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25.1 Functional Genomics: Gene Expression on a Genomic Scale • Functional genomics refers to those areas that deal with the function or expression of genomes • All transcripts an organis[r]
(1)Lecture PowerPoint to accompany Molecular Biology Fifth Edition Robert F Weaver Chapter 25 Genomics II: Functional Genomics, Proteomics, and Bioinformatics Copyright © The McGraw-Hill Companies, Inc Permission required for reproduction or display (2) 25.1 Functional Genomics: Gene Expression on a Genomic Scale • Functional genomics refers to those areas that deal with the function or expression of genomes • All transcripts an organism makes at any given time is an organism’s transcriptome • Use of genomic information to block expression systematically is called genomic functional profiling • Study of structures and functions of the protein products of genomes is proteomics 24-2 (3) Transcriptomics • This area is the study of all transcripts an organism makes at any given time • Create DNA microarrays and microchips that hold 1000s of cDNAs or oligos – Hybridize labeled RNAs from cells to these arrays or chips – Intensity of hybridization at each spot reveals the extent of expression of the corresponding gene • Microarray permits canvassing expression patterns of many genes at once • Clustering of expression of genes in time and space suggest products of these genes collaborate in some process 24-3 (4) Oligonucleotides on a Glass Substrate 24-4 (5) Serial Analysis of Gene Expression • Serial Analysis of Gene Expression (SAGE) allows us to determine: – Which genes are expressed in a given tissue – The extent of that expression • Short tags, characteristic of particular genes, are generated from cDNAs and ligated together between linkers • These ligated tags are then sequenced to determine which genes are expressed and how abundantly 24-5 (6) SAGE 24-6 (7) Cap Analysis of Gene Expression (CAGE) • CAGE gives the same information as SAGE about which genes are expressed and how abundantly, in a given tissue • It focuses on the 5’-ends of mRNAs, which allows for the identification of transcription start sites and may help in locating pormoters 24-7 (8) Whole Chromosome Transcription Mapping • High density whole chromosome transcriptional mapping studies have shown a majority of sequences in cytoplasmic poly(A)RNAs derive from non-exon regions of human chromosomes • Almost half of the transcription from these same chromosomes is nonpolyadenylated • Results indicate that great majority of stable nuclear and cytoplasmic transcripts in these chromosomes come from regions outside exons • Helps to explain the great differences between species whose exons are almost identical 24-8 (9) Transcription maps of 10 Human Chromosomes 24-9 (10) Genomic Functional Profiling • Genomic functional profiling can be performed in several ways – A type of mutation analysis, deletion analysis mutants created by replacing genes one at a time with antibiotic resistance gene flanked by oligomers serving as barcode for that mutant – A functional profile can be obtained by growing the whole group of mutants together under various conditions to see which mutants disappear most rapidly 24-10 (11) RNAi Analysis • Another means of genomic functional analysis on complex organisms can be done by inactivating genes via RNAi • An application of this approach targeting the genes involved in early embryogenesis in C elegans has identified: – 661 important genes – 326 are involved in embryogenesis 24-11 (12) Tissue-Specific Functional Profiling • Tissue-specific expression profiling can be done by examining a spectrum of mRNAs whose levels are decreased by an exogenous miRNA • Then compare to the spectrum of expression of genes at the mRNA level in various tissues • If that miRNA causes a decrease in the levels of mRNAs naturally low in cells expressing the miRNA – Suggests that the miRNA is at least a partial cause of those natural low levels • This type of analysis has implicated – miR-124 in destabilizing mRNAs in brain tissue – miR-1 in destabilizing mRNAs in muscle tissue 24-12 (13) Locating Target Sites for Transcription Factors • ChIP-chip analysis can be used to identify DNAbinding sites for activators and other proteins • Small genome organisms - all of the intergenic regions can be included in the microarray • If genome is large, that is not practical • To narrow areas of interest can use CpG islands – These are associated with gene control regions – If timing/conditions of activator’s activity are known, control regions of genes known to be activated at those times, or under those conditions, can be used 24-13 (14) Locating Target Sites for Transcription Factors • Tag sequencing, or ChIPSeq, in which chromatin pieces precipitated by ChIP are repeatedly sequenced, can also be used to identify transcription factor-binding sites • Knowledge of the sequence of multiple mammalian genomes allows one to narrow the search for human transcription factor binding sites by beginning with conserved regions of the genome • In addition, it is easier to search for cis-regulatory modules (CRMs), which contain several transcription factor binding sites 24-14 (15) Locating enhancers that bind unknown proteins • There are still many enhancers whose protein partners are unknown • Pennachio and colleagues started the search for vertebrate enhancers by looking for highly conserved non-coding DNA regions in 2006 • The strategy had a remarkably high success rate but has a drawback in that it only detects highly conserved sequences and not all important control regions are conserved 24-15 (16) Locating promoters that bind unknown proteins • Ren and colleagues performed a genome-wide search for human promoters and were surprised to find that many genes have alternative promoters located hundreds of bases away from their primary promoters • Class II promoters can be identified using ChIPchip analysis with an anti-TAF1 antibody • In one study using human fibroblasts, over 9,000 promoters were identified and over 1600 genes had multiple promoters 24-16 (17) In Situ Expression Analysis • The mouse can be used as a human surrogate in large-scale expression studies that would be ethically impossible to perform on humans • Scientists have studied the expression of almost all the mouse orthologs of the genes on human chromosome 21 – Expression followed through various stages of embryonic development – Catalogued the embryonic tissues in which these genes are expressed 24-17 (18) Single-Nucleotide Polymorphisms (SNPs) • Single-nucleotide polymorphisms can probably account for many genetic conditions caused by single genes and even some by multiple genes • Might be able to predict response to a drug • Haplotype map with over million SNPs makes it easier to sort out important SNPs from those with no effect 24-18 (19) Structural Variation • Structural variation is a prominent source of variation in human genomes – – – – Insertions Deletions Inversions Rearrangements of DNA chunks • Some structural variation can, in principle, predispose certain people to contract diseases – Some variation is presumably benign – Some also is demonstrably beneficial 24-19 (20) 25.2 Proteomics • The sum of all proteins produced by an organism is its proteome • Study of these proteins, even smaller subsets, is called proteomics • Such studies give a more accurate picture of gene expression than transcriptomics studies 24-20 (21) Protein Separations and Analysis • Current research in proteomics requires first that proteins be resolved, sometimes on a massive scale – Best tool for separation of many proteins at once is 2D gel electrophoresis • After separation, proteins must be identified – Best method of identification involves digestion of proteins one by one with proteases – Then identify the peptides by mass spectrometry • In the future, microchips with antibodies attached may allow analysis of proteins in complex mixtures without separation 24-21 (22) Quantitative Proteomics • To determine the changes in protein levels upon perturbation of a cell culture, one can label the cells under the first condition with a light isotopic tag, and under the second condition with a heavy isotopic tag • If the proteins are labeled in vivo, the cell cultures can be mixed, the proteins can be extracted and fragmented by proteolysis and upon further separation can be subjected to mass spectronomy • The ratio of heavy to light peak areas will reflect the change in protein concentration as the growth conditions change 24-22 (23) Comparative Proteomics • What makes a worm a worm and a fly a fly? • Mass spectrometry data can be used to compare protein concentrations in two different organisms • This type of analysis was applied to C.elegans and Drosophila to reveal that the concentrations of orthologous proteins are correlated much better than the orthologous mRNAs in the two organisms 24-23 (24) Protein Interactions • Most proteins work with other proteins to perform their functions • Several techniques are available to probe these interactions • Yeast two-hybrid analysis has been used for some time, now other methods are available – Protein microarrays – Immunoaffinity chromatography with mass spectrometry – Other combinations 24-24 (25) Detecting Protein-Protein Interactions 24-25 (26) 25.3 Bioinformatics • Bioinformatics involves the building and use of biological databases – Some of these databases contain the DNA sequences of genomes – Essential for mining the massive amounts of biological data for meaningful knowledge about gene structure and expression 24-26 (27) Finding Regulatory Motifs in Mammalian Genomes Using computational biology techniques, Lander and Kellis have discovered highly conserved sequence motifs in mammalian species, including humans: – In the promoter regions, these motifs probably represent binding sites for transcription factors – 3’-UTRs motifs probably represent binding sites for miRNAs 24-27 (28) Using the Databases • The National Center for Biological Information (NCBI) website contains a vast store of biological information, including genomic and proteomic data • Start with a sequence and discover the gene to which it belongs, then compare that sequence with that of similar genes • Query the database with a topic for information • View structures of protein in 3D by rotating the structure on your computer screen 24-28 (29)