Biology subjects

Tan, X.

Publications and source records attributed to Tan, X..

At least 73 records · Page 4Linked to original sources

bioRxiv2023

A PARP14/TARG1-Regulated RACK1 MARylation Cycle Drives Stress Granule Dynamics in Ovarian Cancer Cells

Mono(ADP-ribosyl)ation (MARylation) is emerging as a critical regulator of ribosome function and translation. Herein, we demonstrate that RACK1, an integral component of the ribosome, is MARylated on three acidic residues by the mono(ADP-ribosyl) transferase (MART) PARP14 in ovarian cancer cells. MARylation of RACK1 is required for stress granule formation and promotes the colocalization of RACK1 in stress granules with G3BP1, eIF3{eta}, and 40S ribosomal proteins. In parallel, we observed reduced translation of a subset of mRNAs, including those encoding key cancer regulators (e.g., AKT). Treatment with a PARP14 inhibitor or mutation of the sites of MARylation on RACK1 blocks these outcomes, as well as the growth of ovarian cancer cells in culture and in vivo. To re-set the system after prolonged stress and recovery, the ADP-ribosyl hydrolase TARG1 deMARylates RACK1, leading to the dissociation of the stress granules and the restoration of translation. Collectively, our results demonstrate a therapeutically targetable pathway that controls stress granule assembly and disassembly in ovarian cancer cells. SummaryWe have discovered a druggable PARP14/TARG1-regulated pathway that mediates site- specific mono(ADP-ribosyl)ation of RACK1, a ribosomal protein. This pathway controls stress granule assembly and disassembly, as well as the translation of a subset of mRNAs, to modulate the growth of ovarian cancer cells in culture and in vivo.

cell biology↗

bioRxiv2023

Generalization of deep learning models for predicting spatial gene expression profiles using histology images: A breast cancer case study

Spatial transcriptomics is a breakthrough technology that enables spatially-resolved measurement of molecular profiles in tissues, opening the opportunity for integrated analyses of morphology and transcriptional profiles through paired imaging and gene expression data. However, the high cost of generating data has limited its widespread adoption. Predicting gene expression profiles from histology images only can be an effective and cost-efficient in-silico spatial transcriptomics solution but is computationally challenging and current methods are limited in model performance. To advance research in this emerging and important field, this study makes the following contributions. We first provide a systematic review of deep learning methods for predicting gene expression profiles from histology images, highlighting similarities and differences in algorithm, model architecture, and data processing pipelines. Second, we performed extensive experiments to evaluate the generalization performance of the reviewed methods on several spatial transcriptomics datasets for breast cancer, where the datasets are generated using different technologies. Lastly, we propose several ideas for model improvement and empirically investigate their effectiveness. Our results shed insight on key features in a neural network model that either improve or not the performance of in-silico spatial transcriptomics, and we highlight challenges in developing algorithms with strong generalization performance. Key MessagesO_LIWe comprehensively compared the performance of existing methods for predicting spatial gene expression profiles from histology images C_LIO_LIWe assessed the roles of different algorithms, model architectures, and data processing pipelines to model performance C_LIO_LIWe performed extensive experiments to evaluate the generalization of the models on in-distribution and out-of-distribution spatial transcriptomics datasets C_LIO_LIWe proposed several strategies for improving existing models and empirically investigated their effectiveness C_LI

bioinformatics↗

bioRxiv2023

Alternative splicing of a chromatin modifier alters the transcriptional regulatory programs of stem cell maintenance and neuronal differentiation

Development of embryonic stem cells (ESCs) into neurons requires intricate regulation of transcription, splicing, and translation, but how these processes interconnect is not understood. We found that polypyrimidine tract binding protein 1 (PTBP1) alters splicing of DPF2, a subunit of BAF chromatin remodeling complexes. Dpf2 exon 7 is inhibited by PTBP1 to produce the DPF2-S isoform early in development. During neuronal differentiation, loss of PTBP1 allows exon 7 splicing, resulting in a longer DPF2-L isoform. Gene expression changes are induced by DPF2-L in ESC, and by DPF2-S in neurons. In ESC, chromatin immunoprecipitation locates DPF2-S but not DPF2-L at sites bound by pluripotency transcription factors. In neuronal progenitors, DPF2-S sites coincide with NFI protein binding, and DPF2-L sites with CTCF. DPF2-S sites show enhancer chromatin modifications, while DPF2-L sites show modifications associated with promoters. In sum, alternative splicing events during neuronal development impact chromatin organization by altering BAF complex targeting.

molecular biology↗

bioRxiv2023

TIGIT drives the immunosuppressive environment by downregulation of metalloproteinases MMP2 and MMP14 in perihilar cholangiocarcinoma.

BackgroundCheckpoint blockade in cholangiocarcinoma (CCA) is promising; however, little is known about the response to treatment in CCA subtypes. In this study, we investigated the spatial immune environment in combination with checkpoint expression in perihilar CCA (pCCA). Materials & MethodsThe levels of checkpoint molecules (PD-1, PD-L1, PD-L2, LAG-3, ICOS, TIGIT, TIM-3, and CTLA-4), macrophages (CD68), and T cells (CD4 and CD8) were assessed by multiplex immunofluorescence (mIF) in 50 patients. We investigated the transcriptomic profile using the NanoString Cancer Progression Panel, and validation was performed by mIF on tissue sections from 24 patients. ResultsThe expression of checkpoint molecules TIGIT, CTLA-4, and LAG-3 alone and in combination with other checkpoint molecules was more abundant in the Central Tumor (CT) and Invasive Margin (IM) than in peritumoral tissue (PT) (CD4 and CD8 TIGIT p<0.0001 for both CD4 and CD8 CTLA-4, p<0.0001 and p < 0.001, respectively, and CD8 LAG-3 p < 0.05). MMP2 and MMP14 were differentially expressed in patients with high TIGIT expression. ConclusionThe immune environment in pCCA is characterized by the expression of multiple checkpoints, demonstrating the complexity of ICI treatment. High TIGIT expression drives an immunosuppressive environment by modulating the extracellular matrix. Future clinical trials in pCCA could consider TIGIT as a therapeutically relevant target for (combination) treatment.

cancer biology↗

bioRxiv2023

Zfp318 controls the recallability of memory B cells

Expression of the transcriptional regulator ZFP318 is induced in germinal center (GC)- exiting memory B cell precursors and memory B cells (MBCs). Using a conditional ZFP318 fluorescence reporter that also enables ablation of ZFP318-expressing cells, we found that ZFP318-expressing MBCs were highly enriched with GC-derived cells. Although ZFP318-expressing MBCs constituted only a minority of the antigen-specific MBC compartment, their ablation severely impaired recall responses. Deletion of Zfp318 did not alter the magnitude of primary responses but markedly reduced MBC participation in recall. CD40 ligation promoted Zfp318 expression, whereas B-cell receptor (BCR) signaling was inhibitory. Enforced ZFP318 expression enhanced recall performance of MBCs that otherwise responded poorly. ZFP318-deficient MBCs expressed less mitochondrial genes, had structurally compromised mitochondria, and were susceptible to reactivation-induced cell death. The abundance of ZFP318-expressing MBCs, instead of the number of antigen-specific MBCs, correlated with the potency of prime-boost vaccination. Therefore, ZFP318 controls the MBC recallability and represents a quality checkpoint of humoral immune memory.

immunology↗

bioRxiv2023

Methoxsalen inhibited the acquisition of nicotine self-administration: attenuation by cotinine replacement in rats

Cigarette smoking remains the leading preventable cause of disease and death. Nicotine is the primary reinforcing ingredient in cigarettes sustaining addiction. Cotinine is the major metabolite of nicotine that produces a myriad of neurobehavioral effects. Cotinine supported self-administration and rats with a history of intravenous self-administration of cotinine exhibited relapse-like drug-seeking behavior, suggesting cotinine may also be reinforcing. To date, a potential contribution of cotinine to nicotine reinforcement remains unknown. Nicotine metabolism is mainly catalyzed by hepatic CYP2B1 enzyme in the rat and methoxsalen is a potent CYP2B1 inhibitor. The study tested the hypothesis that methoxsalen inbibits nicotine metabolism and self-administration, and that cotinine replacement attenuates the inhibitory effects of methoxsalen. Acute methoxsalen decreased plasma cotinine levels and increased nicotine levels following subcutaneous nicotine injection. Repeated methoxsalen reduced the acquisition of nicotine self-administration, leading to fewer nicotine infusions, disruption of lever differentiation, smaller total nicotine intake, and lower plasma cotinine levels. On the other hand, methoxsalen did not alter nicotine self-administration during the maintenance phase despite great reduction of plasma cotinine levels. Cotinine replacement by mixing cotinine with nicotine for self-administration dose-dependently increased plasma cotinine levels, counteracted effects of methoxsalen, and enhanced the acquisition of self-administration. Neither basal nor nicotine-induced locomotor activity was altered by methoxsalen. These results indicate that methoxsalen depressed cotinine formation from nicotine and the acquisition of nicotine self-administration, and that replacement of plasma cotinine attenuated the inhibitory effects of methoxsalen, suggesting that cotinine may contribute to the development of nicotine reinforcement.

neuroscience↗

bioRxiv2023

STimage:robust, confident and interpretable models for predicting gene markers from cancer histopathological images

Spatial transcriptomic (ST) imaging and sequencing data enable us to link tissue morphological features with thousands of previously unseen gene expression values, opening a new horizon for understanding tissue biology and achieving breakthroughs in digital pathology. Deep learning models are emerging to predict gene expression or classify cell types using images as the sole input. Such models hold significant potential for clinical applications, but require improvements in interpretability and robustness. We developed STimage as a comprehensive suite of models for both regression (predicting gene expression) and classification (mapping tissue regions and cell types) tasks. STimage is the first to thoroughly address robustness (uncertainty) and interpretability. For robustness, STimage predicts gene expression based on parameter distributions rather than fixed data points, allowing for generalisation at a population scale. STimage estimates uncertainty from the data (aleatoric) and from the model (epistemic) for each of thousands of imaging tiles. STimage achieves interpretability by analysing model attribution at a single-cell level, and in the context of histopathological annotation. While existing models focus on predicting highly variable genes, STimage predicts functional genes and identifies highly predictable genes. Using diverse datasets from three cancers and one chronic disease, we assessed the models performance on in-distribution and out-of-distribution samples. STimage is robust to technical variations across platforms, data types, sample preservation methods, and disease types. Further, we implemented an ensemble approach, incorporating pre-trained foundation models, to improve performance and reliability, especially in cases with small training datasets. With single-cell resolution Xenium data, STimage could classify cell types for millions of individual cells. Applying STimage to proteomics data such as CODEX, we found that STimage can predict gene expression consistent with protein expression patterns. Finally, we showed that using STimage-predicted values based solely on imaging input, we could stratify patient survival groups. Overall, STimage advances spatial transcriptomics by improving the prediction of gene expression from traditional histopathological images, making it more accessible for tissue biology research and digital pathology applications.

bioinformatics↗

bioRxiv2023

Efficacy of a phage cocktail preparation for chronic lung infection with Pseudomonas aeruginosa in mice

Pseudomonas aeruginosa is a leading cause of hospital-acquired infections, and the emergence of multi-drug resistant strains has prompted the search for alternative treatments such as phage therapy. In this study, we combined host range and genomic information to design a four-phage cocktail that effectively killed several clinical strains (79%, 23/29) of P. aeruginosa. We demonstrated that the cocktail, composed of three novel phages (PA_ZH1, PA_GL1, and PA_CQ9) and one previously characterized phage (PA_LZ7), was able to lyse P. aeruginosa both in planktonic cultures and in alginate microbeads (an in vivo-like biofilm model). Additionally, we showed that the phage cocktail administered intranasally or intraperitoneally effectively rescued mice from chronic lung infection with P. aeruginosa. Our work explores the potential use of phages as an alternative therapeutic agent against chronic lung infections caused by P. aeruginosa strains.

microbiology↗

bioRxiv2023

Global Marine Cold Seep Metagenomes Reveal Diversity of Taxonomy, Metabolic Function, and Natural Products

Cold seeps in the deep sea are closely linked to energy exploration as well as global climate change. The alkane-dominated chemical energy-driven model makes cold seeps an oasis of deep-sea life, showcasing an unparalleled reservoir of microbial genetic diversity. By analyzing 113 metagenomes collected from 14 global sites across 5 cold seep types, we present a comprehensive Cold Seep Microbiomic Database (CSMD) to archive the genomic and functional diversity of cold seep microbiome. The CSMD includes over 49 million non-redundant genes and 3175 metagenome-assembled genomes (MAGs), which represent 1897 species spanning 106 phyla. In addition, beta diversity analysis indicates that both sampling site and cold seep type have substantial impact on the prokaryotic microbiome community composition. Heterotrophic and anaerobic metabolisms are prevalent in microbial communities, accompanied by considerable mixotrophs and facultative anaerobes, indicating the versatile metabolic potential in cold seeps. Furthermore, secondary metabolic gene cluster analysis indicates that at least 98.81% of the sequences encode potentially novel natural products. These natural products are dominated by ribosomal processing peptides, which are widely distributed in archaea and bacteria. Overall, the CSMD represents a valuable resource which would enhance the understanding and utilization of global cold seep microbiomes.

microbiology↗

bioRxiv2023

Active-feedback 3D single-molecule tracking using a fast-responding galvo scanning mirror

Real-time three-dimensional single-particle tracking (RT-3D-SPT) allows continuous detection of individual freely diffusing objects with high spatiotemporal precision by applying closed-loop active feedback in an optical microscope. However, the current tracking speed in RT-3D-SPT is primarily limited by the response time of control actuators, impeding long-term observation of fast diffusive objects such as single molecules. Here, we present an RT-3D-SPT system with improved tracking performance by replacing the XY piezoelectric stage with a galvo scanning mirror with an approximately five-time faster response rate (~5 kHz). Based on the previously developed 3D single-molecule active real-time tracking (3D-SMART), this new implementation with a fast-responding galvo mirror eliminates the mechanical movement of the sample and allows more rapid response to particle motion. The improved tracking performance of the galvo mirror-based implementation is verified through simulation and proof-of-principle experiments. Fluorescent nanoparticles and ~ 1 kB double-stranded DNA molecules were tracked via both the original piezoelectric stage and new galvo mirror implementations. With the new galvo-based implementation, notable increases in tracking duration, localization precision, and the degree to which the objects are locked to the center of the detection volume were observed. These results suggest faster control response elements can expand RT-3D-SPT to a broader range of chemical and biological systems.

biophysics↗

bioRxiv2023

Phage administration with repeated intravenous doses leads to faster phage clearance in mammalian hosts

ObjectivesPhage therapy has shown a great promise for the treatment of multidrug-resistant bacterial infections. However, the lack of a thorough and organized understanding of phage-body interactions has limited its clinical application. MethodsHere, we administered different purified phages (Salmonella phage SE_SZW1, Acinetobacter phage AB_SZ6, and Pseudomonas phage PA_LZ7) intravenously to healthy animals (rats and monkeys) to evaluate the phage-induced host responses and phage pharmacokinetics (PK) with different intravenous (IV) doses in healthy animals. The plasma and the organs were sampled after different IV doses to determine the phage biodistribution, the phage-induced cytokines, and antibodies. The potential side effects of phages on animals were assessed. ResultsA non-compartment model revealed that the plasma phage titer gradually decreased over time following a single dose. Repeated doses caused that the plasma phage titer at 5 minutes dropped 2-3 Log10 compared to the first dose regardless of phage types in rats. Host innate immune responses were activated including the upregulated expression (>10-fold) of TNF- and splenic enlargement following repeated doses. Phage-specific neutralization antibodies in animals receiving phages were detected. Similar results were obtained from monkeys. ConclusionsThe mammalian bodies were well-tolerant to the administered phages. The animal responses to the phages and the phage biodistribution profiles could have a significant impact on the efficacy of phage therapy.

pharmacology and toxicology↗

bioRxiv2023

A toxic ankyrin cysteine protease effector RipBH of brown rot triggered autophagy-associated cell death

Potato brown rot, caused by Ralstonia solanacearum, is one of the most destructive diseases of potatoes. The pathogen could hide in the tuber, leading to the rotting tubers. However, few mechanisms of pathogenesis in tubers caused by brown rot were reported. Here, we identified a highly virulent type III effector RipBH, which is not only required for the pathogenesis of potato brown rot but also displays strong cell toxicity in yeast and tobacco. We found RipBH is a novel structural cysteine protease with a large ankyrin repeat domain that contains 10 ankyrin repeats, we named it as an ankyrin cysteine protease. Biochemical analysis showed that all the ankyrin repeats are required for virulence, and the first five ankyrin repeats are indispensable for auto-cleavage site recognition. Further analysis showed that RipBH triggered autophagy-associated cell death. The ankyrin cysteine protease effector existed extensively in plant and animal pathogens suggesting the ankyrin cysteine protease effectors are functionally essential for pathogen pathogenesis. Our study enhances our understanding of this type of cysteine protease and illustrates the pathogenesis of cysteine protease in potato brown rot.

microbiology↗

bioRxiv2023

A robust Platform for Integrative Spatial Multi-omics Analysis to Map Immune Responses to SARS-CoV-2 infection in Lung Tissues

The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal samples, bronchoalveolar lavage fluid, and similar, but a robust platform for deep tissue characterisation of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterise and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected samples, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.

bioinformatics↗

bioRxiv2023

Deep spatial-omics to aid personalization of precision medicine in metastatic recurrent Head & Neck Cancers

Immune checkpoint inhibitor (ICI) modality has had a limited success (<20%) in treating metastatic recurrent Head & Neck Oropharyngeal Squamous cell carcinomas (OPSCCs). To improve response rates to ICIs, tailored approaches capable to capture the tumor complexity and dynamics of each patients disease are needed. Here, we performed advanced analyses of spatial proteogenomic technologies to demonstrate that: (i) compared to standard histopathology, spatial transcriptomics better-identified tumor cells and could specifically classify them into two different metabolic states with therapeutic implications; (ii) our new method (Spatial Proteomics-informed cell deconvolution method or SPiD) improved profiling of local immune cell types relevant to disease progression, (iii) identified clinically relevant alternative treatments and a rational explanation for checkpoint inhibitor therapy failure through comparative analysis of pre- and post-failure tumor data and, (iv) discovered ligand-receptor interactions as potential lead targets for personalized drug treatments. Our work establishes a clear path for incorporating spatial-omics in clinical settings to facilitate treatment personalization.

cancer biology↗

bioRxiv2023

Co-evolution integrated deep learning framework for variants generation and fitness prediction

Pandemic caused by viral protein is characterized by waves of transmission triggered by new variants replacing old ones, resulting in immune escape and threatening public health. Therefore, there is an obvious need to accurately identify the vital mutation sites and understand the complex patterns of mutation effect of viral protein. However, existing work do not explicitly modelling vital positions functioning for virus fitness, leading to large search space with money- and time-consuming search cost. Here, we propose EVPMM (evolutionary integrated viral protein mutation machine), a co-evolution profiles integrated deep learning framework for dominant variants forecasting, vital mutation sites prediction and fitness landscape depicting. It consists of a position detector to directly detect the functional positions as well as a mutant predictor to depict fitness landscape. Moreover, pairwise dependencies between residues obtained by a Markov Random Field are also incorporated to promote reasonable variant generation. We show that EVPMM significantly outperforms existing machine learning algorithms on mutation position detection, residue prediction and fitness prediction accuracies. Remarkably, there is a highly agreement between positions identified by our method with current variants of concern and provides some new mutation pattern hypothesis. The method can prioritize mutations as they emerge for public health concern.

bioinformatics↗

bioRxiv2023

Cannabidiol sensitizes TRPV2 channels to activation by 2-APB

The cation-permeable TRPV2 channel is essential for cardiac and immune cells. Cannabidiol (CBD), a non-psychoactive cannabinoid of clinical relevance, is one of the few molecules known to activate TRPV2. Using the patch-clamp technique we discover that CBD can sensitize current responses of the rat TRPV2 channel to the synthetic agonist 2-aminoethoxydiphenyl borate (2- APB) by over two orders of magnitude, without sensitizing channels to activation by moderate (40 C) heat. Using cryo-EM we uncover a new small-molecule binding site in the pore domain of rTRPV2 that can be occupied by CBD in addition to a nearby CBD site that had already been reported. The TRPV1 and TRPV3 channels share >40% sequence identity with TRPV2 are also activated by 2-APB and CBD, but we only find a strong sensitizing effect of CBD on the response of mouse TRPV3 to 2-APB. Mutations at non-conserved positions between rTRPV2 and rTRPV1 in either the pore domain or the CBD sites failed to confer strong sensitization by CBD in mutant rTRPV1 channels. Together, our results indicate that CBD-dependent sensitization of TRPV2 channels engages multiple channel regions and possibly involves more than one CBD and 2-APB sites. The remarkably robust effect of CBD on TRPV2 and TRPV3 channels offers a promising new tool to both understand and overcome one of the major roadblocks in the study of these channels - their resilience to activation.

biophysics↗

bioRxiv2023

Phylogenomics reveals pervasive ancient introgression in the radiation of macaques

The genus Macaca, with 23 species assigned into four to seven species groups, exhibits the largest geographic range and represents the most successful adaptive radiation of nonhuman primates. Here, we conducted phylogenomic analyses of 16 newly generated and eight published macaque genomes and found a strong support for the division of this genus into seven species groups. Both ancient hybrid and incomplete lineage sorting (ILS) have contributed to the radiation and evolution of macaques. Particularly, the contradicting phylogenetic positions among silenus/nigra, fascicularis/mulatta and arctoides/sinica lineages were likely resulted from high level of ILS and potential hybridization between the ancestors of the arctoides/sinica and silenus/nigra lineages. Furthermore, an integrated scenario for macaque radiation is reconstructed by the help of the dated phylogenetic tree combined with documented history records. This study provides insights into ancient introgression involved in the radiation of macaques, which may help us to understand the rapid speciation of nonhuman primates.

evolutionary biology↗

bioRxiv2022

A Host-Harbored Metabolic Susceptibility of Coronavirus Enables Broad-Spectrum Targeting

Host-based antivirals could offer broad-spectrum therapeutics and prophylactics against the constantly-mutating viruses including the currently-ravaging coronavirus, yet must target cellular vulnerabilities of viruses without grossly endangering the host. Here we show that the master lipid regulator SREBP1 couples the phospholipid scramblase TMEM41B to constitute a host "metabolism-to-manufacture" cascade that maximizes membrane supplies to support coronaviral genome replication, harboring biosynthetic enzymes including Lipin1 as druggable viral-specific-essential (VSE) host genes. Moreover, pharmacological inhibition of Lipin1, by a moonlight function of the widely-prescribed beta-blocker Propranolol, metabolically uncouples the SREBP1-TMEM41B cascade and consequently exhibits broad-spectrum antiviral effects against coronaviruses, Zika virus, and Dengue virus. The data implicate a metabolism-based antiviral strategy that is well tolerated by the host, and a potential broad-spectrum medication against current and future coronavirus diseases.

cell biology↗