Biology subjects

zhang, s.

Publications and source records attributed to zhang, s..

15 recordsLinked to original sources

bioRxiv2026

Migrasomes May Originate from Cilia and Represent an Alternative Ciliary Form

Abstract Background Migrasomes, the newly discovered vesicular organelles, have long been speculated to originate from membrane dynamics during cell migration. Here we present integrated imaging and multi-omics evidence supporting a ciliary origin of migrasomes. The shared vesicular trafficking pathways between ciliogenesis and migrasome biogenesis (trans-Golgi/endosomal systems) prompted us to investigate their spatiotemporal coupling mechanism in disease models. Methods Using high-resolution SEM and fluorescence confocal microscopy (FCM), we visualized cilium-like structures with enlarged tips adjacent to migrasome-like vesicles across multiple cell types. TMT-based quantitative proteomics was used to identify proteins shared among migrasomes from different cell types and examine their associations with ciliary proteins. DMBA-induced breast cancer and femoral fracture models were used to investigate the cilia-migrasome association during tumor metastasis and fracture healing. Results Bioinformatics analysis showed a highly positive incidence correlation between cilia and migrasome genes in 31 types of 7858 human normal tissues and organs and various cell types. In 32 cancer tissues, cilia and migrasome genes were still strongly but abnormally correlated. Signaling enrichment by ARL13B and TSPAN4 showed that cilia-migrasome association is related to epithelial-mesenchymal transition (EMT). By analyzing the ARL13B and PIGK expression, we found the abnormal cilia-migrasome association in metastatic breast cancer and positive cilia-migrasome association in fracture healing bone. Conclusion Our findings support a ciliary origin of migrasomes, pointing to a previously unrecognized pathway of organelle biogenesis. Notably, the ciliary-migrasomal association correlates with two distinct pathological/physiological contexts: (1) aberrant epithelial-mesenchymal transition (EMT) in metastatic breast cancer, and (2) accelerated regeneration during fracture healing and tissue repair. This dual correlation not only establishes a mechanistic link between ciliary function and migrasome biogenesis but also highlights their divergent roles in disease progression versus tissue homeostasis. Investigating the spatiotemporal dynamics of this association may provide novel therapeutic targets for metastatic malignancies and regenerative medicine.

cell biology↗

bioRxiv2026

Kinematically-Dominated Regime Shapes Cell Traction Force Dynamics under Osmotic Shock

Adherent cells must continuously adapt to rapid environmental fluctuations to preserve mechanical integrity. However, conventional theoretical frameworks predominantly rely on quasi-static assumptions, thereby limiting their ability to capture the transient dynamics of cellular responses to high-rate perturbations, such as acute osmotic shocks. To address this limitation, we developed a biophysical model incorporating a Hill-type law that couples active cytoskeletal mechanics to cell-edge velocity, explaining the counterintuitive experimental observation that traction forces transiently decrease during rapid hypotonic swelling despite an increase in cell size. We further construct a kinematic-geometric phase diagram that connects classical quasi-static theories with our dynamic model. Moreover, we show that traction-force dynamics are sensitive to the loading rate and amplitude of osmotic shock. In addition, stiffness-associated simulations and supporting perturbation evidence suggest a cellular stiffness-associated recovery trend in which stiffer cells exhibit faster volume recovery driven by stronger recoil of hydrostatic pressure.

biophysics↗

bioRxiv2026

NUMonomer enables accurate and scalable nucleic acid structure prediction from primary sequence alone

Accurate and efficient prediction of three-dimensional nucleic acid structures can accelerate functional characterization and enable downstream applications. Recent deep-learning methods have substantially improved nucleic acid structure prediction by incorporating auxiliary inputs such as multiple sequence alignments, secondary-structure annotations, and representations from pretrained language models. However, prediction accuracy remains limited, and generating these auxiliary inputs can be computationally expensive. Here we show that learning the hierarchical organization of experimentally determined structures across multiple scales, from recurring local conformations to global fold topologies, together with exploiting representations shared between RNA and single-stranded DNA, improves model generalization. Guided by these findings, we developed NUMonomer, an end-to-end deep-learning framework trained with input sequences spanning thousands of nucleotides on a joint RNA and single-stranded DNA dataset to predict nucleic acid structures directly from sequence. Despite requiring no auxiliary inputs, NUMonomer matches or outperforms leading prediction methods on benchmarks comprising CASP16 RNA targets and non-redundant sets of experimentally determined RNA and single-stranded DNA structures, with particularly pronounced improvements for longer RNAs. Its efficient and scalable architecture also reduces inference costs by approximately two orders of magnitude relative to the evaluated methods, enabling large-scale structure prediction. Together, these findings provide insight into generalization in biomolecular structure learning and establish NUMonomer as a practical framework for nucleic acid structure prediction.

molecular biology↗

bioRxiv2025

Analysis of Nutritional Components in Brown Sugar and Its Effects on Anti-fatigue, Alleviation of Qi Deficiency and Blood Stasis, and Antioxidant Effects

Brown sugar (BS), Red granulated sugar (RGS) and White sugar(WS) are three key sweeteners and food additives in China. Notably, BS possesses significant nutritional value and has been widely used in Chinese medicine for its supportive therapeutic properties.This study evaluated the anti-fatigue, the amelioration of qi deficiency and blood stasis, as well as the antioxidant activities of these sugars using a zebrafish model. BS and RGS demonstrated significantly greater efficacy than WS in terms of both anti-fatigue effects and amelioration of qi Deficiency and blood stasis in the zebrafish model. Antioxidant assays revealed that BS, RGS, and WS (500 g/mL) significantly enhanced superoxide dismutase (SOD) activity in zebrafish embryos compared with the control group. Furthermore, the mineral composition of sugar were determined. Nine mineral elements--potassium (K), calcium (Ca), sodium (Na), magnesium (Mg), iron (Fe), zinc (Zn), copper (Cu), phosphorus (P), and selenium (Se)--were analyzed. BS exhibited markedly higher concentrations of P, Ca, Fe, and Mg than RGS and WS, while selenium (detected at 50 mg/kg) was exclusively detected in BS. Intriguingly, RGS contained four-fold higher potassium levels than BS. Total polyphenol content, quantified by Folin-Ciocalteu assay, was 4.35 g/kg in BS and 1.53 g/kg in RGS, with no detectable polyphenols in WS.

biochemistry↗

bioRxiv2025

A new nitrogen biochemical process: Oxidization of ammonium into nitrogen gas using nitrate

The nitrifying bacteria oxidize ammonium into nitrite or nitrate under aerobic conditions, while the anaerobic ammonia-oxidizing bacteria utilize nitrite to oxidize ammonium into nitrogen gas under anaerobic conditions. There is no biochemical process that can directly oxidize ammonium into nitrogen gas using nitrate under anaerobic conditions. In this study, mature anaerobic ammonium oxidation (anammox) granular sludge was inoculated in an anaerobic nitrogen-removal system, while nitrate and ammonium were used as the influent substrates. The experiment was conducted for 537 days at a temperature of 30-32{degrees}C and a pH of 8.0-9.0. The transformation from nitrite-anammox to nitrate-anammox was stably achieved on the 350th day. The experimental results showed that, nitrate directly oxidized ammonium into nitrogen gas under anaerobic conditions. The produced gas consisted of 96.3% nitrogen and 3.7% carbon dioxide. The removal ratio of ammonium to nitrate was approximately 1.67:1, and the total inorganic nitrogen removal rate reached up to 290.20 mg/(L{middle dot}d). Unlike the nitrite-anammox reaction, this reaction was accompanied by an acid production process, which caused a decrease in pH. When the substrate was changed to nitrite and ammonium, the total nitrogen removal rate of only 1.1-1.3 mg/(L{middle dot}d) was achieved. This new biochemical reaction of nitrogen was defined as nitrate-anammox. The study reveals a new pathway of nitrogen transformation, providing novel insights into the global nitrogen cycle.

microbiology↗

bioRxiv2024

ORMDL3 restrains type-I interferon signaling and anti-tumor immunity by promoting RIG-I degradation

Mounting evidence has demonstrated the genetic association of ORMDL3 (ORMDL Sphingolipid Biosynthesis Regulator 3) gene polymorphisms with bronchial asthma and a diverse set of inflammatory disorders. However, its role in type I interferon (IFN) signaling remains poorly defined. Herein, we report that ORMDL3 is a negative modulator of the type I IFN signaling by interacting with MAVS (Mitochondrial Antiviral Signaling protein) and subsequently promoting the proteasome-mediated degradation of RIG-I (Retinoic Acid-Inducible Gene I). Immunoprecipitation coupled with mass spectrometry (IP-MS) assays uncovered that ORMDL3 binds to USP10 (Ubiquitin-Specific Protease 10), which forms a complex with and stabilizes RIG-I through decreasing its K48-linked ubiquitination. ORMDL3 thus disrupts the interaction between USP10 and RIG-I, thereby promoting RIG-I degradation. Additionally, subcutaneous syngeneic tumor models in C57BL/6 mice revealed that inhibition of ORMDL3 enhances anti-tumor efficacy by augmenting the proportion of cytotoxic CD8 positive T cells and IFN production in the tumor microenvironment (TME). Collectively, our findings reveal the pivotal roles of ORMDL3 in maintaining antiviral innate immune responses and anti-tumor immunity.

molecular biology↗

bioRxiv2024

Human DDIT4L intron retention contributes to cognitive impairment and amyloid plaque formation.

Cognitive impairment and amyloid plaques are the most important clinical and neuropathological feature for dementia, especially in Alzheimers disease (AD). However, the etiology of dementia is complicated. The present study reveals that an aberrant splicing of DDIT4L, the isoform DDIT4L intron retention (DIR), occurs in AD patients. Homozygous DIR-knock-in (KI) mice showed DIR expression in hippocampal neurons, marked cognitive impairment, augmented A{beta} deposition and enhanced Tau phosphorylation. The DIR colocalized with thioflavin S-positive plaques and gelsolin in AD patients. The DIR induced A{beta} deposition and cognitive impairment by interacting with gelsolin. Moreover, DIR interacted with GluA1, the subunit of the AMPA receptor, contributing to synaptic deficiency and cognitive impairment. Furthermore, an anti-DIR monoclonal antibody (mAb) alleviated cognitive impairment and reduced A{beta} deposition and Tau phosphorylation. Thus, DIR contributes to cognitive impairment and amyloid plaques, and could be a potential therapeutic target for dementia.

neuroscience↗

bioRxiv2023

Liver regeneration by a population of midzone-located mesenchymal-hepatocyte hybrid cells

Regenerative medicine relies on deep understanding of the mechanisms of organ repair and regeneration. The liver, an organ with critical metabolic functions carried out by hepatocytes located in zones 1-3 of liver lobules, has the capacity to fully regenerate itself, which is mainly attributable to midzone hepatocytes. Yet, how differentiated midzone hepatocytes execute transzone regeneration and quickly recover most of the liver mass remains a myth. Here, we uncover a mesenchymal-hepatocyte population (13.7% of total hepatocytes) that are derived from Twist2-lineage EpCAM+ progenitors, midzone-located, highly polyploidy, and equipped with great mitogenic and migratory capabilities to the detriment of metabolism. They regenerate about 50% of new hepatocytes and repopulate zones 1 and 3 in liver regeneration. Mechanistically, expansion of these cells is negatively controlled by Notch1 signaling. This study has thus uncovered a hepatocyte subpopulation with great proliferation potential and important mechanisms of liver regeneration.

cell biology↗

bioRxiv2022

GraphPred: An approach to predict multiple DNA motifs from ATAC-seq data using graph neural network and coexisting probability

Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) utilizes hyperactive Tn5 transposase to cut open chromatin and reveal chromatin accessibility at a genome-wide level. ATAC-seq can reveal more kinds of transcription factor binding regions than Chromatin immunoprecipitation sequencing (ChIP-seq) and DNase I hypersensitive sites sequencing (DNase-seq). Transcription factor binding sites (TFBSs) prediction is a crucial step to reveal the functions of TFs from the high throughput sequencing data. TFBSs of the same TF tend to be conserved in the sequence level, which is named motif. Several deep learning models based on the convolutional neural networks are used to find motifs from ATAC-seq data. However, these methods didnt take into account that multiple TFs bind to a given sequence and the probability that a fragment of a given sequence is a TFBS. To find binding sites of multiple TFs, we developed a novel GNN model named GraphPred for TFBSs prediction and finding multiple motifs using the coexisting probability of k-mers. In the light of the experiment results, GraphPred can find more and higher quality motifs from 88 ATAC-seq datasets than comparison tools. Meanwhile, GraphPred achieved an area of eight metrics radar (AEMR) score of 2.31.

bioinformatics↗

bioRxiv2022

CIS calibrates GM-CSF signalling strength to regulate macrophage polarization via a STAT5-IRF8 axis

The cytokine granulocyte-macrophage-colony stimulating factor (GM-CSF) possesses the ability to differentiate macrophages (MOs) with opposing functions, namely proinflammatory M1-like and immunosuppressive M2-like. Despite the importance and opposing functional outcomes of these processes, the intrinsic mechanism that regulates the functional polarization of MOs under GM-CSF signaling remains elusive. Here we show that GM-CSF induced MOs polarisation resulted in the expression of the Cytokine-inducible SH2-containing protein (CIS), and that CIS deficiency diverted differentiation of monocytes into immunosuppressive M2-like MOs expression. CIS deficiency resulted in the hyperactivation of the JAK-STAT5 signaling pathway, consequently promoting the downregulation of the transcription factor Interferon Regulatory Factor 8 (IRF8). Loss and gain of function approaches highlighted IRF8 as a critical instructor of the M1-like polarisation program. In vivo, CIS deficiency led to skewing to M2-like macrophages, which induced strong Th2 immune responses characterised by the development of severe experimental asthma. Collectively, we reveal a CIS-censored mechanism interpreting the opposing actions of GM-CSF in MO differentiation and uncovering its role in controlling allergic inflammation.

immunology↗

bioRxiv2021

Anatomical and Functional Gradients Shape Dynamic Functional Connectivity in the Human Brain

Large-scale biophysical circuit models can provide mechanistic insights into the fundamental micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity. By allowing local synaptic properties to vary across brain regions, recent large-scale circuit models have demonstrated better fit to empirical observations, such as inter-regional synchrony averaged over several minutes, i.e. static functional connectivity (FC). However, most previous models do not capture how inter-regional synchrony patterns vary over timescales of seconds, i.e., time-varying FC dynamics. Here we developed a spatially-heterogeneous large-scale dynamical circuit model that allowed for variation in local circuit properties across the human cortex. We showed that parameterizing local circuit properties with both anatomical and functional gradients was necessary for generating realistic static and dynamical properties of resting-state fMRI activity. Furthermore, empirical and simulated FC dynamics demonstrated remarkably similar sharp transitions in FC patterns, suggesting the existence of multiple attractors. We found that time-varying regional fMRI amplitude tracked multi-stability in FC dynamics. Causal manipulation of the large-scale circuit model suggested that sensory-motor regions were a driver of FC dynamics. Finally, the spatial distribution of sensory-motor drivers matched the principal gradient of gene expression that encompassed certain interneuron classes, suggesting that heterogeneity in excitation-inhibition balance might shape multi-stability in FC dynamics.

neuroscience↗

bioRxiv2020

JavaDL: a Java-based Deep Learning Tool to Predict Drug Responses

MotivationAccurate prediction of drug response in each patient is the holy grail in personalized medicine. Recently, deep learning techniques have been witnessed with revival in a variety of areas such as image processing and genomic data analysis, and they will be useful for the coming age of big data analysis in pharmaceutical research and chemogenomic applications. This provides us an impetus to develop a novel deep learning platform to accurately and reliably predict the response of cancer to different drug treatments. ResultsIn this study, we describe a Java-based implementation of deep neural network (DNN) method, termed JavaDL, to predict cancer responses to drugs solely based on their chemical features. To this end, we devised a novel cost function by adding a regularization term which suppresses overfitting. We also adopted an "early stopping" strategy to further reduce overfit and improve the accuracy and robustness of our models. Currently the software has been integrated with a genetic algorithm-based variable selection approach and implemented as part of our JavaDL package. To evaluate our program, we compared it with several machine learning programs including SVM and kNN. We observed that JavaDL either significantly outperforms other methods in model building and prediction or obtains better results in handling big data analysis. Finally, JavaDL was employed to predict drug responses of several highly aggressive triple-negative breast cancer cell lines, and the results showed robust and accurate predictions with r2 as high as 0.80. AvailabilityThe program is freely available at https://imdlab.mdanderson.org/JavaDL/JavaDL.php.

bioinformatics↗

bioRxiv2020

Genetically Defined, Syngeneic Organoid Platform for Developing Combination Therapies for Ovarian Cancer

The paucity of genetically informed, immune-competent tumor models impedes evaluation of conventional, targeted, and immune therapies. By engineering mouse fallopian tube (FT) organoids using lentiviral gene transduction and/or CRISPR/Cas9 mutagenesis, we generated multiple high grade serous ovarian carcinoma (HGSOC) models exhibiting mutational combinations seen in patients. Detailed analysis of homologous recombination (HR)-proficient (Tp53-/-;Ccne1OE;Akt2OE; KrasOE), HR-deficient (Tp53-/-;Brca1-/-;MycOE) and unclassified (Tp53-/-;Pten-/-;Nf1-/-) organoids revealed differences in in vitro properties and tumorigenicity. Tumorigenic organoids had variable sensitivity to HGSOC chemotherapeutics and evoked distinct immune microenvironments. These findings enabled development of a chemotherapy/immunotherapy regimen that yielded durable, T-cell dependent responses in Tp53-/-;Ccne1OE;Akt2OE;Kras HGSOC; by contrast, Tp53-/-;Pten-/-;Nf1-/- tumors failed to respond. Genotype-informed, syngeneic organoid models could provide an improved platform for rapid evaluation of tumor biology and therapeutics. HIGHLIGHTSO_LIOrthotopic injection of genetically defined fallopian tube organoids yield HGSOC. C_LIO_LIOvarian tumors with different genotypes evoke distinct immune microenvironments C_LIO_LICombining Gemcitabine, anti-PD-L1, and anti-CTLA-4 result in complete responses in Tp53-/-;Ccne1OE;Akt2OE;KrasOE organoid-derived HGSOC C_LIO_LITherapeutic response is tumor genotype-specific C_LI

cancer biology↗

bioRxiv2019

Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies

The risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Data on multiple traits is often collected for many complex diseases in order to obtain a better understanding of the diseases. Examination of gene-environment interactions (GxEs) for multiple traits can yield valuable insights about the etiology of the disease and increase power in detecting disease associated genes. Most existing methods focus on testing gene-environment interaction (GxE) for a single trait. In this study, we develop novel approaches to test GxEs for multiple traits in sequencing association studies. We first perform transformation of multiple traits by using either principle component analysis or standardization analysis. Then, we detect the effect of GxE for each transferred phenotypic trait using novel proposed tests: testing the effect of an optimallyweighted combination of GxE (TOW-GE) and/or variable weight TOW-GE (VW-TOW-GE). Finally, we employ the Fishers combination test to combine the p-values of TOW-GE and/or VW-TOW-GE. Extensive simulation studies based on the Genetic Analysis Workshop 17 data show that the type I error rates of the proposed methods are well controlled. Compared to the existing interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are only rare risk and protective variants; VW-TOW-GE is more powerful when there are both rare and common risk and protective variants. Both TOW-GE and VW-TOW-GE are robust to directions of effects of causal GxEs. Application to the COPDGene Study demonstrates that our proposed methods are very powerful.

genetics↗

bioRxiv2019

A General Statistic to Test an Optimally WeightedCombination of Common and/or Rare Variants

Both genome-wide association study and next generation sequencing data analyses are widely employed in order to identify disease susceptible common and/or rare genetic variants in many large scale genetic studies. Rare variants generally have large effects though they are hard to detect due to their low frequency. Currently, many existing statistical methods for rare variants association studies employ a weighted combination scheme, which usually puts subjective weights or suboptimal weights based on some ad hoc assumptions (e.g. ignoring dependence between rare variants). In this study, we analytically derive optimal weights for both common and rare variants and propose a General and novel approach to Test association between an Optimally Weighted combination of variants (G-TOW) in a gene or pathway for a continuous or dichotomous trait while easily adjusting for covariates. We conduct extensive simulation studies to evaluate the performance of G-TOW. Results of the simulation studies show that G-TOW has properly controlled type I error rates and it is the most powerful test among the methods we compared, when testing effects of either both rare and common variants or rare variants only. We also illustrate the effectiveness of G-TOW using the Genetic Analysis Workshop 17 (GAW17) data. In addition, we applied G-TOW and other competitive methods to test association for schizophrenia. The G-TOW have successfully verified genes FYN and VPS39 which are associated with schizophrenia reported in existing publications. Both of these genes are missed by the weighted sum statistic (WSS) and the sequence kernel association test (SKAT). G-TOW also showed much stronger significance (p-value=0.0037) than our previously developed method named Testing the effect of an Optimally Weighted combination of variants (TOW) (p-value=0.0143) on gene FYN. FYN is a member of the protein-tyrosine kinase oncogene family that phosphorylates glutamate metabotropic receptors and ionotropic N-methyl-d-aspartate (NMDA) receptors. NMDA modulates trafficking, subcellular distribution and function. It is involved in neuronal apoptosis, brain development and synaptic transmission and lower expression, which has been observed in the platelets of schizophrenic patients compared with controls. The application for schizophrenia indicates that G-TOW is a powerful tool in genome-wide association studies.

genetics↗