Biology subjects

Forrest, W. F.

Publications and source records attributed to Forrest, W. F..

4 recordsLinked to original sources

bioRxiv2026

Joint analysis of multiply perturbed cells improves statistical power and cost efficiency in Perturb-seq

Perturb-seq measures transcriptomic responses to genetic perturbations at scale, but conventional designs that enrich for one guide RNA per cell remain resource-intensive. Standard analyses discard cells carrying multiple guides, further limiting the usable yield from each experiment. Here, we characterize how incorporating these guide multiplets affects signal recovery, information loss, and cost reduction. At the highest guide burden, cells showed increased stress and suppressed cell-cycle progression. We develop PerturbMatch, a scalable statistical framework to analyze guide multiplets. Among different classes of guide multiplets, doublets and triplets recovered perturbation responses more accurately than higher-order multiplets. Across three 5000-gene Perturb-seq screens with increasing guide loading, per-cell costs decreased by up to 81% while information loss remained within 1.5-fold of the loss observed between technical replicates. In existing genome-wide Perturb-seq data, incorporating previously discarded guide multiplets increased usable cell numbers and improved statistical power. Compared with a singlet holdout set, adding guide multiplets moved signal recovery closer to the theoretical expected reproducibility. Overall, we recommend a design that intentionally includes single-guide cells, guide doublets, and guide triplets to improve cost efficiency while preserving signal recovery.

genomics↗

bioRxiv2024

Engineering ERα degraders with pleiotropic ubiquitin ligase ligands maximizes therapeutic efficacy by co-opting distinct effector ligases

Proximity-inducing compounds that modulate target protein homeostasis are an emerging therapeutic strategy [1]. While the inherent complexity of these bifunctional compounds poses challenges for rational design and bioavailability, their composition also provides opportunities to co-opt specific cellular proteins to maximize therapeutic impact. Here, we systematically evaluate the cellular efficacy, biophysical mechanisms, and therapeutic benefits of a series of bifunctional degrader compounds, that are all engineered with the Estrogen Receptor-alpha (ER)-inhibitor endoxifen linked to different bioactive ubiquitin ligase ligands. Bifunctional ER degraders that incorporate CRL4-CRBN-binding ligands promoted the most potent ER degradation, whereas those incorporating either CRL2-VHL- or IAP-binding ligands maximized the depth of ER degradation. Notably, ER degraders containing pan-IAP antagonist ligands significantly decreased the proliferation of ER-dependent cells relative to clinical-stage ER-degraders, including the SERDs fulvestrant and GDC-9545 and the bifunctional degrader ARV-471. Mechanistic studies revealed that pan-IAP antagonist-based ER degraders uniquely promote TNF-dependent cell death, unlike the clinical-stage comparators. Remarkably, the pan-IAP antagonist-ER-degraders co-opt distinct effector ligases to achieve dual therapeutic effects: they harness XIAP within tumor cells to promote ER degradation, and activate cIAP1/2 within tumor and immune cells to induce TNF that drives tumor cell death. Our studies demonstrate a broader concept that co-opting the discrete functions of a selected set of cellular effectors, while simultaneously modulating therapeutic target protein homeostasis, are dual strategies that can be leveraged to maximize the efficacy of induced proximity therapeutics.

cancer biology↗

bioRxiv2021

MiDAS - Meaningful Immunogenetic Data at Scale

Human immunogenetic variation in the form of HLA and KIR types has been shown to be strongly associated with a multitude of immune-related phenotypes. We present MiDAS, an R package enabling statistical association analysis and using immunogenetic data transformation functions for HLA amino acid fine mapping, analysis of HLA evolutionary divergence as well as HLA-KIR interactions. MiDAS closes the gap between inference of immunogenetic variation and its efficient utilization to make meaningful discoveries.

bioinformatics↗

bioRxiv2018

Multiple-gene targeting and mismatch tolerance can confound analysis of genome-wide pooled CRISPR screens

Genome-wide loss-of-function screens using the CRISPR/Cas9 system allow the efficient discovery of cancer cell vulnerabilities. While several studies have focused on correcting for DNA cleavage toxicity biases associated with copy number alterations, the effects of sgRNAs co-targeting multiple genomic loci in CRISPR screens have not been discussed yet. In this work, we analyze CRISPR essentiality screen data from 391 cancer cell lines to characterize biases induced by multi-target sgRNAs. We investigate two types of multi-targets: on-targets predicted through perfect sequence complementarity, and off-targets predicted through sequence complementarity with up to two nucleotide mismatches. We found that the number of on-targets and off-targets both increase sgRNA activity in a cell line-specific manner, and that existing additive models of gene knockout effects fail at capturing genetic interactions that may occur between co-targeted genes. We use synthetic lethality between paralog genes to show that genetic interactions can introduce biases in essentiality scores estimated from multi-target sgRNAs. We further show that single-mismatch tolerant sgRNAs can confound the analysis of gene essentiality and lead to incorrect co-essentiality functional networks.

bioinformatics↗