Biology subjects

Zhao, H.

Publications and source records attributed to Zhao, H..

At least 325 records · Page 18Linked to original sources

bioRxiv2023

Competitive fitness and homologous recombination of SARS-CoV-2 variants of concern

SARS-CoV-2 variants continue to emerge and cocirculate in humans and wild animals. The factors driving the emergence and replacement of novel variants and recombinants remain incompletely understood. Herein, we comprehensively characterized the competitive fitness of SARS-CoV-2 wild type (WT) and three variants of concern (VOCs), Alpha, Beta and Delta, by coinfection and serial passaging assays in different susceptible cells. Deep sequencing analyses revealed cell-specific competitive fitness: the Beta variant showed enhanced replication fitness during serial passage in Caco-2 cells, whereas the WT and Alpha variant showed elevated fitness in Vero E6 cells. Interestingly, a high level of neutralizing antibody sped up competition and completely reshaped the fitness advantages of different variants. More importantly, single clone purification identified a significant proportion of homologous recombinants that emerged during the passage history, and immune pressure reduced the frequency of recombination. Interestingly, a recombination hot region located between nucleotide sites 22995 and 28866 of the viral genomes could be identified in most of the detected recombinants. Our study not only profiled the variable competitive fitness of SARS-CoV-2 under different conditions, but also provided direct experimental evidence of homologous recombination between SARS-CoV-2 viruses, as well as a model for investigating SARS-CoV-2 recombination. ImportanceSARS-CoV-2 variants or subvariants keep emerging and the epidemic strains keeps changing in humans and animals. The continued replacement of the epidemic strains was attributed to higher competitive fitness evolved by the newly appeared ones than the older ones, but which factors affect the final outcomes are still not entirely clear. In this study, we performed in vitro coinfection and serial passage with three VOCs and WT under different conditions. Our results showed that the competition outcomes of these viral strains varied in different cell lines or under different immune pressure, confirming the probable effects of these two factors for the competitive fitness of different SARS-CoV-2 viral strains. Meanwhile, strikingly, we found that coinfection and serial passage with different SARS-CoV-2 viral strains can mimic the recombination process of SARS-CoV-2 occurred in coinfection individual, indicating it is a novel model to investigate the SARS-CoV-2 recombination mechanism.

microbiology↗

bioRxiv2023

Population genomics and epigenomics provide insights into the evolution of facultative asexuality in plants

Many organisms, particularly plants, are facultatively asexual. Because asexual reproduction is beneficial for the short term but costly for the long term, during the range expansion, natural selection should act on the genetic controls of asexuality in these facultatively asexual organisms. However, the genomic evidence of natural selection driving asexuality remains scarce, and the evolutionary consequences of facultatively asexuality on the genomic and epigenomic diversity remain controversial. Here, by analyzing population genomes and epigenomes of Spirodela polyrhiza (L.) Schleid., a facultatively asexual plant that flowers rarely, we show that this plant has the lowest genomic diversity and DNA methylation levels among all studied multicellular eukaryotes. Within species, demographic history and the frequency of asexual reproduction jointly determined intra-specific variations of genomic diversity and DNA methylation levels. Genome-wide scans revealed that multiple genes involved in flowering and embryogenesis were under positive selection, consistent with the hypothesis that natural selection drove the evolution of asexuality during the recent habitat expansions in this plant. Together, these results provide new insights into the evolution of plant reproductive systems and suggest that natural selection can drive the evolutionary changes of asexuality, which in turn alters the levels of genomic and epigenomic diversity.

evolutionary biology↗

bioRxiv2023

Accurately identifying nucleic-acid-binding sites through geometric graph learning on language model predicted structures

The interactions between nucleic acids and proteins are important in diverse biological processes. The high-quality prediction of nucleic-acid-binding sites continues to pose a significant challenge. Presently, the predictive efficacy of sequence-based methods is constrained by their exclusive consideration of sequence context information, whereas structure-based methods are unsuitable for proteins lacKing Known tertiary structures. Though protein structures predicted by AlphaFold2 could be used, the extensive computing requirement of AlphaFold2 hinders its use for genome-wide applications. Based on the recent breaKthrough of ESMFold for fast prediction of protein structures, we have developed GLMSite, which accurately identifies DNA and RNA-binding sites using geometric graph learning on ESMFold predicted structures. Here, the predicted protein structures are employed to construct protein structural graph with residues as nodes and spatially neighboring residue pairs for edges. The node representations are further enhanced through the pre-trained language model ProtTrans. The networK was trained using a geometric vector perceptron, and the geometric embeddings were subsequently fed into a common networK to acquire common binding characteristics. Then two fully connected layers were employed to learn specific binding patterns for DNA and RNA, respectively. Through comprehensive tests on DNA/RNA benchmarK datasets, GLMSite was shown to surpass the latest sequence-based methods and be comparable with structure-based methods. Moreover, the prediction was shown useful for the inference of nucleic-acid-binding proteins, demonstrating its potential for protein function discovery. The datasets, codes, together with trained models are available at https://github.com/biomed-AI/nucleic-acid-binding.

bioinformatics↗

bioRxiv2023

SPPUSM: An MS/MS spectra merging strategy for improved low-input and single-cell proteome identification

Single and rare cell analysis provides unique insights into the investigation of biological processes and disease progress by resolving the cellular heterogeneity that is masked by bulk measurements. Although many efforts have been made, the techniques used to measure the proteome in trace amounts of samples or in single cells still lag behind those for DNA and RNA due to the inherent non-amplifiable nature of proteins and the sensitivity limitation of current mass spectrometry. Here, we report an MS/MS spectra merging strategy termed SPPUSM (same precursor-produced unidentified spectra merging) for improved low-input and single-cell proteome data analysis. In this method, all the unidentified MS/MS spectra from multiple test files are first extracted. Then, the corresponding MS/MS spectra produced by the same precursor ion from different files are matched according to their precursor mass and retention time (RT) and are merged into one new spectrum. The newly merged spectra with more fragment ions are next searched against the database to increase the MS/MS spectra identification and proteome coverage. Further improvement can be achieved by increasing the number of test files and spectra to be merged. Up to 18.2% improvement in protein identification was achieved for 1 ng HeLa peptides by SPPUSM. Reliability evaluation by the "entrapment database" strategy using merged spectra from human and E. coli revealed a marginal error rate for the proposed method. For application in single cell proteome (SCP) study, identification enhancement of 28%-61% was achieved for proteins for different SCP data. Furthermore, a lower abundance was found for the SPPUSM-identified peptides, indicating its potential for more sensitive low sample input and SCP studies.

molecular biology↗

bioRxiv2023

Electron microscopic and crystallographic studies of bacteriophage Sf6 procapsid-like particles assembled from heterologously expressed capsid protein gp5

Many double-stranded DNA (dsDNA) viruses undergo a capsid maturation process during assembly of infectious virus particles, which involves transformation of a metastable capsid precursor called procapsid into a stable, DNA-filled capsid usually with a larger size and a more angular shape. Sf6 is a tailed dsDNA bacteriophage that infects Shigella flexneri. The phage Sf6 capsid protein gp5 was heterologously expressed and purified. Electron microscopy showed that the gp5 spontaneously assembled into spherical, procapsid-like particles. We also observed tube-like and cone-shaped particles reminiscent of human immunodeficiency virus. The gp5 procapsid-like particles were crystallized and crystals diffracted beyond 4.3 [A] resolution. X-ray data at 5.9 [A] resolution were collected with a completeness of 31.1% and an overall Rmerge of 15.0%. The crystals belong to the space group C2 with unit cell dimensions of a=973.326 [A], b=568.234 [A], c=565.567 [A], and {beta}=120.540{degrees}. Self-rotation function showed the 532 symmetry, confirming formation of icosahedral particles. The particle was situated at the origin of the crystal unit cell with the icosahedral 2-fold axis coinciding with the crystallographic b axis, and there is a half of the icosahedral particle in the crystallographic asymmetric unit.

biochemistry↗

bioRxiv2023

Comprehensive Characterization and Global Transcriptome Analyses of Human Fetal Liver Terminal Erythropoiesis

The fetal liver (FL) is the key erythropoietic organ during fetal development, but knowledge on human FL erythropoiesis is very limited. In this study, we sorted primary erythroblasts from FL cells and performed RNA sequencing analyses. We found that temporal gene expression patterns reflected changes in function during primary human FL terminal erythropoiesis. Notably, expression of genes enriched in proteolysis and autophagy was upregulated in orthochromatic erythroblasts (OrthoE), suggesting involvement of these pathways in enucleation. We also performed RNA sequencing of in vitro cultured erythroblasts derived from FL CD34+ cells. Comparison of transcriptomes between the primary and cultured erythroblasts revealed significant differences, indicating impacts of the culture system on gene expression. Notably, lipid metabolism gene expression was increased in cultured erythroblasts. We further immortalized erythroid cell lines from FL and cord blood (CB) CD34+ cells (FL-iEry and CB-iEry, respectively). FL-iEry and CB-iEry are immortalized at the proerythroblast stage and can be induced to differentiate into OrthoE, but their enucleation ability is very low. Comparison of transcriptomes between OrthoE with and without enucleation capability revealed downregulation of pathways involved in chromatin organization and mitophagy in OrthoE without enucleation capacity, indicating that defects in chromatin organization and mitophagy contribute to the inability of OrthoE to enucleate. Additionally, the expression levels of HBE1, HBZ, and HBG2 were upregulated in FL-iEry compared with CB-iEry, and this was accompanied by downregulation of BCL11A and upregulation of LIN28B and IGF2BP1. Our study provides new insights into human FL erythropoiesis and rich resources for future studies.

cell biology↗

bioRxiv2023

SoyDNGP: A Web-Accessible Deep Learning Framework for Genomic Prediction in Soybean Breeding

Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a Convolutional Neural Networks (CNN)-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its lower parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP consistently outperformed its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including Cotton, Maize, Rice, and Tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGPs potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we have designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two primary features: Trait Lookup, offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and Trait Prediction, allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction and genomic analysis, SoyDNGP opens up new possibilities in the quest for efficient and optimized soybean breeding.

plant biology↗

bioRxiv2023

Structure-based virtual screening identifies small molecule inhibitors of O-fucosyltransferase SPINDLY

Protein O-glycosylation is a nutrient-signaling mechanism that plays essential roles in maintaining cellular homeostasis across different species. In plants, SPINDLY (SPY) and SECRET AGENT (SEC) catalyze posttranslational modifications of hundreds of intracellular proteins by O-fucose and O-linked N-acetylglucosamine, respectively. SPY and SEC play overlapping roles in cellular regulation and loss of both SPY and SEC causes embryo lethality in Arabidopsis. Using structure-based virtual screening of chemical libraries followed by in vitro and in planta assays, we identified a SPY O-fucosyltransferase inhibitor (SOFTI). Computational analyses predicted that SOFTI binds to the GDP-fucose-binding pocket of SPY and competitively inhibits GDP-fucose binding. In vitro assays confirmed that SOFTI interacts with SPY and inhibits its O-fucosyltransferase activity. Docking analysis identified additional SOFTI analogs that showed stronger inhibitory activities. SOFTI treatment of Arabidopsis seedlings decreased protein O-fucosylation and caused phenotypes similar to the spy mutants, including early seed germination, increased root hair density, and defect in sugar-dependent growth. By contrast, SOFTI had no visible effect on the spy mutant. Similarly, SOFTI inhibited sugar-dependent growth of tomato seedlings. These results demonstrate that SOFTI is a specific SPY O-fucosyltransferase inhibitor and a useful chemical tool for functional studies of O-fucosylation and potentially for agricultural management.

plant biology↗

bioRxiv2023

The involvement of the human prefrontal cortex in the emergence of visual awareness

Exploring the neural mechanisms of awareness is a fundamental task of cognitive neuroscience. There is an ongoing dispute regarding the role of the prefrontal cortex (PFC) in the emergence of awareness, which is partially raised by the confound between report- and awareness-related activity. To address this problem, we designed a visual awareness task that can minimize report-related motor confounding. Our results show that saccadic latency is significantly shorter in the aware trials than in the unaware trials. Local field potential (LFP) data from 6 patients consistently show early (200-300 ms) awareness-related activity in the PFC, including event-related potential and high-gamma activity. Moreover, the awareness state can be reliably decoded by the neural activity in the PFC since the early stage, and the neural pattern is dynamically changed rather than being stable during the representation of awareness. Furthermore, the enhancement of dynamic functional connectivity, through the phase modulation at low frequency, between the PFC and other brain regions in the early stage of the awareness trials may explain the mechanism of conscious access. These results indicate that the PFC is critically involved in the emergence of awareness.

neuroscience↗

bioRxiv2023

Signal Improved ultra-Fast Light-sheet Microscope (SIFT) for large tissue imaging

Light-sheet fluorescence microscopy (LSFM) in conjunction with tissue clearing techniques enables morphological investigation of large tissues faster and with excellent optical sectioning. Recently, cleared tissue axially swept light-sheet microscope (ctASLM) demonstrated three-dimensional isotropic resolution in millimeter-scaled tissues. But ASLM based microscopes suffer from low detection signal and slow imaging speed. Here we report a simple and efficient imaging platform that employs precise control of two fixed distant light-sheet foci to carry out ASLM. This allowed us to carry out full field of view (FOV) imaging at 40 frames per second (fps) which is a four-fold improvement compared to the current state-of-the-art. In addition, in a particular frame rate, our method doubles the signal compared to the current ASLM technique. To augment the overall imaging performance, we also developed a deep learning based tissue information classifier that enables faster determination of tissue boundary. We demonstrated the performance of our imaging platform on various cleared tissue samples and demonstrated its robustness over a wide range of clearing protocols.

biophysics↗

bioRxiv2023

GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning

Molecule property prediction has gained significant attention in recent years. The main bottleneck is the label insufficiency caused by expensive lab experiments. In order to alleviate this issue and to better leverage textual knowledge for tasks, this study investigates the feasibility of employing natural language instructions to accomplish molecule-related tasks in a zero-shot setting. We discover that existing molecule-text models perform poorly in this setting due to inadequate treatment of instructions and limited capacity for graphs. To overcome these issues, we propose GIMLET, which unifies language models for both graph and text data. By adopting generalized position embedding, our model is extended to encode both graph structures and instruction text without additional graph encoding modules. GIMLET also decouples encoding of the graph from tasks instructions in the attention mechanism, enhancing the generalization of graph features across novel tasks. We construct a dataset consisting of more than two thousand molecule tasks with corresponding instructions derived from task descriptions. We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks. Experimental results demonstrate that GIMLET significantly outperforms molecule-text baselines in instruction-based zero-shot learning, even achieving closed results to supervised GNN models on tasks such as toxcast and muv.1

bioinformatics↗

bioRxiv2023

Altered methionine metabolism impacts phenylpropanoid production and plant development in Arabidopsis thaliana

Phenylpropanoids are specialized metabolites derived from phenylalanine. Glucosinolates are defense compounds derived mainly from methionine and tryptophan in Arabidopsis. It was previously shown that the phenylpropanoid pathway and glucosinolate production are metabolically linked. The accumulation of indole-3-acetaldoxime (IAOx), the precursor of tryptophan-derived glucosinolates, represses phenylpropanoid biosynthesis through accelerated degradation of phenylalanine-ammonia lyase (PAL). As PAL functions at the entry point of the phenylpropanoid pathway which produces indispensable specialized metabolites such as lignin, aldoxime-mediated phenylpropanoid repression is detrimental to plant survival. Although methionine-derived glucosinolates in Arabidopsis are abundant, any impact of aliphatic aldoximes (AAOx) derived from aliphatic amino acids such as methionine on phenylpropanoid production remains unclear. Here, we investigate the impact of AAOx accumulation on phenylpropanoid production using Arabidopsis aldoxime mutants, ref2 and ref5. REF2 and REF5 metabolize aldoximes to respective nitrile oxides redundantly, but with different substrate specificities. ref2 and ref5 mutants have decreased phenylpropanoid contents due to the accumulation of aldoximes. As REF2 and REF5 have high substrate specificity toward AAOx and IAOx respectively, it was assumed that ref2 accumulates AAOx, not IAOx. Our study indicates that ref2 accumulates both AAOx and IAOx. Removing IAOx partially restored phenylpropanoid production in ref2, but not to the wild-type level. However, when AAOx biosynthesis was silenced, phenylpropanoid production and PAL activity in ref2 were completely restored, suggesting an inhibitory effect of AAOx on phenylpropanoid production. Further feeding studies revealed that the abnormal growth phenotype commonly observed in Arabidopsis mutants lacking AAOx production is a consequence of methionine accumulation. Significance StatementAliphatic aldoximes are precursors of various specialized metabolites including defense compounds. This study reveals that aliphatic aldoximes repress phenylpropanoid production and that altered methionine metabolism affects plant growth and development. As phenylpropanoids include vital metabolites such as lignin, a major sink of fixed carbon, this metabolic link may contribute to available resource allocation during defense.

plant biology↗

bioRxiv2023

GTPBP8 is required for mitoribosomal biogenesis and mitochondrial translation

Mitochondria contain a multi-copy genome and a distinct set of ribosomes devoted to the exclusive synthesis of proteins that are essential for oxidative phosphorylation. The assembly of mitoribosomes for mitochondrial translation is a poorly understood process. In this study, we identify the uncharacterized GTP-binding protein 8 (GTPBP8) as a mitoribosomal assembly factor that specifically associates with the mitoribosomal large subunit. Genetic depletion of GTPBP8 causes an aberrant accumulation of the large mitoribosomal subunit at a late assembly stage and reduces the level of fully assembled 55S mitoribosomes, resulting in impaired mitochondrial translation and function. Together, our findings uncover an important role for human GTPBP8 in the processes of mitoribosomal assembly and mitochondrial translation. Key pointsO_LIGTPBP8 is a novel GTPase residing in the matrix peripherally bound to the inner mitochondrial membrane C_LIO_LIGTPBP8 specifically associates with the large mitoribosome subunit through interactions with large mitoribosomal proteins assembled at late stages. C_LIO_LIGTPBP is essential for maturation of the mitoribosomal large subunit and monosome formation C_LI

cell biology↗

bioRxiv2023

ZIPPI: proteome-scale sequence-based evaluation of protein-protein interaction models

Predicting protein-protein interactions (PPI) is a challenging problem of central importance in fundamental biology. With the increasing number of available PPI prediction methods and databases, an effective evaluation model would be extremely valuable. Here we introduce ZIPPI (Z-score for Information about Protein-Protein Interfaces), which evaluates structural models of a complex based on sequence co-evolution and conservation involving residues that are in contact in the interface. The interface Z-score (ZIPPI score) is calculated by comparing metrics for interface contacts to metrics obtained from randomly chosen surface residues. Since contacting residues are defined by the structural model, this obviates the need of accounting for indirect interactions with methods such as Direct Coupling Analysis. Although ZIPPI relies on species-paired multiple sequence alignments, its focus on contacting interfacial residues and the avoidance of direct coupling methods makes it computationally efficient. The performance of ZIPPI is evaluated through applications to experimentally determined complexes from the Protein Data Bank (PDB) and to decoys from the Critical Assessment of PRedicted Interactions (CAPRI) experiment. We demonstrate how ZIPPI can be implemented on a genome-wide scale by calculating scores for millions of structural models of protein-protein interactions in the E. coli interactome as predicted by PrePPI. Many PrePPI predictions filtered by ZIPPI score are novel. In all, this proteome-scale method shows promising feasibility for applications to the full human protein interactome, which is not yet accessible to deep learning methods.

systems biology↗

bioRxiv2023

A landing pad system for multicopy gene integration in Issatchenkia orientalis

The robust nature of the non-conventional yeast Issatchenkia orientalis allows it to grow under highly acidic conditions and therefore, has gained increasing interest in producing organic acids using a variety of carbon sources. Recently, the development of a genetic toolbox for I. orientalis, including an episomal plasmid, characterization of multiple promoters and terminators, and CRISPR-Cas9 tools, has eased the metabolic engineering efforts in I. orientalis. However, multiplex engineering is still hampered by the lack of efficient multicopy integration tools. To facilitate the construction of large, complex metabolic pathways by multiplex CRISPR-Cas9-mediated genome editing, we developed a bioinformatics pipeline to identify and prioritize genome-wide intergenic loci and characterized 47 sites. These loci are screened for guide RNA cutting efficiency, integration efficiency of a gene cassette, the resulting cellular fitness, and GFP expression level. We further developed a landing pad system using components from these well-characterized loci, which can aid in the integration of multiple genes using single guide RNA and multiple repair templates of the users choice. We have demonstrated the use of the landing pad for simultaneous integrations of 2, 3, 4, or 5 genes to the target loci with efficiencies greater than 80%. As a proof of concept, we showed how the production of 5-aminolevulinic acid can be improved by integrating five copies of genes at multiple sites in one step. We have further demonstrated the efficiency of this tool by constructing a metabolic pathway for succinic acid production by integrating five gene expression cassettes using a single guide RNA along with five different repair templates, leading to the production of 9 g/L of succinic acid in batch fermentations. This study demonstrates the effectiveness of a single gRNA-mediated CRISPR platform to build complex metabolic pathways in non-conventional yeast. This landing pad system will be a valuable tool for the metabolic engineering of I. orientalis. HIGHLIGHTSO_LIIn silico screening was performed to identify 204 unique guide RNAs in the intergenic regions of the genome. C_LIO_LI27 loci demonstrated high integration efficiency (>80%) and can be used for efficient gene or long pathway ([~]18 kb) integration. C_LIO_LIAn array of landing pad systems was installed at four loci for multiplex engineering. C_LIO_LIMulticopy integration of the gene cassettes (GFP, ALAS) resulted in a proportional increase in GFP fluorescence and 5-ALA production. C_LIO_LIA five-gene biosynthetic pathway was integrated into the chromosome in one step. C_LIO_LIThis is the first study reporting the development of the landing pad system in Issatchenkia orientalis. C_LI

synthetic biology↗

bioRxiv2023

LabelSeg: segment annotation for tumor copy number alteration profiles

1Somatic copy number alterations (SCNA) are a predominant type of oncogenomic alterations that affect a large proportion of the genome in the majority of cancer samples. Current technologies allow high-throughput measurement of such copy number aberrations, generating results consisting of frequently large sets of SCNA segments. However, the automated annotation and integration of such data are particularly challenging because the measured signals reflect biased, relative copy number ratios. In this study, we introduce labelSeg, an algorithm designed for rapid and accurate annotation of CNA segments, with the aim of enhancing the interpretation of tumor SCNA profiles. Leveraging density-based clustering and exploiting the length-amplitude relationships of SCNA, our algorithm proficiently identifies distinct relative copy number states from individual segment profiles. Its compatibility with most CNA measurement platforms makes it suitable for large-scale integrative data analysis. We confirmed its performance on both simulated and sample-derived data from The Cancer Genome Atlas (TCGA) reference dataset, and we demonstrated its utility in integrating heterogeneous segment profiles from different data sources and measurement platforms. Our comparative and integrative analysis revealed common SCNA patterns in cancer and protein-coding genes with a strong correlation between SCNA and mRNA expression, promoting the investigation into the role of SCNA in cancer development.

bioinformatics↗

bioRxiv2023

ERG-driven prostate cancer emerges from basal-luminal hybrid cells

To gain insight into how ERG translocations cause prostate cancer, we performed single cell transcriptional profiling of an autochthonous mouse model at an early stage of disease initiation. Despite broad expression of ERG in all prostate epithelial cells, proliferation was enriched in a small, stem-like population with mixed-luminal basal identity (called intermediate cells). Through a series of lineage tracing and primary prostate tissue transplantation experiments, we find that tumor initiating activity resides in a subpopulation of basal cells that co-express the luminal genes Tmprss2 and Nkx3.1 (called BasalLum) but not in the larger population of classical Krt8+ luminal cells. Upon ERG activation, BasalLum cells give rise to the highly proliferative intermediate state, which subsequently transitions to the larger population of Krt8+ luminal cells characteristic of ERG-positive human cancers. Furthermore, this proliferative population is characterized by an ERG-specific chromatin state enriched for NFkB, AP-1, STAT and NFAT binding, with implications for TF cooperativity. The fact that the proliferative potential of ERG is enriched in a small stem-like population implicates the chromatin context of these cells as a critical variable for unmasking its oncogenic activity.

cancer biology↗

bioRxiv2023

Targeted inhibition of SCF-SKP2 confers anti-tumor activities resulting in a survival benefit in osteosarcoma

Osteosarcoma(OS) is a highly aggressive bone cancer for which treatment has remained essentially unchanged for decades. Although OS is characterized by extensive genomic heterogeneity and instability, RB1 and TP53 have been shown to be the most commonly inactivated tumor suppressors in OS. We previously generated a mouse model with a double knockout (DKO) of Rb1 and Trp53 within cells of the osteoblastic lineage, which largely recapitulates human OS with nearly complete penetrance. SKP2 is a repression target of pRb and serves as a substrate recruiting subunit of the SCFSKP2 complex. In addition, SKP2 plays a central role in regulating the cell cycle by ubiquitinating and promoting the degradation of p27. We previously reported the DKOAA transgenic model, which harbored a knock-in mutation in p27 that impaired its binding to SKP2. Here, we generated a novel p53-Rb1-SKP2 triple-knockout model (TKO) to examine SKP2 function and its potential as a therapeutic target in OS. First, we observed that OS tumorigenesis was significantly delayed in TKO mice and their overall survival was markedly improved. In addition, the loss of SKP2 also promoted an apoptotic microenvironment and reduced the stemness of DKO tumors. Furthermore, we found that small-molecule inhibitors of SKP2 exhibited anti-tumor activities in vivo and in OS organoids as well as synergistic effects when combined with a standard chemotherapeutic agent. Taken together, our results suggest that SKP2 inhibitors may reduce the stemness plasticity of OS and should be leveraged as next-generation adjuvants in this cancer.

cancer biology↗