外生菌根真菌生物地理学研究进展

外生菌根真菌对植物矿质营养、生态系统物质循环、物种演化进程等具有十分重要的作用,但其生物地理学研究长期滞后于动植物。扩散和隔离是解释外生菌根真菌生物地理分布格局的重要理论。古地质、古气候和宿主植物是外生菌根真菌地理分布格局形成的重要推动因子。基因组学、生物信息学技术与生物地理学方法相互结合和补充,可以用来研究一些复杂的真菌生物地理学问题。文中详细阐述了外生菌根真菌生物地理学研究的重要过程和现状,如真菌物种界定、常用数据分析方法、起源和演化规律及其在生物多样性保护方面的应用等,并初步探讨了其未来的发展方向。     

真菌群体基因组学研究进展

近年来,随着第二代高通量测序技术的出现和发展,测序成本不断降低,完成全基因组测序的真菌物种迅速增加。以大规模测序为基础的群体基因组学,也逐渐应用于解析真菌的群体结构、物种形成、种群分化和位点特异性效应。本文综述了群体基因组学在工业真菌、病原真菌、食用真菌、共生真菌及其在表型性状遗传基础解析中的研究进展,并对其今后的发展方向进行了展望。     

Metabarcoding技术在真菌多样性研究中的应用

由于受到气候变化、土地利用变化及环境污染等诸多因素的干扰,真菌多样性受到不容忽视的威胁,亟需得到保护。构建物种数据库是实现真菌多样性研究和保护的重要前提。近年来兴起的DNA条形码及metabarcoding技术能够在很大程度上弥补传统鉴定方法的缺陷,可对真菌物种进行大规模、准确、快速、高效地鉴定。本文梳理了metabarcoding技术在真菌物种多样性评估、真菌多样性影响机制和真菌古生态重建等研究中的应用,同时强调了metabarcoding技术用于真菌多样性研究尚处于初期阶段,在构建有效参照数据库、优化实验流程以及升级生物信息学工具等方面仍需要进一步的完善。建议加强真菌分类学家、生态学家以及计算机工具研发工程师之间的合作,共同解决metabarcoding技术在真菌多样性研究及应用中面临的问题,为宏观尺度上真菌多样性保护提供更加科学的依据。     

植物泛基因组研究进展与展望

随着测序技术和生物信息学的快速发展,已有数百种植物的参考基因组被测序,极大地促进了植物功能基因组学、进化遗传学和分子育种学等领域的蓬勃发展。然而,随着研究的深入,越来越多的证据表明来自单一个体的参考基因组远不能代表整个物种的遗传多样性,由此催生了泛基因组（Pan-genome）的概念,并已成功应用于20余种植物的研究,揭示了丰富的遗传变异,发掘了大量的新基因,深化了对相关物种遗传多样性的认识。本文简述了泛基因组的概念、构建方法以及在当前植物研究中的应用现状,最后对其未来发展进行了展望。     

生物信息学与eQTL作图:问题和展望

阐述生物信息学在基因表达QTL作图中的应用。主要对后基因组时代的研究的新领域——遗传基因组学的概念的提出、研究方向和发展进行论述,同时,就eQTL作图的统计学策略、生物信息学软件工具以及遗传网络构建等方面进行讨论,并对今后遗传基因组学的发展前景进行分析。     

中草药表观基因组学研究现状与展望

表观遗传学的实质是非DNA突变引起可继承表型变化的遗传现象.表观基因组学是在基因组水平研究表观遗传变异的科学.中草药表观基因组学是以药用植物和药用真菌为研究对象,利用分子生物学、生物信息学及"组学"相关技术,在全基因组水平上,对表观遗传修饰调控中草药次生代谢、生长发育、胁迫响应的分子机制以及中草药活性成分对生物体表观调控的药理机制进行研究的一门学科,将为解析环境因子在中药材道地性品质形成中的作用机制及中药药理新机制提供理论基础.比较表观遗传学是中草药表观基因组学研究的主要方法.本文综述了中草药表观基因组学的研究现状,探讨并展望了中草药表观基因组学研究相关成果在阐明中药材道地性的生物学实质以及中药药理新机制等方面的理论意义和应用前景.     

基因组学相关概念及其研究进展

２０世纪以来,生命科学得到了空前的发展,大量新的概念不断涌现并相应产生了许多新兴学科,本文试图对当今研究的热点领域－基因组学,包括结构基因组学,比较基因组学,功能基因组学和较新兴的蛋白质组学、功能蛋白质组学及其相关概念、营养基因组学、生物信息学及其相关概念和近期进展做一介绍。     

外生菌根共生:共生真菌多样性及菌根形成的分子机制

大约2%的维管植物能够与17～18个目约280个属的真菌形成外生菌根关系.外生菌根关系的形成、宿主植物的专一化和宿主转移等事件可能是共生真菌物种分化的重要驱动力.比较基因组学研究发现,外生菌根真菌丢失了大量与植物细胞壁物质降解相关的基因.在外生菌根形成过程中,真菌与宿主植物之间借助多样化的信号分子完成相互识别,并且外生菌根真菌能够借助分泌效应蛋白抑制宿主植物的防御反应,促进菌根形成.本文对外生菌根真菌的多样性和分布规律,以及外生菌根形成的分子机制等方面进行了综述,并据此对后续研究提出展望.     

基因组学时代的真菌分类学:机遇与挑战

真菌物种的形态特征有限,加之形态滞后和形态可塑性,仅靠外部形态、内部结构及生理生化指标,很难把握真菌的系统亲缘。应用DNA测序、基因组测序、比较基因组学及生物信息学等技术,研究人员可以快速识别真菌演化中出现的数量众多的单系支系,为建立各分类等级的新分类单元提供有力证据,为真菌分类学研究带来了新的希望和活力。自2000年以来,在真菌界至少发表了1新亚界、4新门、7新亚门、19新纲、9新亚纲、40余新目等高级分类单元。近3年来,我国发表了20余个真菌新属,其中绝大多数属的建立都有分子证据支持。可以预见,大量的新种、新属、新科乃至更高级分类单元将会在今后10年内持续发现和建立。这必将大大促进真菌分类学的发展,完善现有的真菌分类系统。我们应该顺势而上,利用我国丰富的真菌资源,为真菌分类学的发展做出应有贡献。与此同时,真菌分类学也面临着十分严峻的挑战。挑战主要来自3个方面,一是研究变得越来越综合,不但需要有相应的研究经费支持,而且要求从事该领域的研究人员技术更全面、知识更广博及知识更新速度更快捷;二是新物种描述进度偏慢,远远不能满足人们对物种认识和利用的日益增长的需要;三是研究人员亟需创新研究模式,以新技术、新思路、新机制来构建新的真菌分类学,加速新物种的发现和描述进度,最终为社会进步和科学发展服务。     

结构基因组学研究与核磁共振

各种生物的基因组DNA测序计划的完成,将结构生物学带入了结构基因组学时代.结构基因组学是对所有基因组产物结构的系统性测定,它运用高通量的选择、表达、纯化以及结构测定和计算分析手段,为基因组的每个蛋白质产物提供实验测定的结构或较好的理论模型,这将加速生命科学各个领域的研究.生物信息学、基因工程、结构测定技术等的发展为结构基因组学研究提供了保证.近年来核磁共振在技术方法上的进展,使其成为结构基因组学高通量结构分析中的一个关键方法.     

Digitizing life at the level of the cell: high-performance laser-scanning microscopy and image analysis for in toto imaging of development

The field of biological imaging is progressing at an amazing rate. Advances in both laser-scanning microscopy and green fluorescent protein (GFP) technology are combining to make possible imaging-based approaches for studying developmental mechanisms that were previously impossible. Modern confocal and multi-photon microscopes are pushing the envelope of speed, sensitivity, spectral resolution, and depth resolution to allow in vivo imaging of whole, live embryos at cellular resolution over extended periods of time. In toto imaging, in which nearly every cell in an embryo or tissue can be tracked through space and time during development, may become a standard technique for small transparent embryos such as zebrafish and early stage chick and mouse embryos. GFP and its spectral variants can be used to mark a wide range of in vivo biological information for in toto imaging including gene expression patterns, mutant phenotypes, and protein subcellular localization patterns. Combining in toto imaging and GFP transgenic approaches on a large scale may usher in an explosion of in vivo, developmental data as has happened in the past several years with genomic data. There are significant challenges that must be met to reach these goals. This paper will discuss the current state-of-the-art, the challenges, and the prospects of in toto imaging in the areas of imaging, image analysis, and informatics.     

Alterations of plasma inflammatory biomarkers in the healthy and chronic obstructive pulmonary disease patients with or without acute exacerbation

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of mortally and morbidity, associated with acute exacerbations (AECOPD) resulted from smoking, infection or air pollution. Systemic inflammation has been considered as one of major pathophysiologic alterations in AECOPD. The present study aimed at developing disease-specific biomarker evaluation by integrating proteomic profiles of inflammatory mediators in AECOPD with clinical and biological informatics. Plasma samples from 18 subjects including healthy people or patients with stable COPD or AECOPD were collected to measure 507 inflammatory mediators using antibody microarray. Clinical informatics was achieved by a Digital Evaluation Score System (DESS) for assessing severity of patients. 20 mediators were significantly different between 3 groups (p<0.05), of which, Cerberus 1, Growth Hormone R, IL-1F6, IL-17B R, IL-17D, IL-19, Lymphotoxin beta, MMP-10, Thrombopoietin and TLR4 were correlated with DESS scores (p<0.05). There was a down-regulation of systemic inflammatory responses in AECOPD. The integration of proteomic profile with clinical informatics as part of clinical bioinformatics is important to screen disease-specific and disease-staged biomarkers. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

Bioimage informatics: a new area of engineering biology

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called 'bioimage informatics'. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.     

Study of the urine proteome in healthy humans

A new branch of molecular biology, proteomics, has been developed recently due to a success in genomics and informatics. Proteomics is currently solving problems of the full proteome mapping of various biological substances, e.g., body fluids, cells, and tissues in the normal state and pathology; and also search for biomarkers of pathologies, including tumors. Data on the urine proteome have been analyzed in this review. Analysis of the methods used in proteomics, including sample preparation, study strategy, as well as published data on urine proteome over the past five years are presented.     

Open data and algorithms for open science in AI-driven molecular informatics

Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.     

生物多样性信息学:一个正在兴起的新方向及其关键技术

生物多样性科学和生物信息学是生命科学中两个极为重要也是十分活跃的交叉学科,生物多样性信息学则是目前正在兴起的一个新方向,基发展必将进一步深化信息技术在生物多样性研究中的应用。本文简要介绍了国内外该领域的主要目标与进展,讨论了有关的关键技术（如数据库间的互操作与数字图书馆）,并列出了两个原型系统（Ｓｐｅｃｉｅｓ２０００和ＧＢＩＦ）和其他相关系统的网址。     

Reappraising a decade old explanatory model for pharmacognosy

It has been a decade since a tripod model of pharmacognosy – organism, biological activity, chemical structure – was proposed. Since then advances in all disciplines have taken science into the 21st century. What are the implications for pharmacognosy? In this paper we expand the previous model, adding a new module with focus on informatics to encompass results of new technical and theoretical advancements for drug discovery.     

Recent advances in i-Gene tools and analysis: microarrays, next generation sequencing and mass spectrometry

Recent advances in technology and associated methodology have made the current period one of the most exciting in molecular biology and medicine. Underlying these is an appreciation that modern research is driven by increasing large amounts of data being interpreted by interdisciplinary collaborative teams which are often geographically dispersed. The availability of cheap computing power, high speed informatics networks and high quality analysis software has been essential to this as has the application of modern quality assurance methodologies. In this review, we discuss the application of modern 'High-Throughput' molecular biological technologies such as 'Microarrays' and 'Next Generation Sequencing' to scientific and biomedical research as we have observed. Furthermore in this review, we also offer some guidance that enables the reader as to understand certain features of these as well as new strategies and help them to apply these i-Gene tools in their endeavours successfully. Collectively, we term this 'i-Gene Analysis'. We also offer predictions as to the developments that are anticipated in the near and more distant future.     

脂肪酶催化合成生物柴油的瓶颈问题及其对策研究进展

生物柴油，一种新型的清洁能源燃料，具有可再生、可生物降解、环境友好等优良的品性，可部分或全部替代石化柴油。碱催化法、脂肪酶催化法及超临界法是合成生物柴油的主要工艺，其中脂肪酶催化法是一种节能型、环保型工艺，在节能和环保方面,有着碱催化法无可比拟的优越性，具有良好的工业应用前景。但目前在实现产业化的进程中仍存在如酶成本高、稳定性较差、甲醇对酶的失活效应及反应时间长等瓶颈问题。通过固定化技术和全细胞催化剂的采用、甲醇流加方式的改进、溶剂工程的改善及酰基受体和耐醇酶的开发等技术手段，结合固定床生物反应器，较好地解决了这些瓶颈问题，从而推进了酶催化法合成生物柴油的工业化进程。本文主要对酶法合成生物柴油工艺存在的主要问题及相应对策研究进展进行概括介绍，并对其工业化发展前景进行讨论。