Search bioRxiv⌕ Search

Biology subjects

Chen, S.

Publications and source records attributed to Chen, S..

At least 109 records · Page 6Linked to original sources

Multidimensional Mechanistic Study of Panax Notoginseng Saponins in the Treatment of Alcohol-Induced Osteonecrosis of the Femoral Head: Integrating Network Pharmacology, Molecular Dynamics Simulation and In Vivo Validation

BackgroundAlcohol-induced osteonecrosis of the femoral head (AIONFH) is an orthopedic disorder from chronic alcohol abuse, characterized by disrupted femoral head blood supply, osteocyte death and structural collapse. Current hip-preserving therapy is unsatisfactory, and most patients eventually require total hip arthroplasty. Panax Notoginseng Saponins (PNS), the core active component of Panax notoginseng, exerts pro-angiogenic and anti-osteocyte apoptosis effects, but its specific therapeutic mechanism remains unclear. ObjectiveThis study used network pharmacology, molecular dynamics simulation and animal experiments to identify PNSs active components, core targets and key pathways for AIONFH, verify its in vivo efficacy, and provide a scientific basis for clinical application. MethodsPNS active components, their targets and AIONFH-related targets were screened from databases; intersection targets constructed an interaction network, core targets were screened by three machine learning algorithms, with concurrent GO and KEGG analysis. Molecular docking was performed between core targets and PNS components; Gromacs 2022 conducted 100 ns simulation to evaluate complex stability. AIONFH rat models were grouped with 4-week intragastric intervention; pathology, immunofluorescence and PCR were used for detection. Results and DiscussionNetwork pharmacology identified 127 PNS targets and 18 intersections with 672 AIONFH targets. Six core targets (including FGF2, HSD11B1) were screened; KEGG indicated VEGF pathway as key. Ginsenoside Re bound HSD11B1 with the lowest binding energy (-12.4 kcal/mol), and 100 ns simulation confirmed complex stability. Animal experiments showed PNS improved trabecular structure and regulated osteocyte activity. PNS treats AIONFH via multi-component, multi-target mode, core mechanism being osteocyte apoptosis inhibition. Results and DiscussionNetwork pharmacology screening identified 127 potential targets of PNS, and 18 potential intersection targets were obtained by overlapping with 672 AIONFH-related targets. Six core targets including FGF2 and HSD11B1 were screened out by machine learning, and KEGG analysis indicated that the VEGF pathway and other pathways were the key signaling pathways for PNS action. Molecular docking showed that Ginsenoside Re had the lowest binding energy with HSD11B1 (-12.4 kcal/mol), and 100 ns molecular dynamics simulation confirmed the stable conformation of this complex. Animal experiments demonstrated that PNS could improve trabecular bone structure and regulate osteocyte activity. In summary, PNS exerts a therapeutic effect on AIONFH through a multi-component, multi-target and multi-pathway mode, with the core mechanism of inhibiting osteocyte apoptosis.

pharmacology and toxicology↗

Evaluation of Diffusion Tensor Imaging in the Corpus Callosum on a Portable 100 mT MRI System

Low-cost portable MRI has the potential to improve the accessibility of MRI, but new acquisition methods and protocols must be developed and evaluated to accommodate the reduction in SNR and greater impact of system imperfections. Diffusion tensor imaging (DTI) is a candidate tool for monitoring population health, but the bias and variance of quantitative diffusion tensor-derived metrics must be evaluated prior to designing such studies. DTI of the corpus callosum was performed on an in-house, portable 100 mT MRI system using a slab diffusion weighted Fast Spin Echo with radiofrequency (RF) encoding. Slice coverage was restricted to the corpus callosum to shorten scan time and reduce sensitivity to large rigid motion. In vivo DTI images were obtained in two healthy volunteers with nominal voxel size 50 mm3, scan time 25 minutes, and two different volunteers using nominal voxel size 25 mm3, scan time 35 minutes. Mean diffusivity (MD) and fractional anisotropy (FA) coefficients of variation were estimated in the 50 mm3 acquisition using a bootstrap approach and compared to resolution-matched data obtained on a conventional 1.5T system. MD / FA maps were compared quantitatively and qualitatively. Mean MD values in the corpus callosum obtained on the 100 mT system were within 10% of the reference 1.5T acquisition, but FAs were underestimated by 20-30%. The corpus callosum median MD coefficient of variation was 3.7%, and the median FA coefficient of variation was 7.5%. FA maps obtained at 100 mT had an elevated FA noise floor and color FA maps had lower apparent resolution but some white matter tracts were still distinguishable. HighlightsO_LIDiffusion Tensor Imaging (DTI) of the corpus callosum was performed on a portable 100 mT MRI scanner with 50 mm3 voxels in 25 minutes scan time. C_LIO_LIMean Diffusivity estimates in the corpus callosum obtained at 100 mT and 1.5T differed by less than 0.1 x 10-3 mm2/s. C_LIO_LISome white matter tracts were visible in color Fractional Anisotropy maps obtained at 100 mT but FA maps were underestimated by 20- 30% when compared to a resolution-matched 1.5T acquisition, and had lower apparent resolution. C_LI

bioengineering↗

DMN Connectivity Predicts Location-Learning-Related Attentional Capture Vulnerability

Learned spatial regularities can reduce attentional capture by salient distractors, but observers vary in how strongly such regularities shape distractor interference. We asked whether this variability is associated with default mode network (DMN) functional connectivity, a network measure linked to individual differences in attention, internally guided cognition, and contextual memory. Using an existing fMRI visual-search dataset (N = 33), we tested whether task-run DMN connectivity was related to capture by distractors assigned to a learned spatial-probability history. Participants belonged to two training groups that differed in which distractor dimension carried the spatial bias: same-dimension distractors in one group and different-dimension distractors in the other. This design allowed us to ask whether the DMN association followed the design-defined biased dimension rather than general distractibility or a fixed physical distractor feature, while considering that the biased dimension also involved greater exposure. Higher within-DMN connectivity was associated with larger capture scores for the biased distractor dimension, whereas capture by the unbiased dimension showed little corresponding relation. This association remained positive after controls for inter-trial priming, head motion, nuisance signals, task-regressor residualization, global-signal regression, motion scrubbing, and trial-level exposure history. Exploratory edge-level analyses provided descriptive convergence, showing that capture-associated positive edges were concentrated around the DMN. These findings suggest that DMN connectivity may relate to individual differences in vulnerability to distractors embedded in learned spatial-probability histories.

neuroscience↗

Pre-stimulus Cortical State Modulates Dimension-Specific Attentional Capture

Suppressing visual distraction depends on observers goals and learning, but also on the brain state in which the next display arrives. We reanalyzed an fMRI visual-search dataset (N = 34) to ask whether pre-stimulus global signal (GS), a broad state-sensitive BOLD measure, predicts performance and attention-network engagement during learned distractor suppression. Participants searched for an orientation target while ignoring salient distractors defined in the same orientation dimension or a different color dimension; across groups, spatial regularities were assigned to same- or different-dimension distractors. Behavioral and fMRI contrasts reproduced the expected functional architecture: same-dimension distractors slowed responses and recruited a frontoparietal capture-related network, whereas different-dimension distractors produced little interference. Learned spatial suppression was clearest for same-dimension distractors, which produced larger costs and stronger visual and frontoparietal responses in the rare than frequent region. Within this architecture, GS predicted performance in opposite directions across learning contexts: higher GS preceded slower responses when same-dimension distractors carried the spatial regularity, but faster responses when different-dimension distractors did. Higher GS also preceded weaker same-dimension distractor-evoked frontoparietal activation, without a reliable group difference in that neural effect. Default-mode-network activity showed the same behavioral pattern as GS, whereas pupil size showed weaker group-level convergence and its RT slopes did not correlate with GS-RT slopes across participants. Learned distractor suppression therefore appears embedded in a broader state-performance mapping, linking pre-stimulus cortical state to how learned priority settings guide the next trial.

neuroscience↗

Origin and evolution of grapevine genomes

Grapevines (Vitis) belonging to grape family (Vitaceae) are symbolic fruit crops pivotal to human civilization. The evolutionary history of grapevines divergent from other Vitaceae plants remains mysterious, requiring a family-wide whole-genome phylogenomic analysis. Here, we conduct chromosome-level phylogenomics to investigate the origin and evolution of grapevines using 29 genome assemblies of five genera Vitis, Parthenocissus, Ampelopsis, Tetrastigma, and Cissus, 27 of which are newly released in this study. Phylogenomic and macrosynteny analysis unanimously support Ampelopsis as a sister lineage to Parthenocissus, placing both closer to Vitis, with introgression and incomplete lineage sorting contributing to these relationships. Ancestral genome reconstruction delineates the major chromosome rearrangement events in Vitaceae karyotype evolution, highlighting the conserved karyotype in Vitis and the extensive karyotypic reorganization in Tetrastigma and Cissus. Pan-3D genome analysis highlights the contributions of structural variants (SVs) to the variation of A/B compartments and topologically associated domains (TADs), revealing a strong purifying selection of SVs at TAD boundaries. We further demonstrate that Helitron transposons drive the expansion and expression regulation of NLR immune-receptor genes in Vitis. Importantly, we discovered an NLR gene VbRpv35 from wild grapevine V. bellula resistant to downy mildew (DM), whose heterologous expression in V. vinifera confers enhanced DM resistance. Taken together, we provide phylogenomic insight into the origin and evolution of grapevines and valuable resources for grapevine improvement and understanding angiosperm evolution.

genomics↗

Inhibition of CKAMP44 attenuated seizure activity via protein phosphatase 3 regulatory subunit B-mediated GluA1 phosphorylation and synaptic transmission

Temporal lobe epilepsy (TLE) is a complex neurological disorder characterized by spontaneous recurrent seizures and its underlying mechanism remains elusive. This study aimed to investigate the role of cystine-knot AMPAR modulating protein 44 (CKAMP44) in the pathological process of TLE and its potential as a therapeutic target using kainic acid (KA)-induced epilepsy mouse model of TLE. Our results showed that CKAMP44 protein and mRNA expression was significantly increased and primarily localized to neurons during the chronic phase of TLE. Nkx2-1 regulated the transcription of CKAMP44 in the hippocampus brain tissues of KA-induced TLE mice. Inhibition of CKAMP44 suppressed seizure susceptibility and severity in the KA-induced epilepsy mice via behavioral and local field potential monitoring. Furthermore, inhibition of CKAMP44 decreased frequency and amplitudes of spontaneous excitatory postsynaptic currents indicating that the excitatory synaptic transmission was reduced in an in vitro epilepsy model. Mechanistically, inhibition of CKAMP44 specifically upregulated the membrane surface expression of GluA1 and the phosphorylation level of GluA1-ser831 by downregulating protein phosphatase 3 regulatory subunit B(PPP3r2) expression. Overexpression of PPP3r2 downregulated the phosphorylation level and surface expression of GluA1, which ultimately exacerbated the seizure activity suppressed by CKAMP44 knockdown. Collectively, our results indicate that CKAMP44 may be a potential therapeutic target for the treatment of TLE.

pathology↗

Identification of a neural circuit that enables safe, long-term torpor in mice

Torpor, a state of regulated hypothermia and hypometabolism, is a critical survival strategy in certain mammals. Previous studies established the primacy of neuron control in initiating acute torpor. However, whether neuromodulation can safely sustain torpor over the long term, and the specific governing pathways, remain unanswered challenges limiting its translational potential. Here we identify a distinct preoptic neuronal population expressing General control nonderepressible 2 ( G neurons) in mice that is essential and sufficient for torpor induction. Strikingly, sustained and selective activation of G neurons induces a stable, weeks-long torpid state (G neuron-driven long-term torpor, GLT), from which animals arouse without obvious behavioral deficits or tissue pathology. In contrast, long-term torpor driven by pan-neuronal activation of the same brain region (PLT) causes multiple post-recovery damages, underscoring the unique safety profile of selective pathway of G neurons. In a mouse cancer model, GLT directly suppressed tumor proliferation and markedly sensitized tumors to chemotherapy, achieving profound therapeutic outcomes. Together, we delineate a distinct neuronal population safely enabling prolonged torpor. This establishes a specific paradigm for inducing a stable and sustained hypometabolic state, providing a transformative platform for fundamental physiological research and pioneering clinical strategies against chronic pathologies such as cancer.

neuroscience↗

The tongue-brain axis mediates a hidden amino acid appetite

Selecting a diet containing all essential amino acids (EAAs) is critical for health. Following EAA deprivation, animals can select a nutritiously complete food source; however, the underlying mechanisms in vertebrates remain unclear. In mice, we show that leucine deficiency activates hypothalamic agouti-related protein (AgRP) neurons, which project to the paraventricular thalamus (PVT) via {gamma}-aminobutyric acid and are required for EAA deficiency-induced leucine appetite in mice. Furthermore, the peripheral tongue amino acid sensor general control nonderepressive-2 (GCN2) mediates acute EAA appetite via AgRP neurons. Together, these findings identify a tongue-AgRP-PVT circuit underlying EAA appetite, which is important for the rapid and accurate selection of essential nutrients.

neuroscience↗

Revisiting the Monascus genus (Eurotiales, Aspergillaceae): A Multilocus Phylogenetic Approach to Species Delimitation

Monascus spp. are economically important filamentous fungi that have been utilized in the production of beneficial metabolites such as Monascus pigments and monacolin K, as well as in the brewing of some Asian fermented foods. The delimitation of Monascus species has traditionally relied on phenotypic traits; however, this morphological classification approach is susceptible to subjective judgments and variations in cultural conditions and also may not necessarily be related to the actual genetic relationship. Consequently, synonymy and misidentification frequently occur in Monascus taxonomy, highlighting the urgent need for a convenient and reliable classification system for this genus. In this study, a phylogenetic analysis of 82 representative Monascus strains, encompassing all previously recognized species of the genus, was conducted based on the concordance of five gene genealogies (BenA, CaM, ITS, LSU, and RPB2) to clarify species delimitation and resolve phylogenetic relationships within Monascus. The results revealed that the genus Monascus is resolved into 11 species, which are clustered into two sections: Floridani (including M. argentinensis, M. flavipigmentosus, M. floridanus, M. lunisporas, M. mellicola, M. pallens, and M. recifensis) and Rubri (including M. pilosus, M. purpureus, M. ruber, and M. sanguineus). M. pilosus and M. sanguineus were reaffirmed as distinct species due to their well-supported and divergent phylogenetic lineages. Additionally, M. albidulus, M. anka, M. barkeri, and M. fumeus are synonymized with M. pilosus, while M. aurantiacus and M. rutilus are synonyms of M. purpureus. Finally, a comprehensive list of accepted Monascus species along with their corresponding barcode sequence data is provided.

microbiology↗

KIF18A Inhibition as a Therapeutic Strategy in Cancers with Rb Pathway Inactivation

KIF18A inhibition has emerged as a therapeutic strategy for chromosomally unstable cancers, but clinical development is limited by the absence of a deployable predictive biomarker. Here we identify strong, diffuse p16INK4a expression, a well-established surrogate marker of Rb-pathway inactivation, as a predictive biomarker of response to KIF18A inhibition, and show that Rb-pathway inactivation marks a biologically distinct subset of cancers sensitive to this therapeutic approach. In sensitive models, low Rb activity is associated with robust spindle assembly checkpoint signaling and prolonged mitotic arrest following KIF18A inhibition. Weakening the spindle assembly checkpoint in this context is sufficient to confer resistance. Across three independent pan-cancer sensitivity datasets generated with distinct KIF18A inhibitors, Rb-pathway altered models were significantly more sensitive than histology-matched Rb-intact comparators, with the strongest association observed in cancers harboring direct RB1 loss or inactivating mutation. Guided by this mechanism, we retrospectively analyzed p16INK4a expression by immunohistochemistry (IHC) in pre-treatment tumor biopsies from 79 heavily pre-treated high-grade serous ovarian cancer patients across three dose-escalation or expansion cohorts and treated with two different KIF18A inhibitors (sovilnesib and VLS-1488) sharing a common mechanism of action. p16INK4a-high tumors showed substantially higher objective response rates than their p16INK4a-low counterparts (36.0% versus 2.2%; P = 0.0002) and markedly longer progression-free survival (median 24.3 versus 7.9 weeks; hazard ratio, 0.16; P < 0.0001). These findings establish p16INK4a as a mechanistically-based, clinically implementable biomarker of clinical response to KIF18A inhibition that is poised to support pan-cancer development of KIF18A inhibitors guided by Rb-pathway inactivation.

cancer biology↗

Disabling Muller Glia Preserves Retinal Function After Retinal Injury

We developed a physiologically relevant light damage model in pigmented mice and determined how Muller glial (MG) Dicer1 loss impacts retinal structure and function after injury. A moderate light damage paradigm (5,000 lux, 4 hours) was developed in pigmented mice carrying the RPE65 Leu450 variant. MG-specific Dicer1 conditional knockout (cKO) mice across three Cre lines (Rlbp1-CreER, Glast-CreER, Ascl1-CreER) were subjected to light damage at different developmental stages. Retinal structure and function were assessed using optical coherence tomography (OCT), histology, and electroretinography (ERG). Preconditioning and double-damage paradigms were included as controls. The model induced progressive photoreceptor degeneration characterized by early functional decline, followed by structural loss and delayed inner retinal impairment. Across all lines with Dicer loss in MG, retinal structure and function were better preserved following injury than in light-damaged controls. The most sustained protective phenotype was observed in the Rlbp1-CreER-driven line. Inner retinal function (Vmax) was consistently maintained despite reduced photoreceptor input. This phenotype was independent of age, timing of MG manipulation, or baseline retinal condition and was not reproduced by preconditioning paradigms. Dicer-deficient MG displayed reduced glial fibrillary acidic protein (GFAP) immunoreactivity, indicating a potential suppression of glial reactivity. However, the absence of a neuroprotective phenotype following preconditioning suggests that reduced GFAP expression alone is insufficient to account for the observed retinal preservation. Collectively, these findings demonstrate that MG-specific Dicer1 deletion is associated with a neuroprotective retinal phenotype characterized by preserved inner retinal function and reduced secondary degeneration. These findings establish a glia-driven component of retinal degeneration and demonstrate that altering the MG injury response can preserve retinal function following injury.

neuroscience↗

Learning to select computations in recurrent neural circuits

Two hallmarks of biological computation are its flexibility and efficiency. These features are often attributed to cognitive control processes that balance external utility against computational cost. However, how the brain could implement such adaptive control remains unknown. Here, we provide one possible answer by combining the computational theory of rational meta-reasoning with a meta-learning algorithm recently proposed as a model of prefrontal cortex. This yields a recurrent neural network model that learns to select computations. In simple choice tasks, the model approximates the algorithms and representations of optimal symbolic models and reproduces neural dynamics observed in macaque orbitofrontal cortex. In multi-step planning tasks, the model replicates key behavioral signatures of human planning strategies and captures human neural dynamics associated with step-by-step mental simulation. Our framework unifies meta-reasoning and meta-learning by showing that learning to reason can be understood as learning to learn from information generated by ones own cognitive operations, providing a mechanistic account of how adaptive control of thought can be implemented in neural systems.

neuroscience↗

Specific Aneuploidies Predict Immune Evasion and Poor Immunotherapy Response in Melanoma

Melanoma is one of the leading cancer types treated with immune checkpoint blockade (ICB), yet a substantial proportion of patients fail to respond. While tumor mutational burden and PD-L1 expression are established ICB biomarkers, they leave large gaps in predictive accuracy. Somatic copy number alterations (SCNAs) are pervasive in melanoma but their role in shaping the tumor immune microenvironment (TME) and predicting immunotherapy outcomes has been insufficiently characterized. Here we present KaryoTME, an integrated computational framework that systematically links SCNAs to immune phenotypes using genomic, transcriptomic, and clinical data from over 15,000 patients. Applying this framework to skin melanoma (SKCM) within a pan-cancer context, we identify arm-level chromosome 1q gain and 9p loss as the most prominent SCNA events associated with an immune-cold tumor microenvironment. These alterations act through distinct mechanisms: 9p loss preferentially depletes NK and CD8+ T cells, whereas 1q gain is more strongly associated with reduced anti-tumor immune cell infiltration. At the focal level, regions 1q21 and 1q42 show the strongest immune-suppressive associations in melanoma. Applying the TUSON-Immune algorithm, we predict candidate Tumor immune Suppressor Genes (TiSG) and immune Oncogenes (iOG) within these chromosomal regions, revealing enrichment for pathways including IFN signaling, JAK/STAT pathway, and immune-suppressive cytokine secretion. Critically, 1q gain emerged as a strong and independent predictor of poor survival following anti-PD-1/PD-L1 therapy across two independent clinical cohorts: the MSK-IMPACT cohort (p = 0.018, N = 77) and a large real-world Caris Life Sciences dataset (HR = 1.2, p = 0.002, N = 1,167). Multivariate analysis confirmed that 1q gain predicts poor outcomes independently of CD8+ T-cell infiltration, B-cell infiltration, tumor mutational burden, and PD-L1 status. These findings establish chromosome 1q gain as a compelling biomarker of immunotherapy resistance in melanoma and highlight aneuploidies as underappreciated drivers of immune evasion in this disease.

cancer biology↗

Atomic structure and dynamics of the mechanosensitive channel MscL from E. coli by cryo-EM and solid-state NMR

Mechanosensitive channels are central to cellular responses to membrane tension, yet the structural basis of their gating remains incompletely understood. Here, we determine the structures of wild-type and G22S mutants of MscL from E. coli (EcMscL) by cryo-EM in peptide-based lipid nanodiscs and complement them with solid-state NMR measurements in liposomes to capture their dynamics in a native-like membrane environment. The cryo-EM structures reveal a closed conformation, whereas analysis of the low-threshold G22S mutant by NMR uncovers widespread conformational changes in both cytoplasmic and periplasmic regions. These data indicate enhanced dynamics and conformational heterogeneity in the mutant, revealing the early transitions from the closed towards the open state. Together, our results establish a synergistic framework integrating cryo-EM and NMR to resolve both structure and dynamics of mechanosensitive channels, and identify lipid-protein interactions as key determinants of MscL gating and mechanosensitivity. Our study further provides a quantitative benchmark for computational investigations of mechanogating and lays the foundation for the rational design of channels with tunable gating kinetics. TeaserBy integrating cryo-EM and solid-state NMR, we reveal how lipid-coupled dynamics prime MscL for opening, capturing the earliest transitions from closed to active states.

biophysics↗

VeloTrace Reconciles Divergent Velocity and Trajectory in Single-cell Transcriptomics with Deep Neural ODE

Cellular identity and fate transitions are governed by continuous molecular processes that form dynamic trajectories within a high-dimensional transcriptomic landscape. Existing methods attempt to model these dynamics from two complementary perspectives: trajectory inference and velocity modeling. Ideally, velocity and trajectory are dual aspects of transcriptomic dynamics where velocity is tangent to trajectory everywhere. This inherent connection between velocity and trajectory is currently absent in transcriptomic analysis. Splicing velocity are precision-limited to inadequately-sequenced genes, while trajectory inference prioritizes the modeling of global trends while omitting local dynamics. This divergence breaks the geometric continuity between local velocities and global trajectories, hindering the reliable interpretation of developmental dynamics. To reconcile trajectory inference and RNA velocity, we introduce VeloTrace, a framework that unifies them through Neural Ordinary Differential Equations (NeuralODEs). VeloTrace learns a continuous-time velocity field whose integral curves constitute the trajectory itself, while ensuring that velocities are tangent to integral paths everywhere. Leveraging a splicing quality score, VeloTrace incorporates high-quality splicing velocity as partial supervision for velocity orientation and grounding. During optimization, VeloTrace incorporates a Monte Carlo multi-time-frame supervision strategy to ensure coherence between local and global trajectorys and suppress sequencing-induced stochastic diffusion. Through refining the velocity field and cell-specific parameters for pseudo-time, expression, and velocity, VeloTrace reconstructs a smooth, local-and-global-coherent velocity-vector-guided flow in the transcriptomic latent space. This strategy ensures a complementary integration of velocity and trajectory, imputing the transcriptional kinetics for genes of insufficient strength, whose kinetics cannot be accurately portrayed by splicing velocity. In simulation benchmarks, VeloTrace captured the transcriptional dynamics of all expressed genes, even those with inadequate sequencing coverage, producing velocity directions that were most consistent with the true direction and every-where tangential across the entire process, outperforming state-of-the-art methods, including scVelo, UniTVelo, VeloVI and scTour. VeloTrace uniquely reconciles RNA velocity and trajectory inference, creating a velocity field where each cell can infer past and future transitions from its current state. Moreover, VeloTrace extends reliable velocity estimation to a broader set of genes. When applied to mouse neural stem cell differentiation data, it successfully recovers dynamics of driver genes for two developmental lineages, including those with low expression, shedding light on their regulatory roles during differentiation. This unified framework lays the foundation for more accurate modeling of gene regulation and cell fate decisions in complex biological systems.

bioinformatics↗

FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) integrate genome wide association studies with expression quantitative trait locus reference panels to identify genes associated with traits of interest. However, linkage disequilibrium and correlated gene expression can induce spurious TWAS signals, motivating fine mapping methods to prioritize putatively causal genes within associated loci. The rapid growth of large-scale phenomic resources (e.g. electronic health records (EHRs)) has shifted genetic studies from single-trait analyses to phenome-wide investigations that jointly evaluate many closely related phenotypes. We introduce FM-GPT (Fine-mapping of causal Genes for Phenome-wide Transcriptome-wide association studies), a novel Bayesian fine mapping method for prioritizing causal genes across multiple correlated phenotypes with potentially mixed outcome types (e.g., binary, count or continuous) in phenome-wide TWAS. FM-GPT performs gene-guided dimension reduction of the phenotypes and reveals pleiotropic or phenotype-specific effects of the identified genes. In simulations, FM-GPT identified true causal genes more accurately than other fine mapping methods while controlling false positives. We applied FM-GPT to two applications using data from UK Biobank: a brain-wide genetic analysis of MRI data derived regional cortical thickness measures and a phenome-wide genetic analysis of clinical phenotypes derived from EHR data. FM-GPT greatly narrowed down the set size of putatively causal genes and identified: 1. genes with pleiotropic effects on regional cortical thickness across the cerebral cortex, including five genes BCAS3, LRRC37A, NOS2P3, ARL17B and UBB on chromosome 17 regulating neuronal morphology and cortical organization; and 2. genes that influence multiple medical conditions across the circulatory, metabolic, digestive, respiratory and genitourinary systems, revealing two major axes of variation among these conditions that point to a potential trade-off in gene regulation between immune and metabolic functions. These results highlight FM-GPTs power to disentangle complex gene-phenotype relationships in large-scale phenome-wide studies, uncovering shared biological mechanisms across diverse human traits and advancing translational and comorbidity research. Author SummaryWe developed a novel fine mapping method called FM-GPT, to identify putatively causal genes from correlated noise influencing a wide range of human traits and diseases with potentially mixed outcome types (e.g., binary, count or continuous). The rapid expansion of large-scale phenomic datasets has shifted the single-trait genetic studies to phenome-wide analyses, enabling the study of genetic architecture across many related traits simultaneously. FM-GPT performs gene-guided dimension reduction of the phenotypes and reveals pleiotropic or phenotype-specific effects of the identified causal genes. When applied to the UK Biobank data, FM-GPT greatly narrowed down the set size of putatively causal genes compared to other methods. The tool identified genes with pleiotropic effects on regional cortical thickness that regulate neuronal morphology and cortical organization across the cerebral cortex. It also identified genes that influence multiple medical conditions spanning the circulatory, metabolic, digestive, respiratory, and genitourinary systems. Among these conditions, two major axes of variation emerged, revealing a potential trade-off in gene regulation between immune and metabolic functions. This work provides a clearer picture of shared biological mechanisms across traits and diseases, advancing translational research and the understanding of comorbidity.

bioinformatics↗

Transition metal-triggered immunity via an Arabidopsis NLR pair

Plants are challenged by multiple biotic (e.g. pathogens) and abiotic (e.g. heavy metals) stressors. Some transition metals can enhance plants defense against pathogens, but the mechanism remains unclear. Here, we demonstrate that an Arabidopsis head-to-head gene pair of intracellular nucleotide-binding leucine-rich repeat (NLR) receptors, both expressed in the endodermis of roots, antagonistically control transition metal-triggered immunity. One NLR, STM2 binds transition metal ions, such as Cd2+, Cu2+ and Zn2+, with its LRR domain to enhance its NAD+ hydrolytic activity and immune responses via the EDS1/PAD4/ADR1 module, triggering enhanced resistance to bacterial wilt Ralstonia solanacearum. The other NLR, STM1 suppresses STM2 to protect plants from transition metal-triggered immunity and growth inhibition in the presence of excess metals. STM1 also dampens resistance to the pathogen. Our study defines an NLR activated by transition metals and reveals a trade-off between susceptibility to pathogens and sensitivity to transition metals that are pervasive in soil.

plant biology↗

Topographic structure and function of locus coeruleus norepinephrine neurons

Norepinephrine (NE) is released throughout most of the central nervous system by neurons in the locus coeruleus (LC). We found a relationship between the morphologies, gene expression, and activity of LC-NE neurons in mice. Axonal projections of individual neurons were extensive but largely confined to subsets of brain regions. Axonal projections and graded gene expression correlated with locations of cell bodies in LC. In a behavioral task requiring ongoing learning from actions, neurons in dorsal LC projecting to the cerebral cortex were excited when mice made a different choice from the previous one and by reward prediction errors, a signal driving learning. Background activity of neurons in ventral LC was higher when mice ignored stimuli indicating potential reward availability. These observations reveal a topographically organized structure and function of a neurotransmitter system and show that it contains learning signals for flexible behavior.

neuroscience↗