Biology subjects

Forget, A.

Publications and source records attributed to Forget, A..

3 recordsLinked to original sources

bioRxiv2024

A PERTURBATION CELL ATLAS OF HUMAN INDUCED PLURIPOTENT STEM CELLS

Towards comprehensively investigating the genotype-phenotype relationships governing the human pluripotent stem cell state, we generated an expressed genome-scale CRISPRi Perturbation Cell Atlas in KOLF2.1J human induced pluripotent stem cells (hiPSCs) mapping transcriptional and fitness phenotypes associated with 11,739 targeted genes. Using the transcriptional phenotypes, we created a minimum distortion embedding map of the pluripotent state, demonstrating rich recapitulation of protein complexes, such as strong co-clustering of MRPL, BAF, SAGA, and Ragulator family members. Additionally, we uncovered transcriptional regulators that are uncoupled from cell fitness, discovering potential novel pluripotency (JOSD1, RNF7) and metabolic factors (ZBTB41). We validated these findings via phenotypic, protein-interaction, and metabolic tracing assays. Finally, we propose a contrastive human-cell engineering framework (CHEF), a machine learning architecture that learns from perturbation cell atlases to predict perturbation recipes that achieve desired transcriptional states. Taken together, our study presents a comprehensive resource for interrogating the regulatory networks governing pluripotency.

bioengineering↗

bioRxiv2024

Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines

This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools, status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Healths (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.

systems biology↗

bioRxiv2022

Structural insights into regulation of the PEAK3 scaffold by 14-3-3

The three members of the PEAK family of pseudokinases (PEAK1, PEAK2, and PEAK3) are molecular scaffolds that have recently emerged as important regulatory nodes in signaling pathways that control cell migration, morphology, and proliferation, and they are increasingly found to be mis-regulated in human cancers. While no structures of PEAK3 have been solved to date, crystal structures of the PEAK1 and PEAK2 pseudokinase domains revealed their dimeric organization. It remains unclear how dimerization plays a role in PEAK scaffolding functions, as no structures of PEAK family members in complex with their binding partners have been solved. Here, we report the cryo-EM structure of the PEAK3 pseudokinase, also adopting a dimeric state, and in complex with an endogenous 14-3-3 heterodimer purified from mammalian cells. Our structure reveals an asymmetric binding mode between PEAK3 and 14-3-3 stabilized by one pseudokinase domain and the Split HElical Dimerization (SHED) domain of the PEAK3 dimer. The binding interface is comprised of a canonical primary interaction involving two phosphorylated 14-3-3 consensus binding sites located in the N-terminal domains of the PEAK3 monomers docked in the conserved amphipathic grooves of the 14-3-3 dimer, and a unique secondary interaction between 14-3-3 and PEAK3 that has not been observed in any previous structures of 14-3-3/client complexes. Disruption of these interactions results in the relocation of PEAK3 to the nucleus and changes its cellular interactome. Lastly, we identify Protein Kinase D as the regulator of the PEAK3/14-3-3 interaction, providing a mechanism by which the diverse functions of the PEAK3 scaffold might be fine-tuned in cells.

biophysics↗