More Like this

1. Engineering activatible promoters for scalable and multi-input CRISPRa/i circuits

   Burbano, D.A. (January 2023, Proceedings of the National Academy of Sciences of the United States of America)

   Dynamic, multi-input gene regulatory networks are ubiquitous in nature. Multi-layer CRISPR-based genetic circuits hold great promise for building gene regulatory networks akin to those found in naturally-occurring biological systems. We develop an approach for creating high-performing activatable promoters that can be assembled into deep, wide, and multi-input CRISPR-activation and -interference (CRISPRa/i) gene regulatory networks. By integrating sequence-based design and in-vivo screening, we engineer activatable promoters that achieve up to 1000-fold dynamic range in an E. coli-based cell-free system. These new components enable CRISPRa gene regulatory networks that are six layers deep and four branches wide. We show the generalizability of the promoter engineering workflow by improving the dynamic range of the light-dependent EL222 optogenetic system from 6-fold to 34-fold. Additionally, high dynamic range promoters enable CRISPRa systems mediated by small molecules and protein-protein interactions. We apply these tools to build input-responsive CRISPRa/i gene regulatory networks, including feedback loops, logic gates, multi-layer cascades, and dynamic pulse modulators. Our work provides a generalizable approach for the design of high dynamic range activatable promoters and enables new classes of gene regulatory functions in cell-free systems. 

   more » « less
2. Lentiviral CRISPRa/i in the adult prairie vole brain: modulating neuronal gene expression without DNA cleavage

   https://doi.org/10.3389/fgeed.2025.1602983  

   Loth, Meredith K; Mesch, Kendall T; Herrera-Garcia, Celine; Brusman, Liza E; Donaldson, Zoe R (May 2025, Frontiers in Genome Editing)

   Prairie voles (Microtus ochrogaster) are a powerful model for studying the neurobiology of social bonding, yet tools for region- and cell type-specific gene regulation remain underdeveloped in this species. Here, we present a lentivirus-mediated CRISPR activation and interference (CRISPRa/i) platform for somatic gene modulation in the prairie vole brain. This system enables non-mutagenic, titratable regulation of gene expression in the adult brain without germline modification. Our dual-vector system includes one construct expressing dCas9-VPR (VP64-p65-Rta) referred to as CRISPRa or dCas9-KRAB-MeCP2 (Kruppel-associated box-methyl CpG binding protein 2), referred to as CRISPRi under a neuron-specific promoter, and a second construct delivering a U6-driven sgRNA (single guide RNA) alongside an elongation factor 1 alpha (EF1α)-driven mCherry reporter. We detail the design, production, and stereotaxic delivery of these tools and demonstrate their application by targeting four genes implicated in social behavior (Oxtr, Avpr1a, Drd1,andDrd2) across two mesolimbic brain regions: the nucleus accumbens and ventral pallidum. Gene expression analyses confirmed robust, bidirectional transcriptional modulation for selected targets, establishing a proof of concept for CRISPRa/i in this non-traditional model. The dual-vector design is readily adaptable to other gene targets, cell types, and brain regions, and can be multiplexed to provide a flexible and scalable framework for investigating gene function in behaviorally relevant circuits. These advances represent the first successful implementation of somatic CRISPRa/i in prairie voles and expand the genetic toolkit available for this species. 

   more » « less
3. scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets

   https://doi.org/10.3390/ijms241713339  

   Dautle, Madison; Zhang, Shaoqiang; Chen, Yong (September 2023, International Journal of Molecular Sciences)

   Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among gene modules. As state-of-the-art applications, we first applied scTIGER to scRNA-seq datasets of prostate cancer cells, and successfully identified the dynamic regulatory networks of AR, ERG, PTEN and ATF3 for same-cell type between prostatic cancerous and normal conditions, and two-cell types within the prostatic cancerous environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks for BDNF, CREB1 and MAPK4. Additionally, scTIGER demonstrates robustness against high levels of dropout noise in scRNA-seq data. 

   more » « less
4. Highly efficient CRISPR systems for loss-of-function and gain-of-function research in pear calli

   https://doi.org/10.1093/hr/uhac148  

   Ming, Meiling; Long, Hongjun; Ye, Zhicheng; Pan, Changtian; Chen, Jiali; Tian, Rong; Sun, Congrui; Xue, Yongsong; Zhang, Yingxiao; Li, Jiaming; et al (June 2022, Horticulture Research)

   Abstract CRISPR/Cas systems have been widely used for genome engineering in many plant species, while their potentials have remained largely untapped in fruit crops, particularly in pear, due to the high levels of genomic heterozygosity and difficulties in tissue culture and stable transformation. To date, only few reports on application of CRISPR/Cas9 system in pear have been documented with a very low editing efficiency. Here, we report a highly efficient CRISPR toolbox for loss-of-function and gain-of-function research in pear. We compared four different CRISPR/Cas9 expression systems for loss-of-function analysis and identified a potent system that showed nearly 100% editing efficiency for multi-site mutagenesis. To expand targeting scope, we further tested different CRISPR/Cas12a and Cas12b systems in pear for the first time, albeit with low editing efficiency. In addition, we established a CRISPR activation (CRISPRa) system for multiplexed gene activation in pear calli for gain-of-function analysis. Furthermore, we successfully engineered the anthocyanin and lignin biosynthesis pathways using both CRISPR/Cas9 and CRISPRa systems in pear calli. Taken together, we build a highly efficient CRISPR toolbox for genome editing and gene regulation, paving the way for functional genomics studies as well as molecular breeding in pear. 

   more » « less
5. Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks

   https://doi.org/10.1146/annurev-arplant-081320-090914  

   Alvarez, Jose M.; Brooks, Matthew D.; Swift, Joseph; Coruzzi, Gloria M. (May 2021, Annual Review of Plant Biology)

   null (Ed.)

   All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets—at both the local and genome-wide levels—and using them to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology. Expected final online publication date for the Annual Review of Plant Biology, Volume 72 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

   more » « less