Differential regulation of alternative promoters emerges from unified kinetics of enhancer-promoter interaction

Harnessing promoter elements to enhance gene editing in plants: perspectives and advances

Genome-edited plants, endowed with climate-smart traits, have been promoted as tools for strengthening resilience against climate change. Successful plant gene editing (GE) requires precise regulation of the GE machinery, a process controlled by the promoters, which drives its transcription through interactions with transcription factors (TFs) and RNA polymerase. While constitutive promoters are extensively used in GE constructs, their limitations highlight the need for alternative approaches. This review emphasizes the promise of tissue/organ specific as well as inducible promoters, which enable targeted GE in a spatiotemporal manner with no effects on other tissues. Advances in synthetic biology have paved the way for the creation of synthetic promoters, offering refined control over gene expression and augmenting the potential of plant GE. The integration of these novel promoters with synthetic systems presents significant opportunities for precise and conditional genome editing. Moreover, the advent of bioinformatic tools and artificial intelligence is revolutionizing the characterization of regulatory elements, enhancing our understanding of their roles in plants. Thus, this review provides novel insights into the strategic use of promoters and promoter editing to enhance the precision, efficiency and specificity of plant GE, setting the stage for innovative crop improvement strategies.

Regulation of alternative splicing in Catharanthus roseus in response to methyl jasmonate modulation during development and stress resilience

Catharanthus roseus has various terpenoid indole alkaloids (TIAs) with adaptive mechanisms to withstand both biotic and abiotic stresses. We investigated the effects of methyl jasmonate (MeJA) on alternative splicing (AS) mechanisms in C. roseus to identify differentially expressed alternatively spliced (DAS) genes following MeJA treatment. We found pairs of co-expressed splicing factors (SFs) and DAS genes and potential roles of co-expressed SFs in the maturation of their respective transcripts. Twenty two clusters encompassing 17 MeJA-responsive DAS genes co-expressed with 10 SF genes. DAS genes, C3H62 , WRK41 , PIL57 , NIP21 , and EDL6 , exhibited co-expression with the SF genes SR34a , DEAD29 , SRC33 , DEAH10 , and DEAD29 , respectively. These gene pairs are implicated in plant developmental processes and/or stress responses. We suggest that MeJA activates the expression of genes encoding SFs that are regulated in tandem with their co-expressed DAS genes and MeJA may enhance the regulatory frameworks that control splicing mechanisms, resulting in the generation of specific mRNA isoforms. This triggers the expression of particular DAS gene variants to allow the plant to effectively respond to environmental stimuli and developmental signals. Our study advances our understanding on how MeJA modulates alternative splicing in C. roseus , potentially influencing various aspects of plant physiology and metabolism. It is recommended that future studies focus on validating the functional relationships between the identified SF/DAS gene pairs and their specific roles in plant development and stress responses, and exploring the potential of manipulating these splicing mechanisms to enhance the production of valuable TIAs in C. roseus .

Uncovering dynamic transcriptional regulation of methanogenesis via single-cell imaging of archaeal gene expression

Archaeal methanogenesis is a dynamic process regulated by various cellular and environmental signals. However, understanding this regulation is technically challenging due to the difficulty of measuring gene expression dynamics in individual archaeal cells. Here, we develop a multi-round hybridization chain reaction (HCR)-assisted single-molecule fluorescence in situ hybridization (FISH) method to quantify the transcriptional dynamics of 12 genes involved in methanogenesis in individual cells of Methanococcoides orientis. Under optimal growth condition, most of these genes appear to be expressed in a temporal order matching metabolic reaction order. Interestingly, an important environmental factor, Fe(III), stimulates cellular methane production without upregulating methanogenic gene expression, likely through a Fenton-reaction-triggered mechanism. Through single-cell clustering and kinetic analyses, we associate these gene expression patterns to a dynamic mixture of distinct cellular states, potentially regulated by a set of shared factors. Our work provides a quantitative framework for uncovering the mechanisms of metabolic regulation in archaea.

An Integrated Database for Exploring Alternative Promoters in Animals

Alternative promoter (AP) events, as a major pre-transcriptional mechanism, can initiate different transcription start sites to generate distinct mRNA isoforms and regulate their expression. At present, hundreds of thousands of APs have been identified across human tissues, and a considerable number of APs have been demonstrated to be associated with complex traits and diseases. Recent researches have also proven important effects of APs on animals. However, the landscape of APs in animals has not been fully recognized. In this study, 102,349 AP profiles from 23,077 samples across 12 species were systematically characterized. We further identified tissue-specific APs and investigated trait-related promoters among various species. In addition, we analyzed the associations between APs and enhancer RNAs (eRNA)/transcription factors (TF) as a means of identifying potential regulatory factors. Integrating these findings, we finally developed Animal-APdb, a database for the searching, browsing, and downloading of information related to Animal APs. Animal-APdb is expected to serve as a valuable resource for exploring the functions and mechanisms of APs in animals.

mRNA Transcript Variants Expressed in Mammalian Cells

Gene expression or gene regulation studies often assume one gene expresses one mRNA. However, contrary to the conventional idea, a single gene in mammalian cells can express multiple transcript variants translated into several different proteins. The transcript variants are generated through transcription from alternative start sites and alternative post-transcriptional processing of the precursor mRNA (pre-mRNA). In addition, gene mutations and RNA editing further enhance the diversity of the transcript variants. The transcript variants can encode proteins with various domains, expanding the functional repertoire of a single gene. Some transcript variants may not encode proteins but function as non-coding RNAs and regulate gene expression. The expression level of the transcript variants may vary between cell types or within the same cells under different biological conditions. Transcript variants are characteristic of cell differentiation in a particular tissue, and the variants may play a key role in normal development and aging. Studies also reported that some transcript variants may have roles in disease pathogenesis. The biological significances urge studying the complexity of gene expression at the transcript level. This article updates the molecular basis of transcript variants in mammalian cells, including the formation mechanisms and potential roles in host biology. Gaining insight into the transcript variants will not only identify novel mechanisms of gene regulation but also unravel the role of the variants in health and disease.

Piwi regulates the usage of alternative transcription start sites in the Drosophila ovary

Alternative transcription initiation, which refers to the transcription of a gene from different transcription start sites (TSSs), is prevalent across metazoans and has important biological functions. Although transcriptional regulation has been extensively studied, the mechanism that selects one TSS over others within a gene remains elusive. Using the Cap Analysis of Gene Expression sequencing (CAGE-seq) method, we discovered that Piwi, an RNA-binding protein, regulates TSS usage in at least 87 genes. In piwi-deficient Drosophila ovaries, these genes displayed significantly altered TSS usage (ATU). The regulation of TSS usage occurred in both germline and somatic cells in ovaries, as well as in cultured ovarian somatic cells (OSCs). Correspondingly, RNA Polymerase II (Pol II) initiation and elongation at the TSSs of ATU genes were affected in germline-piwi-knockdown ovaries and piwi-knockdown OSCs. Furthermore, we identified a Facilitates Chromatin Transcription (FACT) complex component, Ssrp, that is essential for mRNA elongation, as a novel interactor of Piwi in the nucleus. Temporally controlled knockdown of ssrp affected TSS usage in ATU genes, whereas overexpression of ssrp partially rescued the TSS usage of ATU genes in piwi mutant ovaries. Thus, Piwi may interact with Ssrp to regulate TSS usage in Drosophila ovaries by affecting Pol II initiation and elongation.

Fluctuating Chromatin Facilitates Enhancer-Promoter Communication by Regulating Transcriptional Clustering Dynamics

Enhancers regulate gene expression by forming contacts with distant promoters. Phase-separated condensates or clusters formed by transcription factors (TFs) and cofactors are thought to facilitate these enhancer-promoter (E-P) interactions. Using polymer physics, we developed distinct coarse-grained chromatin models that produce similar ensemble-averaged Hi-C maps but with "stable" and "dynamic" characteristics. Our findings, consistent with recent experiments, reveal a multistep E-P communication process. The dynamic model facilitates E-P proximity by enhancing TF clustering and subsequently promotes direct E-P interactions by destabilizing the TF clusters through chain flexibility. Our study promotes physical understanding of the molecular mechanisms governing E-P communication in transcriptional regulation.

Spectral neural approximations for models of transcriptional dynamics

The advent of high-throughput transcriptomics provides an opportunity to advance mechanistic understanding of transcriptional processes and their connections to cellular function at an unprecedented, genome-wide scale. These transcriptional systems, which involve discrete stochastic events, are naturally modeled using chemical master equations (CMEs), which can be solved for probability distributions to fit biophysical rates that govern system dynamics. While CME models have been used as standards in fluorescence transcriptomics for decades to analyze single-species RNA distributions, there are often no closed-form solutions to CMEs that model multiple species, such as nascent and mature RNA transcript counts. This has prevented the application of standard likelihood-based statistical methods for analyzing high-throughput, multi-species transcriptomic datasets using biophysical models. Inspired by recent work in machine learning to learn solutions to complex dynamical systems, we leverage neural networks and statistical understanding of system distributions to produce accurate approximations to a steady-state bivariate distribution for a model of the RNA life cycle that includes nascent and mature molecules. The steady-state distribution to this simple model has no closed-form solution and requires intensive numerical solving techniques: our approach reduces likelihood evaluation time by several orders of magnitude. We demonstrate two approaches, whereby solutions are approximated by 1) learning the weights of kernel distributions with constrained parameters or 2) learning both weights and scaling factors for parameters of kernel distributions. We show that our strategies, denoted by kernel weight regression and parameter-scaled kernel weight regression, respectively, enable broad exploration of parameter space and can be used in existing likelihood frameworks to infer transcriptional burst sizes, RNA splicing rates, and mRNA degradation rates from experimental transcriptomic data.

Enhancer selectivity across cell types delineates three functionally distinct enhancer-promoter regulation patterns

BACKGROUND

Multiple enhancers co-regulating the same gene is prevalent and plays a crucial role during development and disease. However, how multiple enhancers coordinate the same gene expression across various cell types remains largely unexplored at genome scale.

RESULTS

We develop a computational approach that enables the quantitative assessment of enhancer specificity and selectivity across diverse cell types, leveraging enhancer-promoter (E-P) interactions data. We observe two well-known gene regulation patterns controlled by enhancer clusters, which regulate the same gene either in a limited number of cell types (Specific pattern, Spe) or in the majority of cell types (Conserved pattern, Con), both of which are enriched for super-enhancers (SEs). We identify a previously overlooked pattern (Variable pattern, Var) that multiple enhancers link to the same gene, but rarely coexist in the same cell type. These three patterns control the genes associating with distinct biological function and exhibit unique epigenetic features. Specifically, we discover a subset of Var patterns contains Shared enhancers with stable enhancer-promoter interactions in the majority of cell types, which might contribute to maintaining gene expression by recruiting abundant CTCF.

CONCLUSIONS

Together, our findings reveal three distinct E-P regulation patterns across different cell types, providing insights into deciphering the complexity of gene transcriptional regulation.

Using steady-state formula to estimate time-dependent parameters of stochastic gene transcription models

When studying stochastic gene transcription, it is important to understand how system parameters are temporally modulated in response to varying environments. Experimentally, the dynamic distribution data of RNA copy numbers measured at multiple time points are often fitted to stochastic transcription models to estimate time-dependent parameters. However, current methods require determining which parameters are time-dependent, as well as their analytical formulas, before the optimal fit. In this study, we developed a method to estimate time-dependent parameters in a classical two-state model without prior assumptions regarding the system parameters. At each measured time point, the method fitted the dynamic distribution data using a steady-state distribution formula, in which the estimated constant parameters were approximated as time-dependent parameter values at the measured time point. The accuracy of this method can be guaranteed for RNA molecules with relatively high degradation rates and genes with relatively slow responses to induction. We quantify the accuracy of the method and implemented this method on two sets of dynamic distribution data from prokaryotic and eukaryotic cells, and revealed the temporal modulation of transcription burst size in response to environmental changes.

Shaping the scaling characteristics of gap gene expression patterns in Drosophila

How patterns are formed to scale with tissue size remains an unresolved problem. Here we investigate embryonic patterns of gap gene expression along the anterior-posterior (AP) axis in Drosophila. We use embryos that greatly differ in length and, importantly, possess distinct length-scaling characteristics of the Bicoid (Bcd) gradient. We systematically analyze the dynamic movements of gap gene expression boundaries in relation to both embryo length and Bcd input as a function of time. We document the process through which such dynamic movements drive both an emergence of a global scaling landscape and evolution of boundary-specific scaling characteristics. We show that, despite initial differences in pattern scaling characteristics that mimic those of Bcd in the anterior, such characteristics of final patterns converge. Our study thus partitions the contributions of Bcd input and regulatory dynamics inherent to the AP patterning network in shaping embryonic pattern's scaling characteristics.

Hippocampal ceRNA networks from chronic intermittent ethanol vapor-exposed male mice and functional analysis of top-ranked lncRNA genes for ethanol drinking phenotypes

The molecular mechanisms regulating the development and progression of alcohol use disorder (AUD) are largely unknown. While noncoding RNAs have previously been implicated as playing key roles in AUD, long-noncoding RNA (lncRNA) remains understudied in relation to AUD. In this study, we first identified ethanol-responsive lncRNAs in the mouse hippocampus that are transcriptional network hub genes. Microarray analysis of lncRNA, miRNA, circular RNA, and protein coding gene expression in the hippocampus from chronic intermittent ethanol vapor- or air- (control) exposed mice was used to identify ethanol-responsive competing endogenous RNA (ceRNA) networks. Highly interconnected lncRNAs (genes that had the strongest overall correlation to all other dysregulated genes identified) were ranked. The top four lncRNAs were novel, previously uncharacterized genes named Gm42575, 4930413E15Rik, Gm15767, and Gm33447, hereafter referred to as Pitt1, Pitt2, Pitt3, and Pitt4, respectively. We subsequently tested the hypothesis that CRISPR/Cas9 mutagenesis of the putative promoter and first exon of these lncRNAs in C57BL/6J mice would alter ethanol drinking behavior. The Drinking in the Dark (DID) assay was used to examine binge-like drinking behavior, and the Every-Other-Day Two-Bottle Choice (EOD-2BC) assay was used to examine intermittent ethanol consumption and preference. No significant differences between control and mutant mice were observed in the DID assay. Female-specific reductions in ethanol consumption were observed in the EOD-2BC assay for Pitt1, Pitt3, and Pitt4 mutant mice compared to controls. Male-specific alterations in ethanol preference were observed for Pitt1 and Pitt2. Female-specific increases in ethanol preference were observed for Pitt3 and Pitt4. Total fluid consumption was reduced in Pitt1 and Pitt2 mutants at 15% v/v ethanol and in Pitt3 and Pitt4 at 20% v/v ethanol in females only. We conclude that all lncRNAs targeted altered ethanol drinking behavior, and that lncRNAs Pitt1, Pitt3, and Pitt4 influenced ethanol consumption in a sex-specific manner. Further research is necessary to elucidate the biological mechanisms for these effects. These findings add to the literature implicating noncoding RNAs in AUD and suggest lncRNAs also play an important regulatory role in the disease.