Biology subjects

Jin, H.

Publications and source records attributed to Jin, H..

At least 91 records · Page 5Linked to original sources

bioRxiv2023

CSTA: A Biomarker Regulating Esophageal Stricture Epithelial Barrier, Potential Medication Target

Esophageal strictures resulting from ESD/EMR in the esophagus are distressing, with uncertain causes and treatments due to epithelial barrier dysfunction. We explored cystatin A (CSTA) as a novel biomarker for the esophageal epithelial barrier. CSTA regulates the barrier and is influenced by tacrolimus (FK506). Analyzing biopsy samples by proteomics, we identified CSTA as an esophageal epithelial biomarker. Knocking down CSTA reduced responsiveness to triamcinolone acetonide (TA) and lowered expression of proteins related to cell-cell junctions and barrier function during inflammation. These findings suggest that down-regulate CSTA weakens tight junctions and the esophageal barrier, leading to strictures. Notably, FK506 showed promise in rescuing CSTA-knockdown cells by increasing the expression of proteins related to cell-cell junctions and barrier function. This highlights FK506 as a potential therapy for esophageal strictures in patients with CSTA mutations.

cell biology↗

bioRxiv2023

Beta-catenin inhibition disrupts the homeostasis of osteogenic/adipogenic differentiation leading to the development of glucocorticoid-induced osteonecrosis of femoral head

Glucocorticoid-induced osteonecrosis of the femoral head (GONFH) is a common refractory joint disease characterized by bone damage and the collapse of femoral head structure. However, the exact pathological mechanisms of GONFH remain unknown. Here, we observed abnormal osteogenesis and adipogenesis associated with decreased {beta}-catenin in the necrotic femoral head of GONFH patients. In vivo and in vitro studies further revealed that glucocorticoid exposure disrupted osteogenic/adipogenic differentiation of bone marrow mesenchymal cells (BMSCs) by inhibiting {beta}-catenin signaling in glucocorticoid-induced GONFH rats. Col2+ lineage largely contributes to BMSCs, and was found an osteogenic commitment in the femoral head through 9 months of lineage trace. Specific deletion of {beta}-catenin in Col2+ cells shifted their commitment from osteoblasts to adipocytes, leading to a full spectrum of disease phenotype of GONFH in adult mice. Overall, we uncover that {beta}-catenin inhibition disrupting the homeostasis of osteogenic/adipogenic differentiation contributes to the development of GONFH, and identify an ideal genetic modified mouse model of GONFH.

pathology↗

bioRxiv2023

Multimodal single-cell profiling of T cell specificity and reactivity in lung cancer

Adoptive transfer of autologous tumor-infiltrating lymphocyte T cells (TILs) offers one of the most promising approaches for cancer immunotherapy. However, high variability in patient responses highlight the need for an enhanced understanding of the transcriptional phenotypes of TILs and reactivity of their T cell receptors (TCR). Here, we employ single-cell multiomics approaches and TCR functional screening to investigate TILs from treatment-naive non-small cell lung cancer patients. This comprehensive analysis integrates scRNA-seq, scTCR-seq, and scATAC-seq, enabling a high-resolution examination of TILs within lung cancer tissue, as well as the adjacent non-tumor tissue. We apply a cellular functional screening platform to identify reactive TCRs that represent >1,000 TILs and have specificity towards a multitude of targets, including primary tumor cells, neoantigens, tumor-associated antigens, and viral antigens. Tumor-reactive TILs were primarily associated with dysfunctional phenotypes, whereas viral antigen-reactive TCRs were found in effector phenotype clusters. Key marker genes were identified and used to construct a tumor or viral reactivity score. Comparing clones shared in tumor and non-tumor tissue, a higher fraction of exhausted cells was observed in the tumor tissue, whereas non-tumor adjacent tissue possessed more effector cells, thus providing insight into potential sources for therapeutic T cells. Elucidating the specific T cell populations within TILs and their associated TCRs may support strategies to enhance the efficacy of TIL-based therapies. Graphical AbstractO_ST_ABSMultimodal single cell profiling and reactivity testing of TILsC_ST_ABS(A) CD8+ T cells of treatment naive non-small cell lung cancer patients and adjacent lung tissue were isolated by fluorescence-activated cell sorting (FACS) and were then subjected to scRNA-seq + scTCR-seq or scATAC-seq. (B) TCRs were functionally screened using a cellular platform (TnT cells) and target cells (tumor cells, antigen-pulsed antigen-presenting cells, PBMCs) by flow cytometry and deep sequencing. (C) scRNA-seq + scATAC-seq allowed trajectory inference of transcription factors and genes along pseudotime. (D) Gene scores for tumor- and virus-reactivity were developed by combining functional reactivity and transcriptomic profiling for each CD8+ T cell. (E) TIL scRNA-seq pre and post IL-2 treatment in tumor suspension displayed as alluvial plot shows change of clonal cell state composition. O_FIG O_LINKSMALLFIG WIDTH=196 HEIGHT=200 SRC="FIGDIR/small/560863v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@12fcc20org.highwire.dtl.DTLVardef@1027dccorg.highwire.dtl.DTLVardef@911711org.highwire.dtl.DTLVardef@18171e5_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2023

Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites

Metabolic pathways are a human-defined grouping of life sustaining biochemical reac-tions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic inter-pretation. To address these shortcomings, various machine learning models, including those trained on data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), have been developed to pre-dict the pathway involvement of metabolites based on their chemical descriptions; however, these prior models are based on old metabolite KEGG-based datasets, including one benchmark dataset that is invalid due to the presence of over 1500 duplicate entries. Therefore, we have developed a new benchmark dataset derived from the KEGG following optimal standards of scientific compu-tational reproducibility and including all source code needed to update the benchmark dataset as KEGG changes. We have used this new benchmark dataset with our atom coloring methodology to develop and compare the performance of Random Forest, XGBoost, and multilayer perceptron with autoencoder models generated from our new benchmark dataset. Best overall weighted average performance across 1000 unique folds was an F1-score of 0.8180 and Matthews correlation coeffi-cient of 0.7933, which was provided by XGBoost binary classification models for 11 KEGG-defined pathway categories.

bioinformatics↗

bioRxiv2023

Two functionally distinct HEATR5 protein complexes are defined by fast-evolving co-factors in yeast

The highly conserved HEATR5 proteins are best known for their roles in membrane traffic mediated by the adaptor protein complex-1 (AP1). HEATR5 proteins rely on fast-evolving co-factors to bind to AP1. However, how HEATR5 proteins interact with these co-factors is unknown. Here, we report that the budding yeast HEATR5 protein, Laa1, functions in two biochemically distinct complexes. These complexes are defined by a pair of mutually exclusive Laa1-binding proteins, Laa2 and the previously uncharacterized Lft1/Yml037c. Despite limited sequence similarity, biochemical analysis and structure predictions indicate that Lft1 and Laa2 bind Laa1 via structurally similar mechanisms. Both Laa1 complexes function in intra-Golgi recycling. However, only the Laa2-Laa1 complex binds to AP1 and contributes to its localization. Finally, structure predictions indicate that human HEATR5 proteins bind to a pair of fast-evolving interacting partners via a mechanism similar to that observed in yeast. These results reveal mechanistic insight into how HEATR5 proteins bind their co-factors and indicate that Laa1 performs functions besides recruiting AP1.

cell biology↗

bioRxiv2023

Discovery of fibrotic intervertebral disc in mice

Intervertebral discs are crucial to spine flexibility and stability during locomotion, but the mechanism of their mechanoadaptation to stress remains inadequately elucidated. We discovered spontaneous collagenesis in discs at the tail base in mice occurring from 14 days post-natal development. These discs termed as type C, differ from typical discs by exhibiting intensive collagen II and reduced aggrecan deposition contributing to the stiffening of the extracellular matrix (ECM), without typical degenerative markers. The reinforced mechanical properties enable type C discs to withstand high mechanical stress during flexion. Further analysis revealed that type C discs experience a turnover in cell composition, with the emergence of Procr+ progenitor cells and depletion of notochord cells. We demonstrated the essential role of mechanical stress in type C disc formation, suggesting a mechanoadaptive process rather than a pathological condition. Mechanistically, we identified TRPV4, as pivotal factors in collagen II synthesis in type C discs, highlighting the role of mechanotransduction in this adaptation. Our findings introduce a novel mechanoadaptive process of intervertebral disc by orchestrating collagenesis, advancing understanding of diverse disc functions in spine development and homeostasis, thereby providing insights for leveraging this mechanism to address spinal disorders.

pathology↗

bioRxiv2023

Metabolic reprogramming of cancer cells by JMJD6-mediated pre-mRNA splicing is associated with therapeutic response to splicing inhibitor

Dysregulated pre-mRNA splicing and metabolism are two hallmarks of MYC-driven cancers. Pharmacological inhibition of both processes has been extensively investigated as potential therapeutic avenues in preclinical and clinical studies. However, how pre-mRNA splicing and metabolism are orchestrated in response to oncogenic stress and therapies is poorly understood. Here, we demonstrate that Jumonji Domain Containing 6, Arginine Demethylase and Lysine Hydroxylase, JMJD6, acts as a hub connecting splicing and metabolism in MYC-driven neuroblastoma. JMJD6 cooperates with MYC in cellular transformation by physically interacting with RNA binding proteins involved in pre-mRNA splicing and protein homeostasis. Notably, JMJD6 controls the alternative splicing of two isoforms of glutaminase (GLS), namely kidney-type glutaminase (KGA) and glutaminase C (GAC), which are rate-limiting enzymes of glutaminolysis in the central carbon metabolism in neuroblastoma. Further, we show that JMJD6 is correlated with the anti-cancer activity of indisulam, a "molecular glue" that degrades splicing factor RBM39, which complexes with JMJD6. The indisulam-mediated cancer cell killing is at least partly dependent on the glutamine-related metabolic pathway mediated by JMJD6. Our findings reveal a cancer-promoting metabolic program is associated with alternative pre-mRNA splicing through JMJD6, providing a rationale to target JMJD6 as a therapeutic avenue for treating MYC-driven cancers.

cancer biology↗

bioRxiv2023

Small RNAs from fungal pathogen Botrytis cinerea ride in extracellular vesicles to enter plant cells through clathrin-mediated endocytosis

Small RNAs (sRNAs) of the fungal pathogen Botrytis cinerea can enter plant cells and hijack host Argonaute protein 1 (AGO1) to silence host immunity genes. However, the mechanism by which these fungal sRNAs are secreted and enter host cells remains unclear. Here, we demonstrate that B. cinerea utilizes extracellular vesicles (EVs) to secrete Bc-sRNAs, which are then internalized by plant cells through clathrin-mediated endocytosis (CME). The B. cinerea tetraspanin protein, Punchless 1 (BcPLS1), serves as an EV biomarker and plays an essential role in fungal pathogenicity. We observe numerous Arabidopsis clathrin-coated vesicles (CCVs) around B. cinerea infection sites and the colocalization of B. cinerea EV marker BcPLS1 and Arabidopsis CLATHRIN LIGHT CHAIN 1, one of the core components of CCV. Meanwhile, BcPLS1 and the B. cinerea-secreted sRNAs are detected in purified CCVs after infection. Arabidopsis knockout mutants and inducible dominant-negative mutants of key components of CME pathway exhibit increased resistance to B. cinerea infection. Furthermore, Bc-sRNA loading into Arabidopsis AGO1 and host target gene suppression are attenuated in those CME mutants. Together, our results demonstrate that fungi secrete sRNAs via EVs, which then enter host plant cells mainly through CME.

plant biology↗

bioRxiv2023

An intrinsically Disordered RNA Binding Protein Modulates mRNA Translation and Storage

Many proteins with intrinsically disordered regions interact with cytoplasmic ribosomes. However, many of the molecular functions related to these interactions are unclear. In this study, using an abundant RNA-binding protein with a structurally well-defined RNA recognition motif and an intrinsically disordered RGG domain as a model system, we investigated how this protein modulates mRNA storage and translation. Using genomic and molecular approaches, we show that the presence of Sbp1 slows ribosome movement on cellular mRNAs and promotes polysome stalling. Sbp1-associated polysomes display a ring-shaped structure in addition to a beads-on-string morphology visualized under electron microscope. Moreover, post-translational modifications at the RGG motif play important roles in directing cellular mRNAs to either translation or storage. Finally, binding of Sbp1 to the 5UTRs of mRNAs represses both cap-dependent and cap-independent translation initiation of proteins functionally important for general protein synthesis in the cell. Taken together, our study demonstrates an intrinsically disordered RNA binding protein regulates mRNA translation and storage via distinctive mechanisms under physiological conditions and establishes a framework with which functions of important RGG-proteins can be investigated and defined.

biochemistry↗

bioRxiv2023

Identification of RNA-Binding Protein Targets with HyperTRIBE in Saccharomyces cerevisiae

As a mater regulator in cells, RNA-binding protein (RBP) plays critical roles in organismal development, metabolism and various diseases. It controls gene expression at multiple levels mostly by specific recognition of target RNA. The traditional CLIP-seq method to detect transcriptome-wide RNA targets of RBP is less efficient in yeasts due to their cell walls. Here, we established an efficient HyperTRIBE (Targets of RNA-binding proteins Identified By Editing) in yeast, by fusing a RBP to the hyper active catalytic domain of human RNA editing enzyme ADAR2 and expressing the fusion protein in yeast cells. The target transcripts of RBP were marked with new RNA editing events and identified by high-throughput sequencing. We successfully applied TRIBE to identifying the RNA targets of two yeast RBPs, KHD1 and BFR1. The antibody-free HyperTRIBE has competitive advantages including low background, high sensitivity and reproducibility, and a simple library preparation procedure, which provides a reliable strategy for RBP target identification in Saccharomyces cerevisiae.

molecular biology↗

bioRxiv2023

The Role of Terminal Uridyl Transferases in the Circadian Rhythm

The 3 terminal oligo-uridylation, a post-transcriptional mRNA modification, is conserved among eukaryotes and drives mRNA degradation, thereby affecting several key biological processes such as animal development and viral infection. Our TAIL-seq experiment of mouse liver mRNA collected from six zeitgeber times reveals transcripts with rhythmic poly(A) tail lengths and demonstrates that overall 3 terminal uridylation frequencies at mRNA poly(A) tail very-ends undergo rhythmic change. Consistently, major terminal uridylyl transferases, TUT4 and TUT7, have cycling protein expression in mouse liver corresponding to 3 terminal uridylation rhythms, indicating that the cycling expression of TUTases correlates with the rhythmic pattern of uridylation. Furthermore, the double knockdown of TUT4 and TUT7 in U2OS cells lengthens the circadian period and decreases the rhythmic amplitude of clock gene expression. Our work thoroughly profiles the dynamic changes in poly(A) tail lengths and terminal modifications and uncovers uridylation as a post-transcriptional modulator in the mammalian circadian clock.

molecular biology↗

bioRxiv2023

Bayesian Inference of a Spectral Graph Model for Brain Oscillations

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which can not be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

neuroscience↗

bioRxiv2023

The Human Brain Connectome Weighted by the Myelin Content and Total Intra-Axonal Cross-Sectional Area of White Matter Tracts

A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features. Computational models support in vivo investigation of the mechanisms mediating this relationship but currently lack the requisite biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features to assess their potential joint utilization in computational models. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, tractometry-derived networks weighted by either tensor-based metrics or neurite density show no relationship with whole-brain functional connectivity. We conclude that structure-function brain models are likely to be improved by the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content. We anticipate that the proposed microstructure-weighted computational modeling approach will support mechanistic understanding of the structure-function relationship of the human brain. AUTHOR SUMMARYFor computational network models to provide mechanistic links between brain structure and function, they must be informed by networks in which edge weights quantify structural features relevant to brain function. Here, we characterized several weighted structural networks capturing multiscale features of white matter connectivity. We describe these networks in terms of edge weight distribution, variance and network topology, as well as their relationships with each other, edge length and function. Overall, these findings support the joint use of structural networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts in structure-function models. This thorough characterization serves as a benchmark for future investigations of weighted structural brain networks.

neuroscience↗

bioRxiv2023

Dynamic functional connectivity MEG features of Alzheimer's disease

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimers disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have high sensitivity and specificity in distinguishing AD from healthy subjects. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimers disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

neuroscience↗

bioRxiv2023

Paradoxical activation of oncogenic signaling as a cancer treatment strategy

Cancer homeostasis depends on a balance between activated oncogenic pathways driving tumorigenesis and engagement of stress-response programs that counteract the inherent toxicity of such aberrant signaling. While inhibition of oncogenic signaling pathways has been explored extensively, there is increasing evidence that overactivation of the same pathways can also disrupt cancer homeostasis and cause lethality. We show here that inhibition of Protein Phosphatase 2A (PP2A) hyperactivates multiple oncogenic pathways and engages stress responses in colon cancer cells. Genetic and compound screens identify combined inhibition of PP2A and WEE1 as synergistic in multiple cancer models by collapsing DNA replication and triggering premature mitosis followed by cell death. This combination also suppressed the growth of patient-derived tumors in vivo. Remarkably, acquired resistance to this drug combination suppressed the ability of colon cancer cells to form tumors in vivo. Our data suggest that paradoxical activation of oncogenic signaling can result in tumor suppressive resistance.

cancer biology↗

bioRxiv2023

Heat dissipation from photosynthesis contributes to maize thermoregulation under suboptimal temperature conditions

The extent to which plants thermoregulate to maintain relatively stable metabolic function in response to gradual and rapid temperature changes that jeopardize crop production is unclear. Maize thermoregulation was investigated based on leaf temperature (TL) measurements and its relationship with photochemistry and stomatal conductance (gs) under dry and wet soil scenarios. Seasonal climatology was simulated in a growth chamber according to Beijings climatology with extreme "hot days" based on historical maxima. Maize behaved as a limited homeotherm, an adaptive strategy to maintain photosynthesis around optimum temperatures (Topt). Plants on drier soil had lower thermoregulatory capacity, with reduced gs, photosynthesis and transpiration, which impacted final yields, despite acclimation with a higher Topt to sustained stress. On hot days thermoregulation was affected by heat stress and water availability, suggesting that strong and frequent heatwaves will reduce crop activity although increased temperatures could bring photosynthesis closer to Topt in the region. We propose a novel mechanism to explain thermoregulation from the contribution of heat dissipation via non-photochemical quenching (NPQ) to TL, supporting our hypothesis that NPQ acts as a negative feedback mechanism from photosynthesis by increasing TL in suboptimal conditions. These results could help to design adaptation strategies based on deficit irrigation. HighlightMaize was able to maintain leaf temperatures in narrower ranges than air temperatures by dissipating sunlight not used in photosynthesis as heat energy with a key role of transpiration cooling to sustain optimum photosynthesis temperature.

plant biology↗

bioRxiv2023

Contrasting patterns of somatic mutations in neurons and glia reveal differential predisposition to disease in the aging human brain

Characterizing the mechanisms of somatic mutations in the brain is important for understanding aging and disease, but little is known about the mutational patterns of different cell types. We performed whole-genome sequencing of 71 oligodendrocytes and 51 neurons from neurotypical individuals (0.4 to 104 years old) and identified >67,000 somatic single nucleotide variants (sSNVs) and small insertions and deletions (indels). While both cell types accumulate mutations with age, oligodendrocytes accumulate sSNVs 69% faster than neurons (27/year versus 16/year) whereas indels accumulate 42% slower (1.8/year versus 3.1/year). Correlation with single-cell RNA and chromatin accessibility from the same brains revealed that oligodendrocyte mutations are enriched in inactive genomic regions and are distributed similarly to mutations in brain cancers. In contrast, neuronal mutations are enriched in open, transcriptionally active chromatin. These patterns highlight differences in the mutagenic processes in glia and neurons and suggest cell type-specific, age-related contributions to neurodegeneration and oncogenesis.

neuroscience↗

bioRxiv2023

Artificial nanovesicles for dsRNA delivery in spray induced gene silencing for crop protection

Spray-Induced Gene Silencing (SIGS) is an innovative and eco-friendly technology where topical application of pathogen gene-targeting RNAs to plant material can enable disease control. SIGS applications remain limited because of the instability of dsRNA, which can be rapidly degraded when exposed to various environmental conditions. Inspired by the natural mechanism of crosskingdom RNAi through extracellular vesicle trafficking, we describe herein the use of artificial nanovesicles (AVs) for dsRNA encapsulation and control against the fungal pathogen, Botrytis cinerea. AVs were synthesized using three different cationic lipid formulations, DOTAP + PEG, DOTAP, and DODMA, and examined for their ability to protect and deliver dsRNA. All three formulations enabled dsRNA delivery and uptake by B. cinerea. Further, encapsulating dsRNA in AVs provided strong protection from nuclease degradation and from removal by leaf washing. This improved stability led to prolonged RNAi-mediated protection against B. cinerea both on pre- and post-harvest plant material using AVs. Specifically, the AVs extended the protection duration conferred by dsRNA to 10 days on tomato and grape fruits and to 21 days on grape leaves. The results of this work demonstrate how AVs can be used as a new nanocarrier to overcome dsRNA instability in SIGS for crop protection.

plant biology↗