Biology subjects

Tan, Y.

Publications and source records attributed to Tan, Y..

At least 127 records · Page 7Linked to original sources

bioRxiv2022

Neuron Navigator 1 Regulates Learning, Memory, and the Response to Multiple Potentially Addictive Drugs

Genetic variation accounts for much of the risk for developing a substance use disorder (SUD). Inbred mouse strains exhibit substantial and heritable differences in the extent of voluntary cocaine intravenous self-administration (IVSA). Computational genetic analysis of IVSA data obtained from an inbred strain panel identified Nav1, a member of the neuron navigator family that regulates dendrite formation and axonal guidance, as a candidate gene. To test this hypothesis, we generated and characterized Nav1 knockout (KO) mice. Nav1 KO mice exhibited increased cocaine intake during IVSA testing. Surprisingly, Nav1 KO mice also displayed a reduced susceptibility to become opioid dependent or develop opioid-induced hyperalgesia after chronic morphine administration, and had impaired spatial learning/memory. Immunohistochemistry and electrophysiology studies revealed that inhibitory synapse density in the cortex of Nav1 KO mice was reduced, and excitatory synaptic transmission was increased in the Nav1 KO cortex and hippocampus. Transcriptomic analysis revealed that Nav1 KO mice had a marked increase in excitatory neurons in a deep cortical layer. Collectively, our results indicate that Nav1 regulates learning, memory, and the response to multiple addictive drugs, and that changes in the excitatory and inhibitory synaptic balance in the cortex and hippocampus could possibly mediate these phenotypic effects.

genetics↗

bioRxiv2022

A bioinformatics approach to systematically analyze the molecular patterns of monkeypox virus-host cell interactions

Monkeypox has been spreading worldwide since May 2022, when the World Health Organization (WHO) declared the outbreak of a "Public health emergency of international concern". The spread of monkeypox has posed a serious threat to the health of people all over the world but few studies have been carried out on it, and the molecular mechanism of monkeypox after infection remains unclear. We therefore implemented a transcriptome analysis to identify signaling pathways and biomarkers in monkeypox-infected cells to help understand monkeypox-host cell interactions. In this study, the dataset GSE36854 and GSE11234 were obtained from GEO. Among them, 84 significantly different genes were identified in the dataset GSE36854, followed by KEGG, GO analysis protein-protein interaction (PPI) construction and Hub gene extraction. We also analyzed the expression regulation patterns of hub genes and screened the drugs targeting the hub genes. The results showed that monkeypox-infected cells significantly activated the cellular immune response, and induced inflammatory response. IER3, IFIT2, IL11, ZC3H12A, EREG, IER2, NFKBIE, FST, IFIT1 and AREG were the top 10 hub genes, in which anti-viral gene IFIT1 and IFIT2 were significantly suppressed. AP-26113 and itraconazole promoting the expression of IFIT1 and IFIT2 may be used as new candidates for the treatment of monkeypox viral infection. Our results provide a new entry point for understanding the mode of interaction between monkeypox virus and its host.

microbiology↗

bioRxiv2022

Guild-level microbiome signature associated with COVID-19 severity and prognosis

COVID-19 severity has been associated with alterations of the gut microbiota. However, the relationship between gut microbiome alterations and COVID-19 prognosis remains elusive. Here, we performed a genome-resolved metagenomic analysis on fecal samples collected from 300 in-hospital COVID-19 patients at time of admission. Among the 2,568 high quality metagenome-assembled genomes (HQMAGs), Redundancy Analysis identified 33 HQMAGs which showed differential distribution among mild, moderate, and severe/critical severity groups. Random Forest model based on these 33 HQMAGs classified patients from different severity groups (average AUC = 0.79). Co-abundance network analysis found that the 33 HQMAGs were organized as two competing guilds. Guild 1 harbored more genes for short-chain fatty acid biosynthesis, and fewer genes for virulence and antibiotic resistance, compared with Guild 2. Random Forest regression showed that these 33 HQMAGs at admission had the capacity to predict 8 clinical parameters, which are predictors for COVID-19 prognosis, at Day 7 in hospital. Moreover, the dominance of Guild 1 over Guild 2 at admission predicted the death/discharge outcome of the critical patients (AUC = 0.92). Random Forest models based on these 33 HQMAGs classified patients with different COVID-19 symptom severity, and differentiated COVID-19 patients from healthy subjects, non-COVID-19, and pneumonia controls in three independent datasets. Thus, this genome-based guild-level signature may facilitate early identification of hospitalized COVID-19 patients with high risk of more severe outcomes at time of admission.

microbiology↗

bioRxiv2022

DUnet: A deep learning guided protein-ligand binding pocket prediction

Investigating protein-ligand binding sites is the key step in engineering protein/enzyme activity and selectivity. In this study, we developed a 3D convolutional neural network DUnet that derived from DenseNet and UNet for predicting the protein-ligand binding sites. To train DUnet, the features of protein 3D structure were extracted by describing the atomic physical characters, and the ligand binding sites were used as training labels. DUnet was trained using three dataset, the scPDB dataset (collecting of protein-ligand complexes from Protein Data Bank), scPDB and SC6K (collecting of protein-ligand complexes deposited after January 1st, 2018 from Protein Data Bank) datasets, and scPDB and its derived dataset by rotating the samples in the dataset. DUnet displayed better performance than the current state-of-art methods during the benchmark test using independent validation sets, and enlarging the training set contributed to better accuracy. We developed a small dataset contains commonly used industrial enzymes for testing DUnet and found that it was also accurate in predicting the substrate binding sites. We experimentally characterized the substrate binding sites of microbial transglutaminase according to the prediction and showed the significance of these sites. Finally, DUnet was used to predict the ligand binding sites of Swiss-Prot annotated proteins.

bioinformatics↗

bioRxiv2022

The cosmopolitan appendicularian Oikopleura dioica reveals hidden genetic diversity around the globe

Appendicularian tunicates are some of the most abundant mesozooplankton organisms with key roles in marine trophic webs and global carbon flux. Like most appendicularians with cosmopolitan distributions, Oikopleura dioica Fol, 1872 is considered a single species worldwide based on morphological features that distinguish them from other appendicularians. Despite their abundance however, there are still only [~]70 described appendicularian species, compared with over 2,800 ascidian tunicates. Here we perform a molecular phylogenetic, morphological, and reproductive assessment of O. dioica specimens collected from the Ryukyu Archipelago, mainland Japan, and Europe. The specimens are morphologically very similar, with only detailed examination of the oikoplastic epithelium and quantitative measurements revealing minor distinguishing characteristics. Phylogenetic analyses of the ribosomal gene loci and mitochondrial cytochrome oxidase I (COI) gene strongly indicate that they form three separate genetic clades despite their morphological similarities. Finally, in vitro crosses between the Ryukyu and mainland Japanese specimens show total prezygotic reproductive isolation. Our results reveal that the current taxonomic O. dioica classification likely hides multiple cryptic species, highlighting the genetic diversity and complexity of their population structures. Cryptic organisms are often hidden under a single species name because their morphological similarities make them difficult to disinguish and their correct identification is fundamental to understanding Earths biodiversity. O. dioica is an attractive model to understand how morphological conservation can be maintained despite pronounced genetic divergence.

evolutionary biology↗

bioRxiv2022

Dissecting the genetic basis of the heterosis of Y900, an elite super-hybrid rice

Y900 is one of the top hybrid rice varieties with a yield exceeding 15 t/hm2. To dissect the mechanism of heterosis, the male parent line R900 and female parent line Y58S were sequenced using long-read and Hi-C technology. High-quality reference genomes of sizes of 396.41 Mb and 398.24 Mb were obtained for R900 and Y58S, respectively. Genome-wide variations between the parents were systematically identified, including 1,367,758 SNPs and 299,149 Indels. No megabase level structural variations exist. >75% of genes exhibited variation between the two parents. Compared with other two-line hybrids sharing the same female parent, the Geng/japonica-type genetic components from different male parents showed an increasing trend from phase 2-4 super-hybrid rice; Transcriptome analysis revealed that additive and dominance effects are the main genetic effects that constitute the heterosis of Y900. Allele-specific expression patterns and expression regulation patterns are quite dynamic in different tissues. For young panicle tissues, cis-regulation is dominant, while trans-regulation is more popular in leaf issues. Overdominance is more likely regulated by the trans-regulation mechanism. The differential gene expression and regulation pattern are closely related to Geng/japonica introgression. Additionally, R900 contained several excellent japonica haplotypes, such as NAL1, OsSPL13, Ghd8, OsBRI1, and DTH2, which make a good complement to Y58S. The fine tune mechanism through dynamic expression or regulation pattern change, especially on some key functional genes, is the base for heterosis.

plant biology↗

bioRxiv2022

Single-shot Volumetric Chemical Imaging by Mid- Infrared Photothermal Fourier Light Field Microscopy

Three-dimensional molecular imaging of living organisms and cells plays a significant role in modern biology. Yet, current volumetric imaging modalities are largely fluorescence-based and thus lack chemical content information. Mid-infrared photothermal microscopy as a new chemical imaging technology provides infrared spectroscopic information at sub-micrometer spatial resolution. Here, by harnessing thermosensitive fluorescent dyes to sense the mid-infrared photothermal effect, we demonstrate mid-infrared photothermal Fourier light field (MIP-FLF) microscopy for single-shot volumetric infrared spectroscopic imaging at the speed of 8 volumes per second and sub-micron spatial resolution. Protein contents in bacteria and lipid droplets in living pancreatic cancer cells are visualized. Altered lipid metabolism in drug-resistant pancreatic cancer cells is observed with the MIP-FLF microscope.

biochemistry↗

bioRxiv2022

Markonv: a novel convolutional layer with inter-positional correlations modeled

Deep neural networks equipped with convolutional neural layers have been widely used in omics data analysis. Though highly efficient in data-oriented feature detection, the classical convolutional layer is designed with inter-positional independent filters, hardly modeling inter-positional correlations in various biological data. Here, we proposed Markonv layer (Markov convolutional neural layer), a novel convolutional neural layer with Markov transition matrices as its filters, to model the intrinsic dependence in inputs as Markov processes. Extensive evaluations based on both synthetic and real-world data showed that Markonv-based networks could not only identify functional motifs with inter-positional correlations in large-scale omics sequence data effectively, but also decode complex electrical signals generated by Oxford Nanopore sequencing efficiently. Designed as a drop-in replacement of the classical convolutional layer, Markonv layers enable an effective and efficient identification for inter-positional correlations from various biological data of different modalities. All source codes of a PyTorch-based implementation are publicly available on GitHub for academic usage.

bioinformatics↗

bioRxiv2022

Iterative transcription factor screening enables rapid generation of microglia-like cells from human iPSC

The ability to differentiate stem cells into human cell types is essential to define basic mechanisms and therapeutics, especially for cell types not routinely accessible by biopsies. But while engineered expression of transcription factors (TFs) identified through TF screens has been found to rapidly and efficiently produce some cell types, generation of other cell types that require complex combinations of TFs has been elusive. Here we develop an iterative, pooled single-cell TF screening method that improves the identification of effective TF combinations using the generation of human microglia-like cells as a testbed: Two iterations identified a combination of SPI1, CEBPA, FLI1, MEF2C, CEBPB, and IRF8 as sufficient to differentiate human iPSC into microglia-like cells in 4 days. Characterization of TF-induced microglia demonstrated molecular and functional similarity to primary microglia. We explore the use of single-cell atlas reference datasets to confirm identified TFs and how combining single-cell TF perturbation and gene expression data can enable the construction of causal gene regulatory networks. We describe what will be needed to fashion these methods into a generalized integrated pipeline, further ideas for enhancement, and possible applications.

bioengineering↗

bioRxiv2022

A single-cell atlas of bobtail squid visual and nervous system highlights molecular principles of convergent evolution

Although the camera-type eyes of cephalopods and vertebrates are a canonical example of convergent morphological evolution, the cellular and molecular mechanisms underlying this convergence remain obscure. We used genomics and single cell transcriptomics to study these mechanisms in the visual system of the bobtail squid Euprymna berryi, an emerging cephalopod model. Analysis of 98,537 cellular transcriptomes from the squid visual and nervous system identified dozens of cell types that cannot be placed in simple correspondence with those of vertebrate or fly visual systems, as proposed by Ramon y Cajal and J.Z. Young. Instead, we find an unexpected diversity of neural types, dominated by dopamine, and previously uncharacterized glial cells. Surprisingly, we observe changes in cell populations and neurotransmitter usage during maturation and growth of the visual systems from hatchling to adult. Together these genomic and cellular findings shed new light on the parallel evolution of visual system complexity in cephalopods and vertebrates.

evolutionary biology↗

bioRxiv2022

The Balance between Saturated and Unsaturated Fatty Acids Regulates Ovarian Cancer Cell Fate

Fatty acids are an important source of energy and a key component of phospholipids in membranes and organelles. Saturated (SFAs) are converted into unsaturated fatty acids (UFAs) by stearoyl Co-A desaturase (SCD), an enzyme highly active in cancer. Here we studied how the balance between SFAs and UFAs maintained by SCD impacts cancer cell survival and tumor progression. SCD depletion or inhibition caused lower levels of UFAs vs. SFAs and altered fatty acyl chain plasticity, as demonstrated by lipidomics and stimulated Raman spectroscopy (SRS). Further, the loss of equilibrium between UFAs and SFAs resulting from SCD knock down triggered endoplasmic reticulum (ER) stress response with brisk activation of IRE1/XBP1 and PERK/eIF2/ATF4 axes. Stiff and disorganized ER membrane was visualized by electron microscopy and SRS imaging in cells in which SCD was knocked down. The induction of long-term mild ER stress or short-time severe ER stress by the increased levels of SFAs and loss of UFAs led to cell death. However, ER stress and apoptosis could be readily rescued by supplementation with UFAs and re-equilibration of SFA/UFA levels. The effects of SCD knockdown or inhibition observed in vitro, translated into suppression of intraperitoneal tumor growth in xenograft models. Furthermore, a combined intervention using an SCD inhibitor and an SFA enriched diet, initiated ER stress in tumors growing in vivo and potently blocked their dissemination. In all, our data support SCD as a key regulator of the cancer cell fate under metabolic stress and point to new treatment strategies targeting the lipid balance. Significance StatementWe show that the balance between saturated and unsaturated fatty acids tightly regulated by the desaturase SCD impacts the survival of cancer cells; increased levels of unsaturation being protective against ER stress induced apoptosis. Decreasing fatty acid unsaturation, either through SCD depletion or through SCD inhibition coupled with a dietary intervention blocks tumor progression in vivo. Our findings support the concept of targeting the lipid balance as a new target in cancer.

cancer biology↗

bioRxiv2022

Characterizing transition cells in developmental processes from scRNA-seq data

Multi-cellular organism development involves orchestrated gene regulations of different cell types and cell states. Single-cell RNA-Seq, enable simultaneous observation of cells in various states, making it possible to study the underlying molecular mechanisms. However, most of the analytical methods do not make full use of the dynamics captured. Here, we model single-cell RNA-seq data obtained from a developmental process as a function of gene regulatory network using stochastic differential equations (SDEs). Based on dynamical systems theory, we showed that pair-wise gene expression correlation coefficients can accurately infer cell state transitions and validated it using mouse muscle cell regeneration scRNA-seq data. We then applied our analytical framework to the PDAC (Pancreatic ductal adenocarcinoma) mouse model scRNA-seq data. Through transition cells found in the pancreatic preinvasive lesions scRNA-seq data, we can better explain the heterogeneity and predict distinct cell fate even at early tumorigenesis stage. This suggests that the biomarkers identified by transition cells can be potentially used for diagnosis, prognosis and therapeutics of diseases.

bioinformatics↗

bioRxiv2022

Circular mRNA encoded PROTAC (RiboPROTAC) as a new platform for the degradation of intracellular therapeutic targets

Although proteolysis targeting chimera (PROTAC) technology that hijacking the ubiquitin-proteasome system (UPS) to artificially degrade protein is an emerging promising technology for many undruggable targets, it still faces several challenges, such as the difficulty to select high specificity molecule to protein of interest (POI), and only few of the E3-ligase are suitable for PROTAC mediated protein degradation. Protein-based PROTAC, termed BioPROTAC, owns the advantage of specificity but lacks membrane permeability. Here, we develop a new targeted protein degrading platform, which we termed RiboPROTAC, by encoding BioPROTAC protein degraders with circular mRNA (cmRNA) and delivering intracellularly. In this way, intracellular protein GFP and nuclear protein PCNA were successfully degraded. For PCNA targeting, not only the level of target protein is strongly decreased, but also the cell function related is strongly interfered, such as cell proliferation, apoptosis, as well as the cell cycle. What is more, for the first time, we observed that degrading PCNA with RiboPROTAC causes immunogenic cell death (ICD), although not strong, but was verified in vivo. Importantly, our in vivo results demonstrated that cmRNA encoded PCNA degrader exhibited significant tumor suppression effect, thus we provide the first proof of concept for the application of RiboPROTAC as potential mRNA therapeutic. We consider that RiboPROTAC is a new and superior PROTAC technology for targeting the undruggable targets.

molecular biology↗

bioRxiv2022

StrainPanDA: linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data

BackgroundMicrobial strains of variable functional capacities co-exist in microbiomes. Current bioinformatics methods of strain analysis cannot provide the direct linkage between strain composition and their gene contents from metagenomic data. MethodsHere we present StrainPanDA (Strain-level Pangenome Decomposition Analysis), a novel method that uses the pangenome coverage profile of multiple metagenomic samples to simultaneously reconstruct the composition and gene content variation of co-existing strains in microbial communities. ResultsWe systematically validate the accuracy and robustness of StrainPanDA using synthetic datasets. To demonstrate the power of gene-centric strain profiling, we then apply StrainPanDA to analyze the gut microbiome samples of infants, as well as patients treated with fecal microbiota transplantation. We show that the linked reconstruction of strain composition and gene content profiles is critical for understanding the relationship between microbial adaptation and strain-specific functions (e.g., nutrient utilization, pathogenicity). ConclusionsStrainPanDA can be applied to metagenomic datasets to detect association between molecular functions and microbial/host phenotypes to formulate testable hypotheses and gain novel biological insights at the strain or subspecies level.

bioinformatics↗

bioRxiv2022

Metagenomic and Metabolomic Analyses Reveal Synergistic Effects of Fecal Microbiota Transplantation and Anti-PD-1 Therapy on Treating Colorectal Cancer

Anti-PD-1 immunotherapy has saved numerous lives of cancer patients; however, it only exerts efficacy in 10-15% of patients with colorectal cancer. Fecal microbiota transplantation (FMT) is a potential approach to improving the efficacy of anti-PD-1 therapy, whereas the detailed mechanisms and the applicability of this combination therapy remain unclear. In this study, we evaluated the synergistic effect of FMT with anti-PD-1 in curing colorectal tumor-bearing mice using a multi-omics approach. Mice treated with the combination therapy showed superior survival rate and tumor control, compared to the mice received anti-PD-1 therapy or FMT alone. Metagenomic analysis showed that composition of gut microbiota in tumor-bearing mice treated with anti-PD-1 therapy was remarkably altered through receiving FMT. Particularly, Bacteroides genus, including FMT-increased B. thetaiotaomicron, B. fragilis, and FMT-decreased B. ovatus might contribute to the enhanced efficacy of anti-PD-1 therapy. Furthermore, metabolomic analysis upon mouse plasma revealed several potential metabolites that upregulated after FMT, including punicic acid and aspirin, might promote the response to anti-PD-1 therapy via their immunomodulatory functions. This work broadens our understanding of the mechanism by which FMT improves the efficacy of anti-PD-1 therapy, which may contribute to the development of novel microbiota-based anti-cancer therapies.

microbiology↗

bioRxiv2022

Annelid functional genomics reveal the origins of bilaterian life cycles

Indirect development with an intermediate larva exists in all major animal lineages1, making larvae central to most scenarios of animal evolution2-12. Yet how larvae evolved remains disputed. Here we show that temporal shifts (i.e., heterochronies) in trunk formation underpin the diversification of larvae and bilaterian life cycles. Combining chromosome-scale genome sequencing in the slow-evolving annelid Owenia fusiformis13 with transcriptomic and epigenomic profiling during the life cycles of this and two other annelids, we found that trunk development is deferred to pre-metamorphic stages in the feeding larva of O. fusiformis, but starts after gastrulation in the non-feeding larva with gradual metamorphosis of Capitella teleta and the direct developing embryo of Dimorphilus gyrociliatus. Accordingly, the embryos of O. fusiformis develop first into an enlarged anterior domain that forms larval tissues and the adult head. Notably, this also occurs in the so-called "head larvae" of other bilaterians14,15, with whom O. fusiformis larva shows extensive transcriptomic similarities. Together, our findings suggest that the temporal decoupling of head and trunk formation, as maximally observed in "head larvae", allowed larval evolution in Bilateria, thus diverging from prevailing scenarios that propose either co-option10,11 or innovation12 of gene regulatory programmes to explain larva and adult origins.

genomics↗

bioRxiv2022

Transcription factor determines the sources of extracellular vesicle DNA

Extracellular vesicle DNAs (evDNAs) possess the important diagnostic value for multiple diseases and play roles for horizontally transferring genetic materials among cells. In this study, we have found that transcription factor FOXM1 can mediate the loading of certain chromatin genes or DNA fragments (named FOXM1-chDNAs) to extracellular vesicles (EVs). FOXM1 interacts with LC3 in nucleus and FOXM1-chDNAs (such as DUX4 gene and Telomere DNA) are specified by FOXM1 and translocated to cytoplasm. These DNAs are released to EVs through the process of an LC3-involved autophagosome-multivesicular body (MVB) transport. The roles of FOXM1 on loading FOXM1-chDNAs to EVs are further confirmed by DNA-FISH experiments, tracing the translocation of selected chromatin loci with the TetO/TetR-GFP method, and PCR analysis of the DNA samples from MVBs and EVs. Furthermore, disrupting the expression of FOXM1 or the process of autophagosome-MVB transport impairs the loading of FOXM1-chDNAs to EVs. This discovery suggests that transcription factor FOXM1 contributes the constitution of evDNAs from nuclear chromatin, providing the first example to explain how chromatin DNA fragments are specified and loaded to EVs. It also provide a foundation to further explore the roles of evDNAs in biological processes such as the horizontal gene transfer.

cell biology↗

bioRxiv2021

Annotation of Spatially Resolved Single-cell Data with STELLAR

Accurate cell type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset as well as discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues, and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6 million spatially resolved single cells with dramatic time savings.

bioinformatics↗