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Rovsing, A. B.

Publications and source records attributed to Rovsing, A. B..

4 recordsLinked to original sources

Current challenges in GWAS integration and fine-mapping for variant interpretation

Over the past two decades, genome-wide association studies (GWAS) have identified thousands of trait- and disease-associated loci. However, the mechanistic understanding of these loci remains incomplete, which limits our ability to understand gene regulation and cellular programs underlying complex traits, predict disease risk, and develop therapeutics targeted to root causes. Here, we describe the current challenges for using GWAS to prioritize variants for functional follow-up experiments. These challenges span multiple domains, including limitations in data sharing and harmonization, limitations of statistical and functional fine-mapping, and the ambiguity in the added value of emerging deep learning frameworks for variant effect prediction as a complementary approach alongside traditional statistical genetics methods. We analyze these variant prioritization methods and suggest a multi-modal approach for resolving GWAS loci to a focused set of high-confidence variants for functional exploration. Fully realizing the potential of GWAS will require harmonized summary statistics and broader sharing of in-sample linkage disequilibrium (LD) data to enable robust and scalable causal variant prioritization.

genetics↗

Rational design of synthetic proteins using a genome-scale CRISPR screen

Protein structure prediction using deep learning has revolutionized protein design. Yet, our understanding of protein function remains a key limitation for designing novel proteins that perform complex biological tasks. Here, we adopt a massively-parallel, function-first approach to rationally design synthetic proteins. Using genome-scale CRISPR activation, we overexpress [~]19,000 human proteins and measure their impact on precise gene editing. We identify over 800 native proteins that promote homology-directed repair. Using top candidates, we then design synthetic genome editors -- Targeted Repair fUsion Editors (TruEditors) -- by fusing full-length proteins or smaller core domains to the Cas9 nuclease. We develop 12 unique TruEditors that improve precise gene editing in diverse cell types and at genomic loci where existing methods for precise gene editing fail. Using affinity proteomics, we show that these synthetic proteins work by coordinating with endogenous DNA repair complexes. The delivery of TruEditors via mRNA more than doubles the rate of chimeric antigen receptor (CAR) insertion into the TRAC locus of primary human T cells, enhancing CAR T cell-directed tumor cell killing, and improves precise editing in human pluripotent stem cells more than three-fold. Overall, our study demonstrates that genome-wide protein overexpression screens can guide the rational design of synthetic proteins for specific biological tasks.

bioengineering↗

Multiparametric optimization of human primary B-cell cultures using Design of Experiments

B cells are essential in the immune system, driving antibody production, cytokine secretion, and antigen presentation. Studies in mouse models have illuminated key mechanisms underlying B-cell activation, differentiation, class-switch recombination, and somatic hypermutation. However, the extent to which these findings translate to human biology remains unclear. To address this, we developed a human primary B-cell culture system using feeder cells engineered to express CD40L, supplemented with the cytokines BAFF, IL-4, and IL-21. Using a Design of Experiments (DOE) approach, we optimized critical parameters and dissected the individual contributions of each specific factor. Our results reveal that BAFF plays a negligible role, and IL-21 has more subtle effects, whereas CD40L and IL-4 are critical determinants of cell viability, proliferation and IgE class-switching. Furthermore, we find that engineered feeder cells can serve equally well as a source of cytokines, but providing these in purified form increases the flexibility of the system. This platform enables detailed investigation of human B-cell biology, offering insights into intrinsic and extrinsic regulators of antibody responses and providing a foundation for in vitro production of human primary antibodies.

immunology↗

Resistance to vincristine in cancerous B-cells by disruption of p53-dependent mitotic surveillance

The frontline therapy R-CHOP for patients with diffuse large B-cell lymphoma (DLBCL) has remained unchanged for two decades despite numerous phase III clinical trials investigating new alternatives. Multiple large studies have uncovered genetic subtypes of DLBCL enabling a targeted approach. To further pave the way for precision oncology, we perform genome-wide CRISPR screening to uncover the cellular response to one of the components of R-CHOP, vincristine, in the DLBCL cell line SU-DHL-5. We discover important pathways and subnetworks using gene-set enrichment analysis and protein-protein interaction networks and identify genes related to mitotic spindle organization that are essential during vincristine treatment. Inhibition of KIF18A, a mediator of chromosome alignment, using the small molecule inhibitor BTB-1 causes complete cell death in a synergistic manner when administered together with vincristine. We also identify the genes KIF18B and USP28 for which CRISPR/Cas9-directed knockout induces vincristine resistance across two DLBCL cell lines. Mechanistic studies show that lack of KIF18B or USP28 counteracts a vincristine-induced p53 response involving the mitotic surveillance pathway (USP28-53BP1-p53). Collectively, our CRISPR screening data uncover potential drug targets and mechanisms behind vincristine resistance, which may support the development of future drug regimens. Key pointsO_LIInhibition of the mitotic surveillance pathway (USP28-53BP1-p53) and KIF18B induces resistance to vincristine C_LIO_LISubstantial synergistic effects observed when using the KIF18A-inhibitor BTB-1 with vincristine in eradicating GCB-subtype DLBCL cells C_LI

cancer biology↗