Biology subjects

Li, Z.

Publications and source records attributed to Li, Z..

At least 577 records · Page 32Linked to original sources

bioRxiv2024

PhenoScreen: A Dual-Space Contrastive Learning Framework-based Phenotypic Screening Method by Linking Chemical Perturbations to Cellular Morphology

Phenotypic drug discovery (PDD) screens compounds in cellular models that represent disease-relevant phenotypes, offering a compelling alternative to traditional target-based approaches. Unlike conventional methods, where compounds act on a single predefined target, PDD identifies compounds capable of exerting therapeutic effects through multiple targets and mechanisms. This makes PDD particularly valuable for discovering first-in-class drugs, especially for diseases with poorly understood molecular mechanisms or those lacking validated therapeutic targets. By enabling broader exploration of biological systems and uncovering multi-target drugs (polypharmacology), PDD provides a powerful strategy for tackling complex diseases. In this study, we introduce PhenoScreen, a deep learning framework designed to advance PDD by utilizing large-scale compound-phenotype association data. Through contrastive learning, PhenoScreen connects chemical space with cellular morphological profiles, allowing for accurate prediction of compound-induced phenotypic changes. PhenoScreen can also accurately identfiy lead compounds which could induce user defined phenotypic shift but more novel scaffolds using different levels of phenotypic information reflected by diverse compounds. The model was validated across multiple screening tasks and successfully predicted active compounds inducing user-specified phenotypes with varying inhibitory effects in the osteosarcoma phenotypic model. Further, other than showing effectiveness to osteosarcoma, our experiments also showed that PhenoScreen demonstrated strong generalization to rhabdomyosarcoma, and the active compound we screened had an IC50 of up to 1.842 M, suggesting its ability to capture key phenotypic features shared across cancer cells. These results underscore PhenoScreens potential to accelerate drug discovery by identifying novel therapeutic pathways and increasing the diversity of viable drug candidates. PhenoScreen is accessible online via our groups web server for compound virtual screening at https://bailab.siais.shanghaitech.edu.cn/services/PhenoScreen/, and the source codes are available at https://github.com/Shihang-Wang-58/PhenoScreen.

bioinformatics↗

bioRxiv2024

Classical statistical methods are powerful for the identification of novel targets for the survival of breast cancer patients

Breast cancer is a leading cause of cancer-related deaths among women. The identification of survival-related target genes is critical for improving the prognosis and outcomes of breast cancer patients. Many methods have been applied to this investigation, such as bioinformatics and machine learning approaches, yet few targets identified from these approaches have been applied in clinics. Here, we present a novel approach by using classical statistical methods of Kolmogorov-Smirnov (KS) test and Jensen-Shannon (JS) divergence to analyse the survival time and gene expression data of breast cancer patients (BRCA) from The Cancer Genome Atlas (TCGA). These methods help compare the survival time distributions and differentiate patients into high and low-risk groups based on gene expression profiles. 1,124 survival-related genes were identified based on the KS test and 18 from JS divergence values. We also identified the optimal thresholds of the expression level of these target genes, which enabled the best separation of survival groups for all breast cancer patients and each subtype of breast cancer patients. These targets were further validated through bootstrapping to ensure that significant results are not due to chance. By comparing those survival targets from previous studies, we found two were novel targets, and two were consistent with previous reports. Overall, our study provides a novel approach for identifying survival targets for breast cancer patients by integrating a series of classical statistical methods, such as the KS test, JS divergence, and bootstrapping. Our approach could also be applied to identifying the survival targets for other cancer types and provide valuable insights into cancer research and clinical applications.

bioinformatics↗

bioRxiv2024

The aberrant language network dynamics in autism ages 5-40 years

Language impairments across both structural components and pragmatic use are frequently observed in individuals with autism spectrum disorder (ASD). These difficulties are thought to stem from atypical brain development and abnormal network interactions, yet an integrative network-level model accounting for such impairments remains lacking. To bridge this gap, we applied the dynamic meta-networking framework of language, a theoretical model capturing domain-segregation dynamics during rest, to examine age-related changes (5-60 years) in cortical language networks in individuals with ASD. To further probe the biological underpinnings of these dynamics, we quantified spatial correspondences between network state hubs and gene co-expression modules as well as neurotransmitter receptor distributions. We identified distinct language meta-states characterized by domain-segregation connectivity patterns, which exhibited spatial alignment with gene co-expression modules and neurotransmitter systems. Individuals with ASD showed state-dependent developmental trajectories marked by age-related hypo- and hyper-connectivity. Critically, these network alterations strongly predicted verbal IQ and communicative difficulties, but were unrelated to social functioning or stereotyped behaviors. Our findings provide novel evidence that language-related network dynamics in ASD are developmentally altered, biologically grounded, and selectively linked to verbal and communicative impairments. These results advance a network-level model of language dysfunction in ASD and highlight potential mechanistic pathways for targeted interventions. Lay SummaryLanguage challenges are common in autism and can affect both learning language and using it in everyday conversations. In people aged 5-60, we found distinct, age-changing patterns in how the brains language areas connect at rest; these patterns align with basic biology (genes and brain chemicals) and strongly relate to verbal ability and daily communication, but not to social difficulties or repetitive behaviors. These results suggest clear, brain-based targets for earlier and more tailored support for language problems in autism.

neuroscience↗

bioRxiv2024

Identification and characterization of UBA5 inhibitors

UBA5 is a critical E1-activating enzyme in the UFMylation pathway, a post-translational modification process implicated in neurodegenerative diseases and cancers. In this study, we developed a high-throughput screening (HTS) assay to identify inhibitors of UBA5 from a library of blood-brain barrier (BBB)-permeable compounds. The assay, based on the AMP-Glo kit, enabled the identification of five novel UBA5 inhibitors belonging to three distinct chemical scaffolds with low micromolar IC50 values. These inhibitors demonstrated selectivity for UBA5 over other E1 enzymes, such as UBA1, and showed efficacy in inhibiting endogenous UFMylation in HEK293T cells. The identified inhibitors not only provided valuable tools for studying UFMylation but also represent potential therapeutic candidates for diseases associated with dysregulated UFMylation, such as Alzheimers disease and cancer. Further optimization of these compounds is necessary to enhance their potency and pharmacokinetic properties. Our findings highlight UBA5 as a promising therapeutic target and pave the way for the development of selective inhibitors that can modulate UFMylation in human diseases.

biochemistry↗

bioRxiv2024

Integrated systems biology identifies disruptions in mitochondrial function and metabolism as key contributors to heart failure with preserved ejection fraction (HFpEF)

BackgroundHeart failure with preserved ejection fraction (HFpEF) accounts for [~]50% of HF cases, with no effective treatments. The ZSF1-obese rat model recapitulates numerous clinical features of HFpEF including hypertension, obesity, metabolic syndrome, exercise intolerance, and LV diastolic dysfunction. Here, we utilized a systems-biology approach to define the early metabolic and transcriptional signatures to gain mechanistic insight into the pathways contributing to HFpEF development. MethodsMale ZSF1-obese, ZSF1-lean hypertensive controls, and WKY (wild-type) controls were compared at 14w of age for extensive physiological phenotyping and LV tissue harvesting for unbiased metabolomics, RNA-sequencing, and assessment of mitochondrial morphology and function. Utilizing ZSF1-lean and WKY controls enabled a distinction between hypertension-driven molecular changes contributing to HFpEF pathology, versus hypertension + metabolic syndrome. ResultsZSF1-obese rats displayed numerous clinical features of HFpEF. Comparison of ZSF1-lean vs WKY (i.e., hypertension-exclusive effects) revealed metabolic remodeling suggestive of increased aerobic glycolysis, decreased {beta}-oxidation, and dysregulated purine and pyrimidine metabolism with few transcriptional changes. ZSF1-obese rats displayed worsened metabolic remodeling and robust transcriptional remodeling highlighted by the upregulation of inflammatory genes and downregulation of the mitochondrial structure/function and cellular metabolic processes. Integrated network analysis of metabolomic and RNAseq datasets revealed downregulation of nearly all catabolic pathways contributing to energy production, manifesting in a marked decrease in the energetic state (i.e., reduced ATP/ADP, PCr/ATP). Cardiomyocyte ultrastructure analysis revealed decreased mitochondrial area, size, and cristae density, as well as increased lipid droplet content in HFpEF hearts. Mitochondrial function was also impaired as demonstrated by decreased substrate-mediated respiration and dysregulated calcium handling. ConclusionsCollectively, the integrated omics approach applied here provides a framework to uncover novel genes, metabolites, and pathways underlying HFpEF, with an emphasis on mitochondrial energy metabolism as a potential target for intervention.

molecular biology↗

bioRxiv2024

Deletion of Zyxin Reduces Endothelial Inflammation and Mitigates Atherosclerosis

BACKGROUNDOscillatory shear stress (OSS)-induced endothelial inflammation plays a critical role in the pathogenesis of atherosclerosis. However, the involvement of endothelial zyxin, a mechanosensor, in OSS-associated atherosclerosis and its underlying mechanisms remains unclear. METHODSTo investigate the role of zyxin in vivo, ZyxiniECKOApoE-/- mice were utilized in atherosclerosis model induced by carotid artery ligation; in vitro, endothelial cells were subjected to disturbed flow in Ibidi systerm. RESULTSZyxin was significantly upregulated in the OSS regions of both human and mouse arteries. The specific deletion of zyxin in endothelial cells (ECs) in ApoE-/- (ZyxiniECKOApoE-/-) mice reversed the ECs activation and atherosclerosis induced by OSS. In vitro studies indicated that the absence of zyxin reduced the induction of adhesion molecules and pro-inflammatory cytokines stimulated by OSS. Mechanistic investigations demonstrated that 14-3-3{beta} facilitated yes-associated protein (YAP) phosphorylation at Serine 127, which played a critical role in retaining YAP within the cytoplasm. Under OSS stimulation, zyxin inhibited the phosphorylation of YAP at Serine 127 through its interaction with 14-3-3{beta}, rather than direct regulation of YAP. This inhibition enhanced YAPs nuclear translocation and promoted endothelial inflammation. Furthermore, it was shown that rosuvastatin inhibited zyxin expression in human umbilical vein endothelial cells and the vascular endothelium of ApoE-/- mice. This inhibition led to decreased levels of inflammatory markers and adhesion molecules associated with atherosclerotic lesions observed in the partially ligated left common carotid arteries of ApoE-/- mice. CONCLUSIONSThis study further confirms that zyxin is a mechanoreceptor in endothelial cells and elucidates the indispensable role of the zyxin-14-3-3{beta}-YAP axis in endothelial inflammation and atherogenesis. This indicates that Zyxin plays a crucial role in protecting against atherosclerosis.

cell biology↗

bioRxiv2024

Bio-medical Big Data Operating System (Bio-OS): An Integrated Data Mining Environment for Data Intensive Scientific Research

The advent of high throughput sequencing has ushered life science and clinical research into the era of big data, posing significant challenges for reproducibility due to the complexity of data integration and analysis. Although the FAIR principles advocate for the transparent and reliable sharing of scientific data, their implementation remains hampered by technical barriers. The Global Alliance for Genomics and Health (GA4GH) has made strides in standardizing data and tools, yet a comprehensive solution for reproducibility is lacking. In response, we present BioOS, an open source, cloud native Biomedical big data Operating System. This system encapsulates study components data, code, tools, and environments into workspaces, enhancing reproducibility and validation. BioOS employs JSON Schema for machine readability and includes a Hierarchy Hash Mechanism to ensure data integrity. Adhering to GA4GH protocols, BioOS simplifies complex technological implementations, making advanced research tools accessible. Demonstrated through representative workspaces, BioOS fosters seamless research replication, peer review, and editorial evaluation. Its cloud native infrastructure supports dynamic resource allocation, enabling efficient handling of large scale analyses. By integrating AI driven Large Language Models, BioOS enhances user interaction and operational flexibility. As an evolving open source platform, BioOS exemplifies a transformative approach to biomedical research, aligning with FAIR principles and advancing the AI for Science paradigm, thus promoting a more connected, efficient, and impactful research environment.

bioinformatics↗

bioRxiv2024

Generation of cultured beef from bovine embryonic stem cells

Bovine embryonic stem cells (bESCs) serve as an optimal source for generating muscle and fat tissues. However, systematic methodologies for creating cultured beef from bESCs have not yet been reported. Here, we successfully established a bESC line and formulated a specialized culture medium that supports cell pluripotency in both two-dimensional (2D) and three-dimensional (3D) cultures. Additionally, we have also developed serum-free and transgene-free protocols that efficiently differentiate bESCs into muscle and fat cells. These differentiated cells were validated at the transcriptional and protein levels. Furthermore, we devised a method using high moisture stretched plant-based protein scaffolds for incorporating differentiated muscle and fat cells derived from bESCs, producing an innovative plant-cell hybrid cultured beef. These advancements provide a solid foundation for future cultured beef production using pluripotent stem cells.

bioengineering↗

bioRxiv2024

Submillimeter diffusion MRI using an in-plane segmented 3D multi-slab acquisition and denoiser-regularized reconstruction

High-resolution diffusion MRI (dMRI) provides valuable insights into brain microstructure, particularly at submillimeter resolutions, where it enables more precise delineations of curved and crossing white matter pathways. However, achieving high-quality submillimeter dMRI in-vivo poses significant challenges due to the intrinsically low signal-to-noise ratio (SNR), along with the long echo spacing, readout time, and TE required for the large matrix size, leading to significant image distortion, T2* blurring, and T2 signal decay. In this study, we propose a novel acquisition and reconstruction framework to overcome these challenges. Based on numerical simulations, we introduce an in-plane segmented 3D multi-slab acquisition that leverages the optimal SNR efficiency of 3D multi-slab imaging while reducing echo spacing, readout times, and TE using in-plane segmentation. This approach minimizes distortion, improves image sharpness, and enhances SNR. Additionally, we develop a denoiser-regularized reconstruction to suppress noise while maintaining data fidelity, which reconstructs high-SNR images without introducing substantial blurring or bias. Comprehensive in-vivo experiments demonstrate that our method consistently produces high-quality dMRI data at 0.65 mm and 0.53 mm isotropic resolutions on a 3T scanner. The submillimeter dMRI datasets reveal richer microstructural details, reduce gyral bias, and improve U-fiber mapping compared to prospectively acquired 1.22 mm diffusion data. Our method demonstrates robustness at 7T and generates high-SNR 0.61 mm diffusion datasets, showing excellent agreement with previous post-mortem studies at the same scanner. Implemented using the open-source, scanner-agnostic framework Pulseq, our approach may facilitate broader adoption across different scanner platforms to benefit a wider range of applications. These results underscore the potential of our method to advance medical image analysis and neuroscientific research on human brain connectivity.

neuroscience↗

bioRxiv2024

Bond-centric modular design of protein assemblies

We describe a modular bond-centric approach to protein nanomaterial design inspired by the rich diversity of chemical structures that can be generated from the small number of atomic valencies and bonding interactions. We design protein building blocks with regular coordination geometries and bonding interactions that enable the assembly of a wide variety of closed and opened nanomaterials using simple geometrical principles. Experimental characterization confirms successful formation of more than twenty multi-component polyhedral protein cages, 2D arrays, and 3D protein lattices, with a high (10-50 %) success rate and electron microscopy data closely matching the corresponding design models. Because of the modularity, individual building blocks can assemble with different partners to generate distinct regular assemblies, resulting in an economy of parts and enabling the construction of reconfigurable systems.

biochemistry↗

bioRxiv2024

The utility of single-cell RNA sequencing data in predicting plant metabolic pathway genes

It is an ever challenging task to make genome-wide predictions for plant metabolic pathway genes (MPGs) encoding enzymes that catalyze the biosynthesis of plant natural products. Here, starting from 1,129 benchmark MPGs that have experimental evidence in Arabidopsis thaliana, we investigate the utilities of single-cell RNA sequencing (scRNA-seq) data--a recently arisen omics data that has been used in several other fields--in predicting MPGs using five machine learning (ML) algorithms that support multi-label tasks. Compared with traditional bulk RNA-seq data, scRNA-seq data lead to different but comparable co-expression networks among MPGs within metabolic classes, and significantly higher prediction accuracy of MPGs into classes. Prediction accuracy for individual metabolic classes is not associated with the co-expression network tightness, but correlated with the number of MPGs within each class, indicating that including more benchmark genes in the future will improve the MPG prediction. Splitting the RNA-seq data into genetic background/condition or tissue-specific subsets can improve the gene co-expression network tightness and MPG prediction accuracy for some classes; scRNA-seq-based models still outperform bulk RNA-seq-based models for most classes when corresponding subsets are used. In addition, deep learning approaches outperform classical machine learning approaches; approaches implemented in an ensembled workflow AutoGluon tend to have severe overfitting issues potentially due to the relative scarcity of benchmark MPGs within classes. Our results demonstrate the superiority of scRNA-seq data over bulk RNA-seq data in predicting MPGs into metabolic classes, and propose that scRNA-seq data should be included in the future to advance the identification of plant MPGs.

plant biology↗

bioRxiv2024

Neural correlates differ between crystallized and fluid intelligence in adolescents

Fluid and crystallized intelligence are acknowledged as distinct facets of cognitive ability during brain development, but the specific neural substrates and molecular mechanisms underlying them remain unclear. This study used a sample comprising 7471 young adolescents (mean age 9.87 {+/-} 0.62 years) from the ABCD cohort to elucidate the differential neural correlates of fluid and crystallized intelligence. Our findings indicated that micro-level brain MRI phenotypes such as water diffusivity were closely associated with fluid intelligence, whereas macro-level brain MRI phenotypes such as gray matter cortical thickness were indicative of crystallized intelligence. We further investigated the molecular mechanisms underlying fluid and crystallized intelligence by correlating the characteristic MRI markers with spatial transcriptome profiles and PET imaging. Results showed that fluid intelligence had significant associations with serotonin and glutamate system, while crystallized intelligence was related to serotonin, dopamine and acetylcholine system. Furthermore, we examined the impacts of lifestyle factors on these two forms of intelligence and how the molecular pathways mediated these impacts. Our investigation suggested that physical activities, screen use and sleep duration influenced fluid intelligence mainly through mGlu5 receptors and crystallized intelligence through 5HT1a and D2 receptors. In conclusion, these findings illustrated a distinct neural basis between fluid and crystallized intelligence from the perspectives of neuroimaging, neurotransmitters, and lifestyles in young adolescents.

neuroscience↗

bioRxiv2024

The Composition of the Bacterial Community in Raw Milk from Holstein Dairy Cattle Correlated with the Occurrence of Klebsiella pneumoniae Clinical Mastitis Infections

Klebsiella pneumoniae is a common, opportunistic bacterial pathogen that can cause severe clinical mastitis in dairy cattle. Optimizing the bovine udder microbiome to resist mastitis pathogens is a growing area of research; however, previous work has not examined which members of the mammary microbiome may have antagonistic interactions with K. pneumoniae. In this study, we collected quarter-level milk samples from Holstein dairy cows in Quebec, Canada every two weeks for 14 months and analyzed differences in the milk microbiome between samples that were collected from healthy quarters, quarters that developed subclinical mastitis, and quarters that experienced K. pneumoniae clinical mastitis (KP-CM) (n = 512 milk samples). The occurrence of subclinical mastitis did not cause significant differences in the alpha-diversity of the milk microbiome, nor did subclinical mastitis alter the interactions between taxa in the microbiome. However, the occurrence of KP-CM caused reductions in Shannon diversity in raw milk relative to healthy milk and altered the interactions between taxa. Specifically, K. pneumoniae showed negative interactions with the genus Aerococcus. The negative interactions between Aerococcus spp. and K. pneumoniae in the context of the bovine milk microbiome should be analyzed further.

microbiology↗

bioRxiv2024

CREATE: cell-type-specific cis-regulatory elements identification via discrete embedding

Identifying cis-regulatory elements (CREs) within non-coding genomic regions--such as enhancers, silencers, promoters, and insulators--is pivotal for elucidating the intricate gene regulatory mechanisms underlying complex biological traits. The current prevalent sequence-based methods often focus on singular CRE types, limiting insights into cell-type-specific biological implications. Here, we introduce CREATE, a multimodal deep learning model based on the Vector Quantized Variational AutoEncoder framework, designed to extract discrete CRE embeddings and classify multiple CRE classes using genomic sequences, chromatin accessibility, and chromatin interaction data. CREATE excels in accurate CRE identification and exhibits strong effectiveness and robustness. We showcase CREATEs capability in generating comprehensive CRE-specific feature spectrum, offering quantitative and interpretable insights into CRE specificity. By enabling large-scale prediction of CREs in specific cell types, CREATE facilitates the recognition of disease- or phenotype-related biological variabilities of CREs, thereby expanding our understanding of gene regulation landscapes.

bioinformatics↗

bioRxiv2024

PHLOWER - Single cell trajectory analysis using Decomposition of the Hodge Laplacian

Multi-modal single-cell sequencing, which captures changes in chromatin and gene expression in the same cells, is a game changer in the study of gene regulation in cellular differentiation processes. Computational trajectory analysis is a key computational task for inferring differentiation trees from this single-cell data, though current methods struggle with complex, multi-branching trees and multi-modal data. To address this, PHLOWER (decomPosition of the Hodge Laplacian for inferring trajectOries from floWs of cEll diffeRentiation) leverages the harmonic component of the Hodge decomposition on simplicial complexes to infer trajectory embeddings. These natural representations of cell differentiation facilitate the estimation of their underlying differentiation trees. We evaluate PHLOWER through benchmarking with multi-branching differentiation trees and using novel kidney organoid multi-modal and spatial single-cell data. These demonstrate the power of PHLOWER in both the inference of complex trees and the identification of transcription factors regulating off-target cells in kidney organoids.

bioinformatics↗

bioRxiv2024

Metabolic vulnerabilities in Down syndrome B-cell acute lymphoblastic leukemia can be targeted using Venetoclax

Children with Down syndrome (DS) and B-cell acute lymphoblastic leukemia (B-ALL) are at increased risk for treatment-related mortality and relapse, highlighting the need for new therapies. Leukemia cell lines (CLs) have been fundamental to understanding therapeutic responses to pharmacological agents. We generated three DS B-ALL CLs characterized with diverse genomic alterations, including IGH::CRLF2 rearrangement (BCR::ABL1 like), mutations in FLT3 and TP53, and a novel ERG::CEBPD rearrangement. DS CLs had diminished proliferation, metabolism, and mitochondrial function when compared to non-DS (NDS) CLs and interestingly, these findings were similar to NDS Philadelphia chromosome-like (Ph-like) B-ALL CLs. Based on similar mitochondrial defects and prior preclinical data using Venetoclax for Ph-like B-ALL, we hypothesized that Venetoclax would be effective in DS. Intriguingly, Venetoclax was more effective in DS when compared to both NDS and Ph-like CLs. Efficacy was observed in DS patient derived xenografts (PDXs) and diagnostic/relapsed patient samples treated with Venetoclax, which synergized with Trametinib and Vincristine. Mass spectrometry-based multiomics analyses in DS and NDS B-ALL patient samples revealed an enriched metabolite profile in DS, particularly in the hubs of glucose metabolism and polyunsaturated phosphatidylcholines and phosphatidylinositols. Transcriptome analyses in DS B-ALL patients (n=249) supported enhanced glucose and fatty acid metabolism. Glucose regulated B-ALL viability through de novo serine biosynthesis. Targeting serine synergized with Venetoclax in DS B-ALL CLs. In summary, we have generated novel tools for studying DS B-ALL and identify altered metabolism in DS that responds to Venetoclax.

cancer biology↗

bioRxiv2024

Evaluation of scalp-based targeting methods of DLPFC for TMS therapy: EEG cap F3, Beam F3, CPC F3, and adjusted Beam F3

BackgroundRepetitive transcranial magnetic stimulation targeting the left dorsolateral prefrontal cortex (DLPFC) offers a promising approach for treating depression. Scalp-based targeting methods, including EEG Cap, Beam F3, CPC F3, and adjusted Beam F3, have evolved to improve clinical application. ObejectivesTo evaluate and compare the accuracy, reliability, speed, and training efficacy of these four methods. MethodsThree trained technicians conducted repeated DLPFC targeting measurements on 10 3D-printed head models. Accuracy was assessed by calculating targeting error and electric field strength at the subgenual anterior cingulate cortex anti-correlated peak. Reliability was evaluated through inter- and intra-rater variability, and training efficacy was examined by comparing targeting errors between trained technicians and novices. ResultsCPC F3, Beam F3, and adjusted Beam F3 demonstrated comparable accuracy in terms of electric field strength, but CPC F3 had the lowest targeting error (4.2 mm). CPC F3 also exhibited the highest reliability (inter-rater variability at 3.3 mm, intra-rater variability at 2.3 mm). The EEG cap was the fastest method (80 seconds), while CPC F3 was faster than Beam F3 but slower than EEG Cap. No significant differences in targeting error between trained technicians and novices were noted with CPC F3 and adjusted Beam F3. ConclusionThe comparative analysis of four scalp-based targeting methods provides insights that can enhance clinical decision-making and the practical application. While accuracy among CPC F3, Beam F3, and adjusted Beam F3 was comparable, CPC F3 emerges as highly practical due to its significantly better reliability, training efficacy, moderate speed, and straightforward implementation.

neuroscience↗

bioRxiv2024

Mechanistic Insights into the Stimulation of the Histone H3K9 Methyltransferase Clr4 by Proximal H3K14 Ubiquitination

H3K9 methylation is an evolutionarily conserved hallmark of heterochromatin and plays crucial roles in chromosome segregation, genome stability, and gene expression regulation. Clr4 is the sole histone methyltransferase responsible for catalyzing H3K9 methylation in Schizosaccharomyces pombe. Clr4 K455/K472 automethylation and histone H3K14 ubiquitination (H3K14Ub) are vital activators of the catalytic activity of Clr4, ensuring appropriate heterochromatin deposition and preventing deleterious gene silencing. While the mechanism by which automethylation activates Clr4 was recently elucidated, the mechanism of the significantly pronounced stimulatory effect of H3K14Ub on Clr4 remains unclear. Here we determined the crystal structures of Clr4 bound to ubiquitinated and unmodified H3 peptides at resolutions of 2.60 [A] and 2.39 [A], respectively. Our structures reveal a synergistic mechanism underlying the stronger stimulatory effect by H3K14Ub compared to automethylation: site-specific ubiquitination constrained by the H3K14 linkage increases substrate affinity through multivalent interactions between ubiquitin and Clr4. Additionally, H3K14Ub facilitates the allosteric transition of Clr4 from an inactive apo conformation to a hyperactive "catalyzing state", accompanied by conformational changes in the C-SET-insertion (SI) region, complete release of the autoregulatory loop (ARL), and retraction of the {beta}9/10 loop. Finally, we propose a structural model for the Clr4 catalytic-regulatory cycle, depicting varying levels of conformational regulation mediated by automethylation and ubiquitination. This work provides structural insights into the interplay between different histone modifications and their collective impact on epigenetic regulation.

molecular biology↗