{"paper_id":"2b3d3fe2-eacf-46f7-80bb-0e2fa6fab35a","body_text":"Page 1 of 27 \n \n 1 \nLocal translational program at the muscle-tendon junction endows domain identity in 2 \nmuscle syncytia 3 \n 4 \n 5 \nShort Title: Translation control of muscle-tendon junction 6 \n 7 \nJuliana de Carvalho Neves1,2,3,4,†, Nour El Khazen1,2,3,4,†, Coalesco Smith1,2,3,4,‡, Vedran Franke6,‡, 8 \nSakulrat Mankhong 1,2,3,4, Edgar Jauliac 7, Laura Yedigaryan 2,3,4,5, Elise Lefebvre 2,3,4, Altuna 9 \nAkalins6, Pascal Maire7, and Minchul Kim1,2,3,4,* 10 \n 11 \n 12 \nAffiliations  13 \n 14 \n1, Lab of syncytial cell biology, IGBMC, Institute of Genetics and of Molecular and Cellular 15 \nBiology, 67400 Illkirch, France 16 \n2, Universite´ de Strasbourg, Strasbourg, France  17 \n3, CNRS, UMR 7104, 67400 Illkirch, France 18 \n4, INSERM, UMR-S 1258, 67400 Illkirch, France 19 \n5, Lab of Pathophysiology of neuromuscular diseases, IGBMC, Institute of Genetics and of 20 \nMolecular and Cellular Biology, 67400 Illkirch, France 21 \n6, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, 22 \nHelmholtz Society, Berlin, Germany 23 \n7, Université Paris Cité, CNRS, Inserm, Institut Cochin, F-75014 Paris, France 24 \n 25 \n*, corresponding author. Email: kimm@igbmc.fr 26 \n†, co-first authors 27 \n‡, co-second authors 28 \n 29 \n 30 \n 31 \n 32 \n 33 \n 34 \n 35 \n 36 \n 37 \n 38 \n 39 \n 40 \n 41 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 2 of 27 \n \nAbstract 42 \nHow cells establish specialized subdomains is a fundamental question in cell and tissue biology. 43 \nSkeletal muscle fibers, among the largest cells in the body, are multinucleated and form distinct 44 \nregions such as the neuromuscular and myotendinous junctions (MTJ), the latter forming a critical 45 \ninterface between muscle and tendon that transmits contractile force. While transcriptional 46 \nheterogeneity among myonuclei has been described, whether local translation contributes to domain 47 \nidentity remains unknown, largely due to the lack of tools for domain-specific manipulation. Here, 48 \nwe introduce MTJ-AAV, a viral system that enables selective genetic targeting of MTJ myonuclei. 49 \nThis approach allowed MTJ -specific ribosome tagging and revealed extensive translational 50 \nregulation underlying MTJ biology and its remodeling during exercise. Interestingly, untranslated 51 \nregions of these transcripts were sufficient to control regionalized translation. Notably, the KLF -52 \nfamily transcription factors emerged as translationally upre gulated targets at the MTJ, where they 53 \ndrive local gene expression. Our findings establish local translation as a key layer of subcellular 54 \nspecialization and provide a versatile toolkit for dissecting spatial molecular regulation within 55 \nmuscle syncytia. 56 \n 57 \nIntroduction 58 \nTo precisely coordinate diverse biochemical activities and biological functions, cells must establish 59 \nspecialized subcellular domains. How these domains form, how they are maintained and remodeled 60 \nremain a fundamental question in cell biology. Skeletal muscle cells, or myofibers, provide a unique 61 \nmodel to study this process because of their exceptionally large cytoplasm, which contains hundreds 62 \nto thousands of nuclei . Different myofiber regions interact with distinct external cues, giving rise 63 \nto specialized functional domains. For example, myofiber central regions interact with motor 64 \nneurons, forming the neuromuscular junction (1, 2), while their ends attach to tendons, creating the 65 \nmyotendinous junction (MTJ) (3, 4). The MTJ plays a critical role in dissipating the mechanical 66 \nforces generated during contraction and transmitting them to tendons. Consequently, the MTJ is 67 \nhighly susceptible to injury, and its dysfunction leads to muscle rupture (5). Understanding how 68 \nthese muscle domains are regulated not only advances our knowledge of myology and disease, but 69 \nalso provides broader insights into how cells spatially and functionally organize their intracellular 70 \nspace. 71 \nPrevious studies using single -nucleus RNA sequencing (snRNA -Seq) have shown that muscle 72 \nnuclei at specialized domains adopt distinct gene expression programs, conceptually resembling 73 \ncellular differentiation in multicellular tissues (6-9). These studies highlight transcriptional control 74 \nas an important mechanism for domain formation. However, the contribution of post-transcriptional 75 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 3 of 27 \n \nregulation remains largely unexplored. Translational regulation , the control of protein synthesis 76 \nfrom existing mRNA , has emerged as a central mechanism across diverse biological contexts, 77 \nincluding gametogenesis, neurodevelopment, cancer, and inflammatory signaling (10-14). In  78 \nmuscle, translational profiling has thus far been limited to whole myofibers (15-17), leaving little 79 \ninsight into how localized protein synthesis supports muscle domain biology . Interestingly, 80 \nalthough the MTJ undergoes structural remodeling during exercise and aging (18, 19), snRNA-Seq 81 \nstudies have not detected major transcriptomic changes in MTJ myonuclei (7, 20). This discrepancy 82 \nsuggests that post-transcriptional regulation, particularly translation, may play a key role in these 83 \nadaptations.  84 \nConventional genetic approaches in the muscle field lack spatial resolution and target all myonuclei 85 \nindiscriminately. The distinct lack of genetic tools that can manipulate specific domains is the major 86 \nroadblock preventing the field from studying regionalized molecular events in myofibers. To 87 \novercome this limitation, we reasoned that promoters of MTJ-specific genes identified from 88 \nsnRNA-Seq datasets could be leveraged to drive gene expression specifically within this domain. 89 \nHere, we develop a versatile adeno-associated virus (AAV) system that enables expression of 90 \ngenetic tools specifically at the MTJ. Using this platform, we profile the local translational 91 \nlandscapes of the MTJ and uncover extensive translational regulation that becomes markedly 92 \namplified during endurance exercise. We further demonstrate that untranslated regions (UTRs) of 93 \nMTJ-enriched transcripts are sufficient to confer localized translation and that KLF transcription 94 \nfactors are translationally upregulated to drive local gene expression. Together, these findings 95 \nreveal a previously unrecognized layer of spatial regulation underlying this specialized muscle 96 \ndomain, and provide a powerful toolkit for dissecting subcellular regulation in a syncytium. 97 \n 98 \n 99 \n 100 \n 101 \n 102 \n 103 \n 104 \n 105 \n 106 \n 107 \n 108 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 4 of 27 \n \nResults  109 \n 110 \nDevelopment of MTJ-AAV 111 \nTo selectively target the MTJ, we focused on Tigd4, a highly specific marker of MTJ nuclei (6). 112 \nWe synthesized a fragment of the Tigd4 promoter, spanning from the transcription start site to -3.3 113 \nkb, and cloned it into an AAV plasmid to drive the expression of super -fold GFP (sfGFP) fused 114 \nwith four SV40-derived nuclear localization signals (NLS). The 3.3 kb length was chosen to 115 \naccommodate AAV genome size constraints. The resulting pAAV -Tigd4-NLS-sfGFP construct 116 \nwas packaged i nto the MyoAAV4a serotype (21) and intramuscularly injected into 10 -week-old 117 \nwild-type ( WT) mice (Fig. 1A). As a control, we performed parallel experiments using the 118 \nconventional CK8 promoter, which is active in all myonuclei. 119 \nAs expected, CK8 promoter-driven AAV broadly labeled the entire muscle tissue with NLS-sfGFP 120 \nwhen tibialis anterior  (TA) muscles were examined one -month post -injection. In contrast, the 121 \nTigd4 promoter restricted reporter expression to the MTJ, identifiable by characteristic DAPI -122 \nstained structures penetrating into the muscle (Fig. 1B). Quantification of labeling efficiency using 123 \nGFP immunostaining combined with RNAscope for the MTJ markers Tigd4 and Col22a1 revealed 124 \nthat approximately 80% of MTJ nuclei (defined by co-expression of Tigd4 and Col22a1) were GFP-125 \npositive (Fig. 1C  and 1D). Conversely, only ~ 20% of GFP -positive nuclei were not MTJ nuclei, 126 \nindicating high labeling specificity. Importantly, all GFP-positive nuclei were myonuclei as they 127 \nalso expressed Ttn, a pan-myonuclei marker (Fig. 1D), and located adjacent to the MTJ.  128 \nTo confirm the molecular identity of the labeled nuclei via an orthogonal method, we isolated GFP-129 \npositive nuclei by FACS (Fig. 1E) and performed bulk RNA sequencing (Fig. 1 F). The analysis 130 \nshowed that many representative MTJ markers, including Tigd4, Col22a1, Resf1, Lama2 and 131 \nAnkrd1, to be enriched in GFP-positive nuclei from the Tigd4 promoter-driven AAV compared to 132 \nthose labeled by the CK8 promoter (Fig. 1F). From here on, we refer to our new AAV system that 133 \nuses Tigd4 promoter as ‘MTJ-AAV’. 134 \nWe next tested whether we could express other genetic tools at the MTJ as it will broaden the utility 135 \nof MTJ -AAV. As such, we asked whether MTJ-restricted recombination could be achieved  by 136 \ndelivering Cre  recombinase. MTJ -AAV carrying Cre -NLS was intramuscularly injected into 137 \nRosa26-LSL-H2B-GFP reporter mice (Fig. 2A), resulting in GFP labeling around the MTJ (Fig. 138 \n2B). Quantification using MTJ markers showed a similar labeling efficiency  of ~75% (Fig. 2C). 139 \nHowever, off -target labeling was higher in this model, with ~40% of GFP -positive nuclei not 140 \ncorresponding to MTJ nuclei (Fig. 2C).  Similar to our previous approach, all labelled nuclei were 141 \nTtn-positive, confirming their myonuclear identity. 142 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 5 of 27 \n \nTo investigate why there was higher off -target labelling with Cre compared with NLS -sfGFP, we 143 \nfirst tested whether Cre protein diffuses to neighboring nuclei. Cre/GFP co-immunohistochemistry 144 \nrevealed that only ~56% of GFP -positive nuclei exhibited a detectable Cre signal (Fig. S1A  and 145 \nS1B). Given the ~40% off -target labeling, this result suggests that Cre protein remains largely 146 \nrestricted to MTJ nuclei with minimal  diffusion, although we cannot rule out the possibility that 147 \nundetectable amounts of Cre protein contribute to leaky recombination.  Next, we tested whether 148 \nH2B-GFP protein is more diffusive than the NLS-sfGFP protein used earlier by performing a side-149 \nby-side comparison . Expressing H2B -GFP via MTJ -AAV resulted in a similar ~80% labeling 150 \nefficiency but a higher off-target labeling rate of ~40% compared to ~20% with NLS-sfGFP (Fig. 151 \nS1C). These findings indicate that diffusion properties of expressed factors  can vary and  require 152 \ncareful assessment before downstream experiments. 153 \nFinally, w e tested whether MTJ -AAV could be delivered systemically  (Fig. 2D) . Intra-orbital 154 \ninjection of MTJ -AAV carrying Cre -NLS into the H2B -GFP reporter successfully labeled MTJ 155 \nnuclei across multiple muscles, including the gastrocnemius and diaphragm (Fig. 2E).  156 \n 157 \nGenetic labeling of MTJ ribosomes and translational profiling 158 \nAlthough protein synthesis is central to cellular identity and function, regionalized translation at the 159 \nMTJ has not been explored, largely due to the absence of suitable tools. We hypothesized that 160 \ncombining MTJ-AAV with genetic ribosome tagging would enable selective profiling of ribosome-161 \nassociated transcripts. 162 \nWe therefore intramuscularly injected MTJ-AAV Cre-NLS or CK8-Cre-NLS into LSL-HA-Rpl22 163 \n(RiboTag) mice  (22), in which  Cre-mediated recombination introduces an HA tag into the 164 \nendogenous Rpl22 ribosomal subunit (Fig. 3A). HA immunohistochemistry confirmed the tight 165 \nexpression of HA-Rpl22 at the MTJ (Fig. 3B).  We also performed the same experiment in Ribo -166 \nTrap mice ( Rosa26-LSL-GFP-L10a) (23) and observed similar results. However, GFP -L10a 167 \ndisplayed broader expression than HA -Rpl22 (data not shown), likely due to its overexpression 168 \nfrom the Rosa26 locus. For this reason, we focused on the RiboTag model for subsequent analysis. 169 \nTo isolate ribosome -associated transcripts, TA tissue  lysates were subjected to anti -HA 170 \nimmunoprecipitation. Bioanalyzer profiles confirmed robust RNA recovery from Cre -delivered 171 \nRiboTag muscles, with no detectable RNA in saline -injected controls (Fig. 3C). RNA sequencing 172 \nwas then performed to map the MTJ translational landscape (n = 4 for whole muscle, n = 3 for 173 \nMTJ). Notably, MTJ markers Tigd4 and Col22a1 were among the most enriched riboso me-174 \nassociated transcripts, validating the specificity of our dataset (Fig. 3D and 3E). 175 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 6 of 27 \n \nStrikingly, ~80% of MTJ ribosome -enriched transcripts (17 0 of 209; fold -change > 2, p < 0.05) 176 \nhave not been identified as transcriptionally enriched in published snRNA-Seq datasets or in our 177 \nbulk RNA-Seq from GFP -positive MTJ nuclei  in Figure 1F  (Fig. S2A). This indicates that they 178 \ncorrespond to transcripts that are translationally controlled at the  MTJ (Supplementary Table 1). 179 \nConsistent with MTJ function, Gene Ontology analysis of these genes showed enrichment for those 180 \nrelated to extracellular matrix biology, cytoskeletal organization, and plasma membrane structur e 181 \n(Fig. 3E). Intriguingly, angiogenesis-related genes were also enriched, though the significance of 182 \nthis remains unclear. Manual inspection identified additional targets of diverse function, including 183 \nthe E3 ligase s Klhl42, Rnf152 and Rnf43 and transcription factors such as Klf2 and Klf5. 184 \nConversely, translationally repressed genes were mainly linked to mitochondria and sarcoplasmic 185 \nreticulum biology (Fig. S2B and S2C), suggesting distinct organelle composition or density at the 186 \nMTJ. 187 \n 188 \nRewiring of translational program upon sustained exercise 189 \nEndurance exercise remodels the MTJ by increasing the depth and branching of its interdigitated 190 \nstructures (24). To examine whether this event involves translational control, we subjected whole-191 \nmuscle and MTJ ribosome-tagged mice to an endurance training regimen, followed by Ribo some 192 \nimmunoprecipitation and sequencing (Fig. 3A).  193 \nWe first identified a set of genes that were translationally induced throughout the muscle in response 194 \nto exercise, mainly being  inflammatory mediators ( Nfkbia, Nfkb2, Stat4 , Socs3 ) (Fig. S 2D). 195 \nNotably, many of these were also induced in the M TJ translatome upon exercise, indicating a 196 \ngenuine muscle-wide response (Fig. S2D and S2E). Indeed, pathway analysis of genes upregulated 197 \nin both CK8 and MTJ datasets revealed strong enrichment for cytokine-related signaling pathways 198 \n(Fig. S2F). 199 \nWe next co mpared MTJ ribosome -enriched transcripts to CK8 -labeled ribosomes in exercised 200 \nmuscles (Fig. 3G). The number of MTJ -enriched genes increased substantially under exercise 201 \n(1,054 versus 209 in sedentary conditions), with significant overlap between the two s ets (Fig. 202 \nS2G). Importantly, even shared targets exhibited markedly stronger enrichment after training (Fig. 203 \n3H). For instance, Cpne2 increased from a 1.3-fold enrichment (log₂ scale) at rest to 2.4-fold after 204 \ntraining, while Klf5 rose from 1.1- to 2.9-fold (Supplementary Table 1).  Together, these findings 205 \ndemonstrate that endurance exercise profoundly rewires the translational landscape at the MTJ, 206 \namplifying a pre-existing local translational program to meet the heightened mechanical demands 207 \nof sustained activity. 208 \n 209 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 7 of 27 \n \nRole of untranslated regions in conferring translational specificity 210 \nNext, we asked how  certain transcripts are selected for local translation . Untranslated regions 211 \n(UTRs) often contain regulatory elements that control where and how efficiently mRNAs are 212 \ntranslated (25, 26 ). To test whether such elements mediate local translation at the MTJ, we 213 \ngenerated translation reporters based on MTJ-enriched genes. We selected Arhgap27 and Cpne2 as 214 \nmodels because they were not previously identified as transcriptional markers of MTJ myonuclei 215 \nbut showed enrichment in our RiboTag-Seq profiling. Moreover, their UTRs are sufficiently long 216 \n(~1 kb) to harbor potential regulatory motifs yet compact enough to remain compatible with AAV 217 \npackaging constraints. Arhgap27 encodes a poorly characterized Rho GTPase –activating protein 218 \n(27), whereas Cpne2 encodes a similarly underexplored calcium -dependent phospholipid-binding 219 \nprotein that links membranes to the cy toskeleton (28). Both genes therefore represent plausible 220 \neffectors of MTJ function, where precise coordination between cytoskeletal and membrane 221 \ndynamics is essential. 222 \nWe constructed AAVs in which the pan-myonuclear CK8 promoter drives an NLS-sfGFP reporter 223 \neither alone or fused to the 5′ and 3′ UTRs of Arhgap27 or Cpne2 (Fig. 4A). In the absence of 224 \nUTRs, the reporter protein was uniformly expressed throughout the myofiber, whereas inclusion of 225 \nthe UTRs resulted in markedly enriched expression at the fiber  tips (Fig. 4B). RNAscope analysis 226 \nconfirmed that GFP transcripts remained uniformly distributed even in the presence of the UTRs 227 \n(Fig. 4C), indicating that localization occurs at the translational level rather than through 228 \ntranscriptional control or mRNA transport.  Therefore, the UTRs of Cpne2 and Arhgap27 are 229 \nsufficient to confer MTJ-specific translation. 230 \n 231 \nTranslational control of KLF contributes to MTJ specific gene expression 232 \nWhile previous snRNA-Seq studies identified hundreds of MTJ -enriched transcripts, they did not 233 \nuncover any transcription factors that are strongly and uniquely enriched at the MTJ. This contrasts 234 \nwith the neuromuscular junction, where key transcription factors such as Etv4 and Etv5 are 235 \nspecifically expressed (6-9). However, motif analysis of a published snATAC-Seq dataset predicted 236 \na strong enric hment of KLF -binding motifs in MTJ -specific open chromatin regions  (8). We 237 \nverified this with an independently acquired snATAC -Seq dataset (full data to be published 238 \nelsewhere): 8,601 MTJ-specific chromatin accessible regions were identified that majorly l ocated 239 \nin promoters (~50% of all peaks) (Fig. 5A). Motif analysis of the accessible regions predicted KLF 240 \nfactors as the top candidates (Fig. 5B). These analyses raise the question of how KLF factors might 241 \nselectively function at the MTJ. 242 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 8 of 27 \n \nIn this light, we  were intrigued to find that KLF factors, Klf2 and Klf5, to be enriched  in our 243 \nRiboTag-Seq datasets of the MTJ  (Fig. 3E and 3G). We first confirmed the enrichment of Klf2 244 \nprotein at the MTJ. Klf2 immunohistochemistry in tissue sections showed higher Klf2 signal in the 245 \nnuclei located at the MTJ (Fig. S3A). We also surgically fractionated MTJ and non-MTJ areas using 246 \nSoleus muscle. We chose this muscle type because of its flat -shaped MTJ anatomy, making the 247 \nfractionation more suitable. Western blotting for Co l22a1 confirmed the validity of this strategy, 248 \nwhere Klf2 protein was also found to be more abundant at the MTJ fraction (Fig. S3B). 249 \nTo probe KLF function while circumventing redundancy issue among KLF family members, we 250 \nemployed a dominant-negative (DN) approach. We fused the DNA-binding domain of Klf2 to the 251 \ntranscriptional repressor domain of Drosophila Engrailed (Fig. 4C), a strategy previously validated 252 \nin other biological contexts (29). For proper comparison, we intramuscularly injected a control virus 253 \n(Tigd4 promoter expressing NLS-sfGFP) on one hindlimb, and KLF2-DN virus on the contralateral 254 \nmuscle (Fig. 5C). Targeted expression of Klf2 -DN at the MTJ suppressed MTJ gene expression, 255 \nparticularly for genes containing KLF motifs identified in snATAC-Seq datasets like Col22a1 and 256 \nTigd4 (Fig. 5D and S3C). A reduction in Col22a1 expression was also observed at the protein level 257 \nby immunohistochemistry, displaying reduced signals in KLF-DN transduced muscles (Fig. S3D). 258 \nTherefore, KLF transcription factors are translationally controlled at the MTJ, where they drive a 259 \ndownstream transcriptional program required for MTJ specialization (Fig. 5E). 260 \n 261 \nDiscussion 262 \nOur study establishes a framework for investigating regulatory mechanisms that allow the 263 \nformation of s pecialized domains in multinucleated muscle fibers. By developing a viral system 264 \nwith MTJ specificity, we achieved domain-restricted genetic manipulations and ribosome tagging. 265 \nThese approaches uncovered extensive translational regulation at the myotendino us junction . A 266 \nbroad range of protein are controlled by this mechanism, encompassing extracellular matrix and 267 \ncytoskeletal regulators as well as transcription factors. Previous studies largely attributed the 268 \ndomain formation to a transcriptional specialization of local myonuclei . The results extend our 269 \nunderstanding of how muscle domains are formed and maintained, and expand the growing 270 \nappreciation that translational control represents a central regulatory layer in cell and tissue biology. 271 \nMechanistically, our data indicate that untranslated regions of MTJ-enriched genes play a key role 272 \nin conferring domain-restricted translation. Moreover, we show that the MTJ translational program 273 \nis intensified by endurance exercise, indicating that this mechanism is dyn amically regulated 274 \naccording to physiological demand. The precise cis -regulatory motifs and RNA -binding proteins 275 \ninvolved remain to be identified. Future studies should determine how specific transcripts are 276 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 9 of 27 \n \nselected for local translation  (for example, through UTR motifs, RNA secondary structures, 277 \nribosome heterogeneity, or other mechanisms) and how these processes integrate with tissue-level 278 \nsignals at the MTJ such as tendon-derived or mechanical cues (Fig. 5E).  279 \nWe also found that KLF transcription factors are translationally upregulated at the MTJ and 280 \ncontribute to local gene expression. This explains why KLF motifs are highly enriched in accessible 281 \nchromatin regions of MTJ nuclei , although Klf transcript levels at the MTJ ar e similar to those 282 \nobserved in bulk myonuclei . However, KLF factors are not strictly MTJ -exclusive; they are also 283 \ndetectable in non -MTJ nuclei, albeit at lower levels. This suggests that KLF expression alone is 284 \ninsufficient to convey MTJ identity and must act in concert with additional, yet unidentified 285 \nregulators to establish the full MTJ transcriptome. 286 \nA key innovation of our work is the flexibility of the MTJ -AAV strategy. Beyond transcriptional 287 \nand translational profiling, it can be adapted to deliver diverse genetic tools to investigate the MTJ. 288 \nMoreover, the same strategy could be extended to other muscle domains, such as the neuromuscular 289 \njunction, and to other syncytial systems. For instance, distinct nuclear subtypes have been described 290 \nin placental syncytiotrophoblasts by snRNA -Seq (30), suggesting that similar approaches could 291 \nilluminate nuclear subtype -specific regulation  in these contexts as well. Nonetheless, such 292 \napplications must be pursued with caution, as biomolecular diffusion can confound specificity, as 293 \ndemonstrated by our comparisons of NLS -sfGFP versus H2B -GFP and Rpl22 -HA versus GFP -294 \nL10a. With careful validation, however, MTJ-AAV and related strategies promise to be come 295 \npowerful tools for dissecting the spatial logic of gene regulation in syncytial cells. 296 \n 297 \nMaterials and Methods 298 \n 299 \nAnimals 300 \nWild-type C57BL/6N mice were purchased from Charles River. Rosa26-LSL-H2B-GFP mice were 301 \ndescribed previously (6). RiboTag mice were obtained from Jackson Laboratory (#029977). Mice 302 \nwere housed under standard conditions: constant ambient temperature (23 °C), humidity (56%), and 303 \na 12 -hour light/dark cycle (lights on at 6:00 am, off at 6:00 pm). Animals were euthanized by 304 \ngradual CO₂ inhalation (up to 100%) over a 3-minute period. All procedures were approved by the 305 \ninstitutional animal ethics committee  of IGBMC (Comite d'Ethique). Animal health and welfare 306 \nwere continuously monitored by trained staff and veterinarians to minimize suffering. 307 \n 308 \n 309 \n 310 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 10 of 27 \n \nAAV construction and generation 311 \n3.3kb of Tigd4 promoter was synthesized by GenScript and cloned into pAAV plasmid using 312 \npAAV-MCS2 (Addgene) as the backbone. Recombinant adeno -associated virus (rAAV) were 313 \ngenerated by a triple transfection of HEK2 93T/17 cell line using Polyethylenimine (PEI) 314 \ntransfection reagent and the 3 following plasmids: the expression plasmids (pAAV), the 315 \npMyoAAV4A encoding Rep and Cap genes, and the pHelper (Agilent) encoding the adenovirus 316 \nhelper functions. 48 h after transfection, rAAV vectors were harvested from cell lysate and treated 317 \nwith Benzonase (Merck) at 100U/mL. They were further purified by gradient ultra -centrifugation 318 \nwith Iodixanol (OptiprepTM density gradient medium) followed by dialysis and concentration 319 \nagainst Dulbecco’s Phosphate Buffered Saline (DPBS) using centrifugal filters (Amicon Ultra -15 320 \nCentrifugal Filter Devices 100K, Millipore). Viral titres were quantified by Real -Time PCR using 321 \nthe LightCycler480 SYBR Green I Master (Roche) and primers targeting  respective insert 322 \nsequences ( e.g., sfGFP). Titers are expressed as genome copies per milliliter (GC/mL).  The 323 \npMyoAAV4A plasmid was self -constructed by IGBMC’s molecular biology platform (31), 324 \nbenchmarking the published construct (21).  325 \n 326 \nAAV injection 327 \nMice were anesthetized via intraperitoneal injection of a ketamine/xylazine mixture composed of 328 \n10% ketamine (100 mg/mL), 5% xylazine (20 mg/mL), and 85% sterile NaCl, administered at a 329 \ntotal dose of 100 µL per 10 g of body weight. Anesthesia depth was verified by the absence of pedal 330 \nreflex before proceeding with the injections. 331 \nFor intramuscular (IM) injection, the skin over the target muscle was disinfected with 70% ethanol 332 \nunder anesthesia. MyoAAV4a vectors carrying different constructs were injected intramu scularly 333 \ninto the tibialis anterior muscle using a 30 -gauge needle. Each construct was administered at its 334 \nrespective final viral genome (vg) dose per muscle: 5 × 10¹⁰ - 10¹¹ vg, depending on the viral 335 \nconcentration and experimental design. The total injec tion volume per muscle was adjusted with 336 \nsterile NaCl to ensure a constant final volume of 20 µL across all conditions. Contralateral muscles 337 \nreceived vehicle or a control vector where applicable. Muscles were collected at 1 -month post-338 \ninjection for molecular and histological analyses. 339 \nFor systemic administration, anesthetized mice were placed in a prone position, and MyoAAV4a 340 \nparticles were delivered via retro -orbital sinus injection using a 30 -gauge insulin syringe. A total 341 \nvolume of 100 µL corresponding to 1.5 x 1013 vg per mouse was injected slowly over approximately 342 \n15 seconds to minimize reflux. Animals were monitored until full recovery and returned to their 343 \ncages. Tissue collection was performed at 2 months post-injection. 344 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 11 of 27 \n \nRibosome immunoprecipitation and sequencing (RiboTag-Seq) 345 \n10 weeks old homozygous RiboTag mice were intramuscularly injected with CK8 - or MTJ-AAV 346 \ncarrying Cre recombinase. TA muscles were harvested one month later, snap-frozen, and stored at 347 \n–80°C until processing. 348 \nFrozen tissues were thawed on ice and transferred to homogenization tubes containing silica beads 349 \n(Precellys P000918 -LYSK1-A). For the MTJ -AAV condition, lysates were pooled from four 350 \ninjected muscles from different mice. For the CK8 -AAV condition, lysates were pooled from two 351 \ninjected muscles and two un-injected WT muscles to normalize total lysate concentration between 352 \nconditions, as this might affect HA-based immunoprecipitation efficiency and purity. 353 \nEach sample was homogenized in 1 ml lysis buffer containing 50 mM Tris-HCl (pH 7.5), 100 mM 354 \nKCl, 12 mM MgCl₂, 1% NP -40, 1 mM DTT, 200 U/ml RNAsin (Promega), 1 mg/ml heparin 355 \n(Sigma), 100 µg/ml cycloheximide (Sigma), and protease inhibitor cocktail (Roche).  DTT, 356 \nRNAsin, heparin, cycloheximide and protease inhibitor were added freshly before use. Tissues were 357 \nminced with sterile scissors and incubated on ice for 20 minutes before homogenization using the 358 \nPrecellys Evolution system (5,000 rpm, 25 seconds, twice, with 10-minute intervals on ice). Lysates 359 \nwere clarified by centrifu gation at 10,000g for 20 minutes at 4°C, and the supernatants were 360 \ntransferred to fresh tubes. 361 \nProtein concentration was determined via Bradford assay (Bio-Rad). Approximately 3 mg of lysate 362 \nwas incubated with 5 µl HA antibody (Covance, clone 12CA5) for 4 hours at 4°C with gentle 363 \nrotation. Meanwhile, magnetic Protein A/G beads (Pierce) were equilibrated in lysis buffer. Forty 364 \nmicroliters of beads were added to each sample, followed by overnight incubation at 4°C with 365 \nrotation. 366 \nBeads were washed three times with high-salt buffer (same as lysis buffer, but with 300 mM KCl 367 \nand 0.5 mM DTT), each wash involving 5 minutes of rotation. After the final wash, beads were 368 \nresuspended in 800 µl high-salt buffer and transferred to a new tube. RNA was extracted by adding 369 \n350 µl of RLT buffer (Qiagen RNeasy kit) and following the manufacturer’s protocol, including 370 \nDNase treatment. Purified RNA was stored at –80°C until further use. 371 \nLibrary preparation was performed at the GenomEast platform at the Institute of Genetics and  372 \nMolecular and Cellular Biology using Illumina Stranded Total RNA Prep Ligation with Ribo-Zero 373 \nPlus (Reference Guide - PN 1000000124514). Total RNA-Seq libraries were generated from 50 ng 374 \nof total RNA using Illumina Stranded Total RNA Prep, Ligation with Ri bo-Zero Plus kit and IDT 375 \nfor Illumina RNA UD Indexes, Ligation (Illumina, San Diego, USA), according to manufacturer’s 376 \ninstructions. DNA library were  amplified using 14 cycles of PCR. Surplus PCR primers were 377 \nfurther removed by two successive purifications  using SPRIselect beads (Beckman -Coulter, 378 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 12 of 27 \n \nVillepinte, France). The final libraries were checked for quality  and quantified using Bioanalyzer 379 \n2100 system (Agilent technologies, Les Ulis, France).  Libraries were sequenced on an Illumina 380 \nNextSeq 2000 sequence r as paired -end 50 base reads. Image  analysis and base calling were 381 \nperformed using RTA version 2.7.7 and BCL Convert version 3.8.4. 382 \n 383 \nEndurance exercise 384 \nRiboTag mice were intramuscularly injected with CK8 -Cre-NLS or Tigd4 -Cre-NLS AAV. One 385 \nmonth post-injection, the mice underwent an endurance exercise regimen using an animal treadmill 386 \n(LE8710, Panlab, Harvard Apparatus, Spain). To ensure proper acclimatization, the mice were 387 \nintroduced to the treadmill environment over a period of three days prio r to the start of the 388 \nexperimental protocol. During this familiarization phase, the treadmill operated at a low speed of 389 \n10–12 cm/s for 15 minutes each day. The exercise training protocol began at 12 cm/s for 15 minutes, 390 \nincreased to 20 cm/s for 50 minutes, and finished with a 5-minute cooldown at 10 cm/s. All exercise 391 \nsessions were consistently conducted at the same time of day for 4 weeks (5 days consecutively and 392 \n2 days rest). 393 \n 394 \nFACS isolation and low-input RNA-Seq 395 \nTA muscles injected with CK8 -NLS-sfGFP or Tigd4 -NLS-sfGFP AAVs were collected, snap 396 \nfrozen, and stored at –80°C until further use. The TA muscles were minced and incubated in 300µl 397 \nof cold hypotonic buffer (250mM sucrose, 10mM KCl, mM MgCl2, 10mM Tris-HCl pH 8, 25mM 398 \nHEPES pH 8, 0.3% Triton x-100, 0.2mM PMSF, 0.1 mM DTT, and 0.2U/µL RNase inhibitor) for 399 \n5 mins. Samples were then transferred to 2ml ‘Tissue homogenizing CKMix’ (Bertin Technologies) 400 \nwith an additional 700µl hypotonic buffer. Following a further 15mins incubation, the samples were 401 \nthen homogenized with the ‘Precellys 24 tissue homogenizer’ (Bertin Technologies) for 25s at 402 \n5,000rpm. The homogenized samples were then passed through a 100µm filter (Sysmex) followed 403 \nby a 20µm filter (Sysmex) before t he nuclei were pelleted by centrifugation at 400g for 10 min at 404 \n4 °C. The pellets were then washed and resuspended in a washing buffer (2% BSA in PBS + RNase 405 \ninhibitor 0.2U/µL). The centrifugation and wash steps were then repeated before the homogenized 406 \nsamples were passed through a ‘5ml polystyrene round -bottom tube with cell -strainer cap’ 407 \n(Corning). DAPI was then added to the resuspended nuclei at a final concentration of 200mM. The 408 \nisolated nuclei were sorted with FACs Aria Fusion 2022, with BD FACSDiva and FlowJo (v10) 409 \nsoftware, to sort out the DAPI and GFP+ nuclei. Approximately 1,000 nuclei were collected from 410 \neach sample. The sorted nuclei were immediately collected in 11.5µL of CDS sorting solution, 411 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 13 of 27 \n \nSMART-Seq® mRNA LP (with UMIs) recipe (Takara), bri efly spun down, then flash -frozen on 412 \ndry ice. The frozen isolated nuclei were then stored at -80°C until further use. 413 \nLibrary preparation was performed at the GenomEast platform at the Institute of Genetics and 414 \nMolecular and Cellular Biology using Takara Bio USA, Inc., SMART-Seq mRNA User Manual - 415 \nPN 062223 + Illumina Nextera XT DNA Library Prep Kit (Reference Guide - PN 15031942). Full 416 \nlength cDNA were  generated from 100 to 2000 nuclei using SMART -Seq mRNA (Takara Bio 417 \nEurope, Saint Germain en Laye, France) according to manufacturer’s instructions with 11 cycles of 418 \nPCR for cDNA amplification by Seq -Amp DNA polymerase. The entire volume of each pre -419 \namplified cDNA was then used as input for Tn5 transposon tagmentation followed by 12 cycles of 420 \nlibrary amplifica tion using Nextera XT DNA Library Preparation  Kit and IDT for Illumina 421 \nDNA/RNA UD Indexes, Tagmentation (Illumina, San Diego, USA). Following  purification with 422 \nSPRIselect beads (Beckman -Coulter, Villepinte, France), the size and concentration of  libraries 423 \nwere assessed by capillary electrophoreris (Bioanalyzer 2100 system, Agilent technologies, Les 424 \nUlis, France). Full length cDNA was generated from from 100 to 2000 nuclei using SMART -Seq 425 \nmRNA Kit (Takara Bio Europe, Saint Germain en Laye, France) according to manufacturer’s 426 \ninstructions with 11  cycles of PCR amplification by Seq -Amp polymerase. Totality of pre -427 \namplified cDNA were then used as  input for Tn5 transposon tagmentation followed by 12 cycles 428 \nof library amplification using Nextera XT DNA  Library Preparation Kit and IDT for Illumina 429 \nDNA/RNA UD Indexes (Illumina, San Diego, USA). Following purification with SPRIselect beads 430 \n(Beckman-Coulter, Villepinte, France), the size and concentration of libraries were assessed using 431 \nBioanalyzer 2100 system (Agilent technologies, Les Ulis, France). Libraries were sequenced on an 432 \nIllumina NextSeq 2000 sequencer as paired -end 50 base reads. Image  analysis and base calling 433 \nwere performed using RTA version 2.7.7 and BCL Convert version 3.8.4. 434 \n 435 \nBioinformatic analyses of RNA-Seq datasets 436 \nAll sequencing  was performed on an Illumina NextSeq 2000 platform in a 2x50bp paired -end 437 \nconfiguration. For RiboTag-Seq, sequencing data was processed processed using PiGx -RNA-seq 438 \n(30277498) pipeline. In short, the data was mapped onto the GRCm39/mm11 version of the mouse 439 \ntranscriptome (downloaded from the ENSEMBL database 29155950) using SALMON (28263959). 440 \nThe quantified data was processed using tximport (26925227), and the differential expression 441 \nanalysis was done using DESeq2 (25516281).  Genes with less than 5 reads in all biological 442 \nreplicates of one condition were filtered out before the analysis. Two groups of differentially 443 \nexpressed genes were defined - a relaxed set containing genes with an absolute log2 fold change of 444 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 14 of 27 \n \n0.5, and a stringent set containing games with an absolute log2 fold change of 1. The fold change 445 \nwas deemed significant if the adjusted p-value was less than 0.05 (Benjamini-Hochberg corrected). 446 \nFor bulk RNA -Seq, r eads were preprocessed with cutadapt  4.2 to remove adaptor sequences, 447 \npoly(A) tails, and low-quality bases. Reads shorter than 40 bp were discarded. The remaining reads 448 \nwere mapped to Mus musculus rRNA sequences using bowtie2 2.3.5 (32) and reads aligning to 449 \nthese sequences were removed. The filtered reads were then aligned to the Mus musculus reference 450 \ngenome (GRCm39 assembly) using STAR 2.7.10b (33). Gene-level quantification was performed 451 \nwith HTSeq-count 1.99.2 (34) in “union” mode, using Ensembl 111 annotations. Differential gene 452 \nexpression analysis was carried out with the DESeq2 1.34.0 (35) R/Bioconductor package using 453 \ndefault parameters. P -values were adjusted for multiple testing with the Benjamini -Hochberg 454 \nmethod.  455 \nGene ontology and pathway reactome analyses were performed using 456 \n‘https://maayanlab.cloud/Enrichr/’ website. 457 \n 458 \nAnalyses of snATAC-Seq dataset and motif enrichment 459 \nSingle-nucleus ATAC-seq (snATAC-seq) libraries were processed using Signac v1.10.0 and Seurat 460 \nv4.3.0 in R. Raw peak files were imported and converted to GRanges ob jects for genomic 461 \ncoordinate standardization. Cell -type-specific open chromatin peaks were identified for 462 \nmyotendinous junction (MTJ) nucleus through Seurat object integration with parallel 463 \ntranscriptomic data (GEX), utilizing clustering and differential a ccessibility analysis to isolate 464 \npeaks enriched in MTJ -specific clusters. From the total accessible chromatin landscape, 465 \nmyotendinous junction-specific peaks were extracted by filtering for regions showing significant 466 \naccessibility exclusively in MTJ nucle us relative to other muscle nucleus populations. Peak 467 \ncoordinates were standardized using StringToGRanges coordinate conversion (mm10 genome 468 \nbuild). Selected peaks were then used as input for downstream transcription factor binding site 469 \ndiscovery via motif scanning with JASPAR 2020 vertebrate PWMs. 470 \nTranscription factor binding site (TFBS) enrichment analysis was performed on MTJ-specific open 471 \nchromatin regions using a computational motif -scanning approach. MTJ -specific accessible 472 \nchromatin peaks were identi fied from ATAC -seq data and used as input for systematic motif 473 \ndiscovery. Vertebrate transcription factor position weight matrices (PWMs) were obtained from the 474 \nJASPAR 2020 database using TFBSTools (v1.38.0). All vertebrate PWMs with non -redundant 475 \nversions were retrieved, encompassing 746 motif models representing diverse transcription factor 476 \nfamilies. Genomic sequences corresponding to each peak region were extracted from the mouse 477 \nreference genome (mm10) using BSgenome.Mmusculus.UCSC.mm10 (v1.4.3). Motif scanning 478 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 15 of 27 \n \nwas performed with motifmatchr (v1.22.0) using the matchMotifs() function, which implements a 479 \nlog-odds scoring algorithm based on PWM models. For each peak -motif pair, a match score was 480 \ncomputed by sliding the PWM across both DNA strands. Binding sites exceeding the default log-481 \nodds threshold (typically p < 10⁻⁴) were classified as putative TFBS occurrences. 482 \nResults were integrated into a Seurat object (v4.3.0) using the Signac framework (v1.10.0) for 483 \nchromatin accessibility data. A binary motif occurrence matrix was constructed with dimensions n 484 \npeaks × m motifs, where each entry indicates presence (1) or absence (0) of a motif within a given 485 \npeak. This matrix was stored as a ChromatinAssay object within the Seurat framework, enabling 486 \ndownstream integration with single-cell chromatin accessibility profiles when applicable. 487 \nGlobal motif enrichment was calculated as the proportion of peaks containing each motif: 488 \nEnrichment (%) = (Number of peaks with motif / Total peaks) × 100 489 \nMotifs were ranked by fr equency of occurrence across all analyzed peaks compared to random 490 \noccurences. The top-ranking motifs representing the most enriched transcription factor binding sites 491 \nin MTJ -specific open chromatin were identified for downstream validation and functional 492 \ninterpretation. 493 \nMTJ-specific accessible chromatin peaks were functionally annotated using ChIPseeker v1.36.0 494 \nwith the mouse genome annotation database (TxDb.Mmusculus.UCSC.mm10.knownGene and 495 \norg.Mm.eg.db). Peak coordinates were mapped to their nearest geno mic features, including 496 \npromoters (±3 kb from transcription start sites), gene bodies, introns, and intergenic regions, using 497 \nannotatePeak(). The distribution of peaks across genomic features was visualized with annotation 498 \npie charts to assess regulatory l andscape composition. Gene symbols associated with each peak 499 \nwere extracted, and genes with multiple associated peaks (≥150 peaks per gene) were identified as 500 \nhigh-confidence MTJ regulatory targets. This threshold -based filtering enriched for genes with 501 \nextensive regulatory architecture characteristic of master regulators and structural genes defining 502 \nmyotendinous junction identity. 503 \nAll analyses were performed in R (v4.3.1). The complete analysis pipeline, including motif 504 \noccurrence matrices and enrichment statistics, was saved for reproducibility and downstream 505 \nchromVAR-based activity inference. 506 \n 507 \nHistology and imaging 508 \nFor histological analysis, samples were rapidly embedded in  Cryomatrix (Epredia) in cryomolds 509 \nthen snap-frozen in isopentane pre-cooled in liquid nitrogen for approximately 15 seconds until the 510 \ncryomatrix solidified completely.  Samples embedded in cryomatrix were equilibrated to  cryostat 511 \ntemperature (–20 °C) prior to sectioning. Transverse cryosections were cut at 10 μm thickness using 512 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 16 of 27 \n \na Leica CM3050S cryostat (Leica Microsystems, Germany) and collected on Superfrost  Plus 513 \nadhesion microscope slides (Epredia). Sections were then stored at –20 °C for 30 mins then stored 514 \nat -80 °C until further use. 515 \nFor RNAscope, the tissue sections were fixed with  4% PFA in PBS for 15 minutes at 4  °C. After 516 \ntwo times washing with PBS, the sections were serially dehydrated through increasing ethanol 517 \n(50%, 70%, 100%) 5 minutes each. Subsequently, RNAscope was performed according to 518 \nmanufacturer’s guideline using the RNAscope Multiplex Fluorescent Reagent Kit v2 (Bio-Techne 519 \n323100). Proteinase IV was used for our procedure. The following RNAscope probes were used 520 \nfor our study: Col22a1 (590911-C2), Tigd4 (598761), Ttn (483031), GFP (409011) and Engrailed 521 \n(newly designed for this study). After all RNAscope procedures, samples were counterstained with 522 \nDAPI and mounted using Prolong Gold Antifade (Thermofisher scientific). When combined with 523 \nantibody staining, tissue sections were processed to blocking buffer (see below) after the last wash 524 \nand proceeded to regular immunohistochemistry. 525 \nFor immunofluorescence staining, transverse cryosections were fixed  in 4% paraformaldehyde 526 \n(PFA) for 10 minutes at room temperature (RT), followed by three washes in PBT (PBS containing 527 \n0.1% Tween-20) for 5 minutes each. Permeabilization was performed in  PBX (PBS containing 528 \n0.5% Triton X -100) for 6 minutes, after which  sections were rinsed in PBS for 5 minutes.  Non-529 \nspecific binding was blocked by incubating the sections in  blocking buffer (PB S containing 5% 530 \nBSA, 3% horse serum and 0.1% Triton X-100) for 1 hour at RT. Sections were then incubated with 531 \nprimary antibody diluted in blocking buffer overnight at 4 °C in a  humidified chamber.  The 532 \nfollowing day, slides were washed three times in PBT f or 10 minutes each, then incubated with 533 \nfluorophore-conjugated secondary antibody diluted in blocking buffer for 1 hour at RT. DAPI (1 534 \nμg/mL) was included in the secondary incubation step for nuclear counterstaining. Finally, sections 535 \nwere washed three tim es in PBT for  10 minutes each in the dark, mounted with ProLong Gold 536 \nAntifade, and stored at 4 °C until imaging. The following antibodies were used in this study: GFP 537 \n(Aves Labs; 1:500), Klf2 (Cell Signaling, 1:500), Dystrophin (Abcam, 1:250), and Col22a1 (Gift 538 \nfrom Manuel Koch, 1:1000). 539 \nImages were acquired on a Leica confocal at the IGBMC imaging core facility. 20× oil-immersion 540 \nobjective was used. Fluorescent signals were sequentially detected using appropriate laser lines and 541 \nemission filters for DAPI, Opal 520, Opal 570 and Opal 690  (Opal chemicals were from Akoya 542 \nBiosciences) to avoid channel bleed -through. Laser power, gain, and offset were  optimized and 543 \nmaintained constant across samples. Imaging was  performed using the tile scan mode to capture 544 \nlarge tissue areas, with a frame size of 520 × 520 pixels and processed using imageJ. 545 \n 546 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 17 of 27 \n \nWestern blotting 547 \nTA tissues were lysed as described in the ‘Ribosome immunoprecipitation and sequencing’ section. 548 \nLysates were denatured by adding Laemmli buffer and boiling for 10 minutes. Denatured samples 549 \nwere separated by SDS -PAGE (Bio -Rad) and transferred into nitrocellulose membrane 550 \n(Amarsham). Transferred membranes were blocked for one hour with 5% skim milk in TBS plus 551 \n0.1% Tween-20 (TBST) at RT. Afterwards, primary antibodies were incubated in 5% BSA in TBST 552 \nsupplemented with 0.1% sodium azide overnight in cold room with gentle rocking. After three 553 \ntimes of washing with TBST (10 minutes each), membranes were incubated with secondary 554 \nantibodies (anti-mouse or anti -rabbit HRP; Cell Signaling) diluted in skim milk (1:5000) for one 555 \nhour in RT. After three times washing with TBST, membranes were developed using 556 \nchemiluminescence (ECL substrate; Pierce) and Amarsham ImageQuant 800. For Streptavidin-557 \nHRP, antibody was incubated for one hour in skim milk after blocking.  558 \nThe following antibodies were used in this study: Col22a1 (Abcam; 1:500), β-actin (Cell Signaling; 559 \n1:1000), Klf2 (Cell Signaling, 1:500), HA (Covance, 1:2000), and Streptavidin -HRP (Sigma, 560 \n1:5000). 561 \n 562 \nStatistical analysis 563 \nGraph generation and statistical analyses were performed using GraphPad Prism as described in 564 \neach figure legend.  565 \n 566 \nReferences 567 \n1. L. A. Tintignac, H. R. Brenner, M. A. Ruegg, Mechanisms Regulating Neuromuscular 568 \nJunction Development and Function and Causes of Muscle Wasting. Physiol Rev 95, 809-569 \n852 (2015). 570 \n2. L. Li, W. C. Xiong, L. Mei, Neuromuscular Junction Formation, Aging, and Disorders. 571 \nAnnu Rev Physiol 80, 159-188 (2018). 572 \n3. B. Charvet, F. 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Genome Biol 15, 550 (2014). 644 \n 645 \nAcknowledgments 646 \n 647 \nFunding: We thank grant supports to M.K. from the  648 \nEuropean Research Council ERC-StG 101039531 (M.K)  649 \nANR-22-CE13-0023-03 MYODOM (M.K) 650 \nANR t-ERC StG 2021 (M.K) 651 \nLABEX INTR (M.K) 652 \nUniversity of Strasbourg IDEX Attractivitae (M.K) 653 \nINSERM ATIP-Avenir (M.K)  654 \nAFM-Telethon Trampoline 24287 and n°22AA003-00 (M.K)  655 \nGrand Est PhD fellowship (N.E.K)  656 \nFondation pour la Recherche Médicale postdoctoral fellowship (S.M)  657 \nAFM n°28842 (P.M)  658 \nANR-21-CE14-0042-01 MOTOMYO (P.M) 659 \n 660 \nAuthor contributions:  661 \nConceptualization: M.K 662 \nMain Experiments: J.N and N.E.K 663 \nOther Experiments: C.S, S.M, and L.Y 664 \nBioinformatic analyses: V.F and E.J 665 \nSupervision: P.M and A.A 666 \nMethodology (AAV generation): E.L 667 \nWriting: M.K 668 \n 669 \nCompeting interests:  670 \nAll other authors declare they have no competing interests. 671 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 20 of 27 \n \n 672 \nData and materials availability:  673 \nAll data and materials used for this study are available from the corresponding author upon 674 \nreasonable request. All NGS datasets have been deposited to EBI (RNA -Seq: E-MTAB-16305; 675 \nRiboTag-Seq under sedentary: E -MTAB-16304; RiboTag -Seq after exercise: E -MTAB-16306). 676 \nThe codes used in this study are available in Github server 677 \n(https://github.com/BIMSBbioinfo/MTJ_Figures). All data are available in the main text or the 678 \nsupplementary materials. 679 \n 680 \n 681 \n 682 \n 683 \n 684 \n 685 \n 686 \n 687 \n 688 \n 689 \n 690 \n 691 \n 692 \n 693 \n 694 \n 695 \n 696 \n 697 \n 698 \n 699 \n 700 \n 701 \n 702 \n 703 \n 704 \n 705 \n 706 \n 707 \n 708 \n 709 \n 710 \n 711 \n 712 \n 713 \n 714 \n 715 \n 716 \n 717 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 21 of 27 \n \nFigures and Tables 718 \n 719 \n 720 \n 721 \n 722 \nFigure 1. Development of MTJ-AAV  723 \n(A) Schematic of AAV constructs driven by CK8 or Tigd4 promoters and the expected expression 724 \npattern of the 4×NLS-sfGFP reporter.  725 \n(B) Tile-scan images of tibialis anterior (TA) muscles injected with the AAVs shown in (A).  The 726 \nright panel shows a magnified view of the boxed region.  727 \n(C) Combined GFP immunostaining and RNAscope detection of Col22a1 and Tigd4 transcripts. 728 \nArrows indicate nuclei co-expressing all three markers.  729 \n(D) Quantification of labeling efficiency and specificity from experiments in (C) (n = 5).  730 \n(E) Representative FACS plots of CK8- and Tigd4-NLS-sfGFP injected TA muscles. GFP+ nuclei 731 \nwere isolated and subjected to bulk RNA-Seq.  732 \n(F) Heatmap showing genes enriched or depleted in GFP + nuclei labeled by the Tigd4 promoter 733 \nrelative to the CK8 promoter. Selected MTJ marker genes are indicated.  734 \nScale bars, 100 µm. 735 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 22 of 27 \n \n 736 \n 737 \n 738 \nFigure 2. MTJ-AAV enables Cre-mediated recombination specifically at the MTJ 739 \n(A) Schematic of the AAV construct in which t he Tigd4 promoter drives expression of Cre -NLS. 740 \nWhen injected into Rosa26-LSL-H2B-GFP reporter mice, Cre activity induces H2B-GFP labeling 741 \nof MTJ nuclei.  742 \n(B) Tile-scan images of TA muscles injected with MTJ -AAV-Cre-NLS. Reporter mice injected 743 \nwith an empt y MyoAAV4a vector (containing only the Tigd4 promoter without Cre) showed no 744 \nH2B-GFP expression.  745 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 23 of 27 \n \n(C) Quantification of labeling efficiency and specificity from experiments in (B) (n = 4).  746 \n(D) Schematic of systemic delivery of MTJ -AAV-Cre-NLS into Rosa26-LSL-H2B-GFP reporter 747 \nmice via intra-orbital injection.  748 \n(E) Representative images showing MTJ -specific myonuclear labeling in the gastrocnemius and 749 \ndiaphragm muscles. Similar results were obtained in three independent animals.  750 \nScale bars, 100 µm. 751 \n 752 \n 753 \n 754 \n 755 \n 756 \n 757 \n 758 \n 759 \n 760 \n 761 \n 762 \n 763 \n 764 \n 765 \n 766 \n 767 \n 768 \n 769 \n 770 \n 771 \n 772 \n 773 \n 774 \n 775 \n 776 \n 777 \n 778 \n 779 \n 780 \n 781 \n 782 \n 783 \n 784 \n 785 \n 786 \n 787 \n 788 \n 789 \n 790 \n 791 \n 792 \n 793 \n 794 \n 795 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 24 of 27 \n \n 796 \n 797 \n 798 \nFigure 3. MTJ-AAV reveals a specialized translatome of MTJ  799 \n(A) Experimental workflow for MTJ -targeted ribosome labeling and translatome profiling in 800 \nsedentary and exercised muscles.  801 \n(B) HA immunohistochemistry confirming ribosome labeling along the MTJ. The right panel shows 802 \na magnified view of the boxed region in the middle image. Scale bar, 100 µm.  803 \n(C) Bioanalyzer profile of RNA recovered after HA immunoprecipitation.  804 \n(D) Heatmap showing representative MTJ -ribosome-enriched transcripts under sedentary 805 \nconditions. Known transcriptional MTJ markers are indicated in red.  806 \n(E) Volcano plot comparing CK8- and MTJ-ribosome associated transcripts in sedentary muscles. 807 \n(F) Gene Ontology analysis of translationally enriched MTJ targets.  808 \n(G) Volcano plot comparing CK8- and MTJ-ribosome associated transcripts following endurance 809 \nexercise.  810 \n(H) Exercise amplifies the degree of translational enrichment for MTJ -specific targets relative to 811 \nsedentary conditions. 812 \n 813 \n 814 \n 815 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 25 of 27 \n \n 816 \n 817 \n 818 \n 819 \nFigure 4. UTRs of translational target transcripts enable localized protein synthesis at the 820 \nMTJ 821 \n(A) Schematic representation of MTJ translational reporter constructs.  822 \n(B) Single extensor digitorum longus (EDL) myofibers were isolated one month after intramuscular 823 \ninjection of the AAVs shown in (A) and analyzed by epifluorescence microscopy.  824 \n(C) EDL myofibers from (B) were fixed and subjected to RNAscope detection of GFP mRNA to 825 \nassess transcript distribution.   826 \nScale bars, 100 µm. 827 \n 828 \n 829 \n 830 \n 831 \n 832 \n 833 \n 834 \n 835 \n 836 \n 837 \n 838 \n 839 \n 840 \n 841 \n 842 \n 843 \n 844 \n 845 \n 846 \n 847 \n 848 \n 849 \n 850 \n 851 \n 852 \n 853 \n 854 \n 855 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 26 of 27 \n \n 856 \n 857 \n 858 \n 859 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint \n\nPage 27 of 27 \n \nFigure 5. KLF transcription factors contribute to the MTJ transcriptome 860 \n(A) Classification of MTJ -specific ATAC -seq peaks according to their genomic locations and 861 \nsequence elements.  862 \n(B) De novo motif analysis of MTJ-specific ATAC-seq peaks identifying KLF family transcription 863 \nfactors as top candidates.  864 \n(C) Schematic of KLF inhibition strategy using a dominant -negative (DN) construct expressed at 865 \nthe MTJ.  866 \n(D) Expression of KLF2 -DN at the MTJ suppresses MTJ marker genes Tigd4 and Col22a1, as 867 \ndetermined by RNAscope. KLF2 -DN expression was detected via RNAscope targeting the 868 \nEngrailed (En) sequence. The result was reproduced in three independent animals.  869 \n(E) Model summarizing MTJ -specific translational regulation and its impact on local 870 \ntranscriptional control through KLF factors. Local signals, such as mechanical or tendon -derived 871 \ncues, activate this translational program to reinforce domain specialization.  872 \nScale bars, 100 µm. 873 \n 874 \n 875 \n 876 \n.CC-BY 4.0 International licenseavailable 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 made \nThe copyright holder for this preprintthis version posted January 2, 2026. ; https://doi.org/10.64898/2026.01.01.697307doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}