Molecular cloning of the mouse CCK gene: expression in different brain regions and during cortical development.

In this paper we describe experiments that address specific issues concerning the regulation of the mouse cholecystokinin gene in brain and intestine. The mouse cholecystokinin gene was cloned and sequenced. Extensive homology among the mouse, man and rat genes was noted particularly in the three exons and the regions upstream of the RNA start site. RNAse protection assays for each of the three exons were used to demonstrate that CCK is expressed in only a subset of tissues and that the same cap site and splice choices are used in brain, intestine as well as in cerebellum, cortex, midbrain, hypothalamus and hippocampus. CCK RNA was also noted to be detectable in kidney. Thus the same gene using the same promoter is expressed in subsets of cells that differ in their biochemical, morphologic and functional characteristics. The level of expression of CCK was also monitored during mouse cortical development and the appearance of CCK RNA was compared to glutamate decarboxylase (GAD), enkephalin and somatostatin. It was noted that each of these cortical markers was first expressed at different times during cortical development. The appearance of CCK RNA during intestinal development was also measured and found to precede appearance in cortex by several days.     

Evolutionary rate heterogeneity in proteins with long disordered regions

The dominant view in protein science is that a three-dimensional (3-D) structure is a prerequisite for protein function. In contrast to this dominant view, there are many counterexample proteins that fail to fold into a 3-D structure, or that have local regions that fail to fold, and yet carry out function. Protein without fixed 3-D structure is called intrinsically disordered. Motivated by anecdotal accounts of higher rates of sequence evolution in disordered protein than in ordered protein we are exploring the molecular evolution of disordered proteins. To test whether disordered protein evolves more rapidly than ordered protein, pairwise genetic distances were compared between the ordered and the disordered regions of 26 protein families having at least one member with a structurally characterized region of disorder of 30 or more consecutive residues. For five families, there were no significant differences in pairwise genetic distances between ordered and disordered sequences. The disordered region evolved significantly more rapidly than the ordered region for 19 of the 26 families. The functions of these disordered regions are diverse, including binding sites for protein, DNA, or RNA and also including flexible linkers. The functions of some of these regions are unknown. The disordered regions evolved significantly more slowly than the ordered regions for the two remaining families. The functions of these more slowly evolving disordered regions include sites for DNA binding. More work is needed to understand the underlying causes of the variability in the evolutionary rates of intrinsically ordered and disordered protein.     

Evolutionary rate depends on number of protein-protein interactions independently of gene expression level

Background

Whether or not a protein's number of physical interactions with other proteins plays a role in determining its rate of evolution has been a contentious issue. A recent analysis suggested that the observed correlation between number of interactions and evolutionary rate may be due to experimental biases in high-throughput protein interaction data sets.

Discussion

The number of interactions per protein, as measured by some protein interaction data sets, shows no correlation with evolutionary rate. Other data sets, however, do reveal a relationship. Furthermore, even when experimental biases of these data sets are taken into account, a real correlation between number of interactions and evolutionary rate appears to exist.

Summary

A strong and significant correlation between a protein's number of interactions and evolutionary rate is apparent for interaction data from some studies. The extremely low agreement between different protein interaction data sets indicates that interaction data are still of low coverage and/or quality. These limitations may explain why some data sets reveal no correlation with evolutionary rates.

     

A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies

Over the last decade, gene expression microarrays have had a profound impact on biomedical research. The diversity of platforms and analytical methods available to researchers have made the comparison of data from multiple platforms challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and 'in-house' platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by quantitative real-time (QRT)-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent preprocessing, commercial arrays were more consistent than in-house arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms.     

Alterations in metabolism and gene expression in brain regions during cuprizone-induced demyelination and remyelination

Exposure of mice to the copper chelator, cuprizone, results in CNS demyelination. There is remyelination after removal of the metabolic insult. We present brain regional studies identifying corpus callosum as particularly severely affected; 65% of cerebroside is lost after 6 weeks of exposure. We examined recovery of cerebroside and ability to synthesize cerebroside and cholesterol following removal of the toxicant. The temporal pattern for concentration of myelin basic protein resembled that of cerebroside. We applied Affymetrix GeneChip technology to corpus callosum to identify temporal changes in levels of mRNAs during demyelination and remyelination. Genes coding for myelin structural components were greatly down-regulated during demyelination and up-regulated during remyelination. Genes related to microglia/macrophages appeared in a time-course (peaking at 6 weeks) correlating with phagocytosis of myelin and repair of lesions. mRNAs coding for many cytokines had peak expression at 4 weeks, compatible with intercellular signaling roles. Of interest were other genes with temporal patterns correlating with one of the three above patterns, but of function not obviously related to demyelination/remyelination. The ability to correlate gene expression with known pathophysiological events should help in elucidating further function of such genes as related to demyelination/remyelination.     

Evolutionary significance of gene expression divergence

Recent large-scale studies of evolutionary changes in gene expression among mammalian species have led to the proposal that gene expression divergence may be neutral with respect to organismic fitness. Here, we employ a comparative analysis of mammalian gene sequence divergence and gene expression divergence to test the hypothesis that the evolution of gene expression is predominantly neutral. Two models of neutral gene expression evolution are considered: 1-purely neutral evolution (i.e., no selective constraint) of gene expression levels and patterns and 2-neutral evolution accompanied by selective constraint. With respect to purely neutral evolution, levels of change in gene expression between human-mouse orthologs are correlated with levels of gene sequence divergence that are determined largely by purifying selection. In contrast, evolutionary changes of tissue-specific gene expression profiles do not show such a correlation with sequence divergence. However, divergence of both gene expression levels and profiles are significantly lower for orthologous human-mouse gene pairs than for pairs of randomly chosen human and mouse genes. These data clearly point to the action of selective constraint on gene expression divergence and are inconsistent with the purely neutral model; however, there is likely to be a neutral component in evolution of gene expression, particularly, in tissues where the expression of a given gene is low and functionally irrelevant. The model of neutral evolution with selective constraint predicts a regular, clock-like accumulation of gene expression divergence. However, relative rate tests of the divergence among human-mouse-rat orthologous gene sets reveal clock-like evolution for gene sequence divergence, and to a lesser extent for gene expression level divergence, but not for the divergence of tissue-specific gene expression profiles. Taken together, these results indicate that gene expression divergence is subject to the effects of purifying selective constraint and suggest that it might also be substantially influenced by positive Darwinian selection.     

A semi-parametric statistical model for integrating gene expression profiles across different platforms

Determining differentially expressed genes (DEGs) between biological samples is the key to understand how genotype gives rise to phenotype. RNA-seq and microarray are two main technologies for profiling gene expression levels. However, considerable discrepancy has been found between DEGs detected using the two technologies. Integration data across these two platforms has the potential to improve the power and reliability of DEG detection. We propose a rank-based semi-parametric model to determine DEGs using information across different sources and apply it to the integration of RNA-seq and microarray data. By incorporating both the significance of differential expression and the consistency across platforms, our method effectively detects DEGs with moderate but consistent signals. We demonstrate the effectiveness of our method using simulation studies, MAQC/SEQC data and a synthetic microRNA dataset. Our integration method is not only robust to noise and heterogeneity in the data, but also adaptive to the structure of data. In our simulations and real data studies, our approach shows a higher discriminate power and identifies more biologically relevant DEGs than eBayes, DEseq and some commonly used meta-analysis methods.     

Seasonal changes in patterns of gene expression in avian song control brain regions

Photoperiod and hormonal cues drive dramatic seasonal changes in structure and function of the avian song control system. Little is known, however, about the patterns of gene expression associated with seasonal changes. Here we address this issue by altering the hormonal and photoperiodic conditions in seasonally-breeding Gambel's white-crowned sparrows and extracting RNA from the telencephalic song control nuclei HVC and RA across multiple time points that capture different stages of growth and regression. We chose HVC and RA because while both nuclei change in volume across seasons, the cellular mechanisms underlying these changes differ. We thus hypothesized that different genes would be expressed between HVC and RA. We tested this by using the extracted RNA to perform a cDNA microarray hybridization developed by the SoNG initiative. We then validated these results using qRT-PCR. We found that 363 genes varied by more than 1.5 fold (>log(2) 0.585) in expression in HVC and/or RA. Supporting our hypothesis, only 59 of these 363 genes were found to vary in both nuclei, while 132 gene expression changes were HVC specific and 172 were RA specific. We then assigned many of these genes to functional categories relevant to the different mechanisms underlying seasonal change in HVC and RA, including neurogenesis, apoptosis, cell growth, dendrite arborization and axonal growth, angiogenesis, endocrinology, growth factors, and electrophysiology. This revealed categorical differences in the kinds of genes regulated in HVC and RA. These results show that different molecular programs underlie seasonal changes in HVC and RA, and that gene expression is time specific across different reproductive conditions. Our results provide insights into the complex molecular pathways that underlie adult neural plasticity.     

Evolutionary rate depends on number of protein-protein interactions independently of gene expression level: Response

A response to Fraser HB, Hirsh AE: Evolutionary rate depends on number of protein-protein interactions independently of gene expression level. BMC Evol Biol 2004, 4: 13