Interaction Patterns Research Articles

Unraveling the Structural Basis of Biased Agonism in the β2-Adrenergic Receptor Through Molecular Dynamics Simulations.

Biased agonism in G protein-coupled receptors is a phenomenon resulting in the selective activation of distinct intracellular signaling pathways by different agonists, which may exhibit bias toward either Gs, Gi, or arrestin-mediated pathways. This study investigates the structural basis of ligand-induced biased agonism within the context of the β2-adrenergic receptor (β2-AR). Atomistic molecular dynamics simulations were conducted for β2-AR complexes with two stereoisomers of methoxynaphtyl fenoterol (MNFen), that is, compounds eliciting qualitatively different cellular responses. The simulations reveal distinct interaction patterns within the binding cavity, dependent on the stereoisomer. These changes propagate to the intracellular parts of the receptor, triggering various structural responses: the dynamic structure of the intracellular regions of the (R,R)-MNFen complex more closely resembles the "Gs-compatible" and "β-arrestin-compatible" conformation of β2-AR, while both stereoisomers maintain structural responses equidistant from the inactive conformation. These findings are confirmed by independent coarse-grained simulations. In the context of deciphered molecular mechanisms, Trp313 plays a pivotal role, altering its orientation upon interactions with (R,R)-MNFen, along with the Lys305-Asp192 ionic bridge. This effect, accompanied by ligand interactions with residues on TM2, increases the strength of interactions within the extracellular region and the binding cavity, resulting in a slightly more open conformation and a minor (by ca. 0.2 nm) increase in the distance between the TM5-TM7, TM1-TM6, TM6-TM7, and TM1-TM5 pairs. On the other hand, an even slighter decrease in the distance between the TM1-TM4 and TM2-TM4 pairs is observed.

The relationship between academic performance and the interaction patterns of online students in learning management system

The relationship between academic performance and the interaction patterns of online students in learning management system

Explicit description of viral capsid subunit shapes by unfolding dihedrons.

Viral capsid assembly and the design of capsid-based nanocontainers critically depend on understanding the shapes and interfaces of constituent protein subunits. However, a comprehensive framework for characterizing these features is still lacking. Here, we introduce a novel approach based on spherical tiling theory that explicitly describes the 2D shapes and interfaces of subunits in icosahedral capsids. Our method unfolds spherical dihedrons defined by icosahedral symmetry axes, enabling systematic characterization of all possible subunit geometries. Applying this framework to real T = 1 capsid structures reveals distinct interface groups within this single classification, with variations in interaction patterns around 3-fold and 5-fold symmetry axes. We validate our classification through molecular docking simulations, demonstrating its consistency with physical subunit interactions. This analysis suggests different assembly pathways for capsid nucleation. Our general framework is applicable to other triangular numbers, paving the way for broader studies in structural virology and nanomaterial design.

Prediction for children with autism spectrum disorder based on digital behavioral features during free play.

Play is an indispensable and meaningful activity in children's daily life. Research has shown that autistic children often exhibit differences in play development. The core traits of autism, such as distinct patterns in social interaction and communication, focused interests, and repetitive behaviors, frequently manifest in their play. Therefore, play may serve as an insightful measure of these differences. Unlike previous studies focusing on play behaviors only, we explored other behaviors associated with autism during free play, and constructed a clinical prediction model for effectively screening autistic children. Participants, including 123 autistic children and 123 neurotypical children aged 1-6 years, engaged in a 1.5-min free play with fixed toys, which was videotaped. A novel behavior-coding scheme was used to code these videos for 19 autistic behaviors, including play. The coding details of the 19 behaviors were then converted and expanded to 81 digital behavior indicators, including counts, duration, and proportion. The autistic children showed less functional play and imaginative play and reduced social communication and interactions, such as eye contact, facial expressions, and vocalizations, compared to the neurotypical children during free play. Furthermore, 5 behavioral indicators were selected for the prediction model through stepwise logistic regression, including 1 on socially oriented vocalizations and 4 on count and duration of functional play. The receiver operating characteristic (ROC) curve revealed a good prediction performance with an area under the curve (AUC) of 0.826, a sensitivity of 85.4%, and a specificity of 68.3%. Our findings highlight differences in play performance and social communication and interactions during free play among autistic children. Based on these findings, we constructed a good clinical prediction model, which might be a potential digital tool used by clinicians to effectively screen autistic children.

The Consequences of Reunifications in Chinese History: The Founding of the Sui vs. the Founding of the Song

Although all reunifications in Chinese history can be conceptualized as adhering on the surface to similar patterns, in fact each was the consequence of a complex assortment of contingent circumstances. Moreover, the particularities of the founding years of a dynasty had a wide-ranging impact on subsequent historical development. This article compares the circumstances and process of the founding of the Sui to the circumstances and process of the founding of the Song in order to show how differences between them might explain the very different political cultures of the Sui-Tang period as compared to the Song period. Thus, the pre-dynastic size of the north Chinese elite and patterns of inter-state interactions had repercussions on the subsequent geographic distribution of political power. In addition, distinct strategies of political legitimation at the inception of each of the two dynasties help account for strikingly different attitudes concerning the relationship between the past and the present, as well as the desirability of transformative and fundamental sociopolitical change.

DeepRSMA: a cross-fusion based deep learning method for RNA-small molecule binding affinity prediction.

RNA is implicated in numerous aberrant cellular functions and disease progressions, highlighting the crucial importance of RNA-targeted drugs. To accelerate the discovery of such drugs, it is essential to develop an effective computational method for predicting RNA-small molecule affinity (RSMA). Recently, deep learning based computational methods have been promising due to their powerful nonlinear modeling ability. However, the leveraging of advanced deep learning methods to mine the diverse information of RNAs, small molecules and their interaction still remains a great challenge. In this study, we present DeepRSMA, an innovative cross-attention-based deep learning method for RSMA prediction. To effectively capture fine-grained features from RNA and small molecules, we developed nucleotide-level and atomic-level feature extraction modules for RNA and small molecules, respectively. Additionally, we incorporated both sequence and graph views into these modules to capture features from multiple perspectives. Moreover, a Transformer-based cross-fusion module is introduced to learn the general patterns of interactions between RNAs and small molecules. To achieve effective RSMA prediction, we integrated the RNA and small molecule representations from the feature extraction and cross-fusion modules. Our results show that DeepRSMA outperforms baseline methods in multiple test settings. The interpretability analysis and the case study on spinal muscular atrophy (SMA) demonstrate that DeepRSMA has the potential to guide RNA-targeted drug design. The codes and data are publicly available at https://github.com/Hhhzj-7/DeepRSMA. Supplementary data are available at Bioinformatics online.

A quick response, but not too much: Experienced mangabeys (Cercocebus torquatus) lose interest in contact call exchanges that do not respect this "conversational rule".

Several non-human primate species engage in vocal exchanges of contact calls, throughout the day in peaceful contexts. These vocal exchanges have been compared to human conversations because vocalizations are uttered in turn-taking: a temporal pattern where interlocutors minimize silences and avoid overlaps. But observing such a pattern in the spontaneous production of a species, as is the case with red-capped mangabeys (Cercocebus torquatus), is not enough to make it a rule. Another prerequisite is that the pattern is expected by the animal. Here, we conducted a playback experiment using the violation-of-expectation paradigm to test whether captive red-capped mangabeys react differently to usual vs unusual interactive temporal patterns. We played back vocal exchanges with usual minimized response time (0.5 sec), with unusual longer response time (1.5 sec) and with unusual call overlap to 12 adult captive male mangabeys. For each individual, we measured the occurrences and durations of head orientation toward the loudspeaker after the stimuli. The interest of individuals varied according to the vocal exchange temporal pattern in interaction with their age. Indeed, the older (and thus more socially experienced) an individual was, the less interested he became after an unusual vocal exchange, i.e. a vocal exchange with call overlap or with a delayed response time. These findings suggest that experience shapes attention towards more socially relevant situations, and thus that turn-taking can be qualified as a social rule.

AI Promoted Virtual Screening, Structure-Based Hit Optimization, and Synthesis of Novel COVID-19 S-RBD Domain Inhibitors.

Coronavirus disease 2019 (COVID-19) is caused by a new, highly pathogenic severe-acute-respiratory syndrome coronavirus 2 (SARS-CoV-2) that infects human cells through its transmembrane spike (S) glycoprotein. The receptor-binding domain (RBD) of the S protein interacts with the angiotensin-converting enzyme II (ACE2) receptor of the host cells. Therefore, pharmacological targeting of this interaction might prevent infection or spread of the virus. Here, we performed a virtual screening to identify small molecules that block S-ACE2 interaction. Large compound libraries were filtered for drug-like properties, promiscuity and protein-protein interaction-targeting ability based on their ADME-Tox descriptors and also to exclude pan-assay interfering compounds. A properly designed AI-based virtual screening pipeline was applied to the remaining compounds, comprising approximately 10% of the starting data sets, searching for molecules that could bind to the RBD of the S protein. All molecules were sorted according to their screening score, grouped based on their structure and postfiltered for possible interaction patterns with the ACE2 receptor, yielding 31 hits. These hit molecules were further tested for their inhibitory effect on Spike RBD/ACE2 (19-615) interaction. Six compounds inhibited the S-ACE2 interaction in a dose-dependent manner while two of them also prevented infection of human cells from a pseudotyped virus whose entry is mediated by the S protein of SARS-CoV-2. Of the two compounds, the benzimidazole derivative CKP-22 protected Vero E6 cells from infection with SARS-CoV-2, as well. Subsequent, hit-to-lead optimization of CKP-22 was effected through the synthesis of 29 new derivatives of which compound CKP-25 suppressed the Spike RBD/ACE2 (19-615) interaction, reduced the cytopathic effect of SARS-CoV-2 in Vero E6 cells (IC50 = 3.5 μM) and reduced the viral load in cell culture supernatants. Early in vitro ADME-Tox studies showed that CKP-25 does not possess biodegradation or liver metabolism issues, while isozyme-specific CYP450 experiments revealed that CKP-25 was a weak inhibitor of the CYP450 system. Moreover, CKP-25 does not elicit mutagenic effect on Escherichia coli WP2 uvrA strain. Thus, CKP-25 is considered a lead compound against COVID-19 infection.

Church Union Movement and the Establishment of the “United Church of Christ in Japan”

The establishment of the United Church of Christ in Japan (nihon kirisuto kyōdan 日本基督教団) marked the culmination of the Church Union Movement in Imperial Japan. Although the Church Union Movement can be traced back to the Meiji era, no significant breakthroughs were made until 1939 due to the refusal of some denominations. In this article, I aim to clarify the process and causes behind the formation of the united church, while also attempting to understand the interaction pattern between the State and Christianity under an increasing wartime totalitarian regime. In April 1939, the Diet passed the Religious Organizations Law (syūkyō dantai hō 宗教団体法), a bill aimed at strengthening state control over religions, which required Christian denominations to establish religious organizations. With the war intensifying Japan’s antagonism toward Western countries, Christianity as a foreign religion faced progressive attacks from the nationalist sects. Some denominations, like the Salvation Army, were accused of espionage due to their international connections and were monitored by gendarmerie (kenpeitai 憲兵隊). Facing harsh pressure, Christians sought to project a patriotic image, ultimately leading to the formation of the United Church as a survival strategy amidst a hostile social-political environment.

Medicinal Chemistry Insights in Neuronal Nitric Oxide Synthase Inhibitors Containing Nitrogen Heterocyclic Compounds: A Mini Review.

Many scientific reports over the last two decades have focused on the discovery and development of novel nNOS inhibitors. The structural identity of isoforms, bioavailability, pharmacokinetic, and safety profile issues remain major obstacles in the discovery of more potent and selective nNOS inhibitors. This review aims to provide an in-depth overview of the molecular interaction patterns between nNOS active site and inhibitors containing structurally diverse nitrogen heterocyclic compounds and highlight the structural properties needed to develop selective nNOS inhibitors. Previously published data allowed the usage of the structure-driven approach in the designing of selective nNOS inhibitors, which relies on the specific structural features required to achieve isoform-selectivity towards nNOS. The incorporation of chiral pyrrolidine ring, two aminopyridine heads, or a specific amino tail group, along with the inhibitor's capacity to adopt the curled conformation in the nNOS environment significantly strengthens the molecular interaction between the inhibitor and nNOS residues by forming specific electrostatic interactions and non-bonded contacts that are vital for isoform selectivity. Additional structure-activity relationship investigations are necessary to elucidate more structural characteristics that will ultimately resolve the exact structural basis required for isoform-selective inhibition of nNOS.

The impact of digital communication and offline social interactions on depressive symptoms in Chinese older people

BackgroundIn a special period of lack of offline social interaction (pandemic), the mentality of older people is changing quietly. This study aims to dissect the impact of these changes on their mental health.MethodUtilizing data from the China Health and Retirement Longitudinal Study (CHARLS 2020), this research included 7,784 participants aged over 60 years. It evaluated the prevalence of depressive symptoms, and assessed the relative effects of these interactions on depressive symptoms.Results(1) a depressive symptom prevalence of 40.65%; (2) a modest engagement in digital communication, with only 20.39% of the older participating; (3) varying prevalences of depressive symptoms across groups, with notable differences depending on the type and combination of social interactions. Specifically, the prevalence was 21.7% among those engaging only in digital communication, and varied from 21.7 to 32.0% among other groups, highlighting the significant impact of social interaction patterns on depressive symptoms. Statistical analysis confirmed the significance of these findings (χ2 = 42.415, p < 0.001). (4) In the first model, digital communication was associated with a lower likelihood of depressive symptoms (OR = 0.820, 95%CI: 0.707–0.950, p < 0.05). The second model showed no significant effect of offline social interactions on depressive symptoms (OR = 0.124, 95%CI: 0.917–1.143, p = 0.678). The third model demonstrated that the inclusion of offline social interaction variables did not significantly alter the beneficial effect of digital communication (OR = 0.820, 95%CI: 0.707–0.952, p < 0.05). (5) The results of the 1:1 matching analysis indicated that digital communication significantly alleviated depressive symptoms in older people (ATT = −0.054, p < 0.05). Conversely, offline social interactions did not significantly affect the depressive symptoms of this demographic (ATT = −0.028, p > 0.05).ConclusionThe depressive symptoms among older people in China has greatly increased during 2020 which year the COVID-19 pandemic was in the early stage, accompanied by considerable changes in their modes of social interaction. Our findings suggest that the influences of digital communication and offline social interactions on depressive symptoms may have operated independently during the pandemic. The potential of digital communication engagements in enhancing mental health, particularly in exceptional circumstances such as during a pandemic, underscores the need for further exploration.

Molecular Dynamics Simulations of Nucleosomes Containing Histone Variant H2A.J

Histone proteins form the building blocks of chromatin—nucleosomes. Incorporation of alternative histone variants instead of the major (canonical) histones into nucleosomes is a key mechanism enabling epigenetic regulation of genome functioning. In humans, H2A.J is a constitutively expressed histone variant whose accumulation is associated with cell senescence, inflammatory gene expression, and certain cancers. It is sequence-wise very similar to the canonical H2A histones, and its effects on the nucleosome structure and dynamics remain elusive. This study employed all-atom molecular dynamics simulations to reveal atomistic mechanisms of structural and dynamical effects conferred by the incorporation of H2A.J into nucleosomes. We showed that the H2A.J C-terminal tail and its phosphorylated form have unique dynamics and interaction patterns with the DNA, which should affect DNA unwrapping and the availability of nucleosomes for interactions with other chromatin effectors. The dynamics of the L1-loop and the hydrogen bonding patterns inside the histone octamer were shown to be sensitive to single amino acid substitutions, potentially explaining the higher thermal stability of H2A.J nucleosomes. Taken together, our study demonstrated unique dynamical features of H2A.J-containing nucleosomes, which contribute to further understanding of the molecular mechanisms employed by H2A.J in regulating genome functioning.

Exploring the connections: Elementary teachers’ narratives of Mexican deaf children’s play behaviours, social interactions, and communication development

ABSTRACT This study explores the experiences and perspectives of eight elementary teachers regarding the play behaviours of deaf children in their classrooms in Mexico City. Through a narrative inquiry design, this research comprehensively explains teachers’ insights and observations on social interactions and communication development among deaf children. The findings indicate patterns in social interactions related to play behaviours, language development through imitation, and communication variations contingent on proficiency in Lengua de Señas Mexicana (LSM). The study emphasises the necessity of creating inclusive educational environments and providing appropriate support for the communication development of deaf children. Employing a qualitative research approach, this study involves in-depth interviews with Deaf elementary school teachers with experience working with deaf children. This study aims to elucidate teachers’ perceptions and interpretations of language acquisition and play behaviours concerning social interactions and communication development by analysing their narratives. Three key dimensions emerged from the narratives: linguistic proficiency, play behaviours, and social engagement, which intersect and influence each other. The findings hold significant implications for educators, policymakers, and parents, offering valuable insights that can inform the development of more effective instructional strategies and support systems tailored to the unique needs of deaf children in educational settings.

Active enhancers: recent research advances and insights into disease.

Precise regulation of gene expression is crucial to development. Enhancers, the core of gene regulation, determine the spatiotemporal pattern of gene transcription. Since many disease-associated mutations are characterized in enhancers, the research on enhancer will provide clues to precise medicine. Rapid advances in high-throughput sequencing technology facilitate the characterization of enhancers at genome wide, but understanding the functional mechanisms of enhancers remains challenging. Herein, we provide a panorama of enhancer characteristics, including epigenetic modifications, enhancer transcripts, and enhancer-promoter interaction patterns. Furthermore, we outline the applications of high-throughput sequencing technology and functional genomics methods in enhancer research. Finally, we discuss the role of enhancers in human disease and their potential as targets for disease prevention and treatment strategies.

Anion and Substituents Effect on Spectral-Kinetic and Biological Characteristics of Spiropyran Salts.

Spiropyran salts containing a cationic vinyl-3H-indolium moiety are characterized by NIR absorption and fluorescence of their merocyanine forms. This feature makes them promising fluorescent probes and markers for bioimaging. The article focuses on the synthesis and study of the spectral, kinetic and toxic characteristics of such compounds with respect to various substituents in different moieties and the type of anion. A detailed analysis of the acquired data made it possible to draw some important conclusions regarding the influence of structural factors, which will be very useful for the further rational design of such derivatives. In particular, it was shown that the counterion has minimal effect on the spectral and kinetic characteristics of the dyes but dramatically affects the toxicity of the compounds. Following selection of the most appropriate compounds, bioimaging experiments were carried out to visualize planktonic bacteria and bacterial biofilms of E. coli and A. calcoaceticus. The ability to visualize biofilms is critical for the diagnosis of chronic diseases. By the results of molecular docking a theoretical interaction pattern between spiropyran molecules and DNA was proposed.

Symmetry and Complexity in Gene Association Networks Using the Generalized Correlation Coefficient

High-dimensional gene expression data cause challenges for traditional statistical tools, particularly when dealing with non-linear relationships and outliers. The present study addresses these challenges by employing a generalized correlation coefficient (GCC) that incorporates a flexibility parameter, allowing it to adapt to varying levels of symmetry and asymmetry in the data distribution. This adaptability is crucial for analyzing gene association networks, where the GCC demonstrates advantages over traditional measures such as Kendall, Pearson, and Spearman coefficients. We introduce two novel adaptations of this metric, enhancing its precision and broadening its applicability in the context of complex gene interactions. By applying the GCC to relevance networks, we show how different levels of the flexibility parameter reveal distinct patterns in gene interactions, capturing both linear and non-linear relationships. The maximum likelihood and Spearman-based estimators of the GCC offer a refined approach for disentangling the complexity of biological networks, with potential implications for precision medicine. Our methodology provides a powerful tool for constructing and interpreting relevance networks in biomedicine, supporting advancements in the understanding of biological interactions and healthcare research.

An interactive dashboard for analyzing user interaction patterns in the i2b2 clinical data warehouse.

Clinical data warehouses provide harmonized access to healthcare data for medical researchers. Informatics for Integrating Biology and the Bedside (i2b2) is a well-established open-source solution with the major benefit that data representations can be tailored to support specific use cases. These data representations can be defined and improved via an iterative approach together with domain experts and the medical researchers using the platform. To facilitate these discussions, it is important to understand how users interact with the system. The objective of this work was to develop metrics for describing user interactions with clinical data warehouses in general and i2b2 in particular. Moreover, we aimed to develop a dashboard featuring interactive visualizations that inform data engineers and data stewards about potential improvements. We first identified metrics for different data usage dimensions and extracted the relevant metadata about previous user queries from the i2b2 database schema for further analysis. We then implemented associated visualizations in Python and integrated the results into an interactive dashboard using Dash. The identified categories of metrics include frequency of use, session duration, and use of functionality and features. We created a dashboard that extends our local i2b2 data warehouse platform, focusing on the latter category, further broken down into the number of queries, frequently queried concepts, and query complexity. The implementation is available as open-source software. A range of metrics can be derived from metadata logged in the i2b2 database schema to provide data engineers and data stewards with a comprehensive understanding of how users interact with the platform. This can help to identify the strengths and limitations of specific instances of the platform for specific use cases and aid their iterative improvement.

River stabilization reshaped human-nature interactions in the Lower Yellow River Floodplain

Floodplains are crucial agricultural and populated areas worldwide. Rivers typically shape human activities within floodplains through water supply and flood risk, forming unique human-nature interaction patterns. Given that river systems have undergone significant transformations globally, understanding the response of these interactions to hydrological changes is elementary. Here, using the Lower Yellow River Floodplain (LYRF) as a case, where continuous levees distinctly outline the river-influenced floodplain from a homogenous cultivated plain and the river's hydrology has undergone dramatic alterations since the 1990s, we analyzed how the human-nature interactions respond to river hydrological changes. This study found that the flood-prone nature of the Lower Yellow River has weakened from 1986 to 2021 as shown by decreased surface water extent and inundated extent of cropland. The intensity of agriculture inside the LYRF has been lower but experienced faster development than outside, minimizing the disparity between them. Meanwhile, human activities in the floodplain have moved closer to the river. These changes in human-nature interactions in the LYRF can be explained by the river stabilization caused by upstream regulation, underscoring the significance of integrated river governance in managing human-nature system in floodplain areas.

Acetylcholinesterase – glucose-regulated protein 78 binding site prediction, a hope to cure neurological disorders such as Alzheimer’s disease

Cerebral amyloid plaques in the brain define the elderly neuralgic disorder, Alzheimer’s disease (AD). The enzyme Acetylcholinesterase (AChE) was reported to play a vital role in AD. It was shown that AChE induces amyloid fibril formation forming highly toxic AChE-Amyloid-β (Aβ) complexes. AChE can accelerate amyloid formation, and its inhibition could prevent such alterations to the enzyme. Understanding the proteostasis of AChE and its binding site to cellular chaperone GRP78 (Glucose-regulated protein 78) would help find a treatment for AD. In this study, the state of the art computational tools were utilized to predict the binding location of AChE that can stably associate with the cellular chaperone, GRP78. Sequence comparison along with molecular docking predicts two binding locations on AChE (C69–C96 and C257–C272) that could bind to GRP78 substrate binding domain β (SBDβ). The analysis of the docking data suggests that the former location has the best average binding affinity value (-12.16 kcal/mol) and average interaction pattern (13.9 ± 3.5 H-bonds, 5.5 ± 1.4 hydrophobic contacts, and 1.4 ± 1.2 salt bridges).

Using single-sample networks and genetic algorithms to identify radiation-responsive genes in rice affected by heavy ions of the galactic cosmic radiation with different LET values

IntroductionHeavy ions of the galactic cosmic radiation dominate the radiation risks and biological effects for plants under spaceflight conditions. However, the biological effects and sensitive genes caused by heavy ions with different linear energy transfer (LET) values have not been thoroughly studied.MethodsTo comprehensively analyze the biological effects of heavy ions with different LET values on rice under spaceflight conditions, we utilized the Shijian-10 recoverable satellite (SJ-10) to transport the dehydrated rice seeds on a 12.5-day mission in a 252 km low Earth orbit (LEO), and obtained rice plants hit by individual heavy ions with LET values ranging from 18 keV/μm to 213 keV/μm. The transcriptome and methylation sequencing were conducted on above plants, and a bioinformatics pipeline based on single-sample networks (SSNs) and genetic algorithms (GA) was developed to analyze the multi-omics expression profiles in this work. Note that SSNs can depict the gene interaction patterns within a single sample. The LET regression models were constructed from both gene expression and interaction pattern perspectives respectively, and the radiation response genes that played significant roles in the models were identified. We designed a gene selection algorithm based on GA to enhance the performance of LET regression models.ResultsThe experimental results demonstrate that all our models exhibit excellent regression performance (R2 values close to 1), which indicates that both gene expressions and interaction patterns can reflect the molecular changes caused by heavy ions with different LET values. LET-related genes (genes exhibiting strong correlation with LET values) and radiation-responsive genes were identified, primarily involved in DNA damage and repair, oxidative stress, photosynthesis, nucleic acid metabolism, energy metabolism, amino acid/protein metabolism, and lipid metabolism, etc. DNA methylation plays a crucial role in responding to heavy ions stressors and regulates the aforementioned processes.DiscussionTo the best of our knowledge, this is the first study to report the multi-omics changes in plants after exposure to heavy ions with different LET values under spaceflight conditions.