Biology subjects

Li, C.

Publications and source records attributed to Li, C..

At least 775 records · Page 43Linked to original sources

bioRxiv2022

Decoding the IGF1 Signaling Gene Regulatory Network Behind Alveologenesis from A Mouse Model of Bronchopulmonary Dysplasia

Lung development is precisely controlled by underlying Gene Regulatory Networks (GRN). Disruption of genes in the network can interrupt normal development and cause diseases such as bronchopulmonary dysplasia (BPD)-a chronic lung disease in preterm infants with morbid and sometimes lethal consequences characterized by lung immaturity and reduced alveolarization. Here, we generated a transgenic mouse exhibiting a moderate severity BPD phenotype by blocking IGF1 signaling in secondary crest myofibroblasts (SCMF) at the onset of alveologenesis. Using approaches mirroring the construction of the model GRN in sea urchins development, we constructed the IGF1 signaling network underlying alveologenesis using this mouse model that phenocopies BPD. The constructed GRN, consisting of 43 genes, provides a birds-eye view of how the genes downstream of IGF1 are regulatorily connected. The GRN also reveals a mechanistic interpretation of how the effects of IGF1 signaling are transduced within SCMF from its specification genes to its effector genes and then from SCMF to its neighboring alveolar epithelial cells with WNT5A and FGF10 signaling as the bridge. Consistently, blocking WNT5A signaling in mice phenocopies BPD as inferred by the network. A comparative study on human samples suggests that a GRN of similar components and wiring underlies human BPD. Our network view of alveologenesis is transforming our perspective to understand and treat BPD. This new perspective calls for the construction of the full signaling GRN underlying alveologenesis, upon which targeted therapies for this neonatal chronic lung disease can be viably developed.

developmental biology↗

bioRxiv2022

A new demethylase gene OsDML4 involved in high temperature induced floury endosperm formation in rice

High temperature (HT) can affect the accumulation of seed storage materials and cause adverse effects on the yield and quality in rice. DNA methylation plays an important role in plant growth and development. However, the temperature and DNA methylation interaction on rice seed development has not been studied yet. Here, we identified a new demethylase gene OsDML4 and discovered its function on cytosine demethylation to affect the endosperm formation during the grain filling. Knockout of OsDML4 induced floury endosperm only under HT, which resulted from dramatically reduced the transcription and accumulation of glutelins and 16-kDa prolamin. The expression of two important transcription factors RISBZ1 and RPBF was significantly declined in the osdml4 mutants. The absence of OsDML4 also caused adverse effects on the formation of protein bodies (PBs), the number of PB-II was greatly decreased and incomplete PB-II with empty space and abnormally shaped PB-II were observed in the osdml4 mutants. Whole-genome bisulfite sequencing analysis of seeds at 15 days after pollination revealed much higher global methylation levels of CG, CHG and CHH contexts in the osdml4 mutants compared to wild type (WT). Moreover, the methylation status of RISBZ1 promoter was hypermethylated but RPBF promoter was nearly unchanged. No significant difference was detected between WT and the osdml4 mutants under room temperature. In conclusion, our study demonstrates a novel OsDML4-mediated epigenetic regulatory mechanism involving in the formation of floury endosperm, which will provide a new perspective in regulating endosperm development and the accumulation of SSPs in rice.

plant biology↗

bioRxiv2022

Explainable t-SNE for single-cell RNA-seq data analysis

BackgroundSingle-cell RNA (scRNA-seq) sequencing technologies trigger the study of individual cell gene expression and reveal the diversity within cell populations. To measure cell-to-cell similarity based on their transcription and gene expression, many dimension reduction methods are employed to retrieve corresponding low-dimensional embeddings of input scRNA-seq data to conduct clustering. However, the methods lack explainability and may not perform well with scRNA-seq data because they are not customized for high-dimensional sparse scRNA-seq data. ResultsIn this study, we propose an explainable t-SNE: cell-driven t-SNE (c-TSNE) that fuses cell differences reflected from biologically meaningful distance metrics for input data. Our study shows that the proposed method not only enhances the interpretation of the original t-SNE visualization but also demonstrates favorable single cell segregation performance on benchmark datasets compared to state-of-the-art peers. The robustness analysis shows that the proposed cell-driven t-SNE demonstrates robustness to dropout and noise in clustering. It provides a novel and practical way to investigate the interpretability of t-SNE in scRNA-seq data analysis. Unlike the general assumption that the explainability of a machine learning method may need to compromise with learning efficiency, the proposed explainable t-SNE improves both clustering efficiency and explainability. More importantly, our work suggests that widely used t-SNE can be easily misused in existing scRNA-seq analysis, because its default Euclidean distance can bring biases or meaningless results in cell difference evaluation for high-dimensional sparse scRNA-seq data. To the best of our knowledge, it is the first explainable t-SNE proposed in scRNA-seq analysis and will inspire other explainable machine learning method development in the field. ConclusionThe proposed explainable t-SNE outperforms classic t-SNE and its peers in meaningful visualization and segregation. The poor performance of the classic t-SNE highlights the importance of developing explainable machine learning methods in scRNA-seq analysis. The explainable t-SNE is a data-centric customized ML enhance efficiency in data analysis through bringing more biological insights and interpretations.

bioinformatics↗

bioRxiv2022

Cell- and non-cell-autonomous ARF3 coordinates meristem proliferation and organ patterning in Arabidopsis

In cell-cell communication, non-cell-autonomous transcription factors play vital roles in controlling plant stem cell fate. We previously reported that AUXIN RESPONSE FACTOR 3 (ARF3), a member of the ARF family with critical roles in floral meristem maintenance and determinacy, has a distinct accumulation pattern that differs from the expression domain of its encoding gene in the shoot apical meristem (SAM). However, the biological meaning of this difference is obscure. Here, we demonstrate that ARF3 expression is mainly activated at the periphery of the SAM by auxin, where ARF3 cell-autonomously regulates the expression of meristem-organ boundary-specific genes, such as CUP-SHAPED COTYLEDON1-3 (CUC1-3), BLADE ON PETIOLE1-2 (BOP1-2) and TARGETS UNDER ETTIN CONTROL3 (TEC3) to determine organ patterning. We also show that ARF3 is translocated into the organizing center, where it represses cytokinin activity and WUSCHEL expression to regulate meristem activity non-cell-autonomously. Therefore, ARF3 acts as a molecular link that mediates the interaction of auxin and cytokinin signaling in the SAM while coordinating the balance between meristem maintenance and organogenesis. Our findings reveal an ARF3-mediated coordination mechanism through cell-cell communication in dynamic SAM maintenance.

developmental biology↗

bioRxiv2022

A new class of protein sensor links spirochete pleomorphism, persistence, and chemotaxis

Pathogenic spirochetes can alter their morphologies and behaviors to infect and survive within their hosts. Previous reports demonstrate that the formation of so-called round bodies and biofilms, and chemotaxis are involved in spirochete pathogenesis. Here, in the spirochete Treponema denticola, we report a direct link between these cellular states that involves a new class of protein sensor (CheWS) with hitherto unclear function. Using cryo-EM methods, protein modeling, bioinformatics, genetics methods, and behavioral assays we demonstrate that spirochetes regulate these behaviors in response to the small molecule s-adenosylmethionine (SAM) via a SAM sensor that is anchored to chemotaxis arrays. CheWS influences chemotaxis, biofilm and round body formation under nonstressed conditions by a novel sporulation-like mechanism. Taken together, we establish an improved model for round body formation, we discovered a direct link between this SAM sensor and changes in cellular states, as well as characterized a new sensor class involved in chemotaxis.

microbiology↗

bioRxiv2022

Neuronal cell-type and circuit basis for visual predictive processing

Bayesian Brain theory suggests brain utilises predictive processing framework to interpret the noisy world1-11. Predictive processing is essential to perception, action, cognition and psychiatric disease12, but underlying neural circuit mechanisms remain undelineated. Here we show the neuronal cell-type and circuit basis for visual predictive processing in awake, head-fixed mice during self-initiated running. Preceding running, vasoactive intestinal peptide (VIP)-expressing inhibitory interneurons (INs) in primary visual cortex (V1) are robustly activated in absence of structured visual stimuli. This pre-running activation is mediated via distal top-down projections from frontal, parietal and retrosplenial areas known for motion planning, but not local excitatory inputs associated with the bottom-up pathway. Somatostatin (SST) INs show pre-running suppression and post-running activation, indicating a VIP-SST motif. Differential VIP-SST peri-running dynamics anisotropically suppress neighbouring pyramidal (Pyr) neurons, preadapting Pyr neurons to the incoming running. Our data delineate key neuron types and circuit elements of predictive processing brain employs in action and perception. One Sentence SummaryVIP-SST-Pyr microcircuit in visual predictive processing during voluntary running

neuroscience↗

bioRxiv2022

An ultra-sensitive and easy-to-use multiplexed single-cell proteomic analysis

Proteins analysis from an average cell population often overlooks the cellular heterogeneity of expressed effector molecules, and knowledge about the regulations of key biological processes may remain obscure. Therefore, the necessity of single-cell proteomics (SCP) technologies arises. Without microfluidic chip, expensive ultrasonic equipment, or reformed liquid chromatogram (LC) system, we established an Ultra-sensitive and Easy-to-use multiplexed Single-Cell Proteomic workflow (UE-SCP). Specifically, the flexible sorting system ensured outstanding cell activity, high accuracy, remarkable efficiency, and robustness during single-cell isolation. Multiplex isobaric labeling realized the high-throughput analysis in trapped ion mobility spectrometry coupled with quadrupole time-of-flight mass spectrometry (timsTOF MS). Using this pipeline, we achieved single-cell protein quantities to a depth of over 2,000 protein groups in two human cell lines, Hela and HEK-293T. A small batch experiment can identify and quantify more than 3200 protein groups in 32 single cells, while a large batch experiment can identify and quantify about 4000 protein groups in 96 single cells. All the 128 single cells from different cell lines could been unsupervised clustered based on their proteomes. After the integration of data quality control, data cleaning, and data analysis, we are confident that our UE-SCP platform will be easy-to-marketing popularization and will promote biological applications of single-cell proteomics.

systems biology↗

bioRxiv2021

Broad neutralization of SARS-CoV-2 variants by an inhalable bispecific single-domain antibody

The effectiveness of SARS-CoV-2 vaccines and therapeutic antibodies has been limited by the continuous emergence of viral variants, and by the restricted diffusion of antibodies from circulation into the sites of respiratory virus infection. Here, we report the identification of two highly conserved regions on Omicron variant RBD recognized by broadly neutralizing antibodies. Based on this finding, we generated a bispecific single-domain antibody that was able to simultaneously and synergistically bind these two regions on a single Omicron variant RBD as revealed by Cryo-EM structures. This inhalable antibody exhibited exquisite neutralization breadth and therapeutic efficacy in mouse models of SARS-CoV-2 infections. The structures also deciphered an uncommon cryptic epitope within the spike trimeric interface that may have implications for the design of broadly protective SARS-CoV-2 vaccines and therapeutics.

immunology↗

bioRxiv2021

Reversable deformation of artificial cell colony for muscle behavior mimicry triggered by actin polymerization

The mimicry of living tissues from artificial cells is beneficial to understanding the interaction mechanism among cells, as well as holding great potentials in the tissue engineering field. Self-powered artificial cells capable of reversible deformation are developed by encapsulating living mitochondria, actin proteins, and methylcellulose. Upon the addition of pyruvate molecules, the mitochondria produce ATP molecules as energy sources to trigger the polymerization of actin. ATP molecules were produced by mitochondria (2.76x1010/ml) with the concentrations of 35.8{+/-}3.2 {micro}M, 158.2{+/-}19.3 {micro}M and 200.7{+/-}20.1 M by adding pyruvate molecules with the concentration of 3 M, 12 M and 21 M, respectively. The reversible deformation of artificial cells is experienced with spindle shape resulting from the polymerization of actins to form filaments adjacent to the lipid bilayer, subsequently back to spherical shape resulting from the depolymerization of actin filaments upon laser irradiations. The linear colonies composed of these artificial cells exhibit collective contraction and relaxation behavior to mimic muscle tissues. At the stage of maximum contraction, the long axis of each GUV is in parallel to each other. All colonies are synchronized in the contraction phase. The deformation of each GUV in the colonies is influenced by its adjacent GUVs. The muscle-like artificial cell colonies paved the path to develop sustainably self-powered artificial tissues in the field of tissue engineering.

bioengineering↗

bioRxiv2021

Variation of female pronucleus reveal oocyte or embryo abnormality: An expert experience deep learning of non-dark box analysis

BackgroundPronuclear assessment appears to have the ability to distinguish good and bad embryos in the zygote stage, but paradoxical results were obtained in clinical studies. This situation might be caused by the robust qualitative detection of the development of dynamic pronuclei. Here, we aim to establish a quantitative pronuclear measurement method by applying expert experience deep learning from large annotated datasets. MethodsConvinced handle-annotated 2PN images (13419) were used for deep learning then corresponded errors were recorded through handle check for subsequent parameters adjusting. We used 790 embryos with 52479 PN images from 155 patients for analysis the area of pronuclei and the pre-implantation genetic test results. Establishment of the exponential fitting equation and the key coefficient {beta} 1was extracted from the model for quantitative analysis for pronuclear(PN) annotation and automatic recognition. FindingsBased on the female original PN coefficient {beta}1, the chromosome-normal rate in the blastocyst with biggest PN area is much higher than that of the blastocyst with smallest PN area (58.06% vs. 45.16%, OR=1.68 [1.07-2.64]; P=0.031). After adjusting coefficient {beta}1 by the first three frames which high variance of outlier PN areas was removed, coefficient {beta}1 at 12 hours and at 14 hours post-insemination, similar but stronger evidence was obtained. All these discrepancies resulted from the female propositus in the PGT-SR subgroup and smaller chromosomal errors. Conclusion(s)The results suggest that detailed analysis of the images of embryos could improve our understanding of developmental biology. FundingNone

genetics↗

bioRxiv2021

Single-cell multi-omic velocity infers dynamic and decoupled gene regulation

Single-cell multi-omic datasets, in which multiple molecular modalities are profiled within the same cell, provide a unique opportunity to discover the relationships between cellular epigenomic and transcriptomic changes. To realize this potential, we developed MultiVelo, a mechanistic model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression from single-cell data, providing a quantitative summary of the temporal relationship between epigenomic and transcriptomic changes. Incorporating chromatin accessibility data significantly improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Fitting MultiVelo on single-cell multi-omic datasets from brain, skin, and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. Our model also identifies four types of cell states-two states in which epigenome and transcriptome are coupled and two distinct decoupled states. The parameters inferred by MultiVelo quantify the length of time for which genes occupy each of the four states, ranking genes by the degree of coupling between transcriptome and epigenome. Finally, we identify time lags between transcription factor expression and binding site accessibility and between disease-associated SNP accessibility and expression of the linked genes. We provide an open-source Python implementation of MultiVelo on PyPI and GitHub (https://github.com/welch-lab/MultiVelo).

bioinformatics↗

bioRxiv2021

Potential inhibitors for blocking the interaction of the coronavirus SARS-CoV-2 spike protein and its host cell receptor ACE2

The outbreak of SARS-CoV-2 continues to pose a serious threat to human health and social and economic stability. In this study, we established an anti-coronavirus drug screening platform based on the Homogeneous Time Resolved Fluorescence (HTRF) technology and the interaction between the coronavirus S protein and its host receptor ACE2. This platform is a rapid, sensitive, specific, and high throughput system. With this platform, we screened two compound libraries of 2,864 molecules and identified three potential anti-coronavirus compounds: tannic acid (TA), TS-1276 (anthraquinone), and TS-984 (9-Methoxycanthin-6-one). Our in vitro validation experiments indicated that TS-984 strongly inhibits the interaction of the coronavirus S-protein and the human cell ACE2 receptor. This data suggests that TS-984 is a potent blocker of the interaction between the S-protein and ACE2, which might have the potential to be developed into an effective anti-coronavirus drug. SIGNIFICANCEThe ongoing pandemic of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has made a serious threat to public health worldwide. Given the urgency of the situation, researchers are attempting to repurpose existing drugs for treating COVID-19. In this present study, we screened two compound libraries of 2,864 molecules and identified a potent inhibitor (TS-984) for blocking the coronavirus S-protein and the human cell ACE2 receptor. TS-984 might have the potential to be developed into an effective anti-coronavirus drug for treating COVID-19.

pharmacology and toxicology↗

bioRxiv2021

LKB1 Is Physiologically Required for Sleep from Drosophila to the Mouse

LKB1 is known as a master kinase for 14 kinases related to the adenosine monophosphate (AMP)-activated protein kinase (AMPK). Two of them (SIK3 and AMPK) have previously been implicated in sleep regulation. We generated loss-of-function (LOF) mutants for Lkb1 in both Drosophila and mice. Sleep was reduced in Lkb1-mutant flies and in flies with neuronal deletion of Lkb1. Sleep was reduced in mice after virally mediated reduction of Lkb1 in the brain. Electroencephalography (EEG) analysis showed that non-rapid eye movement (NREM) sleep and sleep need were both reduced in Lkb1-mutant mice. These results indicate that LKB1 plays a physiological role in sleep regulation conserved from flies to mice.

neuroscience↗

bioRxiv2021

Leptin-mediated suppression of food intake by conserved Glp1r-expressing neurons prevents obesity

The adipose-derived hormone leptin acts via its receptor (LepRb) in the brain to control energy balance. A previously unidentified population of GABAergic hypothalamic LepRb neurons plays key roles in the restraint of food intake and body weight by leptin. To identify markers for candidate populations of LepRb neurons in an unbiased manner, we performed single-nucleus RNA-sequencing of enriched mouse hypothalamic LepRb cells, as well as with total hypothalamic cells from multiple mammalian species. In addition to identifying known LepRb neuron types, this analysis identified several previously unrecognized populations of hypothalamic LepRb neurons. Many of these populations display strong conservation across species, including GABAergic Glp1r-expressing LepRb (LepRbGlp1r) neurons that express more Lepr and respond more robustly to exogenous leptin than other LepRb populations. Ablating LepRb from these cells provoked hyperphagic obesity without impairing energy expenditure. Conversely, reactivating LepRb in Glp1r-expressing cells decreased food intake and body weight in otherwise LepRb-null mice. Furthermore, LepRb reactivation in GABA neurons improved energy balance in LepRb-null mice, and this effect required the expression of LepRb in GABAergic Glp1r-expressing neurons. Thus, the conserved GABAergic LepRbGlp1r neuron population plays crucial roles in the control of food intake and body weight by leptin.

neuroscience↗

bioRxiv2021

Revised International Staging System (R-ISS) stage-dependent analysis uncovers oncogenes and potential immunotherapeutic targets in multiple myeloma

Multiple myeloma (MM), characterized by high intratumour heterogeneity, accounts for [~]10% of all haematologic malignancies. Stratified by the Revised International Staging System (R-ISS), little is known about R-ISS-related plasma cell (PC) heterogeneity, gene expression modules in cytotoxic T/NK cells and immunoregulatory ligands and receptors. Herein, we constructed a single-cell transcriptome atlas of bone marrow in normal and R-ISS-staged MM patients. Focusing on PCs, we identified and validated a subset of GZMA+ cytotoxic PCs. In addition, a malignant PC population with high proliferation capability (proliferating PCs) was associated with unfavourable prognosis and EBV infection in our collected samples. Ribonucleotide Reductase Regulatory Subunit M2 (RRM2), a specific marker of proliferating PCs, was shown to induce MM cell line proliferation and serve as a detrimental marker in MM. Subsequently, three R-ISS-dependent gene modules in cytotoxic CD8+ T and NKT cells were identified and functionally analysed. Finally, cell-cell communication between neutrophils and proliferating PCs with cytotoxic CD8+ T and NKT cells was investigated, which identified intercellular ligand receptors and potential immunotargets such as SIRPA-CD47 and TIGIT-NECTIN3. Collectively, this study provides an R-ISS-related single-cell MM atlas and reveals the clinical significance of two PC clusters, as well as potential immunotargets in MM progression.

cancer biology↗

bioRxiv2021

Dihydroartemisinin broke immune evasion through YAP1/JAK1/STAT1, 3 pathways to enhance anti-PD-1 therapy in hepatocellular carcinoma

The efficacy of anti-PD-1 therapy is not as expected in patients with hepatocellular carcinoma (HCC). Yes-associated protein 1 (YAP1) was overexpressed and activated in HCC. This study aimed to investigate the potential mechanism and inhibitor of YAP1 on immune evasion, and promote anti-PD-1 therapy in HCC. Here, we showed that dihydroartemisinin (DHA), an FDA approved drug, directly suppressed YAP1 expression, leading to break immune evasion in liver tumor niche, characterized by decreased PD-L1 in liver tumor cells and increased CD8+ T cell infiltration. Mechanismly, YAP1 is not only directly related to PD-L1, but also involved in activating the JAK1/STAT1, 3 pathways. Moreover, Yap1 knockout elevated CD4+ and CD8+ T cells in liver tumor niche of Yap1LKO mice. Consistently, verteporfin, YAP1 inhibitor, decreased TGF-{beta} in liver tumor niche and exhausted CD8+ T cells in spleen. Furthermore, DHA combined with anti-PD-1 treatment promoted CD4+ T cell infiltration in the spleen and CD8+ T cells in tumor tissues. Thus, we provide a new combined therapeutic strategy for anti-PD-1 with DHA, a potent YAP1 inhibitor, in HCC.

cancer biology↗

bioRxiv2021

Effect of gastrointestinal alterations mimicking elderly conditions on in vitro digestion of meat and soy proteins

We evaluated the digestion of meat (chicken, beef, and pork) and soy proteins under in vitro conditions mimicking gastrointestinal (GI) conditions of adults (control, C) and elderly with achlorhydria (EA). The changes in degree of hydrolysis (DH), SDS-PAGE profiles, peptide concentration, and proteomics profiles during the digestion process were investigated. Digestion under the EA condition markedly decreased the DH of all protein sources, especially for soy protein. SDS-PAGE profiling and proteomics showed that myofibrillar/sarcoplasmic protein from meat and glycinin/beta-conglycinin from soy were the proteins most affected by the different digestive conditions. Our results indicated that the difference in the digestibility of meat protein between EA and control conditions gradually narrowed from the gastric to the intestinal phase, while a pronounced difference between control and EA conditions was maintained also in the intestinal phase. This work provides new insights of value for future dietary recommendations for elderly individuals.

biochemistry↗

bioRxiv2021

ME-ACP: Multi-view Neural Networks with Ensemble Model for Identification of Anticancer Peptides

Cancer remains one of the most threatening diseases, which kills millions of lives every year. As a promising perspective for cancer treatments, anticancer peptides (ACPs) overcome a lot of disadvantages of traditional treatments. However, it is time-consuming and expensive to identify ACPs through conventional experiments. Hence, it is urgent and necessary to develop highly effective approaches to accurately identify ACPs in large amounts of protein sequences. In this work, we proposed a novel and effective method named ME-ACP which employed multi-view neural networks with ensemble model to identify ACPs. Firstly, we employed residue level and peptide level features preliminarily with ensemble models based on lightGBMs. Then, the outputs of lightGBM classifiers were fed into a hybrid deep neural network (HDNN) to identify ACPs. The experiments on independent test datasets demonstrated that ME-ACP achieved competitive performance on common evaluation metrics.

bioinformatics↗