Biology subjects

Yang, J.

Publications and source records attributed to Yang, J..

At least 271 records · Page 15Linked to original sources

bioRxiv2025

Global profiling of the proteome and acetylome in mice with abdominal aortic aneurysms

BackgroundAbdominal Aortic Aneurysm (AAA) is a life-threatening vascular condition characterized by a progressive dilation of the abdominal aorta and a high risk of rupture. Currently, no effective pharmacological therapies are available, and treatment relies mainly on surgical intervention. The lack of drug targets is largely due to limited understanding of the molecular mechanisms underlying AAA development. This study aims to identify potential therapeutic targets by performing comprehensive proteomic and acetylomic analyses in a mouse model of AAA. MethodsWe conducted global proteomic and acetylomic profiling on abdominal aortic tissues collected from an AAA mouse model. A total of 7,858 proteins and 1,790 acetylated proteins encompassing 4,581 acetylation sites were quantified. Bioinformatics analyses were performed to investigate the regulatory networks and biological pathways associated with these proteins and post-translational modifications. ResultsIntegrated analysis revealed that histone-related proteins were significantly enriched in pathways co-regulated by the proteome and acetylome. These pathways are influenced by histone acetyltransferases and deacetylases, implicating histone acetylation in AAA-related inflammatory and immune processes. Notably, the deacetylases Sirt2 and Sirt5 were identified as potential regulators of neutrophil extracellular trap (NET) formation through suppression of histone acetylation. ConclusionsThis study provides the first comprehensive proteomic and acetylomic dataset for abdominal aortic tissues in a mouse model of AAA, substantially expanding the mouse acetylation database. The results highlight the central role of histone modifications in immune dysregulation and inflammation, and propose Sirt2 and Sirt5 as candidate therapeutic targets that may inhibit NET formation and slow AAA progression. These findings suggest new avenues for drug development targeting the sirtuin family, although further experimental validation and cross-species studies are needed to confirm translational relevance. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/657536v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1c397a3org.highwire.dtl.DTLVardef@188180org.highwire.dtl.DTLVardef@de1a59org.highwire.dtl.DTLVardef@193e740_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Towards Unmanned Proteomics Data Generation: A Fully Automated Sample-to-Data System for Proteomic Experiments

The {pi}-HuB project requires proteomic measurements of millions of low-input samples that are challenging with current technology. Here, we introduce a pilot {pi}-HuB data factory that features a fully automated sample-to-data system to enable high-throughput digitization of human samples. Our platform, as a proof-of-concept towards unmanned proteomics, was demonstrated to show longitudinal robustness and applied for spatial proteomics and the profiling of plasma and cell lines, thereby serving as the workhorse to drive {pi}-HuB.

biochemistry↗

bioRxiv2025

Towards Unified Neural Decoding with Brain Functional Network Modeling

Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that constructs a whole functional brain network model by integrating intracranial neurophysiological recordings across multiple individuals. MIBRAIN leverages self-supervised learning to derive generalized neural prototypes and supports group-level analysis of brain-region interactions and inter-subject neural synchrony. To validate our framework, we recorded stereoelectroencephalography (sEEG) signals from a cohort of individuals performing Mandarin syllable articulation. Both real-time online and offline decoding experiments demonstrated significant improvements in both audible and silent articulation decoding, enhanced decoding accuracy with increased multi-subject data integration, and effective generalization to unseen subjects. Furthermore, neural predictions for regions without direct electrode coverage were validated against authentic neural data. Overall, this framework paves the way for robust neural decoding across individuals and offers insights for practical clinical applications.

neuroscience↗

bioRxiv2025

Generating dynamic structures through physics-based sampling of trRosettaX2-predicted inter-residue geometries

Deep learning methods like AlphaFold2 have achieved remarkable breakthroughs in predicting static protein structures. However, accurately modeling alternative conformations and dynamic structures remains a significant challenge. Here, we introduce trRosettaX2-Dynamics (trX2-D), a novel approach to addressing this challenge through physics-based iterative sampling of the trRosettaX2-predicted inter-residue geometric distributions. trX2-D was first pre-trained on high-resolution X-ray structures and then fine-tuned on [~]7,000 dynamic NMR structures, enhancing its inherent ability to predict alternative conformations and dynamic structures. Leveraging a transformer-based neural network, it first predicts an initial set of inter-residue geometric constraints, which are then sampled to generate dynamic structures iteratively, without requiring any prior knowledge of native structural states. Comprehensive benchmarks on three datasets, two for alternative conformations and one for dynamic structures, demonstrate that trX2-D shows promise in predicting alternative conformations and capturing structural dynamics. This work illustrates the potential of combining deep learning predictions with physics-based sampling for advancing the prediction of protein dynamic structures.

bioinformatics↗

bioRxiv2025

Multiscale Modeling Uncovers Macrophage Infiltration and TNF-α Signaling Networks for Targeting in Inflammatory Breast Cancer Tumor Emboli

Inflammatory breast cancer (IBC) tumors are characterized by diffuse clusters of cells found in dermal tissue and lymphatic vessels, known as tumor emboli. Thus, IBC needs a novel treatment because it is the most aggressive breast cancer subtype. We hypothesized that the interaction between tumor emboli and the tumor immune microenvironment (TiME) fosters survival signaling, leading to the aggressiveness of the IBC. In this study, ex vivo tumor emboli were generated from patient-derived cell lines cultured in a lymphatic-like environment, which was compared to 2D monolayer cultures, revealing upregulation of TNFR signaling networks, CXCL8, and immune cell chemotaxis genes. Spatial immunophenotyping of IBC patient tumors demonstrated high levels of CD163+ tumor-associated macrophages (TAMs). Furthermore, intravital imaging of CX3cr1GFP mice confirmed macrophage movement toward tumor cell clusters. Finally, targeting macrophage-associated TNF--signaling using Birinapant, a SMAC mimetic, inhibited the tumor emboli phenotype in vivo, identifying Birinapant as a potential therapeutic agent to disrupt this signaling axis.

cancer biology↗

bioRxiv2025

COVID-19 induces persistent transcriptional changes in adipose tissue that are not associated with Long COVID

Long COVID is a heterogeneous condition characterized by a wide range of symptoms that persist for 90 days or more following SARS-CoV-2 infection. Now more than five years out from the onset of the SARS-CoV-2 pandemic, the mechanisms driving Long COVID are just beginning to be elucidated. Adipose tissue has been proposed as a potential reservoir for viral persistence and tissue dysfunction contributing to symptomology seen in Long COVID. To test this hypothesis, we analyzed subcutaneous adipose tissue (SAT) from two cohorts: participants with subacute COVID-19 (28-89 days post-infection) compared to pre-pandemic controls, and participants with Long COVID compared to those with those classified as "indeterminate" based on the RECOVER-Adult Long COVID Research Index (12-47 months post-infection). We found no evidence of persistent SARS-CoV-2 RNA in adipose tissue in any participant. SAT from participants with subacute COVID-19 displayed significant transcriptional remodeling, including depleted immune activation pathways and upregulated Hox genes and integrin interactions, suggesting resident immune cell exhaustion and perturbations in tissue function. However, no consistent changes in gene expression were observed between Long COVID samples and samples from indeterminant participants. Thus, SAT may contribute to inflammatory dysregulation following COVID-19, but does not appear to play a clear role in Long COVID pathophysiology. Further research is needed to clarify the role of adipose tissue in COVID-19 recovery.

immunology↗

bioRxiv2025

The G57 genotype of the BJ/94-like H9N2 lineage exhibits increased replication and virulence in chickens compared to the G1 Middle East Group B lineage

Avian influenza H9N2 viruses cause significant economic losses to the poultry industry and pose a public health risk due to their potential to reassort with other avian influenza viruses, generating strains with zoonotic and pandemic potential. Two major H9N2 lineages dominate globally: the G1 lineage (genotype G1-B), prevalent in the Middle East, Africa and the Indian subcontinent, and the BJ/94 lineage (predominantly genotype G57), dominant in China, Vietnam, South Korea, Indonesia, and the Far East. We investigated replication, transmission, and pathogenicity of representatives of these two lineages, linking genotype to phenotype. The G57 strain A/Ck/Vietnam/H7F-14-BN4-315/2014 (Vietnam/315) was more lethal to chicken embryos than the G1-B strain A/chicken/Pakistan/UDL-01/2008 (Pakistan/UDL-01). Vietnam/315 exhibited higher replication in both directly infected and contact chickens, with increased virus shedding from the oropharynx and cloaca. In contrast, Pakistan/UDL-01 virus was primarily shed from the oropharynx, highlighting differences in replication, tissue tropism and transmission. Gene analysis showed the M gene of Vietnam/315 enhanced replication in primary chicken kidney cells, whereas the PB2, HA, NA, and M genes promoted increased replication in Madin-Darby Canine Kidney cells. Both viruses showed preferential binding to avian-like receptors over human-like receptors. However, Vietnam/315, however, exhibited higher neuraminidase activity and a more acid-stable HA (pH fusion 5.2) than Pakistan/UDL-01 virus. These findings suggest G57 genotype viruses possess greater replication and transmission fitness than G1-B viruses in vivo, ex vivo and in vitro. Reassortment events involving G1-B strains acquiring G57 genes may enhance replication and virulence, potentially increasing the risk of animal and human infection. ImportanceH9N2 avian influenza viruses are widespread in poultry, resulting in significant economic losses and occasional human infections. Different genetic variants dominate in various regions, but their ability to cause disease and spread in poultry remains unclear. These viruses can exchange genes with other avian influenza strains, altering their infectivity and transmission. This study compared two major H9N2 genotypes: G1-B (common in the Indian subcontinent and the Middle East) and G57 (dominant in China and Vietnam). The G57 virus showed higher replication in laboratory tests and infected chickens, shedding more virus through the orofecal route. It also exhibited a stronger attachment to bird cells and had a more acid-stable HA, suggesting an increased potential for infection and spread. Our findings indicate that G57 genotype viruses are more infectious and adaptable in poultry than G1 viruses. Gene exchange with other avian influenza strains may generate more virulent viruses with increased transmission potential. This study supports the risk assessment of emerging strains and enhances disease mitigation strategies.

microbiology↗

bioRxiv2025

Intravenous BCG immunization drives naive CD4+ T cell to Th1 differentiation via CD4+ T cell epigenetic reprogramming

Intravenous (IV) administration of live Bacille Calmette-Guerin (BCG) boosts potent immunity through coordinated interactions of innate immune response and adaptive immune response for BCG antiviral activity. The underlying mechanisms by which BCG initiates CD4+ T response remain incompletely understood. Here, we show that IV administration of BCG drives naive CD4+ T cell differentiation to Th1 cell through epigenetic reprogramming. Single-cell transcriptomic and epigenomic analyses revealed that IV BCG immunization induced metabolic reprogramming in monocytes and increased the proportion of T cells. The immunization induced chromatin openness for transcription factors regulating naive CD4+ T cells differentiation into Th1 cells. Importantly, we observed increases in Stat1, Irf1 and interferon-stimulated genes such as Igtp in Th1 cells, leading to upregulation of interferon related gene expression for antiviral response by BCG immunization. Activated transcription factors like Irf1 in CD4+ T cells promotes Il12rb1 expression to facilitate IL-12 signaling and drive naive CD4+ T cells differentiation into Th1 cells. We showed that BCG immunization reduced respiratory syncytial virus (RSV) infection. Thus, transcription regulation through epigenetic reprogramming plays a critical role in BCG-induced CD4+ T cell differentiation and to the broad antiviral activity. SignificanceBacille Calmette-Guerin (BCG) vaccination confers nonspecific protection against heterologous pathogens. Recent work demonstrated that coordinated interactions of innate immune and persistent CD4+ T cell response plays a critical role in BCG induced antiviral activity. In our study, we investigated BCG immunization on epigenetic reprogramming of CD4+ T cells and found that BCG immunization elicited antiviral activity by provoking T cell proliferation. We observed chromatin openness favoring naive T cell differentiation to Th1 response. Importantly, we observed STAT1 and interferon regulatory factor-1 (Irf1) activation. These findings provide mechanistic insights into BCG-induced Th1 response through epigenetic reprogramming and highlight the potential of BCG in offering a long-term protection against emerging and reemerging pathogens. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/655674v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@1c3cb94org.highwire.dtl.DTLVardef@16a8e8forg.highwire.dtl.DTLVardef@b58b88org.highwire.dtl.DTLVardef@dbc994_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIIntravenous BCG immunization drives naive CD4+ T cell differentiation to Th1 response; C_LIO_LIBCG alters chromatin accessibility for transcriptional factor expression favoring Th1 response through a Irf1-Il12rb1-IL12 feedback loop; C_LIO_LIThe immunization elicits antiviral activity against RSV infection; C_LI

immunology↗

bioRxiv2025

Riemann-GNN: Causal Reasoning on Hyperbolic Riemannian Manifolds for Interpretable Drug-Disease Prediction

The high cost and low success rate of de novo drug development have driven increasing interest in drug repurposing. Computational approaches to drug repurposing leverage large-scale biomedical knowledge graphs (KGs) to identify new indications for existing drugs. However, the structural heterogeneity, relation sparsity, and limited interpretability of current models remain significant challenges. In this study, we propose Riemann-GNN, an end-to-end framework that combines hyperbolic geometry, graph neural networks (GNNs), and causal inference for interpretable drug repurposing. Riemann-GNN embeds multi-relational biomedical KGs into a hyperbolic Riemannian manifold modeled by the Poincare ball, effectively capturing the hierarchical structure of biomedical entities. On top of this geometry, we introduce a causal reasoning module that extracts disease-centric subgraphs and applies intervention-based inference using structural causal models. This module provides mechanistic explanations for predicted drug-disease associations. We evaluate our model on PrimeKG, a comprehensive biomedical KG with over 129,000 nodes and 4 million edges, achieving AUROC = 0.971, AUPRC = 0.963, and Recall@20 = 0.847, significantly outperforming several state-of-the-art baselines. In a case study on amyotrophic lateral sclerosis (ALS), Riemann-GNN identifies 20 novel candidate drugs--such as Bosutinib, Masitinib, and Tamoxifen--and provides interpretable causal paths explaining their therapeutic relevance. Several predictions without existing ALS labels in the KG are supported by literature evidence, while others suggest new hypotheses for future validation. These results demonstrate the utility of integrating hyperbolic representation learning with causal semantics for robust and explainable biomedical AI.

bioinformatics↗

bioRxiv2025

Rapid and accurate protein structure database search using inverse folding model and contrastive learning

Protein structure database search has become increasingly challenging due to the growing number of experimental and computational structures. We introduce mTM-align2, a novel two-step approach for rapid and accurate protein structure database search. In the first step, protein structures are first transformed into embeddings using a pre-trained inverse folding model (ESM-IF) and 3D Zernike polynomials. The ESM-IF embeddings are further optimized through a contrastive learning network, which is trained on [~]7 million structure pairs. Structures with similar embeddings are returned on the fly in this step. The second step employs a rapid structure alignment program to refine top candidates, ensuring high precision and producing high-quality alignments. Extensive benchmarks reveal that mTM-align2 performs competitively compared to other leading methods, completing monomeric structure search in seconds with over 90% precision for the top 10 hits. The t-SNE visualization of the mTM-align2 embeddings for thousands of structures demonstrates that our embeddings are structurally informed, capturing the global structural features. It uncovers insights such as structure misclassifications and ambiguous structural class boundaries. A web server for mTM-align2 is accessible at https://yanglab.qd.sdu.edu.cn/mTM-align/.

bioinformatics↗

bioRxiv2025

Select azo compounds post-translationally modulate HTRA1 abundance and activity potentially through interactions at the trimer interface

High-temperature requirement protein A1 (HTRA1) is a secreted serine protease with diverse substrates, including extracellular matrix proteins, proteins involved in amyloid deposition, and growth factors. Accordingly, HTRA1 has been implicated in a variety of neurodegenerative diseases including a leading cause of blindness in the elderly, age-related macular degeneration (AMD). In fact, genome wide association studies have identified that the 10q26 locus which contains HTRA1 confers the strongest genetic risk factor for AMD. A recent study has suggested that AMD-associated risk alleles in HTRA1 correlate with a significant age-related defect in HTRA1 synthesis in the retinal pigmented epithelium (RPE) within the eye, possibly accounting for AMD susceptibility. Thus, we sought to identify small molecule enhancers of HTRA1 transcription and/or protein abundance using an unbiased high-throughput screening approach. To accomplish this goal, we used CRISPR/Sp.Cas9 engineering to introduce an 11 amino acid luminescent peptide tag (HiBiT) onto the C-terminus of HTRA1 in immortalized ARPE-19 cells. Editing was very efficient ([~]88%), verified by genomic DNA analysis, short interfering RNA (siRNA), and HiBiT blotting. Nineteen-hundred and twenty compounds from two libraries were screened. An azo compound with reported anti-amyloidogenic and cardioprotective activity, Chicago Sky Blue 6B (CSB), was identified as an enhancer of endogenous HTRA1 secretion (2.0 {+/-} 0.3 fold) and intracellular levels (1.7 {+/-} 0.2 fold). These results were counter-screened using HiBiT complement factor H (CFH) edited ARPE-19 cells, verified using HiBiT blotting, and were not due to HTRA1 transcriptional changes. Importantly, serine hydrolase activity-based protein profiling (SH-ABPP) demonstrated that CSB does not affect HTRA1s specific activity. However, interestingly, in follow-up studies, Congo Red, another azo compound structurally similar to CSB, also substantially increased intracellular HTRA1 levels (up to 3.6 {+/-} 0.3 fold) but was found to significantly impair HTRA1 enzymatic reactivity (0.45 {+/-} 0.07 fold). Computational modeling of potential azo dye interaction with HTRA1 suggests that CSB and Congo Red can bind to the non-catalytic face of the trimer interface but with different orientation tolerances and interaction energies. These studies identify select azo dyes as HTRA1 chemical probes which may serve as starting points for future HTRA1-centered small molecule therapeutics.

biochemistry↗

bioRxiv2025

Divergent conformational mechanisms of JAK2V617F and TYK2V678F mutations in Thrombocythemia and related disorders: Molecular Dynamics insights into pathway activation and therapeutic targeting

Thrombocythemia and related disorders are primarily driven by persistent activation of the JAK-STAT signaling pathway, which is frequently associated with valine-to-phenylalanine (VF) point mutations in the JAK2 and TYK2 proteins. However, the precise mechanisms underlying the sustained downstream pathway activation induced by these mutations remain unclear. To investigate this, we employed molecular dynamics simulations and computational biophysical approaches to examine how VF mutations sustain JAK-STAT pathway activation. Our analyses, encompassing energetics, interaction forces, and surface electrostatic charge distributions, revealed that these mutations predominantly alter the spatial conformations and stability of the proteins by modulating inter-dimeric interaction forces and binding free energies. Specifically, the V617F mutation in JAK2 enhanced the stability of the JAK2 dimer and suppressed the activity of the JH2 pseudokinase domain. Similarly, the V678F mutation in TYK2 reducing the binding strength of the JH1 kinase domain and the binding free energy, increasing the binding affinity at the dimer interface. Both mutations ultimately resulted in heightened JH1 kinase domain activity and aberrant activation of downstream signaling pathways. Electrostatic analyses further demonstrated that JAK2V617F strengthened attractive forces within the dimer and TYK2V678F mutation mitigated repulsive forces. Decreased binding free energy confirmed enhanced dimer stability in JAK2V617F and TYK2V678F mutants, driven by distinct mechanisms: reinforced JH2 domain interactions in JAK2V617F and domain compaction with reoriented interfaces in TYK2V678F. These insights delineate the mutation-specific allosteric networks that underpin the pathogenesis of thrombocythemia. Furthermore, they establish a molecular foundation for designing isoform-selective inhibitors that target conformational vulnerabilities, thereby presenting novel therapeutic opportunities. Author SummaryO_LIThe VF mutations located in JAK2 and TYK2 stabilizes the JH2 pseudokinase domain and enhances dimer interfaces to disrupt autoinhibition in JAK2, while it induces structural reorganization of the dimer FERM/SH2 domains and JH1 kinase conformation to promote substrate accessibility in TYK2. C_LIO_LIWe developed a novel multiscale analyses framework, integrating conformational analysis, electrostatic analysis, hydrogen bond and salt bridges, and free energy decomposition, to collaboratively elucidate the effects of VF mutations on the protein multimers. C_LIO_LIThese findings highlights divergent allosteric networks underlying pathogenic signaling, offering a molecular mechanisms for designing isoform-selective inhibitors targeting conformational vulnerabilities in myeloproliferative disorders. C_LI

biophysics↗

bioRxiv2025

Transient centrosome loss in cultured prostate epithelial cells induces chromosomal instability to produce an oncogenic genotype that correlates with poor clinical outcomes

Chromosomal instability (CIN) is a hallmark of prostate cancer that strongly correlates with metastatic burden and appears prominently in both primary cancer and metastatic disease. Low Gleason score primary prostate tumors display pervasive centrosome loss, a known mechanistic driver of CIN, that disrupts normal spindle assembly and increases mitotic errors. Previously, we found that transient depletion of centrosomes in immortalized, non-tumorigenic prostate epithelial cells (PrEC) induced a burst of CIN, generating cell lines capable of forming xenograft tumors. Here, we use a multi-omics approach to identify the oncogenic alterations caused by transient centrosome loss. By integrating genomic and transcriptomic data of the prostate lines, we identified a consensus set of focal copy-number variations (CNVs) induced by centrosome loss in cultured cells that are also detectable within a subset of samples from a prostate cancer patient cohort. Using this CNV signature, we were able to derive a unique transcriptomic signature from prostate cancer patient samples that showed strong predictive value for adverse clinical outcomes. In summary, our experimental system uses centrosome loss to promote a punctuated burst of genomic crisis that is characteristic of genome evolution during prostate cancer progression. Consequently, this prostate cancer model produced recurrent structural variations that are detectable in patient samples and associate with worse outcomes. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/653266v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@ac4c9org.highwire.dtl.DTLVardef@fd9961org.highwire.dtl.DTLVardef@160e20corg.highwire.dtl.DTLVardef@198bf0e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Triple-N Dataset: Non-human Primate Neural Responses to Natural Scenes

Understanding the neural mechanisms of visual perception requires data that encompass both large-scale cortical activity and the fine-grained dynamics of individual neurons. While the Natural Scenes Dataset (NSD) has provided substantial insights into visual processing in humans (Allen et al., 2022), its reliance on functional magnetic resonance imaging (fMRI) limits the exploration of individual neuron contributions. To bridge this gap, we present a new dataset: the Triple-N (Non-human Primate Neural Responses to Natural Scenes) dataset, which extends the NSD framework to non-human primates, incorporating single-neuron activity and fMRI recorded from the inferotemporal (IT) cortex. Using Neuropixels probes, we recorded neural responses from five macaques as they passively viewed 1,000 shared NSD images. The dataset includes 59 sessions across 27 sub-regions, capturing over 30,000 visual responsive units. Many recordings were obtained from fMRI-defined category-selective regions, such as face-, body-, scene-, and color-selective areas. Our dataset enables in-depth exploration of neural responses at multiple levels - from population dynamics to single-neuron activity, providing new insights into various aspects of visual processing, including the heterogeneity of object selectivity within functional regions and the temporal dynamics of responses to natural images. Furthermore, the dataset enables joint cross-species analyses, by integrating the macaque neural recordings with human fMRI data, offering a framework for comparing and aligning visual representations across primate species. Overall, our dataset provides a valuable resource for advancing our understanding of visual perception, bridging the gap between large-scale neuroimaging and fine-grained electrophysiological signals, while also facilitating the development of computational models of the high-level visual system.

neuroscience↗

bioRxiv2025

FASN Inactivation-induced Progranulin (GRN) Expression Promotes Lysosome-dependent Cell Death to Suppress Leukemogenesis

Cancer cells rely on lipogenesis in addition to exogenous lipid uptake, and fatty acid synthase (FASN) is aberrantly overexpressed in myeloid leukemia. However, the precise role of FASN in leukemogenesis in vivo remains elusive. Here, we demonstrated that FASN is essential for leukemogenesis. Ablation of FASN impeded leukemic cell growth, survival, clonogenicity in vitro, and the leukemic burden in vivo. Conditional knockout of FASN barely affected hematopoiesis but significantly attenuated the leukemic progression of MLL-AF9 transplants. By screening a library of platensimycin derivatives, we identified compound MS-C19 as a potent FASN inhibitor. Pharmacological targeting of FASN by MS-C19 suppressed leukemic cell growth and clonogenicity in clinical AML blasts. Mechanistically, both MS-C19 treatment and FASN deficiency triggered the activation of lysosomal and inflammatory gene expression. Loss of FASN led to lysosomal membrane permeabilization and subsequent lysosome-associated cell death, but not lysosome biogenesis. We further identified that GRN, a lysosomal and neuroinflammatory gene, was potently transcribed by TFEB upon FASN loss. Depletion of GRN significantly reversed the inhibitory effects caused by FASN knockdown. Our work demonstrates that FASN is a therapeutic target for myeloid leukemia, and inactivation of FASN by the lead compound MS-C19 provides an alternative approach for leukemic intervention. Key PointsO_LIGenetic ablation of FASN induces apoptosis, impedes leukemia cell growth, and improves the survival of leukemic xenografts. C_LIO_LITargeting FASN by the compound MS-C19, a derivative of the natural microbe antibiotic platensimycin, reduces the cell survival of leukemic cell lines, the clinical leukemic blasts, and MLL-AF9 transformed murine leukemic cells. C_LIO_LIFASN inhibition induces lysosome-dependent cell death that relies on Progranulin (GRN) expression. C_LI

cancer biology↗

bioRxiv2025

Spatial profiling of chromatin accessibility reveals alteration of glial cells in Alzheimer's disease mouse brain

Abnormal epigenetic modifications, including altered chromatin accessibility, have been implicated in the development and progression of Alzheimers disease (AD). In this study, we applied spatially resolved chromatin accessibility profiling by performing spatial assay for transposase-accessible chromatin using sequencing (ATAC-seq) to analyze brain tissues from 5xFAD AD model and C57BL/6J control mice. Our analysis identified seven major cell types across 11 brain regions and further characterized glial subtypes--microglia and astrocytes-- revealing subtype-specific chromatin accessibility changes in 5xFAD mice relative to controls. These alterations were associated with AD-related pathways, including neuroinflammation and immune dysregulation, and synaptic dysfunction and neuronal signaling in microglia, as well as lysosomal and proteasomal activity, lipoprotein metabolism, and mitochondrial dysfunction in astrocytes. We also characterized cell-type-specific enrichments of motifs and transcription factors, including enrichment of Bcl11a in microglia. Furthermore, we linked chromatin accessibility changes in 5xFAD mice to human AD risk genes, highlighting altered epigenetic signatures in genes such as Trem2. Supporting the spatial ATAC-seq findings, flow cytometry validated a selective increase in VISTA (encoded by Vsir) protein expression in 5xFAD microglia, supporting the spatial ATAC-seq findings and indicating a shift toward a pro-inflammatory, disease-associated microglial (DAM-like) phenotype. These findings underscore the utility of spatial chromatin accessibility profiling for uncovering brain-region specific cell identities and epigenetic mechanisms underlying AD pathogenesis.

bioinformatics↗

bioRxiv2025

A synthetic biology toolkit for interrogating plasmid-dependent methylotrophy and enhancing methanol-based biosynthesis of Bacillus methanolicus

Bacillus methanolicus represents a thermophilic methylotroph whose methanol utilization depends on plasmid-encoded genes. It serves as a unique model for deciphering plasmid-dependent methylotrophy and an ideal chassis for low-carbon biomanufacturing using CO2-derived C1 substrates. Despite its evolutionary uniqueness and industrial potential, the lack of synthetic biology tools has hindered both mechanistic understanding and strain engineering. Here, we present a comprehensive synthetic biology platform comprising a high-efficiency electroporation protocol, a CRISPR method enabling robust and multiplex genome editing, diverse neutral loci for gene integration and overexpression, and a cloud-based genome-scale metabolic model iBM822 for user-friendly biodesign. Leveraging this toolkit, we systematically dissected plasmid-dependent methylotrophy, host restriction-modification systems, and functional significance of the chromosomal methylotrophic genes through targeted deletion. To address plasmid loss-induced strain degeneration, we integrated the large endogenous plasmid pBM19 into the chromosome for stable and intact methylotrophic growth. Finally, by integrating metabolic modeling with CRISPR editing, we engineered L-arginine feedback regulation to achieve the first L-arginine biosynthesis from methanol. This study establishes a synthetic biology framework for B. methanolicus, promoting mechanistic exploration of methylotrophy and low-carbon biomanufacturing.

synthetic biology↗

bioRxiv2025

Neanderthal introgressed ancestry reveals human genomic regions enriched with recessive deleterious mutations

Negative selection on deleterious mutations plays a key role in shaping human genetic variation, yet the dominance effects of these mutations remain poorly understood because existing statistical methods often cannot distinguish dominance effects from the overall selective effects. In this work, we take a fundamentally different approach by leveraging the distribution of Neanderthal ancestry across the human genome. Simulations show that recessive deleterious mutations can increase archaic introgressed ancestry through heterosis in the absence of positive selection, in contrast to the depletion expected under additive effects. We use this signal to develop DominL, a machine learning classifier trained on simulations of human demography with Neanderthal introgression to identify megabase-scale genomic windows enriched with recessive mutations. DominL demonstrates robust accuracy, with particularly high power in exon-dense regions. Applied to 7 non-African populations from the 1000 Genomes Project, DominL identifies approximately 3-9% of the genome as enriched for recessive mutations, with most high-confidence regions shared across populations. Predicted regions show patterns consistent with expected signatures from recessive mutations, including weakened background selection, depletion of runs of homozygosity, and enrichment of non-additive trait-associated variants. These regions also contain genes associated with metabolic and immune-related functions.

evolutionary biology↗