{"paper_id":"2455c9a0-aa1c-468d-805b-3e2b0d8f3fbd","body_text":"1 \nStress granule sequestration of OXPHOS gene transcripts exacerbates glycolytic 2 \nrestriction 3 \n 4 \nWanling Zheng1,2,3,#, Ruoqing Xu1,2,3,#, Maoguang Xue1,2,3, Xiaoyu Liu1,2,3, Yinglong 5 \nGao2,4,5,6, Min-Xin Guan1,2,3, Jun Ma1,2,3,* and Feng He1,2,3,*  6 \n 7 \n1Department of Obstetrics and Gynecology, the Fourth Affiliated Hospital of School 8 \nof Medicine, Zhejiang University, Yiwu, Zhejiang 322000, China 9 \n2Institute of Genetics, International School of Medicine, Zhejiang University, Yiwu, 10 \nZhejiang 322000, China 11 \n3Center for Genetic Medicine, International Institutes of Medicine, Zhejiang 12 \nUniversity, Yiwu, Zhejiang 322000, China 13 \n4Department of Pediatric Surgery, The Children’s Hospital, Zhejiang University 14 \nSchool of Medicine, Hangzhou, 310052 15 \n5National Clinical Research Center for Child Health, Hangzhou, 310052 16 \n6National Regional Medical Center for Children, Hangzhou, 310052 17 \n 18 \n# Equal contributions 19 \n* Correspondence to: jun_ma@zju.edu.cn (J.M.) or feng_he@zju.edu.cn (F.H.) 20 \n  21 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nAbstract 22 \nStress granules (SGs) are dynamic organelles formed under cellular stress and they 23 \nare generally regarded as protective entities.  Meanwhile, their role in pathogenesis is 24 \nbecoming increasingly recognized, but the underlying mechanisms remain elusive due 25 \nto the diverse nature of both stress types and biological contexts.  Here we investigate 26 \nSG dynamics and temporal changes in bulk and SG-associated transcriptomes under 27 \ndifferent regimens that inhibit glycolysis.  We subject cells to either single assaults of 28 \nglucose depletion (GD) or 2-deoxy-D-glucose addition (2DG) or a combined 29 \ntreatment (GD+2DG).  We find that SGs formed under these conditions exhibit 30 \ndistinct properties, including eIF2α phosphorylation dependency, mRNA composition, 31 \nand capacity to disassembly.  Our results show that SGs induced by GD+2DG 32 \nuniquely trap oxidative phosphorylation (OXPHOS) gene transcripts, leading to 33 \nmitochondrial dysfunction.  We provide evidence suggesting that the persistency of 34 \nSGs formed under GD+2DG treatment is interwoven with mitochondrial dysfunction 35 \nresulting in heightened apoptosis, effects that can also be recreated under single 36 \nassaults when combined with mitochondrial inhibition.  Our findings suggest that SG 37 \nformation induced by inhibiting a single metabolic pathway can widen its impact in 38 \nintensifying cellular metabolic stress under specific conditions, providing mechanistic 39 \ninsights into the paradoxical dual nature of SGs in stress response and pathology. 40 \n 41 \nIntroduction 42 \nEukaryotic cells respond to stress by compartmentalizing translationally aberrant 43 \nribonucleoproteins into stress granules (SGs), which are dynamically assembled and 44 \ndisassembled to regulate mRNA and protein availability (Van Leeuwen et al. 2019; 45 \nCampos-Melo et al. 2021).  This process enables cells to conserve energy, survive 46 \nstress, and recover efficiently.  However, dysregulation in SG assembly or 47 \ndisassembly can render cells vulnerable to stress, contributing to the pathogenesis of 48 \nvarious diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal 49 \ndementia (FTD) (Zhang et al. 2019; Parameswaran et al. 2023; Buchan et al. 2013; 50 \nCui et al. 2023; Wolozin et al. 2019; Cui et al. 2024).  Understanding the molecular 51 \nmechanisms governing SG dynamics is therefore critical to elucidation of the 52 \nunderlying causes of these disorders. 53 \n 54 \nSG formation as a cellular process is mediated by diverse signaling pathways 55 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nmolecularly, depending on the precise nature and context of the stress.  For instance, 56 \nunder arsenite-induced oxidative stress or heat shock, SG assembly is primarily 57 \ntriggered by the phosphorylation of the translation initiation factor eIF2α 58 \n(Frydrýšková et al. 2020; Aulas et al. 2017; Szczerba et al. 2023).  Beyond this 59 \ncanonical pathway, alternative mechanisms, such as the activation of 4EBP1 and 60 \ndisruption of the eIF4F complex, have also been implicated in SG formation under 61 \nconditions such as selenite exposure, hydrogen peroxide treatment, or complete 62 \nglycolysis inhibition (Tauber et al. 2020; Emara et al. 2012; Wang et al. 2022; 63 \nFujimura et al. 2012).  These findings highlight both versatility and specificity of SG 64 \nresponses to cellular stress. 65 \n 66 \nThe characteristics of SGs can also be influenced by the intensity and duration of the 67 \nstress.  Time-course analyses reveal that under arsenite exposure, the size and 68 \ndynamics of the SG core, marked by the G3BP1 protein, remain stable over 2 hours, 69 \nsuggesting minimal changes in their biochemical state (Wheeler et al. 2016).  In 70 \ncontrast, heat shock-induced SGs exhibit significant changes in their core size over 71 \ntime, accompanied by a proteomic transition as the stress prolongs (Mateju et al. 72 \n2017; Hu et al. 2023).  Furthermore, the dynamics of SG dissolution upon alleviation 73 \nof the stress or cellular adaptation to the stress may also rely on context-specific 74 \nmechanisms, which can involve distinct signaling pathways, molecular chaperons, 75 \nand macromolecule degradation systems (Jia et al. 2024; Hofmann et al. 2021).  76 \nHowever, most of the existing studies have focused on acute stress-induced SGs, and 77 \na comprehensive knowledge is currently lacking with regard to the dynamics and 78 \ncomposition of SGs formed under chronic stress (Hofmann et al. 2012; Cherkasov et 79 \nal. 2013; Huang et al. 2020; Reineke et al. 2018, 2019; Youn et al. 2018, 2019; Khong 80 \net al. 2017; Namkoong et al. 2018; Somasekharan et al. 2020; Frydrýšková et al. 81 \n2020). 82 \n 83 \nNutrient starvation and mitochondrial inhibition have emerged as powerful paradigms 84 \nfor studying chronic stress-induced SGs (Reineke et al. 2018; Fu et al. 2016; 85 \nEiermann et al. 2022; Wang et al. 2022; Aguilera-Gomez et al. 2017; Amen et al. 86 \n2021; Sfakianos et al. 2018; Pernin et al. 2024).  For example, when glucose, serum, 87 \nglutamine and pyruvate were deprived all together, SGs assembled slowly, reached 88 \npeak formation at 8 hours, persisted at 16 hours, and disassembled upon addition of 89 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nglucose (Amen et al. 2021; Reineke et al. 2018).  However, existing models are 90 \ncomplicated by the fact that energy depletion—due to either nutrient withdrawal or 91 \nmitochondrial dysfunction—not only represents a specific type of stress but also 92 \nimpairs the cell’s ability to assemble and disassemble SGs in response to other 93 \nstressors (Jain et al. 2016; Pernin et al. 2024; Wang et al. 2022; Eum et al. 2020).  94 \nHere we investigate mechanisms underlying SG dynamics under glycolytic inhibition, 95 \naimed at disentangling these complexities to provide insights into chronic stress 96 \nresponses. 97 \n 98 \nIn this study, we compare SG dynamics under different long-term glycolytic 99 \ninhibition regimens.  We show that while treatments of glucose depletion (GD) or 2-100 \ndeoxy-D-glucose (2DG) addition induce SGs that dissipate as stress prolongs from 8 101 \nto 24 hours, the combined GD+2DG treatment results in a persistent presence of SGs 102 \nand an increase in apoptotic cells over time.  Time-resolved bulk RNA-seq reveals a 103 \nunique upregulation of oxidative phosphorylation (OXPHOS) pathway activity under 104 \nGD+2DG.  However, this upregulation seems to be a futile response since transcripts 105 \nof many OXPHOS genes are specifically sequestered within SGs, as evidenced by 106 \nG3BP1-APEX2-enriched RNA-seq.  We verify a reduced expression of OXPHOS 107 \nproteins and mitochondrial dysfunction, properties that differentiate cells that are 108 \ntreated with GD+2DG from those under single treatments.  We suggest that 109 \nmitochondrial defects in these cells were responsible for SG persistence, and such SG 110 \npersistency can be recapitulated in cells under single glycolytic inhibition with 111 \nmitochondrial disruption.  Our findings support a feedback model in which SG 112 \nformation itself has a role in widening the impact of glycolytic restriction through 113 \nlimiting OXPHOS gene expression, creating a scenario of a deepened, perpetuating 114 \ncellular stress. 115 \n 116 \nResults 117 \nGD, 2DG and GD+2DG induce SGs with distinct formation kinetics  118 \nThe aim of this study was to compare how cells may form stress granules (SGs) in 119 \nresponse to different assaults that inhibit a common metabolic pathway, glycolysis.  120 \nHere we investigated this question through subjecting 143B cells to either single or 121 \ncombined assaults.  For single assaults, cells were treated with glucose depletion 122 \n(GD) or with addition of 2-deoxy-D-glucose (2DG), a glucose analog that directly 123 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nblocks glycolysis.  For the double assault, 2DG was supplemented while glucose was 124 \nwithdrawn from the medium (GD+2DG).  We examined the characteristics of SG core 125 \nduring their formation using G3BP1 as the marker (Figure 1A).  ~20% of the cells 126 \nunder the treatment of GD or 2DG became SG-positive (SG+) at the peak time of 8 h 127 \nand 1 h, respectively, while 75% cells were SG+ under the treatment of GD+2DG at 128 \nthe peak time of 1 h (Figure 1B).  The number of SGs per cell and the average size of 129 \nSGs were comparable between the two single-assault groups but distinct from the 130 \nGD+2DG group (Figure 1C-D).  Based on these SG characteristics and those detailed 131 \nin the following sections, we regard GD as chronic-mild stress, 2DG as acute-mild 132 \nstress, and GD+2DG as acute-severe stress.  As explained further below, we propose 133 \nthat the previously reported eSG (Wang et al. 2022) is a result of a further exacerbated 134 \nstress state that goes beyond a mere glycolysis restriction. 135 \n 136 \nThe pathway to SG formation can be either dependent or independent of 137 \nphosphorylation of the translation initiation factor eIF2α (p-eIF2α).  Western blot 138 \nanalysis revealed a significant increase of p-eIF2α/eIF2α ratio in both single-assault 139 \ngroups but not in GD+2DG (Figure 1E).  We verified this phenomenon at the single-140 \ncell level through immunofluorescence staining.  By comparing the signals of anti-p-141 \neIF2α and O-propargyl-puromycin (OPP) between SG+ and SG- cells, we observed a 142 \ncorrelation of SG presence to both p-eIF2α elevation and reduction in protein 143 \nsynthesis under single but not double assaults (Figure 1F-G).  Consistently, GADD34, 144 \na factor involved in p-eIF2α dephosphorylation, was reduced under single but not 145 \ndouble assaults (Figure S1).  To verify the distinct dependences of p-eIF2α in SG 146 \nformation in our system, we treated cells with ISRIB, an antagonist of ISR that acts 147 \ndownstream to all eIF2α kinases and specifically reverses the cellular effects of p-148 \neIF2α (Sidrauski et al. 2015).  Our results show that ISRIB effectively reduced both 149 \nthe fraction of SG+ cells and the level of p-eIF2α under single assaults, an effect that 150 \nwas diminished under GD+2DG (Figure 1H-J).  Together, these results suggest that 151 \ncells respond differently to a set of assaults that share the common target of 152 \nglycolysis.  For convenience, we refer to SGs formed under single and double assaults 153 \nin our system as Type I and Type II SGs, respectively.  154 \n 155 \nOXPHOS gene transcripts are compartmentalized in Type II SGs  156 \nThe protective role of SGs has been attributed to the compartmentalization of RNPs 157 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nthat can protect them from degradation and preserve their availability for future use if 158 \nand when cells recover (Das et al. 2022).  While the protein composition of SGs has 159 \nbeen extensively studied, the specific mRNA species associated with SGs may also be 160 \ninformative about the specificity or diversity of responses to the inducing stress.  To 161 \ninvestigate the mRNA composition of SGs induced under our conditions, we 162 \ngenerated an G3BP1-APEX2 fusion protein in a biotin-based proximity labeling 163 \nexperiment for enriching SG-associated mRNAs.  Immunofluorescence staining 164 \nshowed subcellular colocalization between the fusion protein and the biotinylation 165 \nsignal (Figure 2A).  We sequenced cDNA libraries prepared from bulk and G3BP1-166 \nassociated mRNAs under no treatment (UT), GD, 2DG or GD+2DG at the peaking 167 \ntime for SG+ cells.  Pairwise correlation between independent replicates (Pearson’s 168 \ncorrelation ρ > 0.995) indicated an excellent data reproducibility.  Importantly, only a 169 \nminimal level of correlation (ρ = 0.172~0.371) was observed between the bulk-seq 170 \nand APEX-seq results within each pair (Figure 2B).  These results support a selective 171 \nenrichment of G3BP1-associated mRNAs under our experimental conditions.  172 \n 173 \nBased on a hierarchical clustering analysis, we found that, among all G3BP1-174 \nassociated transcriptomes, the GD+2DG groups were most distant from the other 175 \ngroups (Figure 2B).  To further evaluate the difference in the mRNA contents, we 176 \nenriched G3BP1-associated transcriptomes by comparing each of the three stress 177 \nconditions against those from UT.  Such an analysis led to the identification of 1540, 178 \n5172 and 3798 stress-dependent, G3BP1-associated genes under GD, 2DG and 179 \nGD+2DG, respectively (Figure 2C).  We referred to these stress-dependent genes as 180 \nSDGs.  Among them, 458 genes were shared among all three conditions.  Functional 181 \nannotation analysis revealed that these common SDGs were enriched in “cell cycle”, 182 \n“TGF-β signaling pathway”, “RNA degradation”, “valine, leucine and isoleucine 183 \ndegradation” and “human immunodeficiency virus 1 infection” (Figure 2D).  These 184 \nresults, which confirm our recently reported effect of long-term glucose depletion on 185 \ncell cycle progression (Zheng et al. 2023; see also Figure S4 for cell cycle analysis of 186 \nthe current study), are supportive of the hypothesis that SGs store away transcripts 187 \nwhich encode proteins with “unwanted” functions at the time of stress. 188 \n 189 \nTranscript length is one of the features that determine the selectivity of mRNAs 190 \nsorting into SGs.  Here we evaluated the transcript length distributions of SDGs 191 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nidentified from the three conditions of glycolytic stress.  We found that, similar to 192 \nendoplasmic reticulum (ER) stress or arsenite exposure (Namkoong et al. 2018; 193 \nKhong et al. 2017; Ren et al. 2023; Van Treeck et al. 2018), all three glycolytic 194 \nassaults resulted in SDGs whose transcripts were much longer than those of their non-195 \nSDG counterparts (Figure 2E).  However, SDGs induced by GD+2DG had 196 \nsignificantly shorter transcript lengths than those induced by GD or 2DG alone 197 \n(Student’s t-test p = 0.004 and 0.03, respectively).  This finer selection of SDG 198 \ncontents likely reflects a tailored response strategy to the double assault.  Indeed, 199 \nfunctional annotation analysis showed that SDGs identified from GD+2DG were 200 \nsignificantly involved in neurodegenerative diseases such as amyotrophic lateral 201 \nsclerosis (ALS), Huntington disease (HD) and Parkinson disease (PD; Figure 2F).  202 \nAmong the specifically-enriched pathways, genes involved in oxidative 203 \nphosphorylation (OXPHOS) have the shortest transcript lengths, and many of these 204 \ngenes are shared by the pathways of ALS, HD, PD, and reactive oxygen species 205 \n(ROS; Figure 2G).  Together, our results suggest a qualitative difference in mRNA 206 \ncomposition of SGs between single and double glycolytic assaults and, more 207 \nimportantly, a selective sequestration of OXPHOS gene transcripts by SGs induced by 208 \nthe double assault. 209 \n 210 \nMitochondrial function is defective in cells that form Type II SGs 211 \nThe preferential enrichment of OXPHOS genes in SGs formed under the double 212 \nassault suggests a possibility of mitochondrial dysfunction.  To test this, we first 213 \nperformed Western blot analysis on two selected proteins that are encoded by SDGs: 214 \nNDUFB4 serves as an accessory subunit of NADH dehydrogenase (mitochondrial 215 \ncomplex I), and COX6B1 constitutes a critical subunit of cytochrome c oxidase 216 \n(complex IV).  The levels of both proteins were significantly reduced under GD+2DG 217 \nbut not GD or 2DG alone (Figure 3A-B).  To investigate whether the selective 218 \nsequestration of the transcripts of mitochondrial SDGs led to a general mitochondrial 219 \ndysfunction, we determined the levels of three other representative mitochondrial 220 \nproteins not encoded by SDGs: TOM20 is a subunit of the outer mitochondrial 221 \nmembrane translocase (TOM complex), ND6 is a mitochondrial-encoded subunit of 222 \ncomplex I, and ATP6 is a mitochondrial-encoded subunit of ATP synthase (complex 223 \nV).  Our results showed a significant reduction in the level of these mitochondrial 224 \nproteins under GD+2DG but not GD or 2DG alone (Figure 3C-D).  Consistently, 225 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nGD+2DG treatment yielded a most prominent reduction in both TOM20 226 \nimmunofluorescence staining signal and mitochondrial area size in SG+ cells relative 227 \nto SG- cells (Figure 3E-F).  Interestingly, we also detected a higher mitochondrial 228 \nproximity of Type II SGs than those of SGs (Figure 3G), a phenomenon that has been 229 \nreported to link SGs to metabolic remodeling (Amen et al. 2021).  As expected of a 230 \nmitochondrial dysfunction, the levels of intracellular ATP and mitochondrial 231 \nrespiration were significantly reduced under GD+2DG relative to GD or 2DG alone 232 \n(Figure 3H-J).  Together, these results document a unique feature of Type II SGs, a 233 \npreferential retention of mitochondria-related mRNAs, and the accompanying 234 \nmitochondrial defects. 235 \n 236 \nType II SGs differ from Type I SGs in their failure to dissolve during prolonged 237 \ntreatment 238 \nPrevious studies suggest that intracellular ATP deficiency can prevent conventional 239 \nSGs from disassembling (Eum et al. 2020; Jain et al. 2016; Wang et al. 2022).  Based 240 \non our finding that mitochondrial defects were linked only to SGs formed under 241 \nGD+2DG, we predicted that SGs formed under either GD or 2DG alone would 242 \nengage in disassembly without the release of glycolytic insult, i.e., during prolonged 243 \ntreatment.  Therefore, we analyzed the fate of SGs by extending the duration of 244 \ntreatments to 24 h.  Under GD alone, the number of SG+ cells, the average SG 245 \nnumber and the average SG size were all significantly reduced after 8 h (Figure 4A).  246 \nAt 24h, SG+ cells approached a minimum level (~0.95%) that was basically a 247 \nbackground level seen in untreated cells (~1%).  Similarly, under 2DG alone, these 248 \nSG features continuously declined after the peaking time at 1 h (Figure 4B).  In 249 \ncontrast, under GD+2DG, SGs were maintained even at 24 h in all examined cells (N 250 \n= 1,091; Figure 4C).  These differences in SG dynamics between GD or 2DG alone 251 \nand GD+2DG were also observed in HeLa cells (Figure S5). 252 \n 253 \nGiven our result that eIF2α phosphorylation and translation repression are 254 \ndifferentially impacted by double or single assaults, we sought to evaluate the 255 \ndynamic properties of these processes during prolonged treatments.  We detected a 256 \ndecline in p-eIF2α under GD or 2DG alone, which was correlated with SG 257 \ndissociation dynamics (Figure 4D-E), whereas p-eIF2α exhibited a continuous 258 \nincrease under GD+2DG despite its insignificance at the time of SG formation 259 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n(Figure 4F).  Puromycin incorporation assays documented consistently continuous 260 \ndeclines in global protein synthesis in all three conditions (Figure 4G-I), supporting 261 \nthat translation repression can be achieved through both p-eIF2α-dependent and -262 \nindependent pathways.  Together, these results document a dynamic disassembly 263 \nprocess for Type I SGs formed under sustained single glycolytic assaults, a process 264 \nlacking in cells that form Type II SGs. 265 \n 266 \nCells forming Type II SGs upregulate the bulk mRNA expression of OXPHOS 267 \ngenes 268 \nTo analyze transcriptional responses to different glycolytic assaults as a function of 269 \ntime, we generated bulk mRNA-seq datasets from cells under GD, 2DG or GD+2DG 270 \nfrom different durations.  We were particularly interested in comparing the two 271 \ndistinct phases of SG lifecycle: formation (0~8, 0~1 and 0~1 h under GD, 2DG and 272 \nGD+2DG, respectively) and dissolution/persistence (8~16, 1~4 and 1~4 h under GD, 273 \n2DG and GD+2DG, respectively).  We performed a time-series clustering analysis to 274 \nidentify gene ontologies (GOs) with specific dynamic patterns: rise-falling, fall-rising, 275 \nmonotonically rising, and monotonically falling (Figures 5A; see Materials and 276 \nMethods).  A total of 9 pathways shared the same dynamics in cells under all three 277 \ntreatments (Figure 5B), the majority of which (8/9) were monotonically rising such as 278 \n“IRES dependent viral translational initiation” and “eukaryotic translation initiation 279 \nfactor 3 complex Eif3m” (Figure 5C).  This result supports the known mechanism of 280 \nIRES-mediated translation upon stress (Yang et al. 2019; Lacerda et al. 2019) as a 281 \ncommon cellular response to different glycolytic assaults. 282 \n 283 \nThere were 26 pathways that shared the same dynamics between GD alone and 2DG 284 \nalone but not with GD+2DG.  Among these, 3 monotonically-rising pathways were 285 \nrelated to ER stress (Figure 5D-E), indicative of a transcription-level response to 286 \nelevated p-eIF2α and reduced protein synthesis under either single assault.  In 287 \ncontrast, among the pathways with a unique temporal pattern in cells under GD+2DG, 288 \nthe top significant GOs were related to mitochondrial functions, including 289 \n“mitochondrial protein containing complex”, “aerobic respiration” and “oxidative 290 \nphosphorylation” (Figure 5F).  The majority of genes in these pathways exhibited an 291 \noverall increasing trend as a function of treatment duration under GD+2DG but not 292 \nunder GD or 2DG alone (Figure 5G).  It is particularly worth noting that this 293 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nexpression increase in OXPHOS genes coincided with the acute trapping of their 294 \ntranscripts by SGs (Figure 2F-G).  Given the observation of mitochondrial defects 295 \nincluding the reduced expression of OXPHOS genes, we postulate that cells were 296 \nmaking a compensatory response to the double glycolytic assault, a response that 297 \nwould only end up being futile. 298 \n 299 \nMitochondrial inhibition prevents Type I SG dissolution during prolonged 2DG 300 \ntreatment 301 \nTo test whether mitochondrial defects under the double assault may underlie the 302 \ninability of cells to reverse SGs, we analyzed cells under treatments of 2DG along 303 \nwith inhibitors that target different mitochondrial respiratory complexes.  If our 304 \nhypothesis is correct, we would expect mitochondrial inhibition to render SGs formed 305 \nunder Type I condition non-dissolvable.  Our results show that, while each 306 \nmitochondrial inhibitor alone showed little inducement of SGs, combining a 307 \nmitochondrial inhibitor with 2DG led to a dramatic induction of SGs within 1 h 308 \n(Figure 6A).  In addition, both the fraction of SG+ cells and the p-eIF2α level were 309 \npersistent during the prolonged treatments (Figure 6A-B).  Furthermore, in ρ0 cells, 310 \nwhich are depleted of mitochondrial DNA, GD alone induced SGs that shared similar 311 \ndynamics with those formed in 143B cells under GD+2DG (Figure 6C-D). 312 \n 313 \nTo test whether a lack of any specific OXPHOS gene activity may lead to a similar 314 \nphenotype, we combined siRNA knockdown with 2DG treatment.  Here we tested 315 \ntwo OXPHOS SDGs (NDUFB4 and COX6B1) and two OXPHOS non-SDGs 316 \n(NDUFB6 and COX6B2).  Figures 6E-I show that all of these siRNAs significantly 317 \ndecreased the protein level of TOM20 and, importantly, SGs formed under these 318 \nconditions showed Type II hallmarks, including persistence at 24 h and proximal 319 \nmitochondrial association (see also Figure 6E-F for a detectable difference in 320 \ncharacteristics of SGs between SDG knockdown and non-SDG knockdown at 1 h).  321 \nTogether, these results support an involvement of mitochondrial activity in SG 322 \ndissolution under prolonged glycolytic assaults, an engagement that, under the double 323 \nassault, is diminished by SG sequestration of OXPHOS transcripts.  324 \n 325 \nDissolution of Type I SGs requires HSP70 activity whose inhibition alters 326 \nOXPHOS gene expression 327 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nOur results described thus far document a defining difference between single and 328 \ndouble glycolytic assaults, i.e., the ability of SGs to dissociate under prolonged 329 \ntreatments (Figure 4A-C).  To gain insights into the dissolution process of SGs formed 330 \nunder these assaults, we examined the potential dependence of SG clearance on 331 \nautophagy and the HSP70 chaperone, respectively.  We found that, while wortmannin, 332 \na potent autophagy inhibitor, affected neither SG formation nor dissolution (Figure 333 \n7A), siRNA against HSP A1A and VER-155008 targeting HSP70 both effectively 334 \npreserved the fraction of SG+ cells during prolonged treatment of GD or 2DG alone 335 \n(Figure 7B-C).  Importantly, the expression level of HSP70 was significantly 336 \nincreased in cells undergoing SG dissolution but not in cells during prolonged 337 \nGD+2DG treatment (Figure 7D).  These results suggest a crucial role of HSP70 338 \nactivity in SG dissolution during prolonged glycolytic stress. 339 \n 340 \nTo explore HSP70-mediated regulation of SG dissolution during prolonged glycolytic 341 \nstress, we performed bulk RNA-seq of cells that were preconditioned with HSP A1A 342 \nsiRNA and then co-treated with GD for different durations (GD+siHSP).  Differential 343 \nexpression analysis between GD and GD+siHSP at each corresponding time point 344 \nshowed a significant enrichment of downregulated genes in OXPHOS at 0 and 16 h 345 \nbut not at 8 h (Figure 7E), suggesting a requirement of HSP70 activity for OXPHOS 346 \ngene expression before the onset of glycolytic stress and during SG dissolution when 347 \nthe stress prolonged.  In addition, by intersecting SDGs identified from G3BP1-348 \nAPEX2 experiments and HSP70-dependent SG-dissolution genes (upregulated at 16 h 349 \nrelative to 8 h under GD but not under GD+siHSP), we obtained a list of 19 genes, 350 \nwhich might be sequestered by SGs under the control of HSP70 (Figure 7F).  Among 351 \nthese genes, SP ATA18 encodes the protein MIEAP, which promotes accumulation of 352 \nlysosomal proteins in mitochondrial matrix and elimination of damaged proteins 353 \ninside mitochondria (Ikari et al. 2024; Gaowa et al. 2018).  RT-qPCR experiments 354 \nconfirmed that SP ATA18 was significantly increased from 8 to 16 h under GD only 355 \nwhen HSP70 activity was intact (Figure 7G).  Therefore, HSP70 may mediate the 356 \nexpression of mitochondrial quality control genes such as SP ATA18 to control SG 357 \ndissolution. 358 \n 359 \nCell survival under prolonged glycolytic assaults is correlated with SG 360 \ndissolution  361 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nTo test whether SG dissolution is an adaptive function that supports cell survival 362 \nduring prolonged glycolytic assaults, we performed flow cytometry with propidium 363 \niodide and Annexin V staining.  We detected a pronounced fraction of apoptotic cells 364 \nat 24 h under GD+2DG, but not under GD or 2DG alone (Figure 8A).  Consistently, 365 \ngenes involved in apoptosis were enriched in upregulated genes under prolonged 366 \nGD+2DG but not under prolonged GD or 2DG alone according to bulk mRNA-seq 367 \nand RT-qPCR experiments (Figure 8B-C).  Furthermore, when the dissolution of Type 368 \nI SGs was blocked by HSP70 inhibition (GD+VER or 2DG+VER), the fraction of 369 \napoptotic cells was significantly increased (Figure 8D).  Together these results show 370 \nthat cell survival is correlated with SG dissociation under prolonged glycolytic 371 \nassaults, and that persistent SGs represent a sign for cells reaching an impasse. 372 \n 373 \nDiscussion 374 \nIn this study, we document a divergent formation of two distinct types of SGs under 375 \nglycolytic inhibition.  Type I SGs, which are induced by single assaults (GD or 2DG), 376 \nresemble arsenite-induced SGs in their dependence on eIF2α phosphorylation for 377 \nassembly and HSP70 activity for disassembly.  In contrast, Type II SGs, which are 378 \ninduced by GD+2DG and previously referred to as energy deficiency-induced SGs 379 \n(eSGs), exhibit a distinct biochemical state characterized by the sequestration of 380 \nOXPHOS gene transcripts and mitochondrial dysfunction.  Our comparative analysis 381 \nof these two types of SGs suggests a feedback loop between the formation of Type II 382 \nSGs and mitochondrial dysfunction, a regulatory loop that can render Type II SGs 383 \nnon-dissociable under sustained stress (see Figure 9 for a graphic model).  Effectively 384 \nthe sequestration of OXPHOS transcripts within SGs under double assault widens the 385 \nimpact of glycolytic inhibition to further exacerbate energy deficit and prevent SG 386 \ndisassembly.  This feedback loop highlights the dual roles of SGs in stress adaptation 387 \nand pathogenesis, providing insights into the molecular mechanisms underlying SG-388 \nassociated diseases.   389 \n 390 \nThe dynamic and reversible nature of physiological SGs enables cells to adapt to and 391 \nrecover from stressful conditions.  However, the persistence of SGs containing 392 \npathological contents can lead to cell dysfunction and death (Ivanov et al. 2019; 393 \nZhang et al. 2019; Mahboubi et al. 2017; Sato et al. 2024).  Such a paradoxical 394 \ndivergence highlights a delicate control of the quality and dynamics of SGs.  In fact, 395 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\ndisassembly upon the release of the inducing stressors such as sodium arsenite and 396 \nheat shock has been defined as a key feature of acute SGs (Marmor-Kollet et al. 2020; 397 \nHofmann et al. 2021; Buchan et al. 2009).  Interestingly, SGs that are induced by the 398 \nproteasome inhibitor MG132 can also disassemble during a long-term treatment 399 \nwithout eliminating the stressor (Ganassi et al. 2016; Mazroui et al. 2007; Wang et al. 400 \n2022).  Similarly, our Type I SGs, but not Type II SGs, are dissolved within 24 h 401 \nwhen glycolytic restriction remains in effect.  Therefore, different regimens of 402 \nglycolytic stress induce distinct SG responses, resembling a physiological-to-403 \npathological transition that accompanies disease progression. In this context, it is 404 \nworth noting that, while the initiating differences between the single and double 405 \nassaults on glycolytic inhibition might not be major on their own, the positive 406 \nfeedback loop that involves the sequestration of OXPHOS mRNAs and the persistent 407 \nnature of type II SGs likely have contributed significantly to the bifurcation in the SG 408 \ntypes. 409 \n 410 \nIt has been shown that severe energy deficiency can prevent arsenite-induced SGs 411 \nfrom disassembling (Jain et al. 2016; Wang et al. 2022).  Our time-resolved 412 \ntranscriptomic analysis shows that the bulk mRNA level of OXPHOS genes exhibits 413 \nan increase along the GD+2DG treatment time, but this increase in mRNA expression 414 \nis ultimately unsuccessful because their proteins remain at a reduced level and 415 \nmitochondrial functions remain impaired.  We suggest that the preferential 416 \nsequestration of OXPHOS gene transcripts by Type II SGs is responsible for 417 \nundercutting the effect of the compensatory transcriptional upregulation, leading to a 418 \nperpetual mitochondrial dysfunction and energy stress, accompanied by a persistence 419 \nof SGs and increased cell death. 420 \n 421 \nThere is a long-standing hypothesis that SGs serve as temporal storages and silent 422 \nsites of untranslated mRNAs and unused RNA-binding proteins (Kedersha et al. 423 \n2002; Ivanov et al. 2019).  Our APEX2-based transcriptomic analysis uncovers 424 \ndistinct profiles of G3BP1-associated transcripts between Type I and Type II SGs.  425 \nNotably, Type II SGs preferentially sequester shorter transcripts, including OXPHOS 426 \ngenes, despite the conventional preference for long transcripts due to enhanced RNA-427 \nRNA interactions (Campos-Melo et al. 2021; Ren et al. 2023; Lee et al. 2019; Khong 428 \net al. 2017).  This unique mRNA composition reflects a specific cellular response to 429 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\ncomplete glycolytic inhibition and further underscores the role of Type II SGs in 430 \nmodulating mitochondrial function. 431 \n 432 \nIn conclusion, our work suggests a mechanism through which SG formation can 433 \ntransition between their protective and deleterious roles.  Such a transition can be 434 \nachieved through the operation of a feedback loop between SG dynamics and 435 \nmitochondrial function. Our study provides a fresh perspective for understanding the 436 \npathogenesis of SG-associated diseases and potential therapeutic targets. 437 \n 438 \nMaterials and Methods 439 \nCell culture and treatment 440 \n143B and HeLa cells were cultured in DMEM (Gibco) with 10% FBS, 100 U/ml 441 \npenicillin and 100 U/ml streptomycin under 5% CO₂ at 37°C.  The mitochondrial 442 \nDNA-less ρ⁰ 206 cells, derived from 143B cells, were cultured under the same 443 \ncondition except an addition of 50 μg/ml uridine.  For glycolysis inhibition, cells were 444 \nrinsed in PBS and then transferred to glucose-free DMEM, DMEM with 25 mM 2-445 \ndeoxy-D-glucose (2DG), or glucose-free DMEM with 2DG for various durations as 446 \ndescribed in main text.  For mitochondrial inhibition, cells were rinsed in PBS and 447 \nthen transferred to DMEM with 1 μM rotenone, 5 μM antimycin A or 1.5 μM 448 \noligomycin.  For p-eIF2α inhibition, cells were rinsed in PBS and then transferred to 449 \nDMEM with 500 nM ISRIB (MCE, HY-12495A) for 1 h.  For autophagy inhibition, 450 \ncells were rinsed in PBS and then transferred to DMEM with 1 μM wortmannin 451 \n(MCE, HY-10197) for 8 h.  For HSP70 inhibition, cells were rinsed in PBS and then 452 \ntransferred to DMEM with 50 μM VER-155008 (MCE, HY-10941). 453 \n 454 \nsiRNA transfection 455 \nAll siRNAs were designed by DSIR (http://biodev.extra.cea.fr/DSIR/DSIR.html) and 456 \nsynthesized by GenePharma. For transfection, cells were treated with an siRNA at a 457 \nfinal concentration of 50 nM using jetPRIME (Polyplus) for 36 h before other 458 \ntreatments.  The oligo sequences are listed in Table S1. 459 \n 460 \nQuantitative RT-PCR 461 \nCells were seeded in 6-well plates to reach approximately 80~90% confluence, and 462 \ntotal RNA was extracted using TRIzol (TaKaRa, 9109).  The purified RNA was 463 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nquantified by NanoDrop Spectrophotometer, and 1 μg RNA was subjected to reverse 464 \ntranscription using ABScript III RT Master Mix with gDNA Remover (ABclonal, 465 \nRK20429). Quantitative PCR was performed using 2× Universal SYBR Green Fast 466 \nqPCR Mix (ABclonal, RK21203).  For each experimental group, three independent 467 \nreverse transcription experiments were conducted and β-actin was used as an internal 468 \ncontrol.  The primers are listed in Table S1. 469 \n 470 \nWestern blot 471 \nCells were washed with PBS and then lysed in RIPA buffer (FUDE, FD009) with 472 \nBenzonase Nuclease (Beyotime, D7121) and complete Protease and phosphatase 473 \ninhibitor cocktail (Beyotime, P1048). Lysates were loaded onto 10% SDS-PAGE and 474 \nproteins were transferred to PVDF membranes (Millipore, IPVH00010).  Membranes 475 \nwere incubated with rocking first in TBST with 5% milk at room temperature for 1 h, 476 \nthen in TBST with 5% milk and primary antibody at 4°C overnight.  After three 477 \nwashes, membranes were incubated in TBST with secondary antibody and 5% milk at 478 \nroom temperature for 1 h. After another three washes, ECL Western Blotting Substrate 479 \n(Vazyme Biotech, E412-01) were used for detection.   480 \n 481 \nThe following primary antibodies were used: HRP-conjugated β-Actin Rabbit mAb 482 \n(1:5000, ABclonal, AC028), HRP-conjugated β-Tubulin Mouse mAb (1:5000, 483 \nABclonal, AC030), rabbit anti LC3B (1:1000, ABclonal, A19665), mouse anti 484 \nHSP70/HSPA1 (1:2000, ABclonal, A1507), rabbit anti HSP70 (1:2000, ABclonal, 485 \nA23457), rabbit anti phospho-eIF2α (S51; 1:1000, Cell Signaling, 3398), mouse anti 486 \nphospho-EIF2S1 (Ser51) (1:1000, proteintech, 68023-1-Ig), rabbit anti EIF2S1 487 \n(1:1000, proteintech, 82936-1-RR), rabbit anti EIF2S1/ EIF2A (1:1000, proteintech, 488 \n11170-1-AP), rabbit anti GADD34 (1:1000, proteintech, 10449-1-AP), mouse anti 489 \nTOM20 (1:1000, proteintech, 66777-1-Ig), rabbit anti TOM20 (1:1000, proteintech, 490 \n11802-1-AP), rabbit anti ATP6 (1:1000, ABclonal, A23150), rabbit anti COX6B1 491 \n(1:1000, proteintech, 11425-1-AP), rabbit anti MT-ND6 (1:1000, ABclonal, A17991), 492 \nrabbit anti NDUFB4 (1:1000, proteintech, 27931-1-AP), rabbit anti COX6B2 (1:1000, 493 \nproteintech, 11437-1-AP), rabbit anti NDUFB6 (1:1000, proteintech, 16037-1-AP). 494 \n 495 \nImmunofluorescence staining 496 \nCells were washed in PBS, fixed in 4% paraformaldehyde at room temperature for 20 497 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nmin, and rinsed in PBS buffer containing 0.1% Triton X-100 (PBST) for 10 min.  498 \nThen cells were blocked in PBST with 3% BSA for 1 h and incubated at 4°C 499 \novernight with primary antibody. After three washes, cells were incubated in PBST 500 \nwith secondary antibody and 3% BSA at room temperature for 1 h.  After another 501 \nthree washes, cells were mounted in Antifade Mounting Medium with DAPI 502 \n(Beyotime, P0131). 503 \n 504 \nThe following primary antibodies were used: rabbit anti G3BP1 (1:200, ABclonal, 505 \nA3968), mouse anti TOM20 (1:200, proteintech, 66777-1-Ig), rabbit anti TOM20 506 \n(1:200, proteintech, 11802-1-AP), 488-conjugated G3BP1 pAb (1:200, proteintech, 507 \nCL488-13057), mouse anti p-EIF2S1 (Ser51) (1:200, proteintech, 68023-1-Ig), rabbit 508 \nanti p-eIF2α (1:200, Cell Signaling, 3398), mouse anti Myc tag (1:500, proteintech, 509 \n60003-2-Ig). 510 \n 511 \nOPP staining 512 \nO-propargyl puromycin (OPP) staining was performed using Click-iT™ Plus Alexa 513 \nFluor™ 555 Picolyl Azide Toolkit (Thermo Fisher, C10642) according to the 514 \nmanufacturer’s instruction.  After three washes, cells were mounted in Antifade 515 \nMounting Medium with DAPI (Beyotime, P0131). 516 \n 517 \nImage analysis and quantification 518 \nFor each glass slide, >= 5 different fields were imaged by Olympus FV1000 Confocal 519 \nMicroscope or Nikon Instruments A1 Confocal Laser Microscope.  ImageJ tools were 520 \nused to identify individual cells (based on DAPI signals) and quantify SG 521 \ncharacteristics (based on G3BP1 signals).  Cells with at least five G3BP1-positive 522 \nspots detected in the cytoplasm were considered as SG+.  In each SG+ cell, the 523 \nnumber of SGs was measured using Analyze Particles and the aggregated area size of 524 \nSGs was measured using ROI Manager.      525 \n 526 \nFor quantification of p-eIF2α and OPP, the fluorescence intensities within each 527 \nidentified cell were summed, background-subtracted and normalized to the cell area 528 \nsize using ImageJ.  For quantification of TOM20, the total area size of fluorescent 529 \nsignals was measured by ROI Manager and normalized to the cell area. 530 \n 531 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nRNA sequencing 532 \nTotal RNA was extracted using RNAiso Plus (TaKaRa, 9109).  The libraries were 533 \ngenerated using V AHTS Universal V8 RNA-seq Library Prep Kit for Illumina 534 \n(Vazyme, NR605).  Sequencing was performed on Novaseq 6000 (Nanjing Jiangbei 535 \nNew Area Biopharmaceutical Public Service Platform).  Read quality was assessed 536 \nusing fastqc and adaptor sequences were removed using trim_galore v0.6.10.  Then 537 \nreads were aligned to GRCh38 using hisat2 v2.2.1, and summarized using 538 \nfeatureCounts v2.0.1.  Quantification of transcript isoforms was performed using 539 \nStringTie v2.2.1 for assembly and analysis.  Differential expression analysis was 540 \nconducted using edgeR v4.4.0 with a cutoff fold change > 2 and adjusted p-value < 541 \n0.05.  Functional annotation, including Gene Ontology (GO) and Kyoto Encyclopedia 542 \nof Genes and Genomes (KEGG) enrichment analyses, was carried out using 543 \nclusterProfiler v4.14.3 with adjusted p-value < 0.05. 544 \n 545 \nAPEX-based proximity labeling and RNA sequencing analysis 546 \nG3BP1-APEX2-Myc was generated by an in-frame fusion of G3BP1, APEX2 and 547 \nMyc tag in pAcGFP1-N1 vector.  After transfection, cells were cultured for 48 h to 548 \nhave adequate expression, and then subjected to different experimental treatments.  549 \nThe resulting samples were biotin-labeled as previously described (Somasekharan et 550 \nal. 2020).  For immunofluorescence staining analysis, rabbit anti G3BP1 (1:200, 551 \nABclonal, A3968), mouse anti Myc tag (1:500, proteintech, 60003-2-Ig) and Alexa 552 \nFluor™ 647-conjugated streptavidin (1:400, Thermo Fisher, S21374) antibodies was 553 \nused.  For RNA-seq analysis, biotinylated RNAs were pulled down using C1 554 \nStreptavidin beads (Thermo Fisher) according to the manufacturer’s instruction.  To 555 \nidentify stress-dependent G3BP1-associated genes (SDGs), we performed differential 556 \nexpression analysis by comparing APEX-seq under a given stress condition over 557 \nAPEX-seq under no treatment in edgeR v4.4.0 with a cutoff fold change > 2 and 558 \nadjusted p-value < 0.05. 559 \n 560 \nTime-series RNA sequencing analysis 561 \nWe generated bulk mRNA-seq datasets from cells before, at and after the SG peaking 562 \ntime under GD (0, 8 and 16 h), 2DG (0, 1 and 4 h) or GD+2DG (0, 1 and 4 h).  For 563 \neach dataset, we used GSV A v2.0.1 to calculate Geneset Activity Score (GAS) of all 564 \nGO terms, limma v3.62.1 to identify GO terms with differential GASs (fold change > 565 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n2 and adjusted p-value < 0.05), and Mfuzz v2.66.0 to cluster differential GOs with the 566 \nsame dynamic patterns of GAS. 567 \n 568 \nFACS analysis of ROS, apoptosis and cell cycle 569 \nCells were treated with Reactive Oxygen Species Assay Kit (Beyotime, S0035M), 570 \nAnnexin V-FITC assay kit (Beyotime, C1062) or Cell Cycle and Apoptosis Analysis 571 \nKit (Beyotime, C1052) according to the manufacturer’s instructions, respectively, and 572 \nthen fluorescence was quantified by flow cytometry. 573 \n 574 \nMeasurement of ATP 575 \nCells were assayed by Enhanced ATP Assay Kit (Beyotime, S0027) according to the 576 \nmanufacturer’s instruction. 577 \n 578 \nMeasurement of Oxygen Consumption rate 579 \nCells were plated onto a Seahorse XF96 Cell Culture Microplate (Agilent) at a density 580 \nof 1.0 × 104 cells/well.  After an overnight incubation with 5% CO₂ at 37 °C, the 581 \nculture medium was replaced by the assay medium containing 1 mM pyruvate, 4 mM 582 \nglutamine, and with or without 25 mM glucose (as the control and the GD group 583 \nrespectively).  Then sequential treatments with 1 µM oligomycin, 2 µM FCCP, and 1 584 \nµM rotenone+antimycin A allowed for generating the full profile of OCR. 585 \n 586 \nStatistics 587 \nEach sequencing data has at least two replicate samples.  Each quantitative 588 \nexperiment has at least three independent samples.  Unless otherwise stated in the 589 \nlegend, all quantitative results were presented as mean ± standard error of the mean 590 \n(SEM), and analyses of the mean were presented as unpaired two-tailed Student's t-591 \ntest.  592 \n 593 \nData availability 594 \nAll raw RNA-seq data generated in this study have been submitted to the NCBI 595 \nBioProject database under accession number PRJNA1289020. 596 \n 597 \nAcknowledgements 598 \nThis study was supported by the National Natural Science Foundation of China 599 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n(32470584) and the National Key R&D Program of China (2021YFC2700403).  We 600 \nacknowledge support of Zhejiang University School of Medicine affiliated Women’s 601 \nHospital.  602 \n 603 \nAuthor contributions 604 \nW.Z., J.M. and F.H. conceived the study and designed the experiments; W.Z., M.X. 605 \nand X.L. performed experiments and generated data; W.Z. and R.X. analyzed the data 606 \nand generated all figures; Y .G. and M.G. provided technical and managerial support; 607 \nJ.M. and F.H. acquired funding; W.Z., R.X., J.M. and F.H. wrote the paper and all 608 \napproved the paper. 609 \n 610 \nCompeting interests 611 \nThe authors declare no competing interests. 612 \nFigures 613 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n614 \nFigure 1. SGs are differentially formed under different assaults against 615 \nglycolysis. 616 \n(A) Representative images of SG formation (blue: DAPI, red: G3BP1) in 143B cells 617 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nunder no treatment (UT), glucose deprivation for 8 h (GD), treatment with 2DG for 1 618 \nh (2DG), and the combined treatment for 1 h (GD+2DG).  Scale bar: 20 μm.  Inset 619 \nbox: a zoom-in view of SGs. 620 \n(B) Percentages of SG+ cells. Mean ± SEM (standard error of the mean) was 621 \ncalculated from three independent replicate experiments under each condition.  622 \nStudent’s t-test: **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05, ns. p > 0.05 623 \n(same throughout the manuscript). 624 \n(C) Number of SGs per SG+ cell.  For each condition, ≥ 49 individual cells were used 625 \nfor quantification. 626 \n(D) Aggregated area proportion of SGs per SG+ cell. For each condition, ≥ 49 627 \nindividual cells were used for quantification. 628 \n(E) Western blot analysis of p-eIF2α (serine 51) and total eIF2α. Each condition has 3 629 \nindependent replicate experiments.  Quantification was performed as p-eIF2α/eIF2α 630 \nratio. 631 \n(F) Representative images of fluorescent immunostaining (blue: DAPI, red: G3BP1, 632 \ngreen: p-eIF2α) in SG- and SG+ cells under the four conditions, respectively.  For 633 \neach bar, average background-subtracted p-eIF2α intensity per unit cell area size was 634 \nquantified from ≥ 10 individual cells.     635 \n(G) Representative images of fluorescent immunostaining (blue: DAPI, red: G3BP1, 636 \ngreen: OPP) in SG- and SG+ cells under the four conditions, respectively.  For each 637 \nbar, average background-subtracted OPP intensity per unit cell area size was 638 \nquantified from ≥ 10 individual cells. 639 \n(H) Representative images of SG formation (blue: DAPI, red: G3BP1) in cells under 640 \ndifferent glycolytic assaults combined with ISRIB.  Each condition has 3 independent 641 \nreplicate experiments. 642 \n(I-J) Western blot analysis and quantification of p-eIF2α and total eIF2α in cells under 643 \ndifferent glycolytic assaults combined with ISRIB. Each condition has 3 independent 644 \nreplicate experiments.  645 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n646 \nFigure 2. Different glycolytic assaults result in different SG-associated 647 \ntranscripts. 648 \n(A) Colocalization of G3BP1-APEX2-Myc fusion proteins with biotinylation signals 649 \n(blue: DAPI, red: G3BP1, gray: MYC, green: biotin) in cytoplasm and SGs under UT, 650 \nGD, 2DG, and GD+2DG.  Scale bar: 10 μm. 651 \n(B) Hierarchical cluster analysis on pair-wise dissimilarities (calculated as 1 - 652 \nPearson’s correlation coefficient) among all bulk and G3BP1-APEX2-Myc-enriched 653 \nRNA-seq datasets.  Each condition has 2 independent replicates. 654 \n(C) Venn diagram shows the numbers of stress-dependent G3BP1-associated genes 655 \n(SDGs) resulted from GD, 2DG, and GD+2DG.   656 \n(D) Gene pathways enriched with common SDGs among GD, 2DG, and GD+2DG.  657 \nBlue dots represent enriched pathways, with darker colors indicating higher 658 \nsignificance, and gray dots denote genes within specific pathway. 659 \n(E) Transcript lengths of SDGs identified from GD, 2DG and GD+2DG and the other 660 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nexpressing genes (non-SDGs).  Shown are the results based on the lengths of all 661 \nquantified transcript isoforms.  The trend remains consistent whether using transcript 662 \nisoforms with the longest total length or the longest CDS (Figure S2).  663 \n(F) Gene pathways enriched with SDGs identified from GD, 2DG and GD+2DG, 664 \nrespectively. 665 \n(G) Transcript length distributions of SDGs in the GD+2DG-enriched pathways of 666 \nOXPHOS, PD, HD, ALS, and ROS.  667 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n 668 \nFigure 3. Mitochondrial dysfunction under double assault against glycolysis. 669 \n(A-B) Western blot analysis of NDUFB4 and COX6B1, which were identified as 670 \nSDGs under GD+2DG. 671 \n(C-D) Western blot analysis of TOM20, ND6 and ATP6, which were identified as 672 \nnon-SDGs.  673 \n(E-F) Representative images of immunofluorescence staining against G3BP1 (gray) 674 \nand TOM20 (red and binary). Quantification was performed by normalizing the 675 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\naggregated area size of identified mitochondria using TOM20 with the cell size. N ≥ 7 676 \nfor each data point. 677 \n(G) Pearson’s correlation coefficients show pixel intensity correlation between the 678 \ntwo channels within single cells of panel E.  The analysis was performed by 679 \n“Colocalization Finder” in ImageJ.  Each dot represents one single cell. 680 \n(H) Relative levels of intracellular ATP under GD, 2DG and GD+2DG at the 681 \ncorresponding SG peaking times.  682 \n(I) Oxygen consumption rate (OCR) was measured in cells sequentially treated with 683 \noligomycin, trifluoromethoxy carbonylcyanide phenylhydrazone (FCCP), and 684 \nrotenone+antimycin A.  Errorbars represent SEM computed from three independent 685 \nmeasurements. 686 \n(J) Relative rates of basal respiration, maximal respiration, ATP production and proton 687 \nleak, normalized to the corresponding measurements in the side-by-side UT samples. 688 \n  689 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n690 \nFigure 4. Distinct stress granule dynamics of cells under different assaults 691 \nagainst glycolysis. 692 \n(A-C) Representative images and quantifications of SG formation (blue: DAPI, red: 693 \nG3BP1) in 143B cells treated by GD (A), 2DG (B), and GD+2DG (C) for different 694 \ndurations. 695 \n(D-F) Western blot analysis of p-eIF2α (serine 51) and total eIF2α in cells treated by 696 \nGD, 2DG, GD+2DG for different durations.  Quantification shown on the right was 697 \nperformed by normalizing p-eIF2α to eIF2α.  Errorbars represent SEM computed 698 \nfrom three independent replicates. 699 \n(G-I) Western blot analysis of puromycin incorporation assay in cells treated by GD, 700 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n2DG, GD+2DG for different durations.  Quantification shown on the right was 701 \nperformed by normalizing each set of experiments to the control group at 0 h.  702 \nErrorbars represent SEM computed from three independent replicates.   703 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n 704 \nFigure 5. Transcriptomic dynamics identifies upregulation of OXPHOS genes as 705 \na specific response to double glycolytic assaults.     706 \n(A) Geneset Activity Score (GAS) analysis identifies four clusters of gene ontologies 707 \n(GOs) with distinct temporal patterns during the prolonged glycolytic assaults: rise-708 \nfalling (I, dark brown), fall-rising (II, dark green), monotonically rising (III, dark 709 \nblue), monotonically falling (IV , dark purple).  X axis represents time points in 710 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nrelation to the SG peaking time; y axis represents changes in GAS, defined as the 711 \ntemporal variation in GSV A-derived scores of GO terms; each line represents one GO 712 \nterm.  Shown is the result from GD; see Figure S3 for the results from 2DG and 713 \nGD+2DG. 714 \n(B) Venn diagrams illustrate the numbers of GOs with specific temporal patterns that 715 \nare common or unique in cells under GD, 2DG and GD+2DG.   716 \n(C) Gene pathways that share the same temporal pattern under all three conditions.  717 \nColor codes are the same as in (A).  718 \n(D) RT-qPCR analysis of DNAJC3, DNAJB11 and PPIB, three genes identified in (E).  719 \nErrorbars represent one standard deviation computed from 3 independent replicate 720 \nexperiments. 721 \n(E) Gene pathways that share the same temporal pattern between GD alone and 2DG 722 \nalone but not GD+2DG. Color codes are the same as in (A).  723 \n(F) Gene pathways that exhibit a specific temporal pattern under GD+2DG but 724 \nbehave differently under GD or 2DG alone.  Color codes are the same as in (A).  725 \n(G) Heat maps showing scaled expression of OXPHOS genes during the prolonged 726 \ntreatments under GD, 2DG and GD+2DG.  727 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n728 \nFigure 6. Mitochondrial inhibition renders SGs formed under 2DG treatment 729 \nnon-dissociable. 730 \n(A) Representative images and quantifications of SG formation (blue: DAPI, red: 731 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nG3BP1) in 143B cells treated with 2DG and mitochondrial inhibition (1 μM rotenone, 732 \n5 μM antimycin A, or 1.5 μM oligomycin). Scale bar: 20 μm 733 \n(B) Western blot analysis of p-eIF2α (S51) and eIF2α in 143B cells treated with 2DG 734 \nand mitochondrial inhibition. 735 \n(C-D) Representative images and quantification of SG formation (blue: DAPI, red: 736 \nG3BP1) and p-eIF2α in ρ0 cells were treated with GD for different durations.  Scale 737 \nbar: 20 μm. 738 \n(E-F) Representative images and quantification of immunofluorescence staining 739 \nagainst G3BP1 (red) and TOM20 (gray) in 143B cells treated with 2DG and a siRNA 740 \ntargeting negative control (NC), NDUFB4, COX6B1, NDUFB6, or COX6B2.  Scale 741 \nbar: 20 μm. 742 \n(G) Pearson’s correlation coefficients quantify the spatial overlap between SGs 743 \n(G3BP1 intensity) and mitochondria (TOM20 intensity) across treatments.  Each dot 744 \nrepresents measurements from one single cell. 745 \n(H) Western blot analysis of TOM20, NDUFB4, NDUFB6, COX6B1, and COX6B2 746 \nproteins in 143B cells treated with or without 2DG and a siRNA. 747 \n(I) Quantification of TOM20 protein levels in panel H.  748 \n  749 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n750 \nFigure 7. HSP70 is responsible for SG dissociation during prolonged treatments. 751 \n(A-C) Representative images and quantifications of SG formation (blue: DAPI, red: 752 \nG3BP1) in 143B cells treated with GD+ wortmannin (A), GD+HSP A1A siRNA (B) 753 \nand 2DG+VER-155008 (C). Each experimental condition has at least 2 independent 754 \nreplicate experiments. 755 \n(D) Western blot analysis of HSP70 in cells treated by GD, 2DG, GD+2DG for 756 \ndifferent durations. 757 \n(E) Functional enrichment analysis of differential expression genes between GD and 758 \nGD+siHSP at each corresponding time point.  759 \n(F) Venn diagram shows the numbers of genes from SDGs under GD, upregulated at 760 \n16 h relative to 8 h under GD (GD-rising) and upregulated at 16 h relative to 8 h 761 \nunder GD+siHSP (GD+siHSP-rising).  Left panel represents the top 5 genes from the 762 \nintersection of SDGs under GD and HSP70-dependent SG-dissolution genes 763 \n(upregulated at 16 h relative to 8 h under GD but not under GD+siHSP), ranked by 764 \nthe differences in upregulation levels between GD and GD+siHSP.  765 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n(G) RT-qPCR analysis of SP ATA18 identified in (F). Errorbars represent the standard 766 \ndeviation calculated from 3 independent replicate experiments. 767 \n 768 \n 769 \n  770 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n771 \nFigure 8. Cell survival under prolonged glycolytic assaults. 772 \n(A) Flow cytometry analysis of apoptosis using Annexin V and propidium iodide 773 \nstaining in cells under UT, GD, 2DG, and GD+2DG for 8 or 24h.  Errorbars represent 774 \nSEM computed from three independent replicates. 775 \n(B) Gene pathways enriched with upregulated genes under prolonged GD (24h), 2DG 776 \n(8h) and GD+2DG (8h), respectively. 777 \n(C) Heatmap for fold changes of genes involved in apoptosis (resulted from (B)) 778 \nunder prolonged GD, 2DG and GD+2DG, compared to the control.  RT-qPCR 779 \nconfirmed the increase in the mRNA levels of BCL2L11, FOS and ITPR1, three genes 780 \nin the apoptosis pathway, under GD+2DG but not under GD or 2DG alone. 781 \n(D) Flow cytometry analysis of apoptosis in cells treated with VER-155008, 782 \nGD+VER, and 2DG+VER.  Errorbars represent SEM computed from three 783 \nindependent replicates. 784 \n  785 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n 786 \nFigure 9. A model showing the formation of two distinct types of SGs under 787 \nglycolytic inhibition. 788 \nThe double assault (glucose depletion + 2DG treatment) leads to the generation of 789 \nType II SGs, which specifically sequester mRNA molecules related to oxidative 790 \nphosphorylation and intensify cellular energy deficits.  This energy crisis drives Type 791 \nII SGs into a non-dissolvable state.  By contrast, single glycolytic assaults lead to the 792 \nformation of Type I SGs, which can naturally dissolve during prolonged treatment.  793 \nType I SGs may be transformed into the non-dissolvable state by additional 794 \nmitochondrial inhibition (or HSP70 dysfunction).  This model depicts the scenario 795 \nwhere SGs serve as both a stress responder and a driver of metabolic collapse, 796 \noffering mechanistic insight into the association of pathological SGs.  797 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n798 \nFigure S1. GADD34 is reduced and negatively correlated with p-eIF2α under 799 \nsingle glycolytic assaults but not under double assault. 800 \n(A-B) Western blot analysis of GADD34, p-eIF2α, and eIF2α in cells under GD, 2DG 801 \nor GD+2DG.  Quantification was performed by normalizing p-eIF2α to eIF2α and 802 \nnormalizing GADD34 to its corresponding internal control.  Mean ± SEM was 803 \ncalculated from three independent replicate experiments. 804 \n  805 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n 806 \nFigure S2. Comparison of transcript lengths under different conditions. 807 \n(A, C): Transcript lengths of SDGs resulting from GD, 2DG, GD+2DG and non-808 \nSDGs, measured using the longest total length (A), or the longest CDS (C), 809 \nrespectively. 810 \n(B, D): Distribution of transcript lengths for SDGs enriched in specific pathways 811 \nunder GD+2DG, including OXPHOS, PD, HD, ALS, and ROS, measured using the 812 \nlongest total length (B) or the longest CDS (D), respectively. 813 \n  814 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n 815 \nFigure S3. Time-course Geneset Activity Score (GAS) analysis under different 816 \nconditions. 817 \n(A-B) Same as Figure 5A but results from 2DG (A) and GD+2DG (B), respectively. 818 \n  819 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n820 \nFigure S4. Cell cycle is similarly disturbed by different glycolytic assaults. 821 \n(A-B) Flow cytometry analysis of cell cycle using PI staining in cells under GD for 8h 822 \n(A) and GD+2DG for 1h (B).  Both treatments present a moderate increase in the 823 \nproportion of G1 cells: from 28.63 ± 7.72% to 32.61 ± 6.29% under GD and from 824 \n27.12 ± 6.21% to 32.32 ± 4.41% under GD+2DG. 825 \n  826 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\n827 \nFigure S5. Distinct stress granule responses of HeLa cells under different 828 \nglycolytic assaults. 829 \n(A-C) Same as Figure 2A-C but results from HeLa cells. 830 \n  831 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint \n\nTable S1. Primer and oligo sequences. 832 \nRT-qPCR primers \nGene Forward primer 5’-3’ Reverse primer 5’-3’ \nDNAJC3 CTGCAGTACGAAGGTGCTGA ACGGCAGCATGAAACTGAGA \nDNAJB11 ATCAAAGTTGTCAAGCACCC\nA \nGGCCTGGTGATCTTATCCCG \nPPIB GCGGCCGATGAGAAGAAGA CGTAGATGCTCTTTCCTCCTGT \nBCL2L11 GCTACCAGATCCCCGCTTTT CCTGCCTCATGGAAGCCATTG \nFOS TGGCGTTGTGAAGACCATGA AGTTGGTCTGTCTCCGCTTG \nITPR1 GAGTTTCAGCCCTCAGTGGA GCAGAGTGGTGGGATCTAGC \nSPATA18 GAAGAGAACACCCTTCCCGC TGATCACACGTGTTTGTGTTG\nT \nsiRNAs \nGene Sequence 5’-3’ \nNC UUCUCCGAACGUGUCACGUTT \nHSP70/HSPA1A CGUCCAUGGUGCUGACCAAGA \nCOX6B1 GCGAUAUCUCUGUGUGCGAAU \nCOX6B2 CGUGGAUGUUGGAUGUGGAAG \nNDUFB4 AGAUGUCGUUCCCAAAGUAUA \nNDUFB6 GAAAGAAUUUCCUGAUCAACA \n.CC-BY 4.0 International licenseperpetuity. 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It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted September 2, 2025. ; https://doi.org/10.1101/2025.09.02.673658doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}