Biology subjects

Jores, T.

Publications and source records attributed to Jores, T..

8 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2024

Small DNA elements that act as both insulators and silencers in plants

Insulators are cis-regulatory elements that separate transcriptional units, whereas silencers are elements that repress transcription regardless of their position. In plants, these elements remain largely uncharacterized. Here, we use the massively parallel reporter assay Plant STARR-seq with short fragments of eight large insulators to identify more than 100 fragments that block enhancer activity. The short fragments can be combined to generate more powerful insulators that abolish the capacity of the strong viral 35S enhancer to activate the 35S minimal promoter. Unexpectedly, when tested upstream of weak enhancers, these fragments act as silencers and repress transcription. Thus, these elements are capable of both insulating or repressing transcription dependent upon regulatory context. We validate our findings in stable transgenic Arabidopsis, maize, and rice plants. The short elements identified here should be useful building blocks for plant biotechnology efforts.

plant biology↗

bioRxiv2023

Cooperativity and additivity in plant enhancers

Enhancers are cis-regulatory elements that shape gene expression in response to numerous developmental and environmental cues. In animals, several models have been proposed to explain how enhancers integrate the activity of multiple transcription factors. However, it remains largely unknown how plant enhancers integrate transcription factor activity. Here, we use Plant STARR-seq to characterize three light-responsive plant enhancers--AB80, Cab-1, and rbcS-E9--derived from genes active in photosynthesis. Saturation mutagenesis reveals mutations, many of which cluster in short regions, that strongly reduce enhancer activity in the light, in the dark or in both conditions. When tested in the light, these mutation-sensitive regions do not function on their own; rather, cooperative interactions with other such regions are required for full activity. Epistatic interactions occur between mutations in adjacent mutation-sensitive regions, and the spacing and order of mutation-sensitive regions in synthetic enhancers affects enhancer activity. In contrast, when tested in the dark, mutation-sensitive regions act independently and additively in conferring enhancer activity. Taken together, this work demonstrates that plant enhancers show evidence for both cooperative and additive interactions among their functional elements. This knowledge can be harnessed to design strong, condition-specific synthetic enhancers.

plant biology↗

bioRxiv2023

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↗

bioRxiv2022

EUGENe: A Python toolkit for predictive analyses of regulatory sequences

Deep learning (DL) has become a popular tool to study cis-regulatory element function. Yet efforts to design software for DL analyses in genomics that are Findable, Accessible, Interoperable and Reusable (FAIR) have fallen short of fully meeting these criteria. Here we present EUGENe (Elucidating the Utility of Genomic Elements with Neural Nets), a FAIR toolkit for the analysis of labeled sets of nucleotide sequences with DL. EUGENe consists of a set of modules that empower users to execute the key functionality of a DL workflow: 1) extracting, transforming and loading sequence data from many common file formats, 2) instantiating, initializing and training diverse model architectures, and 3) evaluating and interpreting model behavior. We designed EUGENe to be simple; users can develop workflows on new or existing datasets with two customizable Python objects, annotated sequence data (SeqData) and PyTorch models (BaseModel). The modularity and simplicity of EUGENe also make it highly extensible and we illustrate these principles through application of the toolkit to three predictive modeling tasks. First, we train and compare a set of built-in models along with a custom architecture for the accurate prediction of activities of plant promoters from STARR-seq data. Next, we apply EUGENe to an RNA binding prediction task and showcase how seminal model architectures can be retrained in EUGENe or imported from Kipoi. Finally, we train models to classify transcription factor binding by wrapping functionality from Janngu, which can efficiently extract sequences in BED file format from the human genome. We emphasize that the code used in each use case is simple, readable, and well documented (https://eugene-tools.readthedocs.io/en/latest/index.html). We believe that EUGENe represents a springboard toward a collaborative ecosystem for DL applications in genomics research. EUGENe is available for download on GitHub (https://github.com/cartercompbio/EUGENe) along with several introductory tutorials and for installation on PyPi (https://pypi.org/project/eugene-tools/).

bioinformatics↗

bioRxiv2021

Synthetic Promoter Designs Enabled by a Comprehensive Analysis of Plant Core Promoters

Targeted engineering of plant gene expression holds great promise for ensuring food security and for producing biopharmaceuticals in plants. However, this engineering requires thorough knowledge of cis-regulatory elements in order to precisely control either endogenous or introduced genes. To generate this knowledge, we used a massively parallel reporter assay to measure the activity of nearly complete sets of promoters from Arabidopsis, maize and sorghum. We demonstrate that core promoter elements - notably the TATA-box - as well as promoter GC content and promoter-proximal transcription factor binding sites influence promoter strength. By performing the experiments in two assay systems, leaves of the dicot tobacco and protoplasts of the monocot maize, we detect species-specific differences in the contributions of GC content and transcription factors to promoter strength. Using these observations, we built computational models to predict promoter strength in both assay systems, allowing us to design highly active promoters comparable in activity to the viral 35S minimal promoter. Our results establish a promising experimental approach to optimize native promoter elements and generate synthetic ones with desirable features.

plant biology↗

bioRxiv2020

Structural basis of client specificity in mitochondrial membrane-protein chaperones

Chaperones are essential for assisting protein folding, and for transferring poorly soluble proteins to their functional locations within cells. Hydrophobic interactions drive promiscuous chaperone-client binding, but our understanding how additional interactions enable client specificity is sparse. Here we decipher what determines binding of two chaperones (TIM8{middle dot}13, TIM9{middle dot}10) to different integral membrane proteins, the alltransmembrane mitochondrial carrier Ggc1, and Tim23 which has an additional disordered hydrophilic domain. Combining NMR, SAXS and molecular dynamics simulations, we determine the structures of Tim23/TIM8{middle dot}13 and Tim23/TIM9{middle dot}10 complexes. TIM8{middle dot}13 uses transient salt bridges to interact with the hydrophilic part of its client, but its interactions to the trans-membrane part are weaker than in TIM9{middle dot}10. Consequently, TIM9{middle dot}10 is outcompeting TIM8{middle dot}13 in binding hydrophobic clients, while TIM8{middle dot}13 is tuned to few clients with both hydrophilic and hydrophobic parts. Our study exemplifies how chaperones fine-tune the balance of promiscuity vs. specificity.

biophysics↗

bioRxiv2020

Identification of plant enhancers and their constituent elements by STARR-seq in tobacco leaves

Genetic engineering of cis-regulatory elements in crop plants is a promising strategy to ensure food security. However, such engineering is currently hindered by our limited knowledge of plant cis-regulatory elements. Here, we adapted STARR-seq -- a technology for the high-throughput identification of enhancers -- for its use in transiently transformed tobacco leaves. We demonstrate that the optimal placement in the reporter construct of enhancer sequences from a plant virus, pea and wheat was just upstream of a minimal promoter, and that none of these four known enhancers was active in the 3'-UTR of the reporter gene. The optimized assay sensitively identified small DNA regions containing each of the four enhancers, including two whose activity was stimulated by light. Furthermore, we coupled the assay to saturation mutagenesis to pinpoint functional regions within an enhancer, which we recombined to create synthetic enhancers. Our results describe an approach to define enhancer properties that can be performed in potentially any plant species or tissue transformable by Agrobacterium and that can use regulatory DNA derived from any plant genome. One-sentence summaryWe developed a high-throughput assay in transiently transformed tobacco leaves that can identify enhancers, characterize their functional elements and detect condition-specific enhancer activity.

plant biology↗