Biology subjects

Bogard, N.

Publications and source records attributed to Bogard, N..

5 recordsLinked to original sources

bioRxiv2025

Massively parallel assay of human splice variants reveals cis-regulatory drivers of disease-associated and cell type-specific splicing regulation

Splice-disrupting variants (SDVs) underlie many human diseases, yet systematic functional maps of their effects across cell types remain limited. We developed Cell-type Oriented Massively Parallel reporter Assay of Splicing Signatures (COMPASS) to measure splicing outcomes for 87,546 single and double variants across more than 1,700 genes in five human cell lines of diverse tissue origin. COMPASS targets disease relevant gene sets, including ACMG actionable genes and SFARI autism-associated genes, enabling systematic dissection of splicing impacts in health and disease. Our measurements reveal numerous SDVs, including ClinVar variants currently classified as variants of uncertain significance. Using prime editing, we validate variant effects in the genome for ClinVar variants in BIN1, a gene implicated in Alzheimers disease, cancer, and cardiac pathology. Benchmarking our MPRA against state-of-the-art predictive models shows that while these approaches capture broad variant effects, important gaps remain. Our data reveal putative RNA-binding protein motifs whose disruption drives splicing changes, providing mechanistic insight into variant impact. Finally, cross-cell line comparisons reveal a distinct subset of variants that drive cell type-specific splicing programs. This study delivers the largest cell type-resolved, base-resolution atlas of splicing variant effects to date, providing a resource to support variant reclassification in clinical genomics and aid in the selection of therapeutic targets. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/681955v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@81765dorg.highwire.dtl.DTLVardef@181e55dorg.highwire.dtl.DTLVardef@f96eaeorg.highwire.dtl.DTLVardef@1d16d4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

Switching circuits: Modulating NK cell response to TGF-β using novel receptors

Engineered natural killer (NK) cells offer a promising treatment strategy for multiple therapeutic areas, yet tuning cell function to diverse environmental and inhibitory contexts remains a significant challenge. An attractive strategy for functional rescue in the presence of inhibitory ligands is the use of switch receptors, surface-expressed chimeric receptors that convert an external inhibitory cue into an intracellular activation signal. Here, we discover novel engineered switch receptors in NK cells that are responsive to TGF-{beta}, a soluble inhibitory factor present in many therapeutic contexts. Through a pooled screen of an 11,131 member library, we identified multiple novel signaling endodomains and endodomain combinations that improve both the acute cytotoxicity and persistence of NK cells in the presence of TGF-{beta}, demonstrating a novel and flexible approach to switch receptor discovery.

immunology↗

bioRxiv2023

TRAFfic signals: High-throughput CAR discovery in NK cells reveals novel TRAF-binding endodomains that drive enhanced persistence and cytotoxicity

Natural killer (NK) cells are a promising alternative therapeutic platform to CAR T cells given their favorable safety profile and potent killing ability. However, CAR NK cells suffer from limited persistence in vivo, which is, in part, thought to be the consequence of limited cytokine signaling. To address this challenge, we developed an innovative high-throughput screening strategy to identify CAR endodomains that could drive enhanced persistence while maintaining potent cytotoxicity. We uncovered a family of TRAF-binding endodomains that outperform benchmarks in primary NK cells along dimensions of persistence and cytotoxicity, even in low IL-2 conditions. This work highlights the importance of cell-type-specific cell therapy engineering and unlocks a wide range of high-throughput molecular engineering avenues in NK cells.

immunology↗

bioRxiv2019

Deep exploration networks for rapid engineering of functional DNA sequences

Engineering gene sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence generation. The generated sequences can however get stuck in local minima, have low diversity and their fitness depends heavily on initialization. Here, we develop deep exploration networks (DENs), a type of generative model tailor-made for searching a sequence space to minimize the cost of a neural network fitness predictor. By making the network compete with itself to control sequence diversity during training, we obtain generators capable of sampling hundreds of thousands of high-fitness sequences. We demonstrate the power of DENs in the context of engineering RNA isoforms, including polyadenylation and cell type-specific differential splicing. Using DENs, we engineered polyadenylation signals with more than 10-fold higher selection odds than the best gradient ascent-generated patterns and identified splice regulatory elements predicted to result in highly differential splicing between cell lines.

genomics↗

bioRxiv2018

Predicting the Impact of cis-Regulatory Variation on Alternative Polyadenylation

Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over three million APA reporters, built by inserting random sequence into twelve distinct 3UTR contexts. Predictions are highly accurate across both synthetic and genomic contexts; when tasked with inferring APA in human 3UTRs, APARENT outperforms models trained exclusively on endogenous data. Visualizing features learned across all network layers reveals that APARENT recognizes sequence motifs known to recruit APA regulators, discovers previously unknown sequence determinants of cleavage site selection, and integrates these features into a comprehensive, interpretable cis-regulatory code. Finally, we use APARENT to quantify the impact of genetic variants on APA. Our approach detects pathogenic variants in a wide range of disease contexts, expanding our understanding of the genetic origins of disease.

genomics↗