As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs(lncRNAs),N6-methyladenosine(m^(6)A)RNA methylation has been shown to participate in essential biological processes.Recent studies have ...As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs(lncRNAs),N6-methyladenosine(m^(6)A)RNA methylation has been shown to participate in essential biological processes.Recent studies have revealed the distinct patterns of m^(6)A methylome across human tissues,and a major challenge remains in elucidating the tissue-specific presence and circuitry of m^(6)A methylation.We present here a comprehensive online platform,m^(6)A-TSHub,for unveiling the context-specific m^(6)A methylation and genetic mutations that potentially regulate m^(6)A epigenetic mark.m^(6)A-TSHub consists of four core components,including(1)m^(6)A-TSDB,a comprehensive database of 184,554 functionally annotated m^(6)A sites derived from 23 human tissues and 499,369 m^(6)A sites from 25 tumor conditions,respectively;(2)m^(6)A-TSFinder,a web server for high-accuracy prediction of m^(6)A methylation sites within a specific tissue from RNA sequences,which was constructed using multi-instance deep neural networks with gated attention;(3)m^(6)ATSVar,a web server for assessing the impact of genetic variants on tissue-specific m^(6)A RNA modifications;and(4)m^(6)A-CAVar,a database of 587,983 The Cancer Genome Atlas(TCGA)cancer mutations(derived from 27 cancer types)that were predicted to affect m^(6)A modifications in the primary tissue of cancers.The database should make a useful resource for studying the m^(6)A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue(or cancer type).m^(6)A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m^(6)ats.展开更多
Fear extinction is an important form of emotional learning, and affects neural plasticity. Cue fear extinction is a classical form of inhibitory learning that can be used as an exposure-based treatment for phobia, bec...Fear extinction is an important form of emotional learning, and affects neural plasticity. Cue fear extinction is a classical form of inhibitory learning that can be used as an exposure-based treatment for phobia, because the long-term extinction memory produced during cue fear extinction can limit the over-expression of fear. The expression of this inhibitory memory partly depends on the context in which the extinction learning occurs. Studies such as transient inhibition, electrophysiology and brain imaging have proved that the hippocampus - an important structure in the limbic system - facilitates memory retrieval by contextual cues. Mediation of the hippocampus-medial prefrontal lobe circuit may be the neurobiological basis of this process. This article has reviewed the role of the hippocampus in the learning and retrieval of fear extinction. Contextual modulation of fear extinction may rely on a neural network consisting of the hippocampus, the medial prefrontal cortex and the amygdala.展开更多
The concept of synthetic lethality(SL)has been successfully used for targeted therapies.To further explore SL for cancer therapy,identifying more SL interactions with therapeutic potential are essential.Recently,graph...The concept of synthetic lethality(SL)has been successfully used for targeted therapies.To further explore SL for cancer therapy,identifying more SL interactions with therapeutic potential are essential.Recently,graph neural network-based deep learning methods have been proposed for SL prediction,which reduce the SL search space of wet-lab based methods.However,these methods ignore that most SL interactions depend strongly on genetic context,which limits the application of the predicted results.In this study,we proposed a graph recurrent network-based model for specific context-dependent SL prediction(SLGRN).In particular,we introduced a Graph Recurrent Network-based encoder to acquire a context-specific,low-dimensional feature representation for each node,facilitating the prediction of novel SL.SLGRN leveraged gate recurrent unit(GRU)and it incorporated a context-dependent-level state to effectively integrate information from all nodes.As a result,SLGRN outperforms the state-of-the-arts models for SL prediction.We subsequently validate novel SL interactions under different contexts based on combination therapy or patient survival analysis.Through in vitro experiments and retrospective clinical analysis,we emphasize the potential clinical significance of this context-specific SL prediction model.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.32100519 and 31671373)the Scientific Research Foundation for Advanced Talents of Fujian Medical University(Grant No.XRCZX2021019)the XJTLU Key Program Special Fund(Grant Nos.KSF-T-01,KSF-E-51,and KSF-P-02),China.
文摘As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs(lncRNAs),N6-methyladenosine(m^(6)A)RNA methylation has been shown to participate in essential biological processes.Recent studies have revealed the distinct patterns of m^(6)A methylome across human tissues,and a major challenge remains in elucidating the tissue-specific presence and circuitry of m^(6)A methylation.We present here a comprehensive online platform,m^(6)A-TSHub,for unveiling the context-specific m^(6)A methylation and genetic mutations that potentially regulate m^(6)A epigenetic mark.m^(6)A-TSHub consists of four core components,including(1)m^(6)A-TSDB,a comprehensive database of 184,554 functionally annotated m^(6)A sites derived from 23 human tissues and 499,369 m^(6)A sites from 25 tumor conditions,respectively;(2)m^(6)A-TSFinder,a web server for high-accuracy prediction of m^(6)A methylation sites within a specific tissue from RNA sequences,which was constructed using multi-instance deep neural networks with gated attention;(3)m^(6)ATSVar,a web server for assessing the impact of genetic variants on tissue-specific m^(6)A RNA modifications;and(4)m^(6)A-CAVar,a database of 587,983 The Cancer Genome Atlas(TCGA)cancer mutations(derived from 27 cancer types)that were predicted to affect m^(6)A modifications in the primary tissue of cancers.The database should make a useful resource for studying the m^(6)A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue(or cancer type).m^(6)A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m^(6)ats.
基金the National Natural Science Foundation of China, No. 30670704
文摘Fear extinction is an important form of emotional learning, and affects neural plasticity. Cue fear extinction is a classical form of inhibitory learning that can be used as an exposure-based treatment for phobia, because the long-term extinction memory produced during cue fear extinction can limit the over-expression of fear. The expression of this inhibitory memory partly depends on the context in which the extinction learning occurs. Studies such as transient inhibition, electrophysiology and brain imaging have proved that the hippocampus - an important structure in the limbic system - facilitates memory retrieval by contextual cues. Mediation of the hippocampus-medial prefrontal lobe circuit may be the neurobiological basis of this process. This article has reviewed the role of the hippocampus in the learning and retrieval of fear extinction. Contextual modulation of fear extinction may rely on a neural network consisting of the hippocampus, the medial prefrontal cortex and the amygdala.
基金supported by the National Key Research and Development Program of China(2023YFC2604400)the National Natural Science Foundation of China(62103436)。
文摘The concept of synthetic lethality(SL)has been successfully used for targeted therapies.To further explore SL for cancer therapy,identifying more SL interactions with therapeutic potential are essential.Recently,graph neural network-based deep learning methods have been proposed for SL prediction,which reduce the SL search space of wet-lab based methods.However,these methods ignore that most SL interactions depend strongly on genetic context,which limits the application of the predicted results.In this study,we proposed a graph recurrent network-based model for specific context-dependent SL prediction(SLGRN).In particular,we introduced a Graph Recurrent Network-based encoder to acquire a context-specific,low-dimensional feature representation for each node,facilitating the prediction of novel SL.SLGRN leveraged gate recurrent unit(GRU)and it incorporated a context-dependent-level state to effectively integrate information from all nodes.As a result,SLGRN outperforms the state-of-the-arts models for SL prediction.We subsequently validate novel SL interactions under different contexts based on combination therapy or patient survival analysis.Through in vitro experiments and retrospective clinical analysis,we emphasize the potential clinical significance of this context-specific SL prediction model.
