Biology subjects

Adhikary, R.

Publications and source records attributed to Adhikary, R..

3 recordsLinked to original sources

bioRxiv2026

Impact of competition between precursor and mature microRNAs on stochastic gene expression

MicroRNAs (miRNAs) are key post-transcriptional regulators, processed from precursor miRNAs (pre-miRNAs) into mature miRNAs through nuclear and cytoplasmic proteins. Recent evidence shows that pre-miRNAs and mature miRNAs can compete for the same target mRNAs, yet the impact of this miRNA maturation-driven competition on gene expression noise remains unknown. We address this in widespread feedback motifs where both pre- and mature miRNAs degrade a proteins transcripts, and the protein itself either activates or represses miRNA transcription. Using a mathematical model, we show that miRNA maturation tunes the behavior of positive or negative feedback loops, which function as bistable switches or oscillators at the mean-field level, respectively. The relative degradation of mature versus pre-miRNAs and the mRNA-miRNA co-degradation rates can jointly modulate the parameter regions of bistability or oscillations. Moreover, for positive feedback, stochastic simulations reveal that bimodal mRNA distributions emerge near the saddle-node bifurcation boundaries, but not always within the bistable regions. Bimodal mRNA distributions also appear for negative feedback, but outside the region of limit cycles. Importantly, in both feedback types, such noise-induced bimodality emerges in regions where mean-field analysis predicts no bistability or limit cycles. These results demonstrate that noise-induced phenotypic variability cannot necessarily be linked to underlying deterministic bifurcations and elucidate how miRNA maturation shapes stochastic gene expression in regulatory motifs relevant to development and disease.

biophysics↗

bioRxiv2025

A simplified hybrid capture approach retains high specificity and enables PCR-free workflow

Hybrid capture is a critical technology for selective enrichment of genomic regions of interest, enabling cost-effective focused sequencing in both clinical and research applications. We present a simplified hybrid capture approach that eliminates complexities typically associated with hybridization-based selection methods by directly loading the hybridization product onto the sequencing flow cell. The workflow removes bead-based steps, washes, and post-hybridization PCR, while retaining high capture specificity and library complexity. The approach is enabled by the development of a streptavidin flow cell surface, a method to circularize and amplify captured targets on the flow cell, and a fast hybridization protocol. We demonstrate application across targeted panel sizes and improvements to library complexity and variant calling. We also show how the approach can be used to create an entirely PCR-free targeted sequencing workflow that further improves variant calling and enables the detection of repeat expansions.

genomics↗

bioRxiv2022

Effects of microRNA-mediated negative feedback on gene expression noise

Micro-RNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally in eukaryotes by binding with target mRNAs and preventing translation. miRNA-mediated feedback motifs are ubiquitous in various genetic networks which control cellular decision-making. A key question is how such a feedback mechanism may affect gene expression noise. To answer this, we have developed a mathematical model to study the effects of a miRNA-dependent negative feedback loop on mean expression and noise in target mRNAs. Combining analytics and simulations, we show the existence of an expression threshold demarcating repressed and expressed regimes in agreement with earlier studies. The steady-state mRNA distributions are bimodal near the threshold, where copy numbers of mRNAs and miRNAs exhibit enhanced anticorrelated fluctuations. Moreover, variation of negative-feedback strength shifts the threshold locations and modulates the noise profiles. Notably, the miRNA-mRNA binding affinity and feedback strength collectively shape the bimodality. We also compare our model with a direct auto-repression motif, where a gene produces its own repressor. Auto-repression fails to produce bimodal mRNA distributions as found in miRNA-based indirect repression, suggesting the crucial role of miRNAs in creating phenotypic diversity. Together, we demonstrate how miRNA-dependent negative feedback modifies the expression threshold and leads to a broader parameter regime of bimodality compared to the no-feedback case.

biophysics↗