Biology subjects

Lahiri, A.

Publications and source records attributed to Lahiri, A..

At least 19 recordsLinked to original sources

bioRxiv2026

The human circular Tau 12->7 RNA is translated in mouse neurons and promotes tau aggregation

In addition to numerous mRNA isoforms, the human microtubule associated protein tau (MAPT) gene generates circular RNAs that are not known to be expressed in rodents. Using adeno associated viruses, we expressed human circular Tau RNA generated from backsplicing of exon 12 to exon 7 (circTau 12->7) in mouse brain. We observed rolling circle translation of circTau 12->7, similar to cultured human HEK293 cells. Despite using a ubiquitously active promoter, circTau 12->7 protein and a control circEGFP protein were only observed in mouse neurons, indicating neuron-specific translation of these circular RNAs. CircTau 12->7 expression resulted in tau aggregates resembling neurofibrillary tangles in mouse brain that included endogenous mouse tau protein. Our data support a model where neuron-specific translation of circTau RNAs induces 'linear', mRNA-translated tau aggregation, which could contribute to tauopathies, including Alzheimer's disease. Thus, targeting circTau RNAs could be a therapeutic approach for Alzheimer's disease.

neuroscience↗

bioRxiv2026

ddkg.skill: A Compositional Agent Skill for Translating Biomedical and Bioinformatics Questions into Cypher for the Data Distillery Knowledge Graph

Biomedical knowledge graphs can connect information across genes, phenotypes, tissues, pathways, experiments, and clinical resources, but they are difficult to query correctly without detailed knowledge of the graph. Large language models can help write Cypher code for knowledge graphs, yet a query focused on a bioinformatics task that looks reasonable may still use the wrong identifier, relationship direction, source, intermediate node, or output unit if the user is not completely trained on the schema. We developed ddkg.skill, an Agent Skill that can be loaded into a compatible LLM session to support querying of the NIH Common Fund Data Ecosystem Data Distillery Knowledge Graph (DDKG), a biomedical property graph integrating more than 180 ontologies, over 40 genomics datasets, and dozens of additional cross-domain biomedical datasets. Rather than utilizing a simple markdown prompt, ddkg.skill contains a controller, a compositional library of DDKG-specific references and structured tables, primary documentation, validated query patterns, a routing table, and a Python script that checks the internal links among these materials. The skill build for the December 2025 DDKG release contains 38 bundled files and 239 routing relationships and is identified by checksum so users can state the exact build used to compose a query. The evaluation reported here was performed separately on an earlier build with 38 files and 227 routing entries. In that orthogonal nine-test evaluation against a live December 2025 DDKG instance, five of seven tests targeting sources absent from the worked examples produced correct executed results. The evaluation also exposed a failed query, a cross-source comparison that was not biologically well posed, and errors in the skills own reference material that informed later revisions. The skill guides an AI through entity resolution, graph inspection, query construction, and validation for biomedical and bioinformatics queries, while keeping the DDKG itself as the source of returned results. This design provides a portable and versioned method for giving general-purpose AI systems practical knowledge of a complex biomedical knowledge graph to empower complex bioinformatics data integration. Three executed biomedical use cases further demonstrate cross-species phenotype-to-expression querying, genomic overlap between 4DN chromatin-loop anchors and GTEx eQTLs, and integration of congenital heart phenotype associations with ClinVar and GTEx heart evidence.

bioinformatics↗

bioRxiv2026

MOSAIC: Learning Graph Node Embeddings from Spectrally Isolated Dominant Subspaces of Accumulated Diffusion Operators

Graph embedding transforms complex networks into low-dimensional representations for analysis and downstream machine learning. Existing methods often rely on eigenvalue ordering, random-walk sampling, or neural optimization. We introduce MOSAIC, Multistep Operator Spectral Accumulation with Isolated Components, an unsupervised framework that constructs an accumulated diffusion operator from successive powers of a graph transition matrix. MOSAIC uses Krylov subspace analysis to identify dominant spectral components according to their convergence behavior. This approach emphasizes persistent and spectrally separated diffusion modes rather than selecting embedding coordinates solely by eigenvalue magnitude. We evaluated MOSAIC using a biomedical graph projection derived from the Data Distillery Knowledge Graph that contained 5,171 human genes and 10,147 phenotypes. Across embedding dimensions from 16 to 128, MOSAIC achieved node-type classification AUROCs from 0.9863 to 0.9926 and exceeded FastRP, Node2Vec, HashGNN, and GraphSAGE. For gene-phenotype link prediction, validation and test associations were withheld before embedding generation. Under progressive removal of all edges or specific edge classes, MOSAIC retained more node-label information than FastRP, including after extreme graph sparsification. In a focused DICER1 analysis, 106 of 137 documented phenotype associations received scores above 0.90, demonstrating concordance with known pleiotropic biology and prioritizing additional relationships for independent evaluation. These results support MOSAIC as a fully unsupervised framework for learning informative graph representations. Although demonstrated using a biomedical graph projection, MOSAIC is domain independent and can be applied to other complex networks

bioinformatics↗

bioRxiv2025

Recurrent acquisition of nuclease-protease pairs in antiviral immunity

Antiviral immune systems diversify by integrating new genes into existing pathways, creating new mechanisms of viral resistance. We identified genes encoding a predicted nuclease paired with a trypsin-like protease repeatedly acquired by multiple, otherwise unrelated antiviral immune systems in bacteria. Cell-based and biochemical assays revealed the nuclease is a proenzyme that cleaves DNA only after activation by its partner protease. Phylogenetic analysis showed that two distinct immune systems, Hachiman and AVAST, use the same mechanism of proteolytic activation despite their independent evolutionary origins. Examination of nuclease-protease inheritance patterns identified caspase-nuclease (canu) genomic loci that confer antiviral defense in a pathway reminiscent of eukaryotic caspase activation. These results uncover the coordinated activities of pronucleases and their activating proteases within different immune systems and show how coevolution enables defense system innovation.

microbiology↗

bioRxiv2025

A miniature CRISPR-Cas10 enzyme confers immunity by an inverse signaling pathway

Microbial and viral co-evolution has created immunity mechanisms involving oligonucleotide signaling that share mechanistic features with human anti-viral systems1. In these pathways, including CBASS and type III CRISPR systems in bacteria and cGAS-STING in humans, oligonucleotide synthesis occurs upon detection of virus or foreign genetic material in the cell, triggering the antiviral response2-4. In a surprising inversion of this process, we show here that the CRISPR-related enzyme mCpol synthesizes cyclic oligonucleotides constitutively as part of an active mechanism that maintains cell health. Cell-based experiments demonstrated that the absence or loss of mCpol-produced cyclic oligonucleotides triggers cell death, preventing spread of viruses that attempt immune evasion by depleting host cyclic nucleotides. Structural and mechanistic investigation revealed mCpol to be a di-adenylate cyclase whose product, c-di-AMP, prevents toxic oligomerization of the effector protein 2TM{beta}. Analysis of cells by fluorescence microscopy showed that lack of mCpol allows 2TM{beta}-mediated cell death due to inner membrane collapse. These findings unveil a powerful new defense strategy against virus-mediated immune suppression, expanding our understanding of oligonucleotides in cell health and disease. These results raise the possibility of similar protective roles for cyclic oligonucleotides in other organisms including humans.

biochemistry↗

bioRxiv2025

Targeting Colorectal Cancer Liver Metastasis Through Repurposing Metabolic and Immune Inhibitors : A Theoretical Study

Liver metastasis from colorectal cancer is a major cause of death in patients with advanced colorectal cancer (CRC) and remains a difficult challenge in oncology. Inspite of commnedable developments in medical inventions, the complexity and poor prognosis of metastatic stage, alliterative strategies are required to address Colorectal cancer liver metastasis (CRLM). This paper presents a computational study on drug repurposing for CRLM using a Boolean Network model. We comprehensively analyze CRLM signaling pathways such as WNT, PI3K/AKT/mTOR, MAPK, Hedgehog, TGF-{beta}, NF-kB, NOTCH and HGF and target them with metabolic and immune inhibitors. This study utilizes a computational analysis to evaluate single and multi-agent treatments across scenarios involving single and multiple genetic mutations. Our findings highlights the efficacy of metabolic inhibitors such as Simvastatin, Metformin, and predict compatible partner drugs to enhance their effiacy. Additionaly, we predict possible improvements for CRLM treatment using an immunotherapy drug like Pembrolizumab. Overall, this paper provides new insights into drug repurposing to improve CRLM treatment outcomes.

cancer biology↗

bioRxiv2024

Structural and dynamic consequences of inosine, 2'-O-methylation, and N6-methyladenosine modifications in RNA G-quadruplex: A molecular dynamics study

RNA G-quadruplexes (rG4) are stable non-canonical structures, often found to regulate important biological processes such as transcription, translation, splicing, RNA localization, and other steps in gene expression. rG4 structures can also influence DNA-related processes e.g. DNA replication, telomere elongation and homeostasis, and recombination. Due to the involvement of rG4s in such important processes, these structures are potential therapeutic targets against different diseases e.g., viral infection and cancer. Hence, a better understanding of the structure and stability of rG4s and their role in various therapeutically important cellular processes can help in the design of effective therapeutic strategies for targeting such structures. In the present study, we present our findings on the structural and dynamic effect of RNA modifications (inosine; 2'-O-methylation, and m6A-methylation) on RNA G-quadruplex structure from molecular dynamics simulations. Additionally, we also report the dynamic and energetic aspects of inter-quadruplex interactions and the influence of non-G purine tetrads neighboring the inter-quadruplex junction on the interactions.

biophysics↗

bioRxiv2024

Mitochondrial calcium uniporter regulates human fibroblast-like synoviocytes invasion via altering mitochondrial dynamics and dictates rheumatoid arthritis pathogenesis

Rheumatoid arthritis (RA) is characterized by the aggressive migration and invasion of fibroblast-like synoviocytes (FLS) into cartilage and bone, a process that is significantly influenced by mitochondrial calcium uptake. This study highlights the critical role of mitochondrial calcium uniporter (MCU) in regulating FLS migration and mitochondrial dynamics, and its potential as a therapeutic target in RA. Notably, RA-FLS exhibited increased MCU expression and mitochondrial dysfunction compared to controls. Treatment with Ru360, a potent MCU inhibitor significantly reduced RA-FLS migration, calcium influx, and mitochondrial reactive oxygen species (ROS) levels, while restoring mitochondrial morphology and enhancing ATP production. Further analysis of MCU complex expression revealed elevated levels of MCU and other regulatory subunits (EMRE, MICU1, MICU2) in RA-FLS compared to controls, indicating mitochondrial dysfunction in RA. Mechanistically, MCU inhibition altered gene expression related to cytoskeletal dynamics, focal adhesion, and metabolic pathways. In human RA-FLS, MCU inhibition suppressed migration and invasion both in vitro and in vivo. The interaction between MCU and Miro1, essential for mitochondrial transport, was validated, and its disruption impaired FLS migration. Our findings highlight MCU as a promising therapeutic target to inhibit FLS migration and ameliorate RA progression.

immunology↗

bioRxiv2024

The splicing modulator CLK1 is a candidate oncogenic dependency in pediatric high-grade gliomas

BackgroundPediatric brain tumors are the leading cause of disease-related mortality in children, yet many aggressive tumors lack effective therapies. RNA splicing is a hallmark of cancer, but it has not yet been systematically studied in pediatric brain tumors. MethodsWe analyzed 729 pediatric brain tumors spanning histologies and molecular subtypes to quantify differential tumor splicing. We developed the Splicing Burden Index (SBI) to enable cross-sample comparisons and performed hierarchical clustering of highly variable splice events to define splicing-informed tumor groups. These were integrated with clinical outcomes, pathway activity, and proteogenomic data. Recurrent splice events were prioritized for predicted functional impact, and in vitro perturbation studies were performed targeting the splicing kinase CDC-like kinase 1 (CLK1). ResultsSBI revealed substantial inter- and intra-histology heterogeneity. Clusters were enriched for histologies and molecular subtypes, several of which were independently associated with survival beyond histology and clinical covariates. Spliceosome pathway activity varied across clusters and was associated with worse survival, yet was not correlated with SBI, indicating distinct dimensions of splicing dysregulation. Functional prioritization identified a recurrent in CLK1 exon 4, required for canonical kinase activity. CLK1 exon 4 inclusion followed an oncofetal pattern and showed context-dependent associations with outcome distinct from total CLK1 expression. Pharmacologic inhibition and exon 4-specific perturbation of CLK1 reduced tumor cell viability and disrupted cancer-relevant splicing and transcriptional programs. ConclusionsThis study systematically characterizes splicing in pediatric brain tumors, identifies splicing-informed subgroups, and prioritizes CLK1 exon 4 as an oncofetal tumor-specific event, motivating further preclinical exploration. Key Points[bullet] Splicing analysis of 729 pediatric CNS tumors identifies splicing-defined clusters. [bullet]CLK1 exon 4 inclusion is widespread and developmentally regulated. [bullet]Exon-level CLK1 regulation shows context-dependent links to prognosis in aggressive CNS tumors.

genomics↗

bioRxiv2024

The Open Pediatric Cancer Project

BackgroundIn 2019, the Open Pediatric Brain Tumor Atlas (OpenPBTA) was created as a global, collaborative open-science initiative to genomically characterize 1,074 pediatric brain tumors and 22 patient-derived cell lines. Here, we present an extension of the OpenPBTA called the Open Pediatric Cancer (OpenPedCan) Project, a harmonized open-source multi-omic dataset from 6,112 pediatric cancer patients with 7,096 tumor events across more than 100 histologies. Combined with RNA-Seq from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), OpenPedCan contains nearly 48,000 total biospecimens (24,002 tumor and 23,893 normal specimens). FindingsWe utilized Gabriella Miller Kids First (GMKF) workflows to harmonize WGS, WXS, RNA-seq, and Targeted Sequencing datasets to include somatic SNVs, InDels, CNVs, SVs, RNA expression, fusions, and splice variants. We integrated summarized CPTAC whole cell proteomics and phospho-proteomics data, miRNA-Seq data, and have developed a methylation array harmonization workflow to include m-values, beta-vales, and copy number calls. OpenPedCan contains reproducible, dockerized workflows in GitHub, CAVATICA, and Amazon Web Services (AWS) to deliver harmonized and processed data from over 60 scalable modules which can be leveraged both locally and on AWS. The processed data are released in a versioned manner and accessible through CAVATICA or AWS S3 download (from GitHub), and queryable through PedcBioPortal and the NCIs pediatric Molecular Targets Platform. Notably, we have expanded PBTA molecular subtyping to include methylation information to align with the WHO 2021 Central Nervous System Tumor classifications, allowing us to create research-grade integrated diagnoses for these tumors. ConclusionsOpenPedCan data and its reproducible analysis module framework are openly available and can be utilized and/or adapted by researchers to accelerate discovery, validation, and clinical translation.

genomics↗

bioRxiv2024

Hachiman is a genome integrity sensor

Hachiman is a broad-spectrum antiphage defense system of unknown function. We show here that Hachiman comprises a heterodimeric nuclease-helicase complex, HamAB. HamA, previously a protein of unknown function, is the effector nuclease. HamB is the sensor helicase. HamB constrains HamA activity during surveillance of intact dsDNA. When the HamAB complex detects DNA damage, HamB helicase activity liberates HamA, unleashing nuclease activity. Hachiman activation degrades all DNA in the cell, creating phantom cells devoid of both phage and host DNA. We demonstrate Hachiman activation in the absence of phage by treatment with DNA-damaging agents, suggesting that Hachiman responds to aberrant DNA states. Phylogenetic similarities between the Hachiman helicase and eukaryotic enzymes suggest this bacterial immune system has been repurposed for diverse functions across all domains of life.

biochemistry↗

bioRxiv2024

Ready, set, yellow! Color Preference of Indian Free-ranging Dogs

Most of the research on color vision related behaviors in dogs has involved training the dogs to perform visual discrimination tasks. We investigated the meaning of color to untrained Indian free-ranging dogs (FRDs). Using one-time multi-option choice tests for color preference in 134 adult dogs, we found the dogs to prefer yellow objects over blue or gray ones (p < 0.001, Cramers V = 0.433) while there was no preference between blue and gray (N = 102, p = 0.165). We next pitted a yellow object against a gray object that had food. Here, the dogs ignored the food to approach the yellow object first, both when the food was biscuit (N = 52, p < 0.001, Cramers V = 0.576) and chicken (N = 61, p < 0.001, Cramers V = 0.540), indicating the color preference to be quite strong. Color preference has previously been investigated in many other animals and has implications for behaviors like mate choice and foraging. Our study provides a new perspective into the ecology of Indian FRDs and might have implications for pet dogs as well, if they too show this preference. HighlightsO_LIIndian free-ranging dogs (FRDs) show preference toward the color yellow over blue and gray. C_LIO_LIIndian FRDs show no preference between blue and gray colors. C_LIO_LIAttraction towards a yellow object can be stronger than attraction towards food rewards for Indian FRDs. C_LI

animal behavior and cognition↗

bioRxiv2023

Genome-wide Characterization of Diverse Bacteriophages Enabled by RNA-Binding CRISPRi

Bacteriophages constitute one of the largest sources of unknown gene content in the biosphere. Even for well-studied model phages, robust experimental approaches to identify and study their essential genes remain elusive. We uncover and exploit the conserved vulnerability of the phage transcriptome to facilitate genome-wide protein expression knockdown via programmable RNA-binding protein dRfxCas13d (CRISPRi-ART) across diverse phages and their host. Establishing the first broad-spectrum phage functional genomics platform, we predict over 90 essential genes across four phage genomes, a third of which have no known function. These results highlight hidden infection strategies encoded in the most abundant biological entities on earth and provide a facile platform to study them.

microbiology↗

bioRxiv2023

Eukaryotic RNA-guided endonucleases evolved from a unique clade of bacterial enzymes

RNA-guided endonucleases form the crux of diverse biological processes and technologies, including adaptive immunity, transposition, and genome editing. Some of these enzymes are components of insertion sequences (IS) in the IS200/IS605 and IS607 transposon families. Both IS families encode a TnpA transposase and TnpB nuclease, an RNA-guided enzyme ancestral to CRISPR-Cas12. In eukaryotes and their viruses, TnpB homologs occur as two distinct types, Fanzor1 and Fanzor2. We analyzed the evolutionary relationships between prokaryotic TnpBs and eukaryotic Fanzors, revealing that a clade of IS607 TnpBs with unusual active site arrangement found primarily in Cyanobacteriota likely gave rise to both types of Fanzors. The wide-spread nature of Fanzors imply that the properties of this particular group of IS607 TnpBs were particularly suited to adaptation and evolution in eukaryotes and their viruses. Experimental characterization of a prokaryotic IS607 TnpB and virally encoded Fanzor1s uncovered features that may have fostered coevolution between TnpBs/Fanzors and their cognate transposases. Our results provide insight into the evolutionary origins of a ubiquitous family of RNA-guided proteins that shows remarkable conservation across domains of life.

molecular biology↗

bioRxiv2023

Drug Target Identification in Triple Negative Breast Cancer Stem Cell Pathways: a computational study of gene regulatory pathways using Boolean networks

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer associated with an early age of onset, greater propensity towards metastasis, and poorer clinical outcomes. It accounts for 10% to 20% of newly diagnosed breast cancer cases and disproportionately affects individuals from the African American race. While TNBC is sensitive to chemotherapy, it is also prone to relapse. This is because chemotherapy successfully targets the primary TNBC tumor cell but often fails to target the subpopulation of TNBC stem cells. TNBC stem cells display cancerous traits such as cell cycle progression, survival, proliferation, apoptosis inhibition, and epithelial-mesenchymal transition. To study the cancer initiating behavior of the TNBC stem cells, we studied their underlying signaling pathways using Boolean networks(BN). BNs are effective in capturing the causal interactions taking place in signaling pathways. We built the BN from the pathway literature and used it to evaluate the efficacies of eleven targeted inhibitory drugs in suppressing cancer-promoting genes. We simulated the BN when the pathways had single or multiple mutations, with a maximum of three mutations at a time. Our findings indicated that STAT3, GLI, and NF-{kappa}B are the most optimal targets for inhibition. These genes are known regulators of the cancer-promoting genes in the pathway,hence our model agrees with the existing biological literature. Therefore inhibiting these three genes has the potential to prevent TNBC relapse. Additionally, our studies found that drug efficacies decreased as mutations increased in the pathway. Furthermore, we noticed that combinations of drugs performed better than single drugs.

systems biology↗

bioRxiv2023

MACI: A machine learning-based approach to identify drug classes of antibiotic resistance genes from metagenomic data

Novel methodologies are now essential for identification of antibiotic resistant pathogens in order to resist them. Here, we are presenting a model, MACI (Machine learning-based Antibiotic resistance gene-specific drug Class Identification) that can take metagenomic fragments as input and predict the drug class of antibiotic resistant genes. We trained the model to learn underlying patterns of genes using the Comprehensive Antibiotic Resistance Database. It comprises of 116 drug classes with a total of 2960 representative sequences. Among these 116 drug classes, we found 22 categories (contributing approximately 85% of the overall sequence-data) surpassed other 94 drug classes based on the number of fragments. The model showed an average precision of 0.83 and a recall of 0.81 for these 22 drug classes. Moreover, the model predicted multidrug resistant classes with higher performance score (precision and recall: 0.9 and 0.88 respectively) compared to single drug resistant categories (0.77 and 0.75). Post to this, we analysed these 22 drug classes to find out class-specific overlapping patterns of nucleotides that led to accurate classification. This way, we found five drug classes viz. "carbapenem;cephalosporin;penam", "cephalosporin", "cephamycin", "cephalosporin;monobactam;penam;penem", and "fluoroquinolone". Additionally, the positions of these significant patterns corroborated with the functional domains of majority of antibiotic resistance genes in that drug class, indicating their biological importance. These class-specific patterns play a pivotal role in rapid identification of some drug classes comprising antibiotic resistance genes. Further analysis showed that bacterial species, containing these five-drug classes, were found to have well-known multidrug resistance property.

bioinformatics↗

bioRxiv2023

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Further MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web-server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

bioinformatics↗

bioRxiv2023

Structural and thermodynamic consequences of base pairs containing pseudouridine and N1-methylpseudouridine in RNA duplexes

Pseudouridine ({Psi}) is one of the most common post-transcriptional modifications in RNA and has been known to play significant roles in several crucial biological processes. The N1-methyl derivative of pseudouridine i.e N1-methylpseudouridine has also been reported to be important for the stability and function of RNA. Several studies suggest the importance of pseudouridine and N1-methylpseudouridine in mRNA therapeutics. The critical contribution of pseudouridine, especially that of its N1-methyl derivative in the efficiency of the COVID-19 mRNA vaccines, suggests the requirement to better understand the role of these modifications in the structure, stability and function of RNA. In the present study, we have investigated the consequences of the presence of these modifications in the stability of RNA duplex structures by analyzing different structural properties, hydration characteristics and energetics of these duplexes. We have previously studied the structural and thermodynamic properties of RNA duplexes with an internal {Psi}-A pair and reported the stabilizing effect of {Psi} over U (Deb, I. et al. Sci Rep 9, 16278 (2019)). Here, we have extended our work to understand the properties of RNA duplexes with an internal m1{Psi}-A pair and also theoretically demonstrate the effect of substituting internal U-G, U-U and U-C mismatches with the {Psi}-G, {Psi}-U and {Psi}-C mismatches and also with the m1{Psi}-G, m1{Psi}-U and m1{Psi}-C mismatches respectively, within dsRNA. Our results indicate the context-dependent stabilization of base stacking interactions by N1-methylpseudouridine compared to uridine and pseudouridine, presumably resulting from the increased molecular polarizability due to the presence of the methyl group.

biophysics↗