Biology subjects

Tyagi, N.

Publications and source records attributed to Tyagi, N..

7 recordsLinked to original sources

bioRxiv2026

EOLA1 functions in nucleotide salvage through deacetylating free N4-acetylcytidine

RNA-based medicines rely on modified nucleotides to promote immune evasion and in vivo efficacy. Nucleotides generated from RNA degradation are either exported or recycled through metabolically favorable salvage pathways, though whether modified nucleotides are efficiently recycled remains unclear. N4-acetylcytidine (acC) is a naturally occurring modification in rRNA and tRNA that has shown promise in therapeutic mRNA applications. However, N4-acetylation impairs cytidine deamination, the first step in cytidine salvage. Here, we investigate the endogenous mechanisms that enable acC metabolism. Through sensitive sequence and structural analyses, we identify the uncharacterized human ASCH domain protein EOLA1 as a key acC deacetylase in nucleotide salvage. EOLA1 inactivation leads to free intracellular acC accumulation and increased cytotoxicity upon nucleotide export inhibition. While steady-state acC levels in cellular RNAs remain unchanged, EOLA1-dependent regulation of free acC is evident basally and is exacerbated by exogenous mRNA delivery. Proteomic analyses place EOLA1 in proximity to ribosomal proteins, adjacent to endogenous acC sources. In vitro assays confirm EOLA1 specificity for acC, and structural analysis reveals a narrow nucleotide-binding pocket consistent with mononucleotide selectivity. These findings identify EOLA1 as a bona fide acC eraser and uncover a previously unrecognized pathway for recycling modified nucleotides with relevance to therapeutic RNA design.

molecular biology↗

bioRxiv2026

Physics-guided design of intrinsically disordered proteins

Intrinsically disordered protein regions (IDPs) are found across the tree of life and characterized by the lack of a stable 3D fold, encoding function through a vast ensemble of conformations. This plasticity makes rational design of IDPs challenging. Physics-based approaches capturing distinct aspects of sequence composition, charge patterning, and molecular interactions have emerged as powerful predictors of ensemble-derived properties. Here, we present a machine learning framework for proteome-scale de novo IDP design by rationally inverting physics-based models. We first program IDPs to tunably sense and respond to diverse biophysical cues and show that IDP ensembles can directly encode complex signal processing, including threshold detection, bandpass filtering, and Boolean-type multi-input logic. We next engineer multicomponent IDP mixtures with tailored emergent condensate properties, including layering and number of phases, compositional specificity, and RNA-dependent remodeling of structure and composition. Finally, we demonstrate designed IDPs that selectively partition into or deplete from biological condensates in living cells. Together, our framework establishes a flexible and scalable strategy for design of ensemble-derived and collective properties in dynamic biomolecules.

biophysics↗

bioRxiv2025

Regulation of airway fumarate by host and pathogen promotes S. aureus pneumonia

Staphylococcus aureus is a leading cause of healthcare-associated pneumonia, contributing significantly to morbidity and mortality worldwide. As a ubiquitous colonizer of the upper respiratory tract, S. aureus must undergo substantial metabolic adaptation to achieve persistent infection in the distinctive microenvironment of the lung. We observed that fumC, which encodes the enzyme that converts fumarate to malate, is highly conserved with low mutation rates in S. aureus isolates from chronic lung infections. Fumarate, a pro-inflammatory metabolite produced by macrophages during infection, is regulated by the host fumarate hydratase (FH) to limit inflammation. Here, we demonstrate that fumarate, which accumulates in the chronically infected lung, is detrimental to S. aureus, blocking primary metabolic pathways such as glycolysis and oxidative phosphorylation (OXPHOS). This creates a metabolic bottleneck that drives staphylococcal FH (FumC) activity for airway adaptation. FumC not only degrades fumarate but also directs its utilization into critical pathways including the tricarboxylic acid (TCA) cycle, gluconeogenesis and hexosamine synthesis to maintain metabolic fitness and form a protective biofilm. Itaconate, another abundant immunometabolite in the infected airway enhances FumC activity, in synergy with fumarate. In a mouse model of pneumonia, a {Delta}fumC mutant displays significant attenuation compared to its parent and complemented strains, particularly in fumarate- and itaconate-replete conditions. Our findings underscore the pivotal role of immunometabolites in promoting S. aureus pulmonary adaptation.

microbiology↗

bioRxiv2025

The T Cell-Specific miRNA-Target Network in Psoriasis: A Systematic Review

Psoriasis is a chronic skin disease characterized by keratinocyte hyperproliferation, involving T cells as the major players in the manifestation of lesional and systemic inflammation. With the extensive exploration of keratinocyte dysfunctionality, T cell-centric deregulation has gained much focus in the context of disease etiology. miRNAs, the molecular switches known to drive disease manifestation via altering keratinocyte biology, have not been much studied in the context of T cell alterations, with only a few reports. This study aims to explore T cell-associated miRNAs and their target signaling networks to understand their role in modulating psoriasis-specific T cell changes. miRNAs with altered expression in psoriatic T cells were identified through a meta-analysis of the existing literature and datasets. These miRNAs were then used to identify cognate targets and T cell signaling pathways to elucidate T cell-specific cascades influenced by miRNAs, potentially explaining miRNA driven T cell dysregulation in psoriasis. The study identified 14 miRNAs with altered expression in psoriatic T cells and 256 downstream targets involved in 15 T cell-associated signaling pathways. The miRNA-driven targets and pathways identified may dysregulate T cell activation, proliferation, survival, and subset differentiation. These findings suggest multiple miRNA-target signaling axes that require experimental validation to better understand T cell-mediated disease etiology.

systems biology↗

bioRxiv2023

genomicBERT and data-free deep-learning model evaluation

The genome, which serves as the inherent language directing the blueprint of life, offers significant analysis prospects by combining Natural Language Processing (NLP) and machine learning (ML). Integrating biological sequences with other digital healthcare information has potential to transform data-driven diagnostics. Large language models (LLMs) can be harnessed to decode the genomic language. This endeavor encounters three critical challenges: First, long biomolecular sequences require segmentation into smaller subunits, which is non-trivial since many biological "words" remain unknown. Second, the analysis of extended DNA sequences using LLMs demands a compute-intensive infrastructure. Third, ensuring reproducibility and reusability of modeling workflows remains an unresolved issue. To tackle these challenges, we introduce an empirical DNA tokenisation approach and a versatile, semantic-aware, genome language model --genomicBERT. The model is species-agnostic and operates seamlessly at the DNA or RNA levels. By introducing a reduced and specialized DNA vocabulary, our approach minimizes computational overhead and optimizes performance. Our benchmarking demonstrates that the genomicBERT matches or surpasses the performance of contemporary tools on the same datasets under different experimental conditions. To encourage collaboration and ease of access, we introduce genomicBERT as an integral component of the openly accessible conda package, genomeNLP. Validated across diverse case studies, genomicBERT lowers the barriers to decoding genomic language, relying solely on sequence data to extract meaningful insights. HighlightsO_LIThis novel model offers a compelling solution for DNA sequence analysis by significantly reducing model size and computational costs without compromising performance, setting a new standard for efficient model development. C_LIO_LIWe demonstrate that a powerful vocabulary and tokenization method helps to derive patterns from biological sequence data while accounting for hidden semantic rules. C_LIO_LIOur method is agnostic to species or biomolecule type as it is data-driven. Hence, it can be applied to DNA and RNA C_LIO_LIWe validate the important genomicBERT tokens by mapping back to the biologically significant motifs. C_LIO_LIWe present a publicly available genome language modeling toolkit called genomeNLP, specifically designed to combine computational linguistics and genomics, enabling researchers from biology backgrounds to analyze and interpret genomic sequences effectively. C_LI

bioinformatics↗

bioRxiv2021

Comparative analysis of codon usage patterns in SARS-CoV-2, its mutants and other respiratory viruses

The Coronavirus disease 2019 (COVID-19) outbreak caused by Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2) poses a worldwide human health crisis, causing respiratory illness with a high mortality rate. To investigate the factors governing codon usage bias in all the respiratory viruses, including SARS-CoV-2 isolates from different geographical locations (~62K), including two recently emerging strains from the United Kingdom (UK), i.e., VUI202012/01 and South Africa (SA), i.e., 501.Y.V2 codon usage bias (CUBs) analysis was performed. The analysis includes RSCU analysis, GC content calculation, ENC analysis, dinucleotide frequency and neutrality plot analysis. We were motivated to conduct the study to fulfil two primary aims: first, to identify the difference in codon usage bias amongst all SARS-CoV-2 genomes and, secondly, to compare their CUBs properties with other respiratory viruses. A biased nucleotide composition was found as most of the highly preferred codons were A/U-ending in all the respiratory viruses studied here. Compared with the human host, the RSCU analysis led to the identification of 11 over-represented codons and 9 under-represented codons in SARS-CoV-2 genomes. Correlation analysis of ENC and GC3s revealed that mutational pressure is the leading force determining the CUBs. The present study results yield a better understanding of codon usage preferences for SARS-CoV-2 genomes and discover the possible evolutionary determinants responsible for the biases found among the respiratory viruses, thus unveils a unique feature of the SARS-CoV-2 evolution and adaptation. To the best of our knowledge, this is the first attempt at comparative CUBs analysis on the worldwide genomes of SARS-CoV-2, including novel emerged strains and other respiratory viruses.

bioinformatics↗

bioRxiv2020

Integrated genomic view of SARS-CoV-2 in India

India first detected SARS-CoV-2, causal agent of COVID-19 in late January-2020, imported from Wuhan, China. March-2020 onwards; importation of cases from rest of the countries followed by seeding of local transmission triggered further outbreaks in India. We used ARTIC protocol based tiling amplicon sequencing of SARS-CoV-2 (n=104) from different states of India using a combination of MinION and MinIT from Oxford Nanopore Technology to understand introduction and local transmission. The analyses revealed multiple introductions of SARS-CoV-2 from Europe and Asia following local transmission. The most prevalent genomes with patterns of variance (confined in a cluster) remain unclassified, here, proposed as A4-clade based on its divergence within A-cluster. The viral haplotypes may link their persistence to geo-climatic conditions and host response. Despite the effectiveness of non-therapeutic interventions in India, multipronged strategies including molecular surveillance based on real-time viral genomic data is of paramount importance for a timely management of the pandemic.

microbiology↗