环纹榕传粉榕小蜂的传粉模式

榕小蜂的传粉结构、 传粉行为以及寄主榕树的花药胚珠比是判断榕-蜂互惠系统传粉模式的重要依据。本研究于2010年8月至2011年6月对位于云南西双版纳地区的试验样树环纹榕Ficus annulata进行观察, 对出榕果的环纹榕传粉榕小蜂Deilagaon annulatae进行电镜扫描和室内显微镜下对其进行行为观察。电镜扫描显示： 环纹榕传粉榕小蜂D. annulatae位于胸部的花粉筐消失, 具一片可粘附花粉的毛区, 提示其属于被动传粉的种类。传粉行为观察发现, 该蜂没有主动采集花粉的行为, 显然存在的花粉刷已丧失了主动收集花粉的功能。寄主植物环纹榕F. annulata属于典型的自动散粉让榕小蜂沾附花粉的榕树种类。环纹榕传粉榕小蜂D. annulatae是西双版纳热带地区已知58种传粉榕小蜂中唯一体色为黄色的种类, 该蜂偏爱在低温的夜晚出蜂。除了传粉榕小蜂, 一种金小蜂Lipothymus sp.也在雌花期进入榕果内繁殖, 并且其数量显著高于环纹榕传粉榕小蜂D. annulatae (P<0.0001)。自然单果的繁殖中, 环纹榕传粉榕小蜂的数量显著高于种子数量, 呈现出榕-蜂互惠系统中罕见的传粉榕小蜂主导的局面。综合榕-蜂的传粉特征显示, 环纹榕F. annulata及其传粉榕小蜂D. annulatae互惠系统是被动传粉的模式。     

聚果榕传粉榕小蜂传粉与产卵之间的权衡

【目的】榕树(Ficus)依赖专性榕小蜂(Agaonidae)传粉,同时为传粉榕小蜂提供繁衍后代的场所,两者形成动植物间经典的协同进化关系。在雌花期果内,榕小蜂需在有限的存活时间内完成传粉和产卵,而传粉榕小蜂如何在传粉与产卵之间进行权衡仍然是悬而未解的问题。本研究旨在明确传粉榕小蜂——一种栉颚榕小蜂Ceratosolen sp.在雌雄同株的聚果榕Ficus racemosa雌花期果内的行为活动及繁殖模式。【方法】借助测微尺测量聚果榕榕果雌花花柱长度与传粉榕小蜂(Ceratosolen sp.)产卵器长度,通过显微视频记录传粉榕小蜂在雌花期果内搜索、传粉及产卵行为;结合单果控制性引蜂试验,测定不同阶段榕小蜂个体大小、孕卵量、携粉量,以及雄花期最终繁殖的榕小蜂后代和榕果种子数量。【结果】聚果榕雌花花柱长度存在树间变异,榕小蜂产卵器长度比绝大多数的雌花花柱长,说明该小蜂可以产卵于大部分的雌花子房里。通常个体大的榕小蜂孕卵量更多,但个体大小与携粉量之间相关性不显著。观察发现,榕小蜂进入雌花期榕果内,前6 h集中产卵,可产下孕卵量的95%,平均搜索用时27 s,产卵用时46 s,此期间传粉行为少...     

基于集成的共表达网络分析方法研究3种癌症的肿瘤相关模块

在肿瘤/癌旁基因表达数据中,差异表达 (DE,differential expression) 代表各种生物条件下基因表达水平的变化,而差异共表达 (DC,differential co-expression) 代表基因对之间相关系数的变化。单独的DC和DE研究方法已经被广泛应用于人类疾病研究中。但是,目前仍然缺乏有效整合DC和DE的分析方法。文中提出一个新颖的分析框架DC&DEmodule,该框架可以基于共表达模块整合DC和DE的特征,并同时整合多个肿瘤/癌旁表达谱的信息,用以识别与疾病相关的基因共表达模块,包括激活模块 (肿瘤样本中上调且共表达增强) 和失能模块 (肿瘤样本中下调且失去共表达)。将该框架用于分析肝癌、胃癌和结直肠癌各两组微阵列数据,分别得到肝癌、胃癌和结直肠癌的2、5和2个激活模块以及5、5和1个失能模块。富集分析表明与同类方法相比,文中的方法在检测已知的肿瘤相关通路和发现新通路方面均具有更高的灵敏度。然后,进一步从这3种癌症的激活模块中鉴定出17、69和11个模块关键基因,其中包含53个已报道的预后生物标志物以及3个分别与3种癌症存活率显著相关的新预后标志物。基于关键基因训练了3种癌症的随机森林模型,用于区分TCGA(The Cancer Genome Atlas) 和GEO (Gene Expression Omnibus)数据库中的肿瘤和癌旁样本,结果显示其分类的平均准确性达到了93%。三种癌症的比较为不同癌症的共有和组织特异性机制提供了新的见解。一系列评估表明,DC&DEmodule框架能够整合公共数据库中快速积累的表达谱,发现更多疾病中功能失调的生物过程。     

基因共表达网络分析揭示马铃薯薯块发育特征及原始抗性

转录组测序(RNA-seq)技术提供的全基因组数据信息已广泛应用于研究多个样本之间的基因表达模式和调控机制.通过构建种间或种内基因共表达网络(GCNs)挖掘的表达相关基因在功能上通常是相似的.对于马铃薯(Solanum tuberosum)而言,目前有大量的公共转录组测序数据,但是缺乏针对这些高通量数据构建的GCN网络,因此也无法探索在不同基因型、不同组织以及不同环境条件下基因的表达模式及规律.本研究选取16个公共转录组测序数据库构建了 GCN网络,这些数据库涵盖了来自全球各地的11个马铃薯栽培种.基于两两间基因表达相关性,我们在GCN网络中发现了一些具有特定生物学意义的基因模块.该网络共由14个基因模块组成并富集到植物光合形态建成、薯块休眠解除等多个生理过程,其中一个模块的134个基因在原始栽培种(ssp.Andigena)中特异性高表达,且通过功能富集发现这些基因与马铃薯病害和逆境的抗性相关.该结果揭示了在马铃薯人工驯化期间基因进化压力出现遗传漂移.本研究中基于GCN网络分析揭示了马铃薯种间和种内基因共表达模块的聚类以及不同模块基因间在进化上的分化,为马铃薯基因功能研究提供了新的视角.     

西双版纳聚果榕隐头果内小蜂群落结构及种间关系

聚果榕Ficus racemosa Linn.是雌雄同株榕树,它是西双版纳热带雨林生态系统中的一个常见种群。聚果榕必须依靠聚果榕小蜂Ceratosolen fusciceps Mayr传粉才能获得有性繁殖,而聚果榕小蜂又必须依靠聚果榕隐头果内短柱花繁衍后代,两者间形成了种间专一的互惠共生体系。同时,在其隐头果内还存在一个复杂的非传粉小蜂功能群,它们主要是榕树种子和传粉榕小蜂的寄生者。在云南省西双版纳自治州勐腊县勐仑镇选取了5个样地,对聚果榕单果内小蜂群落组成和种间相互关系进行研究。在不同时间段采集聚果榕单果242个,共收集小蜂366660头。聚果榕隐头果内有6种小蜂,隶属小蜂总科Chalcidoidae中的榕小蜂科Agaonidae、长尾小蜂科Caliimomidae、金小蜂科Pteromalidae,其中榕小蜂科中的C．fusciceps是聚果榕唯一的传粉者。金小蜂科中的Apocryta westwoodi Grandi和Apocryta sp.两个种是榕小蜂的寄生者,它们的寄生是传粉榕小蜂的种群数量减少因素之一。长尾小蜂科中Platyneura agraensis Joseph,Platyneura mayri Rasplus和Platyneura testacea Motschulsky3个种是寄生榕树种子或与榕小蜂争夺食物(瘿花)资源的小蜂类群,它们的出现与发生,致使聚果榕正在发育成种子的长柱小花形成瘿花,同时一部分种类把卵寄生在已被榕小蜂产卵的短柱小花子房中与榕小蜂争夺食物资源,致使榕小蜂食物资源的匮乏而死亡,对传粉小蜂种群有明显的影响。传粉小蜂从花托口钻入隐头果内,在隐头果内的长柱小花传粉和短柱小花子房中产卵,5种非传粉小蜂从隐头果外部把产卵器刺穿果肉把卵产在小花子房上。通过对5块样地隐头果内小蜂群落的综合分析发现,传粉榕小蜂为优势种群,而Apocryta sp.种的数量最少。在传粉及非传粉小蜂自然群体中各种类性比明显具有偏雌现象。小蜂群落表现异常的是聚果榕孤立株的样地,该样地传粉小蜂个体数量明显下降,非传粉小蜂个体数量则增加,各小蜂种群之间竞争激烈,出现C．fusciceps和P．mayri两个优势种。     

对叶榕传粉小蜂性比率的调节和稳定

传粉榕小蜂呈现偏雌的性比率,单双倍体性别决定系统、局域配偶竞争和近交效应被认为是调节偏雌性比率的3个主要机制。通过研究影响对叶榕传粉小蜂性比率的因素,结果表明传粉榕小蜂的偏雌性比率随局域配偶竞争强度的降低而增加;受母代雌蜂交配次数的影响,随着母代雌蜂交配次数的增加,子代的偏雌性比率逐渐降低,这一结果首次揭示了传粉榕小蜂的交配制次数对性比率的影响,并在个体水平上定量了性比率变异与雌蜂交配频次的关系。传粉小蜂的性比率与共生的非传粉小蜂的关系,非传粉小蜂的介入直接减少了传粉小蜂的数量,甚至对传粉小蜂的种群有显著影响,结果发现非传粉小蜂对传粉小蜂雌雄性的分配比率没有显著影响,传粉榕小蜂仍能正常地进行繁殖。传粉与非传粉者小蜂之间作用关系的确定,可为进一步理解两者的稳定共生的机制提供科学证据。     

鸡嗉果榕榕小蜂产卵时序与种群数量分析

榕树与传粉榕小蜂的互惠共生体系被多种非传粉榕小蜂利用。在同一个榕果中,具有较高种群密度的传粉榕小蜂和多种非传粉榕小蜂生存在一起并相互作用。通过野外观察和采样分析的方法,对鸡嗉果榕内5种榕小蜂（传粉榕小蜂Ceratosolen gravelyi;非传粉榕小蜂Apocrypta sp., Philotrypesis dunia, Platyneura cunia和Sycoscaptertr ifemmensis）的产卵行为进行了研究。结果证明,鸡嗉果榕内生活的各种榕小蜂通过比较严格的产卵时序在榕果内产卵以占领不同的生态位,这反映了这些榕小蜂各自的生物学特性。自然种群数量分析表明,单个非传粉榕小蜂物种的种群数量几乎不与传粉榕小蜂种群数量呈负相关,而所有非传粉榕小蜂物种的种群个体总量与传粉榕小蜂的种群个体数呈正相关,这可能是多个物种共存于单个榕果内的进化适应。     

聚果榕传粉榕小蜂传粉与产卵之间的权衡

【目的】榕树(Ficus)依赖专性榕小蜂(Agaonidae)传粉,同时为传粉榕小蜂提供繁衍后代的场所,两者形成动植物间经典的协同进化关系。在雌花期果内,榕小蜂需在有限的存活时间内完成传粉和产卵,而传粉榕小蜂如何在传粉与产卵之间进行权衡仍然是悬而未解的问题。本研究旨在明确传粉榕小蜂——一种栉颚榕小蜂Ceratosolen sp.在雌雄同株的聚果榕Ficus racemosa雌花期果内的行为活动及繁殖模式。【方法】借助测微尺测量聚果榕榕果雌花花柱长度与传粉榕小蜂(Ceratosolen sp.)产卵器长度,通过显微视频记录传粉榕小蜂在雌花期果内搜索、传粉及产卵行为;结合单果控制性引蜂试验,测定不同阶段榕小蜂个体大小、孕卵量、携粉量,以及雄花期最终繁殖的榕小蜂后代和榕果种子数量。【结果】聚果榕雌花花柱长度存在树间变异,榕小蜂产卵器长度比绝大多数的雌花花柱长,说明该小蜂可以产卵于大部分的雌花子房里。通常个体大的榕小蜂孕卵量更多,但个体大小与携粉量之间相关性不显著。观察发现,榕小蜂进入雌花期榕果内,前6 h集中产卵,可产下孕卵量的95%,平均搜索用时27 s,产卵用时46 s,此期间传粉行为少见,花粉筐中携带花粉量亦无明显变化;榕小蜂进果后6-24 h,主要执行传粉,其行为主动,连贯高效,单次传粉用时平均为2 s,最终可传完携粉量的80%。控制引蜂试验也证实榕小蜂进入榕果内前6 h主要执行产卵繁殖后代,之后6-24 h主要执行传粉以繁殖榕树种子。【结论】在雌雄同株的聚果榕雌花期榕果内,榕小蜂先产卵、后传粉。本研究首次展示了传粉榕小蜂在聚果榕雌花期榕果内的产卵和传粉行为,并获得与行为相匹配的产卵量和传粉繁殖量,反映了具主动传粉行为的榕小蜂在传粉和产卵之间存在时间和数量上的权衡。     

影响对叶榕及其传粉者繁殖的生态学因素

在西双版纳,分别统计了对叶榕(Ficus hispida)雌花期雌雄果的进蜂量和花后期雌雄果繁殖的多个特征值,以此来探讨自然条件下,影响对叶榕及其传粉榕小蜂(Ceratosolen solmsi marchali)繁殖的因素。结果表明:单果内有效进蜂数量是影响种子生产和传粉榕小蜂繁殖的首要因素,而雌花期进果的传粉榕小蜂并不是都能全部进入果腔传粉或产卵,大部分蜂还未进到果腔就被夹死在顶生苞片层的通道里,能进入雌果内传粉的榕小蜂为(2.72±2.04)只·果^-1,约占总进蜂量的52%;而在雄果里,能进入果腔的蜂量只有(2.08±1.65)只·果^-1,占35%左右。由于雌果内的雌花显著比雄果内的雌花多,结合单果进蜂量雌多雄少的格局,最终单果生产的种子数量 (1 891.63 ± 471.53)比传粉榕小蜂的数量 (367.20 ± 208.02) 多5倍有余。在雌果里,供给传粉的雌花数量与所生产的种子数量之间呈显著的正相关,而没有接受到花粉或不能正常受精的雌花数量与种子数量呈显著的负相关。雄果不仅生产花粉,也是传粉榕小蜂繁殖的场所,在相关于传粉榕小蜂自身繁殖力的因子中,传粉榕小蜂产卵制造的瘿花数量对其种群数量有最大的影响;影响次之的是发育过程中死亡的个体数量,它可降低30%左右的传粉榕小蜂数量;影响排在第三位的是寄主的雌花数量。此外,3类非传粉者的存在,单果内平均可减少30多只传粉小蜂。     

埃及伊蚊不同组织的基因共表达模式分析

【目的】利用权重基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)探索埃及伊蚊Aedes aegypti不同组织基因共表达模式。【方法】从NCBI SRA数据库中选择埃及伊蚊不同组织的转录组数据中具代表性的9种组织(雌雄成蚊的触角和脑,雌蚊的喙、下颚须和卵巢,雄成蚊的前足、中足、后足和腹部末端)的双端测序数据;经过缺失值移除以及方差计算后,筛选出方差最大的5 000个基因,利用R软件中WGCNA包建立埃及伊蚊成蚊不同组织的基因共表达网络并划分模块;然后利用clusterProfiler包对组织特异性模块内的基因进行GO(Gene Ontology)和KEGG(Kyoto Encyclopediaof Genes and Genomes)富集分析,并用Cytoscape软件中的CytoHubba插件筛选共表达模块内的hub基因。【结果】从埃及伊蚊成蚊不同组织中共鉴定出11个基因共表达模块,在雌蚊触角、喙、卵巢、下颚须以及雄蚊脑、腹部末端组织中各鉴定出1个特异性表达模块,雄蚊前足、中足和后足组织中无特异性表达模块。6个组织特异性表达模块内基因功能注释到组织生物学功能;其中,雌蚊触角特异性green模块内基因具有气味结合和嗅觉受体活性等功能;雌蚊喙特异性purple模块内基因具有丝氨酸型肽链内切酶活性和丝氨酸水解酶活性等功能;雄蚊脑特异性blue模块内基因在生物学过程调节、信号转导和神经系统过程等生物学过程中发挥主要作用。利用CytoHubba进一步鉴定出所选组织特异性共表达模块中具有高连通性的hub基因,包括AAEL010426, AAEL002896, AAEL002600, AAEL000961, AAEL007784和AAEL006429。【结论】本研究依据埃及伊蚊不同组织转录组数据,利用WGCNA方法发现了许多重要的基因共表达模块。本研究的结果为蚊虫基因共表达模式分析提供新思路和方法基础,对探究蚊虫不同组织特有的基因资源信息以及功能基因生物信息学研究有参考价值。     

Identification and functional analysis of spermatogenesis-associated gene modules in azoospermia by weighted gene coexpression network analysis

Nonobstructive azoospermia (NOA) or testicular failure is the most severe form of male infertility. A variety of conditions, both acquired and congenital, can cause azoospermia. However, in a large number of azoospermia patients who are classified as idiopathic cases, the etiology remains poorly understand mainly due to the lack of knowledge of all the genetic causes and molecular mechanisms responsible for spermatogenesis failure. Identification of the key gene modules and pathways-related spermatogenesis failure might help to reveal the mechanisms of idiopathic azoospermia. Therefore, the expression patterns of spermatogenesis-associated genes in NOA were analyzed by weighted gene coexpression network analysis (WGCNA) based on two public microarray data sets (GSE45885 and GSE45887), which included 51 samples and 32,321 genes. We identified a module (turquoise) that was significantly related to the Johnsen score of the testicular samples. In addition, the results of function and pathway enrichment analyses based on the online bioinformatics database Metascape revealed that genes in the turquoise module were mainly related to the process of spermatogenesis and spermatid development. To further identify spermatogenesis-associated genes, a microarray data set (GSE926) of murine testis at different developmental time points was analyzed by WGCNA. The blue module in GSE926 was significantly related to the time of murine testis development. The overlap study and k-core analysis based on protein–protein interaction network revealed that spermatogenesis- and spermatid development–associated genes, including glyceraldehyde-3-phosphate dehydrogenase, ADAM metallopeptidase domain 2, transition protein 1, testis-specific serine kinase 2, transition protein 2, and germ cell-associated 1 (GSG1), were further identified in the selected modules. The expression profile of GSG1 in human testis was chosen for further study using immunochemistry staining. Taken together, these screened gene modules and pathways provided a more detailed genetic and molecular mechanism underlying spermatogenesis failure occurrence and holds promise as potential diagnosis biomarkers and therapeutic targets.     

Redesign, synthesis and functional expression of the 6-deoxyerythronolide B polyketide synthase gene cluster

A generic design of Type I polyketide synthase genes has been reported in which modules, and domains within modules, are flanked by sets of unique restriction sites that are repeated in every module [1]. Using the universal design, we synthesized the six-module DEBS gene cluster optimized for codon usage in E. coli, and cloned the three open reading frames into three compatible expression vectors. With one correctable exception, the amino acid substitutions required for restriction site placements were compatible with polyketide production. When expressed in E. coli the codon-optimized synthetic gene cluster produced significantly more protein than did the wild-type sequence. Indeed, for optimal polyketide production, PKS expression had to be down-regulated by promoter attenuation to achieve balance with expression of the accessory proteins needed to support polyketide biosynthesis.     

与实验条件相关的基因功能模块聚类分析方法

针对细胞内基因功能模块化的现象,定义了“基因功能模块”和“特征功能模块”两个概念,并基于这两个概念提出一种“与实验条件相关的基因功能模块聚类算法”。该算法综合利用基因功能知识与基因表达谱信息,将基因聚类为与实验条件相关的基因功能模块。向基因表达谱中加入水平逐渐升高的数据噪音,根据基因功能模块对数据噪音的抵抗力,确定最稳定的基因功能模块,即特征功能模块。加噪音实验显示,在基因芯片技术可能发生的噪音范围内,该算法对噪音的稳健性优于层次聚类和模糊C均值聚类。将模块聚类算法应用在NCI60数据集上,发现了8个与实验条件高度相关的特征功能模块。     

Development and validation of hub genes for lymph node metastasis in patients with prostate cancer

Lymph node metastasis is one of the most important independent risk factors that can negatively affect the prognosis of prostate cancer (PCa); however, the exact mechanisms have not been well studied. This study aims to better understand the underlying mechanism of lymph node metastasis in PCa by bioinformatics analysis. We analysed a total of 367 PCa cases from the cancer genome atlas database and performed weighted gene co‐expression network analysis to explore some modules related to lymph node metastasis. Gene Ontology analysis and pathway enrichment analysis were conducted for functional annotation, and a protein‐protein interaction network was built. Samples from the International Cancer Genomics Consortium database were used as a validation set. The turquoise module showed the most relevance with lymph node metastasis. Functional annotation showed that biological processes and pathways were mainly related to activation of the processes of cell cycle and mitosis. Four hub genes were selected: CKAP2L, CDCA8, ERCC6L and ARPC1A. Further validation showed that the four hub genes well‐distinguished tumour and normal tissues, and they were good biomarkers for lymph node metastasis of PCa. In conclusion, the identified hub genes facilitate our knowledge of the underlying molecular mechanism for lymph node metastasis of PCa.     

High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles

A considerable portion of patients with colorectal cancer have a high risk of disease recurrence after surgery. These patients can be identified by analyzing the expression profiles of signature genes in tumors. But there is no consensus on which genes should be used and the performance of specific set of signature genes varies greatly with different datasets, impeding their implementation in the routine clinical application. Instead of using individual genes, here we identified functional multi-gene modules with significant expression changes between recurrent and recurrence-free tumors, used them as the signatures for predicting colorectal cancer recurrence in multiple datasets that were collected independently and profiled on different microarray platforms. The multi-gene modules we identified have a significant enrichment of known genes and biological processes relevant to cancer development, including genes from the chemokine pathway. Most strikingly, they recruited a significant enrichment of somatic mutations found in colorectal cancer. These results confirmed the functional relevance of these modules for colorectal cancer development. Further, these functional modules from different datasets overlapped significantly. Finally, we demonstrated that, leveraging above information of these modules, our module based classifier avoided arbitrary fitting the classifier function and screening the signatures using the training data, and achieved more consistency in prognosis prediction across three independent datasets, which holds even using very small training sets of tumors.     

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.