Biology subjects

Chen, S.

Publications and source records attributed to Chen, S..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

Minicollagen expression dynamics reveal a transcriptional program for cnidogenesis in the sea anemone Nematostella vectensis

Cnidae are explosive harpoon-like organelles localized within stinging cells, or cnidocytes, of the phylum Cnidaria (jellyfish, hydroids, sea anemones, and corals). These unique Golgi-derived vesicular structures define the phylum and are prominent examples of an evolutionary cellular novelty. While recent studies have focused on the developmental specification and regulation of cnidocytes more broadly, less is understood about gene expression patterns, structural variations, and toxin repertoires within distinct cnidae subtypes. Here, we determine the transcriptional profile of two major cnidae subtypes in the sea anemone Nematostella vectensis, nematocytes and spirocytes, using the cnidae-specific structural family of proteins called minicollagens. We first define the in vivo expression patterns for three known and three uncharacterized minicollagen orthologs. We show that four minicollagens are broadly expressed throughout ectodermal cnidocytes in developing larvae and primary polyps while two others are restricted to tentacular cnidocytes. Leveraging whole adult scRNA-seq data and two novel transgenic reporter lines, we then demonstrate that the tentacle-restricted cnidocytes are developing spirocytes that are distinguished by expression of the minicollagen NvNcol5. To deepen our analysis of cnidocyte gene expression, we used a customized RNA-FACS-seq pipeline to determine global transcriptional differences between these two subtypes. This approach identified a suite of differentially expressed genes, illuminating spatial and temporal gene expression dynamics across both developing nematocytes and spirocytes. Altogether, our experiments provide fundamental and novel insights into the specialization of cnidarian stinging cells while establishing a rich set of resources for further investigation.

evolutionary biology↗

bioRxiv2026

Senataxin loss induces cGAS–STING-mediated mitochondrial dysfunction

Ataxia with oculomotor apraxia type 2 (AOA2) is a rare neurodegenerative disease caused by loss-of-function mutations in Senataxin, which encodes an RNA:DNA helicase. Many studies on Senataxin loss focus on its putative roles in regulating transcription and RNA transcript localization. However, several phenotypes remain underexplored, including metabolic dysregulation associated with ataxias. Using Senataxin-deficient mouse cells, we observed increased nuclear and genomic instability, as well as innate immune activation via the cGAS-STING axis. We also observed elevated ROS levels, decreased mitochondrial function, and hyperfused mitochondria. Importantly, mitochondrial dysfunction depends on cGAS/STING activity, indicating that the two phenotypes are functionally connected. Senataxin-deficient mice and AOA2 patient cells similarly exhibit spontaneous innate immune activation, and AOA2 patient cells also show decreased mitochondrial function. Our work identifies a previously unseen phenotype for AOA2 in which loss of Senataxin results in mitochondrial dysfunction promoted by cGAS and STING. SummaryThis study demonstrates a previously unidentified phenotype in AOA2 and Senataxin research in which cGAS-STING promotes mitochondrial dysregulation in Setx-/- MEFs. Further, these innate immune activation and mitochondrial dysregulated phenotypes are recapitulated in AOA2 patient cells.

Cell Biology↗

bioRxiv2026

Active non-redundancy and viral orchestration sustain diel microbial successions in the coastal ocean

Ecosystem resilience in dynamic coastal oceans is conventionally ascribed to functional redundancy, where taxonomically distinct microbes buffer environmental fluctuations through interchangeable metabolic roles. In this study, we reveal a deterministic succession architecture sustained by active non redundancy and temporal metabolic coupling, uncovered via autonomous drone array metatranscriptomic sampling in Daya Bay, China. By resolving the transcriptional landscape into six chronometrically phased modules rather than simple time points, we demonstrate a near total renewal of the active gene pool, with greater than 90% of transcribed clusters showing phase exclusivity within approximatively a two-hour window. This radical functional reshaping exposes a tight, scale dependent taxonomic functional coupling where community composition is a strong, linear predictor of metabolic output, peaking at the genus level (p < 0.0001). Functional continuity is maintained not by redundant generalists, but by a precisely sequential relay of specialists, each optimized for transient microniches with minimal overlap between successive phases. Deep learning structural adjudication of the psbA gene pool further reveals viral orchestration of this temporal coupling, where incoming cyanophages introduce structurally distinct protein variants that sustain photosynthetic electron flow under peak irradiance. These findings redefine coastal microbiome stability as a fine-tuned rapid succession of temporal specialists rather than a redundant backdrop. We propose a fundamental revision of marine ecosystem models, shifting from passive buffering frameworks to deterministic, clock driven architectures, which may prove to be critical for forecasting microbiome responses to accelerating short term climate variability.

microbiology↗

bioRxiv2026

Flexible decisions arise from resource-rational memory sampling

Flexible decision making depends on retrieving and recombining memories. Yet because this process unfolds covertly, its governing principles remain unknown. Here we use gaze reinstatement to uncover the hidden dynamics and computational logic of memory retrieval during flexible behavior. As people deliberated on a blank screen, they directed their gaze toward the encoding locations of decision-relevant experiences, and these fixations shaped their evolving choice. A task-optimized recurrent neural network captured both their behavior and gaze patterns by learning to balance retrieval costs against expected gains in decision quality. These results demonstrate that flexible decisions emerge from a resource-rational process in which memories are sampled to construct decision variables on the fly.

neuroscience↗

bioRxiv2026

Multisensory integration of a host metabolite coordinates bacterial predation of macrophages

The crisis of antimicrobial resistance demands new strategies that circumvent conventional killing. Here, we show that Yersinia pseudotuberculosis intercepts host mesifurane, a mimic of quorum-sensing signal AI-2. Rather than acting through a single receptor, mesifurane is directly and independently sensed by three receptor classes, YcrA, NarX, and CusR, that converge on a "Sense, Arm, Recruit" program. YcrA drives chemotaxis toward host cells; NarX-NarP licenses T6SS-mediated killing; and CusR amplifies AI-2 production to recruit more bacteria. YcrA is conserved across Gram-negative pathogens, creating a therapeutic vulnerability. Suramin sodium, a repurposed drug blocking YcrA, disarms this circuit without inhibiting growth. In murine sepsis, suramin sodium conferred 80-90% survival against WHO critical-priority pathogens, including carbapenem-resistant A. baumannii, E. coli, and P. aeruginosa, even when delayed 8 hours post-infection, without selecting for resistance. These findings establish that disrupting sensory perception, rather than viability, provides an anti-infective strategy bypassing resistance while imposing minimal selective pressure.

microbiology↗

bioRxiv2026

Structural basis of substrate recognition and allosteric inhibition in human B0AT2

The SLC6 family is a major target for neuropsychiatric therapeutics. Human B0AT2 (SLC6A15) regulates amino acid homeostasis and glutamatergic transmission and is linked to major depressive disorder, yet its transport and inhibition mechanisms remain unclear. Here we report cryo-EM structures of B0AT2 in apo state and in complex with substrates (proline, leucine, methionine) and inhibitors (loratadine, tiagabine), capturing outward-open, early substrate-bound intermediate, outward-occluded, and inward-open conformations along the transport cycle. These structures reveal an early substrate-sensing mechanism at the substrate-binding pocket (S1), where a substrate-dependent rotameric switch of Phe308 remodels pocket geometry to tune substrate accommodation and selectivity. Loratadine stabilizes an outward-occluded state via allosteric inhibition at the extracellular S2 pocket, whereas tiagabine stabilizes the inward-open state through cooperative multi-site inhibition involving S1 and two previously unrecognized intracellular cavities (S3 and S4). Together with functional assays, these data define the molecular basis of B0AT2 substrate selectivity and state-dependent inhibition. Notably, the two intracellular cavities are conserved across SLC6 transporters, reflecting a shared intracellular vestibular architecture and enabling rational design of conformation-selective modulators for neuropsychiatric disorders.

biophysics↗

bioRxiv2026

Uncovering Pseudotime-Varying Genetic Causal Effects Along Single-Cell Trajectories for Pulmonary Disease Trait

With the increasing accessibility of single-cell RNA sequencing (scRNA-seq) data, cell-type-specific gene expression can be linked to complex traits through pseudo-bulk method, which considered aggregated gene expression from multiple cells of the same annotated cell type per individual and clearly shows the limitation of ignoring intra-individual cell-to-cell variability. Concurrently, pseudotime trajectory inference has gained popularity for its ability to capture continuous biological processes such as cell differentiation and lineage development, instead of individual discrete stages. It is natural to consider whether genetic effects for complex traits, such as individual level disease status, show a dynamic pattern along the inferred trajectories. In this study, we introduce a novel framework that models gene expression as a function of pseudotime along the inferred trajectories. We mapped expression quantitative trait loci (eQTL) effects in the cis-region as functional parameters, which we called "dynamic eQTLs", showing regulatory effects exerted by genetic variants change continuously along the cellular trajectory. For eQTLs of constant effects across pseudotime we leveraged external bulk-eQTL information to enhance the power. Furthermore, we employed significant, variable dynamic eQTLs as instrumental variables to infer causal relationships between gene expression and complex traits. To address challenges inherent to scRNA-seq data--such as sparsity and high variability--we incorporate an empirical likelihood-based inference method, which is non-parametric and self-normalized. Besides, genes associated with trajectory branchpoints may bring confounding, and we also proposed a causal mediation analysis framework to determine whether a gene plays a causal role for the disease directly and indirectly through driving cell fates. Applying our method to scRNA-seq data from human lung tissue of 114 samples (66 pulmonary fibrosis cases and 48 controls), along with meta-analyzed GWAS summary statistics for IPF from 3 studies, we identified pseudotime-dependent causal effects for IPF from genes implicated in the trajectory AT2 - translational AT2 - AT1, which is crucial in lung tissue repair and regeneration. We also found that 30 genes have a mediated effect through cell fates.

genetics↗

bioRxiv2026

PanKbase Integrated Single-Cell Map: A Comprehensive Atlas of Human Pancreatic Islets

Single-cell RNA sequencing (scRNA-seq) of human pancreatic islet tissue is a powerful tool for investigating type 1 diabetes (T1D). However, individual datasets are limited in size and fragmented across donors, laboratories, and experimental conditions. To address this, we constructed a comprehensive, integrated scRNA-seq atlas of isolated human pancreatic islets by collating publicly available data generated from tissue provided by resources including the Human Pancreas Analysis Program, the Integrated Islet Distribution Program, and Prodo Labs. Systematic quality controls were implemented to select high-quality samples, reads, and cells. During integration, we accounted for important variables such as age, sex, body mass index, origin study, treatments, islet distribution resources, and sequencing chemistry. Our single-cell atlas comprises 191 high-quality samples from 140 donors (59 female, 81 male) across five phenotypic groups: no diabetes (controls, n=69), autoantibody positivity without diabetes (n=12), pre-diabetes (n=11), T1D (n=12), and type 2 diabetes (T2D) (n=36). In total, the atlas contains 448,935 cells, capturing 13 distinct populations, including alpha cells (43.3%) and beta cells (26.8%), as well as groups such as immune cells (0.6%). Publicly available at www.pankbase.org, this atlas provides a platform for hypothesis-driven investigation of diabetes pathophysiology and, given rigorous quality control, is well-suited for downstream machine-learning applications. Article HighlightsO_LICurrent scRNA-seq datasets of pancreatic islet tissue are limited in size and scattered across donors, laboratories, and experimental conditions, underscoring the need for a consolidated resource. C_LIO_LIWe harmonized datasets from multiple sources to build a comprehensive single-cell map of isolated human pancreatic islets. C_LIO_LIOur atlas captures 448,935 cells from 191 high-quality samples across 140 donors and multiple phenotypic groups, identifying 13 distinct cell populations. C_LIO_LIAvailable at www.pankbase.org, the atlas provides a scalable, rigorously curated platform to support hypothesis-driven diabetes research and can enable a broad range of downstream computational applications. C_LI

genomics↗

bioRxiv2026

CytoGem-XAI:A Hypergraph Neural Network Framework for Genome-Scale Metabolic Modeling and Interpretable Analysis

Genome-scale metabolic models are essential for understanding cellular metabolism, yet existing deep learning approaches remain black boxes, and traditional flux balance analysis (FBA) cannot provide sample-specific predictions. To our knowledge, CytoGem-XAI is the first framework to combine hypergraph neural network representation with interpretable, FBA-parallel analysis and sample-specific metabolic characterization. Built upon hypergraph representations where reactions are encoded as hyperedges connecting their participating metabolites, CytoGem-XAI introduces three analysis modules: perturbation-based carbon source importance ranking, hard intervention reaction bottleneck identification, and pathway-level topological attribution. Beyond prediction, CytoGem-XAI uniquely enables condition-dependent carbon source essentiality and reaction bottlenecks that vary with genetic background--capabilities absent from both traditional FBA and existing deep learning methods. Trained on 17,400 E. coli growth conditions using 10-fold cross-validation, our framework achieves R2 = 0.862, substantially outperforming AMN (R2 = 0.81, +6.4%), FBA (R2 = 0.62, +39%), and gradient boosting baselines (R2 = 0.71, +21%). Biological validation confirms that CytoGem-XAI identifies known essential carbon sources (e.g., alanine, malate) and rate-limiting enzymes (e.g., TCA cycle), while also revealing N-acetylmuramate--a peptidoglycan precursor--as a previously underappreciated essential nutrient.

bioinformatics↗

bioRxiv2026

Identification of highly immunogenic endogenous dsRNAs from cellular MDA5 filaments

ADAR1 converts adenosine to inosine in endogenous double-stranded RNAs (dsRNAs) to prevent excessive MDA5-driven interferon-stimulated gene expression. The source of endogenous immunogenic dsRNAs remains enigmatic because only a small fraction of ADAR1 substrates activate MDA5, and cellular MDA5 filaments have not been isolated. Here, we couple affinity purification of cellular MDA5 filaments with RNA sequencing to define immunogenic endogenous dsRNAs. Greater than 84% of dsRNAs suppressed by combined DDX3X RNA helicase and ADAR1 base-editing activities were present in MDA5 filaments, compared to less than 1% of dsRNA substrates acted on by ADAR1 alone. Dual substrate dsRNAs consisted of inverted repeats embedded in 3-UTRs with high base-pair complementarity and longer intervening sequences between repeats, with a minor contribution coming from intermolecular dsRNAs formed by sense and antisense transcripts. Moreover, the majority of dual substrate immunogenic dsRNAs were hyperedited in DDX3X mutant cancers. This reveals the identity of endogenous immunogenic dsRNAs and quality control mechanisms underlying their suppression.

cell biology↗

bioRxiv2026

Extracellular NAD(P) activates systemic acquired resistance through LecRK-VI.2-mediated phosphorylation of NPR1

Systemic acquired resistance (SAR) is a long-lasting, broad-spectrum immune response induced in distal tissues by signals generated at primary infection sites. Although numerous mobile immune signals have been implicated in SAR, how these signals are perceived and mechanistically coupled to transcriptional reprogramming in systemic tissues remains poorly understood. Extracellular NAD(P) [eNAD(P)] functions as a key integrative SAR signal that activates immunity through the plasma membrane-localized lectin receptor kinase LecRK-VI.2 and the master immune coactivator NONEXPRESSOR OF PATHOGENESIS-RELATED GENES1 (NPR1). However, the mechanism linking eNAD(P) perception to activation of NPR1 has remained unknown. Here, we show that LecRK-VI.2 constitutively associates with NPR1 and directly phosphorylates NPR1 at T359 and likely S356 upon eNAD(P) perception. NADP+-induced phosphorylation of NPR1 occurs rapidly in vivo and requires LecRK-VI.2. Nonphosphorylatable NPR1 variants abolish eNADP+-induced local and systemic immunity as well as biologically induced SAR, whereas phosphomimetic variants retain NPR1 function. Mechanistically, LecRK-VI.2-mediated phosphorylation promotes NPR1 interaction with TGACG-binding transcription factors (TGAs) and the Mediator subunit MED15, thereby enhancing assembly of a transcriptional activation complex required for defense gene expression. We further demonstrate that NPR1 facilitates TGA-MED15 association in a phosphorylation-dependent manner. Together, these findings establish a receptor-to-coactivator signaling mechanism that directly links extracellular immune signal perception to transcriptional activation. This work closes a major mechanistic gap in the SAR signaling pathway and reveals receptor-mediated coactivator activation as a mechanism for rapid conversion of extracellular immune cues into coordinated transcriptional outputs during systemic immunity.

plant biology↗

bioRxiv2026

Closed-loop neurofeedback reshapes preparatory brain states to bias subsequent pain processing

Pain perception is strongly influenced by neural states preceding stimulus onset, yet whether such preparatory states can be causally reshaped remains unclear. Using a double-blind design, we tested whether closed-loop neurofeedback can reconfigure preparatory brain dynamics to influence subsequent pain processing. Real, but not sham, feedback enabled learning-dependent enhancement of pre-stimulus oscillations in primary somatosensory cortex and their trans-hemispheric propagation. Training also selectively reduced transitions between EEG microstates associated with salience and attentional control, indicating reorganization of preparatory brain states. These neural changes were accompanied by attenuated pain-evoked cortical responses and reduced pain perception. Sequential mediation analyses further revealed that somatosensory enhancement predicted microstate reorganization, which in turn suppressed pain-evoked responses. Together, these findings demonstrate that closed-loop neurofeedback can causally reshape preparatory brain states to bias subsequent sensory processing.

neuroscience↗

bioRxiv2026

Learning residue-level context for modeling protein-protein interactions

Protein language models (PLMs) enable prediction of protein properties by learning residue-level features from sequence, yet most PLM-based approaches to protein-protein interactions aggregate information across entire proteins, limiting resolution and interpretability. Here we present ReCLIP, a transformer-based framework that learns interaction-specific representations at the level of individual residues by combining intra-protein residue neighborhoods with residue-conditioned representations of interaction partners. We show that residue-centered context provides a general framework for modeling protein interactions across diverse biological settings. ReCLIP accurately predicts mutation-induced perturbations (AUROC = 0.973), generalizes to post-translational modifications that do not alter sequence (AUROC = 0.822), and enables zero-shot prediction of peptide-MHC binding across unseen alleles (AUROC up to 0.972). Analysis of learned residue neighborhoods reveals structurally and functionally coherent patterns aligned with known determinants of binding. Applied to clinically annotated genetic variants, ReCLIP identifies disease-associated interaction perturbations that link pathogenic variants to specific molecular interaction contexts. Our results establish a generalizable and interpretable framework for modeling protein interactions and provide insights into how residue-level context shapes interaction specificity and its perturbation.

bioinformatics↗

bioRxiv2026

sstar2: A Python Package for S*-based Archaic Introgression Detection with Machine Learning

Detecting introgressed genomic fragments from unsampled or extinct source populations remains challenging. The S* statistic is widely used for this purpose, but the original sstar implementation relies on generalized additive models to smooth quantile-specific values precomputed from fixed count bins, requiring simulations with fixed numbers of segregating sites. Here, we present sstar2, a Python update that replaces this procedure with quantile regression to directly estimate S* thresholds at specified null quantiles from simulated genomic windows. We benchmarked sstar2 against the original sstar, linear quantile regression, and random forest quantile regression across three demographic models with both phased and unphased simulated data. sstar2 showed the best overall performance among the evaluated methods, with the most pronounced improvement under a challenging demographic model of ghost introgression in bonobos. These results show that sstar2 improves S* threshold calibration while making S*-based introgression analyses more flexible and compatible with modern simulation workflows.

bioinformatics↗

bioRxiv2026

Trustworthy ML/AI for Aging Clocks: Preventing Systematic Prediction Bias in Biological Age Estimation

Machine learning (ML)- and artificial intelligence (AI)-based aging clocks are increasingly used to quantify physiological and molecular aging from omics and medical imaging data as distinct from chronological age. Here, we characterize a fundamental but underappreciated statistical limitation of commonly used ML/AI regression models for continuous outcomes: systematic prediction bias and its propagation to downstream association estimates. This issue becomes more challenging when the true outcome, biological age, is latent and therefore unobserved during ML/AI model training. We demonstrate that systematic prediction bias can distort and, in some cases, even reverse downstream association analyses that use aging clocks as ML/AI-predicted outcomes to assess their associations with exposures or clinical factors. For example, it can produce spurious associations suggesting that older predicted brain age is linked to better cognitive performance, or that older epigenetic age is associated with better kidney function. To address this problem, we introduce a principled and broadly applicable ML/AI regression framework based on constrained optimization, yielding better calibrated aging-clock estimates and valid downstream inference.

bioinformatics↗

bioRxiv2026

Human hindbrain organoids with segment-specific 5-HT neurons for motor modulation

The serotonergic system regulates human mood, reward, and motor control, and its impairment contributes to debilitating psychiatric disorders. Serotonergic neurons (SNs) arise predominantly in the hindbrain and acquire segment-specific identities and functions; however, the lack of human models remains a challenge to understanding the human serotonergic system. Here, we generate human serotonergic-enriched rostral and caudal hindbrain organoids (hsrHOs and hscHOs) that produce segment-specific SNs with regional identities corresponding to rhombomeres r2-r3 and r5-r8, respectively. hsrHOs and hscHOs produce SNs that recapitulate their in vivo counterparts in transcriptomic profiles, function, and ascending or descending projection preferences. Assembly with human neuromusculoskeletal organoids enables modeling of serotonergic modulation of motor control. This system further reveals that deficiency of the schizophrenia risk gene DISC1 disrupts human serotonergic development and impairs motor modulation. Together, our study establishes a human organoid platform for modeling development, circuit function and disease-associated dysfunction of the serotonergic modulatory system.

neuroscience↗

bioRxiv2026

rDNA breaks activate dsRNA pattern recognition through sense-antisense transcription

Myriad DNA damaging chemo- and radio- therapies interfere with ribosomal RNA (rRNA) transcription and processing, yet the biological consequences of these phenomena remain unclear. Here we show that aberrant transcripts emanating from rDNA breaks engage double-stranded RNA pattern recognition receptors, melanoma differentiation-associated protein 5 (MDA5) and retinoic acid-inducible gene I (RIG-I) to activate immune signaling. rDNA damage abolishes full-length rRNA synthesis while generating truncated sense-antisense rRNA transcripts, whose accumulation is restrained by ATM and ATR kinase activities. Purification of the endogenous MDA5-filament coupled with sequencing identified complementary sense and antisense rRNA transcripts that terminate and initiate near the break site, respectively. This implicates aberrant rRNA species as a major source of damage induced endogenous ligands for dsRNA pattern recognition and establishes a mechanism by which nucleolar stress is coupled to immune signaling.

cell biology↗

bioRxiv2026

The molecular architecture of tunneling nanotubes

Tunneling nanotubes (TNTs) are thin intercellular bridges that mediate the exchange of proteins, organelles, and nucleic acids between neighboring cells. They are enriched in tumor cells, implicated in chemotherapy resistance, induced in models of aggregation-based diseases, and their formation is stimulated by viruses that use TNTs to enhance infection. Despite their broad relevance and therapeutic potential, TNT morphology and function remain poorly understood, owing to the absence of clear morphological criteria and the limitations of light microscopy. Here, we establish two complementary systems to study TNT formation and function: stimulation with the pseudorabies viral kinase US3 to model viral transmission, and treatment of acute monocytic leukemia THP-1 cells with daunorubicin to model chemotherapy resistance. Using live-cell imaging, we characterize cytoskeletal organization and bidirectional lysosome transport in both contexts, and apply cryo-correlative light and electron microscopy (cryo-CLEM) with cryogenic electron tomography (cryo-ET) to visualize TNTs in their native state at molecular resolution. We show that TNTs display a rich molecular architecture, comprising actin filaments, microtubules, intermediate filaments, active ribosomes, and diverse organelles including multivesicular bodies, autophagosomes, and lysosomes. Sub-nanometer microtubule reconstructions reveal mixed polarity within individual TNTs, suggesting that both connected cells actively contribute to TNT formation and cargo trafficking. This organization is conserved across both systems, implying that TNT biogenesis reflects a shared cellular program rather than a context-specific response. Our findings provide the first structural framework for TNTs, revealing an unexpectedly rich molecular architecture and laying the groundwork for understanding how TNTs orchestrate intercellular communication in disease.

cell biology↗