Biology subjects

Xiong, J.

Publications and source records attributed to Xiong, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

A Meta-Analysis of the Effects of Chronic Stress on the Prefrontal Transcriptome in Animal Models and Convergence with Existing Human Data

BackgroundChronic stress is a major risk factor for psychiatric disorders, including anxiety, depression, and post-traumatic stress disorder. Chronic stress can cause structural alterations like grey matter atrophy in key emotion-related areas such as the prefrontal cortex (PFC). To identify biological pathways affected by chronic stress in the PFC, researchers have performed transcriptional profiling (RNA-sequencing, microarray) to measure gene expression in rodent models. However, transcriptional signatures in the PFC that are shared across different chronic stress paradigms and laboratories remain relatively unexplored. MethodsWe performed a meta-analysis of publicly available transcriptional profiling datasets within the Gemma database. We identified six datasets that characterized the effects of either chronic social defeat stress (CSDS) or chronic unpredictable mild stress (CUMS) on gene expression in the PFC in mice (n=117). We fit a random effects meta-analysis model to the chronic stress effect sizes (log(2) fold changes) for each transcript (n=21,379) measured in most datasets. We then compared our results with two other published chronic stress meta-analyses, as well as transcriptional signatures associated with psychiatric disorders. ResultsWe identified 133 genes that were consistently differentially expressed across chronic stress studies and paradigms (false discovery rate (FDR)<0.05). Fast Gene Set Enrichment Analysis (fGSEA) revealed 53 gene sets enriched with differential expression (FDR<0.05), dominated by glial and neurovascular markers (e.g., oligodendrocyte, astrocyte, endothelial/vascular) and stress-related signatures (e.g., major depressive disorder, hormonal responses). Immediate-early gene markers of neuronal activity (Fos, Junb, Arc, Dusp1) were consistently suppressed. Many of the identified effects resembled those seen in previous meta-analyses characterizing stress effects (CSDS, early life stress), despite minimal overlap in included samples. Moreover, some effects resembled previous observations from psychiatric disorders, including alcohol abuse disorder, major depressive disorder, bipolar disorder, and schizophrenia. ConclusionOur study demonstrates that chronic stress induces a robust, cross-paradigm PFC signature characterized by down-regulation of glia/myelin and vascular pathways and suppression of immediate-early gene activity, highlighting cellular processes linking chronic stress exposure, PFC dysfunction, and psychiatric disorders. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/683091v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1062c5eorg.highwire.dtl.DTLVardef@4ae148org.highwire.dtl.DTLVardef@c747corg.highwire.dtl.DTLVardef@1b3a713_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIChronic stress is linked to many human psychiatric disorders involving the prefrontal cortex (PFC). C_LIO_LIMeta-analysis showed that stressed mice had less gene expression for glia and neural activity in the PFC. C_LIO_LIPFC gene expression in chronically stressed mice mirrors patterns seen in psychiatric patients. C_LI Plain Language SummaryChronic stress has been shown to have lasting effects on the brain, contributing to cognitive impairments and the risk of psychiatric disorders such as anxiety, depression, and post-traumatic stress disorder. The prefrontal cortex, a brain region that is important for behavioral and attentional control, is sensitive to chronic stress. We combined information from six public mouse datasets to identify consistent effects of chronic stress on gene expression (mRNA) in the prefrontal cortex. In these studies, mice that had experienced chronic stress showed a decreased amount of mRNA related to a variety of non-neuronal support cells (glia) and blood vessels, and general neural activity. The mouse chronic stress signature overlapped with gene-activity patterns reported in human psychiatric conditions, suggesting a biological bridge between chronic stress and the onset of psychopathology.

neuroscience↗

bioRxiv2025

Fixing Hybrid Rice: >99% Efficient Apomixis with Near-Normal Seed Set

Apomixis, a form of clonal seed reproduction, offers a transformative approach to agriculture by enabling the stable fixation of hybrid vigor and elite heterozygosity across generations. However, the practical implementation of synthetic apomixis has been hindered by highly variable clonal efficiency or significant yield penalties. In this study, through integrated transcriptomic analyses, we identified a sperm-specific transcription factor in rice that likely functions as a key initiator of embryogenesis. Ectopic expression of this factor in egg cells effectively induces parthenogenesis and produces haploid progeny. When combined with clonal gametogenesis, this system achieved nearly complete synthetic apomixis, with clonal seed production rates exceeding 99% across all derived hybrid rice lines. Moreover, we generated apomictic hybrid lines that not only consistently produced over 99% clonal seeds but also exhibited seed yields comparable to conventional F1 hybrids. These findings establish a scalable and agriculturally viable platform for synthetic apomixis, achieving stable fixation of heterosis through clonal seeds and paving the way for the commercial deployment of self-perpetuating hybrid crops.

plant biology↗

bioRxiv2025

Computer Vision Methods for Spatial Transcriptomics: A Survey

Spatial transcriptomics (ST) enables the simultaneous measurement of gene expression and spatial localization within tissue sections, providing unprecedented opportunities to dissect tissue architecture and functional organization. As a relatively new omics technology, bioinformatics has driven much of the innovation in ST. However, within these frameworks, "spatial" information is often reduced to locations and relationships between molecular profiles, without fully leveraging the wealth of submicron morphological detail and histological knowledge available. Advances in computer vision-based artificial intelligence (AI) are opening exciting new avenues beyond conventional bioinformatics approaches by modeling complex histological patterns and linking morphology to molecular states. More excitingly, they bring fresh perspectives to potentially address key limitations of ST, including its high cost, limited clinical applicability, and reliance on twodimensional (2D) analysis of inherently three-dimensional (3D) tissues. For instance, models that predict ST directly from histology images enable "virtual sequencing," drastically reducing costs while integrating morphological insights from pathology with molecular biomarkers, thus accelerating clinical translation. Moreover, computer vision techniques can reconstruct pixel-aligned 3D tissue models, overcoming the technical barriers of 2D acquisition and advancing 3D spatial omics analytics. In this paper, we present the first systematic survey of computer vision AI models for ST analytics, categorizing approaches across architectures, learning paradigms, tasks, and datasets, and tracing their technological evolution. We highlight key challenges and future directions, offering a panoramic perspective on vision-driven ST and its potential to transform both basic research and clinical practice. The curated collection of vision-driven spatial transcriptomics papers is available at https://github.com/hrlblab/computer_vision_spatial_omics.

bioinformatics↗

bioRxiv2025

NPAS2 attenuates VSMC phenotypic switching in ascending thoracic aortic aneurysm via LPCAT3/PC-PUFA2S-mediated ferroptosis

Ascending thoracic aortic aneurysm (ATAA) is a life-threatening disorder with limited therapeutic options, critically linked to vascular smooth muscle cell (VSMC) phenotypic switching. Neuronal PAS Domain Protein 2 (NPAS2), a circadian rhythm-related transcription factor, regulates diverse physiological processes. However, the biological functions and underlying regulatory mechanisms of NPAS2 in VSMCs during ATAA pathogenesis remain to be elucidated. NPAS2 expression decreased in ATAA patients, PDGF-BB-treated HASMCs and BAPN-administrated mice. NPAS2 depletion facilitated VSMC phenotype switching. VSMC-specific NPAS2 knockout (NPAS2SMKO) mice exhibited aggravated ATAA. Mechanistically, NPAS2 depletion attenuates its transcriptional repression of LPCAT3, subsequently elevating the accumulation of phosphatidylcholines with two polyunsaturated fatty acyl chains (PC-PUFA2S), thereby promoting ferroptosis-induced VSMC phenotype switching and accelerating ATAA progression. Targeting NPAS2 represents a potential therapeutic strategy for ATAA treatment.

molecular biology↗

bioRxiv2025

Segmentation Matters: Recognizing the Cell Segmentation Challenge in Spatial Transcriptomics

Probe-based in situ hybridization spatial transcriptomics has emerged as a state-of-the-art for neuroscience research. Accurate segmentation of neurons and non-neuronal cells, a critical step for downstream analysis, remains a big challenge. Using human sensory ganglia neurons as an example, we systematically explore this problem. We evaluated multiple automatic segmentation approaches using quantitative performance metrics and downstream results. We show that careful parameter tuning is essential for achieving accurate segmentation; however, even with optimized parameters, different models still yield distinct classes of errors. To mitigate these errors, we propose a manual quality check, which can validate and refine automated segmentation results. As a future direction, integrating multi-modal imaging data with tailored neural networks may further improve segmentation accuracy. In summary, our results argue that each automated segmentation method has distinct strengths, weaknesses, and characteristic error patterns; and that a manual review is necessary.

neuroscience↗

bioRxiv2025

Control of myogenesis by the E3 ubiquitin ligase CUL3-BTBD9

Metazoan development requires that cells adopt specific identities at the right time and place with-in an embryo. Central to the success of this process are posttranslational modifications that control the activity, stability or localization of crucial transducers of differentiation signals. During muscle development, modification of proteins with ubiquitin is known to play an important role, but the enzymatic machinery of ubiquitylation that drives myogenesis remains incompletely understood. Here, we identify CUL3BTBD9 as an E3 ubiquitin ligase that is essential for myogenesis in vitro. CUL3BTBD9 binds and ubiquitylates CAV1, the central component of caveolae that modulate insulin signaling during muscle formation. CUL3BTBD9 and CAV1 are required for insulin-dependent activation of the AKT kinase in myoblasts, thereby safeguarding the ability of muscle precursors to respond to insulin signals. Together, this work identifies CUL3BTBD9 as a regulator of myogenesis that acts by modulating plasma-membrane localized events critical for cell fate specification.

cell biology↗

bioRxiv2025

Bridging 3D Molecular Structures and Artificial Intelligence by a Conformation Description Language

Artificial intelligence, particularly language models (LMs), is reshaping research paradigms across scientific domains. In the fields of chemistry and pharmacy, chemical language models (CLMs) have achieved remarkable success in two-dimensional (2D) molecular modeling tasks by leveraging one-dimensional (1D) representations of molecules, such as SMILES and SELFIES. However, extending these successes to three-dimensional (3D) molecular modeling remains a significant challenge, largely due to the absence of effective 1D representations for capturing 3D molecular structures. To address this gap, we introduce ConfSeq, a novel molecular conformation description language that integrates SMILES with internal coordinates including dihedral angles, bond angles, and pseudo-chirality. This design naturally ensures SE(3) invariance, while preserving the human readability and conciseness characteristic of SMILES. ConfSeq enables the reformulation of a range of 3D molecular modeling tasks, such as molecular conformation prediction, 3D molecular generation, and 3D molecular representation, into sequence modeling problems. Then, by simply employing a standard Transformer architecture, we achieve state-of-the-art performance on various benchmark sets. Furthermore, compared to widely used diffusion-based approaches in 3D molecular modeling, the ConfSeq-based method offers unique advantages in inference efficiency, generation controllability, and enables scoring of generated molecules. We believe that ConfSeq can serve as a foundational tool, advancing the development of sequence-based 3D molecular modeling methods.

bioinformatics↗

bioRxiv2025

Beyond Natural Antibodies: Scaffold-Based Generation of Novel Anti-3CLpro Nanobody Nb01

In the post-pandemic era, continuous mutations and persistent infections of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pose a significant threat to global health, making the normalization of preventive measures imperative. Due to its high conservation, the 3CLpro is relatively stable across all these variants without significant changes, suggesting that drugs targeting this enzyme could be effective against all variant viruses. And compared to traditional antibodies, nanobodies show significant advantages against SARS-CoV-2 and its variants. Traditional antibodies often lose their inhibitory activity due to viral mutation. Nanobodies are characterised by their small size, high stability, high affinity for antigen binding, and high water solubility, which enable them to capture viruses quickly and effectively address the continuous mutations and persistent infections of SARS-CoV-2. In this research, we devised a strategy to produce nanobodies by utilizing a fragment-generating large language model, resulting in the identification of a nanobody named Nb01. Nb01 exhibits potent and broad-spectrum neutralizing activity against numerous SARS-CoV-2 variants. The nanobody Nb01, directed against the SARS-CoV-2 3CLpro, demonstrated efficacy against the majority of prevalent SARS-CoV-2 variants. Notably, it has a higher affinity than the current best performing nanobodies (S43, bn03, R14, and 3-2A2-4) for most of the variants, including the Alpha (27.5%), Gamma (29.7%), Omicron BA.2 (32.2%), BA.4/5 (81.2%), BF.7 (64.2%) and XBB (5.5%) variants. For other variants, Nb01 displayed affinities that were on par with these benchmark nanobodies. In summary, the exceptional specificity, low toxicity, robust stability, and extensive spectrum of Nb01 indicate its potential to be developed as a nanobody therapeutic for the management of SARS-CoV-2 infections and its diverse variants.

bioinformatics↗

bioRxiv2025

Short Report: A Meta-Analysis of the Effects of Sleep Deprivation on the Cortical Transcriptome in Animal Models

Sleep deprivation (SD) causes large disturbances in mood and cognition. The molecular basis for these effects can be explored using transcriptional profiling to quantify brain gene expression. In this report, we used a meta-analysis of public transcriptional profiling data to discover SD effects on gene expression that are consistent across studies and paradigms. To conduct the meta-analysis, we used pre-specified search terms related to rodent SD paradigms to identify relevant studies within Gemma, a database containing >19,000 re-analyzed microarray and RNA-Seq datasets. Eight studies met our systematic inclusion/exclusion criteria. These studies characterized the effect of 18 SD interventions on gene expression in the mouse cerebral cortex (collective n=293). For each gene with sufficient data (n=16,290), we fit a random effects meta-analysis model to the SD effect sizes (log(2) fold changes). Our meta-analysis revealed 182 differentially expressed genes in response to SD (false discovery rate: FDR<0.05), most of which (115/182) showed similar effects (FDR<0.05) in an independent large dataset (GSE114845: n=86 RNA-Seq samples from n=222 mice). Gene-set enrichment analysis revealed down-regulation in pathways related to stress response (e.g., glucocorticoid receptor Nr3c1), vasculature, growth and development, and upregulation related to stress, inflammation, and neuropeptide signalling. Exploratory analyses suggested that recovery sleep (included in six contrasts: range: 1-18 hrs), could reverse the impact of SD on gene expression. Our meta-analysis provides a useful reference database illustrating the diverse molecular impact of SD on the rodent cerebral cortex. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/648791v2_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@52984dorg.highwire.dtl.DTLVardef@8d1174org.highwire.dtl.DTLVardef@174ed85org.highwire.dtl.DTLVardef@195e8bf_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

neuroscience↗

bioRxiv2025

Robust analytical methods for bis(monoacylglycero)phosphate profiling in health and disease

Bis(monoacylglycero)phosphate (BMP), a distinct anionic phospholipid predominantly found in late endosomes and lysosomes, plays a pivotal role in supporting lysosomal functions and maintaining metabolic homeostasis. Its impaired function is associated with an array of disorders, notably neurodegenerative diseases. However, the identification and quantitation of BMP remains difficult due to its structural similarity to isomer phosphatidylglycerol (PG), thus necessitating robust analytical methods for accurate and reliable BMP profiling. In this study, we present comprehensive liquid chromatography - tandem mass spectrometry (MS2) methodologies for the precise and systematic analysis of BMP species in biological samples. We detail LC/MS methods for both an untargeted Orbitrap mass spectrometer and a targeted triple quadrupole (QQQ) mass spectrometer. We utilize differences in polarity and structure to annotate BMPs and PGs based on retention time and positive mode MS2 fragmentation patterns, respectively. Further, we propose a new approach for overcoming common challenges in BMP profiling by leveraging the newly discovered biochemical function of CLN5 as the BMP synthase. Since genetic ablation of CLN5 leads to specific depletion of BMPs but not PGs, we use lipid extracts from CLN5 knockout (KO) and wild-type (WT) cells as biological standards to confidently annotate BMPs as targets with significantly low BMP Identification Index (BMPII), defined as BMPII = CLN5 KO / WT. We additionally propose the BMP enrichment score (BMPES) as a secondary validation metric, defined as lysosomal abundance of BMP / whole-cell abundance. Altogether, this approach constitutes a robust method for BMP profiling and annotation, furthering research into health and disease.

biochemistry↗

bioRxiv2025

scCausalVI disentangles single-cell perturbation responses with causality-aware generative model

Single-cell RNA sequencing provides detailed insights into cellular heterogeneity and responses to external stimuli. However, distinguishing inherent cellular variation from extrinsic effects induced by external stimuli remains a major analytical challenge. Here, we present scCausalVI, a causality-aware generative model designed to disentangle these sources of variation. scCausalVI decouples intrinsic cellular states from treatment effects through a deep structural causal network that explicitly models the causal mechanisms governing cell-state-specific responses to external perturbations while accounting for technical variations. Our model integrates structural causal modeling with cross-condition in silico prediction to infer gene expression profiles under hypothetical scenarios. Comprehensive benchmarking demonstrates that scCausalVI outperforms existing methods in disentangling causal relationships, quantifying treatment effects, generalizing to unseen cell types, and separating biological signals from technical variation in multi-source data integration. Applied to COVID-19 datasets, scCausalVI effectively identifies treatment-responsive populations and delineates molecular signatures of cellular susceptibility. Code availabilitySoftware is available at https://github.com/ShaokunAn/scCausalVI.

bioinformatics↗

bioRxiv2025

Integrate and generate single-cell proteomics from transcriptomics with cross-attention

MotivationSingle-cell RNA sequencing (scRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) have experienced rapid advancements in recent years, accompanied by the development of numerous methods for analyzing scRNA-seq and CITE-seq data. These innovations have enabled deeper insights into cellular heterogeneity and functional phenotypes. However, analyzing scRNA-seq and CITE-seq data within a unified framework remains a significant challenge in the field of single-cell analysis. Specifically, this challenge centers on two primary objectives: aligning scRNA-seq and CITE-seq cells within an integrated representation space and generating antibody-derived tag (ADT) measurements for scRNA-seq cells. ResultsBy incorporating interrelationships between cells into a deep generative model with cross-attention, we introduced scProca to integrate and generate single-cell proteomics from transcriptomics. scProca delivers state-of-the-art performance in both integration and generation tasks across benchmark datasets. Furthermore, scProca can accommodate cells across experimental batches, showcasing its flexibility in complex experimental contexts. AvailabilityThe code of scProca is available at https://github.com/xiongbiolab/scProca, and replication for this study is available at https://github.com/ZzzsHuqiaAao/scProca-reproducibility.

bioinformatics↗

bioRxiv2025

MutBERT: Probabilistic Genome Representation Improves Genomics Foundation Models

MotivationUnderstanding the genomic foundation of human diversity and disease requires models that effectively capture sequence variation, such as single nucleotide polymorphisms (SNPs). While recent genomic foundation models have scaled to larger datasets and multi-species inputs, they often fail to account for the sparsity and redundancy inherent in human population data, such as those in the 1000 Genomes Project. SNPs are rare in humans, and current masked language models (MLMs) trained directly on whole-genome sequences may struggle to efficiently learn these variations. Additionally, training on the entire dataset without prioritizing regions of genetic variation results in inefficiencies and negligible gains in performance. ResultsWe present MutBERT, a probabilistic genome-based masked language model that efficiently utilizes SNP information from population-scale genomic data. By representing the entire genome as a probabilistic distribution over observed allele frequencies, MutBERT focuses on informative genomic variations while maintaining computational efficiency. We evaluated MutBERT against DNABERT-2, various versions of Nucleotide Transformer, and modified versions of MutBERT across multiple downstream prediction tasks. MutBERT consistently ranked as one of the top-performing models, demonstrating that this novel representation strategy enables better utilization of biobank-scale genomic data in building pretrained genomic foundation models. Availabilityhttps://github.com/ai4nucleome/mutBERT Contactyanlinzhang@hkust-gz.edu.cn

bioinformatics↗

bioRxiv2025

Electrospinning of hydroxypropyl chitosan nanofibers for bone regeneration application

To study the effect of electrospinning nanofiber mats synthesized using hydroxypropyl chitosan in conjugation with poly (vinyl alcohol) on the osteogenesis in MC3T3 cell, we fabricated different ratios of hydroxypropyl chitosan (HPCH) and poly (vinyl alcohol) to electrospin the nanofiber mats, the mechanical and physical properties of nanofiber mats were tested by using SEM, FT-IR and conventional methods. Then the effect of the electrospinning mats on osteogenesis in MC3T3 cell were evaluated by Alizarin Red stain, ALP stain and expression of relevant genes. We found that the nanofiber mats fabricated by hydroxypropyl chitosan (50%) and poly (vinyl alcohol)(50%) can induce osteoblast differentiation apparently in MC3T3 cell.

biochemistry↗

bioRxiv2025

Iterative acylation on mature lasso peptides by widespread acetyltransferases for lipolasso production

The biosynthesis of ribosomally synthesized and post-translationally modified peptides (RiPPs) leverages iterative catalysis to enhance structural and biological diversity. Traditionally, iterative enzymes install multiple post-translational modifications (PTMs) on linear peptides, rather than mature RiPPs with intricate three-dimensional structures, which would require complex changes in substrate binding poses. Here, we present a prolific class of GCN5-related N-acetyltransferases (GNATs) that iteratively and consecutively acylate two Lys residues within the loop and ring motifs of lasso peptides, diverging from the typical iterative modification of linear peptides--an unprecedented function for PTM enzymes. Utilizing high-resolution cryo-electron microscopy and enzymatic reconstitution, we mapped the lasso peptide binding pocket of IatT and pinpointed key residues involved in demarcating the two distinct acetylation steps. Structure-based engineering of IatTs acetyl group recognition site expanded the cavity to accommodate longer-chain acyl groups, leading to the creation of lipolasso peptides, a novel class of ribosomal lipopeptide. This engineering strategy can be applied to any RiPP BGC encoding GNAT, facilitating the efficient diversification of rare ribosomal lipopeptides. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/630886v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1d88af9org.highwire.dtl.DTLVardef@18c1298org.highwire.dtl.DTLVardef@118b9f3org.highwire.dtl.DTLVardef@580a50_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

AI-Generated Hallmarks of Aging and Cancer: A Computational Approach Using Causal Emergence and Dependency Networks

This study introduces "hallmarks engineering," a computational approach to generate quantifiable hallmarks of aging and cancer. We evaluated these hallmarks using genome-wide DNA methylation data from ten age-related diseases. Causal emergence analysis revealed that hallmark-level features show stronger disease associations than individual genes, with improvements up to 9.7 orders of magnitude. Hallmark-based models achieved comparable predictive performance with fewer predictors compared to regular pathway-based models. Dependency network analysis uncovered regulatory networks with power-law distributions and identified top-level "super-regulators" such as genomic stability. Notably, the inclusion of neurodegenerative and cancer hallmarks enhanced representation for their respective disease categories. Our findings suggest that top-down modeling using computationally generated hallmarks may reveal common mechanisms across multiple diseases, offering a promising approach for modeling multimorbidity.

bioinformatics↗

bioRxiv2024

Pressorum Sensing: Growth-induced Compression Activates cAMP Signaling in Pseudomonas aeruginosa

Bacteria employ various strategies to coordinate population-level behaviors, with quorum sensing being a well-established mechanism. Here, we report a novel population-level regulatory mechanism in Pseudomonas aeruginosa, which we term pressorum sensing. This mechanism allows bacteria to modulate their collective behavior in response to growth-induced mechanical compression in confined spaces. Using a highly sensitive cAMP biosensor in combination with microfluidics, we demonstrate that when compressive forces reach approximately 30 nN, P. aeruginosa cells rapidly increases intracellular cAMP levels via the Pil-Chp chemosensory system. This response leads to up-regulation of the Type III Secretion System, a key virulence factor. Unlike quorum sensing, which relies on diffusible chemical signals, pressorum sensing utilizes mechanical cues to gauge population density and spatial confinement. In bacterial colonies, this mechanism generates striking spatial patterns of cAMP signaling, including traveling rings that coincide with step-like structures in colony morphology. Our findings reveal a previously unknown link between mechanical compression and bacterial virulence, providing new insights into how P. aeruginosa coordinates population-level responses in confined environments. This work also expands our knowledge of mechanogenetics and opens up new possibilities in synthetic biology and bioengineering applications.

microbiology↗

bioRxiv2024

Next Generation Aging Clock: A Novel Approach to Decoding Human Aging Through Over 3000 Cellular Pathways

This paper introduces Next Generation Aging Clock Models, a new approach aimed at improving disease prediction by defining aging clocks for specific cellular components or pathways, rather than giving a single value for the entire human body. The methodology consists of two stages: a pre-training stage that creates 3,028 generic pathway aging models by integrating genome-wide DNA methylation data with gene ontology and pathway databases, and a fine-tuning stage that produces 30,280 disease-specific pathway aging models using DNA methylation profiles from 3,263 samples across 10 age-related diseases. Our findings show the models predictive power for various diseases. For example, the aging index of blood vessel endothelial cell migration can predict Atherosclerosis with an odds ratio of 80. Alzheimers disease can be predicted by the aging index of response to DNA damage stimulus, Major Depressive Disorder by the organization of the mitochondrion, breast cancer by DNA repair, and the severity of COVID-19 by neutrophil degranulation, with an odds ratio of 8.5. Additionally, a global analysis revealed that aging-related diseases can be categorized into nucleus aging (such as Alzheimers disease) and cytoplasm aging (such as Parkinsons disease). This model provides a comprehensive view of aging from the organelle to the organ level using just a blood or saliva sample. This innovative approach is expected to be a valuable tool for research into aging-related diseases and for personalized aging interventions.

systems biology↗