Higher-order Structure Research Articles

Research progress on the effect of sperm chromatin integrity on function and its detection methods.

Sperm chromatin not only carries genetic information such as paternal DNA, but also carries structural proteins, epigenetic information, and higher-order chromatin structures (such as matrix attachment regions and telomeres), etc. These information play an important role in embryonic development. This article mainly reviews the effects of these different information carried by sperm chromatin on sperm function and embryonic development and the research progress of related detection methods, in order to provide a theoretical basis and scientific diagnosis and treatment strategies for the etiology screening of clinical infertility, embryo arrest and recurrent miscarriage, so as to improve the pregnancy outcomes of natural conception and assisted reproduction. Keywords: sperm chromatin; epigenetics; sperm DNA damage; sperm function; higher-order chromatin structures.

Bacterial chromatin proteins, transcription, and DNA topology: Inseparable partners in the control of gene expression.

DNA in bacterial chromosomes is organized into higher-order structures by DNA-binding proteins called nucleoid-associated proteins (NAPs) or bacterial chromatin proteins (BCPs). BCPs often bind to or near DNA loci transcribed by RNA polymerase (RNAP) and can either increase or decrease gene expression. To understand the mechanisms by which BCPs alter transcription, one must consider both steric effects and the topological forces that arise when DNA deviates from its fully relaxed double-helical structure. Transcribing RNAP creates DNA negative (-) supercoils upstream and positive (+) supercoils downstream whenever RNAP and DNA are unable to rotate freely. This (-) and (+) supercoiling generates topological forces that resist forward translocation of DNA through RNAP unless the supercoiling is constrained by BCPs or relieved by topoisomerases. BCPs also may enhance topological stress and overall can either inhibit or aid transcription. Here, we review current understanding of how RNAP, BCPs, and DNA topology interplay to control gene expression.

Polymerization of ZBTB transcription factors regulates chromatin occupancy

BCL6, an oncogenic transcription factor (TF), forms polymers in the presence of a small-molecule molecular glue that stabilizes a complementary interface between homodimers of BCL6's broad-complex, tramtrack, and bric-à-brac (BTB) domain. The BTB domains of other proteins, including a large class of TFs, have similar architectures and symmetries, raising the possibility that additional BTB proteins self-assemble into higher-order structures. Here, we surveyed 189 human BTB proteins with a cellular fluorescent reporter assay and identified 18 ZBTB TFs that show evidence of polymerization. Through biochemical and cryoelectron microscopy (cryo-EM) studies, we demonstrate that these ZBTB TFs polymerize into filaments. We found that BTB-domain-mediated polymerization of ZBTB TFs enhances chromatin occupancy within regions containing homotypic clusters of TF binding sites, leading to repression of target genes. Our results reveal a role of higher-order structures in regulating ZBTB TFs and suggest an underappreciated role for TF polymerization in modulating gene expression.

Relationship between the nanometer-scale structures of amylopectin molecules and temperature dependence of internal structures of starch granules in endosperm of starch branching enzyme 2b (be2b) allelic mutant lines from japonica rice

We analyzed the relationship between the nanometer-scale structure of amylopectin and physicochemical properties such as thermal properties of starch granules and higher-order internal structure of starch granules evaluated by using synchrotron radiation X-ray scattering and scanning electron microscopy. In this study, a line of allelic mutants of rice lacking in starch branching enzyme IIb (BEIIb) activity in their endosperm were used as starch sources. The crystal lattice changed from A-type to C- and B-type. The nanoscale cluster size, and higher-order structures of the starch granules varied among mutant lines. The thermal gelatinization properties of the rice-derived starch samples, as well as the dependence of this process on their crystal structure, were evaluated using differential scanning calorimetry and synchrotron X-ray scattering. After gelatinization, several nm-scaled density fluctuations owing to the formation of gel balls were observed. These fluctuations of all starch samples correlated with the cluster size decreased with the temperature.

Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction.

Dysregulation of miRNAs is closely related to the progression of various diseases, so identifying disease-related miRNAs is crucial. Most recently proposed methods are based on graph reasoning, while they did not completely exploit the topological structure composed of the higher-order neighbor nodes and the global and local features of miRNA and disease nodes. We proposed a prediction method, MDAP, to learn semantic features of miRNA and disease nodes based on various meta-paths, as well as node features from the entire heterogeneous network perspective, and node pair attributes. Firstly, for both the miRNA and disease nodes, node category-wise meta-paths were constructed to integrate the similarity and association connection relationships. Each target node has its specific neighbor nodes for each meta-path, and the neighbors of longer meta-paths constitute its higher-order neighbor topological structure. Secondly, we constructed a meta-path specific graph convolutional network module to integrate the features of higher-order neighbors and their topology, and then learned the semantic representations of nodes. Thirdly, for the entire miRNA-disease heterogeneous network, a global-aware graph convolutional autoencoder was built to learn the network-view feature representations of nodes. We also designed semantic-level and representation-level attentions to obtain informative semantic features and node representations. Finally, the strategy based on the parallel convolutional-deconvolutional neural networks were designed to enhance the local feature learning for a pair of miRNA and disease nodes. The experiment results showed that MDAP outperformed other state-of-the-art methods, and the ablation experiments demonstrated the effectiveness of MDAP's major innovations. MDAP's ability in discovering potential disease-related miRNAs was further analyzed by the case studies over three diseases.

Does the Airport Servicescape Affect the Relationship between Airport Outcome Quality and Passenger Satisfaction?

Airports promote passenger satisfaction with superior service. Airport outcome quality (AOQ) is a part of Airport Service Quality (ASQ), which impacts daily service performance. In this study, the airport servicescape influenced the link between airport outcome quality and passenger satisfaction. The model was developed using the Hierarchical Service Quality Model (HSQM) and Stimulus-Organism-Response (SOR) theory, incorporating two direct and moderated paths. The study utilized a convenience sampling technique and conducted a cross-sectional survey of international travellers at critical airports in Tanzania. 389 respondents were evaluated using the PLS-SEM technique. Significant enhancements have been made in the direct and moderated relationships between airport outcome quality and passenger satisfaction. The airport environment was more effectively managed than the direct route. The environment of an airport improves the satisfaction of international airport passengers. Moreover, these findings have implications for theory, methodology, management, and policy. The study enhances the partial least squares research methodologies, specifically for higher-order structures. The study shows airport architects and officials that the airport servicescape is crucial for improving customer satisfaction at airport terminals.

Multi-stream graph attention network for recommendation with knowledge graph

In recent years, the powerful modeling ability of Graph Neural Networks (GNNs) has led to their widespread use in knowledge-aware recommender systems. However, existing GNN-based methods for information propagation among entities in knowledge graphs (KGs) may not efficiently filter out less informative entities. To address this challenge and improve the encoding of high-order structure information among many entities, we propose an end-to-end neural network-based method called Multi-stream Graph Attention Network (MSGAT). MSGAT explicitly discriminates the importance of entities from four critical perspectives and recursively propagates neighbor embeddings to refine the target node. Specifically, we use an attention mechanism from the user's perspective to distill the domain nodes' information of the predicted item in the KG, enhance the user's information on items, and generate the feature representation of the predicted item. We also propose a multi-stream attention mechanism to aggregate user history click item's neighborhood entity information in the KG and generate the user's feature representation. We conduct extensive experiments on three real datasets for movies, music, and books, and the empirical results demonstrate that MSGAT outperforms current state-of-the-art baselines.

Chemically reactive and aging macromolecular mixtures. I. Phase diagrams, spinodals, and gelation

Multicomponent macromolecular mixtures often form higher-order structures, which may display non-ideal mixing and aging behaviors. In this work, we first propose a minimal model of a quaternary system that takes into account the formation of a complex via a chemical reaction involving two macromolecular species; the complex may then phase separate from the buffer and undergo a further transition into a gel-like state. We subsequently investigate how physical parameters such as molecular size, stoichiometric coefficients, equilibrium constants, and interaction parameters affect the phase behavior of the mixture and its propensity to undergo aging via gelation. In addition, we analyze the thermodynamic stability of the system and identify the spinodal regions and their overlap with gelation boundaries. The approach developed in this work can be readily generalized to study systems with an arbitrary number of components. More broadly, it provides a physically based starting point for the investigation of the kinetics of the coupled complex formation, phase separation, and gelation processes in spatially extended systems.

Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition.

https://github.com/kuanglab/GraphTucker.

Molecular evolution of the mammalian kinetochore complex.

Mammalian centromeres are satellite-rich chromatin domains that serve as sites for kinetochore complex assembly. Centromeres are highly variable in sequence and satellite organization across species, but the processes that govern the co-evolutionary dynamics between rapidly evolving centromeres and their associated kinetochore proteins remain poorly understood. Here, we pursue a course of phylogenetic analyses to investigate the molecular evolution of the complete kinetochore complex across primate and rodent species with divergent centromere repeat sequences and features. We show that many protein components of the core centromere associated network (CCAN) harbor signals of adaptive evolution, consistent with their intimate association with centromere satellite DNA and roles in the stability and recruitment of additional kinetochore proteins. Surprisingly, CCAN and outer kinetochore proteins exhibit comparable rates of adaptive divergence, suggesting that changes in centromere DNA can ripple across the kinetochore to drive adaptive protein evolution within distant domains of the complex. Our work further identifies kinetochore proteins subject to lineage-specific adaptive evolution, including rapidly evolving proteins in species with centromere satellites characterized by higher-order repeat structure and lacking CENP-B boxes. Thus, features of centromeric chromatin beyond the linear DNA sequence may drive selection on kinetochore proteins. Overall, our work spotlights adaptively evolving proteins with diverse centromere-associated functions, including centromere chromatin structure, kinetochore protein assembly, kinetochore-microtubule association, cohesion maintenance, and DNA damage response pathways. These adaptively evolving kinetochore protein candidates present compelling opportunities for future functional investigations exploring how their concerted changes with centromere DNA ensure the maintenance of genome stability.

METTL3-mediated chromatin contacts promote stress granule phase separation through metabolic reprogramming during senescence

METTL3 is the catalytic subunit of the methyltransferase complex, which mediates m6A modification to regulate gene expression. In addition, METTL3 regulates transcription in an enzymatic activity-independent manner by driving changes in high-order chromatin structure. However, how these functions of the methyltransferase complex are coordinated remains unknown. Here we show that the methyltransferase complex coordinates its enzymatic activity-dependent and independent functions to regulate cellular senescence, a state of stable cell growth arrest. Specifically, METTL3-mediated chromatin loops induce Hexokinase 2 expression through the three-dimensional chromatin organization during senescence. Elevated Hexokinase 2 expression subsequently promotes liquid-liquid phase separation, manifesting as stress granule phase separation, by driving metabolic reprogramming. This correlates with an impairment of translation of cell-cycle related mRNAs harboring polymethylated m6A sites. In summary, our results report a coordination of m6A-dependent and -independent function of the methyltransferase complex in regulating senescence through phase separation driven by metabolic reprogramming.

The nested structures of higher-order interactions promote the cooperation in complex social networks

As a powerful tool for exploring non-pairwise interactions, higher-order networks deepen our understanding of how cooperation emerges in complex systems. However, nearly all researches concerning the cooperative evolution on higher-order networks have neglected the prevalence of nested structures in real-world interactions — for instance, students are members of classes, which are components of grades, and these grades, in turn, are part of schools and so on. In this paper, we propose a general and scalable method to investigate the influence of nested structures in higher-order interactions on group cooperation. Specifically, by adjusting the proportion of lower-order nested relations (e.g., pairwise links) within higher-order structures, we find that higher-order interactions are more favorable to the cooperation if they comprise a rich nested dyadic structure. In addition, for the first time, we introduce higher-order network motifs to examine the contributions of various nested structures to cooperation. It is interesting to observe that the more uniform the nested structure, the more it promotes the cooperation. Our study offers a novel perspective to examine the evolution of cooperative behaviors in more complex realistic systems.

Synthesis of ABC/ACB-Type Triblock Copolymers Composed of Polystyrene and Polymethacrylate via Living Anionic Polymerization and Analyzing the Effect of Casting Solvents on Higher-Order Structures

Synthesis of ABC/ACB-Type Triblock Copolymers Composed of Polystyrene and Polymethacrylate via Living Anionic Polymerization and Analyzing the Effect of Casting Solvents on Higher-Order Structures

Online Mixed-Bed Ion Exchange Chromatography for Native Top-Down Proteomics of Complex Mixtures.

Native top-down mass spectrometry (nTDMS) allows characterization of protein structure and noncovalent interactions with simultaneous sequence mapping and proteoform characterization. The majority of nTDMS studies utilize purified recombinant proteins, with significant challenges hindering application to endogenous systems. To perform native top-down proteomics (nTDP), where endogenous proteins from complex biological systems are analyzed by nTDMS, it is essential to separate proteins under nondenaturing conditions. However, it remains difficult to achieve high resolution with MS-compatible online chromatography while preserving protein tertiary structure and noncovalent interactions. Herein, we report the use of online mixed-bed ion exchange chromatography (IEC) to enable separation of endogenous proteins from complex mixtures under nondenaturing conditions, preserving noncovalent interactions for nTDP analysis. We have successfully detected large proteins (>146 kDa) and identified endogenous metal-binding and oligomeric protein complexes in human heart tissue lysate. The use of a mixed-bed stationary phase allowed retention and elution of proteins over a wide range of isoelectric points without altering the sample or mobile phase pH. Overall, our method provides a simple online IEC-MS platform that can effectively separate proteins from complex mixtures under nondenaturing conditions and preserve higher-order structure for nTDP applications.

Predicting the Morphology of Multiphase Biomolecular Condensates from Protein Interaction Networks

Phase-separated biomolecular condensates containing proteins and RNAs can assemble into higher-order structures by forming thermodynamically stable interfaces between immiscible phases. Using a minimal model of a protein/RNA interaction network, we demonstrate how a “shared” protein species that partitions into both phases of a multiphase condensate can function as a tunable surfactant that modulates the interfacial properties. We use Monte Carlo simulations and free-energy calculations to identify conditions under which a low concentration of this shared species is sufficient to trigger a wetting transition. We also describe a numerical approach based on classical density functional theory to predict concentration profiles and surface tensions directly from the model protein/RNA interaction network. Finally, we show that the wetting phase diagrams that emerge from our calculations can be understood in terms of a simple model of selective adsorption to a fluctuating interface. Our work shows how a low-concentration protein species might function as a biological switch for regulating multiphase condensate morphologies. Published by the American Physical Society 2024

Beyond the Usual Suspects: Examining the Role of Understudied Histone Variants in Breast Cancer.

The incorporation of histone variants has structural ramifications on nucleosome dynamics and stability. Due to their unique sequences, histone variants can alter histone-histone or histone-DNA interactions, impacting the folding of DNA around the histone octamer and the overall higher-order structure of chromatin fibers. These structural modifications alter chromatin compaction and accessibility of DNA by transcription factors and other regulatory proteins to influence gene regulatory processes such as DNA damage and repair, as well as transcriptional activation or repression. Histone variants can also generate a unique interactome composed of histone chaperones and chromatin remodeling complexes. Any of these perturbations can contribute to cellular plasticity and the progression of human diseases. Here, we focus on a frequently overlooked group of histone variants lying within the four human histone gene clusters and their contribution to breast cancer.

The structure of emotion regulation strategies among Korean adults

Emotion regulation is a fundamental process that involves monitoring, evaluating, and modifying emotional responses to achieve personal goals. Recently, a growing body of research has challenged the exclusive focus on individual emotion regulation strategies due to conceptual and empirical overlaps among them. This study explored the higher-order factor structure of emotion regulation strategies within the South Korean cultural context. Through a survey of 450 Korean adults and utilizing exploratory and confirmatory factor analyses, we examined the structure of emotion regulation strategies. Our results indicate that a four-factor model fit the data best. Regarding maladaptive emotion regulation strategies, our study identified two higher-order factors: disengagement and cognitive perseverance strategies. Notably, we also found two higher-order factors of adaptive strategies in the South Korean context: acceptance-based regulation and effortful cognitive regulation factors. Through the examination of the fitness of the structure of emotion regulation strategies from a Western sample to a South Korean sample, this study adds to the body of research on emotion regulation, highlighting the significance of targeting higher-order emotion regulation factors in cognitive behavioral therapy.

LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive Learning

Graph collaborative filtering (GCF) has achieved exciting recommendation performance with its ability to aggregate high-order graph structure information. Recently, contrastive learning (CL) has been incorporated into GCF to alleviate data sparsity and noise issues. However, most of the existing methods employ random or manual augmentation to produce contrastive views that may destroy the original topology and amplify the noisy effects. We argue that such augmentation is insufficient to produce the optimal contrastive view, leading to suboptimal recommendation results. In this article, we proposed a L earnable M odel A ugmentation C ontrastive L earning (LMACL) framework for recommendation, which effectively combines graph-level and node-level collaborative relations to enhance the expressiveness of collaborative filtering (CF) paradigm. Specifically, we first use the graph convolution network (GCN) as a backbone encoder to incorporate multi-hop neighbors into graph-level original node representations by leveraging the high-order connectivity in user-item interaction graphs. At the same time, we treat the multi-head graph attention network (GAT) as an augmentation view generator to adaptively generate high-quality node-level augmented views. Finally, joint learning endows the end-to-end training fashion. In this case, the mutual supervision and collaborative cooperation of GCN and GAT achieves learnable model augmentation. Extensive experiments on several benchmark datasets demonstrate that LMACL provides a significant improvement over the strongest baseline in terms of Recall and NDCG by 2.5%–3.8% and 1.6%–4.0%, respectively. Our model implementation code is available at https://github.com/LiuHsinx/LMACL .

Effect of carbon fiber structure of Marimo carbon on polymer electrolyte fuel cell performance

Fibrous carbon has attracted attention for use as a carbon support of polymer electrolyte fuel cells (PEFC). In this study, Marimo carbon (MC), a fibrous carbon, was used as the carbon support. The MC has a higher order structure in which many carbon nanofilaments (CNFs) are grown on the surface of diamond particles (dia.). The MC was synthesized by the contact reaction between a Ni supported oxidized diamond (Ni/O-dia.) and CH4 gas, the carbon source of the CNFs. The flow rate of the CH4 gas during the MC synthesis altered the structure of the CNFs. A thick CNFs was observed in the MC with a low CH4 gas flow rate, which was not observed in the MC with a high CH4 gas flow rate. The MC with the thicker CNFs had wider interspaces between the CNFs and higher Pt supporting amounts. Electrochemical measurements of the Pt support MC (Pt/MC) were evaluated by cyclic voltammetry (CV), linear sweep voltammetry (LSV) and current–voltage (I–V) measurements. The lower CH4 gas flow rate of the MC indicated faster charge transfer. The MC structural and charge transfer changed with the flow rate of the CH4 gas altered the electrochemical active surface area (ECSA), the oxygen reduction reaction (ORR) catalytic activity and the I–V performance of the Pt/MC.

Demonstration of Physicochemical and Functional Similarity of the Biosimilar BAT1806/BIIB800 to Reference Tocilizumab.

Tocilizumab is an immunoglobulin G1 monoclonal antibody targeting the interleukin-6 receptor (IL-6R). BAT1806/BIIB800 (tocilizumab-bavi) has been developed as a biosimilar to the reference product tocilizumab (TCZ). The objective of this study was to demonstrate physicochemical and functional similarity between BAT1806/BIIB800 and TCZ in a comprehensive comparability exercise. A comprehensive panel of over 20 methods was used to generate datasets comparing critical and non-critical product quality attributes for 10 BAT1806/BIIB800 lots and 44 TCZ lots (16 sourced from China, 16 from the EU, and 12 from the US). Primary structure, higher-order structure, and physicochemical properties were assessed using liquid chromatography, mass spectrometry, various spectroscopy techniques/methods, capillary electrophoresis, and thermoanalytical techniques. Fragment antigen-binding (Fab)- and fragment crystallizable (Fc)-mediated biological properties were assessed using cell-based assays, immunoassays, flow cytometry, and kinetic binding assays. BAT1806/BIIB800 and TCZ (irrespective of source) were shown to be similar in terms of structural and functional properties. No differences were observed in terms of the most critical quality attributes, that is, soluble-IL-6R binding and inhibition of IL-6-mediated cell proliferation. BAT1806/BIIB800 and TCZ demonstrated similarity in terms of Fab- and Fc-mediated binding and biological activity. Minor differences were observed in glycosylation (afucosylation and sialylation), glycation, aggregation, and charge variants, which were demonstrated to be not clinically relevant. BAT1806/BIIB800 and TCZ were highly similar for all critical quality attributes. Where differences were observed in less critical quality attributes, additional analytical assessments and clinical study results determined these to be not clinically meaningful.