Biology subjects

Liu, W.

Publications and source records attributed to Liu, W..

At least 253 records · Page 14Linked to original sources

bioRxiv2023

Integrative genomics reveals the polygenic basis of seedlessness in grapevine

Seedlessness is a crucial quality trait in table grape (Vitis vinifera L.) breeding. However, the development of seeds involved intricate regulations, while the polygenic basis of seed abortion remains unclear. Here, we combine comparative genomics, population genetics, quantitative genetics, and integrative genomics to unravel the evolution and polygenic basis of seedlessness in grapes. We generated four haplotype-resolved telomere-to-telomere (T2T) genomes for two seedless grape cultivars, Thompson Seedless (TS, syn. Sultania) and Black Monukka (BM). Comparative genomics identified a [~]4.25 Mb hemizygous inversion on Chr10 specific in seedless cultivars, with seedless-associated genes VvTT16 and VvSUS2 located at breakpoints. Population genomic analyses of 548 grapevine accessions revealed two distinct clusters of seedless cultivars, tracing the origin of the seedlessness trait back to Sultania. Introgression, rather than convergent selection, shaped the evolutionary history of seedlessness in grape improvement. Genome-wide association study (GWAS) analysis identified 110 quantitative trait loci (QTLs) associated with 634 candidate genes, including novel candidate genes, such as three 11S GLOBULIN SEED STORAGE PROTEIN and two CYTOCHROME P450 genes, and well-known genes like VviAGL11. Integrative genomic analyses resulted in 339 core candidate genes categorized into 13 groups related to seed development. Machine learning based genomic selection achieved a remarkable 99% precision in predicting grapevine seedlessness. Our findings highlight the polygenic nature of seedless and provide novel candidate genes for molecular genetics and an effective prediction for seedlessness in grape genomic breeding.

genetics↗

bioRxiv2023

Lactose and its derivatives are a new class of immune checkpoint inhibitors for cancer therapy

Understanding the mechanisms by which the immune system surveils cancer is the key to developing better tumor immunotherapy strategies. By CRISPR/Cas9 screenings, we identified that inactivation of beta-1,4-galactosyltransferase-1 (B4GALT1), a key enzyme in glycoconjugate biosynthesis, leads to enhanced T-cell receptor (TCR) activation and functions of CD8+ T-cells. Via proximity-dependent-intercellular-protein-spreading (PDICPS), cancer cells transfer surface-bound galectin-1 (Gal-1) proteins, which recognize and bind galactosylated membrane proteins, to CD8+ T-cells, thereby suppressing T-cell-mediated cytolysis. B4GALT1-deficiency leads to reduced cell-surface galactosylation and Gal-1 binding of CD8+ T-cells. Proteomic analysis revealed reduced binding of Gal-1 with TCR and its coreceptor CD8 on B4GALT1-deficient CD8+ T-cells, leading to enhanced TCR-CD8 colocalization and T-cell activation. Lactose, a structure-mimicking competitive inhibitor of N-glycan galactosylation, enhances the functions of CD8+ T-cells and tumor immunosurveillance. Results from various preclinical tumor models demonstrate that lactose and its derivatives are a new class of immune checkpoint inhibitors for tumor immunotherapy.

immunology↗

bioRxiv2023

Spatially resolved molecular and cellular atlas of the mouse brain

A comprehensive atlas of genes, cell types, and their spatial distribution across a whole mammalian brain is fundamental for understanding function of the brain. Here, using snRNA-seq and Stereo-seq techniques, we generated a mouse brain atlas with spatial information for 308 cell clusters with single-cell resolution involving over 6 million cells as well as for 29,655 genes. We have identified new astrocyte clusters, and demonstrated that distinct cell clusters exhibit preference for cortical subregions. In addition, we identified 155 genes exhibiting regional specificity in the brainstem, and 513 long non-coding RNA exhibited regional specificity in the adult brain. Parcellation of brain regions based on spatial transcriptomic information showed large overlap with that by traditional method. Furthermore, we have uncovered 411 transcription factor regulons with spatiotemporal specificity during development. Thus, our study has discovered genes and regulon with spatiotemporal specificity, and provided a high-resolution spatial transcriptomic atlas of the mouse brain.

neuroscience↗

bioRxiv2023

Histone marks enable formation of immiscible phase-separated chromatin compartments

Eukaryotic chromatin is organized into compartments for gene expression regulation, but the underlying mechanisms remain unclear. Here, we demonstrate that multivalent H3K27me3 and its reader, CBX7-PRC1 complex, regulate facultative heterochromatin via a phase separation mechanism, similar to constitutive heterochromatin1. Facultative and constitutive heterochromatin represent distinct, coexisting condensates in nuclei. In vitro, H3K27me3- and H3K9me3-marked nucleosomal arrays and their reader complexes can phase separate into immiscible condensates that are analogous to the relationship between facultative and constitutive heterochromatin in vivo. Moreover, overexpression of CBX7-PRC1 causes aberrant chromatin compartmentalization in vivo as demonstrated by H3K9me3 CUT&Tag and up-regulation of genes related to cancer such as acute myeloblastic leukemia (AML). CBX7 inhibitor effectively inhibits cancer cell proliferation possibly through phase separation-mediated compartment reorganization. Our data demonstrated how the specificity of compartmentalization is achieved based on the formation of immiscible phase-separated condensates, and offer novel epigenetic mechanistic insights into tumor development.

biochemistry↗

bioRxiv2023

Giant genes are rare but implicated in cell wall degradation by predatory bacteria

Across the tree of life, gene lengths vary, but most are no more than a few thousand base pairs in length. The largest protein often reported is the [~]40,000 aa eukaryotic Titin. Even larger proteins may occur in the rapidly expanding set of metagenome-derived sequences, but their existence may be obscured by assembly fragmentation. Here, we leverage genome curation to complete metagenome-derived sequences that encode predicted proteins of up to 85,804 aa. Overall, the findings illuminate a huge knowledge gap related to giant proteins. Although predicted proteins of >30,000 aa occur in bacterial phyla such as Firmicutes and Actinobacteria, they are most common in ca. Omnitrophota, ultra small bacteria that adopt predatory lifestyles. All full length giant genes encode numerous transmembrane regions and most encode divergent secA DEAD helicase domains. In silico structural prediction of protein subregions was required to identify domains in unannotated protein segments, and revealed putative domains implicated in attachment and carbohydrate degradation. Many giant genes in new complete and near-complete Omnitrophota genomes occur in close proximity to genes homologous to type II secretion systems as well as carbohydrate import systems. This, in combination with the domain content, suggests that many bacterial giant proteins enable prey adhesion and cell wall digestion during bacterial predation.

microbiology↗

bioRxiv2023

SmCCNet 2.0: an Upgraded R package for Multi-omics Network Inference

SummarySparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience. AvailabilityThis package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https://smccnet.shinyapps.io/smccnetnetwork/.

bioinformatics↗

bioRxiv2023

CLAIR: An integrated lipid database across multiple crop species

Oilseed crops, which are rich in plant lipids, provide essential fatty acids for human consumption and serve as major sources of biofuels and essential raw materials for the chemical industry. As a result of population growth and ecological changes, the demand for vegetable oil is increasingly outpacing supply. A comprehensive understanding of the genes involved in lipid metabolism in oilseed crops and the regulatory relationships among these genes is essential for improving oil content. However, current studies on lipid metabolism genes rely heavily on a decade-old database of genes involved in lipid metabolism in Arabidopsis thaliana. To address this issue, we mined the literature, integrated data from various databases and studies, and aligned homologs of lipid metabolism genes from nine oilseed crops to construct a comprehensive set of lipid metabolism genes in plants. Using this approach, we identified 221 additional lipid metabolism genes. In addition, we created a user-friendly lipid database called CLAIR (Crop Lipid-Associated Information Resource) by integrating and mining multi-omics data from nine major oil crops. The database is available at http://www.clipair.cn/. These resources should facilitate further research and exploration of lipid metabolism in oil crops, ultimately contributing to improved oil production.

plant biology↗

bioRxiv2023

Structural Insights into Dopamine Receptor-Ligand Interactions: From Agonists to Antagonists

This study explores the intricacies of dopamine receptor-ligand interactions, focusing on the D1R and D5R subtypes. Using molecular modeling techniques, we investigate the binding of the pan-agonist rotigotine, revealing a universal binding mode at the orthosteric binding pocket (OBP). Additionally, we analyze the stability of antagonist-receptor complexes with SKF83566 and SCH23390. By examining the impact of specific mutations on ligand-receptor interactions through computational simulations and thermostability assays, we gain insights into binding stability. Our research also delves into the structural and energetic aspects of antagonist binding to D1R and D5R in their inactive states. These findings enhance our understanding of dopamine receptor pharmacology and hold promise for drug development in central nervous system disorders, opening doors to future research and innovation in this field.

pharmacology and toxicology↗

bioRxiv2023

Neoadjuvant Therapy Differentially Remodels Carcinoma and Tumor Microenvironment in Pancreatic Ductal Adenocarcinoma: Insights from Spatially Resolved Transcriptomics

PurposeNeoadjuvant therapy (NAT) is increasingly being used for pancreatic ductal adenocarcinoma (PDAC) treatment. However, its distinct effects on carcinoma cells and the tumor microenvironment (TME) are not fully understood. This study employs spatial transcriptomics and single-cell RNA sequencing to investigate how NAT differentially remodels PDACs carcinoma cells and TME. Experimental DesignWe used spatial transcriptomics to compare gene expression profiles in carcinoma cells and the TME between NAT-treated and NAT-naive PDAC patients and correlated with their clinicopathologic features. Complementary single-nucleus RNA sequencing (snRNA-seq) analysis was conducted to validate our findings and identify cell types driving NAT-induced gene expression alterations. ResultsWe found NAT not only induces apoptosis and inhibits proliferation in carcinoma cells but also significantly remodels the TME. Notably, NAT induces a coordinated upregulation of multiple key complement genes (C3, C1S, C1R, C4B and C7) in the TME, making the complement pathway one of the most significantly affected pathways by NAT. Patients with higher TME complement expression following NAT exhibit improved overall survival; more immunomodulatory and neurotrophic cancer-associated fibroblasts (CAFs); more CD4+ T cells, monocytes and mast cells; and lower immune exhaustion gene expression. snRNA-seq analysis demonstrates C3 complement upregulation specifically in CAFs but not in other stroma cell types. ConclusionsOur findings indicate that NAT may reduce immunosuppression in PDAC by enhancing complement production and signaling within the TME. These findings suggest that local complement dynamics could serve as a novel biomarker for prognosis, evaluating treatment response and resistance, and guiding therapeutic strategies in NAT-treated PDAC patients. Translational RelevanceAs neoadjuvant therapy (NAT) increasingly becomes the preferred approach in treating resectable and borderline resectable pancreatic ductal adenocarcinoma (PDAC), there is a growing demand for novel biomarkers specifically tailored for post-NAT PDAC patients. Our study focused on how NAT differentially remodels the tumor cells and the tumor microenvironment (TME). We demonstrate that NAT can enhance local complement production and signaling in PDACs TME, which is associated with reduced immune exhaustion and improved overall survival. Our results highlight the importance of local complement dynamics in influencing treatment response, resistance, and overall clinical outcomes in PDAC. This new mechanism provides new opportunities in biomarker development which could facilitate more accurate prognostication and precise treatment stratification, particularly for patients undergoing NAT in PDAC.

genomics↗

bioRxiv2023

Protein language models-assisted engineering of Uracil-N glycosylase enables programmable T-to-G and T-to-C base editing

Current base editors use DNA deamination enzymes, such as cytidine deaminase (CBE) or adenine deaminase (ABE), to facilitate transition nucleotide substitutions. Combining CBE or ABE with glycosylase enzymes, including CGBE and AYBE, can induce limited transversion mutations. Nonetheless, a critical demand remains for base editors capable of generating alternative mutation types, such as T>G corrections, which could address over 17% of monogenic SNVs responsible for human genetic diseases. In this study, we leveraged protein language models to engineer a uracil-N-glycosylase (UNG) variant with altered substrate specificities to thymines (eTDG). Notably, after only two rounds of testing fewer than 50 predicted variants, more than 50% exhibited a 1.5-11-fold enhancement in enzymatic activities, a success rate much greater than random mutagenesis. When eTDG was fused with Cas9 nickase without deaminases, it effectively induced programmable T to G or T to C substitutions and precisely corrected db/db diabetic mutation in mice (up to 55%). Our findings not only establish orthogonal strategies for developing novel base editors, but also demonstrate the capacities of protein language models for optimizing enzymes without extensive task-specific training data or laborious experimental procedures.

bioengineering↗

bioRxiv2023

A CLIC1 network coordinates matrix stiffness and the Warburg effect to promote tumor growth in pancreatic cancer

BACKGROUND & AIMSPDAC is characterized by significant matrix stiffening and reprogrammed glucose metabolism, particularly the Warburg effect. However, it is not clear the connection between matrix stiffness and the Warburg effect and the mechanisms of action in tumor progression. METHODSThe relationship between matrix stiffness and the Warburg effect was investigated from clinical, cellular, and bioinformatical perspectives. The ChIP and luciferase reporter gene assays were used to clarify the regulation mechanism of matrix stiffness on the expression of CLIC1. The expression profile and clinical significance of CLIC1 were determined in GEO datasets and a TMA. Loss-of-function and gain-of-function technics were used to determine the in vitro and in vivo functions of CLIC1. GSEA and western blotting revealed the underlying molecular mechanisms. RESULTSPDAC matrix stiffness is closely associated with the Warburg effect, and CLIC1 is a key molecule connecting tumor matrix stiffness and the Warburg effect. Increased CLIC1 expression induced by matrix stiffness correlates with poor prognosis in PDAC. CLIC1 acts as a promoter of glycolytic metabolism and facilitates tumor growth in a glycolysis-dependent manner. Mechanistically, CLIC1 inhibits the hydroxylation of HIF1 via ROS, which then increases the stability of HIF1. Collectively, PDAC cells can sense extracellular matrix stiffness and upregulate the expression of CLIC1, which facilitates the Warburg effect through ROS/HIF1 signaling, thereby supporting tumor growth. CONCLUSIONSIn the context of tumor therapy, targeted approaches can be considered from the perspectives of both extracellular matrix stiffness and tumor metabolism, of which CLIC1 is one of the targets.

cancer biology↗

bioRxiv2023

Identification of visible and near-infrared signature peaks for arboviruses and Plasmodium

Arbovirus and malaria infections affect more than half of the worlds population causing major financial and physical burden. Current diagnostic tools such as microscopy, molecular and serological techniques are technically demanding, costly, or time consuming. Near-infrared spectroscopy has recently been demonstrated as a potential diagnostic tool for malaria and arbovirus and as a screening tool for disease vectors. However, pathogen specific infrared peaks that allow detection of these infections are yet to be described. In this study, we identified unique NIRS peaks from existing laboratory strains of four major arboviruses including Barmah Forest virus (BFV), Dengue virus (DENV), Ross River virus (RRV), Sindbis virus (SINV) and Plasmodium falciparum. Secondly, to determine the diagnostic ability of these peaks, we developed machine learning algorithms using Artificial Neural network (ANN) to differentiate arboviruses from media in which they are grown. Signature peaks for BFV were identified within the visible region at 410, 430, 562 and 588nm and the NIR region at, 946, 958, 1130, 1154 and 1780 nm. DENV related peaks were seen at 410nm within the visible region and 1130 nm within the NIR region. Signature peaks for RRV were observed within the visible region at 410 and 430 nm and within the NIR region at 1130 and 1780 nm, while SINV had a prominent peak at 410 nm within the visible region. Peaks at 514, 528, 547, 561, 582, and 595nm and peaks at 1388, 1432, 1681, 1700, 1721, 1882, 1905, 2245, 2278, 2300 nm were unique for P. falciparum. NIRS predictive sensitivity defined as the ability to predict an arbovirus as an infection was 90% (n = 20) for BFV, 100% (n =10) for RRV and 97.5% (n= 40) for DENV, while infection specificity defined as the ability to predict media as not-infected was 100% (n= 10). Our findings indicate that spectral signatures obtained by NIRS are potential biomarkers for diagnosis arboviruses and malaria. Author summaryMore than half of the world is at risk of contracting either malaria or arboviral infections. In resource limited settings, timely detection of these infections and the ability to screen thousands of people in a day using affordable tools is key to preventing their spread and unprecedented outbreaks. This emphasizes the need to develop portable, rapid, and easy to use diagnostic and surveillance tools. The near-infrared spectroscopy technique has recently been shown to be a potential tool for the diagnosis and surveillance of both malaria and arboviral infections. However, signature spectral biomarkers for these infections remain to be described. This study has identified several spectral biomarkers for DEN, RRV, BVF, SIN arboviruses and P. falciparum parasites. These biomarkers will assist the future assessment of this technique as a diagnostic and or surveillance tool for these infections in the field.

microbiology↗

bioRxiv2023

Proteomic Stratification of Prognosis and Treatment Options for Small Cell Lung Cancer

Small cell lung cancer (SCLC) is a highly malignant and heterogeneous cancer with limited therapeutic options and prognosis prediction models. Here, we analyzed formalin-fixed, paraffin-embedded (FFPE) samples of surgical resections by proteomic profiling, and stratified SCLC into three proteomic subtypes (S-I, S-II, and S-III) with distinct clinical outcomes and chemotherapy responses. The proteomic subtyping was an independent prognostic factor and performed better than current TNM or Veterans Administration Lung Study Group (VALG) staging methods. The subtyping results could be further validated using FFPE biopsy samples from an independent center, extending the analysis to both surgical and biopsy samples. The signatures of the S-II subtype in particular suggest potential benefits from immunotherapy. Differentially overexpressed proteins in S-III, the worst prognostic subtype, allowed us to nominate potential therapeutic targets, indicating that patient selection may bring new hope for previously failed clinical trials. Finally, analysis of an independent cohort of SCLC patients who had received immunotherapy validated the prediction that the S-II patients had better Progression Free Survival (PFS) and Overall Survival (OS) after first-line immunotherapy. Collectively, our study provides the rationale for future clinical investigations to validate the current findings for more accurate prognosis prediction and precise treatments.

cancer biology↗

bioRxiv2023

Identification of the KIF18A alpha-4 helix as a therapeutic target for chromosomally unstable tumor cells

The mitotic kinesin, KIF18A, is required for proliferation of cancer cells that exhibit chromosome instability (CIN), implicating it as a promising target for treatment of a subset of aggressive tumor types. Determining regions of the KIF18A protein to target for inhibition will be important for the design and optimization of effective small molecule inhibitors. In this study, we investigated the effects of mutating S284 within the alpha-4 helix of KIF18A, which was previously identified as a phosphorylated residue. Mutations in S284 cause relocalization of KIF18A from the plus-ends of spindle microtubules to the spindle poles. Furthermore, KIF18A S284 mutants display loss of KIF18A function and fail to support proliferation in CIN tumor cells. Interestingly, similar effects on KIF18A localization and function were seen after treatment of CIN cells with KIF18A inhibitory compounds that are predicted to interact with residues within the alpha-4 helix. These data implicate the KIF18A alpha-4 helix as an effective target for inhibition and demonstrate that small molecules targeting KIF18A selectively limit CIN tumor cell proliferation and result in phenotypically similar effects on mitosis at the single cell level compared to genetic perturbations.

cancer biology↗

bioRxiv2023

Pan-cancer evolution signatures link clonal expansion to dynamic changes in the tumour immune microenvironment

Cancer is an evolutionary process characterised by profound intra-tumour heterogeneity. Intra-tumour heterogeneity can be quantified using in silico estimates of cancer cell fractions of tumour-specific somatic mutations. Here we demonstrate a data-driven approach that uses cancer cell fraction distributions to identify 4 robust pan-cancer evolutionary signatures from an analysis of 4,146 individual tumour samples (TCGA) representing 17 distinct cancer types. Evolutionary signatures defined a continuum of cancer cell fractions representing neutral evolution, clonal expansion and fixation. Correlation of evolutionary signatures with programs representing distinct mutational and biological processes demonstrated that individual tumours enriched for clonal expansions and fixations were associated with immune evasion and distinct changes in the tumour immune microenvironment. We observed a dynamic switch between adaptive and innate immune processes as tumours undergo clonal fixation and escape immune surveillance. We also identify mutational processes underpinning different modes of tumour evolution and demonstrate that switching between adaptive and innate immune cell populations is accompanied by the clonal expansion of driver genes that modulate tumour-stroma interactions1.

bioinformatics↗

bioRxiv2023

Central pore-opening structure and gating of the mechanosensitive PIEZO1 channel

PIEZO1 is a mechanically activated cation channel that undergoes force-induced activation and inactivation to control diverse physiological processes1-4. However, the distinct functional states and gating transitions remain structurally undefined. In response to changes in membrane forces, PIEZO1 transitions from a curved to a flattened shape, which might lead to opening and subsequent inactivation of the ion-conducting pore5,6. Here we employed a PIEZO1 mutant to capture various conformational states including an intermediate state with an opened central pore, allowing us to reveal the gating dynamics of this multiple-gate system. Upon transition from the curved to the partially flattened intermediate state, the trimeric cap undergoes a downward and rotational motion to open the lateral cap-gate, while the loop that connects the cap to the pore-lining inner helix acts as a spring-linker, switching from an extended to a compressed state. Along with the partial flattening of the peripheral blades, these conformational changes collectively dilate the cap-gate, spring-gate and the hydrophobic transmembrane-gate, leading to ion permeation through the transmembrane pore. Upon inactivation, while the blades remain flattened, the cap and spring-linker return to an up and extended state. Molecular dynamics simulations verify the ion-permeating pathway and the distinct ion-conducting states of the assigned close, intermediate, open, and inactivated structures. Mutagenesis and electrophysiological characterizations reveal that domains and residues involved in conformational dynamics of the cap and spring-linker are critical for mechanical activation of PIEZO1. Together, these studies have structurally defined the distinct functional states and the curvature-based activation and inactivation mechanisms of PIEZO1.

neuroscience↗

bioRxiv2023

Discovery of First-in-Class PROTAC Degraders of SARS-CoV-2 Main Protease

We have witnessed three coronavirus (CoV) outbreaks in the past two decades, including the COVID-19 pandemic caused by SARS-CoV-2. Main protease (MPro) is a highly conserved and essential protease that plays key roles in viral replication and pathogenesis among various CoVs, representing one of the most attractive drug targets for antiviral drug development. Traditional antiviral drug development strategies focus on the pursuit of high-affinity binding inhibitors against MPro. However, this approach often suffers from issues such as toxicity, drug resistance, and a lack of broad-spectrum efficacy. Targeted protein degradation represents a promising strategy for developing next-generation antiviral drugs to combat infectious diseases. Here we leverage the proteolysis targeting chimera (PROTAC) technology to develop a new class of small-molecule antivirals that induce the degradation of SARS-CoV-2 MPro. Our previously developed MPro inhibitors MPI8 and MPI29 were used as MPro ligands to conjugate a CRBN E3 ligand, leading to compounds that can both inhibit and degrade SARS-CoV-2 MPro. Among them, MDP2 was demonstrated to effectively reduce MPro protein levels in 293T cells (DC50 = 296 nM), relying on a time-dependent, CRBN-mediated, and proteasome-driven mechanism. Furthermore, MPD2 exhibited remarkable efficacy in diminishing MPro protein levels in SARS-CoV-2-infected A549-ACE2 cells, concurrently demonstrating potent anti-SARS-CoV-2 activity (EC50 = 492 nM). This proof-of-concept study highlights the potential of PROTAC-mediated targeted protein degradation of MPro as an innovative and promising approach for COVID-19 drug discovery.

biochemistry↗

bioRxiv2023

CellPolaris: Decoding Cell Fate through Generalization Transfer Learning of Gene Regulatory Networks

Cell fate changes are determined by gene regulatory network (GRN), a sophisticated system regulating gene expression in precise spatial and temporal patterns. However, existing methods for reconstructing GRNs suffer from inherent limitations, leading to compromised accuracy and application generalizability. In this study, we introduce CellPolaris, a computational system that leverages transfer learning algorithms to generate high-quality, cell-type-specific GRNs. Diverging from conventional GRN inference models, which heavily rely on integrating epigenomic data with transcriptomic information or adopt causal strategies through gene co-expression networks, CellPolaris employs high-confidence GRN sources for model training, relying exclusively on transcriptomic data to generate previously unknown cell-type-specific GRNs. Applications of CellPolaris demonstrate remarkable efficacy in predicting master regulatory factors and simulating in-silico perturbations of transcription factors during cell fate transition, attaining state-of-the-art performance in accurately predicting candidate key factors and outcomes in cell reprogramming and spermatogenesis with validated datasets. It is worth noting that, with a transfer learning framework, CellPolaris can perform GRN based predictions in all cell types even across species. Together, CellPolaris represents a significant advancement in deciphering the mechanisms of cell fate regulation, thereby enhancing the precision and efficiency of cell fate manipulation at high resolution.

bioinformatics↗