Cryo-EM structures of functional and pathological amyloid ribonucleoprotein assemblies.

Amyloids are implicated in neurodegenerative and systemic diseases, yet they serve important functional roles in numerous organisms. Heterogeneous nuclear ribonucleoproteins (hnRNPs) represent a large family of RNA-binding proteins (RBPs) that control central events of RNA biogenesis in normal and diseased cellular conditions. Many of these proteins contain prion-like sequences of low complexity, which not only assemble into functional fibrils in response to cellular cues but can also lead to disease when missense mutations arise in their sequences. Recent advances in cryo-electron microscopy (cryo-EM) have provided unprecedented high-resolution structural insights into diverse amyloid assemblies formed by hnRNPs and structurally related RBPs, including TAR DNA-binding protein 43 (TDP-43), Fused in Sarcoma (FUS), Orb2, hnRNPA1, hnRNPA2, and hnRNPDL-2. This review provides a comprehensive overview of these structures and explores their functional and pathological implications.

Thermally stable high-loading single Cu sites on ZSM-5 for selective catalytic oxidation of NH3.

Rigorous comparisons between single site- and nanoparticle (NP)-dispersed catalysts featuring the same composition, in terms of activity, selectivity, and reaction mechanism, are limited. This limitation is partly due to the tendency of single metal atoms to sinter into aggregated NPs at high loadings and elevated temperatures, driven by a decrease in metal surface free energy. Here, we have developed a unique two-step method for the synthesis of single Cu sites on ZSM-5 (termed CuS/ZSM-5) with high thermal stability. The atomic-level dispersion of single Cu sites was confirmed through scanning transmission electron microscopy, X-ray absorption fine structure (XAFS), and electron paramagnetic resonance spectroscopy. The CuS/ZSM-5 catalyst was compared to a CuO NP-based catalyst (termed CuN/ZSM-5) in the oxidation of NH3 to N2, with the former exhibiting superior activity and selectivity. Furthermore, operando XAFS and diffuse reflectance infrared Fourier transform spectroscopy studies were conducted to simultaneously assess the fate of the Cu and the surface adsorbates, providing a comprehensive understanding of the mechanism of the two catalysts. The study shows that the facile redox behavior exhibited by single Cu sites correlates with the enhanced activity observed for the CuS/ZSM-5 catalyst.

Utilizing the oxygen-atom trapping effect of Co3O4 with oxygen vacancies to promote chlorite activation for water decontamination.

Heterogeneous high-valent cobalt-oxo [≡Co(IV)=O] is a widely focused reactive species in oxidant activation; however, the relationship between the catalyst interfacial defects and ≡Co(IV)=O formation remains poorly understood. Herein, photoexcited oxygen vacancies (OVs) were introduced into Co3O4 (OV-Co3O4) by a UV-induced modification method to facilitate chlorite (ClO2-) activation. Density functional theory calculations indicate that OVs result in low-coordinated Co atom, which can directionally anchor chlorite under the oxygen-atom trapping effect. Chlorite first undergoes homolytic O-Cl cleavage and transfers the dissociated O atom to the low-coordinated Co atom to form reactive ≡Co(IV)=O with a higher spin state. The reactive ≡Co(IV)=O rapidly extracts one electron from ClO2- to form chlorine dioxide (ClO2), accompanied by the Co atom returning a lower spin state. As a result of the oxygen-atom trapping effect, the OV-Co3O4/chlorite system achieved a 3.5 times higher efficiency of sulfamethoxazole degradation (~0.1331 min-1) than the pristine Co3O4/chlorite system. Besides, the refiled OVs can be easily restored by re-exposure to UV light, indicating the sustainability of the oxygen atom trap. The OV-Co3O4 was further fabricated on a polyacrylonitrile membrane for back-end water purification, achieving continuous flow degradation of pollutants with low cobalt leakage. This work presents an enhancement strategy for constructing OV as an oxygen-atom trapping site in heterogeneous advanced oxidation processes and provides insight into modulating the formation of ≡Co(IV)=O via defect engineering.

HNRNPC haploinsufficiency affects alternative splicing of intellectual disability-associated genes and causes a neurodevelopmental disorder.

Heterogeneous nuclear ribonucleoprotein C (HNRNPC) is an essential, ubiquitously abundant protein involved in mRNA processing. Genetic variants in other members of the HNRNP family have been associated with neurodevelopmental disorders. Here, we describe 13 individuals with global developmental delay, intellectual disability, behavioral abnormalities, and subtle facial dysmorphology with heterozygous HNRNPC germline variants. Five of them bear an identical in-frame deletion of nine amino acids in the extreme C terminus. To study the effect of this recurrent variant as well as HNRNPC haploinsufficiency, we used induced pluripotent stem cells (iPSCs) and fibroblasts obtained from affected individuals. While protein localization and oligomerization were unaffected by the recurrent C-terminal deletion variant, total HNRNPC levels were decreased. Previously, reduced HNRNPC levels have been associated with changes in alternative splicing. Therefore, we performed a meta-analysis on published RNA-seq datasets of three different cell lines to identify a ubiquitous HNRNPC-dependent signature of alternative spliced exons. The identified signature was not only confirmed in fibroblasts obtained from an affected individual but also showed a significant enrichment for genes associated with intellectual disability. Hence, we assessed the effect of decreased and increased levels of HNRNPC on neuronal arborization and neuronal migration and found that either condition affects neuronal function. Taken together, our data indicate that HNRNPC haploinsufficiency affects alternative splicing of multiple intellectual disability-associated genes and that the developing brain is sensitive to aberrant levels of HNRNPC. Hence, our data strongly support the inclusion of HNRNPC to the family of HNRNP-related neurodevelopmental disorders.

PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning.

Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA-disease interactions. Although there have been computational methods proposed for the detection of piRNA-disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA-disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA-disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA-disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https://github.com/chenqiuhao/PUTransGCN.

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.

Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.

Spatiotemporal constrained RNA-protein heterogeneous network for protein complex identification.

The identification of protein complexes from protein interaction networks is crucial in the understanding of protein function, cellular processes and disease mechanisms. Existing methods commonly rely on the assumption that protein interaction networks are highly reliable, yet in reality, there is considerable noise in the data. In addition, these methods fail to account for the regulatory roles of biomolecules during the formation of protein complexes, which is crucial for understanding the generation of protein interactions. To this end, we propose a SpatioTemporal constrained RNA-protein heterogeneous network for Protein Complex Identification (STRPCI). STRPCI first constructs a multiplex heterogeneous protein information network to capture deep semantic information by extracting spatiotemporal interaction patterns. Then, it utilizes a dual-view aggregator to aggregate heterogeneous neighbor information from different layers. Finally, through contrastive learning, STRPCI collaboratively optimizes the protein embedding representations under different spatiotemporal interaction patterns. Based on the protein embedding similarity, STRPCI reweights the protein interaction network and identifies protein complexes with core-attachment strategy. By considering the spatiotemporal constraints and biomolecular regulatory factors of protein interactions, STRPCI measures the tightness of interactions, thus mitigating the impact of noisy data on complex identification. Evaluation results on four real PPI networks demonstrate the effectiveness and strong biological significance of STRPCI. The source code implementation of STRPCI is available from https://github.com/LI-jasm/STRPCI.

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity.

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR-epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR-epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR-epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR-epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.

Biclustering data analysis: a comprehensive survey.

Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.

Making ammonia from nitrogen and water microdroplets.

Water (H2O) microdroplets are sprayed onto a magnetic iron oxide (Fe3O4) and Nafion-coated graphite mesh using compressed N2 or air as the nebulizing gas. The resulting splash of microdroplets enters a mass spectrometer and is found to contain ammonia (NH3). This gas-liquid-solid heterogeneous catalytic system synthesizes ammonia in 0.2 ms. The conversion rate reaches 32.9 ± 1.38 nmol s-1 cm-2 at room temperature without application of an external electric potential and without irradiation. Water microdroplets are the hydrogen source for N2 in contact with Fe3O4. Hydrazine (H2NNH2) is also observed as a by-product and is suspected to be an intermediate in the formation of ammonia. This one-step nitrogen-fixation strategy to produce ammonia is eco-friendly and low cost, which converts widely available starting materials into a value-added product.

In situ turning defects of exfoliated Ti3C2 MXene into Fenton-like catalytic active sites.

Controllable in situ formation of nanoclusters with discrete active sites is highly desirable in heterogeneous catalysis. Herein, a titanium oxide-based Fenton-like catalyst is constructed using exfoliated Ti3C2 MXene as a template. Theoretical calculations reveal that a redox reaction between the surface Ti-deficit vacancies of the exfoliated Ti3C2 MXene and H2O2 molecules facilitates the in situ conversion of surface defects into titanium oxide nanoclusters anchoring on amorphous carbon (TiOx@C). The presence of mixed-valence Tiδ+ (δ = 0, 2, 3, and 4) within TiOx@C is confirmed by X-ray photoelectron spectroscopy (XPS) and X-ray absorption fine structure (XAFS) characterizations. The abundant surface defects within TiOx@C effectively promote the generation of reactive oxygen species (ROS) leading to superior and stable Fenton-like catalytic degradation of atrazine, a typical agricultural herbicide. Such an in situ construction of Fenton-like catalysts through defect engineering also applies to other MXene family materials, such as V2C and Nb2C.

Functional aggregation of cell-free proteins enables fungal ice nucleation.

Biological ice nucleation plays a key role in the survival of cold-adapted organisms. Several species of bacteria, fungi, and insects produce ice nucleators (INs) that enable ice formation at temperatures above -10 °C. Bacteria and fungi produce particularly potent INs that can promote water crystallization above -5 °C. Bacterial INs consist of extended protein units that aggregate to achieve superior functionality. Despite decades of research, the nature and identity of fungal INs remain elusive. Here, we combine ice nucleation measurements, physicochemical characterization, numerical modeling, and nucleation theory to shed light on the size and nature of the INs from the fungus Fusarium acuminatum. We find ice-binding and ice-shaping activity of Fusarium IN, suggesting a potential connection between ice growth promotion and inhibition. We demonstrate that fungal INs are composed of small 5.3 kDa protein subunits that assemble into ice-nucleating complexes that can contain more than 100 subunits. Fusarium INs retain high ice-nucleation activity even when only the ~12 kDa fraction of size-excluded proteins are initially present, suggesting robust pathways for their functional aggregation in cell-free aqueous environments. We conclude that the use of small proteins to build large assemblies is a common strategy among organisms to create potent biological INs.

Distinguishing homogeneous advanced oxidation processes in bulk water from heterogeneous surface reactions in organic oxidation.

Clarifying the reaction pathways at the solid-water interface and in bulk water solution is of great significance for the design of heterogeneous catalysts for selective oxidation of organic pollutants. However, achieving this goal is daunting because of the intricate interfacial reactions at the catalyst surface. Herein, we unravel the origin of the organic oxidation reactions with metal oxide catalysts, revealing that the radical-based advanced oxidation processes (AOPs) prevail in bulk water but not on the solid catalyst surfaces. We show that such differing reaction pathways widely exist in various chemical oxidation (e.g., high-valent Mn3+ and MnOX) and Fenton and Fenton-like catalytic oxidation (e.g., Fe2+ and FeOCl catalyzing H2O2, Co2+ and Co3O4 catalyzing persulfate) systems. Compared with the radical-based degradation and polymerization pathways of one-electron indirect AOP in homogeneous reactions, the heterogeneous catalysts provide unique surface properties to trigger surface-dependent coupling and polymerization pathways of a two-electron direct oxidative transfer process. These findings provide a fundamental understanding of catalytic organic oxidation processes at the solid-water interface, which could guide the design of heterogeneous nanocatalysts.

The role of dynamics in heterogeneous catalysis: Surface diffusivity and N2 decomposition on Fe(111).

Dynamics has long been recognized to play an important role in heterogeneous catalytic processes. However, until recently, it has been impossible to study their dynamical behavior at industry-relevant temperatures. Using a combination of machine learning potentials and advanced simulation techniques, we investigate the cleavage of the N[Formula: see text] triple bond on the Fe(111) surface. We find that at low temperatures our results agree with the well-established picture. However, if we increase the temperature to reach operando conditions, the surface undergoes a global dynamical change and the step structure of the Fe(111) surface is destabilized. The catalytic sites, traditionally associated with this surface, appear and disappear continuously. Our simulations illuminate the danger of extrapolating low-temperature results to operando conditions and indicate that the catalytic activity can only be inferred from calculations that take dynamics fully into account. More than that, they show that it is the transition to this highly fluctuating interfacial environment that drives the catalytic process.

Alternative pre-mRNA splicing switch controls hESC pluripotency and differentiation.

Alternative splicing (AS) of pre-mRNAs is a ubiquitous process in mammals that is tightly regulated in a cell type- and cell state-dependent manner. However, the details of how splicing is regulated to impact specific cell fate decisions remains incompletely understood. A study by Yamazaki and colleagues (pp. 1161-1174) in this issue of Genes & Development provides exciting new insight into the role and regulation of splicing in the maintenance of pluripotency of human embryonic stem cells (hESCs). In brief, they show that AS of several genes is robustly regulated upon differentiation of hESCs. One of these genes, T-cell factor 3 (TCF3), is regulated at least in part through the activity of heterogeneous nuclear ribonucleoproteins H1 and F (hnRNP H/F) to control the mutually exclusive expression of the encoded E12 and E47 transcription regulators. The investigators demonstrate that reduced expression of hnRNP H/F favors expression of E47, which in turn decreases E-cadherin expression to promote hESC differentiation. In contrast, high levels of hnRNP H/F induce expression of E12 to maintain pluripotency. Thus, this work provides at least one new link between AS and control of human stem cell fate and suggests a broader role of splicing in pluripotency.

TCF3 alternative splicing controlled by hnRNP H/F regulates E-cadherin expression and hESC pluripotency.

Alternative splicing (AS) plays important roles in embryonic stem cell (ESC) differentiation. In this study, we first identified transcripts that display specific AS patterns in pluripotent human ESCs (hESCs) relative to differentiated cells. One of these encodes T-cell factor 3 (TCF3), a transcription factor that plays important roles in ESC differentiation. AS creates two TCF3 isoforms, E12 and E47, and we identified two related splicing factors, heterogeneous nuclear ribonucleoproteins (hnRNPs) H1 and F (hnRNP H/F), that regulate TCF3 splicing. We found that hnRNP H/F levels are high in hESCs, leading to high E12 expression, but decrease during differentiation, switching splicing to produce elevated E47 levels. Importantly, hnRNP H/F knockdown not only recapitulated the switch in TCF3 AS but also destabilized hESC colonies and induced differentiation. Providing an explanation for this, we show that expression of known TCF3 target E-cadherin, critical for maintaining ESC pluripotency, is repressed by E47 but not by E12.

End-to-end interpretable disease-gene association prediction.

Identifying disease-gene associations is a fundamental and critical biomedical task towards understanding molecular mechanisms, the diagnosis and treatment of diseases. It is time-consuming and expensive to experimentally verify causal links between diseases and genes. Recently, deep learning methods have achieved tremendous success in identifying candidate genes for genetic diseases. The gene prediction problem can be modeled as a link prediction problem based on the features of nodes and edges of the gene-disease graph. However, most existing researches either build homogeneous networks based on one single data source or heterogeneous networks based on multi-source data, and artificially define meta-paths, so as to learn the network representation of diseases and genes. The former cannot make use of abundant multi-source heterogeneous information, while the latter needs domain knowledge and experience when defining meta-paths, and the accuracy of the model largely depends on the definition of meta-paths. To address the aforementioned challenges above bottlenecks, we propose an end-to-end disease-gene association prediction model with parallel graph transformer network (DGP-PGTN), which deeply integrates the heterogeneous information of diseases, genes, ontologies and phenotypes. DGP-PGTN can automatically and comprehensively capture the multiple latent interactions between diseases and genes, discover the causal relationship between them and is fully interpretable at the same time. We conduct comprehensive experiments and show that DGP-PGTN outperforms the state-of-the-art methods significantly on the task of disease-gene association prediction. Furthermore, DGP-PGTN can automatically learn the implicit relationship between diseases and genes without manually defining meta paths.

Attention-based cross domain graph neural network for prediction of drug-drug interactions.

Drug-drug interactions (DDI) may lead to adverse reactions in human body and accurate prediction of DDI can mitigate the medical risk. Currently, most of computer-aided DDI prediction methods construct models based on drug-associated features or DDI network, ignoring the potential information contained in drug-related biological entities such as targets and genes. Besides, existing DDI network-based models could not make effective predictions for drugs without any known DDI records. To address the above limitations, we propose an attention-based cross domain graph neural network (ACDGNN) for DDI prediction, which considers the drug-related different entities and propagate information through cross domain operation. Different from the existing methods, ACDGNN not only considers rich information contained in drug-related biomedical entities in biological heterogeneous network, but also adopts cross-domain transformation to eliminate heterogeneity between different types of entities. ACDGNN can be used in the prediction of DDIs in both transductive and inductive setting. By conducting experiments on real-world dataset, we compare the performance of ACDGNN with several state-of-the-art methods. The experimental results show that ACDGNN can effectively predict DDIs and outperform the comparison models.

Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction.

Drug-target interaction (DTI) prediction is an essential step in drug repositioning. A few graph neural network (GNN)-based methods have been proposed for DTI prediction using heterogeneous biological data. However, existing GNN-based methods only aggregate information from directly connected nodes restricted in a drug-related or a target-related network and are incapable of capturing high-order dependencies in the biological heterogeneous graph. In this paper, we propose a metapath-aggregated heterogeneous graph neural network (MHGNN) to capture complex structures and rich semantics in the biological heterogeneous graph for DTI prediction. Specifically, MHGNN enhances heterogeneous graph structure learning and high-order semantics learning by modeling high-order relations via metapaths. Additionally, MHGNN enriches high-order correlations between drug-target pairs (DTPs) by constructing a DTP correlation graph with DTPs as nodes. We conduct extensive experiments on three biological heterogeneous datasets. MHGNN favorably surpasses 17 state-of-the-art methods over 6 evaluation metrics, which verifies its efficacy for DTI prediction. The code is available at https://github.com/Zora-LM/MHGNN-DTI.