Shaping the landscape of N6-methyladenosine RNA methylation in Arabidopsis.

N 6-methyladenosine (m6A) modification on messenger RNAs (mRNAs) is deposited by evolutionarily conserved methyltransferases (writers). How individual m6A writers sculpt the overall landscape of the m6A methylome and the resulting biological impact in multicellular organisms remains unknown. Here, we systematically surveyed the quantitative m6A methylomes at single-nucleotide resolution and their corresponding transcriptomes in Arabidopsis (Arabidopsis thaliana) bearing respective impaired m6A writers. The m6A sites associated with the five Arabidopsis writers were located mostly within 3' untranslated regions with peaks at around 100 bp downstream of stop codons. m6A predominantly promoted the usage of distal poly(A) sites but had little effect on RNA splicing. Notably, impaired m6A writers resulted in hypomethylation and downregulation of transcripts encoding ribosomal proteins, indicating a possible correlation between m6A and protein translation. Besides the common effects on mRNA metabolism and biological functions uniquely exerted by different Arabidopsis m6A writers compared with their counterparts in human cell lines, our analyses also revealed the functional specificity of individual Arabidopsis m6A writers in plant development and response to stresses. Our findings thus reveal insights into the biological roles of various Arabidopsis m6A writers and their cognate counterparts in other multicellular m6A methyltransferase complexes.

Symmetry of Pyridine Derivatives Controlled Two-Dimensional Nanostructural Diversity by Co-Assembly with Aromatic Carboxylic Acids.

The self-assembly behaviors of aromatic carboxylic acids are commonly investigated at the liquid/solid interfaces because of their rigid skeletons and both hydrogen-bond donors and receptors. However, self-assemblies of aromatic carboxylic acids with low symmetry and interactions between carboxylic acid and pyridine derivatives are worth exploring. In this work, the self-assembled structural transitions of a kind of low-symmetric aromatic carboxylic acid (H4QDA) are regulated by the coadsorption of two pyridine derivatives (DPE and T4PT) with different symmetry, which are investigated by scanning tunneling microscopy under ambient conditions. For the H4QDA/DPE system, the grid structure appears. For the H4QDA/T4PT system, the coassembled morphologies display an obvious concentration dependence. With the increase of solution concentration of T4PT, three coassembled patterns (network structure, chiral linear structure, and brick-like structure) are observed. Corresponding structural models suggest that the O-H···N hydrogen bonds have great contributions to stabilizing these coassembled structures. Our studies will help to explore the complexity, diversity, and functionality of multiple component systems and are conducive to further understanding the underlying mechanisms in the assembly process.

Terminal Group Effect on Two-Dimensional Self-Assembly of Fluorenone-Based Liquid Crystals at the Solid/Liquid Interface.

Self-assemblies of two fluorenone-based derivatives (FE and FEC) consisting of a central 2,7-diphenyl-9-fluorenone polar moiety but differing in the flexible terminal groups were investigated by scanning tunneling microscopy (STM) at the 1-octanoic acid/HOPG interface under different concentrations and density functional theory calculation (DFT). STM results reveal a concentration-dependent polymorphic self-assembly behavior for FE, but without the presence of co-adsorbed solvents. As the concentration decreases, the dimer, bracket-like, and ribbon-like self-assembled structures were observed. On the contrary, FEC molecules assemble into only a type of oval-shaped morphology by the intermolecular N···H-O hydrogen bonds with the solvent molecules. Combined with DFT calculations, it can be deduced that the intermolecular van der Waals forces, dipole-dipole interactions, and hydrogen bonding are the main driving forces to stabilize the molecular packing of fluorenone-based polycatenars with strong polarity. Our work is of significance at the molecular level to further clarify the intermolecular interactions and conformational effects on the formation of molecular packing structures with liquid crystal property.

Gyral peaks and patterns in human brains.

Cortical folding patterns are related to brain function, cognition, and behavior. Since the relationship has not been fully explained on a coarse scale, many efforts have been devoted to the identification of finer grained cortical landmarks, such as sulcal pits and gyral peaks, which were found to remain invariant across subjects and ages and the invariance may be related to gene mediated proto-map. However, gyral peaks were only investigated on macaque monkey brains, but not on human brains where the investigation is challenged due to high inter-individual variabilities. To this end, in this work, we successfully identified 96 gyral peaks both on the left and right hemispheres of human brains, respectively. These peaks are spatially consistent across individuals. Higher or sharper peaks are more consistent across subjects. Both structural and functional graph metrics of peaks are significantly different from other cortical regions, and more importantly, these nodal graph metrics are anti-correlated with the spatial consistency metrics within peaks. In addition, the distribution of peaks and various cortical anatomical, structural/functional connective features show hemispheric symmetry. These findings provide new clues to understanding the cortical landmarks, as well as their relationship with brain functions, cognition, behavior in both healthy and aberrant brains.

Genetic Influence on Gyral Peaks.

Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.

m6Acancer-Net: Identification of m6A-mediated cancer driver genes from gene-site heterogeneous network.

N6-methyladenosine (m6A) is the most abundant eukaryotic modification internal mRNA, which plays the crucial roles in the occurrence and development of cancer. However, current knowledge about m6A-mediated functional circuit and key genes targeted by m6A methylation in cancer is mostly elusive. Thus, here we proposed a novel network-based approach (called m6Acancer-Net) to identify m6A-mediated driver genes and their associated network in specific type of cancer, such as acute myeloid leukemia. m6A-mediated cancer driver genes are defined as genes mediated by m6A methylation, significantly mutated, and functionally interacted in cancer. m6Acancer-Net identified the m6A-mediated cancer driver genes by combining gene functional interaction network with RNA methylation, gene expression and mutation information. A cancer-specific gene-site heterogeneous network was firstly constructed by connecting the m6A site co-methylation network with the functional interaction pruned gene co-expression network generated from large scale gene expression profile of specific cancer. Then, the functional m6A-mediated genes were identified by selecting the m6A regulators as seed genes to perform the random walk with restart algorithm on the gene-site heterogeneous network. Finally, m6A-mediated cancer driver gene subnetworks were constructed by performing the heat diffusion of mutation frequency for functional m6A-mediated genes in protein-protein interaction networks. The experimental results of m6Acancer-Net on the acute myeloid leukemia (AML) and glioblastoma multiforme (GBM) data from TCGA project show that the m6A-mediated caner driver genes identified by m6Acancer-Net are targeted by m6A regulators, and mediate significant cancer-related pathways. They play crucial roles in development and prognostic stratification of cancer. Moreover, 15 m6A-mediated cancer driver genes identified in AML are validated by literatures to mediate AML progress, and 14 m6A-mediated cancer driver genes identified in GBM are validated by literatures to participate in development of GBM. m6Acancer-Net is reliable to identify the functionally significant m6A-mediated driver genes in specific cancer, and it can effectively facilitate the understanding of regulatory and therapeutic mechanism of cancer driver genes in epitranscriptome layer.

m6Aexpress-Reader: Prediction of m6A regulated expression genes by integrating m6A sites and reader binding information in specific- context.

N6-methyladenosine (m6A) is the most abundant form of mRNA modification and plays an important role in regulating gene expression. However, the mechanisms of m6A regulated gene expression in cell or condition specific, are still poorly understood. Even though, some methods are able to predict m6A regulated expression (m6A-reg-exp) genes in specific context, they don't introduce the m6A reader binding information, while this information can help to predict m6A-reg-exp genes and more clearly to explain the mechanisms of m6A-mediated gene expression process. Thus, by integrating m6A sites and reader binding information, we proposed a novel method (called m6Aexpress-Reader) to predict m6A-reg-exp genes from limited MeRIP-seq data in specific context. m6Aexpress-Reader adopts the reader binding signal strength to weight the posterior distribution of the estimated regulatory coefficients for enhancing the prediction power. By using m6Aexpress-Reader, we found the complex characteristic of m6A on gene expression regulation and the distinct regulated pattern of m6A-reg-exp genes with different reader binding. m6A readers, YTHDF2 or IGF2BP1/3 all play an important role in various cancers and the key cancer pathways. In addition, m6Aexpress-Reader reveals the distinct m6A regulated mode of reader targeted genes in cancer. m6Aexpress-Reader could be a useful tool for studying the m6A regulation on reader target genes in specific context and it can be freely accessible at: https://github.com/NWPU-903PR/m6AexpressReader.

m6A-express: uncovering complex and condition-specific m6A regulation of gene expression.

N6-methyladenosine (m6A) is the most abundant form of mRNA modification and controls many aspects of RNA metabolism including gene expression. However, the mechanisms by which m6A regulates cell- and condition-specific gene expression are still poorly understood, partly due to a lack of tools capable of identifying m6A sites that regulate gene expression under different conditions. Here we develop m6A-express, the first algorithm for predicting condition-specific m6A regulation of gene expression (m6A-reg-exp) from limited methylated RNA immunoprecipitation sequencing (MeRIP-seq) data. Comprehensive evaluations of m6A-express using simulated and real data demonstrated its high prediction specificity and sensitivity. When only a few MeRIP-seq samples may be available for the cellular or treatment conditions, m6A-express is particularly more robust than the log-linear model. Using m6A-express, we reported that m6A writers, METTL3 and METTL14, competitively regulate the transcriptional processes by mediating m6A-reg-exp of different genes in Hela cells. In contrast, METTL3 induces different m6A-reg-exp of a distinct group of genes in HepG2 cells to regulate protein functions and stress-related processes. We further uncovered unique m6A-reg-exp patterns in human brain and intestine tissues, which are enriched in organ-specific processes. This study demonstrates the effectiveness of m6A-express in predicting condition-specific m6A-reg-exp and highlights the complex, condition-specific nature of m6A-regulation of gene expression.

Gyral peaks: Novel gyral landmarks in developing macaque brains.

Cerebral cortex development undergoes a variety of processes, which provide valuable information for the study of the developmental mechanism of cortical folding as well as its relationship to brain structural architectures and brain functions. Despite the variability in the anatomy-function relationship on the higher-order cortex, recent studies have succeeded in identifying typical cortical landmarks, such as sulcal pits, that bestow specific functional and cognitive patterns and remain invariant across subjects and ages with their invariance being related to a gene-mediated proto-map. Inspired by the success of these studies, we aim in this study at defining and identifying novel cortical landmarks, termed gyral peaks, which are the local highest foci on gyri. By analyzing data from 156 MRI scans of 32 macaque monkeys with the age spanned from 0 to 36 months, we identified 39 and 37 gyral peaks on the left and right hemispheres, respectively. Our investigation suggests that these gyral peaks are spatially consistent across individuals and relatively stable within the age range of this dataset. Moreover, compared with other gyri, gyral peaks have a thicker cortex, higher mean curvature, more pronounced hub-like features in structural connective networks, and are closer to the borders of structural connectivity-based cortical parcellations. The spatial distribution of gyral peaks was shown to correlate with that of other cortical landmarks, including sulcal pits. These results provide insights into the spatial arrangement and temporal development of gyral peaks as well as their relation to brain structure and function.

Funm6AViewer: a web server and R package for functional analysis of context-specific m6A RNA methylation.

MOTIVATION: N 6-methyladenosine (m6A) is the most abundant mammalian mRNA methylation with versatile functions. To date, although a number of bioinformatics tools have been developed for location discovery of m6A modification, functional understanding is still quite limited. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, there is an urgent need for user-friendly tools to identify and explore the functional relevance of context-specific m6A methylation to gain insights into the epitranscriptome layer of gene expression regulation. RESULTS: We introduced here Funm6AViewer, a novel platform to identify, prioritize and visualize the functional gene interaction networks mediated by dynamic m6A RNA methylation unveiled from a case control study. By taking the differential RNA methylation data and differential gene expression data, both of which can be inferred from the widely used MeRIP-seq data, as the inputs, Funm6AViewer enables a series of analysis, including: (i) examining the distribution of differential m6A sites, (ii) prioritizing the genes mediated by dynamic m6A methylation and (iii) characterizing functionally the gene regulatory networks mediated by condition-specific m6A RNA methylation. Funm6AViewer should effectively facilitate the understanding of the epitranscriptome circuitry mediated by this reversible RNA modification. AVAILABILITY AND IMPLEMENTATION: Funm6AViewer is available both as a convenient web server (https://www.xjtlu.edu.cn/biologicalsciences/funm6aviewer) with graphical interface and as an independent R package (https://github.com/NWPU-903PR/Funm6AViewer) for local usage. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction and interpretation of cancer survival using graph convolution neural networks.

The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic cancers have a much lower median survival rate that has not improved much over the last forty years. This has imposed the challenge of finding gene markers for early cancer detection and treatment strategies. Different methods including regression-based Cox-PH, artificial neural networks, and recently deep learning algorithms have been proposed to predict the survival rate for cancers. We established in this work a novel graph convolution neural network (GCNN) approach called Surv_GCNN to predict the survival rate for 13 different cancer types using the TCGA dataset. For each cancer type, 6 Surv_GCNN models with graphs generated by correlation analysis, GeneMania database, and correlation + GeneMania were trained with and without clinical data to predict the risk score (RS). The performance of the 6 Surv_GCNN models was compared with two other existing models, Cox-PH and Cox-nnet. The results showed that Cox-PH has the worst performance among 8 tested models across the 13 cancer types while Surv_GCNN models with clinical data reported the best overall performance, outperforming other competing models in 7 out of 13 cancer types including BLCA, BRCA, COAD, LUSC, SARC, STAD, and UCEC. A novel network-based interpretation of Surv_GCNN was also proposed to identify potential gene markers for breast cancer. The signatures learned by the nodes in the hidden layer of Surv_GCNN were identified and were linked to potential gene markers by network modularization. The identified gene markers for breast cancer have been compared to a total of 213 gene markers from three widely cited lists for breast cancer survival analysis. About 57% of gene markers obtained by Surv_GCNN with correlation + GeneMania graph either overlap or directly interact with the 213 genes, confirming the effectiveness of the identified markers by Surv_GCNN.

FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes.

MOTIVATION: As the most abundant mammalian mRNA methylation, N6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive. RESULTS: To address this, we propose in this paper FunDMDeep-m6A a novel pipeline for identifying context-specific (e.g. disease versus normal, differentiated cells versus stem cells or gene knockdown cells versus wild-type cells) m6A-mediated functional genes. FunDMDeep-m6A includes, at the first step, DMDeep-m6A a novel method based on a deep learning model and a statistical test for identifying differential m6A methylation (DmM) sites from MeRIP-Seq data at a single-base resolution. FunDMDeep-m6A then identifies and prioritizes functional DmM genes (FDmMGenes) by combing the DmM genes (DmMGenes) with differential expression analysis using a network-based method. This proposed network method includes a novel m6A-signaling bridge (MSB) score to quantify the functional significance of DmMGenes by assessing functional interaction of DmMGenes with their signaling pathways using a heat diffusion process in protein-protein interaction (PPI) networks. The test results on 4 context-specific MeRIP-Seq datasets showed that FunDMDeep-m6A can identify more context-specific and functionally significant FDmMGenes than m6A-Driver. The functional enrichment analysis of these genes revealed that m6A targets key genes of many important context-related biological processes including embryonic development, stem cell differentiation, transcription, translation, cell death, cell proliferation and cancer-related pathways. These results demonstrate the power of FunDMDeep-m6A for elucidating m6A regulatory functions and its roles in biological processes and diseases. AVAILABILITY AND IMPLEMENTATION: The R-package for DMDeep-m6A is freely available from https://github.com/NWPU-903PR/DMDeepm6A1.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods.

N6-methyladenosine (m6A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m6A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m6A levels are controlled and whether and how regulation of m6A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m6A-regulated genes and m6A-associated disease, which includes Deep-m6A, the first model for detecting condition-specific m6A sites from MeRIP-Seq data with a single base resolution using deep learning and Hot-m6A, a new network-based pipeline that prioritizes functional significant m6A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m6A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m6A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m6A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway. The m6A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m6A regulatory functions and its roles in diseases.

MeT-DB V2.0: elucidating context-specific functions of N6-methyl-adenosine methyltranscriptome.

Methyltranscriptome is an exciting new area that studies the mechanisms and functions of methylation in transcripts. A knowledge base with the systematic collection and curation of context specific transcriptome-wide methylations is critical for elucidating their biological functions as well as for developing bioinformatics tools. Since its inception in 2014, the Met-DB (Liu, H., Flores, M.A., Meng, J., Zhang, L., Zhao, X., Rao, M.K., Chen, Y. and Huang, Y. (2015) MeT-DB: a database of transcriptome methylation in mammalian cells. Nucleic Acids Res., 43, D197-D203), has become an important resource for methyltranscriptome, especially in the N6-methyl-adenosine (m6A) research community. Here, we report Met-DB v2.0, the significantly improved second version of Met-DB, which is entirely redesigned to focus more on elucidating context-specific m6A functions. Met-DB v2.0 has a major increase in context-specific m6A peaks and single-base sites predicted from 185 samples for 7 species from 26 independent studies. Moreover, it is also integrated with a new database for targets of m6A readers, erasers and writers and expanded with more collections of functional data. The redesigned Met-DB v2.0 web interface and genome browser provide more friendly, powerful, and informative ways to query and visualize the data. More importantly, MeT-DB v2.0 offers for the first time a series of tools specifically designed for understanding m6A functions. Met-DB V2.0 will be a valuable resource for m6A methyltranscriptome research. The Met-DB V2.0 database is available at http://compgenomics.utsa.edu/MeTDB/ and http://www.xjtlu.edu.cn/metdb2.

lncRNA_Mdeep: An Alignment-Free Predictor for Distinguishing Long Non-Coding RNAs from Protein-Coding Transcripts by Multimodal Deep Learning.

Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing the lncRNA functional mechanism. However, the experimental identification of lncRNAs is expensive and time-consuming. In this study, we presented an alignment-free multimodal deep learning framework (namely lncRNA_Mdeep) to distinguish lncRNAs from protein-coding transcripts. LncRNA_Mdeep incorporated three different input modalities, then a multimodal deep learning framework was built for learning the high-level abstract representations and predicting the probability whether a transcript was lncRNA or not. LncRNA_Mdeep achieved 98.73% prediction accuracy in a 10-fold cross-validation test on humans. Compared with other eight state-of-the-art methods, lncRNA_Mdeep showed 93.12% prediction accuracy independent test on humans, which was 0.94%~15.41% higher than that of other eight methods. In addition, the results on 11 cross-species datasets showed that lncRNA_Mdeep was a powerful predictor for predicting lncRNAs.

Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information.

BACKGROUND: Long non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. However, using experiments to explore the lncRNA-disease associations is expensive and time consuming. RESULTS: In this study, we developed a novel method to identify potential lncRNA-disease associations by Integrating Diverse Heterogeneous Information sources with positive pointwise Mutual Information and Random Walk with restart algorithm (namely IDHI-MIRW). IDHI-MIRW first constructs multiple lncRNA similarity networks and disease similarity networks from diverse lncRNA-related and disease-related datasets, then implements the random walk with restart algorithm on these similarity networks for extracting the topological similarities which are fused with positive pointwise mutual information to build a large-scale lncRNA-disease heterogeneous network. Finally, IDHI-MIRW implemented random walk with restart algorithm on the lncRNA-disease heterogeneous network to infer potential lncRNA-disease associations. CONCLUSIONS: Compared with other state-of-the-art methods, IDHI-MIRW achieves the best prediction performance. In case studies of breast cancer, stomach cancer, and colorectal cancer, 36/45 (80%) novel lncRNA-disease associations predicted by IDHI-MIRW are supported by recent literatures. Furthermore, we found lncRNA LINC01816 is associated with the survival of colorectal cancer patients. IDHI-MIRW is freely available at https://github.com/NWPU-903PR/IDHI-MIRW .

m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks.

As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m6A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Specifically, m6A-Driver integrates the PPI network and the predicted differential m6A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m6A-driven network of m6A-driven genes. To evaluate the performance, we applied m6A-Driver to build the context-specific m6A-driven networks for 4 known m6A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.

Species -Shared and -Unique Gyral Peaks on Human and Macaque Brains.

Cortical folding is an important feature of primate brains that plays a crucial role in various cognitive and behavioral processes. Extensive research has revealed both similarities and differences in folding morphology and brain function among primates including macaque and human. The folding morphology is the basis of brain function, making cross-species studies on folding morphology important for understanding brain function and species evolution. However, prior studies on cross-species folding morphology mainly focused on partial regions of the cortex instead of the entire brain. Previously, we defined a whole-brain landmark based on folding morphology: the gyral peak. It was found to exist stably across individuals and ages in both human and macaque brains. In this study, we identified shared and unique gyral peaks in human and macaque, and investigated the similarities and differences in the spatial distribution, anatomical morphology, and functional connectivity of them.

Defining context-dependent m6A RNA methylomes in Arabidopsis.

N6-Methyladenosine (m6A) prevalently occurs on cellular RNA across almost all kingdoms of life. It governs RNA fate and is essential for development and stress responses. However, the dynamic, context-dependent m6A methylomes across tissues and in response to various stimuli remain largely unknown in multicellular organisms. Here, we generate a comprehensive census that identifies m6A methylomes in 100 samples during development or following exposure to various external conditions in Arabidopsis thaliana. We demonstrate that m6A is a suitable biomarker to reflect the developmental lineage, and that various stimuli rapidly affect m6A methylomes that constitute the regulatory network required for an effective response to the stimuli. Integrative analyses of the census and its correlation with m6A regulators identify multiple layers of regulation on highly context-dependent m6A modification in response to diverse developmental and environmental stimuli, providing insights into m6A modification dynamics in the myriad contexts of multicellular organisms.

N6-methyladenosine-mediated feedback regulation of abscisic acid perception via phase-separated ECT8 condensates in Arabidopsis.

N6-methyladenosine (m6A) is the most abundant internal modification in eukaryotic mRNAs, yet how plants recognize this chemical modification to swiftly adjust developmental plasticity under environmental stresses remains unclear. Here we show that m6A mRNA modification and its reader protein EVOLUTIONARILY CONSERVED C-TERMINAL REGION 8 (ECT8) act together as a key checkpoint for negative feedback regulation of abscisic acid (ABA) signalling by sequestering the m6A-modified ABA receptor gene PYRABACTIN RESISTANCE 1-LIKE 7 (PYL7) via phase-separated ECT8 condensates in stress granules in response to ABA. This partially depletes PYL7 mRNA from its translation in the cytoplasm, thus reducing PYL7 protein levels and compromising ABA perception. The loss of ECT8 results in defective sequestration of m6A-modified PYL7 in stress granules and permits more PYL7 transcripts for translation. This causes overactivation of ABA-responsive genes and the consequent ABA-hypersensitive phenotypes, including drought tolerance. Overall, our findings reveal that m6A-mediated sequestration of PYL7 by ECT8 in stress granules negatively regulates ABA perception, thereby enabling prompt feedback regulation of ABA signalling to prevent plant cell overreaction to environmental stresses.