{"paper_id":"4f4fcfc6-0a3c-4e49-b602-89f40c744f2d","body_text":"1 \nThe 18S rRNA methyltransferase, BUD23, is required for appropriate lipid and \nmitochondrial metabolism \n \nNoelia Martinez-Sanchez1, Anneke Brümmer2,3, Nichola J. Barron4, Daniel B. Rosoff1, \nAngélica Liechti2, Maria Voronkov1,5, Edward A. Hayter4, Sébastien Chamois2, René Dreos2, \nNicolas Guex3, Elspeth Johnson1, Matthew Baxter1, Kerri Smith6, Rebecca C Northeast4, Gina \nGalli6, Leanne Hodson1, David Gatfield2, David W. Ray1, David A. Bechtold4 \n \nAffiliations \n1 National Institute for Health and Care Research Oxford Health Biomedical Research Centre \nand John Radcliffe Hospital, Oxford, UK; Oxford Centre for Diabetes, Endocrinology and \nMetabolism and Oxford Kavli Institute for Nanoscience Discovery, University of Oxford, \nOxford, UK. \n \n2 Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland \n \n3 Bioinformatics Competence Center, University of Lausanne, 1015 Lausanne, Switzerland \n \n4 Centre for Biological Timing, School of Medical Sciences, Faculty of Biology, Medicine and \nHealth, University of Manchester, Manchester, M13 9PT, UK \n \n5 Current address: Proteomics Facility, The Francis Crick Institute, London NW1 1AT, UK \n6 Division of Cardiovascular Sciences, School of Medical Sciences , Faculty of Biology, \nMedicine and Health, University of Manchester, Manchester, M13 9PT, UK  \n Joint senior and corresponding authors \n \nCorrespondence \ndavid.gatfield@unil.ch \ndavid.ray@ocdem.ox.ac.uk \ndavid.bechtold@manchester.ac.uk \n \n  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 2 \nSummary  \nEfficient energy metabolism is essential for health, and its dysregulation drives conditions like \ncardiometabolic disease  and obesity. Delivery of regulatory control through translation and \nribosome function is emerging as important.  Here, we identify the rRNA methyltransferase \nBUD23 as a potent regulator of cellular and systemic energy homeostasis. Adipocyte-specific \ndeletion of BUD23 in mice regulates lipid and mitochondrial metabolism  resulting in a \npronounced lean phenotype and resistance to diet -induced obesity. Mechanistically, BUD23 \nmodulates translation initiation and efficiency of mRNAs with specific features – including short \n5’ UTR length and GC-rich post-initiation codon usage  - characteristic of mitochondrial and \nlipogenic proteins.  Human genetic analyses and drug-target Mendelian Randomisation \nsupport a role for  BUD23 in cardiometabolic traits and diseases. Together, our findings \nuncover a conserved translational control mechanism that regulates energy metabolism and \noffers a potential therapeutic target. \n \nKeywords \nenergy metabolism, ribosome, adipose, liver, translation, RNA methyltransferase, mTOR \n \nIntroduction \nDespite recent advances in weight-loss therapies, maintaining long-term energy balance and \nmetabolic health remains a major challenge. Fundamental questions also persist around the \nmolecular and cellular events which set the metabolic programmes of diverse cell types across \nthe body and how they adapt to nutrient excess or deprivation. While transcriptional regulation \nhas been a m ajor focus, growing evidence points to selective control over messenger RNA \n(mRNA) translation as a key layer of metabolic regulation 1. Ribosomes, traditionally viewed \nas uniform translation machines, are now recognised to exhibit compositional and functional \nheterogeneity, enabling selective translation of specific mRNA subsets tailored to cell identifiy \nand function 2-5.  \nRibosomal RNA (rRNA) plays a central role in ribosomal structure and function, ensuring \nproper positioning of mRNA and transfer RNA (tRNA) to facilitate accurate codon recognition, \nwhich is crucial for maintaining translation fidelity. The small ribosomal subunit (40S) is tasked \nwith decoding the genetic information . Its decoding centre closely monitors the base pairing \nbetween the mRNA codon and the anticodon of the aminoacyl -tRNA, thus ensuring correct \namino acid incorporation into the growing peptide chain. Eukaryotic rRNA harbours over 200 \npost-transcriptionally modified nucleotides 6 – most commonly involving methylation – many \nof which occur at highly conserved and functionally important sites. Recent studies suggest \nthese modifications can be dynamic  7, raising the possiblilty that rRNA modifications are  a \npotential driver of ribosomal heterogeneity 7. However, the functional impact of many such \nmodifications and their potential role in selective translational control remain unclear.  \nBUD23 (also known as WBSCR22/MERM1) is a highly conserved rRNA methyltransferase \nthat has been implicated in the biogenesis and maturation of the 40S subunit  and that acts \ntogether with its obligate dimerization partner, TRMT112, to modify guanosine 1639 (G1639) \non human 18S rRNA to N(7) -methylguanosine (m7G) 8-10. This specific modification has long \nbeen recognised, but remains of unknown functional consequence  to translation. Our prior \nwork demonstrated that global  Bud23 deletion in mice results in embryonic lethality, and its \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 3 \ncardiomyocyte-specific deletion results in early postnatal death from cardiac failure , \nassociated with altered mitochondrial function and insufficiency in cardiomyocyte energy \nproduction 11. These severe phenotypes limited mechanistic exploration.  \nHere, we investigate the role of BUD23 in energy metabolism using tissue-specific deletion in \nadipocytes and hepatocytes – key metabolic cell types. We show that BUD23 is a critical \nregulator of lipid and mitochondrial metabolism, with distinct effects depending on cell context. \nMechanistically, we uncover a BUD23-dependent program of selective translational efficiency. \nBUD23-sensitive transcripts share distinct 5′ mRNA features, incuding short 5′ untranslated \nregions (UTRs), the presence  of upstream open reading frames (uORFs), and high GC \ncontent immediately downstream of the translation start codon. These features are enriched \namong nuclear-encoded mitochondrial genes and lipogenic regulators, explaining metabolic \nphenotypes of BUD23 loss. S tructural modelling of 48S pre-initiation complexes further \nsupports a role for the BUD23 -catalyzed m7G1639 modificaiton in translation initiation, via \ndirect contacts with the initiator tRNA. Finally, human genetic analyses and Mendelian \nrandomization indicate that BUD23 expression is associated with cardiometabolic traits \nincluding hepatic lipid content, body mass index, and obesity. Together, our findings identify \nBUD23-directed rRNA modification as a central regulator of selective translation and cellu lar \nenergy state, with broad relevance to metabolic health and disease. \nResults \nBud23 is essential for normal lipid distribution and storage in white adipose tissue. \nTo interrogate the role of BUD23 in regulating energy metabolism in mammals, we generated \nmice with selective deletion of Bud23 in white and brown adipocytes using established \ntransgenic lines (Bud23fl/fl 11; AdipoqCre 12; Figure S1A,B).  \nUnder ad libitum feeding conditions with standard chow diet, growth and b ody weight were \nsimilar between male mice lacking Bud23 in adipocytes (Bud23fl/fl;AdipoqCre, designated herein \nas Ad KO) and their littermate controls ( Bud23fl/fl, designated Ad WT; Figure 1A ). However, \nanalyses of body composition revealed a striking phenotype, wherein AdKO mice exhibited a \npronounced attenuation of fat mass accumulation and a significant increase in lean mass \nrelative to controls (Figure 1B). This lean phenotype in  AdKO mice was not associated with \naltered food intake ( Figure 1C ), nutrient absorption (as judged by  faecal energy content; \nFigure 1D ), or locomotor activity ( Figure 1E). We did observe a  small, and statistically \nsignificant, increase in daytime b ody temperature in the AdKO mice (Figure S1C). However, \nhousing the animals at thermoneutral  conditions (28°C for 6 weeks) failed to  normalise \ngenotype differences in fat mass (Figure S1D,E), indicating that altered thermoregulation did \nnot underly the altered adiposity of the AdKO mice. A similar lean phenotype was observed in \nfemale AdKO animals (Figure S1F,G). \nWe next profiled metabolic gas exchange and energy expenditure. Despite the lean \nphenotype, AdKO mice presented lower daily energy expenditure (Figure 1F). Furthermore, \nthe mice exhibited a blunted diurnal profile in respiratory exchange ratio (RER) due principally \nto a significant reduction in RER at night relative to control mice (Figure 1G,H). RER broadly \nreflects fuel utilisation (carbohydrate oxidation: RER ~1 vs fatty acid oxidation: RER ~0.7), and \nRER values >1 can indicate elevated rates of lipogenesis  13. Our findings thus  suggest that \nthe mice lacking BUD23 activity in adipocytes exhibit increased reliance on fatty acid oxidation \nand, possibly, reduced rates of de novo lipogenesis (DNL), both of which would contribute to \nthe reduced fat mass. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 4 \nAttenuated whole body fat mass in the AdKO mice was reflected in a significant reduction in \nsubcutaneous and visceral white adipose tissue (WAT) depots , starting  at early age  and \ngreatly accentuated in older mice  (Figure 1B ,I; Figure S1H,I). Interestingly, histological \nanalyses of gonadal WAT (gWAT) sections revealed relatively normal adipocyte morphology \nin the remaining WAT of these animals (Figure 1I, insets; Figure S1J,K). Serum triglycerides \n(TG) were unchanged between genotypes, yet AdKO presented with significantly lower serum \nlevels of non-esterified fatty acids (NEFA) and glycerol , even at relatively early age (Figure \n1J). This suggests that reduced fat storage was not due to excessive lipolysis. To characterize \nadipose function more broadly, we next assessed circulating adipokine levels. In line with the \nlean phenotype, l eptin, adiponectin, and resistin  levels were all reduced in the  AdKO mice  \n(Figure 1K), although the magnitude of effect implicates a more direct impact to adipokine \nproduction. Together, these findings highlight the profound disturbance in WAT function of \nAdKO animals. \nGiven the significant attenuation of lipid storage  in WAT , w e next investigated possible \ncompensatory accumulation of lipids within brown adipose tissue (BAT) and liver. Evident at \nthe point of tissue dissection and further confirmed by histological examination, BAT showed \naccentuated lipid accumulation (“ whitening”; Figure 1L ) in the AdKO mice compared to \ncontrols. Similarly, the livers of these mice showed increased weight and TG content (Figure \n1M,N). These findings suggest that attenuated WAT lipid storage capacity upon loss of BUD23 \nfunction drives excess secondary storage in organs such as liver and BAT.  Stable isotope \nlabelling with ²H₂O (in drinking water, 48 hr) and [¹³C]-labelled D-glucose (bolus administration \nfollowing short-term fasting) was used to assess rates of DNL across these tissues . Higher \nrates of hepatic DNL were confirmed by increased  incorporation of both labels into palmitic \nacid (16:0) in the AdKO mice relative to controls (Figure 1O,P). By contrast, no differences \nwere observed in BAT (Figure 1O,P), although local alteration in rates of DNL may be masked \nby uptake of lipids from the liver . The observed increase in size, TG content , and DNL in the \nliver underscores a shift in lipid metabolism and storage dynamics in response to adipocyte -\nspecific Bud23 deletion. \nIn the AdKO model, the deletion of Bud23 occurs in both white and brown adipocytes. However, \nthe phenotypic outcomes described above revealed striking differences, with attenuated and \naccentuated lipid accumulation  in WAT and BAT , respectively. We hypothesised that the \nwhitening of BAT in the AdKO mice may be secondary to defective lipid storage in WAT. To \naddress this possibility, we generated a Bud23fl/fl;Ucp1Cre mouse line (BAT KO), in which Bud23 \nis selectively deleted from only brown adipocytes. No differences in body weight, adiposity, or \nWAT depot weight  were apparent in the mice  (Figure S2A-D). However, intrascapular BAT \ntissue was significantly smaller and showed no evidence of whitening  in the BAT  KO mice \ncompared with littermate controls (Figure S2E). The animals showed no overt differences in \nmetabolic or thermogenic phenotype (Figure S2F-G). These analyses highlight that while loss \nof Bud23 clearly impacts BAT, the accentuated lipid accumulation in this tissue observed in \nthe AdKO mice is secondary to WAT dysfunction. \nIn an attempt to  drive lipid accumulation in adipose tissues, we placed AdWT and AdKO mice \nonto high-fat diet (HFD ; 60% energy from fat) . As expected, control mice gained significant \nbody weight and fat mass over the 9-week HFD treatment (Figure 1Q-S). In contrast, AdKO \nmice were highly resistant to diet -induced obesity, accumulating significantly less fat  mass. \nThese findings demonstrate that adipocyte-specific Bud23 deficiency impairs WAT lipid \nstorage irrespective of whether the dietary energy source is carbohydrate-rich or fat-rich. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 5 \nIn summary, our results demonstrate that BUD23 is essential for lipid accumulation in WAT. \nThe absence of complete adipocyte loss  implies that explanations, such as defects in cell \ndifferentiation or survival, are unlikely to account for the observations. Instead, BUD23 \nappears to play a role in maintaining long-term lipid storage in WAT and preservation of mature \nwhite adipocyte function. \n \nBud23 is required for white adipocyte function, but not differentiation \nWe next isolated stromal vascular fraction (SVF) cells from gWAT of Ad KO and AdWT mice to \ndirectly examine adipocyte differentiation capacity  in vitro . No defect s in adipocyte \ndifferentiation efficiency, lipid accumulation, or lipid droplet formation in cells derived from AdKO \nanimals were observed  (Figure S3A,B ). Furthermore, within these newly differentiated \nadipocytes, expression levels of endocrine hormones and lipid metabolism  enzymes were \nnormal (Figure S3C). Hence, the lean phenotype observed in AdKO mice in vivo is not due to \nan impaired capacity of adipocytes to differentiate. \nTranscriptomics analyses on gWAT isolated from  ~13-week old AdKO and AdWT mice (thus \nprior to the profound differences in fat mass  that develop with age)  revealed widespread \ndifferential gene expression associated with Bud23 deletion (Figure 2A). We first assessed \nwhether tissue cellular composition contributed to the transcriptional signatures observed. We \ntherefore performed virtual cytometry analyses using CIBERSORT deconvolution  14, and \nleveraging published single-cell RNA-seq data from mouse gWAT 15 (Figure 2B). While these \nanalyses did not reveal profound changes to cell type composition , including adipocyte \nprecursors, notable exceptions emerged. This included a significant reduction in  lipid \nscavenging adipocyte (LSA) and lipogenic adipocyte  (LGA) subpopulations, and  a strong \nincrease in stressed lipid scavenging adipocytes  (SLSA) (Figure 2B ,C). Although these \nanalyses only infer cell ular composition, the results align well with the observed in vivo  \nphenotype. LGAs, which  are characterised by high expression of genes involved in lipid \nbiosynthesis and insulin responsiveness, likely contribute to DNL and efficient lipid storage 15. \nTheir depletion in  AdKO mice, coupled with the concomitant increase in SLSAs, suggests a \nfailure to properly manage and store lipids in the absence of BUD23. \nWe next undertook pathway enrichment on the gWAT transcriptomics. I ngenuity Pathway \nAnalysis (IPA) revealed a striking downregulation of mitochondrial pathways, broadly affecting \nkey metabolic processes, including respiratory electron transport, ATP synthesis, fatty acid β-\noxidation, and branched-chain amino acid degradation  (Figure 2D; Figure S3D). Increased \nmitochondrial dysfunction  was also predicted . Examination of key differentially expressed  \ngenes involved in adipocyte function, lipid metabolism , and mitochondrial activity revealed \ntheir extensive downregulation  in AdKO (Figure 2E). The reduced expression of  adipokine \ngenes, such as Leptin (Lep), Adiponectin (Adipoq) and Resistin (Retn), was also consistent \nwith the observed endocrine profiles of AdKO mice (Figure 1K). Importantly, Bud23 deletion \ndid not  cause uniform repression of all adipocyte-specific or metabolic genes. Instead, \nselective impact to key metabolic pathways, such as fatty acid synthesis , was evident . For \nexample, Fatty acid synthase  (FAS), key enzyme in fatty acid synthesis , exhibited a striking \ndecrease in protein abundance in Ad KO WAT, while other markers remained relatively \nunchanged (Figure 2F). Analysis of mature adipocytes isolated directly from gWAT of Ad KO \nand AdWT mice confirmed that observations made in whole tissue  RNA-seq reflected Bud23-\ndependent reprogramming of white adipocytes per se (Figure 2G). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 6 \nGiven the well -established influence of insulin over adipocyte function, we assess ed insulin \nsensitivity in AdKO and AdWT mice. In vivo, clearance of circulating glucose in response to acute \ninsulin administration was significantly attenuated in the knockout mice when compared to \ncontrols (Figure 2H). In vitro, gWAT explants derived from AdKO mice showed a profoundly \nattenuated induction of Dgat2, Fasn, and Fabp4 following insulin treatment, compared to the \nresponse in AdWT explants (Figure 2I). \nWe concluded from the above experiments that Bud23 exerts profound influence over white \nadipocyte function, yet its effects are selective, with mitochondrial metabolism and lipid \nbiosynthesis as key targets. \n \nBUD23 influence over lipid metabolism and mitochondrial function is conserved across \nwhite and brown adipose \nWhite and brown adipocytes diverge in functional specialisation, with white adipocytes central \nto storage and liberation of energy (principally as TG/ FAs) based on changing energy \ndemands, while brown adipocytes oxidise FAs almost exclusively for the generation of heat. \nTherefore, we next assessed BAT function in the  AdKO mice to identify shared and divergent \nroles of BUD23 between these two adipose depots. \nAs highlighted above  (Figure S1D), Ad KO mice exhibited relatively normal profiles in body \ntemperature suggesting maintained BAT function. This was confirmed through direct \nassessment of interscapular BAT temperature under normal housing conditions (~22°C±2°C; \nFigure 3A,B). Moreover, both genotypes robustly maintained body temperature throughout \nthe cold challenge (Figure 3C,D). In line with robust thermogenic capacity, BAT Ucp1 did not \ndiffer between the genotypes at the level of RNA or protein expression ( Figure S4B, D); \nalthough significant differences were observed in other thermogenesis-related transcripts.  We \ntherefore undertook RNA-seq analyses on BAT tissues derived from Ad KO and control mice, \nwhich identified significant numbers of differentially expressed RNAs ( Figure 3E). IPA \npathways analyses of the RNA-seq data revealed clear enrichment of mitochondrial protein-\nencoding transcripts – characterized by the downregulation of key metabolic pathways such \nas fatty acid oxidation, the tricarboxylic acid (TCA) cycle, the respiratory chain, and branched-\nchain amino acid catabolism (Figure 3F).  \nGiven that BUD23 is an enzyme known to modify rRNA near the ribosomal decoding centre 9, \naltered translation is a likely consequence of Bud23 deletion. Indeed, proteomic analyses of \nidentified widespread differential protein expression between AdKO and Ad WT-derived BAT \n(Figure 3G). IPA pathway analyses revealed a significant enrichment for mitochondrial \nproteins and those involved in branch ed chain amino acid (BCAA) metabolism (Figure 3H). \nDownregulation of BCAA upon loss of Bud23 has been consistently observed between WAT \nand BAT analyses, with pronounced reduction in the expression of key enzymes such as \nBCAT2 observed (Figure S4E). Interestingly, the proteomics analysis of BAT also identified \ntwo ribosome/translation-associated pathways as significantly dysregulated: the Regulation of \neIF4 and p70S6K Signalling  and mTOR Signalling  pathways. Both pathways involve \noverlapping gene sets that include ribosomal protein genes, translation initiation factors , and \nkey signalling components critical for regulating both global and transcript-specific translation \nin response to hormones, nutrients , stress, and other signals. These findings were similarly \nreflected in ontology analyses which highlighted the significant down -regulation of proteins \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 7 \ninvolved in mitochondrial function, lipid metabolism and ribosomal subunit biogenesis (Figure \nS4D). \nAssessment of genotype-specific differences in global protein abundance revealed a particular \nimpact on expression of small subunit ribosomal proteins (RPS) and mitochondrial proteins \n(based on MitoCarta3.0; Figure 3I). RPS protein changes were expected given the known \nrole of BUD23 in small ribosomal subunit maturation 8,16,17, yet the clear impact of Bud23 loss \non mitochondrial genes and proteins prompted us to assess BAT mitochondrial content. This \nrevealed a significant reduction in mitochondrial DNA copy number ( Figure 3J) and reduced \nmitochondrial area assessed using electron microscopy (EM; Figure 3K,L) in Bud23-deficient \nBAT. \nThus, across both WAT and BAT, BUD23 function has relatively select influence over  \nmitochondrial factors and lipid metabolism . Recent evidence highlights an important role for \nmitochondrial dynamics and its physical interaction with the endoplasmic reticulum (ER) and \nlipid droplets (LD) in regulating cellular energy metabolism 18,19 20. Intriguingly, proteins which \nwere significantly down-regulated in BAT of AdKO mice included several factors which dictate \nthese inter-organelle interactions (e.g. MFN1/2, OPA1, MIGA2), and up-regulation in LD coat \nproteins (e.g. PLIN2, PLIN5). \nTogether, our results demonstrate that the loss of Bud23 from adipocytes drives pronounced \nimpact to mitochondrial, lipogenic, and ribosomal protein expression. Given BUD23’s role in \n18S rRNA modification, the observed phenotypes  likely stem from altered translational \ndynamics as a primary mechanism. Our proteomic data are consistent with this model, \nrevealing coordinated shifts in protein abundance consistent with disrupted translation. \nHowever, widespread transcriptional changes and the broad metabolic disruption observed \nupon Bud23 deletion likely inlcude both primary and secondary effects, limiting resolution of \ndirect versus indirect targets in the adipose tissue models. \n \nHepatic t argeting of Bud23 reveals selective impact to energy homeostasis and \nmitochondrial metabolism \nDue to its relatively homogenous cellular composition, large size, and high metabolic activity, \nwe considered the liver well-suited for investigating the  functional impact of  BUD23 on \ntranslation. To this end, we generated a  hepatocyte-specific Bud23 deletion by crossing \nBud23fl/fl mice with the well-established inducible AlbCreERT2 line 21. Tamoxifen administration \nto Bud23fl/fl;AlbCreERT2 (LivKO) and control Bud23fl/fl (LivWT) animals resulted in a robust reduction \nin Bud23 expression in LivKO animals (Figure S5A,B).  \nWe first profiled the LivKO model for overt metabolic phenotypes. Under standard conditions, \nno significant differences between LivKO and LivWT mice were observed for body weight, body \ncomposition, energy expenditure , RER, or food intake (Figure 4A-F). Interestingly, despite \nthis overall metabolic similarity, hepatic TG levels were elevated in LivKO mice (Figure 4G). \nNotably, in response to metabolic challenges driving either positive or negative energy balance \n(HFD-feeding and fasting, respectively) , Bud23-dependent phenotypes emerged. Firstly, \nduring 8 weeks of HFD feeding, Liv KO and LivWT mice showed similar profiles of body weight \ngain (Figure 4G), yet hepatic TG levels were still elevated in Liv KO (Figure 4H). Secondly, \nwhen subject to an extended fast (48 h), Liv KO mice exhibited  significantly attenuated -\nhydroxybutyrate production, despite showing similar levels of body weight loss and \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 8 \nhypoglycaemia (Figure 4K-L). Both findings are consistent with altered mitochondrial activity \n(e.g. -oxidation, BCAA) and lipid handling.  \nGiven our findings in WAT and BAT, we examined the impact of Bud23 loss on mitochondrial \ncontent, structure  and function  in the livers of Liv KO and Liv WT mice. Firstly, overall \nmitochondrial copy number was unchanged (Figure S5C). However, EM analyses revealed a \nslight reduction in mitochondrial area in LivKO, although this difference did not reach statistical \nsignificant (Figure 4M,N). Given that mitochondrial morphology is associated with changes in \nfunction 22,23, we next assessed isolated hepatic mitochondria l respiration  using the \nOROBOROS platform (Figure 4O-Q). Respiration rates measured in isolated mitochondria in \nthe presence of complex I substrates and saturating levels of ADP (State III respirat ion; \nOXPHOS) were comparable between genotypes. However, a significant elevation in State IV \nrespiration (LEAK) reflecting proton leak -driven respiration was evident in the mitochondria \nisolated from LivKO mice. This in turn resulted in a significantly reduced respiratory control ratio \n(RCR), strongly suggesting that mitochondrial ATP production will be less efficient in Liv KO \ntissue, driven by an increase in proton leak.  \nCollectively, our findings demonstrate that mitochondrial d ysfunction and lipid metabolism \nalterations are common features of Bud23 deficiency across tissues. \n \nHepatic Bud23 loss drives selective impact on translation \nWe next interrogated the molecular mechanism of BUD23 action in the liver . To assess its \nimpact on translation, we quantified mRNA abundances and ribosome occupancy using RNA-\nseq and ribo-seq, respectively, in liver tissue from LivKO and LivWT animals. To capture early \neffects in translational reprogramming while minimizing secondary effects, we used a relatively \nearly timepoint (3 weeks post-tamoxifen induction), and given the well-documented rhythmicity \nof hepatic metabolic activity, gene expression, and translation 24, we collected samples at two \ntimes of day corresponding to fasted (Zeitgeber Time ZT6; mid-light phase) and fed (ZT18 ; \nmid-dark phase) states (Figure S6A). Principal component analyses (PCA) revealed clear \nseparation based on genotype (LivKO vs LivWT; PC1), followed by time-of-day (ZT6 vs ZT18; \nPC2) for the RNA-seq (Figure S6B) and ribo-seq (Figure S6C) datasets. In contrast, the PCA \nfor translation efficiencies (TE s; ratio of ribo-seq to RNA -seq counts ) indicated a strong \ngenotype effect, with less influence of time (Figure S6D). Therefore, unless otherwise noted, \nsubsequent analyses were performed using combined timepoint data. \nRNA-seq and ribo-seq analyses revealed widespread Bud23-dependent differential gene \nexpression and ribosome occupancy  (Figure 5A,B ). A subset of genes exhibited altered \nribosome engagement, and thus significant change in TE (decreased TE: n=105; increased \nTE: n=179; Figure 5C). GO term analyses of each dataset identified key functional transcript \ngroups (Figure 5D). Notably, genes encoding ribosomal proteins (RPs) and other ribosome-\nrelated factors  were associated with increased  transcript abundance  and TE, possibly \nreflecting a compensatory response to inactivation of BUD23. In contrast, downregulated \ntranscripts were enriched for  metabolic and mitochondrial functions . T hese metabolic GO \nterms did not reach statistical significance for the differential TE cluster, although several \nindividual genes of note did emerge including Agpat2, Agpat3, Sirt3, RXRa. \nWe further examined translational changes  across all nuclear -encoded genes specifying \nmitochondrial proteins (from MitoCarta 3.0  25). RNA-seq (Figure 5E), ribo-seq (Figure 5F), \nand TE ( Figure 5G ) all revealed significant downregulation in Liv KO samples for this \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 9 \nmitochondrial gene set , whereas ribosome-related transcripts showed  strong upregulation, \nparticularly evident in TE . Many mRNAs encoding RPs and translation factors contain 5’ -\nterminal oligopyrimidine (TOP) motif s, which are key targets of  mTOR-mediated translation \nregulation, allowing cells to adapt protein synthesis to metabolic/environmental changes 26. \nUnder normal conditions, RP mRNAs are highly abundant and exhibit rhythmic translation, \nwith lower translation rates during the fasting (light) phase and upregulated translation upon \nfeeding in the dark phase 24,27,28. We found Bud23 deficiency to affect strongly this regulation; \nthus, i n Liv WT tissue, the TEs for a set of 5’ -TOP mRNAs showed significant time -of-day \ndependent increase (from ZT6 to ZT18), consistent with mTOR -driven translational control \nassociated with feeding state (Figure 5H ). In LivKO samples, TEs were strikingly elevated \nacross both timepoint s and the rhythmic patterns was strongly blunted . This suggests  that \nBUD23 function is required for normal  translational regulation of 5’-TOP mRNAs. \nFor TE calculations, ribosome footprint reads are counted over the entire CDS, without \nconsidering local variations in ribosome occupancy. However, changes in ribosome dwelling \ncan provide deeper insights into translation regulation and kinetics. To assess whether BUD23 \ninfluences specific decoding events, we applied a pipeline to infer elongation dynamics based \non transcriptome-wide codon occupancies associated with the ribosomal E-, P-, and A-sites \n29. This analysis confirmed known differences in decoding speeds across codons in both LivKO \nand Liv WT samples, but revealed only minor genotype-associated changes (Figure S6E,F), \nsuggesting that overall translation elongation dynamics remain largely robust . A metagene \napproach was next used to visualize transcriptome-wide ribosome footprint distributions along \nthe 5′ UTR, CDS , and 3′ UTR.  This analysis revealed two striking differences between  \ngenotypes (Figure 5I). First, in LivWT, we observed the expected low, but readily detectable \nfootprint coverage on 5′ UTRs , consistent with pervasive upstream open reading frame \n(uORF) translation and as seen in  previous liver ribo -seq datasets 24. In contrast, LivKO \nsamples showed a significant depletion of  5′ UTR-mapping reads (Figure 5I,J). Second, the \nLivWT metagene plot displayed the characteristic “translational ramp” – a region of elevated \nribosome density at the beginning of the CDS, which gradually declines as elongation \nprogresses (Figure 5I). This ramp was largely absent in LivKO samples (Figure 5I, inset), with \nsignificantly reduced ribosome occupancy within the first 20 codons post-initiation (Figure 5J). \nThe reduced 5′ UTR occupancy and loss of the ramp in Liv KO suggests a disruption in early \nribosome dynamics, reinforcing the idea that BUD23 influences translation initiation rather \nthan elongation. \nTo understand how BUD23 activity may impact  initiation, we examined available ribosomal \nstructures from cryo-electron microscopy datasets capturing key intermediates of the human \ninitiation pathway 30. In these structures, m 7G1639 is located in immediate vicinity to  the P-\nsite-bound initiator-tRNA (Met-tRNAiMet), interacting with C41 of its anticodon stem loo p via \nhydrogen bonding  (Figure 5K , S7). The C41 residue is critical for maintaining the unique \nconformation of initiator -tRNA, distinguishing it from elongator tRNAs, and ensuring proper \nstart codon recognition  31. Our structural analyses indicate that t he N7-methyl group  of \nm7G1639 fills a hydrophobic pocket that helps stabilize this interaction, while the positive \ncharge conferred onto the guanosine ring by the methyl group (quaternary nitrogen  at N7) \nenables cation-π stacking with Trp35 of ribosomal protein sE25/RPS25, particularly in the late \ninitiation complex  48S-5 (Figure 5K ). This interaction likely  further secures G1639 in an \noptimal orientation for initiator -tRNA placement. In the absence of m 7G1639 methylation (as \nin Bud23 knockouts), it is likely that weakened interaction at this site would alter the precision \nand stability of initiator-tRNA engagement at the start codon. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 10 \nIn summary, we conclude d that loss of BUD23 function leads to a  selective changes to \ntranslation, particularly impacting mRNA encoding ribosomal proteins and mitochondrial \ncomponents. Mechanistically, the association of BUD23 loss with lower 5′ UTR occupancy \nand loss of a translational ramp, as well as structural considerations, suggest predominant \neffects on initiation. \n \nGC content, 5′ UTR length and uORF content predict mRNA responsiveness to BUD23 \nand are associated with selective effects on mitochondrial transcripts \nWe next examined whether changes in CDS TE, reduced ribosome occupancy over the first \n60 nt after translation initiation (i.e., absence of ramp; hereafter referred to as ‘Start TE’), and \ndecreased 5′ UTR coverage (“outcomes” in Figure 6A, upper) were associated with specific \ntranscript features that predict mRNA responsiveness to BUD23 activity (“features” in Figure \n6A, lower). Correlation analyses indicated that genotype-related fold changes (FCs) in 5′ UTR \ncoverage, Start TE ( corrected for CDS TE effects), and CDS TE were largely independent \n(Figure S8A-E), allowing them to be handled as individual outcomes. \nWe assessed several transcript characteristics potentially associated with these outcomes \n(Figure 6A , lower).  Specifically, 5′ UTR -related features  included the presence of uORFs  \n(initiated by ATG or C/G/T TG); 5′ UTR length (based on mouse liver CAGE data); and 5′ UTR \nGC content. In the CDS start codon environment , we quantified the Kozak sequence score; \nthree proxies for RNA structure – GC-content in a 60 nt window preceding the initiation codon \n(-60 nt) or following it (+60 nt, ramp region), and RNA folding minimal free energy (MFE) over \nthe 60 nt window ; as well as codon bias (low scores indicate higher proportion of  rare \ncodons). \nTranscriptome-wide analyses showed that lower 5′ UTR  coverage in Liv KO was primarily \ncorrelated with 5′ UTR length and the presence of translated uORFs, and anticorrelated with \n5′ UTR GC content  (Figure 6B, S8F,G). For CDS TE and normalised Start TE we detected \nstrong anticorrelation with GC content (in the 5′ UTR and around the initiation codon), as well \nas with MFE. Kozak score (i.e., CDS initiation codon optimality) and bias in codon composition \nshowed no, or only weak, correlations. As expected, se veral of the  analysed features are \nintrinsically correlated with each other – for example, UTR length and uORF frequency, or GC \ncontent in the 5′ UTR  and around the initiation codon ( Figure S8H). To disentangle these \neffects, we applied linear regression modelling to identify the primary predictors of variance in \nthe translation outcomes. The model identified 5′ UTR GC content as the predominant \npredictive feature throughout (Figure S8I) with variable additional contributions from 5’ UTR \nlength/CAGE distance, RNA folding MFE, and other predictors to lesser extent. \nWe observed globally lower CDS TE for m itochondrial protein mRNAs in Liv KO liver tissue  \n(Figure 5G ). M oreover, the metagene plot on mitochondrial transcripts also revealed \nparticularly marked footprint reduction directly downstream of the CDS start codon (Figure \n6C). We therefore examined if these effects on mitochondrial mRNAs were associated with \nany of the above transcript features. In these analyses (Figures 6D-F, S8J-O), we observed \nthat the GC content in the 60 nt downstream of the start codon ( Figure 6D) was specifically \nelevated among mitochondrial transcripts.  This effect was accompanied by increased RNA \nfolding MFE (Figure S8L) and pronounced codon bias in the first 60 nt of mitochondrial protein \nCDS (Figure 6E). Metagene analyses on transcript sets selected for high vs. low +60 GC \ncontents further revealed the profound influence that GC content after the start codon has over \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 11 \nribosomal occupancies post-initiation (Figure S8P). For mitochondrial transcripts, GC-content \nis indeed specifically increased in the post -initiation region ( Figure S8Q), where codon \ncomposition is biased towards GC-rich codons for alanine, arginine and leucine (Figure 6G). \nThese are common amino acids in the amphip athic α-helices that make up mitochondrial \ntargeting signals 32. Furthermore, the association between high +60 GC content with reduced \nribosome occupancy post-initiation seen across hundreds of mitochondrial protein transcripts \n(Figure 6H) extended to the translation level of the CDS as a whole – mitochondrial transcripts \nscoring very high for +60 GC content thus also showed particularly reduced CDS TEs (Figure \n6I).  \nAnother striking mitochondrial transcript feature was short 5′ UTRs – an effect that was highly \nsignificant (Figure 6F) and correlated with diminished 5′ UTR ribosome occupancy in Liv KO \n(Figure 6J). When we further correlated 5′ UTR length with TE changes occurring on the CDS, \nwe observed that many mitochondrial transcripts that carried extremely short  UTRs (Figure \n6J) showed specifically strong reduction in CDS translation  in Liv KO tissue (Figure 6K ). \nTranscripts with very short 5′ UTRs (<30 nt) are  thought to be unable to assemble the \ntranslation initiation machinery via the conventional pathway and rely on specialised , poorly \nunderstood mechanisms to thread the mRNA into the 40S subunit to engage on 5′ cap -\nproximal start codons  33. Our data would  suggest that BUD23 is important  for efficient \ntranslation of such transcripts, and the enrichment of mitochondrial transcripts among mRNAs \nwith very short 5′ UTRs likely further contributes to their preferential deregulation in LivKO mice. \nWith the identification of  initiation as  a BUD23 -responsive mechanism, and three likely \ncorrelates of transcript selectivity – GC content at the initiation codon, 5′ UTR length and uORF \ncontent – we next selected prototypic mRNAs to examine footprint distributions along the \ntranscripts. For example, Bola3 – encoding an essential mitochondrial [Fe-S] cluster delivery \nfactor genetically linked to multiple mitochondrial dysfunctions syndrome (MMDS) 34 – has \namong the highest +60 GC contents (82%; Figure 6H) and shortest 5′ UTRs (<25 nt; Figure \n6K), and it exhibited an almost 4-fold reduction in translation efficiency on the CDS ( Figure \n6L); the read distribution along the mRNA confirmed the dramatic loss in ribosome occupancy \npost-initiation, as well as  low coverage over the remainder of the CDS ( Figure 6M). Other \nmitochondrial transcripts where short 5′ UTR and high +60 GC content synergised (Figure \n6I,K) and CDS TE was reduced (Figure 6L), were, for example, Cox6a1 (a nuclear-encoded \nsubunit of the terminal enzyme of the respiratory chain, Cytochrome c oxidase, COX) (Figure \n6N) or Ak3 (an adenylate kinase necessary for TCA cycle  activity 35) (Figure 6O). In other \ncases, short 5′ UTR was not associated with elevated GC content – e.g. Uqcc2 (required for \nmitochondrial respiratory chain complex III assembly, 36) (Figure 6P ), which showed very \nstrongly reduced translation efficiency in Liv KO (Figure 6L). We also examined examples for \ngenes with strong LivKO-specific reduction in ribosome occupancy on the 5′ UTR (Figure 6R-\nT). We found evidence that this was indeed frequently due to low ribosome occupancy on \nuORFs, as shown for the cases of Cisd3 37, Etfdh 38, and Igfbp4. Notably, all the implicated \nuORFs were localised very close to the mRNAs’ 5′ cap  (Figure 6R-T). \nTaken together, the above analyses established that GC content in the early coding region, 5′ \nUTR length, and uORF content are key determinants of BUD23 -dependent translational \ncontrol of initiation. These features not only predict mRNA responsiveness but also explain \nthe preferential impact of BUD23 loss on mitochondrial transcripts, whose structural \ncharacteristics render them particularly reliant on efficient initiation mechanisms. \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 12 \nBUD23 Expression is linked to human cardiometabolic and systemic traits \nGiven the metabolic and mitochondrial phenotype observed in Bud23-knockout mouse \nmodels, we sought to examine whether genetically predicted BUD23 expression in humans is \nassociated with cardiometabolic diseases and related biomarkers. To do this, we applied drug-\ntarget Mendelian randomization (MR) , a method that uses germline genetic variants as \ninstrumental variables to estimate the causal effect of modulating gene expression on disease \nrisk 39,40. Specifically, we modeled BUD23 expression in whole blood using cis-eQTL variants \nwithin the BUD23 locus and assessed its effects on major cardiometabolic outcomes, including \ncoronary artery disease (CAD), obesity, metabolic -associated steatotic liver dise ase \n(MASLD), cirrhosis, and circulating biomarkers of lipid, glucose, and liver function. \nGenetically increased BUD23 expression in whole blood was found to be inversely associated \nwith coronary artery disease (CAD), metabolic -associated steatotic liver disease (MASLD), \nand liver enzymes, including alanine aminotransferase (ALT) and aspartate aminotransferase \n(AST), suggesting a protective effect (Figure 7A). We additionally observed significant inverse \nassociations with hypertension, aligning with lower systolic blood pressure (SBP) levels, as \nwell as a reduction in myocardial infarction (MI) risk. By contrast, increased BUD23 expression \nwas positively associated with body mass index (BMI) and obesity, indicating a potential trade-\noff between adiposity and cardiometabolic protection. Notably, effect estimates were \nconcordant across both primary and replication GWAS sources, ind icating robustness to \npopulation-specific biases and strengthening confidence in the inferred causal effects. \nWe next explored whether the genetic impact of BUD23 is tissue-dependent by repeating the \nMR analyses using eQTLs for BUD23 expression in adipose (subcutaneous and visceral), \nskeletal muscle, heart (atrial appendage), and liver (Figure S9C). The direction and magnitude \nof effects in adipose tissues closely mirrored those in whole blood, particularly for obesity, \nlipids, and MASLD, suggesting shared metabolic pathways. In contrast, muscle -derived \nBUD23 instruments yielded directionally opposite effects, part icularly for BMI and T2D. This \ndivergence may reflect tissue -specific roles of BUD23, where increased BUD23 in muscle \ncould alter translation of mitochondrial factors, with down-stream impact to energy efficiency. \nTo move beyond cardiometabolic phenotypes and capture the broader impact of BUD23 on \nhuman health, we performed a phenome -wide MR scan across ~1,200 outcomes from the \nMillion Veteran Program (MVP) among participants of European ancestry  41. This analysis \nrevealed significant enrichment of genetically proxied BUD23 expression in traits related to \nmetabolic, hepatic, and cardiovascular function, including liver fat, dyslipidemia, hypertension, \nand inflammatory biomarkers ( Figure 7B). To contextualize these findings anatomically, we \nvisualized the top FDR-significant outcomes (FDR<0.05 per disease category) across disease \ndomains using a homunculus -style plot ( Figure 7C ). The most prominent effects were \nobserved in endocrine/metabolic, hepatic, and cardiopulmonary systems, reinforcing the \nhypothesis that BUD23 is a key regulator of systemic energy homeostasis.  \nTogether, these findings integrate human genetic evidence with in vivo  functional data, \nsupporting a role for BUD23 in metabolic regulation and suggesting its potential relevance to \ncardiometabolic health and disease. \n \n \nDiscussion \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 13 \nBUD23 is a highly conserved 18S ribosomal RNA methyltransferase  that catalyzes the \nformation of N7-methylguanosine (m7G) at position G1639 within the small ribosomal subunit \nand contributes to 40S subunit maturation (White et al. 2008). This modification is located near \nthe decoding centre , adjacent to  the P- (peptidyl) and E- (exit) sites, and is unique in that it \nintroduces a positive charge and alters local steric constraints within a structurally restricted \nregion. These local change s likely perturb ribosome -mRNA interactions in a manner that \nselectively alters translation dynamics. However, the physiological role of BUD23, particularly \nin mammalian tissues, has remained largely unexplored.  \nIn this study , we show that BUD23 exerts selective translational control critical for \nmitochondrial function and lipid metabolism, with profound effects in white and brown adipose \ntissue as well as liver. In WAT, Bud23 deletion causes striking mitochondrial dysfunction and \nloss of lipid droplet formation in vivo, despite relatively preserved adipocyte differentiation in \nvitro. In contrast, i n BAT, loss of Bud23 results in lipid accumulation and tissue whitening, \nlikely reflecting impaired mitochondrial beta -oxidation. BAT is rich in mitochondria and relies \nheavily on mitochondrial respiration to fuel its thermogenic function  (Cannon,Nedergaard, \n2004). In our model, however, thermogenic function remains largely preserved, pointing to a \ndistinct and intriguing mitochondrial phenotype. In summary, t he opposing adipocyte \nphenotypes – lipid loss in WAT and lipid accumulation in BAT – underscore a shared \nmitochondrial dysfunction but reveal tissue -specific consequences, potentially driven by \ndifferences in mitochondrial content and the energy storage vs. expenditure roles of these \nadipose depots.  \nThe liver exhibits a milder phenotype, but mitochondrial defects remain a primary outcome. \nThis is supported by our ribosome profiling data that revealed changes to the  translation of \nmRNAs specifying nuclear-encoded mitochondrial proteins. Mechanistically, our findings \nsupport a model in which BUD23-modified ribosomes are optimized for translation of a critical \nsubset of mitochondrial mRNAs . These transcripts often bear structural and sequence \nfeatures that can challenge canonical initiation, such as very short 5′ UTRs and high GC \ncontent immediately downstream of  the initiation codon. This GC bias is a predictable \nconsequence of the amino acid composition of N-terminal mitochondrial targeting sequences, \nwhich are often amphipathic and rich in arginine and leucine. These features may  predispose \nsuch transcripts to inefficient initiation, making them particularly dependent on BUD23 -\nmodified ribosomes for effective translation. This model provides a mechanistic explanation \nfor the coherent mitochondrial dysfunction across tissues.     \nWe also uncover a disruption in energy -sensitive translational regulation. Normally, 5′ TOP -\ncontaining mRNAs – mostly encoding ribosomal proteins and translation factors – respond \ndynamically to nutrient availability via mTOR signalling. In Bud23-deficient livers, this \nregulation is abrogated: 5′ TOP mRNA  translation remains elevated during the light/fasting \nphase, indicating a decoupling of nutrient sensing and translational control. This may reflect \naltered ribosome activity due to altered 40S maturation or mRNA engagement during initiation, \nsuggesting that Bud23 contributes to the broader translational architecture that  enables \nmetabolic adaptation. While the precise integration of global and transcript-selective effects \nremains unclear, the observed specificity for mitochondrial mRNAs is not readily explained by \ngeneral effects in ribosome biogenesis alone . These findings argue for an active regulatory \nrole for BUD23’s methyltransferase activity in shaping the translational landscape. \nAn intriguing possibility  raised by our findings is that m7G1639 modification may not be \nconstitutive but instead dynamically regulated. Although  BUD23 is generally considered a \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 14 \nconstitutive factor, growing evidence from other rRNA-modifying enzymes suggests that  \nactivity and  expression may vary across tissues and physiological  states. Indeed, viral \nmanipulation of the BUD23-dependent methylation site has recently been described 42, raising \nthe possibility of endogenous regulation. If so, dynamic installation of this rRNA modification \ncould provide a powerful mechanism to tune ribosome activity and selectively modulate \ntranslation of metabolically critical transcripts. This raises the exciting possibility that BUD23 \nitself may be modulated by metabolic inputs, forming a feedback loop between nutrient state, \ntranslational output, and energy homeostasis.  Moreover, a  broader rRNA-modification \nnetwork – including other TRMT112 partners such as METTL5 43 – may have evolved to tune \nribosomal activity according to metabolic needs. \nFinally, our study connects these mechanistic insights to human health. While BUD23  has \npreviously been associated with cancer and inflammatory disease  44-46, its role in metabolic \norgans had not been clearly defined beyond our previous work 11. Notably, BUD23 is located \nwithin the critical deletion region of Williams-Beuren Syndrome (WBS), a multisystem disorder \ncharacterized by cardiovascular and metabolic phenotypes 47. Although multiple genes are \ndeleted in WBS, our finding that BUD23 is strongly associated with liver fat content (MASLD), \nbody mass index , and obesity suggest s it may meaningfully contribute to the metabolic \nfeatures of this syndrome. Extending our large-scale genetic analysis to the related METTL5 \nrevealed partially overlapping protective associations, whereas TRMT112 show ed minimal \neffects. These findings integrate functional and genetic data to support a key role for BUD23 \nin metabolic homeostasis and underscore its potential relevance to cardiometabolic health \nand disease.  \nTogether, our findings define BUD23 as a key regulator of ribosome specialization, selectively \nenhancing translation of mitochondrial mRNAs critical for energy metabolism. By linking \nnutrient sensing, translational control, and mitochondrial function, BUD2 3 emerges as a \ncentral node in the regulation of metabolic homeostasis. \n \nAcknowledgment \nWe thank and acknowledge support of core facilities at the University of Manchester: \nBioinformatics Core Facility, UoM Genomic Technologies Core Facility, Biological Services \nUnit, and Histological Services Unit, and at the University of Oxford: Dr. Anne Clark  and the \nElectron Facility (Dunn School) for their advice on  Electron Microscopy imaging, Amy Barret \nfor her support with biochemistry analysis,  and The Target Discovery Institute Mass \nSpectrometry group for the support on proteomic analysis. We thank Dr. Ana Domingos for \nthermal imaging equipment use . D.G. acknowledges funding by the University of Lausanne \nand by the Swiss National Science Foundation (SNSF, grants 212423, 10002692, and NCCR \nRNA & Disease,  205601). D.A.B acknowledges funding by the Medical Research Council \n(MRC)  (MR/P00279X/1). D.W.R acknowledges funding by NIHR Oxford Health Biomedical \nResearch Centre (NIHR203316) and  MRC (MR/W019000/1 and MR/V034049/1). \n \nAuthor contributions \nConception: D.B., D.R., and D.G. Methodology: N.M-S, A.B., N.J.B., D.B.R, A.L, M.V., E.A.H.,  \nS.C., R.D., N.G.,  E.J., M.B., K.S., R.C.N., G.G., L.H. O riginal draft: N. M-S, D.B., D.R., and \nD.G. Revisions: N. M-S, A.B., L.H., D.B., D.R., and D.G. Supervision: D.B., D.R., and D.G. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 15 \n \nDeclaration of interests \nThe authors declare no competing interests. \n \nReferences \n1. 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It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 19 \nMaterial and Methods   \nAnimals \nAdipoqCREBud23fl/fl (AdKO), UCP1CREBud23fl/fl (UCPKO) and AlbCreERT2Bud23fl/fl (LivKO) \nmice were generated by crossing AdipoqCre (JAX laboratory, Strain #:028020), UCP1Cre \n(JAX laboratory, Strain #:024670) and AlbCreERT2 (given by Prof. Pierre Chambon, GIE -\nCERBM (IGBMC)) respectively, with Bud23flox/flox (generated at the University  of \nManchester (UK) 11. In all studies, CRE-negative littermates (Bud23fl/fl) were used as controls \n(AdWT; UCPWT; LivWT). All mice were group -housed in 12:12 light/dark cycles, under \ncontrolled temperature (22±2C) and humidity with ad libitum access to standard laboratory \nchow, unless stated otherwise. All studies used both male and female mice, unless otherwise \nstated. All experiments were carried out in accordance with the Animals (Scientific \nProcedures) Act 1986 (UK) under Home Office Project License PDC3CD59F (University of \nOxford) and PP1136445 (University of Manchester) and a pproval from local ethical review \nbodies.  \nIn vivo treatmenst and studies \nTransgene induction: LivWT and LivKO mice were treated daily with tamoxifen (Sigma, T5648) \nfor 5 days (i.p., 0.1mg/day in sesame oil, Sigma S3547). \nHigh fat diet challenge: Adult mice (10 -13 weeks of age) were given ad libitum access to a \nhigh fat diet (60% energy from fat, DIO Rodent Purified Diet, IPS Ltd) for a period of 9 -12 \nweeks. \nMetabolic phenotyping: Body composition was assessed using an EchoMRI (Echo Medical \nSystems, E26-258-MT). Physiological (metabolic gas exchange) and behavioural (food and \nwater intake, locomotor activity) rhythms were measured using the Phenomaster indirect \ncalorimetry system (TSE Systems). Mice were individually housed and acclimatised for 24hr. \nwith O2 consumption (VO2), CO2 production (VCO2) and energy expenditure (EE) recorded \nevery 2 min for >72 hr. RER was derived from these measures (VCO2/VO2). \nCold and thermoneutrality challenges : For cold challenge, mice (~13 weeks of age) were \nindividually housed within a Phenomaster system, and following >2 days acclimatisation were \nexposed to an abrupt d rop in ambient temperature to 4 C for 6 hours. For thermoneutrality \nstudies, group housed mice were placed ~29C (+/- 1C) ambient temperature for 7 weeks.  \nInsulin Tolerance Test: Blood glucose was measured from tail blood using the Aviva Accuchek \nmeter (Roche). For the insulin tolerance test, mice were fasted from ZT0, then injected with \n0.75 IU/kg human recombinant insulin (I2643, Sigma-Aldrich) at ZT6 (time ‘0 min’). \nThermal imaging & Body temperature. BAT temperature of free moving AdWT and AdKO mice \nwere measured using a thermal camera (Flir). Mice were shaved to expose the interscapular \nregion >2 d prior to thermal imaging to avoid stress-induced BAT activity. Average temperature \nwas calculated using Flir Tools software, where the average temperature was measured from \na minimum of four images per mouse. Rectal temperature of unanaesthetised AD mice was \nmeasured using mouse rectal probe (RET -3, Type T Thermocouple, World Precision \nInstruments) connected to a BAT -12 Microprobe Thermometer (Physitemp Ins truments).  \nRecording of body temperature and activity was carried out via surgically implanted \nradiotelemetry devices (TA -F10, Data Sciences International) . Following >10days recovery, \nbody temperature was recorded every 5 min for >5 d. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 20 \n \nLabelled substrates study . Male adult AD WT / AD KO animals were given  deuterated water \n(2H2O) (CK Isotopes, Ibstock,UK) in the drinking water (25%vol/vol, ad libitum) for 48 hrs. After \nthis period, they were fasted for 4 hours and then received an oral bolus of labelled glucose \n(D-glucose; U -13C6, Cambridge Isotope Laboratories, INC). Tissues were collected 2 hrs \npost-glucose administration. \nPrimary cell culture \nAdipose tissue fractionation: Brown adipose tissue (BAT) was collected and washed in PBS \nsupplemented with amphotericin B from mice aged between 9 -14 wks old. The tissue was \nminced and digested in 1.5mg/ml collagenase H (25 min, shaking incubator 170rpm, 37°C ). \nCollagenase solution was neutralised with DMEM ( containing 20% FBS, 1% PS, 5µg/ml \namphotericin B) and passed through a 100µm mesh filter. Cells were centrifuged (250g, RT 8 \nmin) to separate the stromal vascular fraction (SVF) from the floating mature adipocyte layer. \nThe SVF was collected and cultured until confluent . For BAT differentiation cells were plated \nat 50,000 cells/well , cultured for 3d prior to treatment with the differentiation cocktail (IBMX \n0.5mM, indomethacin 0.125mM, dexamethasone 1µM, rosiglitazone 2.8µM, T3 1nM, insulin, \n20nM) in DMEM (with 20% FBS, 1% PS). 5d post-plating, media was replaced with DMEM \n(with rosiglitazone 2.8µM, T3 1nM,  insulin 20nM, 10% FBS, 1% PS). From 7d post-plating, \nmedia was replaced with Rosiglitazone (2.8µM) and Insulin ( 20nM) in DMEM + 10% FBS + \n1% PS. \nFor g onadal white adipose tissue (gWAT) tissue was washed in PBS supplemented with \namphotericin B, then minced and digested and washed as above. The gWAT SVF pellet was \nthen collected and resuspended in DMEM (glutamax) + 20% FBS + 1% PS prior to isolation \nwith the adipose tissue progenitor isolation kit (Miltenyi Biotec)  according to manufacturer’s \ninstructions. The purified progenitor population was cultured for 24 hrs in DMEM (20% FBS , \n1% PS) before being replaced and maintained in DMEM (20% FBS only ). Once confluent, \ncells were plated at 50,000 cells/well. Differentiation was triggered 3d later using differentiation \ncocktail (IBMX 0.5mM, Dexamethasone 1µM, Rosiglitazone 4µg/ml, insulin 5µg/ml in DMEM, \n20% FBS). On day 6 and day 8 post -plating, media was replaced with DMEM (20% FBS , \n5µg/ml insulin).  \nOil Red O and BODIPY/Hoechst staining . Differentiated cells were stained using Hoechst \n33342, Trihydrochloride, Trihydrate and BODIPY 493/503  (4,4-Difluoro-1,3,5,7,8-\nPentamethyl-4-Bora-3a,4a-Diaza-s-Indacene; Fisher Scientific UK Ltd.) at 1:2000. Images \nwere taken using the EVOS ™ M5000 Imaging system . For Oil Red O staining (ORO), cells \nwere washed in PBS and fixed in 10% Formalin (Merck Life Science UK Limited) for 30 mins. \nCells were rinsed with deionised water and incubated with 60% isopropanol for 5 min  before \nbeing stained with ORO solution for 15 minutes. Cells were wrinsed with d eionised water \nbefore being imaged on the EVOS ™ M5000 Imaging system. Following imaging, the ORO \nwas quantified by the addition of 100% isopropanol for 5 minutes. The absorbance of the \nextracted ORO solution was measured at 492nm on a plate reader. \nMature adipocyte studies. Following a method adapted from Collins et al., 2010, gWAT was \ncollected from adult mice and washed in Hanks’ Balanced Salt Solution (Sigma). Next, tissue \nwas minced and digested in 1 mg/ml collagenase (Collagenase H, Sigma) for 30 min in a \nshaking incubator at 170 rpm, 37°C. The sample was then centrifuged at 1000 rpm for 5 min \nat 4°C. MA (floating layer) and stromal vascular fraction (SVF) (cell pellet) were collected \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 21 \nseparately, lysed in TRIzol Reagent (Invitrogen), and stored at −80°C before proceeding to \nRNA extraction.  To remove excess lipid from MA fractions, samples were centrifuged (full \nspeed, 5 min, room temperature) prior to chloroform addition. RNA extraction was then carried \nout as per the manufacturer’s TRIzol protocol, up to the stage of removing the isopropran ol \nphase, which was transferred to Reliaprep columns (Promega) for on -column DNase \ntreatment, clean-up, and elution as per manufacturer’s protocol. \nCell culture RNA extraction. RNA was extracted from cells using the ReliaPrep ™ RNA cell \nmini prep system (Promega UK Ltd.) following the manufacturer’s instructions. Samples were \nDNAse treated during the extraction procedure. RNA was eluted from the column in a final \nvolume of 15µL and RNA concentration and purity was assessed using  a NanoDrop 2000c \nUV/IV Spectrophotometer. \nRNA extraction (tissue) \nTotal RNA was isolated by using Trizol Reagent (Invitrogen; Carlsbad, CA, USA) according to \nthe manufacturer’s protocol (RNA was precipitated with chloroform and isopropanol, washed \nwith 75% ethanol, and finally dissolved in RNase -free water). RNA concentr ation and purity \nwas assessed using a NanoDrop 2000c UV/IV Spectrophotometer. Before the \nretrotranscription, RNA was DNAse treated using RQ1 DNAse (Promega) following \nmanufacturer’s protocol.  \nRT-PCR. Samples were DNase -treated (RQ1 RNase -Free DNase, Promega, Madison, WI) \nprior to cDNA conversion High Capacity RNA -to-cDNA kit (Applied Biosystems). qPCR was \nperformed using a GoTaq qPCR Master Mix (Promega, Madison, WI) and primers listed in \nAppendix Adipocyte NR1D1 dictates adipose tissue expansion during obesity using a Step \nOne Plus (Applied Biosystems) qPCR machine. Relative quantities of gene expression were \ndetermined using the [delta][delta] Ct method and normalised with the use of a geometr ic \nmean of the housekeeping genes Hprt, Ppib, and Actb. The fold difference of expression was \ncalculated relative to the values of control groups. \nRNA-Sequencing \nBAT: RNA was extracted from BAT using the SV Total RNA Isolation System (Promega) \naccording to manufacturer’s instructions. RNA yield was quantified by TapeStation (Agilent), \nto ensure it was of sufficient quality for sequencing. Library preparation and sequencing for \nthe Illumina HiSeq  4000 platform were performed by Novogene. Raw FASTQ files were \nprocessed through a standard pipeline by Novogene to generate a list of counts. Gene lists \nwere analysed for differential expression using a combination of techniques, including edgeR \n48. \ngWAT. Adipose tissue was collected from adult male mice  (13 wks of age) at ZT8 and flash-\nfrozen. Total RNA was extracted and DNase-treated as described above. Biological replicates \nwere taken forward individually to library preparation and sequencing. For library preparation, \ntotal RNA was submitted to the Genomic Technologie s Core Facility (GTCF). Quality and \nintegrity of the RNA samples were assessed using a 2200 TapeStation (Agilent Technologies) \nand then libraries generated using the TruSeq Stranded mRNA assay (Illumina, Inc) according \nto the manufacturer’s protocol. Brief ly, total RNA (0.1–4 μg) was used as input material from \nwhich polyadenylated mRNA was purified using poly-T, oligo-attached, magnetic beads. The \nmRNA was then fragmented using divalent cations under elevated temperature and then \nreverse-transcribed into first strand cDNA using random primers. Second strand cDNA was \nthen synthesised using DNA Polymerase I and RNase H. Following a single ‘A’ base addition, \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 22 \nadapters were ligated to the cDNA fragments, and the products then purified and enriched by \nPCR to create the final cDNA library. Adapter indices were used to multiplex libraries, which \nwere pooled prior to cluster generation using a cBot instrument. The loaded flow cell was then \npaired-end sequenced (76 + 76 cycles, plus indices) on an Illumina HiSeq4000 instrument. \nFinally, the output data was demultiplexed (allowing one mismatch)  and BCL -to-Fastq \nconversion performed using Illumina’s bcl2fastq software, version 2.17.1.14. \nGenomic DNA extraction and Mitochondria Copy Number analysis \nGenomic DNA (gDNA) was extracted using phenol -chloroform. Tissue was homogenized in \nTE buffer and incubate at 55°C for 3 hours with proteinase K and 20% SDS. After that, 1 \nvolume of phenol:chloroform:isoamyl alcohol (25:24:1; Thermo Fisher) per sample was added \nand vortexed for 20 seconds. Samples were transfered into phase lock tubes (Qiagen) prior \nto centrifugation at 16,000g for 5 mins. The aqueous phase was carefully removed and \ntransferred to a fresh tube. 1 μl GlycoBlue, half a volume of 7.5M ammonium acetate, and 2.5 \nvolumes of 100% ethanol were added. The sample was kept at -80°C for one hour to facilitate \nDNA precipitation. The sample was then centrifuged at 16,000g at 4°C for 30 mins to pellet \nthe DNA. The pellet was washed in ethanol once and left  to air dry. Purified genomic DNA \nwas resuspended in nuclease-free water. \nMitochondria Copy Number was analysed by qPCR by measuring the mitochondrial genome \ngen mt-ND1 and normalizing the values by genomic GAPDH.  \n \nRibosome profiling from mouse liver samples \nLivers from Bud23fl/fl;AlbCreERT2 (LivKO) and age-matched Bud23fl/fl (LivWT) animals (both groups \ntamoxifen-treated) were collected at diurnal Zeitgeber timepoints ZT6 and ZT18 (3 male mice \nper genotype and timepoint) and flash -frozen in liquid nitrogen. Using ~200 mg of frozen \nsample per liver, tissue lysates were prepared and r ibosome footprints were generated \n(RNase I) and purified, all according to previously described protocols as in Janich et al. 24. \nAn aliquot of the same lysate as for footprint generation was used to purify matching total RNA \npreparations, of which 1 μg was chemically fragmented for RNA -seq library preparation, also \nas described in Janich et al. Size-selected, fragmented RNA and footprint samples were then \nsubjected to library preparation protocols identical to those used in our previous studies (e.g. \n49. In this protocol, sample barcodes and unique molecular identifiers (UMIs) are included in \nthe initial adaptors ligated to the RNA molecules (similar to 50), allowing for multiplexing at an \nearly stage, before rRNA depletion (6 samples were multiplexed for each of the final four \nlibraries). The amplification of the libraries was carried out using i5 and i7 indexed primers (12 \nPCR cycles). The libraries were sequenced on a NovaSeq6000 (Illumina). \nRibosome profiling data mapping \nRead mapping was performed essentially following our published protocol 51. Briefly, reads \nwere trimmed from adapter sequence using cutadapt (version : 3.5; options: --match-read-\nwildcards --overlap 8 --discard-untrimmed --minimum-length 30) and quality filtered using \nfastx_toolkit (version: 0.0.14; options: -Q33 -q 30 -p 90). UMIs were extracted from each read \nwith UMItools (version: 1.0.0git; options:  extract --extract-method string --bc-pattern \nNNNNNNNNCCCCC --3prime --filter-cell-barcode –error-correct-cell). Then, reads where \nsize-selected for monosome footprints (size 26 to 35). Subsequently, to estimate rRNA and \ntRNA contamination, reads were mapp ed to human and mouse rRNA and mouse tRNA \ndatabases using bowtie2 (version: 2.3.5; options: -p 2 -L 15 -k 20 --trim5 2). Reads that failed \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 23 \nto map to these, were then mapped to the mouse transcript database (Ensembl database v. \n100). Barcode demultiplexing was carried out with UMItools (options: group --\nmethod=directional --per-cell –read-length) and deduplication with an in -house script. For \neach gene, only one transcript isoform was considered, namely the primary isoform based on \nclassification by the APPRIS database (PMID 34755885). In case several transcript isoforms \nwere annotated as primary by APPRIS, the one with the longest coding r egion was selected. \nFor Ribo-Seq reads, the ribosome A -site was assumed to cover nucleotide positions 15 -17, \nand Ribo-Seq reads were counted for each gene that overlapped with their A -site the coding \nregion. For RNA-Seq, all reads were counted that aligned to a gene. \n \nData and code availability \nFor liver ribo-seq and matching RNA-seq data, raw sequencing data files will be deposited in \nNCBI’s Gene Expression Omnibus (GEO) archive. The scripts for data analysis will be \nabailable from https://github.com/gatfieldlab/. \nPCA analysis \nPCA analysis was done in python3 using the scikit -learn package 52: Scikit-learn: Machine \nLearning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011] considering genes with \nany reads in at least 10 out of 12 samples. Read counts were normalized by sizefactors \nobtained by DESeq2 53. Translation efficiencies were calculated by dividing normalized Ribo-\nSeq read counts by normalized RNA-Seq read counts for genes with any RNA-Seq reads. \nDifferential expression and ribosome occupancy analysis \nDifferential expression and ribosome occupancy analysis was done using DESeq2  53 \nconsidering RNA -Seq and Ribo -Seq read counts, respectively. Differential translation \nefficiency analysis was done in python3 considering genes with reads in all RNA -Seq and \nRibo-Seq samples. An independent T -test was used to compare translation efficienci es in \nBud23-KO with wildtype samples, and p-values were FDR corrected. Genes with corrected p-\nvalue < 0.05 and absolute log2 -fold change > 1.5 were considered significantly differentially \ntranslated. \nGO analysis and gene sets \nGO enrichment analysis was done in python3 using gene ontology gene sets (downloaded \nfrom MSigDB: https://www.gsea-msigdb.org/gsea/index.jsp) with at least 50 genes. For each \ngene set, the proportion of genes contained in the set among significantly changed genes (in \nKO versus WT, based on RNA-Seq, Ribo-Seq or TE) was compared with the proportion for all \ngenes using Fisher’s exact test, and p-values were FDR-corrected. If all genes of a smaller \ngene set were contained in a larger gene set and both gene sets  were significantly depleted \nor enriched, only the larger gene set was retained. The full list of significant gene sets is \nprovided as Supplemental Table S XX and selected representative gene sets are shown in \nFigure 5D.  \nIn Figure 5 E -G, the ribosome gene set is from the GO cellular component (CC) ontology, \nexcluding mitochondrial ribosomal genes (Mrp genes), and the mitochondrial gene set is from \nMitoCarta3.0 (downloaded from \nhttps://personal.broadinstitute.org/scalvo/MitoCarta3.0/Mouse.MitoCarta3.0.xls) \nCodon dwell time estimation \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 24 \nCodon dwell times (DTs) were estimated using the RiboDT pipeline  54,  based on Ribo -Seq \nreads. In Figure S6E, DTs when assuming an interaction between P- and A-sites are shown, \nas well as the difference between both (KO - WT).   \nMeta-profile of ribosome occupancy around CDS starts and ends \nFor each Ribo -Seq read, the A -site was assigned to start at the nucleotide at positions 15. \nThe number of A -sites of ribosomes were counted at each nucleotide in a region from 100 \nnucleotides upstream to 200 nucleotides downstream of annotated start codons  and in a \nregion from 200 nucleotides upstream to 100 nucleotides downstream of annotated stop \ncodons. For each gene, A-site counts of different samples were normalized by their sizefactors \n(obtained from DESeq2), summed over all Liv KO or all Liv WT samples, and then z -scored \nrelative to the profile counts for wildtype. Genes with A -site counts at more than 25 positions \nwere considered. Figure 5I shows the average (line) and standard error (shaded area around \nline) for LivKO and LivWT samples. A one-sample t-test was used to test, at each position, if the \ndistribution of differences of z -scored A-site counts between KO and wildtype for all genes \nwas significantly smaller or larger than 0 (i.e. no difference). The p-values for all positions were \nFDR corrected and considered significant when < 0.05.   \nSequence properties of transcripts and their correlation with changes in translation \nefficiency (TE) for specific gene regions \nWe quantified several sequence properties of transcripts and examined their correlation with \nTE fold-changes in specific gene regions: in 5’ UTRs, in coding regions, and in first or last 60 \nnucleotides of coding regions. We considered the following transcr ipt sequence properties: \ndistance of start codon from mRNA 5’ end (5’ UTR length), number of upstream open reading \nframes (uORFs) in 5’ UTRs starting with a AUG start codons, an in -frame stop codons, and \nribosome A site occupancy, , number of uORFs  starting with a (C/G/U)UG start codon, Kozak \nscore, codon optimality score (for first 20 codons), GC content for different regions (5’ UTRs, \n60 nucleotides upstream or downstream of start codons or both), absolute minimum free \nenergy (MAF) for folded RNA within 60 nucleotides upstream or downstream of start codons \nor 120 nucleotides surrounding start codons).  \nAs 5’ UTR length we considered two measures: the distance between the annotated start \ncodon and the annotated mRNA 5’ end, and the distance between the annotated start codon \nand the start of transcription as indicated by CAGE (Cap analysis of gene expressi on) data \nobtained from mouse liver by the Fantom Consortium (data downloaded from \nhttps://hgdownload.soe.ucsc.edu/gbdb/mm10/fantom5/liver%252c%2520adult%2520pregna\nnt%2520day01.CNhs10466.508-5B2.mm10.ctss.fwd.bw and \nhttps://hgdownload.soe.ucsc.edu/gbdb/mm10/fantom5/liver%252c%2520adult%2520pregna\nnt%2520day01.CNhs10466.508-5B2.mm10.ctss.rev.bw). In particular, we assigned the \ntranscription start site to the position with the maximum number of CAGE reads (at least 10 \nreads) within the first annotated exon and 500 nucleotides upstream of it. \nThe codon optimality score was determined by summing up the relative abundances of \ncodons, corresponding to each codon within the first 20 codons, where the relative codon \nabundances were calculated from codons in expressed genes and weighted by the gene \nexpression level in wildtype samples. \nSpearman correlations between transcript sequence properties and TE fold -changes were \ncalculated for genes with more than 1 normalized RNA -Seq read in KO and WT, more than \n10 normalized Ribo-Seq reads in KO or WT, and a ratio of normalized Ribo-Seq to RNA-Seq \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 25 \nreads larger than 0.5 for KO or WT. As several sequence properties correlated across \ntranscripts, we selected representative sequence properties. \n \nLinear regression model to explain TE FCs with sequence properties \nLinear regression was used to select gene sequence properties that together contribute to \nexplaining the variance in TE FC and to quantify the total fraction of the variance explained by \nthese combined gene sequence properties. To select contributing seque nce properties, we \nperformed a forward feature selection procedure, where in each step the sequence property \nmaximizing the model correlation with TE FC was added. We used a five-fold cross-validation \nto evaluate the model performances, with regression coe fficients being estimated on 80% of \nthe data and correlations of the parametrized models with TE FCs being evaluated on the \nremaining 20% of the data. Splitting of the data was random and repeated 50 times. A new \ngene sequence property was added to the model if it increased the explained variance of TE \nFCs by at least 1% compared to the explained variance by the previous model. \n \nStable isotope analysis/ Gas chromatography   \nThe Folch chloroform-methanol (2:1; v/v) method was used to extract total lipid from tissue \nlysates (PMID – 13428781). Solid-phase extraction was used to separate  lipid fractions and \nfatty acid methyl esters (FAMEs) prepared as previously described 55. Deuterium incorporation \nfrom 2H2O in plasma water (Finnigan GasBench II; Thermo Fisher Scientific, Paisley, UK).  \nTissue palmitate 13C (from U136 glucose) and D2 (from D2O) enrichment was determined by \nGC-mass spectrometry (GC -MS) (Agilent Technologies; CA, USA) with monitoring ions with \nmass-to-charge ratios (m/z) of 270 (M+0), 271 (M+1) and 272 (M+2).  \nWestern blot \nTotal protein was isolated from gWAT and liver using protein extraction RIPA supplemented \nwith protease and phosphatase inhibitors in a tissue homogenizer. Protein quantification was \nperformed following the Bradford method. Protein lysates were subjected to SDS -PAGE, \nelectrotransferred and blocked with milk/BSA. Primary antibodies were incubated overnight at \n4C: DGAT2, FAS, ATGL, LPL. Fluorescence secondary antibodies were incubated 1 hour at \nroom temperature and imagines were taken using Biorad Gel Doc system. Band signal was \nquantified by densitometry using ImageJ 1.33 software, values were expressed in relatio n to \nb-actin/ total protein ponceau. Representative images for all proteins are shown. \nHistology \nHaematoxylin and Eosin (H&E) staining was carried out on paraffin embedded sections using \nthe Leica ST5010 Autostainer XL. Images were acquired on a 3D -Histech Pannoramic-250 \nmicroscope slide-scanner using a 20x/ 0.80 Plan Apochromat objective (Zeiss). Snapshots of \nthe slide-scans were taken using the SlideViewer software (3D-Histech). Further imaging was \nalso carried out on the Zeiss AX10. Adipocyte diameter was quantified using ImageJ and the \nAdiposoft plugin version 1.16. \nProteomics -  Mass Spectrometry \nAdipose tissue homogenates were processed for in -solution protein digestion. Proteins were \nreduced with 5 mM DTT (60 min, room temperature) and alkylated with 20 mM iodoacetamide \n(60 min). Proteins were precipitated using methanol/chloroform, and pellets w ere \nresuspended in 6 M urea, then diluted to <1 M prior to protein quantification (Bradford assay). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 26 \nSamples were digested overnight at 37°C with trypsin (1:50 enzyme -to-protein ratio) and \ndesalted using Pierce C18 tips. Peptides were analysed by LC-MS/MS using an UltiMate 3000 \nRSLC system coupled to a Q Exactive HF mass spectrometer (Thermo Scientific). Separation \nwas performed on a 75 mm × 250 μm, 1.7 μm CSH C18 column over a 120 -minute multistep \nacetonitrile/formic acid gradient at 300 nL/min. MS1 scans were acquired at 120,000 resolution \nwith a 300 –1750 m/z window, followed by data -dependent MS2 scans (top 12 precursors, \nresolution 60,000, dynamic exclusion 15 s). Normalised collision energy was stepped around \n28, and ionisation was set at 1,900 V. Mass spectra were analysed using MaxQuant version \n1.6.0.16 \nBlood biochemistry \nBlood serum was acquired via cardiac puncture. Blood was allowed to clot at room \ntemperature for 30 mins, before centrifugation at 2,000g for 10 mins. Serum was collected and \nthe pellet discarded. Metabolites (including glucose, glycerol, NEFA, TG, lactate, cholesterol, \n3-OHB, CRP, Urea, HDL)  were assessed via ILab 650 Automatic Biochemistry Analyzer \nClinical Chemistry System. \nAdiponectin ELISA \nAdiponectin in cell culture supernatant was measured using the mouse Adiponectin/Acrp30 \nDuoSet ELISA (Bio -Techne Ltd.) according to manufacturer’s instructions. Cell culture \nsupernatant was diluted 1:500 to fall within the range of the assay.  \nBio-Plex \nSerum samples were analysed using the Bio -Plex Pro Mouse Diabetes 8 -Plex assay kit \n(171F7001M, Bio-Rad Laboratories Ltd) and the Bio-Plex 200 system (Bio-Rad Laboratories \nLtd.). The assay was run according to the manufacturer’s instructions and at the \nrecommended dilution for serum samples. \nTriglyceride assay \nTriglyceride in tissue lysates was measured using a Cayman’s Triglyceride calorimetric assay \n(CAY700190-96 wells, Cambridge Bioscience Ltd.) according to the manufacturer’s \ninstructions. Lysates were prepared from ~100mg pieces of liver homogenised in lysing matrix \nD tubes (MP Biomedicals) with NP40 substitute assay reagent supplemented with a cOmplete \nmini EDTA protease inhibitor tablet (Merck Life Science UK Limited). Lysates were diluted 1:3 \nto fall within the range of the assay.  \nElectron Microscopy \nLivers were dissected from adult mice, after PBS perfusion and immersed in fixative solution \n(2.5% glut/4% formaldehyde) for 1 hr at room temperature and then kept at 4C until \nprocessing.  \nFixed samples were processed with microwave assistance using a Leica AMW according to \nthe following steps. Samples were washed with buffer (0.1 M sodium cacodylate buffer pH \n7.2), stained with 1% osmium tetroxide and 1.5% potassium ferrocyanide in buffer, then rinsed \nwith MilliQ water. Samples were further stained with 2% uranyl acetate in water, rinsed again \nwith MilliQ water, then dehydrated through an ethanol series (30%, 50%, 70%, 90%, 95% and \nabsolute ethanol). Samples were then infiltrated with 25%, 5 0%. 75% and finally 100% low \nviscosity resin (TAAB) in ethanol. Samples were removed from the microwave processor and \nsubmerged in fresh 100% resin, then placed on a rotator overnight. Samples were incubated \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 27 \non the rotator for the following 4 days, with changes into fresh resin twice per day. The samples \nwere embedded on the afternoon of the final day and polymerised at 60°C for 48 hours. \nSections of 90 nm were cut from the resin blocks using a Leica UC7 Ultra microtome and \ncollected onto 3 mm copper grids. The sections were then post -stained with lead citrate and \nimaged using a JEOL Flash 120kV TEM equipped with a Gatan Rio camera. Analysis was \ndone using Image J.  \nStructural models \nFor structural models in Figure 5K and S7, PDB entries  8pj1-5 were loaded in Swiss -\nPdbViewer 56 and  the m 7G1639 methyl group was added by superposing the guanine \naromatic ring of pdb entry 5H3T chain A:MGT801 onto PDB entries 8pj1 -5 G1639 guanine \naromatic rings. The molecular surface of 18S rRNA (chain A) was computed excluding G1639 \nto show the space availabl e to perfectly accomodate m 7G1639. Chains e (40S ribosomal \nprotein S25 / eS25), f (40S ribosomal protein S18 / uS13), w (initiator Met-tRNA) and 7 (mRNA) \nwere drawn in purple, teal, dark green, yellow ribbon, respectively. \nData analysis and statistics \nData are expressed as mean SEM. Error bars represent SEM. Statistical significance was \ndetermined by Student t test (when two groups were compared), ANOVA (when more than \ntwo groups were compared) followed by post hoc Bonferroni test. P  0.05 was considered \nsignificant. \n \n  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\nFigure 1. Loss of Bud23 function drives profound metabolic phenotype and shift \nin body composition.\n0 1 2 3 4 5 6 7 8 9\n15\n20\n25\n30\nWeeks of HFD\nBody Weight (g)\nFemales weight tracking\n0 4 8\n0\n2\n4\n6\n8\n10\nWeeks of HFD\nFat Weight (g)\nFemales fat weight\n0 4 8\n10\n12\n14\n16\n18\n20\nWeeks of HFD\nLean Weight (g)\nFemales lean weight\n0 1 2 3 4 5 6 7 8 9\n20\n25\n30\n35\n40\n45\nWeeks of HFD\nBody Weight (g)\nMales weight tracking\n0 4 8\n0\n5\n10\n15\nWeeks of HFD\nFat Weight (g)\nMales fat weight\n****\n0 4 8\n10\n15\n20\n25\n30\nWeeks of HFD\nLean Weight (g)\nMales lean weight\nB C\nF G\nD\nE H\nK\nL\nI J\nA\nM\n N O\nQ\nADKOADWT\nADWT\nADKO\n0 1 2 3 4 5 6 7 8 9\n15\n20\n25\n30\nWeeks of HFD\nBody Weight (g)\nFemales weight tracking\n0 4 8\n0\n2\n4\n6\n8\n10\nWeeks of HFD\nFat Weight (g)\nFemales fat weight\n0 4 8\n10\n12\n14\n16\n18\n20\nWeeks of HFD\nLean Weight (g)\nFemales lean weight\n0 1 2 3 4 5 6 7 8 9\n20\n25\n30\n35\n40\n45\nWeeks of HFD\nBody Weight (g)\nMales weight tracking\n0 4 8\n0\n5\n10\n15\nWeeks of HFD\nFat Weight (g)\nMales fat weight\n****\n0 4 8\n10\n15\n20\n25\n30\nWeeks of HFD\nLean Weight (g)\nMales lean weight\n***\n0 1 2 3 4 5 6 7 8 9\n15\n20\n25\n30\nWeeks of HFD\nBody Weight (g)\nFemales weight tracking\n0 4 8\n0\n2\n4\n6\n8\n10\nWeeks of HFD\nFat Weight (g)\nFemales fat weight\n0 4 8\n10\n12\n14\n16\n18\n20\nWeeks of HFD\nLean Weight (g)\nFemales lean weight\n0 1 2 3 4 5 6 7 8 9\n20\n25\n30\n35\n40\n45\nWeeks of HFD\nBody Weight (g)\nMales weight tracking\n0 4 8\n0\n5\n10\n15\nWeeks of HFD\nFat Weight (g)\nMales fat weight\n****\n0 4 8\n10\n15\n20\n25\n30\nWeeks of HFD\nLean Weight (g)\nMales lean weight\nWeeks on HFD Weeks on HFDWeeks on HFD\nR\n0 1 2 3 4 5 6 7 8 9\n15\n20\n25\n30\nWeeks of HFD\nBody Weight (g)\nFemales weight tracking\n0 4 8\n0\n2\n4\n6\n8\n10\nWeeks of HFD\nFat Weight (g)\nFemales fat weight\n0 4 8\n10\n12\n14\n16\n18\n20\nWeeks of HFD\nLean Weight (g)\nFemales lean weight\n0 1 2 3 4 5 6 7 8 9\n20\n25\n30\n35\n40\n45\nWeeks of HFD\nBody Weight (g)\nMales weight tracking\n0 4 8\n0\n5\n10\n15\nWeeks of HFD\nFat Weight (g)\nMales fat weight\n****\n0 4 8\n10\n15\n20\n25\n30\nWeeks of HFD\nLean Weight (g)\nMales lean weight\nWeeks on HFD\n*\nBAT (% 13C incorp.)\nLiver (% 13C incorp.)\nLiver (% newly \nsynthesized 16:0)\n0\n10\n20\n30\n40\n0\n5\n10\n15\nBAT (% newly \nsynthesized 16:0)\nTG (µmol/L)Liver Weight\n(% BW)\nLiver TG\n(mg/g tissue)\nNEFA (µmol/L)\nGlycerol (µmol/L)\nP\nS\nADKO\nADWT\nLeptin ( pg/ml)\nAdiponectin (µg/ml)\nResistin (ng/ml)\n RER\nRER\nEnergy Expenditure (kcal/h)\nLocomotor Activity (AU)\nLocomotor Activity (AU)\nLean Mass (g)\nFat Mass (g)\nBody Weight (g)\nFat Mass (g)\nLean Mass (g)\nFat Mass (g)\nFat Mass (g)\nFood Intake (g)\nFaecal energy (kJ/g)\nDay Night\nDay NightDay Night Day Night\nADWT\nADKO\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\nF\nH\nETC, ATP synthesis, heat production\nMetabolism of vitamins and cofactors\nAdipocytes\nIMC\nM⏀\nB CA\nE\nG\nFigure 2. Bud23 is required for normal white adipocyte function\nAdipocytes\nM⏀\nAdipocytePrecursors\nEndothelial\nIMCEndothelial\nAdipocytePrecursors\nI\nlog2(FC ADKO vs ADWT)\n-log10(padj)\nn=1535 n=1514\nn=15839\nADKO\nADWT\nADKO\nADWT\nADKOADWT\nADKOADWT\nAdipoq\nFabp4\nFasn\nAtgl\nBud23\n0.0\n0.5\n1.0\n1.5\n2.0\n2.5Relative Expression\nAtgl Dgat2 Fasn Lpl\n0\n1\n2\n3\n4\n5\nRelative expression\nLPLDGAT2ATGL FAS\nRelative Expression (AU) Fabp4 Fasn Bud23AtglAdipoq\nInsulin -   + -   + -   + -   + -   + -   +\nRelative Expression (RQ)\n∗∗∗ ∗∗∗\n∗∗∗\n∗ ∗\n∗∗∗\nAdipocyte function & \nLipid metabolism\nMitochondrial metabolism\nD\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n0.0\n0.5\n1.0\n1.5\n2.0\n2.5Area (μm2)\n0.0\n0.4\n0.8\n1.2\n1.6Area (μm2)\n✱✱✱\n0.0\n0.5\n1.0\n1.5\n2.0\nRelative Gene Expression\n✱\nFigure 3. Bud23 directs brown adipose tissue morphology, lipid metabolism and \nmitochondrial function.\nE\nG H\nJ K\n L\nlog2(FC ADKO vs. ADWT)\n-log10(padj)\nn=414 n=293\nn=2510\n(     )\nF\nADKO\nADWTADKOADWT\n-log10(padj)\nlog2(FC ADKO vs ADWT)\nn=2038 n=2284\nn=14616\nA B C\n37.3\n33.9\n30.6\n27.3\n23.9\n20.6\nADKO\n37.3\n33.9\n30.6\n27.3\n23.9\n20.6\nADWT\nns ns\nBATRectal\nTemperature (°C)\n50\n40\n30\n20\n10\n0\nBody Temperature (°C) ns\nD\nI\nlog2(FC ADKO vs. ADWT)\nmitochondria (n=554)\nall (n=1775)\nRPS (n=30)\np= 5.5e-10 \np< 1.0e-15 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\nA B C D E F\nFigure 4. Bud23 directs a selective impact to liver function and proteome\nLivWT\nM Q\nLEAK (nmol/min/mg)\n0\n5\n10\n15\n20\n25\n*\nOXPHOS (nm ol/m in/m g)0\n20\n40\n60\n80\n100\nRCR\nPO\n*\nRER\nFood Intake (g)\nEnergy Expenditure\n(kcal/h)\nLean Mass (g)\nFat Mass (g)\nBody Weight (g)\nDay Night Day Night Day Night\n∗ ∗\n0.0\n0.2\n0.4\n0.6\n0.8\n1.0Area (μm2)\n0.0\n0.2\n0.4\n0.6\n0.8\n1.0Circularity\n0.0\n0.5\n1.0\n1.5\n2.0\n2.5Area (μm2)\n0\n20\n40\n60\n80\n100\n120\n140TAG (mg/g tissue)\n0.05\nLiver TG\n(mg/g tissue)\nG\nLivKO\n0\n20\n40\n60Body Weight (g)\nBody Weight (g)\nH\n0\n100\n200\n300\n400\n500TAG (mg/g tissue)\n0.06\nLiver TG\n(mg/g tissue)\nI✽\nLivWT\nLivKO\nFed 48h Fast Fed 48h Fast48h Fast\nBlood Glucose (mM)\nβ-hydroxybutyrate (mM)\nBody Weight Loss (g)\nK J L\nLivWT LivKO\nN\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\nFigure 5. Hepatic Bud23 loss drives selective impact on translation efficiency\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\nFigure 6. Selective sensitivity of mitochondrial transcripts to Bud23 loss dictated \nby mRNA features\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\nFigure 7. Human genetics\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n0.0\n0.4\n0.8\n1.2\n1.6Normalised counts\n✱✱✱\n0.0\n0.2\n0.4\n0.618s/28s  Ratio\n0.06\nFigureS1. Loss of Bud23 function drives profound metabolic phenotype.\nJ\nIH\nK\nGF\nADWT ADKO\nADKO\nADWT\nADWT ADKO\nBody Weight (g)\n0 1 2 3 4 5 6\n20\n22\n24\n26\n28\nWeeks on thermoneutrality\nBody Weight (g)\nD E\n34\n35\n36\n37\n38\n39\nWeek 0-7Weeks at Thermoneutral\nBody Temperature ( °C)\nAdWT AdKO\nbp\n1500\n500\n300\nA B\nADKO\nADWT\nBody Temperature ( °C)\nC\nAdWT AdKO AdWT AdKO\ngWAT BAT Liver\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n5 6 7 8 9 10 11 12 13 14 15\n0\n10\n20\n30\n40\nAge (weeks)\nBody Weight (g)\nscWAT gWAT Liver BAT\n0.0\n0.1\n0.2\n0.5\n1.0\n1.5\n2.0Grams\n✱\n0.0\n0.5\n1.0\n1.5Relative Gene Expression 0.07\nDay Night\n0.4\n0.6\n0.8\n1.0\n1.2RER\n✱\n✱\n0\n1\n2\n3\n4\n5Fat masst (g)\n0\n10\n20\n30Lean mass (g)\n0.80\n0.85\n0.90\n0.95\n1.00\n1.05RER\n35\n36\n37\n38\n39Body Temperature (oC)\n30\n32\n34\n36\n38\n40Body Temperature (oC)\n✱✱✱✱\n✱✱✱✱\nFigureS2. Loss of BUD23 in brown adipocytes drives a mild phenotype\nBATWT BATKO\nA B C\nD E\nF G\nBATWT\nBATKO\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\nB\nC\nA\nFigure S3. Bud23 is not required for adipocyte differentiation\nD\nADWT ADKOADWT\nADKO\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n0\n5\n10\n15fpkms\n✱✱✱✱\nUcp1 Dio2 Adrb3 Cidea Nor1 Pgc1a Ppary\n0\n2\n4\n6\nRelative Gene Expression ✱\n✱✱\n✱✱\n✱\nFigure S4. Bud23 directs brown adipose tissue morphology, lipid metabolism and \nmitochondrial function.\nB\nLipid Metabolism Glucose Metabolism\nMitochondrial Metabolism DynamicsMaintenance\nMitochondrial\nSig\nC D\nA ADKO\nADWT\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n0.0\n0.5\n1.0\n1.5Relative Gene Expression\n0\n500\n1000\n1500\n2000AUC\n0 30 60 90 120\n0\n2\n4\n6\n8\n10\n12\n14\nTime (mins)\nGlucose (mmol/L)\nFigureS5. Bud23 targeting in Liver\n0.0\n0.5\n1.0\n1.5\nRelative Gene Expression ✱✱✱\nA C\nD\nB\nbp\n500\n300\nLivWT LivKO LivKO LivKO LivWT \nLivKO\nLivWT\nLivKO\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\nFigureS6. Extended Figure 5a\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\nFigureS7. Extended Figure 5b\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\nFigureS8. Extended Figure 6\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\nFigureS9. Human genetics\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 28 \nFigure Legends \nFigure 1. Loss of Bud23 function drives profound metabolic phenotype and shift in \nbody composition. \n(A) Body weight change of Bud23 fl/fl AdipoqCre+ (ADKO n=9) mice compared to littermate \ncontrols, Bud23fl/fl (ADWT, n= 11). (B) Body composition of ADKO and ADWT, with total fat mass \n(left and middle panels) and lean mass (right panel) . Histograms reflect body composition at \n24 wks. (C, D) Food intake (C) and faecal energy content (D) of ADWT and ADKO  mice  (food \nintake, n=6 per group; faecal energy content, n=5-7 mice/group). (E-H) Locomotor activity (E), \nEnergy Expenditure (F) and Respiratory Exchange Ratio (RER) (G,H) in ADWT and ADKO  mice  \n(n=6 mice/group). (I) Representative picture and histology of the gonadal white adipose tissue \n(gWAT) of AD WT and AD KO  mice. (J, K) Circulating serum lipids (triglyceride, NEFA and \nglycerol) and adipokines (leptin, adiponectin and resisting) in AD WT and ADKO  mice (n= 5 -7 \nmice/group). (L) Representative picture and histology of Brown Adipose Tissue (BAT) of ADWT \nand AD KO  mice mice. (M, N) Liver weight and triglyceride content of AD WT and AD KO mice \n(males, n=6 -8 mice/group). (O, P) Percentage of lipogenesis de novo in liver and Brown \nAdipose Tissue (BAT) in AD WT and AD KO  mice (n=5 mice/group) (O=% newly synthesised \n16:00 from 13C glucose; P= % newly synthesised 16:00). (Q) Body weight of ADKO  and ADWT \nduring 9 wks of high fat diet (HFD) feeding. (R) Fat and lean mass of male ADKO and ADWT on \nhigh fat diet (HFD). (S) Fat mass of female ADKO and ADWT mice on high fat diet (HFD). \nFigure 2. Bud23 is required for normal white adipocyte function. \n(A) Volcano plot of differentially expressed genes from RNA sequencing in AD KO  gonadal \nWhite Adipose Tissue (gWAT) relative to control. Significantly up - (right) and down - (left) \nregulated transc ripts are indicated in red (n=4 -5 mice/group ). (B) Virtual cytometry plot \ndisplaying the different cell types from RNA sequencing data in gWAT  from ADWT  and  ADKO  \nmice (n= 4-5 mice/group ). (C) Percentage of lipid -scavenging adipocytes, stressed lipid -\nscavenging adipocytes and lipogenic adipocytes from gonadal White Adipose Tissue RNA \nsequencing data in ADWT  and ADKO  mice (n=4-5 mice/group). (D) Gene ontology enrichment \nof major pathways affected by Bud23 deletion in gWAT. adipocytes (columns represents \nproportion of pathway, black dot represents p value).  (E) Specific gene expression changes \nof genes related to Adipocyte function, Lipid metabolism and Mitochondrial function shown as \nLog2 fold change (FC), in gWAT. (F) Western blot representative images and quantification of \nATGL, DGAT2, FAS, LPL in gonadal White Adipose Tissue from AD WT  or ADKO  mice (n=4-5 \nmice/group). (G) Relative gene expression of adipogenic genes in isolated mature adipocytes \n(from gWAT) from AD WT  control or AD KO mice (n = 3 mice/group). (H) Intraperitoneal insulin \ntolerance test (G) and Area Under the Curve (AUC) of ADWT mice and ADKO (n = 6 for control \ngroup; n = 6 for knock-out group). (I) Relative gene expression of adipogenic genes in gonadal \nWhite Adipose Tissue Explants from ADWT   control or ADKO mice treated with Insulin or vehicle  \n(n = 3-6 mice/group). \nFigure 3. Bud23 directs brown adipose tissue morphology, lipid metabolism and \nmitochondrial function. \n(A, B) Representative Infrared thermal images of Brown Adipose Tissue (BAT) skin area and \nrectal and BAT temperature of AD WT and AD KO  mice (n=5 -6 mice/group). (C, D ) Body \nTemperature of AD WT and AD KO mice  exposed to 4 C during 6 hours.  ( E) Volcano plot of \ndifferentially expressed genes from RNA sequencing in AD KO Brown Adipose Tissue (BAT) \nrelative to AD WT  BAT. (n = 8 mice/group). (F) Gene ontology enrichment of major pathways \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 29 \naffected by Bud23 deletion in adipocytes (columns represent proportion of pathway, dots \nrepresent p value). (G) Volcano plot of total proteins from Proteomoics analysis in AD KO BAT \nrelative to AD WT  BAT. ( n= 7-8 mice/group). (H) Gene ontology analysis of major protein \npathways affected by Bud23 deletion in adipocytes (columns represent proportion of pathway, \ndots represent p value). (I) Genotype-specific differences in global protein abundance (RPS= \nsmall subunit ribosome).  (J) BAT mitochondria copy number of AD WT and ADKO mice. (K-L) \nRepresentative electron microscopy images ( K), mitochondrial area quantification (average \nper mouse) ( L,  left panel),  and area of all mitochondria analysed ( L,  right panel)  of BAT \nslices from ADWT and ADKO. \n \nFigure 4. Bud23 directs a selective impact to liver function and proteome. \n(A-C) Body weight, lean mass and fat mass at terminal point of Bud23 fl/fl AlbCreCre+ mice \n(Bud23 knock-out targeted to hepatocytes (Liv KO), compared to littermate controls, Bud23 fl/fl \n(LivWT) (n=5-6 mice group). (D-F) Energy expenditure, respiratory exchange ratio (RER) and \nfood intake of Liv KO, compared to littermate controls, Liv WT (n=5-6 mice/group). (G) Hepatic \ntriglycerides levels of LivKO mice and their littermate controls, LivWT (n=6 mice/group). (H, I) \nBody weight and hepatic triglycerides leves of LivKO mice and their littermate controls on High \nFat Diet (HFD). (K-L) Body weight loss, blood glucose and -hydroxybutirate levels of LivKO \nmice and their littermate controls, at feeding state and after 48 hours fasting.  (M, N) \nRepresentative electron microscopy images (M) and area of all mitochondria analysed (N)  of \nliver tissue slices from LivWT and LivKO. (O-Q) OROBOROS mitochondrial respiration analysis \nof isolated mitochondria from liver of LivKO and their control littermates \n \nFigure 5. Hepatic Bud23 drives selective impact on translation efficiency. \n(A) Volcano plot of differentially expressed genes in liver from RNA sequencing in Liv KO vs. \nLivWT. Significantly up- (right) and down- (left) regulated transcripts with adjusted p value <0.05 \nand absolute log2 fold change >1.5 are indicated in red. All 6 samples, irrespective of the \nZeitgeber time points (3 for ZT6 and 3 for ZT18), were compared betwe en genotypes. ( B) \nVolcano plot as in ( A) but for Ribo -seq data.  (C) Volcano plot as in ( A) and ( B) but for \ntranslation efficiencies (TE, ratio of normalised Ribo-seq to RNA-seq reads calculated per liver \nsample). (D) GO-term analysis on differentially expressed gene sets shown in (A) - (C). Most \nsignificant enrichments are shown. Size of circles indicates the number of genes within a \ncategory; colour coding indicates statistical significance, as illustrated in the le gend in lower \npart of panel.  (E) Boxplot representation of log 2 fold-changes between Liv KO vs. LivWT for all \ntranscripts, mitochondrial transcripts (annotation from MitoCarta3.0), and ribosomal \ntranscripts (from GO cellular component ). Log2 fold changes were calculated between all 6 \nsamples of each genotype. Boxes cover the interquartile range, with whisters extending to \n1.5x interquartile range; median marked in red. p -values indicate significance assessed by a \nranksum test.  (F) Boxplot representation as in ( E) for Ribo -seq data. ( G) Boxplot \nrepresentation as in (E) for translation effficiency. (H) Translation efficiency in LivKO (pink) and \nLivWT (dark green) at ZT6 (light phase) and ZT18 (dark phase ) of selected ribosomal \nprotein/translation factor mRNAs (n=42) that contain a 5’-TOP sequence and that were \npreviously identified as particularly rhythmic in their TE 24. For each transcript, TE is expressed \nrelative to the level in LivWT at ZT6, which was set to 100%. (I) Metagene plot aligning ribosome \nfootprint A-sites relative to coding sequence (CDS) start (left) and stop (right) codons, for LivKO \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 30 \n(pink) and Liv WT (dark green). Transcripts are included with A sites at 25 or more positions \nwithin the shown range of nucleotides (n=8591). Dots below the plot indicate positions with a \nratio between A sites for Liv KO and Liv WT significantly higher (green) or lower (blue) than 0 \ncalculated using a directional one -sample t test. The inset at the top right shows a zoom on \nthe area around the start codon, indicating a specific decrease of ribosomes in LivKO over the \nfirst ~20 codons and the 5’ UTR. (J) Quantification of glob al read distribution in LivKO (pink) \nand LivWT (dark green) for 5’ UTR, CDS (all and first 20 codons in light colors) and 3’ UTR. \nError bars indicate the standard deviation across the 6 samples per genotype. P -values \nindicate statistical significance assessed by a two -tailed independent t-test. (K) Structural \nmodel of human late initiation complex 48S-5 (PDB 8pj5) at the site of m7G1639. Highlighted \nresidues apart from m 7G1639 (with N7 methyl group marked by blue halo) are Met -tRNAiMet \n(backbone traced in green; anticodon loop in orange), tRNA residue C41 that forms extensive \nhydrogen bonds with m 7G1639. Trp35 of eS25/RPS25  is placed for cation-π stacking onto \nm7G1639. mRNA trace is marked in yellow and 18S rRNA surface in grey.  \n \nFigure 6. Selective BUD23 regulation is associated with specific transcript features.  \n(A) Schematic of analysis of correlation between transcript features and BUD23 -selective \nregulation. Three “Outcomes”, shown at the top of figure, were extracted from the ribo -seq \nprofiles and quantified transcriptome-wide – translation efficiency (TE) change on the 5’ UTR \n(indicative of uORF translation), TE change on the whole CDS (i.e. change in overall \ntranslation rate) and specifically within the first 20 codons (the “ramp effect”). These were \ncorrelated with “Features” of transcripts, pertaining to the 5’ UTR – presence of ATG, (C/G/T) \nTG, UTR length as determined from cap analysis of gene expression (CAGE) data, GC \ncontent – or the CDS initiation codon environment – KOZAK score, GC content, minimal free \nenergy of folding (MFE), codon bias. (B) Spearman correlation coefficients between transcript \nsequence features  (y-axis) and translation efficiency log2 fold -changes (beween Liv KO and \nLivWT) at 5’ UTRs (first panel), CDSs (second panel), and the Start codon region (first 20 \ncodons) relative to the whole CDS (third panel). TE fold changes were calculated as the \naverage over the separate TE fold changes for the two Zeitgeber time points (ZT6 and ZT18). \nBars are coloured according to correlation p -value. (C) Metagene plot aligning ribosome \nfootprint A-sites relative to coding sequence (CDS) around start (left panels) and stop (right \npanels) codons, for Liv KO (pink) and Liv WT (dark green) for all transcripts (upper panels) and \ntranscripts encoding mitochondrial proteins (based on MitoCarta 3.0; lower panels) . See Fig. \n2I for additional information. (D) Analysis of GC content within the 60 nt after the start codon, \nfor transcripts encoding mitochondrial proteins (red) vs. all transcripts (black), indicating \nsignificantly increased GC-content in the mitochondrial group. In the upper part of the panel, \nthe number of transcripts used for the analysis (n), their median (also shown as diamond \nsymbol in graph) and the p -value for the difference using a ranksum test are given. ( E) As in \n(D), for codon bias/selectivity for the first 20 codons. Mitochondrial groupis composed of  \nsignificantly less abundant codons.  (F) As in (E), for 5’ UTR length as determined from cap \nanalysis of gene expression (CAGE) data. Mitochondrial protein mRNAs are characterised by \nsignificantly shorter 5’ UTRs. (G) Analysis of codon composition of first 20 codons (left panel) \nand whole CDS (right panel), comparing all mRNAs (x-axis) with mitochondrial protein mRNAs \n(y-axis). Codons are labelled with a 20% higher (red) or lower (blue) abundance in \nmitochondrial protein mRNAs that in all mRNAs. More abundant codons correspond largely to \nGC-rich codons associated with amphiphilic helices of mitochondrial targeting signals \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 31 \nenriched in the first 20 codons of mitochondrial protein mRNAs. (H) Scatter plot of translation \nefficiency log2 fold-changes at the first 20 codons relative to whole CDS, beween Liv KO and \nLivWT, and GC -content in the same +60 nt region of the transcript. All transcripts and  \nmitochondrial protein mRNAs are shown as blue  and red circles, respectively. The local \nregression fits (LOESS) for all and mitochondrial transcripts are shown in pale blue and pink, \nrespectively. Spearman correlation coefficient and p -value of correlation are given above, \nalongside number of transcripts used in the analysis. Specific genes/transcripts are \nhighlighted in ochre.  (I) As in (H), but showing correlation between whole CDS translation \nefficiency fold -change (Liv KO vs. Liv WT)and GC -content of +60 nt region.  (J) As in (H), but \nshowing correlation between 5’ UTRs translation efficiency fold-change (LivKO vs. LivWT) and \n5’ UTR length/CAGE distance.  (K) As in (H), but showing correlation between CDS fold -\nchange (Liv KO vs. Liv WT) and 5’ UTR length/CAGE distance.  (L) Quantification of CDS \ntranslation efficiency change (Liv KO vs. LivWT) for selected transcripts labelled in panels (H) - \n(K). Values for ZT6 and ZT18 are plotted individually as circles, with orange bars showing the \nmean. (M) Genome browser tracks showing read pile -up along Bola3 (BolA familiy member \n3) transcript in LivWT (upper track, dark green) and LivKO (lower track, pink). Grey track shows \nCAGE signal from mouse liver FANTOM5 data (i.e. this 5’ UTR is extremely short). Transcript \nmodel and scale bar shown in blue. Green shading placed on first 20 codons, with GC content \nwritten above (82%). Data from ZT18 are plotted, combining reads from the three biological \nreplicates. (N) As in (M) for Cox6a1 (Cytochrome c oxidase subunit 6A1). This transcript has \nvery high GC content and very short 5’ UTR. (O) As in (M) for Ak3 (Adenylate kinase 3). This \ntranscript h as very high GC content and relatively short 5’ UTR.  (P) As in (M) for Uqcc2 \n(Ubiquinol-cytochrome c reductase complex assembly factor 2) . This transcript has average \nGC content yet very short 5’ UTR.  (Q) As in (M) for Fasn (Fatty Acid synthase) . Fasn has \naverage GC and a relatively short 5’ UTR. (R) As in (M) for Cisd3 (CDGSH iron sulfur domain \n3). Theposition of a uORF is indicated by orange shading.  (S) As in (R) for Etfdh (Electron \ntransferring flavoprotein, dehydrogenase) . (T) As in (R) for Igfbp4 (Insulin-like growth factor \nbinding protein 4). \n \nFigure 7. Human Genetic Evidence Linking Whole Blood BUD23 Expression to \nCardiometabolic Disease Outcomes. \nThis figure presents Mendelian Randomization (MR) analyses modeling the effect of \ngenetically increased BUD23 expression in whole blood on human disease and biomarker \ntraits. (A) Forest plots display MR estimates (Beta ± 95% CI) across cardiometabolic outcomes \nin both primary and replication GWAS datasets. Estimates reflect the direction and magnitude \nof association per unit increase in genetically predicted BUD23 expression. ( B) Enrichment \nanalysis summarizing phenome -wide MR associations across ~1,200 tra its in the Million \nVeteran Program (MVP), grouped by clinical domain. Bar heights represent enrichment \nsignificance, indicating domains with disproportionate representation of associated traits. ( C) \nHomunculus schematic mapping representative traits that surpassed FDR correction (FDR < \n0.05) within each disease category. Arrows denote the direction of association per increased \nBUD23 expression: upward arrows indicate positive associations with the o utcome, while \ndownward arrows indicate negative associations. \n \nSupplementary Figure Legends  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 32 \nFigure S1. Loss of Bud23 function drives profound metabolic phenotype. \n(A) Representative agarose gel image of the recombination to ensure knockdown in gonadal \nWhite Adipose Tissue (gWAT) and Brown Adipose Tissue (BAT) (liver was included as a \nnegative control). Band at approximately 350bp in gWAT and BAT of ADKO animals indicated \nrecombination. ( B) Bud23 expression from RNAseq normalised counts gonadal White \nAdipose Tissue (gWAT) from AD WT or ADKO. (C) Body temperature over 24 hours and total \nday/night of AD WT or ADKO. (D, E) Body temperature, body weight change and fat mass of \nADWT or ADKO at thermoneutrality (28 C). (F, G) Body weight and total fat mass of female \nADWT or ADKO at age 12 weeks and 24 weeks. ( H, I) Tissue mass and body mass of male \nADKO mice and their littermate controls fl at 10 weeks old and 24 weeks old.  (J, K ) H&E \nrepresentative images and Percentage of different Adipocyte diameter (um) in gonadal White \nAdipose Tissue (gWAT) of ADKO . \n \nFigure S2. Loss of BUD23 in brown adipocytes drives a mild phenotype.  \n(A) Relative Bud23 expression in BAT of BATWT and BATKO mice. (B, C) Body weight change \nand fat and lean mass of BATKO, compared to littermate controls BATWT. (D) Fat depots mass \nof BATWT and BAT KO mice.  (E) Representative picture and histology of the Brown Adipose \nTissue (BAT) of BATKO and BATWT. (F, G) Respiratory Exchange Ratio (RER) and body \ntemperature over 24 hours and at day/night of BATKO and BATWT. \n \nFigure S3. Bud23 is not required for adipocyte differentiation. \n(A, B ) Representative pictures of Oil Red O (ORO) staining (A) and quantification (B) in \ndifferentiated gWAT adipocytes from AD WT and AD KO mice. (C) Relative mRNA expression \nlevels of endocrine hormones, lipolitic and lipogenic genes in gonadal White Adipocytes \n(gWAT) pre-adipocytes from AD WT and ADKO mice, before and after differentiation  (each dot \nrepresents a biological replicate).  (D) Gene Ontology (GO) terms for Biological Process, \nCellular Component and Reactome are shown, with directionality indicated as downregulated \nor upregulated, the number of proteins associated with each GO term and the corresponding \nfalse discovery rate (FDR) values. \n \nFigure S4. Bud23 directs brown adipose tissue morphology, lipid metabolism and \nmitochondrial function \n(A) Bud23 expression from RNAseq in BAT of AD WT and AD KO mice. (B) Relative mRNA \nexpression levels of themogenic genes in BAT from ADWT and ADKO mice. (C) Gene Ontology \n(GO) terms for Biological Process, Cellular Component and Reactome are shown, with \ndirectionality indicated as downregulated or upregulated, the number of proteins associated \nwith each GO term and the corresponding false discovery rate (FDR) values, in BAT in AD KO \nmice. (D) Specific gene expression changes of genes related to glucose and lipid metabolism \n(left panel) and Mitochondrial function and metabolism (right panel) shown as Log2 fold \nchange (FC), in BAT in ADKO mice \n \nFigure S5. Bud23 targeting in Liver. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 33 \n(A, B ) Representative agarose gel image of the recombination to ensure knockdown and \nrelative Bud23 epression in liver of LIVWT and LIVKO. (C) Mitochondria copy number of liver in \nliver of LIVWT and LIVKO mice. (D) Glucose levels during Insulin Tolerance Test and Area Under \nthe Curve (AUC) LIVWT and LIVKO mice on High Fat Diet (HFD). \n \nFigure S6. Extended Figure 5A. \n(A) Schematic showing the design of the ribosome profiling experiment. ( B) Principal \ncomponent analysis on the RNA -seq data, showing separation on PC1 by genotype – LivKO \n(pink) vs. LivWT (dark green) – and by timepoint on PC2. (C) As in (B) for the ribosome footprint \ndata. (D) As in (B) for translation efficiencies (i.e., ratio of normalized ribo -seq to RNA -seq \nreads per gene). In this analysis, main separation occurs by genotype (PC1) with little \ninfluence by ZT. (E) Analysis of dwell times of elongating ribosomes using the RiboDT pipeline \n29. Upper two sets show heat-maps of dwell times for E- P- and A-sites in LivKO and LivWT data \n(ZT6 and ZT18 combined data) and indicate very similar preference for codon occupancies \nbetween genotypes. Lower part shows fold-change differences in dwell times, LivKO vs. LivWT, \nagain indicating relatively mild differences in codon-specific dwelling between genotypes. (F) \nBox plots of dwell time log2 fold changes between genotypes (same as lower panel of (E)) at \nE-, P- and A-sites indicate that overall codon-specific variation appears more prominent at the \nP-site as compared to E- or A-sites. \n \nFigure S7. Extended Figure 5B. \nOverview of structural models of human early (48S -1) to late initiation complexes (48S -5) \nemphasizing m7G1639 surroundings (based on PDB structures 8pj1-5). Left part of the Figure \nillustrates the sequence of 48S state transitions to 80S formation with major rearrangements \nbetween them. Middle panels are zoom -out, and right panels zoom-in views of the structural \nmodels. Important highlighted residues apart from m7G1639 (with N7 methyl group marked by \nblue halo in some of the panels) are Met -tRNAiMet (backbone traced in dark green; anticodon \nloop in orange), tRNA residue C41 that forms extensive hydrogen bonds with m 7G1639; \neS25/RPS25 (backbone traced in purple) Trp35 is progressively brought close to m7G1639 \nup to a point where it makes  cation-π stacking onto m 7G1639 in the last structure). The C -\nterminal region of 40S ribosomal protein RPS18/uS13 backbone is visible as a teal ribbon, \nmRNA trace is marked in yellow and 18S rRNA surface in grey.  \n \nFigure S8. Extended Figure 6. \n(A) Complementary to Fig. 6B, the graph shows a correlation analysis between 5’ UTR TE \nfold-change and Start TE fold-change, for all (blue), mitochondrial (red) and ribosomal (green) \ntranscripts. The local regression fit (LOESS) is shown as line for each transcript group. \nSpearman correlation coefficient and p -value of correlation are given above, alongside \nnumber of tran scripts used in the analysis. (B) As in (A) for correlation of 5’ UTR TE fold -\nchange and CDS TE fold -change. (C) As in (A) for correlation of Start TE fold -change and \nwhole CDS TE fold-change. (D) As in (A) for correlation of Start TE fold-change and Start TE \nfold-change relative to whole CDS TE fold -change. (E) As in (A) for correlation of Start TE \nfold-change (ncorrected for CDS TE fold-change) and CDS TE fold -change. (F) Left panels: \nMetagene plot aligning ribosome footprint A -sites relative to coding sequence (CDS) around \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint \n\n 34 \nstart and stop codons, for Liv KO (pink) and Liv WT (dark green) for transcripts with specifically \nhigh (upper) and low (lower) GC content in 5’ UTR. Right panels: Alignment of reads to the \nannotated transcriptional start site (TSS), and plotted separately for reads that fall into \nannotated 5’ UTR (left) and CDS (right). Black line shows cumulative density of 5’ UTRs (left) \nand CDS starts (right). (G) As in (F), but for transcripts with particularly long (upper) or short \n(lower) 5’ UTRs according to the CAGE data. (H) Cross-correlation analysis between transcript \nsequence features considered in the analysis.  (I) Linear regression model’s prediction of TE \nFC variances explained by different transcript sequence features (color -coded; listed at \nbottom). TE FCs are predicted for 5’ UTRs, CDSs and Start regions (first 20 codons, relative \nto whole CDS FCs). Total explained variance by a combination of transcript sequence features \nis given at the right of the bars.  (J) Analysis of GC content within the 60 nt upstream of the \ninitiation codon, for transcripts encoding mitochondrial proteins (red) vs. all transcripts (black), \nindicating no significant differences in GC-content in the mitochondrial group. In the upper part \nof the panel, the number of transcripts used for t he analysis (n), their median (also shown as \ndiamond symbol in graph) and the p -value for the difference from a ranksum test are given. \n(K) As in (J), for GC content of the full 5’ UTR, for transcripts encoding mitochondrial proteins \n(red) vs. all transcripts (black). (L) As in (J), for Minimal Free Energy (MFE) of RNA folding in \nthe window +/- 60 nt around the CDS initiation codon. (M) As in (J) for ATG count within the \n5’ UTR (and taking into account ribosome footprint coverage, serving as a proxy for AUG -\ninitiated uORFs). (N) As in (M), but for alternative uORF start codons, (C/G/T)TG.  (O) As in \n(J) for Kozak score. (P) As in (F), but for transcripts with particularly high (upper) or low (lower) \nGC content in the +60 nt region. (Q) Percentages of all (blue) and of mitochondrial protein \ntranscripts (red) with G or C nucleotides at positions around the start and stop codons. A 10 \nnt moving window was used for averaging to smoothen the curves. It is evident that \nmitochondrial transcripts show higher GC content at the CDS beginning as compared to all \ntranscripts.   \n \nFigure S9. Mendelian Randomization Analysis of Whole Blood METTL5 and TRMT112 \nExpression Across Cardiometabolic Traits and Tissues. \nThis figure presents Mendelian Randomization (MR) analyses examining the association of \ngenetically predicted expression of METTL5 and TRMT112 in whole blood with \ncardiometabolic outcomes. ( A) shows forest plots of MR estimates (Beta ± 95% CI) for \nMETTL5 expression across primary and replication GWAS datasets. ( B) presents the \ncorresponding MR results for TRMT112. ( C) displays a heatmap comparing the Z -scores \n(Beta/SE) for BUD23 across cardiometabolic tissues, including whole blood, subcutaneous \nand visceral adipose tissue, skeletal muscle, and heart atrial appendage. Asterisks (*) denote \nnominal significance (P < 0.05), and grey cells indicate unavailable inst ruments. (D) shows \nenrichment analyses for METTL5 and TRMT112, respectively, across predefined clinical \ncategories in the Million Veteran Program (MVP). Bar heights reflect enrichment significance, \nand domains meeting Bonferroni or nominal thresholds are h ighlighted. All effect directions \ncorrespond to associations per increased gene expression in whole blood. \n \n \n \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted May 19, 2025. ; https://doi.org/10.1101/2025.05.16.654455doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}