Biology subjects

Yang, Y.

Publications and source records attributed to Yang, Y..

At least 937 records · Page 52Linked to original sources

bioRxiv2022

Impaired neuromodulator crosstalk delays vigilance-dependent astroglia Ca2+ activation in mouse models of Alzheimer's disease

Degeneration in neuronal nuclei producing the neuromodulators acetylcholine and norepinephrine is a hallmark of Alzheimers disease (AD). Therapeutic interventions that increase acetylcholine in brain ameliorate AD symptoms in human patients, and augmenting norepinephrine restores cognitive function in mouse models of AD as well as Down Syndrome, the most frequent cause of early onset AD. A prominent cellular target of noradrenergic and potentially cholinergic signaling during states of heightened vigilance are astroglia and recent studies indicate that astroglia Ca2+ dynamics in awake mice contribute to optimal cognitive performance. Here we tested the hypothesis that vigilance-dependent Ca2+ signaling in mouse primary visual cortex astrocytes is altered in mouse models of AD and provide mechanistic insight into upstream neuromodulator signaling that shapes astrocyte Ca2+ dynamics in healthy and AD conditions. In two mouse models of AD (APPswe/PSEN1dE9 and AppNL-F KI), we consistently observed delayed and less coordinated astrocyte Ca2+ elevations in response to locomotion, a well-controlled behavioral paradigm triggering widespread Ca2+ activation in astroglia throughout the brain. Combining pharmacological and genetic manipulations, we found that noradrenergic signaling to astrocytes was facilitated by cholinergic signaling, but this neuromodulator crosstalk was impaired in AppNL-F mice. Pharmacological facilitation of norepinephrine release rescued delayed and less coordinated astrocyte Ca2+ activation in AppNL-F mice and suggests that astrocytes preserve a functional reserve that can be recruited even during late-stage disease. Our findings of delayed and less coordinated astroglia Ca2+ activation predict impaired noradrenergic signaling and may contribute to the cognitive decline in AD.

neuroscience↗

bioRxiv2022

Probabilistic embedding and clustering with alignment for spatial transcriptomics data integration with PRECAST

Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.

genomics↗

bioRxiv2022

CARD8 negatively regulates NLRP1 inflammasome activation level by interaction with NLRP1

NLRP1 inflammasome functions as canonical cytosolic sensor in response to intracellular infections and is implicated in auto-inflammatory diseases. But the regulation and signal transduction mechanisms of NLRP1 are incompletely understood. Here, we show that the T60 variant of CARD8, but not the canonical T48 isoform, negatively regulates NLRP1 inflammasome activation by directly interacting with the receptor molecule NLRP1 and inhibiting inflammasome assembly. Furthermore, our results suggest that the different ASC preference in three types of inflammasomes, namely ASC-indispensable NLRP1 inflammasome, ASC-dispensable mNLRP1b inflammasome and ASC-independent CARD8 inflammasome, is mainly caused by the CARD domain, not the UPA subdomain. Based on the systematic site-directed mutagenesis and structural analysis, we find that the signal transduction of NLRP1 inflammasome relies on multiple interaction surfaces on its death domain superfamily member CARD domain. Finally, our results partly explain the mechanism of the NLRP1 mutation-derived auto-inflammatory diseases caused by the overactivation of the NLRP1 inflammasome. In conclusion, our study not only reveals how CARD8 downregulates NLRP1 inflammasome activation, but also provides insights into the mechanisms of CARD-containing inflammasome assembly.

immunology↗

bioRxiv2022

Structural basis of the M-N protein interaction in a SARS-COV-2-related Betacoronavirus

The membrane (M) protein is the most abundant structural protein of coronaviruses including SARS-COV-2 and plays a central role in virus assembly through its interaction with various partner proteins. However, mechanistic details about how M protein interacts with others remain elusive due to lack of high-resolution structures. Here, we present the first crystal structure of a coronavirus M protein from Pipistrellus bat coronavirus HKU5 (batCOV5-M), which is closely related to SARS-COV-2 M protein. Furthermore, an interaction analysis indicates that the carboxy-terminus of the batCOV5 nucleocapsid (N) protein mediates its interaction with batCOV5-M. Combined with a computational docking analysis an M-N interaction model is proposed, providing insight into the mechanism of M protein-mediated protein interactions.

biophysics↗

bioRxiv2022

The cynosure of CtBP: evolution of a bilaterian transcriptional corepressor

Evolution of sequence-specific transcription factors clearly drives lineage-specific innovations, but less is known about how changes in the central transcriptional machinery may contribute to evolutionary transformations. In particular, transcriptional regulators are rich in intrinsically disordered regions that appear to be magnets for evolutionary innovation. The C-terminal Binding Protein (CtBP) is a transcriptional corepressor derived from an ancestral lineage of alpha hydroxyacid dehydrogenases; it is found in mammals and invertebrates, and features a core NAD-binding domain as well as an unstructured C-terminus (CTD) of unknown function. CtBP can act on promoters and enhancers to repress transcription through chromatin-linked mechanisms. Our comparative phylogenetic study shows that CtBP is a bilaterian innovation whose CTD of about 100 residues is present in almost all orthologs. CtBP CTDs contain conserved blocks of residues and retain a predicted disordered property, despite having variations in the primary sequence. Interestingly, the structure of the C-terminus has undergone radical transformation independently in certain lineages including flatworms and nematodes. Also contributing to CTD diversity is the production of myriad alternative RNA splicing products, including the production of "short" tailless forms of CtBP in Drosophila. Additional diversity stems from multiple gene duplications in vertebrates, where up to five CtBP orthologs have been observed. Vertebrate lineages show fewer major modifications in the unstructured CTD, possibly because gene regulatory constraints of the vertebrate body plan place specific constraints on this domain. Our study highlights the rich regulatory potential of this previously unstudied domain of a central transcriptional regulator.

evolutionary biology↗

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

Daily administration of low-dose daunorubicin or doxorubicin inhibits hypoxia-inducible factor 1 and tumor vascularization

Using a hypoxia-inducible factor 1 (HIF-1)-dependent luciferase reporter in Hep3B human hepatocellular carcinoma cells, we screened over 3,000 drugs that have been used in clinical trials and identified multiple anthracyclines as inhibitors of HIF-1 activity. Anthracyclines interfered with the ability of HIF-1 to bind to DNA. Daily injection of tumor-bearing mice with anthracyclines at low dose inhibited expression of the luciferase reporter and HIF-1 target genes that encode vascular endothelial growth factor A (VEGFA; ligand of VEGFR2), stromal-derived factor 1 (SDF-1; ligand of CXCR4), and stem cell factor (SCF; ligand of CD117) in tumor tissue. Increased numbers of circulating CXCR4+/Sca1+, VEGFR2+/CD34+, and VEGFR2+/CD117+ cells were demonstrated in immunodeficient mice bearing prostate cancer xenografts but not in tumor-bearing mice treated with anthracyclines, which also significantly inhibited angiogenesis in tumor tissue. Our findings indicate that HIF-1 inhibition underlies the anti-angiogenic effect associated with daunorubicin or doxorubicin metronomic therapy and suggest that these drugs may be particularly effective in patients with high levels of HIF-1 in their diagnostic tumor biopsy.

cancer biology↗

bioRxiv2022

Cytomulate: Accurate and Efficient Simulation of CyTOF data

Recently, many analysis tools have been devised to offer insights into data generated via Cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data where the ground truth is generally unknown. In this paper, we develop Cytomulate, a reproducible and accurate simulation algorithm of CyTOF data, which could serve as a foundation for future method development and evaluation. We demonstrate that Cytomulate can capture various characteristics of CyTOF data and is superior in learning overall data distributions than single-cell RNA-seq-oriented methods such as scDesign2, Splatter and generative models like LAMBDA.

bioinformatics↗

bioRxiv2022

NSUN2-mediated mRNA m5C Modification Regulates the Progression of Hepatocellular Carcinoma

RNA modification affects many biological processes and physiological diseases. The 5-methylcytosine (m5C) modification regulates the progression of multiple tumors. However, its characteristics and functions in hepatocellular carcinoma (HCC) remain largely unknown. Here, we found that HCC tissues had a higher m5C methylation level than the adjacent normal tissues. Transcriptome analysis revealed that a major fraction of the hypermethylated genes participated in the phosphokinase signaling pathways, such as the Ras and PI3K-Akt pathways. The m5C methyltransferase NSUN2 was highly expressed in HCC tissues consistently. Interestingly, the expression of many oncogenes was positively correlated with the expression of NSUN2, including GRB2, RNF115, AATF, ADAM15, RTN3, and HDGF. Real-time PCR assays further revealed that the expression of the mRNA of GRB2, RNF115, and AATF decreased significantly with the depletion of NSUN2 in HCC cells. Furthermore, NSUN2 could regulate the cellular sensitivity of HCC cells to sorafenib via modulating the Ras signaling pathway. Moreover, knocking down NSUN2 caused cell cycle arrest. Our study demonstrated a vital role of NSUN2 in the progression of HCC.

cancer biology↗

bioRxiv2022

The Arabidopsis D27like1 is a novel Isomerase that Contributes to SL Biosynthesis and Negatively Impacts ABA Level

The enzyme DWARF27 (D27) catalyzes the reversible isomerization of all-trans- into 9-cis-{beta}-carotene, initiating strigolactone (SL) biosynthesis. Genomes of higher plants encode two D27-homologs, D27-like1 and -like2, with unknown functions. Here, we investigated the enzymatic activity and biological function of the Arabidopsis D27-like1. In vitro enzymatic assays and Expression in Synechocystis sp. PCC6803 revealed a yet not reported 13-cis/15-cis/9-cis- and a 9-cis/all-trans-{beta}-carotene isomerization. Although disruption of AtD27-like1 did not cause SL deficiency phenotypes, overexpression of AtD27-like1 in the Atd27 mutant restored the more-branching phenotype, indicating a contribution of AtD27-like1 to SL biosynthesis. Accordingly, generated Atd27 Atd27like1 double mutants showed more pronounced branching phenotype, compared to Atd27. The contribution of AtD27-like1 to SL biosynthesis is likely due to its formation of 9-cis-{beta}-carotene that was present at higher levels in AtD27-like1 overexpressing lines. In contrast, AtD27-like1 expression correlated negatively with the content of 9-cis-violaxanthin, a precursor of abscisic acid (ABA), in shoots. Consistently, ABA levels were higher in shoots and also in dry seeds of the Atd27like1 and Atd27 Atd27like1 mutants. Transgenic lines expressing {beta}-glucuronidase (GUS) driven by the AtD27LIKE1 promoter and transcript analysis performed with hormone-treated Arabidopsis seedlings unraveled that AtD27LIKE1 is expressed in different tissues and regulated ABA and auxin. Taken together, our work revealed a cis/cis-{beta}-carotene isomerase activity that affects the content of both cis-carotenoid derived plant hormones ABA and SLs.

plant biology↗

bioRxiv2022

Cross-modal Graph Contrastive Learning with Cellular Images

Constructing discriminative representations of molecules lies at the core of a number of domains such as drug discovery, material science, and chemistry. State-of-the-art methods employ graph neural networks (GNNs) and self-supervised learning (SSL) to learn the structural representations from unlabeled data, which can then be fine-tuned for downstream tasks. Albeit powerful, these methods that are pre-trained solely on molecular structures cannot generalize well to the tasks involved in intricate biological processes. To cope with this challenge, we propose using high-content cell microscopy images to assist in learning molecular representation. The fundamental rationale of our method is to leverage the correspondence between molecular topological structures and the caused perturbations at the phenotypic level. By including cross-modal pre-training with different types of contrastive loss functions in a unified framework, our model can efficiently learn generic and informative representations from cellular images, which are complementary to molecular structures. Empirical experiments demonstrated that the model transfers non-trivially to a variety of downstream tasks and is often competitive with the existing SSL baselines, e.g., a 15.4% absolute Hit@10 gains in graph-image retrieval task and a 4.0% absolute AUC improvements in clinical outcome predictions. Further zero-shot case studies show the potential of the approach to be applied to real-world drug discovery.

bioinformatics↗

bioRxiv2022

Multi-task learning for single-cell multi-modality biology

Current biotechnologies can simultaneously measure multi-modality high-dimensional information from the same cell and tissue samples. To analyze the multi-modality data, common tasks such as joint data analysis and cross-modal prediction have been developed. However, current analytical methods are generally designed to process multi-modality data for one specific task without considering the underlying connections between tasks. Here, we present UnitedNet, a multi-task deep neural network that integrates the tasks of joint group identification and cross-modal prediction to analyze multi-modality data. We have found that multi-task learning for joint group identification and cross-modal prediction significantly improves the performance of each task. When applied to various single-cell multi-modality datasets, UnitedNet shows superior performance in each task, achieving better unsupervised and supervised joint group identification and cross-modal prediction performances compared with state-of-the-art methods. Furthermore, by considering the spatial information of cells as one modality, UnitedNet substantially improves the accuracy of tissue region identification and enables spatially resolved cross-modal prediction.

genomics↗

bioRxiv2022

Feasibility of 3T layer-dependent fMRI with GE-BOLD using NORDIC and phase regression

IntroductionFunctional MRI with spatial resolution in the submillimeter domain enables measurements of activation across cortical layers in humans. This is valuable as different types of cortical computations, e.g., feedforward versus feedback related activity, take place in different cortical layers. Layer-dependent fMRI (L-fMRI) studies have almost exclusively employed 7T scanners to overcome the reduced signal stability associated with small voxels. However, such systems are relatively rare and only a subset of those are clinically approved. In the present study, we examined the feasibility of L-fMRI at 3T using NORDIC denoising. Methods5 healthy subjects were scanned on a Siemens MAGNETOM Prisma 3T scanner. To assess across-session reliability, each subject was scanned in 3-8 sessions on 3-4 consecutive days. A 3D gradient echo EPI (GE-EPI) sequence was used for BOLD acquisitions (voxel size 0.82 mm isotopic, TR = 2.2 s) using a block designed finger tapping paradigm. NORDIC denoising was applied to the magnitude and phase time series to overcome limitations in tSNR and the denoised phase time series were subsequently used to correct for large vein contamination through phase regression. Results and conclusionNORDIC denoising resulted in temporal signal-to-noise ratio (tSNR) values comparable to or higher than commonly observed at 7T. Layer-dependent activation profiles could thus be extracted robustly, within and across sessions, from regions of interest located in the hand knob of the primary motor cortex (M1). Phase regression led to substantially reduced superficial bias in obtained layer profiles, although residual macrovascular contribution remained. We believe the present results support the feasibility of L-fMRI at 3T, which might help make L-fMRI available to a much wider community.

neuroscience↗

bioRxiv2022

A flexible, efficient, and scalable platform to produce circular RNAs as new therapeutics

Messenger RNA (mRNA) has recently emerged as a new drug modality with great therapeutic potential. However, linear mRNAs are relatively unstable and require base modification to reduce their immunogenicity, imposing a limitation to the broad application. The circular RNA (circRNA) presents a better alternative for prolonged expression of the proteins. However the in vitro circularization of RNA at industrial scale is technically challenging, and the direct comparison of efficacy between the circRNA and linear mRNA drugs is lacking. Here we developed a new self-catalyzed system to efficiently produce circRNAs in a co-transcriptional fashion. By rational sequence design, we can efficiently produce scarless circRNAs that do not contain foreign sequences. The resulting circRNAs are very stable and have low immunogenicity, enabling prolonged protein translation in different cells without cellular toxicity. The circRNAs encapsulated in lipid nanoparticles can be efficiently delivered into mice to direct robust protein expression with improved duration and efficiency. Finally, the circRNAs encoding RBD of SARS-CoV-2 S protein induced strong antibody productions, with neutralization antibody titers higher than the preclinical data from the linear mRNAs. Collectively, this study provided a general platform for efficient production of circRNAs with robust activity in directing protein production, demonstrating the potential of circRNAs as the new generation of mRNA therapy.

bioengineering↗

bioRxiv2022

Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning

More than one-third of the proteins contain metal ions in the Protein Data Bank. Correct identification of metal ion-binding residues is important for understanding protein functions and designing novel drugs. Due to the small size and high versatility of metal ions, it remains challenging to computationally predict their binding sites from protein sequence. Existing sequence-based methods are of low accuracy due to the lack of structural information, and time-consuming owing to the usage of multi-sequence alignment. Here, we propose LMetalSite, an alignment-free sequence-based predictor for binding sites of the four most frequently seen metal ions (Zn2+, Ca2+, Mg2+ and Mn2+). LMetalSite leverages the pretrained language model to rapidly generate informative sequence representations and employs transformer to capture long-range dependencies. Multi-task learning is adopted to compensate for the scarcity of training data and capture the intrinsic similarities between different metal ions. LMetalSite was shown to surpass state-of-the-art structure-based methods by more than 19.7%, 14.4%, 36.8%, and 12.6% in AUPR on the four independent tests, respectively. Further analyses indicated that the self-attention modules are effective to learn the structural contexts of residues from protein sequence.

bioinformatics↗

bioRxiv2022

Structural analysis of cholesterol binding and sterol selectivity by ABCG5/G8

The ATP-binding cassette (ABC) sterol transporters are responsible for maintaining cholesterol homeostasis in mammals by participating in reverse cholesterol transport (RCT) or transintestinal cholesterol efflux (TICE). The heterodimeric ABCG5/G8 carries out selective sterol excretion, preventing the abnormal accumulation of plant sterols in human bodies, while homodimeric ABCG1 contributes to the biogenesis and metabolism of high-density lipoproteins. A sterol-binding site on ABCG5/G8 was proposed at the interface of the transmembrane domain and the core of lipid bilayers. In this study, we have determined the crystal structure of ABCG5/G8 in a cholesterol-bound state. The structure combined with amino acid sequence analysis shows that in the proximity of the sterol-binding site, a highly conserved phenylalanine array supports functional implications for ABCG cholesterol/sterol transporters. Lastly, in silico docking analysis of cholesterol and stigmasterol (a plant sterol) suggests sterol-binding selectivity on ABCG5/G8, but not ABCG1. Together, our results provide a structural basis for cholesterol binding on ABCG5/G8 and the sterol selectivity by ABCG transporters.

biochemistry↗

bioRxiv2022

SpatialDEG: Identification of differentially expressed genes by leveraging spatial information in spatially resolved transcriptomic studies

MotivationSpatially resolved transcriptomics (SRT) technologies have been developed to simultaneously profile gene expression while retaining physical information. To explore differentially expressed genes using SRT in the context of various conditions, statistical methods are needed to perform spatial differential expression analysis. ResultsWe propose that a new probabilistic framework, spatialDEG, can perform differential expression analysis by leveraging spatial information on gene expression with spatial information. SpatialDEG utilizes the average information algorithm and can be scalable to tens of thousands of genes. Comprehensive simulations demonstrated that spatialDEG can identify genes differentially expressed in tissues across different conditions with a controlled type-I error rate. We further applied spatialDEG to analyze datasets for human dorsolateral prefrontal cortex and mouse whole liver. AvailabilityThe R package spatialDEG can be downloaded from https://github.com/Shufeyangyi2015310117/spatialDEG.

genetics↗