Biology subjects

Wu, Y.

Publications and source records attributed to Wu, Y..

At least 397 records · Page 22Linked to original sources

bioRxiv2024

KlebPhaCol: A community-driven resource for Klebsiella research identified a novel gut phage order associated with the human gut

The growing threat of multidrug-resistant Klebsiella pneumoniae, coupled with its role in gut colonisation, has intensified the search for new treatments, including bacteriophage therapy. Despite increasing documentation of Klebsiella-targeting phages, clinical applications remain limited, with key phage-bacteria interactions still poorly understood. A major obstacle is fragmented access to well-characterised phage-bacteria pairings, restricting the collective advancement of therapeutic and mechanistic insights. To address this gap, we created the Klebsiella Phage Collection (KlebPhaCol), an open-source resource comprising 53 phages and 74 Klebsiella isolates, all fully characterised. These phages span five families - including a novel order, Felixvirales, associated with the human gut - and target 27 sequence types (including ST258, ST11, ST14) and 28 capsular-locus types (including KL1 and KL2), across six Klebsiella species. Freely accessible at www.klebphacol.org, KlebPhaCol invites the scientific community to both use and contribute to this resource, fostering collaborative research and a deeper understanding of Klebsiella-phage interactions beyond therapeutic use.

microbiology↗

bioRxiv2024

Cavin1 binding nanobodies reveal structural flexibility and regulated interactions of the N-terminal coiled-coil domain

Caveolae are abundant plasma membrane structures that regulate signalling, membrane homeostasis, and mechanoprotection. Their formation is driven by caveolins and cavins and their coordinated interactions with lipids. We have developed nanobodies against the trimeric HR1 coiled-coil domain of Cavin1. We identify specific nanobodies that do not perturb Cavin1 membrane binding and localise to caveolae when expressed in cells. The crystal structure of a nanobody HR1 complex reveals a symmetric 3:3 architecture as validated by mutagenesis. In this structure, the C-terminal half of the HR1 domain is disordered suggesting the nanobody has stabilised an open conformation previously identified as important for membrane interactions. A phosphomimic mutation in a Thr-Ser pair proximal to this region reveals selective regulation of Cavin2/Cavin3 association. These studies provide new insights into Cavin domains required for assembly of multiprotein caveolar assemblies and describe new nanobody tools for structural and functional studies of caveolae. SUMMARY STATEMENTNanobodies are reported that can label the Cavin1 protein and caveola structures in cells.

cell biology↗

bioRxiv2024

Notch Signaling Drives Pro-Regenerative and Migratory Traits in Glandular Stem/Progenitor Cells

Organoid models have advanced our understanding of adult stem/progenitor cell dynamics and function. However, uncovering the regulatory mechanisms of scarce and often quiescent stem cells in organs like the salivary glands remains challenging. Using single-cell RNA sequencing and bulk ATAC and RNA-sequencing analysis, we conducted in-depth profiling of the cellular populations and key signaling pathways characterizing a mouse submandibular salivary gland organoid (mSGO) model at different temporal stages and in response to radiation damage. We identified Sox9- and Itgb1-expressing cells as the most primitive adult stem/progenitor populations and uncover novel stemness and migratory roles for Cd44-expressing cells. Moreover, we revealed that Notch signaling is essential for maintaining self-renewal and migration potential of these stem/progenitor cells post-irradiation. Extending these findings to patient-derived mSGOs, as well as murine and patient-derived mammary and thyroid gland organoids, we confirmed Notch signaling as a conserved regulator of stem/progenitor cell function under migrative and regenerative conditions.

cell biology↗

bioRxiv2024

Microbial virulome and resistome in deep-sea cold seeps pose minimal public health risks

Deep-sea cold seeps host high microbial biomass and biodiversity that thrive on hydrocarbon and inorganic compound seepage, exhibiting diverse ecological functions and unique genetic resources. However, potential health risks from pathogenic or antibiotic-resistant microorganisms in these environments remain largely overlooked, especially during resource exploitation and laboratory research. Here, we analyzed 165 metagenomes and 33 metatranscriptomes from 16 global cold seep sites to investigate the diversity and distribution of virulence factors (VFs), antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs). A total of 2,353 VFs are retrieved in 689 MAGs, primarily associated with non-pathogenic functions like adherence. Additionally, cold seeps harbor nearly 100,000 ARGs, as important reservoirs, with high-risk ARGs presenting at low abundance. Compared to other environments, microorganisms in cold seeps exhibit substantial differences in VF and ARG counts, with potential horizontal gene transfer facilitating their spread. These virulome and resistome profiles provide valuable insights into the evolutionary and ecological implications of pathogenicity and antibiotic resistance in extreme deep-sea ecosystems. Collectively, these results indicate that cold seep sediments pose minimal public health risks, shedding lights on environmental safety in deep-sea resource exploitation and research. ImportanceUnderstanding pathogenicity and antibiotic resistance in environmental reservoirs like deep-sea cold seeps is critical for assessing public health risks, particularly with increasing human activities such as deep-sea mining. This study offers the first comprehensive analysis of virulome, resistome, and mobilome profiles in cold seep microbial communities. While cold seeps act as reservoirs for diverse ARGs, high-risk ARGs are rare, and most VFs contribute to non-pathogenic ecological functions. These results highlight the minimal threat to public health posed by cold seeps, providing a reference for monitoring the spread of pathogenicity and resistance in extreme ecosystems and informing environmental safety assessments during deep-sea resource exploitation.

microbiology↗

bioRxiv2024

Effects of Bacterial Wilt on Community Composition and Diversity of Culturable Endophytic Fungi in Alpinia galanga

Hongdoukou plant (Alpinia galanga Willd.) is a perennial herbaceous plant that usually has a stable microflora living in the inter-root and stem and leaf tissues, which assists the host in normal growth and development. The bacterial wilt disease that had been investigated in planting bases of A. galanga, was a new soil-borne disease infected by pathogenic bacteria (Ralstonia solanacearum (Smith) Yabuuchi et al.), which disrupts the A. galanga-microbe-soil microecological balance. For this reason, it is important to study the changes of the community composition and diversity of endophytic fungi in healthy and diseased A. galanga (HDK_J and HDK_B), and to mine the active endophytic fungal resources in order to lay the foundation for exploring the functional microbial communities for artificial synthesis. All experimental materials were from the healthy and diseased plant clusters of A. galanga in the traditional planting areas in Guangdong Province. The endophytic fungi were isolated from the stems and leaves of A. galanga by separated plant tissue blocks cultured in PDA medium. The colonization rate (CR) was calculated, and the isolated strains were amplified by PCR with the ITS1/ITS4 genes as the fungal molecular markers, to identify the endophytic fungi species, and to analyze the diversity of endophytic fungi in the healthy and diseased clusters of HDK_J and HDK_B, and the indexes included: relative segregation frequency (SF), relative abundance (RA) plotted by ggplot2 under R language, Shannon-Wiener diversity index (H'), Simpson index (D), Margalef richness index (R) and Pielou uniformity index (J), and finally, a phylogenetic tree of the endophytic fungi of healthy and diseased A. galanga was constructed by MEGA. A total of 685 endophytic fungal strains were isolated from the stems and leaves of healthy (HDK_J) and diseased (HDK_B) A. galanga, of which 27 endophytic fungal species were identified in healthy A. galanga. and 8 in diseased A. galanga, which belonged to 3 Phyla, 6 Classes and 13 Families. At the genus level, the RA of HDK_B endophytic fungi were only 38.93% of HDK_J, with significant difference (P<0.05), indicating that the composition and abundance of endophytic fungi in HDK_B were lower than that in HDK_J. Meanwhile, the endophytic fungi species isolated from the stems and leaves of the HDK_J and HDK_B were the same, but the RA of leaf organs was higher than that of stems, with the RA in the leaves in HDK_J higher than that of stems by 124.23%, and the RA of leaves in HDK_B higher than that of stems by 78.23%. The RA of HDK_J leaves was 78.08% higher than that of stems. all the diversity indexes of HDK_J were higher than that of HDK_B, with significant differences (P<0.05), and the Shannon-Wiener index (H'), Simpsons index (D), and Margalefs index (R) behaved as follows: HDK_J leaves (1.16, 0.87, 5.12) > HDK_J stems (0.82, 0.72, 4.22) > HDK_B leaves (0.57, 0.24, 2.97) > HDK_B stems (0.33, 0.14, 2.25), whereas Pielous index (J) indicated that the endophytic fungal species were more uniformly distributed in HDK_J leaves, and there was not much difference in the homogeneity of the leaves of the HDK_J stems and the HDK_B stems. The mapping phylogenetic trees showed there were 4 major development branches of the species on endophytic fungi in HDK_J, and especially, there were many long development branches under the Ascomycota. The genetic relationships among different species were completely revealed in phylogenetic trees. The average genetic similarity among genera was 52.80%. Conversely, there only were1major branch (Ascomycota), and few sub-branches under the Ascomycota in HDK_B. The average genetic similarity among genera was 84.59%, and the difference was small. The bacterial wilt significantly affected the composition and RA of endophytic fungi in A. galanga. The diversity indices showed decreasing trend in A. galanga after infected by R. solanacearum. The dominant species were changed. The parts of sensitive endophytic fungi were disappeared. This result will be helpful for the study on the relationship between the artificial minimal microbial community and the role of the host as well as the study on synthetic microbiomics.

microbiology↗

bioRxiv2024

Functional split-tRNA: a new perspective on codon decoding mechanism

The translation machinery is required to process all codon triplets without exception while maintaining high speed and accuracy, despite orders-of-magnitude differences in cognate pairing stability. For stability-based selection to be efficient, the range of pairing stabilities must be narrowed by raising the lower bound and lowering the upper bound. The constrained structure and intramolecular cooperativity of tRNA complicate understanding of how it modulates codon-anticodon stability and whether it affects selection kinetics beyond codon recognition. To address these questions, we engineered functional split-tRNAs bearing a dangling anticodon in place of the anticodon loop. Our results demonstrate that split-tRNA supports in vitro translation nearly as efficiently as intact synthetic tRNA, challenging the notion that tRNA strain is essential for triggering GTP hydrolysis in response to codon recognition. Using split-tRNA architecture, we found that codon-anticodon stability is likely modulated by the dipole moments of adjacent nucleobases. Our kinetic modeling aligns with a conformational selection mechanism, where the decoding site fluctuates between open and closed states, and the correct codon-anticodon minihelix acts as an allosteric effector that permits its spontaneous closure and stabilizes the closed state. Overall, our data challenge the notion that tRNA is an active player in the selection process.

molecular biology↗

bioRxiv2024

Linking spatial drug heterogeneity to microbial growthdynamics in theory and experiment

Diffusion and migration play pivotal roles in microbial communities - shaping, for example, colonization in new environments and the maintenance of spatial structures of biodiversity. While previous research has extensively studied free diffusion, such as range expansion, there remains a gap in understanding the effects of biologically or physically deleterious confined environments. In this study, we examine the interplay between migration and spatial drug heterogeneity within an experimental meta-community of E. faecalis, a Gram-positive opportunistic pathogen. When the community is confined to spatially-extended habitats ( islands) bordered by deleterious conditions, we find that the population level response depends on the trade-off between the growth rate within the island and the rate of transfer into regions with harsher conditions, a phenomenon we explore by modulating antibiotic concentration within the island. In heterogeneous islands, composed of spatially patterned patches that support varying levels of growth, the populations fate depends critically on the specific spatial arrangement of these patches - the same spatially averaged growth rate leads to diverging responses. These results are qualitatively captured by simple simulations, and analytical expressions which we derive using first-order perturbation approximations to reaction-diffusion models with explicit spatial dependence. Among all possible spatial arrangements, our theoretical and experimental findings reveal that the arrangement with the highest growth rates at the center most effectively mitigates population decline, while the arrangement with the lowest growth rates at the center is the least effective. Extending this approach to more complex experimental communities with varied spatial structures, such as a ring-structured community, further validates the impact of spatial drug arrangement. Our findings suggest new approaches to interpreting diverging clinical outcomes when applying identical drug doses and inform the possible optimization of spatially-explicit dosing strategies. Author summaryIn this study, we develop an automated platform to experimentally investigate short-term population growth and migration dynamics under spatial drug heterogeneity. Our findings reveal that the collective spatial response of the population can vary significantly, even with the same migration rate and averaged drug dose, due to different spatial drug arrangements. By constructing a simple reaction-diffusion model, we observed that simulated short-term spatial growth rate closely matches the experimental data. Furthermore, this short-term spatial growth rate aligns well with the long-term spatial growth rate, defined by the largest eigenvalue, as the spatial system quickly enters the equilibrium growth state. Using concepts from perturbation theory, we derived an analytical relationship between the boundary diffusion effect, homogeneous growth effect, and heterogeneous effect. Our results highlight that in spatially-extended habitats, the spatial growth response is an emergent property. The bacterial population quickly enters equilibrium growth, suggesting that the spatial growth rate measured at an ecological scale may be used to predict resistance evolutionary behavior.

biophysics↗

bioRxiv2024

Astrocyte-specific secretome profiling reveals its correlation with neurological disorders

Secreted proteins mediate intercellular communication throughout the lives of multicellular organisms. However, due to the lack of new technology for secreted protein capturing, the progress of "secretomics lags behind. Here, we report a two-step secretome enrichment method (tsSEM) combining unnatural amino acid labeling and click chemistry-based biorthogonal reaction, which enables in vitro secretome profiling in the presence of serum. Using this novel method, we systematically investigated the secretome of human iPSCs-derived astrocytes (iAst) in different disease models and identified a panel of astrocyte-secreted proteins that are responsible for its non-cell autonomous toxicity under disease conditions. Furthermore, we validated two astrocytes-derived novel neurotrophic proteins, FAM3C and KITLG, which we identified from disease models, and found that they could boost neurite outgrowth, protect neurons, and promote neural progenitor proliferation. Our study highlights the utility of secretome profiling of iAst and demonstrates its applications in disease study and target identification and validation in drug development.

neuroscience↗

bioRxiv2024

SuperWater: Predicting Water Molecule Positions on Protein Structures by Generative AI

Water molecules play a significant role in maintaining protein structural stability and facilitating molecular interactions. Accurate prediction of water molecule positions around protein structures is essential for understanding their biological roles and has significant implications for protein engineering and drug discovery. Here, we introduce SuperWater, a novel generative AI framework that integrates a score-based diffusion model with equivariant graph neural networks to predict water molecule placements around proteins with high accuracy. SuperWater surpasses existing methods, delivering state-of-the-art performance in both crystal water coverage and prediction precision, achieving water localization within 0.3 {+/-} 0.06 {degrees}A of experimentally validated positions. We demonstrate the capabilities of SuperWater through case studies involving protein hydration, protein-ligand binding, and protein-protein binding sites. This framework can be adapted for various applications, including structural biology, binding site prediction, multi-body docking, and water-mediated drug design.

bioinformatics↗

bioRxiv2024

WMH-DualTasker: A Dual-Task Deep Learning Model with Self-supervised Consistency for Automated Segmentation and Visual Rating of White matter Hyperintensities - a Multicentre study

BackgroundWhite matter hyperintensities (WMH) are neuroimaging markers linked to an elevated risk of cognitive decline. WMH severity is typically assessed via visual rating scales and through volumetric segmentation. While visual rating scales are commonly used in clinical practice, they offer limited descriptive power. In contrast, supervised volumetric segmentation requires manually annotated masks, which is labor-intensive and challenging to scale for large studies. Therefore, our goal was to develop an automated deep learning model that can provide accurate and holistic quantification of WMH severity with minimal supervision. MethodsWe developed WMH-DualTasker, a deep learning model that simultaneously performs voxel-wise segmentation and visual rating score prediction. The model employs self-supervised learning with transformation-invariant consistency constraints, using WMH visual ratings (ARWMC scale, range 0-30) from clinical settings as the sole supervisory signal. Additionally, we assessed its clinical utility by applying it to identify individuals with mild cognitive impairment (MCI) and to predict dementia conversion. FindingsThe volumetric quantification performance of WMH-DualTasker was either superior to or on par with existing supervised methods, as demonstrated on the MICCAI-WMH dataset (N=60, Dice=0.602) and the SINGER dataset (N=64, Dice=0.608). Furthermore, the model exhibited strong agreement with clinical visual rating scales on an external dataset (SINGER, MAE=1.880, K=0.77). Importantly, WMH severity metrics derived from WMH-DualTasker improved predictive performance beyond conventional clinical features for MCI classification (AUC=0.718, p<0.001), MCI conversion prediction (AUC=0.652, p<0.001) using the ADNI dataset. InterpretationsWMH-DualTasker substantially reduces the reliance on labor-intensive manual annotations, facilitating more efficient and scalable quantification of WMH severity in large-scale population studies. This innovative approach has the potential to advance preventive and precision medicine by enhancing the assessment and management of vascular cognitive impairment associated with WMH. Code and model weights are publicly available at https://github.com/hzlab/WMH-DualTasker.

neuroscience↗

bioRxiv2024

LTB4 is converted into a potent human neutrophil NADPH oxidase activator via a receptor transactivation mechanism in which the BLT1 receptor activates the free fatty acid receptor 2

The endogenous neutrophil chemoattractant leukotriene B4 (LTB4) is a biased signalling agonist that potently increases the intracellular concentration of free calcium ions ([Ca2+]i), but alone is a weak activator of the neutrophil superoxide anion (O2-)-generating NADPH oxidase. However, in this study we show that an allosteric modulator of the free fatty acid 2 receptor (FFA2R) converts LTB4 into a potent NADPH oxidase activating agonist. While an allosteric modulation of FFA2R was required for LTB4 receptor 1 (BLT1R)-mediated activation of the NADPH oxidase, the LTB4-induced increase in [Ca2+]i was not affected by the modulator. Thus, the biased BLT1R signalling pattern was altered in the presence of the allosteric FFA2R modulator, being biased with a preference towards the signals that activate the NADPH oxidase relative to an increase in [Ca2+]i. Both BLT1R and FFA2R belong to the family of G protein-coupled receptors (GPCRs), and our results show that a communication between the activated BLT1R and the allosterically modulated FFA2Rs generates signals that induce NADPH oxidase activity. This is consistent with a previously described receptor transactivation (crosstalk) model whereby the function of one neutrophil GPCR can be regulated by receptor downstream signals generated by another GPCR. Furthermore, the finding that an allosteric FFA2R modulator sensitises not only the response induced by orthosteric FFA2R agonists but also the response induced by LTB4, violates the receptor restriction properties that normally define the selectivity of allosteric GPCR modulators.

immunology↗

bioRxiv2024

Curcumin attenuates microglia-mediated chronic neuropathic pain through CDK5 /p35 signaling pathway

Curcumin is a phenolic compound derived from turmeric, one of the main ingredients of curry powder, which is widely used for its antioxidant, anti-inflammatory and immunomodulatory effects. Curcumin has been reported to help relieve pain, such as neuropathic pain caused by injury or disease, but the specific mechanism of its antinociceptive effect on pathological pain is unclear. Cyclin-dependent kinase 5 (Cdk5) is a key control point for the release of neurotransmitters from presynaptic vesicles. Cdk5 and its activator p35 couple to regulate key signaling cascades, thereby participating in the pain process. In this study, we established a NP model by chronic constriction injury (CCI) of the bilateral sciatic nerve in rats and evaluated behavioral hyperalgesia using mechanical and hot and cold tests. Protein expression and distribution were evaluated using western blotting and immunofluorescence. The results showed that Iba-1 and Cdk5/p35 were co-localized in the dorsal horn and dorsal root ganglia, respectively. After CCI, the expression of Cdk5 and p35 was upregulated in the dorsal horn and dorsal root ganglia, while intraperitoneal injection of curcumin significantly reversed the activation of Cdk5/p35 protein and alleviated the hyperalgesia in rats. In addition, the injection of curcumin reduced the co-localization expression of Iba-1 and Cdk5/p35, indicating that curcumin inhibited the activation of Cdk5/p35 protein in the dorsal horn and dorsal root ganglia, thereby affecting the activation of microglia, thereby having a destructive effect on the neuronal cell plasticity and synaptic structure remodeling in the development of NP. Our study provides new evidence that Cdk5/p35 in the dorsal horn and dorsal root ganglia is related to the occurrence of NP, introduces microglia as the basis for the long-term maintenance of NP, and provides insights into the molecular mechanisms involved in the analgesic effect of curcumin. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/623498v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@125fc20org.highwire.dtl.DTLVardef@cba7d3org.highwire.dtl.DTLVardef@772291org.highwire.dtl.DTLVardef@17e87e3_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

ScisTree2: An Improved Method for Large-scale Inference of Cell Lineage Trees and Genotype Calling from Noisy Single Cell Data

In a multicellular organism, cell lineages share a common evolutionary history. Knowing this history can facilitate the study of development, aging, and cancer. Cell lineage trees represent the evolutionary history of cells sampled from an organism. Recent developments in single-cell sequencing have greatly facilitated the inference of cell lineage trees. However, single-cell data are sparse and noisy, and the size of single-cell data is increasing rapidly. Accurate inference of cell lineage tree from large single-cell data is computationally challenging. In this paper, we present ScisTree2, a fast and accurate cell lineage tree inference and genotype calling approach based on the infinite-sites model. ScisTree2 relies on an efficient local search approach to find optimal trees. ScisTree2 also calls single-cell genotypes based on the inferred cell lineage tree. Experiments on simulated and real biological data show that ScisTree2 achieves better overall accuracy while being significantly more efficient than existing methods. To the best of our knowledge, ScisTree2 is the first model-based cell lineage tree inference and genotype calling approach that is capable of handling datasets from tens of thousands of cells or more.

bioinformatics↗

bioRxiv2024

Paired and solitary ionocytes in the zebrafish olfactory epithelium

The sense of smell is generated by electrical currents that are influenced by the concentration of ions in olfactory sensory neurons and mucus. In contrast to the extensive morphological and molecular characterization of sensory neurons, there has been little description of the cells that control ion concentrations in the zebrafish olfactory system. Here, we report the molecular and ultrastructural characterization of zebrafish olfactory ionocytes. Transcriptome analysis suggests that the zebrafish olfactory epithelium contains at least three different ionocyte types, which resemble Na+/K+-ATPase-rich (NaR), H+-ATPase-rich (HR), and Na+/Cl- cotransporter (NCC) cells, responsible for calcium, pH, and chloride regulation, respectively, in the zebrafish skin. In the olfactory epithelium, NaR-like and HR-like ionocytes are usually adjacent to one another, whereas NCC-like cells are usually solitary. The distinct subtypes are differentially distributed: NaR-like/HR-like cell pairs are found broadly within the olfactory epithelium, whereas NCC-like cells reside within the peripheral non-sensory multiciliated cell zone. Comparison of gene expression and serial-section electron microscopy analysis indicates that the NaR-like cells wrap around the HR-like cells and are connected to them by shallow tight junctions. The development of olfactory ionocyte subtypes is also differentially regulated, as pharmacological Notch inhibition leads to a loss of NaR-like and HR-like cells, but does not affect NCC-like ionocyte number. These results provide a molecular and anatomical characterization of olfactory ionocytes in a stenohaline freshwater teleost. The paired ionocytes suggest that both transcellular and paracellular transport regulate ion concentrations in the olfactory epithelium, while the solitary ionocytes may enable independent regulation of ciliary beating.

neuroscience↗

bioRxiv2024

Jade1 and the HBO1 complex are spatial-selective cofactors of Oct4

Oct4 is a master regulator of pluripotency. Potential Oct4 interactors have been cataloged but the manner and significance of these interactions are incompletely defined. Oct4 is capable of binding to DNA in multiple configurations, however the relationship between these configurations and cofactor recruitment (and hence transcription output) are unknown. Here, we show that Oct4 interacts with common and unique proteins when bound to DNA in different configurations. A unique protein is Jade1, a component of HBO histone acetyltransferase complexes. Jade1 preferentially associates with Oct4 when bound to More palindromic Octamer-Related Element (MORE) DNA sequences that bind Oct4 dimers. Surprisingly, we find that the Oct4 N-terminal activation domain, rather than facilitating Jade1 binding, serves as an autoinhibitory domain that dampens the interaction. ChIP-seq using HBO1, the enzymatic component of the complex, identifies a preference for binding adjacent to Oct4 at MORE sites. Using purified recombinant proteins and nucleosome complexes, we show that the HBO1 complex acetylates histone H3K9 within nucleosomes more efficiently when Oct4 is co-bound to a MORE. An Oct4 mutant with superior MORE binding characteristics also shows superior ability to catalyze H3K9 acetylation. Jade1 knockdown reduces H3K9Ac at regions where Oct4 binds a MORE but not a simple octamer. Cryo-electron microscopy reveals that Oct4 bound to a MORE near the nucleosome entry/exit site partially unwinds DNA from nucleosome core particles, and identifies additional mass associated with the HBO1 complex. These results identify a novel mechanism of transcriptional regulation by Oct4.

molecular biology↗

bioRxiv2024

Decoding Liver Cancer Prognosis: From Multi-omics Subtypes, Prognostic Models to Single Cell Validation

PurposeHepatocellular carcinoma (HCC) is a highly aggressive tumor characterized by significant heterogeneity and invasiveness, leading to a lack of precise individualized treatment strategies and poor patient outcomes. This necessitates the urgent development of accurate patient stratification methods and targeted therapies based on distinct tumor characteristics. Experimental DesignBy integrating gene expression data from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO), we identified subtypes through a multi-omics consensus clustering approach amalgamated from 10 clustering techniques. Subsequently, we developed a prognostic model, employing machine learning algorithms, based on subtype classification features. Finally, by analyzing single cell sequencing data, we investigated the mechanisms driving prognostic variations among distinct subtypes. ResultsFirst, we developed a novel consensus clustering method that categorizes liver cancer patients into two subtypes, CS1 and CS2. Second, we constructed a prognostic prediction model, which demonstrated superior predictive accuracy compared to several models published in the past five years. Finally, we observed differences between CS1 and CS2 in various metabolic pathways, biological processes, and signaling pathways, such as fatty acid metabolism, hypoxia levels, PI3K-AKT and MIF signaling pathway.

bioinformatics↗

bioRxiv2024

DNA-SaM, a robust system for large-scale data storage

DNA data storage offers a viable strategy to address the impending data explosion. Early attempts to harness DNA as a storage medium have encountered scalability limitations, largely due to the complexity of codec algorithms, the generation of biochemically harmful sequences and lack of a robust architecture. We present "DNA-SaM", a novel system designed for DNA data storage, which achieves linear computational complexity and strict bio-constraint adherence, ensuring high coding efficiency and fidelity. It encoded data at speeds surpassing classic systems by over 2 orders of magnitude, with this superiority changes across various encoding algorithms. Importantly, DNA-SaM effectively eliminates any sequence that could be deleterious to in vitro and in vivo biochemical processes, including homopolymer runs, tandem repeat motifs, and potential promoter sequences, etc. It also involves an advanced DNA data storage architecture that incorporates a two-tiered indexing system and a novel "storage unit" distribution paradigm for large-scale data storage. It is further validated by practical data storage both in vitro and in vivo with a 100% success rate. Our system is capable of storing data over 1039 PB, which marks a critical advancement in the scalability of DNA-based data storage.

synthetic biology↗

bioRxiv2024

In vivo genome editing of human hematopoietic stem cells for treatment of blood disorders by mRNA delivery

Ex vivo autologous hematopoietic stem cells (HSCs) gene therapy provides promising new treatments for hematological disorders. However, current methods involve complex processes and chemotherapeutic conditioning, leading to limited accessibility for treatment and significant side effects. Here, we developed an antibody-free targeted lipid nanoparticles (LNPs) for mRNA delivery to HSCs in vivo, enabling efficient base editing of the HBG target in human HSCs to reactivate fetal hemoglobin in derived erythroid cells. Delivery of ABE8e/sgRNA mRNA with optimized structure LNPs achieves efficient in vivo base editing of HBG in transfusion-dependent {beta}-thalassemia (TDT) patients derived HSCs engrafted in immunodeficient NCG-X mice, showing restored globin chain balance in erythroid cells. Our research indicated that utilizing LNPs for delivery of genome editor achieves efficient editing of endogenous genes of human HSCs. Notably, this non-viral delivery system eliminates the need for harvesting or mobilizing HSCs, providing a potent and one-time treatment potential for blood disorders like sickle cell disease (SCD) and TDT. HighlightsO_LIAntibody-free targeted LNPs system achieves efficient mRNA delivery and base editing in human bone marrow cells including HSCs via one-time intravenous injection. C_LIO_LIDelivery of ABE8e/sgRNA mRNA with optimized LNPs achieves efficient in vivo base editing of HBG in TDT patients derived HSCs engrafted in immunodeficient NCG-X mice, showing restored globin chain balance in erythroid cells. C_LIO_LILNPs efficiently deliver gene editing system to the BM for in vivo editing of human HSCs, providing preclinical evidence for the next generation non-viral gene therapy for blood disorders. C_LI

bioengineering↗