Embedded Files
Multiomics dissection
of hierarchical translatome reprogramming
via regulome- and epitranscriptome-mediated
translational control
Selected Research Questions and Preliminary Analyses
Selected Research Questions and Preliminary Analyses
GCN2 / ATF4 / METTL3 / CREBBP / THRB axis (proof of concept)
Central Hypothesis: Based on my previous studies on thyroid hormone–dependent regulomes, it is hypothesized that translational regulons (RMTC) comprising transcripts with long, highly structured 5′UTRs containing multiple uORFs, IRES elements, and translation-inhibitory motifs constitute a pool of translationally inactive mRNAs that can be selectively and rapidly activated under defined physiological or pathological conditions, such as mitosis, cellular differentiation, or the integrated stress response (ISR). This context-dependent recruitment into active translation is enabled by EMTRAC-driven RNA modification states, which can affect secondary structure formation, modulate RMTC-defined cis-regulatory architectures and promote selective engagement of sequence- and modification-specific trans-acting factors.
Central Research Question.: How do EMTRAC-dependent RNA modification states cooperate with RMTC-defined cis-regulatory architectures to enable selective, context-dependent activation of translationally repressed mRNA pools during physiological and pathological cellular transitions?
Translationally important models: a) cancer, which alters translational selectivity by changing the composition of 5′UTRs isoforms through the usage of alternative transcription start sites (TSSs), alternative splicing, as well as aberrant RNA modifications including (m6A); b) Amino acids deprivation–induced Integrated Stress Response (ISR) in cancer cells, mediating a switch from cap-dependent to cap-independent translation. Supplementary models: c) megakaryocytes → platelets (platelet biogenesis) and tumor-educated platelets (TEPs), which are anucleate cells lacking transcription but capable of translation in response to tumor-derived signals; d) enucleated reticulocytes; e) neurons (localized translation in axons, dendrites and dendritic spines); f) oocytes and early embryos prior to maternal-to-zygotic transition as well as post-meiotic spermatids / spermiogenesis; g) mitochondrial translation (mitoribosomes); h) quiescent and senescent cellular states; i) viral models including internal ribosome entry site (IRES)-driven translation.
Selected pathway: GCN2, a protein kinase that senses amino acid scarcity activates the Integrated Stress Response (ISR), which preferentially triggers translation of a subset of mRNAs, including ATF4 - referred to as the ISR translational regulome. METTL3, an RNA methyltransferase, deposits N6-methyladenosine (m6A) on target mRNAs, guiding their translation or decay during ISR. Notably, m6A modifications play opposing roles on different targets: they enhance cap-independent, m6A-mediated translation of CREBBP, a coactivator of multiple transcription factors including CREB, THRB, and ATF4 (Chen et al. 2025), while simultaneously inhibiting uORF-dependent cap-independent translation of ATF4 (Zhou et al. 2018). Amino acid deprivation not only activates GCN2 and the ISR but also limits amino acids as essential substrates for thyroid hormone synthesis, and thereby closes the regulatory loop of the GCN2-THRB axis, linking nutrient sensing to hormonal control (Zhang et al 2025, Zhang et al. 2026)
Methods: Translational regulome / translatome analysis methods: DIFFREG (Differential Translational Regulome) approach, ribosome profiling (Ribo-seq), polysome profiling, TRAP-seq, puromycin labeling (SUnSET), reporter assays (luciferase / fluorescent), nascent peptide labeling (BONCAT), RNA immunoprecipitation (RIP), proximity-specific ribosome profiling, 5′ UTR mapping using CAGE and long-read RNA sequencing (Nanopore/PacBio, 5′UTR-targeted oligonucleotide modulation (dGoligo/eRNA), single-cell multiomics, bulk RNA-seq integration. Epitranscriptome / RNA modification analysis tools: Illumina miCLIP-m6A, GLORI 2.0, EpiPlex assay, CHEUI, LC-MS/MS, Arraystar Inc Epitranscriptomic Array, Merk Magna MeRIP-seq m6A kit, Epigentek Elisa, EpiNext CUT&RUN RNA m6A-Seq Kit, Thermofisher N6-Methyladenosine (m6A), Monoclonal Antibody, Abcam Anti-N6-methyladenosine (m6A) antibody, Abcam m6A methylation, SELECT, and other methods including MAZTER-seq, DART-seq, RNA modification profiling, absolute m6A quantification, single-cell epitranscriptomics, cap-independent translation analysis, alternative splicing, nuclear export, RNA stability assays.
Impact: Understanding this interaction emphasizes how regulome- and epitranscriptome-mediated translational control shapes stress-responsive and metabolic gene programs, offering new insights into health-related processes, biomarkers, and therapeutic targets.
In Brief:
m6A enhances cap-independent (m6A-induced) trasnlation of CREBBP (Chen et al. 2025)
m6A inhibits cap-independent (uORF-induced) trasnlation of ATF4 (Zhou et al. 2018)
ISR Translational Regulome - a subset of mRNAs preferentionally translated during Integrated Stress Resposne. ATF4 is one of the proteins selectively translated during ISR, and further acts as a transcription factor to activate ATF4-responsive genes (the ATF4 transcriptional regulome).
ISR induction: include amino-acid deprivation such as dietary sulfur amino acid restriction (Jonsson et al 2018); supraphysiological concentrations (SPCs) of thyroid hormone (T3) (Ishaq et al. 2016, Ishaq et al 2020), ROS (Wang et al. 2016), Halofuginone - a GCN2 agonist (Paul et al 2024), JPH203 - a selective LAT1 amino acid transporter inhibitor (Zhang et al. 2026), RAF inhibitors (Gilley et al 2024)
GCN2 (EIF2AK4, eukaryotic translation initiation factor 2‑alpha kinase 4), is a serine/threonine-protein kinase that senses amino acid deprivation and activates the Integrated Stress Response (ISR) to regulate gene expression, with a role in: cacer survival (Gold and Masson 2022); thyroid cancer (Zheng et al. 2025) tumor angiogenesis (Wang et al 2013); cardiovascular diseases (Kalinin et al 2026); viral infection (Gibbs et al. 2024, Cosnefroy et al 2013); age related diseases and neurodegeneration (Altintas and MacArthur 2024); neuropathy (Mikesell et al 2025, Chen et al. 2024, Master et al 2023); immunotherapy (Paul et al 2024); longevity, metabolic fitness, stress resistance (Gallinetti et al 2013), wound healing and cytoskeleton reorganization (Miles at al 2021)
ATF4 (Activating Transcription Factor 4) is a transcription factor preferentially translated during the Integrated Stress Response (a member of the ISR translational regulome) that regulates genes involved in amino acid metabolism, redox homeostasis, and stress adaptation. Role in cancer induced angiogenesis (Chen et al. 2017), thyroid tumorigenesis (Krishnamoorthy et al 2019); thyroid hormone synthesis (Yu et al. 2023) , neurodegeneration (Adjibade and Mazroui, 2026)
METTL3 (Methyltransferase Like 3) is an RNA methyltransferase that deposits N6-methyladenosine (m6A) marks on target RNAs, modulating the epitranscriptome to regulate mRNA stability and translation. Role in cancer immunotherapy (Yu et al 2024), protein synthesis in cancer (Kovalski at al 2022), translational repression of ATF4 (Zhou et al. 2018), enhancement of CREBBP translation (Chen et al. 2025).
CREBBP (CREB Binding Protein) is a histone acetyltransferase (HAT) and transcriptional coactivator of THRB, extensively methylated, that regulates gene expression by modifying chromatin structure. Role in triple negative breast cancer (Peck et al. 2021), lymphoma (Jiang et al 2017, Nie et al. 2021).
THRB, the nuclear receptor for thyroid hormones, mediates transcriptional regulation upon binding its ligand, thyroid hormone (TH) - T3, which is synthesized from Tyrosine → Thyroglobulin (TG) → MIT/DIT → T4 → T3 in peripheral tissues (Master and Nauman, 2014), with a role in: thyroid cancer (Zhang et al. 2026), renal cancer (Master et al 2010, Master et al. 2016, Wojcicka et al. 2014). KEGG database shows that the TH pathway is extensively regulated by epitranscriptomicly modified transcripts, as shown in the analysis below.
Goal-1: Define disease-associated translational regulons (analogous to transcriptional regulons, such as the Single-cell Regulons Atlas of Pancreatic Cancer).
Goal-2: Define disease-associated epitranscriptomes for the diagnosis and treatment of diseases, including cancer (analogous to the EPIGLIO clinical trial project).
Both aims: Generate atlases of translation associated biomarkers and potential therapeutic targets, starting from cancer models.
Figure 1. Preliminary STRING analysis of the genes within the CREBBP/THRB axis showed direct interactions among the components, forming an integrated network centered on CREBBP.
Description: Integrative clustering analysis of disease- and translation-related gene modules. Using network-based clustering (MCL; stochastic flow–based detection of natural clusters), the analyzed gene set segregated into five biologically coherent modules reflecting distinct but interconnected regulatory programs. Cluster 1 (15 genes) was strongly enriched for the endoplasmic reticulum unfolded protein response, indicating activation of ISR-linked proteostasis and translational stress pathways. Cluster 2 (11 genes) mapped to inflammatory bowel disease–associated signaling, consistent with immune and inflammatory regulatory circuits intersecting with translational control. Cluster 3 (6 genes) formed a compact thyroid cancer–related module, supporting disease-specific translational and epitranscriptomic reprogramming in endocrine malignancies.
Cluster 4 (4 genes) was highly specific for RNA N6-methyladenosine machinery, encompassing m6A methyltransferase complex components and mRNA demethylase activity, directly implicating epitranscriptomic regulation as a core organizing principle. Cluster 5 (3 genes; DHX58, EIF2AK2, MGA) represented a focused antiviral and stress-sensing translational control module, linking RNA helicase activity, eIF2α kinase signaling and transcriptional repression. Together, these clusters delineate convergent ER stress, immune–inflammatory, endocrine cancer, and epitranscriptomic control axes, highlighting RMTC/EMTRAC-driven modular organization of disease-relevant translatomic states.
CREBBP / THRB axis feedback loop
Backround: CREBBP (CREB-binding protein) is a well-characterized histone acetyltransferase (HAT) that acetylates histones such as H3K27, thereby promoting chromatin relaxation and transcriptional activation (Liu et al., 2025). Notably, CREBBP expression is extensively regulated at the epitranscriptomic level, where increased N6-methyladenosine (m6A) modifications enhance CREBBP translation, thereby promoting the transcription of key beiging-related genes through increased chromatin accessibility (Chen et al. 2025).
On the other hand, it is well established that thyroid hormone receptors (TRs: THRA, THRB) regulate gene expression through protein–protein interactions involving the recruitment of chromatin-modifying complexes, including histone acetyltransferase (HATs) such as CREBBP and histone deacetylases (HDACs), which dynamically control the transcription of thyroid hormone (TH: T3)-responsive genes (Saponaro et al., 2020), including TRs themself (autoregulation, Sakurai et al 2992, Singh et al 2014).
Scientific gap. Despite evidence of functional interplay between CREBBP and TH signaling (Ritter et al., 2025), there is currently no clear evidence demonstrating how CREBBP itself is regulated by T3. Typically, TRs recognize TH response elements (TREs) within regulatory regions of responsive genes, however, no canonical TREs have been identified in the promoter region of CREBBP.
Importantly, a growing body of literature indicates that functional TREs are frequently located not only in promoters but also within intronic regions. For instance, a major TRE was identified in the third intron of the rat growth hormone gene (Sap et al., 1990), while TH-dependent activation via intronic TREs has been demonstrated for the MBD3 gene (Fu et al., 2020) and SPAG7 during Xenopus metamorphosis (Fu et al., 2022).
Similarly, regulation of the CPT1α gene involves TREs located both in the promoter and first intron (Jansen et al., 2000), and genome-wide analyses confirm that TRs frequently bind to non-promoter regions, including introns (Zhang et al., 2015).
Methods. In light of these observations, an in silico analysis of the CREBBP gene, including its promoter and intron regions (NCBI Gene ID: 1387), was performed to identify potential canonical TRE motifs. The analysis was based on established consensus sequences AGGTCANNNNAGGTCA and AGGNCANNNNAGGNCA (Taylor et al., 2023; Flammat et al 2022). Toos: JASPAR, Oligo v 6.71, NCBI Gene analysis software. The analysis was perforemd by Adam Master.
Results. Bioinformatic analysis revealed the presence of one perfect canonical DR4-type TRE sequence (CAGGC-AGGTCACCTGAGGTCA-GGCGT) located on the reverse strand within intron 1 (between exons 1 and 2, CREBBP transcript variant 1) of the CREBBP gene, which is encoded on the negative strand of chromosome 16 (NCBI Reference Sequence: NC_000016.10). This motif maps to genomic coordinates 3864837–3864861, including flanking regions (positive strand of chromosome 16, negative strand on the gene). Additionally, a second TRE-like motif differing by a single nucleotide from the consensus sequence (AGCTC-AGGCCAAAGTAGGTCA-CCACC) was identified within intron 27 (between exons 27 and 28 CREBBP transcript variant 1) of the CREBBP gene. This sequence is also located on the reverse strand relative to the gene and corresponds to genomic coordinates 3732031–3732055 on the positive strand of NC_000016.10. Despite a single nucleotide variation, this motif remains consistent with the reported TRE consensus definition as described by Taylor et al., 2023.
Figure 2A. CREBBP (NCBI Gene) with perfect canonical DR4-type TRE sequences identified in silico within introns 1 and 27 (marked in red).
Figure 2B. CREBBP and THRB transcript modifications according to RMBase v3.0.
In conclusion, this analysis is the first to demonstrate the presence of canonical TRE consensus motifs within two introns of the CREBBP gene. These findings suggest that TRβ may not only recruit CREBBP protein to TRE-containing gene complexes (Saponaro et al., 2020) but could also act directly at the CREBBP locus, binding intronic TREs, promoting chromatin relaxation and facilitating transcription.
The CREBBP mRNA may then be subject to epitranscriptomic modifications, facilitating m6A-dependent translation (Chen et al. 2025), thereby establishing a T3-dependent feedback loop in the thyroid hormone/CREBBP axis. Functional validation however will be required to confirm these mechanistic insights and determine the extent of this regulatory interplay.
Gantt Chart: Work Packages, Methods, and Milestones
Gantt Chart: Work Packages, Methods, and Milestones
RMTC & EMTRAC - mediated translational control hierarchy
RMTC & EMTRAC - mediated translational control hierarchy
At the global level, translational output is governed by kinetic uORF-based cis-acting sensors decoded by ternary-complex availability and stress-activated trans-acting pathways, while EMTRAC contributes a universal m⁷G-cap–dependent competence layer controlled by dedicated capping machinery and mTOR-regulated cap accessibility.
At the intermediate level, translational regulons defined by shared cis-acting elements within 5′UTRs are decoded by functionally specialized ribosomes and a broad set of trans-acting initiation, remodeling, and 3′UTR-associated factors, while EMTRAC-driven rRNA and tRNA modifications program ribosomal capacity to selectively translate structurally complex regulon transcripts in response to signaling cues.
At the local level, transcript-specific cis-acting features integrate with EMTRAC-encoded, sequence- and structure-dependent RNA modifications that are written, erased, and interpreted by dedicated enzymatic machineries, enabling precise, context-dependent fine-tuning of translation efficiency and isoform-specific protein output.
Table 1. Conceptual Description: Translational control (TCtrl) constitutes a hierarchical regulatory system that determines protein output independently of transcriptional changes. At its core, TCtrl integrates two interdependent decision layers: regulome-mediated translational control (RMTC), which decodes RNA-encoded regulatory logic, and epitranscriptome-mediated translational control (EMTRAC), which programs RNA modification states that shape translational competence.
At the global level, TCtrl operates as a system-wide decision gate. Translational initiation is primarily determined by ternary complex availability and stress-activated signaling pathways. Cis-acting kinetic sensors encoded within mRNAs, most prominently overlapping uORF architectures, are decoded by trans-acting stress kinases and initiation factors, enabling selective translation under conditions of globally reduced protein synthesis. In parallel, EMTRAC contributes a universal competence layer through m⁷G-cap formation and cap-dependent initiation machinery, establishing the baseline capacity for translation.
At the intermediate level, translational control is organized into translational regulons—cohorts of mRNAs sharing common cis-acting elements embedded within 5′UTRs. These elements encode structural and sequence features that define competitive translation under specific cellular states. Decoding of regulons is executed by functionally specialized ribosomes, initiation and elongation factors, RNA helicases, 3′UTR-associated complexes, and non-coding RNAs, while signaling pathways such as mTOR–S6K modulate translational capacity. At this level, EMTRAC-driven rRNA and tRNA modifications program ribosomal properties, enabling preferential translation of structurally complex regulon transcripts.
At the local level, translational output is fine-tuned in a transcript-specific manner. Unique RNA sequences, structures, and isoform-specific untranslated regions integrate with site-specific RNA modifications, including m⁶A, m¹A, m⁵C, m⁶Am, ac⁴C, and pseudouridylation. These modifications are dynamically written, erased, and interpreted by dedicated enzymatic machineries that recognize local sequence and structural context, allowing precise control of individual mRNA translation and protein isoform production.
Together, this hierarchical organization enables translational reprogramming, whereby cells selectively maintain or enhance translation of defined transcripts despite global translational repression. This decoupling of mRNA abundance from protein output underlies the translatomic paradox, a phenomenon particularly evident in stress responses, development, and cancer, where adaptive protein synthesis supports survival, plasticity, and therapy resistance.
This structural–functional stratification provides a conceptual and practical framework for disentangling information-encoding RNA features from the executive machineries that decode them. By explicitly separating cis-acting regulatory architectures from trans-acting effectors, the table enables systematic identification of regulatory bottlenecks, layer-specific vulnerabilities, and points of functional distortion across global, intermediate, and local levels of translational control. This distinction is critical for experimental design, as it allows targeted perturbation of either informational RNA elements or their decoding machineries, facilitating causal inference rather than correlative observation. In the context of discovery-driven research and grant planning, this framework supports rational prioritization of interventions, hypothesis-driven mapping of regulatory rewiring, and identification of novel biomarkers and therapeutic targets arising from layer-specific dysregulation within RMTC and EMTRAC.
Regulome-mediated translational control (RMTC)
Regulome-mediated translational control (RMTC)
Translational Regulons Classified by 5′UTR Cis-Acting Elements and their Modifications
Translational Regulons Classified by 5′UTR Cis-Acting Elements and their Modifications
Table 2. Translational regulons of cellular transcripts. This table summarizes translational regulons, defined as groups of mRNAs that are coordinately and selectively translated in response to specific cellular states or stimuli. Each regulon represents a set of transcripts whose translation is modulated at the post-transcriptional level, rather than through changes in transcription. These states can be physiological, such as mitosis or nutrient sufficiency, stress-related (ISR) or heat shock, hypoxia, or pathological, including viral infection or other cellular insults. The table lists the regulon name, the key transcripts included, and the underlying mechanisms of translational control mediated by cis-acting elements including uORFs, IRES, G-quadruplexes, m6A-dependent regulation, TOP motifs, or other RNA secondary structures.
Similarly to cellular RNAs, virions use the same cis-acting motifs, to enable host regulon-specific translation, including viral: Poliovirus IRES (Poliovirus, Coxsackievirus, Enterovirus; IRES-mediated), EMCV IRES (Encephalomyocarditis virus; IRES-mediated), HCV IRES (Hepatitis C virus; IRES-mediated), Rhinovirus IRES (Rhinovirus; IRES-mediated), Dicistrovirus IGR IRES (Cricket paralysis virus, other Dicistroviridae; eIF-independent), KSHV uORFs (Kaposi's sarcoma-associated herpesvirus; uORF-mediated), HCMV uORFs (Human cytomegalovirus; uORF-mediated), HBV uORF (Hepatitis B virus; uORF-mediated), SIV uORFs (SIV Rev/Env; uORF-mediated), Caliciviridae TURBS (Norovirus, Murine norovirus; ribosome reinitiation), Influenza B TURBS (Influenza B virus; TURBS-mediated), Sarbecovirus SPEAR (SARS-CoV-2 subgenomic RNAs; IRES-like), 3′‑CITE elements (Plant viruses; cap-independent enhancer), Bovine coronavirus uORFs (Bovine coronavirus; uORF-mediated), Prototype foamy virus uORFs (Foamy virus; uORF-mediated), Rice tungro virus uORFs (Rice tungro virus; uORF-mediated), Alphavirus stem–loops (Alphavirus 26S mRNA; structured RNA), West Nile virus G-quadruplex (West Nile virus NS5; G-quadruplex-mediated).
Independently of the classification of the translational regulons based on cis-acting elements, a similar classification can be made according to trans-acting factors that target these elements or shared consensus sequences, regulating the protein synthesis. Such trans-acting factors may include lncRNAs, small non-coding RNAs (including microRNAs), RNA-binding proteins (RBPs), heterogeneous nuclear ribonucleoproteins (hnRNPs), and some metabolites, iron, magnesium, potassium, S-adenosylmethionine (SAM), flavin mononucleotide (FMN), thiamine pyrophosphate (TPP), cobalamin (vitamin B12), and nucleotides like guanosine tetraphosphate (ppGpp).
Table 3. Cis-acting elements (sequence motifs) within 5′ untranslated regions (5′UTRs) across diverse RNAs that may define translational regulons, i.e., groups of transcripts coordinately regulated at the level of translation due to shared sequence features.
Table 4. Protein trans-acting factors that recognize cis-acting elements within 5′ untranslated regions (5′UTRs) and regulate translational regulons; additional trans-acting factors may include nucleic acids (e.g., small non-coding RNAs and long non-coding RNAs), ions, metabolites, and other molecules (not shown).
Epitranscriptome-mediated translational control (emtrac)
Epitranscriptome-mediated translational control (emtrac)
RNA Modifications - Frequency
RNA Modifications - Frequency
Figure 3. Statistics for RNA Modification Positions (left) and Statistics for RNPs of different RNAs (right) according to RMBase v3.0.
For further details, see: Xuan J, Chen L, Chen Z, Pang J, Huang J, Lin J, Zheng L, Li B, Qu L, Yang J. RMBase v3.0: decoding the landscape, mechanisms, and functions of RNA modifications. Nucleic Acids Res. 2024;52(D1):D273–D284. doi:10.1093/nar/gkad1070. PMID: 37956310; PMCID: PMC10767931.
Table 5: Epitranscriptomic Modifications of Selected Transcripts of Project Interest According to RMBase v3.0. Top modified transcripts are encoded by the PCDHGA (protocadherin gamma cluster), MUC16 (CA-125), MKI67, and CREBBP genes.
Table 5B: RNA modifications modulating ΔG-dependent secondary structure formation and stability of RNA structures. RNA modifications can modulate the formation of RNA secondary structures that influence translation initiation. In this framework, RNA modifications can be viewed as covalently attached and dynamically reversible regulatory marks that behave analogously to trans-acting factors in regulating RNA structural states, thereby modulating the functional output of RNA cis-acting elements in controlling translation efficacy. The table contains a ranked (+9 to -9) and quantitative nearest-neighbor–based thermodynamic assessment describing the effects of RNA base and ribose modifications on secondary structure stability. The rank reflects the relative magnitude and direction of each modification’s effect on RNA stability. The “Modification” column lists the standard abbreviation of each modified nucleotide. The “Full name” column specifies the complete chemical identity. The “Effect” column classifies each modification as stabilizing, neutral, or destabilizing. The “ΔΔG (kcal/mol)” column reports the estimated local change in Gibbs free energy per modification site based on nearest-neighbor thermodynamic approximations. The “Mechanism” column describes structural effects on base pairing, base stacking, backbone conformational restriction, and solvation/electrostatics. The “Dominant contribution to ΔG” column indicates whether the effect is primarily driven by enthalpic (ΔH), entropic (−TΔS), or mixed contributions, all relative to unmodified RNA reference states where ΔΔG = 0.
Table 6: Epitranscriptomic (total RNA) modifications according to RMBase v3.0.
For further details, see: Xuan J, Chen L, Chen Z, Pang J, Huang J, Lin J, Zheng L, Li B, Qu L, Yang J. RMBase v3.0: decoding the landscape, mechanisms, and functions of RNA modifications. Nucleic Acids Res. 2024;52(D1):D273–D284. doi:10.1093/nar/gkad1070. PMID: 37956310; PMCID: PMC10767931.
Table 7: Top RNA modifications by mRNA region (5'UTR, CDS, 3'UTR).
For further details, see: Qiu L, Jing Q, Li Y, Han J. RNA modification: mechanisms and therapeutic targets. Mol Biomed. 2023;4(1):25. doi:10.1186/s43556-023-00139-x. PMID: 37612540; PMCID: PMC10447785.
Tables 7A-7G. Top RNA modifications across major RNA classes, including mRNA, tRNA, rRNA, microRNA, snoRNA, and lncRNA.
For further details, see: Qiu L, Jing Q, Li Y, Han J. RNA modification: mechanisms and therapeutic targets. Mol Biomed. 2023;4(1):25. doi:10.1186/s43556-023-00139-x. PMID: 37612540; PMCID: PMC10447785.
Table 7H. Selected genes involved in RNA modification (human epitranscriptome core)
Genes encode proteins involved in RNA modification pathways across nucleotides (A, C, U, G), specifying modification type, full chemical identity, and RNA substrate context (mRNA, tRNA, rRNA, snRNA, cap-associated RNA). Chemical group denotes modification chemistry (methylation, deamination, acetylation, isomerization, sulfur/selenium incorporation). Class defines functional role of gene products (writers, erasers, readers, cofactors, complexes). Gene set represents core RNA modification machinery, gene products constitute potential biomarkers and/or therapeutic targets due to functional involvement in epitranscriptomic regulation and disease-associated RNA remodeling.
Functional significance of the epitranscriptome as an integrated regulatory layer,
encompassing m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing.
Functional significance of the epitranscriptome as an integrated regulatory layer,
encompassing m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing.
Figure 4A. STRING analysis of the top 50 most extensively modified RNAs (including total modifications: m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing) according to RMBase 3.0.. MCL clustering was applied to identify natural clusters based on stochastic flow (inflation parameter = 1.8). The top clusters are enriched in cadherin-mediated cell adhesion and chromatin modification-related processes. Collectively, the most modified transcripts includes gamma-protocadherins, a subgroup of the protocadherin family, which are calcium-dependent cell adhesion molecules primarily involved in neuronal development, synapse formation, and neural circuit specificity.
Figure 4B. STRING analysis of the top 100 most extensively modified RNAs (including total modifications: m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing) according to RMBase 3.0. MCL clustering was applied to identify natural clusters based on stochastic flow (inflation parameter = 1.8). The top cluster is enriched in chromatin organization-related processes.
Figure 4C. STRING analysis of the top 100 most extensively modified RNAs (including total modifications: m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing) according to RMBase 3.0. MCL clustering was applied to identify natural clusters based on stochastic flow (inflation parameter = 1.8). The top cluster is enriched in chromatin organization-related processes.
Figure 4D. STRING analysis of the top 400 most extensively modified RNAs (including total modifications: m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing) according to RMBase 3.0. MCL clustering was applied to identify natural clusters based on stochastic flow (inflation parameter = 1.8). The top cluster is enriched in chromatin organization-related processes.
Figure 4E. STRING analysis of the top 800 most extensively modified RNAs (including total modifications: m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing) according to RMBase 3.0. MCL clustering was applied to identify natural clusters based on stochastic flow (inflation parameter = 1.8). The top cluster is enriched in chromatin organization-related processes.
Figure 4F. STRING analysis of the top 1600 most extensively modified RNAs (including total modifications: m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing) according to RMBase 3.0. MCL clustering was applied to identify natural clusters based on stochastic flow (inflation parameter = 1.8). The top cluster is enriched in chromatin organization and chromatin modification related and transcription coregulator processes.
In conclusion, based on String analysis (MCL clustering, inflation parameter: 1.8), the most frequent epitranscriptomic modifications (including m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing, all together) occur on transcripts belonging to clusters that consistently appear across analyses of the top 50, 100, 200, 400, 800, and 1600 most extensively modified RNAs (RMBase 3.0): 1) protocadherin-mediated processes, 2) chromatin organization and DNA repair 3) cell cycle and 4) several clusters involved in cytoskeleton reorganization.
1. PROTOCADHERIN CLUSTER. In particular, the most frequent epitranscriptomic modifications are enriched on transcripts encoding protocadherins, including PCDH9, PCDHA1, PCDHA10, PCDHA11, PCDHA2, PCDHA3, PCDHA4, PCDHA5, PCDHA6, PCDHA7, PCDHA8, PCDHA9, PCDHGA1, PCDHGA10, PCDHGA11, PCDHGA12, PCDHGA2, PCDHGA3, PCDHGA4, PCDHGA5, PCDHGA6, PCDHGA7, PCDHGA8, PCDHGA9, PCDHGB1, PCDHGB2, PCDHGB3, PCDHGB4, PCDHGB5, PCDHGB6 and PCDHGB7, FAT1 and FAT4. They are included in homophylic cell adhesion cluster (String clustering and Gene Onthology). These genes are involved in cadherin-mediated cell-cell adhesion, neuronal connectivity, and synaptic specificity. The modifications may influence transcript stability and translation, potentially impacting tissue patterning, neuronal network formation, and intercellular communication. A number of protocadherins are involved in cancer such as PCDHGA9 that plays a role of tumor suppressor and represses epithelial-mesenchymal transition as well as is used as prognostic biomarker (Weng et al 2020)
2. CHROMATIN REMODELING CLUSTER. Further analysis showed that the epitranscriptomic modifications are enriched on transcripts encoding genes involved in chromatin organization, primarily linked to histone modification processes including histone methylation, acetylation, deacetylation, and chromatin remodeling. Histone methylation is mediated by KMT2A, KMT2C, KMT2D, NSD1, SETD2, EHMT1, EHMT2, DOT1L, KDM5B, KDM5C, KDM4B, KDM2A, KDM3B, and KDM2B. Histone acetylation and deacetylation are regulated by HATs EP300, CREBBP, KAT6A, and KAT8, together with corepressors NCOR1 and NCOR2. Chromatin structure and transcriptional accessibility are further modulated by remodeling factors such as BRD4, BPTF, SMARCA2, SMARCA4, SMARCC1, CHD1, CHD2, CHD3, CHD4, CHD6, CHD7, CHD8, PHF3, ASH1L, TRRAP, EP400, and SRCAP. Notably, DNMT1 is the only gene from this list directly associated with DNA methylation, whereas the majority act on histones, thereby influencing transcriptional regulation and genome stability.
3. MITOTIC CELL CYCLE CLUSTER.The epitranscriptomic modifications are enriched on transcripts encoding genes involved in cytoskeleton organization and mitotic spindle regulation, including STAG1, STAG2, SMC1A, TACC2, CLASP2, KIF2A, ROCK1, ROCK2, DNM2, FLNA, MYH10, and TAOK1. These genes collectively regulate actin and microtubule dynamics, spindle assembly, chromosome segregation, and focal adhesion, integrating cytoskeletal remodeling with cell division. Key signaling components such as ROCK1, ROCK2, TAOK1 control Rho GTPase-mediated actin contractility, while spindle and cohesion regulators like STAG1/2, SMC1A, TACC2, CLASP2, KIF2A ensure accurate chromosome alignment and segregation. Vesicle trafficking and motor proteins (DNM2, MYH10, FLNA) further coordinate cytoskeletal architecture with intracellular transport. Epitranscriptomic regulation of these transcripts may therefore influence cell division fidelity, cytoskeletal dynamics, intracellular trafficking, and mechanotransduction, highlighting a critical link between RNA modifications and cellular structural integrity.
4. CYTOSKELETON REORGANIZATION CLUSTER. The epitranscriptomic modifications are enriched on transcripts encoding genes involved in cytoskeleton reorganization, including RB1CC1, RDX, FOXO3, RABEP1, TSC1, DRAM1, RICTOR, MAP1B, MAP1A, TSC2, RPTOR, MTOR, PTK2, PTPN11, CD44, EGFR, TLN1, TLN2, FN1, VCL, ITGA3, ROCK1, ROCK2, IQGAP1, DOCK1, FLNA, FLNB, ACTN1, and MYH9, among others. These genes collectively regulate actin cytoskeleton dynamics, focal adhesion assembly, and cell–matrix interactions, integrating extracellular signals with intracellular structural remodeling. A prominent feature of this cluster is the involvement of key signaling pathways, including PI3K/AKT/mTOR (MTOR, RPTOR, RICTOR, AKT2, AKT3), receptor tyrosine kinase signaling (EGFR, FGFR1, IGF1R, MET), and Rho GTPase-mediated cytoskeletal control (DOCK1, ARHGAP35, ARHGEF2, ROCK1, ROCK2). Additionally, components of vesicle trafficking and endocytosis (DNM2, CLTC, EEA1, RABEP1, AP2B1) indicate tight coupling between membrane dynamics and cytoskeletal remodeling. Epitranscriptomic regulation of these transcripts may therefore impact cell adhesion, migration, mechanotransduction, and intracellular transport, highlighting a coordinated control of cellular architecture and signaling responsiveness.
Further studies are required to elucidate the interplay between epitranscriptomic regulation and chromatin (histone) remodeling, protocadherin-mediated cell adhesion, cell cycle and cytoskeleton reorganization. Epitranscriptomic modifications enable rapid recruitment of specific transcripts for translation without the need for changes at the transcriptional level, providing a fast and dynamic layer of gene expression control. These modifications appear to preferentially regulate the translation of transcripts encoding key regulatory and structural proteins, including chromatin modifiers, protocadherins, and cytoskeleton-associated factors, suggesting a selective post-transcriptional control of gene expression programs, cell–cell interactions, and dynamic cellular responses.
Pathway clustering of the top 2% most modified transcripts, including m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing, integrated across multiple databases.
Figures 5A–5L. Pathway clustering of the top 2% most modified transcripts (RMBase 3.0) across multiple databases (including Gene Ontology Biological Processes, KEGG, Reactome, and others) revealed clusters consistent with STRING analysis, although in different rank order, as well as additional novel clusters, including the thyroid hormone signaling pathway (KEGG).
Functional significance of individual epitranscriptomic modifications,
including m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing, analyzed separately (String/Biological Processes/Gene Ontology )
Functional significance of individual epitranscriptomic modifications,
including m6A, m1A, m5C, m7G, pseudouridine, 2′-O-methylation, and RNA editing, analyzed separately (String/Biological Processes/Gene Ontology )
Figure 6A. m6A
Biological Processes/Gene Ontology analysis (via String) of the top 2% most extensively m6A-modified RNAs (RMBase 3.0) revealed top clusters enriched in: a) homophilic cell adhesion, b) chromatin organization, and c) chromosome organization. This clusters corresponds closely to the one observed in STRING analysis of the top RNAs with all modifications combined (protocadherins and chromatin organization). The predominance of m6A modification may largely account for these results. Additionally, KEGG pathway enrichment revealed clusters including Thyroid hormone signaling, Lysine degradation, and Focal adhesion, whereas Reactome revealed: Chromatin modifying enzymes, Signaling by Rho GTPases (RHOA), SUMO E3 ligases (not shown here).
Figure 6B. m1A
Biological Processes/Gene Ontology analysis (via String) of the top 2% most extensively m1A-modified RNAs (RMBase 3.0) revealed top clusters enriched in: a) regulation of execution phase of apoptosis and b) negative regulation of execution phase of apoptosis. Additionally, other databases revealed: receptor antagonist activity, Humanin family and Acetylation clusters (not shown here). It appears that this modification is related to apoptosis. m1A is considered one of the most destabilizing modifications in RNA secondary structure formation (see Table 5B), thereby potentially facilitating translation of selected groups of transcripts (regulons).
Figure 6C. m5C
Biological Processes/Gene Ontology analysis (via String) of the top 2% most extensively m5C-modified RNAs (RMBase 3.0) revealed top clusters enriched in: a) cell division and b) cellular nitrogen metabolism. It appears that m5C may underlie the cell division cluster observed in STRING analysis of RNAs with all modifications combined. Additionally, other databases revealed: Pancreatitis, and Carboxypeptidase activation peptide, Pancreatic secretion, Transcriptional Regulation by TP53, Cell Cycle Checkpoints, Hypermelanotic macule, acetylation, methylation clusters. (not shown here)
Figure 6D. m7G
Biological Processes/Gene Ontology analysis (via String) of the top 2% most extensively m7G-modified RNAs (RMBase 3.0) revealed top clusters enriched in: a) mRNA processing and metabolic processes b) RNA splicing. Additionally, other databases revealed: RNA binding, Cortical cytoskeleton, RNA transport, Metabolism of RNA, mRNA splicing (not shown). It appears that this modification is strongly related to RNA processing.
Figure 6E. Ψ pseudouridine
Biological Processes/Gene Ontology analysis (via String) of the top 2% most extensively 2′-O-methyl-modified RNAs (RMBase 3.0) revealed top clusters enriched in: a) Smooth endoplasmic reticulum. This modification, however, is predominantly observed on tRNA, snoRNA, and rRNA, rather than on protein-coding transcripts. Additionally, other databases revealed: testis/gonad tissue localization, Phosphoprotein, Alzheimer clusters (not shown). Pseudouridine is considered one of the most stabilizing modifications in RNA secondary structure formation (see Table 5B), thereby potentially increasing RNA structural stability.
Figure 6F. 2′-O-methylation
Biological Processes/Gene Ontology analysis (via String) of the top 2% most extensively 2′-O-methyl-modified RNAs (RMBase 3.0) revealed top clusters enriched in: a) cytoplasmic protein translation, b) amide and organonitrogen compound biosynthesis. Additionally, other databases revealed: Structural constituent of ribosome, RNA binding, Ribosome, Peptide chain elongation, cellular and viral RNA translation, Multisystem proteinopathy, Diamond-Blackfan anemia, lymphoblast, prostate, mutism, Ribonucleoprotein, FASN, Tubulins, Tubulin/FtsZ family C-terminal domain, telomere maintenance clusters (not shown here). It appears that this modification is strongly related to protein translation.
Figure 6G. RNA editing
Biological Processes/Gene Ontology analysis (via String) of the top 2% most extensively RNA edited (A→I, C→U) RNAs (RMBase 3.0) revealed top clusters enriched in: a) protein localization, b) protein transport, c) nitrogen compound transport. Additionally, other databases revealed: GTPase activator activity, K homology RNA-binding domain, and U1-like zinc finger, Brain, Central nervous system, Smoking behaviour, Cognition, Alternative splicing, Phosphoprotein, G protein-coupled receptor signaling pathway, Phosphatidylinositol metabolic process, Histone lysine methylation (not shown here). It appears that this modification is strongly related to protein localization.
Biomedical applications
Biomedical applications
MOLECULAR TARGETS & BIOMARKERS
MOLECULAR TARGETS & BIOMARKERS
Table 8. Clinical trials (ClinicalTrials.gov) including epitranscriptomic targets, including studies such as investigations of the efficacy and mechanism of METTL3 peptide inhibitors in enhancing anti-tumor immune responses by reshaping the tumor microenvironment NCT06762925.
Similarly to epitranscriptomic targets, the US database contains a growing number of clinical trials focusing on selected cis-acting elements and trans-acting factor targets (components of regulons).
Table 9. Potential epitranscriptomic targets in cancer and associated references (PubMed IDs).
Table 10. Potential nucleic acids-based next-generation therapeutics targeting RNA cis-acting elements and other nucleic acid based therapeutics including mRNA, self-amplifying RNAs, AAV, CRISPR sgRNAs.
Table 11. Potential nucleic acids-based next-generation therapeutics targeting RNA cis-acting elements and other nucleic acid based therapeutics including mRNA, self-amplifying RNAs, AAV, CRISPR sgRNAs.
For a detailed discussion of translational control mechanisms underlying this framework, see our related publications, inventions, and RESEARCH.
REFERENCES
REFERENCES
Regulome and epitranscriptome mediated translational control
Regulome and epitranscriptome mediated translational control
Akirtava C, McManus CJ. Control of translation by eukaryotic mRNA transcript leaders-Insights from high-throughput assays and computational modeling. Wiley Interdiscip Rev RNA. 2021 May;12(3):e1623. doi: 10.1002/wrna.1623. Epub 2020 Aug 31. PMID: 32869519; PMCID: PMC7914273.
Ankit Sharma, Madankumar Ghatge, Vrushali Deshpande, Rajani Kanth Vangala. A systems biology approach to elucidate the post-translational regulome of coronary artery disease. bioRxiv 2022.02.09.479759; doi: https://doi.org/10.1101/2022.02.09.479759
Atrian F, Ramirez P, De Mange J, Marquez M, Gonzalez EM, Minaya M, Karch CM, Frost B. m6A-dependent circular RNA formation mediates tau-induced neurotoxicity. bioRxiv [Preprint]. 2024 Jan 26:2024.01.25.577211. doi: 10.1101/2024.01.25.577211. PMID: 38328044; PMCID: PMC10849734.
Bai Y, Li K, Peng J, Yi C. m6A modification: a new avenue for anti-cancer therapy. Life Med. 2023 Apr 8;2(1):lnad008. doi: 10.1093/lifemedi/lnad008. PMID: 39872957; PMCID: PMC11749794.
Baleriola J, Walker CA, Jean YY, Crary JF, Troy CM, Nagy PL, Hengst U. Axonally synthesized ATF4 transmits a neurodegenerative signal across brain regions. Cell. 2014 Aug 28;158(5):1159-1172. doi: 10.1016/j.cell.2014.07.001. PMID: 25171414; PMCID: PMC4149755.
Bassell GJ, Warren ST. Fragile X syndrome: loss of local mRNA regulation alters synaptic development and function. Neuron. 2008 Oct 23;60(2):201-14. doi: 10.1016/j.neuron.2008.10.004. PMID: 18957214; PMCID: PMC3691995.
Basu A, Yang JY, Tsirukis VE, Loiacono A, Koch G, Khwaja IA, Krishnamurthy M, Fazio N, White E, Jha A, Shah S, Takmil C, Bagdas D, Demirer A, Master A, Natke E, Honkanen R, Huang L, Rigas B. Phosphosulindac (OXT-328) prevents and reverses chemotherapy induced peripheral neuropathy in mice. Front Neurosci. 2024 Jan 29;17:1240372. doi: 10.3389/fnins.2023.1240372. PMID: 38347876; PMCID: PMC10860339.
Behrens A, Rodschinka G, Nedialkova DD. High-resolution quantitative profiling of tRNA abundance and modification status in eukaryotes by mim-tRNAseq. Mol Cell. 2021 Apr 15;81(8):1802-1815.e7. doi: 10.1016/j.molcel.2021.01.028. Epub 2021 Feb 12. PMID: 33581077; PMCID: PMC8062790.
Bisogno LS, Keene JD. RNA regulons in cancer and inflammation. Curr Opin Genet Dev. 2018 Feb;48:97-103. doi: 10.1016/j.gde.2017.11.004. Epub 2017 Nov 22. PMID: 29175729; PMCID: PMC6489128.
Borden KLB, Volpon L. The diversity, plasticity, and adaptability of cap-dependent translation initiation and the associated machinery. RNA Biol. 2020 Sep;17(9):1239-1251. doi: 10.1080/15476286.2020.1766179. Epub 2020 Jun 4. PMID: 32496897; PMCID: PMC7549709.
Bugaut A, Balasubramanian S. 5'-UTR RNA G-quadruplexes: translation regulation and targeting. Nucleic Acids Res. 2012 Jun;40(11):4727-41. doi: 10.1093/nar/gks068. Epub 2012 Feb 20. PMID: 22351747; PMCID: PMC3367173.
Chen CK, Cheng R, Demeter J, Chen J, Weingarten-Gabbay S, Jiang L, Snyder MP, Weissman JS, Segal E, Jackson PK, Chang HY. Structured elements drive extensive circular RNA translation. Mol Cell. 2021 Oct 21;81(20):4300-4318.e13. doi: 10.1016/j.molcel.2021.07.042. Epub 2021 Aug 25. PMID: 34437836; PMCID: PMC8567535.
Chen QM. The Odds of Protein Translation Control Under Stress. Antioxid Redox Signal. 2024 Jun;40(16-18):943-947. doi: 10.1089/ars.2023.0478. Epub 2024 Apr 24. PMID: 38573012; PMCID: PMC11538090.
Das S, Vera M, Gandin V, Singer RH, Tutucci E. Intracellular mRNA transport and localized translation. Nat Rev Mol Cell Biol. 2021 Jul;22(7):483-504. doi: 10.1038/s41580-021-00356-8. Epub 2021 Apr 9. Erratum in: Nat Rev Mol Cell Biol. 2021 Jul;22(7):505. doi: 10.1038/s41580-021-00374-6. PMID: 33837370; PMCID: PMC9346928.
Dassi E, Malossini A, Re A, et al. AURA: Atlas of UTR Regulatory Activity. Bioinformatics. 2012;28(1):142–143. Available from: http://aura.science.unitn.it
Dever TE, Ivanov IP, Hinnebusch AG. Translational regulation by uORFs and start codon selection stringency. Genes Dev. 2023 Jun 1;37(11-12):474-489. doi: 10.1101/gad.350752.123. Epub 2023 Jul 11. PMID: 37433636; PMCID: PMC10393191.
Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, et. al. Landscape of transcription in human cells. Nature. 2012 Sep 6;489(7414):101-8. doi: 10.1038/nature11233. PMID: 22955620; PMCID: PMC3684276.
Dolatshahi M, Salehipour A, Saghazadeh A, Sanjeari Moghaddam H, Aghamollaii V, Fotouhi A, Tafakhori A. Thyroid hormone levels in Alzheimer disease: a systematic review and meta-analysis. Endocrine. 2023 Feb;79(2):252-272. doi: 10.1007/s12020-022-03190-w. Epub 2022 Sep 27. PMID: 36166162.
Eleftheriou M, Russell J, Tzelepis K. Epitranscriptomic advances in normal and malignant hematopoiesis. Leukemia. 2025 Dec;39(12):2848-2857. doi: 10.1038/s41375-025-02765-6. Epub 2025 Sep 19. PMID: 40973767; PMCID: PMC12634440.
Enríquez JA, Fernández-Silva P, Garrido-Pérez N, López-Pérez MJ, Pérez-Martos A, Montoya J. Direct regulation of mitochondrial RNA synthesis by thyroid hormone. Mol Cell Biol. 1999 Jan;19(1):657-70. doi: 10.1128/MCB.19.1.657. PMID: 9858589; PMCID: PMC83923.
Fernandopulle MS, Lippincott-Schwartz J, Ward ME. RNA transport and local translation in neurodevelopmental and neurodegenerative disease. Nat Neurosci. 2021 May;24(5):622-632. doi: 10.1038/s41593-020-00785-2. Epub 2021 Jan 28. PMID: 33510479; PMCID: PMC8860725.
Gadekar VP, Munk AW, Miladi M, Junge A, Backofen R, Seemann SE, Gorodkin J. Clusters of mammalian conserved RNA structures in UTRs associate with RBP binding sites. NAR Genom Bioinform. 2024 Aug 9;6(3):lqae089. doi: 10.1093/nargab/lqae089. PMID: 39131818; PMCID: PMC11310781.
Gay DM, Lund AH, Jansson MD. Translational control through ribosome heterogeneity and functional specialization. Trends Biochem Sci. 2022 Jan;47(1):66-81. doi: 10.1016/j.tibs.2021.07.001. Epub 2021 Jul 23. PMID: 34312084.
Gebert LFR, MacRae IJ. Regulation of microRNA function in animals. Nat Rev Mol Cell Biol. 2019 Jan;20(1):21-37. doi: 10.1038/s41580-018-0045-7. PMID: 30108335; PMCID: PMC6546304.
Girón-Pïllado M, Cruz-Bautista I, Saavedra-González V, Atisha-Fregoso Y, Barraza G, Aguilar-Salinas CA, Hernández-Molina G. Autoimmune Thyroid Disease in Primary Sjögren's Syndrome: Real-life Screening Practice and Clinical Outcomes. Curr Rheumatol Rev. 2022;18(3):272-277. doi: 10.2174/1573397118666220127105546. PMID: 35086454.
Girón-Pïllado M, Cruz-Bautista I, Saavedra-González V, Atisha-Fregoso Y, Barraza G, Aguilar-Salinas CA, Hernández-Molina G. Autoimmune Thyroid Disease in Primary Sjögren's Syndrome: Real-life Screening Practice and Clinical Outcomes. Curr Rheumatol Rev. 2022;18(3):272-277. doi: 10.2174/1573397118666220127105546. PMID: 35086454.
Grillo G et al. UTRdb and UTRsite (RELEASE 2010): a collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs. Nucleic Acids Research 38(Database issue):D75–D80 (2010).
Harvey RF, Smith TS, Mulroney T, Queiroz RML, Pizzinga M, Dezi V, Villenueva E, Ramakrishna M, Lilley KS, Willis AE. Trans-acting translational regulatory RNA binding proteins. Wiley Interdiscip Rev RNA. 2018 May;9(3):e1465. doi: 10.1002/wrna.1465. Epub 2018 Jan 17. PMID: 29341429; PMCID: PMC5947564.
Heneka MT, Gauthier S, Chandekar SA, Hviid Hahn-Pedersen J, Bentsen MA, Zetterberg H. Neuroinflammatory fluid biomarkers in patients with Alzheimer's disease: a systematic literature review. Mol Psychiatry. 2025 Jun;30(6):2783-2798. doi: 10.1038/s41380-025-02939-9. Epub 2025 Mar 6. PMID: 40050444; PMCID: PMC12092255.
Ho JJD, Man JHS, Schatz JH, Marsden PA. Translational remodeling by RNA-binding proteins and noncoding RNAs. Wiley Interdiscip Rev RNA. 2021 Sep;12(5):e1647. doi: 10.1002/wrna.1647. Epub 2021 Mar 10. PMID: 33694288.
Hu H, Song H, Han B, Zhao H, He J. Tumor-Educated Platelet RNA and Circulating Free RNA: Emerging Liquid Biopsy Markers for Different Tumor Types. Front Biosci (Landmark Ed). 2024 Feb 22;29(2):80. doi: 10.31083/j.fbl2902080. PMID: 38420812.
Ingolia NT, Brar GA, Stern-Ginossar N, Harris MS, Talhouarne GJ, Jackson SE, Wills MR, Weissman JS. Ribosome profiling reveals pervasive translation outside of annotated protein-coding genes. Cell Rep. 2014 Sep 11;8(5):1365-79. doi: 10.1016/j.celrep.2014.07.045. Epub 2014 Aug 21. PMID: 25159147; PMCID: PMC4216110.
Iwata M, Kosai K, Ono Y, Oki S, Mimori K, Yamanishi Y. Regulome-based characterization of drug activity across the human diseasome. NPJ Syst Biol Appl. 2022 Nov 7;8(1):44. doi: 10.1038/s41540-022-00255-4. PMID: 36344521; PMCID: PMC9640590.
Jia X, He X, Huang C, Li J, Dong Z, Liu K. Protein translation: biological processes and therapeutic strategies for human diseases. Signal Transduct Target Ther. 2024 Feb 23;9(1):44. doi: 10.1038/s41392-024-01749-9. PMID: 38388452; PMCID: PMC10884018.
Jiang X, Liu B, Nie Z, Duan L, Xiong Q, Jin Z, Yang C, Chen Y. The role of m6A modification in the biological functions and diseases. Signal Transduct Target Ther. 2021 Feb 21;6(1):74. doi: 10.1038/s41392-020-00450-x. PMID: 33611339; PMCID: PMC7897327.
Karsh J, Pavlidis N, Weintraub BD, Moutsopoulos HM. Thyroid disease in Sjögren's syndrome. Arthritis Rheum. 1980 Nov;23(11):1326-9. doi: 10.1002/art.1780231118. PMID: 7447970.
Karsh J, Pavlidis N, Weintraub BD, Moutsopoulos HM. Thyroid disease in Sjögren's syndrome. Arthritis Rheum. 1980 Nov;23(11):1326-9. doi: 10.1002/art.1780231118. PMID: 7447970.
Keene JD. RNA regulons: coordination of post-transcriptional events. Nat Rev Genet. 2007 Jul;8(7):533-43. doi: 10.1038/nrg2111. PMID: 17572691
Kenessey A, Ojamaa K. Thyroid hormone stimulates protein synthesis in the cardiomyocyte by activating the Akt-mTOR and p70S6K pathways. J Biol Chem. 2006 Jul 28;281(30):20666-20672. doi: 10.1074/jbc.M512671200. Epub 2006 May 22. PMID: 16717100.
Khan MA, Mohammad T, Alalami ZS, Hassan MI. Iron activates Alzheimer's amyloid precursor protein synthesis through an iron responsive element (IRE) mRNA binding to translation initiation factor. Int J Biol Macromol. 2025 Sep;321(Pt 3):146231. doi: 10.1016/j.ijbiomac.2025.146231. Epub 2025 Jul 22. PMID: 40706937.
Kwantwi LB. Exosome-mediated crosstalk between tumor cells and innate immune cells: implications for cancer progression and therapeutic strategies. J Cancer Res Clin Oncol. 2023 Sep;149(11):9487-9503. doi: 10.1007/s00432-023-04833-9. Epub 2023 May 8. PMID: 37154928; PMCID: PMC11796874.
Lamping JP, Krebber H. The hidden power of antisense long non-coding RNAs: a dive into a novel regulatory layer mediated by double-stranded RNA formation. RNA Biol. 2025 Dec;22(1):1-16. doi: 10.1080/15476286.2025.2530797. Epub 2025 Jul 9. PMID: 40629869; PMCID: PMC12247107.
Leppek K, Das R, Barna M. Functional 5' UTR mRNA structures in eukaryotic translation regulation and how to find them. Nat Rev Mol Cell Biol. 2018 Mar;19(3):158-174. doi: 10.1038/nrm.2017.103. Epub 2017 Nov 22. Erratum in: Nat Rev Mol Cell Biol. 2018 Oct;19(10):673. doi: 10.1038/s41580-018-0055-5. PMID: 29165424; PMCID: PMC5820134.
Li C, Chen K, Li X, Xiong X. Epitranscriptome-epigenome interactions in development and disease mechanisms. Trends Genet. 2025 Aug;41(8):691-705. doi: 10.1016/j.tig.2025.04.009. Epub 2025 May 15. PMID: 40374434.
Li J, Singh U, Arendsee Z, Wurtele ES. Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data. Front Genet. 2021 Aug 16;12:722981. doi: 10.3389/fgene.2021.722981. PMID: 34484307; PMCID: PMC8415361.
Li W, Deng X, Chen J. RNA-binding proteins in regulating mRNA stability and translation: roles and mechanisms in cancer. Semin Cancer Biol. 2022 Nov;86(Pt 2):664-677. doi: 10.1016/j.semcancer.2022.03.025. Epub 2022 Apr 2. PMID: 35381329; PMCID: PMC9526761.
Li X, Gui Z, Han Y, Yang X, Wang Z, Zheng L, Zhang L, Wang D, Fan X, Su L. Comprehensive analysis of dysregulated exosomal long non-coding RNA networks associated with arteriovenous malformations. Gene. 2020 May 15;738:144482. doi: 10.1016/j.gene.2020.144482. Epub 2020 Feb 19. PMID: 32087271.
Li Y, Dou X, Liu J, Xiao Y, Zhang Z, Hayes L, Wu R, Fu X, Ye Y, Yang B, Ostrow LW, He C, Sun S. Globally reduced N6-methyladenosine (m6A) in C9ORF72-ALS/FTD dysregulates RNA metabolism and contributes to neurodegeneration. Nat Neurosci. 2023 Aug;26(8):1328-1338. doi: 10.1038/s41593-023-01374-9. Epub 2023 Jun 26. PMID: 37365312; PMCID: PMC11361766.
Lin J, Chen Y, Zhang Y, Lin H, Ouyang Z. Deciphering the role of RNA structure in translation efficiency. BMC Bioinformatics. 2022 Dec 23;23(Suppl 3):559. doi: 10.1186/s12859-022-05037-7. PMID: 36564729; PMCID: PMC9783404
Liu S, Zhao M, Yang X, Liu Y, Xu H, Yan H, Xu Y. Exosomal microRNA biomarkers in Alzheimer's disease: Systematic review and meta-analysis. Clin Chim Acta. 2026 Jan 1;578:120542. doi: 10.1016/j.cca.2025.120542. Epub 2025 Aug 7. PMID: 40783152.
Marro S, Chiabrando D, Messana E, Stolte J, Turco E, Tolosano E, Muckenthaler MU. Heme controls ferroportin1 (FPN1) transcription involving Bach1, Nrf2 and a MARE/ARE sequence motif at position -7007 of the FPN1 promoter. Haematologica. 2010 Aug;95(8):1261-8. doi: 10.3324/haematol.2009.020123. Epub 2010 Feb 23. PMID: 20179090; PMCID: PMC2913073.
Martinez De La Cruz B, Markus R, Malla S, Haig MI, Gell C, Sang F, Bellows E, Sherif MA, McLean D, Lourdusamy A, Self T, Bodi Z, Smith S, Fay M, Macdonald IA, Fray R, Knight HM. Modifying the m6A brain methylome by ALKBH5-mediated demethylation: a new contender for synaptic tagging. Mol Psychiatry. 2021 Dec;26(12):7141-7153. doi: 10.1038/s41380-021-01282-z. Epub 2021 Oct 19. PMID: 34663904; PMCID: PMC8872986.
Master A, Huang L, Tsirukis VE, Loiacono A, Khawja A, Takmil C, Tsioulias A, Honkanen R, Rigas B . Transcriptomic landscape of the Dorsal Root Ganglia (DRG) and the Sciatic Nerve (SN) in a mouse model of Paclitaxel-Induced Peripheral Neuropathy (PIPN). PLoS One in preparation (2026). Transcriptome datasets published on Dec 08 2023, NCBI GEO acc. no.: GSE249643
Master A, Huang L, Tsirukis VE, Loiacono A, Khawja A, Takmil C, Tsioulias A, Honkanen R, Rigas B . Transcriptomic landscape of the Dorsal Root Ganglia (DRG) and the Sciatic Nerve (SN) in a mouse model of Paclitaxel-Induced Peripheral Neuropathy (PIPN). PLoS One in preparation. Abstract published on Dec 08 2023, NCBI GEO acc. no.: GSE249643
Master A, Huang W, Huang L, Honkanen R, Rigas B. An Improved Ocular Impression Cytology Method: Quantitative Cell Transfer to Microscope Slides Using a Novel Polymer. Curr Eye Res. 2022 Jan;47(1):41-50. doi: 10.1080/02713683.2021.1951300. Epub 2021 Nov 27. PMID: 34841993; PMCID: PMC8792174.
Master A, Huang W, Huang L, Honkanen R, Rigas B. An Improved Ocular Impression Cytology Method: Quantitative Cell Transfer to Microscope Slides Using a Novel Polymer. Curr Eye Res. 2022 Jan;47(1):41-50. doi: 10.1080/02713683.2021.1951300. Epub 2021 Nov 27. PMID: 34841993; PMCID: PMC8792174.
Master A, Huang W, Huang L, Li W, Saglam S, Honkanen R, Rigas B. Simplified ex-vivo drug evaluation in ocular surface cells: Culture on cellulose filters of cells obtained by impression cytology. Exp Eye Res. 2021 Dec;213:108827. doi: 10.1016/j.exer.2021.108827. Epub 2021 Nov 3. PMID: 34742691.
Master A, Huang W, Huang L, Li W, Saglam S, Honkanen R, Rigas B. Simplified ex-vivo drug evaluation in ocular surface cells: Culture on cellulose filters of cells obtained by impression cytology. Exp Eye Res. 2021 Dec;213:108827. doi: 10.1016/j.exer.2021.108827. Epub 2021 Nov 3. PMID: 34742691.
Master A, Kontzias A, Huang L, Huang W, Tsioulias A, Zarabi S, Wolek M, Wollocko BM, Honkanen R, Rigas B. The transcriptome of rabbit conjunctiva in dry eye disease: Large-scale changes and similarity to the human dry eye. PLoS One. 2021 Jul 29;16(7):e0254036. doi: 10.1371/journal.pone.0254036. PMID: 34324523; PMCID: PMC8321226.
Master A, Kontzias A, Huang L, Huang W, Tsioulias A, Zarabi S, Wolek M, Wollocko BM, Honkanen R, Rigas B. The transcriptome of rabbit conjunctiva in dry eye disease: Large-scale changes and similarity to the human dry eye. PLoS One. 2021 Jul 29;16(7):e0254036. doi: 10.1371/journal.pone.0254036. PMID: 34324523; PMCID: PMC8321226.
Master A, Nauman A. Gene expression regulation by long naturally occurring antisense transcripts [Regulacja ekspresji genów przez długie naturalnie występujące antysensowne transkrypty]. Post. Biol. Kom. (PBK) 2014;41(1):3-28. Review.
Master A, Nauman A. Genomic context and expression regulation of nuclear thyroid hormone receptors by long naturally occurring antisense transcripts [Genomowy kontekst i regulacja ekspresji receptorów hormonu tarczycy przez długie naturalnie występujące antysensowne transkrypty]. Post. Biol. Kom. (PBK) 2014; 41(1):29-58. Review.
Master A, Nauman A. Molecular mechanisms of protein biosynthesis initiation - biochemical and biomedical implications of a new model of translation enhanced by the RNA hypoxia response element (rHRE) [Molekularne mechanizmy inicjacji biosyntezy białek - biochemiczne i biomedyczne implikacje nowego modelu translacji wzmacnianej przez element RNA odpowiedzi na hipoksję (rHRE)]. Postepy Biochem. 2014;60(1):39-54. Review. The Polish Biochemical Society has granted this work with Boleslaw Skarzynski Award for the best article published in a quarterly journal “Postepy Biochemii” published in year 2014
Master A, Nauman A. THRB (Thyroid Hormone Receptor, Beta). Atlas Genet Cytogenet Oncol Haematol (AGCOH). 2014; 18(6). https://atlasgeneticsoncology.org/gene/42554/thrb-(thyroid-hormone-receptor-beta)
Master A, Wójcicka A, Giżewska K, Popławski P, Williams GR, Nauman A. A Novel Method for Gene-Specific Enhancement of Protein Translation by Targeting 5’UTRs of Selected Tumor Suppressors. PLoS ONE. 2016 May 12; 11(5): e0155359.
Master A. Nucleic acid molecule designed for selective enhancement of protein synthesis. Polish patent PL237080B1 (P.393170). Filed 12 Apr 2010; published 21 Oct 2020. Developed within Adam Master’s grant project Dz.3.4/ML/2/05 (ZPORR). Patent Office of the Republic of Poland.
Master A. Opinion on novelty of concept of invention PL237080B1: document submitted to Polish Patent Office (available as internal file and referenced by patent attorney). (Patent Office of the Republic of Poland, Opinion on novelty of concept of invention PL237080B1. 2024). (Direct link not indexed in PubMed / DOI — use as grey literature citation).
Mateusz Garbulowski, Riccardo Mosca, Carlos J. Gallardo-Dodd, Claudia Kutter, Erik L. L. Sonnhammer. Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer. bioRxiv 2024.09.04.611258 doi: https://doi.org/10.1101/2024.09.04.611258
Meyer KD, Patil DP, Zhou J, Zinoviev A, Skabkin MA, Elemento O, Pestova TV, Qian SB, Jaffrey SR. 5' UTR m(6)A Promotes Cap-Independent Translation. Cell. 2015 Nov 5;163(4):999-1010. doi: 10.1016/j.cell.2015.10.012. Epub 2015 Oct 22. PMID: 26593424; PMCID: PMC4695625.
Mikesell AR, Meyer AR, García G, Frietze LR, Stucky CL, Pan T, Campbell ZT. GCN2 regulates paclitaxel-induced neuropathic pain. Br J Pharmacol. 2025 Dec;182(23):5913-5930. doi: 10.1111/bph.70154. Epub 2025 Aug 13. PMID: 40803344.
Milenkovic I, Cruciani S, Llovera L, Lucas MC, Medina R, Pauli C, Heid D, Muley T, Schneider MA, Klotz LV, Allgäuer M, Lattuca R, Lafontaine DLJ, Müller-Tidow C, Novoa EM. Epitranscriptomic rRNA fingerprinting reveals tissue-of-origin and tumor-specific signatures. Mol Cell. 2025 Jan 2;85(1):177-190.e7. doi: 10.1016/j.molcel.2024.11.014. Epub 2024 Dec 10. PMID: 39662470.
Milenkovic I, Novoa EM. Dynamic rRNA modifications as a source of ribosome heterogeneity. Trends Cell Biol. 2025 Jul;35(7):604-614. doi: 10.1016/j.tcb.2024.10.001. Epub 2024 Nov 4. PMID: 39500673.
Mir DA, Ma Z, Horrocks J, Rogers A. Stress-Induced Eukaryotic Translational Regulatory Mechanisms. J Clin Med Sci. 2024;8(2):1000277. Epub 2024 Jun 24. PMID: 39364184; PMCID: PMC11448810.
Nagaraj S, Zoltowska KM, Laskowska-Kaszub K, Wojda U. microRNA diagnostic panel for Alzheimer's disease and epigenetic trade-off between neurodegeneration and cancer. Ageing Res Rev. 2019 Jan;49:125-143. doi: 10.1016/j.arr.2018.10.008. Epub 2018 Nov 2. PMID: 30391753.
Nappi A, Moriello C, Morgante M, Fusco F, Crocetto F, Miro C. Effects of thyroid hormones in skeletal muscle protein turnover. J Basic Clin Physiol Pharmacol. 2024 Sep 20;35(4-5):253-264. doi: 10.1515/jbcpp-2024-0139. PMID: 39297559.
Park SM, Kang TI, So JS. Roles of XBP1s in Transcriptional Regulation of Target Genes. Biomedicines. 2021 Jul 8;9(7):791. doi: 10.3390/biomedicines9070791. PMID: 34356855; PMCID: PMC8301375.
Patent: EP3313989B1 Modified CRISPR RNA and modified single CRISPR RNA and uses thereof. European patent EP3313989B1, publication 25 Dec 2024; priority and family data listed under EP3313989C0. Inventor and assignee: Ionis Pharmaceuticals, Inc.
Patent: WO2023111337A1 Antisense oligonucleotide. World Intellectual Property Organization (WIPO) patent application WO2023111337A1, filed 16 Dec 2022, published 22 Jun 2023. F. Hoffmann-La Roche AG, Hoffmann La Roche Inc.
Peer E, Moshitch-Moshkovitz S, Rechavi G, Dominissini D. The Epitranscriptome in Translation Regulation. Cold Spring Harb Perspect Biol. 2019 Aug 1;11(8):a032623. doi: 10.1101/cshperspect.a032623. PMID: 30037968; PMCID: PMC6671940.
Pino LK, Canzani D, Gutierrez A, Robbins J, McEllin B, Hubbard E, Broderick E, Prymolenna A, Tatka L, Paez JS, Mercenne G, Siebenthall K, Fondrie WE, Federation AJ. Native, Spatiotemporal Profiling of the Global Human Regulome. bioRxiv [Preprint]. 2025 Jun 25:2025.06.14.659727. doi: 10.1101/2025.06.14.659727. PMID: 40667134; PMCID: PMC12262435.
Pupak A, Rodríguez-Navarro I, Sathasivam K, Singh A, Essmann A, Del Toro D, Ginés S, Mouro Pinto R, Bates GP, Vang Ørom UA, Martí E, Brito V. m6A modification of mutant huntingtin RNA promotes the biogenesis of pathogenic huntingtin transcripts. EMBO Rep. 2024 Nov;25(11):5026-5052. doi: 10.1038/s44319-024-00283-7. Epub 2024 Oct 11. PMID: 39394467; PMCID: PMC11549361.
Razumova E, Makariuk A, Dontsova O, Shepelev N, Rubtsova M. Structural Features of 5' Untranslated Region in Translational Control of Eukaryotes. Int J Mol Sci. 2025 Feb 25;26(5):1979. doi: 10.3390/ijms26051979. PMID: 40076602; PMCID: PMC11900008.
Roy B. Effects of mRNA Modifications on Translation: An Overview. Methods Mol Biol. 2021;2298:327-356. doi: 10.1007/978-1-0716-1374-0_20. PMID: 34085254.
Ruden DM. Uncovering the Epitranscriptome: A Review on mRNA Modifications and Emerging Frontiers. Genes (Basel). 2025 Aug 12;16(8):951. doi: 10.3390/genes16080951. PMID: 40870000; PMCID: PMC12385769.
Shi Z, Fujii K, Kovary KM, Genuth NR, Röst HL, Teruel MN, Barna M. Heterogeneous Ribosomes Preferentially Translate Distinct Subpools of mRNAs Genome-wide. Mol Cell. 2017 Jul 6;67(1):71-83.e7. doi: 10.1016/j.molcel.2017.05.021. Epub 2017 Jun 15. PMID: 28625553; PMCID: PMC5548184.
Sloan KE, Warda AS, Sharma S, Entian KD, Lafontaine DLJ, Bohnsack MT. Tuning the ribosome: The influence of rRNA modification on eukaryotic ribosome biogenesis and function. RNA Biol. 2017 Sep 2;14(9):1138-1152. doi: 10.1080/15476286.2016.1259781. Epub 2016 Dec 2. PMID: 27911188; PMCID: PMC5699541.
Sokoloff L, Roberts PA, Januska MM, Kline JE. Mechanisms of stimulation of protein synthesis by thyroid hormones in vivo. Proc Natl Acad Sci U S A. 1968 Jun;60(2):652-9. doi: 10.1073/pnas.60.2.652. PMID: 5248822; PMCID: PMC225096.
Sol N, Wurdinger T. Platelet RNA signatures for the detection of cancer. Cancer Metastasis Rev. 2017 Jun;36(2):263-272. doi: 10.1007/s10555-017-9674-0. PMID: 28681241; PMCID: PMC5557864.
Tata JR, Widnell CC. Ribonucleic acid synthesis during the early action of thyroid hormones. Biochem J. 1966 Feb;98(2):604-20. doi: 10.1042/bj0980604. PMID: 5941352; PMCID: PMC1264884.
Thomas-Jardin S, Suresh S, Arce A, Novaresi N, Deng Q, Stein E, Thomas L, Lewis C, Ahn C, Evers BM, Akbay EA, Salvatierra ME, Lu W, Khan K, Solis Soto LM, Wistuba II, Minna JD, O'Donnell KA. The Integrated Stress Response Pathway Coordinates Translational Control of Multiple Immune Checkpoints in Lung Cancer. Cancer Res. 2025 Jul 15;85(14):2574-2590. doi: 10.1158/0008-5472.CAN-24-3844. PMID: 40327600; PMCID: PMC12260515.
Tidu A, Martin F. The interplay between cis- and trans-acting factors drives selective mRNA translation initiation in eukaryotes. Biochimie. 2024 Feb;217:20-30. doi: 10.1016/j.biochi.2023.09.017. Epub 2023 Sep 21. PMID: 37741547.
Tidu A, Martin F. The interplay between cis- and trans-acting factors drives selective mRNA translation initiation in eukaryotes. Biochimie. 2024 Feb;217:20-30. doi: 10.1016/j.biochi.2023.09.017. Epub 2023 Sep 21. PMID: 37741547.
Wang J, Wang W, Ma F, Qian H. A hidden translatome in tumors-the coding lncRNAs. Sci China Life Sci. 2023 Dec;66(12):2755-2772. doi: 10.1007/s11427-022-2289-6. Epub 2023 May 5. PMID: 37154857.
Wang S, Sun S. Translation dysregulation in neurodegenerative diseases: a focus on ALS. Mol Neurodegener. 2023 Aug 25;18(1):58. doi: 10.1186/s13024-023-00642-3. PMID: 37626421; PMCID: PMC10464328.
Wang SF, Chen MS, Chou YC, Ueng YF, Yin PH, Yeh TS, Lee HC. Mitochondrial dysfunction enhances cisplatin resistance in human gastric cancer cells via the ROS-activated GCN2-eIF2α-ATF4-xCT pathway. Oncotarget. 2016 Nov 8;7(45):74132-74151. doi: 10.18632/oncotarget.12356. PMID: 27708226; PMCID: PMC5342041.
Wang X, Zhao BS, Roundtree IA, Lu Z, Han D, Ma H, Weng X, Chen K, Shi H, He C. N(6)-methyladenosine Modulates Messenger RNA Translation Efficiency. Cell. 2015 Jun 4;161(6):1388-99. doi: 10.1016/j.cell.2015.05.014. PMID: 26046440; PMCID: PMC4825696.
Wang YH, Chen X, Bai YZ, Gao P, Yang Z, Guo Q, Lu YY, Zheng J, Liu D, Yang J, Tu PF, Zeng KW. Palmitoylation of PKCδ by ZDHHC5 in hypothalamic microglia presents as a therapeutic target for fatty liver disease. Theranostics. 2024 Jan 1;14(3):988-1009. doi: 10.7150/thno.89602. PMID: 38250049; PMCID: PMC10797291.
Wojda U. Alzheimer's disease lymphocytes: potential for biomarkers? Biomark Med. 2016;10(1):1-4. doi: 10.2217/bmm.15.79. Epub 2015 Dec 7. PMID: 26640978.
Wojsiat J, Laskowska-Kaszub K, Mietelska-Porowska A, Wojda U. Search for Alzheimer's disease biomarkers in blood cells: hypotheses-driven approach. Biomark Med. 2017 Oct;11(10):917-931. doi: 10.2217/bmm-2017-0041. Epub 2017 Oct 4. PMID: 28976776.
Wojsiat J, Zoltowska KM, Laskowska-Kaszub K, Wojda U. Oxidant/Antioxidant Imbalance in Alzheimer's Disease: Therapeutic and Diagnostic Prospects. Oxid Med Cell Longev. 2018 Jan 31;2018:6435861. doi: 10.1155/2018/6435861. PMID: 29636850; PMCID: PMC5831771.
Wolozin B, Ivanov P. Stress granules and neurodegeneration. Nat Rev Neurosci. 2019 Nov;20(11):649-666. doi: 10.1038/s41583-019-0222-5. Epub 2019 Oct 3. PMID: 31582840; PMCID: PMC6986315.
Wu CH, Yeh CT, Lin KH. Thyroid hormones suppress FOXM1 expression to reduce liver cancer progression. Oncol Rep. 2020 Oct;44(4):1686-1698. doi: 10.3892/or.2020.7716. Epub 2020 Aug 5. PMID: 32945493.
Xia X, Lei L, Qin W, Wang L, Zhang G, Hu J. GCN2 controls the cellular checkpoint: potential target for regulating inflammation. Cell Death Discov. 2018 Feb 14;4:20. doi: 10.1038/s41420-017-0022-5. PMID: 29531817; PMCID: PMC5841328.
Xuan JJ, et al. RMBase v3.0: Decode the landscape, mechanisms and functions of RNA modifications. Nucleic Acids Research. 2023 Nov 13. Available at http://bioinformaticsscience.cn/rmbase/
Xue S, Tian S, Fujii K, Kladwang W, Das R, Barna M. RNA regulons in Hox 5' UTRs confer ribosome specificity to gene regulation. Nature. 2015 Jan 1;517(7532):33-8. doi: 10.1038/nature14010. Epub 2014 Nov 19. PMID: 25409156; PMCID: PMC4353651.
Yang J, Kim KS, Iyirhiaro GO, Marcogliese PC, Callaghan SM, Qu D, Kim WJ, Slack RS, Park DS. DJ-1 modulates the unfolded protein response and cell death via upregulation of ATF4 following ER stress. Cell Death Dis. 2019 Feb 12;10(2):135. doi: 10.1038/s41419-019-1354-2. PMID: 30755590; PMCID: PMC6372623.
Yang Y, Wang Q. Three genes expressed in relation to lipid metabolism considered as potential biomarkers for the diagnosis and treatment of diabetic peripheral neuropathy. Sci Rep. 2023 May 29;13(1):8679. doi: 10.1038/s41598-023-35908-9. PMID: 37248406; PMCID: PMC10227002.
Ye C, Yu X, Yan J, Ye G, Shao Y. Biological roles and potential clinical values of snoRNAs in Cancer. Int J Biol Macromol. 2025 Sep 12;328(Pt 2):147654. doi: 10.1016/j.ijbiomac.2025.147654. Epub ahead of print. PMID: 40946988.
Yu D, Dai N, Wolf EJ, Corrêa IR Jr, Zhou J, Wu T, Blumenthal RM, Zhang X, Cheng X. Enzymatic characterization of mRNA cap adenosine-N6 methyltransferase PCIF1 activity on uncapped RNAs. J Biol Chem. 2022 Apr;298(4):101751. doi: 10.1016/j.jbc.2022.101751. Epub 2022 Feb 19. PMID: 35189146; PMCID: PMC8931429.
Yuan S, Zhou G, Xu G. Translation machinery: the basis of translational control. J Genet Genomics. 2024 Apr;51(4):367-378. doi: 10.1016/j.jgg.2023.07.009. Epub 2023 Aug 1. PMID: 37536497.
Zambrano A, García-Carpizo V, Villamuera R, Aranda A. Thyroid hormone increases bulk histones expression by enhancing translational efficiency. Mol Endocrinol. 2015 Jan;29(1):68-75. doi: 10.1210/me.2014-1235. PMID: 25422881; PMCID: PMC5414774.
Zhou ZD, Tan EK. Iron regulatory protein (IRP)-iron responsive element (IRE) signaling pathway in human neurodegenerative diseases. Mol Neurodegener. 2017 Oct 23;12(1):75. doi: 10.1186/s13024-017-0218-4. PMID: 29061112; PMCID: PMC5654065.
Fu L, Crawford L, Tong A, Luu N, Tanizaki Y, Shi YB. Sperm associated antigen 7 is activated by T3 during Xenopus tropicalis metamorphosis via a thyroid hormone response element within the first intron. Dev Growth Differ. 2022 Jan;64(1):48-58. doi: 10.1111/dgd.12764. PMID: 34862790; PMCID: PMC8810736.
Fu L, Li C, Na W, Shi YB. Thyroid hormone activates Xenopus MBD3 gene via an intronic TRE in vivo. Front Biosci (Landmark Ed). 2020 Jan 1;25(3):437-451. doi: 10.2741/4812. PMID: 31585895; PMCID: PMC8251660.
Sap J, de Magistris L, Stunnenberg H, Vennström B. A major thyroid hormone response element in the third intron of the rat growth hormone gene. EMBO J. 1990 Mar;9(3):887-96. doi: 10.1002/j.1460-2075.1990.tb08186.x. PMID: 2155782; PMCID: PMC551749.
Zhang A, Sieglaff DH, York JP, Suh JH, Ayers SD, Winnier GE, Kharitonenkov A, Pin C, Zhang P, Webb P, Xia X. Thyroid hormone receptor regulates most genes independently of fibroblast growth factor 21 in liver. J Endocrinol. 2015 Mar;224(3):289-301. doi: 10.1530/JOE-14-0440. Epub 2014 Dec 11. PMID: 25501997.
Jansen MS, Cook GA, Song S, Park EA. Thyroid hormone regulates carnitine palmitoyltransferase Ialpha gene expression through elements in the promoter and first intron. J Biol Chem. 2000 Nov 10;275(45):34989-97. doi: 10.1074/jbc.M001752200. PMID: 10956641.
Taylor E, Wynen H, Heyland A. Thyroid hormone membrane receptor binding and transcriptional regulation in the sea urchin Strongylocentrotus purpuratus. Front Endocrinol (Lausanne). 2023 May 26;14:1195733. doi: 10.3389/fendo.2023.1195733. PMID: 37305042; PMCID: PMC10250714.
Liu R, Li J, Nian Q, Tu G, Wang Z, Zhang R, Li G. CREB binding protein (CREBBP): Structure-based perspectives for the development of clinical inhibitors. Transl Oncol. 2025 Nov;61:102507. doi: 10.1016/j.tranon.2025.102507. Epub 2025 Aug 18. PMID: 40829266;
Saponaro F, Sestito S, Runfola M, Rapposelli S, Chiellini G. Selective Thyroid Hormone Receptor-Beta (TRβ) Agonists: New Perspectives for the Treatment of Metabolic and Neurodegenerative Disorders. Front Med (Lausanne). 2020 Jul 9;7:331. doi: 10.3389/fmed.2020.00331. PMID: 32733906; PMCID: PMC7363807.
Sordyl D, Boileau E, Bernat A, Maiti S, Mukherjee S, Moafinejad SN, Farsani MA, Shavina A, Cappannini A, Agostini G, Conticello SG, Stefaniak F, Dieterich C, Purta E, Bujnicki JM. MODOMICS: a database of RNA modifications and related information. 2025 update and 20th anniversary. Nucleic Acids Res. 2026 Jan 6;54(D1):D219-D225. doi: 10.1093/nar/gkaf1284. PMID: 41277531; PMCID: PMC12807697.
Boileau E, Wilhelmi H, Busch A, Cappannini A, Hildebrand A, Bujnicki JM, Dieterich C. Sci-ModoM: a quantitative database of transcriptome-wide high-throughput RNA modification sites. Nucleic Acids Res. 2025 Jan 6;53(D1):D310-D317. doi: 10.1093/nar/gkae972. PMID: 39498498; PMCID: PMC11701610.
Xuan J, Chen L, Chen Z, Pang J, Huang J, Lin J, Zheng L, Li B, Qu L, Yang J. RMBase v3.0: decode the landscape, mechanisms and functions of RNA modifications. Nucleic Acids Res. 2024 Jan 5;52(D1):D273-D284. doi: 10.1093/nar/gkad1070. PMID: 37956310; PMCID: PMC10767931.
29 refereed publications, 30 > abstracts, 4 patents
H-Index range: 11–16 (11/22/2024), depending on bibliographic databases, as some papers or journals may not be indexed. Citation counts can vary across different databases due to their unique indexing criteria. See Bibliographic Databases
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