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Zhang, H.

Publications and source records attributed to Zhang, H..

At least 199 records · Page 11Linked to original sources

Metabolic Perturbation Exacerbates Sinoatrial Node Dysfunction in Heart Failure

Heart failure (HF) affects approximately 6.2 million people in the United States, with a 5-year mortality exceeding 50%. Bradyarrhythmia, a known complication in HF due to sinoatrial node (SAN) dysfunction (SAND), increases the morbidity and mortality of HF patients. Insights into the mechanistic underpinnings of SAND in HF could therefore uncover vital therapeutic targets to improve clinical outcomes. The SAN cells are endowed with a dense mitochondrial network crucial for sustaining their pacemaking function on a beat-to-beat basis. We have previously demonstrated significant disruptions in the mitochondrial-sarcoplasmic reticulum connectomics, resulting in abnormal mitochondrial Ca2+ handling and impaired mitochondrial function in HF. Here, we hypothesize that the metabolic perturbation is one of the critical mechanisms underlying SAND. To this end, we took advantage of a multi-omics approach combined with ultra-resolution imaging and functional analyses to decipher the metabolic shift that transpires in the HF SAN. Our findings revealed significant metabolic remodeling within the SAN mitochondria in HF, with a diminished reliance on fatty acid {beta}-oxidation, enhanced utilization of ketone bodies, and heightened dependence on carbohydrate catabolism. Notably, metabolomics analyses identified the pronounced increase of glucosylceramides and ceramides as one of the mechanisms leading to mitochondrial dysfunction. We directly test this hypothesis and demonstrate that ceramides induce a dose-dependent metabolic shift from oxidative phosphorylation to glycolysis. Importantly, these alterations lead to a significant impairment in SAN automaticity in a dose-dependent manner. Collectively, the findings support the notion that ceramides are not only markers of metabolic derangement, but also active mediators of mitochondrial and metabolic dysfunction in the SAN. Overall, the study provides evidence that ceramides may be a potential therapeutic target for mitigating SAND in HF.

physiology↗

ATLAS: Population-Level Disease Locus Discovery via Differential Attention in Genomic Language Models

Identifying disease-associated genetic variants remains a key challenge in genomics, especially in small cohorts or for rare and complex mutation types where genome-wide association studies (GWAS) often fall short. We introduce ATLAS, a population-level framework that leverages attention signals from pretrained genomic language models (gLMs) to detect disease-associated genes and loci directly from raw sequences--without requiring explicit variant calls or supervised training. ATLAS first performs gene-level differential attention analysis to prioritize candidate genes, followed by base-level analysis to localize disease-associated regions at single-haplotype resolution. We validate ATLAS on synthetic and {beta}-thalassemia datasets, demonstrating robust performance across diverse allele frequencies (down to 10%), cohort sizes (below 200 individuals per group), and genomic scales. Compared to GWAS, ATLAS achieves higher recall of known loci and captures haplotype-specific signals missed by traditional methods. Cross-model benchmarking further shows that precise localization depends on both model size and pretraining on diverse human genomes. In summary, ATLAS offers a scalable, sequence-native alternative to traditional statistical genetics.

genetics↗

Protenix-v1: Toward High-Accuracy Open-Source Biomolecular Structure Prediction

We introduce Protenix-v1 (PX-v1), the first fully open-source structure prediction model to attain superior performance to AlphaFold3 while strictly adhering to the same training data cutoff, model size, and inference budget. Beyond standard evaluations, we highlight the effectiveness of inference-time scaling behavior of Protenix-v1, demonstrating that increasing the sampling budget yields consistent improvements in prediction quality--a behavior previously observed in AlphaFold3 and largely absent from prior open-source models. In addition to improved accuracy, Protenix-v1 incorporates key capabilities including protein template integration and RNA MSA support. Furthermore, to better support real-world applications such as drug discovery, we additionally release Protenix-v1-20250630, a variant trained on a larger dataset (cutoff: June 30, 2025), delivering further improved prediction accuracy. Finally, we identify limitations in existing benchmarking practices and provide updated evaluation tools and year-stratified benchmarks to support more reliable and transparent assessment. Collectively, these contributions provide a robust foundation for the Protenix series and the broader field.

bioinformatics↗

Intestinal epithelial Casd1 influences mucus sialic acid O-acetylation and tissue damage susceptibility toward large-intestinal mucosal insults.

The intestinal mucus network, primarily composed of O-glycosylated MUC2 mucin polymers, is essential for protecting the gastrointestinal tract from microbial threats. Sialic acid (Sia), a terminal monosaccharide on complex O-glycans, plays a key role in maintaining mucus integrity and is frequently modified by Casd1-dependent O-acetylation (OAc). Despite its prevalence, the biological significance of sialic acid OAc (OAc-Sia) modifications in human and murine mucus remains unclear. We hypothesized that OAc-Sia variants on mucus interact with the microbiota and are required for optimal mucus barrier function and host-microbe homeostasis in the colon. To test this, we profiled OAc-Sia on human and mouse MUC2 in situ using viral-derived probes with bacterial FISH and confocal microscopy; generated intestinal epithelial cell (IEC)-specific Casd1 null mice (IEC Casd1-/-); performed sialylomic and O-glycomic HPLC-MS analyses; assessed microbial communities by 16S rRNA sequencing with quantitative microbial profiling (QMP); and evaluated disease susceptibility using DSS colitis and Citrobacter rodentium infection models. Results revealed that both human and murine mucins are extensively O-acetylated and interact with the microbiota, suggesting biological relevance. IEC Casd1-/- mice were viable and displayed a complete loss of mucin OAc-Sia, indicating Casd1 is the sole contributor to OAc-status. Unexpectedly, mucus function was intact in IEC Casd1-/- mice, with no difference in structure or quality vs. WT co-housed littermates.16S rRNA analysis showed a modest but significant sex-specific reduction of microbial loads in male IEC Casd1-/-mice, and a clear trend toward reduced Turicibacter spp. vs. WT mice in both male and females, without impacting overall short-chain fatty acid (SCFA) production. DSS treatment led to more severe and extensive tissue damage in IEC Casd1-/- mice. C. rodentium infection led to increased damage in the cecum and distal colon of IEC Casd1-/- mice without affecting pathogen load, suggesting that OAc-Sia status has a role in tolerance defense. These findings establish intestinal epithelial Sia O-acetylation as a component dispensable for mucus and host-microbe homeostasis at baseline, but important in limiting damage to mucosal inflammatory insults.

cell biology↗

Decoupling Structure and Property in Discrete Protein Diffusion: Alignment Dynamics and Collapse Mechanisms

Direct Preference Optimization (DPO) has emerged as a powerful paradigm for aligning generative models, yet its temporal optimization dynamics in the discrete diffusion space of proteins remain poorly understood. Existing approaches often assume that maintaining structural integrity while optimizing physicochemical properties requires simultaneous, tightly coupled reinforcement learning constraints. In this work, we challenge this assumption by uncovering a fundamental temporal decoupling between structural and functional alignment. Using antibody design as a testbed, extensive trajectory analysis reveals two distinct regimes: (1) Instant Structural Alignment, where the strong generative prior of discrete diffusion rapidly eliminates structural hallucinations via denoising within the first few epochs; and (2) Slow Property Adaptation, where physicochemical attributes improve gradually over a prolonged optimization window. We further identify a critical transition point around Epoch 50, which empirically defines a Pareto-optimal boundary between property improvement and structural stability. Beyond this point, continued optimization induces a sharp phase transition into a Structural Collapse regime. To isolate the physical driver underlying this collapse, we introduce a counterfactual preference experiment targeting negative charge. We observe a striking symmetrical collapse: while hydrophilicity optimization induces a Poly-Arginine (+) degeneration, negative charge optimization drives a Poly-Aspartate (-) degeneration. Despite opposite physicochemical trajectories, including extreme shifts in isoelectric point (> 11 vs. < 4.5), both regimes converge to the same structural failure. This symmetry demonstrates that generic Coulombic repulsion, rather than residue-specific bias, constitutes the fundamental physical constraint being violated. Our findings reveal that discrete diffusion models possess strong intrinsic structural robustness, enabling minimalist alignment strategies provided optimization halts before this physical boundary. More broadly, this work offers a mechanistic warning against unchecked reward optimization in biological generation, illustrating a concrete manifestation of Goodharts Law in protein design. Code and data are available at https://github.com/Wu-Junqi/DPO-Protein-Diffusion.

bioinformatics↗

Decomposing multi-scale dynamic regulation from single-cell multiomics with scMagnify

Deciphering the highly coupled regulatory circuits that drive cellular dynamics remains a fundamental goal in biology. However, capturing the multi-scale time-lagged dynamics and combinatorial regulatory logic of gene regulation remains computationally challenging. Here we present scMagnify, a deep-learning-based framework that leverages multiomic single-cell assays of chromatin accessibility and gene expression via nonlinear Granger causality to reconstruct and decompose multi-scale gene regulatory networks (GRNs). Benchmarking on both simulated and real datasets demonstrates that scMagnify achieves superior performance. scMagnify employs tensor decomposition to systematically identify combinatorial TF modules and their activation profiles across different time-lags. It enables a hierarchical dissection of the regulatory landscape-- from the activity of individual regulator to the combinatorial logic of regulatory modules and intercellular communications. We applied scMagnify to human hematopoiesis and mouse pancreas development, where it successfully recovered known lineage-driving regulators and provided novel insights into the combinatorial logic that governs cell fate decisions. Furthermore, in the context of kidney injury, scMagnifys intracellular communication module mapped key signaling-to-transcription cascades linking microenvironment cues to pathological epithelial cell changes. In summary, scMagnify provides a powerful and versatile computational framework for dissecting the multi-scale regulatory logic that governs complex biological processes in development and disease.

bioinformatics↗

Ripk1 Deficiency or K376R Mutation in Dendritic Cells Promotes Potent Antitumor Immunity

Dendritic cells (DCs) are critical for initiating adaptive immunity through antigen presentation and T cell priming. While immunogenic cell death (ICD) enhances anti-tumor immunity, how programmed cell death in DCs shapes tumor immune responses remains unclear. Receptor-interacting protein kinase 1 (RIPK1) regulates apoptosis, necroptosis, and inflammation. Here, we show that mice with disrupted RIPK1 ubiquitination on K376 (Ripk1fl/K376R Cd11c-Cre, Ripk1DC-KO/K376R) develop inflammation and autoimmunity, phenocopying DC-specific RIPK1-deficient(Ripk1fl/fl Cd11c-Cre, Ripk1DC-KO) mice. These phenotypes were rescued by the genetic ablation of Ripk3 or Mlkl, establishing that RIPK1 ubiquitination is essential for suppressing DC necroptosis to maintain immune homeostasis. Remarkably, both Ripk1DC-KO and Ripk1DC-KO/K376R mice exhibit robust resistance to tumor growth, characterized by significantly enhanced T cell infiltration and activation. Mechanistically, we show that Ripk1 deficiency or the loss of its K376 ubiquitination augments antigen-presenting capacity of DCs through both necroptosis-dependent and -independent pathways. Furthermore, adoptive transfer of T cells from Ripk1DC-KO mice further amplifies anti-tumor immunity in recipient hosts. These results identify RIPK1 as a critical immunological checkpoint in DCs that constrains their immunogenic potential. Targeting this pathway represents a promising therapeutic strategy to potentiate T cell-mediated cancer immunotherapy.

immunology↗

eScreen: a deep learning framework for functionally decoding the regulatory genome at single-nucleotide resolution

The human genome is densely populated with cis-regulatory elements (CREs), yet deciphering their functional regulatory syntax and combinational logic remains a fundamental challenge. Here, we integrate 379 genome-scale CRISPR screen experiments, encompassing 21 million perturbations across 23 cell types, to construct a compendium of 41,239 high-confidence functional CREs from 530,527 candidates. Leveraging this resource, we develop eScreen, a deep learning model built on the StripedHyena2 architecture to functionally decode the regulatory genome at single-nucleotide resolution. eScreen achieves three primary functions: (1) predicts genome-wide cell-type-specific CRE functional activity with high accuracy, outperforming existing models; (2) provides mechanistic interpretation of regulatory syntax at single-nucleotide resolution; (3) dissects the functional organization of enhancer clusters through in silico perturbation analysis. We perform multiple independent CRISPR knockout, CRISPR interference (CRISPRi), and base editing screens to validate these functions of eScreen both at scale and on individual cases. Furthermore, we provide an interactive web server (https://escreen.huanglabxmu.com/) for the community to access the integrated CRISPR screen resources and eScreen functions. Collectively, our work establishes a highly precise and convenient tool to decode the causal effects of the regulatory genome.

genomics↗

Film Recall Reveals Intact Event Memory but Impaired Sequence Memory in Temporal Lobe Epilepsy Patients

BACKGROUND AND OBJECTIVESPatients with epilepsy (PWE), especially temporal lobe epilepsy (TLE), experience impaired memory for personally experienced events. However, current assessments of episodic memory are limited in their ecological validity with a potential to miss detection of subtle cognitive decline. We conducted an exploratory study to determine whether a naturalistic film-viewing task with open-ended spoken recall could detect memory differences between TLE patients and healthy controls (HCs). METHODSTLE patients (ages 18-60, fluent in English, not legally blind) were recruited from a Level 4 Epilepsy Center (2018-2024). TLE diagnosis was based on seizure semiology, MRI Brain, and EEG. TLE patients scored [&ge;]22/30 on the Montreal Cognitive Assessment (MOCA); HCs scored [&ge;]26/30. Subjects watched 6 short films and then freely recalled film details. Spoken responses were recorded, transcribed, segmented, and scored for film- and event-level recall. Recall order was assessed using the Damerau-Levenshtein distance. Semantic and causal centrality were quantified using sentence embeddings and rater-identified causal links, respectively. Beta regression with cluster-robust standard errors assessed group and centrality effects on recall probability. Beta regression evaluated the influence of age, MOCA, and testing platform on sequence recall error. RESULTSWe recruited 51 subjects (27 TLEs; 24 HCs, 70.1% F, mean 29.9 {+/-}8.3 years). TLE patients and HCs showed similar recall of films (HC 89% {+/-}11% vs TLE 88% {+/-}18%, p = 0.54), coarse events (HC 50% {+/-}16% vs TLE 44% {+/-}18%, p = 0.19) and fine events (HC 25%{+/-}10% vs. TLE 22%{+/-}12%, p=0.17). Both groups recalled high causal centrality events better. For coarse event sequence recall, TLE patients showed a numerical trend toward greater sequence errors compared to HCs (HC 10.8% {+/-} 10.5% vs. TLE 19.5% {+/-} 18%, p = 0.06), although this difference did not reach statistical significance. However, TLE patients showed significantly greater fine event sequence errors at recall than HCs (HC 15% {+/-}13% vs 23% {+/-}18%, p = 0.02, Hedges g = 0.85, Cliffs {delta} = 0.51), with RTLE demonstrating more sequence errors than HCs (15%{+/-}13 vs. 29%{+/-}21% p = 0.021) Age, education, MOCA, and performance on standard verbal and visual memory tasks were unrelated to film, event, and sequence recall performance. DISCUSSIONWe demonstrate that a short film task with spontaneous spoken recall can identify sequence memory impairment in TLE patients despite intact film- and event-level recall. Sequence memory may represent a subtle manifestation of memory impairment that is not detected by standard cognitive testing. Key PointsO_LIWe asked whether a naturalistic film recall task could detect episodic memory impairment in a temporal lobe epilepsy cohort. C_LIO_LIPatients with temporal lobe epilepsy showed comparable film and event recall compared to healthy controls but were found to have impaired sequence memory. C_LIO_LISequential memory for temporal order is an overlooked aspect of episodic memory that may detect subtle memory decline. C_LI

neuroscience↗

The genetic repertoire of the deep sea: from sequence to structure and function

The deep sea as the largest and maybe most hostile environment on Earth is still underexplored especially regarding its genetic repertoire. Yet, previous work has revealed significant habitat-specific deep-sea biodiversity. Here, we present an integrated deep-sea genetic dataset comprising 502 million nonredundant genes from 2,138 samples and 2.4 million predicted structures, and used it to link specific protein structures with genetic variants associated with life in the deep sea and to assess their biotechnology potential. Combining global sequence analysis with biophysical and biochemical measurements revealed unprecedented sequence diversity, yet substantial structural conservation of proteins. Especially proteins involved in replication, recombination, and repair were identified to be under rapid evolution and with specialized properties. Among these, a structurally divergent helicase exhibited advantages in controlling nanopore sequencing speed. Thus, our work positions the deep sea as a unique evolutionary engine that generates and hosts genetic diversity and bridges genetic knowledge with biotechnology.

bioinformatics↗

JAK-STAT Pathway Heterogeneity Governs Immunotherapy Response in Breast Cancer

The JAK-STAT pathway (JSP) is a well-known oncogenic cascade; however, recent clinical trials have detected JSP upregulation in breast cancer following anti-PD1 immunotherapy. This paradoxical observation warrants further investigation into JSPs intercellular heterogeneity, tumor dynamics, molecular mechanisms, and clinical implications for immunotherapy. JSP expression showed dynamic shifts during breast cancer progression, with higher levels in T cells and para-cancerous epithelial cells. In tumor cells, elevated JSP highly correlated with malignant phenotypes. JSP-high tumor cells overexpressed oncogenic pathways, while exhibiting increased immunosuppressive signaling via MIF-CD74 signaling axis. In T cells, higher JSP levels were associated with enhanced cytotoxic activity, improved differentiation, and reduced exhaustion, reflecting robust anti-tumor immunity. Analysis of immunotherapy datasets revealed that higher JSP levels were associated with improved responses towards PD-1 inhibitors, particularly in triple-negative breast cancer (TNBC) patients, with JSP serving as a predictive biomarker for immunotherapy sensitivity. As a key JSP component, STAT4 exerts dual roles in breast cancer: it drives tumorigenesis in malignant cells, sustains breast epithelial cell proliferation, and bolsters T cell anti-tumor functionality--while also acting as a highly accurate biomarker for predicting immunotherapy response. This indicates that JSP targeting demands a nuanced approach: broad inhibition may impair anti-tumor immunity, and optimized therapeutic strategies paired with precise biomarkers are critical to maximize JSPs utility in breast cancer immunotherapy. Our findings highlight JSPs functional heterogeneity in epithelial, tumor, and T cells, with high JSP activity correlating with enhanced immunotherapy efficacy in breast cancer. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/703506v3_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@c3a850org.highwire.dtl.DTLVardef@15f2f0borg.highwire.dtl.DTLVardef@99065org.highwire.dtl.DTLVardef@1e6eb6c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

Selective Glucocorticoid Receptor Modulators of Immune Checkpoint Function

Glucocorticoids (GCs) coordinate immunity, inflammation, and metabolism through allosteric regulation of the glucocorticoid receptor (GR) transcription factor. GCs are indispensable anti-inflammatory drugs yet linking specific ligand-receptor structural states to specific biological outcomes has remained a major barrier to designing safer, more selective therapies. Using structure-based design, we developed selective glucocorticoid receptor modulators (SGRMs) of immune function by extending a steroidal scaffold from the ligand-binding pocket into an adjacent solvent channel. These SGRMs suppressed T cell pro-inflammatory cytokines and promoted differentiation of memory precursor T cells while showing minimal induction of M2 macrophage polarization or T cell checkpoint proteins PD-1 and CTLA-4, all key targets of immunotherapy. Molecular dynamics simulations revealed that solvent-channel substituents function as a lever arm to drive dynamic oscillations in the steroid core, thereby allosterically tuning GR activity states. Systematic perturbation of immune cells with a graded series of ligands enabled a ligand perturbation with machine learning (LPML) framework to map coregulated responses across cell types and identified effector T cell gene networks tightly coupled with immune checkpoint induction. This approach outlines a general strategy for decoding the logic of allosteric drug action, enabling the rational design of SGRMs with tailored immunomodulatory profiles.

systems biology↗

3D Cell-Matrix Mechanical Interaction Models for Cancer Invasion and Drug Evaluation

Cancer cells breach the extracellular matrix (ECM) using both protease-driven degradation and force-driven physical remodeling, yet most anti-metastatic drug screens still rely on biochemical assays that overlook cell-matrix mechanical reciprocity. Here, we present a fully synthetic 3D invasion platform based on cellular force-responsive polyisocyanide (PIC) hydrogels that isolates biophysical invasion mechanisms. Cell-generated forces align and densify the PIC fibrous network, reproducing hallmark matrix remodeling seen in the tumor microenvironment. A constitutive model, parameterized by the critical stress for strain stiffening effect, links matrix nonlinear elasticity to pericellular stiffening, long-range mechanotransmission, and intercellular coupling. Using this system, we show that breast cancer cells invade by pulling and pushing the network even when matrix metalloproteinases are inhibited, revealing a physical bypass of protease blockade. Accordingly, broad-spectrum metalloproteinase inhibitors that suppress invasion in Matrigel fail to inhibit invasion here, exposing a limitation of current drug-evaluation pipelines. In co-culture, cancer-associated fibroblasts markedly accelerate invasion by generating aligned fiber tracks through higher contractility, implicating CAF-driven mechanical remodeling as a key route for breaching barriers during metastasis. The platform is thermoresponsive, compatible with standard Transwell formats, enables direct imaging of fiber architecture and invasion fronts, and decouples biophysical from biochemical cues for mechanism-aware, animal-free assessment of anti-metastatic therapies.

cancer biology↗

An agentic framework turns patient-sourced records into a multimodal map of ALS heterogeneity

ALS progression is multidimensional, yet fragmented records and scalar outcomes obscure how patients move through disease states and how those states relate to molecular variation. MEDSTREM converts patient-held medical-record images into standardised longitudinal data, enabling bottom-up cohort construction. Using MEDSTREM-structured records from more than 8,000 AskHelpU participants together with PRO-ACT and Answer ALS, we developed DynaALS, the ALS Disease Dynamics Atlas. DynaALS represents ALS as a dynamic patient-state manifold that captures distinct directions of deterioration and patient movement between them over time. DynaALS retrieved population-referenced future states and decoded them into multidimensional clinical profiles without requiring patient-specific longitudinal histories. Motor-neuron RNA and chromatin profiles linked DynaALS states to developmental and regulatory programs, while neuromuscular-organoid single-cell multi-omics converged on a neural-developmental Netrin-DCC signalling axis across interacting cell types. By coupling MEDSTREM-enabled data construction to dynamic state modelling, DynaALS establishes a transferable patient-state engine for predictive and biologically interpretable disease models.

bioinformatics↗

Engineering physically optimized T cells for increased sampling of complex tumor microenvironments

Pancreatic ductal adenocarcinoma (PDA) remains highly lethal, in part, because its dense fibroinflammatory stroma restricts therapy distribution, including adoptive T cell immunotherapies where direct interactions between T and carcinoma cells are essential for effective therapy. While T cell function must be maintained once effector-target engagement occurs, without inducing co-localization subsequent cytotoxic function steps cannot be undertaken. We therefore developed a strategy to "physically optimize" T cells to more effectively sample complex tumor volumes. Informed by pharmacologic perturbations and mathematical modeling we shifted T cell phenotype through expression of constitutively activated RhoA to increase cortical contractility, activation, migration, and sampling in PDA, while showing decreases in exhaustion markers. In CAR T cells this results in more efficient targeting through decreased sampling time and increased engagement with carcinoma cells, consistent with modeling predictions. This significantly increases T cell infiltration and distribution in PDA, resulting in improved tumor control in vivo, suggesting that this is an effective strategy to overcome stromal constraints, improve tumor engagement, and enhance the therapeutic performance of engineered T cell therapies in solid tumors.

bioengineering↗

A general and extensible algorithmic framework to biological sequence alignment across scales and applications

Sequence alignment underpins nearly every facet of modern genomics, from genetic testing and cancer profiling to functional genome annotation. Yet, despite decades of algorithmic innovation, most existing aligners remain narrowly optimized for specific tasks, fragmenting analytical workflows and limiting reproducibility. Here we introduce the Versatile Alignment Toolkit (VAT), a unified algorithmic framework that generalizes existing seeding and genome-indexing strategies within a single, transparent architecture. VAT employs a novel multi-view indexing scheme that integrates multiple seeding strategies and supports run-time seed-length parameterization without reindexing. A radix clustering algorithm based on hardware-efficient in-register bitonic sort accelerates multi-view table construction, ensuring scalability across large datasets. VAT delivers consistently high performance across diverse alignment tasks, including short- and long-read mapping, homology search, and whole-genome alignment, while maintaining algorithmic simplicity and adaptability. By bridging previously isolated alignment paradigms, VAT substantially reduces workflow complexity, enhances computational efficiency, and establishes an extensible foundation for future sequencing technologies. We anticipate this unification will set a new standard for flexible and reproducible sequence alignment in biomedical research and clinical genomics.

bioinformatics↗

Spatial mapping of RNA turnover kinetics and regulatory landscapes of mRNA stability in the mammalian brain

Spatial and activity-dependent gene regulation in the mammalian brain requires coordinated control of RNA synthesis and degradation 1,2, yet spatially resolved measurement of RNA turnover kinetics in complex tissues remains technically challenging 3. Here, we present spatial NT-seq, an approach that integrates transgenesis-free metabolic RNA labeling with in situ chemical recoding to spatially co-map RNA abundance and turnover kinetics in the mouse brain. By distinguishing newly synthesized from pre-existing RNAs, this method reveals spatially resolved transcriptional and post-transcriptional responses to electroconvulsive stimulation (ECS), a treatment for refractory depression. We uncover pronounced spatial heterogeneity in RNA turnover, with the dentate gyrus (DG) exhibiting elevated basal RNA turnover and robust ECS-induced responses. These findings reveal a "kinetics scaling" mechanism of coordinated regulation of RNA synthesis and decay, by which DG cells can rapidly remodel their transcript pools in responses to external stimuli or differentiation signals 4,5. Machine learning applied to in vivo RNA kinetics landscapes further identifies sequence features and post-transcriptional regulators underlying region-and cell-type-specific control of mRNA stability. Together, this integrated experimental and computational framework, in vivo Timescope, enables transcriptome-wide mapping of RNA turnover kinetics and the regulatory architecture of RNA stability across spatial and cellular contexts, providing new insights into the spatiotemporal regulation of RNA dynamics in brain function and disease.

genomics↗

Driving theta-gamma oscillations modulates extrasynaptic GABAergic tone: a tACS-TMS study

BACKGROUNDTheta-gamma phase-amplitude coupled ({theta}{gamma}-PAC) oscillations in primary motor cortex (M1) have been shown to support motor skill acquisition. Past research has shown that driving gamma activity at the theta peak (TGP), but not the theta trough (TGT) using transcranial alternating current stimulation (tACS) enhances motor learning (Akkad et al., 2021). However, the neurophysiological mechanisms underlying this phase-specific effect remain unclear. METHODSIn a double-blind, sham-controlled, cross-over study, twenty-two healthy participants received 20 minutes of 75Hz/6Hz TGP-tACS, TGT-tACS, or sham stimulation over M1. We used paired-pulse transcranial magnetic stimulation (TMS) to assess GABAergic and NMDAR-mediated activity before, during, and after tACS. Outcome measures included short-interval intracortical inhibition at 1ms (SICI1ms; extrasynaptic GABAergic tone) and 2.5ms (SICI2.5ms; synaptic GABAA activity), intracortical facilitation at 12ms (ICF12ms; NMDAR activity), and motor evoked potential (MEP) amplitude (corticospinal excitability). RESULTSTGP-tACS selectively decreased SICI1ms, a putative marker of extrasynaptic GABAergic tone (main effect of Stimulation: p=.021), with significant differences between TGP and TGT during late stimulation (p=.047). No significant effects were observed on corticospinal excitability, synaptic GABAergic activity (SICI2.5ms), or NMDAR signalling (ICF12ms). CONCLUSIONSDriving theta-gamma oscillations at the theta peak using tACS specifically modulates extrasynaptic GABAergic tone in M1 without affecting corticospinal excitability or synaptic inhibition. Given that reductions in GABAergic signalling supports motor learning, these findings provide a neurophysiological mechanism for the phase-specific behavioural effects of {theta}{gamma}-PAC tACS and suggest a potential therapeutic approach for facilitating motor recovery after stroke. HighlightsO_LITheta-gamma peak tACS selectively reduces extrasynaptic GABA in human motor cortex C_LIO_LIOnly gamma at the peak, not trough, of theta stimulation modulates GABA C_LIO_LINo effects on corticospinal excitability, synaptic GABA, or NMDAR signalling C_LIO_LItACS-TMS reveals mechanism for phase-dependent motor learning effects C_LI

neuroscience↗