Biology subjects

Yang, P.

Publications and source records attributed to Yang, P..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Statins inhibit onco-dimerization of the 4Ig isoform of B7-H3

B7-H3 (CD276), a member of the B7-family of immune checkpoint proteins, has been shown to have immunological and non-immunological effects promoting tumorigenesis [1, 2] and expression correlates with poor prognosis for many solid tumors, including cervical, ovarian and breast cancers [3-6]. We recently identified a tumor-cell autochthonous tumorigenic role for dimerization of the 4Ig isoform of B7-H3 (4Ig-B7-H3) [7], where 4Ig-B7-H3 dimerization in cis activated tumor-intrinsic cellular proliferation and tumorigenesis pathways, providing a novel opportunity for therapeutic intervention. Herein, a live cell split-luciferase complementation strategy was used to visualize 4Ig-B7-H3 homodimerization in a high-throughput small molecule screen (HTS) to identify modulators of this protein-protein interaction (PPI). Notably, the HTS identified several compounds that converged on lipid metabolism (including HMG-CoA reductase inhibitors, also known as statins) as significant inhibitors of 4Ig-B7-H3 dimerization (p < 0.01). In vitro and in vivo murine studies provided evidence that statin-mediated disruption of 4Ig-B7-H3 dimerization was associated with anti-tumor effects. Statin-mediated anti-cancer efficacy was selective for B7-H3-expressing tumors and retrospective analysis of clinical tumor specimens supported the hypothesis that concurrent statin use enhanced clinical outcomes for patients in a B7-H3 restricted manner. Thus, disruption of 4Ig-B7-H3 dimerization provides an unanticipated molecular mechanism linking statin use in cancer therapy and prevention with immune checkpoint.

cancer biology↗

bioRxiv2024

CLUEY enables knowledge-guided clustering and cell type detection from single-cell omics data

Clustering is a fundamental task in single-cell omics data analysis and can significantly impact downstream analyses and biological interpretations. The standard approach involves grouping cells based on their gene expression profiles, followed by annotating each cluster to a cell type using marker genes. However, the number of cell types detected by different clustering methods can vary substantially due to several factors, including the dimension reduction method used and the choice of parameters of the chosen clustering algorithm. These discrepancies can lead to subjective interpretations in downstream analyses, particularly in manual cell type annotation. To address these challenges, we propose CLUEY, a knowledge-guided framework for cell type detection and clustering of single-cell omics data. CLUEY integrates prior biological knowledge into the clustering process, providing guidance on the optimal number of clusters and enhancing the interpretability of results. We apply CLUEY to both unimodal (e.g. scRNA-seq, scATAC-seq) and multimodal datasets (e.g. CITE-seq, SHARE-seq) and demonstrate its effectiveness in providing biologically meaningful clustering outcomes. These results highlight CLUEY on providing the much-needed guidance in clustering analyses of single-cell omics data. CLUEY package is available from https://github.com/SydneyBioX/CLUEY.

bioinformatics↗

bioRxiv2024

A Watershed Algorithm GUI for Personalized fMRI-guided rTMS Target

Personalized repetitive transcranial magnetic stimulation (rTMS) increasingly relies on resting-state functional magnetic resonance imaging (fMRI) to select stimulation sites, yet most pipelines depend on user-defined thresholds and atlas masks, which can shift individualized targets. We propose a watershed-based approach, implemented in a graphical user interface, that performs threshold-independent segmentation of functional images to support rTMS target localization. As a proof-of-concept, we focused on Alzheimers disease-related circuits within the default mode network, designating the posterior cingulate cortex (PCC) as the deep effective region and the inferior parietal lobule (IPL) as the superficial stimulation target. In a cohort of 21 healthy participants, quantitative comparison with a conventional threshold-based, mask-constrained peak strategy revealed high concordance for PCC peaks but a median spatial displacement of 6.0 mm (95% CI: 0.0-12.7 mm) for IPL targets. Qualitative examples further illustrate that watershed segmentation reduces bias from neighboring functional clusters, truncation by atlas boundaries, and ambiguity among multiple local peaks. By decoupling target definition from user-chosen thresholds and packaging the method in an accessible toolbox, this framework offers a generalizable tool for individualized fMRI-guided rTMS.

neuroscience↗

bioRxiv2024

Newly discovered and conserved role of IgM against viral infection in an early vertebrate

IgM emerged in jawed vertebrates 500 Mya and remains the most evolutionarily conserved antibody class. However, despite extensive studies on IgM as an ancient antiviral weapon in warm-blooded vertebrates, its role and mechanisms in combating viral infections in early vertebrates remain poorly understood. Here, we found that significant virus-specific sIgM titers were generated in serum and gut mucus of teleost fish that survived infection, and fish lacking sIgM were more susceptible to viral infection. These results challenge the paradigm that IgM and IgT are specialized to systemic and mucosal immunity, respectively. More crucially, our study provides further insight into the role of sIgM in viral neutralization and firstly clarified the mechanism through which teleost sIgM blocks viral infection by directly targeting viral particles. From an evolutionary perspective, our findings indicate that sIgM in both primitive and modern vertebrates follow conserved principles in the development of specialized antiviral immunity.

immunology↗

bioRxiv2024

BrainSTEM: A multi-resolution fetal brain atlas to assess the fidelity of human midbrain cultures

Many midbrain dopaminergic neuron (mDA) differentiation protocols aimed at Parkinsons disease (PD) modeling and cell replacement therapy have been developed. However, comprehensive evaluations of the transcriptomic fidelity of these protocols at the single-cell level against a common in vivo reference have been lacking. To this end, we constructed an integrated human fetal whole-brain atlas and a midbrain subatlas to use as a standard of comparison. From the whole-brain atlas, we observed distinct brain-region-specific gene expression in most neural cell types, emphasizing the need to first evaluate in vitro protocols at the whole-brain level to identify midbrain-associated cells. These cells are then mapped to the midbrain subatlas for more refined neuronal subtype specification and trajectory analysis specific to the midbrain. We surveyed all publicly available single-cell datasets of human midbrain culture models and performed the two-tier mapping. Using this biologically-driven multi-resolution mapping strategy which we termed BrainSTEM (brain Single-cell Two tiEr Mapping), we confirmed the presence of multiple midbrain cell types ( on-target), but also a substantial proportion of cells associated with non-midbrain regions and subtypes ( off-target). This leads to an overall inflation of mDA presence, stemming from non-midbrain-associated cells, across all published protocols. BrainSTEM thus offers an unbiased framework for understanding the current state of midbrain models and aids the improvement of midbrain differentiation protocols for PD studies.

bioinformatics↗

bioRxiv2024

Multi-task benchmarking of single-cell multimodal omics integration methods

Single-cell multimodal omics technologies have empowered the profiling of complex biological systems at a resolution and scale that were previously unattainable. These biotechnologies have propelled the fast-paced innovation and development of data integration methods, leading to a critical need for their systematic categorisation, evaluation, and benchmark. Navigating and selecting the most pertinent integration approach poses a significant challenge, contingent upon the tasks relevant to the study goals and the combination of modalities and batches present in the data at hand. Understanding how well each method performs multiple tasks, including dimension reduction, batch correction, cell type classification and clustering, imputation, feature selection, and spatial registration, and at which combinations will help guide this decision. This study develops a much-needed guideline on choosing the most appropriate method for single-cell multimodal omics data analysis through a systematic categorisation and comprehensive benchmarking of current methods. The Stage 1 protocol for this Registered Report was accepted in principle on 30th July 2024. The protocol, as accepted by the journal, can be found at https://springernature.figshare.com/articles/journal_contribution/Multi-task_benchmarking_of_single-cell_multimodal_omics_integration_methods/26789902.

bioinformatics↗

bioRxiv2024

A neural basis for mutant ATAXIN-1 induced respiratory dysfunction in mouse models of Spinocerebellar ataxia type 1

Spinocerebellar ataxia type 1 (SCA1), a dominantly inherited neurodegenerative disorder caused by an expanded trinucleotide repeat in the ATAXIN-1 (ATXN1) gene, is characterized by motor dysfunction, cognitive impairment, and death from compromised swallowing and respiration. To delineate specific cell types that contribute to respiratory dysfunction, we utilized the floxed conditional knock-in f-ATXN1146Q/2Q mouse. Whole body plethysmography during spontaneous respiration and respiratory challenge showed that f-ATXN1146Q/2Q mice exhibit a spontaneous respiratory phenotype characterized by elevated respiratory frequency, volumes, and respiratory output. Consequently, the ability of f-ATXN1146Q/2Q mice to increase ventilation during the challenge is impaired. To investigate the role of mutant ATXN1 expression in neural and skeletal muscle lineages, f-ATXN1146Q/2Q mice were bred to Nestin-Cre and Acta1-Cre mice respectively. These analyses revealed that the abnormal spontaneous respiration in f-ATXN1146Q/2Q mice involved two aspects: a behavioral phenotype in which SCA1 mice exhibit increased motor activity during respiratory testing and functional dysregulation of central respiratory control centers. Both aspects of spontaneous respiration were partially ameliorated by removing mutant ATXN1 from neural, but not skeletal muscle, cell lineages.

neuroscience↗

bioRxiv2024

Developmental variability in cotton fiber cell wall properties linked to important agronomic traits

The economic value of cotton is based on its long, thin, strong, and twisted trichoblasts that emerge from the ovule epidermis. The mature dried fiber cell reflects the outcome of a rapid tapering of the nascent trichoblast, weeks of polarized diffuse growth, followed by a transition to persistent secondary cell wall synthesis. Highly conserved and dynamic microtubule and cellulose microfibril-based anisotropic growth control modules are central to all of these phases. In this paper, we developed novel quantitative phenotyping and computational modeling pipelines to analyze fiber growth behaviors at a daily resolution. We uncovered unexpected variability in growth rate, cell wall properties, and cell geometry across a critical window of fiber development. Finite element computational modeling of fiber growth was used to analyze the instability of cell diameter control and predict how spatial gradients of fiber and matrix material properties can interact to dictate the patterns of shape change. As an initial step toward gaining insight into the molecular orchestration of cellulose biosynthesis, expression profiles of a broad set of relevant genes were quantified across the same developmental timeline and correlated with fiber phenotypes. This analysis identified specific candidate genes that may serve as targets for fiber quality improvement.

plant biology↗

bioRxiv2024

ThunderBolt: An interactive data sharing and analysis platform for large-omics experiments

SummaryMass-spectrometry (MS) datasets present a unique set of challenges that make in-depth bioinformatics analysis non-trivial, with analysis requiring both expertise and time. Often these datasets have unique structures that need to be dealt with on an individual basis. Currently, tools providing a fast, interactive and guided way of exploring and analysing these data sets are not readily available. To this end, we have developed ThunderBolt: a highly interactive, point-and-click web-based application providing both bioinformaticians and biologists with a platform for i) searching and comparing multiple omics datasets, ii) fast data exploration and quality control, iii) interactive visualization, iv) pre-processing, v) statistical analysis and vi) functional and network enrichment analysis of large proteomics datasets using the Shiny framework. AvailabilityThunderBolt is a shiny-application accessible at https://thunderbolt.sydney.edu.au/ Contactjames.burchfield@sydney.edu.au Supplementary information

systems biology↗

bioRxiv2024

A high-resolution model of gene expression during Gossypium hirsutum (cotton) fiber development

Cotton fiber development relies on complex and intricate biological processes to transform newly differentiated fiber initials into the mature, extravagantly elongated cellulosic cells that are the foundation of this economically important cash crop. Here we extend previous research into cotton fiber development by employing controlled conditions to minimize variability and utilizing time-series sampling and analyses to capture daily transcriptomic changes from early elongation through the early stages of secondary wall synthesis (6 to 24 days post anthesis; DPA). A majority of genes are expressed in fiber, largely partitioned into two major coexpression modules that represent genes whose expression generally increases or decreases during development. Differential gene expression reveals a massive transcriptomic shift between 16 and 17 DPA, corresponding to the onset of the transition phase that leads to secondary wall synthesis. Subtle gene expression changes are captured by the daily sampling, which are discussed in the context of fiber development. Coexpression and gene regulatory networks are constructed and associated with phenotypic aspects of fiber development, including turgor and cellulose production. Key genes are considered in the broader context of plant secondary wall synthesis, noting their known and putative roles in cotton fiber development. The analyses presented here highlight the importance of fine-scale temporal sampling on understanding developmental processes and offer insight into genes and regulatory networks that may be important in conferring the unique fiber phenotype.

cell biology↗

bioRxiv2024

Systematic evaluation of blastoid models of early human development

Recent generation of stem cell-derived blastoids that recapitulate the architecture and cellular constitution of the human blastocyst offers an experimental model for the elucidation of the biology of early human development. To evaluate the fidelity of the blastoids for modelling human blastocysts, we first established a reference map of cell identity and lineage differentiation of the human blastocysts through the integration and curation of single-cell transcriptome data of ex vivo blastocysts profiled at a range of developmental timepoints in culture. We next use this reference map to assess the coverage and the authenticity of cell lineages and the progression of lineage differentiation of various blastoid models generated with different cellular sources and protocols. This reference map generated from this study enables the benchmarking of blastoids that may guide the optimization of the protocol for the generation of high-fidelity blastoid models that faithfully recapitulate the natural human blastocyst.

systems biology↗

bioRxiv2024

SpaNorm: spatially-aware normalisation for spatial transcriptomics data

Library size normalisation is necessary to enable comparisons between observations in transcriptomic datasets. Numerous methods have been developed to normalise these effects with sample and gene specific adjustments. However, in spatial transcriptomics data, normalisation is complicated by the fact that spatial region-specific library size confounds biology. The most popular approach of adapting methods developed for single-cell RNA-seq data has been shown to excessively remove biological signals associated with spatial domains and thus results in poorer downstream domain identification. To this end, we propose the first spatially-aware normalisation method, SpaNorm. SpaNorm concurrently models spatial library size effects and the underlying smooth biology, to tease apart these effects, and thereby remove library size effects without removing biology. This is achieved through optimal decomposition of spatially smooth variation into those related and unrelated to library size and the use of location-specific scaling factors. Using 27 tissue samples from 6 datasets spanning 4 spatial platforms, we show that SpaNorm outperforms current state of the art methods at retaining biological information in the form of spatial domains and spatially variable genes (SVGs) better than 4 commonly used single-cell normalisation approaches. SpaNorm is versatile and it can be used for both spot-based and subcellular spatial transcriptomics data. Notably, the benefit of using SpaNorm is more pronounced for the latter data such as those from Xenium, STOmics and CosMx platforms for which the proportion of genes exhibiting region-specific library size effect is higher. SpaNorm works equally well with segmented cell-level data and spot-based data where each spot contains multiple cells.

bioinformatics↗

bioRxiv2024

Structural and functional analysis of the Nipah virus polymerase complex

Nipah virus (NiV) is a bat-borne, zoonotic RNA virus that is among the most pathogenic viruses known to humans. The NiV polymerase, which mediates viral genome replication and mRNA transcription, is a drug target. However, NiV polymerase structures were previously unavailable. We determined the cryo-EM structure of the NiV polymerase complex, comprising the large protein (L) and its associated co-factor (P), and performed structural, biophysical, and functional analyses of the NiV polymerase. The complex assembles with a long P tetrameric coiled-coil that is capped by a bundle of -helices that we show are likely dynamic in solution. Highly conserved zinc-binding modules in the capping domain and a large insert in the RdRp palm domain that is short or absent in most other non-segmented negative strand RNA viruses are critical for replication and transcription. Our findings have the potential to aid in the rational development of drugs to combat NiV infection.

microbiology↗

bioRxiv2024

Targeting protein tyrosine phosphatase non-receptor type 2 with a novel inhibitor for AML therapy

Acute myeloid leukemia (AML) is a fatal disease characterized by a bleak prognosis. For over four decades, treatment options for AML have been constrained to administering high-dose cytotoxic chemotherapy. However, the emergence of drug resistance and the resultant toxic side effects have created an urgent necessity for identifying novel therapeutic targets. In this study, non-receptor protein tyrosine phosphatase type 2 (PTPN2) is highly expressed in AML. Remarkably, knock down of PTPN2 expression markedly mitigated AML burden both in vitro and in vivo. Additionally, we unveiled the direct interaction between PTPN2 and C-MYC, establishing C-MYC as a direct acting substrate of PTPN2. Then, we performed a small-molecule compound screening to identify K73, a selective inhibitor of PTPN2. At the same time, K73 showed a good safety profile and exhibited strong activity against AML in vivo. In conclusion, we have pinpointed a considerable therapeutic potential in targeting PTPN2 for AML treatment and discovered a novel class of selective PTPN2 inhibitors suitable for AML therapy.

cancer biology↗

bioRxiv2024

Colorectal ALOX15 as a host factor determinant of EPA and DHA effects on colorectal carcinogenesis

Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), omega-3 polyunsaturated fatty acids ({omega}-3 PUFAs) derived from fish oil, are widely used as dietary supplements and FDA-approved treatments for hypertriglyceridemia. However, studies investigating the effects of EPA and DHA on colorectal carcinogenesis (CRC) have yielded conflicting results. The factors that determine these discrepant results remain unknown. Resolvins, oxidative metabolites of EPA and DHA, inhibit key pro-tumorigenic cytokine and chemokine signaling of colorectal cancer (e.g., IL-6, IL-1{beta}, and CCL2). 15-lipoxygenase-1 (ALOX15), a critical enzyme for resolvin generation is commonly lost during human CRC. Whether ALOX15 expression, as a host factor, modulates the effects of EPA and DHA on CRC remains unknown. Therefore, we evaluated the effects of ALOX15 transgenic expression in colonic epithelial cells on resolvin generation by EPA and DHA and CRC in mouse models representative of human CRC. Our results revealed that 1) EPA and DHA effects on CRC were diverse, ranging from suppressive to promotive, and these effects were occasionally altered by the formulations of EPA and DHA (free fatty acid, ethyl ester, triglyceride); 2) EPA and DHA uniformly suppressed CRC in the presence of intestinal ALOX15 transgenic expression, which induced the production of resolvins, decreased colonic CCL3-5 and CXCL-5 expression and tumor associated macrophages while increasing CD8 T cell abundance in tumor microenvironment; and 3) RvD5, the predominant resolvin produced by ALOX15, inhibited macrophage generation of pro-tumorigenic cytokines. These findings demonstrate the significance of intestinal ALOX15 expression as a host factor in determining the effects of EPA and DHA on CRC. SignificanceEicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are widely used as dietary supplements and FDA-approved treatments for hypertriglyceridemia. Studies of EPA and DHA effects on colorectal carcinogenesis (CRC) have revealed inconsistencies; factors determining the direction of their impact on CRC have remained unidentified. Our data show that EPA and DHA effects on CRC were divergent and occasionally influenced by their formulations. More importantly, intestinal 15-lipoxgenase-1 (ALOX15) expression modulated EPA and DHA effects on CRC, leading to their consistent suppression of CRC. ALOX15 promoted EPA and DHA oxidative metabolism to generate resolvins, which inhibited key pro-tumorigenic inflammatory cytokines and chemokines, including IL-6. IL-1{beta}, and CCL2. ALOX15 is therefore an important host factor in determining EPA and DHA effects on CRC.

cancer biology↗

bioRxiv2024

Sacrificing Adaptability for Functionality: The Ivory Tower of Macular Muller Cells

The predilection of many retinal diseases for the macula suggests it may be less resistant to stress than the peripheral retina. Profiling of single-cell level transcriptional changes found that the peripheral retina exhibited more transcriptional changes than the macula in response to stress. One pronounced change was in a subgroup of Muller cells (MCs) that were dominant in the peripheral retina. Genes more abundantly expressed in peripheral MCs were mainly associated with stress responses and were more influenced by light stress. In contrast, genes highly expressed in MCs that predominated in the macula had roles in cellular function and were less influenced by light stress. Metallothionein 1, A Kinase Anchor Protein 12 and MAF BZIP Transcription Factor F were more abundantly expressed in peripheral MCs than in macular MCs. Knockdown of these genes in primary MCs reduced their viability under stress. Our findings indicate that macular MCs are more directed toward maintaining retinal function rather than mounting a stress response when exposed to stress, which may contribute to the maculas vulnerability to degenerative diseases.

neuroscience↗

bioRxiv2024

Characterization of the mesendoderm progenitors in the gastrulating mouse embryo

A population of putative mesendoderm progenitor cells that can contribute cellular descendants to both mesoderm and endoderm lineages is identified. These progenitor cells are localized to the anterior primitive streak and the adjacent epiblast of E7.0-E7.5 mid-to late-gastrula stage embryos. Lineage tracing in vivo revealed that putative mesendoderm progenitors that are marked by Mixl1 and Mesp1 activity contribute descendants to the endoderm layer. Analysis of the role of Mixl1 transcription factor in endoderm differentiation of the mouse epiblast stem cells revealed the choice for endoderm or mesoderm cell fate depends on the timing of activation of Mixl1 upon exit from pluripotency, suggesting Mixl1 function may underpin the divergence of the mesendoderm progenitor to mesoderm and endoderm lineages. The knowledge gained on the spatial, temporal, and lineage attribute of mesendoderm progenitors enriches our mechanistic understanding of germ layer allocation and endoderm differentiation of mesendoderm progenitor in embryonic development and lineage allocation of primed state pluripotent stem cells in vitro.

developmental biology↗

bioRxiv2024

Towards anchoring evolutionary fitness for protein stability with virtual chemical environment recovery

Predicting the protein stability changes upon mutations is one of the effective ways to improve the efficiency of protein engineering. Here, we propose a dual-view ensemble learning-based framework, DVE-stability, for mutation-induced protein stability change prediction from single sequence. DVE-stability integrates the global and local dependencies of mutations to capture the intramolecular interactions from two views through ensemble learning, in which a structural microenvironment simulation module is designed to indirectly introduce the information of structural microenvironment at the sequence level. DVE-stability achieved state-of-the-art prediction performance on 7 single-point mutation benchmark datasets, and comprehensively surpassed other methods on 5 of them. Furthermore, DVE-stability outperformed other methods comprehensively through zero-shot inference on multiple-point mutation prediction task, demonstrating superior model generalizability to capture the epistasis of multiple-point mutations. More importantly, DVE-stability exhibited superior generalization performance in predicting rare beneficial mutations that are crucial for practical protein directed evolution scenarios. In addition, DVE-stability identified important intramolecular interactions via attention scores, demonstrating interpretable. Overall, DVE-stability provides a flexible and efficient tool for mutation-induced protein stability change prediction in an interpretable ensemble learning manner.

bioinformatics↗