Biology subjects

Wei, Y.

Publications and source records attributed to Wei, Y..

At least 199 records · Page 11Linked to original sources

bioRxiv2022

In vivo base editing rescues photoreceptors in a mouse model of retinitis pigmentosa

Retinitis pigmentosa (RP) is a group of retinal diseases that cause the progressive death of retinal photoreceptor cells and eventually blindness. Mutations in the {beta}-domain of the phosphodiesterase 6 (Pde6b) gene are among the most identified causes of autosomal recessive RP. Here, we report a base editing approach in which adeno-associated virus (AAV)-mediated adenine base editor (ABE) delivery to postmitotic photoreceptors is used to correct the Pde6b mutation in a retinal degeneration 10 (rd10) mouse model of RP. Subretinal delivery of AAV8-ABE corrects Pde6b mutation with up to 37.41% efficiency at the DNA level and up to 91.95% efficiency at the cDNA level, restores PDE6B expression, preserves photoreceptors and rescues visual function. RNA-seq reveals upregulation of genes associated with phototransduction and photoreceptor survival. Our data demonstrate that base editing is a potential gene therapy that could provide durable protection against RP.

genetics↗

bioRxiv2022

Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer

Neoadjuvant chemotherapy (NAC) is a standard treatment option for locally advanced breast cancer. However, not all patients benefit from NAC; some even get worse outcomes after therapy. Hence, predictors for treatment benefit are crucial for guiding clinical decision-making. Here, we investigated the predictive potentials of breast cancer stromal histology via a deep learning (DL)-based approach and proposed the tumor-associated stroma score (TS-score) for predicting pathological complete response (pCR) to NAC with a multi-center dataset. The TS-score is demonstrated to to be an independent predictor of pCR as it not only outperformed the baseline variables and stromal tumor-infiltrating lymphocytes (sTILs) but also significantly improved the prediction performance of the baseline variable-based model. Further, we discovered that unlike lymphocyte, collagen and fibroblasts in stroma were likely associated with poor response to NAC. The TS-score has potentials to be a candidate for better stratification of breast cancer patients in NAC settings.

pathology↗

bioRxiv2022

Combined transcriptome and metabolome analysis of a new species of microalgae from Tibetan plateau and its response to sewage treatment

Microalgae are pivotal in maintaining water quality in the lakes and rivers of Qinghai-Tibet plateau. The optimum sewage treatment conditions for Desmodesmus sp. are, temperature: 20-25{degrees}C, light intensity: 3000-8000 lx, and pH: 7.0-7.5, identified based on orthogonal experiments. The maximum removal rate of total nitrogen, total phosphorus, and chemical oxygen demand was more than 95% in the actual sewage treatment. The sewage treatment capacity of Desmodesmus sp. from plateau is higher than that from plains under the same treatment conditions. To identify the differentially expressed genes and metabolites in Desmodesmus sp. in response to sewage treatment, a combination of metabolomics and transcriptomics was employed to the microalgae with and without sewage treatment. The results showed that the oxidative phosphorylation, photosynthesis, and propanoate metabolism pathways were the most significantly enriched pathways in sewage treatment. Further, the metabolism of adenosine diphosphate, 2-oxobutanoate, and succinate were significantly upregulated, downregulated, and both upregulated and downregulated, respectively, as shown by the combined transcriptome and metabolome analysis. Additionally, we found that sewage treatment could also induce numerous changes in the primary metabolism, such as carbohydrate, fatty acid biosynthesis, and amino acid metabolism when compared with control. Overall, our results should improve fundamental knowledge of molecular responses to Desmodesmus sp. in sewage treatment and contribute to the design of strategies in microalgae response to sewage treatment.

ecology↗

bioRxiv2022

An engineered xCas12i with high activity, high specificity and broad PAM range

The type-V CRISPR effector Cas12i, with its smaller size, short crRNA guiding, and self-processing features, is a potentially versatile genome editing tool. By screening Cas12i proteins from a metagenomic database, we identified a natural variant with high activity in mammalian cells, named as xCas12i. We further engineered the PAM-interacting, REC, and RuvC domains for enhanced cleavage activity and specificity. This variant, named as high-fidelity Cas12Max, exhibited robust genome editing activity and minimal off-target activity with a broad 5-TN recognition profile. With the fusion of deaminase TadA8e and further optimization of xCas12i, the base editor dCas12i-Tad8e also showed the high editing efficiency. This study provides highly efficient and specific tools for gene therapy.

bioengineering↗

bioRxiv2022

Specificity and overlap in the genetic architectures of functional and structural connectivity within cerebral resting-state networks

The functional connectivity and dynamics of resting-state networks (RSN-FC) are vital for cognitive functioning. RSN-FC is heritable and partially translates to the anatomical architecture of white matter, but the genetic component of structural connections of RSNs (RSN-SC) and their potential genetic overlap with RSN-FC remains unknown. Here we perform genome-wide association studies (Ndiscovery=24,336; Nreplication=3,412) and in silico annotation on RSN-SC and RSN-FC. We identify the first genes for visual network-SC, that are involved in axon guidance and synaptic functioning and show that genetic variation in RSN-FC impacts biological processes related to brain disorders that have previously been associated with FC alterations in those same RSNs. Correlations of the genetic components of RSNs are mostly observed within the functional domain, whereas less overlap is observed within the structural domain and between the functional and structural domains. This study advances the understanding of the complex functional organization of the brain and its structural underpinnings from a genetics viewpoint.

genetics↗

bioRxiv2022

Functional Contribution of Cancer-Associated Fibroblasts in Glioblastoma

The abundance and biological contribution of cancer associated fibroblasts (CAFs) in glioblastoma are poorly understood. Here, we applied single-cell RNA sequencing and spatial transcriptomics analyses to identify and characterize CAFs in human glioblastoma tumors and then performed functional enrichment analysis and in vitro assays to investigate their interactions with malignant glioblastoma cells. We found that CAF abundance was significantly correlated with tumor grade, poor clinical outcome, and activation of extracellular matrix remodeling, using three large databases containing bulk RNA-sequencing data and clinical information. Proteomic analysis of the CAFs and their secretome revealed fibronectin (FN1) as a strong candidate mediating CAF functions. This was validated using in vitro cellular models, which demonstrated that CAF conditioned media and recombinant FN1 could facilitate the migration and invasion of glioblastoma cells. In addition, we showed that CAFs were more abundant in the mesenchymal-like state (or subtype) than in other states of glioblastomas, while cell lines resembling the proneural-state responded to the CAF signaling better in terms of the migratory and invasive phenotypes. Investigating the in-situ expression of gene markers specifically associated with CAFs and mesenchymal malignant cells further indicated that CAFs were enriched in the perinecrotic and pseudopalisading zones of human tumors, where mesenchymal-like glioblastoma cells co-resided and thus likely interacted. Overall, this study characterized the molecular features and functional impacts of CAFs in glioblastoma, alluding to a novel cell-to-cell interaction axis mediated by CAFs in the glioblastoma microenvironment.

cancer biology↗

bioRxiv2022

DeepAcr: Predicting Anti-CRISPR with Deep Learning

As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e., CRISPR-Cas), showing great potential for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging for its high variability and fast evolution. Existing biological studies often depend on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number of pairs in reality. Computational methods usually struggle with prediction performance. To tackle these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves impressive performance. On both the cross-fold and cross-dataset validation, our method outperforms the previous state-of-the-art methods significantly. Impressively, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. Combined with AlphaFold prediction, further motif analysis and docking experiments demonstrate that AcrNET captures the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly. With the impressive prediction capability, AcrNET can serve as a valuable tool for anti-CRISPR study and new anti-CRISPR discovery, with a free webserver at https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/.

bioinformatics↗

bioRxiv2022

Time-series monitoring of transgenic maize seedlings phenotyping exhibiting glyphosate tolerance

Developing herbicide resistant cultivars is one of the effective methods to solve the safety problem caused by the use of herbicide. In this study, hyperspectral image was used to develop more robust leaf chlorophyll content (LCC) prediction model based on different datasets to finally analyze the response of LCC to glyphosate-stress. Chlorophyll a fluorescence (ChlF) was used to dynamically monitor the photosynthetic physiological response of transgenic glyphosate-resistant and wild glyphosate-sensitive maize seedlings, and applying chemometrics methods to extract time-series features to screen resistant cultivars. Both the proposed two transfer strategies achieved the best prediction of LCC with a coefficient of determination value of 0.84, and relative root mean square error of 4.03 for the prediction set. Based on the predicted LCC and ChlF data, we found the antioxidant system of glyphosate-sensitive plants is too fragile to protect themselves from the damage, while glyphosate-resistant plants could overcome it by activating more powerful antioxidant system. {varphi}E0, VJ, {psi}E0, and M0 could be used to indicate damage caused by glyphosate to differentiate resistant cultivars. This study provided a new methodology to monitor LCC to finally analyze glyphosate tolerance in a time-series manner, and verified the feasibility of ChlF in crop breeding. HighlightThis study proposed a new methodology to monitor leaf chlorophyll content to finally analyze glyphosate tolerance in vivo, and verified the feasibility of chlorophyll a fluorescence in crop breeding.

plant biology↗

bioRxiv2022

The identification of two newly discovered fluorescent proteins in human glioblastoma

Glioblastoma (GBM) is a common malignancy featured by an extremely strong proliferation with enriched genetic information. Due to the rapid proliferation and the unstable genome-induced evolutionary potential, unique proteins may be expressed in GBM cells under the certain influence of the microenvironment. We therefore speculated that fluorescent proteins exist in GBM cells. During the immunofluorescence staining assay, we accidently discovered autofluorescence in primary GBM cells without fluorescent labeling, which were further validated as 2 newly discovered fluorescent proteins excited by 467 nm and 378 nm wavelength, respectively, namely human fluorescent protein and (HFP1, HFP2). Fluorescence colocalization and fluorescence resonance energy transfer (FRET) results showed the tight interaction of HFP1 and HFP2, and their synergistic effect. Our results for the first time identified 2 newly discovered fluorescent proteins in GBM cells, and clarified their chemical properties.

cancer biology↗

bioRxiv2022

The infiltration pattern of microenvironmental cells and different immune escape mechanisms in colorectal cancer

BackgroundThe tumour microenvironment (TME) plays a crucial role in tumour progression and metastasis. However, the infiltration pattern of TME cells in CRC samples and the immune escape mechanism underneath have not been studied sufficiently. MethodsTranscriptomic data from public datasets were retrieved online. In total, 1802 samples from the microarray dataset and 619 samples from the TCGA dataset were enrolled. The ssGSEA algorithm and unsupervised clustering were used for TME cells infiltration speculation and infiltration pattern recognition. ResultsCRC samples can be classified into three distinct TME cell subtypes. Subtype 1, the immune-active subtype, was characterised by high infiltration of activated adaptive immune cells. Subtype 2, the immune-desert subtype, featured high tumour purity and low infiltration of immune and stromal cells. And subtype 3, stroma-rich subtype, had high infiltration of stromal cells. The stroma-rich subtype conferred a significantly worse prognosis. Three subtypes had different immune escape mechanisms. The immune-active subtype has the highest immune checkpoint expression level. In comparison, the immune-desert subtype had the lowest immunogenicity and the defective antigen presentation. And the stroma-rich subtype lacked activated immune cells. ConclusionsDistinct TME cell subtypes and immune escape mechanisms may provide inspiration and direction for further researches on CRC immunotherapy.

cancer biology↗

bioRxiv2022

Arbidol inhibits esophageal squamous cell carcinoma growth in vitro and in vivo through suppressing ataxia telangiectasia and Rad3-related protein kinase

BackgroundEsophageal cancer has a global impact on human health due to its high incidence and mortality. Therefore, there is an urgent need to develop new drugs to treat or prevent the prominent pathological subtype of esophageal cancer, esophageal squamous cell carcinoma. Based upon a screening of drugs approved by the Food and Drug Administration, we discovered that Arbidol could effectively inhibit the proliferation of esophageal squamous cell carcinoma in vitro and in vivo. MethodWe first conducted a series of cell-based assays and found that Arbidol treatment inhibited the proliferation and colony formation ability of ESCC cells and promoted G1 phase cell cycle arrest. Phospho-proteomics experiments and in vitro kinase assays were subsequently performed using cell lysates derived from Arbidol-treated cells in order to identify the underlying growth inhibitory mechanism. Finally, a PDX model was used to verify the inhibitory effect of Arbidol in vivo. FindingArbidol inhibits proliferation of ESCC in vitro and in vivo through the DNA replication pathway and is associated with the cell cycle. InterpretationArbidol is a potential ATR inhibitor via binding to ATR kinase to reduce the phosphorylation and activation of MCM2 at Ser108. FundingThis research was supported by the National Natural Science Foundation of China (grant number 81872335), the National Natural Science Youth Foundation (grant number 81902486), and the Natural Science Foundation of Henan (grant number 161100510300).

cancer biology↗

bioRxiv2022

Discovery of therapeutic agents targeting PKLR for NAFLD using drug repositioning

Background & AimsNon-alcoholic fatty liver disease (NAFLD) encompasses a wide spectrum of liver pathologies. However, not medical treatment has been approved for the treatment of the disease. In our previous study, we found PKLR could be a potential target for treatment of NALFD. Here, the aim is to investigate the effect of PKLR in in vivo model and perform drug repositioning to identify a drug candidate for treatment of NAFLD. MethodsBiopsies from liver, muscle, white adipose tissue and heart were obtained from control and PKLR knockout mice fed with chow and high sucrose diets. Lipidomics as well as transcriptmics analyses were conducted using these tissue samples. In addition, a computational drug repositioning analysis was performed and drug candidates were identified. The drug candidates were finally tested in both in vitro and in vivo models to evaluated their toxicity and efficacy. ResultsThe Pklr KO reversed the increased hepatic triglyceride level in mice fed with high sucrose diet and partly recovered the transcriptomic changes in liver as well as other three tissues. Both liver and white adipose tissues exhibited dysregulated circadian transcriptomic profiles, and these dysregulations were reversed by hepatic knockout of Pklr. In addition, 10 small molecule drugs were identified as potential inhibitor of PKLR by the drug repositioning pipeline, and two of them significantly inhibited both the PKLR expression and triglyceride level in in vitro model. Finally, the two selected small molecule drugs were evaluated in in vivo rat models and it was demonstrated that these drugs attenuated hepatic steatosis without side effect on other tissues. ConclusionIn conclusion, our study provided biological insights about the critical role of PKLR in NAFLD progression and proposed a treatment strategy for NAFLD patients, which has been validated in preclinical experiment.

systems biology↗

bioRxiv2022

Single-nucleus transcriptomic analysis reveals divergence of glial cells in peripheral somatosensory system between human and mouse

Glial cells play a crucial role in regulating physiological and pathological functions, such as sensation, infections, acute injuries, and chronic neurodegenerative disorders. Glial cells include astrocytes, microglia, and oligodendrocytes in the central nervous system (CNS) and satellite glial cells (SGCs) in the peripheral nervous system (PNS). Despite the understanding of glial subtypes and functional heterogeneity in animal models achieved by single-cell or single-nucleus RNA sequencing, no research has investigated the transcriptomic profiles of glial cells in the human PNS and spinal cord. Here, we used high-throughput single-nucleus RNA sequencing to map the cellular and molecular heterogeneity of SGCs in the human dorsal root ganglion (DRG) and astrocytes, microglia, and oligodendrocytes in the human spinal cord. To explore the conservation and divergence across species, we compared these human findings with those from mice. Additionally, the expression profiles of risk genes of common DRG and spinal cord diseases in glial cells were compared between humans and mice. As a result, little SGCs heterogeneity was found in both human and mouse DRG. In the human spinal cord, astrocytes, microglia, and oligodendrocytes were respectively divided into six distinct transcriptomic subclusters. In the mouse spinal cord, astrocytes, microglia, and oligodendrocytes were divided into six, five, and six distinct transcriptomic subclusters, respectively. The comparative results revealed substantial heterogeneity in all glial cells between humans and mice. Notably, we also identified transcriptomic heterogeneity in several classical genes and risk genes for neurological disorders across humans and mice. Together, the present data comprehensively profiled glial cell heterogeneity and provides a powerful resource for investigating the cellular basis of glial-related physiological and pathological conditions in peripheral somatosensory system.

neuroscience↗

bioRxiv2022

RaPID-Query for Fast Identity by Descent Search and Genealogical Analysis

The size of genetic databases has grown large enough such that, genetic genealogical search, a process of inferring familial relatedness by identifying DNA matches, has become a viable approach to help individuals finding missing family members or law enforcement agencies locating suspects. However, a fast and accurate method is needed to search an out-of-database individual against millions of individuals in such databases. Most existing approaches only offer all-vs-all within panel match. Some prototype algorithms offer 1-vs-all query from out-of-panel individual, but they do not tolerate errors. A new method, random projection-based identical-by-descent (IBD) detection (RaPID) query, referred as RaPID-Query, is introduced to make fast genealogical search possible. RaPID-Query method identifies IBD segments between a query haplotype and a panel of haplotypes. By integrating matches over multiple PBWT indexes, RaPID-Query method is able to locate IBD segments quickly with a given cutoff length while allowing mismatched sites in IBD segments. A single query against all UK biobank autosomal chromosomes can be completed within 2.76 seconds CPU time on average, with the minimum 7 cM IBD segment length and minimum 700 markers. Using the same criteria, RaPID-Query can achieve 0.099 false negative rate and 0.017 false positive rate at the same time on a chromosome 20 sequencing panel having 92,296 sites, which is comparable to the state-of-the-art IBD detection method Hap-IBD. For the relatedness degree separation experiments, RaPID-Query is able to distinguish up to fourth degree of the familial relatedness for a given individual pair, and the area under the receiver operating characteristic curve values are at least 97.28%. It is anticipated that RaPID-Query will make genealogical search convenient and effective, potentially with the integration of complex inference models.

bioinformatics↗

bioRxiv2021

Comparative analysis of single cell lung atlas of bat, cat, tiger and pangolin

Horseshoe bats (Rhinolophus sinicus) might help maintain coronaviruses severely affecting human health, such as SARS-CoV and SARS-CoV-2. It has long been suggested that bats may be more tolerant of viral infection than other mammals due to their unique immune system, but the exact mechanism remains to be fully explored. During the COVID-19 pandemic, multiple animal species were diseased by SARS-CoV-2 infection, especially in the respiratory system. Herein, single-cell transcriptomic data of the lungs of a horseshoe bat, a cat, a tiger, and a pangolin were generated. The receptor distribution of twenty-eight respiratory viruses belonging to fourteen viral families were characterized for the four species. Comparison on the immune-related transcripts further revealed limited cytokine activations in bats, which might explain the reason why bats experienced only mild diseases or even no symptoms upon virus infection. Our findings might increase our understanding of the immune background of horseshoe bats and their insensitivity to virus infections.

bioinformatics↗

bioRxiv2021

High-fidelity Cas13 variants for targeted RNA degradation with minimal collateral effect

CRISPR-Cas13 systems have recently been employed for targeted RNA degradation in various organisms. However, collateral degradation of bystander RNAs has imposed a major barrier for their in vivo applications. We designed a dual-fluorescent reporter system for detecting collateral effects and screening Cas13 variants in mammalian cells. Among over 200 engineered variants, several Cas13 variants (including Cas13d and Cas13X) exhibit efficient on-target activity but markedly reduced collateral activity. Furthermore, transcriptome-wide off-targets and cell growth arrest induced by Cas13 are absent for these variants. Importantly, high-fidelity Cas13 variants show comparable RNA knockdown activity with wild-type Cas13 but no detectable collateral damage in transgenic mice and adeno-associated virus-mediated somatic cell targeting. Thus, high-fidelity Cas13 variants with minimal collateral effect are now available for targeted degradation of RNAs in basic research and therapeutic applications.

molecular biology↗

bioRxiv2021

Molecular mechanisms governing circulating immune cell heterogeneity across different species revealed by single cell sequencing

BackgroundImmune cells play important roles in mediating immune response and host defense against invading pathogens. However, insights into the molecular mechanisms governing circulating immune cell diversity among multiple species are limited. MethodsIn this study, we compared the single-cell transcriptomes of 77{square}957 immune cells from 12 species using single-cell RNA-sequencing (scRNA-seq). Distinct molecular profiles were characterized for different immune cell types, including T cells, B cells, natural killer cells, monocytes, and dendritic cells. ResultsResults revealed the heterogeneity and compositions of circulating immune cells among 12 different species. Additionally, we explored the conserved and divergent cellular crosstalk and genetic regulatory networks among vertebrate immune cells. Notably, the ligand and receptor pair VIM-CD44 was highly conserved among the immune cells. ConclusionsThis study is the first to provide a comprehensive analysis of the cross-species single-cell atlas for peripheral blood mononuclear cells (PBMCs). This research should advance our understanding of the cellular taxonomy and fundamental functions of PBMCs, with important implications in evolutionary biology, developmental biology, and immune system disorders.

cell biology↗

bioRxiv2021

Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration

We have entered the multi-omics era, and we can measure cells from different aspects. When dealing with such multi-omics data, the first step is to determine the correspondence among different omics. In other words, we should match data from different spaces corresponding to the same object. This problem is particularly challenging in the single-cell multi-omics scenario because such data are very sparse with extremely high dimensions. Secondly, matched single-cell multi-omics data are rare and hard to collect. Furthermore, due to the limitations of the experimental environment, the data are usually highly noisy. To promote the single-cell multi-omics research, we overcome the above challenges, proposing a novel framework to align and integrate single-cell RNA-seq data and single-cell ATAC-seq data. Our approach can efficiently map the above data with high sparsity and noise from different spaces to a low-dimensional manifold in a unified space, making the downstream alignment and integration straightforward. Compared with the other state-of-the-art methods, our method performs better on both simulated and real single-cell data. On the real data, the performance improvement on accuracy over the previous methods is up to 55.7% regarding scRNA-seq and scATAC-seq data integration. Downstream trajectory inference analysis shows that our tool can transfer the labels from scRNA-seq to scATAC-seq with very high accuracy, which indicates our methods effectiveness.

bioinformatics↗