Aβ aggregates, especially the low-molecular weight oligomers, are the primary toxic agents in AD and hence, there is increasing interest in understanding their formation and behavior. Aggregation is a nucleation-dependent process in which the pre-nucleation events are dominated by Aβ homotypic interactions. Dynamic flux and stochasticity during pre-nucleation renders the reactions susceptible to perturbations by other molecules. In this context, we investigate the heterotypic interactions between Aβ and fatty acids (FAs) by two independent tool-sets such as reduced order modelling (ROM) and ensemble kinetic simulation (EKS). We observe that FAs influence Aβ dynamics distinctively in three broadly-defined FA concentration regimes containing non-micellar, pseudo-micellar or micellar phases. While the non-micellar phase promotes on-pathway fibrils, pseudo-micellar and micellar phases promote predominantly off-pathway oligomers, albeit via subtly different mechanisms. Importantly off-pathway oligomers saturate within a limited molecular size, and likely with a different overall conformation than those formed along the on-pathway, suggesting the generation of distinct conformeric strains of Aβ, which may have profound phenotypic outcomes. Our results validate previous experimental observations and provide insights into potential influence of biological interfaces in modulating Aβ aggregation pathways.Aggregation of amyloid  Brains of AD patients contain large amounts of proteinacious plaques mainly comprised of insoluble Aβ fibril deposits. Monomeric Aβ peptides (Aβ40 and Aβ42) spontaneously undergo aggregation towards large fibrils in a nucleation dependent manner. The effects of nucleation on aggregation dynamics have been extensively studied8 that point to a key rate-limiting step for the formation of nucleus/nuclei13. Therefore, the dynamics associated with reactions leading up to nucleation is critical for aggregation. Energetically, the pre-nucleation phase is nebulous with monomers and oligomers  involved in a dynamic flux dominated by stochastic interactions16. Furthermore, intrinsic disorder and amphipathic nature of Aβ facilitate multiple phase transitions and heterogeneous interactions during nucleation, making the process particularly sensitive to environmental factors such as pH, ionic strength, temperature and other interacting partners22. This is significant because smaller, soluble aggregates have emerged as the primary neurotoxic agents responsible for memory loss in AD26. Furthermore, it is clear that oligomers may not be the obligatory intermediates to fibril formation, and that the oligomers can also be populated along alternate pathways of aggregation (off-pathways)32. Off-pathway oligomers differ from those formed along the on-pathway resulting in multiple conformational variants with distinct biochemical and cellular properties. Given the conformational diversity among oligomers, it is imperative to determine the factors that affect dynamics involved in such oligomer formation to establish a framework of molecular mechanisms that better defines amyloid progression.Aggregation of Aβ pre-nucleation dynamics are the anionic surfactants such as fatty acids and lipids39. Lipids and fatty acids are profoundly important in physiological contexts as they are abundant in both brain tissues and CSF41. The amphipathic Aβ peptide is known to have strong affinity for membranes and hence, these interactions may affect the pre-nucleation dynamics. Several reports also demonstrate the effects of phospholipids on Aβ aggregation44. Similarly, both polyunsaturated (PUFAs) and saturated fatty acids are also known to have significant effects on the AD brain46. Kumar and co-workers have previously reported the generation of 4–5 mers and 12–24 mers from lauric acid along a non-fibril formation pathway22. More importantly, using carbon chain lengths of C9-C12 fatty acids (FAs), they established that below (non-micellar), near (pseudo-micellar) and above (micellar) their respective critical micelle concentrations (CMCs), FAs generate Aβ oligomers or fibrils via distinct pathways22. A schematic of the switching dynamics from on- to the off-pathway with increasing concentration of FAs is shown in Fig. FA concentration while the vertical arrows conceptually depict the dynamical aggregation regimes of the system with the inclusion of monomeric Aβ. It can be observed that low FA concentration involves the non-micellar phase where only the on-pathway reactions are active; while high FA concentration (beyond the CMC) involves the micellar phase where only the off-pathway reactions are active (with on-pathway being completely switched off). The middle phase of the FA concentration arrow denotes the near-CMC  phase and is the most interesting one; here, both on- and off-pathway reactions are active and the monomeric Aβ may form aggregated oligomers from both pathways simultaneously (as denoted by the arrows).One class of biologically important interacting partners that affect Aβ and FAs and how phase transitions of FAs modulate Aβ aggregation. We first use a reduced order model (ROM) and a corresponding mathematical analysis rooted in a linear stability argument to identify the switching behavior from on- to off-pathway in the presence of micellar interfaces. The ROM can be thought of as a toy-version of the far more complex biophysical problem. However, despite considering only 4-species in this model, the ROM still deals with a highly non-linear problem which captures the overall aggregation dynamics of the Aβ system and allows for rigorous examination of features such as dynamic stability which would be intractable in the case of a very fine grained model. Mathematical biologists consistently simplify complex biological problems to gain initial insight into problems, to understand the nature of equilibria, stability, bifurcations in the larger system, before venturing to speak of specific details which is the objective of the ROM. This analysis was used as the basis to further develop a more detailed model, which was built around the discoveries of the ROM and experimental data.In this report, we sought to understand the dynamics of heterotypic interactions between AFAs significantly and consistently modulate Aβ aggregation by altering the pathways. These results are in agreement with the reported in vitro experimental observations, and provides detailed molecular insights into the heterotypic interactions between Aβ and FAs. These insights also reveal that generation of conformationally-distinct oligomeric strains (produced by the oligomers involved in the on- and off-pathways) are highly likely in physiological environments containing lipid and FA interfaces due to the dynamics involved in the heterotypic FA-Aβ interactions.Secondly, using an ensemble kinetic simulation (EKS) model, we demonstrate that in three distinct phase-transition regimes, A1 denotes the Aβ monomers while FA . An are the on-pathway oligomers while n monomeric units. Finally, i = 1, 2, 3). The details of the modeling methodology are elaborated in the Methods section. We are aware that the on-pathway fibrils are considerably larger than the terminal species of the off-pathway, i.e. size of (An) ≫ size of (A′n) or n ≫ n′. In the ROM analysis however, both pathways terminate at the same value of n since we believe this to be sufficient to elucidate the competition between the two pathways in our model. Since the only “bridge” from the on- to off-pathway is between the monomeric species, once the reaction chooses the path from We examined the sets of on- and off-pathway reactions shown below. The model system considered is as follows:Methods section, Fig. λi (i = 1–4), of the Jacobian matrix for the mass action kinetic equations  dominate in magnitude and also become more stable than transcritical-like bifurcation at α3 = 1.0.Based on the results of our analysis presented in the quations –21. Thesquations –21. It stranscritical bifurcation refers to a stable equilibrium and an unstable equilibrium which exchange stabilities with respect to some parameter47. In our model system, the switching of stabilities is revealed by α3, which, by its definition, contains the ratio of the forward to backward rate constants for this reaction, as well as the fatty acid concentration which enhances the formation of the off-pathway species. Figure Bn and Note that in the dynamical systems literature, α3 = 1.87  were observed with λ2 eventually being more negative than λ1, i.e. off-pathway eventually becomes more stable for sufficiently large α3. However, for 12 ≤ n ≤ 20, the on-pathway eventually becomes more stable. The physical significance of the multiple switching, in particular the one beyond α3 = 1 still eludes us and it remains to be verified if this an indication of some underlying bifurcation in the system or is simply an artifact of the nonlinear system or an indication of some nucleation type event observed in our previous work using similar methods48.We explore the effect of β system and can help explain the switching dynamics considering only the CMC; (ii) The non-dimensional parameter α3 is seen to be the key control parameter in this analysis indicating that FA concentration, along with the ratio of the rate constants along this “bridge” reaction pathway determine the ultimate fate of the system. (iii) The presence of a second, discontinuous switching of eigenvalues at α3 ≥ 1.87 potentially indicates a different biophysical phenomenon occurring along the off-pathway implicating the involvement of additional species that may play an important role in the FA dynamics. To explore the dynamics between on- and off-pathways of Aβ aggregation, we then conducted a more detailed ensemble simulation using chemical kinetics under varying concentration of FAs.The results of our stability analysis with support from the Nash equilibrium for this system (see Methods section), allows us to draw the following conclusions: (i) The simplified, though highly nonlinear system of ROM equations with 4-species captures the essential dynamics of the AFAs, we used the EKS approach. EKS approach involves numerical ordinary differential equation (ODE)-based simulations that encompasses ensembles of the dynamic species involved in the reaction flux. Fundamental aspects of EKSs were used in the on-pathway model that we had previously reported48. In this report, we have expanded the EKS model to simulate various phase-transition regimes of FAs and Aβ, especially along off-pathway (elaborated in the Methods section). Before modeling on- and off-pathway dynamics, it is imperative to simulate the phase-transitions of FAs during micelle formation. We specifically chose three concentration regimes at which FAs show distinct phase changes: low concentrations where the FAs are non-micellar (FAn), near their CMCs where they are pseudo-micellar (FApm), and high concentration where they are fully micellar (FAm). We conjecture the nomenclature, pseudo-micelles to define the dynamic state between a non-micelle and a micelle, which seemed to affect Aβ in a distinct way22. The hydrodynamic radii (Rh) estimates of FApm and FAm indicated that the former is ~6–7 times larger than the latter, confirming the loosely-held, amorphous state of FApm , we find the forward rate constants during the nucleation stage (knuon) is considerably lower than the forward rate constant (kfbon) of elongation stage, as expected  in the solution. We hypothesize that FAns interact with Aβ and catalyze aggregation by altering the rate constants along the on-pathway by a factor of K, resulting in a change in the final fibril concentration. The corresponding set of reactions for this model are shown in Appendix-C under Supplementary Information. We obtain the value of K < 1 for the best fit with the experimental data pointing to a slowdown in the elongation stage of the on-pathway , despite no change in the lag time  by a factor of K, which did not fit the fibril growth curve for the C12 FAn case and concomitantly could not validate this behavior (data not shown). Next, upon varying the forward elongation rate constant (kfbon), a small decrease in the forward elongation rate constant render higher ThT intensity with delayed saturation time, and without changing the lag time. This suggests that the addition of FAn molecules effectively decrease the forward elongation rate constants. According to the Lee’s and our own on-pathway models49, the elongation stage (ie. A12s) resulting in higher ThT intensity/fibril growth. The increased number of A12s resulting from these reactions then react with one another as well as the on-pathway oligomers during the pre-nucleation stage  to generate higher order aggregates with concomitant higher ThT intensities. The predicted K value for different initial FAn concentrations is shown in Supplementary Table FAn are shown in Fig. R2 values obtained for the fits .We further consider two hypothetical but physically relevant scenarios in which varying rates of entry of AFAn set-ups. We considered four oligomers for this test: A1, A4, A8, A12; in each case, these ratios rise above the value of 1, showing that the presence of more below-CMC oligomers than on-pathway oligomers in the system as time increases. Note that these were generated from two stand-alone simulations, one for the control and the other one for FAn as in both cases only on-pathway oligomers are produced. The results clearly suggest that the FAn scenario produces more oligomers (and possibly more fibrils) with time which supports the experimental observation of increased ThT intensity for FAn. Additionally, as expected the ratios are smaller for larger oligomers; this is simply because of the reaction order as fewer oligomers of larger size are formed in the system. Thus our reaction hypothesis of a slowdown of the elongation stage in the presence of FAns, although counter-intuitive, accurately validates the experimental observations of increased ThT intensity.In Fig. , we repoFA concentration around the CMC increases the concentration of FApm in the bulk solution, the presence of which shifts the reaction flux towards predominantly off-pathway as established experimentally51 . Furthermore, previous reports identified that 12–24 mers (LFAOs) are the predominant species generated in the presence of C12 FApm51. Our simulations predict the presence of multiple conformers within the oligomer of a specific size (strains) as observed before52. The 12–24 mer LFAOs show conformational dynamics , more the saturation is delayed for the ThT growth curve. As expected, our predicted FApm concentration is highest near the CMC, and the concentration starts to decrease when total FA concentration is either increased or decreased. The predicted FApm concentration for each fatty acid chain length at the near CMC zone experiments is shown in Table Comparing the ots Fig. , we addiFAm) only produces 22  interactions of Aβ with non-micellar FAs (FAn) generate predominantly high molecular weight, on-pathway aggregates. (b) Interactions of Aβ with FAs near the latter’s CMC (FApm) generate predominantly low molecular weight oligomers (12–24 mers) along off-pathway along with some minor quantities of on-pathway fibrils. (c) Interactions of Aβ with high concentrations of FAs  exclusively generate off-pathway 4–5 mer oligomers without any fibrils suggesting that the on-pathway was completely turned off.The results reported here by two independent methods (ROM and EKS) reaffirm the experimental observations that FAs dictate switching of Aβ aggregation pathways. Furthermore, the report demonstrates that under three distinct phases, FAn, FApm, and FAm, the FAs not only modulate Aβ aggregation pathways but also promote low molecular weight oligomers, which are increasingly known to be the main pathogenic species in AD and other related neurodegenerative disorders. Specifically under pseudo-micellar conditions, where FApm undergoes a distinct phase transition, they preferentially promote and stabilize low-molecular weight oligomers. The ability of certain phases of FAs, or in general any biological surfactants, to promote the formation of low molecular weight, off-pathway presents high significance in AD pathology. This is because oligomers generated in this manner could lead to the formation of various toxic ‘conformeric strains’ of Aβ aggregates, which could manifest in distinct phenotypic manifestations observed in AD. Indeed, Dean et al., have shown that LFAOs, which are generated in the presence of C12 FAs, are able to propagate morphologically distinct fibrils and caused excessive cerebral amyloid angiopathy (CAA) in transgenic mice brains, which was absent in similar reactions seeded with on-pathway fibrils52.Perhaps most significantly, simulations from both ROM and EKS, as well as the previously demonstrated experimental evidence have identified that both concentration and phase-transitions of β-FA interactions. Some of the salient dynamics involved in the modulation of Aβ aggregation by FAs in distinct phases include: (i) in the presence of FAn, the free FAs seem to slow the kinetics of elongation (or post-nucleation) stage along the on-pathway, (ii) in the presence of FAm, aggregation does not proceed beyond β by FAm as Aβ-FAmm complex. Further investigation of this complex by molecular dynamics based studies to infer why such complexes cannot aggregate is on-going and will be reported at a later date, and (iii) in the presence of FApm, Aβ forms A4 during the pre-nucleation phase followed by a slower progression to 51, which renders them incapable of aggregating further.Mechanistically, simulations reported here have revealed a deeper understanding of Aβ or FAs, a constant influx of Aβ monomers as well as pseudo-micellar and micellar FAs were considered. Incorporating specific entry rates for Aβ and FAs, we observe that the presence of pseudo-micellar phase influences the switching of pathways more than that induced by the monomers. However, we observed that even a slight increase in the entry rates of either FA or Aβ is enough to switch the dynamics preferentially towards the off-pathway. This is evident in Fig. FApm/FAm rates. The corresponding oligomer concentration ratios along off- and on-pathway show that the on-pathway is active only when FApm entry rates are very low , there is a higher propensity of the system to switch to the off-pathway even with lower monomer or micelle entry rates.In addition to these insights, EKS model also considered a physiological scenario in which instead of constant amount or Alow Fig. . After 50 h Fig. . The dynβ-FA interactions, which seems to depend critically on the concentration and phase of FAs. Furthermore, the switching dynamics in conjunction with the constant infusion of Aβ monomers and FAs in their distinct phases illustrate the potential of biological interfaces to influence Aβ into forming several different conformationally distinct aggregates. Such conformeric strains could then influence phenotypic outcomes in AD pathology.Overall, this report has brought forth several interesting observations on the dynamics of Aβ monomer (50 μM) buffered in 20 mM Tris, pH 8.0, and kept at 37°C in quiescent conditions. Separate reactions were started in a staggered fashion such that at the time of data collection, the sample would reach the desired time point.C12:0 fatty acid (5 mM) with 50 mM NaCl was added to freshly purified Aβ, stock concentrations of C9:0, C10:0, C11:0, and C12:0 were diluted to the appropriate concentration in 50 mM NaCl buffered in 20 mM Tris, pH 8.0.DLS data was collected using a Zetasizer Nano S instrument . At each time point, DLS was collected by averaging 24 runs of 5 s each with a pre equilibration time of 30 s. This average was used to determine the diameter (nm) using the volume (%) function. For determining micelle size without Aμm nitrocellulose membrane (BioRad) and boiled for 1 min in a microwave oven in 1X PBS, followed by blocking for 1.5 h in 1X PBS containing 5% nonfat dry milk with 1% tween 20. Blots were then probed overnight at 4 °C with a 1:6000 dilution of Ab5 monoclonal antibody, which detects amino acids 1–16 of Aβ. Blots were then incubated with a 1:6000 dilution of anti-mouse, horseradish peroxidase conjugated secondary antibody and developed with ECL reagent (Thermo Scientific).LFAO preparations were staggered in start such that at the time of initiating SDS-PAGE experiments, each sample was at the desired time point. Samples were diluted into 1X Laemmli loading buffer with 1% SDS, and loaded onto 4–20% BioRad TGX gels without boiling. Pre-stained MW markers  were run in parallel for MW determination. Proteins were transferred to 0.2 The system of differential equations corresponding to Eqns A0 (the equilibrium concentration of monomers) and The above equations are better suited for analysis in non-dimensional form. If we chose the characteristic concentration and time as s’, now can be written asThe corresponding dimensionless differential equations, in terms of dimensionless time ‘In the equations –7, if weHowever, the on-pathway cannot be switched off and always persists. In this case the equilibrium solution is given by the pair Be1,e indicates that these species are in equilibrium.Turning our attention now to the more general case of the non-dimensional equations –12, the 55, then the equilibrium α3 = 1 which also will be seen to have special significance in terms of the stability of the system. It follows that when α3 > 1, then the off-pathway species dominate creating more equilibrium concentrations of α3 < 1, on-pathway dominates resulting in greater production of It should also be noted that if one treats the on- and off-pathway dynamics as a “competition” in accordance with the term used in game-theoryWe define n = 4. Also, here The system of equations –7 were so(ε) becomesB given byTo study the stability of the four species, we linearly perturb the system which is mathematically represented byλi  of the matrix B. This is best done numerically. In the figures below we discuss the results of the computations. In particular, the term α3 is varied throughout the analysis since it captures a significant dynamical feature of this problem; the switching from on-pathway to off-pathway. Over all, we choose the following ranges for our parameters: i = 1, 2. Also, base case defined by the choice n = 4 and Be1, = 1 and then study the sensitivity of the system and our results to each of the parameters.The stability of the equilibrium is found from computing the eigenvalues, in-vitro experiment using the non-esterified fatty acids of chain length C9 to C12 revealed a few crucial characteristics of Aβ42 aggregation22:FA concentration less than CMC range (FAn), only on-pathway aggregates were produced. Furthermore an increase in ThT intensity than the control experiment was detected.At FApm), off-pathway fibrils of length 12–24 mers were noticed with very few fibrils being produced leading to the conclusion that the on-pathway was almost switched off.At near CMC (FA concentration higher than CMC (FAm), off-pathway oligomers of length 4–5 mers were observed while no fibrils were produced, i.e., the on-pathway was switched off completely.At The detailed simulations and subsequent analysis are based on prior work where the FAs) in the solution from where the micelle formation starts. Therefore, if high concentration of surfactants are introduced into the solution (FAm), it directly forms micelles. At FAn and FApm concentrations, ideally there should not be any micelles present in the solution whereas at FAm range, almost all of the FAs should convert into micelles. As discussed earlier, at FApm, the FAs loosely form pseudo-micelles that are six-seven times larger than regular micelles. It can hence be hypothesized that as the FA concentration is increased beyond the CMC, extra FA molecules bind to the pseudo-micelles, which may then undergo structural change to form more stable and compact structured micelles  that were considered in the ROM model.Before establishing a proper modeling of the off-pathway, we wish to explore the formation of micelles first. Ideally, CMC is the concentration of the surfactants  through monomer addition and eventually form fibrils; the modeling abstraction considers A12 as fibrils, F. These reactions mainly depend on the monomer concentration and are slow being termed as pre-nucleation. Next, the higher order oligomers (A12) react with the monomers and other on-pathway aggregates  and get elongated. The rates of these reactions depend on both monomer and fibril concentration. The pre-nucleation stage is slower, whereas the elongation phase is rapid, thereby causing the sigmoidal growth of the fibril concentration over time. The nucleation number of on-pathway reactions is taken as 12 as reported in ref. The on-pathway reaction model is motivated by our previous works in refs FAn range, we assume the reactions to be exactly similar to the on-pathway model; however the rate constants for each phase of the sigmoidal ThT growth curve were varied by a factor of K. The corresponding reaction model is shown in Appendix-C under Supplementary Information.At the FAm range, the four Aβ monomers directly convert into the off-pathway oligomer . In the next stage, A12). This results in a 6-fold increase in the oligomer size hosted by the pseudo-micelles as compared to the micelles  from the on-pathway; (ii) forward and backward rate constants of elongation reactions  from the on-pathway; (iii) forward and backward rate constants of pre-nucleation in the off-pathway (kcon and kcon_); (iv) forward and backward rate constants of nucleation reactions ; (v) forward and backward rate constants of elongation reactions  (vi) forward and backward rate constants of lateral association reactions ; (vii) forward and backward rate constants of secondary fragmentation ; (viii) the fatty acid effect on the on-pathway rate constant parameter, K; and finally (ix) the pseudo-micelle concentration: p. Note that, the estimation of the pseudo-micelle concentration from the CMC values of fatty acids poses a different problem and needs controlled experimentation; however, it is biophysically not possible to exactly measure the pseudo-micelle concentration. We could only measure the diameter of pseudo-micelles. Hence we considered p as a free parameter along with the other rate constants to estimate the pseudo-micelle concentration.Thus, the model at the near-CMC range deals with several rate constants and parameters. There are four rate constants for on-pathway reactions: (i) forward and backward rate constants of pre-nucleation reaction (First, determine the rate constants for the on-pathway reactions from control experiment data.K for the FAn experimental data by using the estimated on-pathway rate constants.Estimate the parameter FApm experimental data utilizing the estimated on-pathway rate constants above.Estimate the rate constants of off-pathway aggregation with FAm concentration using all the estimated rate constants.Validate the formation of off-pathway oligomers (It is difficult to estimate the proper rate constant values of all these parameters at once. Hence, we follow our divide and conquer strategy from ref. R2 value between the simulated curve and experimental data was calculated for the different rate constant combinations. We solved these differential equations for various rate constant combinations with each rate constant ranging from 10−5 to 105 and the pseudo-micelle concentration p from 1 to 100 μM with multiples of 5. After that, these rate constants were manually fine-tuned to better match the experimental data and obtain better rate constant estimates. The best-fitted simulation parameters are taken as estimated rate constants of these reactions; each of these parameters are reported in the Appendices under the corresponding reaction models. Moreover, all the rate constants estimated here by fitting with experimental ThT intensity plots required a mapping of the cumulative effects of the concentration of higher order ThT positive oligomers to the ThT values; such mapping is explained in Appendix-F in the Supplementary Information.We first calculated the reaction flux for all reactions at a particular stage; then using these reaction fluxes, the differential equations for the rate of change of concentration is formulated for each oligomer. Next, these differential equations were solved using MATLAB’s ode solver and the Supplementary Information


The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered. Metabolism consists of complex interactions across various organs and body fluids, which poses a substantial challenge for the analysis of metabolic data. To address this problem, Jan Krumsiek from Helmholtz Zentrum München and colleagues used metabolomics measurements of plasma, urine, and saliva from 1000 people to statistically reconstruct a map of interactions in human metabolism. Based on this map, a novel approach that identifies highly correlated biochemical modules that are associated with a given phenotype, was tested for gender and insulin-like growth factor I (IGF-I). The identified modules provided insights into the interaction between metabolome and phenotype that reach beyond what can be found by commonly used statistical approaches for metabolomics. The approach is generic and can be readily applied to new datasets by other colleagues from the field. The metabolome is a readout of the biochemical transformations that involve small molecules in a body fluid or organ, and it reflects a snapshot of the state of a biological system.11 With continuous technical advancements and decreasing costs, datasets with simultaneous metabolomics measurement should become available rapidly, as can be seen by the increasing research in this field.16Most published metabolomics studies focused on only one body fluid, usually blood or urine; however, phenotypes usually have links to metabolism in multiple fluids simultaneously. For example, we reported multifluid associations for type 2 diabetes in two recent studies.17 Systematic module identification algorithms are well established for omics data,22 but have rarely been applied to high-throughput metabolomics data. A few metabolomics studies proceeded toward this objective by finding clusters in metabolite correlation networks, and by subsequently performing enrichment analyses with respect to a certain phenotype;26 however, none of these studies performed a systematic phenotype-driven module search. Moreover, these analyses were performed in only one single fluid.Phenotype associations in such large-scale, heterogeneous metabolomics datasets can be expected to be substantially complex, spanning functional modules, possibly across multiple fluids Fig. . Functio28 A module search would lead to numerous results covering the majority of the metabolic network ('dense' associations), thereby impeding interpretation by their sheer quantity  associations with metabolomics data are usually straightforward; however, phenotypes such as gender or BMI have been described to associate with more than a third to half of the blood metabolome.ity Fig. . To solvIn this study, we present a method for the systematic phenotype-driven identification of modules from multifluid metabolomics data, operating both at the single metabolite and at the pathway level. Specifically, we created a hierarchical map of multifluid metabolomics correlations as a template for the underlying metabolic network. Based on this network, we automatically extracted modules associating with two example phenotypes.29 Specifically, we estimated Gaussian graphical models (GGMs) based on partial correlations at the metabolite level and at two pathway levels: 'super-pathways' representing metabolite classes such as 'Lipid' or general metabolic processes such as 'Energy' and 'sub-pathways' representing biochemical subclasses or processes within a super-pathway such as 'Lysolipid' or 'TCA cycle,' respectively. The three networks  together depict the hierarchical map.To create this hierarchical map, we generated data-driven multifluid networks from blood, urine, and saliva metabolomics data of the German Study of Health in Pomerania (SHIP-TREND) cohort.20 and applied it to serum measurements of insulin-like growth factor I (IGF-I) and gender. IGF-I is a growth hormone with high sequence homology to insulin. It participates in numerous biochemical processes, in particular in the stimulation of cell growth and proliferation, and has been found to be associated with various disorders such as diabetes, cardiovascular diseases, and cancer.34 Despite its key roles in various biochemical processes, mapping IGF-I−metabolite associations onto a metabolite network in a previous study on the same dataset from the SHIP cohort resulted in only a relatively small number of blood and urine metabolites.34 Thus, IGF-I here serves as a trait with sparse associations. For gender associations, on the other hand, we found associations with a major part of the metabolic network,26 thus representing a trait with dense associations.Moreover, we developed a module search algorithm inspired by Chuang et al.Our analysis was based on data from the SHIP-TREND cohort. The dataset comprised 906 individuals, 512 females and 394 males, for which fasting plasma, urine, and saliva samples were available. Untargeted metabolomics measurements were performed by ultra-high liquid-phase chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). Data preprocessing included run-day normalization, dilution factor normalization , log transformation, outlier handling, and handling of missing values. After preprocessing, 610 known metabolites and 387 metabolites, whose chemical structures had not been identified yet, were available for further analysis. For each metabolite, knowledge-based pathway annotations from the metabolomics platform (Metabolon Inc.) were used. Each known metabolite was annotated with one of 73 'sub-pathways', which represent metabolic pathways or biochemical subclasses of the compounds . In addition, each sub-pathway was assigned to one of eight broad 'super-pathways' . These pathway annotations have been frequently used in previous studies that investigated data from the same platform . This is reflected by low-explained variances for super-pathway eigenmetabolites  How similar are the intrafluid correlation structures when comparing the three different fluids? (ii) How can the crosstalk between fluids be characterized?For all the three fluids, we determined the fluid-specific correlations, i.e., those exclusively occurring in only one body fluid, and the correlations shared between at least two fluids Figs. . At all At the metabolite level, 266, 447, and 207 intrafluid edges were exclusively found in plasma, urine, and saliva, respectively. A pairwise comparison of the fluids yielded 57 edges that occurred in at least two body fluids, with the majority (31) shared between plasma and urine Fig. . Plasma At the sub-pathway level, we found 69 fluid-specific and 10 shared edges Fig. . Only oncollapsed to 10 interfluid links between super-pathways . Notably, a single metabolite is scored by its univariate association with the phenotype. In a final consolidation step, overlapping optimal modules, for instance, those obtained from neighboring seed nodes, are identified and combined into a maximal module.We developed a procedure to identify modules associated with a phenotype at different levels of the hierarchical map. The algorithm is graphically outlined in Supplementary Information p-value of a module had to be lower than the significance level of 0.05 divided by the number of network nodes (Bonferroni correction at node level). In addition, we required each module’s score to be higher than the maximum score observed across all single components of the module.We followed a conservative multiple testing correction approach: To be significant, the The procedure was applied to two phenotypes at all the three levels : IGF-I as a phenotype with sparse associations, and gender as a trait with dense associations.34 Here, we additionally integrated metabolomics measurements from saliva. Notably, in the work by Knacke et al., IGF-I associations were analyzed for males and females separately. In our study, however, the results of a module search stratified by gender were mostly covered by modules from a joint gender analysis, which is why the latter analysis was chosen. Furthermore, Knacke et al. used a more relaxed multiple testing correction (FDR at 0.05), while in this study we applied the conservative Bonferroni correction, since we expected a substantially increased statistical power for the module-identification approach.Associations of IGF-I with blood and urine metabolites in the SHIP-TREND dataset were investigated in a previous study by Knacke et al.At the metabolite level, our algorithm identified six modules associated with IGF-I Fig. . For thep-value. This can be attributed to the reduction of statistical noise when aggregating concentrations of multiple metabolites.The six identified modules demonstrate that the module-identification algorithm enhances classical association analysis in several ways: It detected modules that (i) cover multiple pathways (see modules A and F) and (ii) span multiple body fluids (see modules B–E). (iii) Moreover, the algorithm was able to dissect apparently related but distinct processes. For example, modules C and E were in close proximity in the network and both contained a steroid amongst unidentified metabolites; however, the identification of two distinct modules suggested that they reflect two different processes that are independently associated with IGF-I levels. (iv) The algorithm increased the statistical power in several cases. For modules A–C, none of the single metabolites inside the modules was significant, whereas the entire module showed a significant 39 In our study, we identified a multifluid module containing a cluster of plasma and urine epiandrosterone and androsterone metabolites (module B). IGF-I has also been linked to the maintenance of physiological mitochondrial function via regulation of the expression of the mitochondrial pyrimidine nucleotide carrier PNC1.40 The association of single blood metabolites from the pyrimidine pathway with IGF-I has already been reported by Knacke et al. In the present study, we additionally observed that the aggregation of several blood and urine metabolites from this pathway (module D) yielded a considerably lower p-value than the single components, further supporting the link between pyrimidines and IGF-I. Both modules B and D contain metabolites from plasma and urine, indicating that not only the concentration levels of the respective metabolites in these fluids but also their crossfluid transport processes might be associated with IGF-I.Beyond the advantages of our approach compared to classical association analysis, we found a series of biologically interesting results. Initially, we were able to confirm previously identified IGF-I associations. For instance, it has been reported that there is a complex interplay between sex hormones and IGF-I.We also detected IGF-I associations, that to the best of our knowledge, have not been reported previously. We found a saliva module (A) comprising three amino acids, 2-hydroxyglutarate, a lipid, and laurylsulfate, a xenobiotic, each of which alone was not significantly associated with the phenotype. Associations of these metabolites with IGF-I have not previously been reported, in particular not in human saliva. Module F contained the xenobiotic 2-ethylhexanoate (EHA) and the fatty acid caprylate (8:0), neither of which has been reported with IGF-I to date; however, in this case, the module score seems to be mainly driven by EHA, while the fatty acid only contributes marginally to the score.Finally, we investigated the effects of oral hygiene on the modules identified for IGF-I by correcting for the teeth brushing behavior of the study participants in the module identification process. Exactly the same modules were found, indicating that oral hygiene has no effects on metabolic changes related to IGF-I  of gender-associated modules  comprising plasma and urinary lipids of ‘Fatty acid, Amino’, which to the best of our knowledge, have not been reported before.Similar to the IGF-I results, the method identified both previously reported and novel phenotype associations. A well-known metabolome–gender association, the steroid pathway, was thereby detected as a multifluid module, spanning the plasma and urine pathways (Module H). We also detected three intrafluid lipid modules (B–D), showing multiple processes within this pathway in which men and women differ. Modules C and D comprised pathways from blood, while module B consisted of salivary ‘Fatty acid, monohydroxy’ and ‘Fatty acid, Dicarboxylate’. Several metabolites of these pathways in blood and saliva were found to associate with gender in previous studies,3). Since the set of measured metabolites differ between SHIP-TREND and QMDiab due to different profiling platforms, we only considered metabolites measured in both cohorts for an appropriate comparison. We generated a new hierarchical map based on the reduced SHIP-TREND dataset comprising 752 metabolites in total (490 known and 262 unknown), which were grouped into 134 different sub-pathways. The module search algorithm was run at the sub-pathway level of this newly generated hierarchical map for both SHIP-TREND and QMDiab. One-third of the gender-associated modules identified in the reduced SHIP-TREND were replicated in QMDiab . In the sub-pathway network, crossfluid correlations mostly connected the same sub-pathways . Such correlations between biochemically closely related molecules may arise due to transport and exchange processes across the fluids.We then performed a module identification approach based on the hierarchical map. We found that IGF-I was associated with rather local parts of the metabolic network, while at the more global level (sub-pathways and super-pathways) fewer modules were detected. In contrast, for gender, we identified a large number of modules (73) in the fine-grained metabolite network. At the sub-pathway level, these numerous modules were fused into 13 sub-pathway modules, facilitating biological interpretation by providing a better overview over parts of metabolism affected by gender. Two modules were detected at the coarse super-pathway level, but did not promote biological interpretation in this case.34 Moreover, to the best of our knowledge, in this study, IGF-I associations were analyzed in saliva for the first time. For gender, for instance, we showed that the association with histidyl peptides appears across human blood and urine. For both the phenotypes, the identification of multifluid modules suggested that not only the concentration levels of the respective metabolites in the corresponding fluids, but also the transport and exchange processes between the fluids were associated with the phenotypes. These types of findings could only be leveraged through a module search on multifluid data.For both IGF-I and gender, we could confirm previously reported associations. In addition, our analyses extended these findings to multiple fluids. For example, we could extend the association between IGF-I and plasma pyrimidine metabolites to urine, which has already been reported by Knacke et al.45 which were originally assigned to two different pathways according to sub-pathway definitions in this study.Our results demonstrated an increase in statistical power for the phenotype-driven module identification approach compared to classical analysis. We found significant modules comprising components that were not significantly associated with the phenotype when the single components were considered alone . Moreover, by combining metabolite groups from different body fluids, statistical power is also substantially increased compared to the results from just a single fluid . This increase is most likely due to the reduction in statistical noise while aggregating measurements of multiple metabolites. Another major advantage of the module approach lies in overcoming borders of pathway definitions, which are inherently arbitrary for any commonly available metabolite-centric or process-centric pathway annotations. Our algorithm recognizes the phenotype-driven interplay of pathways and merges them, if appropriate, as shown in gender module I. This module reflected the well-known gender association with metabolites from caffeine metabolism,46 For future studies, other pathway definitions, such as in the HMDB47 or MetaCyc48 could be used; however, since a large number of measured metabolites will not be covered in those databases,49 this approach would currently result in a substantial loss of information. (ii) Following the eigengene approach,50 we defined a pathway representative as the first principal component of its metabolites. To capture a higher degree of variance explained, multivariate association methods, such as canonical correlation analysis51 and O2-PLS52 could be adapted to model the relationships between the two pathways. (iii) Our module identification approach is only suitable for finding modules where all components show the same direction of association with the phenotype , while opposing effects will cancel out. A possible solution to this restriction would be the use of the multivariate modeling approaches mentioned in the previous point. (iv) We used data from the Qatar Metabolomics Study on Diabetes  We used pathway annotations provided by the metabolomics platform, which are analogous to KEGG pathways.In conclusion, we introduced a hierarchical map, that besides serving as a template of human metabolism for the module identification algorithm, can also be a valuable, downloadable resource for future studies, since it allows for a fundamental understanding of the complex correlation structure within and across multiple body fluids. Based on this map, we proposed an approach for the phenotype-driven identification of modules spanning multiple pathways and multiple body fluids. These modules provide deeper insights into mechanistic aspects of phenotype associations. Importantly, our module approach is generic, and therefore widely applicable. An R implementation of the algorithm is freely available as 34 Briefly, metabolomics measurements were performed for a subset of 1000 participants without self-reported diabetes. The dataset included 561 females and 439 males with an age distribution of 50.14 ± 13.17 (mean ± SD) and 50.08 ± 14.24, and a BMI distribution of 26.99 ± 5.12 and 27.85 ± 3.7, respectively. Fasting (≥8 h) plasma and urine samples were collected between 07:00 and 12:00 am. Blood was sampled from the cubital vein of subjects in a supine position. Samples were stored at −80 °C. Stimulated saliva was collected with a commercially available collection system . The subjects chewed a plain cotton roll for exactly 1 min to stimulate salivation. The rolls with the absorbed saliva were placed into the Salivette® and immediately centrifuged at 1000×g for 20 min at 4 °C to remove food remnants, insoluble material, and cell debris. The resulting supernatant was stored at −80 °C. Samples were analyzed on an untargeted metabolomics platform established by Metabolon Inc.  with ultra-high liquid-phase chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) in both positive and negative modes. The measurements were performed at the Genome Analysis Center, Helmholtz Zentrum, Munich, yielding a total of 1665 metabolites across all fluids, of which 1190 represented unique metabolites. Blood IGF-I concentrations were determined by automated two-site chemiluminescent immunoassays on the IDS-iSYS kit .Metabolomics data were obtained from the Study of Health in Pomerania (SHIP-TREND), conducted between 2008 and 2011 in West Pomerania, Germany, with 4420 participants. The study was approved by the local ethics committee and conformed to the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants. Details about sample acquisition and experimental procedures can be found elsewhere.53 PQN was, moreover, used to normalize saliva measurements for dilution variations. For the PQN procedure, first a ‘pseudo-sample’ (reference) was calculated as the mean of all metabolites with no missing entries for all participants . Subsequently, a dilution factor was estimated as the median quotient between the reference and each sample. Finally, all measurements were divided by the respective dilution value. Of note, urine creatinine and the estimated urinary dilution factor were substantially correlated  within the SHIP-TREND data  and 991 unique metabolites from all three body fluids. Probabilistic quotient normalization (PQN) was then applied to urine samples to account for diurnal variation. PQN has previously been shown to be superior to common creatinine scaling.2-transformed. Multivariate outlier detection  was performed separately for all fluids using an algorithm proposed by Filzmoser et al. (2008),54 implemented in the pcout function within the R package mvoutlier. Briefly, this algorithm calculates an outlier score for each sample using principal component analysis and the Mahalanobis distance on a robustly scaled data matrix. Default parameters were used for the identification process, and the exclusion criterion was set to 4 SD. As a result, 13, 8, and 16 samples from plasma, urine, and saliva, respectively, were excluded from further analyses. After these preprocessing steps, the dataset comprised 906 individuals for which fasting plasma, urine, and saliva samples, as well as IGF-I measurements were available.All metabolite levels and serum IGF-I measurements were log56 To ensure robust parameter estimation for the truncated normal distributions, this procedure was only applied to metabolites for which the respective run day contained more than 10 nonmissing concentration values. Remaining missing values were imputed with the mice R package (version 2.22) with predictive mean matching as an elementary imputation model. Note that we also used the stringent threshold of 20% to exclude variables with missing values, because estimating  correlations based on too many imputed values that in turn were generated by mice using the covariance structure of the data might introduce unwanted bias. The final imputed dataset consisted of 906 samples and 997 metabolites.Since for the network inference procedure a fully observed data matrix is required, missing values were imputed by the following procedure: All metabolites with more than 20% missing values (320) were excluded from the dataset to avoid false positive results and to preserve statistical power. The first step of imputation was performed per run day on the log-transformed raw data . Following the assumptions that missing values occur due to a detection threshold and that metabolites are log-normally distributed, each missing value was replaced by a random value drawn from the censored part of a normal distribution reconstructed by maximum-likelihood estimation.unknowns), and thus, cannot be assigned to any pathway for which reason they were excluded from the pathway analyses. A detailed list of metabolites and their annotated pathways is provided in Supplementary Information Each metabolite with known chemical structure (610 metabolites) was annotated with one of the 73 sub-pathways , and one of eight more general super-pathways . The remaining 387 metabolites have unknown chemical structure  using the GeneNet R package, version 1.2.12. GGMs are based on partial correlations, which represent the linear associations between two variables corrected for all remaining variables in multivariate Gaussian distributions. We included age, gender, and BMI as standard covariates into the model. Edges between metabolites or sub-pathways were assigned if both their Pearson correlations and their partial correlations were statistically significant with α = 0.05 after the Bonferroni correction for p is the number of metabolites or sub-pathways, respectively.Two networks were inferred using GGMs, one for metabolite concentrations  and one for the sub-pathway collapsed into a super-pathway network. To this end, a link between two nodes was drawn if there was at least one connection between any two sub-pathways assigned to the two respective super-pathways in the underlying sub-pathway GGM.To obtain a global view of connections between the super-pathways, the sub-pathway GGM was candidate module M, a representative value RM is defined as the average of scaled intensities (average z-score) of all metabolites in M. If M consists of sub-pathways, then the representative is calculated as the mean z-score of all metabolites in the set union of all sub-pathways. Notably, for pathway network estimation, a pathway representative was defined as the sub-pathway eigenmetabolite based on the assumption that pathway components share common chemical and biological properties. In contrast, we chose to use mean z-scores as module representatives, since modules are considerably more heterogeneous.For a M is obtained from the multivariable linear regression modelRM is the aforementioned representative value, βM,0 is the intercept, βM,1,…,βM,4 are the regression coefficients for each independent variable, P is the phenotype of interest, and ϵM is a normally distributed error term. The module score is then defined as the negative logarithmized p-value of the coefficient βM,1, which represents the magnitude of phenotype association. Notably, the score of a single component equals its negative logarithmized p-value from a univariate analysis. Furthermore, the scoring function for gender does not contain gender as a covariate.The score of each candidate module optimal module. In every iteration, each neighboring node of the candidate module is added and the score of the extended module is calculated. The neighbor leading to the highest score improvement is then added to the module. Furthermore, a neighbor is only added if the score of the new module is higher than the scores of all single components. The algorithm terminates if no further improvements can be made. In a final step, overlapping optimal modules from different seed nodes are combined into a single module , which is rescored by the scoring function.Given the scoring function and an initial node (seed node), a greedy search procedure is performed to identify an α = 0.05 after the Bonferroni correction for the total number of nodes in the underlying network. The proposed algorithm is visually described in Supplementary Information For the identification of IGF-I-associated and gender-associated modules, the procedure was applied to all three networks, namely, the metabolite, the sub-pathway, and the super-pathway networks. To assess the significance of the modules, a conservative multiple testing correction procedure was used with a significance level of The data that support findings of this study are available from the Ernst-Moritz-Arndt-Universität Greifswald but restrictions apply to the availability of these data (the informed consent given by the study participants does not cover data posting in public databases), which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Ernst-Moritz-Arndt-Universität Greifswald or can be directly applied for via www.fvcm.med.uni-greifswald.de/dd_service/data_use_intro.php?lang=ger.An implementation of the generic approach and an example script is freely available as supplementary material (Supplementary Information Supplementary Material


By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR  annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities.WikiPathways ( Similar to the pathway data, the code base is open source and associated formats and ontologies conform to open standards, allowing other developers to participate and join the team (github.com/wikipathways). Pathways are encoded in GPML format and created with PathVisio . Reactome pathways have been updated to version 61 using an extended version of the previously reported Reactome2GPML converter  Biochemical Pathways Part I (Homo sapiens). onverter . This coHomo sapiens pathways, such as the Glycolysis and Gluconeogenesis pathway  Glycolysis and Gluconeogenesis (H. sapiens). wikipathways.org/instance/WP534_r94762). We now discriminate between the straight-chain (open) and cyclic (closed) forms of glucose-6-phosphate and fructose-6-phosphate and their conversions into one another. We hope that this and future work on carbohydrates in our pathways will result in a better visualization of data from the glycomics field. In order to let WikiPathways tell the full biological story of a pathway, all elements need to be specified as a gene, protein, metabolite, or pathway node, and associated with an identifier. Further curation work was aimed at improving the annotation of pathways, e.g. by annotating interactions in pathways with their directionality information, such as the direction of enzymatic reactions and the involved enzyme(s). Part of the growth, therefore, is due to converting metabolites represented as text labels to metabolite nodes. Figure Additionally, a considerable contribution to the growth comprises improved annotation and curation efforts. We improved the biological annotation of several small carbohydrates for multiple doi.org/10.6084/m9.figshare.3578313.v1).In order to link other databases and to map experimental data onto pathways, it is crucial to annotated WikiPathways nodes with identifiers. BridgeDb is the identifier mapping tool used to translate identifiers in pathways to those in experimental data sets and third party databases . The patwikipathways.org/pathway-finder/demo), which can be used to find pathways based on indirect queries, such as targets of specific microRNAs or which metabolites are converted by specific enzymes.Besides updated content, biologists and chemists will also benefit from updates to the WikiPathways website itself. For example, a new search tool is available called Pathway Finder (groups.google.com/forum/#!forum/wikipathways-discuss) with new communication channels. First, we implemented an updated request form (goo.gl/forms/FE6Ab347OLa7cBzr1), where anyone can suggest any published pathway figure to be digitized and made machine readable. We will prioritize requested pathways and assign pathway modeling tasks to people in our curation team. Given that thousands of pathway images are available, such prioritization is needed (surveymonkey.com/r/wikipathways). We use the responses to gauge how WikiPathways is used in research in order to prioritize new feature development.Feedback and requests from the WikiPathways community are important to us. To facilitate more interaction between the developers, curators, authors, and users, on generic and specific questions about the WikiPathways project, tools, and pathways, we have complemented our discussion group , which aims to provide comprehensive and easy-to-use training. The Academy covers all aspects of pathway authoring, from basic concepts and terminology to various authoring tools. Designed as a step-wise path (academy.wikipathways.org/path.html), the Academy starts with a ‘Biology 101’ primer on pathway concepts, followed by a ‘Walk along a pathway’, introducing the user to reading and interpreting pathways, and then using ‘Pathway Building Blocks’ to finally build ‘My First Metabolic Pathway’. Authors are also trained on how to leverage the WikiPathways website to create an account, publish pathways and complete pathways with ontology categories, descriptions and more.To support the authoring of new pathways, we created a new interactive training environment called WikiPathways Academy . The protocol consists of seven basic and five optional tasks, covering screening of new pathways and recent edits, orienting new users, fixing poorly annotated pathways and nodes, and evaluating the content of pathway collections for downstream analysis. Within each task, the curator is presented with relevant pathways one-by-one in a built-in pathway viewer, from which they can seamlessly open the pathway at WikiPathways to make necessary changes. Each task contains a detailed description of the curation steps involved, in order to assure a consistent workflow. The QA protocol is open to anyone interested in improving pathway content. We also continuously recruit a dedicated team of curators, whom rotate on a weekly basis and are displayed as the ‘Curator of the Week’ on the front page of the WikiPathways website.Taking advantage of the new training platform, we have redesigned our Quality Assurance (QA) protocol as an interactive experience at WikiPathways Academy, (jenkins.io) and uses the WikiPathways RDF that is generated from the GPML twice every day , while this system also informs curators if a failure is detected via a push message.The curators are further supported by automated testing. A test suite is run by a build service using the Jenkins software (very day . The suiphysiology.case.edu/research/labs/garvin-lab) to develop resources for renal genomics research. As a first step, we created a new Renal Genomics portal at WikiPathways , which showcases 11 pathways. This effort aims to produce high-quality pathways with tissue/cell-specificity for kidney disease and biology, and to attract researchers in this domain to collaborate on pathway modeling. As part of an ongoing collaboration, a portal was created for pathway content relevant to the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium . CPTAC aims to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis. Pathways in the portal are annotated with post-translational modifications at the protein level (states), such as phosphorylation, methylation and ubiquitination, to facilitate data overlay and integration with other CPTAC tools.New portals have been set up to support specific communities. A new collaboration started with scientists from the Garvin lab at Case Western Reserve (pathwaypresenter.jcbwndsr.com), has been developed to create visual presentations of the pathway diagrams from WikiPathways. With this tool, presentation slides can be created where nodes in the pathway are for example highlighted or hidden. This tool will soon be released as an integrated presentation builder and viewer launched from any pathway page at WikiPathways.org. Second, a recent analysis showed that 150 pathways have been cited in literature by their pathway identifier. We want to provide different citation styles, compatible with programs that make use of e.g. BibTeX and Endnote, for individual pathways. By adoption of data citation standards, we want to enable researchers to effortlessly and accurately acknowledge pathway authors.First, a new tool, named Pathway Presenter (data.wikipathways.org). Each release includes the updated contributed pathway content that has undergone community curation and internal quality assurance to ensure (i) standard identifiers for genes, proteins and metabolites, (ii) properly modeled interaction and reactions, (iii) adequate descriptions and tags, (iv) literature references and (v) ontology term annotations. These releases currently provide the pathway content in formats for computational analysis  and for visualization (SVG). Daily releases and other formats  are also available from the main download page (wikipathways.org/download).We initiated a new archive for monthly releases of WikiPathways content at (www.openapis.org), which is available at webservice.wikipathways.org. Matching client side libraries for Java, JavaScript, Python, and R have been updated and are available at (github.com/wikipathways/) under wikipathways-api-client-java, wikipathways-api-client-js, wikipathways-api-client-py and rwikipathways. With the use of OpenAPI tools, client code can be automatically generated for other popular programming languages, if needed.Web services are available for programmatic access to WikiPathways content. We also now provide interactive Swagger documentation using OpenAPI (wikidata.org/wiki/User:Pathwaybot/query_examples) demonstrate the advantages.Noteworthy is the recent license change of the WikiPathways content. After community requests to include pathway content in Wikidata, and consultation with the author community, the CC-BY license was replaced by the more liberal CCZero waiver. With this waiver, WikiPathways is placed as completely as possible in the public domain, so that others may freely build upon, enhance and reuse our works for any purposes without restriction under copyright or database law. This change enables inclusion in Wikidata and benefits other projects that reuse the pathways. The availability of the human WikiPathways in Wikidata allows enriching these pathways with the other biomedical data , and a fgithub.com/wikipathways/wikipathways.org/issues).In addition to the aforementioned feedback mechanisms, developers are further supported by the ability to request new features and provide feedback regarding WikiPathways via the issue tracker on GitHub (fdp.wikipathways.org). The FDP implements the FAIR principles by the use of rich metadata and resource description, using linked data approaches and public ontologies. The FDP describes the data resources, provides clear copyright and license information, provenance information about when the data was generated and by whom, and points to a download location. This information allows search engine crawlers to find and index the resources, implementing the findability requirement of the FAIR principles.To further automate dissemination, we adopted the recently introduced FAIR principles, which propose to enhance the Findability, Accessibility, Interoperability, and Reusability of metadata . To achiA development regarding semantic web formats is a project to create nanopublications of facts captured in WikiPathways, such as which biological entities are interacting with each other. Nanopublications have a rich provenance model, allowing stating the origin of the fact, linking to specific research papers, or even to experiments ,33.The WikiPathways project is thriving. The FAIR and open science approach and the extensive community support continues to trigger growth of the project and the database content. In the following years, our growth will be supported by recently renewed funding. The updates presented here, for biologist, chemists, authors, curators, and data scientists, demonstrate the success of our approach and open up new ways in which biological complexity can be represented and reused by others. Examples of such complexity include post-translational modifications affecting protein activity and the temporal dynamics of processes. Our recent efforts around the content and curation of metabolic pathways show a high potential for adoption by the metabolism and metabolomics communities and those applying these technologies. In fact, the demonstrated continued growth in content and features since the 2016 update shows we are getting closer to reaching our commitment to capture every pathway of interest and share them in as many useful ways as possible.


CSMD1, which encodes a negative regulator of complement activation. Pathways for glycosaminoglycan (GAG) synthesis exhibited strong association in multiple contexts. Taken together, highly associated pathways suggested a pathogenesis mechanism where maternal immune activation causes disruption of neurogenesis  and deficits in cortical interneurons, leading to symptoms triggered by synaptic pruning. Increased risks arise from GAG deficiencies causing complement activation and excessive microglial action. Analysis of PTSD data sets suggested an etiology common to SZ/BP: interneuron deficiency can also lead to impaired control of fear responses triggered by trauma. We additionally found PTSD risk factors affecting synaptic plasticity and fatty acid signaling, consistent with the fear extinction model. Our results suggest that much of the missing heritability of psychiatric disorders resides in non-additive interaction effects.Genetic susceptibility factors behind psychiatric disorders typically contribute small effects individually. A possible explanation for the missing heritability is that the effects of common variants are not only polygenic but also non-additive, appearing only when interactions within large groups are taken into account. Here, we tested this hypothesis for schizophrenia (SZ) and bipolar disorder (BP) disease risks, and identified genetic factors shared with posttraumatic stress disorder (PTSD). When considered independently, few single-nucleotide polymorphisms (SNPs) reached genome-wide significance. In contrast, when SNPs were selected in groups (containing up to thousands each) and the collective effects of all interactions were estimated, the association strength for SZ/BP rose dramatically with a combined sample size of 7187 cases and 8309 controls. We identified a large number of genes and pathways whose association was significant only when interaction effects were included. The gene with highest association was  Extensions of the independent loci (IL) analysis by pairwise testing may partially capture such effects, but in the largest SZ meta-analysis to date, it was reported that pairwise tests did not yield new associations.4 In this work, we characterized non-additive interaction effects of common variants on SZ and BP risks using a recently developed algorithm ,10 which greatly extends the scale of interacting partners simultaneously considered beyond SNP pairs by using the regularized inference of high-dimensional interactions within large SNP groups. Over-fitting is avoided by optimizing the risk prediction quantified by the area under the curve (AUC) of receiver operating characteristics, estimated from cross-validation. We refer to such optimized inference of the aggregated sum of all possible interaction effects as the collective loci inference.Schizophrenia (SZ) and bipolar disorder (BP) are severe psychiatric disorders with overlapping symptoms affecting ~1% of the population.11 as the main cause underlying the disease phenotypes.Using moderately sized collections of SZ and BP case–control samples, we first found that standard IL analyses without interaction could detect only marginal levels of association. In contrast, inclusion of interaction effects led to the emergence of a large number of highly significant SNP groups. These collectively interacting genetic factors (genes and pathways) comprised not only loci with documented genetic association but also varied arrays of neurodevelopmental pathways, whose roles in SZ/BP phenotypes have so far been inferred largely through evidence from non-genetic studies. In particular, the pathway groups collectively point to the insufficient development of cortical inhibitory interneurons13 Recent genome-wide association studies identified potential risk loci,18 but connections to fear conditioning/extinction, a powerful model for PTSD,19 are yet to be established. We analyzed two PTSD data sets using our algorithm and found a significant overlap of associated neurodevelopmental pathways with SZ/BP results, which provides an explanation for the observed comorbidity of these phenotypes.22 Our results further suggest significant association of pathways central to fear conditioning/extinction, including AMPA receptor trafficking, γ-aminobutyric acid (GABA) synthesis and degradation, and fatty acid-regulated nociceptive signaling.We further supported this main conclusion by analyzing posttraumatic stress disorder (PTSD) data sets. PTSD is characterized by the inability to recover from trauma-induced symptoms, including flashback and avoidance.23 We used the quality control-filtered GAIN data for autosomal chromosomes. For the non-GAIN sample, we applied minor allele frequency >0.01, call rate >95% and Hardy–Weinberg (HW) equilibrium P>10−6 filters to get 714 608 SNPs common to European American (EA) and African American (AA) groups. We then combined GAIN and non-GAIN samples for EA and AA groups separately  BP data and followed quality control steps as described:25 we first identified SNPs with independent-SNP P-value Pi<10−3 and removed variants with poor clustering in call signals. We further removed SNPs with high apparent association in isolation to obtain 458 846 SNPs in total. We then identified 343 070 SNPs common to GAIN and WT samples for further analysis  (dbGaP accession number phs000021.v3.p2) and non-GAIN studies (phs000167.v1.p1) data.parately  and seleanalysis . We trea26 by selecting trauma-exposed subjects with known PTSD status, divided into EA and AA groups based on the stratification information of the original data  data set16 (phs000864.v1.p1), selecting subjects meeting DSM-IV criteria A1 (524 cases and 3304 controls). We used principal component analysis along with self-reported ethnicity to stratify subjects into EA, AA and AS (Asian/others) groups  by selecting 443 198 SNPs shared by both data sets, containing five distinct subgroups .We formed PTSD case and control groups from the alcohol, cocaine and opioid dependence (ACOD) data set) groups . We sele9 to account for population heterogeneity, similar in spirit to other meta-analysis algorithms28 except that it is also applicable to interacting SNPs . The AUC versus P-value relationship (We used 1730 Reactome pathwaystionship  was obtahttp://www.github.com/BHSAI/GeDI.The updated version of the software GeDI (genotype distribution-based inference) is available at 23 comprising EA and AA subpopulations with 3971 cases and 3666 controls in total. For BP, we combined the GAIN samples (EA and AA)24 with WT data25  each had weak levels of association  studies.30 The Molecular Genetics of Schizophrenia and GAIN sub-samples used in our study are contained in the PGC studies, which, however, are much larger in combined sample sizes. For SZ, the association levels of a number of loci showed considerable overlap with those in the PGC  with permutation resampling and found −log10P to be highly linear for AUC >0.51  is P<2.5 × 10−5 or AUC >0.53 because significance tests were performed once for each pathway . SZ and BP outcomes were similar , indicating common disease mechanisms. In contrast to SZ and BP data sets, we observed a strong increase in association strength with the inclusion of interactions in SZ+BP (P~10−21) of the signaling by G-protein-coupled receptor group and metabolism of carbohydrates (AUC=0.612 (0.009), P~10−21), the latter again suggesting the importance of proteoglycans. We used two representative pathways of different sizes to examine the performance of collective inference with varying sample sizes, level of linkage disequilibrium and models , consistent with the strong association of GAG metabolism and O-linked glycosylation in ErbB4 , HIVEP1 (P=9 × 10−6) and PTPRD (P=4 × 10−5), respectively, while the distribution of AUCs for pathways was similar to those of SZ and BP in PTPRD gene is third in ranking within the SZ+BD results (P=1 × 10−4), peroxisome proliferator-activated receptor α (PPARA) activates gene expression (AUC=0.568 (0.027), P=2 × 10−5) and mitogen-activated protein kinase (MAPK) kinase kinase (MAP3K)8-dependent MAPK1/3 activation (AUC=0.551 (0.017), P=1 × 10−5) for the three data sets, respectively. The top two pathways each in MRS and ACOD+MRS exceeded the Bonferroni threshold. We also estimated false discovery rates61 using the AUC-P regression and found lowest false discovery rate of 0.14, 0.019 and 0.014 for ACOD, MRS and ACOD+MRS data sets, respectively. For the latter two data sets, there were 188 and 591 pathways, respectively, with false discovery rate <0.05 .The level of IL association in the two PTSD data sets, ACOD results . We anal results : the top results , constitte <0.05 . We comp64 The other pathways of interest were lipid metabolism, transport and signaling pathways, in addition to those involved in neurodevelopment previously observed in SZ/BP: regulation of TP53 activity through association with co-factors, G2 phase (cell cycle) and post-chaperonin tubulin-folding pathway. Many of these pathways were also present in the MRS results  suggests the importance of GAG molecules in SZ.40 We noted in our results at least three distinct contexts in which GAGs act as negative regulators: preventing premature senescence during neurogenesis, guiding migration and suppressing phagocytosis. The high association of CSGALNACT1 (P~10−17, slightly weaker but comparable to pathways shown in CSMD1 (71 Our results are thus consistent not only with many previous observations of the association of this gene to SZ35 and BP,39 but also with the recent work on SZ pathogenesis implicating C4 in microglial phagocytosis.72Evidence suggesting that mast cell activation results from maternal immune activation includes elevated levels of interleukin-6 and tumor necrosis factor during pregnancy, in which neurogenesis and migration occur during its first and second/third trimesters, respectively.GALNACT1  further in CSMD1 , encodes73 in the etiology of SZ/BP and PTSD to replicate some of our SZ/BP findings in PTSD. The case group sample size of ACOD+MRS is ~20% of SZ+BP (PTPRD was scored high in both SZ+BP (We took advantage of the suspected commonalitiesof SZ+BP  and the of SZ+BP  is consiof SZ+BP . Despiteth SZ+BP  and ACODth SZ+BP . More imth SZ+BP , includith SZ+BP . When vi2+ influx leads to AMPA receptor trafficking into postsynaptic density, activation of MAPK, transcription and translation of long-term potentiation-associated proteins, as well as structural remodeling via Rho GTPase-controlled signaling for actin polymerization.62 The robust presence of AMPA receptor trafficking pathways and the high associations of MAPK and Rho GTPase pathways (62 as supported by the association of GABA synthesis and degradation pathways in MRS results (74 which is consistent with degradation of ECM in A second prominent feature in the PTSD-associated pathways is synaptic plasticity , which ipathways  are cons results . Structu66 Polyunsaturated fatty acids are released from membrane by the action of PLA2, often coupled to neurotransmitter receptors (including NMDAR75 and opioid receptors;76 phospho-PLA2 pathway in 77 and regulates pain signals78 by activating PPAR-α, which acts as an analgesic factor inhibiting nociceptive responses.79 The strong association of PPAR-α pathway (80 and inhibition of fatty acid amide hydrolase degrading PPAR-α ligands enhances memory acquisition.81 It is also notable that the phosho-PLA2 pathway (82 by inhibiting GABAergic antinociceptive pathway.76We also noted high association in fatty acid/phospholipid metabolism and signaling  known to pathway  thus sug pathway  occurs iIn conclusion, by testing groups of SNPs for collective association strengths while including non-additive interaction effects, we demonstrated that analyses of moderately sized samples can allow for the discovery of a rich array of pathway groups associated with psychiatric disorders.


Further metabolic analysis suggested that a linear relationship existed between the relative change of hydrogen yield in the formate pathway or NADH pathway and the relative change of NADH yield or ATP yield. Thus, this finding provides new insight into the role of cellular reducing power and energy level in the hydrogen metabolism. It also establishes a rationale for improving hydrogen production from a global perspective.The formate pathway and NADH pathway as two common hydrogen-producing metabolic pathways have been well characterized to understand and improve biohydrogen production. These two pathways have been thought to be separate and have been independently investigated. However, in this study, perturbation of genes ( In such a context, hydrogen can be a promising future energy carrier, which is pollution free for its combustion only generating water without any byproduct2. At present, 40% of hydrogen is produced from natural gases, 30% from heavy oil and naphtha, 18% from coal, and 4% from electrolysis3. However, these production methods require highly intensive energy sources. It is worthless to produce clean energy by consuming much energy and still cause environmental problems. In the present scenario, biohydrogen production is of great importance to be an alternative4.Fossil fuels have been the conventional and principal sources to satisfy the world’s demand of energy. However, their intensive use has caused environmental problems4. There are three basic production methods: direct biophotolysis, photofermentation, and dark fermentation. Compared to the first two methods, dark fermentation seems to be the most practical and promising method on account of having no need for direct solar energy input, while having a wide substrate range from energy crops to waste streams, and requiring only simple reactor technology. However, the dark fermentation method is currently limited by low hydrogen yield6. Therefore, many studies focus on how to improve the hydrogen yield of dark fermentation by metabolic engineering.Compared to conventional production methods, biological hydrogen production can use renewable resources as the reactant and be operated at ambient temperature and pressure, which means much less energy input and lower costEscherichia coli, Clostridium sp.7, Enterobacter sp.8, and Rhodobacter sp.9. Although each strain has its different metabolic features, there are two main hydrogen-producing pathways in common: the formate pathway and NADH pathway  and hydrogenase (H2ase) -NAD+ reductase and ferredoxin hydrogenase. Many steps have been made to regulate the metabolism of NADH to enhance the H2 production16.The key element of the formate pathway is the formate hydrogen lyase (FHL) complexKlebsiella pneumoniae, a higher temperature can enhance the hydrogen-producing NADH pathway, while the flux in the formate pathway reach their maximum at pH 7.0–7.517. Ma et al. found that the hydrogen production fluxes in the formate pathway and the NADH pathway both increased when NADH dehydrogenase was impaired in Enterobacter aerogenes18. It seems that regulation of the NADH pathway is accompanied by the perturbation of the formate pathway.The formate pathway and NADH pathway have been thought to be independent, but recent studies demonstrated that there might be a link between them Fig. . In Niu’E. aerogenes, was used. Two hydrogen-producing pathways, the formate pathway and the NADH pathway, coexist in E. aerogenes. Genes related to the formate pathway or NADH pathway were perturbed, and then the hydrogen production was investigated. Cellular energy metabolism was further explored by metabolic analysis. Findings obtained in this study could help to thoroughly understand how perturbation of formate pathway and NADH pathway act on the hydrogen metabolism.To figure out the puzzle of the interaction between the formate pathway and the NADH pathway, further research on metabolic regulation of fermentative hydrogen production is needed. In this study, a representative hydrogen-producing bacterium, facultatively anaerobic E. aerogenes, a fourfold strategy was adopted (Table hycA (FHL repressive regulon gene), overexpression of fdhF (formate dehydrogenase H gene), or overexpression of fhlA (FHL activator protein FHLA gene) made the hydrogen yields increase from the formate pathway but decrease from the NADH pathway. Second, mutations directly related to the NADH pathway were then evaluated. The most NADH-consuming pathway is lactate production pathway. The knockout of ldhA (lactate dehydrogenase gene) greatly improved the hydrogen yield from the NADH pathway, but the hydrogen yield from the formate pathway had no change. The nuoB gene encoding the NADH dehydrogenase/NADH-quinone oxidoreductase was impaired, which made the hydrogen yields from the formate pathway and NADH pathway both increase. The third task was directly altering both the formate pathway and NADH pathway at the same time. Knockout of hybO (uptake hydrogenase gene) improved the hydrogen yields from both the formate pathway and NADH pathway. An enzyme called NAD+-dependent formate dehydrogenase can work on both formate and NADH, which uses formate to regenerate NADH from NAD+ 19. The gene fdh1 encoding NAD+-dependent formate dehydrogenase from Candida boidinii was expressed into wild-type E. aerogenes. It resulted in the increase of hydrogen yields from the formate pathway but the decrease of hydrogen yields from the NADH pathway. The fourth strategy was alternating the whole cellular metabolism that might indirectly affect the hydrogen production. A reported global regulator NarP encoded by narP is a transcriptional regulator of many anaerobic electron transport and fermentation-related genes20. The polyphosphate kinase (PPK) encoded by ppk regulates cellular energy level22. Overexpression of narP or ppk improved the hydrogen yields from both formate pathway and NADH pathway. The relative change of hydrogen yield in the NADH pathway was bigger than that in the formate pathway. Further metabolite analysis  related to the formate pathway or NADH pathway 25 and focA (formate transmembrane protein)26, as well as overexpression of fhlA 28, fdhF (formate dehydrogenase H)29, modE 26 and selC (tRNA related to formate dehydrogenase)26.In the formate pathway, the structure and the mechanism of the FHL at the gene level is already clear, such as the sequence of its operons and regulons. Therefore, many genetic engineering studies about the FHL-related genes have been done to regulate the metabolism of the formate pathway to enhance the hydrogen production  pool keeps a dynamic balance. Therefore, many steps have been made to regulate the metabolism of NADH to enhance the HhycA, fdhF, fhlA, ldhA, nuoB, hybO, fdh1, narP, and ppk) directly or indirectly related to the formate pathway or NADH pathway were perturbed. The genes hycA, fdhF and fhlA are directly related to the formate pathway, and the genes ldhA and nuoB are directly related to the NADH pathway. Furthermore, the genes hybO and fdh1 are directly related to both the formate pathway and NADH pathway. The genes narP and ppk alternate cellular global metabolism, which are indirectly related to hydrogen-producing pathways. The exploration of the perturbation of those genes further confirmed that these two pathways mutually impacted each other or were regulated together. The increased ratios of total hydrogen yield by the knockout of the nuoB gene or the hybO gene were much higher than that by the knockout of other genes, as shown in Table E. aerogenes, which may be the reason why overexpression of fdhF, fhlA or fdh1 in nuoB-deficient or hybO-deficient strains almost did not affect the hydrogen production, as shown in Table E. aerogenes are lactate, ethanol, acetate, succinate, and 2,3-butanediol.Unfortunately, because of the complexity of cellular metabolism, it was hard to describe the interaction between the formate pathway and the NADH pathway for the hydrogen production in a quantitative way. The two substrates, formate and NADH, are both important metabolites in the complicated cellular metabolic network. It might be more feasible to understand the hydrogen metabolism not only from the local point of view but also from the global point of view.In this work, genes 2SO4 2.0 g, KH2PO4 14.0 g, K2HPO4.3H2O 6.0 g, and MgSO4 0.2 g6. All chemicals were of analytical grade. The glucose was sterilized individually by autoclave. The initial pH value of the medium was controlled at 6.0 by the addition of NaOH or HCl.The culture medium (1 liter) consisted of glucose 15.0 g, tryptone 5.0 g, (NH660), 1 mL of the culture as the inoculum was transferred into the serum bottle. The whole operation was done under anaerobic conditions. The plasmids were induced by 0.5 mM IPTG. The inducers were added into the culture at the beginning.Twenty mL of medium was put into the serum bottle with a total volume of 70 mL. The serum bottle was air-sealed with a butyl rubber stopper and degassed with 100% of aseptic nitrogen for 5 minutes. The cultivation was performed at 37 °C on a 170 rpm shaker for 15 h. When the bacterial cells cultivated in a 100 mL Erlenmeyer flask containing 20 mL medium reached 1.0 in terms of optical density at 660 nm (OD600) were measured with an ultraviolet-visible spectrophotometer . The dry cell weight of E. aerogenes was then calculated by the equation of dry cell weight (g/L) = 0.132 × OD600. The glucose concentration was measured by 3,5-dinitrosalicylic acid (DNS) colorimetry32.Cell densities at 600 nm  equipped with a thermal conductivity detector (TCD) and a 2 m × 3 mm (i.d.) stainless-steel column packed with TDX-01 (80~100 mesh). The temperatures of the injector, detector, and column were kept at 120, 120, and 80 °C, respectively. Nitrogen was used as a carrier gas at a flow rate of 10 mL/min.4 was used as a mobile phase at a flow rate of 1 mL/min. The retention times of succinate, lactate, formate, acetate, 2,3-butanediol, and ethanol were 6.681 min, 8.666 min, 9.404 min, 10.088 min, 11.746 min, and 13.670 min, respectively.The concentrations of organic acids and alcohols in metabolites were analyzed by high-performance liquid chromatography (HPLC) (Shimadzu 10 A) equipped with a refractive index detector and a Shimadzu SCR-102H column after pretreatment with a 0.45 μm membrane filter. Five mM HClO2 yield produced by formate pathway = (acetate + ethanol-formate)/consumed glucose). The hydrogen yield produced by the NADH pathway was calculated from the sum of the pathways producing NADH minus those consuming NADH -)/consumed glucose).The hydrogen yield produced by the formate pathway was calculated from the sum of final acetate and ethanol minus the residual formate (HSupplementary Info