Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.     

微小染色体维系蛋白3基因在斑马鱼早期胚胎发育过程中的表达

目的:制备地高辛标记的微小染色体维系蛋白3(MCM3)基因的RNA探针,研究MCM3在斑马鱼早期发育中的时空表达。方法:收集并固定受精后24 h时期的野生型斑马鱼胚胎,提取总RNA,制备DIG标记的MCM3 RNA反义探针,整胚原位杂交,研究MCM3在斑马鱼胚胎早期发育过程的表达。结果:斑马鱼的MCM3氨基酸序列与小鼠、人具有高度同源性,通过不同时期胚胎的原位杂交,发现MCM3在早期发育过程中普遍性表达,胚胎受精后0~2 hMCM3在增殖性区域泛表达,受精后14~22 h在中枢神经系统、发育未成熟的眼部、体节及增殖性区域表达,受精后24 h在血液、中枢神经、翼板中脑、视觉盖及增殖性区域表达,受精后48 h在头部及肛门增殖性区域表达。结论:明确了MCM3在斑马鱼胚胎发育过程中的表达模式,证明其与早期斑马鱼发育细胞增殖密切相关,为研究该基因功能提供了一定的理论基础。     

FLT4 基因在斑马鱼早期发育过程中的表达

目的:研究FLT4(VEGFR3)基因在斑马鱼早期发育过程中的表达。方法:提取斑马鱼胚胎的总RNA,制备地高辛标记的FLT4RNA反义探针,WISH(整胚胎原位杂交)研究FLT4在斑马鱼早期发育过程中的表达。结果:成功合成FLT4基因探针,获得FLT4基因在斑马鱼早期发育过程中的表达情况:FlT4在2-cell、32-cell、oblong、shield期前普遍性表达(0.75h、1.7h、3.7h、6h);24h在头部表达较多,特别是在ICM(intermediate cell mass,中间细胞群)区处有特异性表达;48h、72h在头部表达均较高表达。结论:FLT4在早期参与了血管的形成和造血的发生,在脑部和肾小管的发育中可能起到了重要作用。     

Removing the bottlenecks of cell culture metabolomics: fast normalization procedure,correlation of metabolites to cell number,and impact of the cell harvesting method

Introduction

Although cultured cells are nowadays regularly analyzed by metabolomics technologies, some issues in study setup and data processing are still not resolved to complete satisfaction: a suitable harvesting method for adherent cells, a fast and robust method for data normalization, and the proof that metabolite levels can be normalized to cell number.

Objectives

We intended to develop a fast method for normalization of cell culture metabolomics samples, to analyze how metabolite levels correlate with cell numbers, and to elucidate the impact of the kind of harvesting on measured metabolite profiles.

Methods

We cultured four different human cell lines and used them to develop a fluorescence-based method for DNA quantification. Further, we assessed the correlation between metabolite levels and cell numbers and focused on the impact of the harvesting method (scraping or trypsinization) on the metabolite profile.

Results

We developed a fast, sensitive and robust fluorescence-based method for DNA quantification showing excellent linear correlation between fluorescence intensities and cell numbers for all cell lines. Furthermore, 82–97 % of the measured intracellular metabolites displayed linear correlation between metabolite concentrations and cell numbers. We observed differences in amino acids, biogenic amines, and lipid levels between trypsinized and scraped cells.

Conclusion

We offer a fast, robust, and validated normalization method for cell culture metabolomics samples and demonstrate the eligibility of the normalization of metabolomics data to the cell number. We show a cell line and metabolite-specific impact of the harvesting method on metabolite concentrations.

     

Expression profiles of ndel1a and ndel1b, two orthologs of the NudE-Like gene, in the zebrafish

NudE-Like (NDEL1/NUDEL), through its interaction with LIS1 and DISC1, has been implicated in the etiology of neurological disorders such as lissencephaly and schizophrenia, respectively. Subsequently, a large portion of the research done on the function of NDEL1 has been specifically targeted to its role in brain development while ignoring its function in other developing and adult tissues. To begin a more global exploration of NDEL1's function, this study characterizes the developmental expression pattern of the NDEL1 orthologs in the zebrafish embryo. Our bioinformatic analyses identified two NDEL1 orthologs in the zebrafish, ndel1a and ndel1b. ndel1a is expressed predominantly in the anterior central nervous system (CNS), trigeminal ganglia, and eyes while ndel1b is expressed in the developing somites and, later, in the CNS. In addition to the spatial differences in their expression patterns, these genes are also individually regulated in their temporal expression. Both are expressed maternally but at later time-points there are subtle differences. ndel1a expression is lost between 6 and 12 hpf but then increases to a higher, near steady state, level from 72 to 120 hpf. ndel1b expression decreases from 3 to 36 hpf and subsequently increases from 36 to 120 hpf. The non-overlapping expression patterns of these two orthologs may indicate that they have split the functional role of the one NDEL1 gene present in mammalian species. The temporal and spatial regulation of these two orthologs will aid in the characterization of the multiple functions of this gene in both the developing and mature organism.     

Knockdown of two splice variants of Ca(2+)/calmodulin-dependent protein kinase Iδ causes developmental abnormalities in zebrafish, Danio rerio

We isolated cDNA clones for zebrafish Ca(2+)/calmodulin-dependent protein kinase I (zCaMKI) δ isoforms by expression screening using cDNA library from embryos at 72-h post-fertilization (hpf). There are two splice variants with different C-terminal sequences, comprising of 392 and 368 amino acids, and they are designated zCaMKIδ-L (long form) and zCaMKIδ-S (short form), respectively. Although recombinant zCaMKIδ-L and zCaMKIδ-S expressed in Escherichia coli showed essentially the same catalytic properties including substrate specificities, they showed different spatial and temporal expression. Western blotting analysis using the isoform-specific antibodies revealed that zCaMKIδ-L clearly appeared from 36hpf but zCaMKIδ-S began to appear at 60hpf and thereafter. zCaMKIδ-S was predominantly expressed in brain, while zCaMKIδ-L was widely distributed in brain, eye, ovary and especially abundantly expressed in skeletal muscle. The gene knockdown of zCaMKIδ using morpholino-based antisense oligonucleotides induced significant morphological abnormalities in zebrafish embryos. Severe phenotype of embryos exhibited short trunk, kinked tail and small heads. These phenotypes could be rescued by coinjection with the recombinant zCaMKIδ, but not with the kinase-dead mutant. These results clearly indicate that the kinase activity of zCaMKIδ plays a crucial role in the early stages in the embryogenesis of zebrafish.     

A multi-analytical platform approach to the metabonomic analysis of plasma from normal and Zucker (fa/fa) obese rats

Plasma obtained from 20 week old normal Wistar-derived and Zucker (fa/fa) rats was analysed using a number of different analytical methodologies to obtain global metabolite profiles as part of metabonomic investigations of animal models of diabetes. Samples were analysed without sample pre-treatment using 1H NMR spectroscopy, after acetonitrile solvent protein precipitation by ultra-performance liquid chromatography-MS (UPLC-MS) and after acetonitrile protein precipitation and derivatisation for capillary gas chromatography-MS (GC-MS). Subsequent data analysis using principal components analysis revealed that all three analytical platforms readily detected differences between the plasma metabolite profiles of the two strains of rat. There was only limited overlap between the metabolites detected by the different methodologies and the combination of all three methods of metabolite profiling therefore provided a much more comprehensive profile than would have been provided by their use individually.     

斑马鱼胚胎发育过程中TGF-1基因的表达特征分析

为研究转化生长因子 (Transforming growth factor , TGF)1对斑马鱼胚胎发育的调控作用, 通过NCBI获得TGF-1基因序列, TGF-1 cDNA全长1571 bp, 编码377个氨基酸。系统进化树分析发现, TGF-蛋白按照不同的类型严格聚类, 斑马鱼TGF-1与其他鱼类的TGF-1聚集到一个分支, 在进化中非常保守。对斑马鱼胚胎进行RT-PCR和Real-Time PCR检测显示, TGF-1基因为母源表达基因, 在分节期之前的表达水平比较低, 而从咽囊期开始持续高水平的表达。胚胎整体原位杂交发现, TGF-1基因在斑马鱼24 hpf 胚胎中开始有特异信号出现, TGF-1基因的表达主要分布在腮弓、侧线原基、耳囊、嗅觉基板、心脏和前肾等处, 表明TGF-1基因可能参与斑马鱼胚胎免疫调节、循环系统发育和侧线形成。用低氧处理斑马鱼胚胎, 发现低氧处理24h后斑马鱼胚胎发育延迟。利用Real-Time PCR和胚胎整体原位杂交检测发现, 低氧处理后发育延迟的斑马鱼胚胎中TGF-1 mRNA表达量较常氧组显著降低。以上结果表明, TGF-1基因参与斑马鱼胚胎发育调控, 并且可能与低氧处理后斑马鱼胚胎发育延迟有关。研究结果将为深入研究斑马鱼TGF-1基因的功能奠定基础。       

The biological interpretation of metabolomic data can be misled by the extraction method used

The field of metabolomics is getting more and more popular and a wide range of different sample preparation procedures are in use by different laboratories. Chemical extraction methods using one or more organic solvents as the extraction agent are the most commonly used approach to extract intracellular metabolites and generate metabolite profiles. Metabolite profiles are the scaffold supporting the biological interpretation in metabolomics. Therefore, we aimed to address the following fundamental question: can we obtain similar metabolomic results and, consequently, reach the same biological interpretation by using different protocols for extraction of intracellular metabolites? We have used four different methods for extraction of intracellular metabolites using four different microbial cell types (Gram negative bacterium, Gram positive bacterium, yeast, and a filamentous fungus). All the quenched samples were pooled together before extraction, and, therefore, they were identical. After extraction and GC?CMS analysis of metabolites, we did not only detect different numbers of compounds depending on the extraction method used and regardless of the cell type tested, but we also obtained distinct metabolite levels for the compounds commonly detected by all methods (P-value?<?0.001). These differences between methods resulted in contradictory biological interpretation regarding the activity of different metabolic pathways. Therefore, our results show that different solvent-based extraction methods can yield significantly different metabolite profiles, which impact substantially in the biological interpretation of metabolomics data. Thus, development of alternative extraction protocols and, most importantly, standardization of sample preparation methods for metabolomics should be seriously pursued by the scientific community.     

Intra- and inter-metabolite correlation spectroscopy of tomato metabolomics data obtained by liquid chromatography-mass spectrometry and nuclear magnetic resonance

Nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LCMS) are frequently used as technological platforms for metabolomics applications. In this study, the metabolic profiles of ripe fruits from 50 different tomato cultivars, including beef, cherry and round types, were recorded by both ¹H NMR and accurate mass LC-quadrupole time-of-flight (QTOF) MS. Different analytical selectivities were found for these both profiling techniques. In fact, NMR and LCMS provided complementary data, as the metabolites detected belong to essentially different metabolic pathways. Yet, upon unsupervised multivariate analysis, both NMR and LCMS datasets revealed a clear segregation of, on the one hand, the cherry tomatoes and, on the other hand, the beef and round tomatoes. Intra-method (NMR–NMR, LCMS–LCMS) and inter-method (NMR–LCMS) correlation analyses were performed enabling the annotation of metabolites from highly correlating metabolite signals. Signals belonging to the same metabolite or to chemically related metabolites are among the highest correlations found. Inter-method correlation analysis produced highly informative and complementary information for the identification of metabolites, even in de case of low abundant NMR signals. The applied approach appears to be a promising strategy in extending the analytical capacities of these metabolomics techniques with regard to the discovery and identification of biomarkers and yet unknown metabolites. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.