Multi-Omics Revealed Molecular Mechanisms Underlying Guard Cell Systemic Acquired Resistance

Systemic Acquired Resistance (SAR) improves immunity of plant systemic tissue after local exposure to a pathogen. Guard cells that form stomatal pores on leaf surfaces recognize bacterial pathogens via pattern recognition receptors, such as Flagellin Sensitive 2 (FLS2). However, how SAR affects stomatal immunity is not known. In this study, we aim to reveal molecular mechanisms underlying the guard cell response to SAR using multi-omics of proteins, metabolites and lipids. Arabidopsis plants previously exposed to pathogenic bacteria Pseudomonas syringae pv. tomato DC3000 (Pst) exhibit an altered stomatal response compared to control plants when they are later exposed to the bacteria. Reduced stomatal apertures of SAR primed plants lead to decreased number of bacteria in leaves. Multi-omics has revealed molecular components of SAR response specific to guard cells functions, including potential roles of reactive oxygen species (ROS) and fatty acid signaling. Our results show an increase in palmitic acid and its derivative in the primed guard cells. Palmitic acid may play a role as an activator of FLS2, which initiates stomatal immune response. Improved understanding of how SAR signals affect stomatal immunity can aid biotechnology and marker-based breeding of crops for enhanced disease resistance.     

Energy Metabolites as Biomarkers in Ischemic and Dilated Cardiomyopathy

With more than 25 million people affected, heart failure (HF) is a global threat. As energy production pathways are known to play a pivotal role in HF, we sought here to identify key metabolic changes in ischemic- and non-ischemic HF by using a multi-OMICS approach. Serum metabolites and mRNAseq and epigenetic DNA methylation profiles were analyzed from blood and left ventricular heart biopsy specimens of the same individuals. In total we collected serum from n = 82 patients with Dilated Cardiomyopathy (DCM) and n = 51 controls in the screening stage. We identified several metabolites involved in glycolysis and citric acid cycle to be elevated up to 5.7-fold in DCM (p = 1.7 × 10⁻⁶). Interestingly, cardiac mRNA and epigenetic changes of genes encoding rate-limiting enzymes of these pathways could also be found and validated in our second stage of metabolite assessment in n = 52 DCM, n = 39 ischemic HF and n = 57 controls. In conclusion, we identified a new set of metabolomic biomarkers for HF. We were able to identify underlying biological cascades that potentially represent suitable intervention targets.     

多组学数据整合分析和应用研究综述

随着组学新测序技术的不断涌现和推广,产生了大量的组学数据,这些数据对人们深入研究和揭示生命奥秘有着极重要的意义。利用多组学数据整合技术分析生命科学问题可获得更丰富更全面的生命系统相关信息,已成为研究者探索生命机制的新方向。介绍了多组学数据整合分析的研究背景和研究意义,综述了近年来多组学数据整合分析的方法和相关领域的应用研究,探讨了多组学数据整合分析方法当前所存在的问题以及未来展望。     

跨模态脑图谱数据融合研究进展

脑图谱是研究脑结构和功能及脑疾病的基础,不同类型的脑图谱从不同角度提供了脑的组织模式或连接信息。随着图像采集和生物检测技术的发展,不同模态的脑影像和生物组学数据迅速增长。相较于单模态,多模态融合数据能够同时考察不同模态数据间的多元化信息,挖掘蕴含的未知新信息。因此,开展跨模态脑图谱数据融合研究有助于更全面地理解大脑的结构和功能,并辅助加深对脑发育、老化和病变机理的理解。本文根据参与融合的模态是否具有空间信息,将近年来有代表性的跨模态脑图谱融合技术分为脑影像融合和脑数据融合两大类。脑影像融合是指对宏观脑影像(磁共振等)和组织学脑影像(胞体染色、轴突染色等)等具有空间信息的数据进行融合,构建涵盖脑结构和功能信息的跨模态多尺度脑图谱,为研究宏观特征的介观机制以及介观特征的宏观表征提供了重要途径。脑数据融合是指对缺乏脑空间信息的生物大数据,包括基因组、电生理、认知和行为等,利用脑图谱提供精细空间信息,挖掘高维、异构生物大数据蕴含的信息,明确脑图谱的生理意义,并提升其应用价值。本文将针对这两类融合类型阐述国内外有代表性的研究进展,并对比国内外研究现状的差异。此外,为促进跨模态脑图谱数据融合领域的交流和发展,总结了部分有代表性的大样本公开数据集。最后讨论了当前该领域待解决的问题以及未来的发展趋势。     

多组学技术解析发酵水产食品风味形成机理研究进展

发酵水产食品风味形成机制复杂,制备和发酵过程中的原料、所用发酵剂以及设备和加工工艺中的多种微生物相互作用,导致形成的风味成分种类多样,从单一层面对不同发酵水产食品风味成分解析较为困难。近年来,通过利用对不同层面准确解析的组学技术,研究基因表达调控、蛋白质转录翻译及相互作用,并对代谢物进行定性及定量分析,可用于明确特征风味成分,揭示风味形成机制。因此,多组学技术可以用于动态检测发酵过程中水产品风味成分变化并解析风味形成机制、构建风味化合物代谢网络,探究风味相关微生物及酶作用关系。本文综述水产品发酵中风味形成的主要代谢途径、多组学技术应用于解析水产品发酵过程中风味形成的研究进展,以及多组学技术在发酵水产品风味研究中的重要作用。     

蛋白质组学技术在藻类研究中的应用现状与展望

藻类具有复杂多样的进化历史和生物学特征,不仅在生态系统中扮演着重要角色,而且具有许多独特的基因和生物过程。随着后基因时代的到来,组学技术受到各界学者的高度重视,近年来在藻类研究中也得到了应用。高通量技术在藻类研究领域中的应用,也大大促进了藻类蛋白质组学的发展。本文综述了蛋白质组学技术在藻类品质差异鉴定、养殖胁迫作用、生理机制方面的研究进展,并对其发展方向和应用前景进行了展望,为从事藻类组学的研究者提供参考。     

基因调控网络中的癌症标记物预测方法

基于多组学的癌症标记物识别对癌症分子机制的研究具有重要的意义,但是当前大部分工作都是结合蛋白质相互作用数据进行的,故提出一种新型的基于多基因调控网络和多组学数据的研究方法,用于分析癌症的分子机制以及预测生物分子标记物。该方法首先整合多组学数据,以胃癌和食管癌为例,分别构建了胃癌和食管癌的癌症特异性网络;然后在这两个网络上进行加权共表达网络分析,采用层次聚类划分模块,计算模块的第一主成分和所有已知癌症标记物的关系,以此为据筛选出癌症特异的模块;最后再提取疾病特异的生物通路,使用相似性评估方法识别潜在的癌症标记物。实验结果表明,该方法预测的特异性模块具有功能特性,并且在模块内部使用皮尔逊相关系数法进行预测的结果更准确。     

癌症多组学数据深度自编码器整合分型方法

在癌症研究中,随着高通量测序技术发展已经产生了海量的复杂数据。尽管有了一些利用深度学习和统计学方法进行多组学数据整合的研究,但目前仍缺乏较为有效率的整合方法。因此提出一种基于深度自编码器的多组学数据整合方法（deep autoencoder for multi-omics integration,DAEMI）。它利用自编码器中的瓶颈层,学习多组学数据的特征表示。与先前利用深度学习整合的研究相比,DAEMI可以发现明显生存差异的癌症亚型。同时因为不需要生存数据来选择特征,DAEMI可以使用更多特征进行[K]均值聚类,进而完成癌症分型任务。将DAEMI应用于模拟数据集与四个癌症数据集实验,通过与高阶路径相似度网络的融合模型（HOPES）、相似性网络融合（SNF）、iClusterPlus和moCluster进行比较,结合模拟数据集测试结果与真实癌症数据集测试结果来看,DAEMI要优于其他方法。相应的生物功能分析揭示,神经退行性疾病与线粒体功能障碍可能与癌症共享某些生物学通路。     

Multi-omics differential gene regulatory network inference for lung adenocarcinoma tumor progression biomarker discovery

A systematic method was proposed to infer differential gene regulatory networks (GRNs) among lung adenocarcinoma (LUAD) samples at different stages by integrating multi-omics data to uncover significant network features and to identify tumor progression (TP) biomarker genes. The mRNA expressions, copy number variations, and DNA methylations of two independent LUAD cohorts (TCGA and SPORE) at stages I, II, and III were used, respectively. As results, the transition from normal to early onset was showed to be critical to reveal the pathogenesis of LUAD; 61 genes were identified as TP-related biomarkers, including two types of microRNAs of ABLIM2 and ZFAS1. These identified biomarkers may set light on the underlying mechanism of LUAD TP and may serve as potential drug targets for new treatments. Moreover, our study provides a general framework for TP biomarker identification for other types of cancer, which may improve the mechanism research for cancer development.