Biology subjects

Jin, S.

Publications and source records attributed to Jin, S..

At least 73 records · Page 4Linked to original sources

bioRxiv2023

Melatonin alleviates valproic acid-induced neural tube defects by modulating Src/PI3K/ERK signaling and oxidative stress

Neural tube defects (NTDs) represent a developmental disorder of the nervous system that can lead to significant disability in children and impose substantial social burdens. Valproic acid (VPA), a widely prescribed first-line antiepileptic drug for epilepsy and various neurological conditions, has been associated with a fourfold increase in the risk of NTDs when used during pregnancy. Consequently, urgent efforts are required to identify innovative prevention and treatment approaches for VPA-induced NTDs. Studies have demonstrated that the disruption in the delicate balance between cell proliferation and apoptosis is a crucial factor contributing to NTDs induced by VPA. Encouragingly, our current data reveal that melatonin (MT) exerts significant inhibition on apoptosis while promoting the restoration of neuroepithelial cells proliferation impaired by VPA. Moreover, further investigations demonstrate that MT substantially reduces the incidence of neural tube malformations resulting from VPA exposure, primarily achieved by suppressing apoptosis through the modulation of intracellular reactive oxygen species levels. In addition, the Src/PI3K/ERK signaling pathway appears to play a pivotal role in VPA-induced NTDs, with a significant inhibition observed in the affected samples. Notably, MT treatment successfully reinstates the Src/PI3K/ERK signals, thereby offering a potential underlying mechanism for MTs protective effects against VPA-induced NTDs. In summary, our current study substantiates the considerable protective potential of MT in mitigating VPA-triggered NTDs, thereby offering valuable strategies for the clinical management of VPA-related birth defects.

developmental biology↗

bioRxiv2023

Using graph-based model to identify cell specific synthetic lethal effects

Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cells survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we utilized various cell-line specific omics data to design a deep learning model for predicting SL pairs on particular cell-lines. By incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. Our approach allows for prediction of SL pairs in a cell-specific manner and enhances cancer precision medicine. The code and data of our approach can be found at https://github.com/promethiume/SLwise HighlightsO_LIFew computational methods can systematically predict SL pairs at a cell-specific level, and their performance may not generalize well to clinical scenarios due to the heterogeneity of cancer types. C_LIO_LIThe SLWise utilizes various cell-line specific omics data to design a deep learning model with a graph-based representation and self-attention mechanism. C_LIO_LIThis approach allows for the prediction of SL pairs in a cell-specific manner, providing valuable insights on effectively identifying the cell-type specific SL targets for personalized treatment strategies. C_LI

bioinformatics↗

bioRxiv2023

A genome-wide single-cell 3D genome atlas of lung cancer progression

Although three-dimensional (3D) genome structures are altered in cancer cells, little is known about how these changes evolve and diversify during cancer progression. Leveraging genome-wide chromatin tracing to visualize 3D genome folding directly in tissues, we generated 3D genome cancer atlases of murine lung and pancreatic adenocarcinoma. Our data reveal stereotypical, non-monotonic, and stage-specific alterations in 3D genome folding heterogeneity, compaction, and compartmentalization as cancers progress from normal to preinvasive and ultimately to invasive tumors, discovering a potential structural bottleneck in early tumor progression. Remarkably, 3D genome architectures distinguish histologic cancer states in single cells, despite considerable cell-to-cell heterogeneity. Gene-level analyses of evolutionary changes in 3D genome compartmentalization not only showed compartment-associated genes are more homogeneously regulated, but also elucidated prognostic and dependency genes in lung adenocarcinoma and a previously unappreciated role for polycomb-group protein Rnf2 in 3D genome regulation. Our results demonstrate the utility of mapping the single-cell cancer 3D genome in tissues and illuminate its potential to identify new diagnostic, prognostic, and therapeutic biomarkers in cancer.

cell biology↗

bioRxiv2023

An aptamer-mediated base editing platform for simultaneous knock-in and multiple gene knockout for allogeneic CAR-T cells generation

Gene editing technologies hold promise for enabling the next generation of adoptive cellular therapies. Conventional gene editing platforms that rely on nuclease activity, such as Clustered regularly interspaced short palindromic repeats-CRISPR associated protein 9 (CRISPR-Cas9), allow efficient introduction of genetic modifications; however, these modifications occur via the generation of DNA double-strand breaks (DSBs) and can lead to unwanted genomic alterations and genotoxicity. Here, we apply the novel modular RNA aptamer-mediated Pin-point base editing platform to simultaneously introduce multiple gene knockouts and site-specific integration of a transgene in human primary T cells. We demonstrate high editing efficiency and purity at all target sites and significantly reduced frequency of chromosomal translocations compared to the conventional CRISPR-Cas9 system. Site-specific knock-in of a chimeric antigen receptor (CAR) and multiplex gene knockout are achieved within a single intervention and without the requirement for additional sequence-targeting components. The ability to perform complex genome editing efficiently and precisely highlights the potential of the Pin-point platform for application in a range of advanced cell therapies.

molecular biology↗

bioRxiv2023

Native nanoproteomics captures the structure and dynamics of endogenous protein complexes in human heart tissue

Protein complexes are highly dynamic entities that display substantial diversity in their assembly, post-translational modifications, and non-covalent interactions, allowing them to play critical roles in various biological processes. The heterogeneity, dynamic nature, and low abundance of protein complexes in their native states present tremendous challenges to study using conventional structural biology techniques. Here we develop a "native nanoproteomics" strategy for the native enrichment and subsequent native top-down mass spectrometry (nTDMS) of of low-abundance protein complexes. Specifically, we demonstrate the first comprehensive characterization of the structure and dynamics of cardiac troponin (cTn) complexes directly from human heart tissue. The endogenous cTn complex is effectively enriched and purified using peptide-functionalized superparamagnetic nanoparticles under non-denaturing conditions to enable the isotopic resolution of cTn complexes, revealing their complex structure and assembly. Moreover, nTDMS elucidates the stoichiometry and composition of the heterotrimeric cTn complex, localizes Ca2+ binding domains (II-IV), defines cTn-Ca2+ binding dynamics, and provides high-resolution mapping of the proteoform landscape. This native nanoproteomics strategy opens a new paradigm for structural characterization of low-abundance native protein complexes.

biochemistry↗

bioRxiv2023

Protein multiple conformations prediction using multi-objective evolution algorithm

MotivationWith the breakthrough of AlphaFold2 and the publication of AlphaFold DB, the protein structure prediction has made remarkable progress, which may further promote many potential applications of proteomics in all areas of life. However, it should be noted that AlphaFold2 models tend to represent only a single static structure, and accurately predicting multiple conformations remains a challenge. Therefore, it is essential to develop methods for predicting multiple conformations, which enable us to gain knowledge of multiple conformational states and the broader conformational landscape to better understand the mechanism of action. ResultsIn this work, we proposed a multiple conformational states folding method using the distance-based multi-objective evolutionary algorithm framework, named MultiSFold. First, a multi-objective energy landscape with multiple competing constraints generated by deep learning is constructed. Then, an iterative modal exploration and exploitation strategy based on multi-objective optimization, geometric optimization and structural similarity clustering is designed to perform conformational sampling. Finally, the final population is generated using a loop-specific perturbation strategy to adjust the spatial orientations. MultiSFold was compared with state-of-the-art methods on a developed benchmark testset containing 81 proteins with two representative conformational states. Based on the proposed metric, the success ratio of MultiSFold predicting multiple conformations was 70.4% while that of AlphaFold2 was 9.88%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to produce conformations spanned the range between two experimental structures. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate that the TM-score of MultiSFold is 2.97% and 7.72% higher than that of AlphaFold2 and RoseTTAFold, respectively, supporting our hypothesis that multiple competing optimization objectives can further assist conformational search to improve prediction accuracy.

bioinformatics↗

bioRxiv2023

Biomaterial based implants caused remote liver fatty deposition through activated blood-derived Kupffer cells

Understanding the foreign-body response (FBR) of biomaterials is a prerequisite for the prediction of its clinical application, and the present assessments mainly rely on in vitro cell culture and in situ histopathology. However, remote organs responses after biomaterials implantation is unclear. Here, by leveraging body-wide-transcriptomics data, we performed in-depth systems analysis of biomaterials - remote organs crosstalk after abdominal implantation of polypropylene and silk fibroin using a rodent model, demonstrating local implantation caused remote organs responses dominated by acute-phase responses, immune system responses and lipid metabolism disorders. Of note, liver function was specially disturbed, defined as hepatic lipid deposition. Combining flow cytometry analyses and liver monocyte recruitment inhibition experiments, we proved that blood derived monocyte-derived Kupffer cells in the liver underlying the mechanism of abnormal lipid deposition induced by local biomaterials implantation. Moreover, from the perspective of temporality, the remote organs responses and liver lipid deposition of silk fibroin group faded away with biomaterial degradation and restored to normal at end, which highlighted its superiority of degradability. These findings were further indirectly evidenced by human blood biochemical examination from 141 clinical cases of hernia repair using silk fibroin mesh and polypropylene mesh. In conclusion, this study provided knowledge of biomaterials-body interactions. It is of great important for future development of biomaterial devices for clinical application. One Sentence SummaryAbdominal local biomaterials implantation induces remote organ fatty deposition through activated blood-derived Kupffer cells.

pharmacology and toxicology↗

bioRxiv2023

BOLD Moments: modeling short visual events through a video fMRI dataset and metadata

Studying the neural basis of human dynamic visual perception requires extensive experimental data to evaluate the large swathes of functionally diverse brain neural networks driven by perceiving visual events. Here, we introduce the BOLD Moments Dataset (BMD), a repository of whole-brain fMRI responses to over 1,000 short (3s) naturalistic video clips of visual events across ten human subjects. We use the videos extensive metadata to show how the brain represents word- and sentence-level descriptions of visual events and identify correlates of video memorability scores extending into the parietal cortex. Furthermore, we reveal a match in hierarchical processing between cortical regions of interest and video-computable deep neural networks, and we showcase that BMD successfully captures temporal dynamics of visual events at second resolution. With its rich metadata, BMD offers new perspectives and accelerates research on the human brain basis of visual event perception.

neuroscience↗

bioRxiv2023

Astroglial exosome HepaCAM signaling and ApoE antagonization coordinates early postnatal cortical pyramidal neuronal axon growth and dendritic spine formation

Developing astroglia play important roles in regulating synaptogenesis through secreted and contact signals. Whether they regulate postnatal axon growth is unknown. By selectively isolating exosomes using size-exclusion chromatography (SEC) and employing cell-type specific exosome reporter mice, our current results define a secreted astroglial exosome pathway that can spread long-range in vivo and stimulate axon growth of cortical pyramidal neurons. Subsequent biochemical and genetic studies found that surface expression of glial HepaCAM protein essentially and sufficiently mediates the axon-stimulating effect of astroglial exosomes. Interestingly, apolipoprotein E (ApoE), a major astroglia-secreted cholesterol carrier to promote synaptogenesis, strongly inhibits the stimulatory effect of astroglial exosomes on axon growth. Developmental ApoE deficiency also significantly reduces spine density of cortical pyramidal neurons. Together, our study suggests a surface contact mechanism of astroglial exosomes in regulating axon growth and its antagonization by ApoE, which collectively coordinates early postnatal pyramidal neuronal axon growth and dendritic spine formation.

neuroscience↗

bioRxiv2023

High-content image-based CRISPR screening reveals regulators of 3D genome architectures

Three-dimensional (3D) genome organization becomes altered during development, aging, and disease1-23, but the factors regulating chromatin topology are incompletely understood and currently no technology can efficiently screen for new regulators of multiscale chromatin organization. Here, we developed an image-based high-content screening platform (Perturb-tracing) that combines pooled CRISPR screen, a new cellular barcode readout method (BARC-FISH), and chromatin tracing. We performed a loss-of-function screen in human cells, and visualized alterations to their genome organization from 13,000 imaging target-perturbation combinations, alongside perturbation-paired barcode readout in the same single cells. Using 1.4 million 3D positions along chromosome traces, we discovered tens of new regulators of chromatin folding at different length scales, ranging from chromatin domains and compartments to chromosome territory. A subset of the regulators exhibited 3D genome effects associated with loop-extrusion and A-B compartmentalization mechanisms, while others were largely unrelated to these known 3D genome mechanisms. We found that the ATP-dependent helicase CHD7, the loss of which causes the congenital neural crest syndrome CHARGE24 and a chromatin remodeler previously shown to promote local chromatin openness25-27, counter-intuitively compacts chromatin over long range in different genomic contexts and cell backgrounds including neural crest cells, and globally represses gene expression. The DNA compaction effect of CHD7 is independent of its chromatin remodeling activity and does not require other protein partners. Finally, we identified new regulators of nuclear architectures and found a functional link between chromatin compaction and nuclear shape. Altogether, our method enables scalable, high-content identification of chromatin and nuclear topology regulators that will stimulate new insights into the 3D genome functions, such as global gene and nuclear regulation, in health and disease.

genetics↗

bioRxiv2022

Regulation of Nuclear Transcription by Mitochondrial RNA

Chromatin-associated RNAs (caRNAs) form a relatively poorly recognized layer of the epigenome. The caRNAs reported to date are transcribed from the nuclear genome. Here, leveraging a recently developed assay for detection of caRNAs and their genomic association, we report that mitochondrial RNAs (mtRNAs) are attached to the nuclear genome and constitute a subset of caRNA, which we termed mt-caRNA. In four human cell types analyzed, mt-caRNAs preferentially attach to promoter regions. In human endothelial cells (ECs), the level of mt-caRNA-promoter attachment changes in response to environmental stress that mimics diabetes. Suppression of a non-coding mt-caRNA in ECs attenuates stress-induced nascent RNA transcription from the nuclear genome, including that of critical genes regulating cell adhesion, and abolishes stress-induced monocyte adhesion, a hallmark of dysfunctional ECs. Finally, we report increased nuclear localization of multiple mtRNAs in the ECs of human diabetic donors, suggesting many mtRNA translocate to the nucleus in a cell stress and disease-dependent manner. These data nominate mt-caRNAs as messenger molecules responsible for mitochondrial-nuclear communication and connect the immediate product of mitochondrial transcription with the transcriptional regulation of the nuclear genome.

genomics↗

bioRxiv2022

Deep Learning model accurately classifies metastatic tumors from primary tumors based on mutational signatures

Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the therapeutic treatment of cancers. Even though somatic mutations have been directly linked to tumorigenesis and metastasis, it is less explored whether the metastatic events can be identified through genomic mutation signatures, a concise representation of the mutational processes. Here, applying mutation signatures as input features calculated from Whole-Exome Sequencing (WES) data of TCGA and other metastatic cohorts, we developed MetaWise, a Deep Neural Network (DNN) model. This model accurately classified metastatic tumors from primary tumors. Signatures of non-coding mutations also have a major impact on the model performance. SHapley Additive exPlanations (SHAP) and Local Surrogate (LIME) analysis into the MetaWise model identified several mutational signatures directly correlated to metastatic spread in cancers, including APOBEC-mutagenesis, UV-induced signatures and DNA damage response deficiency signatures.

cancer biology↗

bioRxiv2022

Amyloid beta peptides (Aβ) from Alzheimer's disease neuronal secretome induce endothelial activation in a human cerebral microvessel model

In Alzheimers disease (AD), secretion and deposition of amyloid beta peptides (A{beta}) have been associated with blood-brain barrier dysfunction. However, the role of A{beta} in endothelial cell (EC) dysfunction remains elusive. Here we investigated AD mediated EC activation by studying the effect of A{beta} secreted from human induced pluripotent stem cell-derived cortical neurons (hiPSC- CN) harboring a familial AD mutation (Swe+/+) on human brain microvascular endothelial cells (HBMECs) in 2D and 3D perfusable microvessels. We demonstrated that increased A{beta} levels in Swe+/+ conditioned media (CM) led to stress fiber formation and upregulation of genes associated with endothelial inflammation and immune-adhesion. Perfusion of A{beta}-rich Swe+/+ CM induced acute formation of von Willebrand factor (VWF) fibers in the vessel lumen, which was attenuated by reducing A{beta} levels in CM. Our findings suggest that A{beta} can trigger rapid inflammatory and thrombogenic responses within cerebral microvessels, which may exacerbate AD pathology.

bioengineering↗

bioRxiv2022

Porous microneedle patch with sustained exosomes delivery repairs severe spinal cord injury

1.Mesenchymal stem cell-derived exosome (MSC-EXO) transplantation has been suggested as an efficacious treatment to suppress spinal cord injury (SCI)-triggered neuroinflammation. However, an ethically acceptable method to continuously deliver MSC-EXOs to acute spinal lesions, without damaging nearby tissues/axons, has never been achieved. In this study, we fabricated a device comprising a patch containing MSCs and a microneedle array (MN-MSC patch) to treat severe SCI. When topically applied to an acute spinal lesion beneath the spinal dura, the soft microneedle (MN) array with reasonable mechanical strength avoided damaging the nearby spinal tissues, and the porous microstructure of MNs facilitated highly efficient MSC-EXO delivery. With the capacity for sustained delivery of MSC-EXOs, the MN-MSC patch was evaluated in a contusive rat SCI model. The MSCs encapsulated in the patch could survive for at least 7 days, encompassing the optimal time window for downregulating SCI-triggered neuroinflammation. As a result, MN-MSC patch treatment led to reduced cavity and scar tissue formation, greater angiogenesis, and improved survival of nearby tissues/axons. Remarkably, rats treated by this method achieved superior muscle control and exhibited robust hindlimb locomotion functional recovery. Conclusively, the MN-MSC patch device proposed here overcomes the current dilemma between treatment efficacy and ethical issues in treating acute SCI.

bioengineering↗

bioRxiv2022

NOTCH3 active immunotherapy reduces NOTCH3 deposition in brain capillaries in a CADASIL mouse model

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common monogenic form of familial small vessel disease and no preventive or curative therapy is available. CADASIL is caused by mutations in the NOTCH3 gene, resulting in a mutated NOTCH3 receptor, with aggregation of the NOTCH3 extracellular domain (ECD) around vascular smooth muscle cells. In this study we have developed a novel active immunization therapy specifically targeting CADASIL-like aggregated NOTCH3 ECD. Immunizing CADASIL TgN3R182C150 mice with aggregates composed of CADASIL-R133C mutated and wild type EGF1-5 repeats for a total of four months resulted in a marked reduction (38-48%) in NOTCH3 deposition around brain capillaries, increased microglia activation and lowered serum levels of NOTCH3 ECD. Active immunization did not impact body weight, general behavior or the number and integrity of vascular smooth muscle cells in the retina, suggesting that the therapy is tolerable. This is the first therapeutic study reporting a successful reduction of CADASIL-like NOTCH3 accumulation in mice supporting further development towards clinical application for the benefit of CADASIL patients.

neuroscience↗

bioRxiv2022

Injectable hydrogel embedded with mesenchymal stem cells repairs severe spinal cord injury

Mesenchymal stem cell (MSC) transplantation was suggested as a promising approach to treat spinal cord injury (SCI). However, the heterogeneity of MSC and the lack of appropriate delivery methods impede its clinical application. To tackle these challenges, we first generated human MSCs derived from a single cell with a great homogeneity of batch quality and then developed a biocompatible injectable hydrogel to embed these cells to treat severe SCI. In a clinically relevant rat severe SCI model, we showed that the injection of MSCs with injectable hydrogel into the lesion site promoted robust functional recovery, while the intrathecal delivery of MSCs only resulted in limited therapeutic effects. Mechanistically, the hydrogel protected MSCs from the damage of harmful neuroinflammatory microenvironment in the spinal cord lesion. The hydrogel with the survived MSCs ameliorates the neuroinflammatory microenvironment of spinal cord lesion, preventing cavity formation and leads to the remnant of spared axons/tissues, which results in a better prognosis in the end.

bioengineering↗

bioRxiv2022

Improving sequence alignments with AlphaFold2 regardless of structural modeling accuracy

Protein sequence alignments are essential to structural, evolutionary and functional analysis but their accuracy is often limited by sequence similarity unless molecular structures are available. Protein structures predicted at experimental grade accuracy, as achieved by AlphaFold2, could therefore have a major impact on sequence analysis. Here, we find that multiple sequence alignments estimated on AlphaFold2 predictions are almost as accurate as alignments estimated on experimental structures and significantly superior to sequence-based alignments. We also show that AlphaFold2 structural models of relatively low quality can be used to obtain highly accurate alignments. These results suggest that, besides structure modeling, AlphaFold2 encodes higher-order dependencies that can be exploited for sequence analysis.

bioinformatics↗

bioRxiv2022

Improving and evaluating deep learning models of cellular organization

MotivationCells contain dozens of major organelles and thousands of other structures, many of which vary extensively in their number, size, shape and spatial distribution. This complexity and variation dramatically complicates the use of both traditional and deep learning methods to build accurate models of cell organization. Most cellular organelles are distinct objects with defined boundaries that do not overlap, while the pixel resolution of most imaging methods is not sufficient to resolve these boundaries. Thus while cell organization is conceptually object-based, most current methods are pixel-based. Using extensive image collections in which particular organelles were fluorescently-labeled, deep learning methods can be used to build conditional autoencoder models for particular organelles. A major advance occurred with the use of a U-net approach to make multiple models all conditional upon a common reference, unlabeled image, allowing the relationships between different organelles to be at least partially inferred. ResultsWe have developed improved GAN-based approaches for learning these models and have also developed novel criteria for evaluating how well synthetic cell images reflect the properties of real images. The first set of criteria measure how well models preserve the expected property that organelles do not overlap. We also developed a modified loss function that allows retraining of the models to minimize that overlap. The second set of criteria uses object-based modeling to compare object shape and spatial distribution between synthetic and real images. Our work provides the first demonstration that, at least for some organelles, deep learning models can capture object-level properties of cell images. AvailabilityA Reproducible Research Archive containing all source code, generated images and analysis results will be made available at http://murphylab.cbd.cmu.edu/Software upon publication. Contactmurphy@cmu.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗