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Anastasia, E.

Publications and source records attributed to Anastasia, E..

3 recordsLinked to original sources

Gigabase-scale deletion scanning of the human genome

What fraction of the human genome is essential for cellular viability? To date, essentiality in human cells has been mapped almost exclusively at the level of individual open reading frames (ORFs). Whether noncoding regions and broader architectural features of the genome are required for human cells to remain viable, and where the boundaries of any such regions lie, remains largely unexplored. Here we introduce Shred-seq, which couples Type I-C CRISPR-Cas3-mediated deletions with phage polymerase-based genotyping to enable large-scale deletion scans of the human genome. Shred-seq leverages thousands of genomically integrated, mapped target sites as launchpads for Cas3 to initiate unidirectional deletions ranging in size from hundreds of base pairs (bp) to hundreds of kb. Breakpoints are directly captured at high resolution by in vitro or in situ transcription from flanking phage polymerase promoters, enabling bulk or single-cell phenotyping, respectively. In this proof-of-concept, we generate and genotype 36,257 independent deletions originating from 9,604 Cas3 launchpads, individually spanning 100 bp to 500 kb, collectively covering 461 Mb (14% of the human genome), and totaling to 2.55 Gb of deleted sequence ([~]10-fold coverage of these regions). Surviving deletions are depleted not only for essential protein-coding genes, but also for active, conserved and mutation-constrained non-coding sequences, directly quantifying purifying selection across both coding and noncoding intervals. Deletion length distributions further enable annotation-agnostic fine mapping of essential region boundaries and establish an empirical lower bound on the fraction of the human genome required for cellular viability. Finally, we demonstrate compatibility with single-cell RNA-seq (scRNA-seq), enabling direct linkage of specific deletions to transcriptional phenotypes. Together, Shred-seq provides a scalable platform for the systematic dissection of genome architecture and noncoding function, as well as for generating training data for predictive and generative models of genome structure-function, analogous to the roles that conventional Perturb-seq screens play for ORFs. By enabling the empirical delimitation of the genomic content required for human cellular viability, Shred-seq may also open a path to the construction of a minimal human genome.

genomics↗

GCAD: a Computational Framework for Mammalian Genetic Program Computer-Aided Design

Genetic programs can direct living systems to perform diverse, pre-specified functions. As the library of parts available for building such programs continues to expand, computation-guided design is increasingly helpful and necessary. Predictive models aid the challenging design process, but iterative simulation and experimentation are intractable for complex functions. Computer-aided design accelerates this process, but existing tools do not yet capture the behavior of mammalian-specific parts and population-level effects needed for mammalian synthetic biologists. To address these needs, we developed a framework for mammalian genetic program computer-aided design. Starting with a user-defined design specification to quantify circuit performance, the framework uses a genetic algorithm to search through possible designs. Circuit space is defined by a library of experimentally characterized parts and dynamical systems models for gene expression in a heterogeneous cell population. We developed this genetic algorithm using a directed graph-based formulation with biologically constrained rules to explore regulatory connections and parts. We evaluated the framework for design problems of varying complexity, including programs we describe as an amplifier, signal conditioner, and pulse generator, demonstrating that the algorithm can successfully find optimal circuit designs. Finally, we experimentally evaluated selected circuits, demonstrating the path from a predicted circuit design to experimental testing and highlighting the importance of characterization for enabling predictive design. Overall, this framework establishes general approaches that can be refined and expanded, accelerating the design and implementation of mammalian genetic programs.

synthetic biology↗

The yEvo Mutation Browser: Enhancing student understanding of experimental evolution and genomics through interactive data visualization

Experimental evolution is a powerful method for studying the relationship between genotype and phenotype by observing how populations genetically adapt to controlled selective pressures. In educational settings, this approach also offers a dynamic way for students to engage with molecular genetics. One such educational effort, known as "yEvo" (yeast evolution), introduces experimental evolution into high school classrooms, allowing students to evolve the bakers yeast Saccharomyces cerevisiae under various stressors and investigate the resulting genetic changes. While the hands-on experiments have been successful in fostering student interest and understanding of evolution, the downstream data analysis (interpreting whole-genome sequencing results of evolved yeast compared to the ancestor) remains a challenge. Students often struggle to grasp the significance of their mutated genes and lack the broader context to determine which mutations are most phenotypically relevant. To address these issues, we developed the yEvo Mutation Browser, an intuitive web tool designed to assist students and researchers alike in visualizing and contextualizing genome sequencing data. Developed using R Shiny, this tool features an interactive chromosome map displaying mutated genes, graphs categorizing mutation types, a gene viewer illustrating specific mutation sites within genes, and a protein view that maps specific mutations onto protein structures. The app also features an option for non-yEvo-affiliated users to upload their own experimental evolution or genetic screen datasets and compare them with all yEvo data collected since 2018. The yEvo Mutation Browser streamlines data interpretation, helping students understand how organisms employ diverse genetic strategies to adapt to environmental stress. In the future, this framework could be adapted for use with other model organisms, offering a valuable resource for both genetics research and education. SignificanceBringing novel research into the classroom can transform how students learn science. The yEvo project engages high school students in experimental evolution, enabling them to witness natural selection in action and connect genetic mutations to evolutionary outcomes. By providing an accessible web-based tool, the "yEvo Mutation Browser," this work lowers barriers to interpreting genome sequencing data, allowing students, teachers, and researchers alike to explore how yeast populations adapt to diverse stress conditions. This resource not only strengthens STEM education by making cutting-edge genetics more approachable, but also builds a shared dataset that enriches the broader yeast genetics community.

scientific communication and education↗