Gene Expression. Following identification of the entire novel gene sequence, gene expression is determined in a number of tissues including brain, liver, pancreas, and muscle. In addition, gene expression under a variety of metabolic disturbances including obesity, fasting, aver-feeding and sever diabetes is determined. The pattern o£ gene expression is then examined to identify leads for future experiments aimed at identifying the function of the novel gene. A major advance in technology in the area of gene expression is ‘real-time PCR’ which is now routinely available in our laboratory and allows accurate and rapid determination of gene expression. Following detailed animal experiments in Psammomys obesus and to confirm the importance of the identified gene in human metabolism, human RNA blots are used to examine gene expression in a variety of human tissues.
Gene Expression. Clones isolated from the fed flea gut cDNA expression library will be expressed in E. coil and the resulting fusion proteins characterized by Western blot analysis with immune sera. To verify that the recombinant clones encode the protein of interest, fusion protein expressed by the purified recombinant phage is bound to a nitrocellulose filter and used to affinity-purify clone- specific Antibodies from the
Gene Expression. Clones isolated from the fed flea gut cDNA expression library will be expressed in E. coli and the resulting fusion proteins characterized by Western blot analysis with immune sera. To verify that the recombinant clones encode the protein of interest, fusion protein expressed by the purified recombinant phage is bound to a nitrocellulose filter and used to affinity-purify clone- specific antibodies from the original immune sera. The monospecific antibodies are then eluted and used in Western blot analysis to identify the molecular weight of the native flea gut antigen encoded by the recombinant. The clones will be subcloned into a vector system optimized for large scale production of fusion protein that can be purified by affinity chromotography.
Gene Expression. The transcriptome maps presented revealed localised differences in the expres- sion levels between different tissues and different developmental stages. These differences were shown to follow certain trends at the level of isochores, as discussed in the previous section. In order to attribute the differences at the isochore level to differences at the gene level, the expression data was mapped onto the NCBI Consensus Coding Sequence Database (CCDS) and normalised for the length of each coding sequence (CDS) and the total number of reads mapped onto the coding sequences of the tissue (Equation 3.4). Figure 3.5 demonstrates that the normalised expression of genes, averaged per isochore, follows similar patterns as those shown for the expression at isochore level, after the gene density was factored out (Fig. 3.4), albeit the correlations are consid- erably weaker. Here again, despite the adult tissues’ showing a positive correlation between the gene expression and the GC content of the isochores, the gene expression during development appears to be independent of the base composition of the isochores, with genes in GC-poor isochores matching in expression level the genes in GC-rich isochores. Therefore the positive correlation between the ex- pression level and the GC content observed in the adult tissues is the result of differential regulation of genes in GC-poor and GC-rich isochores, possibly at the level of entire isochores, as discussed previously.
Gene Expression. Genetic and pharmacological mechanisms to control gene expression will be presented, including antisense oligonucleotides, inhibitory RNAs (e.g. short-hairpin RNA) and microRNA.
Gene Expression. Gene expression is the process of using a gene’s information in the synthesis of a functional gene product (usually a protein). Gene expression is the most fundamental level in genetics since the genotype gives rise to the phenotype through gene expression. By the definition of gene expression, the amount of functional gene products (usually proteins) should be measured. However, the measurement for functional gene products is difficult and often the abundance of messenger RNA (mRNA), an intermediate product positively correlated with functional gene product, is measured to determine the intensity for gene expression. Gene expression microarray and RNA-Seq are actually two different methods measuring the intensity of mRNA. Gene expression microarray uses an “array”, a collection of microscopic DNA spots attached to a solid surface, to hybridize cDNAs (converted from mRNAs) for target genes. On the other hand, RNA-Seq sequences cDNA and all the sequence fragments (Reads) will be aligned to a reference genome to reflect the intensity of gene expression. Preprocessing and normalization are extremely important topics for both microarray and RNA-Seq data (Ghosh & Qin, 2010). In this dissertation, we skip the preprocessing and normalization steps and focus on the downstream analyses, assuming our data have already been properly preprocessed and normalized. After appropriate preprocessing and normalization, microarray data can be summarized into an I by K matrix that stores log transformed gene expression levels across I genes and K samples. Basic statistical framework: Microarray analysis An important task of analyzing gene expression data from different conditions is to identify DE genes in an experiment that compares two groups (conditions) of samples. We define the two groups as the control group and the treatment group. Let Xijk denotes the normalized log expression value, where i denotes different genes, j denotes different conditions (control group or treatment group), k denotes different replicates. i = 1, 2…I, j = 1, 2. k = 1, 2….n. The basic assumption for the log gene expression value is:
