Tomokazu Konishi

Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment

BackgroundTo cancel experimental variations, microarray data must be normalized prior to analysis. Where an appropriate model for statistical data distribution is available, a parametric method can normalize a group of data sets that have common distributions. Although such models have been proposed for microarray data, they have not always fit the distribution of real data and thus have been inappropriate for normalization. Consequently, microarray data in most cases have been normalized with non-parametric methods that adjust data in a pair-wise manner. However, data analysis and the integration of resultant knowledge among experiments have been difficult, since such normalization concepts lack a universal standard.ResultsA three-parameter lognormal distribution model was tested on over 300 sets of microarray data. The model treats the hybridization background, which is difficult to identify from images of hybridization, as one of the parameters. A rigorous coincidence of the model to data sets was found, proving the models appropriateness for microarray data. In fact, a closer fitting to Northern analysis was obtained. The model showed inconsistency only at very strong or weak data intensities. Measurement of z-scores as well as calculated ratios was reproducible only among data in the model-consistent intensity range; also, the ratios were independent of signal intensity at the corresponding range.ConclusionThe model could provide a universal standard for data, simplifying data analysis and knowledge integration. It was deduced that the ranges of inconsistency were caused by experimental errors or additive noise in the data; therefore, excluding the data corresponding to those marginal ranges will prevent misleading analytical conclusions.

Allantoin, a stress-related purine metabolite, can activate jasmonate signaling in a MYC2-regulated and abscisic acid-dependent manner.

Highlight Allantoin, a stress-related purine metabolite, can activate JA responses via ABA in Arabidopsis, suggesting its possible involvement in the homeostasis of these phytohormones and their interplay in stress signaling.

Caloric restriction-associated remodeling of rat white adipose tissue: effects on the growth hormone/insulin-like growth factor-1 axis, sterol regulatory element binding protein-1, and macrophage infiltration

The role of the growth hormone (GH)-insulin-like growth factor (IGF)-1 axis in the lifelong caloric restriction (CR)-associated remodeling of white adipose tissue (WAT), adipocyte size, and gene expression profiles was explored in this study. We analyzed the WAT morphology of 6–7-month-old wild-type Wistar rats fed ad libitum (WdAL) or subjected to CR (WdCR), and of heterozygous transgenic dwarf rats bearing an anti-sense GH transgene fed ad libitum (TgAL) or subjected to CR (TgCR). Although less effective in TgAL, the adipocyte size was significantly reduced in WdCR compared with WdAL. This CR effect was blunted in Tg rats. We also used high-density oligonucleotide microarrays to examine the gene expression profile of WAT of WdAL, WdCR, and TgAL rats. The gene expression profile of WdCR, but not TgAL, differed greatly from that of WdAL. The gene clusters with the largest changes induced by CR but not by Tg were genes involved in lipid biosynthesis and inflammation, particularly sterol regulatory element binding proteins (SREBPs)-regulated and macrophage-related genes, respectively. Real-time reverse-transcription polymerase chain reaction analysis confirmed that the expression of SREBP-1 and its downstream targets was upregulated, whereas the macrophage-related genes were downregulated in WdCR, but not in TgAL. In addition, CR affected the gene expression profile of Tg rats similarly to wild-type rats. Our findings suggest that CR-associated remodeling of WAT, which involves SREBP-1-mediated transcriptional activation and suppression of macrophage infiltration, is regulated in a GH–IGF-1-independent manner.

Neurotrophin-3 Is Involved in the Formation of Apical Dendritic Bundles in Cortical Layer 2 of the Rat

Apical dendritic bundles from pyramidal neurons are a prominent feature of cortical neuropil but with significant area specializations. Here, we investigate mechanisms of bundle formation, focusing on layer (L) 2 bundles in rat granular retrosplenial cortex (GRS), a limbic area implicated in spatial memory. By using microarrays, we first searched for genes highly and specifically expressed in GRS L2 at postnatal day (P) 3 versus GRS L2 at P12 (respectively, before and after bundle formation), versus GRS L5 (at P3), and versus L2 in barrel field cortex (BF) (at P3). Several genes, including neurotrophin-3 (NT-3), were identified as transiently and specifically expressed in GRS L2. Three of these were cloned and confirmed by in situ hybridization. To test that NT-3–mediated events are causally involved in bundle formation, we used in utero electroporation to overexpress NT-3 in other cortical areas. This produced prominent bundles of dendrites originating from L2 neurons in BF, where L2 bundles are normally absent. Intracellular biocytin fills, after physiological recording in vitro, revealed increased dendritic branching in L1 of BF. The controlled ectopic induction of dendritic bundles identifies a new role for NT-3 and a new in vivo model for investigating dendritic bundles and their formation.

Microarray test results should not be compensated for multiplicity of gene contents.

In contrast to its common definition and calculation, interpretation of p-values diverges among statisticians. Since p-value is the basis of various methodologies, this divergence has led to a variety of test methodologies and evaluations of test results. This chaotic situation has complicated the application of tests and decision processes. Here, the origin of the divergence is found in the prior probability of the test. Effects of difference in Pr(H0 = true) on the character of p-values are investigated by comparing real microarray data and its artificial imitations as subjects of Students t-tests. Also, the importance of the prior probability is discussed in terms of the applicability of Bayesian approaches. Suitable methodology is found in accordance with the prior probability and purpose of the test.

A thermodynamic model of transcriptome formation

The genome supplies information on both the quality and quantity of the transcriptome. However, as it remains unknown how a cell determines transcript levels from the genome sequences, despite comprehensive knowledge of the cellular components involved, the quantity information held by the genome cannot as yet be derived from nucleotide sequences. The model presented here explains on a thermodynamic basis how the components decode the genome to form and maintain the transcriptome. The model describes the level of a transcript as a pseudo-equilibrium between velocities of synthesis and degradation, both of which are controlled by sequence-specific interactions between protein factors and nucleic acids. Each of the transcript levels can be described by a single equation expressing a function of the activity concentrations of the protein factors. Quantitative information in the genome can thus be transformed into constants determined from the nucleotide sequences. Using this model, the transcriptome can be traced back to the protein factors and the state of chromosome packaging. The total description of transcript levels allows the model to be verified through comparison of derived hypotheses with comprehensive measurements of the transcriptome. The hypotheses thus derived in the present study are well supported by experimental microarray data, confirming the appropriateness of the model.

Sulfur deficiency–induced repressor proteins optimize glucosinolate biosynthesis in plants

Researchers report how sulfur deficiency affects glucosinolate biosynthesis in plants. Glucosinolates (GSLs) in the plant order of the Brassicales are sulfur-rich secondary metabolites that harbor antipathogenic and antiherbivory plant-protective functions and have medicinal properties, such as carcinopreventive and antibiotic activities. Plants repress GSL biosynthesis upon sulfur deficiency (−S); hence, field performance and medicinal quality are impaired by inadequate sulfate supply. The molecular mechanism that links –S to GSL biosynthesis has remained understudied. We report here the identification of the –S marker genes sulfur deficiency induced 1 (SDI1) and SDI2 acting as major repressors controlling GSL biosynthesis in Arabidopsis under –S condition. SDI1 and SDI2 expression negatively correlated with GSL biosynthesis in both transcript and metabolite levels. Principal components analysis of transcriptome data indicated that SDI1 regulates aliphatic GSL biosynthesis as part of –S response. SDI1 was localized to the nucleus and interacted with MYB28, a major transcription factor that promotes aliphatic GSL biosynthesis, in both yeast and plant cells. SDI1 inhibited the transcription of aliphatic GSL biosynthetic genes by maintaining the DNA binding composition in the form of an SDI1-MYB28 complex, leading to down-regulation of GSL biosynthesis and prioritization of sulfate usage for primary metabolites under sulfur-deprived conditions.

Coincidence between Transcriptome Analyses on Different Microarray Platforms Using a Parametric Framework

A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Microarrays are widely employed to acquire transcriptome information, and several platforms of chips are currently in use. However, discrepancies among studies are frequently reported, particularly among those performed using different platforms, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks are based on different philosophies and yield different results, but all involve normalization against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using several slide-glass-type chips and GeneChip. The model is based on a common statistical characteristic of microarray data, and each set of chip data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other frameworks.

Sterol regulatory element-binding protein-1c orchestrates metabolic remodeling of white adipose tissue by caloric restriction

Caloric restriction (CR) can delay onset of several age‐related pathophysiologies and extend lifespan in various species, including rodents. CR also induces metabolic remodeling involved in activation of lipid metabolism, enhancement of mitochondrial biogenesis, and reduction of oxidative stress in white adipose tissue (WAT). In studies using genetically modified mice with extended lifespans, WAT characteristics influenced mammalian lifespans. However, molecular mechanisms underlying CR‐associated metabolic remodeling of WAT remain unclear. Sterol regulatory element‐binding protein‐1c (Srebp‐1c), a master transcription factor of fatty acid (FA) biosynthesis, is responsible for the pathogenesis of fatty liver (steatosis). Our study showed that, under CR conditions, Srebp‐1c enhanced mitochondrial biogenesis via increased expression of peroxisome proliferator‐activated receptor gamma coactivator‐1α (Pgc‐1α) and upregulated expression of proteins involved in FA biosynthesis within WAT. However, via Srebp‐1c, most of these CR‐associated metabolic alterations were not observed in other tissues, including the liver. Moreover, our data indicated that Srebp‐1c may be an important factor both for CR‐associated suppression of oxidative stress, through increased synthesis of glutathione in WAT, and for the prolongevity action of CR. Our results strongly suggested that Srebp‐1c, the primary FA biosynthesis‐promoting transcriptional factor implicated in fatty liver disease, is also the food shortage‐responsive factor in WAT. This indicated that Srebp‐1c is a key regulator of metabolic remodeling leading to the beneficial effects of CR.

Ascorbic acid deficiency affects genes for oxidation-reduction and lipid metabolism in livers from SMP30/GNL knockout mice.

BACKGROUND We sought to elucidate the effect of an ascorbic acid (AA) deficiency on gene expression, because the water soluble antioxidant AA is an important bioactive substance in vivo. METHODS We performed microarray analyses of the transcriptome in the liver from senescence marker protein-30 (SMP30)/gluconolactonase (GNL) knockout (KO) mice, which are unable to synthesize AA in vivo. RESULTS Our microarray analysis revealed that the AA deficiency increased gene expression related to the oxidation-reduction process, i.e., the nuclear factor, erythroid derived 2, like 2 (Nrf2) gene, which is a reactive oxygen species-sensitive transcriptional factor. Moreover, this AA deficiency increased the expression of genes for lipid metabolism including the cytochrome P450, family 7, subfamily a, polypeptide 1 (Cyp7a1), which is a late-limiting enzyme of the primary bile acid biosynthesis pathway. Although an AA deficiency increased the Cyp7a1 protein level, bile acid levels in the liver and gallbladder decreased. Since Cyp7a1 has a heme iron at the active site, AA must function as a reductant of the iron required for the continuous activation of Cyp7a1. CONCLUSIONS This experimental evidence strongly supports a role for AA in the physiologic oxidation-reduction process and lipid metabolism including bile acid biosynthesis. GENERAL SIGNIFICANCE Although many effects of AA supplementation have been reported, no microarray analysis of AA deficiency in vivo is available. Results from using this unique model of AA deficiency, the SMP30/GNL-KO mouse, now provide new information about formerly unknown AA functions that will implement further study of AA in vivo.