Biology subjects

Yang, G.

Publications and source records attributed to Yang, G..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

FLASH Irradiation Regulates IFN-β induction by mtDNA via Cytochrome c Leakage

Ultrahigh dose rate radiotherapy (FLASH-RT) is under intensive investigation for its biological benefits. The mechanisms underlying its ability to spare normal tissues while suppress tumor growth still remain controversial. Here we reveal that compared to the low dose rate electron irradiation (0.36 Gy/s), FLASH electron irradiation at 61 or 610 Gy/s enhances the cytochrome c leakage from mitochondria in human breast cells MCF-10A, which elicits substantial caspase activation, suppresses both the cytosolic mitochondrial DNA (mtDNA) accumulation and IFN-{beta} secretion. Besides, the deletion of mtDNA severely decreases the radiation-induced cGAS-STING activation. Conversely, the cytochrome c leakage in carcinoma cells MDA-MB-231 post electron irradiation is limited, especially for the case of FLASH irradiation, resulting in less cytosolic cytochrome c but stronger cGAS-STING activation than those in MCF-10A cells. The enhanced difference of cytochrome c leakage between cancer cells and normal cells post FLASH irradiation indicates a potential mechanism of FLASH effect by regulating the apoptotic and inflammatory pathway.

biophysics↗

bioRxiv2024

Fragility of ER homeostatic regulation underlies haploid instability in human somatic cells

Mammalian somatic cells are generally unstable in the haploid state, resulting in haploid-to-diploid conversion within a short time frame. However, cellular and molecular principles that limit the sustainability of somatic haploidy remain unknown. In this study, we found the haploidy-linked vulnerability to ER stress as a critical cause of haploid intolerance in human somatic cells. Pharmacological induction of ER stress selectively induced apoptosis in haploid cells, facilitating the replacement of haploids by co-existing diploidized cells in a caspase-dependent manner. Biochemical analyses revealed that unfolded protein response (UPR) was activated with similar dynamics between haploids and diploids upon ER stress induction. However, haploids were less efficient in solving proteotoxic status, resulting in a bias toward a proapoptotic mode of UPR signaling. Artificial replenishment of chaperone function or inhibition of a UPR signal transducer ATF6 substantially alleviated the haploidy-linked upregulation of proapoptotic signaling and improved haploid cell retention under ER stress. These data demonstrate that the ER stress-driven haploid instability stems from inefficient proteostatic control that alters the functionality of UPR to cause apoptosis selectively in haploids. Interestingly, haploids suffered a higher level of protein aggregation even in unperturbed conditions, and the long-term stability of the haploid state was significantly improved by alleviating their natural proteotoxicity. Based on these results, we propose that the haploidy-specific vulnerability to ER stress creates a fundamental cause of haploid intolerance in mammalian somatic cells. Our findings provide new insight into the principle that places a stringent restriction on the evolution of animal life cycles.

biochemistry↗

bioRxiv2024

GABAAR-PPT1 palmitoylation homeostasis controls synaptic transmission and circuitry oscillation in CLN1 disease

CLN1 disease, also called infantile neuronal ceroid lipofuscinosis, is a fatal neurodegenerative disease caused by mutations in the CLN1 gene encoding palmitoyl protein thioesterase 1 (PPT1). To identify depalmitoylation substrate of PPT1 is crucial to understand CLN1 disease. In this study, we found that PPT1 depalmitoylates GABAAR 1 subunit at Cystein-260, while binding to Cystein-165 and -179. Mutations of PPT1 or its GABAAR 1 subunit binding site result in enhanced inhibitory synaptic transmission, strengthened and oscillation but disrupted phase coupling in CA1 region and impaired learning and memory in 1- to 2-months-old PPT1-deficient and Gabra1em1 mice. Our study highlights the critical role of PPT1 in maintaining GABAAR palmitoylation homeostasis and reveals a previously unknown molecular pathway in PPT1 induced diseases.

neuroscience↗

bioRxiv2024

Scaling Properties for Artificial Neural Network Models of a Small Nervous System

The nematode worm C. elegans provides a unique opportunity for exploring in silico data-driven models of a whole nervous system, given its transparency and well-characterized nervous system facilitating a wealth of measurement data from wet-lab experiments. This study explores the scaling properties that may govern learning the underlying neural dynamics of this small nervous system by using artificial neural network (ANN) models. We investigate the accuracy of self-supervised next time-step neural activity prediction as a function of data and models. For data scaling, we report a monotonic log-linear reduction in mean-squared error (MSE) as a function of the amount of neural activity data. For model scaling, we find MSE to be a nonlinear function of the size of the ANN models. Furthermore, we observe that the dataset and model size scaling properties are influenced by the particular choice of model architecture but not by the precise experimental source of the C. elegans neural data. Our results fall short of producing long-horizon predictive and generative models of C. elegans whole nervous system dynamics but suggest directions to achieve those. In particular our data scaling properties extrapolate that recording more neural activity data is a fruitful near-term approach to obtaining better predictive ANN models of a small nervous system.

neuroscience↗

bioRxiv2024

Cost-benefit Tradeoff Mediates the Rule- to Memory-based Transition during Practice

Practice not only improves task performance but also changes task execution from rule-to memory-based processing by incorporating experiences from practice. However, how and when this change occurs is unclear. We test the hypothesis that strategy transitions in task learning can result from decision-making guided by cost-benefit analysis. Participants learn two task sequences and are then queried about the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis, and the model-predicted strategy transition points align with the observed behavioral slowing. Model comparisons using behavioral data show that strategy transitions are better explained by a cost-benefit analysis across alternative strategies rather than solely on memory strength. Model-guided fMRI findings suggest that the brain encodes a decision variable reflecting the cost-benefit analysis and that different strategy representations are double-dissociated. Further analyses reveal that strategy transitions are associated with activation patterns in the dorsolateral prefrontal cortex and increased pattern separation in the ventromedial prefrontal cortex. Together, these findings support cost-benefit analysis as a mechanism of practice-induced strategy shift.

neuroscience↗

bioRxiv2024

Puzzle Hi-C: an accurate scaffolding software

High-quality, chromosome-scale genomes are essential for genomic analyses. Analyses, including 3D genomics, epigenetics, and comparative genomics rely on a high-quality genome assembly, which is often accomplished with the assistance of Hi-C data. Current Hi-C-assisted assembling algorithms either generate ordering and orientation errors or fail to assemble high-quality chromosome-level scaffolds. Here, we offer the software Puzzle Hi-C, which uses Hi-C reads to accurately assign contigs or scaffolds to chromosomes. Puzzle Hi-C uses the triangle region instead of the square region to count interactions in a Hi-C heatmap. This strategy dramatically diminishes scaffolding interference caused by long-range interactions. This software also introduces a dynamic, triangle window strategy during assembly. Initially small, the window expands with interactions to produce more effective clustering. Puzzle Hi-C outperforms available scaffolding tools.

bioinformatics↗

bioRxiv2024

Transcranial Photobiomodulation on the Left Prefrontal Cortex Enhances Mandarin Chinese L1 and L2 Complex Sentence Processing Performances

This study investigated the causal effect of transcranial photobiomodulation (tPBM) over the left prefrontal cortex (LPFC) on syntactically complex Mandarin Chinese first language (L1) and second language (L2) sentence processing performances. Two (L1 and L2) groups of participants (thirty per group) were recruited to receive the double-blind, sham-controlled tPBM intervention, followed by the sentence processing, the verbal working memory (WM), and the visual WM tasks. Results revealed a consistent pattern for both groups: (a) tPBM enhanced sentence processing performance but not verbal WM and visual WM performance; (b) Participants with lower sentence processing performances under sham tPBM benefited more from active tPBM. Taken together, the current study substantiated that tPBM enhanced L1 and L2 sentence processing ability directly without verbal WM interference, and would serve as a promising and cost-effective noninvasive brain stimulation (NIBS) tool for future applications on upregulating the human language faculty. HighlightsO_LIThe first study that applies tPBM to (complex) sentence processing. C_LIO_LItPBM enhances sentence processing performances in both Mandarin L1 & L2 speakers. C_LIO_LItPBM directly enhances sentence processing without the interference of verbal WM. C_LIO_LIA causal role of LPFC for sentence processing through active tPBM. C_LIO_LIOpening up the promising application prospect for tPBM on sentence processing. C_LI

neuroscience↗

bioRxiv2024

MntJULiP and Jutils: Differential splicing analysis of RNA-seq data with covariates

Differences in alternative splicing patterns can reveal important markers of phenotypic differentiation, including biomarkers of disease. Emerging large and complex RNA-seq datasets from disease and population studies include multiple confounders such as sex, age, ethnicity and clinical attributes, which demand highly specialized data analysis tools. However, few methods are equipped to handle the new challenges. We describe an implementation of our programs MntJULiP and Jutils for differential splicing detection and visualization from RNA-seq data that takes into account covariates. MntJULiP detects intron-level differences in alternative splicing from RNA-seq data using a Bayesian mixture model. Jutils visualizes alternative splicing variation with heatmaps, PCA and sashimi plots, and Venn diagrams. Our tools are scalable and can process thousands of samples within hours. We applied our methods to the collection of GTEx brain RNA-seq samples to deconvolute the effects of sex and age at death on the splicing patterns. In particular, clustering of covariate adjusted data identifies a subgroup of individuals undergoing a distinct splicing program during aging. MntJULiP and Jutils are implemented in Python and are available from https://github.com/splicebox/.

bioinformatics↗

bioRxiv2023

E3 ligase ASB3 downregulates antiviral innate immunity by targeting MAVS for ubiquitin-proteasomal degradation

E3 ubiquitin ligases are very important to regulate antiviral immunity during viral infection. Here, we discovered that Ankyrin repeat and SOCS box-containing protein 3 (ASB3), an E3 ligase, are upregulated in the presence of RNA viruses, particularly Influenza A virus (IAV). Notably, overexpression of ASB3 inhibits type I IFN (IFN-I) responses induced by Sendai virus (SeV) and H9N2, and ablation of ASB3 restores SeV and H9N2 infection-mediated transcription of IFN-{beta} and its downstream interferon-stimulated genes (ISGs). Interestingly, animals lacking ASB3 showed a decreased susceptibility to H9N2 and PR8 infections. Mechanistically, ASB3 interacts with MAVS and directly mediates K48-linked polyubiquitination and degradation of MAVS at K297, thereby inhibiting the phosphorylation levels of TBK1 and IRF3, downregulating downstream antiviral signaling. These findings establish ASB3 as a critical negative regulator in controlling the activation of antiviral signaling and describe a novel function of ASB3 that has not been previously reported. IMPORTANCEIAV is a significant zoonotic pathogen that causes infections of the respiratory system. Hosts have evolved multiple strategies to defend against IAV infection. However, not all host proteins play an active defense role. In this study, we found that the E3 ligase ASB3 regulates antiviral immunity by manipulating MAVS stability. Briefly, overexpression of ASB3 degrades MAVS, thereby promoting viral replication. In contrast, ASB3 deletion restores MAVS expression, upregulating IFN-I responses. Additional research revealed that ASB3 mediates the K48-linked polyubiquitination of MAVS at K297, resulting in ASB3 being degraded via the ubiquitin-proteasome pathway. These findings reveal, for the first time, a novel mechanism by which ASB3 negatively regulates antiviral immunity and provides a potential target for anti-IAV drug development.

immunology↗

bioRxiv2023

AhR ligands from LGG metabolites promote piglet intestinal ILC3 activation and IL-22 secretion to inhibit PEDV infection

In maintaining organismal homeostasis, gut immunity plays a crucial role. The coordination between the microbiota and the immune system through bidirectional interactions regulates the impact of microorganisms on the host. Our research focused on understanding the relationship between substantial changes in jejunal intestinal flora and metabolites and intestinal immunity during porcine epidemic diarrhea virus (PEDV) infection in piglets. We discovered that Lactobacillus rhamnosus GG (LGG) could effectively prevent PEDV infection in piglets. Further investigation revealed that LGG metabolites interact with type 3 innate lymphoid cells (ILC3) in the jejunum of piglets through the aryl hydrocarbon receptor (AhR). This interaction promotes the activation of ILC3 cells and the production of interleukin-22 (IL-22). Subsequently, IL-22 facilitates the proliferation of IPEC-J2 cells and activates the STAT3 signaling pathway, thereby preventing PEDV infection. Moreover, the AhR receptor exerts its influence on various cell types within organoids, including intestinal stem cells (ISCs), Paneth cells, and enterocytes, fostering their growth and development, suggesting a broad impact of AhR on intestinal health. In conclusion, our study demonstrates the ability of LGG to modulate intestinal immunity and effectively prevent PEDV infection in piglets. These findings highlight the potential application of LGG as a preventive measure against viral infections in livestock.

immunology↗

bioRxiv2023

Biosurfactant stabilized nanoemulsions as multifunctional magnetically targeted delivery vehicles

Nanoemulsions have been widely used for pharmaceutic applications. However, there remains a significant challenge to functionalize available pharmaceutical surfactants with targeting or reporter moieties to generate next-generation drug delivery systems. Herein, a designed biosurfactant platform technology, based on our library of -helical peptide AM1 derivatives, was used to prepare multifunctional magnetic nanoemulsion drug delivery systems. Key factors such as electrostatic and steric stabilization of the nanoemulsions were determined using this peptide library. Stabilization of the nanoemulsion was achieved by controlled and tunable loading of PEG-functionalized biosurfactant at the oil-water interface. A model drug and iron oxide nanoparticles were incorporated into the oil core to prepare multifunctional nanoemulsions. In vitro cell uptake experiments using an external magnetic field demonstrated controlled rapid uptake of iron oxide loaded nanoemulsions by SKOV3 cancer and RAW 264.7 macrophage cells. SKOV3 cells demonstrated a slower rate of uptake under a magnetic field when compared to RAW 264.7 cells. This work demonstrates a highly adaptable hierarchical nanoemulsion system that can provide a platform to develop effective multifunctional nanomedicines and tools for biomedical insights. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/568750v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@31c3f2org.highwire.dtl.DTLVardef@1376aecorg.highwire.dtl.DTLVardef@14cb0aeorg.highwire.dtl.DTLVardef@1e37a6e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Brain-Age Prediction: Systematic Evaluation of Site Effects, and Sample Age Range and Size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of site harmonization, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2,101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 years and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1,600 participants. These findings have been incorporated into CentileBrain [https://centilebrain.org/#/brainAGE2], an open-science, web-based platform for individualized neuroimaging metrics.

bioinformatics↗

bioRxiv2023

GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model

Deciphering the universal gene regulatory mechanisms in diverse organisms holds great potential to advance our knowledge of fundamental life process and facilitate research on clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms, resulting in limited collection and integration of complex features on various cell types across species. Recent breakthroughs in single-cell sequencing and advancements in deep learning techniques present an unprecedented opportunity to tackle this challenge. In this study, we developed GeneCompass, the first knowledge-informed, cross-species foundation model pre-trained on an extensive dataset of over 120 million single-cell transcriptomes from human and mouse. During pre-training, GeneCompass effectively integrates four types of biological prior knowledge to enhance the understanding of gene regulatory mechanisms in a self-supervised manner. Fine-tuning towards multiple downstream tasks, GeneCompass outperforms competing state-of-the-art models in multiple tasks on single species and unlocks new realms of cross-species biological investigation. Overall, GeneCompass marks a milestone in advancing knowledge of universal gene regulatory mechanisms and accelerating the discovery of key cell fate regulators and candidate targets for drug development.

bioinformatics↗

bioRxiv2023

CellPolaris: Decoding Cell Fate through Generalization Transfer Learning of Gene Regulatory Networks

Cell fate changes are determined by gene regulatory network (GRN), a sophisticated system regulating gene expression in precise spatial and temporal patterns. However, existing methods for reconstructing GRNs suffer from inherent limitations, leading to compromised accuracy and application generalizability. In this study, we introduce CellPolaris, a computational system that leverages transfer learning algorithms to generate high-quality, cell-type-specific GRNs. Diverging from conventional GRN inference models, which heavily rely on integrating epigenomic data with transcriptomic information or adopt causal strategies through gene co-expression networks, CellPolaris employs high-confidence GRN sources for model training, relying exclusively on transcriptomic data to generate previously unknown cell-type-specific GRNs. Applications of CellPolaris demonstrate remarkable efficacy in predicting master regulatory factors and simulating in-silico perturbations of transcription factors during cell fate transition, attaining state-of-the-art performance in accurately predicting candidate key factors and outcomes in cell reprogramming and spermatogenesis with validated datasets. It is worth noting that, with a transfer learning framework, CellPolaris can perform GRN based predictions in all cell types even across species. Together, CellPolaris represents a significant advancement in deciphering the mechanisms of cell fate regulation, thereby enhancing the precision and efficiency of cell fate manipulation at high resolution.

bioinformatics↗

bioRxiv2023

ER mediates spatial regulation of lysosome-endosome interactions via motion switch at junction sites

Membrane-bound intracellular organelles engage in extensive direct interactions through membrane contact or fusion to work together for vital physiological functions. However, how their interactions are regulated is unclear. Lysosomes, for example, interact with ER and endosomes through membrane contact or fusion/fission to receive macromolecular cargos for recycling and degradation. The interactions are thought to be spatially regulated because participating lysosomes and endosomes must be positioned in close proximity. But the mechanism of spatial regulation is unknown. In this study, we examined how individual lysosomes and endosomes move along the endoplasmic reticulum (ER) network and found primarily two modes of movement. In the fast mode they explore different regions. In the slow mode they often pause and become confined near ER junctions to come near each other for interactions. The pause and confinement of lysosomes and endosomes occur in ER regions with elevated network density and connectivity and are mediated by condensation of the actin cytoskeleton, in which the VAP-STARD3-YWHAH pathway plays a key role. Together, these results reveal that ER mediates spatial regulation of lysosome-endosome interactions. Other organelles such as lipid droplets and peroxisomes are also found to pause near ER junctions. Overall, our findings suggest a general ER-mediated mechanism for spatial regulation of organelle interactions.

cell biology↗

bioRxiv2023

Inverted U-shaped brain development of cognitive control across the human lifespan

Cognitive control plays a pivotal role in guiding human goal-directed behavior. While existing studies have documented an inverted U-shaped trajectory of cognitive control both behaviorally and anatomically, little is known about the corresponding changes in functional brain activation with age. To bridge this gap, we conducted a comprehensive meta-analysis of 129 neuroimaging studies using conflict tasks, encompassing 3,388 participants aged from 5 to 85 years old. We have three major findings: 1) The inverted U-shaped trajectory is the predominant pattern; 2) Cognitive control-related brain regions exhibit heterogeneous lifespan trajectories: the frontoparietal control network follows inverted U-shaped trajectories, peaking between 24 and 40 years, while the dorsal attention network demonstrates no clear trajectories; 3) Both the youth and the elderly show weaker brain activities and greater left laterality than young to middle-aged adults. These results reveal the lifespan trajectories of cognitive control, highlighting heterogeneous fluctuations in brain networks with age. Classification: Biological Sciences/Psychological and Cognitive Sciences

neuroscience↗

bioRxiv2023

ASB3 expression aggravates inflammatory bowel disease by targeting TRAF6 protein stability and affecting the intestinal microbiota

E3 ubiquitin ligase (E3) plays a vital role in regulating inflammatory responses by mediating ubiquitination. Previous studies have shown that ankyrin repeat and SOCS box-containing protein 3 (ASB3) is involved in immunomodulatory functions associated with cancer. However, the impact of ASB3 on the dynamic interplay of microbiota and inflammatory responses in inflammatory bowel disease (IBD) is unclear. Here we systematically identify the E3 ligase ASB3 as a facilitative regulator in the development and progression of IBD. ASB3-/- mice are resistant to DSS-induced colitis. I{kappa}B phosphorylation levels and production of proinflammatory factors IL-1{beta}, IL-6, and TNF- were reduced in colonic tissues of ASB3-/- mice compared to WT mice. This colitis-resistant phenotype was suppressed after coprophagic microbial transfer and reversed after combined antibiotic clearance of the microbiota. Mechanistically, ASB3 specifically catalyzes K48-linked polyubiquitination of TRAF6 in IECs. In contrast, in ASB3-deficient organoids, the integrity of the TRAF6 protein is shielded, consequently decelerating the onset of intestinal inflammation. Taken together, Our findings demonstrates that ASB3 is associated with dysregulation of the colitis microbiota and promotes proinflammatory factors production by disrupting the TRAF6 stability. Strategies to limit the protein level of ASB3 in intestinal epithelial cells may help in the treatment of colitis.

immunology↗

bioRxiv2023

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Transcriptome-wide association study (TWAS) is an emerging model leveraging gene expressions to direct genotype-phenotype association mapping. A key component in TWAS is the prediction of gene expressions; and many statistical approaches have been developed along this line. However, a problem is that many genes have low expression heritability, limiting the performance of any predictive model. In this work, hypothesizing that appropriate denoising may improve the quality of expression data (including heritability), we propose AE-TWAS, which adds a transformation step before conducting standard TWAS. The transformation is composed of two steps by first splitting the whole transcriptome into co-expression networks (modules) and then using autoencoder (AE) to reconstruct the transcriptome data within each module. This transformation removes noise (including nonlinear ones) from the transcriptome data, paving the path for downstream TWAS. We showed two inspiring properties of AE-TWAS: (1) After transformation, the transcriptome data enjoy higher expression heritability at the low-heritability spectrum and possess higher connectivity within the modules. (2) The transferred transcriptome indeed enables better performance of TWAS; and moreover, the newly formed highly connected genes (i.e., hub genes) are more functionally relevant to diseases, evidenced by their functional annotations and overlap with TWAS hits.

bioinformatics↗