Biology subjects

Safaeesirat, A.

Publications and source records attributed to Safaeesirat, A..

4 recordsLinked to original sources

bioRxiv2026

Motif-based model of transcription predicts effects of sequence variants in AR enhancers and reveals distinct functions for AR-associated transcription factors

Androgen receptor (AR)-mediated transcription plays a central role in prostate cancer development and progression, yet the contributions of individual transcription factors (TFs) to AR-dependent enhancer activity remain incompletely understood. Here we use a biophysically motivated, interpretable motif-based model to dissect these contributions from STARR-seq data in LNCaP cells. By fitting the model separately to androgen inducibility and to baseline enhancer activity, we resolve TFs into three functional classes: hormone-dependent drivers, constitutive activators, and dual-role factors that contribute to both. These patterns suggest that inducibility is associated not only with the presence of AR and co-activator motifs, but also with the relative absence of constitutive activators that may saturate enhancer output. We validate the model against an independent saturation-mutagenesis dataset spanning 40 AR enhancers, predicting mutational effects at single-base resolution (AUC = 0.76), and show that direct fitting to these data independently recovers known AR regulators. Finally, we apply the model to prostate cancer GWAS risk alleles in AR binding site regions, prioritizing four candidate variants predicted to reduce the DHT/EtOH enhancer activity ratio at these loci.

genomics↗

bioRxiv2026

Inference of enhancer-specific transcription factor interactions from gene expression data using a biophysical model

Transcription factors (TFs) play a central role in gene expression and regulation. In recent years, numerous experimental techniques have generated large-scale datasets, alongside computational methods aimed at inferring the role of TF-TF interactions in gene regulation. However, these approaches typically yield global interaction patterns across datasets, which may not accurately reflect local regulatory interactions at specific enhancers. Here, we model transcription using an Ising-type biophysical framework and introduce approximations based on its mean-field representation to infer TF-TF interactions at the level of individual enhancers from expression data, such as STARR-seq or fluorescent protein measurements. We validate our approach using simulated data and evaluate the effect of the strengths of TF-TF and TF-DNA interactions on inference accuracy. We then apply the model to experimental fluorescence data of gap genes for the eve stripe-2 (eve2) enhancer in the fruit fly embryo. The model successfully infers the established roles of the gap genes and predicts the possibility of cooperative and antagonistic interactions among them, which can be experimentally investigated.

biophysics↗

bioRxiv2026

Direct Counting of mRNA Copies Inside Individual Lipid Nanoparticles Using In Situ Lysis and Labeling

The optimization of mRNA-lipid nanoparticles (mRNA-LNPs) for therapeutic applications is limited in part by the inadequate characterization of mRNA payload heterogeneity. One current challenge is accurately measuring the number of mRNA copies within individual LNPs, where the standard method of intensity-based mRNA number determination is sensitive to fluorescent dye-dye interactions and heterogeneity of mRNA labeling. Here we present a single-particle microscopy method that combines direct counting of the mRNA copies per LNP with LNP size measurements. While confined in microwells, individual mRNA-LNPs are lysed to release their cargo and stained with a dye such that the number of mRNA molecules in each well can be directly counted using fluorescence microscopy. Since the method stains the mRNA cargo in situ, it enables characterization of LNPs formulated with therapeutic grade (e.g., unlabeled) mRNA. We applied this approach to two Onpattro(R)-based LNP formulations prepared using different formulation buffers, where the two formulations had different average mRNA copy number, particle size, and fraction of LNPs lacking mRNA. The ability to directly count the number of mRNA molecules in LNPs establishes a complimentary method to intensity-based mRNA number determination and supports the characterization and screening of clinically relevant LNP formulations.

biophysics↗

bioRxiv2024

Inference of Transcriptional Regulation From STARR-seq Data

One of the primary regulatory processes in cells is transcription, during which RNA polymerase II (Pol-II) transcribes DNA into RNA. The binding of Pol-II to its site is regulated through interactions with transcription factors (TFs) that bind to DNA at enhancer cis-regulatory elements. Measuring the enhancer activity of large libraries of distinct DNA sequences is now possible using Massively Parallel Reporter Assays (MPRAs), and computational methods have been developed to identify the dominant statistical patterns of TF binding within these large datasets. Such methods are global in their approach and may overlook important regulatory sites which function only within the local context. Here we introduce a method for inferring functional regulatory sites (their number, location and width) within an enhancer sequence based on measurements of its transcriptional activity from an MPRA method such as STARR-seq. The model is based on a mean-field thermodynamic description of Pol-II binding that includes interactions with bound TFs. Our method applied to simulated STARR-seq data for a variety of enhancer architectures shows how data quality impacts the inference and also how it can find local regulatory sites that may be missed in a global approach. We also apply the method to recently measured STARR-seq data on androgen receptor (AR) bound sequences, a TF that plays an important role in the regulation of prostate cancer. The method identifies key regulatory sites within these sequences which are found to overlap with binding sites of known co-regulators of AR. 1 Author SummaryWe present an inference method for identifying regulatory sites within a putative DNA enhancer sequence, given only the measured transcriptional output of a set of overlapping sequences using an assay like STARR-seq. It is based on a mean-field thermodynamic model that calculates the binding probability of Pol-II to its promoter and includes interactions with sites in the DNA sequence of interest. By maximizing the likelihood of the data given the model, we can infer the number of regulatory sites, their locations, and their widths. Since it is a local model, it can in principle find regulatory sites that are important within a local context that may get missed in a global fit. We test our method on simulated data of simple enhancer architectures and show that it is able to find only the functional sites. We also apply our method to experimental STARR-seq data from 36 androgen receptor bound DNA sequences from a prostate cancer cell line. The inferred regulatory sites overlap known important regulatory motifs and their ChIP-seq data in these regions. Our method shows potential at identifying locally important functional regulatory sites within an enhancer given only its measured transcriptional output.

genomics↗