Complementation Test Genetics Example Essay

Complementation Testing

Occasionally, multiple mutations of a single wild type phenotype are observed. The appropriate genetic question to ask is whether any of the mutations are in a single gene, or whether each mutations represents one of the several genes necessary for a phenotype to be expressed. The simplest test to distinguish between the two possibilities is the complementation test. The test is simple to perform --- two mutants are crossed, and the F1 is analyzed. If th e F1 expresses the wild type phenotype, we conclude each mutation is in one of two possible genes necessary for the wild type phenotype. When it is shown that shown genetically that two (or more) genes control a phenotype, the genes are said to form a complementation group. Alternatively, if the F1 does not express the wild type phenotype, but rather a mutant phenotype, we conclude that both mutations occur in the same gene.

These two results can be explained by considering the importance of genes to phenotypic function. If two separate genes are involved, each mutant will have a lesion in one gene while maintaining a wild type copy of the second gene. When the F1 is produc ed, it will expresses the mutant allele of gene A and the wild type allele of gene B (each contributed by one of the mutant parents). The F1 will also express the wild type allele for gene A and the mutant allele for gene B (contributed by the other muta nt parent). Because the F1 is expressing both of the necessary wild type alleles, the wild type phenotype is observed.

Conversely, if the mutations are in the same gene, each homolog will express a mutant version of the gene in the F1. Without a normal functioning gene product in the individual, a mutant phenotype occurs.

Eye color in Drosphila is a good model to demonstrate the complementation test. A wide array of spontaneous mutations have been studied. These experiments demonstrated that five genes (white, ruby, vermillion, garnet and carnation) controlling ey e color reside within 60 cM of each other on the X chromosome. The dominant wild type allele for each gene produces the deep red eyes. The mutant alleles produce a different color. If mutants from any of these five genes are crossed, the F1 would expre ss deep red eye color (wild type phenotype).

Five different alleles (buff, coral, apricot, white and cherry) are also known to exist for the white gene, each representing a mutation at a different position in the gene. If mutant flies for any of the five white alleles are crossed, the F1 offspring would have a mutant eye color. Therefore, two genes involved in the expression of a phenotype complement each other. But complementation bet ween two alleles of the same gene does not occur.

Copyright © 1999. Phillip McClean

Protein–Protein Interactions

Large-scale protein–protein interaction (PPI) mapping complements genetic studies by revealing physical associations and helps to define the physical signaling network. In PPI networks, nodes represent proteins and the edges represent a physical association between them. The methods most widely used to map PPI networks include the yeast two-hybrid (Y2H) system and its derivatives [160, 161], and affinity- or immunoprecipitation followed by mass spectrometry (AP/MS) [162–165]. PPI networks derived from Y2H methods are composed of binary (direct) interactions, whereas those derived from AP/MS techniques can be both direct and indirect, as they identify protein complexes.

Large-scale Y2H studies have been conducted with proteins from Helicobacter pylori[166], yeast [167–169], C. elegans[21,170,171], Drosophila[172–174], and human [175–179]. Strikingly, the three large-scale Drosophila Y2H mapping studies failed to fully recapitulate known signaling pathways. For example, querying the combination of these studies for Raf reveals only interactions with CG15422, Ras, Rhomboid, and Rap2L (http: //itchy.med.wayne.edu/PIM2/PIMtool.html), neglecting to identify most known targets, scaffolds, and co-regulators of Raf activity. Thus, these ‘proteome-scale’ approaches, although they identified highly abundant or strongly interacting cellular components, failed to identify many interactors of signaling components – most likely because of the absence of endogenous signaling contexts [180]. For this reason, MS-based approaches have become more popular, especially as the difficulty of implementation and costs have dropped dramatically.

Comprehensive MS-based PPI mapping has been applied in yeast [181–185]. A global protein kinase and phosphatase interaction network identified 1844 interactions between 887 proteins [181]. The success of MS approaches has been aided by the increased sensitivity of MS technology and implementation of tandem affinity purification (TAP) of protein complexes [186]. Recently, tandem affinity purification followed by MS has been used to isolate protein complexes from Drosophila tissue culture cells and tissues (http: //flybase.org/). ~5000 Drosophila proteins were fused to a FLAG-HA tag so that the fusion proteins could be expressed and recovered with their interacting partners from cells, or from whole transgenic flies. In addition to proteome-scale AP/MS, a number of smaller studies have been conducted in human cells on signaling pathways such as TNF-α and Wnt [187,188], biological processes such as autophagy [189], protein families such as the de-ubiquitinating enzymes [190], and protein complexes such as the RNA–polymerase II and PP2A complexes [191,192].

Both Y2H and AP/MS PPI mapping methods have been applied to the characterization of cellular networks with disease relevance, such as virus–host interactions [193–197]. These proteomic studies have confirmed that cellular processes take place within large networks of interconnected proteins.

PPI approaches, as implemented thus far, have been incomplete for investigations of signal transduction because they (1) do not provide functional information, and (2) often take place outside the context of endogenous signaling. These issues can be addressed by combining proteomics with RNAi. For instance, Y2H was used to identify interactors of the DAF-7/TGF-β pathway in C. elegans, resulting in a network of 59 proteins, and RNAi was used to show that nine novel interactors functionally interact with the TGF-β pathway [198]. Another major study used a pathway-specific approach with liquid chromatography/tandem MS to characterize the interactors of 32 TNF-α/NFκB pathway components in mammalian cells under endogenous signaling conditions [187]. Interactors were identified at baseline and under TNF-α stimulus, revealing 221 interactions. RNAi was then used to determine their influence on signaling output. This study demonstrated the power of pathway-directed proteomics in endogenous signaling contexts. One limitation of this study, however, was the lack of rigorous quantitation of the assembly of signaling complexes. Most signaling complexes are highly dynamic, with components often held in inactive complexes that can change dramatically following stimulation. For example, Raf and KSR are held in separate inactive complexes bound to PP2A core components and 14-3-3 proteins; following stimulation, Ras induces the recruitment of PP2A regulatory subunits to Raf, dephosphorylation of 14-3-3 binding sites, release of 14-3-3 proteins, membrane recruitment, KSR and Raf co-localization, Raf phosphorylation of MEK, and MEK phosphorylation of MAPK [199,200].

RNAi and MS can also be combined by first starting with RNAi and then following up with MS, as has been demonstrated for RTK/ERK signaling at baseline and under insulin stimulation [84,85]. All of the major known components of the pathway were tagged. In addition, a control cell line was engineered to subtract common interactors/contaminants. Altogether, 54 339 peptides were identified representing 12 208 proteins, encompassing an unfiltered network of 5009 interactions among 1188 individual proteins. To provide a ranked list of novel pathway interactors, filtering out sticky proteins found in control preparations and providing a probability that the observed interactor is real, the significance analysis of interactome (SAINT) method was applied to the PPI dataset [181]. Using a SAINT cut-off of 0.83 and a false discovery rate (FDR) of 10%, a filtered PPI network of 386 interactions among 249 proteins surrounding the canonical components of the RTK/Ras/ERK signaling pathway was generated [84]. In this network canonical baits have multiple common interactors, as would be expected from a well-connected signaling pathway (as opposed to unbiased PPI mapping of random protein baits), as well as many unique interactors. Because the baits were purified under two conditions, baseline and insulin stimulation, the dynamics of the mini-proteome during signaling events was uncovered. As a measure of the sensitivity of the TAP/MS approach to network characterization, interactions among the canonical components and their known interactors were extracted. This canonical network recapitulated most of the known RTK-ERK signaling pathway.

Comparing the RTK-ERK PPI network to six unbiased genome-wide RNAi screens revealed that nearly half (119) of the proteins identified by PPI mapping scored in the RNAi screens, which is a significant enrichment relative to the entire genome (19%, p < 7 × 10–25) [187].

A major bottleneck in large-scale proteomics studies is the experimental validation of specific interactors or components of complexes. The combination of AP-MS with RNAi-mediated knockdown provides a way to directly validate specific PPIs. With differential labeling of the two proteomes to be compared (wild-type vs. RNAi knockdown) such analyses have the potential for accurate, highly quantitative results [201,202]. Currently, two major types of labeling technique are used for MS-based proteomics studies: metabolic labeling and chemical labeling [203]. Stable isotope labeling by amino acids in cell culture (SILAC) is considered to be the gold standard in the case of metabolic labeling. Here, isotopically labeled amino acids (for example arginine and lysine labeled with the stable 13C and/or 15N isotope) are incorporated into cellular proteins during normal protein biosynthesis [204]. Thus, the cells to be compared (for example wild-type vs. RNAi) are grown in media containing ‘light’ (normal) and ‘heavy’ (labeled) amino acids, respectively. After labeling, the two cell populations are mixed, fractionated, and subject to MS/MS analysis to quantify the differences between their two proteomes in a highly accurate manner [205,206]. Because the labels are carried by arginine and lysine residues tryptic digestion produces peptides that contain a labeled amino acid at the carboxy terminus. The heavy and light tryptic peptides elute together as pairs separated by a defined mass difference that allows the two proteomes to be distinguished in the MS/MS analyses. SILAC can be multiplexed to allow comparisons between three different proteomes simultaneously. SILAC-based differential labeling combined with RNAi, co-immunoprecipitation and quantitative MS analysis was used to detect and validate the cellular interaction partners of endogenous β-catenin and Cbl proteins in mammalian cells [202]. Alternatively, chemical labeling involves the use of isobaric tagging reagents such as iTRAQ (isobaric tags for relative and absolute quantification) [207] or TMT (tandem mass tags) [208] (see Box 5.1 for definitions) to label peptides after lysis and trypsinization. The peptides in samples to be compared are modified by covalent attachment of a unique tag or label, which enables the quantification of the same peptide across multiple samples. The uniquely labeled samples are combined and run through an MS analysis. Despite bearing distinct tags, the same peptides from the different samples are indistinguishable from each other in the first MS run, because the molecular weight of each tag is the same. However, during MS/MS each tag undergoes fragmentation, releasing a signature reporter ion. The signature reporter ions differ in mass between the tags and their relative levels serve as a measure of differences in the levels of a given peptide between samples. Labeling methods such as these provide a rapid means by which to quantitatively examine global proteome-level changes, and compare, for example, wild-type cells with those subjected to mutations, RNAi knockdown or small molecule treatments.

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