JMJD5 regulates metabolism by inhibiting the Arginine Methyltransferase PRMT6

JMJD5 regulates metabolism by inhibiting the Arginine Methyltransferase PRMT6 | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results JMJD5 regulates metabolism by inhibiting the Arginine Methyltransferase PRMT6 Zaid A Khan , Jair Marques Junior , Edward J Jarman , Philippe Gautier , Chinmay Pednekar , View ORCID Profile Luke Boulter , View ORCID Profile Alexander von Kriegsheim doi: https://doi.org/10.1101/2025.09.27.678987 Zaid A Khan 1 Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh , Edinburgh, EH4 2XR, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jair Marques Junior 1 Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh , Edinburgh, EH4 2XR, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Edward J Jarman 1 Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh , Edinburgh, EH4 2XR, UK 2 Leicester Cancer Research Centre, University of Leicester , Leicester, LE1 5WW, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Philippe Gautier 3 MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh , Edinburgh, EH4 2XR, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chinmay Pednekar 1 Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh , Edinburgh, EH4 2XR, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luke Boulter 1 Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh , Edinburgh, EH4 2XR, UK 3 MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh , Edinburgh, EH4 2XR, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luke Boulter For correspondence: alex.vonkriegsheim{at}ed.ac.uk luke.boulter{at}ed.ac.uk Alexander von Kriegsheim 1 Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh , Edinburgh, EH4 2XR, UK 2 Leicester Cancer Research Centre, University of Leicester , Leicester, LE1 5WW, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexander von Kriegsheim For correspondence: alex.vonkriegsheim{at}ed.ac.uk luke.boulter{at}ed.ac.uk Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract 2-Oxoglutarate-dependent Dioxygenases (2OGDDs) are a family of enzymes requiring molecular oxygen, 2-oxoglutarate, reduced iron, and ascorbic acid to function. This dependency renders them key sensors of the cell’s metabolic state, driving crucial functions when oxygen or metabolic homeostasis is perturbed, including adaptation to low oxygen, epigenetic control of gene transcription, and the reshaping of metabolic pathways. Jumonji-C (JmjC) domain-containing protein 5 (JMJD5), a 2OGDD that alters epigenetic marks, is essential for DNA damage repair and is a key regulator of cell metabolism. Notably, JMJD5 is often lost in hepatocellular carcinoma, which correlates with poor overall survival. Despite its biological significance, the molecular functions of JMJD5 remain unresolved, and its physiological targets are elusive. Here, we identify and characterise a novel signalling pathway where JMJD5 hydroxylates an arginine residue on the protein ISY1. This modification enables ISY1 to bind to and reduce the activity of Protein Arginine N-methyltransferase 6 (PRMT6). Significantly, the inactivation of PRMT6 rescues the majority of the molecular phenotype driven by JMJD5 loss, establishing the JMJD5-ISY1-PRMT6 pathway as the principal executor of JMJD5’s enzymatic function. This signalling pathway clarifies existing controversies regarding JMJD5’s function and identifies PRMT6 as a potential therapeutic target for treating cancers that lack JMJD5. Introduction 2-Oxoglutarate-dependent dioxygenases (2OGDDs) are a diverse family of enzymes crucial for cellular metabolism, epigenetic regulation, and the response to environmental stress ( 1 , 2 ). These enzymes depend on molecular oxygen, 2-oxoglutarate (2OG), reduced iron (Fe2+), and ascorbic acid as cofactors, making them intrinsically sensitive to cellular metabolic and oxygen states. Among these, Jumonji-C (JmjC) domain-containing protein 5 (JMJD5), also known as KDM8, has emerged as a versatile enzyme with significant roles in both normal and pathological conditions. In normal physiology, JMJD5 is vital for embryogenesis, circadian rhythm regulation, DNA damage repair, cell cycle control, and metabolism ( 3 – 6 ). In C. elegans JMJD5’s hydroxylase activity was shown to be required for efficient DNA damage repair, and loss of JMJD5 resulted in hypersensitivity to ionising radiation ( 7 ). In humans, inactivation of its hydroxylase activity leads to severe failure to thrive, intellectual disability, and facial dysmorphism associated with increased DNA replication stress ( 8 ). Depletion in mice leads to severe developmental defects, including embryonic lethality and growth retardation, which may be linked to the dysregulation of p53 ( 9 ). Taken together, these data highlight JMJD5’s role in modulating chromosome stability and metabolic pathways to maintain cellular homeostasis. JMJD5 is highly expressed in liver tissues, with its expression being nearly absent elsewhere. Given this restricted expression, JMJD5’s role in cancer is context-dependent. For instance, in cancers such as lung, breast, colon, prostate, and oral cancer, JMJD5 promotes tumour progression by inhibiting apoptosis, regulating cell cycle progression, enhancing glucose metabolism via the Warburg effect, and facilitating metastasis ( 10 – 13 ). Conversely, in hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC), JMJD5 is often lost during tumour progression, an event associated with a poor prognosis ( 14 , 15 ). This highlights its tumour-suppressive functions in tissues where it has high basal expression. Despite the significant physiological roles of JMJD5, our mechanistic understanding of its functions is both limited and controversial. Although initially identified as a potential histone demethylase targeting H3K36me2 ( 13 ), recent evidence suggests this effect may be indirect and that JMJD5 may have distinct functions. While some studies propose that JMJD5 acts as an arginine-directed peptidase targeting the N-terminus of histones ( 16 , 17 ), the mechanism remains unclear. Structurally, JMJD5 clusters within the protein hydroxylase group of 2OGDDs, and there is compelling biochemical evidence that JMJD5 is indeed a protein hydroxylase. It exhibits arginine hydroxylase activity in vitro, catalysing the stereospecific C3 hydroxylation of arginyl residues in peptides from RCCD1 and RPS6 ( 18 ). However, despite clear activity in vitro, these hydroxylation sites have not been confirmed in cells, suggesting these proteins may not be physiological substrates. While JMJD5 has clear physiological and pathophysiological phenotypes, insights into its role are limited by a lack of mechanistic understanding of its functions. Here, we aim to address this knowledge gap by characterising the role of JMJD5 in liver cells and tumours and elucidating the signalling network that executes these functions. Results To identify pathways regulated by JMJD5 activity in liver cells, we used the hepatocellular carcinoma cell line HepG2 as a model system, as it has not lost endogenous JMJD5 expression. Using CRISPR/Cas9, we knocked out JMJD5 and subsequently rescued the loss by virally transducing either wild-type (WT) turboID-tagged JMJD5 or an enzymatically dead H321A mutant, which cannot chelate the Fe2+ required for activity ( 13 ). The expression level of the rescue constructs was equivalent to endogenous levels, although the H321A mutant consistently expressed at a higher level ( Fig. 1a ). This suggests that overexpression of enzymatically competent JMJD5 may be detrimental, whereas higher levels of the inactive form can be tolerated. Download figure Open in new tab Figure 1: JMJD5 activity regulates metabolic networks in HepG2 cells. a) Expression levels of JMJD5 in HepG2 cells in WT, JMJD5 -/- and cell lines reconstituted with wt (RE) or H321A (MT) JMJD5 b) Heatmap of proteins which significantly change in WT/JMJD5 -/- cells, in triplicates and z-normalised c) GO Biological Processes (GOBP) enriched in protein cluster suppressed by JMJD5 loss d) String Network of suppressed cluster, colour coding of GOBP as indicated e) GOBP enriched in protein cluster induced by JMJD5 loss f) String Network of induced cluster, colour coding of GOBP as indicated JMJD5 loss regulates metabolic networks in HepG2 cells We then quantified the proteome of these four cell lines by mass spectrometry (Data Table 1 ). The loss of JMJD5 altered the expression of a surprisingly large proportion of the proteome. The majority of these changes were rescued by re-expressing WT JMJD5, whereas the H321A mutant failed to rescue the proteome-wide expression changes ( Fig. 1b ). This demonstrates that the enzymatic activity of JMJD5 is essential for driving the majority of its biological functions. Closer inspection of the pathways and networks dysregulated by the loss of JMJD5 revealed that its absence reduced the expression of proteins involved in RNA metabolism, DNA metabolism, and damage repair ( Fig. 1c & d ). Conversely, JMJD5 loss resulted in the induction of proteins involved in numerous metabolic pathways, including those for lipids, amino acids, and sulphur compounds ( Fig. 1e &f). The latter sparked our interest, as sulphur-compound metabolism is prominently involved in one-carbon metabolism and is essential for generating methyl donors for methyltransferases. Given the proposed connection of JMJD5 with regulating histone methylation, we took a closer look at these proteins. We identified a core network of induced proteins that included BHMT and BHMT2 ( Fig. 1g & h ). These enzymes are essential regulators of the methionine cycle in the liver, where they regenerate methionine by catalysing the transfer of a methyl group from betaine to homocysteine ( 19 ). In addition to upregulating BHMT/2, we detected the upregulation of other methyltransferases, such as Glycine N-methyltransferase (GNMT) ( Fig. 1f ). Together, these data suggested that JMJD5 may regulate the epigenetic landscape by altering one-carbon metabolism and the expression levels of key methyltransferases. JMJD5 interacts with ISY1 in an enzyme-activity-dependent manner Our next aim was to identify a mechanistic link between JMJD5’s enzymatic activity and the induction of these metabolic and methylation regulators. Since the WT, but not the enzymatically dead mutant, could rescue the observed changes in BHMT/GNMT expression, we hypothesised that the signalling downstream of JMJD5 must involve a direct substrate of the enzyme. To identify likely substrates, we designed an unbiased interaction proteomic screen based on a substrate-trap approach we previously used to successfully identify substrates of the related hydroxylase FIH ( 20 ). In this screen, we used the inhibitor DMOG to trap the substrate-enzyme complex and included the H321A mutant as a control, as its inability to chelate the catalytic iron ion likely reduces its affinity for substrates ( Fig. 2a ). We transfected Flag-tagged WT or H321A JMJD5, or a vector control, into HEK293 cells in the presence of either DMSO or DMOG. Download figure Open in new tab Figure 2: JMJD5 forms a complex with ISY1. a) Schematic cartoon of substrate-trapping approach b) Right-hand half of Volcano plot JMJD5 WT DMOG/negative Control c) Right-hand half of Volcano plot JMJD5 WT DMOG/JMJD5 H321A DMOG d) Right-hand half of Volcano plot JMJD5 WT DMOG/JMJD5 WT DMSO e) Alphafold model of trimeric complex JMJD5/RCCD1/ISY1 f) Zoom of the JMJD5 active site model illustrating the location of ISY1 R3 g) Quantification of Strepavidin pulldown of cells expressing either a turbo-BirA Turbo ID (NT_TID) or a BirA-Turbo-JMJD5 (JMJD5_TID) construct. Bargraph of ISY1 expression levels as determined by label-free mass spectrometry (LFQ), n=3 h) Cross links detected between JMJD5, RCCD1 and ISY1 as determined by xiSEARCH and visualised by xiVIEW. ISY1 n-terminal crosslink to amino acid 350 (n-t-ISY1-350JMJD5) of JMJD5 highlighted in yellow i) Superimposition of n-t-ISY1-350JMJD5 crosslink onto Alphafold structure ISY1 is purple, JMJD5 is green, Mn2+ is purple dot Comparison of the co-immunoprecipitated complexes from WT JMJD5 versus the control with DMOG treatment identified the broad interactome of JMJD5 ( Fig. 2b , Data Table 2 ). Reassuringly, JMJD5/KDM8 itself and its best-characterised binding partner, RCCD1, were both identified as top interactors. This short-list of interactors was then cross-referenced with two additional comparisons: WT vs H321A ( Fig. 2c ) and WT JMJD5 with vs without DMOG ( Fig. 2d ). We focused on interactors that were enriched with DMOG treatment and showed reduced binding to the H321A JMJD5 mutant. In both comparisons, the putative splicing factor ISY1 was the most significantly enriched protein, suggesting it may be a direct JMJD5 substrate. Although this interaction has not been previously characterised, it was identified in a yeast-two-hybrid screen, suggesting the proteins may bind directly ( 21 ). To generate a structural hypothesis for the complex, we used AlphaFold-3 ( 22 ) to model an interaction between JMJD5, RCCD1, and ISY1 ( Fig. 2e ). Closer inspection of the model revealed that the N-terminus of ISY1 was positioned in close vicinity to the active site, particularly near the transition metal ion (Mn2+) that we included in the model ( Fig. 2f ). The model also predicted two hydrogen bonds between ISY1 and JMJD5 (R3-S318 and K7-E238). To validate this predicted complex, we used several orthogonal methods. Unfortunately, we failed to detect the endogenous complex, even in the presence of DMOG. However, we were able to confirm the interaction using proximity biotinylation catalysed by a turboID-JMJD5 chimaera expressed at near-endogenous levels in our JMJD5 knock-out rescue cells ( Fig. 2g and see Fig. 1a ). Furthermore, to test the prediction that the N-terminus of ISY1 lies near the JMJD5 active site, we employed cross-linking mass spectrometry. We co-transfected RCCD1, EGFP-JMJD5, and ISY1 into HEK293 cells, immunoprecipitated the EGFP-JMJD5 complexes, and cross-linked them using the enrichable cross-linker PhoX ( 23 ). Analysis of the cross-linked peptides revealed one link between the N-terminus of ISY1 and amino acid 350 in JMJD5 (Extended Fig. 1 ). Overlaying this cross-link onto our structural model showed that it was indeed compatible with the AlphaFold prediction ( Figs. 2h & i ), lending further support to our model. JMJD5 hydroxylate Arginine 3 on ISY1 Having partially validated the structure of the complex, we next devised an unbiased screen to identify post-translational modifications on ISY1 that correlate with the co-expression of active JMJD5. Again, using HEK293 cells, we expressed ISY1 in the presence of either WT or H321A JMJD5. We immunoprecipitated ISY1 and, being agnostic to the type or location of modification, used an "open" search strategy to identify all peptides derived from it ( 24 ). We then compared the levels of ISY1 peptides that were differentially abundant between the two conditions. This revealed two peptides with statistically different levels ( Fig. 3a , Data Table 3): an N-terminal peptide (lacking the initial methionine and with N-terminal acetylation) was more abundant when co-expressed with H321A JMJD5, whereas the same peptide harbouring an additional hydroxylation on Arginine 3 (R3) was more abundant with WT JMJD5 ( Fig. 3b ). Assuming roughly equal ionisation efficiencies, we estimated the occupancy of this hydroxylation site to be around 80% when WT JMJD5 was co-expressed ( Fig. 3c ). Comparison of the MS/MS spectra between the hydroxylated and non-hydroxylated peptides confirmed the localisation of the +16 Da modification to R3 ( Fig. 3d ). These data strongly suggest that catalytically active JMJD5 induces R3 hydroxylation on ISY1 to a high stoichiometry. Download figure Open in new tab Figure 3: JMJD5 hydroxylates Arginine 3 of ISY1 a) Volcano plot of ISY1 peptides detected in HEK293 cells co-expressing either JMJD5 WT (JMJD5wt) or JMJD5 H321A (JMJDmut) and quantified by Fragpipe, n=3 b) Bargraph of two peptides differentially expressed in HEK293 cells co-expressing JMJD5wt JMJD5mut c) Bargraph estimated ISY1 hydR3 occupancy in HEK293 cells co-expressing JMJD5wt JMJD5mut d) Representative MS/MS of R3 hydroxylated or non-hydroxylated n-terminal ISY1 peptide Acetyl-ARNAEK from a, 5 ppm mass error e) Quantification of Acetyl-ARNAEK or R3 hydroxylated Acetyl-ARNAEK peptide identified in endogenous ISY1 immunoprecipitated from WT or JMJD5 -/- (JMJD5 KO) HepG2 cells f) Representative MS/MS of R3 hydroxylated or non-hydroxylated n-terminal ISY1 peptide Acetyl-ARNAEK from e, 5 ppm mass error JMJD5 has also been suggested to act as an arginine-directed peptidase. To investigate this, we repeated our search using a semi-specific algorithm that could identify peptides with alternative N-termini. Interestingly, we identified two such peptides, both of which were significantly induced by WT over H321A JMJD5 (Extended Figs. 2a & b). However, the abundance of these cleaved peptides was minuscule compared to the full-length peptide (Fig. S1b), suggesting that this was either a side-reaction of JMJD5 or that the hydroxylation might induce a secondary interaction with a peptidase that becomes rate-limiting under overexpression conditions. To determine if R3 hydroxylation is the terminal modification or if it triggers cleavage endogenously, we immunoprecipitated ISY1 from WT and JMJD5 knock-out HepG2 cells. In WT cells, we were able to identify the hydroxylated peptide but not the non-hydroxylated version. In contrast, in the JMJD5 knock-out line, we identified both forms but with reduced hydroxylation levels ( Figs 3e & f , Data Table 4). Critically, we were unable to detect any peptides consistent with N-terminal cleavage in either condition. This strongly suggests that R3 hydroxylation is catalysed by JMJD5, is present on the endogenous protein, and is the final modification. The residual hydroxylation seen in the knock-out line is likely because it is a cell pool, with approximately 5-10% of cells still expressing JMJD5 that could hydroxylate R3 during the immunoprecipitation protocol, or, alternatively, another hydroxylase may catalyse the reaction at a lower level. Hydroxylation of ISY1 induces an interaction with PRMT6 Hydroxylation sites on amino acid side chains often alter protein-protein interactions, as famously exemplified by the hydroxylation-dependent binding of HIF1α to VHL ( 25 , 26 ). We therefore hypothesised that the hydroxylation of R3 on ISY1 may create or destroy a protein binding site. To identify such hydroxylation-dependent interactions, we devised another interaction-proteomic screen. We transfected HEK293 cells with either WT ISY1 (in the presence of WT or H321A JMJD5 to modulate hydroxylation) or with ISY1 point mutants (R3A, R3K) that cannot be hydroxylated. The presence or absence of hydroxylation on R3 did not fundamentally alter the overall interactome of ISY1, with most interactions retained regardless of hydroxylation status ( Figs 4a & b , Data Table 5). To pinpoint differential binders, we generated a short list of high-confidence ISY1 interactors and used an ANOVA test to find proteins whose binding varied significantly across our conditions ( Fig. 4c ). Download figure Open in new tab Figure 4: R3 Hydroxylation induces a PRTM6/ISY1 complex a) ISY1 interactome in HEK293, right-hand half of Volcano plot ISY1 co-expressing WT JMJD5 over a negative control co-expressing JMJD5, n=3 b) Same as a, only co-expressing H321A JMJD5, n=3 c) Heatmap of ISY1 interacting proteins whose interaction was significantly altered in the indicated groups, n=3, ANOVA corrected q<0.05 d) Western Blot of WT and JMJD5 -/- HepG2 cells, blotted with indicated antibodies. Reassuringly, this analysis confirmed that ISY1 bound to JMJD5/KDM8 and that this interaction was reduced with the H321A mutant. Importantly, mutation of R3 to either A or K completely ablated the binding between ISY1 and JMJD5 ( Fig. 4c ), indicating that this specific residue is essential for the interaction and indirectly confirming that R3 is the major hydroxylation site. We then focused on proteins that bound ISY1 in a hydroxylation-dependent manner. We were unable to detect any proteins that bound specifically to non-hydroxylated ISY1. However, two proteins, APOH and PRMT6, co-immunoprecipitated with ISY1 only when it was hydroxylated ( Fig. 4c ). APOH (apolipoprotein H) is predominantly a plasma protein and an unlikely physiological binder for the nuclear protein ISY1, suggesting it is a spurious interaction. In contrast, Protein Arginine N-methyltransferase 6 (PRMT6) is a predominantly nuclear protein and a known regulator of the epigenetic landscape ( 27 – 29 ). Moreover, PRMT6 has been shown to drive hepatic lipogenesis and alter metabolism as well are regulate DNA-damage repair ( 30 – 32 ), a phenotype reminiscent of pathways that we detected to be impacted upon JMJD5 loss. To explore the structural basis of this interaction, we generated an AlphaFold model of the PRMT6-ISY1 complex (Extended Data 3a). The model predicted a close association of the N-terminus of ISY1 with the C-terminal domain of PRMT6 (Extended Data Fig. 3b), regardless of hydroxylation status. However, when mapping intermolecular hydrogen bonds, the model suggested that the hydroxylation of R3 induces a new hydrogen bond between the N-terminus of ISY1 and PRMT6, potentially explaining why the modification is required for the interaction (Extended Data Fig. 3c). We attempted to validate this complex using cross-linking mass spectrometry but were unable to identify credible linked peptides, possibly due to a weak or transient interaction. PRMT6 is the enzyme that predominantly catalyses the asymmetric dimethylation of arginine (ADMA) residues in proteins. To test whether JMJD5 loss might alter PRMT6 activity, we compared global ADMA levels in WT and JMJD5 knock-out HepG2 cells. We observed that ADMA levels were broadly upregulated in the knock-out cell line ( Fig. 4d ), suggesting that the loss of JMJD5 leads to a gain-of-function or disinhibition of PRMT6 activity. JMJD5 and PRMT6 are components of a novel signalling pathway To determine whether ISY1 and PRMT6 were essential effectors downstream of JMJD5, we aimed to knock them out in both the WT and JMJD5 knock-out cell lines. Loss of ISY1 was lethal in HepG2 cells, consistent with its classification as a common essential gene in the DepMap database ( 33 , 34 ), precluding further study. PRMT6, on the other hand, was readily knocked out by CRISPR. We hypothesised that if PRMT6 is a key downstream effector of JMJD5, then its loss should either rescue or phenocopy the signalling events caused by JMJD5 loss. To test this, we generated two additional cell lines by knocking out PRMT6 in both WT and JMJD5 knock-out HepG2 cells (Extended Fig. 4 ) and analysed the proteomes of all four lines. We first identified proteins whose expression was significantly regulated by the loss of JMJD5 and then assessed how the additional loss of PRMT6 impacted their expression ( Fig. 5a , Data Table 6). While loss of PRMT6 in the WT background had almost no overlap with the JMJD5-loss signature, its ablation in the JMJD5 knock-out background rescued around half of the expression changes induced by JMJD5 loss. We isolated two clusters of proteins: those induced by JMJD5 loss and rescued by PRMT6 loss, and those suppressed by JMJD5 loss and rescued by PRMT6 loss. Gene Ontology analysis of the suppressed-rescued cluster revealed a high enrichment for proteins regulating RNA metabolism and DNA repair ( Fig. 5b ), while the induced-rescued cluster was enriched for sulphur amino acid/compound and lipid metabolism ( Fig. 5c ). This demonstrates that these expression changes are dependent on the JMJD5-PRMT6 pathway. Closer inspection revealed that BHMT and BHMT2, key nodes in liver-specific one-carbon metabolism, were among the proteins whose induction was rescued by PRMT6 loss ( Fig. 5d ). Download figure Open in new tab Figure 5: PRMT6 is a major downstream effector of JMJD5 function a) Heatmap of proteins which significantly change in WT/JMJD5 -/- cells, in triplicate and z-normalised. Green-encircled clusters are rescued by PRMT6 knock-out b) GOBP enriched in protein cluster reduced by JMJD5 loss and rescued by concurrent knockout of PRMT6 c) GOBP enriched in protein cluster induced by JMJD5 loss and rescued by concurrent knockout of PRMT6 d) Protein expression intensities of three proteins involved in the methylation cycle, BHMT/2 and CBS, as determined by quantitative mass spectrometry (QuantUMS), in the four cell lines n=3 e) Western Blot of four HepG2 cell lines, as indicated, blotted with indicated antibodies f) Heatmap of global histone PTM analysis, of modifications significantly regulated by JMJD5 loss across four HepG2 cell lines as indicated, n=3, q<0.05, z-value normalised g-i) Bargraphs of occupancy of selected histone PTM sites as determine by Epiprofile. To confirm that the increase in ADMA upon JMJD5 loss was driven by PRMT6, we performed western blotting on the cell lysates ( Fig. 5e ). As seen before, loss of JMJD5 induced ADMA, and loss of PRMT6 alone marginally reduced ADMA levels in WT cells. Crucially, knocking out PRMT6 in the JMJD5-deficient background completely rescued the ADMA induction, demonstrating that PRMT6 is the primary effector of ADMA methylation driven by JMJD5 loss. Finally, we addressed the long-standing observation that JMJD5 loss induces H3K36me2. This histone mark induction is a consistent phenotype of JMJD5 loss across multiple systems ( 3 , 7 , 13 ), though it is now assumed to be an indirect effect ( 18 ). We sought to determine if we could recapitulate this in HepG2 cells and whether it was dependent on PRMT6. Using a protocol for unbiased profiling of histone PTMs ( 35 ), we found that several H3 PTMs were indeed induced by JMJD5 loss, including methylation on K27, K36, K56, and K79. Significantly, the simultaneous loss of PRMT6 was able to at least partially rescue some of these marks, including H3K79me2, H3K56me2, and partially, H3K36me2 ( Figs 5f - 1 ). Overall, these data demonstrate that a significant proportion of the signalling downstream of JMJD5 is dependent on PRMT6, establishing it as a major effector of the pathway. JMJD5 loss regulates liver tumour growth in a genetically engineered murine model Having established that JMJD5 loss impacts several metabolic pathways frequently dysregulated in cancer, we next wanted to determine whether loss of JMJD5 affects hepatic tumour growth in vivo. JMJD5 has a complex, context-dependent role, acting as either a tumour suppressor or promoter. To determine the relevant context for human HCC, we interrogated the LIHC TCGA database and found that tumours with low JMJD5 expression frequently had mutations in β-catenin and p53 (Extended Data Fig.5). The link to p53 is plausible, as JMJD5 is required for efficient DNA damage repair, and its loss in mice induces p21 and is embryonically lethal, suggesting that p53 ablation may be necessary to tolerate JMJD5 loss. Therefore, to recapitulate this common mutational background, we used a mouse model where p53 was ablated and truncated active CTNNB1 (N90 β-catenin) and c-Myc were expressed ectopically ( Fig. 6a ). Download figure Open in new tab Figure 6: JMJD5 loss alters liver tumour growth and signalling in vivo a) Schematic depiction of experiment b) Representative images of dissected livers of murine models, genetic perturbations as indicated c) Weight as % of total body weight of dissected livers, groups as indicated, n=6 d) Representative H&E histology slice of liver, bottom image is zoom of red rectangle e) Heatmap of proteins which significantly change comparing livers form animal exposed to a scrambled (sgScramble) or a JMJD5-specific gRNA (sgJMJD5), q<0.05, n=6 & 5 and z-normalised f) String Network of cluster induced in sgJMJD5 livers, colour coding of GOBP as indicated in g g) GOBP enriched in protein cluster induced by JMJD5 loss, highlighting lipid and sulphur metabolism normalised h) String Network of cluster suppressed in sgJMJD5 livers, colour coding of GOBP as indicated in i i) GOBP enriched in protein cluster suppressed by JMJD5 loss, highlighting secreted lipoprotein transport proteins j) Violin plot of ADMA expression in livers, determined by Western blotting, n= 6 and 5 Loss of JMJD5 significantly reduced the size and tumour burden of the liver ( Figs 6b & c ). In the livers expressing JMJD5, the majority of the tissue appeared cancerous, whereas the knock-out of JMJD5 reduced the tumour burden of the tumour, with a significant proportion of cells resembling untransformed hepatocytes ( Fig. 6d ). To determine which molecular signalling pathways were dependent on JMJD5 in this murine HCC model, we dissected the HCC tissue and analysed it by proteomics (Data Table 7). Differential expression analysis revealed two clusters of proteins dependent on JMJD5 expression ( Fig. 6e ). Similar to our observations in HepG2 cells, proteins induced upon JMJD5 loss in our in vivo model included those regulating lipid and sulphur compound metabolism ( Figs 6f & g ). Conversely, proteins associated with plasma lipoprotein and lipid transport were significantly downregulated ( Figs 6 h &i ). Overall, at the network level, we observed a high degree of consistency for JMJD5-dependent molecular pathways between HepG2 cells and our genetically engineered murine HCC model. Finally, to determine whether ADMA was regulated by JMJD5 loss in vivo, we quantified it by Western blotting and saw that loss of JMJD5 induced an uptrend of ADMA expression ( Fig. 6 ). JMJD5 loss in liver tumour disrupts one-carbon metabolism, redox balance, amino acids metabolism and lipid homeostasis To investigate the metabolic consequences of JMJD5 deficiency in the liver tumour tissues, we integrated proteomics (Data Table 7), metabolomics (Data Table 8) and lipidomics data (Data Table 9). This combined analysis revealed alterations in redox regulation, one-carbon metabolism, amino acid utilisation, urea cycle and lipid remodelling. Joint pathway analysis of up-regulated significant proteins and metabolite changes identified several significantly dysregulated metabolic pathways, including retinol metabolism, Linoleic acid metabolism, glutathione metabolism, steroid biosynthesis, and cysteine and methionine metabolism ( Fig. 7a ). Retinol metabolism enrichment was supported by the coordinated upregulation of multiple retinol and retinal-processing enzymes, including Rdh7, Rdh16f2, Sardh, and Adh4 ( Fig. 7b ). Adh4 catalyses the oxidation of retinol to retinal, providing substrate for retinoic acid synthesis ( 36 ), a potent regulator of hepatic lipid metabolism and RXR-mediated transcriptional programs that influence peroxisomal function( 37 ). The concurrent elevation of Rdh7 and Rdh16f2 further supports enhanced retinoid turnover, potentially contributing to the transcriptional changes and lipid remodelling observed in JMJD5-deficient livers. In parallel, proteomic analysis revealed broad activation of the glutathione S-transferase (GST) family, with 10 isoforms significantly up-regulated in JMJD5-deficient livers ( Fig. 7c ). GSTs are canonical phase II detoxification enzymes with established roles in carcinogen metabolism and oxidative stress defence( 38 ). Download figure Open in new tab Figure 7: JMJD5 loss in liver tumour disrupts metabolism in vivo a) Joint pathway analysis of up-regulated significant proteins (p <0.05) and metabolite changes (p<0.05) showing the top 10 metabolic pathways b) Expression levels of enzymes involved in retinol metabolism c) Expression levels of glutathione S-transferases d) Area ratio of s-adenosyl methionine (SAM) / s-adenosyl homocysteine (SAH) e) Area ratio of glutathione di-sulfide (GSSG) / Glutathione (GSH) f) Area ratio of adenosine triphosphate (ATP) / adenosine diphosphate (ADP) g) Area ratio of glutamine / glutamate h) log2 quantUMS intensity of Gls2 i) Area ratio of NADP+ / NADPH j) Area ratio of succinate / fumarate k) Area ratio of lactate / pyruvate l) Area ratio of serine / glycine m) Normalised area of acetyl-CoA n) log2 quantUMS intensity of Acly o) log2 quantUMS intensity of Fasn p) log2 quantUMS intensity of Acox1 and Ehhadh q) log2 quantUMS intensity of Acss2 and Acss3 r) String analysis cluster enriched for the GO term “Plasma lipoprotein particle remodeling. Quantification of metabolite ratios revealed significant increases in SAM/SAH, GSSG/GSH, ATP/ADP, PCr/Cr, and Glutamine/Glutamate in the JMJD5-deficient group ( Fig. 7d-g ). These changes reflect elevated methylation potential, increased oxidative stress, and heightened energy potential. Additionally, proteomic changes revealed the upregulation of Gpx1, consistent with increased peroxide detoxification. The increase in Glutamine/Glutamate, alongside Gls2 upregulation ( Fig. 7h ), suggests elevated glutamine uptake and/or accelerated downstream glutamate utilisation for TCA cycle anaplerosis or glutathione synthesis. Conversely, the NADP⁺/NADPH, Succinate/Fumarate, and Serine/Glycine ratios were reduced ( Fig. 7i-l ), consistent with altered TCA cycle dynamics, and altered one-carbon flux. Proteomic analysis revealed upregulation of urea cycle enzymes Arg1, Ass1 and Asl (Data Table 7) and the metabolic profiling confirmed perturbations in amino acid metabolism, with L-arginine and L-cystine exhibiting the most severe depletion. Arginine reduction aligns with increased flux through the urea cycle, while cystine reduction may reflect redox stress and increased glutathione demand. The serine/glycine axis was also disrupted, with reductions in both serine and glycine, suggesting a broader inhibition or rerouting of one-carbon metabolism. The urea cycle intermediate citrulline was slightly depleted, while guanidinosuccinic acid, a marker of elevated nitrogen flux, was strongly elevated, reinforcing the activation of nitrogen disposal mechanisms. Together, the significant increase in acetyl-CoA levels ( Fig. 7m ) and the upregulation of ATP citrate lyase (Acly) ( Fig. 7n ) indicate enhanced cytosolic conversion of citrate to acetyl-CoA, substrate for histone acetylation and fatty acid synthesis, the latter supported by the up-regulation of fatty acid synthase (Fasn) ( Fig. 7o ), fuelling lipid remodelling. Within the TCA cycle, intermediates including malate, succinate, and fumarate were diminished. These data suggest a metabolic rewiring where glutaminolysis and amino acid catabolism are activated, yet TCA cycle intermediates were reduced in spite of the lower succinate/fumarate ratio and high ATP/ADP, suggesting a functional ETC and cataplerosis. JMJD5 deficiency remodels the lipidome Lipidomic profiling uncovered a systematic shift in lipid composition. Among the phospholipids, phosphatidyl choline (PC), phosphatidylethanolamine (PE) and phosphatidyl inositol (PI) species were broadly increased. However, the very long-chain polyunsaturated PC 44:6, PC 44:10, and PE 40:5 was selectively decreased (Extended Data Fig. 6a-c). Phosphatidylethanolamine N-methyltransferase (Pemt), which uses SAM to methylate PE to form PC was significantly elevated, linking one-carbon metabolism to phospholipid biosynthesis. Cardiolipins (CL), especially with high linoleic acid content CL(18:2_18:2_18:2_18:2), were elevated, consistent with mitochondrial membrane adaptation to oxidative stress (Extended Data Fig. 7a). Sphingomyelins (SM) and ceramides (Cer) were also increased (Extended Data Fig. 7b-c). Cholesteryl esters (CE) were significantly reduced, and triacylglycerols (TG) exhibited a chain-length–dependent pattern with an increase in shorter-chain TGs, while long-chain TGs were consistently decreased (Extended Data Fig. 8a-b). The decrease in very long chain fatty acids (VLCFA) lead us to interrogate the proteomics data, and we found significantly increased levels of Acox1 and Ehhadh ( Fig. 7p ), the first and second step in the peroxisomal oxidation of VLCFA, respectively. The peroxisomal β-oxidation of VLCFA exports acetate ( 39 ), later converted to acetyl- CoA in the cytosol by Acss2, and mitochondria by Acss3 ( 40 ), both found increased ( Fig. 7q ), revealing a broad effort to deal with peroxisomal and mitochondrial products. Furthermore, STRING cluster analysis using the down-regulated proteins revealed a group of apolipoproteins and lipid-metabolising enzymes (Apoa4, Pltp, Apom, Lpl, Pla2g7) enriched for the GO biological process “plasma lipoprotein particle remodelling” ( Fig. 7r ), indicating coordinated suppression of lipoprotein metabolism in JMJD5-deficient liver. Together, the lipidomics data revealed a JMJD5-dependent program regulating phospholipid remodelling, VLCFA peroxisomal β-oxidation and fatty acids de novo biosynthesis. Discussion Mutations in several 2-oxoglutarate-dependent dioxygenases (2OGDDs) have been linked to various genetic diseases. For example, mutations in prolyl-hydroxylases are directly linked to diseases through the misregulation of collagen hydroxylation ( 41 – 43 ), providing a clear connection between the loss of hydroxylase activity and the resulting phenotype. Conversely, mutations in JMJD5 cause severe phenotypes ( 8 ), but the direct mechanistic link between the genotype and phenotype has remained elusive. Here, we’ve uncovered a novel signalling pathway that helps explain how inhibiting JMJD5 profoundly impacts the proteome and metabolome. Our data suggest that many of JMJD5’s effects are transduced via a signalling pathway that requires its hydroxylase activity. Specifically, we’ve shown that hydroxylation-dependent complex formation between ISY1 and PRMT6 is a key step. When hydroxylation is reduced, ISY1 fails to sequester PRMT6, which somehow increases PRMT6 activity. This is likely the pathway’s "branch point," as numerous proteins are regulated by asymmetric dimethylarginine (ADMA) modifications driven by PRMT6 ( 44 ). Intriguingly, there’s a significant overlap between the phenotypes of JMJD5 loss and PRMT6 activation, suggesting a causal link between these proteins in a broader physiological context. Furthermore, by identifying PRMT6 as a downstream effector of JMJD5 loss, we can speculate that tumours with low JMJD5 levels could be susceptible to PRMT6 inhibitors, which have been developed as anti-neoplastic agents ( 45 ). The strong phenotype observed from the loss of JMJD5 in our genetically engineered mouse model (GEMM) was unexpected. It dramatically curtailed liver tumour growth, which contradicts what has been observed in human liver tumours, where JMJD5 is frequently lost and low expression is correlated with poor survival ( 14 ). One possible explanation is our model’s staggeringly rapid nature, which required animals to be culled just two weeks after hydrodynamic tail vein injection. As loss of JMJD5 has been shown to induce replication stress and reduce DNA-damage repair, this may explain why a rapid and highly proliferative tumour model would be inhibited by JMJD5 loss. Alternatively, JMJD5 expression might have a "Goldilocks" level that is conducive to tumour growth, and a complete knockout could be anti-tumorigenic. 2OGDDs are a broad family of enzymes whose reliance on metabolites, iron, and molecular oxygen gives them a unique ability to sense the cell and organism’s metabolic and oxygenation states. In this study, we investigated JMJD5’s function using knockout experiments, which provided a broad overview of the signalling pathways it can regulate. While near-complete loss of JMJD5 activity has been shown to occur in genetic diseases as well as hepatic and pancreatic tumours, its precise physiological role remains to be addressed. A notable feature of the 2OGDD family is that some branches, such as the PHD/EglN prolyl-hydroxylases, act as exquisite oxygen sensors because their K m values for molecular oxygen fall within the physiological normoxic to hypoxic range ( 2 ). Other 2OGDDs are more sensitive to changes in metabolite levels, such as product inhibition by succinate or fumarate or substrate inhibition by 2-oxoglutarate (2OG). We don’t have accurate K m values for JMJD5, but the enzyme is closely related to FIH, for which we do have data. Although FIH is a HIF hydroxylase, its activity is remarkably impervious to reduced oxygen levels and product/substrate inhibition by succinate or 2OG. However, FIH activity is highly sensitive to the R - and S -isoforms of 2-hydroxyglutarate (2HG) ( 2 ). R -2HG is an oncometabolite produced at low levels under normal conditions but is strongly induced by oncogenic mutations of isocitrate dehydrogenase (IDH1 and IDH2) ( 46 ). S -2HG, on the other hand, can be generated by malate dehydrogenase or under hypoxic and acidic conditions by lactate dehydrogenase, which catalyses the reduction of 2OG to S-2HG ( 47 ). Assuming that JMJD5 has similar enzymatic affinities, its activity dynamics may be similar to FIH. The induction of S -2HG, driven by lower pH and high LDH activity due to prolonged hypoxia, could be what drives the metabolic adaptations we observed upon JMJD5 loss. While not investigated in detail here, we’ve observed that inactivating JMJD5 induces proteomic changes that indicate it may drive cellular metabolism toward glucose consumption and, rather than shunting carbons toward lactate secretion, promote lipogenesis. This is a process that can generate ATP without oxygen consumption, similar to glycolysis. Whether this occurs physiologically is a topic for future investigation. Our integrated metabolomic, lipidomic and profiling in the GEMM revealed that JMJD5 loss in the liver extends its impact beyond classical one-carbon metabolism and methylation dynamics to retinol metabolism and peroxisomal β-oxidation, suggesting a coordinated transcriptional–metabolic program. The coordinated upregulation of Adh4, Rdh7, and Rdh16f2 indicates an increase in retinal biosynthesis, supplying substrate for RA synthesis. Retinoic acid is a potent ligand for nuclear receptors, including RAR and RXR, which can heterodimerise with PPARα, a master transcriptional regulator of peroxisomal, mitochondrial and microsomal fatty acid oxidation( 48 ) ( 37 , 49 ). Consistent with enhanced PPARα signalling, proteomics revealed significant upregulation of the peroxisomal rate-limiting β-oxidation enzyme Acox1 ( 50 ) and EhhadH, which catalyse the second step of very-long-chain fatty acid degradation. The lipidomic signature revealed the depletion of VLC-PUFA-containing phospholipids (PL) and long to VLCFA-containing TGs, a picture that aligns with enhanced peroxisomal chain shortening and redistribution of fatty acyl flux. The simultaneous increase in acetyl-CoA pool, Acly, and Fasn levels suggests that carbons released from peroxisomal β-oxidation are recycled into lipogenesis, potentially fuelling membrane biogenesis in a methylation-driven context, as supported by elevated SAM/SAH and Pemt expression. In parallel, JMJD5-deficient tumours exhibited broad upregulation of glutathione S-transferases (GSTs), canonical phase II detoxification enzymes that protect against oxidative damage and xenobiotics. Nuclear receptor signalling provides a plausible mechanistic link as the PPARγ–RXR heterodimer has been shown to induce GST expression in response to retinoid agonists such as 9-cis-retinoic acid, which can interconvert with all-trans-RA ( 51 ). Given that RXR is an obligate heterodimeric partner for both PPARγ and PPARα, retinoid-mediated modulation of RXR activity may integrate metabolic and detoxification programs, positioning retinoid–PPAR signalling as a central coordinator of both fatty acid catabolism and antioxidant defence in JMJD5-deficient liver. STRING cluster analysis of down-regulated proteins identified a cluster enriched for the GO biological process “plasma lipoprotein particle remodelling.” This suppression of apolipoproteins and lipid-processing enzymes suggests reduced HDL biogenesis, which may diminish the delivery of fatty acids from the plasma compartment. Such changes may shift the balance of lipid trafficking toward intracellular handling, promoting internal lipid remodelling mechanisms. The integration of redox imbalance, one-carbon metabolism, and lipid remodelling further supports a model in which JMJD5 loss creates a metabolic state optimised for coping with oxidative stress while enabling fatty acid turnover. Retinoic acid–PPAR signalling is known to induce antioxidant genes ( 52 ) alongside peroxisomal β-oxidation enzymes, and our finding of increased GPX1 expression alongside elevated GSSG/GSH fits this adaptive framework. Thus, JMJD5 deficiency appears to shift hepatic metabolism toward a retinoid-driven, PPAR-activated peroxisomal lipid catabolic program, tightly coupled to methyl donor availability and membrane phospholipid remodelling and lipid storage landscape. This raises the intriguing possibility that JMJD5, through PRMT6 inhibition, may restrain retinoid metabolism and peroxisomal β-oxidation, maintaining a balance between lipid catabolism, methylation potential, and redox homeostasis. Loss of this restraint could predispose cells to altered lipid composition and turnover, with potential consequences for membrane integrity, lipid signalling and organelle function. Methods Reagents All reagents were from Sigma if not otherwise stated. Solvents were LC-MS grade Cell lines HEK293t were grown in DMEM 4.5 g/l glucose supplemented with 10% foetal bovine serum and 2mM glutamine, at 37°C and 5% CO2. HepG2 as above, only in 1g/l glucose Plasmids Flag-myc-ISY1 was purchased from Origene. JMJD5 plasmids (wt and H321A) were a kind gift from Yoshihiro Izumiya ( 13 ). Flag-RCCD1 GFP-KDM* was a kind gift from Profs Sun and Wang ( 53 ), CRISPR/Cas9 View this table: View inline View popup Table 1: gRNA guides targeting mouse Jmjd5. Primary antibodies View this table: View inline View popup Download powerpoint Western blot analysis Cleared lysates were resolved on SDS-PAGE acrylamide gels and transferred onto PVDF membranes (Whatman) using Mini Trans-Blot® Electrophoretic Transfer Cell (BIO RAD). Membranes were blocked in 4% BSA for 1h and blotted with primary antibodies (see paragraph below). Immunocomplexes were visualized using ClarityTM Western ECL Substrate (BioRAD) in a ChemidocTM MP (BioRAD) with horseradish peroxidase–conjugated secondary anti-bodies (CST 1:10000). WB quantification was performed using ImageJ. All immunoblots experiments were repeated at least three times (n=3). Gene editing in cells crRNAs (Alt-R CRISPR-Cas9 crRNA) targeting the genes of interest were designed and ordered from IDT (Table #) along with tracrRNA (Alt-R CRISPR-Cas9 tracrRNA) Cas9 (Alt-R™ S.p. Cas9 Nuclease V3; cat# 1081058) and electroporation enhancer (Alt-R™ Cas9 Electroporation Enhancer, 2 nmol; cat# 1075915). Equimolar concentrations of the tracrRNA and crRNA were mixed in a sterile PCR tube and duplex was formed by heating it up at 95°C for five minutes and allowing it to cool down to room temperature. The ribonucleoprotein complex (RNP) was formed by adding 100pmol Cas-9 and 100pmol electroporation enhancer to 120pmol of the duplex and incubating it for ten minutes at room temperature. Cells were grown to 80% confluency in a suitable dish, trypsinised, washed with PBS and counted. Nucleofection was carried out using the SF Cell Line 4D-Nucleofector® X Kit (V4XC-2032). 0.1×106 cells were suspended in 20uL SF Cell Line Nucleofector® Solution supplemented with Supplement 1 and pipetted into the Nucleocuvette® Strip. The RNP was pipetted into the cell suspension and the mixed carefully. The RNP was transfected into the cells using the Lonza 4-D Nucleofector® (AAF-1003X) using the EH-100 program. The cells were immediately supplemented with 80uL fresh media and pipetted into 6-well plates. Cells were incubated for 48-72 hours and media was changed after 24 hours. Knockout was confirmed by western blot, whole expression proteomics or IHC. CRISPR guides table View this table: View inline View popup Download powerpoint Table 2: gRNA guides targeting human Jmjd5. Animal experiments Short guides (sg) sequences targeting the Trp53 gene (p53-SBCrispr), Jmjd5 gene (JMJD5-SBCrispr) (3 unique guides per gene, see table 1 ) or non-targeting short guides (Scramble-SBCrispr) were cloned into CRISPR-SB plasmid (Addgene #177936). Female FVB/N mice from Charles River (4-6 weeks of age) were given hydrodynamic tail vein injections containing DNA plasmids in physiological saline solution at a volume of 10% bodyweight (maximum 2 ml) as previously published ( 54 ). Mice were divided into two groups of 6 mice each. They were injected with solutions containing pCMV(CAT)T7-SB100 (6 µg, Addgene #34879), pT3-N90-beta-catenin (20 µg, Addgene #31785), p53-SBCrispr (20 µg), c-myc-PT3EF1a (20 µg, Addgene #92046) and Scramble-SBCrispr (20 µg) (sgScramble) or JMJD5-SBCrispr (20 µg). After 2 weeks, mice were euthanised by CO2 and death was confirmed by dislocation of the neck. Livers were perfused with PBS. The liver lobes were separated, and the left lobe was fixed with 4% paraformaldehyde overnight. The right, median and caudate lobes were dissected and frozen using isopentane cooled to -78.5°C using dry ice. Expression Proteomics HepG2 cells and liver protein pellets were processed following the PAC digest protocol ( 55 ). Cell were lysed with the PAC lysis buffer (5% SDS, 100 mM Tris pH 8.5, 1 mg/ml chloroacetamide, 1.5 mg/ml Tris (2-carboxyethyl) phosphine). They were heated at 95 degrees for 30 minutes and then sonicated. Lysate was then added to a Kingfisher 96 well deep well plate (Thermo Fisher, UK) and prepared for an 8h digest protocol on the Kingfisher Duo (Thermo Fisher, UK). Lysate was added to Row G with HILIC beads (MagReSyn, UK) and ACN is added to 70% final concentration. Rows D, E, and F are filled with 95% ACN and Rows B and C are filled with 70% EtOH. The digest buffer (1 ug/ml MS grade trypsin in 50 mM Triethyammonium bicarbonate) is added to Row A. Desalted peptides were then loaded onto 25cm Aurora Columns (IonOptiks, Australia) using a RSLC nano uHPLC systems connected to a Fusion Lumos mass spectrometer. Peptides were separated by a 70 min linear gradient from 5% to 30% acetonitrile, 0.5% acetic acid. The mass spectrometer was operated in DIA mode, acquiring a MS 350-1650 Da at 120k resolution followed by MS/MS on 45 windows with 0.5 Da overlap (200-2000 Da) at 30 k with a NCE setting of 28. Files were searched using DIA-NN 1.8 or 2.0 ( 56 ) against the human Uniprot database and a list of common contaminants using pre-set settings. Statistical test and analysis was done on Perseus ( 57 ). Interaction Proteomics Cells were lysed in lysis buffer (1% NP40, 150 mM NaCl, 2 mM EDTA, 1 mM PMSF, 20 mM Tris pH 7.5). Lysates were cleared by centrifugation and pulldowns were performed in a Kingfisher Duo (Thermo Scientific). Cleared lysates were incubated with 5µL of protein G Mag Sepharose™ Xtra beads (GE Healthcare) and 1µg of antibody anti-ISY1 or antibodies against EGFP, Myc (both Chromotek, UK) or Flag (Sigma, UK)pre-coupled to magnetic agarose beads for 1 hour, washed two times Lysis Buffer and three times in TBS and resuspended in 100 µl digestion buffer (2 M urea, 50 mM Tris-HCL pH7.5, 1 mM DTT) with 0.25µg of porcine trypsin MS-grade (Promega). Samples were digested at 37°C for 8 hours. Cysteines were alkylated with iodacetamide (1mg/ml), 30min. and acidified with TFA (final concentration 1% v/v). Peptides were desalted and purified using C18 STAGE tips. The eluted peptides were lyophilised in a vacuum concentrator (LabConco), resuspended in 0.1% TFA and analysed by LC-MS/MS on a Fusion Lumos Mass Spectrometer (Thermo Fisher) using OT/OT DDA acquisition, 120k MS and 30k MS/MS resolution.. Label-free quantification approach was used for protein identification and quantification using FragPipe software ( 58 ). Alternatively, for the ISY1 interactome, LC-MS/MS analyses were performed on a timsTOF SCP Mass Spectrometer (Bruker) coupled to an Evosep One LC system (Evosep). We utilised Aurora Elite CSI analytical columns (IonOpticks, AUR3-15075C18-CSI) and a CaptiveSpray ionisation source. Samples were analysed using the Whisper 40 SPD zoom method, which employed a gradient flow of 200 nL/min with a 31 min method duration. Eluted peptides were analysed with a parallel accumulation-serial fragmentation data-independent acquisition (diaPASEF) method. Isolation windows were optimised for low-input samples using an open-source Python package for dia-PASEF methods with Automated Isolation Design (py_diAID) ( 59 ) Data acquisition: employed a window placement scheme consisting of 2 TIMS ramps with 12 mass ranges per ramp spanning from 327 to 1200 m/z and from 0.6 to 1.60 1/K0 and searched using Spectronaut 20 (Biognosis). Crosslinking Mass Spectrometry Immunoprecipitated complexes were incubated with PhoX (Thermo Fisher) at 2 μg/ml in PBS and rotated at RT for 1 hour. Crosslinker was quenched using 50 mM ammonium bicarbonate for 5 minutes and digested as described above. Crosslinked peptides were enriched as described ( 23 ) but using Zr IMAC beads (MagResyn) rather than Fe. Enriched peptides were loaded onto Evotips and analysed on a timsTOF SCP as described above, but using a DDA PASEF method with 200 ms accumulation, 10 PASEF elutions and a window excluding likely singly charged peptides. The raw data was searched against the human proteome in Fragpipe ( 24 ) to generate a calibrated mxXML that was converted into mgf using ProteoWizard MSConvert ( 60 ). The spectra were then analysed using xiSEARCH ( 61 ) with MS1/MS2 error tolerances of 14 and 15 ppm, respectively. The search was performed with carbamidomethylation of cysteine as a fixed modification and methionine oxidation as a variable modification. The PhoX cross-linker was defined to be reactive with K,S,T,Y residues and protein N-termini, with a score penalty for matches to S,T,Y residues. Modifications related to the cross-linker included linked PhoX (+209.97181 Da), hydrolysed PhoX (+227.98237 Da) and amidated PhoX (+226.99836 Da), searched on protein N-termini and K,S,T,Y residues of linear peptides. Results were then filtered in xiFDR to a 5% false discovery rate at the residue pair level and exported to xiView.org for visualisation. Histone PTM analysis Histones were purified from HepG2 cells as described ( 35 ). Histones were extracted by acid extraction using 0.2 M sulfuric acid. The extracted histones were propionylated and digested with trypsin using a modified bottom-up proteomic approach. Briefly, the histones were first reacted with propionic anhydride to block lysine □-amino groups. Following an overnight trypsin digestion, the resulting peptides were subjected to a second propionylation step to modify the newly formed N-termini. The peptides were then purified and analysed by LC-MS/MS on a Fusion Lumos Mass Spectrometer (Thermo Fisher) using OT/OT DIA acquisition, 60k MS and 30k MS/MS resolution and 50 Da windows. Data was analysed using the Matlab Epiprofile script. Extraction of protein, metabolites and lipids from the liver Fresh frozen liver tissue was weighed and homogenised in methanol (1:20 w/v) using the soft tissue setting on the Precellys homogeniser (cat# P002511-PEVT0-A.0). Methyl tert-butyl ether (MTBE) was added to the methanol in a 3:1 ratio and centrifuged at maximum speed at 4°C for 10 minutes. The supernatant was moved to a new tube, and the pellet was lysed using lysis buffer (5% SDS, 100 mM Tris-HCl pH 8.5, 1 mg/ml CAA, 1.5 mg/ml TCEP), proteins were extracted using the PAC digest protocol mentioned above and LC-MS/MS was performed as mentioned above. LC-MS grade water was added to the supernatant (1:1 water to methanol) and vortexed to mix. The tube was centrifuged at max speed at 4°C for 10 minutes. The solution, now separated into two phases, was pipetted put carefully to collect only the upper phase and moved to a new tube. The lower phase, now containing metabolites was chilled at -80°C for ten minutes to precipitate any remaining protein. The tube was then centrifuged at max speed at 4°C for 10 minutes. 30-50uL of supernatant was used for LC-MS as mentioned above. The upper phase, now containing lipids, was dried using a vacuum centrifuge at room temperature. The dried lipids were reconstituted in minimum volume (100mL) of 1:1 methanol : butanol v/v. The tube was chilled at -80°C for ten minutes to precipitate any remaining protein. The tube was then centrifuged at max speed at 4°C for 10 minutes. 30-50uL of supernatant was used for LC-MS as mentioned above. LC-MS ZIC-pHILIC LC column with Q Exactive or Q Exactive Plus MS (Short Run) were used. SeQuant ZIC-pHILIC guard column 20 x 2.1mm and SeQuant ZIC-pHILIC analytical column 150 x 2.1mm, 5µm were used. 20mM ammonium carbonate, adjusted to approximately pH 9.2 with ammonium hydroxide solution (25%) was used as mobile phase A and 100% acetonitrile for mobile phase B. Positive/negative mass spectrometry was used with no lock masses. The scan range was 70-9000 m/z, and the resolution was 70,000 with the maximum IT of 250ms. Data Analysis The chromatogram was analysed, and the ratios were obtained using Skyline (MacCoss Lab Software). Metaboanalyst or Metabolite AutoPlotter was used to generate graphs. TCGA analysis Normalised STAR-counts of RNA-seq data from the TCGA-LIHC study was used to stratify the data into two groups based on JMJD5 expression using the mclust script ( 62 ). This resulted in 281 low JMJD5 and 93 high JMJD5 samples. 5 samples in the low group and 1 sample in the high group were excluded for having high variance in the data. Mutational data from the study was analysed using the maftools package ( 63 ). 200 FLAG genes which were mutated in both groups were also eliminated from the analysis. The final results were 37 genes frequently mutated in JMJD5 low group (figure). Beta catenin (CTNNB1) was the most frequently mutated gene when JMJD5 was lost in hepatocellular carcinoma patients. Data analysis Statistics Bioinformatic analysis was done on Perseus. Proteomics data was log2 transformed, proteins with x<3 missing values in at least one group were excluded. The remaining missing values were imputed using a random distribution as implemented in Perseus, statistical tests were either ANOVA or Student’s t-test, and multiple testing was corrected using permutation as implemented in Perseus. Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange with the dataset identifier PXD067356 Extended Data Extended Data Fig. 1: MS/MS of crosslinked peptide. Representative MS/MS peptide identified as being crosslinked Extended Data Fig. 2: Non-canonical n-terminal peptides of ISY1. a) Volcano plot of ISY1 peptides detected in HEK293 cells co-expressing either JMJD5 WT (JMJD5wt) or JMJD5 H321A (JMJDmut) and quantified by Fragpipe, n=3 b) Bargraph of three ISY1 n-terminal peptides expressed in HEK293 cells co-expressing JMJD5wt JMJD5mut Extended Data Fig. 3: AlphaFold model of a) dimeric complex ISY1/PRMT6. ISY1 R3 and residues involved in hydrogen-bridges are shown b) AlphaFold score and interaction matrix from a c) Zoom of ISY1 R3 of AlphaFold model of dimeric complex ISY1-hydroxylatedR3/PRMT6. Residues involved in hydrogen bridges are shown Extended Data Fig. 4: Expression of JMJD5 and PRMT6 in HepG2 cell lines a) Bar graph of JMJD5 and PRMT6 expression levels as determined by mass spectrometry Extended Data Fig. 5: Gene mutation enriched in HCC expressing low vs. high JMJD5 mRNA: JMJD5 expression in the TCGA-LIHC data set was used to stratify two groups based on JMJD5 (281 Low and 93 High JMJD5). The table shows 37 genes frequently mutated and enriched (OR) in JMJD5 low. P-Value is adjusted significance * <0.05; **<0.01 Extended Data Fig. 6: Scatter plots of lipid species grouped by class: a) Phosphatidylcholines (PC), b) Phosphatidylethanolamines (PE), and c) Phosphatidylinositol (PI). For each panel, the x-axis shows total acyl-chain length (sum of carbons across both/acyl chains) and the y-axis shows log□ fold change (sgJMJD5 / Scramble). Individual points represent measured lipid species. Nodes colour encodes degree of unsaturation. Nodes size is proportional to statistical significance, plotted as –log□□(p-value). Positive values indicate increased abundance in sgJMJD5 liver relative to control; negative values indicate decreased abundance. Extended Data Fig. 7: Scatter plots of lipid species grouped by class: a) Cardiolipins (CL), b) Sphingomyelins (SM), and c) Ceramides (Cer). For each panel, the x-axis shows total acyl-chain length (sum of carbons across both/acyl chains) and the y-axis shows log□ fold change (sgJMJD5 / Scramble). Individual points represent measured lipid species. Nodes colour encodes degree of unsaturation. Nodes size is proportional to statistical significance, plotted as –log□□(p-value). Positive values indicate increased abundance in sgJMJD5 liver relative to control; negative values indicate decreased abundance. Extended Data Fig. 8: Scatter plots of lipid species grouped by class: a) Cholesterol Esters (CE), and b) Triacylglycerols (TG). For each panel, the x-axis shows total acyl-chain length (sum of carbons across both/acyl chains) and the y-axis shows log□ fold change (sgJMJD5 / Scramble). Individual points represent measured lipid species. Nodes colour encodes degree of unsaturation. Nodes size is proportional to statistical significance, plotted as –log□□(p-value). Positive values indicate increased abundance in sgJMJD5 liver relative to control; negative values indicate decreased abundance. Acknowledgements ZK is supported by the Melville Trust – For the Care and Cure of Cancer PhD studentship. AvK acknowledges the MRC Equipment grant (MR/X01293X/1), BBSRC Alert (BB/X019160/1) and Wellcome Trust Multi-user Equipment Grant (grant ID: 208402/Z/17/Z). EJ is supported by a CRUK BTP grant: (PRCBTP-May24/100001) and a PSC Support research 668 grant (EJPROJ23). A Cancer Research UK Fellowship (C52499/A27948 and PRCBTP-May24/100001) and MRC project grant (MR/Z506199/1) funds LB. 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Share JMJD5 regulates metabolism by inhibiting the Arginine Methyltransferase PRMT6 Zaid A Khan , Jair Marques Junior , Edward J Jarman , Philippe Gautier , Chinmay Pednekar , Luke Boulter , Alexander von Kriegsheim bioRxiv 2025.09.27.678987; doi: https://doi.org/10.1101/2025.09.27.678987 Share This Article: Copy Citation Tools JMJD5 regulates metabolism by inhibiting the Arginine Methyltransferase PRMT6 Zaid A Khan , Jair Marques Junior , Edward J Jarman , Philippe Gautier , Chinmay Pednekar , Luke Boulter , Alexander von Kriegsheim bioRxiv 2025.09.27.678987; doi: https://doi.org/10.1101/2025.09.27.678987 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cell Biology Subject Areas All Articles Animal Behavior and Cognition (7622) Biochemistry (17648) Bioengineering (13870) Bioinformatics (41880) Biophysics (21423) Cancer Biology (18553) Cell Biology (25458) Clinical Trials (138) Developmental Biology (13364) Ecology (19866) Epidemiology (2067) Evolutionary Biology (24290) Genetics (15589) Genomics (22475) Immunology (17711) Microbiology (40327) Molecular Biology (17145) Neuroscience (88472) Paleontology (666) Pathology (2826) Pharmacology and Toxicology (4815) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9815) Zoology (2268)