Biology subjects

Zhang, L.

Publications and source records attributed to Zhang, L..

At least 577 records · Page 32Linked to original sources

bioRxiv2024

Spatiotemporal Single-Cell Roadmap of Human Skin Wound Healing

Wound healing is vital for human health, yet the details of cellular dynamics and coordination in human wound repair remain largely unexplored. To address this, we conducted single-cell and spatial transcriptomics analyses on human skin and acute wound tissues through inflammation, proliferation, and remodeling phases of wound repair from the same individuals, monitoring the cellular and molecular dynamics of human skin wound healing at an unprecedented spatiotemporal resolution. This singular roadmap reveals the cellular architecture of the wound margin and identifies FOSL1 as a critical driver of re-epithelialization. It shows that pro-inflammatory macrophages and fibroblasts sequentially support keratinocyte migration like a relay race across different healing stages. Comparison with single-cell data from venous and diabetic foot ulcers uncovers a link between failed keratinocyte migration and impaired inflammatory response in chronic wounds. Additionally, comparing human and mouse acute wound transcriptomes underscores the indispensable value of this roadmap in bridging basic research with clinical innovations. HighlightsO_LIA spatiotemporal atlas to explore human in vivo gene expression across wound healing C_LIO_LIWound margin architecture unveils a model for human re-epithelialization C_LIO_LIDistinct healing challenges in venous ulcers and diabetic foot ulcers C_LIO_LIUnique human healing traits emerge in cellular heterogeneity and gene expression C_LI

cell biology↗

bioRxiv2024

Computational Modeling shows Confirmation Bias during Formation and Revision of Self-Beliefs

Self-beliefs hinge on social feedback, but their formation and revision are not solely based on new information. Biases, such as confirming initial expectations, can lead to inaccurate self-beliefs. This study uses computational modeling to explore how initial expectations and confidence affect self-belief formation and revision in novel behavioral domains. In the first session, participants developed performance self-beliefs through trial-by-trial feedback. In the second session, feedback contingencies were reversed, requiring belief revision for accurate self-beliefs. Results showed a confirmation bias in belief updating, with initial expectations being linked to biased learning during both formation and revision. Higher confidence was associated with reduced belief revision and on average, self-beliefs persisted despite the conflicting evidence. This study extends the literature on confirmation bias to learning in uncharged, novel behavioral domains. Further, it demonstrates the importance of initial expectations and associated confidence for biased self-belief formation and subsequent learning.

neuroscience↗

bioRxiv2024

Dopamine and temporal discounting: revisiting pharmacology and individual differences

Disorders characterised by changes in dopamine (DA) neurotransmission are often linked to changes in the temporal discounting of future rewards. Likewise, pharmacological manipulations of DA neuro-transmission in healthy individuals modulate temporal discounting, but there is considerable variability in the directionality of reported pharmacological effects, as enhancements and reductions of DA signalling have been linked to both increases and reductions of temporal discounting. This may be due to meaningful individual differences in drug effects and/or false positive findings in small samples. To resolve these inconsistencies, we 1) revisited pharmacological effects of the DA precursor L-DOPA on temporal discounting in a large sample of N = 76 healthy participants (n = 44 male) and 2) examined several putative proxy measures for DA to revisit the role of individual differences in a randomised, double-blind placebo-controlled pre-registered study (https://osf.io/a4k9j/). Replicating previous findings, higher rewards were discounted less (magnitude effect). Computational modelling using hierarchical Bayesian parameter estimation confirmed that the data in both drug conditions were best accounted for by a non-linear temporal discounting drift diffusion model. In line with recent animal and human work, L-DOPA reliably reduced the discount rate with a small effect size, challenging earlier findings in substantially smaller samples. We found no credible evidence for linear or quadratic effects of putative DA proxy measures on model parameters, calling into question the role of these measures in accounting for individual differences in DA drug effects.

neuroscience↗

bioRxiv2024

Machine Learning Uncovers Vascular Endothelial Cell Identity Genes by Expression Regulation Features in Single Cells

Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and many diseases involving cell identity dysregulation. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In alignment with recent reports that cell identity genes are regulated with unique epigenetic signatures, we found cell identity genes exhibit distinctive genetic sequence signatures, e.g., unique enrichment patterns of cis-regulatory elements. Using these genetic sequence signatures, along with gene expression information from single-cell RNA-seq data, enables SCIG to uncover the identity genes of a cell without a need for comparison to other cells. Cell identity gene score defined by SCIG surpassed expression value in network analysis to uncover master transcription factors regulating cell identity. Applying SCIG to the human endothelial cell atlas revealed that the tissue microenvironment is a critical supplement to master transcription factors for cell identity refinement. SCIG is publicly available at https://github.com/kaifuchenlab/SCIG, offering a valuable tool for advancing cell differentiation, development, and regenerative medicine research.

bioinformatics↗

bioRxiv2024

Foundation model for efficient biological discovery in single-molecule data

Modern data-intensive techniques offer ever deeper insights into biology, but render the process of discovery increasingly complex. For example, exploiting the unique ability of single-molecule fluorescence microscopy (SMFM)1-5. to uncover rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches that are difficult to systematize. To facilitate systematic and efficient discovery from SMFM data, we introduce META-SiM, a transformer-based foundation model pre-trained on diverse SMFM analysis tasks. META-SiM achieves high performance--rivaling best-in-class algorithms--on a broad range of analysis tasks including trace selection, classification, segmentation, idealization, and stepwise photobleaching analysis. Additionally, the model produces high-dimensional embedding vectors that encapsulate detailed information about each trace, which the web-based META-SiM Projector (https://www.simol-projector.org) casts into lower-dimensional space for efficient whole-dataset visualization, labeling, comparison, and sharing. Combining this Projector with the objective metric of Local Shannon Entropy enables rapid identification of condition-specific behaviors, even if rare or subtle. As a result, by applying META-SiM to an existing single-molecule Forster resonance energy transfer (smFRET) dataset6, we discover a previously unobserved intermediate state in pre-mRNA splicing. META-SiM thus removes bottlenecks, improves objectivity, and both systematizes and accelerates biological discovery in complex single-molecule data.

biophysics↗

bioRxiv2024

Multimodal Protein-Ligand Contrastive Pretraining for Effective and Efficient Drug Discovery

Recent advancements in genomics and proteomics have identified numerous clinically significant protein targets, with notably 85% categorized as undruggable. These targets present widespread challenges due to their complex structures and dynamics, rendering conventional drug design strategies not always effective. In this study, we introduce Uni-Clip, a contrastive learning framework that incorporates multi-modal features of proteins (structure and residue) and ligands (conformation and graph). Optimized with a specifically designed CF-InfoNCE loss, Uni-Clip enhances the modeling of protein-ligand interactions for both undruggable and druggable proteins. Uni-Clip demonstrates superior performance in benchmark evaluations on widely acknowledged datasets, LIT-PCBA and DUD-E, achieving a 147% and 218% improvements in enrichment factors at 1% compared to baselines. Furthermore, Uni-Clip proves to be a practical tool for various drug discovery applications. In virtual screening for the challenging protein target GPX4 with flat surface, it identified non-covalent inhibitors with an IC50 of 4.17 M, in contrast to the predominantly covalent inhibitors currently known. Through target fishing for benzbromarone, Uni-Clip identified the intrinsically disordered protein c-Myc as a potential target, highlighting benzbromarones potential for repurposing in cancer therapy. Explainable analyses effectively identified binding sites consistent with molecular dynamics and experimental results, even for challenging undruggable targets.

bioinformatics↗

bioRxiv2024

Transient dynamics response analysis of the limb under impact loading

The limb vibrations are initiated at paw-strike in animals normal movement. The short ground contact duration suggests that there exists the transient dynamic response of the impact between the leg and ground, which is a high nonlinear problem and not well understood. The purpose of this study is to investigate the transient dynamic response of the lower limb of the felid during the moving. A kinematic model of the musculoskeletal system of a small felid is constructed from the anatomical measurement data. The elastic moduli are measured and calculated for different parts of the lower limb by a Nano-indentation technique. On the basis of the measured material parameters, the substructure technique is employed to numerically solve the contact-impact behavior of the lower limb. The high speed imaging system and a designed electronic detection system are employed in the experiment to testify the numerical results, which demonstrate that the initial impact has an important influence on the performance of the lower limb during the movement especially the high speed movement, and the paw-pad has good damping effects during the impact. The multiple impacts exist between limbs and the soil, which may provide the feedback energy for the felids high-speed running, though it may also increase the risk of the stress fracture of the limbs.

bioengineering↗

bioRxiv2024

A Self-Assembling Immune-Featured Osteosarcoma Patient/PDX Derived Organoid Model and Biobank for Personalized Immune Therapy

Osteosarcoma (OS) exhibit intra- and inter-heterogeneity, complicating the exploration of effective therapeutic strategies. Traditional in vitro and in vivo models are limited in inheriting biological and genomic heterogeneities of OS patients, even in inheriting the features on tumor microenvironment. The prolonged generation time of current models makes the drug development of OS slow and is not suitable to clinically rapid timing. Here, we introduce methods for generating and biobanking patient/PDX-derived osteosarcoma organoids (OS PD(X)Os) that recapitulate the histological, biological and genomic features of their paired OS patients. OS PD(X)Os can be generated quickly with high reliability in vitro or transplanted to immunodeficient mice. We further demonstrate an immune-featured OS PD(X)O (named iOS) model and its method for testing personalized chemotherapy response, personalized immune therapeutic strategy and target drug development, such as a novel PRMT5MTA inhibitor ARPN2169 on MTAP-deleted OS. Our studies show that iOS models maintain many typical features of OS and could be rapidly employed to investigate patient-specific therapeutic strategies. Additionally, our biobank establishes a rich resource for basic, translational and even clinical OS researches.

cancer biology↗

bioRxiv2024

Neural Mechanism Underlying Successful Classification of Amnestic Mild Cognitive Impairment Using Multi-Sensory-Evoked Potentials

Early detection of amnestic mild cognitive impairment (aMCI) is crucial for timely interventions. This study combines scalp recordings of lateralized auditory, visual, and somatosensory stimuli with a flexible and interpretable support vector machine learning pipeline to differentiate individuals diagnosed with aMCI from healthy controls. Event-related potentials (ERPs) and functional connectivity (FC) matrices from each modality successfully predicted aMCI. Reduced ERP amplitude in aMCI contributed to classification. The analysis of FC using phase-locking value revealed higher FC in aMCI than controls in frontal regions, which predicted worse cognitive performance, and lower FC in posterior regions from delta to alpha frequency. We observe optimal classification accuracy (96.1%), sensitivity (97.7%) and specificity (94.3%) when combining information from all sensory conditions than when using information from a single modality. The results highlight the clinical potential of sensory-evoked potentials in detecting aMCI, with optimal classification using both amplitude and oscillatory-based FC measures from multiple modalities.

neuroscience↗

bioRxiv2024

Mechanistic basis of lineage restriction

Lineage restriction is the biological phenomenon where cells lose developmental potency during differentiation for all except their adopted lineages. Lineage restriction is foundational to multicellularity as it secures the functional identities of the myriad cell types in the body. As yet, the mechanisms of lineage restriction remain enigmatic. By developing Potency-Seq to systematically map transcriptional potency of genes across the genome, we uncovered a link between the restriction of developmental potency of cells and the occlusion of transcriptional potency of their genomes. We further showed that, strikingly, genes can undergo irreversible occlusion of their transcriptional potency simply by chromatinization into the nucleosomal form with unmodified histones. These findings led to a comprehensive mechanistic account of lineage restriction as driven by gene occlusion. Specifically, naive pluripotent stem cells at the onset of development possess the unique capacity to erase occlusion globally to reset full transcriptional potency of the genome, which in turn establishes full developmental potency of the cells. Such deocclusion capacity is abolished when cells progress from naive to primed pluripotency. From this stage onward, lineage-inappropriate genes can undergo nucleosome-mediated occlusion in an irreversible manner when their cognate transcription factors (TFs) and/or placeholder factors (PFs) no longer exist in cells to protect them. Consequently, the transcriptionally potent portion of the genome shrinks progressively and irreversibly during differentiation, thus permanently restricting the developmental potency of differentiating cells. ONE-SENTENCE SUMMARYDevelopmental potency of cells is linked to transcriptional potency of their genomes

developmental biology↗

bioRxiv2024

Twisted Sister1: an agravitropic mutant of bread wheat (Triticum aestivum) with altered root and shoot architectures.

We identified a mutant of hexaploid wheat (Triticum aestivum) with impaired responses to gravity. The mutant named Twisted Sister1 (TS1) had agravitropic roots that were often twisted along with altered shoot phenotypes. Roots of TS1 were insensitive of externally applied auxin with the genetics and physiology suggestive of a mutated AUX/IAA transcription factor gene. Hexaploid wheat possesses over eighty AUX/IAA genes and sequence information did not identify an obvious candidate. Bulked segregant analysis of an F2 population mapped the mutation to chromosome 5A and subsequent mapping located the mutation to a 41 Mbp region. RNA-seq identified the TraesCS5A03G0149800 gene encoding a TaAUX/IAA protein to be mutated in the highly conserved domain II motif. We confirmed TraesCS5A03G0149800 as underlying the mutant phenotype by generating transgenic Arabidopsis thaliana. Analysis of RNA-seq data suggested broad similarities between Arabidopsis and wheat for the role of AUX/IAA genes in gravity responses. Here we show that the sequenced wheat genome along with previous knowledge largely from the model species Arabidopsis, gene mapping, RNA-seq and expression in Arabidopsis have enabled cloning of a key wheat gene defining plant architecture.

plant biology↗

bioRxiv2024

Discovery of ICOS-targeted small molecules using affinity selection mass spectrometry screening

Inducible T cell co-stimulator (ICOS) is a positive immune checkpoint receptor expressed on the surface of activated T cells, which could promote cell function after being stimulated with ICOS ligand (ICOS-L). Although clinical benefits have been reported in the ICOS modulation-based treatment for cancer and autoimmune disease, current modulators are restricted in biologics, whereas ICOS-targeted small molecules are lacking. To fill this gap, we performed an affinity selection mass spectrometry (ASMS) screening for ICOS binding using a library of 15,600 molecules. To the best of our knowledge, this is the first study that utilizes ASMS screening to discover small molecules targeting immune checkpoints. Compound 9 with a promising ICOS/ICOS-L inhibitory profile (IC50 = 29.38 {+/-} 3.41 {micro}M) was selected as the template for the modification. Following preliminary structure-activity relationship (SAR) study and molecular dynamic (MD) simulation revealed the critical role of the ortho-hydroxy group on compound 9 in the ICOS binding, as it could stabilize the interaction via the hydrogen bond formation with residuals on the glycan, and the depletion could lead to an activity lost. This work validates a promising inhibitor for the ICOS/ICOS-L interaction, and we anticipate future modifications could provide more potent modulators for this interaction.

pharmacology and toxicology↗

bioRxiv2024

Design and Biophysical Characterization of Second-Generation Cyclic Peptide LAG-3 Inhibitors for Cancer Immunotherapy

Lymphocyte activation gene 3 (LAG-3) is an inhibitory immune checkpoint crucial for suppressing the immune response against cancer. Blocking LAG-3 interactions enables T cells to recover their cytotoxic capabilities and diminishes the immunosuppressive effects of regulatory T cells. A cyclic peptide (Cys-Val-Pro-Met-Thr-Tyr-Arg-Ala-Cys, disulfide bridge: 1-9) was recently reported as a LAG-3 inhibitor. Based on this peptide, we designed 19 derivatives by substituting tyrosine residue to maximize LAG-3 inhibition. Screening via TR-FRET assay identified 8 outperforming derivatives, with cyclic peptides 12 [Tyr6(L-3-CN-Phe)], 13 [Tyr6(L-4-NH2-Phe)], and 17 [Tyr6(L-3,5-DiF-Phe)] as top candidates. Cyclic peptide 12 exhibited the highest inhibition (IC50 = 4.45 {+/-} 1.36 {micro}M). MST analysis showed cyclic peptides 12 and 13 bound LAG-3 with KD values of 2.66 {+/-} 2.06 {micro}M and 1.81 {+/-} 1.42 {micro}M, respectively, surpassing the original peptide (9.94 {+/-} 4.13 {micro}M). Docking simulations indicated enhanced binding for cyclic peptide 12, with a docking score of -7.236 kcal/mol compared to -5.236 kcal/mol for the original peptide.

pharmacology and toxicology↗

bioRxiv2024

A COVID-19 Rapid Antigen Test Employing Upconversion Nanoparticles

The COVID-19 pandemic has underscored the critical need for rapid and accurate diagnostic tools. Current methods, including PCR and rapid antigen tests (RAT), have limitations in speed, sensitivity, and the requirement for specialized equipment and trained personnel. Nanotechnology, particularly upconversion nanoparticles (UCNPs), offer a promising alternative due to their unique optical properties. UCNPs can convert low-energy near-infrared (NIR) light into higher-energy visible light, making them ideal for use as optical probes in single molecule detection and point of care applications. This study, initiated in early 2020, explores the opportunity of using highly doped UCNPs (40%Yb3+/4%Er3+) in lateral flow assay (LFA) for the early diagnosis of COVID-19. The UCNPs-based LFA testing demonstrated a minimum detection concentration of 100 pg/mL for SARS-CoV-2 antigen and 105 CCID50/mL for inactivated virus. Clinical trials, conducted in Malaysia and Western Australia independently, showed that the technique was at least 100 times more sensitive than commercial RAT kits, with a sensitivity of 100% and specificity of 91.34%. The development process involved multidisciplinary collaborations, resulting in the Virulizer device, an automated strip reader for point-of-care testing. This work sets a reference for future development of highly sensitive and quantitative rapid antigen tests, aiming for the Limits of Detection (LoD) in the range of sub-ng/mL.

bioengineering↗

bioRxiv2024

Metagenome-based diversity and functional analysis of culturable microbes in sugarcane

Sugarcane, widely acknowledged as the foremost crop for sugar and energy production on a worldwide scale, is confronted with many diseases that pose serious threats to its production. Biological control has become more popular as an approach for preventing and controlling diseases because of its environment-friendly characteristics. However, there is a lack of thorough investigation and use of microbial resources in sugarcane. This study conducted a thorough analysis of culturable microbes and their functional features in different tissues and rhizosphere soil of four diverse sugarcane species using metagenomics techniques. The results revealed significant microbial diversity in sugarcanes tissues and rhizosphere soil, including several important biomarker bacterial taxa identified, which are reported to engage in several processes that support plant growth. The LEfSe studies identified unique microbial communities in different parts of the same sugarcane species, particularly Burkholderia, which exhibited significant variations across the sugarcane species. Microbial analysis of carbohydrate-active enzymes (CAZymes) indicated that genes related to sucrose metabolism were mostly present in specific bacterial taxa, including Burkholderia, Pseudomonas, Paraburkholderia, and Chryseobacterium. This study improves understanding of the diversities and functions of endophytes and rhizosphere soil microbes in sugarcane. Moreover, the approaches and findings of this study provide valuable insights for microbiome research and the use of comparable technologies in other agricultural fields.

microbiology↗

bioRxiv2024

Loss function of tumor suppressor FRMD8 confers resistance to tamoxifen therapy via a dual mechanism

Approximately 40% ER-positive breast cancer patients suffer from therapeutic resistance to tamoxifen. Although reduced ER level is the major cause of tamoxifen resistance, the underlying mechanisms remain elusive. Here, we report that FRMD8 raises the level of ER at both transcriptional and post-translational layers. FRMD8 deficiency in MMTV-Cre+; Frmd8fl/fl; PyMT mice accelerates mammary tumor growth and loss of luminal phenotype, and confers tamoxifen resistance. Single-cell RNA profiling reveals that Frmd8 loss decreases the proportion of hormone-sensing differentiated epithelial cells and downregulates the levels of ER. Mechanically, on one hand, loss of FRMD8 inhibits ESR1 transcription via suppressing the expression of FOXO3A, a transcription factor of ESR1. On the other hand, FRMD8 interacts both with ER and UBE3A, and disrupts the interaction of UBE3A with ER, thereby blocking UBE3A-mediated ER degradation. In breast cancer patients, FRMD8 gene promoter is found hypermethylated and low level of FRMD8 predicts poor prognosis. Therefore, FRMD8 is an important regulator of ER and may control therapeutic sensitivity to tamoxifen in ER-positive breast cancer patients. Impact StatementTumor suppressive protein FRMD8 inhibits breast cancer progression by regulating the level of ER and has the possibility to be a potential target for overcoming tamoxifen treatment resistance.

cancer biology↗

bioRxiv2024

Uncovering Individualized Cerebellar Atrophy Pattern and Behavioral Links in Children with Brainstem Tumor

Background and PurposeBrainstem tumors are rare but cause enduring behavioral issues, challenging patients and surgeons. Research on cerebellar changes in these patients limited, despite symptoms similar to cerebellar injuries. This study aims to investigate cerebellar damage pattern resulting from brainstem tumors and its association with behavioral disorders. MethodsIn this study, a U-Net-based segmentation algorithm was used to divide the cerebellum into 26 lobules, which were then used to build a normative model for assessing individual structural deviations. Furthermore, a behavior prediction model was developed using the total outlier count (tOC) index and brain volume as predictive features. ResultsMost patients were found to have negative deviations in cerebellar regions, particularly in anterior lobules like Left V. Higher tOC was significantly associated with severe social problems (r = 0.31, p = 0.001) and withdrawal behavior (r = 0.28, p = 0.001). Smaller size of cerebellar regions strongly correlated with more pronounced social problems (r = 0.27, p = 0.007) and withdrawal behavior (r = 0.25, p = 0.015). Notably, lobules Right X, V, IV, VIIB, Left IX, VIII, and X influenced social problems, while Left V, Right IV, Vermis VI, and VIII impacted withdrawal behavior. ConclusionsOur study revealed cerebellar damage patterns in patients with brainstem tumors, emphasizing the role of both anterior and posterior cerebellar lobes in social problems and withdrawal behavior. This research sheds light on the brain mechanisms underlying complex behavioral disorders in brainstem tumor patients.

neuroscience↗

bioRxiv2024

Normative Growth Modeling of Cortical Thickness Identify Neuroanatomical Variability and Distinct Subtypes in Brainstem Tumor Patients

BackgroundBrainstem tumors can cause structural brain changes, but the resulting heterogeneity within wholebrain structure is not well-studied. This study examines cortical thickness to identify patterns of structural alterations and explore underlying biological subtypes and their associations with clinical factors. Materials and MethodsThis study involved 124 pediatric brainstem tumor patients, aged 4-18 years. Cortical thickness was measured using CAT12 segmentation of 3D T1-weighted structural MRI. A normative model was established using data from 849 healthy children. Deviations in cortical thickness were estimated, and patients were classified into two subtypes using spectral clustering. Clinical statistical analyses were conducted with SPSS 26.0. ResultsThe normative model revealed significant heterogeneity in cortical thickness deviations, which correlated with tumor size and growth patterns. Focal tumors primarily caused negative deviations (t = 3.14, p = 0.02). There was a significant positive correlation between extreme positive deviations and tumor volume (r = 0.214, p = 0.010), and between extreme negative deviations and progression-free survival (r = 0.39, p = 0.008). Two subtypes were identified: Subtype 1, consisting of diffuse tumors with extreme positive deviations, and Subtype 2, consisting of focal tumors with extreme negative deviations. Subtype and tumor growth pattern significantly influenced duration (p < 0.01). The Kaplan-Meier survival curves for Subtype 1 and Subtype 2 demonstrated a significant difference in survival probabilities over time (p = 0.03). ConclusionOverall, this study identifies two major patterns of cortical thickness changes in brainstem tumor patients, enhancing our understanding of their relationship with cortical morphology. The findings suggest that cortical thickness alterations could serve as valuable biomarkers for predicting progression-free survival, which is crucial for clinical assessment and personalized treatment strategies. This research provides new insights into the physiological mechanisms by which brainstem tumors affect brain structure, supporting more precise clinical interventions and efficacy monitoring in the future.

neuroscience↗