Biology subjects

Shen, T.

Publications and source records attributed to Shen, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Cooperative IDR-folded domain interplay governs HP1α phase separation and underlies biphasic regulation by pathogenic poly-PR

HP1 undergoes liquid-liquid phase separation (LLPS), yet the prevailing model, which attributes condensation almost entirely to intrinsically disordered regions (IDRs), leaves the role of its folded domains unresolved. Using solution and solid-state NMR, we show that HP1 LLPS is instead governed by reciprocal interplay between IDRs and folded domains. Within the condensed phase, solid-state NMR reveals a dynamic hierarchy: the chromoshadow domain (CSD) assembles into polymorphic oligomers that form rigid cores, while the phosphorylation sites on the N-terminal extension (NTE) remain highly mobile, challenging their proposed role as stable multivalent hubs. Phosphorylation of the NTE instead promotes LLPS primarily by increasing local rigidity and releasing the NTE from the chromodomain (CD) to relieve autoinhibition, with electrostatic charge contributing secondarily. The C-terminal extension (CTE) suppresses phase separation through two routes: weakening the association between the hinge region (HR) and the CSD, and inhibiting interactions between CSD dimers. ALS-associated poly-PR targets this network at the NTE and exerts concentration-dependent biphasic regulation, promoting LLPS at low concentrations via multivalent bridging and dissolving condensates at high concentrations via saturation, a switch recapitulated in cells and in mouse brain. These findings reframe HP1 condensation from an IDR-driven process to one cooperatively governed by IDR-folded domain interplay.

biochemistry↗

bioRxiv2026

Metabolomics highlights cardiovascular risk factor links to sphingolipid and one-carbon metabolism in asymptomatic adults

Background: Metabolic perturbations associated with cardiovascular disease (CVD) risk factors prior to clinical disease manifestation may inform novel preventative measures. Here we characterize metabolome-wide associations with various cardiometabolic risk factors in an asymptomatic population to develop a mechanistic metabolic framework of preemergent metabolic shifts. Methods: Plasma was collected from a clinically healthy cross-sectional cohort (n = 361; Clinical Trials.gov ID: NCT02367287). Cohort mass spectrometry-based metabolomics (biocrates MxP(R) Quant 500 kits) were complemented with expanded lipidomic coverage for a sex/age-matched subset with LDLc difference > 60 mg/dL (n = 38). LDLc-focused partial least square regressions and discriminate analyses were performed, after false discovery rate corrections. Results: As LDLc levels increased, plasma sphingolipids and cholesteryl esters, and transulfuration products were higher, while ether-linked phosphatidylcholines serine, glycine, alanine, methionine were lower. The subset analysis further highlighted elevations in triglycerides and deoxyceramides with elevated LDLc. When adjusted for alanine concentrations, deoxyceramide concentrations increased as serine declined (p = 0.0012), and the deoxyceramide:ceramide ratios were higher in the high LDLc group (p = 0.039). Lower serine levels were also accompanied by parallel changes in glycine (p= 0.0004), and anti-parallel changes in transsulfuration pathway metabolites particularly when adjusted for ceramide levels. Notably, the alanine:serine ratio was more strongly associated with fasting triglycerides (r = 0.38; p <0.0001) than LDLc. Conclusions: Serine depletion by increased demands of sphingolipid, and possibly transsulfuration pathway, metabolism is associated with elevations in cytotoxic deoxyceramides and reductions in glycine and methionine availability for one-carbon metabolism. These changes were strongly associated with fasting triglycerides and insulin resistance, a key marker of residual CVD risk.

biochemistry↗

bioRxiv2026

Dopamine dips during unrewarded actions promote punishment-resistant reward seeking

Punishment-resistant reward-seeking, a hallmark of addiction, is less prominent in females than males. We found that chronic estradiol manipulations increased punishment resistance in female mice without increasing dopamine peaks on rewarded actions as expected, instead exaggerating dopamine dips on unrewarded actions; optogenetically mimicking these dips accelerated punishment resistance in females and males. These results suggest dopamine dips suppress learning from unrewarded actions, consistent with policy-based accounts of dopamine function.

neuroscience↗

bioRxiv2026

Aberrant cortical subcortical somatosensory network organization is associated with impaired tactile-motion perception in akinetic-rigid Parkinson's disease

BackgroundParkinsons disease (PD) is associated with somatosensory dysfunction, yet higher-order tactile-motion processing and its neural basis remain poorly understood. Here, we combined psychophysical assessment of tactile-motion perception with resting-state functional MRI (rs-fMRI) to investigate how altered sensorimotor network organization relates to tactile perceptual deficits in akinetic-rigid dominant PD (PDAR). MethodsForty-six participants (25 PDAR and 21 healthy controls, HCs) underwent clinical evaluation and resting-state functional MRI scanning. A subset of participants (15 PDAR and 15 HCs) additionally completed a tactile-motion perception task assessing directional bias and speed sensitivity. ROI-to-ROI functional connectivity analyses were performed within a customized 26-node sensorimotor and cortico-basal ganglia-thalamo-cortical (CBGTC) network. ResultsCompared with HCs, PDAR patients exhibited significantly altered tactile-motion perception, including greater directional bias (p = 0.047) and reduced sensitivity to dynamic tactile speed (p < 0.001), reflecting diminished sensitivity to mid-to-high speeds. Resting-state functional connectivity analyses revealed widespread reductions in connectivity within cortical somatosensory regions (Areas 1, 2, 3a, 3b, 5), particularly between primary somatosensory and motor cortices. In contrast, increased connectivity was observed between basal ganglia-thalamic structures and cortical sensorimotor regions, including thalamus-M1 and GPi-sensorimotor connections (all p-FDR < 0.05). ConclusionsThese findings demonstrate a dual pattern of sensorimotor network reorganization in PDAR, characterized by disrupted cortical somatosensory integration alongside enhanced subcortical-cortical coupling. The present study provides evidence that higher-order tactile-motion dysfunction in PD is associated with large-scale abnormalities in distributed sensorimotor networks and highlights the somatosensory system as a potential target for future therapeutic intervention.

neuroscience↗

bioRxiv2026

Visualizing Interchromosomal Interactions at Sub-Megabase Resolution Using Network Clustering Coefficients

Specific interchromosomal interactions involve communication between non-homologous chromosomes, enabling coordinated genomic activities such as gene regulation. However, because these communications are often embedded within a nonspecific and noisy background of contact interactions, it is essential to annotate these interaction patterns at the resolution of genomic positions. Such annotation facilitates clean visualization and comparison with linear genomic features to reveal underlying biological functions. We developed and validated a set of network-based metrics as cross-chromosomal interaction descriptors that bridge complex 3D genome structures and 1D functional genomics. By utilizing graph-theoretic representations, these network-based features succinctly summarize complex inter-chromosomal relationships. We constructed a graph representation of contact interactions derived from Hi-C data and implemented three annotations that capture the distinct "many-body" nature of the interactions. Among these, we demonstrate that {Delta}C4 (a cis-contact-mediated 4-cycle interaction metric) is superior to both {Delta}C3 (a cis-contact-mediated 3-cycle metric) and C4E (a direct 4-cycle metric of trans contacts) at filtering noise and providing the most straightforward interpretation. Applying these metrics to chromosomes 17, 19, and 22 of the GM12878 cell line, we found clear evidence that different chromosomes rely on a shared set of interaction hot spots to communicate. Overall, this network-based framework reveals distinct chromosomal regulation patches and provides insight into how chromosomes associate with each other and organize relative to the nuclear envelope.

genomics↗

bioRxiv2026

Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks

Protein-protein interactions (PPIs) are governed by two fundamental interfacial mechanisms: similarity-driven, often involving symmetric structural motifs, and complementarity-driven, arising from geometric and physicochemical matching between binding surfaces. Despite their biological significance, computational models have largely overlooked the coexistence and interplay of these twofold interaction modes. Here, we introduce DMG-PPI, a dual-channel graph neural network framework that jointly models similarity and complementarity in PPI networks, extending prior heterophilous GNN concepts to explicitly disentangle these dual interaction modes. The model consists of two parallel message-passing pathways: Alignment Message Passing (AMP), which aggregates signals from similar neighbors to capture symmetric interfaces, and Divergence Message Passing (DMP), which contrasts node features to extract complementary binding patterns. The signals captured by AMP and DMP are integrated via an adaptive fusion strategy within each block, and the outputs of blocks are aggregated using the MixHop framework to encode higher-order interaction patterns. DMG-PPI substantially outperforms state-of-the-art methods on classical benchmark datasets, achieving a 7.19% improvement in Micro-F1 over the second-best method. Additionally, the dual-channel framework provides interpretable insights into key binding residues by identifying interfacial mechanisms. Overall, DMG-PPI serves as a powerful tool that reveals the mechanisms behind accurate PPI predictions and facilitates downstream biological analysis. Author summaryProteins interact through multiple interfacial principles rather than a single uniform mechanism, reflecting diverse modes of molecular recognition between binding partners. Accurately modeling this heterogeneity remains a central challenge in protein-protein interaction (PPI) prediction. Existing computational approaches often overlook such complexity and treat all interactions as arising from a dominant pattern. To address this limitation, we present DMG-PPI, a dual-channel graph neural network framework designed to account for distinct interaction principles within a unified model. By modeling both similarity-based and complementarity-based interaction signals, DMG-PPI enables a more comprehensive representation of interaction patterns in PPI networks. Evaluations on widely used benchmark datasets demonstrate that our approach achieves robust and consistent improvements over existing methods. Beyond prediction accuracy, DMG-PPI provides biologically meaningful interpretability by highlighting key residues and revealing distinct interfacial interaction mechanisms, offering valuable insights for downstream structural and functional studies.

bioinformatics↗

bioRxiv2025

Mechanical Disturbance of Trichomes Enhances Cd2+ Uptake via Malate-Mediated Stomatal Opening in Arabidopsis

Phytoremediation of cadmium-contaminated soils relies on efficient metal uptake and translocation in plants. We previously found that gentle mechanical stimulation of non-glandular trichomes by brushing significantly enhances shoot Cd{superscript 2} accumulation in Arabidopsis thaliana, although the underlying physiological mechanism remained unclear. Here, we demonstrate that trichome brushing specifically induces stomatal opening, elevates transpiration, and promotes Cd{superscript 2} uptake, as evidenced by measurements of stomatal aperture, stomatal conductance, transpiration rate, and Cd{superscript 2} concentration in wild-type and mutant leaves. These responses are absent in the glabrous gl1-1 mutant and are not elicited by brushing epidermal pavement cells, indicating a trichome-specific mechanosensory pathway. Mechanistically, this pathway requires MPK3/MPK6 activity and employs malic acid as a key regulator of stomatal aperture, functioning independently of canonical ABA signaling. Our findings uncover a previously unrecognized trichome-to-stomatal signaling cascade that links trichome mechanostimulation to enhanced heavy metal accumulation.

plant biology↗

bioRxiv2025

miR-146a is a Pleiotropic Regulator of Motor Neuron Degeneration

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease affecting motor neurons. Here, we have profiled motor neuron microRNAs (miRNAs) during motor neuron degeneration in vivo to gain a better understanding of ALS pathophysiology. We demonstrate that one miRNA, miR-146a, is downregulated in diseased motor neurons despite upregulation in bulk tissue. Genetic deletion of miR-146a significantly extended survival in SOD1G93A mice with heterozygous animals demonstrating the largest benefit. A corresponding reduction in spinal cord gliosis but not motor neuron loss was observed. Finally, we observed that a proportion of miR-146a knockout animals develop spontaneous paralysis, motor neuron loss and chronic neuroinflammation with advanced age. Together these findings demonstrate that a single miRNA influences multiple aspects of motor neuron disease and highlights the complex role for neuroinflammation in ALS pathogenesis.

neuroscience↗

bioRxiv2025

in vivo Reprogramming of NG2 Glia Improves Bladder Function After Spinal Cord Injury

Neurogenic bladder is a debilitating consequence of spinal cord injury (SCI), with few treatment options that restore voluntary voiding. Here, we show that SOX2-mediated in vivo reprogramming of NG2 glia improves bladder function in a clinically relevant mouse model of contusive SCI. NG2 glia reprogramming induces adult neurogenesis, reduces glial scarring, and significantly improves urinary performance, as measured by voiding assays and conscious cystometry. Functional recovery correlates positively with neurogenesis and negatively with glial scarring. These findings demonstrate that SOX2-mediated glial reprogramming promotes autonomic repair and offers a regenerative strategy for neurogenic bladder after SCI.

neuroscience↗

bioRxiv2025

OmicsSankey: Crossing Reduction of Sankey Diagram on Omics Data

In bioinformatics, Sankey diagrams have been widely used to elucidate complex biological insights by visualizing gene expression patterns, microbial community dynamics, and cellular interactions. However, computational scalability remains a challenge for large-scale biological networks. In this work, we present OmicsSankey, a novel formulation of the layout optimization problem for Sankey Diagrams that employs eigen decomposition as a closed-form solution, addressing graph disconnection through a teleportation mechanism that enhances connectivity and stabilizes eigenvector solutions. Experimental results on synthetic datasets with varying layers and nodes validate the efficacy of OmicsSankey compared to state-of-the-art layout-optimizers. Improving the Sankey layouts for Cell Layers, BioSankey, and Sequence Flow further validates OmicsSankey in enhancing the interpretability of biological insights.

bioinformatics↗

bioRxiv2025

FoldBench: An All-atom Benchmark for Biomolecular Structure Prediction

Accurate prediction of biomolecular complex structures is fundamental for understanding biological processes and rational therapeutic design. Recent advances in deep learning methods, particularly all-atom structure prediction models, have significantly expanded their capabilities to include diverse biomolecular entities, such as proteins, nucleic acids, ligands, and ions. However, comprehensive benchmarks covering multiple interaction types and molecular diversity remain scarce, limiting fair and rigorous assessment of model performance and generalizability. To address this gap, we introduce FoldBench, an extensive benchmark dataset consisting of 1,522 biological assemblies categorized into nine distinct prediction tasks. Our evaluations reveal critical performance dependencies, showing that ligand docking accuracy notably diminishes as ligand similarity to the training set decreases, a pattern similarly observed in protein-protein interaction modeling. Furthermore, antibody-antigen predictions remain particularly challenging, with current methods exhibiting failure rates exceeding 50%. Among evaluated models, AlphaFold 3 consistently demonstrates superior accuracy across the majority of tasks. In summary, our results highlight significant advancements yet reveal persistent limitations within the field, providing crucial insights and benchmarks to inform future model development and refinement.

bioinformatics↗

bioRxiv2025

Prior epigenetic status predicts future susceptibility to seizures in mice

Wide variation of responses to identical stimuli presented to genetically inbred mice suggests the hypothesis that stochastic epigenetic variation during neurodevelopment can mediate such phenotypic differences. However , this hypothesis is largely untested since capturing pre-existing molecular states requires non-destructive, longitudinal recording. Therefore, we tested the potential of Calling Cards (CC) to record transient neuronal enhancer activity during postnatal development, and thereby associate epigenetic variation with a subsequent phenotypic presentation - degree of seizure response to the pro-convulsant pentylenetetrazol. We show that recorded differences in epigenetics at 243 loci predict a severe vs. mild response, and that these are enriched near genes associated with human epilepsy. We also validated pharmacologically a seizure -modifying role for two novel genes, Htr1f and Let7c. This proof-of-principle supports using CC broadly to discover predisposition loci for other neuropsychiatric traits and behaviors. Finally, as, human disease is also influenced by non-inherited factors, similar epigenetic predispositions are possible in humans.

genomics↗

bioRxiv2024

Oncogene-induced senescence and senescence-associated secretory phenotype (SASP) determine the efficacy of palbociclib in PIK3CA mutated colorectal cancer

Palbociclib is an excellent CDK4/6 inhibitor, but its clinical application is mainly limited to the treatment of advanced ER+/HR+ and HER2- breast cancer. Its efficacy in colorectal cancer (CRC) is evaluated in multiple trials and so far remains undetermined. We found that the PIK3CA-mutant CRC cells were insensitive to palbociclib treatment compared with the PIK3CA wild-type counterparts, and they were in an OIS (oncogene-induced senescence) state which was predisposed to a strong senescence-associated secretory phenotype (SASP) upon treatment. These senescent cells excessively secreted various SASP factors LCN2, and ultimately caused palbociclib-resistance via upregulation of EGFR in the non-senescent CRC cells. Importantly, a drug combination screen identified that erlotinib could synergize with palbociclib to overcome the SASP-induced resistance. Overall, we found that PIK3CA mutation-induced senescence compromised the efficacy of palbociclib treatment in CRC but co-targeting EGFR could minimize the OIS-associated side effects while preserving the beneficial effects.

cancer biology↗

bioRxiv2024

Selection efficacy differs between lifestages in thehaploid-diploid Marchantia polymorpha subsp. ruderalis

Ploidy has profound effects on evolution. Perhaps the most compelling effect is related to dominance: In cells with more than one genome copy, the effects of non-dominant deleterious variants are masked to some extent, leading to a reduction in the efficacy of selection. This, in turn, leads to increased levels of nucleotide diversity, and it may also affect nucleotide substitution rates. To test this predicted association between ploidy and the efficacy of selection, we studied genome scale per-gene patterns of genetic diversity and divergence in the genome of the haploid-diploid bryophyte Marchantia. We treated lifestage gene expression bias as a continuous covariate and accounted for genomic autocorrelation patterns using smoothing splines in a general additive regression model (GAM) framework. Consistent with a lower efficacy of purifying selection, we found increased levels of sequence diversity, Wattersons{theta} , and net divergence at non-degenerate sites in genes with diploid-biased gene expression. These genes also showed reduced levels of codon usage bias. In addition, we found chromosome 5 to be an outlier with overall decreased levels of diversity, the site-frequency spectrum skewed towards common alleles, and increased linkage disequilibrium. In this study, we show the utility of generalized additive models in population genomics, and we present evidence for a ploidy associated difference in the efficacy of selection. We discuss parallels to the evolution of (diploid) sex chromosomes and why the patterns observed are unlikely to be mediated by masking.

evolutionary biology↗

bioRxiv2024

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning

Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluated the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. Lung lobes from male and female BALB/c mice were cryosectioned and acquired by high resolution mass spectrometry imaging (MSI). Processed MSI peak annotations were validated by LC-MS/MS data from scraped tissue slides and microdissected lung tissue. Images were normalized and segmented into clusters. Interestingly, segmented clusters overlapped with stained serial tissue sections, enabling statistical analysis across biological replicates for morphologically relevant lung regions. Spatially distinct lipids had higher overall degree of unsaturated fatty acids in distal lung regions compared to proximal regions. Furthermore, the airway and alveolar epithelium exhibited significantly decreased sphingolipid and glycerophospholipid abundance in females, but not in males. We demonstrate the potential role of lipid saturation in healthy lung function and highlight sex differences in regional lung lipid distribution following ozone exposure. Our study provides a framework for future MSI experiments capable of relative quantification across biological replicates and expansion to multiple sample types, including human tissue.

pharmacology and toxicology↗

bioRxiv2024

RTM-align: an improved RNA alignment tool with enhanced short sequence performance via post-standardization and fragment alignment

Understanding the three-dimensional structure of RNA is crucial for studying various biological processes. Accurate alignment and comparison of RNA structures are essential for illustrating RNA functionality and evolution. The existing RNA alignment tools suffer from limitations such as size-dependency of scoring functions and inadequate handling of short RNA fragments, leading to the conflicting interpretation of structural and functional relationships among RNA molecules. Hence, we introduce RTM-align, a novel RNA structural alignment tool enhanced for short RNAs. RTM-align employs the RTM-score, which integrates post-standardization to ensure size-independence and utilizes a fragment alignment strategy that improves alignment accuracy by concentrating on structural motifs and local structural elements. Benchmarking results demonstrate that RTM-align outperforms existing tools in RNA structure comparison, offering a universal scoring scale regardless of RNA length. The improvement is particularly evident in evaluating predicted structures for CASP15 RNA targets, with significant enhancements observed for the short RNA target R1117. RTM-align is expected to significantly improve the accuracy and reliability of RNA structure alignment and comparison, thereby aiding in the deeper understanding and discovery of novel RNA functions and their interactions in biological systems. RTM-align is now available at https://github.com/BEAM-Labs/RTM-align.

bioinformatics↗

bioRxiv2024

The Class III peroxidase OsPrx20 is a key regulator of stress response and growth in rice

In plants, reactive oxygen species maintain strictly low intracellular levels that is prerequisite for their function as second messengers. However, abiotic and biotic stresses are also long-term processes, requiring rapid scavenging of excess intracellular ROS. Plant Class III peroxidases, as multifunctional enzymes, are core enzymes in regulating intracellular ROS homeostasis and are key to regulating the complicated signaling network. Here, we found a rice Class III peroxidase OsPrx20 maintains intracellular ROS homeostasis in different conditions. OsPrx20 is a target of Ca2+/calmodulin-dependent protein kinase (OsDMI3) and phosphorylates its Thr-244 site by OsDMI3. Overexpression of OsPrx20 enhances osmotic stress tolerance but reduces blast resistance, while lack of OsPrx20 is opposite, suggesting OsPrx20 positively regulates osmotic stress tolerance but negatively regulates blast resistance in rice. Meanwhile, overexpression of OsPrx20 enlarges spike size and grain fullness, whereas lack of OsPrx20 leads to dwarfism and smaller spike. In ABA signaling, OsPrx20 Thr-244 phosphorylation is specifically dependent on OsDMI3 to reduce the sensitivity of ABA to seed germination and root growth and to enhance osmotic stress tolerance without affecting spike and grain development. Our study reveals an essential regulatory mechanism that directly activates OsPrx20 in ABA signaling, highlighting the multi-functionality of OsPrx20 in different biological processes.

plant biology↗

bioRxiv2024

PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers

AO_SCPLOWBSTRACTC_SCPLOWPredicting protein-ligand binding affinity is crucial for drug discovery, as it enables efficient identification of drug candidates. We introduce PLAPT, a novel model utilizing transfer learning from pre-trained transformers like ProtBERT and ChemBERTa to predict binding affinities with high accuracy. Our method processes one-dimensional protein and ligand sequences, leveraging a branching neural network architecture for feature integration and affinity estimation. We demonstrate PLAPTs superior performance through validation on multiple datasets, achieving state-of-the-art results while requiring significantly less computational resources for training compared to existing models. Our findings indicate that PLAPT offers a highly effective and accessible approach for accelerating drug discovery efforts.

bioinformatics↗