Epigenetic Activation of WNT5A Drives Glioblastoma Stem Cell Differentiation and Invasive Growth.

Glioblastoma stem cells (GSCs) are implicated in tumor neovascularization, invasiveness, and therapeutic resistance. To illuminate mechanisms governing these hallmark features, we developed a de novo glioblastoma multiforme (GBM) model derived from immortalized human neural stem/progenitor cells (hNSCs) to enable precise system-level comparisons of pre-malignant and oncogene-induced malignant states of NSCs. Integrated transcriptomic and epigenomic analyses uncovered a PAX6/DLX5 transcriptional program driving WNT5A-mediated GSC differentiation into endothelial-like cells (GdECs). GdECs recruit existing endothelial cells to promote peritumoral satellite lesions, which serve as a niche supporting the growth of invasive glioma cells away from the primary tumor. Clinical data reveal higher WNT5A and GdECs expression in peritumoral and recurrent GBMs relative to matched intratumoral and primary GBMs, respectively, supporting WNT5A-mediated GSC differentiation and invasive growth in disease recurrence. Thus, the PAX6/DLX5-WNT5A axis governs the diffuse spread of glioma cells throughout the brain parenchyma, contributing to the lethality of GBM.

ARAF protein kinase activates RAS by antagonizing its binding to RASGAP NF1.

RAF protein kinases are effectors of the GTP-bound form of small guanosine triphosphatase RAS and function by phosphorylating MEK. We showed here that the expression of ARAF activated RAS in a kinase-independent manner. Binding of ARAF to RAS displaced the GTPase-activating protein NF1 and antagonized NF1-mediated inhibition of RAS. This reduced ERK-dependent inhibition of RAS and increased RAS-GTP. By this mechanism, ARAF regulated the duration and consequences of RTK-induced RAS activation and supported the RAS output of RTK-dependent tumor cells. In human lung cancers with EGFR mutation, amplification of ARAF was associated with acquired resistance to EGFR inhibitors, which was overcome by combining EGFR inhibitors with an inhibitor of the protein tyrosine phosphatase SHP2 to enhance inhibition of nucleotide exchange and RAS activation.

Yap1 activation enables bypass of oncogenic Kras addiction in pancreatic cancer.

Activating mutations in KRAS are among the most frequent events in diverse human carcinomas and are particularly prominent in human pancreatic ductal adenocarcinoma (PDAC). An inducible Kras(G12D)-driven mouse model of PDAC has established a critical role for sustained Kras(G12D) expression in tumor maintenance, providing a model to determine the potential for and the underlying mechanisms of Kras(G12D)-independent PDAC recurrence. Here, we show that some tumors undergo spontaneous relapse and are devoid of Kras(G12D) expression and downstream canonical MAPK signaling and instead acquire amplification and overexpression of the transcriptional coactivator Yap1. Functional studies established the role of Yap1 and the transcriptional factor Tead2 in driving Kras(G12D)-independent tumor maintenance. The Yap1/Tead2 complex acts cooperatively with E2F transcription factors to activate a cell cycle and DNA replication program. Our studies, along with corroborating evidence from human PDAC models, portend a novel mechanism of escape from oncogenic Kras addiction in PDAC.

Phosphoantigens glue butyrophilin 3A1 and 2A1 to activate Vγ9Vδ2 T cells.

In both cancer and infections, diseased cells are presented to human Vγ9Vδ2 T cells through an 'inside out' signalling process whereby structurally diverse phosphoantigen (pAg) molecules are sensed by the intracellular domain of butyrophilin BTN3A11-4. Here we show how-in both humans and alpaca-multiple pAgs function as 'molecular glues' to promote heteromeric association between the intracellular domains of BTN3A1 and the structurally similar butyrophilin BTN2A1. X-ray crystallography studies visualized that engagement of BTN3A1 with pAgs forms a composite interface for direct binding to BTN2A1, with various pAg molecules each positioned at the centre of the interface and gluing the butyrophilins with distinct affinities. Our structural insights guided mutagenesis experiments that led to disruption of the intracellular BTN3A1-BTN2A1 association, abolishing pAg-mediated Vγ9Vδ2 T cell activation. Analyses using structure-based molecular-dynamics simulations, 19F-NMR investigations, chimeric receptor engineering and direct measurement of intercellular binding force revealed how pAg-mediated BTN2A1 association drives BTN3A1 intracellular fluctuations outwards in a thermodynamically favourable manner, thereby enabling BTN3A1 to push off from the BTN2A1 ectodomain to initiate T cell receptor-mediated γδ T cell activation. Practically, we harnessed the molecular-glue model for immunotherapeutics design, demonstrating chemical principles for developing both small-molecule activators and inhibitors of human γδ T cell function.

Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

Biolinguistic graph fusion model for circRNA-miRNA association prediction.

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

The NAD+-mediated self-inhibition mechanism of pro-neurodegenerative SARM1.

Pathological degeneration of axons disrupts neural circuits and represents one of the hallmarks of neurodegeneration1-4. Sterile alpha and Toll/interleukin-1 receptor motif-containing protein 1 (SARM1) is a central regulator of this neurodegenerative process5-8, and its Toll/interleukin-1 receptor (TIR) domain exerts its pro-neurodegenerative action through NADase activity9,10. However, the mechanisms by which the activation of SARM1 is stringently controlled are unclear. Here we report the cryo-electron microscopy structures of full-length SARM1 proteins. We show that NAD+ is an unexpected ligand of the armadillo/heat repeat motifs (ARM) domain of SARM1. This binding of NAD+ to the ARM domain facilitated the inhibition of the TIR-domain NADase through the domain interface. Disruption of the NAD+-binding site or the ARM-TIR interaction caused constitutive activation of SARM1 and thereby led to axonal degeneration. These findings suggest that NAD+ mediates self-inhibition of this central pro-neurodegenerative protein.

Design of robust superhydrophobic surfaces.

The ability of superhydrophobic surfaces to stay dry, self-clean and avoid biofouling is attractive for applications in biotechnology, medicine and heat transfer1-10. Water droplets that contact these surfaces must have large apparent contact angles (greater than 150 degrees) and small roll-off angles (less than 10 degrees). This can be realized for surfaces that have low-surface-energy chemistry and micro- or nanoscale surface roughness, minimizing contact between the liquid and the solid surface11-17. However, rough surfaces-for which only a small fraction of the overall area is in contact with the liquid-experience high local pressures under mechanical load, making them fragile and highly susceptible to abrasion18. Additionally, abrasion exposes underlying materials and may change the local nature of the surface from hydrophobic to hydrophilic19, resulting in the pinning of water droplets to the surface. It has therefore been assumed that mechanical robustness and water repellency are mutually exclusive surface properties. Here we show that robust superhydrophobicity can be realized by structuring surfaces at two different length scales, with a nanostructure design to provide water repellency and a microstructure design to provide durability. The microstructure is an interconnected surface frame containing 'pockets' that house highly water-repellent and mechanically fragile nanostructures. This surface frame acts as 'armour', preventing the removal of the nanostructures by abradants that are larger than the frame size. We apply this strategy to various substrates-including silicon, ceramic, metal and transparent glass-and show that the water repellency of the resulting superhydrophobic surfaces is preserved even after abrasion by sandpaper and by a sharp steel blade. We suggest that this transparent, mechanically robust, self-cleaning glass could help to negate the dust-contamination issue that leads to a loss of efficiency in solar cells. Our design strategy could also guide the development of other materials that need to retain effective self-cleaning, anti-fouling or heat-transfer abilities in harsh operating environments.

Genetically Encoded Lizard Color Divergence for Camouflage and Thermoregulation.

Local adaptation is critical in speciation and evolution, yet comprehensive studies on proximate and ultimate causes of local adaptation are generally scarce. Here, we integrated field ecological experiments, genome sequencing, and genetic verification to demonstrate both driving forces and molecular mechanisms governing local adaptation of body coloration in a lizard from the Qinghai-Tibet Plateau. We found dark lizards from the cold meadow population had lower spectrum reflectance but higher melanin contents than light counterparts from the warm dune population. Additionally, the colorations of both dark and light lizards facilitated the camouflage and thermoregulation in their respective microhabitat simultaneously. More importantly, by genome resequencing analysis, we detected a novel mutation in Tyrp1 that underpinned this color adaptation. The allele frequencies at the site of SNP 459# in the gene of Tyrp1 are 22.22% G/C and 77.78% C/C in dark lizards and 100% G/G in light lizards. Model-predicted structure and catalytic activity showed that this mutation increased structure flexibility and catalytic activity in enzyme TYRP1, and thereby facilitated the generation of eumelanin in dark lizards. The function of the mutation in Tyrp1 was further verified by more melanin contents and darker coloration detected in the zebrafish injected with the genotype of Tyrp1 from dark lizards. Therefore, our study demonstrates that a novel mutation of a major melanin-generating gene underpins skin color variation co-selected by camouflage and thermoregulation in a lizard. The resulting strong selection may reinforce adaptive genetic divergence and enable the persistence of adjacent populations with distinct body coloration.

Reversing lysosome-ribosome circuit dysregulation mitigates C9FTD/ALS neurodegeneration and behaviors.

G4C2 repeat expansion in C9orf72 causes the most common familial frontotemporal dementia and amyotrophic lateral sclerosis (C9FTD/ALS). The pathogenesis includes haploinsufficiency of C9orf72, which forms a protein complex with Smcr8, as well as G4C2 repeat-induced gain of function including toxic dipeptide repeats (DPRs). The key in vivo disease-driving mechanisms and how loss- and gain-of-function interplay remain poorly understood. Here, we identified dysregulation of a lysosome-ribosome biogenesis circuit as an early and key disease mechanism using a physiologically relevant mouse model with combined loss- and gain-of-function across the aging process. C9orf72 deficiency exacerbates FTD/ALS-like pathologies and behaviors in C9ORF72 bacterial artificial chromosome (C9-BAC) mice with G4C2 repeats under endogenous regulatory elements from patients. Single nucleus RNA sequencing (snRNA-seq) and bulk RNA-seq revealed that C9orf72 depletion disrupts lysosomes in neurons and leads to transcriptional dysregulation of ribosomal protein genes, which are likely due to the proteotoxic stress response and resemble ribosomopathy defects. Importantly, ectopic expression of C9orf72 or its partner Smcr8 in C9FTD/ALS mutant mice promotes lysosomal functions and restores ribosome biogenesis gene transcription, resulting in the mitigation of DPR accumulation, neurodegeneration as well as FTD/ALS-like motor and cognitive behaviors. Therefore, we conclude that loss- and gain-of-function crosstalk in C9FTD/ALS converges on neuronal dysregulation of a lysosome-ribosome biogenesis circuit leading to proteotoxicity, neurodegeneration and behavioral defects.

A fast and globally optimal solution for RNA-seq quantification.

Alignment-based RNA-seq quantification methods typically involve a time-consuming alignment process prior to estimating transcript abundances. In contrast, alignment-free RNA-seq quantification methods bypass this step, resulting in significant speed improvements. Existing alignment-free methods rely on the Expectation-Maximization (EM) algorithm for estimating transcript abundances. However, EM algorithms only guarantee locally optimal solutions, leaving room for further accuracy improvement by finding a globally optimal solution. In this study, we present TQSLE, the first alignment-free RNA-seq quantification method that provides a globally optimal solution for transcript abundances estimation. TQSLE adopts a two-step approach: first, it constructs a k-mer frequency matrix A for the reference transcriptome and a k-mer frequency vector b for the RNA-seq reads; then, it directly estimates transcript abundances by solving the linear equation ATAx = ATb. We evaluated the performance of TQSLE using simulated and real RNA-seq data sets and observed that, despite comparable speed to other alignment-free methods, TQSLE outperforms them in terms of accuracy. TQSLE is freely available at https://github.com/yhg926/TQSLE.

A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.

MOTIVATION: A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data. RESULTS: In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed. AVAILABILITY: The data and source code can be found at https://github.com/1axin/JSNDCMI.

Kssdtree: an interactive Python package for phylogenetic analysis based on sketching technique.

SUMMARY: Sketching technologies have recently emerged as a promising solution for real-time, large-scale phylogenetic analysis. However, existing sketching-based phylogenetic tools exhibit drawbacks, including platform restrictions, deficiencies in tree visualization, and inherent distance estimation bias. These limitations collectively impede the overall convenience and efficiency of the analysis. In this study, we introduce Kssdtree, an interactive Python package designed to address these challenges. Kssdtree surpasses other sketching-based tools by demonstrating superior performance in terms of both accuracy and time efficiency on comprehensive benchmarking datasets. Notably, Kssdtree offers key advantages such as intra-species phylogenomic analysis and GTDB-based phylogenetic placement analysis, significantly enhancing the scope and depth of phylogenetic investigations. Through extensive evaluations and comparisons, Kssdtree stands out as an efficient and versatile method for real-time, large-scale phylogenetic analysis. AVAILABILITY AND IMPLEMENTATION: The Kssdtree Python package is freely accessible at https://pypi.org/project/kssdtree and source code is available at https://github.com/yhlink/kssdtree. The documentation and instantiation for the software is available at https://kssdtree.readthedocs.io/en/latest. The video tutorial is available at https://youtu.be/_6hg59Yn-Ws.

Mutations in TSPAN12 gene causing familial exudative vitreoretinopathy.

BACKGROUND: To report newly found TSPAN12 mutations with a unique form of familial exudative vitreoretinopathy (FEVR) and find out the possible mechanism of a repeated novel intronic variant in TSPAN12 led to FEVR. RESULTS: Nine TSPAN12 mutations with a unique form of FEVR were detected by panel-based NGS. MINI-Gene assay showed two splicing modes of mRNA that process two different bands A and B, and mutant-type shows replacement with the splicing mode of Exon11 hopping. Construction of wild-type and mutant TSPAN12 vector showed the appearance of premature termination codons (PTC). In vitro expression detection showed significant down-regulated expression level of TSPAN12 mRNAs and proteins in cells transfected with mutant vectors compared with in wild-type group. On the contrary, translation inhibitor CHX and small interfering RNA of UPF1 (si-UPF1) significantly increased mRNA or protein expression of TSPAN12 in cells transfected with the mutant vectors. CONCLUSIONS: Nine mutations in TSPAN12 gene are reported in 9 FEVR patients with a unique series of ocular abnormalities. The three novel TSPAN12 mutations trigger NMD would cause the decrease of TSPAN12 proteins that participate in biosynthesis and assembly of microfibers, which might lead to FEVR, and suggest that intronic sequence analysis might be a vital tool for genetic counseling and prenatal diagnoses.

Metal-Loaded Synthetic Melanin via Oxidative Polymerization of Neurotransmitter Norepinephrine Exhibiting High Photothermal Conversion.

Polydopamine (PDA)-derived melanin-like materials exhibit significant photothermal conversion owing to their broad-spectrum light absorption. However, their low near-infrared (NIR) absorption and inadequate hydrophilicity compromise their utilization of solar energy. Herein, we developed metal-loaded poly(norepinephrine) nanoparticles (PNE NPs) by predoping metal ions (Fe3+, Mn3+, Co2+, Ca2+, Ga3+, and Mg2+) with norepinephrine, a neuron-derived biomimetic molecule, to address the limitations of PDA. The chelation between catechol and metal ions induces a ligand-to-metal charge transfer (LMCT) through the formation of donor-acceptor pairs, modulating the light absorption behavior and reducing the band gap. Under 1 sun illumination, the Fe-loaded PNE coated wood evaporator achieved a high seawater evaporation rate and efficiency of 1.75 kg m-2 h-1 and 92.4%, respectively, owing to the superior hydrophilicity and photothermal performance of PNE. Therefore, this study offers a comprehensive exploration of the role of metal ions in enhancing the photothermal properties of synthetic melanins.

Opposing roles of TGFß and BMP signaling in prostate cancer development.

SMAD4 constrains progression of Pten-null prostate cancer and serves as a common downstream node of transforming growth factor ß (TGFß) and bone morphogenetic protein (BMP) pathways. Here, we dissected the roles of TGFß receptor II (TGFBR2) and BMP receptor II (BMPR2) using a Pten-null prostate cancer model. These studies demonstrated that the molecular actions of TGFBR2 result in both SMAD4-dependent constraint of proliferation and SMAD4-independent activation of apoptosis. In contrast, BMPR2 deletion extended survival relative to Pten deletion alone, establishing its promoting role in BMP6-driven prostate cancer progression. These analyses reveal the complexity of TGFß-BMP signaling and illuminate potential therapeutic targets for prostate cancer.

PRKCI promotes immune suppression in ovarian cancer.

A key feature of high-grade serous ovarian carcinoma (HGSOC) is frequent amplification of the 3q26 locus harboring PRKC-ι (PRKCI). Here, we show that PRKCI is also expressed in early fallopian tube lesions, called serous tubal intraepithelial carcinoma. Transgenic mouse studies establish PRKCI as an ovarian cancer-specific oncogene. Mechanistically, we show that the oncogenic activity of PRKCI relates in part to the up-regulation of TNFα to promote an immune-suppressive tumor microenvironment characterized by an abundance of myeloid-derived suppressor cells and inhibition of cytotoxic T-cell infiltration. Furthermore, system-level and functional analyses identify YAP1 as a downstream effector in tumor progression. In human ovarian cancers, high PRKCI expression also correlates with high expression of TNFα and YAP1 and low infiltration of cytotoxic T cells. The PRKCI-YAP1 regulation of the tumor immunity provides a therapeutic strategy for highly lethal ovarian cancer.

BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks.

Circular RNA (CircRNA)-microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.