Biology subjects

Jin, L.

Publications and source records attributed to Jin, L..

At least 91 records · Page 5Linked to original sources

bioRxiv2023

Mechanism of ozone alleviation of malignant ascites in hepatocellular carcinoma through inhibition of NETs

Malignant ascites in hepatocellular carcinoma is usually a sign of advanced disease and poor prognosis and is also thought to be associated with chronic inflammation mediated by neutrophil extracellular trap networks (NETs). Whereas ozone, a strong oxidant, has significant antibacterial and anti-inflammatory effects, its effectiveness in treating malignant liver ascites is unclear.cOzone showed significant anti-peritoneal fluid production properties in H22 tumor-bearing mice, and ozone reduced peritoneal fluid production by activating AMPK and up-regulating SR-A phagocytosis damage-associated molecular patterns (DAMPs) to reduce the production of NETs. This suggests that ozone could be used as a new drug for the treatment of malignant ascites in hepatocellular carcinoma.

cancer biology↗

bioRxiv2023

How hibernation in frogs drives brain and reproductive evolution in opposing directions

Environmental seasonality can promote the evolution of larger brains through cognitive and behavioral flexibility but can also hamper it when temporary food shortage is buffered by stored energy. Multiple hypotheses linking brain evolution with resource acquisition and allocation have been proposed for warm-blooded organisms, but it remains unclear how these extend to cold-blooded taxa whose metabolism is tightly linked to ambient temperature. Here, we integrated these hypotheses across frogs and toads in the context of varying brumation ( hibernation) durations and their environmental correlates. We showed that protracted brumation covaried negatively with brain size but positively with reproductive investment, likely in response to brumation-dependent changes in the socio-ecological context and associated selection on different tissues. Our results provide novel insights into resource allocation strategies and possible constraints in trait diversification, which may have important implications for the adaptability of species under sustained environmental change.

evolutionary biology↗

bioRxiv2023

Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing

Mapping the connectivity of diverse neuronal types provides the foundation for understanding the structure and function of neural circuits. High-throughput and low-cost neuroanatomical techniques based on RNA barcode sequencing have the potential to map circuits at cellular resolution and a brain-wide scale, but existing Sindbis virus-based techniques can only map long-range projections using anterograde tracing approaches. Rabies virus can complement anterograde tracing approaches by enabling either retrograde labeling of projection neurons or monosynaptic tracing of direct inputs to genetically targeted postsynaptic neurons. However, barcoded rabies virus has so far been only used to map non-neuronal cellular interactions in vivo and synaptic connectivity of cultured neurons. Here we combine barcoded rabies virus with single-cell and in situ sequencing to perform retrograde labeling and transsynaptic labeling in the mouse brain. We sequenced 96 retrogradely labeled cells and 295 transsynaptically labeled cells using single-cell RNA-seq, and 4,130 retrogradely labeled cells and 2,914 transsynaptically labeled cells in situ. We found that the transcriptomic identities of rabies virus-infected cells can be robustly identified using both single-cell RNA-seq and in situ sequencing. By associating gene expression with connectivity inferred from barcode sequencing, we distinguished long-range projecting cortical cell types from multiple cortical areas and identified cell types with converging or diverging synaptic connectivity. Combining in situ sequencing with barcoded rabies virus complements existing sequencing-based neuroanatomical techniques and provides a potential path for mapping synaptic connectivity of neuronal types at scale.

neuroscience↗

bioRxiv2023

HEATR5B associates with dynein-dynactin and selectively promotes motility of AP1-bound endosomal membranes

The dynein motor complex mediates polarised trafficking of a wide variety of organelles, intracellular vesicles and macromolecules. These functions are dependent on the dynactin complex, which helps recruit cargoes to dyneins tail region and activates motor movement. How dynein and dynactin orchestrate trafficking of diverse cargoes is unclear. Here, we identify HEATR5B, an interactor of the AP1 clathrin adaptor complex, as a novel player in dynein-dynactin function. HEATR5B is one of several proteins recovered in a biochemical screen for proteins whose association with the human dynein tail complex is augmented by dynactin. We show that HEATR5B binds directly to the dynein tail and dynactin and stimulates motility of AP1-associated endosomal membranes in human cells. We also demonstrate that the HEATR5B homologue in Drosophila is an essential gene that promotes dynein-based transport of AP1-bound membranes to the Golgi apparatus. As HEATR5B lacks the coiled-coil architecture typical of dynein adaptors, our data point to a non-canonical process orchestrating motor function on a specific cargo. We additionally show that HEATR5B promotes association of AP1 with endosomal membranes in a dynein-independent manner. Thus, HEATR5B co-ordinates multiple events in AP1-based trafficking.

cell biology↗

bioRxiv2023

Three-Dimensional Bioconjugated Liquid-Like Solid (LLS) Enhance Characterization of Solid Tumor - Chimeric Antigen Receptor T cell Interactions

Cancer immunotherapy offers lifesaving treatments for cancers, but the lack of reliable preclinical models that could enable the mechanistic studies of tumor-immune interactions hampers the identification of new therapeutic strategies. We hypothesized 3D confined microchannels, formed by interstitial space between bio-conjugated liquid-like solids (LLS), enable CAR T dynamic locomotion within an immunosuppressive TME to carry out anti-tumor function. Murine CD70-specific CAR T cells cocultured with the CD70-expressing glioblastoma and osteosarcoma demonstrated efficient trafficking, infiltration, and killing of cancer cells. The anti-tumor activity was clearly captured via longterm in situ imaging and supported by upregulation of cytokines and chemokines including IFNg, CXCL9, CXCL10, CCL2, CCL3, and CCL4. Interestingly, target cancer cells, upon an immune attack, initiated an "immune escape" response by frantically invading the surrounding microenvironment. This phenomenon however was not observed for the wild-type tumor samples which remained intact and produced no relevant cytokine response. Single cells collection and transcriptomic profiling of CAR T cells at regions of interest revealed feasibility of identifying differential gene expression amongst the immune subpopulations. Complimentary 3D in vitro platforms are necessary to uncover cancer immune biology mechanisms, as emphasized by the significant roles of the TME and its heterogeneity.

bioengineering↗

bioRxiv2023

Artificial channel separation of monochrome multi-structure microscopy image

Fluorescence microscopy is a powerful tool for life sciences, which employs fluorescent tags to label and observe cellular structures and their dynamics. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live cell applications. Here we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six subcellular structures from three images with only two separate fluorescent labels. DBSN combines the intensity-balance models to compensate for uneven fluorescent labels for different structures and the structure-separation models to extract multiple different structures with the same fluorescent labels. Therefore, DBSN permits the imaging of multiple structures with only one fluorescent label. It significantly reduces photobleaching, breaks the bottleneck of the existing technologies, and would have vast applications in cell biology.

biophysics↗

bioRxiv2022

Complexes Formed by Mixing Tumor-derived Extracellular Vesicles with Polymeric Surfactants for Personalized Therapeutic Vaccine

The personalized therapeutic vaccine is an ideal weapon to eliminate tumors. However, the core steps of manufacturing personalized cancer vaccines are identifying tumor-specific antigens (TSAs, also called neoantigens) and HLA epitope prediction, which is time-consuming and labor-intense. Tumor-derived extracellular vesicles (TEVs) are alternative sources of neoantigens. However, the immunosuppressive nature of TEVs limits their application in such immunotherapy. In this study, we present a new strategy to maintain neoantigens in TEVs and diminish the immunosuppression by deconstructing the structure of TEVs with polymeric surfactant polyethylene glycol-phosphatidylethanolamine (PEG-PE). Together with adjuvant MPLA, the newly formed micelle-like complexes compose a therapeutic vaccine (MLC-V). Results show that MLC-V is capable of eliciting neoantigen-specific T-cell responses, restoring TEV-induced immunosuppression, and preventing lung metastasis of murine melanoma. MLC-V also exhibits outstanding anti-tumor efficacy in multiple tumor models. MLC-V can be used as a personalized therapeutic vaccine in a mimetic pre-clinical MC38 model and the anti-tumor effect of MLC-V was synergistically enhanced by PD-1 mAb. Taken together, the present study demonstrates a time-saving, low-cost, and simplified strategy to produce personalized therapeutic vaccines based on MLC-V platform technology.

immunology↗

bioRxiv2022

Ratio-based quantitative multiomics profiling using universal reference materials empowers data integration

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

systems biology↗

bioRxiv2022

The cellular Notch1 Protein Promotes KSHV reactivation in an Rta-dependent manner.

The cellular Notch signal transduction pathway is intimately associated with infections by Kaposis sarcoma-associated herpesvirus (KSHV) and other gamma-herpesviruses. RBP-Jk, the cellular DNA binding component of the canonical Notch pathway, is the key Notch downstream effector protein in virus-infected and uninfected animal cells. Reactivation of KSHV from latency requires the viral lytic switch protein, Rta, to form complexes with RBP-Jk on numerous sites within the viral DNA. Constitutive Notch activity is essential for KSHV pathophysiology in models of Kaposis sarcoma (KS) and Primary Effusion Lymphoma (PEL), and we demonstrate that Notch1 is also constitutively active in infected Vero cells. Although the KSHV genome contains >100 RBP-Jk DNA motifs, we show that none of the four isoforms of activated Notch can productively reactivate the virus from latency in a highly quantitative trans-complementing reporter virus system. Nevertheless, Notch contributed positively to reactivation because broad inhibition of Notch1-4 with gamma secretase inhibitor (GSI) or expression of dominant negative mastermind-like1 (dnMAML1) coactivators severely reduced production of infectious KSHV from Vero cells. Reduction of KSHV production is associated with gene specific reduction of viral transcription in both Vero and PEL cells. Specific inhibition of Notch1 by siRNA partially reduces production of infectious KSHV, and NICD1 forms promoter-specific complexes with viral DNA during reactivation. We conclude that constitutive Notch activity is required for robust production of infectious KSHV, and our results implicate activated Notch1 as a pro-viral member of a MAML1/RBP-Jk/DNA complex during viral reactivation. ImportanceKaposis sarcoma-associated herpesvirus (KSHV) manipulates the host cell oncogenic Notch signaling pathway for viral reactivation from latency and cell pathogenesis. KSHV reactivation requires that the viral protein Rta functionally interacts with RBP-Jk, the DNA binding component of the Notch pathway, and with promoter DNA to drive transcription of productive cycle genes. We show that the Notch pathway is constitutively active during KSHV reactivation and is essential for robust production of infectious virus progeny. Inhibiting Notch during reactivation reduces expression of specific viral genes yet does not affect growth of the host cells. Although Notch cannot reactivate KSHV alone, the requisite expression of Rta reveals a previously unappreciated role for Notch in reactivation. We propose that activated Notch cooperates with Rta in a promoter-specific manner that is partially programmed by Rtas ability to redistribute RBP-Jk DNA binding to the virus during reactivation.

microbiology↗

bioRxiv2022

Disulfide stabilization reveals conserved dynamic features between SARS-CoV-1 and SARS-CoV-2 spikes.

SARS-CoV-2 spike protein (S) is structurally dynamic and has been observed by cryo-EM to adopt a variety of prefusion conformations that can be categorized as locked, closed and open. The locked conformations feature tightly packed trimers with structural elements incompatible with RBD in "up" position. For SARS-CoV-2 S, it has been shown that the locked conformations are transient under neutral pH. Probably due to their transience, locked conformations remain largely uncharacterized for SARS-CoV-1 S. Intriguingly, locked conformations were the only conformations captured for S proteins of bat and pangolin origin SARS-related coronaviruses. In this study, we introduced x1, x2, and x3 disulfides into SARS-CoV-1 S. Some of these disulfides have been shown to preserve rare locked conformations when introduced to SARS-CoV-2 S. Introduction of these disulfides allowed us to image a variety of locked and other rare conformations for SARS-CoV-1 S by cryo-EM. We identified bound cofactors and structural features that are associated with SARS-CoV-1 S locked conformations. We compare newly determined structures to other available spike structures of Sarbecoviruses to identify conserved features and discuss their possible functions.

biochemistry↗

bioRxiv2022

From comparative gene content and gene order to ancestral contigs, chromosomes and karyotypes

To reconstruct the ancestral genome of a set of phylogenetically related descendant species, we use the Raccroche pipeline for organizing a large number of generalized gene adjacencies into contigs and then into chromosomes. Separate reconstructions are carried out for each ancestral node of the phylogenetic tree for focal taxa. The ancestral reconstructions are monoploids; they each contain at most one member of each gene family constructed from descendants, ordered along the chromosomes. We design and implement a new computational technique for solving the problem of estimating the ancestral monoploid number of chromosomes x. This involves a "g-mer" analysis to resolve a bias due long contigs, and gap statistics to estimate x. We find that the monoploid number of all the rosid and asterid orders is x = 9. We show that this is not an artifact of our method by deriving x {approx} 20 for the metazoan ancestor.

bioinformatics↗

bioRxiv2022

Quartet DNA reference materials and datasets for comprehensively evaluating germline variants calling performance

Current methods for evaluating the accuracy of germline variant calls are restricted to easy-to-detect high-confidence regions, thus ignoring a substantial portion of difficult variants beyond the benchmark regions. We established four DNA reference materials from immortalized cell lines derived from a Chinese Quartet including parents and monozygotic twins. We integrated benchmark calls of 4.2 million small variants and 15,000 structural variants from multiple platforms and bioinformatic pipelines for evaluating the reliability of germline variant calls inside the benchmark regions. The genetic built-in-truth of the Quartet family design not only improved sensitivity of benchmark calls by removing additional false positive variants with apparently high quality, but also enabled estimation of the precision of variants calls outside the benchmark regions. Batch effects of variant calling in large-scale DNA sequencing efforts can be effectively identified with the concurrent use of the Quartet DNA reference materials along with study samples, and can be alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Matched RNA and protein reference materials were also established in the Quartet project, thereby enabling benchmark calls constructed from DNA reference materials for evaluation of variants calling performance on RNA and protein data. The Quartet DNA reference materials from this study are a resource for objective and comprehensive assessment of the accuracy of germline variant calls throughout the whole-genome regions.

bioinformatics↗

bioRxiv2022

Quartet RNA reference materials and ratio-based reference datasets for reliable transcriptomic profiling

As an indispensable tool for transcriptome-wide analysis of differential gene expression, RNA sequencing (RNAseq) has demonstrated great potential in clinical applications. However, the lack of multi-group RNA reference materials of biological relevance and the corresponding reference datasets for assessing the reliability of RNAseq hampers its wide clinical applications wherein the underlying biological differences among study groups are often small. As part of the Quartet Project for quality control and data integration of multiomic profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets using multi-batch RNAseq datasets, providing "ground truth" for benchmarking. Moreover, Quartet-sample-based quality metrics were developed for assessing reliability of RNAseq technology in terms of intra-batch proficiency and cross-batch reproducibility. The small intrinsic biological differences among the Quartet samples enable sensitive assessment of performance of transcriptomic measurements. The Quartet RNA reference materials combined with the reference datasets can be served as unique resources for assessing data quality and improving reliability of transcriptomic profiling.

genetics↗

bioRxiv2022

Predicting Phenotypes From Novel Genomic Markers Using Deep Learning

Genomic selection models use Single Nucleotide Polymorphism (SNP) markers to predict phenotypes. However, these predictive models face challenges due to the high dimensionality of genome-wide SNP marker data. Thanks to recent breakthroughs in DNA sequencing and decreased sequencing cost, the study of novel genomic variants such as Structural Variations (SVs) and Transposable Elements (TEs) become increasingly prevalent. In this paper, we develop a deep convolutional neural network model, NovGMDeep, to predict phenotypes using SVs and TEs markers for genomic selection. The proposed model is trained and tested on samples of A. thaliana and O. sativa using k-fold cross-validation. The prediction accuracy is evaluated using Pearsons Correlation Coefficient (PCC), Mean Absolute Error (MAE), and Standard Deviation (SD) of MAE. The predicted results showed higher correlation when the model is trained with SVs and TEs than with SNPs. NovGMDeep also has higher prediction accuracy when comparing with conventional statistical models. This work sheds light on the unrecognized function of SVs and TEs in genotype-to-phenotype associations, as well as their extensive significance and value in crop development.

bioinformatics↗

bioRxiv2022

Co-targeting BCL-XL and MCL-1 with DT2216 and AZD8055 synergistically inhibits small-cell lung cancer growth without causing on-target toxicities in mice

Small-cell lung cancer (SCLC) is an aggressive malignancy with limited therapeutic options. The dismal prognosis in SCLC is in part associated with an upregulation of BCL-2 family anti-apoptotic proteins, including BCL-XL and MCL-1. Unfortunately, the currently available inhibitors of BCL-2 family anti-apoptotic proteins, except BCL-2 inhibitors, are not clinically relevant because of various on-target toxicities. We, therefore, aimed to develop an effective and safe strategy targeting these anti-apoptotic proteins with DT2216 (our platelet-sparing BCL-XL degrader) and AZD8055 (an mTOR inhibitor) to avoid associated on-target toxicities while synergistically optimizing tumor response. Through BH3 mimetic screening, we identified a subset of SCLC cell lines that is co-dependent on BCL-XL and MCL-1. After screening inhibitors of selected tumorigenic pathways, we found that AZD8055 selectively downregulates MCL-1 in SCLC cells and its combination with DT2216 synergistically killed BCL-XL/MCL-1 co-dependent SCLC cells, but not normal cells. Mechanistically, the combination caused BCL-XL degradation and suppression of MCL1 expression, and thus disrupted MCL-1 interaction with BIM leading to an enhanced apoptotic induction. In vivo, DT2216+AZD8055 combination significantly inhibited the growth of cell line-derived and patient-derived xenografts and reduced tumor burden accompanied with extended survival in a genetically-engineered mouse (GEM) model of SCLC without causing significant thrombocytopenia or other normal tissue injury. Thus, these preclinical findings lay a strong foundation for future clinical studies to test DT2216+mTOR inhibitor combination in a subset of SCLC patients whose tumors are co-driven by BCL-XL and MCL-1.

cancer biology↗

bioRxiv2022

Gell: A GPU-powered 3D hybrid simulator for large-scale multicellular system

As a powerful but computationally intensive method, hybrid computational models study the dynamics of multicellular systems by evolving discrete cells in reacting and diffusing extracellular microenvironments. As the scale and complexity of studied biological systems continuously increase, the exploding computational cost starts to limit large-scale cell-based simulations. To facilitate the large-scale hybrid computational simulation and make it feasible on easily accessible computational devices, we develop a fast and memory-efficient open-source GPU-based hybrid computational modeling platform Gell (GPU Cell), for large-scale system modeling. We fully parallelize the simulations on GPU for high computational efficiency and propose a novel voxel sorting method to further accelerate the modeling of massive cell-cell mechanical interaction with negligible additional memory footprint. As a result, Gell efficiently handles simulations involving tens of millions of cells on a personal computer. We compare the performance of Gell with a state-of-the-art paralleled CPU-based simulator on a hanging droplet spheroid growth task and further demonstrate Gell with a ductal carcinoma in situ (DCIS) simulation. Gell affords ~150X acceleration over the paralleled CPU method with one-tenth of the memory requirement. Author SummaryNumerical cell simulations provide indispensable insight into the cell-to-tumor tissue transition and help reduce biological experimental variables. However, the availability and practicality of large-scale cell simulation tools have been limited by high computational cost, slow performance, or proprietary. Recent developments in open-source simulation codes and GPU implementation have partially addressed the challenge. We further optimized the cell simulation platform for GPU implementation in this work. As a result, benchmark cell simulation experiments can be performed efficiently on a personal computer with a modern GPU. We made the platform open source to encourage community adoption and collective development.

systems biology↗

bioRxiv2022

Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns

Deep learning has shown potential in domains where large-scale annotated datasets are available. However, manual annotation is expensive, time-consuming, and tedious. Pixel-level annotations are particularly costly for semantic segmentation in images with dense irregular patterns of object instances, such as in plant images. In this work, we propose a method for developing high-performing deep learning models for semantic segmentation of wheat heads utilizing little manual annotation. We simulate a computationally-annotated dataset using a few annotated images, a short unannotated video clip of a wheat field, and several video clips from fields with no wheat. This dataset is then used to train a customized U-Net model for wheat head segmentation. Considering the distribution shift between the simulated and real data, we apply three domain adaptation steps to gradually bridge the domain gap. Only using two annotated images, we achieved a Dice score of 0.89 on the internal test set, i.e., images extracted from the wheat field video. The model trained using only two annotated images was evaluated on a diverse external dataset collected from 18 different domains across five countries and achieved a Dice score of 0.73. To further expose the model to images from different growth stages and environmental conditions, we incorporated two annotated images from each of the 18 domains and further fine-tuned the model. This resulted in improving the Dice score to 0.91. These promising results highlight the utility of the proposed approach in the absence of large-annotated datasets. Although the utility of the proposed method is shown on a wheat head dataset, it can be extended to other segmentation tasks with similar characteristics of irregularly repeating patterns of object instances.

plant biology↗

bioRxiv2022

A mathematical approach for optimizing telecentric digital holographic microscope design

Digital holographic microscopy (DHM) has become an attractive imaging tool for the analysis of living cells and histological tissues. The telecentric DHM (TDHM) is a configuration of DHM that lightens the computation load with a priori aberration corrections. However, TDHM requires a well-aligned optical pipeline to optimize its resolution and image quality (IQ), which has traditionally complicated the alignment process. Further deriving from the optical interference functions, we offer a set of methodologies to simplify TDHM design and alignment by determining the optimal +1 order position, which depends on the object-reference beam angle and the interference plane rotation angle. The methods are then experimentally tested and verified on a TDHM system by imaging living HeLa cells in suspension.

biophysics↗