Biology subjects

Jiang, J.

Publications and source records attributed to Jiang, J..

At least 127 records · Page 7Linked to original sources

bioRxiv2025

Phosphoproteomics Uncovers Tumor Heterogeneity and Therapeutic Opportunities in Early-Stage Hepatocellular Carcinoma

Mechanisms driving progression and metastasis in early-stage hepatocellular carcinoma (HCC) remain poorly clarified. Although phosphoproteomics has been applied to broader HCC cohorts, the heterogeneity of phosphorylation signaling in early-stage HCC remains unexplored. Here, we conducted integrated phosphoproteomic and proteomic analysis on early-stage HCC tumors and paired non-tumor tissues. By comparing tumor and non-tumor tissues, we identified markedly reduced phosphorylation of EGFR-mediated canonical ERK/MAPK signaling, alongside elevated atypical MAPK signaling in HCC. To further explore the signaling heterogeneity within tumors, we applied nonnegative matrix factorization (NMF) and consensus-clustering, and identified three distinct phosphoproteomic subtypes with unique phosphorylation signatures and clinical characteristics. Interrogating the most malignant subtype, we found markedly high abundance of phosphorylation of mRNA splicing regulators, and an enrichment of epithelial-mesenchymal transition (EMT) pathways in the proteome, with phosphorylation of the splicing factor SRSF3 emerging as a critical feature. Mechanistically, we show that SRSF3 hyperphosphorylation, driven by SRPK1/CLKs and potential PP1 phosphatase suppression, promotes metastasis and proliferation. Critically, SRPK1/CLKs inhibitors effectively suppressed SRSF3 hyperphosphorylation-mediated migration and invasion of HCC cells. Our study uncovers a new dimension of signaling-driven tumor heterogeneity in early-stage HCC and identifies the SRPK1/CLKs-SRSF3 hyperphosphorylation axis as a key metastatic driver, suggesting a potential therapeutic target for early intervention in HCC.

cancer biology↗

bioRxiv2025

From Local Interactions to Global Patterns: An Analysis of Silk Density in Spider Webs

This study explores the structural complexity of spider webs through information-theoretic and harmonicity-based frameworks to quantify spatial patterns in silk density across different web regions and reveal the underlying resource allocation strategies. Currently, there is no normalized approach for describing web structure and complexity, particularly for sheet-webs, and this methodology allows for non-destructive scanning and quantification of web characteristics. By analyzing the entropy of silk density distributions, a single scalar that captures the heterogeneity of material investment across the entire web, we observed that the entropy values follow a normal distribution with a mean of 1.24 {+/-} 0.22 bits when using 10 quantization levels. In the second part of the paper, by measuring the harmonicity of the silk density, we reveal that the silk density at a given point can be inferred from its neighbors, with an average harmonicity value of 0.0039 {+/-} 0.0017 (fraction of total points in point cloud data). The harmonic behavior is notable for its maximum principle, suggesting that the strongest parts of the web appear at the boundaries, aligning with existing knowledge of spider web construction. These findings provide a new technique for quantifying web-building strategies and offer new insights into spider behavior and evolution.

animal behavior and cognition↗

bioRxiv2025

Artificial Intelligence Apps for Medical Image Analysis using pyCERR and Cancer Genomics Cloud

This work introduces a user-friendly, cloud-based software framework for conducting Artificial Intelligence (AI) analyses of medical images. The framework allows users to deploy AI-based workflows by customizing software and hardware dependencies. The components of our software framework include the Python-native Computational Environment for Radiological Research (pyCERR) platform for radiological image processing, Cancer Genomics Cloud (CGC) for accessing hardware resources and user management utilities for accessing images from data repositories and installing AI models and their dependencies. GNU-GPL copyright pyCERR was ported to Python from MATLAB-based CERR to enable researchers to organize, access, and transform metadata from high dimensional, multi-modal datasets to build cloud-compatible workflows for AI modeling in radiation therapy and medical image analysis. pyCERR provides an extensible data structure to accommodate metadata from commonly used medical imaging file formats and a viewer to allow for multi-modal visualization. Analysis modules are provided to facilitate cloud-compatible AI-based workflows for image segmentation, radiomics, DCE MRI analysis, radiotherapy dose-volume histogram-based features, and normal tissue complication and tumor control models for radiotherapy. Image processing utilities are provided to help train and infer convolutional neural network-based models for image segmentation, registration and transformation. The framework allows for round-trip analysis of imaging data, enabling users to apply AI models to their images on CGC and retrieve and review results on their local machine without requiring local installation of specialized software or GPU hardware. The deployed AI models can be accessed using APIs provided by CGC, enabling their use in a variety of programming languages. In summary, the presented framework facilitates end-to-end radiological image analysis and reproducible research, including pulling data from sources, training or inferring from an AI model, utilities for data management, visualization, and simplified access to image metadata.

bioinformatics↗

bioRxiv2025

Molecular and structural basis of a subfamily of PrfH rescuing both the damaged and intact ribosomes stalled in translation

In bacteria, spontaneous mRNAs degradation and ribotoxin-induced RNA damage are two main biological events that lead to the stall of protein translation. The ubiquitous trans-translation system as well as several alternative rescue factors (Arfs) are responsible for rescuing the stalled ribosomes caused by truncated mRNAs that lack the stop codons. To date, protein release factor homolog (PrfH) is the only factor known to rescue the stalled ribosome damaged by ribotoxins. Here we show that a subfamily of PrfH, exemplified by PrfH from Capnocytophaga gingivalis (CgPrfH), rescues both types of stalled ribosomes described above. Our in vitro biochemical assays demonstrate that CgPrfH hydrolyzes the peptides attached to P-site tRNAs when in complex with both the damaged and intact ribosomes. Two cryo-EM structures of CgPrfH in complex with the damaged and intact 70S ribosomes revealed that CgPrfH employs two different regions of the protein to recognize two different stalled ribosomes to orient the GGQ motif for peptide hydrolysis. Thus, using a combination of bioinformatic, biochemical, and structural characterization described here, we have uncovered a family of ribosome rescue factors that possesses dual activities to resolve two distinct stalled protein translation in bacteria.

biochemistry↗

bioRxiv2025

An Enhanced Conditional Variational Autoencoder-Based Normative Model for Neuroimaging Analysis

Normative modelling in neuroimaging provides a powerful framework for quantifying individual deviations from expected brain measures as a function of relevant covariates. While most established approaches have focused on analysing distinct variables in isolation, there is a growing need for deep learning-based methods capable of handling multiple response variables simultaneously. Conditional variational autoencoders (cVAEs) have previously been applied in this context; however, existing inference methods still face challenges in providing reliable probabilistic predictions. In this study, we introduce an enhanced cVAE-based framework for normative modelling of neuroimaging data. Our key contribution is the development of a novel inference method that integrates latent space sampling and bootstrapping. This method directly leverages the generative nature of cVAEs to estimate the conditional distributions of brain measures. We demonstrate the effectiveness of this approach using white matter hyperintensity (WMH) volumes as a test case for developing, testing, and validating the model. Our dataset includes 8,551 normotensive and 18,180 hypertensive participants from the UK Biobank. We evaluated our method against three well-established and popular normative modelling techniques, including Generalised Additive Models for Location, Scale, and Shape (GAMLSS), Multivariate Fractional Polynomial Regression (MFPR), and Hierarchical Bayesian Regression (HBR). Our results indicate that the proposed cVAE-based framework achieves comparable performance across various metrics, while capturing individual deviations that correlate with the severity of hypertension. The model is particularly well-suited for high-dimensional, non-linear data, making it a robust tool for assessing individual deviations in brain structure. This enhanced normative modelling architecture paves the way for more nuanced and reliable assessments of brain health, with potential implications for personalised medicine and early detection of neurological disorders.

neuroscience↗

bioRxiv2024

Dorsolateral prefrontal cortex TMS evokes responses in the subgenual anterior cingulate cortex: Evidence from human intracranial EEG

Transcranial magnetic stimulation combined with intracranial local field potential recordings in humans (TMS-iEEG) represents a new method for investigating electrophysiologic effects of TMS with spatiotemporal precision. We applied TMS-iEEG to the dorsolateral prefrontal cortex (dlPFC) in two subjects and demonstrate evoked activity in the subgenual anterior cingulate cortex (sgACC). This study provides direct electrophysiologic evidence that dlPFC TMS, as targeted for depression treatment, can modulate brain activity in the sgACC.

neuroscience↗

bioRxiv2024

C. elegans LET-381/FoxF and DMD-4/DMRT control development of the mesodermal HMC endothelial cell

Endothelial cells form the inner layer of blood vessels and play key roles in circulatory system development and function. A variety of endothelial cell types have been described through gene expression and transcriptome studies; nonetheless, the transcriptional programs that specify endothelial cell fate and maintenance are not well understood. To uncover such regulatory programs, we studied the C. elegans Head Mesodermal Cell (HMC), a non-contractile mesodermal cell bearing molecular and functional similarities to vertebrate endothelial cells. Here, we demonstrate that a Forkhead transcription factor, LET-381/FoxF, is required for HMC fate specification and maintenance of HMC gene expression. DMD-4, a Dmrt transcription factor, acts downstream of and in conjunction with LET-381 to mediate HMC fate specification and gene expression maintenance. DMD-4, independently of LET-381, also represses the expression of genes associated with a different, non-HMC, mesodermal fate. Our studies uncover essential roles for FoxF transcriptional regulators in endothelial cell development, and suggest that the identity of FoxF co-functioning target transcription factors promotes specific non-contractile mesodermal fates.

developmental biology↗

bioRxiv2024

Genome-enabled stilbenoid gene cluster discovery in Pleuropterus multiflorus

Stilbenoids, a class of compounds with the stilbene skeleton as part of plant secondary metabolism, are renowned for their diverse health benefits. Among them, 2,3,5,4'-Tetrahydroxystilbene-2-O-{beta}-D-glucoside (THSG), specifically abundant in the famous medicinal plant Pleuropterus multiflorus, exhibits significant pharmacological properties. A selfing progeny of P. multiflorus commercial variety Jinwufugui No.1, which contains high THSG content, was used for genome sequencing. Through the combination of weighted correlation network analysis, genome mining, and enzymatic characterization studies, we identified an unpredicted biosynthetic gene cluster responsible for THSG biosynthesis. This cluster includes a stilbene synthase (PmSTS1), a flavin-containing monooxygenase (PmFMO3), and a UDP-glycosyltransferase (UGT72B90). Our findings suggest this stilbenoid gene cluster is formed by transposable element-mediated gene duplications and neofunctionalization, and regulated by MYB and NAC type transcription factors. Notably, PmFMO3, as the first identified stilbenoid-2-hydroxylase, was confirmed that it can modify the other stilbenoids without the A ring modifications. The novel type of gene cluster, in which FMO serves as the key decorating enzyme, will guide the identification of more biosynthetic gene clusters in plants. In addition, the characterization of PmFMO3 extends our understanding of the FMO gene function in plants, and provides an important catalytic bioparts for the heterologous synthesis of polyhydroxylated stilbenoids.

molecular biology↗

bioRxiv2024

Jan and mini-Jan, a model system for potato functional genomics

Potato (Solanum tuberosum) is the third most important food crop in the world. Although the potato genome has been fully sequenced, functional genomics research of potato lags relative to other major food crops due primarily to the lack of a model experimental potato line. Here, we present a diploid potato line, Jan, which possesses all essential characteristics for facile functional genomics studies. Jan has a high level of homozygosity after seven generations of self-pollination. Jan is vigorous and highly fertile with outstanding tuber traits, high regeneration rates, and excellent transformation efficiencies. We generated a chromosome-scale genome assembly for Jan, annotated genes, and identified syntelogs relative to the potato reference genome assembly DMv6.1 to facilitate functional genomics. To miniaturize plant architecture, we developed two "mini-Jan" lines with compact and dwarf plant stature using CRISPR/Cas9-mediated mutagenesis targeting the Dwarf and Erecta genes related to growth. Mini-Jan mutants are fully fertile and will permit higher-throughput studies in limited growth chamber and greenhouse space. Thus, Jan and mini-Jan provide an outstanding model system that can be leveraged for gene editing and functional genomics research in potato.

plant biology↗

bioRxiv2024

Generative AI divide: How College Students' Backgrounds Affect Their Gen AI Literacy

In the field of education, generative artificial intelligence has a profound impact, mainly reflected in areas such as personalized learning, automated assignment evaluation, content generation, and open educational resources. However, some scholars have begun to discuss whether this technology may lead to new inequalities. Therefore, this study aims to explore how user background affects the performance of generative artificial intelligence technology through empirical research while focusing on the potential educational inequalities that may arise in the era of generative AI. The research found that generative artificial intelligence literacy is correlated with grade level, institutional tier, and academic discipline, with higher-grade students, those from prestigious universities, and those studying Applied Sciences and Natural Sciences demonstrating better performance.

scientific communication and education↗

bioRxiv2024

Decoding task representations that support generalization in hierarchical task

Task knowledge can be encoded hierarchically such that complex tasks can be built by associating simpler tasks. This associative organization supports generalization to facilitate learning of related but novel complex tasks. To study how the brain implements generalization in hierarchical task learning, we trained human participants on two complex tasks that shared a simple task and tested them on novel complex tasks whose association could be inferred via the shared simple task. Behaviorally, we observed faster learning of the novel complex tasks than control tasks. Using electroencephalogram (EEG) data, we decoded constituent simple tasks when performing a complex task (i.e., EEG association effect). Crucially, the shared simple task, although not part of the novel complex task, could be reliably decoded from the novel complex task. This decoding strength was correlated with EEG association effect and behavioral generalization effect. The findings demonstrate how task learning can be accelerated by associative inference. Significance StatementHumans can generalize knowledge of existing tasks to accelerate the learning of new tasks. We hypothesize that this generalization is achieved by building complex tasks that associate simple (sub)tasks that can be reused. Using electroencephalogram (EEG) data, we showed that constituent simple tasks can be decoded from the EEG data of humans learning new complex tasks. Crucially, when participants represent complex tasks as associations between multiple simple tasks, the simple tasks can be decoded from the new complex task, even when they are not part of the new complex task. These findings demonstrate the importance of the reinstatement of simple tasks in task learning through generalization.

neuroscience↗

bioRxiv2024

Dermatopontin-expressing fibroblasts mediate an essential skin macrophage niche

Fibroblasts are present in all tissues and are crucial for maintaining tissue homeostasis. We previously identified fibroblasts marked by Dermatopontin (Dpt) but their role in supporting macrophage homeostasis remains unclear. Here, we generated novel mesenchymal lineage-restricted genetic tools to target Dpt expressing fibroblasts and elucidate their role in supporting skin macrophages. Transcriptional profiling, flow cytometry, and in situ hybridization uncovered two broad populations of F4/80-expressing skin macrophages, denoted by high expression of CD206 and CD64 (CD206hiCD64+), or CD11c. Targeted depletion of Dpt+ fibroblasts resulted in a profound loss of both macrophage populations. Conditional deletion of colony-stimulating factor-1 (Csf1) in Dpt+ fibroblasts revealed that CD206hiCD64+, and not CD11c+, macrophages are acutely dependent on fibroblast-derived Csf1, consistent with their higher expression of the Csf1 receptor. Following Csf1 deletion in Dpt+ fibroblasts, loss of CD206hiCD64+ macrophages were observed across the dermis, dermal white adipose tissue (dWAT), and adventitia, accompanied by a modest upregulation of fibroblast-related and extracellular matrix (ECM) genes and structural changes to the skin. Alterations to the skin network upon loss of fibroblast-derived Csf1 and CD206hiCD64+ macrophages led to a significant delay in wound healing. We also demonstrate the CSF1-CSF1R signaling pathway is functionally relevant in human systemic sclerosis, or scleroderma, as elevated levels of CSF1 produced by fibroblasts and an increased abundance of macrophages both correlate with disease severity. Our findings demonstrate the role of Dpt+ fibroblasts in regulating a Csf1-dependent macrophage niche in skin and orchestrating responses in injury and disease.

immunology↗

bioRxiv2024

Ox-LDL induces a non-inflammatory response enriched for coronary artery disease risk in human endothelial cells

Oxidised low-density lipoprotein cholesterol (ox-LDL) is critical in the initiation and progression of atherosclerosis. While excessive atherogenic lipids in the arterial intima can trigger endothelial dysfunction in advanced lesions, the response of endothelial cells to ox-LDL in the early stages of atherogenesis remains unclear. Here, we conducted a comprehensive, genome-wide multi-omics characterisation of the cellular response to ox-LDL in primary human aortic endothelial cells (HAECs). Our results reveal that the exposure of HAECs to ox-LDL leads to pathogenic changes in metabolism, transcriptome and epigenome, but in the absence of a typical inflammatory endothelial phenotype. An integrative analysis implicates the role of AP-1, NFE-2 and CEBP transcription factors in regulating ox-LDL-induced transcription. We further demonstrate that ox-LDL activates endothelial cell migration through the epigenomic rewiring of transcription factor binding. Notably, these ox-LDL-induced dynamic binding sites are enriched for the genetic risk of coronary artery disease, enabling the discovery of the gene-environment interaction of rs62172376 and ox-LDL at the CALCRL/TFPI locus. Collectively, our findings provide an unbiased understanding of the transcriptional regulation in endothelial cells in response to ox-LDL, together with its interaction with the genetic element of coronary artery disease.

genomics↗

bioRxiv2024

{-}Numb provides a fail-safe mechanism for intestinal stem cell self-renewal in adult Drosophila midgut

Stem cell self-renewal often relies on asymmetric fate determination governed by niche signals and/or cell-intrinsic factors but how these regulatory mechanisms cooperate to promote asymmetric fate decision remains poorly understood. In adult Drosophila midgut, asymmetric Notch (N) signaling inhibits intestinal stem cell (ISC) self-renewal by promoting ISC differentiation into enteroblast (EB). We have previously shown that epithelium-derived BMP promotes ISC self-renewal by antagonizing N pathway activity (Tian and Jiang, 2014). Here we show that loss of BMP signaling results in ectopic N pathway activity even when the N ligand Delta (Dl) is depleted, and that the N inhibitor Numb acts in parallel with BMP signaling to ensure a robust ISC self-renewal program. Although Numb is asymmetrically segregated in about 80% of dividing ISCs, its activity is largely dispensable for ISC fate determination under normal homeostasis. However, Numb becomes crucial for ISC self-renewal when BMP signaling is compromised. Whereas neither Mad RNAi nor its hypomorphic mutation led to ISC loss, inactivation of Numb in these backgrounds resulted in stem cell loss due to precocious ISC-to-EB differentiation. Furthermore, we find that numb mutations resulted in stem cell loss during midgut regeneration in response to epithelial damage that causes fluctuation in BMP pathway activity, suggesting that the asymmetrical segregation of Numb into the future ISC may provide a fail-save mechanism for ISC self-renewal by offsetting BMP pathway fluctuation, which is important for ISC maintenance in regenerative guts.

developmental biology↗

bioRxiv2024

Unraveling transposable element-mediated regulatory landscape in diverse human immune cells

Transposable elements (TEs) are key contributors to genetic novelty. Despite increasing evidence of their importance, their roles in shaping the regulatory landscape of diverse immune cell populations remain largely unclear. Using single-cell multiome data from human peripheral blood mononuclear cells, we annotated the cell-specific cis-regulatory elements for major immune cell populations and identified a highly cell-specific signature of the overrepresented TE families. Focusing on monocytes that bear fast-evolving transcriptomes, we found that high proportions of their enhancers are TE-derived and bound by multiple pioneer transcription factors. Among them, we confirmed that the core myeloid regulator SPI1 can bind and regulate hundreds of TE-derived enhancers, which further affect the expression of adjacent immune genes. Additionally, interspecies comparison reveals that non-conserved monocyte enhancers are frequently generated by lineage-specific TE insertions, and correlate with the evolved gene expression between human and mouse. Overall, our study supports the importance of TEs in shaping the regulatory landscape of diverse immune cell populations.

genomics↗

bioRxiv2024

DEMINERS enables clinical metagenomics and comparative transcriptomic analysis by increasing throughput and accuracy of nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) advances RNA biology but is limited by relatively low basecalling accuracy, low throughput, yet high RNA input and costs. Here we introduce a novel DRS toolkit, DEMINERS, which integrates an RNA multiplexing experimental workflow, a machine-learning barcode classifier based on Random Forest and a novel basecaller built on an optimized convolutional neural network providing an additional species-specific training module. With the increased accuracy in barcode classification and basecalling, DEMINERS can demultiplex up to 24 samples and the required RNA input and running time are both substantially reduced. We demonstrated the applications of DEMINERS in clinical metagenomics, cancer transcriptomics and parallel comparison of transcriptomic features in different biological conditions, revealing altered airway microbial diversity in COVID-19 and a potential role of m6A in increasing transcriptomic diversity in glioma and the mature blood-stage of malaria parasites. Overall, DEMINERS is a simple, robust, high-throughput DRS method for accurately estimating transcript levels, poly(A) lengths, and mutation and RNA modification heterogeneity at single-read level, with minimal sequencing biases.

bioinformatics↗

bioRxiv2024

Optical coherence tomography enables longitudinal evaluation of cell graft-directed remodeling in stroke lesions

Stem cell grafting can promote glial repair of adult stroke injuries during the subacute wound healing phase, but graft survival and glial repair outcomes are perturbed by lesion severity and mode of injury. To better understand how stroke lesion environments alter the functions of cell grafts, we employed optical coherence tomography (OCT) to longitudinally image mouse cortical photothrombotic ischemic strokes treated with allogeneic neural progenitor cell (NPC) grafts. OCT angiography, signal intensity, and signal decay resulting from optical scattering were assessed at multiple timepoints across two weeks in mice receiving an NPC graft or an injection of saline at two days after stroke. OCT scattering information revealed pronounced axial lesion contraction that naturally occurred throughout the subacute wound healing phase that was not modified by either NPC or saline treatment. By analyzing OCT signal intensity along the coronal plane, we observed dramatic contraction of the cortex away from the imaging window in the first week after stroke which impaired conventional OCT angiography but which enabled the detection of NPC graft-induced glial repair. There was moderate, but variable, NPC graft survival at photothrombotic strokes at two weeks which was inversely correlated with acute stroke lesion sizes as measured by OCT prior to treatment, suggesting a prognostic role for OCT imaging and reinforcing the dominant effect of lesion size and severity on graft outcome. Overall, our findings demonstrate the utility of OCT imaging for both tracking and predicting natural and treatment-directed changes in ischemic stroke lesion cores.

neuroscience↗

bioRxiv2024

BrainScale: Enabling Scalable Online Learning in Spiking Neural Networks

Spiking neural networks (SNNs) represent a promising paradigm for understanding brain functions [1, 2] and developing neuromorphic intelligence [3, 4]. However, their potential remains largely unrealized due to a fundamental limitation: the lack of a scalable online learning system capable of supporting large-scale training of complex brain dynamics over long timescales. Current approaches are either limited to offline learning [5], impeding long-time task training; constrained by high memory complexity [6, 7], hindering network scaling; or using oversimplified models [8], failing to capture complex brain dynamics. Here, we introduce Brain-Scale, a system that synergizes model universality, computational efficiency, and engineering usability to enable scalable online learning in SNNs. First, BrainScale introduces standard model abstractions to support the training of diverse spiking networks. Second, BrainScale implements an online learning algorithm with linear memory complexity by exploiting intrinsic properties of SNN dynamics. Third, BrainScale provides an online learning compiler that automates the implementation of online learning for any user-defined models. Extensive evaluations across diverse SNN dynamics and computational tasks demonstrate that BrainScale consistently delivers strong training performance while maintaining extremely low memory usage and high computational efficiency. These properties enable the online training of a whole-brain-scale SNN of the Drosophila brain, accurately capturing functional activities across brain regions. These results position BrainScale as a foundational platform for advancing large-scale brain simulation and neuromorphic computing.

neuroscience↗