedback, we assume m1 and m2 to always be positive, and that the sign of mf decides whether it is a positive or negative feedback loop. Feedback loops depend crucially on the relative strength of the forward versus backward causation. If the feedback term, i.e. the effect of C on B is weak, it approximates to a linear pathway, and if the forward term i.e. effect of B on C is weak, it approximates to a convergent pathway. Therefore, the predictions of linear or convergent pathways can be expected if the forward or feedback links respectively are weak. Additionally, however, the negative feedback pathway is associated with a problem of definition. If the feedback effect of C on B is stronger than the effect of B on C, the signs of the slope in the causal and regression equations could be opposite, implying that while m2 is positive, Mcb could become negative. This happens whenThe causal equations for this model would beB and C are given byThis results in a paradoxical transformation of a causally negative feedback into an apparent positive feedback since the sign of the slope and that of the feedback effect is the same. Further, when the negative feedback is much stronger than the forward effect, the predictions of convergent pathway are more applicable than the predictions of negative feedback pathway. At equilibrium where both For simplification we take Similarly, e2, and would therefore co-vary. The sign of this covariance is decided by the sign of mf, i.e. whether the feedback is positive or negative.It should be noted that Regression parameters can be derived from the above as in Prediction R1: When the feedback is negative The reverse applies for positive feedbacks where Mca > McbMba and in positive feedback |Mca| < McbMba.Prediction R2: In negative feedback mf which is negative for negative feedback and positive for positive feedback.Prediction R3: The sign of this correlation will be decided by the sign of rBC. For smaller rBC it is difficult to make a definite prediction. (b) for negative feedback rBC.Prediction R4: (a) It can be shown that for negative feedbacks eae1e2 are not correlated. At equilibriumThe causal equations for this model would beWe will take e1 decides both e1 and e2, the covariance between e1 and e2 will be mf is negative i.e. for negative feed-forward, and negative when mf positive i.e. positive feed-forward is.Note that since m1 and m2 to be positive and the sign of mf decides whether the feed-forward is positive or negative; a negative feed-forward pathway is once again associated with a problem of definition. If the feed-forward effect of A on C is stronger than that through B, and if their signs are opposite, the signs of slope in the causal and regression equations could be opposites. That is, if mfd1 > m1m2 then Mcb can be negative although the causal relationship between B and C is positive. This results in a paradoxical transformation of a causally negative feed-forward pathway into an effectively positive feed-forward pathway as the product MbaMcb and Mca both have the same sign.For simplifying the definition of feed-forward, we assume Note that while all the expressions are the same as in feedback pathways , the difPrediction R1: For positive feed-forward mf is positive, McbMba < Mca. In the case of negative feed-forward McbMba > Mca but under conditions in which a negative feed-forward transforms into a positive feed-forward, McbMba < m1m2 = Mca.Prediction R2: When Mcb. For a negative feed-forward pathway, under conditions when it transforms into an effective positive feed-forward correlation, Mcb.Prediction R3: For positive feed-forward, we expect a negative correlation, and for negative feed-forward, a positive correlation. Therefore, for positive feed-forward, the correlation rAB.Prediction R4: (a) In the case of positive feed-forward (b) For positive feed-forward 0 is true, it is indicated by green, while H1 being true is indicated by red and H2 being true by yellow.Testing in data needs to be different for equality and inequality predictions. The prediction can serve as the null model wherever equality is predicted but needs to be treated as an alternative hypothesis wherever inequality is predicted. For pathways that predict equality, a two-tailed probability is used, and for pathways predicting one-way inequality, a one tailed test is used. Numerical simulations were run using the causal pathway equations for each of the pathways P1 to P7 to generate data, assuming the errors to be distributed normally around a mean zero. In the results of simulations reported below, the convention consistently followed through Figs 0: 1: 2: R1: H0: Mca = McbMba (green), H1: Mca < McbMba (red) and H2: Mca > McbMba (yellow).R2: Hm1 and m2 are allowed to be positive or negative in the models, the correlation coefficients are multiplied by the sign of the slope Mcb.R3: Since the signs of causal slopes 0: 1: 2: H0: 1: 2: R4a: H0: 1: 2: R4b: HThe results match very well with the predictions in rBC and Y is rBC becomes negative and the predictions of positive feedback and feed-forward respectively apply. It can also be seen that when the ratio is close to zero, predictions of converging pathway hold true.For feedback and feed-forward pathways predictions from R1 to R4a, the X axis is Predictions from R4b for all pathways are shown as scatter plots with Rejection due to overfitting inequality: For all inequality predictions, overfitting is possible. For example, if we expect that ions see , or usinWe used simulations to test the sensitivity and reliability of the analytical predictions. The simulations were run using the causal pathway equations for each of the pathways P1 to P7 to generate data, assuming the errors to be distributed normally around a mean zero. Up to 10000 simulations are run, with each run using randomly drawn parameters and error standard deviations from a given range  . For exaple size . Althougple size . In the ple size . This imr2 values. Feedback is not distinguishable from linear pathways unless the feedback arrow is sufficiently strong. Such limitations are common to all statistical tools, and they need to be used and interpreted in light of the appropriate context and conditions.It can be seen from The predictions derived and tabulated above are based on the typical assumptions of mainstream statistics that the input variables are distributed normally and that all causal links are linear. However, it is important to ask how critical these assumptions are for the predictions to work. In experimental biology, the input variable is often designed to have uniform intervals and is not normally distributed. A moderate deviation from linearity is also common in physiological and other biological systems. If the predictions are too sensitive to these assumptions, they may prove to be of limited use in real-life. We used Monte-Carlo simulations to assess whether the predictions work under moderate deviations from these assumptions. When the input variables were selected randomly from a uniform rather than a Gaussian distribution, all predictions worked with nearly the same differentiating ability (data not shown). Similarly, when non-parametric Spearman ranked correlations were used instead of Pearson’s correlations, the correlation related predictions (from R1 and R4) worked similarly (data not shown). This demonstrates that the tools are not too sensitive to the assumptions of normality of input variable, linearity of relationships, and parametric or non-parametric nature of correlations.Two approaches are possible by which the predictions of a pathway can be tested using real-life data.(i) Based on confidence intervals of correlations and regression slopes: The null hypotheses for every prediction can be tested using calculation of confidence intervals of regression correlation parameters. Simulations have shown that except when the underlying correlation coefficients are too low, this approach can be reliably used to test the predictions. The sensitivity of predictions depends upon the sample size as well as the position in the parameter space  Monte-Carlo simulation approach: An alternative approach, which will be more conservative in rejecting pathways, is the Monte-Carlo approach. Assuming a specific pathway to be true, it is possible to back calculate the causal equation parameters from the regression correlation parameters obtained in the data  and (iii) in a different context below.It is extremely difficult to infer causal relationship between two correlated variables. Although some solutions have been suggested, their applicability is limited , 16. HowTo infer causal pathways within three intercorrelated variables, three alternative approaches are possible. The first approach is to test and resolve between preconceived hypothetical pathways. It is likely that prior knowledge or some insights into mechanisms allow us to start with a few plausible alternative pathways. It is possible to perceive more complex pathways by combinations of the primary pathways that we considered in this paper. For example, a pathway may contain both feedback and feed-forward elements. Such complex or combinational pathways can be used to make a set of predictions by the analytical approach described above and testing these predictions can resolve between pathways. If we do not have such preconceived pathways, it would be necessary to consider all possible combinations of pathways between the three variables and make differential predictions from each of them. In such cases, we must also consider permutations of the variables. At the end, it may not be possible to ascertain a single unique causal pathway since the prediction signatures of some of them may be identical. Nevertheless, it would still be possible to reject some pathways based on their prediction signature. In addition, if available, we can involve a fourth variable correlated to one or more of the three, if there is some pre-existing knowledge about its causal relationship.In complex systems, often there are large causal networks. In such networks, combinations of 3 membered motifs can be identified. Out of the possible pathways among three variables some can be rejected using analysis of the three variables. Bringing in a fourth one can provide additional insights which can be used for cross checking or validating our first set of inferences. In complex causal networks, there can be many such cross check and validation possibilities. For large networks algorithms requiring massive computational power may be needed that may pin down one or a few network structures from the large number of possible ones using combinations of three-member motifs and cross validation facility among the motifs.Apart from some common pathways described above, it is possible that real life problems have some added complications due to which, the standard solution of testing a fixed set of predictions may not be sufficient. However, one can apply similar foundational principles to handle such pathway-specific questions. We will illustrate this using a classical hypothesis that attempts to explain a human physiological state designated as an insulin resistant, hyperinsulinemic, normoglycemic, pre-diabetic state. In this state, the plasma levels of fasting insulin (FI) are raised although fasting glucose (FG) remains normal. The classical interpretation of this state  is that A logical flaw in this interpretation is that, after the glucose levels return to normal, there is no reason why FI should remain high. Insulin has a short half-life of about 6 minutes ,35 and tThe pathway in question is more complex than the basic set of pathways P1 to P7. For regulation of glucose production by the liver and glucose uptake by tissues, there is a dual negative feedback. One feedback is exerted by glucose itself, which enhances tissue uptake and suppresses liver glucose production. The other feedback operates through insulin, which facilitates glucose uptake by insulin-dependent tissues and suppresses liver glucose production. If we ignore the direct glucose feedback and assume that feedback regulation operates only through insulin, then there is a single negative feedback. Thus the pathway can be simplified to the negative feedback pathway P6. If we incorporate dual feedback, as the equations show below, the relationship between insulin resistance and FG is not strictly linear. We could therefore use the standard set of predictions of a negative feedback model, assuming a single feedback. Alternatively, we can use the dual feedback model, and apply simulations to make and test predictions, since empirical estimates for most of the parameters are available from experiments see .However, the main problem in testing these pathways is that we have no direct measure of insulin resistance. HOMA-IR and HOMA-β are believed to measure insulin resistance and β cell response respectively, but they are derived from the other two variables, which makes the problem tricky and circular. We approach the problem using more than one set of assumptions. (i) First, we test the dual feedback pathway  assumingAs a general note, we would like to add that the HOMA indices being ratio measures could be confounding factor. It is known that ratio indices can behave unpredictably in different parameter ranges, and can therefore generate spurious correlations. In our view, this strengthens the case for using the underlying causal relationships between the variables to interpret these correlations in the appropriate context.n individuals from the four samples. (i) Coronary Risk of Insulin Sensitivity in Indian Subjects (CRISIS) Study, Pune, India [We used four data sets of sample studies by two research groups. All the four sets contain individuals with and without overt type 2 diabetes. Since we are addressing the prediabetic state here we have taken the non-diabetic subset of e, India  (n=558).e, India  (n=299).e, India  which hae, India  (n=299).We assume that the standing plasma glucose level is a result of baseline rate of glucose production by the liver; suppression of this production as well as muscle glucose pickup which is proportional to the standing glucose level (direct glucose feedback); the insulin mediated suppression as well as uptake (insulin mediated feedback) and individual variability. The standing insulin levels are a result of glucose stimulated insulin secretion on the one hand and insulin degradation on the other. Thus, the causal equations can be written asK1 denotes the rate constant for negative feedback of glucose on liver glucose production and tissue glucose uptake; K2 denotes the rate constant for insulin-mediated feedback which is proportional to ISENS, the insulin sensitivity of tissues; K3 is the rate constant for glucose-induced insulin release; and d, the rate of insulin degradation.Where G and I are plasma levels of glucose and insulin respectively, and FG and FI are the fasting steady state levels of the same. At steady state, we getIR) which is a reciprocal of insulin sensitivity. Using the reciprocal, we can writeIt can be seen that the steady state glucose level is a function of insulin resistance  FG and FI are poorly related . The gluK3/d. Empirical estimates for both K3 and d are available . Thus, apart from a mismatch between the slope required to cause the observed variation in FI and actual slopes, the slopes expected from the empirical estimates of parameters and those obtained in regression also do not match. The latter mismatch by itself may not be sufficient to reject the pathway since a large measurement error in the X variable, i.e. FG can lead to underestimation of regression slope, but this explanation implies that a substantial part of variation in glucose is independent of insulin resistance, and is akin to random error with respect to the hypothetical causal pathway.By the steady state equations, the slope of the regression of FI on FG should be able see  and therK3. However K3 is a constant in our model, and although it may have some variability in the population, it is uncorrelated with the three variables of concern. Therefore, HOMA-β should show no significant correlation with FG, FI and HOMA-IR. However, in all the four data sets HOMA-β is significantly positively correlated with FI, but negatively correlated with FG and positively correlated with HOMA-IR.HOMA-β in our assumption represents In a negative feedback pathway ulations . The corThus if we assume the two HOMA indices to faithfully represent insulin resistance and beta cell response respectively, then classical pathway needs to be rejected owing to mismatches with many of its predictions.The distribution generated by simulations matches well with the real life correlations for true IR-FG (grey bars and arrows), FG-FI (red bars and arrows), and the product of the two (purple bars and arrows). The correlation between true IR and FI is greater than the product as predicted by the pathway  but the correlation between HOMA-IR and FI (blue bars and arrows) is substantially greater than the predicted leading to an overfitting rejection. This indicates that either HOMA-IR as currently calculated is substantially different from true insulin resistance or the pathway get rejected based on this prediction.m.FG + e, HOMA-IR will be correlated to FG2. Similarly, HOMA-β should be represented as m.FG/(FG – 63)+e. Under normal physiological range, FG > 63 and therefore HOMA-β is a decreasing function of FG. As a result both FI and HOMA-IR should be negatively correlated to HOMA-β. Simulations of the pathway results in a negative correlation between HOMA-IR and HOMA-β as long as the errors are small to moderate. These expectations do not match the empirical data, in which FI and HOMA-IR have significant positive correlations with HOMA-β. Thus, accepting the classical pathway with some allowance for artifacts coming out of the derived variables is not sufficient to explain the empirical correlations.Effects of deriving HOMA-IR and HOMA-β from FG and FI: Since HOMA-IR and HOMA-β are not independently measured but derived from FG and FI measurements, some correlations will follow from the derivations themselves. The overfitting anomaly observed above can be explained as an artifact coming out of the calculation of HOMA-IR. However, some other anomalies remain unexplained. Here we are assuming that the classical pathway is true and therefore, FI is a linear function of FG. If FI is represented as 2 ranges from 0.026 to 0.049, and thus not more than 5% of variance is explained by the relationship.Testing the predictions of the null model: If FG and FI are independent of each other and have some variance around a mean, HOMA-IR is expected to be positively correlated with both since it is a product of the two. FI should be positively correlated with HOMA-β, but FG should be negatively correlated with HOMA-β. In the HOMA-IR- HOMA-β relationship, FI is in the numerator of both. FG is in the numerator of HOMA-IR but in the denominator of HOMA-β. Nevertheless, since the coefficient of variation of FI is substantially greater than that of FG, FI is expected to dominate the relationship and result in a positive correlation between HOMA-IR and HOMA- β. All these predictions are observed in the data . The misIf we consider FG and FI to be independent and HOMA-IR and HOMA-β derived from them, they constitute a convergent pathway that can be tested by the pathway predictions. It can be seen that predictions from R1, R2 and R3 of the convergent pathway are accepted. However, prediction from R4 and the pathway-specific prediction are rejected Tables  and 13. If we ignore the non-linearity of the model and assume HOMA-IR and HOMA-β to faithfully represent insulin resistance and beta cell response, we may use the 4 predictions of the standard negative feedback pathway. It is seen that predictions from R1, R2 and R3 are accepted but the outcome of prediction from R4 is complex . After ce1 and e2 are close to zero Click here for additional data file.S2 Text(DOCX)Click here for additional data file.


Further bioguided fractionation of the extracts of this sponge led us to isolate the first sulfur-containing organic polyarsenicals ever found in Nature. These metabolites, called arsenicin B and arsenicin C, are built on a noradamantane-type framework that is characterized by an unusual As–As bonding. Extensive NMR measurements, in combination with mass spectra, enabled the assignment of the structure for arsenicin B (C3H6As4S2) as 2. The scarcity of arsenicin C and its intrinsic chemical instability only allowed the collection of partial spectral data, which prevented the full structural definition. After the extensive computational testing of several putative structures, structure 3 was inferred for arsenicin C (C3H6As4OS) by comparing the experimental and density functional theory (DFT)-calculated 1H and 13C NMR spectra. Finally, the absolute configurations of 2 and 3 were determined with a combined use of experimental and time-dependent (TD)-DFT calculated electronic circular dichroism (ECD) spectra and observed specific rotations. These findings pose great challenges for the investigation of the biosynthesis of these metabolites and the cycle of arsenic in Nature. Arsenicins B and C showed strong antimicrobial activities, especially against S. aureus, which is comparable to the reference compound gentamycin.Arsenicin A (C Inorganic arsenic compounds are ubiquitous on earth and in the atmosphere. Natural organoarsenicals are also found, originating from the conversion of inorganic arsenic forms by a wide variety of marine organisms and including volatile, non-volatile, and water-soluble polar metabolites . StudiesNonetheless, the knowledge of natural sulfurated organoarsenic compounds remains scanty, and is limited to thioarsenic acid, which is involved in the metabolism of cod-liver arsenolipids in humans , 2-dimet3H6As4O3) from the poecilosclerid sponge Echinochalina bargibanti from the northeastern coast of New Caledonia. Arsenicin A, the first example of a polyarsenical of natural origin, was structurally assigned by extensive NMR and IR spectra measurements and ab initio calculations to have an adamantane-type backbone  oxide, which reduces the risk associated with the handling of these dangerous chemicals. The in vitro cytotoxicity screening of these compounds on a full panel of cancer cell lines at the National Cancer Institute (NCI-USA) indicated that the most lipophilic arsenicin A analogue is responsible for the best growth inhibition of both leukemia and solid tumor cell lines, with ICcompound .Arsenic is the epitome of toxicity and, indeed, all of the arsenic compounds are toxic to some degree. Nevertheless, some arsenicals have been used as therapeutic agents. Significant examples include the synthetic drug salvarsan, which has been used to treat syphilis and trypanosomiasis, which is also known as the sleeping sickness, and arsenic trioxide, which has been exploited in traditional Chinese medicine for a long time. It was also approved for the treatment of acute promyelocytic leukemia by the Food and Drug Administration (FDA) in 2000 and is currently studied for the treatment of solid tumors . In the E. bargibanti provided further advances on polyarsenicals. We describe here the first natural polyarsenicals built on a noradamantane framework, containing unprecedented arsenic–sulfur bonds and a very unusual arsenic–arsenic bond.A careful examination of the minor metabolites isolated by extracts of the New Caledonian sponge E. bargibanti were subjected to bioassay-guided silica flash chromatography (FC). Bioactive fractions from antibacterial and antifungal assays were freed from sterols and carotenoids by reverse-phase FC. Preparative HPLC purification performed under careful handling provided, besides the known arsenicin A (1) + at m/z 407, which was detected as the most intense signal, adduct ions appeared at m/z 423 and m/z 439. The latter signal can be attributed to a [MH(MeOH)]+ ion, since methanol was used as the solvent for direct injection of the sample into the source. In accordance, the intensity of this signal increased upon lowering the applied voltage. Tandem fragmentation (+)APCI-MS/MS experiments, carried out on the m/z 407 signal, revealed the loss of a CH2S fragment. Envisaging sulfur in the place of oxygen atoms, this behavior recalls the fragmentation that is observed for both arsenicin A and its synthetic model compound, where the loss of CH2O and CH3CHO, respectively, was observed 2Cl2/H2O partitioned, and the residue from the evaporation of the organic phase was subjected to gradient flash chromatography , bioguided by both S. aureus and Candida albicans activities. This procedure was then carried out on the active fractions from the whole freeze-dried material. The CH2Cl2 extract (20 g) was subjected to flash chromatography, collecting 26 fractions of 0.1-L each. Combined fractions 6–15 were subjected to reversed-phase flash chromatography , setting free the organoarsenicals (UV spots) from the sterols and carotenoids. The solvent, MeCN, was evaporated at room temperature, and the remaining aqueous residue was extracted with AcOEt and evaporated. The residue was subjected to preparative HPLC purification  with n-hexane/AcOEt 96:4 to give, in the order of elution, arsenicin B , arsenicin C , and arsenicin A . The two minor compounds proved to be labile under the routine aerial workup, so that their isolation from elution by preparative HPLC was repeated under a nitrogen atmosphere in order to minimize oxidative degradation.The sponge (R1858/881m) was collected along the northeastern coast of New Caledonia at 18–25 m depth during the Substances Marines d’Intérêt Biologique (SMIB) program , 439 [(M + H + MeOH)+], 423 [(M + O + H)+], 407 [(M + H)+]; APCI-MS/MS (407): m/z 361 [(M + H -CH2S)+].3)Arsenicin C (. White powder. 3). NMR data: in m/z 392 , 390 , 360 , 344 , 301 , 300 (2), 299 , 283 , 253 (9), 225 (14), 163 (19), 107 , 91 , 89 , 75 . HR–EI–MS: m/z 389.7059 ± 0.007 ; 343.7116 ± 0.001 . APCI-MS (positive ion mode): m/z 391 [(M + H)+]; APCI-MS (positive ion mode): m/z 439 [(M + O + H + MeOH)+], 423 [(M + H + MeOH)+], 407 [(M + O + H)+], 391 [(M + H)+]; APCI-MS/MS (391): m/z 361[(M + H -CH2O)+], 345 [(M + H -CH2S)+], 285, 257; APCI-(MS)3: 391 → 361 → 253 → 225.J), were calculated with three different methods. Methods A and B are non-relativistic and employed the hybrid B3LYP functional. They differ in adopting either the cc-pVTZ basis set (A) or a mixed basis set (B) comprising the pcJ-2 basis set [ref − σ. TMS (tetramethylsilane) was used as the reference compound, with the following σref values for each method. 13C: 184.5978 (A), 181.1052 (B), 186.88 (C); 1H: 31.7660 (A), 31.6927 (B), 31.64 (C).The structure of each species was optimized at the non-relativistic B3LYP/6-311G level, which was previously found to be appropriate for the other arsenicals . Naturalasis set  for S, Oasis set ,31; howeasis set . Geometrasis set , and relasis set  and assoasis set ,35,36,37asis set ,39 modulS. aureus and E. coli) were grown on Mueller Hinton agar plates, and seeded after 24 h of growth at 37 °C on fresh Mueller Hinton agar plates. Candida albicans was grown on Sabouraud Agar plates (pH 6) and also seeded after 24 h of growth at 37 °C on fresh Sabouraud agar plates. Cellulose disks (6-mm diameter) impregnated with the natural compounds (see Bacteria (unds see  and the Echinochalina bargibanti as the first polyarsenic compound in Nature, we report here new sulfurated polyarsenic metabolites, called arsenicin B and arsenicin C, which were isolated from the same sponge. These metabolites differ from the adamantane-type structure of arsenicin A by their noradamantane framework, having a different heteroatoms connectivity, and containing a very unusual As–As bond. Structure 2 for arsenicin B rests on spectral data supported by the X-ray validated structure of a recently reported synthetic product. The incomplete set of spectral data obtained for the thermally labile arsenicin C left the relative positions of oxygen and sulfur atoms unassigned. This issue was circumvented by simulating its NMR spectra with DFT calculations, including heavy-atom spin-orbit effects on 13C shieldings. This allowed us to assign structure 3 to arsenicin C, based on the method validated on a series of trial structures of arsenicin B. The absolute configurations of metabolites 2 and 3 were determined with a combined use of experimental and TD–DFT calculated ECD spectra, and experimentally obtained specific rotations.Following our previous report on arsenicin A, isolated from the poecilosclerid sponge Arsenicins A–C represent a new class of natural products that has no precedent. Great challenges are posed by these unique compounds. The discrimination between the biosynthesis of the arsenicins by either the sponge enzymes or microbial symbionts, as well as the identification of the biosynthetic pathways still remain unresolved. Finally, a rethinking about the recycling of arsenic in Nature must be considered after the discovery of these compounds.Staphylococcus aureus showed that arsenicins A–C have biological effects that are similar to those of the antibiotic gentamycin. Furthermore, the promising results obtained on arsenicin A and its related synthetic compounds embolden also including also sulphurated polyarsenicals in the next studies for the development of new, eventually more potent and selective arsenical agents for therapeutic applications against cancer.The results for antimicrobial evaluation against


Methanothrix concilii, and exoelectrogenic Geobacter lovleyi, were successfully recovered for the reconstruction and analysis of metabolic pathways involved in the transformation of fatty acids to methane. In particular, SPOB degraded propionate into acetate, which was further converted into methane and CO2 by M. concilii via the acetoclastic methanogenesis. Concurrently, G. lovleyi oxidized acetate into CO2, releasing electrons into the extracellular environment. By accepting these electrons through direct interspecies electron transfer (DIET), M. concilii was capable of performing CO2 reduction for further methane formation. Most notably, an alternative RuBisCO-mediated CO2 reduction (the reductive hexulose-phosphate (RHP) pathway) is transcriptionally-active in M. concilii. This RHP pathway enables M. concilii dominance and energy gain by carbon fixation and methanogenesis, respectively via a methyl-H4MPT intermediate, constituting the third methanogenesis route. The complete acetate reduction (2 mole methane formation/1 mole acetate consumption), coupling of acetoclastic methanogenesis and two CO2 reduction pathways, are thermodynamically favorable even under very low substrate condition (down to to 10−5 M level). Such tight interactions via both mediated and direct interspecies electron transfer (MIET and DIET), induced by the conductive GAC promote the overall efficiency of bioenergy processes.In a staged anaerobic fluidized-bed ceramic membrane bioreactor, metagenomic and metatranscriptomic analyses were performed to decipher the microbial interactions on the granular activated carbon. Metagenome bins, representing the predominating microbes in the bioreactor: syntrophic propionate-oxidizing bacteria (SPOB), acetoclastic  Methanogenesis is accomplished by the syntrophic microbial interactions to achieve interspecies electron transfer (IET). Traditionally, IET was thought to be accomplished by syntrophs and methanogens through diffusive carriers . This form of mediated IET (MIET) is constrained by the physical distance between syntrophs and methanogens, and the diffusion rate of electron carriers, creating a metabolic bottleneck2. However, there is growing evidence of an alternative direct interspecies electron transfer (DIET), which could overcome this bottleneck and enhance methane production rate4. DIET occurs in the presence of exoelectrogens, which are capable of shuttling electrons exogenously to methanogens through conductive pili or surfaces5. It is the predominant mechanism responsible for electron exchange in natural methanogenic communities aggregates6 and methanogenic system supplemented with electrically conductive particles such as magnetite (Fe3O4)4.Energy recovery in the form of methane with sewage is of great interest9. Effluent produced by anaerobic fluidized bed bioreactor (AFBR) was treated further by anaerobic fluidized bed ceramic membrane bioreactor (AFCMBR), termed as staged, anaerobic fluidized bed ceramic membrane bioreactor (SAF-CMBR). In these setups, GAC addition was originally conceived to serve as a mechanical scouring agent along membrane for reducing membrane fouling and a carrier for microbial attachment. Recently, we examined the microbial community in a staged anaerobic fluidized-bed ceramic membrane bioreactor (SAF-CMBR) consisting of anaerobic fluidized-bed bioreactor (AFBR) followed by anaerobic fluidized-bed ceramic membrane bioreactor (AFCMBR), fed with acetate and propionate8. This presented an ideal system for investigating syntrophic microbial interactions as these substrates are the key precursors of methanogenesis, driving IET in energy-limited methanogenic systems12. We observed the co-dominance and tight interactions between syntrophic propionate oxidizing bacteria (SPOB), Syntrophobacter and Smithella, acetoclastic methanogen Methanothrix, and exoelectrogen Geobacter on the GAC particles, suggesting that GAC serves as an electrically conductive material for promoting DIET in the SAF-CMBR8. This lends further credence to the growing literature on GAC-facilitated DIET3. Nevertheless, the metabolic interactions, particularly between Methanothrix and Geobacter, have yet to be fully understood. Such energy efficient SAF-CMBR was an ideal system to explore the underlying metabolic mechanisms and to provide their exploitation for the further improvement of the reactor design and operation.Energy efficient anaerobic fluidized membrane bioreactor (AFMBR) systems, which use granular activated carbon (GAC) as the fluidized media, are developed to treat domestic wastewaters with high energy harvestMethanothrix and Geobacter as both species utilize acetate for methanogenesis and respiration14. However, synergetic interaction between these two microbes is also possible. Most interestingly, while Methanothrix is unable to perform hydrogentrophic methanogenesis using CO2 due to the absence of a hydrogen uptake mechanism15, it can form syntrophic association, termed ‘electric syntrophy’, with Geobacter to achieve methane production, in a similar fashion to hydrogentrophic methanogenesis, through DIET-dependent CO2 reduction16. On the other hand, Kono et al. (2017) recently proposed a ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO)-mediated CO2 fixation pathway in many methanogen species including Methanothrix. It could reduce CO2 into various carbon intermediates for important metabolic pathways, such as gluconeogenesis and glycolysis, with acetate or/and hydrogen Kono, Mehrotra, Endo, Kizu, Matusda, Kimura, Mizohata, Inoue, Hasunuma, Yokota, Matsumura and Ashida17. This newly-discovered pathway, termed “reductive hexulose-phosphate (RHP) pathway”, is analogous to the Calvin–Benson cycle in plant photosynthesis, raising our hypothesis that it could be electron-driven . Additionally, this carbon fixation pathway has a potential link with methanogenesis via a formaldehyde intermediate, leading to our speculation of a third methanogenesis route in Methanothrix.Competition may occur between Geobacter and Methanothrix, on GAC surfaces in the propionate- and acetate-fed SAF-CMBR. By employing a combinatorial approach of metagenomics and metatranscriptomics sequencing, this current enquiry aims to disclose potential methane formation pathways, facilitated by MIET and DIET, and their metabolic link in Methanothrix. The efficient and stable operation of methanogenic bioreactors relies heavily on syntrophic-driven IET mechanisms. A deeper understanding of such interactions is therefore critical to ultimately tie ecology to improvements in engineering operation and design.This study examines the interspecies interactions, particularly between Syntrophobacter and Smithella), acetoclastic methanogens (Methanothrix), and the exoelectrogen Geobacter8. To further examine the metabolic interactions between these species, high quality genome bins (with >90% completeness) for these microbes were recovered through metagenomic short reads and hybrid assemblies  genome bin and G. lovleyi genome bin (AFBR_GAC_MaxBin.001), respectively. The acetoclastic methanogenesis, classical CO2 reduction and RHP pathways were also fully recovered from the M. concilii genome bin (AFCMBR_GAC_MaxBin.090)  Fig. . MetatraSyntrophobacter degrade propionate into acetate using methylmalonyl-CoA (MMC) pathway adopted by most SPOB11. While a unique syntrophic propionate oxidizer, Smithella was transcriptionally-active and it degrades propionate into acetate via butyrate using a distinctly different pathway, of which the genes involved are still unclear so far18. On the other hand, the MMC pathway was fully reconstructed in the S. fumaroxidans MPOB genome bin, and these genes were found to be highly expressed with log2 RPKM values of 5.17–10.13   Table ; Fig. 1,20. Collectively, these findings confirmed that a syntrophic interaction was present between GAC-dwelling acetoclastic methanogen M. concilii and SPOB, S. fumaroxidans MPOB and Smithella. sp., to achieve the complete bioconversion of propionate to CH4 and CO2. Given that these microbes were selectively enriched on the GAC in the SFA-CMBR8, it is most likely due to GAC facilitated MIET by enabling the microbes to grow in proximity of each other.In propionate-fed syntrophic community, the metabolic activities of SPOB and methanogens are intimately dependent on each otherG. lovleyi showed the capability of acetate utilization and CO2 production via the tricarboxylic acid (TCA) cycle   Fig. . The higM. concilii genome bin. The DIET-dependent CO2 reduction methanogenesis pathway was also recovered  were highly expressed at levels close to the acetoclastic methanogenesis pathway   and intracellular (CO2 as a byproduct from acetoclastic methanogenesis). In M. concilii, the by-product of MIET facilitated pathway (acetoclastic methanogenesis), CO2, was further utilized in the DIET facilitated pathways (CO2 reduction). By coupling the MIET and DIET, M. concilii could utilize the metabolite, CO2, for additional energy capture. Accordingly, in this SFA-CMBR, such interspecies interactions facilitated DIET-dependent pathway and promoted the overall energy recovery in the form of methane.Why such a substrate-competing relationship exists within a cooperative association? Is it associated with DIET-induced interactions attributed to the electrically-conductive GAC? Indeed, acetoclastic methanogenesis was not the only transcriptionally-active pathway detected in the red Fig. . The gen68) Fig. . Unlike 2 reduction, the RHP pathway for carbon fixation is expected to be widely distributed in methanogenic archaea and the genes in such pathway are conserved in M. concilii17. Indeed, the complete RHP pathway was identified in the M. concilii genome bin  regeneration17. A study analyzing the RHP pathway in vivo for Methanospirillum hungatei showed that a small proportion of carbons fixed by RuBisCO were recycled for RuBP regeneration in the RHP pathway, and while a high proportion of fixed carbons were supplied to gluconeogenesis and glycolysis17. Also, it was proposed that the archaea invested a much smaller fraction of energy in the RHP pathway as compared to plant’s energy investment in the Calvin–Benson cycle17. By accomplishing this carbon fixation pathway with relatively low-energy investment, M. concilii could achieve further cell synthesis, therefore facilitating the dominance of M. concilii in the community and strengthened their overall activities. Hence, it is very likely that the RHP pathway plays an important role in M. concilii anabolism.Besides the classical CObin Fig. . All the.79 Fig. . This prM. concilii mediates methane production. The formaldehyde intermediate has been speculated to act as a metabolic link between the RHP pathway and methanogenesis in methanogens17. Accordingly, formaldehyde released from the RHP cycle can be condensed with tetrahydromethanopterin to form methyl-H4MPT, which is a key methanogenic precursor also central to both the methanogenesis and classical CO2 reduction pathways , which perform formaldehyde condensation, were successfully recovered from the M. concilii genome bin  strongly suggest the involvement of the RHP pathway in methanogenesis. Moreover, similar to the classical DIET-dependent CO2 methanogenesis, the RHP carbon fixation pathway is also an electron-consuming process. This raises the possibility that the RHP pathway could be relying on external electrons received from G. lovleyi through DIET.An important question is if the RHP pathway in bin Fig. . The hig2 reduction to methane (Eq. 2), CO2 fixation via RHP pathway (Eq. 3). At biological conditions (298 K and pH 7.0), the standard Gibbs free energy changes (∆G0′) of Eq. 1–Eq. 3 were calculated . This is reflected in the summation of acetoclastic methanogenesis with classical CO2 reduction (Eq. 4) and RHP pathway (Eq. 5). As shown in Table 0′ values were far below zero. Additionally, the energy released/yielded from the classical CO2 reduction (−86.95 kJ mol−1) and RHP pathway (−53.95 kJ mol−1) are significant and higher than that from acetoclastic methanogenesis, when there is an incoming electron supply for M. concilii through DIET. With facilitation of DIET, methanogens can achieve CO2 reduction and yield more energy compared to conditions without external electrons. The yielded energy in M. concilii results in more methane formation, therefore improving the overall energy recovery efficiency of the AFCMBR.The transcriptional activity of all MIET and DIET-facilitated methanogenesis pathways meant that they were all concurrently happening. Therefore, the thermodynamics of each pathway was explored. Table ed Table . Given t4/CO2 partial pressure ratio of 1–4, which mimics the actual conditions prevalent in the AFCMBR and other anaerobic digestion systems. Figure 4 to CO2 exerted insignificant influence on the energy gain. On the other hand, as shown in Fig. −1) were higher than that of acetoclastic methanogenesis alone without further CO2 reduction (−30 to −40 kJ mol−1). Therefore, when there are electrons available via DIET for M. concilii, the thermodynamic driving force for further CO2 reduction and/or the complete acetate reduction into methane is favorable. Notably, the energy gain from the complete acetate reaction via classical CO2 reduction is higher compared to the one via the RHP pathway, due to the differences of each metabolism involved intrinsically. Similar to the acetoclastic methanogenesis, ∆G′ of the two complete acetate reduction reactions were hardly affected by the partial pressure ratio of CH4/CO2, and their energy gains gently enhances as the acetate concentration increases. Overall, all the three reactions were thermodynamically feasible under this SFA-CMBR even at very low substrate concentrations. The thermodynamics calculation verified the feasibility of the acetoclastic methanogenesis and classical and RHP CO2 reduction pathway under such system conditions. With these two CO2 reduction pathways facilitated by DIET, the energy gain from the complete acetate reduction was higher that acetoclastic methanogenesis alone without further CO2 reduction.To further evaluate thermodynamic feasibility of these reactions in the AFCMBR, the transformed Gibbs free energy values (∆G′) at 298 K and pH 7 were estimated within an acetate concentration range of 0.03 mM–4 mM and a CH−1) SAF-CMBR on Day 162, when the system was at steady state, for metagenomics and transcriptomics sequencing8. Genomic DNA extraction method was described in the previous study8. Shotgun metagenomic library construction and sequencing was performed on Illumina HiSeq. 4000 platform  at BGI , generating paired-end (PE) reads with a read length of 150 base pair (bp). About 50 Gbp of metagenomic data per sample was generated. Meanwhile, 10 kb metagenomic library construction and sequencing were also performed on the PacBio Sequel Platform . Single-end (SE) reads with a mean insert length of 5700 base pair (bp), and 11.87 Gbp (AFBR_GAC) and 5.34 Gbp (AFCMBR_GAC) of metagenomic data were generated.The fluidized GAC samples were taken from the propionate- and acetate-fed  in a volume ratio of 1:1 immediately after sample collection and frozen at −20 °C overnight. Subsequently, the samples were sent to BGI  for total RNA extraction, metatranscriptomic library construction and metatranscriptomic sequencing on Illumina Hiseq. 4000 platform . rRNA was removed with kit after total RNA was collected from. Fragmentation buffer was added for interrupting mRNA to short fragments. Paired-end (PE) with read length of 150 bp and 15.4Gbp of metatranscriptomics data per sample was generated.The two GAC samples were obtained from AFBR and AFCMBR for metatranscriptomics sequencing when CH24. Followig which, digital normalization was performed to remove redundant sequences with khmer scripts (k-mer size 20). Paired-end reads were de novo assembled into long sequence contigs using St. Petersburg genome assembler  based on de Bruijn graph with default settings 26. Meanwhile, a parallel hybrid assembly was performed on the trimmed reads obtained from the Illumina HiSeq platform together with the trimmed reads obtained from the PacBio Sequel Platform using the same parameters. Next, MaxBin was used for binning the assembled contigs into taxonomic bins based on an Expectation-Maximization algorithm27. Then, CheckM was performed to assess the quality of draft genomes using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes28. The recovered genome bins were phylogenetically identified by comparing with reference genomes using PhyloPhlAn29. Prokka (version 1.11)30 was used annotated protein coding genes. Then gene functions were further characterized and functional pathways were reconstructed with BlastKOALA31.For metagenomics analysis, the raw reads were first trimmed with a minimum quality cutoff of 3, and further screened to be at least 78 bp in length, having an average quality score >30 and containing less than 3 ambiguous nucleotides (N’s) using Trimmomatic24. Assembly was performed on the paired-end trimmed reads using Trinity32. Afterward, Sequence Expression AnaLyzer  in the BBTools suite was used to map the assembled metatranscriptomic file against the recovered high-quality genome bins generated from metagenomic analysis pipeline under “ambig modes”33. Genes expression level was evaluated based on generated Reads Per Kilobase Million (RPKM) and calculated as log2 RPKM values.For metatranscriptomics analysis, the raw reads were first trimmed with a minimum quality cutoff of 3, and further screened to be at least 50 bp in length, having an average quality score >30 and containing less than 3 ambiguous nucleotides (N’s) using trimmomaticSupplementary information


Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects.Homo sapiens, two from Danio rerio and one from Mus musculus– and has reproduced findings from the original studies and from independent literature.We introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities  that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets –three from The R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.The online version of this article (10.1186/s12859-018-2487-5) contains supplementary material, which is available to authorized users. Metabolomics is the science that measures lightweight molecules in living organisms and stands as a valuable source of biomarkers and biological knowledge . The preclusterProfiler [Over representation analysis (ORA) approaches are based on testing the proportion of a list of affected metabolites inside a pathway. ORA is available in tools like the web server MetaboAnalyst  and the Profiler . FunctioProfiler  contain FELLA, available in Bioconductor [Here, we introduce the R package onductor , for metFELLA is an R package that performs metabolomics data enrichment starting from (I) a network derived from KEGG and (II) a list of KEGG compounds  to KEGG pathways through intermediate entities, namely reactions, enzymes and KEGG modules, see Fig. The cornerstone of In order to report a sub-network, nodes are ranked according to a scoring function –based on network propagation– and only the top scoring nodes are returned. Two algorithms are supported for propagating the labels from the affected metabolites: a classical heat diffusion approach  and the As exposed in , rankingr is when k nodes are uniformly sampled. The assumption behind this filter is that meaningful inputs encompass metabolites relatively close to each other within the knowledge graph, prone to be reported in large connected components involving most of them.An optional filter allows the removal of small connected components from the reported sub-network. When building the database, a number of random sub-networks are sampled to characterise how infrequent a connected component of order at least FELLA relies on two classes: FELLA.DATA for the internal knowledge representation, based on the igraph R package [FELLA.USER for the user analysis, see Fig.  package , and FELThe function buildGraphFromKEGGREST retrieves the tabular KEGG data for the desired organism and builds the knowledge graph as described in . Then, aFELLA.DATA object is in memory, defineCompounds maps the list of input metabolites, in the form of KEGG identifiers, to the internal representation, providing a FELLA.USER object. Then, the propagation algorithms in [Once the database is loaded, i.e. the ithms in  are run ithms in  whereas ithms in . Both apFinally, the best scoring KEGG entries can be visualised through plot, exported as a sub-network with generateResultsGraph, or in tabular format with generateResultsTable. A dedicated table with the reported enzymes and its associated genes can be obtained with generateEnzymesTable. Alternatively, exportResults allows writing such objects directly to files.FELLA includes an interactive graphical interface, based on the R package shiny [FELLA  and enzymes (tabular format).FELLA has already been discussed and validated in [FELLA is hereby demonstrated on three public human studies on epithelial cells [FELLA reproduces findings from the original publications, not only in the form of pathway hits but also as newly suggested enzymes and metabolites. The Additional file The algorithmic part of dated in . The usaal cells , ovarianal cells  and febral cells . The exaFELLA is applied to two datasets from a gilt-head bream study [FELLA.DATA object for each organism.To demonstrate its usefulness outside human studies, am study  and a moam study . The comFELLA.DATA object. The enrichment  and validated via western blot.The activation of the “glycerophosphocholine synthesis” rather than the “carnitine” response is a main result in the original work . FELLA hFELLA.DATA object. The sub-network is obtained by leaving the default parameters and setting a limit of nlimit = 150 nodes.The second dataset has been extracted from the study on metabolic responses of ovarian cancer cells . OCSCs aSeveral “TCA cycle”-related entities are highlighted, also found by the authors and by previous work . It alsoFELLA.DATA object) has been extracted from the original supplementary data spreadsheet, using all the possible KEGG matches for the “non malaria” patient group. The sub-network is obtained by leaving the default parameters and setting a limit of nlimit = 50 nodes.The metabolites in this example are related to the distinction between malaria and other febrile illnesses . SpecifiIn this case, the depicted subnetwork contains the modules “C21-Steroid hormone biosynthesis, progesterone =>corticosterone/aldosterone” and “C21-Steroid hormone biosynthesis, progesterone =>cortisol/cortisone”, related to the “corticosteroids” as a main pathway reported in the original text. This is part of the also reported “Aldosterone synthesis and secretion”; aldosterone is known to show changes related to fever as a metabolic response to infection . AnotherFELLA. Here, both metabolite lists are used to build suggested sub-networks with the default parameters and fixing nlimit = 250. The FELLA.DATA object is built for the Danio Rerio organism, a common approximation when annotations specific to gilt-head bream are not available. Further details can be found in the vignette .FELLA reports ten out of the eleven metabolites in a connected component involving around 120 nodes. Seven pathways are suggested, from which “Linoleic acid metabolism”, “Biosynthesis of unsaturated fatty acids”, “alpha-Linolenic acid metabolism”, “Glycerophospholipid metabolism” and “Glycine, serine and threonine metabolism” were used to build a comprehensive picture of the metabolic changes in the original manuscript  and especially that of its reaction (top 3% among reactions) are highly suggestive. We also show how other (1) related metabolites, found by leveraging the expression data, and (2) differentially expressed genes, taken from an external study [FELLA.The sub-network found by al study , tend toFELLA, an R package for enriching metabolomics data, focused on interpretability. It can be used either programmatically or through a simple user interface. FELLA offers a comprehensive enrichment by depicting the intermediate reactions, enzymes and modules that link the input metabolites to the relevant pathways. This layout gives a biological picture with information of the pathway overlap and the connections between the entities of interest, while suggesting enzymes and possibly other metabolites for further study. The utility of FELLA has been demonstrated on six public datasets, both with human and non-human organisms, where reported entities include several original findings in addition to results from third studies. FELLA is publicly available in the Bioconductor public repository under the GPL-3 license.We present Project name: FELLAProject home page: https://doi.org/doi:10.18129/B9.bioc.FELLA, https://github.com/b2slab/FELLAOperating system(s): platform independentProgramming language: ROther requirements: noneLicense: GPL-3Restrictions to use by non-academics: those derived by the GPL-3 licenseAdditional file 1FELLA showing fast and concise toy examples of its application. (HTML 2587 kb)User guide within Additional file 2FELLA with background, implementation details and three real examples on its usage. (PDF 1096 kb)User guide within the R package Additional file 3FELLA: two datasets on the effect of oxybenzone exposition on gilt-head bream. (PDF 221 kb)Case study with Additional file 4FELLA: a multi-omic mouse model of non-alcoholic fatty liver disease. (PDF 235 kb)Case study with Additional file 5Descriptive files on the three human datasets: a summary of the inputs (descriptive_input.csv), input and reported subgraph in each dataset , hits discussed in the results section (descriptive_hits.csv). Also contains the database object (fella_data.RData) and metadata about the database (info_fella_data.txt), the KEGG version (info_kegg.txt) and the R session (info_session.txt). (ZIP 525 kb)Additional file 6R workspace from the gilt-head bream datasets. (ZIP 590 kb)Additional file 7R workspace from the mouse model study. (ZIP 829 kb)


Immunoglobulin A nephropathy (IgAN) is the most frequent primary glomerulopathy worldwide. The study aimed to provide potential molecular biomarkers for IgAN management.The public gene expression profiling GSE58539 was utilized, which contained 17 monocytes samples . Firstly, differentially expressed genes (DEGs) between the two kinds of samples were identified by limma package. Afterwards, pathway enrichment analysis was implemented. Thereafter, protein-protein interaction (PPI) network was constructed and key nodes in PPI network were predicted using four network centrality analyses. Ultimately, gene functional interaction (FI) was constructed according to expressions in each sample, and then module network was extracted from FI network.IL6, TNF, IL1B, PRKACA and CCL20 were closely related to pathways such as hematopoietic cell lineage, apoptosis and Toll-like receptor (TLR) signaling pathway. Moreover, 12 genes in the FI network belonged to the 72 identified key nodes, such as CCL20, HDAC10, FPR2 and PRKACA, which were also key genes in 4 module networks.A total of 678 DEGs were screened out, of these, 72 DEGs were identified as crucial nodes in PPI network that could well distinguish IgAN and healthy samples. In particular, IL6, TNF, IL1B, CCL20, PRKACA, FPR2 and HDAC10. These genes might co-involve in pathways such as TLR and apoptosis signaling during IgAN progression.Several crucial genes were identified in monocytes of IgAN patients, such as  MUC20 gene [Worldwide, immunoglobulin A nephropathy (IgAN) is the most frequent primary glomerulopathy. Reportedly, 20–50% of adults who suffered with IgAN would progress to end-stage renal diseases . TherefoC20 gene . HoweverRecently, it is found that abnormality of IgAN disease is related to IgA immune system and peripheral blood leucocytes, especially the peripheral blood mononuclear cells , 12. MonIn the present study, we re-analyzed GSE58539 profiling using a more comprehensive bioinformatics. After identifying the differentially expressed genes (DEGs) in monocytes between IgAN patients and HBDs, functional enrichment and protein-protein interactions (PPIs) network analyses were carried out, followed by key nodes prediction of the network through four network centrality analyses. Notably, in order to reveal potential interactions of DEGs that involved in similar functions and pathways, gene functional interaction (FI) network and the module network analyses were performed based on gene expressions of each sample. The study aimed to further uncover the pathogenesis and progression of IgAN, and thus provide potential molecular biomarkers for the diagnosis and targeting therapy of IgAN.http://www.ncbi.nlm.nih.gov/geo) database. This dataset contained 17 monocytes samples, including 8 monocytes samples isolated from IgAN patients (IgAN group) and 9 monocytes samples isolated from HBDs . The platform of the dataset was Illumina HumanHT-12 V4.0 expression beadchip .The microarray data GSE58539  was downhttp://www.bioconductor.org/packages/release/bioc/html/limma.html) package of R [We used the robust multi-array average (RMA) method in Linear Models for Microarray Analysis  was conducted using gplots tools [Non-paired t-test method in limma package was utilized to calculate significance ts tools  in R pachttp://david.abcc.Ncifcrf.gov/) [http://www.genome.jp/kegg/pathway.html) [p-value < 0.05 were chosen as cut-off criteria.The Database for Annotation, Visualization and Integration Discovery   tool wasay.html)  pathway http://string-db.org/) [http://cytoscape.org/) software [The Search Tool for the Retrieval of Interacting Genes   databasesoftware .The topological property of PPI network makes it possible to investigate key genes in the network. Here, four network centrality analyses were performed to explore the key genes, including degree centrality, betweenness centrality, subgraph centrality and closeness centrality of key genes. Generally, degree was used for describing the importance of protein nodes in the network, and betweenness centrality is a kind of indicator that describes the global topological properties of the network. Besides, subgraph centrality was used to measure the importance of nodes in the network based on the combination of network topology and protein complex information. Closely centricity represented the closely connection degree of a certain node and all other nodes –25.A cytoscape plug-in, CytoNCA , was useBased on gene expression value of each sample, the gene FI network was established using Cytoscape app-ReactomeFI . The inpHere, we obtained a total of 453 up-regulated and 225 down-regulated DEGs. As indicated in the clustering heat map Fig. , these Dp-value = 7.92 × 10− 4; which involved interleukin-6 (IL6), tumor necrosis factor (TNF) and interleukin 1 beta (IL1B)), NOD-like receptor signaling pathway , cytokine-cytokine receptor interaction ), intestinal immune network for IgA production  and apoptosis .Unfortunately, the up-regulated DEGs were not enriched in any pathways. However, the down-regulated DEGs were significantly enriched in nine pathways. The enriched pathways were listed in Table As presented in Fig. IL6, TNF, IL1B, PRKACA, TYK2 and CCL20 were closely related to five the pathways, including NOD-like receptor signaling pathway, cytokine-cytokine receptor interaction, hematopoietic cell lineage, apoptosis, and Toll-like receptor signaling pathway , GPCR-related pathways (module b), RNA binding-related pathways (module c), alcoholism and chromatin modifying enzymes (module d).In the present study, a total of 72 crucial nodes in the PPI network were identified via re-analyzing the dataset GSE58539, which could well distinguish the IgAN and healthy samples. Among which, genes such as IL6 has great roles in inflammation and regulation of immune response [TLR4 mRNA [IL1B gene, is a member of interleukin 1 cytokine family and crucial for the regulation of inflammatory response [IL1A, IL1B and IL6) are always upregulated simultaneously [IL1B is implicated in the TLR-4 induced immune response in chronic pain [IL6 and IL1B were both downregulated and enriched in TLR signaling pathway. These results suggested that IL6 and IL1B might be co-regulated in TLR signaling pathway and contribute to the abnormality of the immune response in monocytes of IgAN patients.The cytokine encoded by response . Toll-liresponse . Increasresponse . ExpressLR4 mRNA . IL1B, eresponse . In respaneously , 35. In nic pain . In our Mycoplasma penetrans-infected IgAN mice model, and the protein is proposed to involve in the induction of renal damage in IgAN [TNF expression is obviously reduced in monocytes of IgAN patients, compared with those of HBDs [TNF is a multifunctional proinflammatory cytokine. Reportedly, TNF expression is dramatically increased in  in IgAN . Moreove in IgAN . However of HBDs . The fin of HBDs . TherefoCCL20 is one of the chemokines that take part in the host response to pathogens invasions by activating inflammatory cells, and it has the similar effects on monocytes [CCL20 in monocytes of IgAN patients might cause alteration in immune response, and thereby influence the IgAN development.CCL20 is a small cytokine that also involves in immune regulation and inflammation . Combinaonocytes . TherefoPRKACA), formyl peptide receptor 2 (FPR2) and histone deacetylase 10 (HDAC10) were firstly predicted in monocytes of IgAN. PRKACA protein encoded by PRKACA gene is one subunit of protein kinase A that participates in apoptosis. At present, PRKACA amplification is served as a method for identifying genetic defect correlated with Cushing’s syndrome [PRKACA have been detected in adenomas of the adrenal cortex [PRKACA mediates apoptosis-related signaling pathways in many cancer diseases, such as breast cancer and follicular thyroid cancer cells [PRKACA was up-regulated and significantly enriched in apoptosis pathway, suggesting it might exert its function in monocytes via regulating apoptosis during IgAN progression. FPR2 is known to activate the G-protein coupled receptor and N-formyl peptide receptor. FPR2 is found in adipose tissues as a receptor for the pro-resolving mediators [HDAC10, containing two catalytic sites, is highly expressed in numerous human tissues such as kidney [HDAC10 is associated with the advanced stage and adverse outcome [HDAC10. In the current study, FPR2 and HDAC10 were hub up-regulated genes in both PPI network and FI network, implying they might co-function in monocytes of IgAN patients. One limitation of this study is the lack of expression validation. However, we will do more experiments to verify our conclusions once we collect the samples in the future.Three novel genes, protein kinase, CAMP-dependent, catalytic, alpha (syndrome . Somaticl cortex . Moreoveer cells , 46. In ediators , which cediators . HDAC10,s kidney . In lung outcome . HoweverIL6, TNF, IL1B, CCL20, PRKACA, FPR2 and HDAC10. They might have co-functions and their dysregulations might alter activities of pathways such as TLR and apoptosis signaling, which might finally promote IgAN progression. The study is of great value for the prediction of key regulators in monocytes of IgAN and the identification of targeting therapeutic management for IgAN.In conclusion, several crucial genes were identified in monocytes of IgAN patients, such as


Chemical activity profiling approaches must consider chemical effects on all pathways operative in mammalian cells. To enable a strategic and maximally efficient chemical profiling of pathway space, we have created the NCATS BioPlanet, a comprehensive integrated pathway resource that incorporates the universe of 1,658 human pathways sourced from publicly available, manually curated sources, which have been subjected to thorough redundancy and consistency cross-evaluation. BioPlanet supports interactive browsing, retrieval, and analysis of pathways, exploration of pathway connections, and pathway search by gene targets, category, and availability of corresponding bioactivity assay, as well as visualization of pathways on a 3-dimensional globe, in which the distance between any two pathways is proportional to their degree of gene component overlap. Using this resource, we propose a strategy to identify a minimal set of 362 biological assays that can interrogate the universe of human pathways. The NCATS BioPlanet is a public resource, which will be continually expanded and updated, for systems biology, toxicology, and chemical genomics, available at  For most of its history, the field of toxicology has focused predominantly on whole-organism studies, with observable histological, behavioral, or developmental endpoints, or “apical endpoints,” being cataloged as occurring after exposure to chemicals. While whole-organism studies have served as the backbone of scientific and regulatory imperatives to protect human health, they suffer from lack of mechanistic insights, high cost, low throughput, and uncertain applicability to human risk assessment. However, unlike systems pharmacology and drug development, toxicology assessment has changed relatively little in the last 50 years  due, in in vitro approaches to toxicology, the United States Tox21 program . The merging procedure was repeated until no two pathways met any of these criteria. After merging, the pathway gene lists were manually curated to correct mis-assigned genes and further remove redundancy (Two pathways were merged into one by merging their gene components when one of four criteria was met: (1) the overlap, defined as the number of genes shared by the two pathways divided by the total number of unique genes in the two pathways, was >90%; (2) the two pathways differ by only one gene; (3) one pathway has <3 genes and all of these genes are contained in the other pathway; (4) the two pathways have >50% overlap in their gene components and Homo sapiens. The pathway names were standardized and corrected for consistent capitalization, biological clarity, usage of Greek letters, and hyphenated terms. The genes in the pathways were also edited to remove withdrawn identifiers and replace obsolete ones. Non-human genes were removed or replaced with corresponding human genes. However, even after the removal of non-human genes, a few pathways from mice and other species remained. Yet, despite the pathway names, all genes in these pathways were human. Therefore, to ameliorate this discrepancy, these pathways were renamed to remove the animal inference. Extremely small pathways that contained only one or two genes were merged with larger pathways, and some pathways with similar names and functions were merged. Eighty-nine sets of pathways had very similar names but different gene lists. For example, “Alzheimer’s disease” has 168 genes, and a separate pathway called “Alzheimer’s disease” has 82 genes, but some genes from the latter set are not among the genes in the former one. To resolve similarly named pathway sets like these, each set was manually examined to establish whether their gene lists had sufficiently similar functions to substantiate merging of these pathways, or whether the functions were sufficiently different, and the pathways should have been kept separate under distinctly different names. To make these decisions, the genes unique to each pathway were uploaded to the DAVID annotation resource2. Using DAVID’s Functional Annotation Clustering tool, the top annotation cluster characterizing the gene list was used to determine the collective function of these genes. Gene Ontology Biological Process terms, KEGG pathways, and BioCarta pathways were preferred when available. Based on these results, pathway sets with gene lists that had sufficiently similar functions were merged. The pathways with gene lists that had distinct functions were preserved as separate pathways and renamed to distinguish them better. In total, 714 of the 1,774 pathway names (40%) were edited. Some pathways were removed or merged with other ones during the process, reducing the total number of pathways to 1,658. The number of unique genes represented in the pathways was reduced from 10,040 to 9,818.After initial merging, BioPlanet included 1,774 pathways that contained 10,040 unique genes, 9,928 of which were assigned to 3 was used to link genes to literature references (PubMed IDs). Pathway names and gene lists were used to search PubMed to find pathway-literature linkages. PubMed IDs shared between genes and pathways were then identified to establish the gene-pathway association. An average of 50 abstracts from each method were spot-checked to ensure the method was producing the correct results. A total of 234,347 unique references were found for all 1,658 pathways, with each pathway having at least one reference. Further curation of the gene–gene interactions within each pathway is currently underway. Publications supporting the interactions selected by the pathway authors are retrieved from the source files and added to the BioPlanet pathways.Literature supporting the pathways and interactions were first added computationally. For the 303 pathways with no literature association found through the automated approach, references were sourced manually. GeneRif4, a small set of high-level functions characterizing an organism, were used to generate the list of pathway tags. Some GO Slim terms were rejected for being too long , only applying to one pathway (“ribosome biogenesis”), or describing processes that do not exist in humans (“photosynthesis”). Disease-related tags were added based on the top-level disease categories at Disease Ontology5. Tags used by the source databases to group pathways were also collected for inclusion. Redundant tags were removed or merged with existing tags. A total of 51 tags were eventually selected, and grouped into seven categories: Major Systems, Cell Cycle, Genetic Information Processing, Metabolism, Development, Signaling, and Disease. The tags in each category are listed in Keyword tags were used to group functionally related pathways into categories. The GO Slim biological processesGO annotations for human genes were used to tag many of the pathways automatically. The tag keywords were first matched manually to GO terms in the top 4 levels of the GO hierarchy. For each GO term, up to three tags were assigned. Most level-4 terms were not manually tagged unless they also occurred in a higher level. These terms were then associated with genes using the GO annotations, and the gene lists for each pathway were used to determine whether enough genes with one tag were present to assign that tag to the whole pathway. Specifically, we required that (1) at least 10% of the genes in the pathway have the same tag and (2) at least four genes have the same tag. The automated GO term method assigned at least one tag to 84% of the pathways. However, the GO term method missed some obvious tags suggested by the pathway titles. For example, “HIV-induced T cell apoptosis” would be expected to get tags for “Infectious disease” (“HIV” in the name), “Immune response” (“T cell”), and “Cell death” (“apoptosis”). For this reason, a second component was added to the automatic tagging algorithm. A list of keywords was created that would be expected to match each tag, and the occurrence of these keywords in the pathway title would assign the corresponding tags. For example, the tag “Nucleic acid metabolism” would be assigned if the pathway title contained words like “Nucleobase,” “Nucleotide,” “Nucleoside,” “Purine,” or “Pyrimidine.” This keyword method assigned at least one tag to 58% of the pathways. The combination of the two methods yielded 92% pathways with at least one tag. To measure how well the automated tagging process worked, 10 pathways were selected for manual review. The results showed that the automated process produced a high false positive (63%) and low false negative rate (30%). For this reason, we decided to manually review all of the tagged pathways, removing tags that seemed irrelevant and adding tags that were missed.6) was used to find relevant disease associations. The list of gene IDs from the pathway was entered into CTD’s gene set analyzer and disease Venn diagram. The first method shows a list of diseases associated with the input gene set, ranked by p-value, and the second method shows the overlap between the input gene set and the disease gene set. Rather than relying on a p-value threshold or minimum number of genes, high-ranking diseases in the list were accepted if they were consistent with the pathway description. This prevented the problem we observed in some of the automated tag assignments, when, for example, a subset of pathway genes may be involved in an Infectious Disease but the corresponding pathway is not primarily associated with any such Infectious Disease. After manual curation, all pathways have at least one tag assigned. The median number of tags per pathway is 5, while the maximum number is 15.A manual workflow was then applied to add missing tags, remove false positive tags, and add disease tags to pathways. For each pathway, one or more summaries of the pathway were found from online scientific sources like PubMed, Entrez Gene  and BioCSince one rationale for creating the BioPlanet is to enable the experimental assessment of chemical modulation of a wide range of human pathways, we next explored the current availability of extant bioassays to probe the 1,658 distinct human pathways. We examined bioassays from four sources, which cover 2,685 gene targets in total (in the order of decreasing priority): (1) assays from the Tox21 program that have been run at NCATS, (2) other NCATS assays, (3) other bioassays in PubChem, and (4) assays from commercial vendors not yet employed by Tox21, NCATS, or PubChem assay providers. Phenotypic assays with no specific gene targets were excluded from the analysis. 7. Genes that cause genetic diseases have been identified in 97% of annotated pathways to date. Disease-related pathways have significantly better assay coverage (89% have at least one bioassay) than pathways that do not contain any disease-related genes (66% have bioassays). Compared to the other PubChem assays, Tox21 and NCATS showed relatively better coverage of disease-related pathways (71% assays are from Tox21 or NCATS) than other pathways (only 40% assays are from Tox21 or NCATS). Next, we examined the assay availability for disease-related and non-related pathways . Of the The ultimate goal of systems pharmacology, of which the Tox21 program is an exemplar, is to characterize the activity of a broad range of chemicals across the full spectrum of 1,658 human pathways. However, since performing 1,658 separate assays is experimentally unfeasible, and given that pathways are overlapping in their component genes and functions, and together constitute an interconnected network web, we reasoned that it should be possible to account for all of pathway space with a reduced number of assays that could cover multiple pathways. We thus sought to define a minimal set of gene targets that could be experimentally assayed and cover all of pathway space with some degree of overlap and redundancy to assure complete coverage.p < 1.0 × 10−4) with genes that have been reported to be essential for the viability of human cells , and the correlation improved to 0.57 when the degree of gene sharing between two pathway assays was >20%. Though this correlation is statistically significant and supports the notion that achievement of a compound’s comprehensive pathway activity footprint via testing in the full 1,658 pathways will be feasible, the extent to which pathway activity may be confidently inferred from activity in other “indicator” assays is unclear and will require experimental testing. One of the major goals of the Tox21 program and other systems biology initiatives is to generate and make public just these kinds of diverse pathway data and predictive algorithms, and experimentally test their utility. As data are generated, they will be linked to the BioPlanet for straightforward browsing and correlation testing by others and ourselves.The underlying premise of testing compounds in a reduced number of assays as a proxy for all biological pathway activity space is that it is possible to identify “indicator pathways” based on genes that regulate/participate in several pathways, such that activity in this indicator assay would allow inference that the compound would be active in other pathways that share this gene product. In this case, screening multiple pathway assays sharing the same gene target(s) would be redundant and thus unnecessary in a global assessment of compound activity on biological space. This premise predicts a positive correlation between the degree of compound activity overlap and the extent of gene sharing of two pathway assays. To test this prediction, we evaluated data generated from screening of the pilot phase Tox21 collection of 2,870 compounds against a set of 25 pathway assays . The deg8, that is publicly available  as BioPlanet 1.0 in recognition of its evolving nature. Like the data that went into creating the current version of BioPlanet, which was derived from the community of scientists worldwide, we view the ongoing curation and growth of the BioPlanet as a community “wiki” type effort, and therefore actively encourage comments, corrections, contributions, and suggestions for additional features via the BioPlanet page at We hope that the research community will find the BioPlanet useful, both for systems biology analyses as well as hypothesis generation. However, beyond its utility as a catalog, we hope that the BioPlanet will facilitate perturbation studies using small molecules, RNAi, gene knockouts, and other forms of biological modulation. The ultimate test of any network map is its ability to predict effects when a node in the network is perturbed. We will be adding capabilities for linking data from small molecule and siRNA screens performed at our Center to the next version of the BioPlanet, and we look forward to linking data obtained by other researchers as well. The current version of BioPlanet contains only human pathways, therefore, as a future endeavor, pathways for other species will be added both for their own importance in biological research and in comparison to their human counterparts, since human-animal pathway differences are likely drivers of non-concordance of chemical effects on humans and animals.in vitro assays to strengthen predictive toxicology methods  screen of the Tox21 “10K” set requires at least one week for each assay even with the ultrahigh-throughput robotic platform being utilized. GWAS studies were rendered practical by the comprehensive cataloging of SNPs and the discovery of the SNPs that are inherited together in haplotype blocks, thus allowing the imputation of SNPs not directly tested via the presence of a reduced numbers of “tag SNPs.” While there are 1,658 total pathways currently characterized, our analysis suggests that assaying only 362 will allow the imputation of activity in the remaining pathways. Importantly, this reduced number, while consistent with current data, will require ongoing data production to test and refine this very concept and the actual number of independent assays required to adequately query all of pathway activity space, with such data being provided in PubChem and other public-facing portals on a continuing basis.In the nearest term, the BioPlanet will find utility in the selection of  methods . An unde effects , 2018. TWhile BioPlanet was initially conceived as a tool to guide systems toxicology efforts, it has implications and applications across the spectrum of systems biology, systems pharmacology, and disease pathophysiology. We look forward to continuing to collaborate with the research community to further develop and populate the BioPlanet, and thus achieve its potential as a resource for discovery.RH coordinated the project, sourced and compiled pathway lists to construct the BioPlanet database, helped with data curation, helped to build the BioPlanet database and browser, performed statistical analysis of all data, and wrote the manuscript. TZ and YW built the BioPlanet database and browser. IG, JG, and JO curated all data and wrote the manuscript. IG generated all pathway diagrams. DN helped with assay vender searching and data curation. D-TN helped to extract data from PubChem and build the BioPlanet browser. RG helped with data curation and the BioPlanet browser. AJ coordinated the data curation. NS helped to coordinate data curation and construction of the BioPlanet browser. AS directed the project and wrote the manuscript. CA conceived and directed the project and wrote the manuscript. All authors reviewed the manuscript.IG, JG, and JO were employed by company Rancho BioSciences. The remaining 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.