Emma Dann

All Publications

• Systematic discovery of pro- and anti-HIV host factors in primary human CD4+ T cells. Cell Rathore, U., Dugan, E., Thornton, H., Kumar, V. E., Dajani, R., Burdick, R. C., Young, J. M., Steinhart, Z., Lao, R., Delviks-Frankenberry, K. A., Choi, W., Henriques, W. S., Echeverria, I., Dann, E., Dureja, I., Pathak, N., Arce, M. M., McKetney, J., Umhoefer, J. M., Parulekar, S., Schmidt, R., Polacco, B. J., Neidleman, J., Montano, M., Nguyen, V. Q., Sali, A., Levy, J. A., Tenthorey, J. L., Cheng, Y., Roan, N. R., Swaney, D. L., Kaake, R. M., Dodgson, S. E., Hiatt, J., Pathak, V. K., Malik, H. S., Krogan, N. J., Marson, A. 2026

  Abstract

  Host factors that promote or restrict human immunodeficiency virus (HIV) infection in human CD4+ T cells have not been comprehensively identified. We employed orthogonal genome-wide CRISPR activation (CRISPRa) and CRISPR knockout screens in primary CD4+ T cells to discover pro- and anti-HIV host factors systematically. Secondary pooled screens and individual perturbations validated high-confidence hits and revealed diverse mechanisms of action. CRISPRa uncovered multiple potent antiviral factors, including PI16, PPID, SHISA3, and ITM2A. PI16 interacts with host factors involved in HIV fusion and inhibits viral entry, whereas PPID (Cyp40), a paralog of the proviral cyclophilin CypA, binds capsid and reduces nuclear import of the HIV core. Structural modeling, evolutionary analyses, and targeted mutagenesis revealed domains and residues required for PPID-mediated HIV restriction, including non-human primate ortholog substitutions that enhance antiviral activity. Together, these data define the functional HIV-host interaction landscape in primary human T cells and uncover new mechanisms modulating infection.

  View details for DOI 10.1016/j.cell.2026.03.046

  View details for PubMedID 42013838

• Causal modelling of gene effects from regulators to programs to traits. Nature Ota, M., Spence, J. P., Zeng, T., Dann, E., Milind, N., Marson, A., Pritchard, J. K. 2025

  Abstract

  Genetic association studies provide a unique tool for identifying candidate causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits. Here we propose approaches to bridge this gap by combining quantitative estimates of gene-trait relationships from loss-of-function burden tests1 with gene-regulatory connections inferred from Perturb-seq experiments2 in relevant cell types. By combining these two forms of data, we aim to build causal graphs in which the directional associations of genes with a trait can be explained by their regulatory effects on biological programs or direct effects on the trait3. As a proof of concept, we constructed a causal graph of the gene-regulatory hierarchy that jointly controls three partially co-regulated blood traits. We propose that perturbation studies in trait-relevant cell types, coupled with gene-level effect sizes for traits, can bridge the gap between genetic association and biological mechanism.

  View details for DOI 10.1038/s41586-025-09866-3

  View details for PubMedID 41372418

  View details for PubMedCentralID 8596853

• Causal modeling of gene effects from regulators to programs to traits: integration of genetic associations and Perturb-seq. bioRxiv : the preprint server for biology Ota, M., Spence, J. P., Zeng, T., Dann, E., Marson, A., Pritchard, J. K. 2025

  Abstract

  Genetic association studies provide a unique tool for identifying causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits. Here we propose new approaches to bridge this gap by combining quantitative estimates of gene-trait relationships from loss-of-function burden tests with gene-regulatory connections inferred from Perturb-seq experiments in relevant cell types. By combining these two forms of data, we aim to build causal graphs in which the directional associations of genes with a trait can be explained by their regulatory effects on biological programs or direct effects on the trait. As a proof-of-concept, we constructed a causal graph of the gene regulatory hierarchy that jointly controls three partially co-regulated blood traits. We propose that perturbation studies in trait-relevant cell types, coupled with gene-level effect sizes for traits, can bridge the gap between genetics and biology.

  View details for DOI 10.1101/2025.01.22.634424

  View details for PubMedID 39896538