Biology subjects

Cui, L.

Publications and source records attributed to Cui, L..

At least 19 recordsLinked to original sources

bioRxiv2026

A brain locus for viable gestation

The expectant mother's body is systemically remodeled by her hormones to weather the physiological vicissitudes of pregnancy. In contrast to other reproductive organs, how the expectant mother's brain promotes these system-wide changes is largely unexplored. Here we show that pregnancy induces profound changes in gene expression and identity of hormone-sensitive neurons and report a previously unknown class of such cells that is required for viable pregnancy. We performed RNA sequencing from late mid-gestation mice of four estrogen receptor alpha-expressing populations from hypothalamus and amygdala that regulate reproductive behaviors altered during gestation. Each of these populations undergoes such large, specific transcriptional shifts during pregnancy that these changes even exceed their transcriptional differences between the sexes. The gene expression changes during pregnancy also imbue particular transcriptomically-defined neuronal types within these four populations with new molecular identities. Targeted ablation of one such neuronal type, POANpy2r cells, precludes implantation and abrogates viable pregnancy. Taken together, we have uncovered an essential role of hormone-sensitive neurons in the brain in sustaining pregnancy. The etiology of spontaneous gestational loss, which afflicts ~15% of human pregnancies, remains idiopathic in many cases, and our findings suggest a brain-based mechanism that contributes to such events. More broadly, we provide a molecular and cellular foundation to study gestational processes in health and disease from the perspective of the pregnant brain-body axis.

neuroscience↗

bioRxiv2026

Preclinical trial supports dual inhibition of BCL2 and Aurora kinase A for MYCN-amplified high-risk neuroblastoma

Purpose: Treatment for children with high-risk neuroblastoma relies on conventional chemotherapy and anti-GD2 immunotherapy. However, 5-year survival is only 50%, with high rates of late effects. Targeted therapy combinations are a major priority for these patients. The BCL2 inhibitor venetoclax, in combination with cyclophosphamide/topotecan, has clinical activity in relapsed and refractory neuroblastoma. We sought more effective and safer venetoclax combinations through systematic preclinical testing. Experimental design: Synergistic combinations were identified by high-throughput screening using patient-derived xenograft (PDX) models and confirmed in vivo. The leading combination (venetoclax-alisertib) was compared to combination chemotherapy in a clinical trial-like study using 22 PDX models, in scheduling experiments designed to reduce short-term toxicity, and in combination with anti-GD2 immunotherapy. BCL2 and Bim-BCL2 complex protein levels were assessed as predictors of sensitivity. Results: In vitro synergy with venetoclax was observed for standard-of-care chemotherapies and targeted agents, including DNA topoisomerase, microtubule, HDAC and Aurora kinase A (AURKA) inhibitors. Venetoclax-alisertib was particularly effective in vivo. In an n=1 study, venetoclax-alisertib induced objective response in all models. Activity was most striking in models of MYCN-amplified disease (n=12), doubling median survival time compared to cyclophosphamide/topotecan, and outperforming venetoclax-cyclophosphamide-topotecan. Efficacy was maintained with discontinuous schedules, minimizing hematological toxicity without substantially compromising activity. PDX-engrafted animals treated with venetoclax-alisertib and anti-GD2 immunotherapy survived tumor-free long-term. BCL2 expression and BCL2-Bim complex levels were of limited value for predicting response. Conclusion: Our findings support advancement of BCL2-AURKA inhibition to clinical trial for neuroblastoma with or without anti-GD2 immunotherapy, particularly in patients with MYCN amplified disease.

cancer biology↗

bioRxiv2026

TORC1 integrates metabolic state transitions during aging

Target of rapamycin complex 1 (TORC1) coordinates nutrient availability with anabolic metabolism, yet how TORC1-linked metabolic states influence cellular aging remains unclear. Using genetic, transcriptomic, metabolomic, and pharmacological analyses in prototrophic Saccharomyces cerevisiae, we identify context-dependent metabolic state transitions that uncouple cellular proliferation from long-term survival during aging. Loss of the SEACIT complex, conserved with mammalian GATOR1, establishes a low-flux metabolic state characterized by coordinated remodeling of nitrogen, nucleotide, and central carbon metabolism during stationary phase. Pharmacological restraint of nucleotide, glycolytic, or sterol metabolism converges on similar adaptive metabolic programs, whereas perturbations that impair mitochondrial respiratory capacity destabilize these states and reduce survival. Integrative analyses in human cancer and primary cells further reveal that diverse metabolic perturbations converge on mTORC1 suppression but generate distinct mitochondrial and stress responses depending on nutrient availability. Together, these findings demonstrate that TORC1 integrates metabolic state transitions in which nutrient availability and mitochondrial function determine cellular survival during aging.

systems biology↗

bioRxiv2026

A layered standards framework for integrating single-cell and spatial omics data into brain cell atlases

The BRAIN Initiative Cell Atlas Network (BICAN) is generating large-scale multimodal datasets to profile cell types in the human, non-human primate, and mouse brain. The diversity of single-cell and spatial transcriptomic and epigenomic assays, combined with varied experimental contexts, multiple data-generating laboratories and distributed infrastructure, poses substantial challenges for data integration and reuse in BICAN. To address this, we implemented a standards framework that enables layered integration of these data into knowledge-ready products for interoperable brain cell atlases. This framework organizes data based on three progressively structured layers. First, we introduced an assay-agnostic modeling layer that unifies the representation of single-cell and spatial omics data using a common set of biological entities and processes assessed by diverse experimental techniques. Second, we implemented harmonized metadata standards that capture key experimental features linked to biospecimen provenance across heterogeneous tissue sources, species, and preparations, supporting integration and validation while minimizing burden on data contributors. Third, we present an extensible representation for data-driven cell type taxonomies that integrates molecular data with annotations, ontology mappings, and evidence. Together, these contributions represent an end-to-end framework that transforms heterogeneous datasets into structured, interoperable resources that support broad community reuse via mapping algorithms, annotation systems, and visualization platforms. This approach links biospecimen provenance with cell-level outputs and embeds these in a standardized taxonomy format, enabling downstream applications such as cross-dataset integration, reference mapping, and knowledge-driven analysis. More broadly, our work demonstrates a generalizable strategy for enabling an efficient data-to-knowledge pipeline in a large-scale consortium setting.

genomics↗

bioRxiv2026

ClumPyCells resolves spatial aggregation in complex tissues overcoming size biases

The spatial arrangement of cells within a tissue microenvironment shapes their interactions and cell states, which are essential for tissue development, homeostasis, and disease. Spatial -omics technologies can precisely map the location of each cell within complex tissue structures, while also profiling their protein content and transcriptional diversity. Various approaches have been developed to analyze spatial patterns of cell aggregation, repulsion, or random distribution within tissues. However, differences in cell morphology within a tissue can introduce significant bias. Cell size in particular is not accounted for and introduces challenges when quantifying the aggregation of cells or their molecular features. To overcome such limitations, we present ClumPyCells: a statistical framework that measures cell and marker aggregation within tissue while correcting for size morphology. ClumPyCells enables interpretation of cell aggregation, bypassing interfering cell types or tissue regions unrelated to the desired spatial correlation. We demonstrate the capabilities of ClumPyCells across several tumor types, including melanoma and colorectal cancer, and spatial -omics technologies such as spatial transcriptomics and proteomics, while benchmarking how cell-size differences contribute to misinterpretations. By correcting for disruptive cell types within a region of interest, ClumPyCells will determine new tissue patterns and structures without morphological interference.

bioinformatics↗

bioRxiv2026

Fear Learning Induced Brain Dynamics Predict Individual Extinction Memory Expression following Transcranial Magnetic Stimulation

Fear learning and extinction unfold as time-dependent processes. Herein, we examined how fear learning dynamically reorganizes brain activity immediately after learning, and whether such reorganization can be modulated with TMS application during extinction learning to prospectively predict extinction-memory expression. Eighty-seven healthy adults completed a three-day Pavlovian threat-learning protocol with resting-state fMRI acquired before and after conditioning (Day 1), dorsolateral prefrontal cortex (DLPFC) transcranial magnetic stimulation (TMS) applied during extinction learning (Day 2), and fMRI during extinction recall and renewal (Day 3). Using coactivation pattern analysis with a hidden Markov model within a 24-nodes threat-circuit parcellation, we identified a fear-learning-induced brain state characterized by global threat-circuit coactivation with heightened engagement and transition uncertainty post conditioning, and a progressive increase in engagement across post-conditioning. Critically, conditioning-induced functional connectivity reorganization within this state predicted individual differences in extinction recall- and renewal-related brain activation under TMS-modulated extinction (cross-validated; recall r = 0.47, p = 0.001; renewal r = 0.37, p = 0.01; permutation-tested), but not under natural extinction. Similar associations were observed between neural features and behavioral expression. These findings demonstrate that fear learning reshapes spontaneous brain-state dynamics and that such learning-induced reorganization serves as an interpretable biomarker for neuromodulation-linked extinction-memory expression.

neuroscience↗

bioRxiv2026

Loss of C3 and CD14 reduces region-specific neuroinflammation in a murine polytrauma model

BackgroundTraumatic brain injury (TBI) together with non-cerebral injuries characterizes the TBI-polytrauma (P-TBI) constellation, which is associated with acute neurological deterioration, delirium and unfavourable prognosis. It is hypothesized that systemic inflammatory mediators my enhances the focal, cerebral neuroimmune reaction with overall detrimental consequences, in particular in terms of acute microglial reactivity. MethodsWe explored the role of the Complement factor 3 (C3) and of the TLR-co receptor cluster of differentiation (CD14) in a murine polytrauma model that involves a mild TBI together with femur fracture, blunt thorax trauma and resuscitated haemorrhagic shock, making use of mice genetically lacking either C3, CD14 or both. ResultsWe show that P-TBI results in a rapid (4h) and brain-wide induction of inflammatory cytokines, although with distinct profiles (TNF and CCL2 having brain-wide involvement and IL-1{beta} restricted to ipsilateral cortex and striatum). TNF and CCL2 mRNA as well as protein synthesis were upregulated in microglia upon P-TBI in cortex, hippocampus and striatum which was fully abolished in the C3-/-CD14-/-animals. The analysis of single-KO animals revealed that induction of TNF and CCL2 was prevented in animals lacking C3, but not CD14, in the contralateral cortex and striatum, with an abolishment in hippocampus in mice lacking both C3 and CD14. In the cortical area of focal lesion neither C3 nor CD14 affected the induction of pro-inflammatory cytokines. ConclusionThus, C3 and CD14 are dispensable for the acute cytokine response to P-TBI in the site of injury but play differential roles across the cortex, hippocampus and striatum for the induction of cytokines in the non-injured parenchyma and in particular in microglia. Thus, interventions on C3 (mainly) and/or CD14 may reduce the encephalopathy risk associated with P-TBI but not the acute response in the injury site, where additional DAMP signalling may offer redundant activation pathways.

neuroscience↗

bioRxiv2026

Sex-specific Genetic Regulatory Effects in Chickens

Sexual dimorphism is a defining vertebrate feature, yet its sex-specific molecular architecture remains poorly understood. Here we established a sex-balanced, uniformly reared chicken cohort to map this landscape, integrating individual whole-genome sequencing with 7,969 bulk and 779,380 single nucleus transcriptomes across 32 tissues from 280 birds. We identified 495,098 independent expression quantitative trait loci for 20,194 genes, including 10,937 loci modulated by cell-type composition. Notably, 340 genes were regulated by 449 loci in a sex-dependent manner, significantly enrichment in endocrine tissues like adipose and the adrenal gland. Furthermore, we fine-mapped 1,219 structural variants, demonstrating their unique roles to tissue- and sex-specific expression beyond SNPs. Ultimately, we showed the utility of these regulatory effects in elucidating the molecular basis of metabolism and complex traits in both chickens and humans. This comprehensive atlas of regulatory effects provides profound insights into the genomic and molecular basis of sexual dimorphism in vertebrates.

genomics↗

bioRxiv2026

Cell-autonomous thermogenesis of macrophage alters its antibacterial function

Dynamic temperature gradients exist across the bodies of endothermic animals, from the core to peripheral organ, resulting in the physiological cold environment in superficial regions. Consequently, macrophages distributed throughout the body must be able to adapt not only to thermoneutral conditions but also to colder environments. In fact, it is known that environmental temperature influences macrophage immune responses. However, the thermo-responsive mechanisms of macrophage have been largely unexplored. Here we show that macrophage themselves maintains intracellular temperature under physiological cold condition by increasing proton leak index (defined as mitochondrial proton leak per spare respiratory capacity). We further identified a contribution of ADP/ATP carrier (AAC) to this increase in proton leak index. This cell-autonomous thermogenesis pathway, which does not depend on neural or hormonal inputs, highlights the potential for local and organ-specific temperature regulation. Moreover, cold stress reduced mitochondrial membrane potential, which in turn suppressed the expression of the antimicrobial peptide Resistin-like molecule alpha (RETNLA) and diminished antibacterial properties. Together, these findings suggest that macrophages generate heat whereas compromising antibacterial properties, thereby increasing susceptibility to bacterial infection in physiological cold environment. This adaptation mechanism may underscore the important role of temperature homeostasis in non-adipocyte cells.

physiology↗

bioRxiv2025

Tissue-to-Bytes: A Catalytic Digital Twin Platform for Consortium-Scale Integration of Single-Cell Omics Data in BRAIN Initiative Cell Atlas Network

Monolithic Electronic Health Record (EHR) architectures remain fundamentally misaligned with high-resolution, specialty-specific clinical workflows, contributing to significant physician burnout and challangies in secondary use of data. This study introduces the Enterprise Health Twin (EHT) framework, a paradigm grounded in the Digital Twin concept, with the goal to transform static clinical ledgers into dynamic decision-support systems. The EHT establishes continuous bidirectional feedback (feed-forward ingestion and feed-backward decision support) between the physical entities of clinical workflows and the digital counterparts of interfaces reflecting the evolving states of the workflow, for each individual patient in the EHT. We conceptualized, prototyped, and operationalized the EHT framework for epilepsy care for seven years, in production, at the point of care. Deployed at the Epilepsy Center of the University of Texas Health Science Center at Houston, a [~]400% increase productivity has been achieved in specialized surgical evaluation throughput without additional staffing. The EHT achieved de novo capture of ontology-anchored structured data at the point of care, mitigating cognitive load while growing the volume of high-resolution, annotated longitudinal datasets for clinical research, and driving an increase of care velocity in a learning healthcare system.

neuroscience↗

bioRxiv2025

Representational Competition of Spatially and Temporally Overlapped Target and Distractor

Representational competition occurs when a task-relevant target stimulus and a distractor overlap in space and time. Given limited neural resources, it is expected that stronger representations of the distractor will result in weaker representations of the target, leading to poorer behavioral performance. We tested this hypothesis by recording fMRI while participants (n = 27) viewed a compound stimulus consisting of randomly moving dots (the target) superimposed on IAPS affective pictures (the distractor). Each trial lasted [~]12 seconds, during which the moving dots and the IAPS pictures were flickered at 4.29 Hz and 6 Hz, respectively. The task was to detect and report brief episodes of coherent motion in the moving dots. Depending on the emotion category of the IAPS pictures, the trials were classified as pleasant, neutral and unpleasant. Focusing on three ROIs: middle temporal cortex (MT), ventral visual cortex (VVC), as well as the primary visual cortex (V1), we performed MVPA analysis to decode the distractor categories in each ROI, and correlated the decoding accuracy, taken to index the strength of distractor representation, with the accuracy in detecting the episodes of coherent motion. The following results were found: (1) the decoding accuracy was above chance level in all ROIs, and (2) in MT and VVC but not in V1, the higher the decoding accuracy, the worse the behavioral performance. These results suggested that distractor information was represented in V1 as well as in the two motion-processing areas, and in the motion-processing areas, stronger representations of the distractor led to poorer ability to process attended information, leading to worse behavioral performance. The hypothesis was thus supported and trade-offs in the fidelity of stimulus representations prompted by neural competition demonstrated.

neuroscience↗

bioRxiv2025

Sourdough starter-inspired living materials grown from probiotic consortia with engineered division-of-labor

Genetic circuits enable precise control over cellular behavior. As circuits become more complex, implementing them within a single cell population becomes difficult. Tasks can instead be distributed in engineered consortia. However, selecting appropriate consortia and harnessing the natural traits of each species for such a division of labor remain unsolved challenges. Here, we report the creation of SINERGY, an engineered synthetic sourdough starter in which functional modules are distributed between the two constituent species. These modules, consisting of sensing, communication, and response, enable in vitro biosensing and in vivo drug delivery and modulate the gut ecosystem in an animal disease model. SINERGY not only preserves the safety and health-promoting properties of the natural components but also endows microbes with programmable functionalities, facilitating broad applications in biomedicine.

synthetic biology↗

bioRxiv2025

Whole genome sequencing reveals the phylogenetic relationship and reticulate evolution of Phrynocephalus lizards in Eurasia

Toad-headed agama (genus Phrynocephalus) is a widely distributed group of lizards across Central Eurasia, inhabiting diverse arid and semi-arid environments from the Caspian Basin to the Qinghai-Tibetan Plateau (QTP). Despite considerable research efforts over the past decades, a robust phylogenetic framework for Phrynocephalus has remained elusive. In this study, we collected 26 samples representing a substantial portion of the distribution of this genus and established a well-supported phylogeny based on whole-genome sequencing and phylogenomic approaches. We found that the Phrynocephalus lizards comprised of three major clades, including the Western Asia, Central Asia and viviparous clades. Furthermore, we provided compelling evidence for an ancient hybridization event that led to the speciation of P. mystaceus, which likely contributed to the observed phylogenetic discordance across the genome. Divergence time estimation and ancestral area reconstruction indicated that Phrynocephalus lizards originated approximately 31.57 million years ago in the Central Asia and Iranian Plateau region, followed by Miocene dispersals into the Arabian Peninsula, the Gobi Desert, and the QTP. Our results illuminate how hybridization and historical range dynamics have jointly driven the origin and diversification of Phrynocephalus, providing new insights into the formation of biodiversity in Eurasia.

bioinformatics↗

bioRxiv2025

Neural representation of emotional valence in human amygdala

The amygdala is a core structure for encoding the affective value of external stimuli. Animal studies suggest that positive and negative emotions are separately encoded by distinct neuronal populations within the amygdala; however, this hypothesis has rarely been tested in humans. The current study examined this hypothesis by comparing the distributed emotion encoding model, as proposed in animal studies, with the univariate emotion encoding model using functional magnetic resonance (fMRI) imaging. More specifically, we applied univariate regression, using average amygdala activation to represent global activation level, and multivariate regression, using distributed voxel-level pattern within the amygdala, to predict normative valence of affective images from the IAPS library. In the core amygdala, the multivariate models prediction performance was not better than that of the univariate model, with weight map analysis revealing an overwhelming predominance of voxels selectively responsive to negative stimuli. When the region of interest was expanded to include voxels with lower anatomical probability of belonging to the amygdala as well as voxels from adjacent areas, the multivariate model significantly outperformed the univariate model, with the voxels selectively responsive to positive valence primarily located in regions surrounding the core amygdala. These findings suggest that in the human amygdala, the core region encodes emotional valence primarily through a global activation signal, rather than distributed patterns consisting of separate clusters of positive and negative voxels, and a more distributed valence representation emerges when voxels surrounding the amygdala are taken into consideration.

neuroscience↗

bioRxiv2025

VO: The Vaccine Ontology

With the widespread use of vaccines in research and clinical settings, there is an urgent need to standardize vaccine representation, integrate information across diverse vaccine types, and support computer-assisted reasoning. Accordingly, we have since 2007 developed the community-based Vaccine Ontology (VO), which aligns with the Basic Formal Ontology and adheres to OBO Foundry principles. VO models ontologically vaccines, vaccine components, vaccine immune responses, vaccine investigation studies and other vaccine-related topics. VO represents more than 10,000 vaccines targeting 289 infectious pathogens and cancers in humans and over 30 nonhuman animal species. VO provides mappings to external resources such as RxNorm, CVX, FDA, and USDA. Various VO use cases exist. VO facilitates vaccine standardization in resources such as the VIOLIN vaccine database, ImmPort, and the Vaccine Adjuvant Compendium (VAC). Semantic queries can be made to query VO. VO has been shown to enhance experimental and clinical vaccine data analysis and vaccine literature mining. Overall, VO standardizes vaccine modeling and representation and greatly supports vaccine AI research in the Semantic Web era.

bioinformatics↗

bioRxiv2025

Mapping and Harmonization of CVX vaccine terms tothe Vaccine Ontology

BackgroundWith many vaccines developed and used, it is critical to standardize vaccine information. The OHDSI OMOP Common Data Model (CDM), widely used to support EHR data integration and analysis, leverages CVX, RxNorm, and RxNorm Extension codes to standardize vaccine-related records. However, these terminologies lack robust semantic relations, making the vaccine classification ineffective in OMOP CDM. To address this issue, our OHDSI Vaccine Vocabulary Working Group proposes to use the Vaccine Ontology (VO) to map these standards and build up its own semantic relations. As a first study of the work, we performed the mapping and alignment of the Vaccine Administered (CVX) codes with the VO using a combination of semi-automatic and manual mapping methods. ResultsA total of 273 CVX terms were first collected and classified. A high-level VO design pattern and an exact one-to-one mapping strategy were developed to guide the CVX-to-VO term mapping. To facilitate the manual mapping and harmonization process, we also developed and evaluated three semi-automated mapping approaches utilizing lexical and semantic information of vaccine concepts to map CVX to VO. These approaches suggested candidate VO mappings for CVX terms and also indicated CVX terms that were unmappable to VO and required new term additions to VO. The application of the best approach to the 2022-10-05 release of VO achieved an accuracy of 85.55% for its suggestions. The suggestions made by the semi-automated approaches were taken into account to further enhance the mappings, which led to our eventual mapping of all CVX terms to the latest version of VO. We innovatively proposed the inclusion of the passive vaccine branch in VO, which includes 24 immunoglobulins and antitoxins from CVX as passive vaccines. A specific CVX-VO OWL file was developed and added to the VO GitHub. Use case queries were developed to demonstrate its support for computer-assisted queries of vaccine groups based on CVX-VO hierarchies. ConclusionAll CVX terms were mapped to the VO using our combined semi-automatic and manual mapping methods. The mapped results enhanced semantic vaccine classification, providing a basis for further OMOP vaccine classification and EHR data analysis.

bioinformatics↗

bioRxiv2025

Expanding the utility of transcriptome analysis for mutation detection in high-risk childhood precision oncology

In precision oncology, whole transcriptome sequencing (RNA-seq) excels at identifying oncogenic fusions. Here, using a cohort of 477 high-risk paediatric tumours, we demonstrate that RNA-seq can identify all mutation classes found previously using whole genome sequencing (WGS) and provides additional functional insights into their pathogenicity. By incorporating reference-guided fusion, and reference-free structural variant (SV) detection algorithms with RNA abundance assessment, RNA-seq identified 96% of SVs and resolved 33 complex SVs that WGS failed to identify. Furthermore, RNA-seq identified 92% of all single nucleotide variants and small insertions and deletions. Importantly RNA-seq informed the pathogenicity assessment in 22% of variants through identification of allele specific expression or the functional consequence of splice-altering variants. The utility of RNA-seq extends beyond fusion identification to the interpretation of mutation pathogenicity and the discovery of important mutations that would otherwise go undetected. We propose that RNA-seq is an indispensable companion to WGS in precision medicine.

genomics↗

bioRxiv2025

Dendritic computation for rule-based flexible categorization

A hallmark of intelligent behavior is the ability to flexibly respond to external sensory inputs based on dynamically changing rules. A central question is how neurons in the brain implement computations underlying intelligent behaviors. The neocortical pyramidal neurons use their elaborated dendritic arbors to segregate a plethora of inputs and dynamically integrate them--a process known as dendritic computation--which may play important roles in rule-dependent sensory processing. However, evidence directly linking dendritic computation with intelligent cognitive behaviors has been absent. Here we combine two-photon imaging and a rule-switching flexible categorization task in mice to show that a projectome-defined extratelencephalic (ET) cortical layer 5 (L5) neurons in the auditory cortex integrate dendritic rule information and somatic sensory input to enable rule-dependent flexible categorization. The apical dendrite and soma within the same ET neurons exhibit distinct compartmental representations for sensory and rule information, with the soma predominantly encoding sensory information and the dendrites representing inferred task rule. Simultaneous optogenetic dendritic inhibition and two-photon imaging revealed that dendritic rule coding is essential for somatic output of flexible categorization. Our findings indicate that nonlinear dendritic integration of rule and sensory information constitutes a neuronal computational mechanism underlying rule-switching flexible decision-making.

neuroscience↗