Biology subjects

Becerra, B.

Publications and source records attributed to Becerra, B..

4 recordsLinked to original sources

bioRxiv2025

In cellulo DNA assembly for targeted genomic integration and rearrangement in human cells

Although therapeutic genome editing holds great potential to remedy diverse inherited and acquired disorders, targeted installation of medium to large sized genomic modifications in therapeutically relevant cells remains challenging. We have developed an approach that permits DNA sequence assembly and integration in human cells leveraging CRISPR-targeted dual flap synthesis. This method, named prime assembly, allows for RNA-programmable site-specific integration of single- or double-stranded DNA fragments. Unlike homology-directed repair, prime assembly was similarly active in dividing and non-dividing cells. We applied prime assembly to perform targeted exon recoding, transgene integration, and megabase-scale rearrangements, including at therapeutically relevant loci in primary human cells. Prime assembly expands the capabilities of genome engineering by enabling the targeted integration of medium to large sized DNA sequences without relying on double-stranded DNA donors, nuclease-driven double strand breaks, or cell cycle progression.

molecular biology↗

bioRxiv2024

CRISPR-CLEAR: Nucleotide-Resolution Mapping of Regulatory Elements via Allelic Readout of Tiled Base Editing

CRISPR tiling screens have advanced the identification and characterization of regulatory sequences but are limited by low resolution arising from the indirect readout of editing via guide RNA sequencing. This study introduces CRISPR-CLEAR, an end-to-end experimental assay and computational pipeline, which leverages targeted sequencing of CRISPR-introduced alleles at the endogenous target locus following dense base-editing mutagenesis. This approach enables the dissection of regulatory elements at nucleotide resolution, facilitating a direct assessment of genotype-phenotype effects.

genomics↗

bioRxiv2023

A molecular glue approach to control the half-life of CRISPR-based technologies

Cas9 is a programmable nuclease that has furnished transformative technologies, including base editors and transcription modulators (e.g., CRISPRi/a), but several applications of these technologies, including therapeutics, mandatorily require precision control of their half-life. For example, such control can help avert any potential immunological and adverse events in clinical trials. Current genome editing technologies to control the half-life of Cas9 are slow, have lower activity, involve fusion of large response elements (> 230 amino acids), utilize expensive controllers with poor pharmacological attributes, and cannot be implemented in vivo on several CRISPR-based technologies. We report a general platform for half-life control using the molecular glue, pomalidomide, that binds to a ubiquitin ligase complex and a response-element bearing CRISPR-based technology, thereby causing the latters rapid ubiquitination and degradation. Using pomalidomide, we were able to control the half-life of large CRISPR-based technologies (e.g., base editors, CRISPRi) and small anti-CRISPRs that inhibit such technologies, allowing us to build the first examples of on-switch for base editors. The ability to switch on, fine-tune and switch-off CRISPR-based technologies with pomalidomide allowed complete control over their activity, specificity, and genome editing outcome. Importantly, the miniature size of the response element and favorable pharmacological attributes of the drug pomalidomide allowed control of activity of base editor in vivo using AAV as the delivery vehicle. These studies provide methods and reagents to precisely control the dosage and half-life of CRISPR-based technologies, propelling their therapeutic development.

bioengineering↗

bioRxiv2021

Probing molecular specificity with deep sequencing and biophysically interpretable machine learning

Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene regulation and signal transduction. Next-generation sequencing (NGS) based assays are increasingly being used to profile these interactions with high-throughput. However, these assays do not provide the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to define sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it determines the absolute affinity of protein-ligand interactions. It can also profile the kinetics of kinase-substrate interactions. By constructing a biophysically robust foundation for profiling sequence recognition, ProBound opens up new avenues for decoding biological networks and rationally engineering protein-ligand interactions.

genomics↗