Biology subjects

Tang, M.

Publications and source records attributed to Tang, M..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

A transgene harboring the human Glucose Transporter1 (GLUT1) gene locus ameliorates disease in GLUT1 deficiency syndrome model mice.

Proper brain function relies on an adequate supply of energy - mainly glucose - to power neuronal activity. Delivery of this nutrient to the neuropil is mediated by the Glucose Transporter1 (GLUT1) protein. Perturbing glucose supply to the brain is profoundly damaging and exemplified by the neurodevelopmental disorder, GLUT1 deficiency syndrome (GLUT1DS). Resulting from haploinsufficiency of the SLC2A1 (GLUT1) gene, GLUT1DS is characterized by intractable infantile-onset seizures and a disabling movement disorder. Ketogenic diets, which supply the brain with an alternate energy source, ketone bodies, are currently the preferred therapeutic option for Glut1DS patients but do not address the underlying cause - low brain glucose - of the disease. One intuitively appealing therapeutic strategy that does, involves restoring GLUT1 levels to the patient brain. Here, we demonstrate that transgenic expression of the human GLUT1 genomic locus in a mouse model of GLUT1DS raises brain GLUT1 levels and reduces disease burden. Augmenting GLUT1 levels in mutants correspondingly raised cerebrospinal fluid (CSF) glucose levels, improved motor performance and reduced the frequency of seizures characteristically observed in GLUT1DS. Interestingly, the increased GLUT1 in mutants harboring the human GLUT1 locus was at least partly the result of an increase in murine Slc2a1 (Glut1) activity, most likely the effect of a long non-coding RNA (lncRNA) embedded in the human transgene. Collectively, our work has not only shown that repleting human GLUT1 mitigates GLUT1DS but also has yielded transgenic mice that constitute a useful tool to test and optimize clinically promising agents designed to stimulate this gene for therapeutic purposes.

neuroscience↗

bioRxiv2025

A therapeutic role for a regulatory glucose transporter1 (Glut1)-associated natural antisense transcript

The mammalian brain relies primarily on glucose for its energy needs. Delivery of this nutrient to the brain is mediated by the glucose transporter-1 (Glut1) protein. Low Glut1 thwarts glucose entry into the brain, causing an energy crisis and, triggering, in one instance, the debilitating neurodevelopmental condition - Glut1 deficiency syndrome (Glut1DS). Current treatments for Glut1DS are sub-optimal, as none address the root cause - low Glut1 - of the condition. Levels of this transporter must respond rapidly to the brains changing energy requirements. This necessitates fine-tuning its expression. Here we describe a long-noncoding RNA (lncRNA) antisense to Glut1 and show that it is involved in such regulation. Raising levels of the lncRNA had a concordant effect on Glut1 in cultured human cells and transgenic mice; reducing levels elicited the opposite effect. Delivering the lncRNA to Glut1DS model mice via viral vectors induced Glut1 expression, enhancing brain glucose levels to mitigate disease. Direct delivery of such a lncRNA to combat disease has not been reported previously and constitutes a unique therapeutic paradigm. Moreover, considering the importance of maintaining homeostatic Glut1 levels, calibrating transporter expression via the lncRNA could become broadly relevant to the myriad conditions, including Alzheimers disease, wherein Glut1 concentrations are perturbed.

neuroscience↗

bioRxiv2025

In vivo validation of the palmitoylation cycle as a therapeutic target in NRAS-mutant cancer

Ras proteins are functionally dependent on one or more lipid modifications.1,2 The dynamic palmitoylation of N-Ras by DHHC palmitoyl acyltransferases and depalmitoylation by ABHD17 serine hydrolases is essential for the growth of NRAS-mutant acute myeloid leukemia (AML) cells.3-6 Here we show that ABD778, an in vivo-active ABHD17 inhibitor, selectively reduces the growth of NRAS-mutant AML and melanoma cell lines and is synergistic with the MEK inhibitor PD0325901 (PD901; mirdametinib). Mechanistically, ABD778 and PD901 induce deep and durable suppression of mitogen activated protein kinase (MAPK) pathway activation. Co-treatment extended the survival of mice transplanted with NrasG12D AMLs, which acquired by-pass mutations at relapse that conferred drug resistance and restored MAPK activation. ABD778 augmented the anti-leukemia activity of PI3 kinase, pan-Ras tri-complex, and FLT3 inhibitors, and restored gilteritinib sensitivity in a patient-derived xenograft model of FLT3 inhibitor resistance. These studies validate the palmitoylation cycle as a therapeutic target in NRAS-mutant cancers.

cancer biology↗

bioRxiv2025

High-throughput, unbiased single-molecule displacement mapping with deep learning reveals spatiotemporal heterogeneities in intracellular diffusivity

Single-molecule displacement/diffusivity mapping (SMdM) has revolutionized the study of molecular motion in live cells by providing high spatial resolution insights. However, its utility is restricted by measurement biases and low throughput, making it challenging to capture temporal dynamics of intracellular diffusivity. Here we report a high-throughput single-molecule diffusivity microscopy approach (Hi-SMdM) that rapidly and unbiasedly maps nanoscale heterogeneities in molecular motion inside mammalian cells, enabling time-resolved imaging of local diffusivity dynamics. Hi-SMdM employs a self-supervised deep-learning denoising framework with a general noise model to effectively restore the scarce signals of fast-moving single molecules, while eliminating artifact motions without relying on spatial redundancies or temporal correlations. It then provides unbiased estimations of nanoscale diffusion coefficient and improve both throughput and temporal resolution by up to an order of magnitude. We demonstrate the versatility of Hi-SMdM through time-resolved mapping of the spatially heterogeneous diffusivity of free proteins in the cytoplasm. Hi-SMdM also unveils the temporal dynamics of intracellular diffusion under hypotonic conditions in live cells. Additionally, Hi-SMdM tracks the spatiotemporal dynamics of intraorganellar diffusivity during rapid rearrangements of the ER network and functional activities of mitochondria. Overall, Hi-SMdM provides exceptional opportunities for high-throughput and unbiased single-molecule diffusivity mapping with excellent spatiotemporal resolution.

biophysics↗

bioRxiv2025

In situ generated vaccine-like pyroptosome for personalized cancer immunotherapy

The efficacy of in situ cancer vaccination has been hampered by poor spatiotemporal orchestration of multiple key steps of cancer-immunity cycle in most tumours and systemic toxicity related to therapeutic strategies. Here, we report a systemic injectable and pyroptosis-enabled nanoadjuvant (SPEN) that precisely evokes the secretion of vaccine-like pyroptosome in tumour area for eliciting robust anti-tumour immunity. SPEN induces vigorous immunogenic pyroptosis, triggering the efficient release of tumour antigen-rich pyroptosomes, DAMPs, and proinflammatory cytokines. Upon the activatable release of a TLR7/8 agonist into pyroptosome, the generated pyroptosome functions as in situ cancer vaccine for cooperatively activating the cancer-immunity cycle while avoiding systemic toxicity. This in situ vaccine boosts both innate and adaptive immune response, facilitating the eradication of primary tumour and long-lasting cancer prevention. Our findings provide new insights into the rational design of pyroptosis-inducing nanomedicines for boosting the cancer-immunity cycle, thus advancing personalized cancer immunotherapy.

cancer biology↗

bioRxiv2025

RNAcare: Integrating Clinical Data with Transcriptomic Evidence using Rheumatoid Arthritis as a Case Study

BackgroundGene expression analysis is a crucial tool for uncovering the biological mechanisms that underlie differences between patient subgroups, offering insights that can inform clinical decisions. However, despite its potential, gene expression analysis remains challenging for clinicians due to the specialised skills required to access, integrate, and analyse large datasets. Existing tools primarily focus on RNA-Seq data analysis, providing user-friendly interfaces but often falling short in several critical areas: they typically do not integrate clinical data, lack support for patient-specific analyses, and offer limited flexibility in exploring relationships between gene expression and clinical outcomes in disease cohorts. Users, including clinicians with a general knowledge of transcriptomics, however, who may have limited programming experience, are increasingly seeking tools that go beyond traditional analysis. To overcome these issues, computational tools must incorporate advanced techniques, such as machine learning, to better understand how gene expression correlates with patient symptoms of interest. ResultsOur RNAcare platform, addresses these limitations by offering an interactive and reproducible solution specifically designed for analysing bulk RNA-Seq data from patient samples in a clinical context. This enables researchers to directly integrate gene expression data with clinical features, perform exploratory data analysis, and identify patterns among patients with similar diseases. By enabling users to integrate transcriptomic and clinical data, and customise the target label, the platform facilitates the analysis of the relationships between gene expression and clinical symptoms, like pain and fatigue. This allows users to generate hypotheses and illustrative visualisations/reports to support their research. As proof of concept, we use RNAcare to link inflammation-related genes to pain and fatigue in rheumatoid arthritis (RA) and detect signatures in the drug response group, confirming previous findings and generating new hypotheses. ConclusionWe present a novel computational platform allowing the interpretation of clinical and transcriptomics data in real-time. The platform can be used for data generated by the user, such as the patient data presented here or using published datasets. The platform is available at https://rna-care.mvls.gla.ac.uk/, with its source code at https://github.com/sii-scRNA-Seq/RNAcare/.

bioinformatics↗

bioRxiv2024

Network-based representation learning reveals the impact of time and diet on the gut microbial and metabolomic environment of infants

BackgroundWhile studies have explored differences in gut microbiome development for infant liquid diets (breastmilk, formula), little is known about the impact of complementary foods on infant gut microbiome development. Here, we investigated how different protein-rich foods (i.e., meat vs. dairy) affect fecal metagenomics and metabolomics during early complementary feeding from 5-12 months in U.S. formula-fed infants from a randomized controlled feeding trial. ResultsWe used a novel network representation learning approach to model the time-dependent, complex interactions between microbiome features, metabolite compounds, and diet. We then used the embedded space to detect features associated with age and diet type and found the meat diet group was enriched with microbial genes encoding amino acid, nucleic acid, and carbohydrate metabolism. Compared to a more traditional differential abundance analysis, which analyzes features independently and found no significant diet associations, network node embedding represents the infant samples, microbiome features, and metabolites in a single transformed space revealing otherwise undetected associations between infant diet and the gut microbiome. ConclusionsOur findings generate new hypotheses regarding the interplay between complementary feeding practices, microbial-metabolic interactions, and infant physiological outcomes. This work highlights the impact of complementary foods on infant gut microbiome development and the potential of using network representation learning to integrate multi-omic data, allowing for greater insight into complex diet, microbial, and metabolite interactions.

microbiology↗

bioRxiv2024

A new synonym for Viburnum erosum (Viburnaceae) in East China, based on morphological and molecular evidence

The critical observations of living plants in the field, along with the examination of type specimens and protologues, led us to conclude that the key characteristics, including the length of the petiole, stipules and peduncles, as well as the shape of leaves of Viburnum fengyangshanense, all fall within the variation range of V. erosum. Additionally, molecular analysis of nuclear ribosomal internal transcribed spacer (nrITS) and three plastid DNA markers (rbcL, matK and ndhF) indicates that V. fengyangshanense and V. erosum are deeply nested within a clade. Therefore, based on morphological and molecular evidence, it is demonstrated that V. fengyangshanense should be regarded as a new synonym of V. erosum.

plant biology↗

bioRxiv2024

A curated collection of transcriptome datasets to study the transcriptional response in blood and nasal samples following viral respiratory inoculation and vaccination

Our understanding of the human immune systems response to viral respiratory tract infections (VRTIs) and vaccines, including the molecular mechanisms and correlates of protection, remains incomplete. Extensive transcriptomic data from inoculation and vaccination studies have been deposited in publicly available databases. However, these studies are often separate and difficult to locate. We provide a curated compendium of public gene expression data repositories for researchers to reanalyze transcriptomes from human whole blood, peripheral blood mononuclear cells (PBMCs), and nasal swab samples. This enables the study of transcriptional responses to viral inoculation or vaccination. This collection includes 18 datasets from inoculation studies and 37 datasets from vaccination studies, sourced from the NCBI Gene Expression Omnibus (GEO), ImmPort Shared Data, and ArrayExpress.

genomics↗

bioRxiv2024

ScReNI: single-cell regulatory network inference through integrating scRNA-seq and scATAC-seq data

Single cells exhibit heterogeneous gene expression profiles and chromatin accessibility, measurable separately via single-cell RNA sequencing (scRNA-seq) and single-cell transposase chromatin accessibility sequencing (scATAC-seq). Consequently, each cell possesses a unique gene regulatory network. However, limited methods exist for inferring cell-specific regulatory networks, particularly through the integration of scRNA-seq and scATAC-seq data. Here, we develop a novel algorithm named single-cell regulatory network inference (ScReNI), which leverages k-nearest neighbors and random forest algorithms to integrate scRNA-seq and scATAC-seq data for inferring gene regulatory networks at the single-cell level. ScReNI is built to analyze both paired and unpaired datasets for scRNA-seq and scATAC-seq. Using these two types of single-cell sequencing datasets, we validate that a higher fraction of regulatory relationships inferred by ScReNI are detected by chromatin immunoprecipitation sequencing (ChIP-seq) data. ScReNI shows superior performance in network-based cell clustering when compared to existing single-cell network inference methods. Importantly, ScReNI offers the unique function of identifying cell-enriched regulators based on each cell-specific network. In summary, ScReNI facilitates the inferences of cell-specific regulatory networks and cell-enriched regulators.

bioinformatics↗

bioRxiv2024

Discovery of novel quinoline papain-like protease inhibitors for COVID-19 through topology constrained molecular generative model

The rapid emergence of drug-resistant SARS-CoV-2 variants poses a persistent challenge to current antiviral strategies. Mutations in key viral targets, including RNA-dependent RNA polymerase (RdRp), main protease (3CLpro), and papain-like protease (PLpro), have been shown to markedly reduce the efficacy of approved therapeutics, highlighting the urgent need for next-generation antivirals capable of overcoming resistance. Here, we report the discovery of a novel class of PLpro inhibitors through an AIDD strategy based on a topology-constrained molecular generative model (Tree-Invent) integrated with structure-guided optimization. Scaffold hopping from a previously reported lead enabled the identification of a quinoline-based chemical series with substantially improved metabolic stability and antiviral potency. Structure-guided optimization yielded compound GZNL-2016, which exhibited potent enzymatic inhibition of PLpro (IC50 = 10.5 nM), robust antiviral activity against multiple SARS-CoV-2 variants, including Omicron BA.5 and XBB.1, and favorable pharmacokinetic properties following oral administration. Notably, GZNL-2016 retained substantial inhibitory activity against the clinically relevant drug-resistant mutant PLpro E167K (IC50 = 480.2 nM; Ki = 439.3 nM), in contrast to previously reported inhibitors that exhibit markedly reduced potency. In a SARS-CoV-2 infection mouse model, oral administration of GZNL-2016 significantly reduced pulmonary viral titers, demonstrating in vivo antiviral efficacy. Collectively, this study establishes an AI-enabled strategy for rapid antiviral discovery and identifies GZNL-2016 as a promising lead compound to address the threat of coronavirus infections caused by drug-resistant mutant SARS-CoV-2 variants. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/611841v3_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@ecbd3eorg.highwire.dtl.DTLVardef@7c663corg.highwire.dtl.DTLVardef@11c0e37org.highwire.dtl.DTLVardef@e950f0_HPS_FORMAT_FIGEXP M_FIG C_FIG We leveraged an AI generative model to discover a novel PLpro inhibitor with excellent liver stability, low CYP, hERG inhibition and reasonable oral PK properties. At the same time, the compound 16 exhibits high efficacy for the resistance mutation E167K, which resulted in severe resistance to the previously reported inhibitor Jun12682 and PF-07957472.

microbiology↗

bioRxiv2024

PerturbAtlas: A Comprehensive Atlas of Public Genetic Perturbation Bulk RNA-seq Datasets

Manipulating gene expression is crucial for understanding gene function, with high-throughput sequencing techniques such as RNA-seq elucidating the downstream mechanisms involved. However, the lack of a standardized metadata format for small-scale perturbation expression datasets in public repositories hinders their reuse. To address this issue, we developed PerturbAtlas, an add-value resource that re-analyzes publicly archived RNA-seq libraries to provide quantitative data on gene expression, transcript profiles, and alternative splicing events following genetic perturbation. PerturbAtlas assists users in identifying trends at the gene and isoform levels in perturbation assays by re-analyzing a curated set of 122,801 RNA-seq libraries across 13 species. This resource is freely available at https://perturbatlas.kratoss.site as both raw data tables and an interactive browser, allowing searches by species, tissue, or genomic features. The results provide detailed information on alterations following perturbations, accessible through both forward and reverse approaches, thereby enabling the exploration of perturbation consequences and the identification of potential causal perturbations. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/605482v1_ufig1.gif" ALT="Figure 1"> View larger version (76K): org.highwire.dtl.DTLVardef@150012org.highwire.dtl.DTLVardef@b7ac25org.highwire.dtl.DTLVardef@86c4feorg.highwire.dtl.DTLVardef@1ef3341_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Longitudinal sequencing reveals polygenic andepistatic nature of genomic response to selection

Evolutionary adaptation to new environments likely results from a combination of selective sweeps and polygenic shifts, depending on the genetic architecture of traits under selection. While selective sweeps have been widely studied, polygenic responses are considered more prevalent but challenging to quantify. The infinitesimal model makes explicit the hypothesis about the dynamics of changes in allele frequencies under selection, where only allelic effect sizes, frequencies, linkage, and gametic disequilibrium matter. Departures from this, like long-range correlations of allele frequency changes, could be a signal of epistasis in polygenic response. We performed an Evolve & Resequence experiment in Drosophila melanogaster exposing flies to a high-sugar diet as a source of environmental stress for over 100 generations. We tracked allele frequency changes in >3000 individually sequenced flies as well as population pools and searched for loci under selection by identifying sites with allele frequency trajectories that differentiated selection regimes consistently across replicates. We estimate that at least 4% of the genome was under positive selection, the result of a highly polygenic response. Most of this response was seen as small but consistent allele frequency changes over time, and there were only a few large allele-frequency changes (selective sweeps). We then searched for signatures of selection on pairwise combinations of alleles in the new environment and found several strong signals of putative epistatic interactions across unlinked loci that were consistent across selected populations. Finally, we measured differentially expressed genes (DEGs) across treatments and show that DEGs are enriched for selected SNPs, suggesting a regulatory basis for the selective response. Our results suggest that epistatic contributions to polygenic selective response are common and lead to detectable signatures.

evolutionary biology↗

bioRxiv2024

FLASH radiotherapy using high-energy X-rays: validation of the FLASH effect triggered by a compact single high-energy X-ray source device

PurposeThis preclinical study aimed to verify the FLASH effect of compact single high-energy X-ray source (CHEXs) and to explore whether three irradiations with single-gantry rotation two 30 s pauses can generate FLASH effect in mice. Materials and methodsThe absolute dose and pulsed beam of the CHEXs were measured using an EBTXD radiochromic film and fast current transformer. Healthy C57BL/6J female mice and a subcutaneous tumor model were irradiated under different conditions: sham (control), FLASH-RT (FLASH1: delivering the total dose in 1 fraction; FLASH3: delivering the total dose with two 30 second pauses to simulate a three-field delivery where the gantry rotation is occurring within 30 seconds), and conventional dose rate radiotherapy (CONV-RT). Various total doses were administered to the corresponding normal tissues (whole thorax, 30 Gy; whole abdomen, 12 Gy; and skin, 36 Gy) and tumors (CT26, 16.5 Gy; and LLC, 18 Gy). Survival status, normal tissue damage, and tumor growth suppression were recorded in each group. ResultsThe average dose rate of the CHEXs exceeded 40 Gy/s. For whole-thorax and skin irradiation, both FLASH1 and FLASH3 demonstrated protective effects. For whole-abdomen irradiation, FLASH1 exhibited a superior protective effect. No significant differences in tumor growth responses were observed between the FLASH1, FLASH3, and CONV-RT groups (P>0.05). ConclusionThis study confirmed that the FLASH effect could be triggered using CHEXs FLASH radiotherapy, and demonstrated that three irradiations with single gantry rotation two 30 s pauses can trigger the FLASH effect, indicating the potential benefit of CHEXs 3D conformal radiotherapy. Our findings indicate that further clinical trials on CHEXs are warranted.

cancer biology↗

bioRxiv2024

PRISM: Multiplexed Profiling of RNA In-Situ through Single-round of Imaging in Three-Dimensional Tissue

Spatial RNA imaging has not been widely adopted because conventional fluorescence microscopy is limited to only a few channels, and the cyclic reactions needed to increase multiplexing in techniques such as sequential fluorescence in-situ hybridization (FISH) require sophisticated instrumentation. Here, we introduce Profiling of RNA In-situ through Single-round iMaging (PRISM), a method that expands coding capacity through color intensity grading. Using a radius vector filtering strategy to ensure the distinguishability of codewords in color space, PRISM achieves up to 64-plex color-barcoded RNA imaging in a single imaging round with conventional microscopes. We validate PRISMs versatility across various tissues by generating a 3D atlas of mouse embryonic development from E12.5 to E14.5, a quasi-3D tumor-normal transition landscape of human hepatocellular carcinoma (HCC), and a 3D cell atlas and subcellular RNA localization landscapes of mouse brain. Additionally, we show the critical role of cancer-associated fibroblasts (CAFs) in mediating immune infiltration and immune response heterogeneity within and between tumor microenvironments.

bioengineering↗

bioRxiv2024

Predictive coding model can detect novelty on different levels of representation hierarchy

Novelty detection, also known as familiarity discrimination or recognition memory, refers to the ability to distinguish whether a stimulus has been seen before. It has been hypothesized that novelty detection can naturally arise within networks that store memory or learn efficient neural representation, because these networks already store information on familiar stimuli. However, computational models instantiating this hypothesis have not been shown to reproduce high capacity of human recognition memory, so it is unclear if this hypothesis is feasible. This paper demonstrates that predictive coding, which is an established model previously shown to effectively support representation learning and memory, can also naturally discriminate novelty with high capacity. Predictive coding model includes neurons encoding prediction errors, and we show that these neurons produce higher activity for novel stimuli, so that the novelty can be decoded from their activity. Moreover, the hierarchical predictive coding networks uniquely perform novelty detection at varying abstraction levels across the hierarchy, i.e., they can detect both novel low-level features, and novel higher-level objects. Overall, we unify novelty detection, associative memory, and representation learning within a single computational framework.

neuroscience↗

bioRxiv2024

Discovery of orally bioavailable SARS-CoV-2 papain-like protease inhibitor as a potential treatment for COVID-19

The RNA-dependent RNA polymerase (RdRp), 3C-like protease (3CLpro), and papain-like protease (PLpro) are pivotal components in the viral life cycle of SARS-CoV-2, presenting as promising therapeutic targets. Currently, all FDA-approved antiviral drugs against SARS-CoV-2 are RdRp or 3CLpro inhibitors. However, the mutations causing drug resistance have been observed in RdRp and 3CLpro from SARS-CoV-2, which makes it necessary to develop antivirals with novel mechanisms. Through the application of a structure-based drug design (SBDD) approach, we discovered a series of novel potent non-covalent PLpro inhibitors with remarkable in vitro potency and in vivo PK properties. The co-crystal structures of PLpro with leads revealed that the residues D164 and Q269 around the S2 site are critical for improving the inhibitors potency. The lead compound GZNL-P36 not only inhibited SARS-CoV-2 and its variants at the cellular level with EC50 ranging from 58.2 nM to 306.2 nM, but also inhibited HCoV-NL63 and HCoV-229E with EC50 of 81.6 nM and 2.66 M, respectively. Oral administration of the compound resulted in significantly improved survival and notable reductions in lung viral loads and lesions in SARS- CoV-2 infection mouse model, consistent with RNA-seq data analysis. Our results indicate that PLpro inhibitor is a promising SARS-CoV-2 therapy.

microbiology↗

bioRxiv2024

Early-life stress alters chromatin modifications in VTA to prime stress sensitivity

Early-life stress increases sensitivity to subsequent stress, which has been observed at behavioral, neural activity, and gene expression levels. However, the molecular mechanisms underlying such long-lasting sensitivity are poorly understood. We tested the hypothesis that persistent changes in transcription and transcriptional potential were maintained at the level of the epigenome, through changes in chromatin. We used a combination of bottom-up mass spectrometry, viral-mediated epigenome-editing, RNA-sequencing, patch clamp electrophysiology of dopamine neurons, and behavioral quantification in a mouse model of early-life stress, focusing on the ventral tegmental area (VTA), a dopaminergic brain region critically implicated in motivation, reward learning, stress response, and mood and drug disorders. We found that early-life stress alters histone dynamics in VTA, including enrichment of histone-3 lysine-4 monomethylation -- associated with open chromatin and primed or active enhancers -- and the H3K4 monomethylase Setd7. Mimicking early-life stress through postnatal overexpression of Setd7 and enrichment of H3K4me1 in VTA sensitizes transcriptional, physiological, and behavioral response to adult stress. These findings link early-life stress experience to long-term stress hypersensitivity within the brains dopaminergic circuitry, providing a mechanism by which early-life stress increases risk for mood and anxiety disorders later in life.

neuroscience↗