Biology subjects

Pellin, D.

Publications and source records attributed to Pellin, D..

6 recordsLinked to original sources

bioRxiv2024

Modeling integration site data for safety assessment with MELISSA

Gene and cell therapies pose safety concerns due to potential insertional mutagenesis by viral vectors. We introduce MELISSA, a statistical framework for analyzing Integration Site (IS) data to assess insertional mutagenesis risk. MELISSA employs regression models to estimate and compare gene-specific integration rates and their impact on clone fitness. We tested MELISSA under three settings. First, we conducted simulation studies to verify its performance under controlled conditions. Second, we characterized the IS profile of a lentiviral vector on Mesenchymal Stem Cells (MSCs) and compared it with that of Hematopoietic Stem and Progenitor Cells (HSPCs), in addition to comparing the in vitro clonal dynamics of MSCs isolated from alternative tissues. Finally, we applied MELISSA to published IS data from patients enrolled in gene therapy clinical trials, successfully identifying both known and novel genes that drive changes in clone growth through vector integration. MELISSA identifies over- and under-targeted genes, estimates IS impact and explores biological relevance through pathway analysis. This offers a quantitative tool for researchers, clinicians, and regulators to bridge the gap between IS data and safety and efficacy evaluation, facilitating the generation of comprehensive data packages supporting Investigational New Drug (IND) and Biologics License (BLA) applications and the development of safe and effective gene and cell therapies.

bioinformatics↗

bioRxiv2024

Scalable assessment of genome editing off-targets associated with genetic variants

Genome editing with RNA-guided DNA binding factors carries risk of off-target editing at homologous sequences. Genetic variants may introduce sequence changes that increase homology to a genome editing target, thereby increasing risk of off-target editing. Conventional methods to verify candidate off-targets rely on access to cells with genomic DNA carrying these sequences. However, for candidate off-targets associated with genetic variants, appropriate cells for experimental verification may not be available. Here we develop a method, Assessment By Stand-in Off-target LentiViral Ensemble with sequencing (ABSOLVE-seq), to integrate a set of candidate off-target sequences along with unique molecular identifiers (UMIs) in genomes of primary cells followed by clinically relevant gene editor delivery. Gene editing of dozens of candidate off-target sequences may be evaluated in a single experiment with high sensitivity, precision, and power. We provide an open-source pipeline to analyze sequencing data. This approach enables experimental assessment of the influence of human genetic diversity on specificity evaluation during gene editing therapy development.

genomics↗

bioRxiv2023

Gene editing without ex vivo culture evades genotoxicity in human hematopoietic stem cells

Gene editing the BCL11A erythroid enhancer is a validated approach to fetal hemoglobin (HbF) induction for {beta}-hemoglobinopathy therapy, though heterogeneity in edit allele distribution and HbF response may impact its safety and efficacy. Here we compared combined CRISPR-Cas9 endonuclease editing of the BCL11A +58 and +55 enhancers with leading gene modification approaches under clinical investigation. We found that combined targeting of the BCL11A +58 and +55 enhancers with 3xNLS-SpCas9 and two sgRNAs resulted in superior HbF induction, including in engrafting erythroid cells from sickle cell disease (SCD) patient xenografts, attributable to simultaneous disruption of core half E-box/GATA motifs at both enhancers. We corroborated prior observations that double strand breaks (DSBs) could produce unintended on- target outcomes in hematopoietic stem and progenitor cells (HSPCs) such as long deletions and centromere-distal chromosome fragment loss. We show these unintended outcomes are a byproduct of cellular proliferation stimulated by ex vivo culture. Editing HSPCs without cytokine culture bypassed long deletion and micronuclei formation while preserving efficient on-target editing and engraftment function. These results indicate that nuclease editing of quiescent hematopoietic stem cells (HSCs) limits DSB genotoxicity while maintaining therapeutic potency and encourages efforts for in vivo delivery of nucleases to HSCs.

genetics↗

bioRxiv2022

Stochastic modelling of cell differentiation networks from partially-observed clonal tracking data

MotivationClarifying how hematopoietic stem cells differentiate into mature cell types is important for understanding how they attain specific functions and offers the potential for therapeutic manipulation. Over the past decades, clonal tracking has proven to be capable of unveiling population dynamics and hierarchical relationships in vivo. For this reason, clonal tracking studies are required for safety and long-term efficacy assessment in gene therapy. However, many standard clonal tracking studies consider only a subset of cell-types and are subject to noise. ResultsIn this work, we propose a stochastic framework that investigates the dynamics of cell differentiation from typical clonal tracking data subject to measurement noise, false-negative errors, and systematically unobserved cell types. Our framework is based on stochastic reaction networks combined with extended Kalman filtering and Rauch-Tung-Striebel smoothing. Our tool can provide statistical support to biologists in gene therapy clonal tracking studies to better understand clonal reconstitution dynamics. AvailabilityThe stochastic framework is implemented in the [Formula] package Karen which is available for download at https://github.com/delcore-luca/Karen. The code that supports the findings of this study is openly available at https://github.com/delcore-luca/CellDifferentiationNetworks. Contactl.del.core@rug.nl

systems biology↗

bioRxiv2022

TRACKING HEMATOPOIETIC STEM CELL EVOLUTION IN A WISKOTT-ALDRICH CLINICAL TRIAL

Hematopoietic Stem Cells (HSC) are the cells that give rise to all other blood cells and, as such, they are crucial in the healthy development of individuals. Wiskott-Aldrich Syndrome (WAS) is a severe disorder affecting the regulation of hematopoietic cells and is caused by mutations in the WASP gene. We consider data from a revolutionary gene therapy clinical trial, where HSC harvested from 3 WAS patients bone marrow have been edited and corrected using viral vectors. Upon re-infusion into the patient, the HSC multiply and differentiate into other cell types. The aim is to unravel the cell multiplication and cell differentiation process, which has until now remained elusive. This paper models the replenishment of blood lineages resulting from corrected HSC via a multivariate, density-dependent Markov process and develops an inferential procedure to estimate the dynamic parameters given a set of temporally sparsely observed trajectories. Starting from the master equation, we derive a system of non-linear differential equations for the evolution of the first- and second-order moments over time. We use these moment equations in a generalized method-of-moments framework to perform inference. The performance of our proposal has been evaluated by considering different sampling scenarios and measurement errors of various strengths using a simulation study. We also compared it to another state-of-the-art approach and found that our method is statistically more efficient. By applying our method to the Wiskott-Aldrich Syndrome gene therapy data we found strong evidence for a myeloid-based developmental pathway of hematopoietic cells where fates of lymphoid and myeloid cells remain coupled even after the loss of erythroid potential. All code used in this manuscript can be found in the online Supplement, and the latest version of the code is available at github. com/dp3ll1n/SLCDP_v1.0.

bioinformatics↗

bioRxiv2018

CRISPR-SURF: Discovering regulatory elements by deconvolution of CRISPR tiling screen data

Tiling screens using CRISPR-Cas technologies provide a powerful approach to map regulatory elements to phenotypes of interest, but computational methods that effectively model these experimental approaches for different CRISPR technologies are not readily available. Here we present CRISPR-SURF, a deconvolution framework to identify functional regulatory regions in the genome from data generated by CRISPR-Cas nuclease, CRISPR interference (CRISPRi), or CRISPR activation (CRISPRa) tiling screens. We validated CRISPR-SURF on previously published and new data, identifying both experimentally validated and new potential regulatory elements. With CRISPR tiling screens now being increasingly used to elucidate the regulatory architecture of the non-coding genome, CRISPRSURF provides a generalizable and accessible solution for the discovery of regulatory elements.

bioinformatics↗