Probing the Druggability Limits for Enzymes of the NAD Biosynthetic

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Probing the druggability limits for enzymes of the NAD biosynthetic network in glioma Jyothi Padiadpu, Madhulika Mishra, Eshita Sharma, Uchurappa Mala, Kumaravel Somasundaram, and Nagasuma Chandra J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.5b00733 • Publication Date (Web): 09 Mar 2016 Downloaded from http://pubs.acs.org on March 11, 2016

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Journal of Chemical Information and Modeling

Probing the Druggability Limits for Enzymes of the NAD Biosynthetic Network in Glioma

Jyothi Padiadpu1,2, Madhulika Mishra1, Eshita Sharma1,3, Uchurappa Mala4, Kumar Somasundaram4 and Nagasuma Chandra1* 1

Department of Biochemistry, IISc, Bangalore 560012, India Supercomputer Education and Research Centre, IISc, Bangalore 560012, India 3 Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX37BN, United Kingdom 4 Department of Microbiology, IISc, Bangalore 560012, India 2

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Correspondence to:

Prof. Nagasuma Chandra, Dept. of Biochemistry, Indian Institute of Science, Bangalore 560 012, INDIA Tel: +91-80-22932892, E-mail:- [email protected]

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Abstract The biosynthesis of NAD constitutes an important metabolic module in the cell, since NAD is an essential cofactor involved in several metabolic reactions. NAD concentrations are known to be significantly increased in several cancers, particularly in glioma, consistent with the observation of up-regulation of several enzymes of the network. Modulating NAD biosynthesis in glioma is therefore an attractive therapeutic strategy. Here we report reconstruction of a biochemical network of NAD biosynthesis consisting of 22 proteins, 36 metabolites and 86 parameters, tuned to mimic the conditions in glioma. Kinetic simulations of the network provide comprehensive insights about the role of individual enzymes. Further, quantitative changes in the same network between different states of health and disease enable identification of drug targets, based on specific alterations in the given disease. Through simulations of enzyme inhibition titrations, we identify NMPRTase as a potential drug target, while eliminating other possible candidates NMNAT, NAPRTase and NRK. We have also simulated titrations of both binding affinities as well as inhibitor concentrations, which provide insights into druggability limits of the target, a novel aspect that can provide useful guidelines for designing inhibitors with optimal affinities. Our simulations suggest that an inhibitor affinity of 10 nM used in a concentration range of 0.1 to 10 µM, achieves a near maximal inhibition response for NMPRTase and increasing the affinity any further is not likely to have a significant advantage. Thus, the quantitative appreciation defines a maximal extent of inhibition possible for a chosen enzyme in the context of its network. Knowledge of this type enables an upper affinity threshold to be defined as a goal in lead screening and refinement stages in drug discovery.


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1. Introduction Nicotinamide adenine dinucleotide (NAD) is an important player in several biological pathways. NAD is used as a substrate in several biochemical reactions including mono- and poly- ADP ribosylation, protein deacetylation and ADP-ribose cyclization in addition to being an essential redox cofactor for many enzymes 1. Such an extensive participation in metabolism, makes NAD an important metabolite in a wide range of biological processes such as cell survival, apoptosis, cell differentiation, aging 2, 3, DNA repair 4, cell signalling 5, transcriptional regulation 6 and immune responses 5. Variations in cellular levels of NAD, therefore has strong implications in health and disease 7. Not surprisingly, patho-physiologies such as certain cancers, neurodegenerative diseases, diabetes and autoimmune disorders exhibit altered NAD levels 8-11. In the recent years, enzymes of the NAD biosynthetic pathway are being explored as drug targets and biomarkers for some of these diseases 12-15. Tumour cells in general, have a high rate of NAD metabolism mainly due to elevated ADPribosylation activity in response to DNA damage 11, 13. An increased dependence on high cellular levels of NAD is observed in glioma as well, consistent with gene expression profiles of glioma tissue using microarrays, which indicate increased levels of several enzymes involved in NAD metabolism 16, 17

. Expression levels of NMPRTase (or visfatin/Pre B-cell enhancing factor1(PBEF1)), an enzyme

in the salvage pathway of NAD, is significantly higher in colorectal cancers 18, malignant brain cancers 17, 19 and chronic myeloid leukaemia 20 suggesting that NMPRTase may be crucial for maintaining cellular NAD levels in tumours 21, 22. Specific inhibition of NMPRTase, by a known inhibitor FK866, has been shown to effect a reduction in cellular NAD levels and promote apoptosis in cancer cells 22-24. Moreover several other enzymes of the NAD biosynthesis pathway show a large change in expression levels from normal to cancer phenotype as in glioma, with increasing levels of expression usually correlating with disease progression to advanced stages 24. Glioblastoma multiforme, a type of brain cancer, that comprises 10-15 % of all cancers, has a poor prognosis, with a median survival time about a year 25-27. Surgery and radiation therapy still remain the mainstay options for treatment and there is a very limited choice for chemotherapy 28, 29. The ACS Paragon Plus Environment

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success rates with these options are still very low, warranting urgent attention to new drug discovery. However, discovering anti-cancer drugs is a highly challenging endeavour due to many factors including complex nature of the disease, lack of complete knowledge of the molecular pathology and difficulties in both the correct choice of the targets as well as in the design of specific drugs 15, 30. Given that glioma exhibits high levels of metabolic dysregulation of the intra- and extracellular environments, targeting critical enzymes of the key metabolic pathways has been suggested as an attractive strategy 31. Methodologies to study metabolic pathways have been well established 32, among which kinetic simulations are the most useful to obtain a quantitative appreciation 33-34. Though drug target identification is now well accepted be a critical step in the drug discovery pipeline, a systematic exploration of the available target space is seldom carried out. Often, targets enter the pipeline based on some prior knowledge of their roles in the given pathology and are then typically taken through a validation process. Validation involving use of in vitro and in vivo methods, is laborious, time consuming and sees high rates of attrition 30. Newer methods are undoubtedly required for the correct choice of the target itself. An emerging trend in drug discovery has been to shift the focus from individual targets to a more comprehensive systems biology perspective 35-37. An understanding of the relative reaction fluxes in a pathway and differences in their flux control can be exploited to identify right strategies for therapeutic intervention 14, 38-40. Here, we seek to identify prioritize drug targets in the NAD biosynthetic pathway in glioma by modelling the pathway dynamics. Further, we probe the druggability limits of the identified target by modelling the response of the pathway as a whole upon drug inhibitions at different points. Towards this, we first construct a systems level model of NAD biosynthesis and compare the flux profiles in normal glial cells to that in glioma and identify NMPRTase to be the top ranked target in the pathway.


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2.

Results

The biochemical network comprehensively captures known reactions in the NAD biosynthesis covering the de novo pyridine ring formation via the kynurenine pathway, utilization of dietary precursor nicotinate through the Preiss-Handler pathway and the utilization of nicotinamide and nicotinamide riboside through the NAD salvage pathway for NAD synthesis, as illustrated in Figure 1. The model also incorporates NAD breakdown by the non-redox NAD utilizing reactions. The constructed biochemical network consists of 25 kinetic reactions involving 22 enzymes, 36 metabolites and 86 kinetic parameters for the corresponding reactions (Table S1-S4). The dynamic model of the pathway reflects the biological processes more realistically than the static topological maps, hence allowing us to comprehend the change in the concentrations of the metabolites in a given condition.


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Figure 1: A schematic illustration of the NAD biosynthesis pathway. NAD+ can be generated by the de novo pathway starting with the precursor tryptophan or by the salvage pathway from the precursors nicotinamide, nicotinic acid, and nicotinamide riboside. Green boxes show the dietary substrates which can be converted to NAD. Blue boxes are enzymes of the kynurenine pathway, yellow boxes are enzymes of the nicotinate pathway, pink boxes are enzymes of the NAD salvage pathway from nicotinamide and nicotinamide riboside and orange boxes are the non-redox NAD utilizing enzymes. NAD is shown in red box, while other intermediate metabolites are shown in brown. (Inset: Diagrammatic representation of the model pathway and the perturbations introduced in the system. Reactions marked in red indicate points of perturbation indicated by ( ) which were subjected to screening for inhibition effects).


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A time course simulation of the kinetic model for the normal cell, (Fig. 2a-2d, Blue curve) revealed the attainment of steady state concentrations of most of the metabolites of the pathway. There was a build-up of metabolites of the kynurenine pathway ending in a linear increase in quinolinate concentrations. Reaction products such as ADP, ADP ribose, pyrophosphate (PPi), ADP ribose (n+1), N2-ADP ribosyl L- arginine, which are not utilized further in the pathway, were observed to be building up and could be used as direct indicators of the activities of their respective kinetic reactions. The cellular NAD concentration attained during the simulations, while maintaining normal cellular levels of substrates as inputs, correlates well with the experimentally observed range 8, 41.

Figure 2: Steady state levels of some key metabolites of the NAD biosynthesis pathway produced during time simulation runs (~ 50000 time units) of the kinetic model of the pathway in a normal cell (blue) versus a cancer cell (red). Activity of the NAD biosynthesis pathway is much higher in a cancer cell as compared to a normal cell. Pyrophosphate levels are an indicator of general activity of the pathway as pyrophosphate is the end product of several key reactions of the pathway. It must be noted that Y- axis is not uniform for different plots in this figure (for clarity). Plots for other metabolites are given in supplementary material (Fig. S1-S2).

A similar simulation was carried out for the pathway in glioma cells, which when compared to the normal cells indicates an almost two-fold increase for the dynamics of the NAD pathway in glioma, resulting in elevated levels of several key metabolites (Figure S1) 17, 19. Figure 2 illustrates concentrations of metabolites over the course of the simulation in both the conditions. The steady state levels of NAD, nicotinamide were found to be nearly two-fold higher in glioma. A steady state ACS Paragon Plus Environment

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was not attained for quinolinate, which is not surprising, since quinolinate utilization outside the NAD biosynthesis pathway is not included in this model. There was no difference in quinolinate levels between normal and glioma cells, indicating that the rate limiting step in NAD biosynthesis occurs after the kynurenine pathway. Thus kyneurine pathway in the network does not seem to contribute significantly to observed elevated NAD levels in glioma, consistent with earlier reports in literature 42. The concentrations of ADP ribose(n+1) was monitored to obtain insights about the activity of NAD utilizing enzyme PARP, which is highly upregulated in all cancers 9. The levels of pyrophosphate (PPi) was also monitored since pyrophosphate is a by-product in several key reactions of the pathway and can be used as an indicator for pathway activity. Our simulations show that ADP ribose(n+1) levels build up to as much as 5-fold higher in glioma as compared to the normal cell. On the other hand, pyrophosphate levels are seen to be about 2-fold higher in glioma over the normal cell levels indicating that overall pathway activity is approximately 2-fold higher in glioma. The reported microarray data shows an increase in expression levels of enzymes involved in NAD production as well as its utilization 19, 43. Yet, the kinetic simulation of the pathway shows a net increase in NAD levels in cancer cells (Figure 2). From the large increase in activity of NAD utilizing enzymes in glioma (as seen from ADP ribose (n+1) levels), it could be supposed that nicotinamide production should be higher in cancer cells as compared to normal ones. However, the similar rates of production obtained from our model indicate that the increased rate of NAD utilization, in glioma is compensated by an increase in the NMPRTase catalysed salvage pathway of nicotinamide utilization to regenerate NAD.

2.1 Experimental validation of the model The quantification assay performed for the comparative analysis of concentrations in normal astrocytes and glioma clearly show enhanced levels of NAD and related metabolites in glioma, thus validating the simulation results. There was a 1.4 to 2 fold increase in NAD levels detected by the NAD cycling assay in the U87 glioma cell lines 24. NAD was quantified in both glioma and normal astrocyte cells. A significant two fold difference (1.88 average) was observed in U87 (glioma cell ACS Paragon Plus Environment

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line) with respect to NHA (normal astrocyte) (Figure 3). No significant difference was observed for NAD/NADH ratio.

Figure 3. Quantification of NAD in U87 and NHA cells. Experiments were repeated three times and shown as an average with ± SEM, ***p < 0.001.

2.2 Identification of potential drug targets A key enzyme in the pathway that has a significant control over NAD concentrations has the potential to serve as a drug target to inhibit NAD biosynthesis in glioma. In order to identify such key steps in the network, a systematic inhibition study was carried out on QPRT, NAPRTase, NMPRTase, NRK and NMNAT enzymes. The first four enzymes were chosen since they are directly involved in converting quinolinate, nicotinate, nicotinamide (Nam) and nicotinamide riboside (NamR) respectively to NAD, while the fifth enzyme NMNAT is involved in the final catalytic step in all these conversions. The inhibition of each enzyme was simulated independently using hypothetical inhibitors ([I] = 1µM, Ki = 1 nM). Graphs plotted for NAD concentration in the presence of various inhibitors show that NMPRTase as well as NAPRTase cause a large reduction in NAD levels (Figure 4A), while inhibition of QPRT, NMNAT and NRK do not show a significant effect. Thus, from the synthetic inhibition study, NMPRTase and NAPRTase are seen to be possible control points for modulating NAD levels in the cells.


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Figure 4: Comparative analysis of inhibition of some key nodes (enzymes) of the NAD biosynthesis pathway. Enzymes QPRT, NAPRTase, NMNAT, NRK and NMPRTase were inhibited using hypothetical non-competitive inhibitors at concentration, [I] = 1 µM and binding affinity, Ki = 1 nM. Steady state levels of NAD, Nicotinamide , ADP ribose (n+1), and NMN , at the end of time simulation runs of the kinetic model of the pathway in normal cell (dark grey) vs. glioma (light grey) is plotted using representative histograms.

Steady state levels of NaAD (Figure S2A) did not show any change upon inhibition of NMNAT, NAPRTase and NRK, but showed a slight fall upon QPRT inhibition and a drastic fall in NAPRTase inhibition. Nicotinamide (Nam) production followed a different trend as Nam production was found to increase significantly with NMPRTase inhibition in both normal as well as in glioma (Figure 4B). This can be expected as nicotinamide is a direct substrate of NMPRTase and is expected to build-up upon NMPRTase inhibition. However, since nicotinamide or vitamin B3 is taken as a dietary ACS Paragon Plus Environment

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supplement and mechanisms exist for degradation of an excess amount of this substance , the buildup of this metabolite in this model is not of much significance42,24. On the other hand, NAPRTase and NMNAT inhibition caused a sharp fall in Nam production. No significant changes were observed with inhibition of QPRT and NRK. The steady state levels of nicotinamide riboside (NamR) (Figure S2B) were found unchanged in inhibition studies with QPRT. There was a fall in NamR levels on NAPRTase and NMPRTase inhibition. The restoring of NamR concentration with NMNAT inhibition can be explained by the accumulation of NMN which favours the NT5C catalysed reaction to produce NamR. The levels of ADPribose(n+1) were studied, as it is an indicator PARP activity in the model (Figure 4C), which was seen to be significantly higher in glioma as compared to normal cells. No change was observed in this metabolite upon QPRT or NMNAT inhibition. NRK inhibition showed a marginal increase in normal cells while cancer cells showed a drastic reduction in the concentration. NMPRTase inhibition showed a 4-fold reduction, while NAPRTase inhibition showed an almost complete loss of activity showing the influence of NMPRTase on the PARP activity. Pyrophosphate (PPi) level (Figure 4D) was monitored as a marker for the activity of the whole pathway as PPi is a common metabolite released in nine reactions of the pathway (Figure 1) , five of which are being studied for inhibition kinetics in the pathway (NAPRTase, NMPRTase, QPRT, the two isozymes of NMNAT- 1 and 2 found in glial cells). Slight increase in PPi levels was observed upon inhibition of either NMNAT or NRK. A slight reduction in PPi production was observed upon QPRT inhibition which ties in with reduction in QPRT catalysed cleavage of PRPP. NMPRTase inhibition showed a reduction in PPi production in normal cells and a much more pronounced effect was seen upon NAPRTase inhibition. This occurs largely due to reduction in metabolite flux throughout the pathway as NAPRTase is the point of entry of nicotinate, one of the major flux determinants of the pathway. Quinolinate concentrations (Figure S2C) were found to remain unchanged in all of the inhibitions. Steady state levels of NMN (Figure 4E) as well as NaMN (Figure S2D) were found to be elevated in NMNAT inhibition, while they remained unchanged for QPRT and NRK inhibition. NMN levels were found to fall in NAPRTase inhibition while NaMN levels ACS Paragon Plus Environment

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were found to fall only in NAPRTase inhibition. The increase in steady state levels of NMN and NaMN with NMNAT inhibition is expected as these substrates are directly utilized by the enzyme. In summary (Figure 4F), NMPRTase inhibition in our simulations is seen to cause a specific reduction of the NAD level in glioma. NAPRTase inhibition leads to complete loss of pathway activity in both normal and cancer cells. To further validate inhibition studies, metabolic control analysis (MCA), a widely used method was performed44, 45. MCA is a classical quantitative approach to understand the extent to which enzymes limit the reaction and metabolite fluxes , which can be further explored to identify important control points in a biochemical network44, 45. NAPRTase and NMPRTase were found to have the top two highest control coefficients with respect to the control of NAD levels. Kinetic simulations as well as MCA analysis were thus consistent and confirmed that both NAPRTase and NMPRTase are the most important controlling factors of the NAD biosynthetic pathway. Of the two, NAPRTase inhibition leads to a complete loss of pathway activity non-selectively while NMPRTase inhibition leads to a more selective and controlled reduction of NAD levels in glioma, clearly indicating NMPRTase to be a better drug target in glioma.

2.3 Estimating druggability limits of NMPRTase in glioma The complexity in the biochemical network owing to its intricate topology and even more so due to the inherent dynamics automatically leads to complex behaviour. For example unlike with a purified enzyme sample in isolation, inhibition of an enzyme in the whole system may not produce a linear effect with respect to a concentration scan. The maximum extent to which a given enzyme can be inhibited in the network can be described as the limits of its druggability, which can be different for different enzymes depending on the interconnections as well as the relative rates of, inter conversion in the network. Druggability itself is defined as the amenability of a target to be manipulated by a small molecule ligand. Druggability is typically studied through ‘precedence information’ of a related target or by using machine learning approaches that can use different physiochemical features.


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Structure-based approaches that analyse ligand binding sites and protein flexibility can access druggability at a much higher resolutions46, 47. NMPRTase being an enzyme has distinct substrate binding sites which can be used for targeting a drug and is also expressed in sufficient quantities, hence meeting the criteria of being a druggable target 31, 48. Using time-course kinetic simulations, here we seek to estimate the druggability limits of NMPRTase in the system. Using the model here, we have simulated titrations with a set of hypothetical inhibitors of varying binding affinities, each at different concentrations. From such an analysis, it would be possible to determine (a) the maximum extent of inhibition that can be seen in the system with druggable inhibitor concentrations and (b) the optimal combination of inhibitor affinity and concentration that can achieve the required effect. Titration studies were performed by monitoring the effect of NMPRTase inhibition on NAD levels in glioma cells using a range of binding affinity values (Ki:10 µM to 0.1 nM) for hypothetical non-competitive inhibitors and, for each inhibitor a continuous range of concentrations from 0.1 nM to 1 µM and then from 1 µM to 10µM were tested and for each Ki maximum extent of inhibition achieved was observed. Titration studies revealed that inhibition of NMPRTase with 1nM affinity inhibitor at 10µM concentration leads to a reduction of NAD levels by 65% in the glioma model. Increasing the concentration of the inhibitor did not lead to any further improvement in inhibition. Increasing the affinity of the inhibitor also did not lead to any further significant reduction in NAD levels. This clearly indicates that 65% reduction in NAD levels is the maximum reduction that is achievable in this model. We this background, next we wanted to test what combination of affinity and concentration of the inhibitor is sufficient to bring about the maximum possible reduction of NAD levels in glioma, which we refer to as druggable limits of NMPRTase (DLNMPRTase). A similar experiment carried out for NAPRTase showed its druggability limits to be about 98%, whereas that for QPRT is only less than 1% (see also Fig 4a (NAD concentration), the latter consistent with an experimental observation in a recent report 42 . From the affinity scan, we observed that inhibitors with Ki of 0.2µM and higher will not be able to reach the 90% of DLNMPRTase even if their concentrations are increased by 10 fold. These are indicated ACS Paragon Plus Environment

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in dashed lines in Figure 5A (Zone-C in Figure 5B). On the other hand, all those inhibitors of Ki with higher affinity of 0.01µM or lower are observed to reach the DLNMPRTase fully (Zone-A in Figure 5B) even in the nanomolar range of inhibitor concentration. Those inhibitors of Ki in range of 0.02µM to 0.1µM are seen to reach upto ~98% of DLNMPRTase with higher concentration of inhibitor (Zone-B in Figure 5B). Increasing the affinity higher than 0.01µM (in Zone A) does not seem to have any benefit, as the DLNMPRTase is already achieved. On the other hand, a trade-off can be found by decreasing the Ki, but keeping the inhibitor concentration within reasonable limits for the Zone –B inhibitors. Of these lines indicates starred(*) in Figure 5A reflects an inhibitor of 0.03µM affinity, which is capable of reaching ~90% of DLNMPRTase at a concentration of 4µM.

Figure 5: Percentage inhibition of NAD biosynthesis in glioma on NMPRTase inhibition using hypothetical noncompetitive inhibitors at concentration, [I] = 10 µM, 5 µM, 1 µM, 0.5 µM, 0.1 µM, 0.05 µM, and 0.01 µM and binding affinity, Ki = 10 µM to 0.1 nM. Y-axis shows percentage inhibition of NAD production at steady state while the X-axis shows the concentration range in µM. Different zones of druggability is shown in Figure 5B. Inhibitors belonging from Zone-C will not achieve DLNMPRTase even at the higher dose of inhibitor, however inhibitors from Zone-A will achive it even in nanomolar concentration range. The optimum can be achieved only for inhibitors of Zone-B range.

Thus these simulations provide an understanding of the maximal extent of inhibition that can be achieved in the pathway, a perspective that would be completely missed out by studying enzymes individually. An inhibitor titration was also performed for a hypothetical inhibitor with a binding affinity similar to a known drug FK866 (Ki for non-competitive inhibition = 0.3 nM) (Figure S4). This study revealed an IC50 value of about 2.5 nM, which correlates with the known IC50 for FK866


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(i.e. 1-3nM) 22, 23, 49. The titrations were tested with hypothetical competitive inhibitors as well and similar trends were observed. It must be pointed out that studying the limits of druggability is a novel aspect that emerges from a systems level study, and can have wide applicability to understand extent of inhibition that can be achieved in vivo for any enzyme. Knowledge of this has major implications to lead identification and lead optimization stages of the discovery pipeline. Simulations here clearly suggest that in this case, efforts to screen for new leads or synthesize analogues of a lead to increase binding affinity beyond 1 nM will have little use.

3. Discussion Given the central role of NAD in triggering and maintaining various cellular activities, it is important to have a quantitative appreciation of the dynamics of the various reactions controlling its levels. A wealth of information is available about the individual enzymes in the pathway in literature 50-53. The model reported here has been built extensively from experimental data from literature and agrees well with our understanding of how NAD is synthesized. The model is a reasonably self-contained module representing NAD homeostasis. A dynamic profile of the network requires us to study the inter-relatedness of the various components in it, which can be achieved effectively through kinetic simulations. Kinetic simulations have provided useful insights in other systems such as glutathione metabolism, various aspects of onecarbon metabolism as well as critical control points in the arachidonic acid pathway 34, 54, 55. In all these and several other cases, a theoretical kinetic model built using experimental information has proved to be useful in explaining, rationalizing and in predicting the outcome of the network as a whole, under a variety of conditions. Several lines of evidence have suggested the involvement of NAD in forming a link between metabolism and regulatory events in health and disease 12. It comes as no surprise therefore, that concentrations of several metabolites in the pathway have been observed to be significantly altered in a range of diseases such as neurodegenerative diseases, cancers and aging 7, 12, 56. Gene expression ACS Paragon Plus Environment

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and proteomics data in various cancers are increasingly becoming available, which indicate that genes for several enzymes are also over-expressed in the pathway especially in cancers such as glioma, while some others also exhibited reduced gene expression. Such metabolic changes in a cancer cell are considered crucial for cell survival. These changes have been incorporated in our model to capture the pathway in glioma cell 19. By comparing the metabolite profiles of the pathway in healthy and glioma states, we obtain an understanding of the alteration in the pathway in glioma and more importantly we can identify the critical points that control the dynamics of the pathway. Simulations show that nicotinamide levels remain the same in both states, although levels of other metabolites such as NAD, NaAD, NMN, NaMN and NR are significantly elevated in cancer cells. The balance in NAD biosynthesis and utilization seem to be maintained in both normal and cancer states, leading to maintenance of nicotinamide levels. An understanding of the relative importance of individual metabolites or enzymes cannot be drawn from a map of a metabolic pathway at the topological level alone, thereby necessitating quantitative kinetic simulations. For example, QPRT, NMNAT and NMPRTase may appear to be equally important for NAD biosynthesis from the topological map. Similarly, an inspection of the reaction velocities suggest that the enzymes QPRT, NMPRTase, NMNAT and NAPRTase, all appear to be rate limiting steps since they all have velocities orders of magnitude lower than the other enzymes in the network. However, kinetic simulations prove that information on individual reactions is not adequate enough to identify critical points in the network, and that it is essential to consider network dynamics for obtaining such insights. Simulations indeed show that NMPRTase turns out to be a more useful strategy for intervention, while the other three enzymes are not. Identification of NMPRTase as a critical enzyme for NAD biosynthesis through our approach in fact validates earlier reports about the enzyme as a potential drug target 12, 48, 57. While inhibiting NAPRTase redcued NAD levels drastically resulting to near zero NAD levels, inhibition of NMPRTase exhibited a possibility of reduction in NAD levels in cancer cells bringing the levels similar to that in normal cells. Although, both NMPRTase and NAPRTase were found to have a strong inhibitory effect on ACS Paragon Plus Environment

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NAD production, NMPRTase turns out to be a better drug target for the following reasons: (a) effect of inhibition of NAPRTase was more drastic, reducing the whole flux of the pathway and thus does not provide an easy handle to inhibit NAD levels in a therapeutic range (b) NAPRTase inhibition causes a substantial generic reduction of most of the pathway metabolites while the effect of NMPRTase inhibition appears to be more specific to reducing steady state NAD levels and (c) NMRPTase inhibition was more selective to the cancer phenotype than to the normal state, conferring a distinct advantage for drug discovery 57. Given that a target for glioma such as this needs to be only tempered down and not totally knocked out, owing to the fact that normal cells require these networks to maintain health, it might be easier to target NMPRTase rather than NAPRTase, since inhibition of the latter has a drastic effect on NAD production. Since cancer cells have higher requirement of NAD as compared to normal cells, the inhibitory potential of the pathway is exploited not by the specificity of interaction but by the demand and supply of NAD 13. Hence, it becomes crucial to identify the level of inhibition required to target the pathway in cancer cells with minimum effect on normal cells. A recent study by Horssen et al. demonstrated enhanced motility in glioma due to increase in NAD+ concentration and NMRPTase inhibiton by FK866 alleviating the invasive behaviour of the tumour 24. A similar trend was observed in pancreatic cancer as well 22.

NAD pathway has been a strategic choice for inhibition in many diseases 12, 48, 58. An increased turnover of NAD has been reported in some other tumour cells such as leukemia, hepatic carcinoma and colorectal cancers as well 17, 18, 20. The pathway model reported here provides a general framework for studying NAD metabolism in other disease phenotypes as well. A novel insight obtained from this study has been to identify the druggability limits for a given enzyme by considering the context of its biochemical network. This information has major implications in drug discovery, since it suggests that there exists an optimal affinity that needs to be set as a goal during ligand screening and lead optimisation phases. Kinetic simulations can thus be used to determine optimum inhibitor affinity and concentration for the design of drugs. This in turn will have major implications to drug safety, since neither the affinity nor the concentration need to be ACS Paragon Plus Environment

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used beyond their thresholds to achieve the desired inhibition. An excess concentration or an excessively strong inhibitor may have problems of exhibiting unintended interactions with other molecules in the system, which can be avoided if optimal values can be identified from simulation studies and implemented in the drug discovery process. It would be conceivable to apply such knowledge to make more informed decisions during lead finding and refinement steps more generally in drug discovery pipelines.

4. Materials and Methods

4.1.1 Model building : KEGG Pathway database 59 was used as a starting point to reconstruct the human NAD Biosynthesis pathway. The network was further elaborated based on information from literature, especially to add the NAD utilization reactions and some of the reactions in the NAD salvage pathway 1, 56, 60, 61. Kinetic parameters (Kcat, Km, Ki) and where available Vmax values for all the reactions of the pathway were collected from BRENDA enzyme database 62 and published experimental studies. To the extent possible parameters reported for normal brain cells were taken for model building. In a few cases where such data was not available, parameters for other cell types have been considered. A full list of the parameters is given as supplementary data (Table S1). For reactions where Vmax was not available it was calculated using the following standard formula, Turnover number = Specific activity * Molecular weight Vmax = Turnover number * [Et] where, [Et] is the total enzyme concentration. The calculated values are also available in the supplementary material (Table S2). Modified Michaelis-Menten equations were derived for all the reactions in the network using random rapid equilibrium kinetics for uni- bi- and tri-substrate reactions as detailed by Segel60. All reactions were irreversible except the reaction catalysed by nicotinamide mononucleotide adenylyltransferase (NMNAT) as this enzyme is known to be reversible at physiological concent rations of substrates and products 63 (Table S4). Initial


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concentrations of all enzymes were set at 1 unit to enable simple representations of fold changes as seen in glioma. Initial concentrations of all input metabolites and currency metabolites were kept close to physiological levels, as obtained from literature (Table S3). Ordinary differential equations representing each reaction were formulated and solved using TeranodeTM software (Seattle, WA, USA).

4.1.2 Rate changes in Glioma cell: The fold change in enzyme concentrations was obtained from a microarray profile for normal glial cells and glioblastoma multiforme 19 obtained from microarray database ONCOMINE 43. The change in enzyme levels for glioma was calculated by the addition of the fold difference in mRNA expression, as obtained from microarray data, to the initial enzyme concentrations (~ 1µM), assuming that the fold change in mRNA is translated to similar change in enzyme. Vmax for all reactions in cancer cell was further calculated using the new enzyme concentrations in the formula Vmax= Kcat[Et] (Table S2).

4.1.3 Simulation: Simulations were carried out using TeranodeTM. Baseline kinetic parameters identified in Table S1-S4 were incorporated into the model and initial concentrations of the metabolites input into the system such as tryptophan, oxygen, nicotinic acid, were kept fixed at their reported physiological levels. The initial concentrations of all other metabolites were kept at zero and the values were allowed to vary over the course of the simulation. The concentration of these metabolites reaches the steady state after duration of 50,000 time units in the simulation. It must be noted that the time unit by itself has no physical significance and does not reflect real time.

4.1.4 Inhibition Analysis: Chosen reactions which regulate NAD biosynthesis (de novo and salvage) from the input metabolites were selected for inhibition studies for identifying potential drug targets in the pathway. Simulation studies of inhibition were performed using non-competitive inhibitors with a hypothetical binding affinity (Ki) of 1 nM at an inhibitor concentration [I] of 1µM. The enzymes ACS Paragon Plus Environment

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chosen for screening for inhibition effects are quinolinate phosphoribosyltransferase (QPRT), nicotinate phosphoribosyltransferase (NAPRTase), nicotinamide phosphoribosyltransferase (NMPRTase), nicotinamide mononucleotide adenylyltransferase (NMNAT) and nicotinamide riboside kinase (NRK). The production of NAD, NamR, Nam, NaAD, ADPribose(n+1), Pyrophosphate (PPi), NMN, NaMN, and quinolinate were monitored over a time course simulation in the presence of inhibitors. 4.1.5 Metabolic control analysis (MCA): MCA was performed using COPASI-4.12.81 software. Flux control coefficients (FCCs) and concentration control coefficients (CCCs) were calculated at the steady-state of the model. Normalized FCCs and CCCs were used for further analysis. 4.1.6 Titration studies: Simulations for inhibitor titrations were performed in a stepwise manner for a range of inhibitors with binding affinities (Ki) varying from 0.1 nM to 10 µM at constant different concentrations of the inhibitors ([I] = 0.01 nM to 10 µM). All equations were formulated as v=

V max [ A][ B ] I   [ I ][ A]   [ I ][ B ]   [ A]   [ B ]   [ A] [ B ]   ( Km a× Kmb ) ×  1 +    +  + + + +    Ki    Ki × Kma   Ki × Kmb   Kma   Kmb   Kma × Kmb 

Since the inhibitors that are being referred to are only hypothetical at this stage and no mechanism can be associated with them, a standard non-competitive inhibition kinetics equation has been chosen.

4.2 Quantification of total NAD in astrocytes and glioma 4.2.1 Cell culture Human Glioma cell line U87MG and Normal healthy astrocytes (NHA)

were

grown

in

Dulbecco’s Modified Eagle Medium (DMEM). Media was supplemented with 10% FBS and antibiotics- penicillin, streptomycin and Gentamycin. cells were grown in serum containing growth media until they reach 80-90% confluence, later washed thoroughly with 1X PBS and used for NAD quantification.

4.2.2 NAD/NADH Quantification Assay The NAD Cycling Enzyme Mix from Sigma was used to quantify the total NAD in the normal as well as glioma cells. The kit is specific for NAD and NADH from samples. The presence of NADP or ACS Paragon Plus Environment

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NADPH does not interfere with the colorimetric quantification process because of the specificity of the enzymes involved in the detection.

4.2.3 NAD quantification Intracellular NAD was quantified according to the manufacturer's instruction of NAD+/NADH quantification kit (Abcam). In short, the cells collected were subject to sonication (2pulse on /off) in NADH/NAD extraction buffer. The supernatant was obtained by centrifugation at 12,000 × g for 5 min. Then 50 µl supernatant sample was mixed with 100 µl NAD Cycling Mix and 2 µl NADH Cycling Enzyme Mix, and incubated at room temperature for 5 min to convert NAD to NADH. After adding 10 µl NADH developer, the optical density was read at 450 nm. The amount of sample NAD was calculated according to the readings to NADH standard curve. The total NAD level was expressed as pmol/106 cells. Samples were measured in triplicate and blanks, without NAD were used for background correction.

4.2.4 Statistical analysis Statistical analysis was performed using Microsoft Excel 2003 (Microsoft). Data were expressed as means ± standard error of mean (S.E.M.). Values between groups were compared using student t-test. P < 0.001 is considered statistically significant.

Abbreviations: IDO -indoleamine 2,3-dioxygenase; NA - nicotinic acid; NaAD - nicotinic acid adenine dinucleotide; NAD - nicotinamide adenine dinucleotide; ADP – Adenosine diphosphate; NADSYN - NAD synthase; Nam - nicotinamide; NamR – Nicotinamide riboside; NaMN - nicotinic acid mononucleotide; NaMNAT, nicotinic acid mononucleotide adenylyltransferase; NMPRTase – nicotinamide phosphoribosyltransferase; NAPRTase - nicotinic acid phosphoribosyltransferase; NMN - nicotinamide mononucleotide; NMNAT - nicotinamide mononucleotide adenylyltransferase; PARP - poly(ADP-ribose) polymerase; PRPP - phosphoribosyl pyrophosphate; QPRT - quinolinic acid phosphoribosyltransferase; NRK – Nicotinamide riboside kinase; NT5C – 5’-nucleotidase. Supplementary Information Supporting Information.doc Figure S1: Metabolite concentrations at normal and cancer conditions

Figure S2: Varying concentrations of the inhibitor at fixed binding affinity similar to the known inhibitor FK866.

Table S1: Kinetic parameters and molecular weights of all the reactions incorporated in the model. ACS Paragon Plus Environment

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Table S2: Difference in enzyme expression levels between normal and glioblastoma multiforme cells and changes in velocity of reactions with change in enzyme levels.

Table S3: Physiological levels of independent variables as inputs in the kinetic model. Table S4: Kinetic equations representing each reaction in the model.

Author’s information JP and ES built the model and carried out most of the simulations and wrote the first draft of the manuscript. MM, UM and JP carried out the experiments. NC generated the idea and closely supervised the project. All authors read and approved the final manuscript.

Acknowledgements Financial support from the Department of Biotechnology, Government of India at the Indian Institute of Science is gratefully acknowledged. We thank Dr. Karthik Raman, currently at IIT Madras for help during the initial stages of this work.

Conflict of interest The authors declare no conflict of interest.

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Probing the Druggability Limits for Enzymes of the NAD Biosynthetic Network in Glioma Jyothi Padiadpu, Madhulika Mishra, Eshita Sharma, Uchurappa Mala, Kumar Somasundaram and Nagasuma Chandra*


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Probing the Druggability Limits for Enzymes of the NAD Biosynthetic

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