The conserved NAD(H)-dependent corepressor CTBP-1 regulates Caenorhabditis elegans life span.

CtBP (C-terminal binding protein) is an evolutionarily conserved NAD(H)-dependent transcriptional corepressor, whose activity has been shown to be regulated by the NAD/NADH ratio. Although recent studies have provided significant new insights into mechanisms by which CtBP regulates transcription, the biological function of CtBP remains incompletely understood. Here, we report that genetic inactivation of the Caenorhabditis elegans homolog, ctbp-1, results in life span extension, which is suppressed by reintroduction of the ctbp-1 genomic DNA encoding wild-type but not NAD(H)-binding defective CTBP-1 protein. We show that CTBP-1 possibly modulates aging through the insulin/IGF-1 signaling pathway, dependent on the forkhead transcription factor DAF-16, but independent of the NAD-dependent histone deacetylase SIR-2.1. Genome-wide microarray analysis identifies >200 potential CTBP-1 target genes. Importantly, RNAi inhibition of a putative triacylglycerol lipase gene lips-7(C09E8.2) but not another lipase suppresses the life span extension phenotype. Consistently, metabolic analysis shows that the triacylglycerol level is reduced in the ctbp-1 deletion mutant, which is restored to the wild-type level by RNAi inhibition of lips-7. Taken together, our data suggest that CTBP-1 controls life span probably through the regulation of lipid metabolism.

Cellular expression of Smarca4 (Brg1)-regulated genes in zebrafish retinas.

BACKGROUND: In a recent genomic study, Leung et al. used a factorial microarray analysis to identify Smarca4 (Brg1)-regulated genes in micro-dissected zebrafish retinas. Two hundred and fifty nine genes were grouped in three-way ANOVA models which carried the most specific retinal change. To validate the microarray results and to elucidate cellular expression patterns of the significant genes for further characterization, 32 known genes were randomly selected from this group. In situ hybridization of these genes was performed on the same types of samples (wild-type (WT) and smarca4a50/a50 (yng) mutant) at the same stages (36 and 52 hours post-fertilization (hpf)) as in the microarray study. RESULTS: Thirty out of 32 riboprobes showed a positive in situ staining signal. Twenty seven out of these 30 genes were originally further classified as Smarca4-regulated retinal genes, while the remaining three as retinal-specific expression independent of Smarca4 regulation. It was found that 90.32% of the significant microarray comparisons that were used to identify Smarca4-regulated retinal genes had a corresponding qualitative expression change in the in situ hybridization comparisons. This is highly concordant with the theoretical true discovery rate of 95%. Hierarchical clustering was used to investigate the similarity of the cellular expression patterns of 25 out of the 27 Smarca4-regulated retinal genes that had a sufficiently high expression signal for an unambiguous identification of retinal expression domains. Three broad groups of expression pattern were identified; including 1) photoreceptor layer/outer nuclear layer specific expression at 52 hpf, 2) ganglion cell layer (GCL) and/or inner nuclear layer (INL) specific expression at both 36 & 52 hpf, and 3) GCL and/or INL specific expression at 52 hpf only. Some of these genes have recently been demonstrated to play key roles in retinal cell-type specification, differentiation and lamination. For the remaining three retinal-specific genes that are independent of Smarca4 regulation, they all had a subtle expression difference between WT and smarca4a50/a50 retinas as detected by in situ hybridization. This subtle expression difference was also detected by the original microarray analysis. However, the difference was lower than the fold change cut-off used in that study and hence these genes were not inferred as Smarca4-regulated retinal genes. CONCLUSIONS: This study has successfully investigated the expression pattern of 32 genes identified from the original factorial microarray analysis. The results have demonstrated that the true discovery rate for identifying Smarca4-regulated retinal genes is 90.3%. Hence, the significant genes from the microarray study are good candidates for cell-type specific markers and will aid further investigation of retinal differentiation.

Expression of acute-phase response proteins in retinal Müller cells in diabetes.

PURPOSE: To characterize the whole spectrum of gene expression changes induced by diabetes in retinal Müller glial cells. METHODS: Müller cells were isolated from the retina of streptozotocin-diabetic and age-matched control rats by gradient centrifugation and immediately processed for RNA isolation. The gene expression profile of Müller cells was studied with the GeneChip Rat Genome oligonucleotide array (Affymetrix, Santa Clara, CA). The upregulation of acute-phase proteins in the retina of diabetic rats was confirmed by Northern and Western blot analyses. Real-time-RT-PCR was used to study the retinal expression of inflammatory cytokines. RESULTS: Gene expression profiling identified 78 genes as differentially expressed in diabetic Müller cells. One third of these genes were associated with inflammation, including a large cluster (18% of the differentially expressed genes) of acute-phase response proteins: alpha2-macroglobulin, ceruloplasmin, complement components, lipocalin-2, metallothionein, serine protease inhibitor-2, transferrin, tissue inhibitor of metalloproteases-1, transthyretin, and the transcription factor C/EBPdelta. Northern and Western blot analyses confirmed the upregulation of alpha2-macroglobulin and ceruloplasmin in the diabetic retina, but not in the cerebral cortex and liver of the same animals. The acute-phase response of Müller cells in diabetes was associated with upregulation of interleukin (IL)-1beta in the retina. CONCLUSIONS: Müller cells acquire a complex and specific reactive phenotype in diabetes characterized by the induction of acute-phase response proteins and other inflammation-related genes. The concomitant upregulation of IL-1beta in the retina of diabetic rats points to this cytokine as a possible mediator of the acute-phase response mounted by Müller cells in diabetes.

Integrating whole-genome expression results into metabolic networks with Pathway Processor.

Genes never act alone in a biological system, but participate in a cascade of networks. As a result, analyzing microarray data from a pathway perspective leads to a new level of understanding the system. The authors' group has recently developed Pathway Processor (http://cgr.harvard.edu/cavalieri/pp.html), an automatic statistical method to determine which pathways are most affected by transcriptional changes and to map expression data from multiple whole-genome expression experiments on metabolic pathways. This unit presents applications of the Pathway Processor software.

Pathway Processor: a tool for integrating whole-genome expression results into metabolic networks.

We have developed a new tool to visualize expression data on metabolic pathways and to evaluate which metabolic pathways are most affected by transcriptional changes in whole-genome expression experiments. Using the Fisher Exact Test, the method scores biochemical pathways according to the probability that as many or more genes in a pathway would be significantly altered in a given experiment by chance alone. This method has been validated on diauxic shift experiments and reproduces well known effects of carbon source on yeast metabolism. The analysis is implemented with Pathway Analyzer, one of the tools of Pathway Processor, a new statistical package for the analysis of whole-genome expression data. Results from multiple experiments can be compared, reducing the analysis from the full set of individual genes to a limited number of pathways of interest. The pathways are visualized with OpenDX, an open-source visualization software package, and the relationship between genes in the pathways can be examined in detail using Expression Mapper, the second program of the package. This program features a graphical output displaying differences in expression on metabolic charts of the biochemical pathways to which the open reading frames are assigned.