Fragile X mental retardation protein regulates glycolytic gene expression under chronic hypoxia

Fragile X mental retardation protein regulates glycolytic gene expression under chronic hypoxia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Fragile X mental retardation protein regulates glycolytic gene expression under chronic hypoxia Kentaro Kawata, Zaijun Zhang, Yoko Ogura, Xiaoning Sun, Atsuko Nakanishi Ozeki, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4221145/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Oxygen shortage, known as hypoxia, occurs commonly in both physiological and pathological conditions. Transcriptional regulation by hypoxia-inducible factors (HIFs) is a dominant regulatory mechanism controlling hypoxia-responsive genes during acute hypoxia; however, recent studies suggest that post-transcriptional regulation, including RNA degradation, also involves hypoxia-induced gene expression during the chronic hypoxia. In this study, we developed a method to quantify the contributions of RNA synthesis and degradation to differential gene expression, and identified 102 genes mainly regulated via RNA degradation under chronic hypoxia in HCT116 cells. Bioinformatics analysis showed that the genes mainly regulated by RNA degradation were involved in glycolysis. Combinatory analysis of experimental approach using RNA interactome capture and statistical analysis using public databases, and followed depletion assays identified that an RNA-binding protein fragile X mental retardation protein (FMRP) enhances the expression of mRNAs encoding rate-limiting enzymes for glycolysis under chronic hypoxia. This study emphasizes the importance of post-transcriptional gene regulation under chronic hypoxia. Glycolysis Hypoxia Metabolic labeling RNA degradation RNA synthesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Aerobic organisms utilize oxygen to produce chemical energy for the tricarboxylic acid cycle (TCA cycle) and electron transport chain 1 – 5 . Adenosine triphosphate (ATP) supplies the chemical energy for diverse biochemical reactions 6 , 7 . Low oxygen conditions, referred to as hypoxia, thus causes a crisis for aerobic organisms. Hypoxia occurs under both physiological and pathological situations 8 , 9 ; for instance, hypoxic conditions are observed in embryonic development 10 , 11 . Normal mammalian development occurs in a hypoxic environment, and the hypoxic environment is absolutely required in aspects of developmental morphogenesis for placental and fetal heart 11 . In addition, an insufficient vascular supply caused by rapid growth of tumor tissues results in hypoxic regions within tumor tissues, thus enhancing the epithelial-to-mesenchymal transition of cells, which in turn increases cell motility and metastasis 12 , 13 . Pathological hypoxic conditions are also observed in chronic heart and kidney diseases 9 and in cardiovascular diseases 14 . To avoid the crisis caused by oxygen deficiency, cells have developed various mechanisms to respond to hypoxia. Regulation of transcription modules by hypoxia-inducible factors (HIFs) is a widely studied hypoxia-response mechanism. Among these, HIF1, which plays a central role in the hypoxia response, was originally identified as a factor regulating expression of erythropoietin ( EPO ) in response to hypoxia 15 – 17 . During hypoxia, the regulatory subunit of HIF1, HIF1α, is stabilized and transported into the nucleus to regulate the transcription of target genes such as VEGF , EPO , and GLUT1 18,19 . HIF1 regulates cell cycle arrest and apoptosis upon hypoxia 20 , 21 , and inflammation during liver injury 22 . Although HIF1 is known to be a dominant regulatory factor in acute hypoxia (several hours), recent studies have suggested that the chronic hypoxic response (several days) is regulated by other mechanisms. HIF1α protein levels increase rapidly at the onset of hypoxia and return to basal levels under prolonged (chronic) hypoxia 23 . We previously reported that chronic hypoxia activates transcription factors other than HIF1, including cAMP-response element binding protein (CREB) and nuclear factor-kB (NF-kB), contributing to tumor malignancy 23 . Despite limited reports, post-transcriptional mechanisms, such as RNA stability, have also been shown to be involved in acute hypoxic responses 24 – 26 . Some hypoxia-responsive RNAs, including VEGF , EPO , and TH , are stabilized under acute hypoxic conditions, and some RNA-binding proteins (RBPs), such as HuR (also known as ELAVL1), are known to be involved in the stability of these RNAs 26 – 30 . These results suggest that gene expression profiles under chronic hypoxia is regulated by distinct mechanisms, including the regulation of RNA stability, in contrast to the HIF1-dominant acute phase. Nevertheless, the landscape of gene expression regulation via post-transcriptional mechanisms such as RNA degradation, in response to chronic hypoxia, remains unclear. Recent developments in techniques, including next-generation sequencing (NGS), have enabled us to measure RNA kinetics, comprising RNA synthesis and degradation, as well as gene expression levels 31 – 40 . For instance, SLAM-seq, TimeLapse-seq, and TUC-seq enable in situ labeling of intracellular RNAs with 4-thiouridine (4sU) followed by base conversion to distinguish newly synthesized RNAs and measuring RNA synthesis rate 41 – 43 . BRIC-seq enables pulsed labeling of intracellular RNAs to quantify RNA degradation rates 44 , 45 . We recently developed Dyrec-seq to quantify RNA synthesis and degradation rates simultaneously and comprehensively by multi-labeling of endogenous RNAs with both 4sU and 5′-bromouridine (BrU) 46 , and revealed that RNA degradation affected chronological gene expression patterns. The combined measurement of these kinetic parameters enables to reveal the contributions of transcriptional and post-transcriptional regulation to differential gene expression. Moreover, recent approaches to the proteome-wide identification of RBPs based on mass spectrometry (MS) enable the quantification of the RNA-binding properties of RBPs, as a major factor regulating RNA kinetics 47 , 48 . These approaches have led to the development of enhanced RNA interactome capture (eRIC)-MS for accurate quantification of the RNA-binding properties of RBPs 49 , enabling us to identify RBPs involved in post-transcriptional regulation under specific conditions. In this study, we hypothesized that post-transcriptional regulation, especially RNA degradation via RBPs, controls the regulated expression of hypoxia-responsive genes in chronic hypoxia. We initially calculated RNA synthesis and degradation simultaneously in HCT116 human colorectal carcinoma-derived cells under chronic hypoxic conditions, and quantified the respective contributions of RNA synthesis and degradation to the differential gene expression. Our study led a novel insight that mRNAs encoding enzymes involved in glycolysis (glycolytic mRNAs) is regulated via RNA degradation through an RBP, named fragile X mental retardation protein (FMRP) which is coded by FMR1 gene. This study highlights the involvement of post-transcriptional regulation via the specific RBP in the enhancement of glycolysis in chronic hypoxia. RESULTS Chronic hypoxia in HCT116 cells To investigate the chronological response of HCT116 cells in response to hypoxic conditions, we cultured the cells under hypoxic condition (1% O 2 ), and examined the temporal changes in RNA and protein expression. HIF1α protein levels increased up to 4 h after hypoxia and then diminished within 48 h (Fig S1 A). RNA levels of the early hypoxia-response factors GLUT1 and CA9 increased continuously from 0 to 48 h (Fig S1 B and S1C), while RNA levels of the chronic hypoxia-response factor MMP1 23 only increased at 48 h (Fig S1 D). We also examined the gene expression profile of HCT116 cells under chronic hypoxia by RNA-seq analysis. To examine differences in gene expression profiles, we analyzed the RNA expression profiles using principal component analysis (PCA) (Fig S1 E and S1F). The first principal component was largely altered at 24 and 48 h after hypoxic treatment, indicating that the hypoxia-induced expression profile changed within 24 h and remained changed until at least 48 h (Fig S1 F). The first principal component was also largely changed at 72 h, possibly reflecting the effects of cell death. On the basis of this PCA, we considered that the cellular state in response to hypoxia reached a steady state between 24 and 48 h. We therefore defined chronic hypoxia as 36 h after applying hypoxic conditions in this study. Simultaneous calculation of RNA synthesis and degradation rates in HCT116 cells under normoxia and chronic hypoxia states We quantified RNA synthesis and degradation rates in HCT116 cells under chronic hypoxia by SLAM-seq, which allowed the identification of newly synthesized transcripts with 4sU labeling 41 . The labelled RNAs were reacted with iodoacetamide (IAA), a thiol-reactive compound, to attach a carboxyamidomethyl group to the thiol group in the incorporated 4sU by nucleophilic substitution (S N 2) reaction (alkylation). Because the alkylated 4sU is paired with guanine instead of adenine during reverse transcription, a 4sU incorporated into the RNA is detected as a mutation (T-to-C conversion). Determination of the 3′ untranslated regions (UTRs) of the alkylated RNA using QuantSeq, a poly(A)-tail dependent massive sequencing technique 50 , enabled us to quantify the expression levels of RNAs and those labeled with 4sU simultaneously (Fig. 1 A and 1 B). We determined the appropriate concentration of 4sU for labeling HCT116 cells by measuring the 4sU incorporation ratio and cell viability under the indicated 4sU concentrations (Fig S1 G and S1H). The incorporation ratio of 4sU reached a plateau at approximately 2.5% with 50 µM 4sU in 12 h (Fig S1 G). Treatment with up to 100 µM 4sU resulted in > 80% of proliferation (Fig S1 H). On the basis of these results, we used 100 µM 4sU for RNA labeling in this study. Moreover, we confirmed efficient alkylation of 4-thiouracil, a nucleobase that compose 4sU, using IAA, by the shift in absorption spectrum (Fig S1 I). High-performance liquid chromatography (HPLC) measurement of nucleoside samples derived from RNA collected from cells labeled with 100 µM 4sU and those alkylated with IAA treatment indicated a clear shift in the maximum absorption wavelength (Fig S1 J), indicating efficient alkylation of 4sU incorporated into the RNAs. T-to-C conversions obtained from the alkylated 4sU-labeled RNAs increased approximately four-fold compared with those obtained from unalkylated RNAs, indicating the identifiability of 4sU-labeled RNAs in NGS (Fig S1 K). Note that the “T-to-C conversion” in RNA-seq reads derived from genes located on the reverse strand for the reference genome is identified as “A-to-G conversion” during bioinformatics analysis. We collected RNAs for SLAM-seq according to the above conditions (Fig. 1 A and 1 B). Briefly, HCT116 cells were cultured under hypoxic (1% O 2 ) and normoxic conditions (21% O 2 ) for 36 h, followed by the addition of 4sU to the medium to a final concentration of 100 µM (Fig. 1 A). RNAs were collected at 0, 1, 2, 4, 8, and 12 h after addition of 4sU, and the purified RNAs were subjected to alkylation of 4sUs with IAA. The collected RNA samples were provided to QuantSeq to determine the sequences of the 3′UTRs. We detected more than 30×10 6 reads in all samples (Fig S2 A). For alignment of the reads using the SLUMDUNK tool 41 on the reference genome (hg38), we found that > 50% of the reads were aligned singly (Fig S2 B). Finally, we counted T-to-C conversions (and A-to-G conversions) detected in the QuantSeq data for the alkylated 4sU-labeled RNA. Detection of T-to-C conversions by the SLAMDUNK tool indicated a labeling-time-dependent increase in T-to-C conversions up to approximately 8.0% (Fig S2 C), resulting in approximately 40% of reads including T-to-C conversions (Fig S2 D). Based on the labeling-time-dependent increase in T-to-C conversions (and A-to-G conversions), we calculated the RNA synthesis and degradation rates in HCT116 cells simultaneously. First, we identified expressed genes based on the QuantSeq results, with gene expression levels quantified as counts per million (CPM). The CPM values were distributed unimodally in a range > 0 in individual samples (Fig S2 E). Here, we considered genes with a CPM > 0 as expressed genes, and identified 11,969 RNAs derived from expressed genes in all samples (Table S1 ). We then simultaneously calculated the synthesis and degradation rates for RNAs derived from individual genes using the SLAM-seq data. The RNA synthesis rate, \({k}_{s}\) , and degradation rate, \({k}_{d}\) , were defined as the abundance of RNA molecules synthesized per min and the ratio of RNA molecules degraded per min, respectively. The expression level of an RNA is determined as the ratio of the RNA synthesis and degradation rates, and the shape of the curve is determined by the degradation rate. Namely, we can calculate the \({k}_{s}\) and \({k}_{d}\) values based on the fitting curve on the temporal increase of newly synthesized RNAs identified based on 4sU incorporation, combined with RNA expression level (see below). When the RNA expression level is at a steady state, the amount of newly synthesized RNA ( \({x}_{t}\) ) at each time point ( \(t\) ) is as follows: $$\begin{array}{c}{x}_{t}=\frac{{k}_{s}}{{k}_{d}}\left(1-{e}^{-{k}_{d}t}\right)\#\dots eq 1\end{array}.$$ We can therefore calculate \({k}_{s}\) and \({k}_{d}\) by fitting the time series of 4sU-labeled RNA and expression levels estimated from SLAM-seq to Eq. 1. Prior to calculating \({k}_{s}\) and \({k}_{d}\) values, we extracted the genes in steady state after 4sU treatment (see Materials and Methods), because a steady state of RNA expression level is required to estimate the \({k}_{s}\) and \({k}_{d}\) values. A total of 9,541 genes were in steady state in both normoxic and hypoxic samples, among the 11,969 genes expressed in all samples (Table S1 ). For the RNAs derived from these genes, we calculated the \({k}_{s}\) and \({k}_{d}\) values at the genome-wide level by fitting the time series of 4sU-labeled RNA to Eq. 1. We also adopted RNAs with a good fit between their actual expression levels and expression levels predicted from the estimated \({k}_{s}\) and \({k}_{d}\) values (see Materials and Methods). We were therefore able to calculate the RNA synthesis and RNA degradation rates of 8,961 and 8,479 RNAs for normoxic and hypoxic samples, respectively (Table S2 ). The estimated \({k}_{s}\) and \({k}_{d}\) values both obeyed a log Gaussian distribution, and no mutual correlation of \({k}_{s}\) and \({k}_{d}\) was observed in either sample (Fig. 1 C and 1 D). Some of differentially expressed genes are regulated via RNA degradation under chronic hypoxia We aimed to estimate the relative contributions of RNA synthesis and degradation to the differential expression of RNAs, based on the calculated RNA synthesis and degradation rates (Fig. 2 A). We identified RNAs that were differentially expressed in chronic hypoxia by setting a threshold change in CPM of > 1.5-fold or < 2/3-fold between normoxic and hypoxic samples and a false discovery rate (FDR) of < 0.05 (paired t -test). A total of 1,330 RNAs among the 11,969 genes expressed in all samples were identified as differentially expressed in hypoxia (Fig. 2 B and Table S3 ), and the RNA synthesis and degradation rates for 1,160 of these RNAs were estimated in both normoxic and hypoxic samples. We then quantified the contributions of RNA synthesis and degradation to the differential expression of RNAs. When \(t\) in Eq. 1 approaches infinity, the abundance of newly synthesized RNAs, \({x}_{t}\) , approaches asymptotically to expression level ( \(X\) ), thus: \(X=\underset{t\to \infty }{\text{lim}}\frac{{k}_{s}}{{k}_{d}}\left(1-{e}^{-{k}_{d}t}\right)\) \(\begin{array}{c}=\frac{{k}_{s}}{{k}_{d}}\#\dots eq 2.\end{array}\) To quantify the relationships of differential RNA expression between two conditions (e.g., normoxia vs. hypoxia) and synthesis or degradation rates, we moved Eq. 2 to log space: $$\begin{array}{c}\varDelta \text{log}\left(X\right)=\varDelta \text{log}\left({k}_{s}\right)-\varDelta \text{log}\left({k}_{d}\right)\#\dots eq 3\end{array}.$$ The sum of the contributions of RNA synthesis and degradation to the differential expression of RNAs should be one, and we therefore defined the contributions of RNA synthesis, \({\rho }_{s}\) , and degradation, \({\rho }_{d}\) , as relative values of \(\varDelta \text{log}\left({k}_{s}\right)\) or \(\varDelta \text{log}\left({k}_{d}\right)\) over \(\varDelta \text{log}\left(X\right)\) : $${\rho }_{s}=\frac{\varDelta \text{log}\left({k}_{s}\right)}{\varDelta \text{log}\left(X\right)}$$ and $${\rho }_{d}=\frac{\varDelta \text{log}\left({k}_{d}\right)}{\varDelta \text{log}\left(X\right)}.$$ These definitions satisfy the premise that the sum of the contributions is equal to one. Estimation of the RNA synthesis and degradation rates thus enabled us to quantify their relative contributions. The estimated \({\rho }_{d}\) values obeyed an exponential unimodal distribution with 0% of mode (Fig. 2 C), indicating that most gene expression levels were regulated via transcriptional regulation. However, 102 RNAs had \({\rho }_{d}\) values > 60%, indicating that their differential expression was mainly regulated via RNA degradation. Moreover, increased RNAs tended to have larger \({\rho }_{d}\) values (Fig. 2 C), indicating that their increased expression in chronic hypoxia tended to be caused by RNA stabilization. Regulation of RNA degradation involves glycolytic enhancement under chronic hypoxia To determine the cellular functions regulated via either RNA synthesis or degradation under chronic hypoxia, we performed functional enrichment analysis for RNAs mainly regulated via either mechanism (Fig. 2 D and S3, and Table S4 ). Differentially expressed RNAs mainly regulated via RNA synthesis were significantly enriched ( FDR < 0.05) in RNA processing, lipid metabolism, and alternative splicing (Fig S3 ), while differentially expressed RNAs mainly regulated via RNA degradation were significantly enriched in glycolysis (Fig. 2 D and S3). We also carried out gene set enrichment analysis (GSEA) 51 based on the \({\rho }_{d}\) values to examine cooperativeness of RNA degradation on cellular functions. GSEA statistically tests the homogeneities of differential expression of RNAs involved in specific biological functions. A uniform distribution in fold change of RNAs involved in a specific term makes the p -value of the term larger, while an uneven distribution makes the p -value smaller. We used the \({\rho }_{d}\) value from change in RNA degradation rates and expression level instead of the fold change, to approach cooperative regulation of RNA degradation (see Materials and Methods). GSEA suggested that RNAs with high \({\rho }_{d}\) values (differential expressed genes mainly regulated by RNA degradation) were significantly related to glycolysis (Fig. 2 E and Table S5 ), as with the results of the functional enrichment analysis. Changes in glycolysis in response to hypoxic conditions are important for cellular adaptation to hypoxia 52 , 53 . Although the enhancement of glycolysis by acute hypoxia for up to a few hours is considered to be predominantly regulated transcriptionally via HIF 52 , the mechanisms regulating glycolysis in chronic hypoxia remain unclear. The current findings suggest that regulation of RNA degradation, rather than transcriptional regulation, is the main mechanism responsible for the enhancement of glycolysis under chronic hypoxia. In support of this finding, metabolic enzymes encoded by mRNAs with large \({\rho }_{d}\) values, i.e., mainly regulated via RNA degradation, were in distributed in glycolysis on Kyoto Encyclopedia of Genes and Genomes (KEGG) Metabolic pathways (hsa01100) (Fig. 3 A and B), consistent with the results of the functional enrichment analysis. Moreover, mRNAs encoding rate-limiting enzymes for glycolysis, such as hexokinase-1 (HK1), phosphofructokinase, liver type (PFKL), and pyruvate kinase M1/2 (PKM), were among those with large \({\rho }_{d}\) values. In contrast, enzymes encoded by RNAs with small \({\rho }_{d}\) values, i.e., mainly regulated by transcriptional regulation, were distributed on lipid and amino acid metabolism. We confirmed that up-regulated mRNAs with high \({\rho }_{d}\) values (red in Fig. 3 A) were mapped mainly to carbon metabolism, thus supporting the idea that mRNAs involved in glycolysis were stabilized during chronic hypoxia. Measurement of individual RNA levels also indicated significant increases in mRNAs encoding rate-limiting enzymes ( HK1 , PFKL , and PKM mRNAs) (Fig. 3 C), and calculation of the half-lives of these mRNAs indicated that they were stabilized in response to chronic hypoxia (Fig. 3 D). Protein levels of HK1, PFKL, and PKM were also increased under the same conditions (Fig. 3 E), accompanied by the accumulation of intracellular lactate (Fig. 3 F). These results suggested that expression levels of mRNAs encoding rate-limiting enzymes for glycolysis were increased by RNA stabilization, resulting in increased abundances of these enzymes to enhance glycolysis. Identification of candidate RBPs involved in chronic hypoxia by eRIC-MS We investigated how the degradation of glycolytic mRNAs was regulated under chronic hypoxia by focusing on RBPs, as proteins involved in the regulation of RNA fate, including degradation 54 , 55 . We examined changes in mRNA binding to individual RBPs under chronic hypoxia using eRIC-MS, in which poly(A)-tailed mRNAs were collected and the proteins bound to these mRNAs were purified and identified by MS (Fig. 4 A, see Materials and Methods). Briefly, we cultured HCT116 cell for 36 h in normoxic or hypoxic conditions, followed by treatment with ultraviolet rays (UV) to crosslink RNA-RBP complexes. We then isolated the poly(A)-tailed RNAs using oligo(dT) and analyzed them using BioAnalyzer. Ribosomal RNAs were removed and poly(A)-tailed RNAs were highly enriched (Fig S4 A). RNase treatment eluted RBPs binding to the poly(A)-tailed RNAs from oligo(dT). We also obtained RBPs from cells without UV crosslinking as negative controls in normoxic or hypoxic conditions. We performed proteomic analysis using MS with technical duplications for all samples under four treatments: normoxia with UV, normoxia without UV, hypoxia with UV, and hypoxia without UV, with biological triplicates, to obtain profiles of RBPs bound to poly(A)-tailed RNAs (Table S6 ). For the obtained MS data, we confirmed the reliability of each profile by comparing the technical duplications, which indicated that the profiles for two samples (2nd sample of normoxia without UV, and 1st sample of hypoxia with UV) were less reproducible (Fig S4 B). We therefore omitted these samples from the following analyses. MS data frequently contain several proteins as noise due to non-specific binding during precipitation and purification. To identify RBPs specifically binding to poly(A)-tailed RNAs, we performed statistical test for the abundance of proteins compared with that in the corresponding negative controls (without UV). Among the obtained RBPs with two or more unique peptides, 349 and 258 were significantly enriched in the normoxic and hypoxic samples with UV crosslinking, respectively (Fig. 4 B, see Materials and Methods). Gene ontology (GO) analysis of the significantly enriched RBPs in the obtained protein samples confirmed that we mainly obtained RBPs from eRIC-MS approach (Fig. 4 C). Based on the eRIC-MS data, we identified RBPs whose binding to poly(A)-tailed RNAs was changed by chronic hypoxia. Comparing the RBP abundances between hypoxic and normoxic conditions indicated that 93 and two RBPs, among the total 351 identified RBPs, were normoxia- and hypoxia-specific, respectively, and 257 RBPs were common to both conditions (Table S7 ). Among the 257 RBPs identified in normoxic and hypoxic conditions commonly, 43 were activated (increased > 1.5-fold) under chronic hypoxic conditions (Fig. 4 D and Table S7 ). Identification of FMRP as stability regulators for glycolytic mRNAs To identify the RBPs responsible for stabilizing glycolytic mRNAs in response to chronic hypoxia, we performed a hypergeometric test integrating databases with the SLAM-seq data (Fig. 5 A). Using the Reactome database 56 – 58 , which collects information on biological pathways, to identify mRNAs encoding glycolytic enzymes, we identified 72 glycolytic mRNAs (Reactome mRNAs). By surveying the Reactome mRNAs among the 6,140 mRNAs stabilized during chronic hypoxia obtained from SLAM-seq analysis, we identified 40 Reactome mRNAs that were stabilized in hypoxia (stabilized Reactome mRNAs). We then aimed to identify candidate RBPs that potentially stabilize these 40 mRNAs. We initially listed the RBP-target mRNAs using 150 RBPs analyzed in the ENCODE eCLIP-seq study 59 , which identified candidate target mRNAs regulated by the 150 ENCODE RBPs. The hypergeometric test showed that 75 of these 150 ENCODE RBPs potentially regulate the 40 stabilized Reactome mRNAs ( FDR < 0.05) (Fig. 5 B). Cross-referencing the 75 ENCODE RBPs extracted from the hypergeometric test (Fig. 5 B) and 43 activated RBPs identified by eRIC-MS (Fig. 4 D) implicated two RBPs, IGF2BP2 and FMRP, potentially regulating the stability of glycolytic mRNAs such as HK1 , PFKL , and PKM (Fig. 5 C, and Table S8 ). We examined the effects of the 2 activated RBPs on expression of the glycolytic mRNAs by depletion assay. As described above, most of the glycolytic mRNAs were increased under chronic hypoxia (Table S2 ). We therefore hypothesized that the activated RBPs IGF2BP2 and FMRP act as stabilizing factors in chronic hypoxia condition for the glycolytic mRNAs, and examined the effects of depletion of the RBPs on the RNAs encoding the rate-limiting enzymes, as representatives of the glycolytic mRNAs. Chronic hypoxia significantly increased the expression levels of all the rate-limiting enzymes compared to normoxia in siControl samples, but knocking-down of FMRP cancelled the increase (Fig. 5 D and S5A). On the contrast, knocking-down of IGF2BP2 did not cancel the increase (Fig S5 B and S5C). These results suggested that FMRP is a stabilizing factor for the rate-limiting enzymes under chronic hypoxia, and the enhancement of glycolysis is regulated by FMRP activated in response to chronic hypoxia to stabilize HK1 , PFKL and PKM (Fig. 6 ). DISCUSSION Differential gene expression, accompanied by responses to extracellular stimulation and physiological processes such as development, is generally considered to be regulated via alterations in gene transcription; however, post-transcriptional regulation, including RNA degradation, has recently been shown to play important roles in the regulation of gene expression 60 – 62 . In this study, we developed a procedure to quantify the respective contributions of RNA synthesis ( \({\rho }_{s}\) ) and RNA degradation ( \({\rho }_{d}\) ) to differential gene expression simultaneously. We identified 1,003 and 102 genes in HCT116 cells whose differential expression levels were mainly regulated via RNA synthesis and degradation, respectively, under chronic hypoxia. Regarding the genes mainly regulated via RNA degradation, functional enrichment analysis revealed that these genes were significantly enriched in glycolysis, and most of these mRNAs were stabilized; i.e., expression levels of glycolytic RNAs were regulated by mRNA stabilization during adaptation to chronic hypoxia. This represents a reasonable strategy for enhancing glycolysis while avoiding further consumption of ATP. Furthermore, integration of eRIC-MS to identify RBPs binding mRNAs under specific conditions, with statistical analysis using public databases followed by depletion assays, indicated that FMRP is an important factor controlling the increase in rate-limiting enzymes for glycolysis under chronic hypoxia via regulation of RNA stability. The current results suggest that glycolysis is enhanced during chronic hypoxia via regulation of mRNA stability by RBP: the RNA stabilizing factor FMRP bind to the mRNAs encoding rate-limiting enzymes for glycolysis in response to chronic hypoxia, to protect the RNA from degradation (Fig. 6 ). FMRP is an RBP related to the incurable neurological disease, fragile X syndrome, and is considered to be responsible for neuronal development and synaptic plasticity via the regulation of mRNA degradation and alternative splicing 63 – 65 . We recently reported that FMRP regulates neuronal differentiation by repressing the nonsense mediated mRNA decay (NMD) pathway 66 and sequestering mRNA from translation and deadenylation 67 . FMRP shapes the proteome and transcriptome under several pathological hypoxic conditions, such as resistance of leukemia cells in a hypoxic microenvironment 68 , acute kidney injury caused by hypoxia-reoxygenation 69 , and encephalopathy of prematurity following perinatal hypoxia–ischemia 70 . Various mechanisms, including remodeling of the translational process, have been proposed to modulate the proteome and transcriptome, but the details remain unclear. The current study showed that FMRP regulated RNA stability in chronic hypoxia, suggesting that FMRP may act as a factor coupling RNA stability and translation in chronic hypoxic responses. Recent developments in measurement techniques have enabled us to acquire information on RNA kinetics, including RNA synthesis and degradation 41 – 45 . Nevertheless, it is independent matters that the RNA kinetics are altered, and that changes in RNA kinetics affect gene expression. For instance, mutation-dependent rapid RNA degradation leads to up-regulated transcription of related genes, including the mutated gene itself. This compensation mechanism allows destabilization of RNA to increase transcription to maintain robust gene expression levels 71 . This report demonstrates that transcription and RNA degradation are highly linked. The current approach for determining the quantity of respective alterations in RNA synthesis and degradation on differential gene expression ( \({\rho }_{s}\) and \({\rho }_{d}\) ) enabled us to clarify the effects of RNA kinetics on gene expression quantitatively and comprehensively. Although this “regulatory analysis approach” has been introduced in metabolic studies 72 , to the best of our knowledge, this is the first time it has been applied to analyze gene expression. Differential gene expression in response to hypoxia is generally considered to be controlled by transcriptional regulation by HIFs; however, the current approach revealed that the expression of glycolytic mRNAs during chronic hypoxia is regulated via changes in RNA stability by RBPs, such as FMRP. RNA kinetics regulates various pathological and physiological cell functions, including responses to extracellular stress or signals and cell differentiation or developmental processes. The current approach will provide substantial insights into how the regulation of RNA kinetics contributes to cellular behavior in relation to pathological and physiological phenomena. In conclusion, we developed a procedure to quantify the respective contributions of RNA synthesis and degradation to differential gene expression, and showed that enhancement of glycolysis in response to chronic hypoxia is regulated via alterations in RNA stability. Moreover, we found that FMRP is involved in increases of mRNAs encoding the rate-limiting enzymes for glycolysis. Regulation of glycolysis plays an important role in the adaptation to hypoxia 73 – 75 . These findings provide substantial insights into cellular regulatory mechanisms under chronic hypoxia, and may thus contribute to the development of cancer treatment strategies and medicines. MATERIALS AND METHODS Human colorectal carcinoma derived HCT116 cell line The human colorectal carcinoma derived HCT116 cell line used in this study were purchased from ATCC, and were passaged and stored in the laboratory. In this study, we used Only cells with less than 20 of passages for the experiments. Cell culture and hypoxia treatment HCT116 cells were cultured with Dulbecco's modified Eagle's medium (DMEM) (Wako, Cat #044-29765), supplying with 10% fetal bovine serum (FBS) (Life Technologies, Cat #F7524). FBS was heat-inactivated at 56℃ for 30 min. HCT116 cells were cultured in a humidified incubator (Thermo Fisher Scientific, MODEL #370, REL #1, S/N #310370-4133) with 5% CO 2 at 37℃. For hypoxia treatment, the cells were precultured for 24 h, followed by culturing in hypoxia chamber (RUSKINN, UM-025, Version #2.0-_CSC2.01) with 1% O 2 , 5% CO 2 , and 94% N 2 at 37℃ for specified time. Collection of RNA and protein samples For collection of RNAs samples, the cells were washed twice with phosphate-buffered saline (PBS) and then total RNA was isolated using RNAiso Plus (Takara, #9109) according to the manufacturer’s protocol. Where appropriate, genomic DNA included in RNA samples are removed by Recombinant DNase I treatment (Recombinant DNase I (Takara, #2270), DNase I Buffer (Takara, #2270), Recombinant RNase Inhibitor (Takara, #2313), 37℃, 1 h), and RNAs were purified using RNAiso Plus again. For collection of protein samples, intracellular proteins were collected using 2×SDS sample buffer (20% glycerol, 100 mM Tris-HCl (pH 6.8), 40 mg/mL SDS, 0.01% Bromophenol Blue, 12% 2-mercaptoethanol), followed by fragmentation of genomic DNAs using sonication (15 sec, three times) and heat denaturation (95℃, 5 min). Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) We reverse-transcribed appropriate amount of total RNA purified from cell lysate using 5×PrimeScript RT Master Mix (Takara, #9109) according to the manufacturer’s protocol, and then quantified expression level of individual genes by PCR using SYBR Premix Ex TaqⅡ (Perfect Real Time) (Takara, RR041) with the cDNA as templates. For the PCR, we used Thermal Cycler Dice Real Time system II (Takara, TP900) with 2 step PCR mode (95 ℃ for 5 sec, and 60 ℃ for 30 sec, 40 cycles). The Expression levels were quantified with ΔΔCt methods against corresponding control condition and internal control. Primer sequences used for the PCR are indicated in Table 1 . Table 1 Primer sequences for quantitative polymerase chain reaction Gene symbol Forward primer Reverse primer GLUT1 GGTTGTGCCATACTCA CAGATAGGACATCCAG CA9 CCTTTGCCAGAGTTGA GCAACTGCTCATAGGC MMP1 GAGATCATCGGGACAACTCTCCTT GTTGGTCCACCTTTCATCTTCATCA HK1 TGAACCGCCTGCGTGATA AATGAGCCAGGGTCTCCTCT PFKL CATCAGCAACAACGTCCCTG GGCCAGGTAGCCACAGTAAC PKM TCCAGGTGAAGCAGAAAGGT TTCTTGCTGCCCAAGGAG ACTB CCAACCGCGAGAAGAT CCAGAGGCGTACAGGG SDS polyacrylamide gel electrophoresis (PAGE), SYPRO Ruby staining, and western plotting (WB) Proteins samples collected from the cells were separated by SDS-PAGE with Mini-protean TGX Gels (BIO-RAD, Cat#4561086) or hand-made gels consisted of 3% stacking gel (for one large gel: ultrapure water 3.0 mL, 40% acrylamide bis mixed 0.34 mL, 4×wide range buffer 1.13 mL, 5% APS 90 µL, TEMED 9.0 µL) and 8% of separating gel (for one large gel: ultrapure water 5.4 mL, 40% acrylamide bis mixed 2.0 mL, 4×wide range buffer 2.5 mL, 5% APS 100 µL, TEMED 6.0 µL), subjected to SYPRO Ruby (Invitrogen, S12000) staining or WB. For SYPRO Ruby staining, the gel was incubated twice in 25 mL of fix solution (12.5 mL 100% methanol, 1.75 mL acetic acid in nuclease-free water) on shaker at RT for 15 min. Then we stained the gel with 25 mL of SYPRO Ruby in microwave for 15 sec, and incubated on shaker at RT for 3 min (avoid light); continued to heat the gel in microwave for 10 sec and incubated on shaker at RT for 3 min (avoid light); after that, heat the gel in microwave for another 10 sec and incubate on shake at RT for 30 min (avoid light). The SYPRO Ruby stained gel was washed with 25 mL wash solution (2.5 mL 100% methanol, 1.75 mL acetic acid in nuclease-free water) on shaker at RT for 30 min, and then stained proteins were detected with LAS 4000 mini (Fujifilm). For WB, the separated proteins are transferred on Immobilon-P PVDF membrane (Millipore, MA) with Trans-Blot SD Semi-Dry Transfer CeLL (Bio-Rad, #1703940) (0.05A, 45 min or 120 min). The membranes were incubated with antibodies diluted to corresponding dilutions (Table 2 ) at room temperature. Corresponding secondary antibodies are applied and then intensities of bands were detected with LAS 4000 mini. Table 2 Properties and dilution of antibodies Antibody Class Dilution Supplier and Cat# Rabbit monoclonal anti-HIF1α IgG 1:1000 Cell signaling technology, Cat#14179s Rabbit monoclonal anti-HK1 IgG 1:1000 Cell signaling technology, Cat#2024s Rabbit monoclonal anti-PFKL IgG 1:1000 Abcam, Cat#ab181064 Mouse monoclonal anti-PKM1/2 IgG 1:5000 Cell signaling technology, Cat#3190s Mouse monoclonal anti-β-actin IgG 1:2000 MBL, Cat#M177-3 Polyclonal anti-rabbit IgG/HRP IgG 1:5000 Cell signaling technology, Cat#7074s RNA sequencing (RNA-seq) analysis We sequenced 100 ng of total RNA samples purified from cell lysate using Nova-seq 6000 (Illumine). The sequencings were requested to Macrogen Japan ( https://www.macrogen-japan.co.jp/ ). Briefly, poly(A)-tailed RNAs were enriched using oligo(dT)-conjugate beads, and qualities of the enriched poly(A)-tailed RNAs were assessed using Bioanalyzer (Agilent). The sequence libraries were prepared with TruSeq stranded mRNA Sample Prep Kit (Illumine, RS-122-2101), and then standard Illumina protocols were used to generate 150-bp paired end read libraries that were sequenced on the Nova-seq 6000 platform. Based on the RNA-seq data, we estimated expression level of individual genes. We used HISAT2 (ver. 2.1.0) 76 with hg38 genomic sequences as references to align the sequenced fragments. The gene expression profiles were quantified using StringTie (ver. 1.3.4d) 77 , 78 . Both alignment and quantification of gene expression profiles were performed with default parameters of HISAT2 and StringTie tools. Principal component analysis (PCA) We performed PCA on the gene expression profiles estimated from the RNA-seq data and eRIC-MS data to identify latent variables in the data sets. PCA was performed using prcomp function in R with default parameters. Each dataset was standardized to make the mean and variance constant. RNA digestion and HPLC measurement We performed HPLC measurement according to a previous report 79 . Total RNAs purified from cell lysate were treated with DNase, followed by digestion to nucleotides (0.1 mM DL-Dithiothreitol solution (DTT) (Sigma-Aldrich, #3483-12-3), 13.8 mM MgCl2 (Wako, #136–03995), 34.6 mM Tris-HCl (pH 7.5) (Invitrogen, #15567027), 1.6U Alkaline Phosphatase (E. coli C75) (BAP) (Takara, #2120), 0.2U Phosphodiesterase I (Worthington, LS003926), 37℃ for 16 h). After purification, peaks of the nucleotides were determined using Prominence HPLC system (Shimadzu) with HPLC buffer A (3% acetonitrile (Wako, #015-08633), 0.1 M TEAA (Wako, #202–02646)) and HPLC buffer B (900 mL acetonitrile (Wako, #015-08633), and 100mL ultrapure water). Concentration of the nucleotides and nucleotide analogs were quantified based on area of the peaks. Alkylation of 4sUracil We alkylated the 20 µg of 4sUracil by reacting with 10 mM of IAA under optimal conditions (50% DMSO (SIGMA #D-8418), 10 mM iodoacetamide (Wako #095-02151), 50 mM sodium phosphate buffer pH8.0 (1M NaH 2 PO 4 (Nacalai Tesgue #317 − 18) 4.66 mL, 1M Na2HPO4 (Wako #196–02835) 340 µL, up to 10mL with UltraPure Distilled Water (Invitrogen #10977-015)), for 15 min at 50℃). The reaction was halted by adding 100 mM of DTT, and the spectrum of absorbance was measured using e-Spect (Malcom). The absorbance at 400 nm of wavelength was measured as the reference wavelength. SLAM-seq RNA labeling and IAA treatment We seeded HCT116 cells with 7.5×10 4 cells/mL of concentration on 12 well plate (Thermo Scientific #150628). Following 24 h of preculture, we cultured the cells under normoxic and hypoxic condition for 36 h. We added final concentration of 100 µM 4sU into the culture medium to label newly synthesized RNAs. The cells were collected after 0, 1, 2, 4, 8, 12 h after the 4sU addition, and the total RNAs were purified. We alkylated the 20 µg of total RNA by reacting with 10 mM of IAA under optimal conditions (50% DMSO (50% DMSO (SIGMA #D-8418), 10 mM iodoacetamide (Wako #095-02151), 50 mM sodium phosphate buffer pH8.0 (1M NaH 2 PO 4 (Nacalai Tesgue #317 − 18) 4.66 mL, 1M Na2HPO4 (Wako #196–02835) 340 µL, up to 10mL with UltraPure Distilled Water (Invitrogen #10977-015)), for 15 min at 50℃). Quality of the total RNAs were assessed after ethanol precipitation. We requested DNA Tech ( https://dnatech.genomecenter.ucdavis.edu/ ) to provide the total RNA to QuantSeq analysis. The QuantSeq analysis were performed twice for each sample. Detection of T-to-C conversion by SLAMDUNK tool We quantified the expression level and newly synthesized RNA level based on the QuantSeq data of each time point using SLAMDUNK tool (ver. 0.3.3) 41 , a pipeline for analysis of SLAM-seq data. Since QuantSeq sequences 3’ end of RNA in poly(A)-dependent manner, we aligned the QuantSeq data to comprehensive 3’ untranslated regions (3’UTRs) sequences generated based on human genome sequences and annotation (hg38) obtained from Ensemble database (release 92) 80 . We performed the alignment with default parameters of the SLAMDUNK tool. Briefly, we trimmed twelve bases from the 5’ end as adaptor-clipped reads, and then removed four and more subsequent adenines from the 3’ end as remaining poly(A)-tail. VarScan (ver. 2.4.1) 81 included in the SLAMDUNK tool regards a mismatch as a SNP when it has 0.8 and more of variant fraction and 10-fold and more coverage cutoff. Through these filters, we counted total number of reads and the number of those including T-to-C conversions aligned on the 3’ UTRs of individual genes. Since 4sU pairs with guanine (G) during reverse transcription instead of adenine (A), the 4sU-labeled reads were identified as those including T-to-C conversions. Since QuantSeq generates one read from one RNA, the number of reads including and not including T-to-C mutations correspond to the numbers of 4sU-labeled and unlabeled RNAs, respectively. Therefore, we counted the numbers of reads and those including T-to-C conversion corresponded to each gene, and normalized as count per million (CPM). Identification of RNAs in a steady state To remove the cells whose expression is fluctuated by biological or mechanical effect during 4sU labeling, we extracted the genes in a steady state with the procedure we developed previously 46 . Briefly, since the expression of a gene in a steady state changes dependent on only white noise, the sum of angles (SoA) formed by the lines connecting each time point is relatively small, whereas the SoA values in a differentially expressed gene (DEG) with a constant trend is relatively large. We calculated the SoA values from the angles formed by the lines connecting certain time points and neighboring ones, within a time series of expression level for each gene. Then, we calculated empirical p -values of the SoA by comparing those when all time points are randomly rearranged. We calculated FDR from the empirical p -values using Storey's procedure 82 . The genes whose FDR values are less than 0.001 were identified as genes in a steady state. Identification of DEGs using paired t -test We identified DEGs based on CPM of individual RNAs inferred from the SLAM-seq data. To avoid effect of 4sU labeling of gene expression, we tested differences in gene expression levels in cells in normoxia and hypoxia with paired t -test for each time point. Based on the p -values, we calculated FDR using Storey procedure 82 . Among the genes expressed in all time points, we identified the genes whose FDR are less than 0.01 as DEGs. Inference of RNA synthesis and degradation rates According to Eqs. 1 and 2, expression level of RNA is determined as ratio of RNA transcription rate, \({k}_{s}\) , and degradation rate, \({k}_{d}\) , and increase curves of 4sU-labeled RNAs depend on \({k}_{d}\) . Therefore, fitting of the time series of 4sU-labeled RNAs on Eq. 1 enables us to infer \({k}_{s}\) and \({k}_{d}\) values. For RNAs derived from genes in steady states, we obtained the \({k}_{s}\) and \({k}_{d}\) values by fitting of the time series of 4sU-labeled RNAs on Eq. 1 according to previous report 46 , in genome-wide manner. Briefly, we performed fitting by combining an evolutionary algorithm (genetic algorithm) and hill climbing ( L-BFGS-B algorithm), and evaluating with the least squares method in Python 2.7. The genetic algorithm was implemented using the DEAP library 83 with a generation number of 200, population number of 50, crossover probability of 0.5, and mutation probability of 0.2. The L-BFGS-B algorithm was implemented using the minimize module in the SciPy package, in which the parameters estimated by the genetic algorithm are given as initial parameters. The fitness in each gene was evaluated as the correlation of actual newly synthesized RNA levels with estimated values. The probability of the null hypothesis that a population correlation coefficient is equivalent to zero was calculated for each gene using the OLS module in the StatsModels package 84 , and the \({k}_{s}\) and \({k}_{d}\) values of the genes whose FDR as determined by Storey's procedure 82 was less than 10 − 5 were extracted. Functional enrichment analysis For functional enrichment analysis of focused genes and protein, we performed functional enrichment analysis using DAVID tool (ver. 6.8) ( https://david.ncifcrf.gov/ ). As functional term, we utilized Biological Process (GOTERM_BP_DIRECT), Cellular Component (GOTERM_CC_DIRECT), Molecular Function (GOTERM_MF_DIRECT), UniProt keywords (UP_KEYWORDS), and KEGG pathway (KEGG_PATHWAY). The p -values indicating enrichment were calculated based on modified hypergeometric test 85 , 86 . We selected optimal gene list as the background. The functional terms whose FDR values are less than thresholds were identified as those significantly enriched. GSEA The relationship between the biological function of genes and \({\rho }_{d}\) values, contribution of RNA degradation rate on differential expression, were examined using GSEA (ver. 4.0.3) 51 . For each individual gene, 1 was used as the control value and genes were ranked based on the ratio of the \({\rho }_{d}\) value to the control (i.e., the original \({\rho }_{d}\) value). Enrichment score (ES) for each term in the GSEA hallmark was calculated using default parameters, and compared with the distribution of ES values for random set of 10,000 genes to calculate empirical p -values. The empirical p -values were corrected as FDR. The terms whose FDR values are less than 0.05 were identified as those significantly enriched. Lactate assay Intracellular lactate was quantified using Lactate Assay Kit-WST (Doujin, #343–09281). The HCT cells were cultured in corresponding conditions with six of replicates and the medium were removed, and cell lysates were prepared with 0.1% Triton solution. We then added 20 µL of lactate standard solution and 80 µL of working solution to 20 µL of the cell lysate, followed by incubation on 37℃ for 30 minutes. The absorbance at 450 nm of wavelength was measured using an absorbance microplate reader (Tecan). eRIC-MS Coupling of the capture probe to Dynabeads Dynabeads™ MyOne™ Carboxylic Acid (Invitrogen, #65012) was washed with double volumes of 100 mM MES (pH 4.8) and vortexed for 5 to 10 sec. We removed the supernatant with a magnet stand and resuspended the Dynabeads in 30 µL of 100mM MES (pH 4.8). Based on previous reports 87 , we utilized HPLC purified capture probe consisted of primary amine and C6 linker, followed by 20 thymidine nucleotides, in which every other nucleotide is a locked nucleic acid (LNA) (Exiqon); /ssH5AmC6-LNA10T10/T(L)TT(L)TT(L)TT(L)TT(L)TT(L)TT(L)TT(L)TT(L)TT(L)T (T(L): LNA thymidine, T: DNA thymidine) 88 . We prepared the LNA oligo(dT) to 97.2 µL/sample with 10.8 µL of 1M MES (pH 4.8) and 27 µL of 500mg/mL N -(3-Dimethylaminopropyl)- N ’-ethylcarbodiimide hydrochloride (EDC) (Sigma-Aldrich, #E7750). The LNA oligo(dT) was coupled on Dynabeads by gently rotating mixture of 600 µL of Dynabeads slurry (10 mg/mL) and 97.2 µL of LNA oligo(dT) (200 µM) at room temperature for 3 h. The Dynabeads were then washed with two volumes of 250 mM Tris buffer (pH 8.0) and 0.01% Tween 20 for more the 30 min twice. The LNA oligo(dT) coupled Dynabeads were stored in 0.1% PBS-Tween at 4 ℃. UV crosslink The medium culturing HCT116 cells on the 15 cm dish was removed and cells were washed with 10 mL cold PBS for twice. To avoid effect of exposure on normoxic environment, the hypoxia treated cells were sealed with hybrid-bag after removing the PBS completely in the hypoxia chamber, and transferred from the chamber. For normoxia treated cells, hybrid-bag was also used in UV crosslink. 2,000 mJ/cm 2 of UV was used in crosslink and irradiation was omitted in controls without UV crosslink (without UV). Immediately before sample collection, we added 1.0 mM DTT (Sigma-Aldrich, #43816) and 0.5 U/µL of recombinant RNase inhibitor (RRI) (Takara, #2313B) into prepared lysis buffer (10 mM Tris-HCl (pH 7.5) (Invitrogen, #15567027), 10 mM NaCl, 0.02% (w/v) Digitonin (Fujifilm, 043-21371), 1 mM EDTA pH 8.0 (Invitrogen, MA)). After irradiation, we opened the sealed hybrid-bag were, and kept the irradiation cell on ice. We then added 2.0 mL of cold hypo lysis buffer and collected cells with scraper (Corning). The cell lysates were mixed for 10 times and transferred into 5 mL centrifuge tubes. Cell lysates from two of 15 cm dishes were collected for one eRIC-MS sample. Above operations were done on ice quickly. Capture of RNA-RBP conjugations Cell lysates were mixed by inversion at 4 ℃ for 10 min. Cell lysates were then centrifuged at 1,000 g for 5 min at 4 ℃ to remove the nucleolus, the supernatants were transferred to new tubes, and continued to centrifuge at 15,000 g for 5 min at 4 ℃ to remove the organelles like mitochondria. We transferred the supernatants to new tubes and added 500 mM of lithium chloride (LiCl) (Sigma-Aldrich, #L9650), and inverted tubes completely before adding 0.5% lithium dodecyl sulfate (LiDS) (Sigma-Aldrich, #L9781). The mixture was Incubated at 60 ℃ for 15 min, and quickly cooled down on ice for 5 min. The mixtures were clarified with centrifuge at 15,000 g for 5 min at 4 ℃ and the supernatants were transferred into new tubes. We stored 40 µL and 50 µL supernatants as input for later RNA and protein analysis, respectively, and DTT was added to the remaining supernatants for a final concentration to be 5 mM. The remaining supernatants were mixed with the LNA oligo(dT) coupled Dynabeads washed with five volumes of the hypo lysis buffer for 3 times before usage, and gently rotated at 40 ℃ for 1 h to capture RNA-protein complexes. Elution of RNA-RBP complex We collected the Dynabeads capturing the RNA-protein complexes with a magnetic stand, and transferred supernatants to new tubes for later analysis. Beads were subjected to successive rounds of washes with wash buffer 1 (20 mM Tris-HCl (pH 7.5), 500 mM LiCl, 1 mM EDTA, 5 mM DTT, and 0.1% (w/v) LiDS), wash buffer 2 (20 mM Tris-HCl (pH 7.5), 500 mM LiCl, 1 mM EDTA, 5 mM DTT, and 0.02% (v/v) NP40), and wash buffer 3 (20 mM Tris-HCl (pH 7.5), 200 mM LiCl, 1 mM EDTA, 5 mM DTT, and 0.02% (v/v) NP40) for twice with gentle rotation at 40 ℃ for each 5 min. Pre-elution was performed in 440 µL nuclease-free water at 40 ℃ for 5 min. Afterwards, the beads suspension was divided into two groups: 400 µL of RNase-mediated elution for protein analysis; and 40 µL of heat-mediated elution for RNA/DNA analyses. For RNase-mediated elution, beads were resuspended in 400 µL of RNase buffer (0.25 µL of RNase mixture in 400 µL nuclease-free water; RNase mixture: 1 µg/µL RNase A, 40 U/µL RNase T1, 50 mm Tris (pH 7.0), 50 mm NaCl (Invitrogen, #AM9759), and 50% glycerol), and incubated at 37 ℃ for 30 min. For heat-mediated elution, beads were resuspended in 40 µL nuclease-free water, and incubated at 95 ℃ for 5 min. We took the supernatants immediately after beads were collected with a magnetic stand. To confirm the effect of above elution, heat-mediated second elution was conducted with above two groups. After that, stored all samples at -80 ℃. MS measurement Following a confirmation that ribosomal RNAs were removed from the heat-mediate elution using BioAnalyzier, and that no abnormalities such as contaminants occurs from a part of the RNase-mediate elution using SDS-page and SYPRO Ruby staining, the SDS in the RNase-mediate elution samples was removed using the methanol–chloroform protein precipitation method. Briefly, four volumes of methanol, one volume of chloroform, and three volumes of water were added to the eluted sample and mixed thoroughly. The samples were centrifuged at 15,000 rpm for 10 min, and the water phase was removed carefully, and then four volumes of methanol was added to the samples, and the samples were centrifuged at 15,000 rpm for 10 min. After that, the supernatant was removed and the pellet was washed with 100% ice-cold acetone once. The precipitated protein was re-dissolved in guanidine hydrochloride and reduced with Tris (2-carboxyethyl) phosphine hydrochloride, alkylated with iodoacetamide, followed by digestion with lysyl endopeptidase and trypsin. The digested peptide mixture was applied to a Mightysil-PR-18 (Kanto Chemical) frit-less column (45×0.150 mm ID), and separated using a 0–40% gradient of acetonitrile containing 0.1% formic acid for 80 min at a flow rate of 100 nL/min, and the eluted peptides were sprayed into a mass spectrometer (Triple TOF 5600+; AB Sciex) directly. MS and MS/MS spectra were obtained using the information-dependent mode. Up to 25 precursor ions above an intensity threshold of 50 counts/sec were selected for MS/MS analyses from each survey scan. All MS/MS spectra were searched against protein sequences of the RefSeq (NCBI) human protein database (RDB) using Proteome discoverer 2.2, and decoy sequences were then selected with FDR < 1%. Identification of RBPs RBPs reliably binding to poly(A)-tailed RNAs were identified by comparing eRIC-MS data from same condition with irradiation (with UV) and without irradiation (without UV). Among the peptides detected in both of with and without UV, signal intensities of those with two and more unique peptides number, using one-side Mann–Whitney U test. The p -values are corrected as FDR with Storey’s procedure 82 . To compensate lower detection power of Mann-Whitney U test, a non-parametric test, we adapted 5% as threshold of the FDR. RBPs including one and more peptides with FDRs less than the threshold were considered as those reliably binding to poly(A)-tailed RNAs. Note that, among the twelve samples (triplication for four conditions), one sample from group of “hypoxia with UV” and the other from group of “normoxia without UV” were excluded from later analysis because of lower reproducibility. Abundance of the RBPs were estimated as the value of signal intensity with the total normalized to 1,000,000. Hypergeometric test to extract RBPs targeting stabilized glycolytic mRNAs The glycolytic mRNAs were identified based on Reactome 56 – 58 , a database for biological pathways ( https://reactome.org/download/current/Ensembl2Reactome_All_Levels.txt ). The data obtained from Reactome database contains Ensembl gene IDs and relating biological pathways. We extracted entries corresponded to Ensembl gene IDs as whole transcriptome (background) of the hypergeometric test. Among these entries, we identified the entries corresponded to “Glycolysis” as glycolytic mRNAs (named as “Reactome mRNAs”), and extracted those whose \({k}_{d}\) values were decreased in chronic hypoxia condition compared with normoxia as stabilized Reactome mRNAs. For each RBP included in ENCODE, a database of functional elements of human genome 59 , we corresponded target RNAs and calculated p -value for enrichment of the stabilized Reactome mRNAs included in the targets against the background with hypergeometric test. Based on the p -values, we calculated FDR using Storey procedure 82 . Depletion assay HCT116 cells were seeded in 24-well plates at concentration of 2.5×10 4 cells/well with DMEM (Wako, Cat# 044-29765) supplying with 10% FBS ((Life Technologies, Cat# F7524), and incubated appropriate time in the humidified incubator (Normoxic condition) or in the hypoxia chamber (hypoxic condition). 1 µM of siRNAs (Table 3 ) were transfected into the HCT116 cells by using Opti-MEM (Gibco, Cat# 31985-070) and Lipofectamine RNAimax Reagent (Invitrogen, REF# 13778-500) according to manufacturer's protocol, followed by 48 h of incubation. The cells were washed twice with PBS and then total RNA was isolated using RNAiso Plus (Takara, #9109) according to the manufacturer’s protocol. Expression level of individual genes were quantified as described above, following the reverse-transcription. Table 3 siRNAs used in this study Target # Merchandise siRNA ID Cat # IGF2BP2 #1 Thermo Fisher, silencer select siRNA s20923 4427037 IGF2BP2 #2 Thermo Fisher, silencer select siRNA s20922 4427037 FMRP #1 Thermo Fisher, silencer select siRNA s53175 4392420 FMRP #2 Thermo Fisher, silencer select siRNA 556458 4399665 Declarations The RNA-seq and SLAM-seq data generated in this study have been submitted to the DNA Data Bank of Japan (DDBJ) Sequence Read Archive (DRA; https://ddbj.nig.ac.jp/DRASearch/) under accession number PRJDB17883. ACKNOWLEDGMENTS: We thank our laboratory personnel for critically reading the manuscript and for their technical assistance with the experiments. The computational analysis was performed using the National Institute of Genetics (NIG) supercomputer system at the Research Organization of Information and Systems (ROIS). This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (grant numbers 17KK0163, 18H02570, 18KT0016, 16H06279, and 20H04838). K.K. received funding from JSPS KAKENHI (grant number 19K16635 and 22H03683), Takeda Science Foundation, and Kowa Life Science Foundation. We also thank Susan Furness, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript. AUTHOR CONTRIBUTIONS: K.K., Z.Z., Y.O., X.S., and N.A. conceived the project. Z.Z., Y.O., X.S., A.N.O., K.T., R.O.M., K.N., N.G., and N.A. designed and performed the experiments. K.K. analyzed the data. S.A. performed MS experiments. K.K., Z.Z., Y.O., X.S., and N.A. wrote the manuscript. ADDITIONAL INFORMATION: The authors declare that they have no conflict of interest. References Arnold, P. K. & Finley, L. W. S. 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Nucleic Acids Res 32 , e64 (2004). https://doi.org/10.1093/nar/gnh056 Additional Declarations No competing interests reported. Supplementary Files SupplInfo1.pdf SupplInfo2.pdf TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx TableS8.xlsx Cite Share Download PDF Status: Published Journal Publication published 17 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 May, 2024 Reviews received at journal 08 May, 2024 Reviews received at journal 03 May, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviewers invited by journal 24 Apr, 2024 Editor assigned by journal 24 Apr, 2024 Editor invited by journal 23 Apr, 2024 Submission checks completed at journal 21 Apr, 2024 First submitted to journal 05 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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2","display":"","copyAsset":false,"role":"figure","size":243027,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figuresmain2.png","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/673f29c2cdc9161ac632c3e4.png"},{"id":55258329,"identity":"49e77bdc-bcd7-47ad-8a93-b605de9f0811","added_by":"auto","created_at":"2024-04-24 21:06:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1214561,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/335157d2665d2ee4037120f1.png"},{"id":55258465,"identity":"9f4902da-edcb-4b28-807c-bbcbe60dce61","added_by":"auto","created_at":"2024-04-24 21:14:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":95268,"visible":true,"origin":"","legend":"\u003cp\u003eLandscape of RBP–mRNA binding under normoxic and hypoxic conditions.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Scheme of eRIC-MS. HCT cells cultured under normoxic or hypoxic conditions are exposed to UV light to crosslink RNAs and RBPs. Cells are lysed under denaturing conditions, and poly(A)-tailed RNAs crosslinked with their binding proteins are captured using oligo(dT) probes. Proteins bound to poly(A)-tailed RNAs are eluted by RNase, followed by MS analysis. (\u003cstrong\u003eB\u003c/strong\u003e) Identification of proteins reliably binding to poly(A)-tailed RNAs. The x- and y-axes in each panel indicate log-fold change and log-FDR, respectively. Blue and orange dots indicate proteins significantly enriched in samples with UV crosslinking compared with proteins in samples without UV (\u003cem\u003eFDR\u003c/em\u003e\u0026lt; 0.05) in normoxic and chronic hypoxic conditions, respectively. (\u003cstrong\u003eC\u003c/strong\u003e) Representative molecular functions involved in RNA binding that were significantly enriched for proteins binding to poly(A)-tailed RNAs. (\u003cstrong\u003eD\u003c/strong\u003e) Distribution of fold change in yield of proteins bound to poly(A)-tailed RNAs from cells under hypoxic conditions compared with yield of those from cells under normoxic conditions. Proteins sorted according to fold changes. Gray and red dots indicate proteins with \u0026lt; 1.5-fold change (no change) and \u0026gt; 1.5-fold change (activated), respectively.\u003c/p\u003e","description":"","filename":"Figuresmain5.png","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/33f1a86dcce30b405aeba1b7.png"},{"id":55258336,"identity":"b50569fa-6eac-4bc7-84c5-6f1b20f5132c","added_by":"auto","created_at":"2024-04-24 21:06:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":111875,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of RBPs regulating RNA abundance of rate-limiting enzymes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Scheme of hypergeometric test to identify candidate RBPs stabilizing glycolytic mRNAs under chronic hypoxia. Among 72 glycolytic mRNAs identified from the Reactome database (Reactome mRNAs), 40 were extracted as stabilized under chronic hypoxia based on SLAM-seq data (stabilized Reactome mRNAs). A total of 150 RBPs studied by the ENCODE project were analyzed to identify their target RNAs. Hypergeometric test of 40 stabilized Reactome mRNAs against target mRNAs of 150 ENCODE RBPs was performed to identify candidate RBPs stabilizing glycolytic mRNAs under hypoxia. \u003cstrong\u003e(B) \u003c/strong\u003eRBPs for which the 40 stabilized Reactome mRNAs were significantly enriched against the target RNAs compared with whole transcriptome. RBPs with FDR \u0026lt; 0.05 are indicated. Red bars indicated the RBPs identified as activated RBPs in eRIC-MC. (\u003cstrong\u003eC\u003c/strong\u003e) Venn diagram of 43 activated RBPs identified based on eRIC-MS (Fig 4D), and 75 ENCODE RBPs for which stabilized Reactome mRNAs were significantly enriched against the target RNAs identified based on the hypergeometric test (Fig 5B). (\u003cstrong\u003eD\u003c/strong\u003e) Differential expression of mRNAs encoding rate-limiting enzymes for glycolysis with depletion of FMRP (siFMRP), in normoxia and hypoxia conditions. The y-axis indicates fold-change of RNA abundance normalized to average RNA abundance with siControl (siCont) in normoxia condition. RNA expression levels were normalized to \u003cem\u003eACTB\u003c/em\u003e in same sample. Error bars indicate standard deviations.\u003c/p\u003e","description":"","filename":"Figuresmain6.png","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/0e2cb5a1e0774fe4330f424b.png"},{"id":55258334,"identity":"db7c997a-5581-451b-9ae9-33b301009eca","added_by":"auto","created_at":"2024-04-24 21:06:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":55611,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of enhancement of glycolysis under chronic hypoxia.\u003c/p\u003e\n\u003cp\u003eSchematic diagram indicating enhancement of glycolysis in chronic hypoxia. Upon chronic hypoxia, FMRP binds to \u003cem\u003eHK1\u003c/em\u003e,\u003cem\u003ePFKL \u003c/em\u003eand \u003cem\u003ePKM\u003c/em\u003e mRNAs to stabilize them, resulting in enhanced glycolysis to adapt to hypoxia.\u003c/p\u003e","description":"","filename":"Figuresmain7.png","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/44e2767cccfb413047ab7998.png"},{"id":81050805,"identity":"87c7a96e-0522-41ea-9be6-b8934a884363","added_by":"auto","created_at":"2025-04-21 16:05:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3024604,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/4feb7d4b-95c9-411d-8be1-86a92cafb88c.pdf"},{"id":55258332,"identity":"62c27885-72be-43e1-9fda-8a8008cc891a","added_by":"auto","created_at":"2024-04-24 21:06:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2203253,"visible":true,"origin":"","legend":"","description":"","filename":"SupplInfo1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/524a5ab767bb40df34b7bd9d.pdf"},{"id":55258464,"identity":"5c64050d-6e57-4c59-a72c-877e05eb5f4f","added_by":"auto","created_at":"2024-04-24 21:14:36","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1804692,"visible":true,"origin":"","legend":"","description":"","filename":"SupplInfo2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/15d3a338e1802715fbba217d.pdf"},{"id":55258342,"identity":"ce8e56fb-dbff-40a1-88cb-aabcab4212c6","added_by":"auto","created_at":"2024-04-24 21:06:37","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4747942,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/671a7a64ae130bbc0270243e.xlsx"},{"id":55258344,"identity":"7d73154f-140f-4f49-a162-d7f937bf6c13","added_by":"auto","created_at":"2024-04-24 21:06:37","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4412906,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/db7c04fc3f4b8ca99e47c982.xlsx"},{"id":55258343,"identity":"e63cc433-cc51-4b36-959d-277c9998eb15","added_by":"auto","created_at":"2024-04-24 21:06:37","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2036765,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/2681000e2923118df5be991c.xlsx"},{"id":55258335,"identity":"43bb7fe8-9b8d-44cf-8adf-6940bd31a571","added_by":"auto","created_at":"2024-04-24 21:06:36","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":128338,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/2cc49e1f23bd03f38d6952be.xlsx"},{"id":55258341,"identity":"462b5287-e781-4f3d-bdd0-101602d40527","added_by":"auto","created_at":"2024-04-24 21:06:37","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":13967,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/bd55f85648ac8a546d448a6b.xlsx"},{"id":55258338,"identity":"791fe199-ee81-4a74-b828-51eebfdc5304","added_by":"auto","created_at":"2024-04-24 21:06:36","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":138376,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/f364253a773ac15af04e8c28.xlsx"},{"id":55258340,"identity":"a4be21cc-4dd3-4b15-b187-3dafb158fb7e","added_by":"auto","created_at":"2024-04-24 21:06:36","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":88900,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/254982c18845238737f3de84.xlsx"},{"id":55258339,"identity":"b69d07df-2739-4341-87e7-dede65785b02","added_by":"auto","created_at":"2024-04-24 21:06:36","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":21883,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4221145/v1/380a96a73e924e8a064e05c7.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fragile X mental retardation protein regulates glycolytic gene expression under chronic hypoxia","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAerobic organisms utilize oxygen to produce chemical energy for the tricarboxylic acid cycle (TCA cycle) and electron transport chain\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Adenosine triphosphate (ATP) supplies the chemical energy for diverse biochemical reactions\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Low oxygen conditions, referred to as hypoxia, thus causes a crisis for aerobic organisms. Hypoxia occurs under both physiological and pathological situations\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e; for instance, hypoxic conditions are observed in embryonic development\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Normal mammalian development occurs in a hypoxic environment, and the hypoxic environment is absolutely required in aspects of developmental morphogenesis for placental and fetal heart\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In addition, an insufficient vascular supply caused by rapid growth of tumor tissues results in hypoxic regions within tumor tissues, thus enhancing the epithelial-to-mesenchymal transition of cells, which in turn increases cell motility and metastasis\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Pathological hypoxic conditions are also observed in chronic heart and kidney diseases\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and in cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo avoid the crisis caused by oxygen deficiency, cells have developed various mechanisms to respond to hypoxia. Regulation of transcription modules by hypoxia-inducible factors (HIFs) is a widely studied hypoxia-response mechanism. Among these, HIF1, which plays a central role in the hypoxia response, was originally identified as a factor regulating expression of erythropoietin (\u003cem\u003eEPO\u003c/em\u003e) in response to hypoxia\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. During hypoxia, the regulatory subunit of HIF1, HIF1α, is stabilized and transported into the nucleus to regulate the transcription of target genes such as \u003cem\u003eVEGF\u003c/em\u003e, \u003cem\u003eEPO\u003c/em\u003e, and \u003cem\u003eGLUT1\u003c/em\u003e\u003csup\u003e18,19\u003c/sup\u003e. HIF1 regulates cell cycle arrest and apoptosis upon hypoxia\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and inflammation during liver injury\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Although HIF1 is known to be a dominant regulatory factor in acute hypoxia (several hours), recent studies have suggested that the chronic hypoxic response (several days) is regulated by other mechanisms. HIF1α protein levels increase rapidly at the onset of hypoxia and return to basal levels under prolonged (chronic) hypoxia\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. We previously reported that chronic hypoxia activates transcription factors other than HIF1, including cAMP-response element binding protein (CREB) and nuclear factor-kB (NF-kB), contributing to tumor malignancy\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite limited reports, post-transcriptional mechanisms, such as RNA stability, have also been shown to be involved in acute hypoxic responses\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Some hypoxia-responsive RNAs, including \u003cem\u003eVEGF\u003c/em\u003e, \u003cem\u003eEPO\u003c/em\u003e, and \u003cem\u003eTH\u003c/em\u003e, are stabilized under acute hypoxic conditions, and some RNA-binding proteins (RBPs), such as HuR (also known as ELAVL1), are known to be involved in the stability of these RNAs\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These results suggest that gene expression profiles under chronic hypoxia is regulated by distinct mechanisms, including the regulation of RNA stability, in contrast to the HIF1-dominant acute phase. Nevertheless, the landscape of gene expression regulation via post-transcriptional mechanisms such as RNA degradation, in response to chronic hypoxia, remains unclear.\u003c/p\u003e \u003cp\u003eRecent developments in techniques, including next-generation sequencing (NGS), have enabled us to measure RNA kinetics, comprising RNA synthesis and degradation, as well as gene expression levels\u003csup\u003e\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. For instance, SLAM-seq, TimeLapse-seq, and TUC-seq enable \u003cem\u003ein situ\u003c/em\u003e labeling of intracellular RNAs with 4-thiouridine (4sU) followed by base conversion to distinguish newly synthesized RNAs and measuring RNA synthesis rate\u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. BRIC-seq enables pulsed labeling of intracellular RNAs to quantify RNA degradation rates\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. We recently developed Dyrec-seq to quantify RNA synthesis and degradation rates simultaneously and comprehensively by multi-labeling of endogenous RNAs with both 4sU and 5\u0026prime;-bromouridine (BrU)\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, and revealed that RNA degradation affected chronological gene expression patterns. The combined measurement of these kinetic parameters enables to reveal the contributions of transcriptional and post-transcriptional regulation to differential gene expression. Moreover, recent approaches to the proteome-wide identification of RBPs based on mass spectrometry (MS) enable the quantification of the RNA-binding properties of RBPs, as a major factor regulating RNA kinetics\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. These approaches have led to the development of enhanced RNA interactome capture (eRIC)-MS for accurate quantification of the RNA-binding properties of RBPs\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, enabling us to identify RBPs involved in post-transcriptional regulation under specific conditions.\u003c/p\u003e \u003cp\u003eIn this study, we hypothesized that post-transcriptional regulation, especially RNA degradation via RBPs, controls the regulated expression of hypoxia-responsive genes in chronic hypoxia. We initially calculated RNA synthesis and degradation simultaneously in HCT116 human colorectal carcinoma-derived cells under chronic hypoxic conditions, and quantified the respective contributions of RNA synthesis and degradation to the differential gene expression. Our study led a novel insight that mRNAs encoding enzymes involved in glycolysis (glycolytic mRNAs) is regulated via RNA degradation through an RBP, named fragile X mental retardation protein (FMRP) which is coded by \u003cem\u003eFMR1\u003c/em\u003e gene. This study highlights the involvement of post-transcriptional regulation via the specific RBP in the enhancement of glycolysis in chronic hypoxia.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eChronic hypoxia in HCT116 cells\u003c/h2\u003e \u003cp\u003eTo investigate the chronological response of HCT116 cells in response to hypoxic conditions, we cultured the cells under hypoxic condition (1% O\u003csub\u003e2\u003c/sub\u003e), and examined the temporal changes in RNA and protein expression. HIF1α protein levels increased up to 4 h after hypoxia and then diminished within 48 h (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). RNA levels of the early hypoxia-response factors \u003cem\u003eGLUT1\u003c/em\u003e and \u003cem\u003eCA9\u003c/em\u003e increased continuously from 0 to 48 h (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB and S1C), while RNA levels of the chronic hypoxia-response factor \u003cem\u003eMMP1\u003c/em\u003e\u003csup\u003e23\u003c/sup\u003e only increased at 48 h (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD). We also examined the gene expression profile of HCT116 cells under chronic hypoxia by RNA-seq analysis. To examine differences in gene expression profiles, we analyzed the RNA expression profiles using principal component analysis (PCA) (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE and S1F). The first principal component was largely altered at 24 and 48 h after hypoxic treatment, indicating that the hypoxia-induced expression profile changed within 24 h and remained changed until at least 48 h (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eF). The first principal component was also largely changed at 72 h, possibly reflecting the effects of cell death. On the basis of this PCA, we considered that the cellular state in response to hypoxia reached a steady state between 24 and 48 h. We therefore defined chronic hypoxia as 36 h after applying hypoxic conditions in this study.\u003c/p\u003e \u003cp\u003eSimultaneous calculation of RNA synthesis and degradation rates in HCT116 cells under normoxia and chronic hypoxia states\u003c/p\u003e \u003cp\u003eWe quantified RNA synthesis and degradation rates in HCT116 cells under chronic hypoxia by SLAM-seq, which allowed the identification of newly synthesized transcripts with 4sU labeling\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The labelled RNAs were reacted with iodoacetamide (IAA), a thiol-reactive compound, to attach a carboxyamidomethyl group to the thiol group in the incorporated 4sU by nucleophilic substitution (S\u003csub\u003eN\u003c/sub\u003e2) reaction (alkylation). Because the alkylated 4sU is paired with guanine instead of adenine during reverse transcription, a 4sU incorporated into the RNA is detected as a mutation (T-to-C conversion). Determination of the 3\u0026prime; untranslated regions (UTRs) of the alkylated RNA using QuantSeq, a poly(A)-tail dependent massive sequencing technique\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, enabled us to quantify the expression levels of RNAs and those labeled with 4sU simultaneously (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). We determined the appropriate concentration of 4sU for labeling HCT116 cells by measuring the 4sU incorporation ratio and cell viability under the indicated 4sU concentrations (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eG and S1H). The incorporation ratio of 4sU reached a plateau at approximately 2.5% with 50 \u0026micro;M 4sU in 12 h (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eG). Treatment with up to 100 \u0026micro;M 4sU resulted in \u0026gt;\u0026thinsp;80% of proliferation (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eH). On the basis of these results, we used 100 \u0026micro;M 4sU for RNA labeling in this study. Moreover, we confirmed efficient alkylation of 4-thiouracil, a nucleobase that compose 4sU, using IAA, by the shift in absorption spectrum (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eI). High-performance liquid chromatography (HPLC) measurement of nucleoside samples derived from RNA collected from cells labeled with 100 \u0026micro;M 4sU and those alkylated with IAA treatment indicated a clear shift in the maximum absorption wavelength (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eJ), indicating efficient alkylation of 4sU incorporated into the RNAs. T-to-C conversions obtained from the alkylated 4sU-labeled RNAs increased approximately four-fold compared with those obtained from unalkylated RNAs, indicating the identifiability of 4sU-labeled RNAs in NGS (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eK). Note that the \u0026ldquo;T-to-C conversion\u0026rdquo; in RNA-seq reads derived from genes located on the reverse strand for the reference genome is identified as \u0026ldquo;A-to-G conversion\u0026rdquo; during bioinformatics analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe collected RNAs for SLAM-seq according to the above conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Briefly, HCT116 cells were cultured under hypoxic (1% O\u003csub\u003e2\u003c/sub\u003e) and normoxic conditions (21% O\u003csub\u003e2\u003c/sub\u003e) for 36 h, followed by the addition of 4sU to the medium to a final concentration of 100 \u0026micro;M (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). RNAs were collected at 0, 1, 2, 4, 8, and 12 h after addition of 4sU, and the purified RNAs were subjected to alkylation of 4sUs with IAA. The collected RNA samples were provided to QuantSeq to determine the sequences of the 3\u0026prime;UTRs. We detected more than 30\u0026times;10\u003csup\u003e6\u003c/sup\u003e reads in all samples (Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). For alignment of the reads using the SLUMDUNK tool\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e on the reference genome (hg38), we found that \u0026gt;\u0026thinsp;50% of the reads were aligned singly (Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). Finally, we counted T-to-C conversions (and A-to-G conversions) detected in the QuantSeq data for the alkylated 4sU-labeled RNA. Detection of T-to-C conversions by the SLAMDUNK tool indicated a labeling-time-dependent increase in T-to-C conversions up to approximately 8.0% (Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC), resulting in approximately 40% of reads including T-to-C conversions (Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eBased on the labeling-time-dependent increase in T-to-C conversions (and A-to-G conversions), we calculated the RNA synthesis and degradation rates in HCT116 cells simultaneously. First, we identified expressed genes based on the QuantSeq results, with gene expression levels quantified as counts per million (CPM). The CPM values were distributed unimodally in a range\u0026thinsp;\u0026gt;\u0026thinsp;0 in individual samples (Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eE). Here, we considered genes with a CPM\u0026thinsp;\u0026gt;\u0026thinsp;0 as expressed genes, and identified 11,969 RNAs derived from expressed genes in all samples (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). We then simultaneously calculated the synthesis and degradation rates for RNAs derived from individual genes using the SLAM-seq data. The RNA synthesis rate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e, and degradation rate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e, were defined as the abundance of RNA molecules synthesized per min and the ratio of RNA molecules degraded per min, respectively. The expression level of an RNA is determined as the ratio of the RNA synthesis and degradation rates, and the shape of the curve is determined by the degradation rate. Namely, we can calculate the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e values based on the fitting curve on the temporal increase of newly synthesized RNAs identified based on 4sU incorporation, combined with RNA expression level (see below). When the RNA expression level is at a steady state, the amount of newly synthesized RNA (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{t}\\)\u003c/span\u003e\u003c/span\u003e) at each time point (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e) is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{x}_{t}=\\frac{{k}_{s}}{{k}_{d}}\\left(1-{e}^{-{k}_{d}t}\\right)\\#\\dots eq 1\\end{array}.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWe can therefore calculate \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e by fitting the time series of 4sU-labeled RNA and expression levels estimated from SLAM-seq to Eq.\u0026nbsp;1. Prior to calculating \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003evalues, we extracted the genes in steady state after 4sU treatment (see Materials and Methods), because a steady state of RNA expression level is required to estimate the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e values. A total of 9,541 genes were in steady state in both normoxic and hypoxic samples, among the 11,969 genes expressed in all samples (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For the RNAs derived from these genes, we calculated the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e values at the genome-wide level by fitting the time series of 4sU-labeled RNA to Eq.\u0026nbsp;1. We also adopted RNAs with a good fit between their actual expression levels and expression levels predicted from the estimated \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e values (see Materials and Methods). We were therefore able to calculate the RNA synthesis and RNA degradation rates of 8,961 and 8,479 RNAs for normoxic and hypoxic samples, respectively (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The estimated \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e values both obeyed a log Gaussian distribution, and no mutual correlation of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e was observed in either sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSome of differentially expressed genes are regulated via RNA degradation under chronic hypoxia\u003c/h2\u003e \u003cp\u003eWe aimed to estimate the relative contributions of RNA synthesis and degradation to the differential expression of RNAs, based on the calculated RNA synthesis and degradation rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We identified RNAs that were differentially expressed in chronic hypoxia by setting a threshold change in CPM of \u0026gt;\u0026thinsp;1.5-fold or \u0026lt;\u0026thinsp;2/3-fold between normoxic and hypoxic samples and a false discovery rate (FDR) of \u0026lt;\u0026thinsp;0.05 (paired \u003cem\u003et\u003c/em\u003e-test). A total of 1,330 RNAs among the 11,969 genes expressed in all samples were identified as differentially expressed in hypoxia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), and the RNA synthesis and degradation rates for 1,160 of these RNAs were estimated in both normoxic and hypoxic samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then quantified the contributions of RNA synthesis and degradation to the differential expression of RNAs. When \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e in Eq.\u0026nbsp;1 approaches infinity, the abundance of newly synthesized RNAs, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{t}\\)\u003c/span\u003e\u003c/span\u003e, approaches asymptotically to expression level (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(X\\)\u003c/span\u003e\u003c/span\u003e), thus:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(X=\\underset{t\\to \\infty }{\\text{lim}}\\frac{{k}_{s}}{{k}_{d}}\\left(1-{e}^{-{k}_{d}t}\\right)\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\begin{array}{c}=\\frac{{k}_{s}}{{k}_{d}}\\#\\dots eq 2.\\end{array}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo quantify the relationships of differential RNA expression between two conditions (e.g., normoxia vs. hypoxia) and synthesis or degradation rates, we moved Eq.\u0026nbsp;2 to log space:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}\\varDelta \\text{log}\\left(X\\right)=\\varDelta \\text{log}\\left({k}_{s}\\right)-\\varDelta \\text{log}\\left({k}_{d}\\right)\\#\\dots eq 3\\end{array}.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe sum of the contributions of RNA synthesis and degradation to the differential expression of RNAs should be one, and we therefore defined the contributions of RNA synthesis, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{s}\\)\u003c/span\u003e\u003c/span\u003e, and degradation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e, as relative values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta \\text{log}\\left({k}_{s}\\right)\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta \\text{log}\\left({k}_{d}\\right)\\)\u003c/span\u003e\u003c/span\u003e over \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta \\text{log}\\left(X\\right)\\)\u003c/span\u003e\u003c/span\u003e:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${\\rho }_{s}=\\frac{\\varDelta \\text{log}\\left({k}_{s}\\right)}{\\varDelta \\text{log}\\left(X\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eand\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${\\rho }_{d}=\\frac{\\varDelta \\text{log}\\left({k}_{d}\\right)}{\\varDelta \\text{log}\\left(X\\right)}.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThese definitions satisfy the premise that the sum of the contributions is equal to one. Estimation of the RNA synthesis and degradation rates thus enabled us to quantify their relative contributions. The estimated \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e values obeyed an exponential unimodal distribution with 0% of mode (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), indicating that most gene expression levels were regulated via transcriptional regulation. However, 102 RNAs had \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e values\u0026thinsp;\u0026gt;\u0026thinsp;60%, indicating that their differential expression was mainly regulated via RNA degradation. Moreover, increased RNAs tended to have larger \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), indicating that their increased expression in chronic hypoxia tended to be caused by RNA stabilization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRegulation of RNA degradation involves glycolytic enhancement under chronic hypoxia\u003c/h2\u003e \u003cp\u003eTo determine the cellular functions regulated via either RNA synthesis or degradation under chronic hypoxia, we performed functional enrichment analysis for RNAs mainly regulated via either mechanism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and S3, and Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Differentially expressed RNAs mainly regulated via RNA synthesis were significantly enriched (\u003cem\u003eFDR\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in RNA processing, lipid metabolism, and alternative splicing (Fig \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), while differentially expressed RNAs mainly regulated via RNA degradation were significantly enriched in glycolysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and S3). We also carried out gene set enrichment analysis (GSEA)\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e based on the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e values to examine cooperativeness of RNA degradation on cellular functions. GSEA statistically tests the homogeneities of differential expression of RNAs involved in specific biological functions. A uniform distribution in fold change of RNAs involved in a specific term makes the \u003cem\u003ep\u003c/em\u003e-value of the term larger, while an uneven distribution makes the \u003cem\u003ep\u003c/em\u003e-value smaller. We used the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e value from change in RNA degradation rates and expression level instead of the fold change, to approach cooperative regulation of RNA degradation (see Materials and Methods). GSEA suggested that RNAs with high \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e values (differential expressed genes mainly regulated by RNA degradation) were significantly related to glycolysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e), as with the results of the functional enrichment analysis.\u003c/p\u003e \u003cp\u003eChanges in glycolysis in response to hypoxic conditions are important for cellular adaptation to hypoxia\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Although the enhancement of glycolysis by acute hypoxia for up to a few hours is considered to be predominantly regulated transcriptionally via HIF\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, the mechanisms regulating glycolysis in chronic hypoxia remain unclear. The current findings suggest that regulation of RNA degradation, rather than transcriptional regulation, is the main mechanism responsible for the enhancement of glycolysis under chronic hypoxia. In support of this finding, metabolic enzymes encoded by mRNAs with large \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e values, i.e., mainly regulated via RNA degradation, were in distributed in glycolysis on Kyoto Encyclopedia of Genes and Genomes (KEGG) Metabolic pathways (hsa01100) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B), consistent with the results of the functional enrichment analysis. Moreover, mRNAs encoding rate-limiting enzymes for glycolysis, such as hexokinase-1 (HK1), phosphofructokinase, liver type (PFKL), and pyruvate kinase M1/2 (PKM), were among those with large \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e values. In contrast, enzymes encoded by RNAs with small \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e values, i.e., mainly regulated by transcriptional regulation, were distributed on lipid and amino acid metabolism. We confirmed that up-regulated mRNAs with high \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e values (red in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) were mapped mainly to carbon metabolism, thus supporting the idea that mRNAs involved in glycolysis were stabilized during chronic hypoxia. Measurement of individual RNA levels also indicated significant increases in mRNAs encoding rate-limiting enzymes (\u003cem\u003eHK1\u003c/em\u003e, \u003cem\u003ePFKL\u003c/em\u003e, and \u003cem\u003ePKM\u003c/em\u003e mRNAs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and calculation of the half-lives of these mRNAs indicated that they were stabilized in response to chronic hypoxia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Protein levels of HK1, PFKL, and PKM were also increased under the same conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), accompanied by the accumulation of intracellular lactate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). These results suggested that expression levels of mRNAs encoding rate-limiting enzymes for glycolysis were increased by RNA stabilization, resulting in increased abundances of these enzymes to enhance glycolysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of candidate RBPs involved in chronic hypoxia by eRIC-MS\u003c/h2\u003e \u003cp\u003eWe investigated how the degradation of glycolytic mRNAs was regulated under chronic hypoxia by focusing on RBPs, as proteins involved in the regulation of RNA fate, including degradation\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. We examined changes in mRNA binding to individual RBPs under chronic hypoxia using eRIC-MS, in which poly(A)-tailed mRNAs were collected and the proteins bound to these mRNAs were purified and identified by MS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, see Materials and Methods). Briefly, we cultured HCT116 cell for 36 h in normoxic or hypoxic conditions, followed by treatment with ultraviolet rays (UV) to crosslink RNA-RBP complexes. We then isolated the poly(A)-tailed RNAs using oligo(dT) and analyzed them using BioAnalyzer. Ribosomal RNAs were removed and poly(A)-tailed RNAs were highly enriched (Fig \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA). RNase treatment eluted RBPs binding to the poly(A)-tailed RNAs from oligo(dT). We also obtained RBPs from cells without UV crosslinking as negative controls in normoxic or hypoxic conditions. We performed proteomic analysis using MS with technical duplications for all samples under four treatments: normoxia with UV, normoxia without UV, hypoxia with UV, and hypoxia without UV, with biological triplicates, to obtain profiles of RBPs bound to poly(A)-tailed RNAs (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the obtained MS data, we confirmed the reliability of each profile by comparing the technical duplications, which indicated that the profiles for two samples (2nd sample of normoxia without UV, and 1st sample of hypoxia with UV) were less reproducible (Fig \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eB). We therefore omitted these samples from the following analyses. MS data frequently contain several proteins as noise due to non-specific binding during precipitation and purification. To identify RBPs specifically binding to poly(A)-tailed RNAs, we performed statistical test for the abundance of proteins compared with that in the corresponding negative controls (without UV). Among the obtained RBPs with two or more unique peptides, 349 and 258 were significantly enriched in the normoxic and hypoxic samples with UV crosslinking, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, see Materials and Methods). Gene ontology (GO) analysis of the significantly enriched RBPs in the obtained protein samples confirmed that we mainly obtained RBPs from eRIC-MS approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eBased on the eRIC-MS data, we identified RBPs whose binding to poly(A)-tailed RNAs was changed by chronic hypoxia. Comparing the RBP abundances between hypoxic and normoxic conditions indicated that 93 and two RBPs, among the total 351 identified RBPs, were normoxia- and hypoxia-specific, respectively, and 257 RBPs were common to both conditions (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). Among the 257 RBPs identified in normoxic and hypoxic conditions commonly, 43 were activated (increased\u0026thinsp;\u0026gt;\u0026thinsp;1.5-fold) under chronic hypoxic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of FMRP as stability regulators for glycolytic mRNAs\u003c/h2\u003e \u003cp\u003eTo identify the RBPs responsible for stabilizing glycolytic mRNAs in response to chronic hypoxia, we performed a hypergeometric test integrating databases with the SLAM-seq data (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Using the Reactome database\u003csup\u003e\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, which collects information on biological pathways, to identify mRNAs encoding glycolytic enzymes, we identified 72 glycolytic mRNAs (Reactome mRNAs). By surveying the Reactome mRNAs among the 6,140 mRNAs stabilized during chronic hypoxia obtained from SLAM-seq analysis, we identified 40 Reactome mRNAs that were stabilized in hypoxia (stabilized Reactome mRNAs). We then aimed to identify candidate RBPs that potentially stabilize these 40 mRNAs. We initially listed the RBP-target mRNAs using 150 RBPs analyzed in the ENCODE eCLIP-seq study\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, which identified candidate target mRNAs regulated by the 150 ENCODE RBPs. The hypergeometric test showed that 75 of these 150 ENCODE RBPs potentially regulate the 40 stabilized Reactome mRNAs (\u003cem\u003eFDR\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCross-referencing the 75 ENCODE RBPs extracted from the hypergeometric test (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and 43 activated RBPs identified by eRIC-MS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) implicated two RBPs, IGF2BP2 and FMRP, potentially regulating the stability of glycolytic mRNAs such as \u003cem\u003eHK1\u003c/em\u003e, \u003cem\u003ePFKL\u003c/em\u003e, and \u003cem\u003ePKM\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, and Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe examined the effects of the 2 activated RBPs on expression of the glycolytic mRNAs by depletion assay. As described above, most of the glycolytic mRNAs were increased under chronic hypoxia (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). We therefore hypothesized that the activated RBPs IGF2BP2 and FMRP act as stabilizing factors in chronic hypoxia condition for the glycolytic mRNAs, and examined the effects of depletion of the RBPs on the RNAs encoding the rate-limiting enzymes, as representatives of the glycolytic mRNAs. Chronic hypoxia significantly increased the expression levels of all the rate-limiting enzymes compared to normoxia in siControl samples, but knocking-down of FMRP cancelled the increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and S5A). On the contrast, knocking-down of IGF2BP2 did not cancel the increase (Fig \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eB and S5C). These results suggested that FMRP is a stabilizing factor for the rate-limiting enzymes under chronic hypoxia, and the enhancement of glycolysis is regulated by FMRP activated in response to chronic hypoxia to stabilize \u003cem\u003eHK1\u003c/em\u003e, \u003cem\u003ePFKL\u003c/em\u003e and \u003cem\u003ePKM\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eDifferential gene expression, accompanied by responses to extracellular stimulation and physiological processes such as development, is generally considered to be regulated via alterations in gene transcription; however, post-transcriptional regulation, including RNA degradation, has recently been shown to play important roles in the regulation of gene expression\u003csup\u003e\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. In this study, we developed a procedure to quantify the respective contributions of RNA synthesis (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{s}\\)\u003c/span\u003e\u003c/span\u003e) and RNA degradation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e) to differential gene expression simultaneously. We identified 1,003 and 102 genes in HCT116 cells whose differential expression levels were mainly regulated via RNA synthesis and degradation, respectively, under chronic hypoxia. Regarding the genes mainly regulated via RNA degradation, functional enrichment analysis revealed that these genes were significantly enriched in glycolysis, and most of these mRNAs were stabilized; i.e., expression levels of glycolytic RNAs were regulated by mRNA stabilization during adaptation to chronic hypoxia. This represents a reasonable strategy for enhancing glycolysis while avoiding further consumption of ATP. Furthermore, integration of eRIC-MS to identify RBPs binding mRNAs under specific conditions, with statistical analysis using public databases followed by depletion assays, indicated that FMRP is an important factor controlling the increase in rate-limiting enzymes for glycolysis under chronic hypoxia via regulation of RNA stability.\u003c/p\u003e \u003cp\u003eThe current results suggest that glycolysis is enhanced during chronic hypoxia via regulation of mRNA stability by RBP: the RNA stabilizing factor FMRP bind to the mRNAs encoding rate-limiting enzymes for glycolysis in response to chronic hypoxia, to protect the RNA from degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). FMRP is an RBP related to the incurable neurological disease, fragile X syndrome, and is considered to be responsible for neuronal development and synaptic plasticity via the regulation of mRNA degradation and alternative splicing\u003csup\u003e\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. We recently reported that FMRP regulates neuronal differentiation by repressing the nonsense mediated mRNA decay (NMD) pathway\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e and sequestering mRNA from translation and deadenylation\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. FMRP shapes the proteome and transcriptome under several pathological hypoxic conditions, such as resistance of leukemia cells in a hypoxic microenvironment\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, acute kidney injury caused by hypoxia-reoxygenation\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, and encephalopathy of prematurity following perinatal hypoxia\u0026ndash;ischemia\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Various mechanisms, including remodeling of the translational process, have been proposed to modulate the proteome and transcriptome, but the details remain unclear. The current study showed that FMRP regulated RNA stability in chronic hypoxia, suggesting that FMRP may act as a factor coupling RNA stability and translation in chronic hypoxic responses.\u003c/p\u003e \u003cp\u003eRecent developments in measurement techniques have enabled us to acquire information on RNA kinetics, including RNA synthesis and degradation\u003csup\u003e\u003cspan additionalcitationids=\"CR42 CR43 CR44\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Nevertheless, it is independent matters that the RNA kinetics are altered, and that changes in RNA kinetics affect gene expression. For instance, mutation-dependent rapid RNA degradation leads to up-regulated transcription of related genes, including the mutated gene itself. This compensation mechanism allows destabilization of RNA to increase transcription to maintain robust gene expression levels\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. This report demonstrates that transcription and RNA degradation are highly linked. The current approach for determining the quantity of respective alterations in RNA synthesis and degradation on differential gene expression (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e) enabled us to clarify the effects of RNA kinetics on gene expression quantitatively and comprehensively. Although this \u0026ldquo;regulatory analysis approach\u0026rdquo; has been introduced in metabolic studies\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, to the best of our knowledge, this is the first time it has been applied to analyze gene expression. Differential gene expression in response to hypoxia is generally considered to be controlled by transcriptional regulation by HIFs; however, the current approach revealed that the expression of glycolytic mRNAs during chronic hypoxia is regulated via changes in RNA stability by RBPs, such as FMRP. RNA kinetics regulates various pathological and physiological cell functions, including responses to extracellular stress or signals and cell differentiation or developmental processes. The current approach will provide substantial insights into how the regulation of RNA kinetics contributes to cellular behavior in relation to pathological and physiological phenomena.\u003c/p\u003e \u003cp\u003eIn conclusion, we developed a procedure to quantify the respective contributions of RNA synthesis and degradation to differential gene expression, and showed that enhancement of glycolysis in response to chronic hypoxia is regulated via alterations in RNA stability. Moreover, we found that FMRP is involved in increases of mRNAs encoding the rate-limiting enzymes for glycolysis. Regulation of glycolysis plays an important role in the adaptation to hypoxia\u003csup\u003e\u003cspan additionalcitationids=\"CR74\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. These findings provide substantial insights into cellular regulatory mechanisms under chronic hypoxia, and may thus contribute to the development of cancer treatment strategies and medicines.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHuman colorectal carcinoma derived HCT116 cell line\u003c/h2\u003e \u003cp\u003eThe human colorectal carcinoma derived HCT116 cell line used in this study were purchased from ATCC, and were passaged and stored in the laboratory. In this study, we used Only cells with less than 20 of passages for the experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and hypoxia treatment\u003c/h2\u003e \u003cp\u003eHCT116 cells were cultured with Dulbecco's modified Eagle's medium (DMEM) (Wako, Cat #044-29765), supplying with 10% fetal bovine serum (FBS) (Life Technologies, Cat #F7524). FBS was heat-inactivated at 56℃ for 30 min. HCT116 cells were cultured in a humidified incubator (Thermo Fisher Scientific, MODEL #370, REL #1, S/N #310370-4133) with 5% CO\u003csub\u003e2\u003c/sub\u003e at 37℃. For hypoxia treatment, the cells were precultured for 24 h, followed by culturing in hypoxia chamber (RUSKINN, UM-025, Version #2.0-_CSC2.01) with 1% O\u003csub\u003e2\u003c/sub\u003e, 5% CO\u003csub\u003e2\u003c/sub\u003e, and 94% N\u003csub\u003e2\u003c/sub\u003e at 37℃ for specified time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCollection of RNA and protein samples\u003c/h2\u003e \u003cp\u003eFor collection of RNAs samples, the cells were washed twice with phosphate-buffered saline (PBS) and then total RNA was isolated using RNAiso Plus (Takara, #9109) according to the manufacturer\u0026rsquo;s protocol. Where appropriate, genomic DNA included in RNA samples are removed by Recombinant DNase I treatment (Recombinant DNase I (Takara, #2270), DNase I Buffer (Takara, #2270), Recombinant RNase Inhibitor (Takara, #2313), 37℃, 1 h), and RNAs were purified using RNAiso Plus again. For collection of protein samples, intracellular proteins were collected using 2\u0026times;SDS sample buffer (20% glycerol, 100 mM Tris-HCl (pH 6.8), 40 mg/mL SDS, 0.01% Bromophenol Blue, 12% 2-mercaptoethanol), followed by fragmentation of genomic DNAs using sonication (15 sec, three times) and heat denaturation (95℃, 5 min).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReverse transcription-quantitative polymerase chain reaction (RT-qPCR)\u003c/h2\u003e \u003cp\u003eWe reverse-transcribed appropriate amount of total RNA purified from cell lysate using 5\u0026times;PrimeScript RT Master Mix (Takara, #9109) according to the manufacturer\u0026rsquo;s protocol, and then quantified expression level of individual genes by PCR using SYBR Premix Ex TaqⅡ (Perfect Real Time) (Takara, RR041) with the cDNA as templates. For the PCR, we used Thermal Cycler Dice Real Time system II (Takara, TP900) with 2 step PCR mode (95 ℃ for 5 sec, and 60 ℃ for 30 sec, 40 cycles). The Expression levels were quantified with ΔΔCt methods against corresponding control condition and internal control. Primer sequences used for the PCR are indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequences for quantitative polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward primer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse primer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLUT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGTTGTGCCATACTCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAGATAGGACATCCAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCTTTGCCAGAGTTGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCAACTGCTCATAGGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAGATCATCGGGACAACTCTCCTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTTGGTCCACCTTTCATCTTCATCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGAACCGCCTGCGTGATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAATGAGCCAGGGTCTCCTCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFKL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCATCAGCAACAACGTCCCTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGCCAGGTAGCCACAGTAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePKM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCCAGGTGAAGCAGAAAGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTCTTGCTGCCCAAGGAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCAACCGCGAGAAGAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCAGAGGCGTACAGGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSDS polyacrylamide gel electrophoresis (PAGE), SYPRO Ruby staining, and western plotting (WB)\u003c/h2\u003e \u003cp\u003eProteins samples collected from the cells were separated by SDS-PAGE with Mini-protean TGX Gels (BIO-RAD, Cat#4561086) or hand-made gels consisted of 3% stacking gel (for one large gel: ultrapure water 3.0 mL, 40% acrylamide bis mixed 0.34 mL, 4\u0026times;wide range buffer 1.13 mL, 5% APS 90 \u0026micro;L, TEMED 9.0 \u0026micro;L) and 8% of separating gel (for one large gel: ultrapure water 5.4 mL, 40% acrylamide bis mixed 2.0 mL, 4\u0026times;wide range buffer 2.5 mL, 5% APS 100 \u0026micro;L, TEMED 6.0 \u0026micro;L), subjected to SYPRO Ruby (Invitrogen, S12000) staining or WB. For SYPRO Ruby staining, the gel was incubated twice in 25 mL of fix solution (12.5 mL 100% methanol, 1.75 mL acetic acid in nuclease-free water) on shaker at RT for 15 min. Then we stained the gel with 25 mL of SYPRO Ruby in microwave for 15 sec, and incubated on shaker at RT for 3 min (avoid light); continued to heat the gel in microwave for 10 sec and incubated on shaker at RT for 3 min (avoid light); after that, heat the gel in microwave for another 10 sec and incubate on shake at RT for 30 min (avoid light). The SYPRO Ruby stained gel was washed with 25 mL wash solution (2.5 mL 100% methanol, 1.75 mL acetic acid in nuclease-free water) on shaker at RT for 30 min, and then stained proteins were detected with LAS 4000 mini (Fujifilm). For WB, the separated proteins are transferred on Immobilon-P PVDF membrane (Millipore, MA) with Trans-Blot SD Semi-Dry Transfer CeLL (Bio-Rad, #1703940) (0.05A, 45 min or 120 min). The membranes were incubated with antibodies diluted to corresponding dilutions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) at room temperature. Corresponding secondary antibodies are applied and then intensities of bands were detected with LAS 4000 mini.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProperties and dilution of antibodies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibody\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDilution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSupplier and Cat#\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRabbit monoclonal anti-HIF1α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell signaling technology, Cat#14179s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRabbit monoclonal anti-HK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell signaling technology, Cat#2024s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRabbit monoclonal anti-PFKL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbcam, Cat#ab181064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMouse monoclonal anti-PKM1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell signaling technology, Cat#3190s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMouse monoclonal anti-β-actin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMBL, Cat#M177-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyclonal anti-rabbit IgG/HRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell signaling technology, Cat#7074s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRNA sequencing (RNA-seq) analysis\u003c/h2\u003e \u003cp\u003eWe sequenced 100 ng of total RNA samples purified from cell lysate using Nova-seq 6000 (Illumine). The sequencings were requested to Macrogen Japan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.macrogen-japan.co.jp/\u003c/span\u003e\u003cspan address=\"https://www.macrogen-japan.co.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Briefly, poly(A)-tailed RNAs were enriched using oligo(dT)-conjugate beads, and qualities of the enriched poly(A)-tailed RNAs were assessed using Bioanalyzer (Agilent). The sequence libraries were prepared with TruSeq stranded mRNA Sample Prep Kit (Illumine, RS-122-2101), and then standard Illumina protocols were used to generate 150-bp paired end read libraries that were sequenced on the Nova-seq 6000 platform. Based on the RNA-seq data, we estimated expression level of individual genes. We used HISAT2 (ver. 2.1.0)\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e with hg38 genomic sequences as references to align the sequenced fragments. The gene expression profiles were quantified using StringTie (ver. 1.3.4d)\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Both alignment and quantification of gene expression profiles were performed with default parameters of HISAT2 and StringTie tools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal component analysis (PCA)\u003c/h2\u003e \u003cp\u003eWe performed PCA on the gene expression profiles estimated from the RNA-seq data and eRIC-MS data to identify latent variables in the data sets. PCA was performed using \u003cem\u003eprcomp\u003c/em\u003e function in R with default parameters. Each dataset was standardized to make the mean and variance constant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRNA digestion and HPLC measurement\u003c/h2\u003e \u003cp\u003eWe performed HPLC measurement according to a previous report\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Total RNAs purified from cell lysate were treated with DNase, followed by digestion to nucleotides (0.1 mM DL-Dithiothreitol solution (DTT) (Sigma-Aldrich, #3483-12-3), 13.8 mM MgCl2 (Wako, #136\u0026ndash;03995), 34.6 mM Tris-HCl (pH 7.5) (Invitrogen, #15567027), 1.6U Alkaline Phosphatase (E. coli C75) (BAP) (Takara, #2120), 0.2U Phosphodiesterase I (Worthington, LS003926), 37℃ for 16 h). After purification, peaks of the nucleotides were determined using Prominence HPLC system (Shimadzu) with HPLC buffer A (3% acetonitrile (Wako, #015-08633), 0.1 M TEAA (Wako, #202\u0026ndash;02646)) and HPLC buffer B (900 mL acetonitrile (Wako, #015-08633), and 100mL ultrapure water). Concentration of the nucleotides and nucleotide analogs were quantified based on area of the peaks.\u003c/p\u003e \u003cp\u003eAlkylation of \u003cb\u003e4sUracil\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe alkylated the 20 \u0026micro;g of 4sUracil by reacting with 10 mM of IAA under optimal conditions (50% DMSO (SIGMA #D-8418), 10 mM iodoacetamide (Wako #095-02151), 50 mM sodium phosphate buffer pH8.0 (1M NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e (Nacalai Tesgue #317\u0026thinsp;\u0026minus;\u0026thinsp;18) 4.66 mL, 1M Na2HPO4 (Wako #196\u0026ndash;02835) 340 \u0026micro;L, up to 10mL with UltraPure Distilled Water (Invitrogen #10977-015)), for 15 min at 50℃). The reaction was halted by adding 100 mM of DTT, and the spectrum of absorbance was measured using e-Spect (Malcom). The absorbance at 400 nm of wavelength was measured as the reference wavelength.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSLAM-seq\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eRNA labeling and IAA treatment\u003c/h2\u003e \u003cp\u003eWe seeded HCT116 cells with 7.5\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells/mL of concentration on 12 well plate (Thermo Scientific #150628). Following 24 h of preculture, we cultured the cells under normoxic and hypoxic condition for 36 h. We added final concentration of 100 \u0026micro;M 4sU into the culture medium to label newly synthesized RNAs. The cells were collected after 0, 1, 2, 4, 8, 12 h after the 4sU addition, and the total RNAs were purified. We alkylated the 20 \u0026micro;g of total RNA by reacting with 10 mM of IAA under optimal conditions (50% DMSO (50% DMSO (SIGMA #D-8418), 10 mM iodoacetamide (Wako #095-02151), 50 mM sodium phosphate buffer pH8.0 (1M NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e (Nacalai Tesgue #317\u0026thinsp;\u0026minus;\u0026thinsp;18) 4.66 mL, 1M Na2HPO4 (Wako #196\u0026ndash;02835) 340 \u0026micro;L, up to 10mL with UltraPure Distilled Water (Invitrogen #10977-015)), for 15 min at 50℃). Quality of the total RNAs were assessed after ethanol precipitation. We requested DNA Tech (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dnatech.genomecenter.ucdavis.edu/\u003c/span\u003e\u003cspan address=\"https://dnatech.genomecenter.ucdavis.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to provide the total RNA to QuantSeq analysis. The QuantSeq analysis were performed twice for each sample.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDetection of T-to-C conversion by SLAMDUNK tool\u003c/h2\u003e \u003cp\u003eWe quantified the expression level and newly synthesized RNA level based on the QuantSeq data of each time point using SLAMDUNK tool (ver. 0.3.3)\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, a pipeline for analysis of SLAM-seq data. Since QuantSeq sequences 3\u0026rsquo; end of RNA in poly(A)-dependent manner, we aligned the QuantSeq data to comprehensive 3\u0026rsquo; untranslated regions (3\u0026rsquo;UTRs) sequences generated based on human genome sequences and annotation (hg38) obtained from Ensemble database (release 92)\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. We performed the alignment with default parameters of the SLAMDUNK tool. Briefly, we trimmed twelve bases from the 5\u0026rsquo; end as adaptor-clipped reads, and then removed four and more subsequent adenines from the 3\u0026rsquo; end as remaining poly(A)-tail. VarScan (ver. 2.4.1)\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e included in the SLAMDUNK tool regards a mismatch as a SNP when it has 0.8 and more of variant fraction and 10-fold and more coverage cutoff. Through these filters, we counted total number of reads and the number of those including T-to-C conversions aligned on the 3\u0026rsquo; UTRs of individual genes. Since 4sU pairs with guanine (G) during reverse transcription instead of adenine (A), the 4sU-labeled reads were identified as those including T-to-C conversions. Since QuantSeq generates one read from one RNA, the number of reads including and not including T-to-C mutations correspond to the numbers of 4sU-labeled and unlabeled RNAs, respectively. Therefore, we counted the numbers of reads and those including T-to-C conversion corresponded to each gene, and normalized as count per million (CPM).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of RNAs in a steady state\u003c/h2\u003e \u003cp\u003eTo remove the cells whose expression is fluctuated by biological or mechanical effect during 4sU labeling, we extracted the genes in a steady state with the procedure we developed previously\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Briefly, since the expression of a gene in a steady state changes dependent on only white noise, the sum of angles (SoA) formed by the lines connecting each time point is relatively small, whereas the SoA values in a differentially expressed gene (DEG) with a constant trend is relatively large. We calculated the SoA values from the angles formed by the lines connecting certain time points and neighboring ones, within a time series of expression level for each gene. Then, we calculated empirical \u003cem\u003ep\u003c/em\u003e-values of the SoA by comparing those when all time points are randomly rearranged. We calculated FDR from the empirical \u003cem\u003ep\u003c/em\u003e-values using Storey's procedure\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. The genes whose FDR values are less than 0.001 were identified as genes in a steady state.\u003c/p\u003e \u003cp\u003eIdentification of DEGs using paired \u003cb\u003et\u003c/b\u003e-test\u003c/p\u003e \u003cp\u003eWe identified DEGs based on CPM of individual RNAs inferred from the SLAM-seq data. To avoid effect of 4sU labeling of gene expression, we tested differences in gene expression levels in cells in normoxia and hypoxia with paired \u003cem\u003et\u003c/em\u003e-test for each time point. Based on the \u003cem\u003ep\u003c/em\u003e-values, we calculated FDR using Storey procedure\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. Among the genes expressed in all time points, we identified the genes whose FDR are less than 0.01 as DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eInference of RNA synthesis and degradation rates\u003c/h2\u003e \u003cp\u003eAccording to Eqs.\u0026nbsp;1 and 2, expression level of RNA is determined as ratio of RNA transcription rate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e, and degradation rate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e, and increase curves of 4sU-labeled RNAs depend on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e. Therefore, fitting of the time series of 4sU-labeled RNAs on Eq.\u0026nbsp;1 enables us to infer \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e values. For RNAs derived from genes in steady states, we obtained the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e values by fitting of the time series of 4sU-labeled RNAs on Eq.\u0026nbsp;1 according to previous report\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, in genome-wide manner. Briefly, we performed fitting by combining an evolutionary algorithm (genetic algorithm) and hill climbing (\u003cem\u003eL-BFGS-B\u003c/em\u003e algorithm), and evaluating with the least squares method in Python 2.7. The genetic algorithm was implemented using the \u003cem\u003eDEAP\u003c/em\u003e library\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e with a generation number of 200, population number of 50, crossover probability of 0.5, and mutation probability of 0.2. The \u003cem\u003eL-BFGS-B\u003c/em\u003e algorithm was implemented using the \u003cem\u003eminimize\u003c/em\u003e module in the \u003cem\u003eSciPy\u003c/em\u003e package, in which the parameters estimated by the genetic algorithm are given as initial parameters. The fitness in each gene was evaluated as the correlation of actual newly synthesized RNA levels with estimated values. The probability of the null hypothesis that a population correlation coefficient is equivalent to zero was calculated for each gene using the \u003cem\u003eOLS\u003c/em\u003e module in the \u003cem\u003eStatsModels\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e, and the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{s}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e values of the genes whose FDR as determined by Storey's procedure\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e was less than 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e were extracted.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eFor functional enrichment analysis of focused genes and protein, we performed functional enrichment analysis using DAVID tool (ver. 6.8) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). As functional term, we utilized Biological Process (GOTERM_BP_DIRECT), Cellular Component (GOTERM_CC_DIRECT), Molecular Function (GOTERM_MF_DIRECT), UniProt keywords (UP_KEYWORDS), and KEGG pathway (KEGG_PATHWAY). The \u003cem\u003ep\u003c/em\u003e-values indicating enrichment were calculated based on modified hypergeometric test\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. We selected optimal gene list as the background. The functional terms whose FDR values are less than thresholds were identified as those significantly enriched.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eGSEA\u003c/h2\u003e \u003cp\u003eThe relationship between the biological function of genes and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e values, contribution of RNA degradation rate on differential expression, were examined using GSEA (ver. 4.0.3)\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. For each individual gene, 1 was used as the control value and genes were ranked based on the ratio of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e value to the control (i.e., the original \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\rho }_{d}\\)\u003c/span\u003e\u003c/span\u003e value). Enrichment score (ES) for each term in the GSEA hallmark was calculated using default parameters, and compared with the distribution of ES values for random set of 10,000 genes to calculate empirical \u003cem\u003ep\u003c/em\u003e-values. The empirical \u003cem\u003ep\u003c/em\u003e-values were corrected as FDR. The terms whose FDR values are less than 0.05 were identified as those significantly enriched.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eLactate assay\u003c/h2\u003e \u003cp\u003eIntracellular lactate was quantified using Lactate Assay Kit-WST (Doujin, #343\u0026ndash;09281). The HCT cells were cultured in corresponding conditions with six of replicates and the medium were removed, and cell lysates were prepared with 0.1% Triton solution. We then added 20 \u0026micro;L of lactate standard solution and 80 \u0026micro;L of working solution to 20 \u0026micro;L of the cell lysate, followed by incubation on 37℃ for 30 minutes. The absorbance at 450 nm of wavelength was measured using an absorbance microplate reader (Tecan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eeRIC-MS\u003c/h2\u003e \u003cdiv id=\"Sec27\" class=\"Section4\"\u003e \u003ch2\u003eCoupling of the capture probe to Dynabeads\u003c/h2\u003e \u003cp\u003eDynabeads\u0026trade; MyOne\u0026trade; Carboxylic Acid (Invitrogen, #65012) was washed with double volumes of 100 mM MES (pH 4.8) and vortexed for 5 to 10 sec. We removed the supernatant with a magnet stand and resuspended the Dynabeads in 30 \u0026micro;L of 100mM MES (pH 4.8). Based on previous reports\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e, we utilized HPLC purified capture probe consisted of primary amine and C6 linker, followed by 20 thymidine nucleotides, in which every other nucleotide is a locked nucleic acid (LNA) (Exiqon); /ssH5AmC6-LNA10T10/T(L)TT(L)TT(L)TT(L)TT(L)TT(L)TT(L)TT(L)TT(L)TT(L)T (T(L): LNA thymidine, T: DNA thymidine)\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. We prepared the LNA oligo(dT) to 97.2 \u0026micro;L/sample with 10.8 \u0026micro;L of 1M MES (pH 4.8) and 27 \u0026micro;L of 500mg/mL \u003cem\u003eN\u003c/em\u003e-(3-Dimethylaminopropyl)-\u003cem\u003eN\u003c/em\u003e\u0026rsquo;-ethylcarbodiimide hydrochloride (EDC) (Sigma-Aldrich, #E7750). The LNA oligo(dT) was coupled on Dynabeads by gently rotating mixture of 600 \u0026micro;L of Dynabeads slurry (10 mg/mL) and 97.2 \u0026micro;L of LNA oligo(dT) (200 \u0026micro;M) at room temperature for 3 h. The Dynabeads were then washed with two volumes of 250 mM Tris buffer (pH 8.0) and 0.01% Tween 20 for more the 30 min twice. The LNA oligo(dT) coupled Dynabeads were stored in 0.1% PBS-Tween at 4 ℃.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eUV crosslink\u003c/h2\u003e \u003cp\u003eThe medium culturing HCT116 cells on the 15 cm dish was removed and cells were washed with 10 mL cold PBS for twice. To avoid effect of exposure on normoxic environment, the hypoxia treated cells were sealed with hybrid-bag after removing the PBS completely in the hypoxia chamber, and transferred from the chamber. For normoxia treated cells, hybrid-bag was also used in UV crosslink. 2,000 mJ/cm\u003csup\u003e2\u003c/sup\u003e of UV was used in crosslink and irradiation was omitted in controls without UV crosslink (without UV). Immediately before sample collection, we added 1.0 mM DTT (Sigma-Aldrich, #43816) and 0.5 U/\u0026micro;L of recombinant RNase inhibitor (RRI) (Takara, #2313B) into prepared lysis buffer (10 mM Tris-HCl (pH 7.5) (Invitrogen, #15567027), 10 mM NaCl, 0.02% (w/v) Digitonin (Fujifilm, 043-21371), 1 mM EDTA pH 8.0 (Invitrogen, MA)). After irradiation, we opened the sealed hybrid-bag were, and kept the irradiation cell on ice. We then added 2.0 mL of cold hypo lysis buffer and collected cells with scraper (Corning). The cell lysates were mixed for 10 times and transferred into 5 mL centrifuge tubes. Cell lysates from two of 15 cm dishes were collected for one eRIC-MS sample. Above operations were done on ice quickly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eCapture of RNA-RBP conjugations\u003c/h2\u003e \u003cp\u003eCell lysates were mixed by inversion at 4 ℃ for 10 min. Cell lysates were then centrifuged at 1,000 g for 5 min at 4 ℃ to remove the nucleolus, the supernatants were transferred to new tubes, and continued to centrifuge at 15,000 g for 5 min at 4 ℃ to remove the organelles like mitochondria. We transferred the supernatants to new tubes and added 500 mM of lithium chloride (LiCl) (Sigma-Aldrich, #L9650), and inverted tubes completely before adding 0.5% lithium dodecyl sulfate (LiDS) (Sigma-Aldrich, #L9781). The mixture was Incubated at 60 ℃ for 15 min, and quickly cooled down on ice for 5 min. The mixtures were clarified with centrifuge at 15,000 g for 5 min at 4 ℃ and the supernatants were transferred into new tubes. We stored 40 \u0026micro;L and 50 \u0026micro;L supernatants as input for later RNA and protein analysis, respectively, and DTT was added to the remaining supernatants for a final concentration to be 5 mM. The remaining supernatants were mixed with the LNA oligo(dT) coupled Dynabeads washed with five volumes of the hypo lysis buffer for 3 times before usage, and gently rotated at 40 ℃ for 1 h to capture RNA-protein complexes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eElution of RNA-RBP complex\u003c/h3\u003e\n\u003cp\u003eWe collected the Dynabeads capturing the RNA-protein complexes with a magnetic stand, and transferred supernatants to new tubes for later analysis. Beads were subjected to successive rounds of washes with wash buffer 1 (20 mM Tris-HCl (pH 7.5), 500 mM LiCl, 1 mM EDTA, 5 mM DTT, and 0.1% (w/v) LiDS), wash buffer 2 (20 mM Tris-HCl (pH 7.5), 500 mM LiCl, 1 mM EDTA, 5 mM DTT, and 0.02% (v/v) NP40), and wash buffer 3 (20 mM Tris-HCl (pH 7.5), 200 mM LiCl, 1 mM EDTA, 5 mM DTT, and 0.02% (v/v) NP40) for twice with gentle rotation at 40 ℃ for each 5 min. Pre-elution was performed in 440 \u0026micro;L nuclease-free water at 40 ℃ for 5 min. Afterwards, the beads suspension was divided into two groups: 400 \u0026micro;L of RNase-mediated elution for protein analysis; and 40 \u0026micro;L of heat-mediated elution for RNA/DNA analyses. For RNase-mediated elution, beads were resuspended in 400 \u0026micro;L of RNase buffer (0.25 \u0026micro;L of RNase mixture in 400 \u0026micro;L nuclease-free water; RNase mixture: 1 \u0026micro;g/\u0026micro;L RNase A, 40 U/\u0026micro;L RNase T1, 50 mm Tris (pH 7.0), 50 mm NaCl (Invitrogen, #AM9759), and 50% glycerol), and incubated at 37 ℃ for 30 min. For heat-mediated elution, beads were resuspended in 40 \u0026micro;L nuclease-free water, and incubated at 95 ℃ for 5 min. We took the supernatants immediately after beads were collected with a magnetic stand. To confirm the effect of above elution, heat-mediated second elution was conducted with above two groups. After that, stored all samples at -80 ℃.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eMS measurement\u003c/h2\u003e \u003cp\u003eFollowing a confirmation that ribosomal RNAs were removed from the heat-mediate elution using BioAnalyzier, and that no abnormalities such as contaminants occurs from a part of the RNase-mediate elution using SDS-page and SYPRO Ruby staining, the SDS in the RNase-mediate elution samples was removed using the methanol\u0026ndash;chloroform protein precipitation method. Briefly, four volumes of methanol, one volume of chloroform, and three volumes of water were added to the eluted sample and mixed thoroughly. The samples were centrifuged at 15,000 rpm for 10 min, and the water phase was removed carefully, and then four volumes of methanol was added to the samples, and the samples were centrifuged at 15,000 rpm for 10 min. After that, the supernatant was removed and the pellet was washed with 100% ice-cold acetone once. The precipitated protein was re-dissolved in guanidine hydrochloride and reduced with Tris (2-carboxyethyl) phosphine hydrochloride, alkylated with iodoacetamide, followed by digestion with lysyl endopeptidase and trypsin. The digested peptide mixture was applied to a Mightysil-PR-18 (Kanto Chemical) frit-less column (45\u0026times;0.150 mm ID), and separated using a 0\u0026ndash;40% gradient of acetonitrile containing 0.1% formic acid for 80 min at a flow rate of 100 nL/min, and the eluted peptides were sprayed into a mass spectrometer (Triple TOF 5600+; AB Sciex) directly. MS and MS/MS spectra were obtained using the information-dependent mode. Up to 25 precursor ions above an intensity threshold of 50 counts/sec were selected for MS/MS analyses from each survey scan. All MS/MS spectra were searched against protein sequences of the RefSeq (NCBI) human protein database (RDB) using Proteome discoverer 2.2, and decoy sequences were then selected with FDR\u0026thinsp;\u0026lt;\u0026thinsp;1%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of RBPs\u003c/h2\u003e \u003cp\u003eRBPs reliably binding to poly(A)-tailed RNAs were identified by comparing eRIC-MS data from same condition with irradiation (with UV) and without irradiation (without UV). Among the peptides detected in both of with and without UV, signal intensities of those with two and more unique peptides number, using one-side Mann\u0026ndash;Whitney U test. The \u003cem\u003ep\u003c/em\u003e-values are corrected as FDR with Storey\u0026rsquo;s procedure\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. To compensate lower detection power of Mann-Whitney U test, a non-parametric test, we adapted 5% as threshold of the FDR. RBPs including one and more peptides with FDRs less than the threshold were considered as those reliably binding to poly(A)-tailed RNAs. Note that, among the twelve samples (triplication for four conditions), one sample from group of \u0026ldquo;hypoxia with UV\u0026rdquo; and the other from group of \u0026ldquo;normoxia without UV\u0026rdquo; were excluded from later analysis because of lower reproducibility. Abundance of the RBPs were estimated as the value of signal intensity with the total normalized to 1,000,000.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eHypergeometric test to extract RBPs targeting stabilized glycolytic mRNAs\u003c/h2\u003e \u003cp\u003eThe glycolytic mRNAs were identified based on Reactome\u003csup\u003e\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, a database for biological pathways (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/download/current/Ensembl2Reactome_All_Levels.txt\u003c/span\u003e\u003cspan address=\"https://reactome.org/download/current/Ensembl2Reactome_All_Levels.txt\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data obtained from Reactome database contains Ensembl gene IDs and relating biological pathways. We extracted entries corresponded to Ensembl gene IDs as whole transcriptome (background) of the hypergeometric test. Among these entries, we identified the entries corresponded to \u0026ldquo;Glycolysis\u0026rdquo; as glycolytic mRNAs (named as \u0026ldquo;Reactome mRNAs\u0026rdquo;), and extracted those whose \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{d}\\)\u003c/span\u003e\u003c/span\u003e values were decreased in chronic hypoxia condition compared with normoxia as stabilized Reactome mRNAs. For each RBP included in ENCODE, a database of functional elements of human genome\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, we corresponded target RNAs and calculated \u003cem\u003ep\u003c/em\u003e-value for enrichment of the stabilized Reactome mRNAs included in the targets against the background with hypergeometric test. Based on the \u003cem\u003ep\u003c/em\u003e-values, we calculated FDR using Storey procedure\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eDepletion assay\u003c/h2\u003e \u003cp\u003eHCT116 cells were seeded in 24-well plates at concentration of 2.5\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells/well with DMEM (Wako, Cat# 044-29765) supplying with 10% FBS ((Life Technologies, Cat# F7524), and incubated appropriate time in the humidified incubator (Normoxic condition) or in the hypoxia chamber (hypoxic condition). 1 \u0026micro;M of siRNAs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were transfected into the HCT116 cells by using Opti-MEM (Gibco, Cat# 31985-070) and Lipofectamine RNAimax Reagent (Invitrogen, REF# 13778-500) according to manufacturer's protocol, followed by 48 h of incubation. The cells were washed twice with PBS and then total RNA was isolated using RNAiso Plus (Takara, #9109) according to the manufacturer\u0026rsquo;s protocol. Expression level of individual genes were quantified as described above, following the reverse-transcription.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esiRNAs used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMerchandise\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003esiRNA ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCat #\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGF2BP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThermo Fisher, silencer select siRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es20923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4427037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGF2BP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThermo Fisher, silencer select siRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es20922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4427037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThermo Fisher, silencer select siRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003es53175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4392420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThermo Fisher, silencer select siRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e556458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4399665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe RNA-seq and SLAM-seq data generated in this study have been submitted to the DNA Data Bank of Japan (DDBJ) Sequence Read Archive (DRA; https://ddbj.nig.ac.jp/DRASearch/) under accession number PRJDB17883.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ACKNOWLEDGMENTS:\u003c/p\u003e\n\u003cp\u003eWe thank our laboratory personnel for critically reading the manuscript and for their technical assistance with the experiments. The computational analysis was performed using the National Institute of Genetics (NIG) supercomputer system at the Research Organization of Information and Systems (ROIS). This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (grant numbers 17KK0163, 18H02570, 18KT0016, 16H06279, and 20H04838). K.K. received funding from JSPS KAKENHI (grant number 19K16635 and 22H03683), Takeda Science Foundation, and Kowa Life Science Foundation. We also thank Susan Furness, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;AUTHOR CONTRIBUTIONS:\u003c/p\u003e\n\u003cp\u003eK.K., Z.Z., Y.O., X.S., and N.A. conceived the project. Z.Z., Y.O., X.S., A.N.O., K.T., R.O.M., K.N., N.G., and N.A. designed and performed the experiments. K.K. analyzed the data. S.A. performed MS experiments. K.K., Z.Z., Y.O., X.S., and N.A. wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ADDITIONAL INFORMATION:\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArnold, P. K. \u0026amp; Finley, L. W. S. Regulation and function of the mammalian tricarboxylic acid cycle. \u003cem\u003eJ Biol Chem\u003c/em\u003e \u003cstrong\u003e299\u003c/strong\u003e, 102838 (2023). https://doi.org/10.1016/j.jbc.2022.102838\u003c/li\u003e\n\u003cli\u003eKrebs, H. A. \u0026amp; Johnson, W. A. Metabolism of ketonic acids in animal tissues. \u003cem\u003eBiochem J\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 645-660 (1937). https://doi.org/10.1042/bj0310645\u003c/li\u003e\n\u003cli\u003eNolfi-Donegan, D., Braganza, A. \u0026amp; Shiva, S. 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I.\u003cem\u003e et al.\u003c/em\u003e Discovery of RNA-binding proteins and characterization of their dynamic responses by enhanced RNA interactome capture. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 4408 (2018). https://doi.org/10.1038/s41467-018-06557-8\u003c/li\u003e\n\u003cli\u003eJacobsen, N.\u003cem\u003e et al.\u003c/em\u003e Direct isolation of poly(A)+ RNA from 4 M guanidine thiocyanate-lysed cell extracts using locked nucleic acid-oligo(T) capture. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, e64 (2004). https://doi.org/10.1093/nar/gnh056\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Glycolysis, Hypoxia, Metabolic labeling, RNA degradation, RNA synthesis","lastPublishedDoi":"10.21203/rs.3.rs-4221145/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4221145/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOxygen shortage, known as hypoxia, occurs commonly in both physiological and pathological conditions. Transcriptional regulation by hypoxia-inducible factors (HIFs) is a dominant regulatory mechanism controlling hypoxia-responsive genes during acute hypoxia; however, recent studies suggest that post-transcriptional regulation, including RNA degradation, also involves hypoxia-induced gene expression during the chronic hypoxia. In this study, we developed a method to quantify the contributions of RNA synthesis and degradation to differential gene expression, and identified 102 genes mainly regulated via RNA degradation under chronic hypoxia in HCT116 cells. Bioinformatics analysis showed that the genes mainly regulated by RNA degradation were involved in glycolysis. Combinatory analysis of experimental approach using RNA interactome capture and statistical analysis using public databases, and followed depletion assays identified that an RNA-binding protein fragile X mental retardation protein (FMRP) enhances the expression of mRNAs encoding rate-limiting enzymes for glycolysis under chronic hypoxia. This study emphasizes the importance of post-transcriptional gene regulation under chronic hypoxia.\u003c/p\u003e","manuscriptTitle":"Fragile X mental retardation protein regulates glycolytic gene expression under chronic hypoxia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-24 21:06:31","doi":"10.21203/rs.3.rs-4221145/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-16T18:36:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-08T23:30:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-03T19:49:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"057dcfc4-04e9-447c-9cdc-9bd15391f933","date":"2024-04-24T17:52:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"be078b74-29c3-46e4-90f0-4c1054fdd3ef","date":"2024-04-24T17:19:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-24T17:00:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-24T16:53:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-23T12:17:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-22T03:51:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-05T06:47:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5dd9dcc9-0d82-4a08-9633-20040dc11ac1","owner":[],"postedDate":"April 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T15:59:59+00:00","versionOfRecord":{"articleIdentity":"rs-4221145","link":"https://doi.org/10.1038/s41598-025-91828-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-17 15:57:22","publishedOnDateReadable":"April 17th, 2025"},"versionCreatedAt":"2024-04-24 21:06:31","video":"","vorDoi":"10.1038/s41598-025-91828-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-91828-w","workflowStages":[]},"version":"v1","identity":"rs-4221145","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4221145","identity":"rs-4221145","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}