Guanine is an inhibitor of c-Jun terminal kinases

Guanine is an inhibitor of c-Jun terminal kinases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Guanine is an inhibitor of c-Jun terminal kinases Jessica Treeby, Sherihan El-Sayed, Samuel Morgan, Sophie Maddock, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6521885/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The toxicity of purine bases adenine and guanine is mostly recognized when associated with inborn errors of purine metabolism such as Lesch-Nyhan syndrome, and metabolic diseases with a lifestyle component including gout. In these pathologies, the peripheral toxicity of purine bases is attributed to the accumulation of their catabolite uric acid in the kidneys, causing nephrolithiasis or crystalluria, and the joints, causing gout. However, inborn errors of purine metabolism also present neurological and neurobehavioral abnormalities including motor disabilities, seizures, hypotonia and dystonia, and self-injurious behaviour. The mechanisms underlying these pathologies is less well-understood but does not seem to be caused by uric acid. In a different context, adenine and guanine have been shown to be cytotoxic and antiproliferative, highlighting their potential use in cancer chemotherapies, but the underlying mechanisms have not been identified. In our previous investigations, we have shown that adenine, a molecule classified as acutely toxic, is an inhibitor of 1-carbon metabolism and biological methylations. Using the same experimental paradigm based on real-time luminometry with mouse embryonic fibroblasts to probe in real-time the potential biological activity of small molecules, complemented with metabolite quantifications, in silico docking predictions, kinase assays and phosphoproteomics, we now reveal that guanine and to a lesser extend adenine are direct inhibitors of c-Jun N-terminal kinases, which may contribute to their toxicity and to the symptoms of Lesch-Nyhan syndrome. Biological sciences/Biochemistry Biological sciences/Biochemistry/Kinases Health sciences/Diseases/Metabolic disorders Biological sciences/Cell biology Biological sciences/Cell biology/Circadian rhythms Biological sciences/Cell biology/Mechanisms of disease Guanine purine JNK MAPK Lesch-Nyhan metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The purine nucleotides ATP and GTP are not only components of nucleic acid DNA and RNA but are also required for many essential processes including energy metabolism and signal transduction. Moreover, the purine nucleoside adenosine is a neurotransmitter(1) and guanosine a neuromodulator(2). It is not surprising, therefore, that the metabolism of purine is tightly regulated. The purine nucleotides GMP and AMP can either be synthesized de novo from inosine monophosophate (IMP) or salvaged from their respective nucleosides (guanosine and adenosine) and bases (guanine and adenine). The degradation of all purines converges in the production of the base xanthine, then uric acid. Considering their importance for cellular metabolism, it is not surprising that disorders of purine metabolism, genetic and life-style related, lead to dramatic pathologies. A chronic dietary excess in purines causes gout due to the accumulation of uric acid in the joints. Similarly, genetic mutations inactivating the enzyme hypoxanthine-guanine phosphoribosyltransferase (HPRT) that salvages guanine to GMP leads to increased degradation of guanine to uric acid, causing gout(3,4). Lesch-Nyhan disease is caused by mutations in HPRT, and while gout may not arise or be detected until late childhood, cognitive impairment, motor disability and self-injurious behaviour including biting of the lips and fingers, head-banging and poking of the eyes occur much earlier(3,5). The mechanisms underlying such neurological symptoms are not well understood but do not seem to involve neurodegeneration or abnormal morphology(6,7). A disruption in the development of the dopaminergic and adrenergic pathway has been proposed, but little evidence of the underlying cause has been provided(8,9). Lesch-Nyhan disease highlights the potential toxicity of purine bases, but the lack of evidence for the mechanisms underlying the toxicity of purines limits treatment opportunities. Adenine and guanine have been shown to be intrinsically cytotoxic in several cell types in vitro , a toxicity that appeared to not depend on the formation of secondary purine metabolites(10-13). In vitro approaches have provided other important clues: cultures of Lesch-Nyhan fibroblasts have established that the severity of the disease is strongly correlated with the inability to salvage guanine by HPRT(14), and HPRT knock-out cell lines have shown that guanine, undetectable in wild-type control cells, considerably increased, associated with slower proliferation rates(15). Investigating the mechanisms underlying purine toxicity, we have previously demonstrated that adenine is an inhibitor of 1-carbon metabolism and cellular methylations(16). Here, using real-time luminometry of cellular circadian rhythms as a toxicity screening, in silico docking predictions, in vitro kinase assays and phosphoproteomics, we report that guanine is an inhibitor of the c-Jun N-terminal kinases JNK1-3, with an IC50 of 100-300 mM. Results To investigate the biological activity and adverse effects of the purine bases adenine, guanine, xanthine and hypoxanthine, we used a real-time luminescence assay with mouse embryonic fibroblasts prepared from PER2::LUC mice carrying a heterozygous chimeric fusion between the endogenous circadian clock protein PER2 and LUCIFERASE(17). Since the circadian clock is based on a transcription-translation feedback loops driving rhythmic gene expression, and controlled by cellular metabolism at all levels from epigenetic to post-translational regulations, this system is particularly well suited to detect and quantify the adverse effects of small molecules and inhibitors, as we have demonstrated previously with adenine and other 1-carbon metabolism inhibitors(16,18-20). As previously observed(16), adenine caused a concentration-dependent lengthening of the circadian period, i.e. a slower biological clock (Figure 1A, B). Guanine was even more potent than adenine, not only causing a significant lengthening of the circadian period (Figure 1A, B) but also accompanied with a decrease in amplitude (Figure 1C), which is a measure of the robustness of circadian oscillations. Xanthine showed a weak but statistically significant shortening of the period, but no concentration-dependent effects were seen (Figure 1A, B). Hypoxanthine did not significantly affect the oscillations, the period or the amplitude of reporter gene oscillations. We further investigated the mechanisms underlying the effects of guanine. Since adenine was shown to disrupt the methyl cycle by inhibiting the enzyme adenosylhomocysteinase (AHCY), leading to changes in 1-carbon metabolites adenosylmethionine (SAM), adenosylhomocysteine (SAH) and methylthioadenosine (MTA)(16), we first hypothesized that guanine had the same mode of action. To investigate this possibility and to gain insights into the metabolic paths of exogenous guanine and adenine, we quantified selected 1-carbon metabolites in cells treated with these 2 purine bases. Adenine caused an increase in SAH (Figure 2A), confirming the inhibition of AHCY previously observed(16), accompanied by an increase in methionine and MTA, suggesting the methionine salvage pathway has been activated. In contrast, guanine had no significant effects on SAH or MTA (Figure 2A), evidence that guanine does not inhibit AHCY. A graphical representation of simplified 1-carbon metabolism is shown in Figure 2B to guide understanding. To gain further insights into how guanine is metabolized in these cells and whether guanine treatment leads to intracellular guanine accumulation, we quantified purine bases and nucleosides (Figure 2C). In cells treated with adenine, we observed a significant increase not only in adenine but also in hypoxanthine and guanine, indicating excess adenine is indirectly converted to guanine and catabolized to hypoxanthine, and/or allosterically stimulates the de novo branch to guanine nucleotides. In contrast, guanine but not adenine significantly increased in cells treated with guanine, indicating there is no conversion of guanine to adenine in these cells. In cells treated with adenine or guanine, AICAR dramatically dropped while glutamine increased, confirming that de novo purine synthesis is suppressed by the salvage of excess purine bases, a known allosteric regulation of purine synthesis(21). While these data together do not support a role for guanine in the direct or indirect inhibition of 1-carbon metabolism, it is possible that the increase in guanine in cells treated with adenine also contributes to the toxicity of adenine. A graphical representation of simplified purine salvage and de novo synthesis is shown in Figure 2D. Next, to probe potential protein targets of guanine, we used SwissTargetPrediction(22) with guanine, which revealed Mitogen-activated protein kinase 9 (MAPK9), also known as c-Jun N-terminal kinase (JNK2) as the top most likely target using mouse data (Probability 0.044), and both MAPK8 (JNK1, 0.044) and MAPK9 (JNK2, 0.044) as the top ranked third and fourth target using human data (Figure 3A). While these probabilities seem low, the actual ranking of these potential targets is the most meaningful parameter(23). For comparison, purine nucleoside phosphorylase (PNP) and thymidine kinase (TK1), enzymes respectively involved in purine and pyrimidine metabolism, were the top first (0.121) and second (0.053) targets using human data (Figure 3A). MAPK8 and MAPK9 are interesting potential targets because their siRNA-mediated knock-down and inhibition with the inhibitor SP600125 have been shown to lengthen the circadian rhythms in vitro (24). JNKs are key kinases regulating development and cell growth, stress response, apoptosis and inflammation(25). MAPK8 and MAPK9 are expressed in different tissues and implicated in various diseases, while MAPK10 is mainly expressed in brain and implicated in the pathogeneses of CNS disorders(26,27). The general structure of JNKs comprise a C-lobe and N-lobe domain (Figure 3B). The ATP binding site is in the hinge region between the two lobes, involving more interaction with N-lobe amino acid residues. It is known JNK inhibitors either bind to the ATP binding pocket or to the allosteric site (Figure 3B). To probe the potential for binding of guanine to JNKs, we performed docking studies using the ATP-binding pocket of the three kinases (from X-ray structures PDB ID: 2GMX(28) for JNK1, 7N8T(29) for JNK2, and 3G90(30) for JNK3), and the allosteric site of JNK1 (PDB ID: 3O2M(31)). For all JNKs, the top-ranked docked pose of guanine was predicted to form hydrogen bonds in the cofactor binding pocket with residues similar to the adenine base of ATP (Figure 3C-E). However, in docking, a flipped orientation of guanine was also predicted as favourable, with the potential to hydrogen bond with the gatekeeper residue (Met108 in JNK1,2 and Met146 in JNK3, Figure 3F-H). Guanine was also predicted to fit with good affinity to the allosteric site of JNK1 with a ChemGauss4 value of -6.7 (Figure 3I and Table 1). For comparison, adenine was docked into the ATP binding site of the JNK isoforms. The top-ranked docked poses of adenine in the three JNKs structures show binding poses similar to the adenine part of AMP (Figure 3J), and possess with slightly lower docking scores than that of the top-ranked poses of guanine (Table 1). Together these data predict a relatively favourable interaction of guanine for all JNKs. Table 1 : ChemGauss4 docking scores of X-ray ligand, guanine and adenine in ATP and non-ATP binding sites of JNK1, JNK2 and JNK3. Isoform PDB ID Gatekeeper X-ray Ligand Guanine (top-ranked pose) Guanine (flipped pose) Adenine (top-ranked pose) JNK1 2GMX(28) Closed -11.8 -7.9 -6.9 -6.9 JNK2 7N8T(29) Closed -12.1 -9.8 -7.2 -7.4 JNK3 3G90(30) Closed -11.9 -8.1 -5.9 -7.1 JNK1 3O2M(31) N/A -7.9 -6.7 - - Next, we determined the inhibitory effects of guanine on JNK1-3 by radiometric in vitro kinase assays. As predicted, guanine inhibited all three JNKs, with an IC50 value of 0.2, 0.18 and 0.33 mM and Hill Slope of -0.67, -0.98 and -1.1 for JNK1, 2 and 3, respectively (Figure 4A). The lower IC 50 and Hill slope for JNK2 are consistent with their more favorable predicted docking scores. Some inhibition of JNKs was also seen with adenine, but with a higher IC50 (Figure 4A) than guanine, with the lowest IC50 for JNK1. Using our previous RNASeq dataset(16), we confirmed that the PER2::LUC cells we used express JNK1 and 2, the latter the most abundantly expressed homologue, but not JNK3 (Fig 4B). We then quantified the phosphoproteome of PER2::LUC cells treated with 0.5 mM guanine or adenine, or with 9 mM SP600125, a compound originally described as a specific JNK inhibitor but also a potent inhibitor of other kinases including Aurora and Casein kinases(32,33). The complete (phospho)proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE(34) partner repository with the dataset identifier PXD062305 and 10.6019/PXD062305. We detected more than 5500 phosphopeptides (Supporting Data, tab 1) but limited our downstream analysis to those 1433 detected in at least 70% of the samples (Supporting Data, tab 2). Out of these, 355 phosphopeptides showed significant changes (p<0.05) in at least one treatment (Figure 4C), but with little overlap between the effects of guanine, adenine or SP600125 (Figure 4D). Comparison between the phosphoproteome and input proteome data (Supporting Data, tab 3) showed efficient enrichment for phosphopeptides, as many proteins with detected phosphopeptides were not detected in the proteome data, and did not reveal overall parallel changes (Figure 4C), evidence that the significant changes in phosphopeptides were mostly due to changes in phosphorylation rather than protein abundance. Indeed, performing the same differential analysis by PhosPIR but with the proteome revealed 332 proteins significantly regulated in at least one treatment (Supporting Data, tab 4), but only 24 of these proteins had significantly regulated phosphopeptides in the phosphoproteome, with the other 243 proteins with significant phosphopeptides not represented among significant proteins (Figure 4E). To predict which kinase(s) might be regulated by guanine, adenine or SP600125, we then performed analysis of our phosphoproteome with KinSwingR(35). This analysis revealed that MAPK10 had a significant (p<0.05) swing score (change in activity) of 1.33 and MAPK8 a swing score of 1.14 with a p value of 0.057 in guanine-treated cells (Figure 4F; Supporting Data, tab 5). MAPK9 had a poor swing score and a p value>0.1. Surprisingly, however, the swing scores for MAPK8 and 10 were positive, indicating an increase in their activity. This is consistent with higher levels of the canonical target JUN S73 phosphorylation in guanine-treated cells compared to control (Supporting Data, tab 6). Although this may appear contradictory with their inhibition, it has been shown that while knock-out of MAPK8 decreases JUN phosphorylation and stability, MAPK9 deficiency or inhibition increases JUN expression, phosphorylation and stability due to a compensatory increase in MAPK8 activity(36,37). Together with the higher expression of MAPK9 in our cells, these observations suggest that MAPK9 was the main homologue inhibited by guanine in our cells (Figure 4B), in line with higher guanine docking affinity (Table 1) and lower EC50 (Figure 4A). In adenine-treated cells, a negative swing score of -1.46 for MAPK8 with a p value of 0.065 was obtained, but MAPK9 and MAPK10 had poor swing score and p value above 0.1 (Figure 4G; Supporting Data, tab 6). In cells treated with SP600125, while the swing scores for MAPK8-10 were poor and their p values was above 0.1, a negative swing score of -2.57 for Casein kinase 1 delta (CSNK1D) with a p value of 0.02 was observed (Figure 4H; Supporting Data, tab 7), which is consistent with a report showing that SP600125 is a more potent inhibitor of CSNK1D than of JNKs(32). To confirm these observations, an increase in JUN and pS73 JUN by immunoblotting was seen in cells treated with guanine or adenine (Figure 5A), suggesting both treatments affect MAPK8-10 activity. Since MAPK8 and MAPK9 are involved in the regulation of circadian rhythms via the phosphorylation of the core clock protein BMAL1(24), we sought to confirm whether BMAL1 phosphorylation was inhibited in guanine- or adenine-treated cells. Indeed, in cells treated with guanine or adenine, the intensity of an upper band, likely corresponding to phosphorylated BMAL1, markedly decreased in intensity (Figure 5A) compared to their respective controls. Together these data demonstrate that guanine, and maybe adenine directly or indirectly, is an inhibitor of MAPK8-10. Lastly, since MAPK9 promotes translation(38), we decided to assess the translation efficiency in guanine-treated cells to provide functional consequences of MAPK9 inhibition. Polysome profiling revealed a deficiency in translation, with an increase in free ribosomal 80S subunits, associated with markedly lower polysome levels (Fig. 5B). Discussion We have shown here that guanine is capable of inhibiting the function of JNKs, which may be of clinical significance for patients with genetic or life-style-related deficiencies in purine metabolism. To probe this possibility further, we mined a published proteomics dataset obtained with a model of Lesch-Nyhan disease based on dopaminergic rat PC6-3 lines carrying different mutations in HPRT(39). Proteins significantly affected by HPRT mutations(39) were used as input for the Expression2Kinases Appyter that predicts upstream kinases likely responsible for observed changes in gene expression(40). Interestingly, MAPK8 and MAPK9 were respectively fifth and second topmost likely kinases (Figure 5C). In cells, ATP is typically in the mM range, meaning that potent ATP-competitive kinase inhibitors typically have a K i in the low micromolar range(41). In vivo conditions in which JNKs inhibition by guanine could occur will thus depend on the levels of both guanine and ATP. Interestingly, HPRT knock-out neural progenitor cells have lower ATP levels(42), suggesting HPRT mutations may present perfect conditions for JNKs inhibition. Our phosphoproteome data do no conclusively demonstrate which JNK isoform is inhibited, because their few substrate phospho-sites in the database used by PhosPIR largely overlap. In our transcriptome data, however, there was evidence that MAPK9 is the dominant homologue expressed in these cells, followed by MAPK8, with MAPK10 being undetectable. In our proteome data (Supporting Data, tab 3), Maxquant was unable to confidently differentiate between MAPK8, 9 and 10, even though one of the two peptides detected, MLVIDPDKR, is specific to MAPK9, with only one amino acid difference with MAPK10. While comparative studies have concluded that MaxQuant has better overall performance than other commercial protein quantification tools(43,44), we decided to re-analyse our MS data with Thermo Fisher Scientific’s Proteome Discoverer (PD), which has been shown to provide better coverage of low abundance proteins(44). PD was able to confirm that MAPK9 was the only JNK to be detected with high confidence in all samples (Supporting Data, tab 8 and 9). This further supports the conclusion that, in our PER2::LUC cells, MAPK9 was the main homologue inhibited by guanine. Moreover, our data do not refute the possibility the guanine (and adenine) may also inhibit other kinases. The catabolism of guanine starts with guanine deaminase (GDA), leading to xanthine, further catabolised to urate by xanthine dehydrogenase (XDH). The development of gout and kidney stones in Lesch-Nyhan patients indicates that salvage of guanine to GMP by HPRT is the main metabolic route for guanine in normal conditions. HPRT deficiency causes elevated levels of it substrates guanine, hypoxanthine and PRPP, especially in the central nervous system(45,46). Although GDA may mitigate the increase in guanine in the brain by degrading it to xanthine(47), it is expressed in neurons but not glia(48), raising the possibility that local, cell-specific accumulation of guanine may occur in Lesch-Nyhan patients, leading to JNKs inhibition as a potential contributor to the symptoms of the disease. This may be in a way similar to what happens in PER2::LUC MEFs treated with guanine. Indeed, from our previously published RNASeq(16) data and the proteome data presented here, of the purine metabolism enzymes shown in Figure 2D, GDA and XDH were not detected, indicating exogenous guanine could not be degraded. It would be interesting to express GDA in these cells to determine whether this provides protection against guanine, since the endogenous expression of HPRT in these cells was not sufficient to protect against exogenous guanine. Guanine, like adenine, caused the lengthening of the circadian period in vitro but our results indicate the mechanisms underlying these effects are different. While adenine acts as a feedback inhibitor of 1-carbon metabolism and methylations, guanine instead inhibits JNKs, leading to the changes in BMAL1 electrophoretic mobility observed in Figure 5A, likely attributable to phosphorylation(24). Within the molecular clockwork, phosphorylation of BMAL1 and its partner CLOCK is circadian-time dependent and inhibits its activity at E-boxes cis-elements in the promoter of target genes(24,49,50). A delay in this phosphorylation-dependent inactivation would lead to a lengthened circadian period, here observed with guanine or previously published when JNKs are inhibited or knocked out/down(24). The observed increase in JUN phosphorylation and the decrease in BMAL1 electromobility upshift in cells treated with adenine or guanine may appear contradictory with an inhibition of JNKs. Unlike JUN that can be phosphorylated by both JNK1 and JNK2, with the compensatory increase in JNK1 when JNK2 is inhibited responsible for the increase in pJUN observed(36,37), it is possible that BMAL1 may be phosphorylated specifically by JNK2, and therefore not affected by the compensatory increase in JNK1. This would ultimately lead to a decrease in BMAL1 phosphorylation due to JNK2 inhibition. In the phosphoproteome analysis from cells treated with guanine (Figure 4F), of note was the negative swing score of both PRKAA1, PRKAA2, subunits of 5' AMP-activated protein kinase (AMPK). These subunits may have been directly inhibited by guanine, or more likely may have been inhibited by changes in the abundance of adenosine nucleotides (ATP, ADP, AMP), known regulators of AMPK(51). In cells treated with adenine, in contrast PRKAA2 had a positive swing score, suggesting AMPK may have been activated instead (Figure 4F). It is possible that excess of guanine nucleotides from salvage in guanine-treated cells may have stimulated the de novo branch of the pathway to ATP, while treatment with adenine instead may have inhibited ATP production. Of note, AICAR, an activator of AMPK(52), was lower in cells treated with guanine or adenine (Figure 2C). It is known that AMPK and JNK pathways interact under metabolic stress(53), which may have further contributed to the changes in the phosphoproteome observed here. Of note, AMPK itself is a regulator of circadian rhythms(54), and changes in AMPK activity may also have contributed to the long circadian period observed in cells treated with adenine or guanine. In conclusion, we have shown here that inhibition of JNKs contributes to the toxicity of purine bases, which further explains why the biosynthesis of purine bases is under strict control. Experimental procedures Cell cultures PER2::LUC MEFS(17) were cultivated and monitored for real-time luminescence as previously described(20). Briefly, cells were seeded into 35 mm dishes (Corning) and allow to grow for 3-4 days to confluence in DMEM/F12 medium (Invitrogen) containing antimycotic/antibiotic (Sigma) and 10% heat-inactivated serum (Gibco). Cells were shocked with 400 nM dexamethasone (Sigma-Aldrich) for 2 h, followed by a medium change including 1 mM beetle luciferin (Promega) and either of the following treatments: adenine, guanine, xanthine and hypoxanthine (Sigma-Aldrich), keeping the concentration of the respective vehicle equal in all dishes (1.6 mM HCl for adenine, 1.6 mM NaOH for guanine, xanthine and hypoxanthine). 35 mm dishes were then sealed with parafilm and transferred to a luminometer (Lumicycle32, Actimetrics) placed in an dry incubator at 35°C. Photons were counted in bins of 2 min at a frequency of 10 min. Period and amplitude were estimated by BioDare2(55). Metabolite quantification by LC-MS/MS PER2::LUC MEFS(17) were cultivated and metabolites were extracted as previously described(20). To repeat, cells cultivated in 10 cm Petri dishes (Corning) for 3-4 days until confluence were treated with guanine, adenine, HCl or NaOH and returned to the incubator for 24 h at 37 °C, 5% CO2. Cells were washed twice with 10 ml 5% mannitol (Sigma-Aldrich), the mannitol was carefully and completely removed before 0.9 ml 100% methanol was added onto the cells, firmly rocking the dish so that the methanol covers the cell monolayer. Dishes were tipped, and 0.6 ml water containing 125 ng/ml BIS-TRIS (Sigma-Aldrich) was added directly into the pool of methanol forming in the corner of the dish before rocking the dish again to cover the cell monolayer. The water/methanol mix was collected from the corner of the tipped dish and transferred to a 1.5 ml microtube. Tubes were kept at room temperature until all dishes were processed, randomly. Samples were centrifuged at 20,000 × g, 4 °C for 30 min, the supernatant transferred to a new tube, centrifuged again at 20,000 × g, 4 °C for 10 min, and the final supernatant transferred to a new 1.5 ml microtube. Prior to analysis, 200 µl of sample was dried in a centrifugal vacuum concentrator and resuspended in 100 µl acetonitrile and water in a ratio of 5:1. The sample was centrifuged at 20,000 × g for 3 min and the top 80 µl was transferred to a glass autosampler vial with 300 µl insert and capped. Liquid chromatography-mass spectrometry analysis was performed using a Thermo-Fisher Ultimate 3000 HPLC system consisting of an HPG-3400RS high-pressure gradient pump, TCC 3000 SD column compartment, and WPS 3000 Autosampler, coupled to a SCIEX 6600 TripleTOF Q-TOF mass spectrometer with TurboV ion source. The system was controlled by SCIEX Analyst 1.7.1, DCMS Link, and Chromeleon Xpress software. A sample volume of 5 μL was injected by pulled loop onto a 5 μL sample loop with 150 μl post-injection needle wash with 9:1 acetonitrile and water. Injection cycle time was 1 min per sample. Separations were performed using an Agilent Poroshell 120 HILIC-Z PEEK-lined column with dimensions of 150 mm length, 2.1 mm diameter, and 2.7 μm particle size equipped with a guard column of the same phase. Mobile phase A was water with 10 mM ammonium formate and 0.1% formic acid, mobile phase B was 9:1 acetonitrile and water with 10 mM ammonium formate and 0.1% formic acid. Separation was performed by gradient chromatography at a flow rate of 0.25 ml/min, starting at 98% B for 3 min, ramping to 5% B over 20 min, hold at 5% B for 1 min, then back to 98% B. Re-equilibration time was 5 min. Total run time including 1 min injection cycle was 30 min. The mass spectrometer was run in positive mode under the following source conditions: curtain gas pressure, 50 psi; ionspray voltage, 5500 V; temperature, 400 °C; ESI nebulizer gas pressure, 50 psi; heater gas pressure, 70 psi; declustering potential, 80 V. Data were acquired in a data-independent manner using SWATH in the range of 50–1000 m/z, split across 78 variable-size windows (79 experiments including TOF survey scan), each with an accumulation time of 20 ms. Total cycle time was 1.66 s. Collision energy of each SWATH window was determined using the formula CE (V) = 0.084 × m/z + 12 up to a maximum of 55 V. Acquired data were processed in MultiQuant 3.0.2. Peaks from MS1 and MS2 data were picked and matched against a metabolite library of 235 standards, based on retention time and mass error of ±0.025 Da. Data exported from MultiQuant 3.0.2 was further sorted, filtered, and scored using a custom VBA macro in Excel, based on presence, peak area, and coelution of precursor and fragment ions. Phospho-proteomic analysis and mass spectrometry in BioMS Samples (frozen cell pellets from 4 independent replicate 15cm Petri dishes per treatment) were lysed in 5% SDS, followed by reduction, alkylation and precipitation with acetone. Samples were then resuspended in Rapigest (Waters) and digested with trypsin overnight. For phospho-peptide enrichment, 95% of each sample volume was desalted (TELOS neo SPE fixed 96-well plates, Cole-Parmer) according to standard protocols, and eluted in phophopeptide enrichment binding solution for processing. The remaining 5% of the digested sample was taken for proteomic analysis. Phospho-peptide enrichment was performed using magnetic microspheres (Ti‐IMAC, ReSyn Bioscience) and a KingFisher Flex (Thermo Scientific) according to facility protocols(56). Phospho-peptides were desalted prior to analysis (TELOS neo SPE fixed 96-well plates, Cole-Parmer). For mass spectrometry peptides were resuspended in 3% (v/v) ACN / 1% (v/v) formic acid and analysed by liquid chromatography-tandem mass spectrometry (LC‐MS/MS) using a Thermo Rapid Separation Liquid Chromatography system (RSLC, Thermo Fisher Scientific) coupled to an Exploris 480 (Thermo Fisher Scientific) mass spectrometer. The RSLC was configured with buffer A as 0.1% formic acid in water and buffer B as 0.1% formic acid in acetonitrile. An injection volume of 2 ul was loaded into the end of a 5 ul loop and reverse flushed on to the analytical column (Waters nanoEase M/Z Peptide CSH C18 Column, 130Å, 1.7 µm, 75 µm X 250 mm) kept at 35 °C at a flow rate of 300 nl/min for 8 min with an initial pulse of 500 nl/min for 0.3 min to rapidly re-pressurise the column. The injection valve was set to load before a separation consisting of a multistage gradient of 2% B to 6% B over 3 minutes, 6% B to 18% B over 67 minutes, 18% B to 29% B over 11 minutes and 29% B to 65% B over 1 minute before washing for 6 minutes at 65% B and dropping down to 2% B in 1 minute. The complete method time was 105 minutes. The analytical column was connected to a Thermo Exploris 480 mass spectrometry system via a Thermo nanospray Flex Ion source via a 20 um ID fused silica capillary. The capillary was connected to a stainless steel emitter with an outer diameter of 150 um and an inner diameter of 30 um (Thermo Scientific, ES542) via a butt-to-butt connection in a steel union using a custom made gold frit (Agar Scientific AGG2440A) to provide the electrical connection. The nanospray voltage was set at 1900 V and the ion transfer tube temperature set to 275 °C. Data was acquired in a data dependent manner using a fixed cycle time of 2 sec, an expected peak width of 15 sec and a default charge state of 2. Full MS data was acquired in positive mode over a scan range of 300 to 1750 Th, with a resolution of 120,000, a normalised AGC target of 300% and a max fill time of 25 mS for a single microscan. Fragmentation data was obtained from signals with a charge state of +2 or +3 and an intensity over 5,000 and they were dynamically excluded from further analysis for a period of 15 sec after a single acquisition within a 10ppm window. Fragmentation spectra were acquired with a resolution of 15,000 with a normalised collision energy of 30%, a normalised AGC target of 300%, first mass of 110 Th and a max fill time of 25 mS for a single microscan. All data was collected in profile mode. The complete (phospho)proteomics raw data have been deposited to the ProteomeXchange Consortium via the PRIDE(34) partner repository with the dataset identifier PXD062305 and 10.6019/PXD062305. Raw files were analysed by MaxQuant v2.6.7.0(57), using parameters listed in the Supporting Method 1 using the UP000000589_10090 fasta reference proteome file available form uniprot.org. The Phospho(STY)Sites.txt output file (Supporting Data, tab 1) was further analysed using PhosPIR(58), limited to the phosphopeptides detected in at least 70% of the samples, normalising the data and imputing the remaining missing values, and using significance cutoff value of p <0.05. For statistical analysis with PhosPIR including KinSwingR, the 3 pairwise comparisons guanine vs. control, adenine vs. control and SP600125 vs. control were setup. Protein identification and quantification by Proteome Discoverer version 3.1.0.638 (Supporting Data, tab 8) was performed using the settings listed in Supporting Data, tab 9, which also include workflow messages. Kinase assays Kinase assays were performed independently by Reaction Biology (RBE, Freiburg, Germany) according to their standard protocol. All protein kinases provided by RBE were expressed in Sf9 insect cells or in E.coli as recombinant GST-fusion proteins or His-tagged proteins, either as full-length or enzymatically active fragments. All kinases were produced from human cDNAs and purified by either GSH-affinity chromatography or immobilized metal. Affinity tags were removed from a number of kinases during purification. The purity of the protein kinases was examined by SDS-PAGE/Coomassie staining, the identity was checked by mass spectroscopy. Adenine, guanine and SP600125 were dissolved and diluted in 100% DMSO to 100x highest assay concentration (1mM, 1 mM, 2 mM, respectively). Prior to testing, the 100% DMSO stock solutions were subjected to a serial, semi-logarithmic dilution using 100 % DMSO as a solvent. A radiometric protein kinase assay ( 33 PanQinase TM Activity Assay) was used for measuring the kinase activity of the three protein kinases. All kinase assays were performed in 96-well ScintiPlates TM from PerkinElmer (Boston, MA, USA) in a 50 ml reaction volume. The reaction cocktail was pipetted in four steps in the following order: 1, 25 ml of assay buffer (standard buffer/[g-33P]-ATP) ; 2, 10 ml of ATP solution (in H2O) ; 3, 5 ml of test compound (in 10 % DMSO) ; 4,10 ml of enzyme/substrate mixture. The assay contained 70 mM HEPES-NaOH, pH 7.5, 3 mM MgCl2, 3 mM MnCl2, 3 mM Na-orthovanadate, 1.2 mM DTT, 50 mg/ml PEG20000, ATP (variable concentrations, corresponding to the apparent ATP-Km of the respective kinase, i.e. 0.3 mM for JNK1, 1.0 mM for JNK2, 0.3 mM for JNK3), [g -33P]-ATP (~3 x 10 5 cpm per well), protein kinase (2.3 nM, 2.0 nM, 2.1 nM for JNK1-3 respectively), and substrate (ATF2, 0.25 mg, 1.0 mg, 2.0 mg for JNK1-3 respectively). The reaction cocktails were incubated at 30°C for 60 minutes. The reaction was stopped with 50 ml of 2 % (v/v) H 3 PO 4 , plates were aspirated and washed two times with 200 ml 0.9 % (w/v) NaCl. Incorporation of 33 Pi was determined with a microplate scintillation counter (Microbeta, Wallac). The median value of the counts in wells without enzyme or test compounds (low control) was subtracted from the median value of the counts in wells with enzyme but without test compounds (high control) to obtain a measure of 100% activity, and from all the other values obtained with added compounds withing the same plate. The residual activity (in %) for each well of a given plate was calculated by using the following formula: Res. Activity (%) = 100 X [(cpm of compound – low control) / (high control – low control)].The residual activities for each concentration and the compound IC50 values were calculated using Quattro Workflow V3.1.1 (Quattro Research GmbH, Munich, Germany; www.quattro-research.com). The fitting model for the IC50 determinations was "Sigmoidal response (variable slope)" with parameters "top" fixed at 100 % and "bottom" at 0 %. The fitting method used was a least-squares fit. As a parameter for assay quality, the Z´-factor(59) for the low and high controls of each assay plate was used. RBE´s criterion for repetition of an assay plate is a Z´-factor below 0.4(60). As an additional quality control, a control inhibitor (Staurosporine) was tested in parallel. The inhibitor IC50 values were in the expected range for each kinase. Immunoblotting Proteins were visualised by Western blot as previously described(16) with some modifications. Confluent PER2::LUC MEFs cultivated in 24-well plates were treated with 0.5 mM guanine, 0.5 mM adenine or respective vehicles ( for 48h in the incubator at 37°C, 5% CO2. Cells were washed once with 1 ml PBS then lysed in the plate with 0.1 ml/well 2X Laemmli buffer (Bio-Rad) supplemented with 20 mM DTT. Cells were scraped out with a pipette tip and transferred to a 1.5 ml microtube, boiled for 10 min at 95°C, vortexed at full speed for 8 sec, and spinned-down before being split into single-use aliquots kept at -20°C. On the day of the immunoblotting, aliquots were boiled again for 10 min at 95°C, vortexed at full speed for 5–10 sec, and spinned-down. Samples (10 ml/well) were loaded into a pre-cast mini-PROTEAN gel (Bio-Rad), run and transferred in a min Trans-blot cell according to manufacturer’s instructions and consumables (Bio-Rad). Membranes were probed with primary antibodies against cJUN (60A8, Cell Signalling #9165 lot 13, 1247 citations, 1:1000), S73p-cJUN (D47G9, Cell Signalling #3270 lot 5, 449 citations, 1:1000), BMAL1 (D2L7G, Cell Signalling #14020 lot 4, 89 citations, 1:1000), Actin (Sigma A5441, >10,000 citations, 1:5000) and secondary (anti-rabbit, Amersham NA934, 1:10,000; anti-mouse, Amersham NA931, 1:50,000) antibodies overnight at 4°C or 1h at room temperature, respectively, and followed by three washes of 10 min at room temperature with Tris Buffered Saline 0.1% Tween20. Proteins were detected using chemiluminescent substrate (Amersham ECL-Prime), pictures of membranes were acquired with a G:Box (Syngene). Molecular docking The structures of JNK1 (PDB ID: 2GMX(28), 3O2M(31)), JNK2 (PDB ID: 7N8T(29)) and JNK3 (PDB ID: 3G90(30)) were prepared using MOE(61) and used for computational docking. Docking was performed using the OpenEye(62) software suite. The OMEGA classic tool(63) was used to create a 3D structure of the compounds using maximum conformations of 500 for each compound. The binding sites were prepared for docking using the make_receptor module. The FRED(64,65) module was used to dock the compounds using the ChemGauss4 scoring function. The best 50 poses for each compound were visualized using Vida 4.4.0(62) and MOE 2024.06(61). The docking process was validated by redocking of the cocrystallized ligand in the binding site for each JNK, where the ligand docked poses were found to reproduce the X-ray orientation. Declarations DATA AVAILABILITY The complete (phospho)proteomics raw data have been deposited to the ProteomeXchange Consortium via the PRIDE(34) partner repository with the dataset identifier PXD062305 and 10.6019/PXD062305. All non-omics data related to this manuscript can be requested form the lead corresponding author at [email protected] . ACKNOWLEDGEMENTS The authors would like to gratefully acknowledge the help of the Genomic Technologies Core Facility, the assistance given by Research IT and the use of the Computational Shared Facility at The University of Manchester. The free academic license from OpenEye (http://www.eyesopen.com) to the Bryce lab at the University of Manchester is acknowledged. FUNDING This research is funded by a Future Leaders Fellowship and its continuation awarded to J.-M.F (MR/S031812/1 and MR/Y003896/1). CONFLICT OF INTEREST The authors declare that they have no conflicts of interest with the contents of this article. AUTHORS CONTRIBUTIONS Jessica Treeby: Investigation, Formal analysis, Writing - Review & Editing. Sherihan El-Sayed : Investigation, Methodology, Software, Formal analysis, Visualization . Samuel Morgan : Investigation, Validation. Sophie Maddock : Investigation, Validation. George Taylor : Methodology, Investigation. Stacey Warwood : Methodology, Investigation. Julian Selley : Methodology, Investigation, Software, Validation, Formal analysis, Data Curation . David Knight : Supervision. Benjamin Saer : Investigation, Supervision. Richard A. Bryce : Supervision, Formal analysis, Visualization, Supervision Writing - Review & Editing . Jean-Michel Fustin: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Visualization, Supervision, Project administration, Funding acquisition. References Latini, S., and Pedata, F. (2001) Adenosine in the central nervous system: release mechanisms and extracellular concentrations. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6521885","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463803649,"identity":"59ad6e4a-3d15-4109-971f-f81e518a2b4b","order_by":0,"name":"Jessica Treeby","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Treeby","suffix":""},{"id":463803652,"identity":"bd74e785-d064-4c48-b80d-e379d8167692","order_by":1,"name":"Sherihan El-Sayed","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Sherihan","middleName":"","lastName":"El-Sayed","suffix":""},{"id":463803653,"identity":"1fa061ff-e766-4ef6-88d3-557c1df6c51b","order_by":2,"name":"Samuel Morgan","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Morgan","suffix":""},{"id":463803655,"identity":"52e2969d-abcd-41bd-ab98-959f76deb66b","order_by":3,"name":"Sophie Maddock","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Sophie","middleName":"","lastName":"Maddock","suffix":""},{"id":463803657,"identity":"d261863a-d04c-45ca-ac9a-7be63a96ab44","order_by":4,"name":"George Taylor","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Taylor","suffix":""},{"id":463803658,"identity":"a628aa43-f1d1-4d46-834a-4f4791ec4873","order_by":5,"name":"Stacey Warwood","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Stacey","middleName":"","lastName":"Warwood","suffix":""},{"id":463803660,"identity":"e335dfb9-0b99-4c3a-a313-29afac0ead80","order_by":6,"name":"Julian Selley","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Julian","middleName":"","lastName":"Selley","suffix":""},{"id":463803663,"identity":"aca2816d-41fc-462c-b6d7-dd6632de98eb","order_by":7,"name":"David Knight","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Knight","suffix":""},{"id":463803665,"identity":"11c0acd2-6938-4859-b163-ad3a0d1c34b5","order_by":8,"name":"Benjamin Saer","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Saer","suffix":""},{"id":463803667,"identity":"875f04a5-2a4f-4584-9dfb-4e4a5b11d02d","order_by":9,"name":"Richard A. Bryce","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"A.","lastName":"Bryce","suffix":""},{"id":463803668,"identity":"2da27df5-481d-49d3-ac72-cf9fa7f44d8f","order_by":10,"name":"Jean-Michel Fustin","email":"data:image/png;base64,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","orcid":"","institution":"University of Manchester","correspondingAuthor":true,"prefix":"","firstName":"Jean-Michel","middleName":"","lastName":"Fustin","suffix":""}],"badges":[],"createdAt":"2025-04-24 14:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6521885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6521885/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-11617-3","type":"published","date":"2025-08-11T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83686616,"identity":"aeab4f08-05b7-467b-8363-d84726d13148","added_by":"auto","created_at":"2025-05-30 18:30:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":309216,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGuanine lengthen the circadian period and decrease the amplitude of oscillations.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e, Dexamethasone-synchronized PER2::LUC MEFs were treated with either adenine, guanine, xanthine or hypoxanthine at the concentrations indicated above and luminescence was acquired in real-time for at least 96 hours (representative results of at least 3 independent experiments, data showing mean of n=4 independent replicate dishes with +SEM shown as a dotted line for each trace). From these traces,\u003cstrong\u003e \u003c/strong\u003eamplitude (\u003cstrong\u003eB\u003c/strong\u003e) and period (\u003cstrong\u003eC\u003c/strong\u003e) were estimated by Biodare2. Data show mean +/- SEM of n=4 independent replicate dishes, analysed by one-way ANOVA followed by Bonferroni’s test, A vs. B vs. C vs. D at least p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6521885/v1/76001669777d9d4f20defaac.jpg"},{"id":83686364,"identity":"7014028d-69f5-4c98-ab18-187e488506ff","added_by":"auto","created_at":"2025-05-30 18:22:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":372642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic response to excess adenine and guanine.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e, Quantification of selected 1-carbon metabolites by HPLC in cells treated with adenine, guanine or their respective vehicle (H, HCl; N, NaOH), data show mean of n=4 independent replicate dishes with +/-SEM analysed by one-way ANOVA followed by Šidák test for the selected A vs. H and G vs. N comparisons. \u003cstrong\u003eB\u003c/strong\u003e, Simplified 1-carbon metabolism, with adenine an inhibitor of SAH hydrolysis by AHCY and SAH an inhibitor of methyltransferases using SAM. \u003cstrong\u003eC\u003c/strong\u003e, Quantification of purine bases and nucleosides by HPLC in cells treated with adenine, guanine or their respective vehicle (H, HCl; N, NaOH), data show mean of n=4 independent replicate dishes with +/-SEM analysed by one-way ANOVA followed by Šidák test for the selected A vs. H and G vs. N comparisons. \u003cstrong\u003eD\u003c/strong\u003e, Simplified purine metabolism, showing the name of enzymes that have been detected in PER2::LUC cells in our proteome dataset (see below) and RNASeq data previously published(16). Guanine deaminase (GDA) and Xanthine dehydrogenase (XDH), essential for guanine and xanthine/hypoxanthine degradation, were not detected. Other abbreviations: PPAT, phosphoribosyl pyrophosphate amidotransferase; ATIC, 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase; ADSS, Adenylosuccinate synthase; ADSL, Adenylosuccinate lyase; ADA, Adenosine deaminase; IMPDH, inosine monophosphate dehydrogenase; GMPS, guanosine monophosphate synthase; AMPD, adenosine monophosphate deaminase; NT5..., 5’-nucleotidase (many homologues present); APRT, adenine phosphoribosyltransferase; PNP, purine nucleoside phosphorylase.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6521885/v1/b5fe4917a393b8cdd475ff24.jpg"},{"id":83686369,"identity":"d87beeef-1b34-4062-921a-ffb46dc1b587","added_by":"auto","created_at":"2025-05-30 18:22:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":200212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrediction of guanine binding in the binding sites of JNKs.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e, results of SwissTargetPrediction tool using human and mouse databases with guanine SMILES formula C1=NC2=C(N1)C(=O)NC(=N2)N. \u003cstrong\u003eB\u003c/strong\u003e, Space filling structure of JNK showing ATP binding site (cyan) using JNK2 X-ray structure (PDB ID: 7N8T(29)); and allosteric binding site (magenta) using JNK1 X-ray structure (PDB ID: 3O2M(31)). The detailed interatomic interactions of AMP (adenosine mono phosphate) in the ATP binding site of JNK2 (PDB ID: 7N8T(29)) is shown in the box. Detailed interatomic interactions of the top-ranked docked pose of guanine in the ATP-binding site of \u003cstrong\u003eC\u003c/strong\u003e, JNK1 (PDB ID: 2GMX(28)), \u003cstrong\u003eD\u003c/strong\u003e, JNK2 (PDB ID: 7N8T(29)) and \u003cstrong\u003eE\u003c/strong\u003e, JNK3 (PDB ID: 3G90(30)). Detailed interatomic interactions of the guanine in its flipped-orientation in the ATP-binding site of \u003cstrong\u003eF\u003c/strong\u003e, JNK1 (PDB ID: 2GMX(28)), \u003cstrong\u003eG\u003c/strong\u003e, JNK2 (PDB ID: 7N8T(29)) and \u003cstrong\u003eH\u003c/strong\u003e, JNK3 (PDB ID: 3G90(30)). \u003cstrong\u003eI\u003c/strong\u003e, Detailed interatomic interactions of the top-ranked docked pose of guanine in the allosteric binding site of JNK1 (PDB ID: 3O2M(31)). \u003cstrong\u003eJ\u003c/strong\u003e, Superposition of the top-ranked docked poses of adenine in the ATP-binding site of JNK1 (violet, PDB ID: 2GMX(28)), JNK2 (gold, PDB ID: 7N8T(29)), JNK3 (pink, PDB ID: 3G90(30)) and AMP (cyan, PDB ID: 7N8T(29)). Dashed lines represent hydrogen bonds and values of the distance between heteroatoms in Å.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6521885/v1/277cfed0fa43234b993018c7.jpg"},{"id":83687163,"identity":"42416aeb-bdfe-4ab7-bafe-5faac1a401da","added_by":"auto","created_at":"2025-05-30 18:46:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":362886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGuanine is an inhibitor of JNKs.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e, Radiometric kinase assays using ATF2 as a substrate. Data show mean +/- S.D. of n=3 independent experiments, with Hill slope and IC50 values extrapolated form the non-linear fit curve. \u003cstrong\u003eB\u003c/strong\u003e, mRNA expression of \u003cem\u003eMapk8\u003c/em\u003e and \u003cem\u003eMapk9\u003c/em\u003ein mouse embryonic fibroblasts, from previously published RNAseq data(16). Data show mean +/- SEM of n = 4 replicate wells. \u003cstrong\u003eC\u003c/strong\u003e, Phosphoproteome (left heatmap) and input proteome (right) of cells treated with vehicle (C), guanine (G), SP600125 (S) or adenine (A). Only the phosphopeptides significantly (p\u0026lt;0.05) regulated in at least one treatment are shown. Data shows mean of n = 4 replicate dishes. \u003cstrong\u003eD\u003c/strong\u003e, Venn diagram with the number of significantly regulated phosphopeptides detected in each condition vs. control. \u003cstrong\u003eE\u003c/strong\u003e, Venn diagram showing the number of significantly (p\u0026lt;0.05) regulated proteins and phosphoproteins. \u003cstrong\u003eF\u003c/strong\u003e, Graph showing KinSwingR results of kinases with a probability of observing such a swing score \u0026lt;0.1 and their predicted swing scores, based on the phosphoproteome signatures of guanine-treated cells, adenine-treated cells (\u003cstrong\u003eG\u003c/strong\u003e), or SP600125-treated (\u003cstrong\u003eH\u003c/strong\u003e) cells. The gray dotted line indicate the significance threshold (probability\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6521885/v1/076193fbf6f4664f589b3e0f.jpg"},{"id":83686368,"identity":"08190d1f-6991-4de7-820a-ca6b5898c8ce","added_by":"auto","created_at":"2025-05-30 18:22:48","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":155963,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConsequences of JNKs inhibition by guanine and potential relevance in a cellular model of Lesch-Nyhan.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e, Representative immunoblotting (experiment performed at least 3 times independently) results showing adenine (A) and guanine (G) cause higher abundance of JUN and pJUN but less intense upshifts in BMAL1 compared to their respective controls HCl (H) and NaOH (N). \u003cstrong\u003eB\u003c/strong\u003e, Representative (experiment performed at least 3 times independently) profiling traces showing a pronounced decrease in light and heavy polysome (LP, HP) associated with an increase in free 60 and 80S ribosome particles. For visualisation purpose, traces were aligned to the trough point between 40S and 60S, but aligning the trace to the lowest point of the heavy polysomes would lead to the same conclusion, i.e. an excess in free ribosome particles not involved in translation. \u003cstrong\u003eC\u003c/strong\u003e, Output of the Expression2Kinases Appyter using the data from Dammer et al. (2015), showing the top five kinases predicted to explain changes in the proteome of HPRT mutant cells. The colours and size of compound bars respectively represent the different libraries used (names in legend) and scores given to that kinase by these libraries.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6521885/v1/cbf9315c28ed43bd6ec30c58.jpg"},{"id":89310801,"identity":"7b1458db-bb67-463d-ae20-53cdd98ca7e2","added_by":"auto","created_at":"2025-08-18 16:10:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2304249,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6521885/v1/6cd73521-6cd6-495c-99b5-d4c0e52b666a.pdf"},{"id":83686371,"identity":"94ce01ed-cbc8-4ed5-be6e-c342e240bb69","added_by":"auto","created_at":"2025-05-30 18:22:50","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":24293978,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6521885/v1/57970626c3ed9cd458c90a5f.xlsx"},{"id":83687038,"identity":"c0bde12f-4089-4b29-9e40-5ec949dd976d","added_by":"auto","created_at":"2025-05-30 18:38:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16607,"visible":true,"origin":"","legend":"","description":"","filename":"Supportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6521885/v1/fa8647d6be42aa87070daa4a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Guanine is an inhibitor of c-Jun terminal kinases","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe purine nucleotides ATP and GTP are not only components of nucleic acid DNA and RNA but are also required for many essential processes including energy metabolism and signal transduction. Moreover, the purine nucleoside adenosine is a neurotransmitter(1) and guanosine a neuromodulator(2). It is not surprising, therefore, that the metabolism of purine is tightly regulated. The purine nucleotides GMP and AMP can either be synthesized \u003cem\u003ede novo\u003c/em\u003e from inosine monophosophate (IMP) or salvaged from their respective nucleosides (guanosine and adenosine) and bases (guanine and adenine). The degradation of all purines converges in the production of the base xanthine, then uric acid.\u003c/p\u003e\n\u003cp\u003eConsidering their importance for cellular metabolism, it is not surprising that disorders of purine metabolism, genetic and life-style related, lead to dramatic pathologies. A chronic dietary excess in purines causes gout due to the accumulation of uric acid in the joints. Similarly, genetic mutations inactivating the enzyme hypoxanthine-guanine phosphoribosyltransferase (HPRT) that salvages guanine to GMP leads to increased degradation of guanine to uric acid, causing gout(3,4). Lesch-Nyhan disease is caused by mutations in HPRT, and while gout may not arise or be detected until late childhood, cognitive impairment, motor disability and self-injurious behaviour including biting of the lips and fingers, head-banging and poking of the eyes occur much earlier(3,5). The mechanisms underlying such neurological symptoms are not well understood but do not seem to involve neurodegeneration or abnormal morphology(6,7). A disruption in the development of the dopaminergic and adrenergic pathway has been proposed, but little evidence of the underlying cause has been provided(8,9).\u003c/p\u003e\n\u003cp\u003eLesch-Nyhan disease highlights the potential toxicity of purine bases, but the lack of evidence for the mechanisms underlying the toxicity of purines limits treatment opportunities. Adenine and guanine have been shown to be intrinsically cytotoxic in several cell types \u003cem\u003ein vitro\u003c/em\u003e, a toxicity that appeared to not depend on the formation of secondary purine metabolites(10-13). \u003cem\u003eIn vitro\u003c/em\u003e approaches have provided other important clues: cultures of Lesch-Nyhan fibroblasts have established that the severity of the disease is strongly correlated with the inability to salvage guanine by HPRT(14), and HPRT knock-out cell lines have shown that guanine, undetectable in wild-type control cells, considerably increased, associated with slower proliferation rates(15).\u003c/p\u003e\n\u003cp\u003eInvestigating the mechanisms underlying purine toxicity, we have previously demonstrated that adenine is an inhibitor of 1-carbon metabolism and cellular methylations(16). Here, using real-time luminometry of cellular circadian rhythms as a toxicity screening, \u003cem\u003ein silico\u003c/em\u003e docking predictions, \u003cem\u003ein vitro\u003c/em\u003e kinase assays and phosphoproteomics, we report that guanine is an inhibitor of the c-Jun N-terminal kinases JNK1-3, with an IC50 of 100-300 mM.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo investigate the biological activity and adverse effects of the purine bases adenine, guanine, xanthine and hypoxanthine, we used a real-time luminescence assay with mouse embryonic fibroblasts prepared from PER2::LUC mice carrying a heterozygous chimeric fusion between the endogenous circadian clock protein PER2 and LUCIFERASE(17). Since the circadian clock is based on a transcription-translation feedback loops driving rhythmic gene expression, and controlled by cellular metabolism at all levels from epigenetic to post-translational regulations, this system is particularly well suited to detect and quantify the adverse effects of small molecules and inhibitors, as we have demonstrated previously with adenine and other 1-carbon metabolism inhibitors(16,18-20).\u003c/p\u003e\n\u003cp\u003eAs previously observed(16), adenine caused a concentration-dependent lengthening of the circadian period, \u003cem\u003ei.e.\u003c/em\u003e a slower biological clock (Figure 1A, B). Guanine was even more potent than adenine, not only causing a significant lengthening of the circadian period (Figure 1A, B) but also accompanied with a decrease in amplitude (Figure 1C), which is a measure of the robustness of circadian oscillations. Xanthine showed a weak but statistically significant shortening of the period, but no concentration-dependent effects were seen (Figure 1A, B). Hypoxanthine did not significantly affect the oscillations, the period or the amplitude of reporter gene oscillations. We further investigated the mechanisms underlying the effects of guanine.\u003c/p\u003e\n\u003cp\u003eSince adenine was shown to disrupt the methyl cycle by inhibiting the enzyme adenosylhomocysteinase (AHCY), leading to changes in 1-carbon metabolites adenosylmethionine (SAM), adenosylhomocysteine (SAH) and methylthioadenosine (MTA)(16), we first hypothesized that guanine had the same mode of action. To investigate this possibility and to gain insights into the metabolic paths of exogenous guanine and adenine, we quantified selected 1-carbon metabolites in cells treated with these 2 purine bases. Adenine caused an increase in SAH (Figure 2A), confirming the inhibition of AHCY previously observed(16), accompanied by an increase in methionine and MTA, suggesting the methionine salvage pathway has been activated. In contrast, guanine had no significant effects on SAH or MTA (Figure 2A), evidence that guanine does not inhibit AHCY. A graphical representation of simplified 1-carbon metabolism is shown in Figure 2B to guide understanding.\u003c/p\u003e\n\u003cp\u003eTo gain further insights into how guanine is metabolized in these cells and whether guanine treatment leads to intracellular guanine accumulation, we quantified purine bases and nucleosides (Figure 2C). In cells treated with adenine, we observed a significant increase not only in adenine but also in hypoxanthine and guanine, indicating excess adenine is indirectly converted to guanine and catabolized to hypoxanthine, and/or allosterically stimulates the \u003cem\u003ede novo\u003c/em\u003e branch to guanine nucleotides. In contrast, guanine but not adenine significantly increased in cells treated with guanine, indicating there is no conversion of guanine to adenine in these cells. In cells treated with adenine or guanine, AICAR dramatically dropped while glutamine increased, confirming that \u003cem\u003ede novo\u003c/em\u003e purine synthesis is suppressed by the salvage of excess purine bases, a known allosteric regulation of purine synthesis(21). While these data together do not support a role for guanine in the direct or indirect inhibition of 1-carbon metabolism, it is possible that the increase in guanine in cells treated with adenine also contributes to the toxicity of adenine. A graphical representation of simplified purine salvage and \u003cem\u003ede novo\u003c/em\u003e synthesis is shown in Figure 2D.\u003c/p\u003e\n\u003cp\u003eNext, to probe potential protein targets of guanine, we used SwissTargetPrediction(22) with guanine, which revealed Mitogen-activated protein kinase 9 (MAPK9), also known as c-Jun N-terminal kinase (JNK2) as the top most likely target using mouse data (Probability 0.044), and both MAPK8 (JNK1, 0.044) and MAPK9 (JNK2, 0.044) as the top ranked third and fourth target using human data (Figure 3A). While these probabilities seem low, the actual ranking of these potential targets is the most meaningful parameter(23). For comparison, purine nucleoside phosphorylase (PNP) and thymidine kinase (TK1), enzymes respectively involved in purine and pyrimidine metabolism, were the top first (0.121) and second (0.053) targets using human data (Figure 3A).\u003c/p\u003e\n\u003cp\u003eMAPK8 and MAPK9 are interesting potential targets because their siRNA-mediated knock-down and inhibition with the inhibitor SP600125 have been shown to lengthen the circadian rhythms \u003cem\u003ein vitro\u003c/em\u003e(24). JNKs are key kinases regulating development and cell growth, stress response, apoptosis and inflammation(25). MAPK8 and MAPK9 are expressed in different tissues and implicated in various diseases, while MAPK10 is mainly expressed in brain and implicated in the pathogeneses of CNS disorders(26,27).\u003c/p\u003e\n\u003cp\u003eThe general structure of JNKs comprise a C-lobe and N-lobe domain (Figure 3B).\u0026nbsp;The ATP binding site is in the hinge region between the two lobes, involving more interaction with N-lobe amino acid residues.\u0026nbsp;It is known JNK inhibitors either bind to the ATP binding pocket or to the allosteric site (Figure 3B). To probe the potential for binding of guanine to JNKs, we performed docking studies using the ATP-binding pocket of the three kinases (from X-ray structures PDB ID: 2GMX(28) for JNK1, 7N8T(29) for JNK2, and 3G90(30) for JNK3), and the allosteric site of JNK1 (PDB ID:\u0026nbsp;3O2M(31)). \u0026nbsp;For all JNKs, the top-ranked docked pose of guanine was predicted to form hydrogen bonds in the cofactor binding pocket with residues similar to the adenine base of ATP (Figure 3C-E). However, in docking, a flipped orientation of guanine was also predicted as favourable, with the potential to hydrogen bond with the gatekeeper residue (Met108 in JNK1,2 and Met146 in JNK3, Figure 3F-H). Guanine was also predicted to fit with good affinity to the allosteric site of JNK1 with a ChemGauss4 value of -6.7 (Figure 3I and Table 1). For comparison, adenine was docked into the ATP binding site of the JNK isoforms. The top-ranked docked poses of adenine in the three JNKs structures show binding poses similar to the adenine part of AMP (Figure 3J), and possess with slightly lower docking scores than that of the top-ranked poses of guanine (Table 1). Together these data predict a relatively favourable interaction of guanine for all JNKs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: ChemGauss4 docking scores of X-ray ligand, guanine and adenine in ATP and non-ATP binding sites of JNK1, JNK2 and JNK3.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIsoform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePDB ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGatekeeper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eX-ray Ligand\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGuanine (top-ranked pose)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGuanine (flipped pose) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdenine (top-ranked pose)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJNK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2GMX(28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClosed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-11.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJNK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7N8T(29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClosed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-12.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-9.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJNK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3G90(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClosed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-11.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJNK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3O2M(31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNext, we determined the inhibitory effects of guanine on JNK1-3 by radiometric \u003cem\u003ein vitro\u003c/em\u003e kinase assays. As predicted, guanine inhibited all three JNKs, with an IC50 value of 0.2, 0.18 and 0.33 mM and Hill Slope of -0.67, -0.98 and -1.1 for JNK1, 2 and 3, respectively (Figure 4A). The lower IC\u003csub\u003e50\u003c/sub\u003e and Hill slope for JNK2 are consistent with their more favorable predicted docking scores. Some inhibition of JNKs was also seen with adenine, but with a higher IC50 (Figure 4A) than guanine, with the lowest IC50 for JNK1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing our previous RNASeq dataset(16), we confirmed that the PER2::LUC cells we used express JNK1 and 2, the latter the most abundantly expressed homologue, but not JNK3 (Fig 4B). We then quantified the phosphoproteome of PER2::LUC cells treated with 0.5 mM guanine or adenine, or with 9 mM SP600125, a compound originally described as a specific JNK inhibitor but also a potent inhibitor of other kinases including Aurora and Casein kinases(32,33). The complete (phospho)proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE(34) partner repository with the dataset identifier PXD062305 and 10.6019/PXD062305.\u003c/p\u003e\n\u003cp\u003eWe detected more than 5500 phosphopeptides (Supporting Data, tab 1) but limited our downstream analysis to those 1433 detected in at least 70% of the samples (Supporting Data, tab 2). Out of these, 355 phosphopeptides showed significant changes (p\u0026lt;0.05) in at least one treatment (Figure 4C), but with little overlap between the effects of guanine, adenine or SP600125 (Figure 4D). Comparison between the phosphoproteome and input proteome data (Supporting Data, tab 3) showed efficient enrichment for phosphopeptides, as many proteins with detected phosphopeptides were not detected in the proteome data, and did not reveal overall parallel changes (Figure 4C), evidence that the significant changes in phosphopeptides were mostly due to changes in phosphorylation rather than protein abundance. Indeed, performing the same differential analysis by PhosPIR but with the proteome revealed 332 proteins significantly regulated in at least one treatment (Supporting Data, tab 4), but only 24 of these proteins had significantly regulated phosphopeptides in the phosphoproteome, with the other 243 proteins with significant phosphopeptides not represented among significant proteins (Figure 4E).\u003c/p\u003e\n\u003cp\u003eTo predict which kinase(s) might be regulated by guanine, adenine or SP600125, we then performed analysis of our phosphoproteome with KinSwingR(35). This analysis revealed that MAPK10 had a significant (p\u0026lt;0.05) swing score (change in activity) of 1.33 and MAPK8 a swing score of 1.14 with a \u0026nbsp;p value of 0.057 in guanine-treated cells (Figure 4F; Supporting Data, tab 5). MAPK9 had a poor swing score and a p value\u0026gt;0.1. Surprisingly, however, the swing scores for MAPK8 and 10 were positive, indicating an increase in their activity. This is consistent with higher levels of the canonical target JUN S73 phosphorylation in guanine-treated cells compared to control (Supporting Data, tab 6). Although this may appear contradictory with their inhibition, it has been shown that while knock-out of MAPK8 decreases JUN phosphorylation and stability, MAPK9 deficiency or inhibition increases JUN expression, phosphorylation and stability due to a compensatory increase in MAPK8 activity(36,37). Together with the higher expression of MAPK9 in our cells, these observations suggest that MAPK9 was the main homologue inhibited by guanine in our cells (Figure 4B), in line with higher guanine docking affinity (Table 1) and lower EC50 (Figure 4A).\u003c/p\u003e\n\u003cp\u003eIn adenine-treated cells, a negative swing score of -1.46 for MAPK8 with a p value of 0.065 was obtained, but MAPK9 and MAPK10 had poor swing score and p value above 0.1 (Figure 4G; Supporting Data, tab 6). In cells treated with SP600125, while the swing scores for MAPK8-10 were poor and their p values was above 0.1, a negative swing score of -2.57 for Casein kinase 1 delta (CSNK1D) with a p value of 0.02 was observed (Figure 4H; Supporting Data, tab 7), which is consistent \u0026nbsp;with a report showing that SP600125 is a more potent inhibitor of CSNK1D than of JNKs(32).\u003c/p\u003e\n\u003cp\u003eTo confirm these observations, an increase in JUN and pS73 JUN by immunoblotting was seen in cells treated with guanine or adenine (Figure 5A), suggesting both treatments affect MAPK8-10 activity. Since MAPK8 and MAPK9 are involved in the regulation of circadian rhythms via the phosphorylation of the core clock protein BMAL1(24), we sought to confirm whether BMAL1 phosphorylation was inhibited in guanine- or adenine-treated cells. Indeed, in cells treated with guanine or adenine, the intensity of an upper band, likely corresponding to phosphorylated BMAL1, markedly decreased in intensity \u0026nbsp;(Figure 5A) compared to their respective controls. Together these data demonstrate that guanine, and maybe adenine directly or indirectly, is an inhibitor of MAPK8-10.\u003c/p\u003e\n\u003cp\u003eLastly, since MAPK9 promotes translation(38), we decided to assess the translation efficiency in guanine-treated cells to provide functional consequences of MAPK9 inhibition. Polysome profiling revealed a deficiency in translation, with an increase in free ribosomal 80S subunits, associated with markedly lower polysome levels (Fig. 5B).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe have shown here that guanine is capable of inhibiting the function of JNKs, which may be of clinical significance for patients with genetic or life-style-related deficiencies in purine metabolism. To probe this possibility further, we mined a published proteomics dataset obtained with a model of Lesch-Nyhan disease based on dopaminergic rat PC6-3 lines carrying different mutations in HPRT(39). Proteins significantly affected by HPRT mutations(39) were used as input for the Expression2Kinases Appyter that predicts upstream kinases likely responsible for observed changes in gene expression(40). Interestingly, MAPK8 and MAPK9 were respectively fifth and second topmost likely kinases (Figure 5C).\u003c/p\u003e\n\u003cp\u003eIn cells, ATP is typically in the mM range, meaning that potent ATP-competitive kinase inhibitors typically have a K\u003csub\u003ei\u003c/sub\u003e in the low micromolar range(41). \u003cem\u003eIn vivo\u003c/em\u003e conditions in which JNKs inhibition by guanine could occur will thus depend on the levels of both guanine and ATP. Interestingly, HPRT knock-out neural progenitor cells have lower ATP levels(42), suggesting HPRT mutations may present perfect conditions for JNKs inhibition.\u003c/p\u003e\n\u003cp\u003eOur phosphoproteome data do no conclusively demonstrate which JNK isoform is inhibited, because their few substrate phospho-sites in the database used by PhosPIR largely overlap. In our transcriptome data, however, there was evidence that MAPK9 is the dominant homologue expressed in these cells, followed by MAPK8, with MAPK10 being undetectable. In our proteome data (Supporting Data, tab 3), Maxquant was unable to confidently differentiate between MAPK8, 9 and 10, even though one of the two peptides detected, MLVIDPDKR, is specific to MAPK9, with only one amino acid difference with MAPK10. While comparative studies have concluded that MaxQuant has better overall performance than other commercial protein quantification tools(43,44), we decided to re-analyse our MS data with Thermo Fisher Scientific’s Proteome Discoverer (PD), which has been shown to provide better coverage of low abundance proteins(44). PD was able to confirm that MAPK9 was the only JNK to be detected with high confidence in all samples (Supporting Data, tab 8 and 9). This further supports the conclusion that, in our PER2::LUC cells, MAPK9 was the main homologue inhibited by guanine. Moreover, our data do not refute the possibility the guanine (and adenine) may also inhibit other kinases.\u003c/p\u003e\n\u003cp\u003eThe catabolism of guanine starts with guanine deaminase (GDA), leading to xanthine, further catabolised to urate by xanthine dehydrogenase (XDH). The development of gout and kidney stones in Lesch-Nyhan patients indicates that salvage of guanine to GMP by HPRT is the main metabolic route for guanine in normal conditions. HPRT deficiency causes elevated levels of it substrates guanine, hypoxanthine and PRPP, especially in the central nervous system(45,46). Although GDA may mitigate the increase in guanine in the brain by degrading it to xanthine(47), it is expressed in neurons but not glia(48), raising the possibility that local, cell-specific accumulation of guanine may occur in Lesch-Nyhan patients, leading to JNKs inhibition as a potential contributor to the symptoms of the disease. This may be in a way similar to what happens in PER2::LUC MEFs treated with guanine. Indeed, from our previously published RNASeq(16) data and the proteome data presented here, of the purine metabolism enzymes shown in Figure 2D, GDA and XDH were not detected, indicating exogenous guanine could not be degraded. It would be interesting to express GDA in these cells to determine whether this provides protection against guanine, since the endogenous expression of HPRT in these cells was not sufficient to protect against exogenous guanine.\u003c/p\u003e\n\u003cp\u003eGuanine, like adenine, caused the lengthening of the circadian period \u003cem\u003ein vitro\u003c/em\u003e but our results indicate the mechanisms underlying these effects are different. While adenine acts as a feedback inhibitor of 1-carbon metabolism and methylations, guanine instead inhibits JNKs, leading to the changes in BMAL1 electrophoretic mobility observed in Figure 5A, likely attributable to phosphorylation(24). Within the molecular clockwork, phosphorylation of BMAL1 and its partner CLOCK is circadian-time dependent and inhibits its activity at E-boxes cis-elements in the promoter of target genes(24,49,50). A delay in this phosphorylation-dependent inactivation would lead to a lengthened circadian period, here observed with guanine or previously published when JNKs are inhibited or knocked out/down(24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe observed increase in JUN phosphorylation and the decrease in BMAL1 electromobility upshift in cells treated with adenine or guanine may appear contradictory with an inhibition of JNKs. Unlike JUN that can be phosphorylated by both JNK1 and JNK2, with the compensatory increase in JNK1 when JNK2 is inhibited responsible for the increase in pJUN observed(36,37), it is possible that BMAL1 may be phosphorylated specifically by JNK2, and therefore not affected by the compensatory increase in JNK1. This would ultimately lead to a decrease in BMAL1 phosphorylation due to JNK2 inhibition.\u003c/p\u003e\n\u003cp\u003eIn the phosphoproteome analysis from cells treated with guanine (Figure 4F), of note was the negative swing score of both PRKAA1, PRKAA2, subunits of 5' AMP-activated protein kinase (AMPK). These subunits may have been directly inhibited by guanine, or more likely may have been inhibited by changes in the abundance of adenosine nucleotides (ATP, ADP, AMP), known regulators of AMPK(51). In cells treated with adenine, in contrast PRKAA2 had a positive swing score, suggesting AMPK may have been activated instead (Figure 4F). It is possible that excess of guanine nucleotides from salvage in guanine-treated cells may have stimulated the \u003cem\u003ede novo\u003c/em\u003e branch of the pathway to ATP, while treatment with adenine instead may have inhibited ATP production. Of note, AICAR, an activator of AMPK(52), was lower in cells treated with guanine or adenine (Figure 2C). It is known that AMPK and JNK pathways interact under metabolic stress(53), which may have further contributed to the changes in the phosphoproteome observed here. Of note, AMPK itself is a regulator of circadian rhythms(54), and changes in AMPK activity may also have contributed to the long circadian period observed in cells treated with adenine or guanine.\u003c/p\u003e\n\u003cp\u003eIn conclusion, we have shown here that inhibition of JNKs contributes to the toxicity of purine bases, which further explains why the biosynthesis of purine bases is under strict control.\u003c/p\u003e"},{"header":"Experimental procedures","content":"\u003cp\u003e\u003cstrong\u003eCell cultures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePER2::LUC MEFS(17) were cultivated and monitored for real-time luminescence as previously described(20). Briefly, cells were seeded into 35 mm dishes (Corning) and allow to grow for 3-4 days to confluence in DMEM/F12 medium (Invitrogen) containing antimycotic/antibiotic (Sigma) and 10% heat-inactivated serum (Gibco). Cells were shocked with 400 nM dexamethasone (Sigma-Aldrich) for 2 h, followed by a medium change including 1 mM beetle luciferin (Promega) and either of the following treatments: adenine, guanine, xanthine and hypoxanthine (Sigma-Aldrich), keeping the concentration of the respective vehicle equal in all dishes (1.6\u0026nbsp;mM HCl for adenine, 1.6\u0026nbsp;mM NaOH for guanine, xanthine and hypoxanthine). 35 mm dishes were then sealed with parafilm and transferred to a luminometer (Lumicycle32, Actimetrics) placed in an dry incubator at 35°C. Photons were counted in bins of 2 min at a frequency of 10 min. Period and amplitude were estimated by BioDare2(55).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolite quantification by LC-MS/MS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePER2::LUC MEFS(17) were cultivated and metabolites were extracted as previously described(20). To repeat, cells cultivated in 10 cm Petri dishes (Corning) for 3-4 days until confluence were treated with guanine, adenine, HCl or NaOH and returned to the incubator for 24 h at 37 °C, 5% CO2. Cells were washed twice with 10 ml 5% mannitol (Sigma-Aldrich), the mannitol was carefully and completely removed before 0.9 ml 100% methanol was added onto the cells, firmly rocking the dish so that the methanol covers the cell monolayer. Dishes were tipped, and 0.6 ml water containing 125 ng/ml BIS-TRIS (Sigma-Aldrich) was added directly into the pool of methanol forming in the corner of the dish before rocking the dish again to cover the cell monolayer. The water/methanol mix was collected from the corner of the tipped dish and transferred to a 1.5 ml microtube. Tubes were kept at room temperature until all dishes were processed, randomly. Samples were centrifuged at 20,000 × g, 4 °C for 30 min, the supernatant transferred to a new tube, centrifuged again at 20,000 × g, 4 °C for 10 min, and the final supernatant transferred to a new 1.5 ml microtube.\u003c/p\u003e\n\u003cp\u003ePrior to analysis, 200 µl of sample was dried in a centrifugal vacuum concentrator and resuspended in 100 µl acetonitrile and water in a ratio of 5:1. The sample was centrifuged at 20,000 × g for 3 min and the top 80 µl was transferred to a glass autosampler vial with 300 µl insert and capped.\u003c/p\u003e\n\u003cp\u003eLiquid chromatography-mass spectrometry analysis was performed using a Thermo-Fisher Ultimate 3000 HPLC system consisting of an HPG-3400RS high-pressure gradient pump, TCC 3000 SD column compartment, and WPS 3000 Autosampler, coupled to a SCIEX 6600 TripleTOF Q-TOF mass spectrometer with TurboV ion source. The system was controlled by SCIEX Analyst 1.7.1, DCMS Link, and Chromeleon Xpress software.\u003c/p\u003e\n\u003cp\u003eA sample volume of 5 μL was injected by pulled loop onto a 5 μL sample loop with 150 μl post-injection needle wash with 9:1 acetonitrile and water. Injection cycle time was 1 min per sample. Separations were performed using an Agilent Poroshell 120 HILIC-Z PEEK-lined column with dimensions of 150 mm length, 2.1 mm diameter, and 2.7 μm particle size equipped with a guard column of the same phase. Mobile phase A was water with 10 mM ammonium formate and 0.1% formic acid, mobile phase B was 9:1 acetonitrile and water with 10 mM ammonium formate and 0.1% formic acid. Separation was performed by gradient chromatography at a flow rate of 0.25 ml/min, starting at 98% B for 3 min, ramping to 5% B over 20 min, hold at 5% B for 1 min, then back to 98% B. Re-equilibration time was 5 min. Total run time including 1 min injection cycle was 30 min.\u003c/p\u003e\n\u003cp\u003eThe mass spectrometer was run in positive mode under the following source conditions: curtain gas pressure, 50 psi; ionspray voltage, 5500 V; temperature, 400 °C; ESI nebulizer gas pressure, 50 psi; heater gas pressure, 70 psi; declustering potential, 80 V.\u003c/p\u003e\n\u003cp\u003eData were acquired in a data-independent manner using SWATH in the range of 50–1000 m/z, split across 78 variable-size windows (79 experiments including TOF survey scan), each with an accumulation time of 20 ms. Total cycle time was 1.66 s. Collision energy of each SWATH window was determined using the formula CE (V) = 0.084 × m/z + 12 up to a maximum of 55 V.\u003c/p\u003e\n\u003cp\u003eAcquired data were processed in MultiQuant 3.0.2. Peaks from MS1 and MS2 data were picked and matched against a metabolite library of 235 standards, based on retention time and mass error of ±0.025 Da. Data exported from MultiQuant 3.0.2 was further sorted, filtered, and scored using a custom VBA macro in Excel, based on presence, peak area, and coelution of precursor and fragment ions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhospho-proteomic analysis and mass spectrometry in BioMS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples (frozen cell pellets from 4 independent replicate 15cm Petri dishes per treatment) were lysed in 5% SDS, followed by reduction, alkylation and precipitation with acetone. Samples were then resuspended in Rapigest (Waters) and digested with trypsin overnight. \u0026nbsp;For phospho-peptide enrichment, 95% of each sample volume was desalted (TELOS neo SPE fixed 96-well plates, Cole-Parmer) according to standard protocols, and eluted in phophopeptide enrichment binding solution for processing. \u0026nbsp;The remaining 5% of the digested sample was taken for proteomic analysis. \u0026nbsp;Phospho-peptide enrichment was performed using magnetic microspheres (Ti‐IMAC, ReSyn Bioscience) and a KingFisher Flex (Thermo Scientific) according to facility protocols(56). \u0026nbsp;Phospho-peptides were desalted prior to analysis (TELOS neo SPE fixed 96-well plates, Cole-Parmer).\u003c/p\u003e\n\u003cp\u003eFor mass spectrometry peptides were resuspended in 3% (v/v) ACN / 1% (v/v) formic acid and analysed by liquid chromatography-tandem mass spectrometry (LC‐MS/MS) using a Thermo Rapid Separation Liquid Chromatography system (RSLC, Thermo Fisher Scientific) coupled to an Exploris 480 (Thermo Fisher Scientific) mass spectrometer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe RSLC was configured with buffer A as 0.1% formic acid in water and buffer B as 0.1% formic acid in acetonitrile. An injection volume of 2 ul was loaded into the end of a 5 ul loop and reverse flushed on to the analytical column (Waters nanoEase M/Z Peptide CSH C18 Column, 130Å, 1.7 µm, 75 µm X 250 mm) kept at 35 °C at a flow rate of 300 nl/min for 8 min with an initial pulse of 500 nl/min for 0.3 min to rapidly re-pressurise the column. The injection valve was set to load before a separation consisting of a multistage gradient of 2% B to 6% B over 3 minutes, 6% B to 18% B over 67 minutes, 18% B to 29% B over 11 minutes and 29% B to 65% B over 1 minute before washing for 6 minutes at 65% B and dropping down to 2% B in 1 minute. The complete method time was 105 minutes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analytical column was connected to a Thermo Exploris 480 mass spectrometry system via a Thermo nanospray Flex Ion source via a 20 um ID fused silica capillary. The capillary was connected to a stainless steel emitter with an outer diameter of 150 um and an inner diameter of 30 um (Thermo Scientific, ES542) via a butt-to-butt connection in a steel union using a custom made gold frit (Agar Scientific AGG2440A) to provide the electrical connection. The nanospray voltage was set at 1900 V and the ion transfer tube temperature set to 275 °C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData was acquired in a data dependent manner using a fixed cycle time of 2 sec, an expected peak width of 15 sec and a default charge state of 2. Full MS data was acquired in positive mode over a scan range of 300 to 1750 Th, with a resolution of 120,000, a normalised AGC target of 300% and a max fill time of 25 mS for a single microscan. Fragmentation data was obtained from signals with a charge state of +2 or +3 and an intensity over 5,000 and they were dynamically excluded from further analysis for a period of 15 sec after a single acquisition within a 10ppm window. Fragmentation spectra were acquired with a resolution of 15,000 with a normalised collision energy of 30%, a normalised AGC target of 300%, first mass of 110 Th and a max fill time of 25 mS for a single microscan. All data was collected in profile mode. The complete (phospho)proteomics raw data have been deposited to the ProteomeXchange Consortium via the PRIDE(34) partner repository with the dataset identifier PXD062305 and 10.6019/PXD062305.\u003c/p\u003e\n\u003cp\u003eRaw files were analysed by MaxQuant v2.6.7.0(57), using parameters listed in the Supporting Method 1 using the UP000000589_10090 fasta reference proteome file available form uniprot.org. The Phospho(STY)Sites.txt output file (Supporting Data, tab 1) was further analysed using PhosPIR(58), limited to the phosphopeptides detected in at least 70% of the samples, normalising the data and imputing the remaining missing values, and using significance cutoff value of p \u0026lt;0.05. For statistical analysis with PhosPIR including KinSwingR, the 3 pairwise comparisons guanine vs. control, adenine vs. control and SP600125 vs. control were setup. Protein identification and quantification by Proteome Discoverer version 3.1.0.638 (Supporting Data, tab 8) was performed using the settings listed in Supporting Data, tab 9, which also include workflow messages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKinase assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKinase assays were performed independently by Reaction Biology (RBE, Freiburg, Germany) according to their standard protocol. All protein kinases provided by RBE were expressed in Sf9 insect cells or in E.coli as recombinant GST-fusion proteins or His-tagged proteins, either as full-length or enzymatically active fragments. All kinases were produced from human cDNAs and purified by either GSH-affinity chromatography or immobilized metal. Affinity tags were removed from a number of kinases during purification. The purity of the protein kinases was examined by SDS-PAGE/Coomassie staining, the identity was checked by mass spectroscopy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdenine, guanine and SP600125 were dissolved and diluted in 100% DMSO to 100x highest assay concentration (1mM, 1 mM, 2\u0026nbsp;mM, respectively). Prior to testing, the 100% DMSO stock solutions were subjected to a serial, semi-logarithmic dilution using 100 % DMSO as a solvent.\u003c/p\u003e\n\u003cp\u003eA radiometric protein kinase assay (\u003csup\u003e33\u003c/sup\u003ePanQinase\u003csup\u003eTM\u003c/sup\u003e Activity Assay) was used for measuring the kinase activity of the three protein kinases. All kinase assays were performed in 96-well ScintiPlates\u003csup\u003eTM\u003c/sup\u003e from PerkinElmer (Boston, MA, USA) in a 50\u0026nbsp;ml reaction volume. The reaction cocktail was pipetted in four steps in the following order: 1, \u0026nbsp;25\u0026nbsp;ml of assay buffer (standard buffer/[g-33P]-ATP) ; 2, 10\u0026nbsp;ml of ATP solution (in H2O) ; 3, 5\u0026nbsp;ml of test compound (in 10 % DMSO) ; 4,10\u0026nbsp;ml of enzyme/substrate mixture. The assay contained 70 mM HEPES-NaOH, pH 7.5, 3 mM MgCl2, 3 mM MnCl2, 3\u0026nbsp;mM Na-orthovanadate, 1.2 mM DTT, 50\u0026nbsp;mg/ml PEG20000, ATP (variable concentrations, corresponding to the apparent ATP-Km of the respective kinase, \u003cem\u003ei.e.\u003c/em\u003e 0.3\u0026nbsp;mM for JNK1, 1.0\u0026nbsp;mM for JNK2, 0.3\u0026nbsp;mM for JNK3), \u0026nbsp;[g\u0026nbsp;-33P]-ATP (~3 x 10\u003csup\u003e5\u003c/sup\u003e cpm per well), protein kinase (2.3 nM, 2.0 nM, 2.1 nM for JNK1-3 respectively), and substrate (ATF2, 0.25\u0026nbsp;mg, 1.0\u0026nbsp;mg, 2.0\u0026nbsp;mg for JNK1-3 respectively).\u0026nbsp;The reaction cocktails were incubated at 30°C for 60 minutes. The reaction was stopped with 50\u0026nbsp;ml of 2 % (v/v) H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, plates were aspirated and washed two times with 200\u0026nbsp;ml 0.9 % (w/v) NaCl. Incorporation of \u003csup\u003e33\u003c/sup\u003ePi was determined with a microplate scintillation counter (Microbeta, Wallac).\u003c/p\u003e\n\u003cp\u003eThe median value of the counts in wells without enzyme or test compounds (low control) was subtracted from the median value of the counts in wells with enzyme but without test compounds (high control) to obtain a measure of 100% activity, and from all the other values obtained with added compounds withing the same plate. The residual activity (in %) for each well of a given plate was calculated by using the following formula: Res. Activity (%) = 100 X [(cpm of compound – low control) / (high control – low control)].The residual activities for each concentration and the compound IC50 values were calculated using \u003cem\u003eQuattro Workflow V3.1.1\u0026nbsp;\u003c/em\u003e(Quattro Research GmbH, Munich, Germany; www.quattro-research.com). The fitting model for the IC50 determinations was \"Sigmoidal response (variable slope)\" with parameters \"top\" fixed at 100 % and \"bottom\" at 0 %. The fitting method used was a least-squares fit.\u003c/p\u003e\n\u003cp\u003eAs a parameter for assay quality, the Z´-factor(59) for the low and high controls of each assay plate was used. RBE´s criterion for repetition of an assay plate is a Z´-factor below 0.4(60). As an additional quality control, a control inhibitor (Staurosporine) was tested in parallel. The inhibitor IC50 values were in the expected range for each kinase.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunoblotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProteins were visualised by Western blot as previously described(16) with some modifications. Confluent PER2::LUC MEFs cultivated in 24-well plates were treated with 0.5 mM guanine, 0.5 mM adenine or respective vehicles ( for 48h in the incubator at 37°C, 5% CO2. Cells were washed once with 1 ml PBS then lysed in the plate with 0.1 ml/well 2X Laemmli buffer (Bio-Rad) supplemented with 20 mM DTT. Cells were scraped out with a pipette tip and transferred to a 1.5 ml microtube, boiled for 10 min at 95°C, vortexed at full speed for 8 sec, and spinned-down before being split into single-use aliquots kept at -20°C. On the day of the immunoblotting, aliquots were boiled again for 10 min at 95°C, vortexed at full speed for 5–10 sec, and spinned-down.\u003c/p\u003e\n\u003cp\u003eSamples (10\u0026nbsp;ml/well) were loaded into a pre-cast mini-PROTEAN gel (Bio-Rad), run and transferred in a min Trans-blot cell according to manufacturer’s instructions and consumables (Bio-Rad). Membranes were probed with primary antibodies against cJUN (60A8, Cell Signalling #9165 lot 13, 1247 citations, 1:1000), S73p-cJUN (D47G9, Cell Signalling #3270 lot 5, 449 citations, 1:1000), BMAL1 (D2L7G, Cell Signalling #14020 lot 4, 89 citations, 1:1000), Actin (Sigma A5441, \u0026gt;10,000 citations, 1:5000) and secondary (anti-rabbit, Amersham NA934, 1:10,000; anti-mouse, Amersham NA931, 1:50,000) antibodies overnight at 4°C or 1h at room temperature, respectively, and followed by three washes of 10 min at room temperature with Tris Buffered Saline 0.1% Tween20. Proteins were detected using chemiluminescent substrate (Amersham ECL-Prime), pictures of membranes were acquired with a G:Box (Syngene).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe structures of JNK1 (PDB ID: 2GMX(28), 3O2M(31)), JNK2 (PDB ID: 7N8T(29)) and JNK3 (PDB ID: 3G90(30)) were prepared using MOE(61) and used for computational docking. Docking was performed using the OpenEye(62) software suite. The OMEGA classic tool(63) was used to create a 3D structure of the compounds using maximum conformations of 500 for each compound. The binding sites were prepared for docking using the make_receptor module. The FRED(64,65) module was used to dock the compounds using the ChemGauss4 scoring function. The best 50 poses for each compound were visualized using Vida 4.4.0(62) and MOE 2024.06(61). The docking process was validated by redocking of the cocrystallized ligand in the binding site for each JNK, where the ligand docked poses were found to reproduce the X-ray orientation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eDATA AVAILABILITY\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe complete (phospho)proteomics raw data have been deposited to the ProteomeXchange Consortium via the PRIDE(34) partner repository with the dataset identifier PXD062305 and 10.6019/PXD062305. All non-omics data related to this manuscript can be requested form the lead corresponding author at [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eACKNOWLEDGEMENTS\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to gratefully acknowledge the help of the Genomic Technologies Core Facility, the assistance given by Research IT and the use of the Computational Shared Facility at The University of Manchester. The free academic license from OpenEye (http://www.eyesopen.com) to the Bryce lab at the University of Manchester is acknowledged.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFUNDING\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis research is funded by a Future Leaders Fellowship and its continuation awarded to J.-M.F (MR/S031812/1 and MR/Y003896/1).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCONFLICT OF INTEREST\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest with the contents of this article.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAUTHORS CONTRIBUTIONS\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJessica Treeby:\u003c/strong\u003e Investigation, Formal analysis, Writing - Review \u0026amp; Editing. \u003cstrong\u003eSherihan El-Sayed\u003c/strong\u003e: Investigation, Methodology, Software, Formal analysis, Visualization . \u003cstrong\u003eSamuel Morgan\u003c/strong\u003e: Investigation, Validation.\u0026nbsp;\u003cstrong\u003eSophie Maddock\u003c/strong\u003e: Investigation, Validation.\u0026nbsp;\u003cstrong\u003eGeorge Taylor\u003c/strong\u003e: Methodology, Investigation.\u0026nbsp;\u003cstrong\u003eStacey Warwood\u003c/strong\u003e: Methodology, Investigation.\u0026nbsp;\u003cstrong\u003eJulian Selley\u003c/strong\u003e: Methodology, Investigation, Software, Validation, Formal analysis, Data Curation .\u0026nbsp;\u003cstrong\u003eDavid Knight\u003c/strong\u003e: Supervision.\u0026nbsp;\u003cstrong\u003eBenjamin Saer\u003c/strong\u003e: Investigation, Supervision.\u0026nbsp;\u003cstrong\u003eRichard A. Bryce\u003c/strong\u003e: Supervision, Formal analysis, Visualization, Supervision Writing - Review \u0026amp; Editing .\u003cstrong\u003e\u0026nbsp;Jean-Michel Fustin:\u003c/strong\u003e Conceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Visualization, Supervision, Project administration, Funding acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLatini, S., and Pedata, F. (2001) Adenosine in the central nervous system: release mechanisms and extracellular concentrations. \u003cem\u003eJ Neurochem\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 463-484\u003c/li\u003e\n\u003cli\u003eLanznaster, D., Dal-Cim, T., Piermartiri, T. C., and Tasca, C. I. (2016) Guanosine: a Neuromodulator with Therapeutic Potential in Brain Disorders. \u003cem\u003eAging Dis\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 657-679\u003c/li\u003e\n\u003cli\u003eTorres, R. J., and Puig, J. G. 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(2012) FRED and HYBRID docking performance on standardized datasets. \u003cem\u003eJournal of Computer-Aided Molecular Design\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 897-906\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Guanine, purine, JNK, MAPK, Lesch-Nyhan, metabolism","lastPublishedDoi":"10.21203/rs.3.rs-6521885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6521885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The toxicity of purine bases adenine and guanine is mostly recognized when associated with inborn errors of purine metabolism such as Lesch-Nyhan syndrome, and metabolic diseases with a lifestyle component including gout. In these pathologies, the peripheral toxicity of purine bases is attributed to the accumulation of their catabolite uric acid in the kidneys, causing nephrolithiasis or crystalluria, and the joints, causing gout. However, inborn errors of purine metabolism also present neurological and neurobehavioral abnormalities including motor disabilities, seizures, hypotonia and dystonia, and self-injurious behaviour. The mechanisms underlying these pathologies is less well-understood but does not seem to be caused by uric acid.\nIn a different context, adenine and guanine have been shown to be cytotoxic and antiproliferative, highlighting their potential use in cancer chemotherapies, but the underlying mechanisms have not been identified.\nIn our previous investigations, we have shown that adenine, a molecule classified as acutely toxic, is an inhibitor of 1-carbon metabolism and biological methylations. Using the same experimental paradigm based on real-time luminometry with mouse embryonic fibroblasts to probe in real-time the potential biological activity of small molecules, complemented with metabolite quantifications, in silico docking predictions, kinase assays and phosphoproteomics, we now reveal that guanine and to a lesser extend adenine are direct inhibitors of c-Jun N-terminal kinases, which may contribute to their toxicity and to the symptoms of Lesch-Nyhan syndrome.","manuscriptTitle":"Guanine is an inhibitor of c-Jun terminal kinases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 18:22:44","doi":"10.21203/rs.3.rs-6521885/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-09T06:27:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-08T13:09:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-06T16:05:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261348224422865209571371744840917600963","date":"2025-05-30T15:47:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121889751127603315900679445685488145243","date":"2025-05-29T18:42:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-28T00:46:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-28T00:31:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-06T12:16:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-06T04:54:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-24T14:43:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b55e932-d775-4c50-b9ce-c5821f2ccd89","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49249558,"name":"Biological sciences/Biochemistry"},{"id":49249559,"name":"Biological sciences/Biochemistry/Kinases"},{"id":49249560,"name":"Health sciences/Diseases/Metabolic disorders"},{"id":49249561,"name":"Biological sciences/Cell biology"},{"id":49249562,"name":"Biological sciences/Cell biology/Circadian rhythms"},{"id":49249563,"name":"Biological sciences/Cell biology/Mechanisms of disease"}],"tags":[],"updatedAt":"2025-08-18T16:07:26+00:00","versionOfRecord":{"articleIdentity":"rs-6521885","link":"https://doi.org/10.1038/s41598-025-11617-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-11 15:57:29","publishedOnDateReadable":"August 11th, 2025"},"versionCreatedAt":"2025-05-30 18:22:44","video":"","vorDoi":"10.1038/s41598-025-11617-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-11617-3","workflowStages":[]},"version":"v1","identity":"rs-6521885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6521885","identity":"rs-6521885","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}