Biology subjects

Santesmasses, D.

Publications and source records attributed to Santesmasses, D..

4 recordsLinked to original sources

bioRxiv2025

Highly Efficient Multiplexed Genome Engineering and Clone Selection to Enable Next Generation Induced Pluripotent Stem Cell (iPSC)-based Cell Therapies

Human induced pluripotent stem cells (iPSCs) have revolutionized regenerative medicine and cellular therapies. To improve the functionality and safety of iPSC-based therapies, genome engineering has been employed to disrupt gene expression and introduce therapeutic transgenes. However, current genome editing methods face significant challenges, including labor-intensive procedures, low efficiency, and safety concerns. Here, we report a novel and efficient approach for multiplex genome engineering in iPSCs using Integrase-mediated Programmable Genomic Integration (I-PGI) technology. I-PGI combines CRISPR-mediated genome editing and site-specific integrases, enabling precise gene insertion without DNA double-strand breaks. By optimizing I-PGI components and protocols, we achieved multiple gene knockouts and knock-ins of up to five transgenes in a single process. Additionally, we developed a streamlined workflow for enrichment, deposition, and screening of single-cell clones with the desired genome edits. This approach significantly accelerates and improves the precision of multiplex iPSC engineering, paving the way for next-generation iPSC-based therapies.

genomics↗

bioRxiv2024

Large Serine Integrase Off-Target Discovery with Deep Learning for Genome Wide Prediction

Large Serine Integrases (LSIs) hold significant therapeutic promise due to their ability to efficiently incorporate gene-sized DNA into the human genome, offering a method to integrate healthy genes in patients with monogenic disorders or to insert gene circuits for the development of advanced cell therapies. To advance the application of LSIs for human therapeutic applications, new technologies and analytical methods for predicting and characterizing off-target recombination by LSIs are required. It is not experimentally tractable to validate off-target editing at all potential off-target sites in therapeutically relevant cell types because of sample limitations and genetic variation in the human population. To address this gap, we constructed a deep learning model named IntQuery that can predict LSI activity genome-wide. For Bxb1 integrase, IntQuery was trained on quantitative off-target data from 410,776 cryptic attB sequences discovered by Cryptic-seq, an unbiased in vitro discovery technology for LSI off-target recombination. We show that IntQuery can accurately predict in vitro LSI activity, providing a tool for in silico off-target prediction of large serine integrases to advance therapeutic applications.

bioinformatics↗

bioRxiv2024

Large Serine Integrase Off-target Discovery and Validation for Therapeutic Genome Editing

While numerous technologies for the characterization of potential off-target editing by CRISPR/Cas9 have been described, the development of new technologies and analytical methods for off-target recombination by Large Serine Integrases (LSIs) are required to advance the application of LSIs for therapeutic gene integration. Here we describe a suite of off-target recombination discovery technologies and a hybrid capture validation approach as a comprehensive framework for off-target characterization of LSIs. HIDE- Seq (High-throughput Integrase-mediated DNA Event Sequencing) is a PCR-free unbiased genome-wide biochemical assay capable of discovering sites with LSI- mediated free DNA ends (FDEs) and off-target recombination events. Cryptic-Seq is a PCR-based unbiased genome-wide biochemical or cellular-based assay that is more sensitive than HIDE-Seq but is limited to the discovery of sites with off-target recombination. HIDE-Seq and Cryptic-Seq discovered 38 and 44,311 potential off-target sites respectively. 2,455 sites were prioritized for validation by hybrid capture NGS in LSI- edited K562 cells and off-target integration was detected at 52 of the sites. We benchmarked the sensitivity of our LSI off-target characterization framework against unbiased whole genome sequencing (WGS) on LSI-edited samples, and off-target integration was detected at 5 sites with an average genome coverage of 40x. This reflects a greater than 10-fold increase in sensitivity for off-target detection compared to WGS, however only 4 of the 5 sites detected by WGS were also validated by hybrid capture NGS. The dissemination of these technologies will help advance the application of LSIs in therapeutic genome editing by establishing methods and benchmarks for the sensitivity of off-target detection.

genomics↗

bioRxiv2019

The tuatara genome: insights into vertebrate evolutionfrom the sole survivor of an ancient reptilian order

The tuatara (Sphenodon punctatus), the only living member of the archaic reptilian order Rhynchocephalia (Sphenodontia) once widespread across Gondwana, is an iconic and enigmatic terrestrial vertebrate endemic to New Zealand. A key link to the now extinct stem reptiles from which dinosaurs, modern reptiles, birds and mammals evolved, the tuatara provides exclusive insights into the ancestral amniotes. The tuatara genome, at [~]5 Gbp, is among the largest vertebrate genomes assembled. Analysis of this genome and comparisons to other vertebrates reinforces the uniqueness of the tuatara. Phylogenetic analyses indicate tuatara diverged from the snakes and lizards [~]250 MYA. This lineage also shows moderate rates of molecular evolution, with instances of punctuated evolution. Genome sequence analysis identifies expansions of protein, non-protein-coding RNA families, and repeat elements, the latter of which show an extraordinary amalgam of reptilian and mammalian features. Sequencing of this genome provides a valuable resource for deep comparative analyses of tetrapods, as well as for tuatara biology and conservation. It also provides important insights into both the technical challenges and the cultural obligations associated with genome sequencing.

genomics↗