Dynamic Gene Regulation Research Articles

Single-Cell Multi-omics Assessment of Spinal Cord Injury Blocking via Cerium-doped Upconversion Antioxidant Nanoenzymes.

Spinal cord injury (SCI) impairs the central nervous system and induces the myelin-sheath-deterioration because of reactive oxygen species (ROS), further hindering the recovery of function. Herein, the simultaneously emergency treatment and dynamic luminescence severity assessment (SETLSA) strategy is designed for SCI based on cerium (Ce)-doped upconversion antioxidant nanoenzymes (Ce@UCNP-BCH). Ce@UCNP-BCH can not only efficiently eliminate the SCI localized ROS, but dynamically monitor the oxidative state in the SCI repair process using a ratiometric luminescence signal. Moreover, the classic basso mouse scale score and immunofluorescence analysis together exhibit that Ce@UCNP-BCH effectively facilitates the regeneration of spinal cord including myelin sheath, and promotes the functional recovery of SCI mice. Particularly, the study combines snATAC-eq and snRNA-seq to reveal the heterogeneity of spinal cord tissue following Ce@UCNP-BCH treatment. The findings reveal a significant increase in myelinating oligodendrocytes, as well as higher expression of myelination-related genes, and the study also reveals the gene regulatory dynamics of remyelination after treatment. Besides, the ETLSA strategy synergistically boosts ROS consumption through the superoxide dismutase (SOD)-related pathways after SOD-siRNA treatment. In conclusion, this SETLSA strategy with simultaneously blocking and dynamic monitoring oxidative stress has enriched the toolkit for promoting SCI repair.

Maternal obesity alters histone modifications mediated by the interaction between Ezh2 and Ampk, impairing neural differentiation in the developing embryonic brain cortex.

Neurodevelopmental disorders have complex origins that manifest early during embryonic growth and are associated with intricate gene regulation dynamics. A perturbed metabolic environment such as hyperglycemia or dyslipidemia, particularly due to maternal obesity, poses a threat to the optimal development of the embryonic central nervous system. Accumulating evidence suggests that these metabolic irregularities during pregnancy may alter neurogenesis pathways, thereby predisposing the developing fetus to neurodevelopmental disorders. One primary mechanism through which such disruptions may occur involves changes in histone modifications resulting from fluctuations in the expression of histone-modifying enzymes or the availability of their substrates. Herein, we have used a rat model of maternal obesity induced by a high-fat diet (HFD) before and during gestation to investigate the cellular and molecular repercussions of maternal obesity on embryonic cortical neurogenesis. Maternal obesity impairs neurogenesis by reducing cell proliferation, increasing neuronal marker expression, and shifting development toward astrogliogenesis. Differentially expressed genes (DEGs) revealed disruptions in key developmental signaling pathways and reduced Akt phosphorylation, particularly at E14.5. These changes were associated with epigenetic alterations, mainly the differential expression and phosphorylation of Ezh2 and subsequent changes in global histone modifications. ChIP-seq revealed reduced H3K27me3 at genes upregulated due to maternal obesity, which could have resulted from reduced expression and increased phosphorylation of Ezh2 at Thr311. Interestingly, Ezh2 also showed increased O-GlcNAcylation in HFD embryos along with increased association with Ampk-Thr172 in accordance with previous studies showing that Ampk catalyzes Ezh2-Thr311p. These results suggest that an epigenetic gene regulatory mechanism mediated by Ampk and Ezh2 interactions resulted in reduced H3K27me3 and de-repression of key developmental genes, which could have led to cell fate changes observed in the developing embryo brain cortex due to maternal obesity.

Temporal Expression Analysis to Unravel Gene Regulatory Dynamics by microRNAs.

MicroRNAs (miRNAs) are a class of small non-coding RNAs (sncRNAs) of length 21-25 nucleotides. These sncRNAs hybridize to repress their target genes and inhibit protein translation, thereby controlling regulatory functions in the cell. Integration of time-series matched small and RNA-seq data enables investigation of dynamic gene regulation through miRNAs during development or in response to a stimulus, such as stress. Here we summarize analysis strategies, such as probabilistic and regression-based models, that take advantage of the temporal dimension to investigate the complexity of miRNA regulation.

An enhancer-promoter-transcription factor module orchestrates plant immune homeostasis by constraining camalexin biosynthesis

Effective plant defense against pathogens relies on highly coordinated regulation of immune gene expression. Enhancers, as cis-regulatory elements, are indispensable determinants of dynamic gene regulation, but the molecular functions in plant immunity are not well understood. In this study, we identified a novel enhancer, CORE PATTERN-INDUCED ENHANCER 35 (CPIE35), which is rapidly activated upon pathogenic elicitation and negatively regulates anti-fungal resistance through modulating WRKY15 expression. During immune activation, CPIE35 activates the transcription of WRKY15 by forming chromatin loops with the promoter of WRKY15 in a WRKY18/40/60-, WRKY33-, and MYC2-dependent manner. WRKY15 directly binds to the promoters of PAD3 and GSTU4, suppressing their expression and leading to the reduced camalexin synthesis and resistance. Interestingly, CPIE35 region is evolutionarily conserved among Brassicaceae plants, and the CPIE35-WRKY15 module exerts similar functions in Brassica napus to negatively regulate anti-fungal resistance. Our work reveals the “enhancer-promoter-transcription factor” regulatory mechanism in maintenance of immune homeostasis, highlighting the importance and conserved role of enhancers in fine-tuning immune gene expression in plants.

Integrating Prior Knowledge Using Transformer for Gene Regulatory Network Inference.

Gene regulatory network (GRN) inference, a process of reconstructing gene regulatory rules from experimental data, has the potential to discover new regulatory rules. However, existing methods often struggle to generalize across diverse cell types and account for unseen regulators. Here, this work presents GRNPT, a novel Transformer-based framework that integrates large language model (LLM) embeddings from publicly accessible biological data and a temporal convolutional network (TCN) autoencoder to capture regulatory patterns from single-cell RNA sequencing (scRNA-seq) trajectories. GRNPT significantly outperforms both supervised and unsupervised methods in inferring GRNs, particularly when training data is limited. Notably, GRNPT exhibits exceptional generalizability, accurately predicting regulatory relationships in previously unseen cell types and even regulators. By combining LLMs ability to distillate biological knowledge from text and deep learning methodologies capturing complex patterns in gene expression data, GRNPT overcomes the limitations of traditional GRN inference methods and enables more accurate and comprehensive understanding of gene regulatory dynamics.

A Computational Pipeline for Identifying Gene Regulatory Networks: A Case Study of Response to Exercise.

Gene regulatory networks are foundational in the control of virtually all biological processes. These networks orchestrate a myriad of cell functions ranging from metabolic rate to the response to a drug or other intervention. The data required to accurately identify these control networks remains very cost and labor intensive typically leading to relatively sparse time course data that is largely incompatible with conventional data-driven model identification techniques. In this work, we combine empirical identification of gene-gene interactions with constraints describing the expected dynamic behavior of the network to infer regulatory dynamics from under-sampled data. We apply this to the identification of gene regulatory subnetworks recruited in groups of subjects participating in several different exercise interventions. Intervention-specific response networks are compared to one another and control actions driving differences are identified. We propose that this approach can extract statistically robust and biologically meaningful insights into gene regulatory dynamics from a dataset consisting of a small number of participants with very limited longitudinal sampling, for example pre- and post- intervention only.

Transitions in development - an interview with Dorit Hockman.

Dorit Hockman is a Senior Lecturer in the Division of Cell Biology, Faculty of Health Sciences at the University of Cape Town, South Africa. Her group studies the dynamics of gene regulation in the developing nervous system. We chatted with Dorit over a video call to find out more about her career path, her return to South Africa to establish her independent research group, and the importance of having supportive colleagues and mentors.

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Across biological systems, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. While low-dimensional dynamics can be extracted using RNA velocity, these algorithms can be fragile and rely on heuristics lacking statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. To address these challenges, we introduce a Bayesian model of RNA velocity that couples velocity field and manifold estimation in a reformulated, unified framework, identifying the parameters of an explicit dynamical system. Focusing on the cell cycle, we implement VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validate its ability to infer cell cycle periods using live imaging. We also apply VeloCycle to reveal speed differences in regionally defined progenitors and Perturb-seq gene knockdowns. Overall, VeloCycle expands the single-cell RNA sequencing analysis toolkit with a modular and statistically consistent RNA velocity inference framework.

A multiplex single-cell RNA-Seq pharmacotranscriptomics pipeline for drug discovery.

The gene-regulatory dynamics governing drug responses in cancer are yet to be fully understood. Here, we report a pipeline capable of producing high-throughput pharmacotranscriptomic profiling through live-cell barcoding using antibody-oligonucleotide conjugates. This pipeline combines drug screening with 96-plex single-cell RNA sequencing. We show the potential of this approach by exploring the heterogeneous transcriptional landscape of primary high-grade serous ovarian cancer (HGSOC) cells after treatment with 45 drugs, with 13 distinct classes of mechanisms of action. A subset of phosphatidylinositol 3-OH kinase (PI3K), protein kinase B (AKT) and mammalian target of rapamycin (mTOR) inhibitors induced the activation of receptor tyrosine kinases, such as the epithelial growth factor receptor (EGFR), and this was mediated by the upregulation of caveolin 1 (CAV1). This drug resistance feedback loop could be mitigated by the synergistic action of agents targeting PI3K-AKT-mTOR and EGFR for HGSOC with CAV1 and EGFR expression. Using this workflow could enable the personalized testing of patient-derived tumor samples at single-cell resolution.

Gene regulatory dynamics during the development of a paleopteran insect, the mayfly Cloeon dipterum.

The evolution of insects has been marked by the appearance of key body plan innovations that promoted the outstanding ability of this lineage to adapt to new habitats, boosting the most successful radiation in animals. To understand the evolution of these new structures, it is essential to investigate which genes and gene regulatory networks participate during the embryonic development of insects. Great efforts have been made to fully understand gene expression and gene regulation during the development of holometabolous insects, in particular Drosophila melanogaster. Conversely, functional genomics resources and databases in other insect lineages are scarce. To provide a new platform to study gene regulation in insects, we generated ATAC-seq for the first time during the development of the mayfly Cloeon dipterum, which belongs to Paleoptera, the sister group to all other winged insects. With these comprehensive datasets along six developmental stages, we characterized pronounced changes in accessible chromatin between early and late embryogenesis. The application of ATAC-seq in mayflies provides a fundamental resource to understand the evolution of gene regulation in insects.

Temporally distinct 3D multi-omic dynamics in the developing human brain

The human hippocampus and prefrontal cortex play critical roles in learning and cognition1,2, yet the dynamic molecular characteristics of their development remain enigmatic. Here we investigated the epigenomic and three-dimensional chromatin conformational reorganization during the development of the hippocampus and prefrontal cortex, using more than 53,000 joint single-nucleus profiles of chromatin conformation and DNA methylation generated by single-nucleus methyl-3C sequencing (snm3C-seq3)3. The remodelling of DNA methylation is temporally separated from chromatin conformation dynamics. Using single-cell profiling and multimodal single-molecule imaging approaches, we have found that short-range chromatin interactions are enriched in neurons, whereas long-range interactions are enriched in glial cells and non-brain tissues. We reconstructed the regulatory programs of cell-type development and differentiation, finding putatively causal common variants for schizophrenia strongly overlapping with chromatin loop-connected, cell-type-specific regulatory regions. Our data provide multimodal resources for studying gene regulatory dynamics in brain development and demonstrate that single-cell three-dimensional multi-omics is a powerful approach for dissecting neuropsychiatric risk loci.

Quantitative analysis of cis-regulatory elements in transcription with KAS-ATAC-seq

Cis-regulatory elements (CREs) are pivotal in orchestrating gene expression throughout diverse biological systems. Accurate identification and in-depth characterization of functional CREs are crucial for decoding gene regulation networks during cellular processes. In this study, we develop Kethoxal-Assisted Single-stranded DNA Assay for Transposase-Accessible Chromatin with Sequencing (KAS-ATAC-seq) to quantitatively analyze the transcriptional activity of CREs. A main advantage of KAS-ATAC-seq lies in its precise measurement of ssDNA levels within both proximal and distal ATAC-seq peaks, enabling the identification of transcriptional regulatory sequences. This feature is particularly adept at defining Single-Stranded Transcribing Enhancers (SSTEs). SSTEs are highly enriched with nascent RNAs and specific transcription factors (TFs) binding sites that define cellular identity. Moreover, KAS-ATAC-seq provides a detailed characterization and functional implications of various SSTE subtypes. Our analysis of CREs during mouse neural differentiation demonstrates that KAS-ATAC-seq can effectively identify immediate-early activated CREs in response to retinoic acid (RA) treatment. Our findings indicate that KAS-ATAC-seq provides more precise annotation of functional CREs in transcription. Future applications of KAS-ATAC-seq would help elucidate the intricate dynamics of gene regulation in diverse biological processes.

Phenotypic memory in quorum sensing.

Quorum sensing (QS) is a regulatory mechanism used by bacteria to coordinate group behavior in response to high cell densities. During QS, cells monitor the concentration of external signals, known as autoinducers, as a proxy for cell density. QS often involves positive feedback loops, leading to the upregulation of genes associated with QS signal production and detection. This results in distinct steady-state concentrations of QS-related molecules in QS-ON and QS-OFF states. Due to the slow decay rates of biomolecules such as proteins, even after removal of the initial stimuli, cells can retain elevated levels of QS-associated biomolecules for extended periods of time. This persistence of biomolecules after the removal of the initial stimuli has the potential to impact the response to future stimuli, indicating a memory of past exposure. This phenomenon, which is a consequence of the carry-over of biomolecules rather than genetic inheritance, is known as "phenotypic" memory. This theoretical study aims to investigate the presence of phenotypic memory in QS and the conditions that influence this memory. Numerical simulations based on ordinary differential equations and analytical modeling were used to study gene expression in response to sudden changes in cell density and extracellular signal concentrations. The model examined the effect of various cellular parameters on the strength of QS memory and the impact on gene regulatory dynamics. The findings revealed that QS memory has a transient effect on the expression of QS-responsive genes. These consequences of QS memory depend strongly on how cell density was perturbed, as well as various cellular parameters, including the Fold Change in the expression of QS-regulated genes, the autoinducer synthesis rate, the autoinducer threshold required for activation, and the cell growth rate.

Effect of network structure on the accuracy of resilience dimension reduction

Dimension reduction is an effective method for system’s resilience analysis. In this paper, we investigate the effect of network structure on the accuracy of resilience dimension reduction. First, we introduce the resilience dimension reduction method and define the evaluation indicator of the resilience dimension reduction method. Then, by adjusting node connections, preferential connection mechanisms, and connection probabilities, we generate artificial networks, small-world networks and social networks with tunable assortativity coefficients, average clustering coefficients, and modularities, respectively. Experimental results for the gene regulatory dynamics show that the network structures with positive assortativity, large clustering coefficient, and significant community can enhance the accuracy of resilience dimension reduction. The result of this paper indicates that optimizing network structure can enhance the accuracy of resilience dimension reduction, which is of great significance for system resilience analysis and provides a new perspective and theoretical basis for selecting dimension reduction methods in system resilience analysis.

A fusion protein approach to integrate antiviral and anti-inflammatory activities for developing new therapeutics against influenza A virus infection

Human interferon α2 (IFNα2) is a cytokine with broad-spectrum antiviral activity, and its engineered forms are widely used to treat viral infections. However, IFNα2 may trigger proinflammatory responses and underlying side effects during treatment. Trefoil factor 2 (TFF2) is a secreted protein with anti-inflammatory properties. Here, we explored whether coupling IFNα2 to TFF2 in a two-in-one fusion form could combine the beneficial effects of both molecules on viral infections toward a more desirable treatment outcome. We engineered two forms of human IFNα2 and TFF2 fusion proteins, IFNα2-TFF2-Fc (ITF) and TFF2-IFNα2-Fc (TIF), and examined their properties in vitro in comparison to IFNα2 and TFF2 alone. RNA-Seq was further used to explore such comparison on dynamic gene regulation at transriptomic level. These in vitro assessments collectively indicated that TIF largely retained the antiviral activity of IFNα2 while being a weaker inflammation inducer, consistent with the presence of TFF2 activity. We further demonstrated the superiority of TIF over IFNα2 or TFF2 alone in treating influenza infection using a mouse infection model. Together, our study provided evidence supporting that, by possessing antiviral activity conferred by IFNα2 with complementation from TFF2 in suppressing the inflammatory side effects, the fusion proteins, particularly TIF, represent more effective agents against influenza and other respiratory viral infections than IFNα2 or TFF2 alone. It implies that merging two molecules with complementary functions holds potential for developing novel therapeutics against viral infections.

Tailored UPRE2 variants for dynamic gene regulation in yeast

Genetic elements are foundational in synthetic biology serving as vital building blocks. They enable programming host cells for efficient production of valuable chemicals and recombinant proteins. The unfolded protein response (UPR) is a stress pathway in which the transcription factor Hac1 interacts with the upstream unfolded protein response element (UPRE) of the promoter to restore endoplasmic reticulum (ER) homeostasis. Here, we created a UPRE2 mutant (UPRE2m) library. Several rounds of screening identified many elements with enhanced responsiveness and a wider dynamic range. The most active element m84 displayed a response activity 3.72 times higher than the native UPRE2. These potent elements are versatile and compatible with various promoters. Overexpression of HAC1 enhanced stress signal transduction, expanding the signal output range of UPRE2m. Through molecular modeling and site-directed mutagenesis, we pinpointed the DNA-binding residue Lys60 in Hac1(Hac1-K60). We also confirmed that UPRE2m exhibited a higher binding affinity to Hac1. This shed light on the mechanism underlying the Hac1-UPRE2m interaction. Importantly, applying UPRE2m for target gene regulation effectively increased both recombinant protein production and natural product synthesis. These genetic elements provide valuable tools for dynamically regulating gene expression in yeast cell factories.

Enhancing Transcription Factor Prediction through Multi-Task Learning (Student Abstract)

Transcription factors (TFs) play a fundamental role in gene regulation by selectively binding to specific DNA sequences. Understanding the nature and behavior of these TFs is essential for insights into gene regulation dynamics. In this study, we introduce a robust multi-task learning framework specifically tailored to harness both TF-specific annotations and TF-related domain annotations, thereby enhancing the accuracy of TF predictions. Notably, we incorporate cutting-edge language models that have recently garnered attention for their outstanding performance across various fields, particularly in biological computations like protein sequence modeling. Comparative experimental analysis with existing models, DeepTFactor and TFpredict, reveals that our multi-task learning framework achieves an accuracy exceeding 92% across four evaluation metrics on the TF prediction task, surpassing both competitors. Our work marks a significant leap in the domain of TF prediction, enriching our comprehension of gene regulatory mechanisms and paving the way for the discovery of novel regulatory motifs.

Dynamics of ER stress-induced gene regulation in plants.

Endoplasmic reticulum (ER) stress is a potentially lethal condition that is induced by the abnormal accumulation of unfolded or misfolded secretory proteins in the ER. In eukaryotes, ER stress is managed by the unfolded protein response (UPR) through a tightly regulated, yet highly dynamic, reprogramming of gene transcription. Although the core principles of the UPR are similar across eukaryotes, unique features of the plant UPR reflect the adaptability of plants to their ever-changing environments and the need to balance the demands of growth and development with the response to environmental stressors. The past decades have seen notable progress in understanding the mechanisms underlying ER stress sensing and signalling transduction pathways, implicating the UPR in the effects of physiological and induced ER stress on plant growth and crop yield. Facilitated by sequencing technologies and advances in genetic and genomic resources, recent efforts have driven the discovery of transcriptional regulators and elucidated the mechanisms that mediate the dynamic and precise gene regulation in response to ER stress at the systems level.

Inflammatory, metabolic, and sex-dependent gene-regulatory dynamics of microglia and macrophages in neonatal hippocampus after hypoxia-ischemia

Neonatal hypoxia-ischemia (HI) is a major cause of perinatal death and long-term disabilities worldwide. Post-ischemic neuroinflammation plays a pivotal role in HI pathophysiology. In the present study, we investigated the temporal dynamics of microglia (CX3CR1GFP/+) and infiltrating macrophages (CCR2RFP/+) in the hippocampi of mice subjected to HI at postnatal day 9. Using inflammatory pathway and transcription factor (TF) analyses, we identified a distinct post-ischemic response in CCR2RFP/+ cells characterized by differential gene expression in sensome, homeostatic, matrisome, lipid metabolic, and inflammatory molecular signatures. Three days after injury, transcriptomic signatures of CX3CR1GFP/+ and CCR2RFP/+ cells isolated from hippocampi showed a partial convergence. Interestingly, microglia-specific genes in CX3CR1GFP/+ cells showed a sexual dimorphism, where expression returned to control levels in males but not in females during the experimental time frame. These results highlight the importance of further investigations on metabolic rewiring to pave the way for future interventions in asphyxiated neonates.

P032 Exploring the genetic sequence of action across the natural history of IBD

Abstract Background We have been very successful at identifying genetic variants associated with inflammatory bowel disease (IBD). From a single locus by the early 2000s, we now know about over 300 regions involved in IBD susceptibility. However, we face two main gaps of knowledge. First, we ignore the mechanisms by which they lead to disease. Second, we ignore their role through the natural history of the disease (e.g., prognosis). To address these gaps, we must integrate inferences from other omic techniques and characterize the genetic sequence of action that takes place across the distinct stages of the disease. Methods We have processed publicly available important studies of the genomics and transcriptomics of IBD to make inferences about the sequence of action of genes involved. In total, we processed the large IBDGC GWAS, five transcriptomics and two proteomic datasets (Table 1). We studied different phases of the disease: preclinical, debut and remission, both in Crohn’s disease and ulcerative colitis. We selected sets of genes associated with stages of IBD by the mentioned omics and looked for the overlaps. Last, we performed transcriptome-wide association studies (TWAS) to study the gene expression levels determined genetically in each individual. Results We report three main observations from this omic-wide exploration on the genetic sequence of action in IBD. First, we observed a lack of overlap in the gene lists that are relevant in each stage (Figure 1). For instance, just 6.4% of genes discovered by GWAS are differentially expressed at the time of diagnosis and/or remission e.g., NOD2, C2, CCL2, IL27, CSF2, CCL11 and BACH2. In turn, gene signatures discovered through transcriptomics are not particularly enriched for GWAS signals. Second, the genetic risk of each individual, measured through polygenic risk scores, does not correlate with the clustering of patients detected molecularly using gene expression. Third, TWAS analysis reveals an intriguing twist, since we observe large differences between the genetically determined component and the actual gene expression observed in each stage. Conclusion Although the genetic makeup of an individual influences expression of key genes involved in IBD, many other genes not detected in genetic studies (GWAS) conform the backbone of molecular alterations present at the time of diagnosis. This global observation challenges the conventional assumption, implying that different actors play a leading role in each stage. Moreover, genetic predisposition is not a strong determinant of the molecular heterogeneity observed in patients across stages. We conclude that the gene regulation dynamics across the natural history of IBD are more intricate than usually assumed.