Biology subjects

Cao, K.

Publications and source records attributed to Cao, K..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Impaired Lef1 activation accelerates iPSC-derived keratinocytes differentiation in Progeria

Hutchinson-Gilford progeria syndrome (HGPS) is a detrimental premature aging disease caused by a point mutation in the human LMNA gene. This mutation results in the abnormal accumulation of a truncated pre-lamin A protein called progerin. Among the drastically accelerated signs of aging in HGPS patients, severe skin phenotypes such as alopecia and sclerotic skins always develop with the disease progression. Here, we study the HGPS molecular mechanisms focusing on early skin development by differentiating patient-derived induced pluripotent stem cells (iPSCs) to a keratinocyte lineage. Interestingly, HGPS iPSCs showed an accelerated commitment to the keratinocyte lineage than the normal control. To study potential signaling pathways that accelerated skin development in HGPS, we investigated the WNT pathway components during HGPS iPSCs-keratinocytes induction. Surprisingly, despite the unaffected {beta}-catenin activity, the expression of a critical WNT transcription factor LEF1 was diminished from an early stage in HGPS iPSCs-keratinocytes differentiation. Chromatin immunoprecipitation (ChIP) experiment further revealed strong bindings of LEF1 to the early-stage epithelial developmental markers K8 and K18 and that the LEF1 silencing by siRNA down-regulates the K8/K18 transcription. During the iPSCs-keratinocytes differentiation, correction of HGPS mutation by Adenine base editing (ABE), while in a partial level, rescued the phenotypes for accelerated keratinocyte lineage-commitment. ABE also reduced the cell death in HGPS iPSCs-derived keratinocytes. These findings brought new insight into the molecular basis and therapeutic application for the skin abnormalities in HGPS.

developmental biology↗

bioRxiv2022

Lysine acetylation regulates antibiotic resistance in Escherichia coli

Antibiotic resistance is increasingly becoming a serious challenge to public health. The regulation of metabolism by post-translational modifications (PTMs) has been widely studied; however, the comprehensive mechanism underlying the regulation of acetylation in bacterial resistance against antibiotics is unknown. Herein, with Escherichia coli as the model, we performed quantitative analysis of the acetylated proteome of wild-type sensitive strain (WT) and ampicillin- (Re-Amp), kanamycin- (Re-Kan), and polymyxin B-resistant (Re-Pol) strains. Based on bioinformatics analysis combined with biochemical validations, we found that a common regulatory mechanism exists between the different resistant strains. Acetylation negatively regulates bacterial metabolism to maintain antibiotic resistance, but positively regulates bacterial motility. Further analyses revealed that key enzymes in various metabolic pathways were differentially acetylated. Particularly, pyruvate kinase (PykF), a key glycolytic enzyme regulating bacterial metabolism, and its acetylated form were highly expressed in the three resistant types and were identified as reversibly acetylated by the deacetylase CobB and the acetyl-transferase PatZ, and also could be acetylated by non-enzyme AcP in vitro. Further, the deacetylation of Lys413 of PykF increased the enzyme activity by changing the conformation of ATP binding site of PykF, resulting in an increase in energy production, which in turn increased the sensitivity of drug-resistant strains to antibiotics. This study provides novel insights for understanding bacterial resistance and lays the foundation for future research on regulation of acetylation in antibiotic-resistant strains. ImportanceThe misuse of antibiotics has resulted in an emergence of a large number of antibiotic-resistant strains, which seriously threaten human health. Bacterial metabolism is tightly controlled by protein post-translational modifications, especially acetylation. However, the comprehensive mechanism underlying regulation of acetylation in bacterial resistance remains unexplored. Here, acetylation was found to positively regulate bacterial motility and negatively regulate energy metabolism, which was common in all the different antibiotic-resistant strains. Moreover, the acetylation and deacetylation process of PykF was uncovered, and deacetylation of the Lys 413 of PykF was found to contribute to bacterial sensitivity to antibiotics. This study provides a new direction for research on development of bacterial resistance through post-translational modifications and provides a theoretical basis for the development of antibacterial drugs.

microbiology↗

bioRxiv2022

uniPort: a unified computational framework for single-cell data integration with optimal transport

Single-cell data integration can provide a comprehensive molecular view of cells. Here we introduce uniPort, a unified single-cell data integration framework which combines a coupled Variational Autoencoder (coupled-VAE) and Minibatch Unbalanced Optimal Transport (Minibatch-UOT). It leverages both highly variable common and dataset-specific genes for integration and is scalable to large-scale and partially overlapping datasets. uniPort jointly embeds heterogeneous single-cell multi-omics datasets into a shared latent space. It can further construct a reference atlas for online prediction across datasets. Meanwhile, uniPort provides a flexible label transfer framework to deconvolute spatial heterogeneous data using optimal transport space, instead of embedding latent space. We demonstrate the capability of uniPort by integrating a variety of datasets, including single-cell transcriptomics, chromatin accessibility and spatially resolved transcriptome data. uniPort software is available at https://github.com/caokai1073/uniPort.

bioinformatics↗

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↗

bioRxiv2021

High Resolution Single Cell Maps Reveals Distinct Cell Organization and Function Across Different Regions of the Human Intestine

The colon is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota, and affects overall health. To better understand its organization, functions, and its regulation at a single cell level, we performed CODEX multiplexed imaging, as well as single nuclear RNA and open chromatin assays across eight different intestinal sites of four donors. Through systematic analyses we find cell compositions differ dramatically across regions of the intestine, demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighborhoods and communities highlighting distinct immunological niches present in the intestine. We also map gene regulatory differences in these cells suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation, and organization for this organ, and serve as an important reference map for understanding human biology and disease.

genomics↗

bioRxiv2021

mTOR-neuropeptide Y signaling sensitizes nociceptors to drive neuropathic pain

Neuropathic pain is a refractory condition that involves de novo protein synthesis in the nociceptive pathway. The mechanistic target of rapamycin (mTOR) is a master regulator of protein translation; however, mechanisms underlying its role in neuropathic pain remain elusive. Using spared nerve injury-induced neuropathic pain model, we found that mTOR is preferentially activated in large-diameter dorsal root ganglion (DRG) neurons and spinal microglia. However, selective ablation of mTOR in DRG neurons, rather than microglia, alleviated neuropathic pain. We show that injury- induced mTOR activation promoted transcriptional induction of NPY likely via signal transducer and activator of transcription 3 (STAT3) phosphorylation. NPY further acted primarily on Y2 receptors (Y2R) to enhance nociceptor excitability. Peripheral replenishment of NPY reversed pain alleviation upon mTOR removal, whereas Y2R antagonists prevented pain restoration. Our findings reveal an unexpected link between mTOR and NPY in promoting nociceptor sensitization and neuropathic pain, through NPY/Y2R signaling-mediated intra-ganglionic transmission.

neuroscience↗

bioRxiv2021

Mitochondrial dynamics regulate genome stability via control of caspase-dependent DNA damage

Mitochondrial dysfunction is interconnected with cancer. Nevertheless, how defective mitochondria promote cancer is poorly understood. We find that mitochondrial dysfunction promotes DNA damage under conditions of increased apoptotic priming. Underlying this process, we reveal a key role for mitochondrial dynamics in the regulation of DNA damage and genome instability. The ability of mitochondrial dynamics to regulate oncogenic DNA damage centres upon the control of minority MOMP, a process that enables non-lethal caspase activation leading to DNA damage. Mitochondrial fusion suppresses minority MOMP, and its associated DNA damage, by enabling homogenous mitochondrial expression of anti-apoptotic BCL-2 proteins. Finally, we find that mitochondrial dysfunction inhibits pro-apoptotic BAX retrotranslocation, causing BAX mitochondrial localization thereby promoting minority MOMP. Unexpectedly, these data reveal oncogenic effects of mitochondrial dysfunction that are mediated via mitochondrial dynamics and caspase-dependent DNA damage.

cell biology↗

bioRxiv2021

Undersampling correction methods to control γ-dependence for comparing β-diversity between regions

Measures of {beta}-diversity are known to be highly constrained by the variation in {gamma}-diversity across regions (i.e., {gamma}-dependence), making it challenging to infer underlying ecological processes. Undersampling correction methods have attempted to estimate the actual {beta}-diversity in order to minimize the effects of {gamma}-dependence arising from the problem of incomplete sampling. However, no study has systematically tested their effectiveness in removing {gamma}-dependence, and examined how well undersampling-corrected {beta}-metrics reflect true {beta}-diversity patterns that respond to ecological gradients. Here, we conduct these tests by comparing two undersampling correction methods with the widely used individual-based null model approach, using both empirical data and simulated communities along a known ecological gradient across a wide range of {gamma}-diversity and sample sizes. We found that undersampling correction methods using diversity accumulation curves were generally more effective than the null model approach in removing {gamma}-dependence. In particular, the undersampling-corrected {beta}-Shannon diversity index was most independent on {gamma}-diversity and was the most reflective of the true {beta}-diversity pattern along the ecological gradient. Moreover, the null model-corrected Jaccard-Chao index removed {gamma}-dependence more effectively than either approach alone. Our validation of undersampling correction methods as effective tools for accommodating {gamma}-dependence greatly facilitates the comparison of {beta}-diversity across regions.

ecology↗

bioRxiv2020

Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona

Single-cell multi-omics sequencing data can provide a comprehensive molecular view of cells. However, effective approaches for the integrative analysis of such data are challenging. Although achieved state-of-the-art performance on single-cell multi-omics data integration and did not require any correspondence information, either among cells or among features, current manifold alignment based integrative methods are often limited by requiring that single-cell datasets be derived from the same underlying cellular structure. To overcome this limitation, we present Pamona, an algorithm that integrates heterogeneous single-cell multi-omics datasets with the aim of delineating and representing the shared and dataset-specific cellular structures. We formulate this task as a partial manifold alignment problem and develop a partial Gromov-Wasserstein optimal transport framework to solve it. Pamona identifies both shared and dataset-specific cells based on the computed probabilistic couplings of cells across datasets, and it aligns cellular modalities in a common low-dimensional space, while simultaneously preserving both shared and dataset-specific structures. Our framework can easily incorporate prior information, such as cell type annotations or cell-cell correspondence, to further improve alignment quality. Simulation studies and applications to four real data sets demonstrate that Pamona can accurately identify shared and dataset-specific cells, as well as faithfully recover and align cellular structures of heterogeneous single-cell modalities in the common space. Pamona software is available at https://github.com/caokai1073/Pamona.

bioinformatics↗

bioRxiv2020

Species packing and the latitudinal gradient in local beta-diversity

The decline in species richness at higher latitudes is among the most fundamental patterns in ecology. Whether changes in species composition across space (beta-diversity) contribute to this gradient of overall species richness (gamma-diversity) remains hotly debated. Previous studies that failed to resolve the issue suffered from a well-known tendency for small samples in areas with high gamma-diversity to have inflated measures of beta-diversity. Here, we provide here a novel analytical test, using beta-diversity metrics that correct the gamma-diversity and sampling biases, to compare beta-diversity and species packing across a latitudinal gradient in tree species richness of 21 large forest plots along a large environmental gradient in East Asia. We demonstrate that after accounting for topography and correcting the gamma-diversity bias, tropical forests still have higher beta-diversity than temperate analogs. This suggests that beta-diversity contributes to the latitudinal species richness gradient as a component of gamma-diversity. Moreover, both niche specialization and niche marginality (a measure of niche spacing along an environmental gradient) also increase towards the equator, after controlling for the effect of topographic heterogeneity. This supports the joint importance of tighter species packing and larger niche space in tropical forests while also demonstrating the importance of local processes in controlling beta-diversity.

ecology↗

bioRxiv2020

Pan-genome analyses of peach and its wild relatives provide insights into the genetics of disease resistance and species adaptation

As a foundation to understand the molecular mechanisms of peach evolution and high-altitude adaptation, we performed de novo genome assembling of four wild relatives of P. persica, P. mira, P. kansuensis, P. davidiana and P. ferganensis. Through comparative genomic analysis, abundant genetic variations were identified in four wild species when compared to P. persica. Among them, a deletion, located at the promoter of Prupe.2G053600 in P. kansuensis, was validated to regulate the resistance to nematode. Next, a pan-genome was constructed which comprised 15,216 core gene families among four wild peaches and P. perisca. We identified the expanded and contracted gene families in different species and investigated their roles during peach evolution. Our results indicated that P. mira was the primitive ancestor of cultivated peach, and peach evolution was non-linear and a cross event might have occurred between P. mira and P. dulcis during the process. Combined with the selective sweeps identified using accessions of P. mira originating from different altitude regions, we proposed that nitrogen recovery was essential for high-altitude adaptation of P. mira through increasing its resistance to low temperature. The pan-genome constructed in our study provides a valuable resource for developing elite cultivars, studying the peach evolution, and characterizing the high-altitude adaptation in perennial crops.

plant biology↗

bioRxiv2020

Unsupervised Topological Alignment for Single-Cell Multi-Omics Integration

Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging. In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex nonlinear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features. UnionCom software is available at https://github.com/caokai1073/UnionCom.

bioinformatics↗

bioRxiv2020

Genomic analyses provide insights into peach local adaptation and responses to climate change

The environment has constantly shaped plant genomes, but the genetic bases underlying how plants adapt to environmental influences remain largely unknown. We constructed a high-density genomic variation map by re-sequencing genomes of 263 geographically representative peach landraces and wild relatives. A combination of whole-genome selection scans and genome-wide environmental association studies (GWEAS) was performed to reveal the genomic bases of peach local adaptation to diverse climates comprehensively. A total of 2,092 selective sweeps that underlie local adaptation to both mild and extreme climates were identified, including 339 sweeps conferring genomic pattern of adaptation to high altitudes. Using GWEAS, a total of 3,496 genomic loci strongly associated with 51 specific environmental variables were detected. The molecular mechanism underlying adaptive evolution of high drought, strong UV-B, cold hardiness, sugar content, flesh color, and bloom date were revealed. Finally, based on 30 years of observation, a candidate gene associated with bloom date advance, representing peach responses to global warming, was identified. Collectively, our study provides insights into molecular bases of how environments have shaped peach genomes by natural selection and adds valuable genome resources and candidate genes for future studies on evolutionary genetics, adaptation to climate changes, and future breeding.

genomics↗

bioRxiv2019

Using Machine Learning to Facilitate Classification of Somatic Variants from Next-Generation Sequencing

BackgroundMolecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories rely on manual screening, which is costly, subjective, and not scalable. Here we present a machine learning-based method to distinguish artifacts from bona fide Single Nucleotide Variants (SNVs) detected by NGS from tumor specimens.\n\nMethodsA cohort of 11,278 SNVs identified through clinical sequencing of tumor specimens were collected and divided into training, validation, and test sets. Each SNV was manually inspected and labeled as either real or artifact as part of clinical laboratory workflow. A three-class (real, artifact and uncertain) model was developed on the training set, fine-tuned using the validation set, and then evaluated on the test set. Prediction intervals reflecting the certainty of the classifications were derived during the process to label \"uncertain\" variants.\n\nResultsThe optimized classifier demonstrated 100% specificity and 97% sensitivity over 5,587 SNVs of the test set. 1,252 out of 1,341 true positive variants were identified as real, 4,143 out of 4,246 false positive calls were deemed artifacts, while only 192(3.4%) SNVs were labeled as \"uncertain\" with zero misclassification between the true positives and artifacts in the test set.\n\nConclusionsWe presented a computational classifier to identify variant artifacts detected from tumor sequencing. Overall, 96.6% of the SNVs received a definitive label and thus were exempt from manual review. This framework could improve quality and efficiency of variant review process in clinical labs.

bioinformatics↗

bioRxiv2019

Trochodendron aralioides, the first chromosome-level draft genome in Trochodendrales and a valuable resource for basal eudicot research

BackgroundThe wheel tree (Trochodendron aralioides) is one of only two species in the basal eudicot order Trochodendrales. Together with Tetracentron sinense, the family is unique in having secondary xylem without vessel elements, long considered to be a primitive character also found in Amborella and Winteraceae. Recent studies however have shown that Trochodendraceae belong to basal eudicots and demonstrate this represents an evolutionary reversal for the group. Trochodendron aralioides is widespread in cultivation and popular for use in gardens and parks.\n\nFindingsWe assembled the T. aralioides genome using a total of 679.56 Gb of clean reads that were generated using both PacBio and Illumina short-reads in combination with 10XGenomics and Hi-C data. Nineteen scaffolds corresponding to 19 chromosomes were assembled to a final size of 1.614 Gb with a scaffold N50 of 73.37 Mb in addition to 1,534 contigs. Repeat sequences accounted for 64.226% of the genome, and 35,328 protein-coding genes with an average of 5.09 exons per gene were annotated using de novo, RNA-seq, and homology-based approaches. According to a phylogenetic analysis of protein-coding genes, T. aralioides diverged in a basal position relatively to core eudicots, approximately 121.8-125.8 million years ago.\n\nConclusionsTrochodendron aralioides is the first chromosome-scale genome assembled in the order Trochodendrales. It represents the largest genome assembled to date in the basal eudicot grade, as well as the closest order relative to the core-eudicots, as the position of Buxales remains unresolved. This genome will support further studies of wood morphology and floral evolution, and will be an essential resource for understanding rapid changes that took place at the base of the Eudicot tree. Finally, it can serve as a valuable source to aid both the acceleration of genome-assisted improvement for cultivation and conservation efforts of the wheel tree.

genomics↗

bioRxiv2019

The soursop genome and comparative genomics of basalangiosperms provide new insights on evolutionaryincongruence

Deep relationships and the sequence of divergence among major lineages of angiosperms (magnoliids, monocots and eudicots) remain ambiguous and differ depending on analytical approaches and datasets used. Complete genomes potentially provide opportunities to resolve these uncertainties, but two recently published magnoliid genomes instead deliver further conflicting signals. To disentangle key angiosperm relationships, we report a high-quality draft genome for the soursop (Annona muricata, Annonaceae). We reconstructed phylogenomic trees and show that the soursop represents a genomic mosaic supporting different histories, with scaffolds almost exclusively supporting single topologies. However, coalescent methods and a majority of genes support magnoliids as sister to monocots and eudicots, where previous whole genome-based studies remained inconclusive. This result is clear and consistent with recent studies using plastomes. The soursop genome highlights the need for more early diverging angiosperm genomes and critical assessment of the suitability of such genomes for inferring evolutionary history.

evolutionary biology↗

bioRxiv2019

Inhibiting TG2 sensitize lung cancer to radiotherapy through interfering TOPOIIα-mediated DNA repair

Radiotherapy is an indispensable strategy for lung cancer, however, treatment failure or reoccurrence is often found in patients due to the developing radioresistance. Novel approaches are required for radiosensitizing to improve the therapeutic efficacy. In present study, we found that transglutaminase 2 (TG2) confers radioresistance in non-small cell lung cancer (NSCLC) cells through regulating TOPOII and promoting DNA repair. Our data showed that TG2 inhibitor or knockdown increased NSCLC radiosensitivity in vivo and in vitro. We found that TG2 translocated into nucleus and located to DSB sites, surprisingly, knockdown TG2 or glucosamine inhibited the phosphorylation of ATM, ATR and DNA-Pkcs. Through IP-MS assay and functional experiments, we identified that TOPOII as an downstream factor of TG2. Moreover, we found that TGase domain account for the interaction with TOPOII. Finally, we found that TG2 expression was correlated with poor survival in lung adenocarcinoma instead of squamous cell carcinoma. In conclusion, we demonstrated that inhibiting TG2 sensitize NSCLC to IR through interfere TOPOII mediated DNA repair, suggesting TG2 as a potential radiosensitizing target in NSCLC.

cancer biology↗

bioRxiv2018

Hydraulic traits are more diverse in flowers than in leaves

Introduction Introduction Methods Results Discussion References The appearance of modern flowers was a turning point in the evolution of flowering plants, enhancing the intimacy and extent of interactions with their animal pollinators and increasing the likelihood of successful outcrossing (Crane et al., 1995; Fenster et al., 2004). Flowers evolved new morphological traits and rapidly diversified, effectively increasing the number of axes along which species could differentiate (Stebbins, 1951; Crepet & Niklas, 2009). Flowers are often noted for their incredible diversity of form, with high amounts of variation existing across phylogenetic scales, from within species to among major clades (Chartier et al., 2014, ...

evolutionary biology↗