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菊花开花遗传调控机制研究进展

张秋玲, 李骏倬, 王钟曼, 戴思兰

张秋玲,李骏倬,王钟曼,戴思兰. 菊花开花遗传调控机制研究进展[J]. 植物科学学报,2023,41(6):768−780. DOI: 10.11913/PSJ.2095-0837.23004
引用本文: 张秋玲,李骏倬,王钟曼,戴思兰. 菊花开花遗传调控机制研究进展[J]. 植物科学学报,2023,41(6):768−780. DOI: 10.11913/PSJ.2095-0837.23004
Zhang QL,Li JZ,Wang ZM,Dai SL. Research progress on the genetic regulatory mechanism of flowering in Chrysanthemum[J]. Plant Science Journal,2023,41(6):768−780. DOI: 10.11913/PSJ.2095-0837.23004
Citation: Zhang QL,Li JZ,Wang ZM,Dai SL. Research progress on the genetic regulatory mechanism of flowering in Chrysanthemum[J]. Plant Science Journal,2023,41(6):768−780. DOI: 10.11913/PSJ.2095-0837.23004

菊花开花遗传调控机制研究进展

基金项目: 国家自然科学基金项目(32371948);北京市科技计划项目(Z191100008519002)。
详细信息
    作者简介:

    张秋玲(1993-),女,博士研究生,研究方向为花卉繁殖与栽培(E-mail:qiulin_zhang@sina.com

    通讯作者:

    戴思兰: E-mail:silandai@sina.com

  • 中图分类号: Q75

Research progress on the genetic regulatory mechanism of flowering in Chrysanthemum

Funds: This work was supported by grants from the National Natural Science Foundation of China (32371948) and Beijing Science and Technology Project (Z191100008519002).
  • 摘要:

    开花是植物发育过程中一个关键的质变过程,是植物从营养生长向生殖生长阶段的转变。对于观赏植物来说,开花的早晚决定了其市场应用和经济价值。植物开花受到内外信号的复杂调控,基于模式植物拟南芥(Arabidopsis thaliana (L.) Heynh)的研究,目前已经阐明了6条主要的开花调控途径,这些途径彼此独立又互相交叉,形成复杂的遗传调控网络。菊花(Chrysanthemum × morifolium Ramat)作为起源于中国的世界名花,是世界花卉市场的重要一员,但因其是典型的短日照植物,不仅增加了生产中开花期调控成本,也限制了菊花的应用范围。本文以高等植物开花遗传调控网络为基础,综述了菊花开花遗传调控机制的研究进展,以期为菊花开花时间改良育种工作提供理论指导,同时也为解析高等植物开花机制提供新见解。

    Abstract:

    Flowering represents a critical transition in plant development, shifting from the vegetative to reproductive growth stages. In ornamental plants, the timing of flowering significantly impacts marketability and economic value. Plant flowering is regulated by complex internal and external signals. Studies on the model plant Arabidopsis thaliana have identified six primary pathways related to flowering regulation. These independent but intersecting pathways form a complex genetic regulatory network. Chrysanthemum × morifolium, a famous flower originating from China, holds a considerable share of the world flower market. However, its typical short-day flowering requirements not only increase production costs but also limit its application scope. Based on the flowering regulatory networks of higher plants, this review discusses current research progress on the genetic regulatory mechanisms underlying chrysanthemum flowering, thus providing theoretical guidance for the breeding and improvement of flowering time, as well as new insights into the flowering mechanisms of higher plants.

  • 生物多样性作为地球上生命体的重要特征之一,是人类社会赖以生存和发展的物质基础[1, 2]。全球范围内的人类活动与快速的气候变化已经造成了生态系统失衡和持续的物种灭绝,导致生物多样性遭受严重的威胁[3, 4]。遗传多样性是生物多样性的重要组成部分,评估遗传多样性已经逐渐成为生态系统稳定性、自然资源与濒危物种保护和利用研究中必不可少的环节之一[5, 6]。因此,保护生物多样性尤其是遗传多样性事关人类福祉,是人类社会可持续发展的必然要求[7]。在此背景下,指导生物多样性保护和维持物种进化潜力的学科—保护生物学(Conservation biology)应运而生[8-10]。随着全球生物多样性面临的危机越来越严峻,该学科已成为生命科学领域中的一个重要分支。

    保护生物学旨在研究人类活动或其他因素对包括物种、群落和生态系统在内的全球生物多样性的潜在影响[8]。早期主要从遗传学角度探究物种亲缘关系、近交繁殖、遗传漂变、基因流、种群动态、遗传结构、遗传多样性等方面[11, 12]。经过数十年分子生物学技术的发展,保护生物学从最初利用形态学、细胞学、同工酶等评估多样性水平,发展到如今利用DNA分子标记鉴定遗传多样性[13]。检测遗传多样性常用的分子标记包括限制性内切酶片段长度多态性(Restriction fragment length polymorphism,RFLP)标记、扩增片段长度多态性标记(Amplified fragment length polymorphism,AFLP),以聚合酶链式反应(Polymerase chain reaction, PCR)技术为核心的随机扩增多态性DNA标记(Random amplified polymorphic DNA, RAPD)、单引物扩增反应(Single primer amplification reaction,SPAR)、简单重复序列(Simple sequence repeat,SSR)以及简单序列重复间区的DNA标记(Inter simple sequence repeat,ISSR)技术等[14]。虽然分子标记技术推动了保护生物学的发展,但由于传统的分子标记数目较少,揭示的遗传变异仅代表物种全基因组水平的极少一部分。此外,常用的分子标记多为中性位点,无法解析自然选择和局域适应驱动的适应性遗传变异的时空格局与机理[15-17]

    下一代测序技术(Next-generation sequencing,NGS)的发展和成熟推动了如简化基因组测序(Restriction-site associated DNA sequencing, RAD)、基因分型测序(Genotyping-by-sequencing,GBS)、全基因组重测序(Whole genome resequencing)等技术的出现[18]。研究人员通过数以万计的单核苷酸多态性(SNP)位点,可检测物种不同种群在基因组水平上的遗传变异,对深入解析物种适应性进化具有重要意义[19]。同时,随着分子生物学和基因组学的发展,能够解析种群遗传变异空间分布特征的景观基因组学得以快速发展,推动适应性进化的分子机理研究跨入了一个新的时代[20-22]。景观基因组学依靠基因型-环境互作关系(Genotype-environment associations,GEAs),通过整合遗传变异和生物的空间分布模型,可从基因组水平研究物种的适应性遗传变异与景观特征之间的相互作用,并可与驱动物种自然选择的重要环境因子进行关联,解析环境因子在塑造物种遗传分布格局中的作用[20, 23]。通过鉴定基因组水平上由于环境变化引起的适应性遗传变异[20, 24],可以深入挖掘当前及未来气候变化下功能基因在适应性进化过程中发挥的作用,这为生物多样性保护带来了新的机遇[25, 26]。越来越多的研究基于景观基因组学的手段解析物种遗传多样性分布格局、遗传适应潜力以及濒危机理等科学问题,并进一步提出科学合理的保护策略[27-30]

    本文首先介绍了景观基因组学方法如何基于基因型-环境关联对物种适应性遗传变异的分布格局进行解析,接着阐述了近年来景观基因组学方法在保护生物学研究中应用的案例,最后,进一步针对景观基因组学方法在保护生物学研究中存在的问题及未来发展趋势提出了建议。

    目前,异常位点(Outlier loci)主要基于遗传分化指数(FST)和基因型-环境互作关系两类方法进行[31-34]。前者通过比较种群内或者种群间的FST,筛选超出中性选择预期的较高或较低的遗传分化位点。这些位点即为可能受到适应性进化影响的异常位点[31, 35]。目前,这类方法常用的分析软件包括BAYESCAN[32]、OutFLANK[36]和PCadapt[37, 38]等。

    BAYESCAN使用贝叶斯方法将FST系数分解为特定种群和特定遗传位点的成分,并估计遗传位点偏离哈迪-温伯格平衡(Hardy-Weinberg equilibrium)的后验概率。显著偏离的位点即被检测为异常位点[32]。该方法考虑有效种群大小和种群的迁移率,但对错误发现率(False discovery rate,FDR)的设置较为敏感,较低的FDR会导致假阳性。OutFLANK是另一种基于FST的方法[36]。该方法通过计算所有个体的FST分布,去除可能受多样性(Diversity selection)或平衡(Balancing selection)选择导致的异常高以及异常低的位点,得到假定的中性位点的零分布(Null distribution)模型,之后再通过该模型在所有位点中检测异常位点。该方法针对复杂进化历史的种群具有较高的检测效率。PCadapt则是一种基于非约束性排序的主成分分析方法,通过对所有可能的主成分进行碎石图(Scree plot)检测,确定可能的种群结构,并选择合适的主成分(K)值[37, 38],然后根据马氏距离(Mahalanobis distance)筛选与种群结构显著相关的异常位点。在种群中存在杂合个体或种群结构连续的情况下,相比其他两类方法,该方法具有更高的检测效率。除了上述分析方法外,ARLEQUIN[31]、FDIST[39]、FLK[40]、XTX[41]以及最新发展的全基因组分化扫描(Genome wide differentiation scan,GWDS)方法[42]也被用于筛选遗传分化异常位点。

    环境因素在不同种群的适应性进化中可能发挥重要作用,上述方法在检测异常位点时并未考虑种群间的环境异质性[43]。基于GEAs的异常位点检测方法可考虑环境因素在塑造不同种群适应性进化中的作用,该方法通过解析种群遗传变异与环境梯度之间的关系,可鉴定与环境梯度存在显著关联的异常位点[19, 24]。环境变量信息(如降水、温度、太阳辐射等)可通过WorldClim气候网站(Https://worldclim.org/)进行下载,并利用R语言[44]或者地理信息系统软件ArcGIS,基于种群经纬度信息提取对应的环境变量。由于环境变量之间的共线性(Collinearity)会对结果造成干扰,在进行分析前,可利用R语言中的usdm函数包剔除共线性较高的环境变量[45]。GEAs常用的分析方法有BAYENV[41, 46]和潜在因素混合模型(Latent factor mixed modelling,LFMM)[33, 47]等。BAYENV是一种贝叶斯方法,通过中性位点建立等位基因频率矩阵作为零分布模型,并基于该模型检验所有遗传位点的等位基因频率与环境变量的相关性。该方法可排除种群历史动态对检测结果的干扰[41]。LFMM基于贝叶斯混合模型,在检测环境变量与遗传变异的相关性时,可将种群结构作为潜在因子引入模型,从而有效地估计由种群进化历史和距离隔离模式引起的随机效应[33]。在更新的2.0版本中,可基于最小二乘法对模型潜在因子进行更准确地评估,在计算速度上也有所提升[47]。除了上述环境关联方法外,BayPass[48]、冗余分析(Redundancy analysis,RDA)[49]、BayeScENV[50]和Samβada[34]等方法也可用于检测与环境关联的异常位点。

    距离隔离模式(Isolation by distance,IBD)反映种群扩散受地理梯度影响而导致的种群遗传分化的局部积累情况[51]。而环境隔离模式(Isolation by environment)则反映不同栖息地的环境选择压力对种群遗传分化的影响[19]。因此,距离和环境隔离模式检测可解析地理或环境梯度在塑造物种遗传变异的空间分布和种群遗传结构中发挥重要作用。

    蒙特尔检测(Mantel test)和冗余分析是进行距离和环境隔离检测应用较广的两类方法[52, 53]。蒙特尔检测是基于种群间的遗传、地理和环境的分化矩阵,分析其相关性来评估种群遗传分化的驱动因素。遗传分化矩阵可利用R语言中的hierfstat函数包进行计算[54]。地理矩阵通常计算种群间的欧氏(Euclidean)距离,可基于种群经纬度信息利用geosphere函数包实现[55]。计算环境矩阵时,考虑到环境变量间的大小差异,需首先对环境变量进行中心化(Center)和标准化(Scale),并基于R语言ecodist函数包中的布雷柯蒂斯(Bray-curtis)方法计算环境矩阵[56]。由于地理和环境变量之间通常存在一定的相关性,因此,在评估距离(环境)矩阵与遗传分化矩阵的相关性时,会进行偏蒙特尔检测(Partial Mantel test)以控制环境(距离)矩阵的影响。蒙特尔检测由于涉及到模型的统计假设问题,检测效率不高,并可能会导致假阳性的发生[57]。RDA是一种基于约束排序(Constrained ordination)的多变量统计方法,可检验遗传变异与地理、气候的多元关系,与蒙特尔检测相比,假阳性率较低[49, 58]。该方法可通过R语言中的vegan函数包实现[59]。冗余分析假设响应变量(Y)和解释变量(X)之间存在线性关系,在应用时将遗传数据作为响应变量矩阵,环境数据(地理、气候)作为解释变量矩阵,并通过环境数据来解释个体之间的遗传变异。考虑到进行冗余分析时,可能存在对环境变量的过拟合(Overfitting)现象,分析前需对环境变量通过前置选择(Forward selection)进行筛选[60]。与偏蒙特尔检测相似,冗余分析通常也进行偏冗余分析(Partial RDA)来控制协变量对遗传分化的影响[58]。此外,典型相关分析(Canonical correlation analysis,CCA)也可用于估算空间和环境变量的相对贡献[61]

    解析物种对环境梯度的响应,并进一步确定可用于预测生物多样性模式的重要环境变量是生态学研究和关注的热点之一[62]。在此背景下,研究人员将遗传和环境数据关联,进行空间建模框架的搭建,从而深入解析景观尺度上物种环境适应性的遗传基础[63]。基于机器学习(Machine-learning)的梯度森林(Gradient forest,GF)[64]和基于矩阵回归(Matrix regression)的广义相异建模(Generalized dissimilarity modelling,GDM)[65]是近年来应用较广的生物多样性建模技术,可鉴定个体或种群的等位基因频率沿着环境梯度的变化趋势。这两类方法在基因-环境关系的探究中适用于明显的非线性关系,在应用于大型基因组数据集时,可处理许多低频等位基因[65]。同时,两者均可将物种不同种群的适应性遗传变异在景观尺度上进行映射。最重要的是,GF和GDM模型可评估未来气候情景下物种不同种群的遗传偏移(Genetic offset),即在当前和未来气候情景下遗传组成的不匹配(Mismatch)程度。遗传偏移越高的种群预计更易受到未来气候变化的影响,种群的遗传脆弱性越大。最近的一项研究将迁移和扩散纳入这两种模型,通过预测种群的正向(Forward)、反向(Reverse)和局域(Local)遗传偏移,可评估种群在原地(in situ)留存或迁移到新的合适栖息地的可能性,新模型可更全面地解析物种应对未来气候变化的适应能力[66]。这两类方法基于个体或种群的等位基因频率与环境数据,可利用R语言中的gradientForest函数包(Http://gradientforest.r-forge.r-project.org/)与gdm函数包[67]实现。

    在当今气候快速变化的背景下,解析物种在未来气候下的适应性进化,是生物多样性保护的关键。因此,我们亟需基于基因型-环境关系建立物种当前气候与种群适应性遗传变异的景观模型,然后基于该模型在空间(物种分布)和时间尺度(未来气候)上进行投影,进一步预测在未来气候变化下物种不同种群的遗传脆弱性[68]。未来气候数据可通过WorldClim气候网站(Https://worldclim.org/)下载,依据温室气体排放量的不同,网站提供了4种共享社会经济路径(Shared socioeconomic pathways,SSPs)情景(SSPs 126、 245、370、585),分别代表碳排放量从低到高。此外,网站提供了未来4个时期(2021-2040、2041-2060、2061-2080、2081-2100)的气候预测。GF、GDM和非适应性风险分析(Risk of non-adaptedness,RONA)是预测未来气候情景下遗传脆弱性应用较广的方法[64, 65, 69]。GF和GDM通过评估种群的遗传偏移程度来预测物种在未来气候下的遗传脆弱性[64, 65],这两类方法在上一节已做过介绍。非适应性风险分析基于当前基因型-环境关系建立线性回归,并以此预测未来气候下的等位基因频率,当前与未来等位基因频率的差值即为RONA值,表示物种为适应未来气候所需的等位基因频率的平均改变。该值越高,表明种群不适应未来气候的风险越高,即遗传脆弱性越大。RONA分析可基于Pina-Martins等[70]开发的Python脚本实现。此外, RDA也可基于建立的基因型-环境关系进行未来气候下的景观预测[58]

    保护生物学是20世纪80年代兴起于美国的一门综合性学科,主要目标是评估人类活动对生物多样性的潜在影响,并提出相关保护策略[8]。基于基因型-环境关联的景观基因组学方法的出现极大推动了保护生物学的发展[20, 22]

    森林是许多陆地生态系统的主体,在保护生物多样性、维持全球碳循环平衡、应对气候变化和全球变暖方面发挥着重要作用[20, 66, 71]。森林树种的边缘种群相对分布于中心的种群来言,可能存在较低的遗传多样性水平[72]。这些种群由于缺少应对未来气候变化的遗传变异,可能具有较高的脆弱性和灭绝风险,应加强对其的保护和管理。如,Yuan等[73]基于全基因组重测序对中国广泛分布的栎属(Quercus)植物麻栎(Q. acutissima Carruthers)27个种群117个个体在未来气候下的适应潜力进行了探究,通过遗传-环境关联分析在麻栎种群中鉴定到与气候适应性相关的多个功能基因,证明了麻栎的多基因适应模式。此外,作者借助GDM分析量化了麻栎应对气候变化的遗传偏移,发现边缘种群面临更高的局部灭绝风险。该研究在基因组水平上为麻栎的环境适应潜力提供了证据,并为物种遗传多样性保护提供了理论依据。Du等[74]对我国西南部生物多样性中心的建群树种栎属川滇高山栎(Q. aquifolioides Rehder & E. H. Wilson)进行了局域适应机理及未来气候变化的适应潜力研究。作者利用65个与干旱胁迫相关的候选基因,检测到川滇高山栎分布范围内不同的遗传变异模式,发现西藏谱系的遗传变异与地理距离相关,而喜马拉雅-横断山脉/川西高原谱系的遗传变异与环境相关,并进一步利用RONA方法检测到种群分布范围最东侧的边缘种群脆弱性较高。该研究为解析树种应对气候选择压力的响应模式提供了参考。珍稀濒危植物作为生物多样性的重要组成部分,一直以来都是保护生物学研究的重点和热点之一。Cao等[75]对东亚第三纪孑遗濒危植物领春木属(Euptelea)现存两物种响应环境变化的基因组变异及未来气候下的脆弱性进行了研究,对中国分布的大果领春木(E. pleiosperma J. D. Hooker & Thomson)和日本分布的多花领春木(E. polyandra Siebold & Zucc.)共171个个体进行简化基因组测序(RAD-seq)。结果表明,中新世晚期的气候震荡和海平面升降促进了领春木属的物种形成,4个与温度相关的气候因子驱使大果领春木种群的局域适应形成,而地理隔离是日本多花领春木种群遗传分化的主要驱动因素。梯度森林进一步检测发现现存的中国中东部种群最易受未来气候变化影响,种群脆弱性较高。该研究促进了人们对东亚孑遗森林树种的物种形成和适应性进化的认识,表明在全球气候变暖背景下我国东部地区孑遗森林树种的保育亟待加强,并为其他孑遗植物类群适应性潜力和灭绝风险区域的评估提供了方法学指导。借助景观基因组学方法,在草本和灌木类群的保护研究中也取得了一定进展。譬如,借助GEAs方法,研究人员在草本植物崖爬藤属(Tetrastigma[76]、报春花属(Primula[77]以及灌木植物金菀木属(Ericameria[78]等物种中鉴定了与环境关联的适应性遗传位点,评估了植物类群在不同气候梯度下的基因组变异以及基因组对未来气候变化的脆弱性,阐明了这些类群的气候适应性遗传机理,为保护管理工作提供了重要基础。

    除植物外,基于景观基因组学方法,在动物保护生物学方面也取得了一定的进展。Maier等[79]利用景观基因组学方法对加利福尼亚州内华达山脉的特有蟾蜍属物种进行了气候变化下适应潜力的预测。作者基于双酶切测序(Double-digest,dd-RAD),通过RDA和贝叶斯关联方法筛选到24个响应气候选择的候选位点,候选位点相关基因参与了对环境变化的响应。接着通过预测在未来气候下的选择压力,并利用基因型-环境关联来估计种群的适应性,确定了蟾蜍分布在北部、东部以及西南低海拔区域等3个适应未来气候的进化单元(Evolutionary units)。该研究为利用景观基因组学方法保护濒危物种蟾蜍提供了一种全面的策略,在其他濒危物种中也具有广泛的适用性。Bay等[80]基于RAD测序研究了对北美候鸟黄莺(Setophaga petechia L.)气候适应的基因组变异,发现在黄莺分布范围内存在显著的距离隔离和环境隔离效应,通过LFMM分析,还检测到降水与黄莺遗传变异的关系最为密切。此外,北美洲西部地区的种群表现出较高的基因组脆弱性,气候变化对这些地区的种群影响较大。该研究为解析鸟类及其他野生动物的基因组脆弱性提供了参考。Jaffé等[81]对南美洲分布的重要传粉者无刺蜜蜂(Melipona subnitida Ducke)的遗传结构及局域适应模式进行了评估,检测到4个遗传谱系,通过检测与温度、降水和森林覆盖度相关的基因组变异,发现这些变异在空间上呈纬度和海拔的分布模式。该研究强调了维持局域适应以及改善不同生境之间的连通性等保护行动对未来蜜蜂授粉的重要作用。气候变化、人类活动等因素对生物多样性构成了严重威胁,基于景观基因组学方法,对物种濒危机理、遗传脆弱性及局域适应等进行研究,可为基因组脆弱区解析、脆弱性种群评估、遗传拯救方案(辅助基因流、辅助迁移等)制定、种质资源储备及保护单元划分等提供关键信息,从而推动保护生物学的快速发展[82-84]

    测序技术的发展和基因组学时代的来临,极大地推动了景观基因组学方法在保护生物学研究中的应用。然而,在应用过程中也存在一些问题。首先,在应用景观基因组学方法解决某一类问题时,有多种方法(如上述异常位点筛选方法)可采纳[22, 85]。由于不同方法基于不同的假设,会存在不同的假阳性现象。因此,通常需综合利用多种方法以获得可靠的结果。其次,以往的景观基因组学研究大多关注基因分化,而较少关注不同种群间的表型分化[86]。未来需要加强同质园和交互移植实验表型数据的获得,并通过基因型-表型关联分析鉴定表型变异的遗传基础,为生物多样性保护和濒危物种的种群复壮提供理论基础。第三,目前多从单一物种角度研究气候变化对生物多样性的影响[19],但同一区域不同物种可能采取不同的气候响应策略,为了探究气候变化对群落生物多样性的影响,更好地指导群落恢复和生境重建工作,未来需利用景观群落基因组学(Landscape community genomics)方法,在群落水平上进行多物种气候响应模式的比较研究[87]。最后,解析种群遗传变异的成因(过去)、遗传多样性的分布格局(当前)及响应未来气候变化的分子机理(未来),可全面了解种群的演化动态,是科学合理地进行生物多样性评估、保护和恢复的基础。景观基因组学研究聚焦于解析物种遗传多样性分布格局,以及响应未来气候变化的分子机理,往往忽略了结合种群遗传变异的历史成因。谱系地理学(Phylogeography)利用基因谱系关系及其时空分布来追溯种群的进化历史,可深入理解种群地理分布格局以及遗传变异的历史成因[88]。只有在全面了解物种进化历史的基础上,对遗传变异与重要环境因子进行关联,才能对物种当前遗传多样性分布格局及未来气候响应机理进行科学的探讨。目前,借助谱系地理学在物种进化历史方面已做了大量的研究和综述,系统阐述了相关类群的谱系地理式样及影响其种群分化形成的地质历史与气候因素[89, 90]。因此,后续工作可整合景观基因组学和谱系地理学,从种群进化历史、适应性遗传变异分布格局及未来气候响应机理方面入手,综合全面地开展保护生物学相关研究。

    总之,未来在基于景观基因组学方法进行保护生物学研究时,首先需了解物种的进化历史(有效种群大小、谱系分化、基因渐渗等),在此基础上,通过综合比较,选择合适的方法在群落水平上解析同一区域或同一属内多个物种的环境响应机理,挖掘机理背后共同的进化动态。同时,应注意物种不同种群间表型数据的获取,以解析其表型变异的遗传基础。最后,进一步预测物种在未来气候情景下的遗传脆弱性和适应风险,评估不同物种的适应潜力,并以此来开展保护单元划分、遗传拯救方案制定、优先保护种群等保护行动。近十年来 ,高通量测序(High-throughput sequencing)技术得到了蓬勃发展,但简化基因组以及基因分型测序由于本质上仍然是基因型数据,缺少物种全基因组信息,且数据可靠性依然存在一定的问题[91]。而全基因组重测序作为目前分辨率最高的基因组学方法,为保护生物学研究提供了充分保障[18]。未来随着测序技术成本的不断降低,必将有越来越多的物种获得全基因组信息,保护生物学研究也将全面进入基因组时代。目前正处于第六次大规模物种灭绝中,全球生物多样性面临严重的危机[92]。通过全面解析物种过去、当前、未来的种群演化动态,并基于此提出生物多样性保护策略,可为保护生物学研究带来新的机遇。

  • 图  1   拟南芥中以光周期途径为主的开花途径和菊花中响应光周期的同源基因

    红线代表在菊花中涉及的研究(参考网站:https://www.wikipathways.org/index.php/Pathway:WP622)。

    Figure  1.   Flowering pathway dominated by photoperiodic pathway in Arabidopsis thaliana and photoperiod-responsive homologous genes in Chrysanthemum

    Red line represents research involving Chrysanthemum (Photoperiodic pathway available online: https://www.wikipathways.org/index.php/Pathway: WP2312).

    图  2   高等植物温度途径及菊花中响应温度变化的同源基因

    红线代表在菊花中涉及的研究。

    Figure  2.   Temperature pathway of higher plants and temperature-responsive homologous genes in Chrysanthemum

    Red line represents research involving Chrysanthemums.

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出版历程
  • 收稿日期:  2022-12-11
  • 修回日期:  2023-01-12
  • 网络出版日期:  2023-03-12
  • 刊出日期:  2024-01-04

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