Biology subjects

You, Z.

Publications and source records attributed to You, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

USP15 REGULATES NEUROINFLAMMATION AND DRIVES PATHOGENESIS IN SYNUCLEINOPATHIES

Neuroinflammation strongly contributes to the pathogenesis of neurological and neurodegenerative diseases, including Parkinson's disease. We show that ablation of Usp15 in astrocytes and in microglia protects against lethal neuroinflammation in vivo. In a mouse model of synucleinopathy, Usp15 deletion diminishes alpha-syn deposits in the brain, slows disease progression, and increases survival time. The neuroprotective effect of Usp15 is associated with differential expression of inflammatory pathways in situ including interferon stimulated genes. These USP15-dependent effects in vivo are recapitulated in vitro in primary human microglia and astrocytes. We detect high USP15 expression in microglia from Parkinson's patients with strong co-expression with LRRK2 and SNCA. In humans, we detect a strong cis-acting eQTL directing high USP15 expression in CD14+ myeloid cells. The allele driving this eQTL is itself associated with increased disease risk, linking myeloid USP15 expression, elevated USP15 plasma levels in Parkinson's patients, to genetic susceptibility.

neuroscience↗

bioRxiv2026

RFOptimization: Guiding Design Optimization with All-Atom Structure Prediction

Biomolecular interactions, including protein--protein interactions, protein--nucleic acid recognition, and protein--small molecule binding, underlie a wide range of biological processes and therapeutic mechanisms. Although recent \emph{de novo} design methods can generate candidate binders for diverse molecular targets, practical design campaigns remain limited by low filter-passing rates and model-specific biases that arise when designs are optimized against a single predictor. Here, we present RFOptimization (RFO), a training-free framework for all-atom biomolecular binder optimization. RFO formulates binder improvement as a residue-wise mutational search problem, sampling candidate substitutions alternately based on gradient-guided sequence optimization using all-atom structure prediction models and a cycling-based sequence redesign strategy that alternates structure generation with an orthogonal predictor and MPNN-based sequence design to improve the \emph{in silico} success rate of RFdiffusion-generated binders within minutes of computation. To reduce overfitting to any individual structure model, candidate mutations are further evaluated with orthogonal AlphaFold3 metrics as final filters. We demonstrate the generality of RFO across diverse design settings, including classical protein binder design, ligand-binding biosensor design, cyclic peptide design, and active site-aware enzyme design.

biochemistry↗

bioRxiv2026

NACraft: Programmatic nucleic-acid aptamer design via all-atom structure-model feedback

Protein-nucleic-acid interactions underpin diverse biological processes and provide a basis for molecular sensing, regulation and therapeutic intervention. However, the coupled dependence of aptamer function on nucleotide sequence, three-dimensional folding and target binding makes rational RNA and DNA binder design challenging. Here we present NACraft, a training-free and programmatic framework for all-atom nucleic-acid aptamer design based on backpropagation through structure-model feedback. By composing binding, sequence-similarity and anti-binding constraints, NACraft supports de novo generation, similarity-guided sampling and target-selective design within a unified optimization framework, without task-specific training or fine-tuning. Computational experiments showed that NACraft generated high-confidence candidates de novo across diverse protein targets, with further improvements achieved through similarity-guided design for both RNA and DNA complexes. Its target-selective design capability was further validated in silico, with 69.44% of paired candidates generated to favour the positive target EGFR over the off-target HER2. Under matched independent AlphaFold3 evaluation, NACraft achieved better performance than ODesign in 10 of 11 NA-12 targets and 17 of 20 protein target-length settings. Together, these results demonstrate the effectiveness and versatility of NACraft and extend structure-model hallucination toward programmatic nucleic-acid aptamer design. Codehttps://github.com/OTEAM-AI4S/NACraft

bioinformatics↗

bioRxiv2026

Ori-Finder-Arch: An Updated Web Server for the Annotation and Visualization of Archaeal Replication Origins

Archaea are promising chassis organisms in biotechnology, and the accurate annotation of their chromosomal replication origins (oriCs) is the key to unlocking their full potential. However, the existing Ori-Finder 2 web server suffers from low accuracy, slow speed, and limited scalability. In this study, we present Ori-Finder-Arch, an updated web server for high-performance oriC prediction in archaea. This pipeline integrates HMMER-based replication initiation protein (RIP) annotation, refined consensus motif recognition, and GC profile-based DNA unwinding element (DUE) detection. On a benchmark set of experimentally validated oriCs, Ori-Finder-Arch achieved a recall of 95.6% and a precision of 86.0%, substantially outperforming Ori-Finder 2 (62.2% and 63.6%, respectively), while running 4.75 times faster and supporting diverse assembly levels. When applied to the available archaeal assemblies, it successfully annotated 17,472 oriCs. Meanwhile, the web server provides interactive visualizations at different levels. In conclusion, Ori-Finder-Arch offers an efficient, accurate, and user-friendly platform for advanced studies of archaeal DNA replication initiation and synthetic biology applications, and is freely available at https://tubic.org/Ori-Finder-Arch/ and https://tubic.tju.edu.cn/Ori-Finder-Arch/.

bioinformatics↗

bioRxiv2026

CSGDA: A Cell State-Guided Graph Domain Adaptation Network for Single-Cell Drug Response Prediction

Intratumoral heterogeneity drives cancer recurrence and metastasis, yet single-cell drug response prediction faces severe "cross-domain" challenges, such as applying in vitro models to in vivo tissues or inferring metastatic resistance from primary tumors. These scenarios trigger distribution shifts arising from heterogeneous sequencing platforms, distinct tissue microenvironments, and metastatic evolution--problems rarely addressed by existing methods. We introduce CSGDA, a cell state-guided graph domain adaptation framework designed to predict drug responses across these biological heterogeneities. CSGDA incorporates biological priors to map gene expression into functional cell states, guiding a structure learning module to construct robust cell topology. To conquer distribution shifts, the model employs graph domain adaptation combined with a novel overlap penalty mechanism. Extensive benchmarks on five scRNA-seq datasets demonstrate that CSGDA outperforms state-of-the-art methods, achieving an average gain of [~]6% in ACC and AUPR. Beyond prediction accuracy, we employed integrated gradients to effectively pinpoint key genes involved in drug resistance within a challenging cross-metastasis cisplatin dataset. These findings underscore CSGDAs superior performance in single-cell drug response prediction and its potential in resolving single-cell heterogeneity, paving the way for precision medicine.

bioinformatics↗

bioRxiv2026

Statistical Methodology for Qualification of a Non-Clinical Risk Assessment Peptide:T Cell Proliferation Assay to Support Decision Making

Antibody- and cell-mediated immune responses against biologics, should they occur, can impact treatment efficacy and potentially pose severe risks to patient safety. Therefore, developers have focused on advancing strategies to mitigate such unwanted immunogenicity. Opportunities to address immunogenicity early in the development process, particularly during the drug design phase, have been identified. In vitro and in silico tools that facilitate the identification and removal of sequence liabilities have been established. For example, human cell-based in vitro T cell assays can be used to identify and remove CD4+ T cell epitopes, which are known to play a critical role in the development of anti-drug antibodies against recombinant proteins products as well as the transgenes of gene and therapy. Despite their widespread use in the industry, most of these assays lack thorough characterization, which undermines confidence in the results and comparability across laboratories. In this study, concepts of immunogenicity bioanalytical assay validation for study design and analysis were applied to characterize an internal CD4+ T cell proliferation assay as fit-for-purpose. A statistical path was applied to establish data acceptance criteria for handling of replicates, positivity and negativity of a signal, and donor cohort size. A Bayesian analysis was also performed and is proposed as an approach for sequence de-risking decision making. The in-depth characterization of the CD4+ T cell proliferation assay described here allows for accurate interpretation of the assay outcomes, thereby enhancing confidence in using this approach for mitigating the immunogenicity of biologics by design.

immunology↗

bioRxiv2026

DeSCENT: Deconvolutional Single-Cell RNA-seq Enhances Transcriptome-based Cancer Survival Analysis

MotivationAccurate cancer survival prediction requires modeling tumor heterogeneity across both population and cell levels. Most cancer survival analyses use tumor transcriptomes only, since cohorts are usually measured with bulk RNA-seq but are rarely recorded with single-cell RNA-seq. This prevents the direct use of cell-level transcriptomes in cancer survival analysis. ResultsTo bridge this gap, we propose using bulk RNA-seq deconvolution algorithms to reconstruct each subjects scRNA-seq profile from their bulk data. Then, by combining both scRNA-seq and bulk RNA-seq together with their survival labels (paired to bulk), we perform multimodal transcriptome-based survival analysis. We built this framework as DeSCENT and evaluated it with common survival models on eight TCGA cancer cohorts. Results showed notable and consistent improvements in C-index over bulk-only models or models using cellular information alone. AvailabilityOur code is available at GitHub: https://github.com/YonghaoZhao722/DeSCENT. Contactzpluo@swjtu.edu.cn; y.he@imperial.ac.uk Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2026

CodonRL: Multi-Objective Codon Sequence Optimization Using Demonstration-Guided Reinforcement Learning

Optimizing synonymous codon sequences to improve translation efficiency, RNA stability, and compositional properties is challenging because the search space grows exponentially with protein length and objectives interact through long range RNA structure. Dynamic programming-based methods can provide strong solutions for fixed objective combinations but are difficult to extend to additional constraints. Deep generative models require large-scale, high-quality mRNA sequence datasets for training, limiting applicability when such data are scarce. Reinforcement learning naturally handles sequential decision-making but faces challenges in codon optimization due to delayed rewards, large action spaces, and expensive structural evaluation. We present CodonRL, a reinforcement learning framework that learns a structural prior for mRNA design from efficient folding feedback and demonstration-guided replay, and then enables user-controlled multi-objective trade-offs during inference. CodonRL uses LinearFold for fast intermediate reward computation during training and ViennaRNA for final evaluation, warms up learning with expert sequences to accelerate convergence for global structure objectives, and introduces milestone-based intermediate rewards to address delayed feedback in long range optimization. On a benchmark of 55 human proteins, CodonRL outperforms GEMORNA, a state-of-the-art codon optimization method, across multiple metrics, achieving 9.5% higher codon adaptation index (CAI), 25.4 kcal/mol more favorable minimum free energy (MFE), and 3.4% lower uridine content on average, while improving codon stabilization coefficient (CSC) in over 90% of benchmark proteins under matched constraints. These gains translate into designs that are predicted to be more efficiently translated, more structurally stable, and less immunogenic, while supporting continuous objective reweighting at inference time.

bioinformatics↗

bioRxiv2026

Data-Driven Retrieval of Effective Point Spread Functions for Super-Resolution Optoacoustic Imaging

Localization-based super-resolution techniques have revolutionized biomedical imaging by surpassing classical diffraction limits. However, their performance is fundamentally constrained by distortions of the point spread function (PSF) induced by the system and the sample, which are particularly prominent in the case of spatially under-sampling. Here we introduce a data-driven effective point spread function retrieval (DEPR) method that directly learns continuous, field-dependent system responses from experimental point source datasets. Through statistical aggregation of thousands of targets and iterative self-supervised refinement, DEPR captures spatially variant imaging characteristics in situ without prior assumptions or external calibrations. When integrated into the localization optoacoustic tomography (LOT) pipeline, DEPR achieves accurate sub-pixel localization despite under-sampled conditions, thus enhancing resolution while reducing computational burden. We demonstrate its efficacy by in vivo imaging of the murine brain microvasculature using microparticle contrast agents in the first (NIR-I) and second (NIR-II) near-infrared windows, achieving significant improvement in data utilization efficiency and substantial reduction of gridded artifacts compared to conventional approaches. The method resolves vascular structures with [~]41 m separation across a [~]3 mm imaging depth range. This framework addresses fundamental challenges shared across diverse localization-based imaging modalities, offering a robust and generalizable strategy for high-precision imaging in complex biological systems.

neuroscience↗

bioRxiv2025

Therapeutic modulation of the calpastatin/calpain pathway restores calpain-mediated synaptic proteolysis and preserves motor neurons survival and function in C9orf72 ALS

A hexanucleotide repeat expansion (GGGGCC) in the C9orf72 gene is the most prevalent genetic cause of ALS, with early neuromuscular junction (NMJ) dysfunction being a key pathological feature. Current therapies provide only limited symptomatic relief, underscoring the need for targeted, mechanism-based interventions. Using a C9orf72 ALS zebrafish model (C9-miR) and patient-derived induced pluripotent stem cell (iPSC) motor neurons, we identified significant downregulation of calpastatin, the endogenous inhibitor of calpains, a calcium-dependent protease family implicated in neurodegeneration. We demonstrate that restoring calpastatin activity with a cell-permeable calpastatin-derived peptide or the small molecule, calpeptin, ameliorates locomotor deficits and NMJ dysfunction in the C9-miR zebrafish model. These interventions enhance synaptic vesicle turnover and quantal release at the NMJ while improving motor neuron excitability and synaptic integrity in iPSC-derived motor neurons. N-terminomic/TAILS mass spectrometry revealed direct calpain-mediated cleavage of synaptic proteins in motor neurons derived from C9orf72 patients. Proteolysis of novel ALS-relevant synaptic and axonal proteins is prevented by calpeptin and calpastatin peptide treatments. Our findings establish the calpastatin as a pivotal regulator of synaptic function in C9orf72-associated ALS and identify it as a promising therapeutic target, offering a novel strategy to restore synaptic transmission and potentially halt disease progression.

neuroscience↗

bioRxiv2025

LRRK2 Kinase Mediates Increased GCase Activity in Microglia in Response to Proinflammatory Stimuli

Variants in the LRRK2 and GBA1 genes are among the most common risk factors associated with Parkinsons disease (PD). Both patients carrying PD-associated variants in GBA1, encoding lysosomal enzyme glucocerebrosidase (GCase), and a subset of non-carrier patients have been shown to have reduced GCase enzymatic activity, suggesting that reduced GCase activity may be a feature of both genetic and a subset of sporadic PD. However, the effect of PD-associated variants in LRRK2, encoding a serine/threonine kinase, on GCase activity remains controversial, with conflicting results in various tissues and cell types. Moreover, rare patients carrying both GBA1 and LRRK2 risk alleles seem to have a more benign disease course than carriers of GBA1 variants alone, suggesting a complex interplay between these two genes in PD. Here we evaluate the effect of LRRK2 kinase activity on GCase activity in human induced pluripotent stem cell (iPSC)-derived microglia (iMGs), a PD-relevant brain cell type expressing high levels of LRRK2. Using CRISPR editing, isogenic control iPSC lines were generated to match PD patient-derived iPSC lines harbouring the LRRK2 p.G2019S, p.M1646T, or p.N551K-p.R1398H protective haplotype variants. Whereas iMGs harbouring the p.M1646T variant, and the protective haplotype, respectively increased and decreased phosphorylation of canonical LRRK2 substrate, Rab10, GCase protein levels and activity were not altered in any of the LRRK2 variant lines. Additionally, whereas pharmacological inhibition of LRRK2 kinase activity had no impact on GCase activity in iMGs under basal conditions, it attenuated the increase in GCase activity elicited in response to interferon {gamma} (IFN{gamma}) treatment. Moreover, GCase activity induced by IFN{gamma} was reduced in PD risk LRRK2 p.M1646T iMGs and increased in p.N551K-p.R1398H protective haplotype iMGs compared to their isogenic corrected controls, congruent with their respective effects on LRRK2 kinase activity and PD risk. Thus, our data suggests a role for LRRK2 kinase activity in regulation of GCase activity in response to neuroinflammation.

neuroscience↗

bioRxiv2025

Benchmarking RNA velocity methods across 17 independent studies

RNA velocity techniques offer great potential for unveiling trajectories of cell state transitions in different biological contexts. While diverse computational methods have been developed, there is no evidence-based guidelines for best-practice in RNA velocity inference. Here we conducted a benchmark study for 14 existing RNA velocity methods in 17 independent datasets. Many validations were done for the first time. We evaluated the performances of each method by measuring accuracy, stability, and usability. Our data showed no single method exhibited superior performance in all the assessments, and unexpected underperformance was observed in certain cases. Especially, the lack of uniformity in the inference results highlights the necessity to compare and control of multiple methods in a single analysis. Our study revealed current limitations and challenges in the RNA velocity methods and informed the best-practice for future studies.

bioinformatics↗

bioRxiv2025

Identification of senescence-related hub genes and potential therapeutic agents for chronic heart failure

BackgroundChronic heart failure (CHF), the terminal phase of cardiovascular disease progression, has emerged as an increasingly severe global public health concern. Despite current therapeutic approaches aimed at symptom relief, their long-term effectiveness remains limited, urgently necessitating the exploration of novel treatment strategies. This study endeavored to explore the role of cellular senescence in CHF, identify the characteristic genes linked to cellular senescence, and predict potential therapeutic agents. MethodsWe acquired CHF-related datasets from the Gene Expression Omnibus database and cell senescence-related genes from the CellAge database to identify differentially expressed cell senescence-related genes. We then conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to elucidate the functions of these differentially expressed genes, constructed a protein-protein interaction network, and screened hub genes. Using receiver operating curve (ROC) analysis, we developed a diagnostic model based on these hub genes. Furthermore, we constructed networks for the hub genes involving miRNAs, lncRNAs, transcription factors, and drugs. Subsequently, we explored the potential mechanism of action of metformin in the treatment of CHF through molecular docking studies. Lastly, we verified the expression of the hub genes in a doxorubicin-induced CHF model in rats. Results We ultimately identified nine hub genes associated with cellular senescence: STAT1, MMP9, MAP2K1, SOCS1, SDC1, MET, EIF4EBP1, ATF3, and NAMPT. These genes exhibited significant differential expression between CHF and normal tissues. The constructed diagnostic model demonstrated robust diagnostic performance in ROC curve analysis, with an area under the curve exceeding 0.7, thereby providing biomarker support for early CHF diagnosis. Furthermore, we identified a regulatory network comprising 94 lncRNAs, 63 miRNAs, and 11 transcription factors and screened 42 potential therapeutic drugs. Subsequent molecular docking simulations revealed that metformin could effectively bind to the hub genes. Finally, we validated the expression of some hub genes in a rodent CHF model, in which gene expression regulation varied across diverse experimental settings. ConclusionsOur research identified nine genes cellular senescence-related genes with potential roles in CHF pathogenesis, offering fresh perspectives concerning the diagnosis and treatment of CHF.

bioinformatics↗

bioRxiv2025

Reconstructing Noisy Gene Regulation Dynamics UsingExtrinsic-Noise-Driven Neural Stochastic Differential Equations

Proper regulation of cell signaling and gene expression is crucial for maintaining cellular function, development, and adaptation to environmental changes. Reaction dynamics in cell populations is often noisy because of (i) inherent stochasticity of intracellular biochemical reactions ("intrinsic noise") and (ii) heterogeneity of cellular states across different cells that are influenced by external factors ("extrinsic noise"). In this work, we introduce an extrinsic-noise-driven neural stochastic differential equation (END-nSDE) framework that utilizes the Wasserstein distance to accurately reconstruct SDEs from trajectory data from a heterogeneous population of cells (extrinsic noise). We demonstrate the effectiveness of our approach using both simulated and experimental data from three different systems in cell biology: (i) circadian rhythms, (ii) RPA-DNA binding dynamics, and (iii) NF{kappa}B signaling process. Our END-nSDE reconstruction method can model how cellular heterogeneity (extrinsic noise) modulates reaction dynamics in the presence of intrinsic noise. It also outperforms existing time-series analysis methods such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). By inferring cellular heterogeneities from data, our END-nSDE reconstruction method can reproduce noisy dynamics observed in experiments. In summary, the reconstruction method we propose offers a useful surrogate modeling approach for complex biophysical processes, where high-fidelity mechanistic models may be impractical.

molecular biology↗

bioRxiv2025

Glucosylceramide induced ectosomes propagate pathogenic α-synuclein in Parkinson's disease

Intercellular transmission of -synuclein contributes to Parkinsons disease pathology. Yet, the mechanisms of -synuclein spread are not fully understood. Here, we used live-cell microscopy to examine the impact of Parkinsons disease associated lipid alterations on -synuclein release. We discovered that increased glucosylceramides induce ectosome shedding from primary neurons, and from dopaminergic neurons derived from Parkinsons disease patient iPSCs harboring mutations in GBA1 (N370S, L444P and W378G) and LRRK2 (G2019S and R1441H) compared to their isogenic control. We show that elevated glucosylceramide similarly increases vesicle release and uptake by other neurons in living mouse brains using 2-photon microscopy. Finally, we show that ectosomes are loaded with pathogenic -synuclein and lead to the transmission of -synuclein pathology to neighbouring neurons. These data reveal ectosomes as the predominant route for -synuclein transmission that can only be appreciated by live-cell imaging technologies.

cell biology↗

bioRxiv2024

Chromosome-level genome assembly of autotetraploid Selenicereus megalanthus and gaining genomic insights into the evolution of trait patterning in diploid and polyploid pitaya species

Yellow pitaya (Selenicereus megalanthus, 2n=4x=44) breeding remains severely hindered due to lacking a reference genome. Here we report yellow pitayas high-quality chromosome-level genome assembly and link the phenotypic trait with genomic data, based on Hi-C, ATAC, and RNA-seq data of specific tissues. We declared yellow pitaya as an autotetraploid with a 7.16 Gb genome size (harboring 27,246 high confidence genes) and majorly evolved from diploid ancestors, which remains unknown. Beyond generating the genome assembly, we explored the 3D chromatin organization which revealed insights into the genome, compartments A (648 and 519), compartments B (728 and 1064), topologically associated domains-TADs (3376 and 2031), and varying numbers of structural variations (SVs) in diploid and polyploid pitaya species, respectively. Overall, TAD boundaries were enriched with the motifs of AP2, WRKY18/60/75, MYB63/116, PHL2, and GATA8 in both pitaya species. By linking the open chromatin genomic structure to function, we identified the major changes in betalains biosynthesis pathway in diploid and polyploid pitaya. Moreover, the higher genetic expression of SmeADH1 [Chr11, Compartment A (135400000 - 135500000), genes inside the TAD region (135480000 - 135520000)], and lower expression of HuDOPA [Chr11, Compartment A (87100000 - 87200000), genes inside the TAD region (87160000-87200000)] acts as a key regulator of yellow and red color on the pericarp of polyploid and diploid pitaya, respectively. In addition, higher expression of HuCYP76AD1 genes in diploid pitaya and lower expression of SmeCYP76AD1 in polyploid pitaya potentially created the difference in the oxidase process that led to the production of betacyanin and betaxanthin, respectively. Furthermore, our results revealed not only the type of motifs that play a potential role in trait patterning but we also further uncovered that motif count in TAD-boundaries may impact the gene expression within the TAD regions of diploid and polyploid pitaya. Our valuable genomic resource and comparison of 3D euchromatin architecture of diploid and polyploid pitaya species will not only aid in the advancement of molecular breeding efforts but also offer insights into the organization of genomes, SVs, compartmentalization (A and B), and TADs, which have the potential to strengthen the idea of TADs-based trait improvement to achieve global food security.

plant biology↗

bioRxiv2024

14-3-3ζ regulates adipogenesis by modulating chromatin accessibility during the early stages of adipocyte differentiation

We previously established the scaffold protein 14-3-3{zeta} as a critical regulator of adipogenesis and adiposity, but the temporal specificity of its action during adipocyte differentiation remains unclear. To decipher if 14-3-3{zeta} exerts its regulatory functions on mature adipocytes or on adipose precursor cells (APCs), we generated Adipoq14-3-3{zeta}KO and Pdgfra14-3-3{zeta}KO mouse models. Our findings revealed a pivotal role for 14-3-3{zeta} in APC differentiation in a sex-dependent manner, whereby male and female Pdgfra14-3-3{zeta}KO mice display impaired or potentiated weight gain, respectively, as well as fat mass. To better understand how 14-3-3{zeta} regulates the adipogenic transcriptional program in APCs, CRISPR-Cas9 was used to generate TAP-tagged 14-3-3{zeta}-expressing 3T3-L1 preadipocytes. Using these cells, we examined if the 14-3-3{zeta} nuclear interactome is enriched with adipogenic regulators during differentiation. Regulators of chromatin remodeling, such as DNMT1 and HDAC1, were enriched in the nuclear interactome of 14-3-3{zeta}, and their activities were impacted upon 14-3-3{zeta} depletion. The interactions between 14-3-3{zeta} and chromatin-modifying enzymes suggested that 14-3-3{zeta} may control chromatin remodeling during adipogenesis, and this was confirmed by ATAC-seq, which revealed that 14-3-3{zeta} depletion impacted the accessibility of up to 1,244 chromatin regions corresponding in part to adipogenic genes, promoters, and enhancers during the initial stages of adipogenesis. Moreover, 14-3-3{zeta}-dependent chromatin accessibility was found to directly correlate with the expression of key adipogenic genes. Altogether, our study establishes 14-3-3{zeta} as a crucial epigenetic regulator of adipogenesis and highlights the usefulness of deciphering the nuclear 14-3-3{zeta} interactome to identify novel pro-adipogenic factors and pathways.

physiology↗

bioRxiv2024

Phosphorylation inhibits intramolecular interactions, DNA-binding and protein interactions of Claspin through disordered/ structured conformation transition

Claspin, known to be highly disordered, plays important roles in replication fork progression, initiation and cellular responses to replication stress. However, regulation of its structure and molecular interactions is not completely understood. We show here, through Proximity-Ligation-Assays, the evidence for intramolecular interaction between the N- and C-terminal segments of Claspin, which depends on the Acidic-Patch [AP] segment near its C-terminus. Interaction of Claspin with DNA and replication factors is highly stimulated in {Delta}AP mutant and by prior dephosphorylation. The wild-type Claspin inhibits the helicase activity of MCM in an AP-dependent manner. {Delta}AP and dephosphorylated Claspin exhibit resistance to trypsin digestion compared to wild-type, suggesting the presence of structural domains in the formers. We propose that Claspin is converted from disordered (closed) to structured (open) conformation at initiation, which stimulates its DNA binding and interaction with replication factors and counteracts its helicase inhibitory activity to trigger initiation of DNA replication.

molecular biology↗