Biology subjects

Yao, J.

Publications and source records attributed to Yao, J..

At least 127 records · Page 7Linked to original sources

bioRxiv2023

KRAS inhibition activates an actionable CD24 do-not-eat-me signal in pancreas cancer

KRASG12C inhibitors (G12Ci) have produced encouraging, albeit modest and transient, clinical benefit in pancreatic ductal adenocarcinoma (PDAC). Identifying and targeting resistance mechanisms to G12Ci treatment is therefore crucial. To better understand the function of KRASG12C and possible G12Ci bypass mechanisms, we developed an autochthonous KRASG12C- driven PDAC model. Compared to the classical KRASG12D PDAC model, the G12C model exhibits slower tumor growth, yet similar histopathological and molecular features. Aligned with clinical experience, G12Ci treatment of KRASG12C tumors produced modest impact despite stimulating a hot tumor immune microenvironment. Immunoprofiling revealed that CD24, a dont eat me signal, is significantly upregulated on cancer cells upon G12Ci treatment. Blocking CD24 enhanced macrophage phagocytosis of cancer cells and significantly sensitized tumors to G12Ci treatment. Similar findings were observed in KRASG12D-driven PDAC. Together, this study reveals common and distinct oncogenic KRAS allele-specific biology and identifies a clinically actionable adaptive mechanism that may improve the efficacy of oncogenic KRAS inhibitor therapy in PDAC. SignificanceGeneration of an autochthonous KRASG12C-driven pancreatic cancer model enabled elucidation of specific effects of KRASG12C during tumor development, revealing CD24 as an actionable adaptive mechanism in cancer cells induced upon KRASG12C inhibition.

cancer biology↗

bioRxiv2023

Mechanisms used for cDNA synthesis and site-specific integration of RNA into DNA genomes by a reverse transcriptase-Cas1 fusion protein

Reverse transcriptase-Cas1 (RT-Cas1) fusion proteins found in some CRISPR systems enable spacer acquisition from both RNA and DNA, but the mechanism of RNA spacer acquisition has remained unclear. Here, we found Marinomonas mediterranea RT-Cas1/Cas2 adds short 3-DNA (dN) tails to RNA protospacers enabling their direct integration into CRISPR arrays as 3-dN-RNA/cDNA duplexes or 3-dN-RNAs at rates comparable to similarly configured DNAs. Reverse transcription of RNA protospacers occurs by multiple mechanisms, including recently described de novo initiation, protein priming with any dNTP, and use of short exogenous or synthesized DNA oligomer primers, enabling synthesis of cDNAs from diverse RNAs without fixed sequence requirements. The integration of 3-dN-RNAs or single-stranded (ss) DNAs is favored over duplexes at higher protospacer concentrations, potentially relevant to spacer acquisition from abundant pathogen RNAs or ssDNA fragments generated by phage-defense nucleases. Our findings reveal novel mechanisms for site-specifically integrating RNA into DNA genomes with potential biotechnological applications.

molecular biology↗

bioRxiv2023

On-Demand Expansion Fluorescence and Photoacoustic Microscopy (ExFLPAM)

Expansion microscopy (ExM) is a promising technology that enables nanoscale imaging on conventional optical microscopes by physically magnifying the specimens. Here, we report the development of a strategy that enables i) on-demand labeling of subcellular organelles in live cells for ExM through transfection of fluorescent proteins that are well-retained during the expansion procedure; and ii) non-fluorescent chromogenic color-development towards efficient bright-field and photoacoustic imaging in both planar and volumetric formats, which is applicable to both cultured cells and biological tissues. Compared to the conventional ExM methods, our strategy provides an expanded toolkit, which we term as expansion fluorescence and photoacoustic microscopy (ExFLPAM), by allowing on-demand fluorescent protein labeling of cultured cells, as well as non-fluorescent absorption contrast-imaging of biological samples.

bioengineering↗

bioRxiv2023

DNAGPT: A Generalized Pretrained Tool for Multiple DNA Sequence Analysis Tasks

Pre-trained large language models demonstrate potential in extracting information from DNA sequences, yet adapting to a variety of tasks and data modalities remains a challenge. To address this, we propose DNAGPT, a generalized DNA pre-training model trained on over 200 billion base pairs from all mammals. By enhancing the classic GPT model with a binary classification task (DNA sequence order), a numerical regression task (guanine-cytosine content prediction), and a comprehensive token language, DNAGPT can handle versatile DNA analysis tasks while processing both sequence and numerical data. Our evaluation of genomic signal and region recognition, mRNA abundance regression, and artificial genome generation tasks demonstrates DNAGPTs superior performance compared to existing models designed for specific downstream tasks, benefiting from pre-training using the newly designed model structure.

genomics↗

bioRxiv2023

A pre-trained large language model for translating single-cell transcriptome to proteome

Proteins are crucial for life, and measuring their abundance at the single-cell level can facilitate a high-resolution understanding of biological mechanisms in cellular processes and disease progression. However, current single-cell proteomic technologies face challenges such as limited coverage, throughput, and sensitivity, as well as batch effects, high costs, and stringent experimental operations. Drawing inspiration from the translation procedure of both natural language processing (NLP) and the genetic central dogma, we propose a pre-trained, large generative model named scTranslator (single-cell translator). scTranslator is align-free and capable of generating multi-omics data by inferring the missing single-cell proteome based on the transcriptome. Systematic benchmarking confirms the accuracy, stability, and flexibility of scTranslator across various quantification techniques, cell types, and conditions. Furthermore, scTranslator has demonstrated its superiority in assisting various downstream analyses and applications, including gene/protein interaction inference, gene pseudo-knockout, cell clustering, batch correction, and cell origin recognition on pan-cancer data.

bioinformatics↗

bioRxiv2023

Pan-genome analysis highlights the role of structural variation in the evolution and environmental adaptation of Asian honeybees

The Asian honeybee, Apis cerana, is an ecologically and economically important pollinator. Mapping its genetic variation is key to understanding population-level health, histories, and potential capacities to respond to environmental changes. However, most efforts to date were focused on single nucleotide polymorphisms (SNPs) based on a single reference genome, thereby ignoring larger-scale genomic variation. We employed long-read sequencing technologies to generate a chromosome-scale reference genome for the ancestral group of A. cerana. Integrating this with 525 resequencing datasets, we constructed the first pan-genome of A. cerana, encompassing almost the entire gene content. We found that 31.32% of genes in the pan-genome were variably present across populations, providing a broad gene pool for environmental adaptation. We identified and characterized structural variations (SVs) and found that they were not closely linked with SNP distributions, however, the formation of SVs was closely associated with transposable elements. Furthermore, phylogenetic analysis using SVs revealed a novel A. cerana ecological group not recoverable from the SNP data. Performing environmental association analysis identified a total of 44 SVs likely to be associated with environmental adaptation. Verification and analysis of one of these, a 330 bp deletion in the Atpalpha gene, indicated that this SV may promote the cold adaptation of A. cerana by altering gene expression. Taken together, our study demonstrates the feasibility and utility of applying pan-genome approaches to map and explore genetic feature variations of honeybee populations, and in particular to examine the role of SVs in the evolution and environmental adaptation of A. cerana.

genomics↗

bioRxiv2023

Lipid Bilayer Strengthens the Cooperative Network of a Membrane-Integral Enzyme

Although membrane proteins fold and function in a lipid bilayer constituting cell membranes, their structure and functionality can be recapitulated in diverse amphiphilic assemblies whose compositions deviate from native membranes. It remains unclear how various hydrophobic environments can stabilize membrane proteins and whether lipids play any role therein. Here, using the evolutionary unrelated -helical and {beta}-barrel membrane proteins of Escherichia coli, we find that the hydrophobic thickness and the strength of amphiphile- amphiphile packing are critical environmental determinants of membrane protein stability. Lipid solvation enhances stability by facilitating residue burial in the protein interior and strengthens the cooperative network by promoting the propagation of local structural perturbations. This study demonstrates that lipids not only modulate membrane proteins stability but also their response to external stimuli.

biophysics↗

bioRxiv2023

Disease associated human TCR characterization by deep-learning framework TCR-DeepInsight

T cell function is defined by both T cell receptors (TCR) and T cell gene expression (GEX). Although single-cell technology enables the simultaneous capture of TCR and GEX information, the lack of a reference atlas and computational tools hinders our ability to uncover the fundamental TCR usage rules and to efficiently characterize disease-associated TCRs (dTCR). Here, through the collection of million-scale single-cell GEX-TCR reference atlas comprising 20 diverse disease conditions, we revealed the intrinsic features of TCR-MHC (Major Histocompatibility Complex) restriction in CD4/CD8 lineages. We observed the higher coherence for TCR/{beta} chains in memory T cells, and detected widely-existing public TCR/{beta} pairs across individuals. Building upon the reference atlas, we introduced TCR- DeepInsight, a deep-learning framework featuring a disease specificity scoring system that enables the characterization of dTCR clusters with similar GEX-TCR. Our study provides a valuable tool for researchers to analyze single-cell GEX-TCR data and identify dTCRs comprehensively and robustly.

bioinformatics↗

bioRxiv2023

Telomere-to-telomere genome of the model plant Physcomitrium patens

The model plant Physcomitrium patens (P. patens) has played a pivotal role in enhancing our comprehension of plant evolution, growth, and development. However, the current genome harbors numerous intricate regions that remain unfinished and erroneous. To address these issues, we present an exemplary assembly of the P. patens nuclear genome, which incorporates telomeres and centromere regions, thereby establishing it as the telomere-to-telomere (T2T) genome in a non-seed plant. This T2T genome not only dispels the prevailing misconception regarding chromosome number in P. patens but also provides indispensable resources for conducting in-depth studies in moss genomics and biology.

genomics↗

bioRxiv2023

Deciphering molecular heterogeneity and dynamics of neural stem cells in human hippocampal development, aging, and injury

While accumulated publications support the existence of neurogenesis in the adult human hippocampus, the homeostasis and developmental potentials of neural stem cells (NSCs) under different contexts remain unclear. Based on our generated single-nucleus atlas of the human hippocampus across neonatal, adult, aging and injury, we dissected the molecular heterogeneity and transcriptional dynamics of human hippocampal NSCs under different contexts. We further identified new specific neurogenic lineage markers that overcome the lack of specificity found in some well-known markers. Based on developmental trajectory and molecular signatures, we found that a subset of NSCs exhibit quiescent properties after birth, and most NSCs become deep quiescence during aging. Furthermore, certain deep quiescent NSCs are re-activated following stroke injury. Together, our findings provide valuable insights into the development, aging, and re-activation of the human hippocampal NSCs, and help to explain why adult hippocampal neurogenesis is infrequently observed in humans.

neuroscience↗

bioRxiv2023

Prefrontal projections modulate recurrent circuitry in insular cortex to support short-term memory

Short-term memory (STM) maintains information during a short delay period. How long-range and local connections interact to support STM encoding remains elusive. Here we tackled the problem focusing on long-range projections from medial prefrontal cortex (mPFC) to anterior agranular insular cortex (aAIC) in head-fixed mice performing an olfactory delayed-response task. Optogenetic and electrophysiological experiments revealed behavioral importance of the two regions in encoding STM information. Spike-correlogram analysis revealed strong local and cross-region functional coupling (FC) between memory neurons encoding the same information. Optogenetic suppression of mPFC-aAIC projections during the delay period reduced behavioral performance, the proportion of memory neurons and memory-specific FC within aAIC, whereas optogenetic excitation enhanced all of them. MPFC-to-aAIC projections also bidirectionally modulated efficacy of STM-information transfer, measured by contribution of FC spiking pairs to coding ability of following neurons. Thus, prefrontal projections modulate functional connectivity and coding ability of insular neurons to support short-term memory.

neuroscience↗

bioRxiv2023

Cross-species single-cell comparison of systemic and cardiac inflammatory responses after cardiac injury

The immune system coordinates the response to cardiac injury and is known to control regenerative and fibrotic scar outcomes in the heart and subsequent chronic low-grade inflammation associated with heart failure. Here we profiled the inflammatory response to heart injury using single cell transcriptomics to compare and contrast two experimental models with disparate outcomes. We used adult mice, which like humans lack the ability to fully recover and zebrafish which spontaneously regenerate after heart injury. The extracardiac reaction to cardiomyocyte necrosis was also interrogated to assess the specific peripheral tissue and immune cell reaction to chronic stress. Cardiac macrophages are known to play a critical role in determining tissue homeostasis by healing versus scarring. We identified distinct transcriptional clusters of monocytes/macrophages in each species and found analogous pairs in zebrafish and mice. However, the reaction to myocardial injury was largely disparate between mice and zebrafish. The dichotomous response to heart damage between the mammalian and zebrafish monocytes/macrophages may underlie the impaired regenerative process in mice, representing a future therapeutic target.

cell biology↗

bioRxiv2023

Histone demethylase KDM2A is a selective vulnerability of cancers relying on alternative telomere maintenance

Telomere length maintenance is essential for cellular immortalization and tumorigenesis. 5% - 10% of human cancers rely on a recombination-based mechanism termed alternative lengthening of telomeres (ALT) to sustain their replicative immortality, yet there are currently no targeted therapies. Through CRISPR/Cas9-based genetic screens in an ALT-immortalized isogenic cellular model, here we identify histone lysine demethylase KDM2A as a molecular vulnerability selectively for cells contingent on ALT-dependent telomere maintenance. Mechanistically, we demonstrate that KDM2A is required for dissolution of the ALT-specific telomere clusters following homology-directed telomere DNA synthesis. We show that KDM2A promotes de-clustering of ALT multitelomeres through facilitating isopeptidase SENP6-mediated SUMO deconjugation at telomeres. Inactivation of KDM2A or SENP6 impairs post-recombination telomere de-SUMOylation and thus dissolution of ALT telomere clusters, leading to gross chromosome missegregation and mitotic cell death. These findings together establish KDM2A as a selective molecular vulnerability and a promising drug target for ALT-dependent cancers.

cancer biology↗

bioRxiv2023

Adaptation to an acid microenvironment promotes pancreatic cancer organoid growth and drug resistance in a p53-dependent manner

The harsh environments in poorly perfused tumor regions have been proposed to select for traits that may drive cancer aggressiveness. Here, we tested the hypothesis that tumor acidosis interacts with driver mutations to exacerbate cancer hallmarks, including drug resistance, in pancreatic cancer. We gradually adapted mouse organoids from normal pancreatic duct (mN) and early PDAC (mP, with KRAS G12V mutation and +/- p53 knockout), from pH 7.4 (physiological level) to 6.7, representing acidic tumor niches. Acid adaptation rewired organoid transcriptional activity, increased viability and, strikingly, increased Gemcitabine- and Erlotinib resistance. Importantly, this response only occurred in organoids expressing wild-type p53 and was most pronounced when acid-adapted cells were returned to physiological pH (mimicking increased perfusion or invasion). While the acid adaptation transcriptional change was overall not highly similar to that induced by drug adaptation of the organoids, acid adaptation induced expression of cytidine deaminase (Cda) and ribonucleotide reductase regulatory subunit M2 (Rrm2), both associated with Gemcitabine resistance, and inhibition of these proteins partially restored Gemcitabine sensitivity. Thus, adaptation to the acidic tumor microenvironment increases drug resistance even after cells leave this niche, and this is in part dependent on acid-adaptation-induced transcriptional upregulation of Cda and Rrm2.

cancer biology↗

bioRxiv2022

A Versatile Deep Graph Contrastive Learning Framework for Single-cell Proteomics Embedding

The advance of single-cell proteomics sequencing technology sheds light on the research in revealing the protein-protein interactions, the post-translational modifications, and the proteoform dynamics of proteins in a cell. However, the uncertainty estimation for peptide quantification, data missingness, severe batch effects and high noise hinder the analysis of single-cell proteomic data. It is a significant challenge to solve this set of tangled problems together, where existing methods tailored for single-cell transcriptome do not address. Here, we proposed a novel versatile framework scPROTEIN, composed of peptide uncertainty estimation based on a multi-task heteroscedastic regression model and cell embedding learning based on graph contrastive learning designed for single-cell proteomic data analysis. scPROTEIN estimated the uncertainty of peptide quantification, denoised the protein data, removed batch effects and encoded single-cell proteomic-specific embeddings in a unified framework. We demonstrate that our method is efficient for cell clustering, batch correction, cell-type annotation and clinical analysis. Furthermore, our method can be easily plugged into single-cell resolved spatial proteomic data, laying the foundation for encoding spatial proteomic data for tumor microenvironment analysis.

bioinformatics↗

bioRxiv2022

Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics

Cells do not live in a vacuum, but in a milieu defined by cell-cell communication that can be measured via emerging high-resolution spatial transcriptomics approaches. However, analytical tools that fully leverage such data for kinetic modeling remain lacking. Here we present Spateo (aristoteleo/spateo-release), a general framework for quantitative spatiotemporal modeling of single-cell resolution spatial transcriptomics. Spateo delivers novel methods for digitizing spatial layers/columns to identify spatially-polar genes, and develops a comprehensive framework of cell-cell interaction to reveal spatial effects of niche factors and cell type-specific ligand-receptor interactions. Furthermore, Spateo reconstructs 3D models of whole embryos, and performs 3D morphometric analyses. Lastly, Spateo introduces the concept of "morphometric vector field" of cell migrations, and integrates spatial differential geometry to unveil regulatory programs underlying various organogenesis patterns of Drosophila. Thus, Spateo enables the study of the ecology of organs at a molecular level in 3D space, beyond isolated single cells.

systems biology↗

bioRxiv2022

Evolutionary trajectory of organelle-derived nuclear DNAs in the Triticum/Aegilops complex species

Organelle-derived nuclear DNAs, nuclear plastid DNAs (NUPTs) and nuclear mitochondrial DNAs (NUMTs), have been identified in plants. Most, if not all, genes residing in NUPTs/NUMTs (NUPGs/NUMGs) are known to be inactivated and pseudogenized. However, the role of epigenetic control in silencing NUPGs/NUMGs and the dynamic evolution of NUPTs/NUMTs with respect to organismal phylogeny remain barely explored. Based on the available nuclear and organellar genomic resources of the Triticum/Aegilops complex species, we investigated the evolutionary fates of NUPTs/NUMTs in terms of their epigenetic silencing and their dynamic occurrence rates in the nuclear diploid genomes and allopolyploid subgenomes. NUPTs and NUMTs possessed similar genomic atlas, including preferential integration to the transposable element-rich intergenic regions and generating sequence variations in the nuclear genome. The global transcriptional silencing of NUPGs/NUMGs with disrupted and intact open reading frames can be mainly attributed to their repressive chromatin states, namely high levels of DNA methylation and low levels of active histone modifications. Phylogenomic analyses suggested that the species-specific and gradual accumulation of NUPTs/NUMTs accompanied the speciation processes. Moreover, based on further pan-genomic analyses, we found significant subgenomic asymmetry in the NUPT/NUMT occurrence, which accumulated during allopolyploid wheat evolution. Our findings provide novel insights into the dynamic evolutionary fates of organelle-derived nuclear DNA in plants.

genomics↗

bioRxiv2022

Deep Domain Adversarial Neural Network for the Deconvolution of Cell Type Mixtures in Tissue Proteome Profiling

Cell type deconvolution is a computational method for the determination/resolution of cell type proportions from bulk sequencing data, frequently used for the analysis of divergent cell types in tumor tissue samples. However, deconvolution technology is still in its infancy for the analysis of cell types using proteomic data due to challenges with repeatability/reproducibility, variable reference standards and the lack of single-cell proteomic reference data. Here, we developed a novel deep learning-based deconvolution method (scpDeconv) tailored to proteomic data. scpDeconv uses an autoencoder to leverage the information from bulk proteomic data to improve the quality of single-cell proteomic data, and employs a domain adversarial architecture to bridge the single-cell and bulk data distributions and transfer labels from single-cell data to bulk data. Extensive experiments validated the performance of scpDeconv in the deconvolution of proteomic data produced from various species/sources and different proteomic technologies. This method should find broad applicability to areas including tumor microenvironment interpretation and clinical diagnosis/classification.

bioinformatics↗