8 resultados para SYBRGreen


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禾谷孢囊线虫严重影响禾谷类作物的产量,在小麦中由禾谷孢囊线虫引起的产量损失可达30-100%。尤其在澳大利亚、欧洲、印度和中东危害严重,目前禾谷孢囊线虫已成为危害我国作物的主要病源。控制禾谷孢囊线虫的方法主要有:作物轮作、杀线虫剂、寄主抗性等等,其中基因工程方法培育抗线虫小麦品种被认为是最经济有效的方法。分离抗禾谷类孢囊线虫基因对揭示抗性基因结构与功能及其表达调控具有重要意义。 尽管小麦是重要的粮食作物,在小麦中已发现的抗禾谷孢囊线虫的基因很少,而比其近缘属如节节麦、易变山羊草、偏凸山羊草中含有丰富的抗源。目前已鉴定出禾谷孢囊线虫抗性位点Cre,并发现了9个禾谷孢囊线虫抗性基因(Cre1,2, 3, 4, 5, 6, 7, 8, and R) ,其中只有Cre1和Cre8直接从普通小麦中获得。从节节麦中获得的Cre3基因能最有效的控制线虫数量,其次是Cre1和Cre8。这些基因的克隆对于了解禾谷孢囊线虫抗性机制及进一步的育种应用都是非常关键的。然而,目前为止仅有Cre3基因通过图位克隆的方法从节节麦中被分离得到。该基因已被克隆得到的多数线虫抗性基因一样均属于核苷酸结合位点区(NBS)-亮氨酸重复序列区(LRR)基因家族。目前,已有很多抗性基因被分离,这些已知的NBS-LRR类抗性基因的保守序列为应用PCR的方法克隆新的抗性基因提供了可能。 因此本课题的目的是采用保守区同源克隆、3′RACE 和5′RACE 等方法从抗禾谷孢囊线虫小麦-易变山羊草小片段易位系E10 中克隆小麦抗禾谷孢囊线虫基因全序列,进而通过半定量PCR 和荧光定量PCR 研究该基因的表达模式。同时通过mRNA 差别显示技术和任意引物PCR(RAP-PCR)技术分离克隆植物禾谷孢囊线虫抗性基因及其相关基因,为阐明植物抗病性分子机制以及改良作物抗病性和作物育种提供基础,为通过分子标记辅助育种和基因工程方法实现高效、定向转移抗病基因到优良小麦品种奠定了重要的理论和物质基础。主要研究结果: 1. 本实验根据此前从抗禾谷孢囊线虫材料E-10 扩增得到的与来自节节麦的抗禾谷孢囊线虫Cre3 基因及其他的NBS-LRR 类抗性基因的NBS 和LRR 保守区序列设计了两对特异性引物,从E10 中扩增到532bp 和1175bp 的两个目标条带,它们有一个32bp 的共同序列,连接构成总长为1675bp 的NBS-LRR 编码区(命名为RCCN)。根据RCCN设计引物,利用NBS-LRR区序列设计引物,通过5′RACE 和3′RACE 技术采用3′-Full RACE Core Set(TaKaRa)和5'-Full RACE Kit (TaKaRa)试剂盒,反转录后通过嵌套引物GSP1 和GSP2 分别进行两轮基因特异性扩增,分别将NBS_LRR 区向5′端和3′端延伸了1173bp 和449bp,并包含了起始密码子和终止密码子。根据拼接的得到的序列重新设计引物扩增进行全基因扩增的结果与上面获得的一致。拼接后得到全长2775 bp 的基因序列(记作CreZ, GenBank 号:EU327996)。CreZ 基因包括完整的开放阅读框,全长2775 bp,编码924个氨基酸。序列分析表明它与已知的禾谷孢囊线虫抗性基因Cre3的一致性很高,并且它与已经报到的NBS-LRR 类疾病抗性基因有着相同的保守结构域。推测CreZ基因可能是一个新的NBS-LRR 类禾谷孢囊线虫抗性基因,该基因的获得为通过基因工程途径培育抗禾谷孢囊线虫小麦新品种奠定了基础,并为抗禾谷孢囊线虫基因的调控表达研究提供了参考。 2. 通过半定量PCR和SYBR Green荧光定量PCR技术对CreZ基因的相对表达模式进行了研究。以α-tubulin 2作为参照,采用半定量PCR 分析CreZ 基因在不同接种时期1d, 5d, 10, 15d 的E-10的根和叶的的表达情况。在内参扩增一致的条件下,CreZ 在E-10的根部随着侵染时间的增加表达量有明显的增加,在没有侵染的E-10的根部其表达量没有明显变化,而在叶中没有检测表达,说明该基因只在抗性材料的根部表达。SYBR Green定量PCR分析接种前后E10根部基因CreZ基因的表达水平为检测CreZ基因的表达建立了一套灵敏、可靠的SYBRGreen I 荧光定量PCR 检测方法。接种禾谷孢囊线虫后E10根内CreZ基因的相对表达水平显著高于接种前。随接种时间的延长持续增加,最终CreZ基因的相对表达量达到未接种的对照植株的10.95倍。小麦禾谷孢囊线虫抗性基因CreZ的表达量与胁迫呈正相关,表明其与小麦的的禾谷孢囊线虫抗性密切相关,推测CreZ基因可能是一个新的禾谷孢囊线虫候选抗性基因。 3. 针对小麦基因组庞大、重复序列较多,禾谷孢囊线虫抗性基因及其相关基因的片断难以有效克隆的问题,通过mRNA 差别显示技术及RAP-PCR 技术分离克隆植物禾谷孢囊线虫抗性及其相关基因。试验最终得到154 条差异表达条带,将回收得到的差异条带的二次PCR 扩增产物经纯化后点到带正电的尼龙膜上,进行反向Northern 杂交筛选,最终筛选得到102 个阳性差异点。将其中81 个进行测序,并将序列提交到Genbank 中的dbEST 数据库,分别获得登录号(FE192210 -FE192265,FE193048- FE193074 )。序列比对分析发现,其中26 个序列与已知功能的基因序列同源;有28 条EST 序列在已有核酸数据库中未找到同源已知基因和EST,属新的ESTs 序列;另外27 个EST 序列与已知核酸数据库中的ESTs 具有一定相似性,但功能未知。其所得ESTs 序列补充了Genbank ESTs 数据库,为今后进一步开展抗禾谷类孢囊线虫基因研究工作打下了基础。结合本试验功能基因的相关信息,对小麦接种禾谷孢囊线虫后产生的抗性机制进行了探讨。接种禾谷孢囊线虫后植物在mRNA 水平上的应答是相当复杂的,同时植物的抗病机制是一个复杂的过程,涉及到多个代谢途径的相互作用。 The cereal cyst nematode (CCN), Heterodera avenae Woll, causes severe yieldreductions in cereal crops. The losses caused by CCN can be up to 30-100% in somewheat fields. At present, cereal cyst nematode has become the major disease sourcein China and it also damaged heavily in Australia, Europe, India and Middle East.The damage caused by CCN can be mitigated through several methods, includingcrop rotation, nematicide application, cultural practice, host resistance, and others.Of these methods, incorporating resistance genes into wheat cultivars and breedingresistant lines is considered to be the most cost-effective control measure forreducing nematode populations. Although wheat is an economically important crop around the world, far fewergenes resistant to CCN were found in wheat than were detected in its relatives, suchas Aegilops taucchi, Aegilops variabilis and Aegilops ventricosa. Cloning these genesis essential for understanding the mechanism of this resistance and for furtherapplication in breeding. Because of the huge genome and high repeat sequencescontent, the efficient methods to clone genes from cereal crops, are still lacking. A resistance locus, Cre, has been identified and 9 genes resistant to CCN (designatedCre1, 2, 3, 4, 5, 6, 7, 8, and R) have been described, in which Cre1 and Cre8 werederived directly from common wheat. The Cre3 locus, which was derived from Ae.tauschii, has the greatest impact on reducing the number of female cysts, followed byCre1 and Cre8. Cloning these genes is essential for understanding the mechanism ofthis resistance and for further application in breeding. However, to this point, only Cre3, a NBS-LRR disease resistance gene, has been obtained through mappingcloning in Ae. tauschii. The majority of nematode resistance genes cloned so far belong to a super familywhich contains highly conserved nucleotide-binding sites (NBS) and leucine-richrepeat (LRR) domains. To date, many NBS-LRR resistance genes have been isolated.The conserved sequences of these recognized NBS-LRR resistance genes provide thepossibility to isolate novel resistance genes using a PCR-based strategy. The aim of the present study was to clone the resistance gene of CCN fromWheat/Aegilops variabilis small fragment chromosome translocation line E10 whichis resistant to CCN and investigate the espression profiles of this gene withsemi-quantitative PCR and real-time PCR. Another purpose of this study is cloningthe relational resistance gene for CCN by mRNA differential display PCR andRAP-PCR. These works will offer a foundation for disease defence of crop andbreeding and directional transferring resistance gene into wheat with geneengineering. Primary results as following: 1.According to the conversed motif of NBS and LRR region of cereal cystnematode resistance gene Cre3 from wild wheat (Triticum tauschlii) and the knownNBS-LRR group resistance genes, we designed two pairs of specific primers for NBSand LRR region respectively. One band of approximately 530bp was amplified usingthe specific primers for conversed NBS region and one band of approximately 1175bpwas amplified with the specific primers for conversed LRR region. After sequencing,we found that these two sequences included 32bp common nucleotide having 1675bpin total, which was registered as RCCN in the Genbank. Based on the conservedregions of known resistance genes, a NBS-LRR type CCN resistance gene analog wasisolated from the CCN resistant line E-10 of the wheat near isogenic lines (NILs), by5′RACE and 3′ RACE.designated as CreZ (GenBank accession number: EU327996) .It contained a comlete ORF of 2775 bp and encoded 924 amino acids. Sequencecomparison indicated that it shared 92% nucleotide and 87% amino acid identitieswith those of the known CCN-resistance gene Cre3 and it had the same characteristic of the conserved motifs as other established NBS-LRR disease resistance genes. 2. Usingα-tubulin 2 as exoteric reference, semi-quantitative PCR and real-timePCR analysis were conducted. The expression profiling of CreZ indicated that it wasspecifically expressed in the roots of resistant plants and its relative expression levelincreased sharply when the plants were inoculated with cereal cyst nematodes. therelative expression level of the 15days-infected E10 is the 10.95 times as that ofuninfected E10,ultimately. It was inferred that the CreZ gene be a novel potentialresistance gene to CCN. 3.We cloned the relational resistance gene for CCN by mRNA differentialdisplay PCR and arbitrarily primed PCR fingerprinting of RNA from wheat whichpossess huge and high repeat sequence content genomes. Total 154 differentialexpression bands were separated and second amplified by PCR. The products werenylon membrane. The 102 positive clones were filtrated by reverse northern dot blotand 81 of those were sent to sequence. The EST sequences were submitted toGenbank (Genbank accession: FE192210 - FE192265, FE193048 - FE193074). Thesequences alignment analysis indicated 26 of them were identical with known genes;28 were not found identical sequence in nucleic acid database; another 27 ests wereidentical with some known ests, but their functions were not clear. These ESTsenriched Genbank ESTs database and offered foundation for further research ofresistance gene of CCN.

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定量描述微生物群落的组成,在微生物生态学的许多研究领域都是非常重要的。然而由于可培养技术的局限性,定量描述微生物群落成为比较困难的事情。最近包括PCR技术在内的分子生物学技术为人们提供了有力的工具,使对微生物群落的分布、丰度等有了进一步的了解。实时荧光定量PCR技术作为核酸定量检测技术,自从发明以来在微生物生态学研究中逐渐得到了广泛的应用。从微生物生态学角度,综述了实时荧光定量PCR技术的原理、发展、优缺点及其在微生物生态学研究中的应用与研究进展,并探讨了实时荧光定量PCR技术的发展和应用前景。

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[目的]探究青海家牦牛HIF-1α基因组织的特异性表达。[方法]应用半定量反转录PCR和实时定量反转录PCR(SYBRGreen)技术对青海家牦牛HIF-1α基因的组织特异性表达进行检测。通过提取不同组织总RNA,经DNase I消化后,用随机引物进行反转录合成cDNA,采用特异性引物分别对HIF-1α和β-actin基因进行RT-PCR和Real Time RT-PCR扩增。[结果]结果表明,HIF-1α基因在心、肝、脾、肺、肾、脑、肌肉、睾丸组织中均有表达,其中以睾丸和脾中HIF-1α基因表达量最高,肌肉的表达量最低。[结论]该研究为进一步揭示HIF-1α在高原土著动物低氧适应过程中的分子机制有着重要的意义。

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Background Chronic myeloproliferative disorders (MPDs) are clonal haematopoietic stem cell malignancies characterised by an accumulation of mature myeloid cells in bone marrow and peripheral blood. Deregulation of the apoptotic machinery may be associated with MPD physiopathology. Aims To evaluate expression of death receptors` family members, mononuclear cell apoptosis resistance, and JAK2 allele burden. Subjects and Methods Bone marrow haematopoietic progenitor CD34 cells were separated using the Ficoll-hypaque protocol followed by the Miltenyi CD34 isolation kit, and peripheral blood leukocytes were separated by the Haes-Steril method. Total RNA was extracted by the Trizol method, the High Capacity Kit was used to synthesise cDNA, and real-time PCR was performed using SybrGreen in ABIPrism 7500 equipment. The results of gene expression quantification are given as 2(-Delta Delta Ct). The JAK2 V617F mutation was detected by real-time allelic discrimination PCR assay. Peripheral blood mononuclear cells (PBMCs) were isolated by the Ficoll-hypaque protocol and cultured in the presence of apoptosis inducers. Results In CD34 cells, there was mRNA overexpression for fas, faim and c-flip in polycythaemia vera (PV), essential thrombocythaemia (ET) and primary myelofibrosis (PMF), as well as fasl in PMF, and dr4 levels were increased in ET. In leukocytes, fas, c-flip and trail levels were increased in PV, and dr5 expression was decreased in ET. There was an association between dr5 and fasl expression and JAK2V617F mutation. PBMCs from patients with PV, ET or PMF showed resistance to apoptosis inducers. Conclusions The results indicate deregulation of apoptosis gene expression, which may be associated with MPD pathogenesis leading to accumulation of myeloid cells in MPDs.

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The dataset is based on samples collected in the framework of the project SESAME, in the Ionian, Libyan and Aegean Sea during March- April 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column. Heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus abundance: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson).

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The SES_GR2_MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the Ionian, Libyan and Aegean Sea during August-September 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column. Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 ?m (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson).

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The HCMR_SES_LAGRANGIAN_GR2_ MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the North Aegean Sea during October 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column as influenced by the BSW. Heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus abundance: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Heterotrophic Nanoflagellate abundance: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6?m and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Ciliate abundance: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Heterotrophic bacteria, Synechococcus, Prochlorococcus bacteria: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Abundance data were converted into C biomass using 250 fgC cell-1 (Kana & Glibert 1987) for Synechococcus, 50 fgC cell-1 (Campbell et al. 1994) for Prochlorococcus and 20fgC cell-1 (Lee & Fuhrman 1987) for heterotrophic bacteria. Heterotrophic Nanoflagellate biomass: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Abundance data were converted into C biomass using 183 fgC µm**3 (Caron et al. 1995). Ciliate biomass: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Ciliate cell sizes were measured and converted into cell volumes using appropriate geometric formulae using image analysis. For biomass estimation, the conversion factor 190 fgC µm**3 was used (Putt and Stoecker 1989).

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The HCMR_SES_LAGRANGIAN_GR1_ MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the North Aegean Sea during April 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column as influenced by the BSW. Heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus abundance: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Heterotrophic Nanoflagellate abundance: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Ciliate abundance: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Heterotrophic bacteria, Synechococcus, Prochlorococcus biomass: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Abundance data were converted into C biomass using 250 fgC cell-1 (Kana & Glibert 1987) for Synechococcus, 50 fgC cell-1 (Campbell et al. 1994) for Prochlorococcus and 20fgC cell-1 (Lee & Fuhrman 1987) for heterotrophic bacteria. Heterotrophic Nanoflagellate biomass: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Abundance data were converted into C biomass using 183 fgC µm**3 (Caron et al. 1995). Ciliate biomass: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Ciliate cell sizes were measured and converted into cell volumes using appropriate geometric formulae using image analysis. For biomass estimation, the conversion factor 190 fgC µm**3 was used (Putt and Stoecker 1989).