Biology subjects

Chen, J.

Publications and source records attributed to Chen, J..

At least 181 records · Page 10Linked to original sources

bioRxiv2026

Categorical Bayes Filtering for Computational Phenotyping in Adaptive Learning

Adaptive learning requires distinguishing environmental volatility from observation stochasticity, two sources of uncertainty that demand opposite adjustments to the learning rate but inflate experienced variance similarly. Disentangling them is computationally difficult with no tractable closed-form solution. Particle-filter methods are the natural tool for this kind of joint inference, but their stochastic likelihoods and non-differentiable objectives force derivative-free fitting protocols and discourage the individual-difference analyses central to cognitive modeling, where small effect sizes leave little room for additional estimator noise. We introduce the Categorical Bayes Filter (CBF), a deterministic alternative that preserves the conditional structure of recent particle-filter accounts but replaces the stochastic outer layer with a categorical distribution on a quantile grid parameterized through differentiable Beta quantile functions. The procedure performs evidence maximization with an exact, deterministic marginal likelihood that is fully differentiable in the grid parameters. In a volatility-stochasticity task with N = 643 participants, fitted CBF dispersion parameters reveal a cross-over phenotyping pattern between volatility-blind and stochasticity-blind subjects that is not recoverable from particle-filter parameters fit to the same data under a state-of-the-art protocol. The deterministic structure also yields a trial-by-trial ambiguity signal that predicts response times not used in fitting. More broadly, the approach opens individual-level analyses in cognitive modeling and computational psychiatry that stochastic methods have effectively foreclosed.

neuroscience↗

bioRxiv2026

Cell-type-resolved RNP topologies reveal dynamic structural mechanisms of splicing and therapeutic targets

Resolving RNA conformations in native ribonucleoprotein (RNP) complexes remains a fundamental challenge. Here, we introduce spatial hydroxyl acylation reversible crosslinking with immunoprecipitation (SHARCLIP) to simultaneously capture RNA-RNA, RNA-protein and protein-protein contacts in cells. SHARCLIP profiling of HNRNPC-associated RNA conformations established a global phased map of ribonucleosomes, resolving a decades-old debate on heterogenous nuclear (hn)RNP assembly. We built a dynamic structural atlas across seven cell lineages for >10,000 RNAs, generating ~200 million contacts, and identifying millions of dynamic loops, steric blockers and conformational switches that control splicing outcome. Deciphering the structural logic of mutually exclusive exons (MXEs) enabled rational design of structure-breaking and stabilizing antisense oligonucleotides (ASOs). We demonstrate effective isoform swapping in 12 genes linked to genetic disorders. SHARCLIP provides a comprehensive roadmap for cellular RNA structural biology and structure-guided RNA therapeutics.

molecular biology↗

bioRxiv2026

Aging and reproductive "rejuvenation" at a single nuclei resolution

Aging is a multifaceted process that occurs on the background of and is significantly driven by transcriptional and cell type abundance changes. Aging-related transcriptional changes may include reduced transcription of maintenance, repair and DNA methylation genes, de-differentiation, or increased transcription of transposons. Unbiased detection of these changes and full understanding of their physiological effects requires single-cell resolution. We studied single-nuclei transcriptomes of a model microcrustacean Daphnia magna sampled from 3 age- and reproductive status groups: young, old reproductively senescent, and old, regaining reproductive function late in life. We detected 17 cell clusters, some identifiable as ovary or fat body-, midgut-, epithelium-, and neural tissue-related, some escaping unambiguous identification. At the same time, some well-characterized cell types were not detected, such as hemocytes or myocytes. We detect significant changes of cell type abundance with age and with reproductive "rejuvenation" in ovary- and gut-related clusters. We also detect several patterns of functional transcriptional differences between age classes with nearly all cell types, with changes between old reproductive and old non-reproductive Daphnia often reversing age-related changes, and often in good concordance with previous bulk RNAseq data.

developmental biology↗

bioRxiv2026

Transferable spatial omics deconvolution with SpaRank

By resolving cell-type compositions from multi-cellular spatial measurements, deconvolution is central to resolving the cellular landscape of complex tissues. Existing deconvolution methods fit continuous expression values and are therefore sensitive to batch effects between single-cell references and spatial data, requiring retraining for each new context. Here we present SpaRank, a context-aware framework that performs spatial deconvolution by representing spots as ranked feature sequences. Adapting the rank-based encodings of single-cell foundation models, this formulation is inherently robust to technical variation, enabling a pretrain-transfer paradigm. On simulated benchmarks, SpaRank achieves strong deconvolution accuracy, robustness to expression perturbations, and substantial computational efficiency. On experimental datasets, pretrained models generalize across diverse biological contexts: a model pretrained on a multi-organ lymphoid atlas accurately resolved cell-type distributions across distinct tissues and sequencing platforms; likewise, a model pretrained on an integrated breast atlas delineated cell-type compositions across normal and malignant disease states. Furthermore, the framework naturally extends to multimodal spatial deconvolution by employing gated fusion to adaptively integrate diverse omics signals, improving accuracy over single-modality approaches. Overall, SpaRank establishes a transferable deconvolution paradigm, enabling unified cellular atlases to support direct, context-aware inference across diverse biological states and profiling modalities.

bioinformatics↗

bioRxiv2026

Ultrasound-led stratification of carpal tunnel syndrome reveals structure-function mismatch

The severity stratification of carpal tunnel syndrome (CTS) relies on ultrasound morphological markers and electromyography. However, it remains unclear how structural imaging can reliably infer functional impairment. Clarifying the structure-function relationship is critical for efficient diagnostic pathways. A retrospective cohort of 55 patients with symptoms related to CTS was analyzed at the Shanghai Sixth Peoples Hospital. All patients were subjected to ultrasound and EMG. 72.7% cases were diagnosed with CTS with a female predominance and equal left-right involvement. Random-forest classifiers were trained using surrogate splits, and performance was evaluated using predictions outside the bag. A full-feature model (34 candidate variables) was compared against a simplified model (8 core variables) capturing the core morphological and electrophysiological features. A residual-based framework was then used to characterize the structure-function mismatch within severity grades (1a-3c). The simplified model improved discriminative performance compared to the full-feature model (AUC 0.789 to 0.824). The simplified model achieved an overall accuracy of 77.3%. Analysis of predicted probability distributions and 10-bin calibration curves indicated stable and clinically interpretable risk estimation in most probability ranges. Permutation-based importance analysis confirmed that both ultrasound and electrophysiological features contributed substantively to prediction. Residual-based grading further revealed structure- function heterogeneity within each main severity grade. CTS severity can be stratified using a limited set of complementary morphological and electrophysiological features. Structure-function mismatch supports an imaging-led initial screening, with electrophysiology reserved for selected patients.

bioengineering↗

bioRxiv2026

An fMRI dataset of verbalized spontaneous thought with annotated transcripts and self-report trait measures

Spontaneous thought is pervasive in everyday human cognition, yet datasets capturing its neural dynamics under minimally interrupted conditions remain limited. The current dataset was acquired from a think-aloud functional MRI experiment in which 118 participants continuously verbalized their spontaneous thoughts during 10-minute scanning sessions. The raw MRI data and verbal transcripts with sentence-level timestamps were previously released and analyzed in our prior study examining neural activity associated with thought transitions. Building on that release, we additionally provide preprocessed MRI data, speech transcriptions with word-level timestamps aligned to image acquisition, large language model-generated ratings of transcribed thoughts across emotional and sensory dimensions, and self-report survey measures assessing personality, mental health, and cognitive abilities. Validation analyses demonstrated activation in expected cortical regions associated with speech production and sensory content identified from transcript annotations, agreement between language model and human ratings, and adequate internal consistency of survey measures, supporting the datasets overall quality. This dataset enables reuse for investigations of spontaneous thought, speech generation, and individual differences using naturalistic functional MRI data.

neuroscience↗

bioRxiv2026

Reverse-engineering amyloid strains with generative protein design

Amyloid fibrils are intrinsically polymorphic protein assemblies that form distinct structural strains linked to diverse biological and pathological outcomes. Yet, the principles governing how sequence encodes diverse fibril architectures, and the extent to which a given fold constrains underlying amino-acid sequence compatibility, remain poorly understood. Here, we apply generative protein design to directly interrogate the sequence-structure relationship of defined fibril architectures, using -synuclein (S), a protein known to form highly polymorphic amyloid fibrils, as a model system. Sampling sequence space under structural constraints reveals a continuous compatibility manifold in which diverse sequences encode a common amyloid architecture. De novo designed sequences assemble into fibrils, often with enhanced aggregation efficiency relative to S. A subset exhibits strain-like behaviour, including similar morphologies, efficient cross-templating, and induction of S cellular propagation, thereby functionally validating structural compatibility with the native fibril fold. Energetic analysis shows that stability is achieved through distinct but compensatory interactions, supporting a non-unique mapping between sequence and structure. Together, our results define a continuous and constrained compatibility landscape underlying amyloid strains, providing a framework for understanding the determinants of polymorphism and establishing generative protein design as a strategy to access this space, interrogate amyloid sequence-structure relationships, and engineer fibrillar protein assemblies and functional biomaterials.

biophysics↗

bioRxiv2026

Coupled Binding and Folding of NS2B/NS3 Protease and Linker Effects Revealed by Topology-based Modeling

Orthoflavivirus, such as West Nile Virus (WNV), dengue virus (DENV) and ZIKA virus (ZIKV), are globally distributed pathogens that pose substantial threats to human health. Currently, there are still no effective antiviral drugs for WNV or ZIKV. Despite the availability of two licensed DENV vaccines, their use remains limited due to potential risks, highlighting an urgent need for antiviral drug development. The highly conserved orthoflavivirus protease NS2B/NS3 is required for viral replication, making it a promising anti-flavivirus target. A major challenge, however, is the highly charged active site of this enzyme, which requires charged chemical matters with low bioavailability. An alternative and more attractive strategy is to target potential allosteric sites or folding intermediate states of the protease. In this work, we employ the topology-based coarse-grained G[o] modeling to explore the coupled binding and folding pathways of WNV NS2B/NS3 protease and study the effects of the widely used experimental construct with a G4SG4 linker between NS2B and NS3 on stability and folding. Our results provide a holistic conformational landscape of the protease binding and folding, including several key intermediate states. We find that the presence of the G4SG4 linker alters the folding pathways and destabilizes the NS2B C-terminus. The latter is consistent with experimental observations that the G4SG4 linked protease has lower activity and adopts an open state without the substrate in crystal structures. Together, these findings provide for the first time a complete picture of the binding and folding of the NS2B/NS3 protease and identify important folding intermediate states that could be targeted for allosteric antiviral drug development. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/722635v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@163c356org.highwire.dtl.DTLVardef@ad7b35org.highwire.dtl.DTLVardef@173ed8aorg.highwire.dtl.DTLVardef@1f026bf_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2026

Not Just Noise: Aperiodic Brain Activity Reflects Corticospinal Excitability

BackgroundElectroencephalography (EEG) can be combined with transcranial magnetic stimulation (TMS) to perform brain-state-dependent stimulation. EEG-TMS studies have shown that corticospinal excitability, as measured via motor evoked potentials (MEPs), is modulated by pre-stimulus periodic EEG features, such as sensorimotor mu-rhythm phase and power. However, the influence of aperiodic brain activity on corticospinal excitability is largely unexplored. ObjectivesWe evaluated the relationship between aperiodic and periodic mu-power, aperiodic exponent, and mu-phase on MEP amplitudes using EEG-TMS. MethodsWe applied 800 single TMS pulses to the left primary motor cortex in 78 healthy adults. We calculated aperiodic/periodic mu-power, aperiodic exponent, and mu-phase for each trial from the pre-stimulus C3-Hjorth transformed EEG. MEP amplitudes were extracted from the right first dorsal interosseous muscle. A linear mixed-effects model assessed relationships between MEP amplitudes and EEG features, with interactions between mu-phase and all other EEG features. ResultsAperiodic and periodic mu-power, aperiodic exponent, and mu-phase significantly modulated MEP amplitudes. Higher aperiodic/periodic mu-power was associated with larger MEP amplitudes, while higher aperiodic exponent was associated with smaller MEP amplitudes. We found a significant interaction effect of aperiodic exponent and mu-phase on MEP amplitude. Aperiodic exponent was negatively associated with MEPs for trough, rising, falling phases, but positively associated with MEPs for peak phase. ConclusionsAperiodic and periodic features of brain activity are reflective of dissociable corticospinal excitability states. Future brain-state-dependent TMS interventions may include aperiodic EEG features, such as aperiodic mu-power and exponent, in addition to the well-established periodic features.

neuroscience↗

bioRxiv2026

CPT1A loss promotes lung metastasis in immune-competent mice via a mechanism of mtDNA release and chronic activation of STING pathway

The metastatic progression of breast cancer involves complex interactions between tumor cells and immune cells, including T cells that exert cytotoxic pressure to limit metastasis. Tumor cells reprogram their metabolism to evade immune surveillance, a critical step to achieving metastatic outgrowth. Using an unbiased CRISPR screen targeting metabolism-related genes and a clinically relevant spontaneous metastasis mouse model, we identified CPT1A, the rate-limiting enzyme in fatty acid {beta}-oxidation, as a suppressor of immune-dependent metastasis. Loss of CPT1A enhances lung metastasis in immunocompetent mice, but not Rag1 KO mice that lack mature lymphocytes. Loss of CPT1A triggers cytosolic mitochondrial DNA (mtDNA) release via the mPTP pore. Cytosolic mtDNA release triggers a STING-dependent inflammatory response, creating an environment that impairs CD8+ T cell function, promoting metastatic outgrowth. Among breast cancer patients, low CPT1A expression correlates with poor survival when CD8+ T cell infiltration is high. These findings reveal an extrinsic role for CPT1A in immune-tumor dynamics and suggest therapeutic opportunities targeting inflammation in metastatic breast cancer.

cancer biology↗

bioRxiv2026

EffectorGeneP: accurate gene annotation in pathogen genomes from infection transcriptomes

Accurate gene annotation is crucial for inference of biological knowledge from genomes. However, non-canonical genes such as orphan or single-exon genes as well as those residing in rapidly evolving regions are routinely dismissed in annotation pipelines. In filamentous pathogen genomes, this disproportionately affects the annotation of genes encoding disease-promoting effector proteins. We introduce EffectorGeneP, a machine learning tool that self-trains on transcript data, predicts the most likely coding sequence from transcripts and effectively separates bona fide genes from transcriptional noise. EffectorGeneP annotates over 95% of known effectors correctly, while other state-of-the-art methods annotate 15%-78%. We show that EffectorGeneP expands the predicted secretome of pathogens by over 50% and that high-throughput screening of an effector library in plant protoplasts uncovers the previously poorly annotated AvrSr26 gene family in the wheat stem rust fungus. EffectorGeneP decodes genomes at unprecedented resolution and will enable the study of biological processes in important pathogen species.

microbiology↗

bioRxiv2026

SPPIDER-seq: Sequence-based partner-aware predictor of protein-protein interaction sites

MotivationSequence-based protein-protein interaction (PPI) site predictors typically analyze proteins in isolation, neglecting partner-specific context that is critical for interface specificity, particularly in transient and disordered interactions. ResultsWe introduce SPPIDER-seq, a partner-aware PPI site prediction framework that combines pretrained ESM-2 embeddings with a cross-attention architecture to enable residue-level conditioning on interacting partners. We curated non-redundant protein-peptide interaction datasets from BioLiP and used them to train and benchmark two complementary models: a receptor-centric model optimized for structured interfaces and a peptide-centric model tailored to disordered, motif-driven binding. On blind benchmarks, SPPIDER-seq achieved AUROC values up to 0.797 and MCC values up to 0.269, outperforming AlphaFold3 on peptide-mediated and disordered interfaces while remaining complementary on globular complexes. Application to 341 TP53 interaction partners revealed coherent, partner-specific interface patterns across both structured and intrinsically disordered regions. Availability and ImplementationSPPIDER-seq models, datasets, and the Python code are freely available on the web at: https://github.com/aporollo-lab/SPPIDER-seq and archived on Zenodo at DOI: 10.5281/zenodo.19835990, corresponding to GitHub release v2.0-manuscript. ContactDr. Aleksey Porollo - porollay@ucmail.uc.edu Dr. Jichao Chen - jichao.chen@cchmc.org Supplementary InformationAvailable online with the manuscript.

bioinformatics↗

bioRxiv2026

ExMODE: A Multi-Omics Repository for Extremophile Adaptation and Bioprospecting

Extreme environments, though hostile to most life forms, host specialized extremophile communities that have redefined biological cognition and emerged as vital biotechnological resources, with their unique adaptive traits and bioactive molecules driving advances in multiple scientific and industrial fields. However, research on extremophiles is hindered by limitations in culture-based methods, fragmented multi-omics data with non-uniform annotation standards across repositories, the lack of cross-extreme comparative research in existing resources, and the singularity of data dimensionality that neglects key structural information, all of which restrict the functional interpretation of extremophile microbes and the exploitation of their bioprospecting potential. To tackle these challenges, we developed ExMODE (https://db.genomics.cn/exmode/), a comprehensive multi-omics database platform dedicated to extremophiles. It centrally integrates multi-omics data from diverse extreme habitats with a standardized annotation framework, resolving data fragmentation and enabling systematic cross-environment comparative analyses to elucidate extremophile adaptive mechanisms. Moreover, ExMODE aggregates multi-dimensional datasets including genes, genomes, secondary metabolite sequences and protein structures, overcoming the constraints of single-dimensional data and significantly improving the efficiency of biotechnological resource discovery from extreme microorganisms.

microbiology↗

bioRxiv2026

Engulfment by brain macrophages in a short-lived vertebrate

Engulfment by macrophages is critical for waste clearance in the vertebrate brain. Understanding clearance mechanisms may open new therapeutic possibilities to counter brain aging and neurodegenerative diseases. However, few in vivo models exist to study engulfment in the brain and characterize this process during aging and across species. Here we present a genetic model for secretion of a fluorescent protein by neurons in the brain of the African turquoise killifish, the shortest-lived vertebrate that can be bred in captivity. We use this model to identify a population of brain macrophages in the killifish responsible for engulfment of material from the brain extracellular space. Intriguingly, many of these cells bear similarities to mammalian border-associated and monocyte-derived macrophages, rare subsets of macrophages in mouse and human brains noted for their engulfment capabilities. We also find that in our model, killifish brain macrophages decline in engulfment capacity with age. This work highlights how vertebrate brain macrophages, particularly those at brain border regions, can play a critical role in clearance and provides an opportunity to test interventions that can boost engulfment by these macrophages to promote brain resilience in old age and disease.

cell biology↗

bioRxiv2026

Proteome-aware organ proxy aging clocks

The development of minimally invasive multi-organ aging clocks, established through the deconvolution of plasma proteomics, has provided a convenient tool to assess the organ heterogeneity of aging. However, prior studies relied on bulk transcriptomic data for organ marker identification, which may lead to the potential misidentification of protein markers, and their research scope was largely confined to a few common diseases. To address these limitations, this study integrated multi-dimensional data to refine organ-enriched marker panels by incorporating organ-specific proteome information, and developed Proteome-Aware Organ Proxy Proteome Aging Clock (PAOPAC). PAOPAC exhibited decelerated biological age corresponding to improved physiological phenotypes across two independent external datasets, demonstrating its generalizability. We then leveraged PAOPAC to generate a comprehensive disease-aging landscape and to investigate the process of chronological and biological aging. Our analyses revealed that the majority of diseases are associated with an accelerated aging phenotype.

systems biology↗

bioRxiv2026

All-in-one, Cas13d-based cell-specific gene knockdown system for zebrafish

Cell type-specific genetic manipulation is essential for dissecting neural circuits and glial biology in vivo; however, methods for achieving and validating cell-specific gene knockdown in zebrafish remain limited. Here, we introduce a CRISPR-Cas13d-based mRNA knockdown platform for zebrafish that leverages RNA targeting, rather than DNA editing, to enable efficient and flexible perturbation of gene function in defined cell types of interest. We engineered an all-in-one vector system for cell type-specific RfxCas13d expression and ubiquitous expression of crispr RNAs (crRNAs) and validated its utility across the major classes of CNS glia: astrocytes, oligodendrocytes, and microglia. Cas13d-mediated knockdown consistently achieved target mRNA depletion and produced heritable reproducible morphological and functional phenotypes, demonstrating the robustness of the approach. This approach complements permanent genome-editing methods such as CRISPR-Cas9 by providing a reliable, efficient, and scalable strategy for RNA-level perturbation. Our Cas13d toolkit expands the repertoire of zebrafish genetic technologies, offering a powerful resource for in vivo cell type-specific studies to uncover cell-autonomous mechanisms and possibility for cell type-specific genetic screens in development and disease.

neuroscience↗

bioRxiv2026

Dysregulated tissue-resident lymphocytes drive age-associated emphysema by impairing alveolar regeneration

Aging is often associated with progressive tissue degeneration and chronic inflammation, yet the role of immune cells in mediating structural and functional decline in organs remains poorly defined. Here, we investigated immune-tissue interactions in the aged lung and identified emphysematous remodeling characterized by alveolar loss. Notably, aged lungs exhibited a marked expansion of tissue-resident lymphocytes (TRLs) with senescent features, accompanied by a significant reduction in alveolar stem/progenitor cell (AT2) abundance. In vivo adoptive T cell transfer and 3D immune-stem cell organoid assays revealed that these expanded TRLs suppressed AT2 growth via secretion of oncostatin M and interferon gamma. In vivo blockade of IL-7 receptor (IL-7R) reduced TRL accumulation in the lungs and ameliorated age-related emphysematous changes, including restoration of alveolar density. Our findings identify TRLs as key drivers of alveolar degeneration in aging and propose IL-7R inhibition as a therapeutic strategy to mitigate pulmonary decline. TeaserBlocking IL-7R clears harmful lymphocytes and helps rebuild the damaged air sacs of the aging lung.

cell biology↗

bioRxiv2026

CellChem: Cellular transcriptional responses reshape molecular representation space for efficient and multi-scale drug discovery

Despite decades of progress in computational drug discovery, deep learning-based molecular representation models remain largely structure-centric, assuming that chemical similarity approximates functional similarity. However, drug effects in cells are shaped not only by chemical similarity but also by molecular interactions in the cellular context. To capture this complexity, we introduce CellChem, a cellular-chemical representation-learning framework that learns cell-guided molecular representations of small-molecule actions within cells. By incorporating large-scale cellular transcriptional profiles during pretraining, CellChem reshapes molecular representation space from structure-centric to a balanced integration of structure and function. The learned CellChem molecular representations exhibit biologically meaningful geometric organization, such that distances between molecules encode not only structural similarity but also similarity in the cellular responses they elicit, independent of downstream tasks. Using downstream derivative models such as cell-guided compound-protein interaction prediction and drug-induced transcriptional response profile generation, CellChem supports highly efficient, multi-scale drug discovery, achieving significantly better performance than traditional models.

molecular biology↗