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Gorjifard, S.

Publications and source records attributed to Gorjifard, S..

4 recordsLinked to original sources

Massively parallel characterization and deep learning of enhancers in plant genomes

Enhancers coordinate gene expression in response to developmental and environmental cues. Because plant enhancers lack the readily detectable molecular hallmarks of animal enhancers, their systematic functional characterization has yet to be accomplished. Here, we characterize the species- and condition-specific enhancer activity of over 350,000 sequences derived from accessible chromatin regions of the Arabidopsis, tomato, maize, and sorghum genomes. Enabled by the massive scale of the data, we developed plantGREP, a deep learning model that predicts enhancer strength and identifies the underlying functional sequence motifs. We apply plantGREP to evolve strong constitutive as well as species- and condition-specific enhancers, and to locate regions with enhancer activity upstream of developmental genes in crop genomes. These results should facilitate the targeted editing of enhancers in crop genomes and the design of cell-type-specific plant enhancers.

plant biology↗

Dosa: A method to covalently barcode proteinsfor high throughput biochemistry

Deep mutational scanning couples a proteins activity to DNA sequencing for high throughput assessment of the effects of all single amino acid substitutions, but it largely uses indirect assays, like growth, as proxy for protein activity. Here, we covalently link variant proteins in vivo to an RNA barcode by fusing them to E. coli tRNA (m5U54) methyltransferase TrmA (E358Q), which forms a covalent bond with a tRNA stem-loop. Following cell lysis, variant proteins are separated in vitro according to their biochemical properties and identified by their barcodes. We use this method, Dosa, to analyze a large pool of FLAG epitope variants for binding to an anti-FLAG antibody, to profile the cleavage preferences of variants of enteropeptidase and human rhinovirus 3C protease, and to measure the solubility of several hundred A{beta}(1-42) variants. This method should be amenable to numerous biochemical assays with proteins produced in E. coli or mammalian cells.

genomics↗

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↗

Features that Govern Terminator Strength in Plants

The 3 end of a gene, often called a terminator, modulates mRNA stability, localization, translation, and polyadenylation. Here, we adapted Plant STARR-seq, a massively parallel reporter assay, to measure the activity of over 50,000 terminators from the plants Arabidopsis thaliana and Zea mays. We characterize thousands of plant terminators, including many that outperform bacterial terminators commonly used in plants. Terminator activity is species-specific, differing in tobacco leaf and maize protoplast assays. While recapitulating known biology, our results reveal the relative contributions of polyadenylation motifs to terminator strength. We built a computational model to predict terminator strength and used it to conduct in silico evolution that generated optimized synthetic terminators. Additionally, we discover alternative polyadenylation sites across tens of thousands of terminators; however, the strongest terminators tend to have a dominant cleavage site. Our results establish features of plant terminator function and identify strong naturally occurring and synthetic terminators.

plant biology↗