Quantitative Surface-Enhanced Raman for Gene Expression Estimation

We demonstrate for the first time, to our knowledge, a unique gene expression assay by surface-enhanced Raman scattering (SERS) using nonfluorescent Raman labels to quantify gene expression at the resolution of alternative splicing using RNA extracted from cancer cells without any amplification steps. Our approach capitalizes on the inherent plasmon-phonon mode of SERS substrates as a self-referencing standard for the detection and quantification of genetic materials. A strategy integrating S1 nuclease digestion with SERS detection was developed to quantify the expression levels of splice junction Δ(9,10), a segment of the breast cancer susceptibility gene 1 (BRCA1) from MCF-7 and MDA-MB-231 cells. Quantification results were cross-validated using two Raman tags and qualitatively confirmed by RT-PCR. Our methodology based on SERS technology provides reliable gene expression data with high sensitivity, bypassing the intricacies involved in fabricating a consistent SERS substrate.     

Vagotomy Affects Lipopolysaccharide-Induced Changes of Urocortin 2 Gene Expression in the Brain and on the Periphery

The corticotropin-releasing hormone family of peptides is involved in regulating the neuroendocrine stress response. Also, the vagus nerve plays an important role in the transmission of immune system-related signals to brain structures, thereby orchestrating the neuroendocrine stress response. Therefore, we investigated gene expression of urocortin 2 (Ucn2) and c-fos, a markers of neuronal activity, within the hypothalamic paraventricular nucleus (PVN), a brain structure involved in neuroendocrine and neuroimmune responses, as well as in the adrenal medulla and spleen in vagotomized rats exposed to immune challenge. In addition, markers of neuroendocrine stress response activity were investigated in the adrenal medulla, spleen, and plasma. Intraperitoneal administration of lipopolysaccharide (LPS) induced a significant increase of c-fos and Ucn2 gene expression in the PVN, and adrenal medulla as well as increases of plasma corticosterone levels. In addition, LPS administration induced a significant increase in the gene expression of tyrosine hydroxylase (TH) and phenylethanolamine N-methyltransferase (PNMT) in the adrenal medulla. In the spleen, LPS administration increased gene expression of c-fos, while gene expression of TH and PNMT was significantly reduced, and gene expression of Ucn2 was not affected. Subdiaphragmatic vagotomy significantly attenuated the LPS-induced increases of gene expression of c-fos and Ucn2 in the PVN and Ucn2 in the adrenal medulla. Our data has shown that Ucn2 may be involved in regulation of the HPA axis in response to immune challenge. In addition, our findings indicate that the effect of immune challenge on gene expression of Ucn2 is mediated by vagal pathways.

     

基于Bootstrap方法的基因表达谱数据互信息估计研究

基因调控网络模型为深入理解生命本质提供了一个新的研究框架和平台。作为基因调控网络模型的其中一种,互信息关联网络模型使用熵和互信息描述基因和基因之间的关联。本文描述了用互信息度量基因表达相似性的方法,提出基于Bootstrap的互信息估计算法,并对产生的偏离现象提出了改进策略。实验结果表明,改进的互信息估计方法可以有效提高基因表达相似性估计的精确度。     

Microarray Analysis of Perinatal-Estrogen-Induced Changes in Gene Expression Related to Brain Sexual Differentiation in Mice

Sexual dimorphism of the behaviors or physiological functions in mammals is mainly due to the sex difference of the brain. A number of studies have suggested that the brain is masculinized or defeminized by estradiol converted from testicular androgens in perinatal period in rodents. However, the mechanisms of estrogen action resulting in masculinization/defeminization of the brain have not been clarified yet. The large-scale analysis with microarray in the present study is an attempt to obtain the candidate gene(s) mediating the perinatal estrogen effect causing the brain sexual differentiation. Female mice were injected with estradiol benzoate (EB) or vehicle on the day of birth, and the hypothalamus was collected at either 1, 3, 6, 12, or 24 h after the EB injection. More than one hundred genes down-regulated by the EB treatment in a biphasic manner peaked at 3 h and 12-24 h after the EB treatment, while forty to seventy genes were constantly up-regulated after it. Twelve genes, including Ptgds, Hcrt, Tmed2, Klc1, and Nedd4, whose mRNA expressions were down-regulated by the neonatal EB treatment, were chosen for further examination by semiquantitative RT-PCR in the hypothalamus of perinatal intact male and female mice. We selected the genes based on the known profiles of their potential roles in brain development. mRNA expression levels of Ptgds, Hcrt, Tmed2, and Nedd4 were significantly lower in male mice than females at the day of birth, suggesting that the genes are down-regulated by estrogen converted from testicular androgen in perinatal male mice. Some genes, such as Ptgds encoding prostaglandin D2 production enzyme and Hcrt encording orexin, have been reported to have a role in neuroprotection. Thus, Ptgds and Hcrt could be possible candidate genes, which may mediate the effect of perinatal estrogen responsible for brain sexual differentiation.     

Using Fluorescence Activated Cell Sorting to Examine Cell-Type-Specific Gene Expression in Rat Brain Tissue

The brain is comprised of four primary cell types including neurons, astrocytes, microglia and oligodendrocytes. Though they are not the most abundant cell type in the brain, neurons are the most widely studied of these cell types given their direct role in impacting behaviors. Other cell types in the brain also impact neuronal function and behavior via the signaling molecules they produce. Neuroscientists must understand the interactions between the cell types in the brain to better understand how these interactions impact neural function and disease. To date, the most common method of analyzing protein or gene expression utilizes the homogenization of whole tissue samples, usually with blood, and without regard for cell type. This approach is an informative approach for examining general changes in gene or protein expression that may influence neural function and behavior; however, this method of analysis does not lend itself to a greater understanding of cell-type-specific gene expression and the effect of cell-to-cell communication on neural function. Analysis of behavioral epigenetics has been an area of growing focus which examines how modifications of the deoxyribonucleic acid (DNA) structure impact long-term gene expression and behavior; however, this information may only be relevant if analyzed in a cell-type-specific manner given the differential lineage and thus epigenetic markers that may be present on certain genes of individual neural cell types. The Fluorescence Activated Cell Sorting (FACS) technique described below provides a simple and effective way to isolate individual neural cells for the subsequent analysis of gene expression, protein expression, or epigenetic modifications of DNA. This technique can also be modified to isolate more specific neural cell types in the brain for subsequent cell-type-specific analysis.     

Changes in Gene Expression of Pial Vessels of the Blood Brain Barrier during Murine Neurocysticercosis

In murine neurocysticercosis (NCC), caused by infection with the parasite Mesocestoides corti, the breakdown of the Blood Brain Barrier (BBB) and associated leukocyte infiltration into the CNS is dependent on the anatomical location and type of vascular bed. Prior studies of NCC show that the BBB comprised of pial vessels are most affected in comparison to the BBB associated with the vasculature of other compartments, particularly parenchymal vessels. Herein, we describe a comprehensive study to characterize infection-induced changes in the genome wide gene expression of pial vessels using laser capture microdissection microscopy (LCM) combined with microarray analyses. Of the 380 genes that were found to be affected, 285 were upregulated and 95 were downregulated. Ingenuity Pathway Analysis (IPA) software was then used to assess the biological significance of differentially expressed genes. The most significantly affected networks of genes were “inflammatory response, cell-to-cell signaling and interaction, cellular movement”, “cellular movement, hematological system development and function, immune cell trafficking, and “antimicrobial response, cell-to-cell signaling and interaction embryonic development”. RT-PCR analyses validated the pattern of gene expression obtained from microarray analysis. In addition, chemokines CCL5 and CCL9 were confirmed at the protein level by immunofluorescence (IF) microscopy. Our data show altered gene expression related to immune and physiological functions and collectively provide insight into changes in BBB disruption and associated leukocyte infiltration during murine NCC.     

植物基因表达的代谢调控

通过分析代谢物、激素和环境因素对植物几种基因表达的影响,就植物基因表达的代谢调控进行了讨论,提出了开展有关这方面研究的一些设想。     

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.Rational and quantitative assessment of metabolic changes in response to genetic modification (GM) is an open question and in need of innovative solutions. Nontargeted metabolite profiling can detect thousands of compounds, but it is not easy to understand the significance of the changed metabolites in the biochemical and biological context of the organism. To better assess the changes in metabolites from nontargeted metabolomics studies, it is important to examine the changed metabolites in the context of the genome-scale metabolic network of the organism.Metabolomics is a technique that aims to quantify all the metabolites in a biological system (Nikolau and Wurtele, 2007; Nicholson and Lindon, 2008; Roessner and Bowne, 2009). It has been used widely in studies ranging from disease diagnosis (Holmes et al., 2008; DeBerardinis and Thompson, 2012) and drug discovery (Cascante et al., 2002; Kell, 2006) to metabolic reconstruction (Feist et al., 2009; Kim et al., 2012) and metabolic engineering (Keasling, 2010; Lee et al., 2011). Metabolomic studies have demonstrated the possibility of identifying gene functions from changes in the relative concentrations of metabolites (metabotypes or metabolic signatures; Ebbels et al., 2004) in various species including yeast (Saccharomyces cerevisiae; Raamsdonk et al., 2001; Allen et al., 2003), Arabidopsis (Arabidopsis thaliana; Brotman et al., 2011), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Zea mays; Riedelsheimer et al., 2012). Metabolomics has also been used to better understand how plants interact with their environments (Field and Lake, 2011), including their responses to biotic and abiotic stresses (Dixon et al., 2006; Arbona et al., 2013), and to predict important agronomic traits (Riedelsheimer et al., 2012). Metabolite profiling has been performed on many plant species, including angiosperms such as Arabidopsis, poplar (Populus trichocarpa), and Catharanthus roseus (Sumner et al., 2003; Rischer et al., 2006), basal land plants such as Selaginella moellendorffii and Physcomitrella patens (Erxleben et al., 2012; Yobi et al., 2012), and Chlamydomonas reinhardtii (Fernie et al., 2012; Davis et al., 2013). With the availability of whole genome sequences of various species, metabolomics has the potential to become a useful tool for elucidating the functions of genes using large-scale systematic analyses (Fiehn et al., 2000; Saito and Matsuda, 2010; Hur et al., 2013).Although metabolomics data have the potential for identifying the roles of genes that are associated with metabolic phenotypes, the biochemical mechanisms that link functions of genes with metabolic phenotypes are still poorly characterized. For example, we do not yet know the principles behind how perturbing the expression of a single gene changes the metabolic system as a whole. Large-scale metabolomics data have provided useful resources for linking phenotypes to genotypes (Fiehn et al., 2000; Roessner et al., 2001; Tikunov et al., 2005; Schauer et al., 2006; Lu et al., 2011; Fukushima et al., 2014). For example, Lu et al. (2011) compared morphological and metabolic phenotypes from more than 5,000 Arabidopsis chloroplast mutants using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS). Fukushima et al. (2014) generated metabolite profiles from various characterized and uncharacterized mutant plants and clustered the mutants with similar metabolic phenotypes by conducting multidimensional scaling with quantified metabolic phenotypes. Nonetheless, representation and analysis of such a large amount of data remains a challenge for scientific discovery (Lu et al., 2011). In addition, these studies do not examine the topological and functional characteristics of metabolic changes in the context of a genome-scale metabolic network. To understand the relationship between genotype and metabolic phenotype, we need to investigate the metabolic changes caused by perturbing the expression of a gene in a genome-scale metabolic network perspective, because metabolic pathways are not independent biochemical factories but are components of a complex network (Berg et al., 2002; Merico et al., 2009).Much progress has been made in the last 2 decades to represent metabolism at a genome scale (Terzer et al., 2009). The advances in genome sequencing and emerging fields such as biocuration and bioinformatics enabled the representation of genome-scale metabolic network reconstructions for model organisms (Bassel et al., 2012). Genome-scale metabolic models have been built and applied broadly from microbes to plants. The first step toward modeling a genome-scale metabolism in a plant species started with developing a genome-scale metabolic pathway database for Arabidopsis (AraCyc; Mueller et al., 2003) from reference pathway databases (Kanehisa and Goto, 2000; Karp et al., 2002; Zhang et al., 2010). Genome-scale metabolic pathway databases have been built for several plant species (Mueller et al., 2005; Zhang et al., 2005, 2010; Urbanczyk-Wochniak and Sumner, 2007; May et al., 2009; Dharmawardhana et al., 2013; Monaco et al., 2013, 2014; Van Moerkercke et al., 2013; Chae et al., 2014; Jung et al., 2014). Efforts have been made to develop predictive genome-scale metabolic models using enzyme kinetics and stoichiometric flux-balance approaches (Sweetlove et al., 2008). de Oliveira Dal’Molin et al. (2010) developed a genome-scale metabolic model for Arabidopsis and successfully validated the model by predicting the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. Other genome-scale models have been developed for Arabidopsis (Poolman et al., 2009; Radrich et al., 2010; Mintz-Oron et al., 2012), C. reinhardtii (Chang et al., 2011; Dal’Molin et al., 2011), maize (Dal’Molin et al., 2010; Saha et al., 2011), sorghum (Sorghum bicolor; Dal’Molin et al., 2010), and sugarcane (Saccharum officinarum; Dal’Molin et al., 2010). These predictive models have the potential to be applied broadly in fields such as metabolic engineering, drug target discovery, identification of gene function, study of evolutionary processes, risk assessment of genetically modified crops, and interpretations of mutant phenotypes (Feist and Palsson, 2008; Ricroch et al., 2011).Here, we interrogate the metabotypes caused by 136 single gene perturbations of Arabidopsis by analyzing the relative concentration changes of 1,348 chemically identified metabolites using a reconstructed genome-scale metabolic network. We examine the characteristics of the changed metabolites (the metabolites whose relative concentrations were significantly different in mutants relative to the wild type) in the metabolic network to uncover biological and topological consequences of the perturbed genes.     

Micromanipulation of Gene Expression in the Adult Zebrafish Brain Using Cerebroventricular Microinjection of Morpholino Oligonucleotides

Manipulation of gene expression in tissues is required to perform functional studies. In this paper, we demonstrate the cerebroventricular microinjection (CVMI) technique as a means to modulate gene expression in the adult zebrafish brain. By using CVMI, substances can be administered into the cerebroventricular fluid and be thoroughly distributed along the rostrocaudal axis of the brain. We particularly focus on the use of antisense morpholino oligonucleotides, which are potent tools for knocking down gene expression in vivo. In our method, when applied, morpholino molecules are taken up by the cells lining the ventricular surface. These cells include the radial glial cells, which act as neurogenic progenitors. Therefore, knocking down gene expression in the radial glial cells is of utmost importance to analyze the widespread neurogenesis response in zebrafish, and also would provide insight into how vertebrates could sustain adult neurogenesis response. Such an understanding would also help the efforts for clinical applications in human neurodegenerative disorders and central nervous system regeneration. Thus, we present the cerebroventricular microinjection method as a quick and efficient way to alter gene expression and neurogenesis response in the adult zebrafish forebrain. We also provide troubleshooting tips and other useful information on how to carry out the CVMI procedure.     

Diabetes Induced Changes in Podocyte Morphology and Gene Expression Evaluated Using GFP Transgenic Podocytes

The effect of diabetes in vivo has not been examined on isolated podocytes. To achieve this, GFP was expressed constitutively in podocytes of PGFP transgenic mice which were bred to OVE mice to produce diabetic OVE-GFP mice. Viewing GFP fluorescence, foot processes of OVE-GFP podocytes were visually and measurably effaced, which did not occur with less severe STZ diabetes. Over 300,000 podocytes were purified from each PGFP mouse but only 49,000 podocytes per diabetic OVE-GFP mouse. The low yield from OVE-GFP mice appeared to be due to more fragile state of most OVE-GFP diabetic podocytes which did not survive the isolation process. Diabetic podocytes that were isolated had high levels of the lipid peroxidation product 4-HNE and they were more sensitive to death due to oxidative stress. Gene array analysis of OVE-GFP podocytes showed strong diabetes induction of genes involved in inflammation. Four CXC chemokines were induced at least 3-fold and the chemokine CXCL1 was shown for the first time to be specifically induced in podocytes by OVE, dbdb and STZ diabetes.     

Methodological Considerations Regarding Single-Cell Gene Expression Profiling for Brain Injury

Genomic microarrays are rapidly becoming ubiquitous throughout a wide variety of biological disciplines. As their use has grown during the past few years, many important discoveries have been made in the fields of central nervous system (CNS) injury and disease using this emerging technology. In addition, single-cell mRNA amplification techniques are now being used along with microarrays to overcome many of the difficulties associated with the cellular heterogeneity of the brain. This development has extended the utility of gene expression profiling and has provided researchers with exciting new insights into the neuropathology of CNS injury and disease at a molecular and cellular level. New methodological, standardization, and statistical techniques are currently being developed to improve the reproducibility of microarrays and facilitate the analysis of large amounts of data. In this review, we will discuss the application of these techniques to experimental, clinically relevant models of traumatic brain injury.     

Gene Expression Profile Changes in Germinating Rice

Water absorption is a prerequisite for seed germination. During imbibition, water influx causes the resumption of many physiological and metabolic processes in growing seed. In order to obtain more complete knowledge about the mechanism of seed germination, two‐dimensional gel electrophoresis was applied to investigate the protein profile changes of rice seed during the first 48 h of imbibition. Thirty‐nine differentially expressed proteins were identified, including 19 down‐regulated and 20 up‐regulated proteins. Storage proteins and some seed development‐ and desiccation‐associated proteins were down regulated. The changed patterns of these proteins indicated extensive mobilization of seed reserves. By contrast, catabolism‐associated proteins were up regulated upon imbibition. Semi‐quantitative real time polymerase chain reaction analysis showed that most of the genes encoding the down‐ or up‐regulated proteins were also down or up regulated at mRNA level. The expression of these genes was largely consistent at mRNA and protein levels. In providing additional information concerning gene regulation in early plant life, this study will facilitate understanding of the molecular mechanisms of seed germination.