Biology subjects

Chen, L.

Publications and source records attributed to Chen, L..

At least 253 records · Page 14Linked to original sources

bioRxiv2025

A Conserved Mycobacterial Nucleomodulin Hijacks the Host COMPASS Complex to Reprogram Pro-Inflammatory Transcription and Promote Intracellular Survival

Nucleomodulins are a class of effector proteins secreted by bacterial pathogens that translocate into the host cell nucleus to modulate nuclear processes. However, their target proteins and underlying molecular mechanisms remain poorly understood in mycobacteria. Herein, we identified a conserved hypothetical protein Rv1075c, designated MgdE, as a nucleomodulin that enhances mycobacterial intracellular survival. MgdE undergoes nuclear translocation via two nuclear localization signals, KRIR108-111 and RLRRPR300-305, and interacts with ASH2L and WDR5, two subunits of the host histone methyltransferase COMPASS complex. This interaction suppresses histone H3 lysine 4 (H3K4) methylation-mediated transcription of pro-inflammatory genes, including IL6 and IL1B, thereby promoting mycobacterial survival in both macrophages and mice. Our study provides the first experimental evidence that a bacterial nucleomodulin facilitates intracellular survival by directly targeting the host COMPASS complex. These findings advance our understanding of mycobacterial pathogenesis by revealing a novel mechanism that contributes to its intracellular survival strategy.

microbiology↗

bioRxiv2025

Deep Blood Proteomics Identifies over 12,000 Proteins Providing Valuable Information about the State of the Human Body

Blood is a valuable resource for clinical research, offering insight into physiological and pathological states. However, the specific proteins detectable in blood and the optimal proteomic methods for their detection have not been rigorously investigated and documented. To address this, we conducted various blood proteomic strategies, including directly blood proteomic analysis, high-abundance protein depletion, low-abundance protein enrichment, and extracellular vesicle enrichment using data-independent acquisition or targeted proteomics. These approaches identified 11,679 protein groups in plasma from healthy individuals. In 136 pancreatic ductal adenocarcinoma whole blood samples, 6,956 protein groups were found, including 678 not seen in healthy samples, expanding the total to 12,357 blood proteins. This represents the most comprehensive blood proteome to date. To support broader access and analysis, we developed the Human Blood Proteome (HuBP) database, detailing protein detectability, abundance, and reproducibility across workflows, sample types, and disease contexts.

systems biology↗

bioRxiv2025

Characterizing the frequency-specific and spatiotemporal dynamics of β-γ Phase-Amplitude Coupling in Parkinson's disease

Cross-frequency coupling (CFC) has been proposed to facilitate neural information transfer across spatial and temporal scales. Phase-amplitude coupling (PAC), a type of CFC in which the amplitude of a faster brain oscillation is coupled to the phase of a slower brain oscillation, is implicated in various higher-order cognitive functions and was shown to be pathologically altered in neurological and psychiatric disease. In Parkinsons disease (PD), the coupling between gamma amplitude (50-150 Hz) to beta phase (13-35 Hz) is exaggerated. Enhanced {beta}-{gamma} PAC was found in the subthalamic nucleus and various cortical sources and shown to be responsive to dopaminergic therapy and deep brain stimulation (DBS). Therefore, exaggerated {beta}-{gamma} PAC has been proposed to be a disease marker and a potential target for brain circuit interventions. Despite these promising findings, a significant knowledge gap remains, as the spatial and frequency-specific dynamics of {beta}-{gamma} PAC and its association with motor symptoms and therapy remain elusive. To address this knowledge gap, we employed high-density electroencephalography (EEG) with advanced source localisation techniques for PD patients at rest. We highlight three key findings: (1) a frequency-specific increase in high {beta} (23-35 Hz)-{gamma} PAC within and between sources of the cortical motor network, (2) a link between elevated high {beta}-{gamma} PAC and bradykinesia and rigidity when OFF medication, but not tremor, and (3) a medication-induced reduction in high {beta}-{gamma} PAC in the supplementary motor area correlating with clinical improvement. Altogether, this study provides novel insights into the pathophysiology of PD as an oscillopathy and identifies high {beta}-{gamma} PAC as a potential marker of Parkinsonian symptoms and treatment effects. This has important implications for invasive as well as non-invasive therapeutic strategies as high {beta}-{gamma} PAC targeting might hold greater promise than targeting {beta}-{gamma} PAC per se.

neuroscience↗

bioRxiv2025

Macromolecular interactions dictate Polycomb-mediated epigenetic repression

The dynamic regulation of epigenetic states relies on complex macromolecular interactions. PRC2, the methyltransferase complex responsible for depositing H3K27me3, interacts with distinct accessory proteins to form the mutually exclusive subcomplexes PHF1-PRC2.1, MTF2-PRC2.1, PHF19-PRC2.1, and PRC2.2. The functions of these subcomplexes are unclear and thought to be highly redundant. Here we show that PRC2 subcomplexes have distinct roles in epigenetic repression of lineage-specific genes and stem cell differentiation. Using a human pluripotent stem cell model, we engineered a comprehensive set of separation-of-function mutants to dissect the roles of individual protein-protein and DNA-protein interactions. Our results show that PRC2.1 and PRC2.2 deposit H3K27me3 locus-specifically, resulting in opposing outcomes in cardiomyocyte differentiation. We find that MTF2 stimulates PRC2.1-mediated repression in stem cells and cardiac differentiation through its interaction with DNA and H3K36me3, while PHF19 antagonizes it. Furthermore, MTF2-PRC2.1 maintains normal cardiomyocyte function. Together, these results reveal the importance and specificity of individual macromolecular interactions in Polycomb-mediated epigenetic repression in human stem cells and differentiation. HighlightsO_LIThe PRC2.1 and PRC2.2 subcomplexes have distinct specificities for H3K27me3 deposition C_LIO_LIThe three PCL accessory proteins have distinct functions in regulating the PRC2 core complex, with MTF2 and PHF19 antagonizing each other C_LIO_LIInteractions between the PCL proteins and the PRC2 core, DNA, and H3K36me3 dictate PRC2 occupancy and activity at developmental genes C_LIO_LIPRC2.1 and PRC2.2 play opposing roles in stem cell cardiomyocyte differentiation C_LIO_LIMTF2 plays key functions in regulating differentiation timing and action potential rhythm in cardiomyocytes C_LI

molecular biology↗

bioRxiv2025

Longitudinal study of liver disease progression in the PEX1-Gly844Asp mouse model of mild Zellweger Spectrum Disorder

IntroductionZellweger spectrum disorder (ZSD) is an autosomal recessive disorder caused by mutations in any of 13 PEX genes encoding proteins required for peroxisome assembly and function. Chronic liver disease is one of the major clinical manifestations in patients and impacts quality of life and survival. However, the pathophysiology of liver disease is ZSD remains largely unknown, and current interventions are limited. To further study the liver disease mechanism, we use the PEX1-Gly844Asp (G844D) mouse model for mild ZSD, which was previously shown to develop hepatomegaly and cholestasis, similar to ZSD patients. MethodsThe natural history of hepatopathy was broadly characterized in PEX1-G844D mice and littermate controls from 1 to 18 months of age using liver histology, electron microscopy, cultured hepatocytes and blood. Metabolite and mechanism analysis included liver functions, respiratory chain dynamics, lipidomics, peroxisome metabolites, gene and protein expression assays. ResultsPEX1-G844D mice featured liver disease progression from hepatomegaly (1 month) to cluster cell death (4 months), hepatosteatosis (6 months), inflammation (8 months), fibrosis, and hepatic cancer (12 and 15 months). Hepatocyte proliferation and reduced glycogen was observed across all ages. Measurement of peroxisomal functions showed defective peroxisomal import and secondary mitochondrial defects in cultured hepatocytes. In blood and liver, plasmalogens were decreased, and C26:0 lyso-phosphatidylcholine and C27 bile acid intermediates were elevated. In liver, we observed accumulation of triglycerides and cholesterol, and reduced membrane phospholipids and sphingolipids. In contrast, in serum we observed reduced triglycerides, cholesterol and membrane lipids. Gene expression profiles confirmed by immunoblotting supported reduced hepatic de novo lipogenesis, increased hepatic lipid uptake and oxidation, PPAR activation, and modulated glucose and glycogen metabolism. Liver X receptor agonist (T0901317) applied to cultured hepatocytes enhanced hepatic lipogenesis and lipid secretion, but aggravated steatosis. ConclusionTaken together, these results suggested the following mechanisms of hepatopathy progression. We propose that global peroxisome dysfunction (1) causes PPAR activation, leading to chronic hyperplasia and partially contributing to disrupted hepatic lipid homeostasis with hepatosteatosis, and (2) underlies chronic hypoglycemia, causing hypoinsulinemia and contributing to reduced hepatic lipogenesis and systemic lipid deficiency. Growth restriction in the mouse model and in ZSD patients could be attributable to systemic lipid deficiency. Our mechanistic delineation of the pathophysiology provides other additional novel potential therapeutic targets to halt liver disease in ZSD.

pathology↗

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

Metabolic modulation of intratumoral cholesterol with gut microbiota for the treatment of colorectal cancer

Excess cholesterol is positively correlated with colorectal cancer (CRC). Current therapeutic strategies for modulating cholesterol levels in CRC are limited and often come with complications. Here, we demonstrated that microbiome shunting of intestinal cholesterol to anticancer metabolites is a safe and applicable therapeutic approach for CRC. By screening major microbial metabolic products of cholesterol, we found that 4-cholesten-3-one (4-C-3) was selectively depleted in fecal samples and tumor tissues of patients with CRC. 4-C-3 exhibits strong antitumor effects on human CRC cell lines, patient-derived organoids, and patient-derived xenograft (PDX) models. Mechanistically, 4-C-3 suppresses CRC tumorigenesis by dually targeting the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homologue (KRAS). 4-C-3 directly binds to EGFR, blocking its signal transduction by inhibiting the binding of EGFR ligands. Additionally, 4-C-3 inhibits oncogenic KRAS variants, such as KRASG12D, by suppressing nucleotide exchange activity and effector engagement. By targeting both EGFR and KRAS, 4-C-3 reduces primary resistance to anti-EGFR therapies caused by KRAS mutations in CRC. As a proof-of-concept study, we showed that delivery of 4-C-3 by Oscillibacter ruminantium, a 4-C-3- producing commensal bacterium that is reduced in CRC patients, or a nonpathogenic Escherichia coli strain engineered to specifically convert intestinal cholesterol into 4-C-3, removed intratumoral cholesterol and led to rapid tumor regression in multiple models of CRC. These results suggest that microbial therapies for restoring intestinal cholesterol homeostasis represent a new therapeutic avenue for CRC.

cancer biology↗

bioRxiv2025

PW-GAN: Pseudo-Warping Field Guided GAN for Unsupervised Denoising of Fetal Brain MRI Images

Fetal brain magnetic resonance imaging is of great importance for prenatal neural disorder diagnosis. To improve signal-to-noise ratio and coverage, fetal brain MRI often uses thick-slice scanning to reduce motion artifacts and ensure image quality for single slices. Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects like fetal MRI. However, raw scans acquired with varying slice thicknesses present substantial disparities in quality when retrospectively reconstructed into isotropic high-resolution volumes (e.g. 0.8 mm slice thickness). In particular, thick-slice acquisitions (e.g. 5-6 mm) tend to yield suboptimal reconstruction results compared to thin-slice scans (e.g. 2-3 mm), often exhibiting residual noise, interpolation-induced artifacts, and inter-slice structural discontinuities. This challenge has highlighted the need to further reduce noise caused by reconstruction errors when the reconstructed volumes are used for downstream tasks. With the success of neural networks in computer vision, deep learning methods based on architectures such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) have achieved outstanding performance in single-image denoising. Nevertheless, CNN-based methods still heavily rely on large-scale datasets comprising noisy-clean image pairs. Although unsupervised GAN variants such as CycleGAN have been developed to mitigate this dependency, the inherent variability of GAN-generated outputs often results in tiny anatomical distortions, significantly limiting the applicability of GAN-based methods. While several approaches have introduced regularizations to address this issue, they largely focus on denoising individual slices, overlooking inter-slice structural inconsistencies that arise from treating slices independently. In this work, we propose Pseudo-Warping Field Guided Unsupervised Generative Adversarial Network (PW-GAN), which formulates post-reconstruction optimization as an unpaired style transfer problem between low-quality and high-quality MRI domains. Moreover, by incorporating a pseudo-deformation field module based on optical flow estimation, our method significantly enhances inter-slice continuity in the reconstructed volumes while effectively suppressing residual noise and interpolation artifacts introduced during the reconstruction process. Evaluations on both simulated and in vivo data demonstrate that our method outperforms existing unsupervised models and achieves performance on par with several state-of-the-art supervised methods.

neuroscience↗

bioRxiv2025

Explainable deep learning for identifying cancer driver genes based on the Cancer Dependency Map

Identifying cancer driver genes and their therapeutic impact remains a core challenge in computational cancer biology. We introduce xNNDriver and xAEDriver, two interpretable neural network frameworks that connect cancer mutations with genome-wide DepMap gene dependencies, pathway activity, and drug-response patterns. xNNDriver is a supervised pathway-guided model that evaluates whether a genes mutation status is encoded in the genome-wide dependency landscape; we interpret model fitness as a driver potential score, which quantifies the strength of this mutation-dependency signal and prioritizes genes with broad functional footprints. Across 3,008 candidate genes, xNNDriver recovers major established drivers and highlights literature-supported candidates, while pathway analyses reveal biologically coherent programs related to metabolism, growth factor signaling, and immune regulation. To capture combinatorial functional states, xAEDriver uses an unsupervised autoencoder to learn Driver Variant Representations (DVRs), latent binary features guided by the frequency distribution of known driver mutations. DVRs capture cell-line-specific dependency patterns and expression patterns and are associated with drug sensitivity and pathway activity. Together, these interpretable deep learning models demonstrate that gene dependency landscapes encode rich, interpretable signals of oncogenic function and provide a hypothesis-generating framework for prioritizing drivers, pathways, and therapeutic vulnerabilities for further experimental validation. Author summaryCancer is often driven by genetic changes that give tumor cells a growth advantage, but finding which changes matter and how they affect cell behavior remains difficult. In this study, we used large-scale gene-editing data from cancer cell lines to ask whether the pattern of genes a cell depends on can reveal information about its cancer-driving alterations. We developed interpretable neural-network models that connect mutation patterns, cell survival dependencies, and biological pathways. One model prioritizes genes whose mutation status leaves a clear functional signature across the cell. A second model summarizes broader, combined driver-like states in each cell line. These summaries were linked to known cancer biology, tissue type, and differences in drug response, suggesting that functional dependency data contain useful clues about cancer mechanisms. By connecting mutation patterns with functional dependencies and pathway activity, our approach helps identify candidate cancer drivers and interpretable drug-response patterns that can guide future experimental studies. Making the models interpretable allows researchers to move from large screening datasets toward biological explanations of cancer-driving processes.

bioinformatics↗

bioRxiv2025

Gene Editing and Aging: A Bibliometric Analysis of Global Trends and Frontier Themes (2015-2024)

ObjectiveThe accumulation of DNA damage and mutations is a key contributor to aging. Recent studies have shown that disrupting the Beclin 1-BCL2 autophagy regulatory complex through gene editing can extend lifespan in mice. The precise application of gene editing technologies offers a promising strategy for aging. This study conducted a bibliometric analysis to map the knowledge landscape of gene editing and aging. MethodsWe retrieved publications related to genome editing and aging from the Web of Science Core Collection, covering the period from 2015 to 2024. The data were analyzed using VOSviewer and R package Bibliometrix. These tools enabled us to identify the most productive researchers, journals, institutions, countries and visualized current trends, emerging research hotspots. ResultsA total of 982 publications on genome editing and aging were identified. The United States (n=285) and China (n=214) form a dual-core structure leading global output. Harvard University (n=116) emerged as the most prolific institution. Scientific Reports was the top-publishing journal, with 23 articles in 2024. ZHANG Y (n=12, citations=102, H-index=6) was identified as the most productive author. KIM Es 2017 publication in Nature Communications (TC=494, TC/year=54.9, NTC=9.33) has had a significant and ongoing impact. The analysis indicates that future directions will include CRISPR optimization and AI-assisted genomic analysis. ConclusionThis study presents the first comprehensive bibliometric analysis and visualization of the knowledge structure in gene editing and aging research up to 2024. It offers researchers a detailed overview of current developments, trends, and emerging frontiers in this rapidly evolving domain.

scientific communication and education↗

bioRxiv2025

Mass spectrometry-based quantification of proteins and post-translational modifications in dried blood: longitudinal sampling of patients with sepsis in Tanzania

The proteomic analysis of blood is routine for disease phenotyping and biomarker development. Whole blood is commonly separated into soluble and cellular fractions. However, this can introduce pre-analytical variability; and analysis of a single component (which is common) may ignore important pathophysiology. We have recently developed methods for the facile processing of dried blood for mass spectrometry-based quantification of the proteome, N-glycoproteome and phosphoproteome. Here, we applied this approach to 38 patients in Tanzania who presented to the hospital with sepsis. Blood was collected on Mitra devices at presentation and 1, 3 and 28-42 days post-enrollment. Processing of 96 total samples was performed in plate-based formats and completed within two days. Approximately 2,000 protein groups and 8,000 post-translational modifications were quantified in 3 LC-MS/MS runs at [~]1.5 hours per sample. Analysis of differential abundance revealed blood proteome signatures of acute phase response and neutrophilic inflammation that partially resolved at the 28-42 day timepoint. Numerous analytes correlated with clinical laboratory values for c-reactive protein and white blood cell counts, as well as the Universal Vital Assessment illness severity score. These datasets serve as proof-of-concept for large scale MS-based (sub)phenotyping of disease using dried blood and are available via the ProteomeXchange consortium (PXD060377).

systems biology↗

bioRxiv2025

CASTOR1: A Novel Tumor Suppressor Linking mTORC1 and KRAS Pathways in Tumorigenesis and Resistance to KRAS-Targeted Therapies in Non-Small Cell Lung Cancer

Cytosolic arginine sensor for mTORC1 Subunit 1 (CASTOR1) functions as a key regulator of mechanistic target of rapamycin complex 1 (mTORC1) signaling. Despite its frequent dysregulation in cancers via mechanisms such as KSHV microRNA-mediated inhibition or AKT-driven phosphorylation and degradation, the impact of CASTOR1 loss on tumor initiation and progression remains poorly understood. Here, we identify CASTOR1 as a critical tumor suppressor in non-small cell lung cancer (NSCLC) by demonstrating that its genetic ablation amplifies tumorigenesis in a KRAS-driven genetically engineered mouse model (GEMM;LSL-KRASG12D). CASTOR1 deficiency markedly enhances lung tumor incidence, accelerates tumor progression, and increases proliferative indices in KRASG12D-driven tumors (KRASG12D;C1KO) compared to CASTOR1 wild type (WT) tumors (KRASG12D;C1WT). Advanced-stage tumors exhibit elevated phosphorylated CASTOR1 (pCASTOR1) and reduced total CASTOR1 levels, suggesting active degradation during tumorigenesis. Mechanistically, CASTOR1 loss amplifies mTORC1 signaling, as evidenced by heightened phosphorylation of downstream effectors 4EBP1 and S6, while also augmenting AKT and ERK activation, uncovering a crosstalk between the PI3K/AKT/mTORC1 and KRAS/ERK pathways. Furthermore, CASTOR1 ablation induces genome instability, which may contribute to enhanced tumor incidence and progression. Importantly, CASTOR1 deficiency confers resistance to KRASG12D-specific inhibitors, while over half of KRASG12D;C1WT tumors also display resistance. Organoids derived from KRASG12D;C1KO and KRASG12D;C1WT tumors reveal a correlation between KRAS inhibitor resistance and hyperactivation of mTORC1, with mTORC1 and PI3K inhibitors sensitizing resistant tumors to KRASG12D-targeted therapies. These findings position CASTOR1 as a novel tumor suppressor that modulates mTORC1 and KRAS signaling to constrain NSCLC progression. Our study further highlights the therapeutic potential of combining mTORC1 or ERK inhibitors with KRAS-targeted therapies for NSCLC characterized by hyperactive KRAS signaling and impaired CASTOR1 activity. HighlightsO_LICASTOR1 functions as a tumor suppressor in NSCLC by limiting KRAS-driven tumor initiation and progression. C_LIO_LICASTOR1 is frequently lost or inactivated in wild-type tumors during tumor progression, contributing to advanced-stage malignancies. C_LIO_LICASTOR1 deficiency amplifies mTORC1 signaling and enhances PI3K/AKT and KRAS/ERK crosstalk, driving tumorigenesis and resistance to KRAS-specific inhibitors. C_LIO_LICombining mTORC1 or PI3K inhibitors with KRAS-targeted therapies effectively overcomes resistance in KRAS-driven NSCLC. C_LI

cancer biology↗

bioRxiv2025

Interrupting Elmsan1 repression of nuclear Acetyl-CoA production therapeutically reprograms cancer cells

Metabolites are essential substrates for epigenetic modifications. Although nuclear acetyl-CoA constitutes a small fraction of the whole cell pool, it regulates cell fate by locally providing histone acetylation substrate. Here, we combined phenotypic chemical screen and genome-wide CRISPR screen to demonstrate a nucleus-specific acetyl-CoA regulatory mechanism that can be modulated to achieve therapeutic cancer cell reprogramming. While previously thought that nucleus-localized pyruvate dehydrogenase complex (nPDC) is constitutively active, we found that nPDC is constitutively inhibited by the nuclear protein ELMSAN1 through direct interaction. Pharmacologic inhibition of the ELMSAN1-nPDC interaction derepressed nPDC activity, enhancing nuclear acetyl-CoA generation and reprogramming cancer cells to a postmitotic state with diminished cell-of-origin signatures. Reprogramming was synergistically enhanced by histone deacetylase 1/2 inhibition, resulting in inhibited tumor growth, durably suppressed tumor-initiating ability, and improved survival in multiple cancer types in vivo, including therapy-resistant sarcoma patient-derived xenografts and carcinoma cell line xenografts. Our findings highlight the potential of targeting ELMSAN1-nPDC as epigenetic cancer therapy.

cancer biology↗

bioRxiv2025

A Versatile Marker-Free Genome Engineering Platform to Overcome Homologous Recombination Bias in Microbes: A Case Study in Synechococcus elongatus UTEX 2973

The single crossover occurring via homologous recombination is a common phenomenon exited among most microbes like Escherichia, Clostridium, Streptococcus, Lactobacillus, and cyanobacteria, threatening the stability of engineered strains. Among them, fast-growing cyanobacterium Synechococcus elongatus UTEX 2973 (Syn2973) is an attractive photosynthetic chassis for CO2 bioconversion. To address the challenge of homologous recombination, we constructed two endogenous plasmid-based shuttle vectors, pSES and pSEL, enabling stable DNA delivery without reliance on homologous recombination. In parallel, two robust counter-selection systems were developed based on sepT2 and rpsL, which, in combination with positive selection markers, significantly improved the screening efficiency of double-crossover mutants. Building upon these tools, we established three marker-free platforms: (i) T4CROSS, which employs two plasmids and four rounds of single crossover; (ii) TRIPLEARM, which uses a single plasmid containing three homologous arms for three rounds of single crossover; and (iii) CRISPRARM, which integrates CRISPR/Cpf1-mediated genome editing with homologous recombination. All three methods successfully repaired mutations in the pilMNOQ pilus gene cluster in Syn2973, restoring natural competence with high efficiency and positive selection rates. As proof of concept, we employed the CRISPRARM platform for a three-step sequential engineering of the sucrose biosynthetic pathway. The final engineered strain produced 7.12 g{middle dot}L-1 of sucrose within four days.

synthetic biology↗

bioRxiv2025

Predicting Early Transitions in Respiratory Virus Infections via Critical Transient Gene Interactions

Early detection of respiratory virus infections, such as influenza A (H3N2), is critical for timely intervention and disease management. Conventional biomarkers often overlook the complex and dynamic nature of gene regulatory changes, while existing predictive models frequently lack automation and robust external validation. Thus, we present CRISGI (Critical tran-Sient Gene Interaction), a computational framework that detects early-warning signals of infection by identifying dynamic changes in gene-gene interactions--termed critical transient interactions--from bulk RNA-seq data. CRISGI leverages critical transition (CT) theory to capture a GRNs unstable intermediate state, known as the CT stage, before irreversible phenotypic shifts. Applied to a human challenge study with H3N2, CRISGI identified 128 critical transition edges (128-TER). These were used to train predictive models capable of forecasting symptom status and onset timing. 128-TER was then validated across six temporal transcriptomic datasets involving three respiratory viruses (H3N2, H1N1, HRV). The 128-TER consistently distinguished symptomatic individuals, predicted infection onset, and revealed phenotype-specific enrichment patterns. Notably, CRISGI captured immune-related transitions involving interferon-stimulated genes (e.g., IFIT1, CXCL10), underscoring their role in early host defense. CRISGI advances early-warning biomarker discovery by integrating interaction-level dynamics and predictive modeling. Its reproducibility across viruses highlights shared immune activation pathways, supporting its utility in both research and clinical contexts.

bioinformatics↗

bioRxiv2025

Nuclear 2'-O-methylation regulates RNA splicing through its binding protein FUBP1

2'-O-methylation (Nm) is an abundant RNA modification exists on different mammalian RNA species. However, potential Nm recognition by proteins has not been extensively explored. Here, we employed RNA affinity purification followed by mass spectrometry to identify Nm-binding proteins. The candidates exhibit enriched binding at known Nm sites. Interestingly, some candidates display nuclear localization and functions. We focused on the splicing factor FUBP1. Electrophoretic mobility shift assay (EMSA) validated preference of FUBP1 to Nm-modified RNA. As FUBP1 predominantly binds intronic regions, we profiled Nm sites in chromatin-associated RNA (caRNA) and found Nm enrichment within introns. Depletion of Nm led to increased exon skipping, suggesting Nm-dependent splicing regulation. The caRNA Nm sites overlap with FUBP1 binding sites, and Nm depletion reduced FUBP1 occupancy on modified regions. Furthermore, FUBP1 depletion induced exon skipping in Nm-modified genes, supporting its role in mediating Nm-dependent splicing regulation. Overall, our findings identify FUBP1 as an Nm-binding protein and uncover previously unrecognized nuclear functions for RNA Nm modification.

molecular biology↗

bioRxiv2025

Knowledge-driven annotation for gene interaction enrichment analysis

Gene Set Enrichment Analysis (GSEA) is a cornerstone for interpreting gene expression data, yet traditional approaches overlook gene interactions by focusing solely on individual genes, limiting their ability to detect subtle or complex pathway signals. To overcome this, we present GREA (Gene Interaction Enrichment Analysis), a novel framework incorporating gene interaction data into enrichment analysis. GREA replaces the binary gene hit indicator with an interaction overlap ratio, capturing the degree of overlap between gene sets and gene interactions to enhance sensitivity and biological interpretability. It supports three enrichment metrics: Enrichment Score (ES), Enrichment Score Difference (ESD) from a Kolmogorov-Smirnov-based statistic, and Area Under the Curve (AUC) from a recovery curve. GREA evaluates statistical significance using both permutation testing and gamma distribution modeling. Benchmarking on transcriptomic datasets related to respiratory viral infections shows that GREA consistently outperforms existing tools such as blitzGSEA and GSEApy, identifying more relevant pathways with greater stability and reproducibility. By integrating gene interactions into pathway analysis, GREA offers a powerful and flexible tool for uncovering biologically meaningful insights in complex datasets. The source code is available at https://github.com/compbioclub/GREA.

bioinformatics↗

bioRxiv2025

RNase H1 counteracts DNA damage and ameliorates SMN-dependent phenotypes in a Drosophila model of Spinal Muscular Atrophy

Spinal Muscular Atrophy (SMA) is caused by a deficiency of the Survival Motor Neuron (SMN) protein. Mutations in SMN disrupt mRNA splicing and translation, leading to maladaptive changes in transcriptomes, proteomes, neuroinflammation, and metabolism, which drive motor neuron degeneration in SMA patients. Using a Drosophila SMA model, we found that systemic depletion of Smn leads to accumulation of RNA:DNA hybrids (R-loops), increased DNA damage, dysregulation of amino acids and sugar metabolism and activation of the innate immune response, recapitulating key pathological features reported in mammalian models and severe SMA patients. Persistent DNA damage in Smn-deficient flies alters cell proliferation rates in larval brains and induces extensive cell death in the developing eye. Importantly here, we show that stimulating the resolution of RNA:DNA hybrids with transgenic human RNAse H1 prevents the accumulation of DNA damage and attenuates the transcriptome and amino acid alterations induced by Smn depletion, mitigating the Smn-dependent cellular and developmental abnormalities, in Smn-deficient flies. Our data suggest that depletion of Smn causes an accumulation of aberrant transcripts and chronic DNA damage, which--along with the altered metabolomic profiles associated with Smn deficiency--trigger systemic inflammatory responses, ultimately affecting neuronal function and survival.

genetics↗