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All the gene-encoded molecules of a cell – proteins and RNAs – are needed in amounts that differ from gene product to gene product and that change over the cell's lifetime. For example, if a cell has too little ribosomal RNA (rRNA), then protein synthesis is retarded; similarly, if molecule X becomes the sole carbon source for a cell and it fails to respond by synthesizing enzymes to catabolize X, then it will be starved for carbon and energy. Because RNAs encode proteins, one way to control the quantity of all gene products is to control the abundance of each kind of RNA.
Opposing forces determine the abundance of RNA: synthesizing RNA increases its cellular concentration, while enzymatic degradation of RNA, cell growth, and cell division reduce its concentration. This chapter is about the molecular machinery that controls rates of RNA synthesis and degradation. Other ways of controlling the amount of gene products are presented elsewhere.
Some of the key concepts of RNA regulation are (1) RNA stability, (2) positive and negative regulatory proteins (activators and repressors) that bind to (3) regulatory DNA sequences (promoters, upstream activating sequences, and enhancers), and (4) a regulatory system, the operon. This chapter focuses on bacteria; other simple forms are considered very briefly.
Abundance of Stable RNAs – rRNA and tRNA
In Escherichia coli undergoing exponential growth, about 97% of RNA is rRNA and tRNA. There are two reasons for this large excess: rRNAs and tRNAs are much more stable than mRNA (less susceptible to degradation), and the rates of synthesis of rRNA and tRNA genes are very high.
There is not a 1:1 relationship between a gene and a trait. Genes are pleiotropic: they affect many traits. Conversely, traits are multigenic – influenced by many genes. The environment, too, plays a role in determining phenotype. Further complexity arises from the quantitative nature of many traits – for example, size, fertility, or the probability of developing a tumor. Genetic effects considered in this chapter include quantitative dominance, epistasis (interactions between genes), penetrance and expressivity (variation in phenotype exhibited by one genotype), and genotype-environment interactions.
Multigenic Determination of Phenotype
Only the nucleic acid sequence of the primary transcript is surely and invariably determined by a single gene. All other classes of phenotype require the participation of two or more genes. However, from a practical standpoint, a single gene often can be considered to code for the amino acid sequence of a polypeptide, as other determinants of amino acid sequence usually do not act in a gene product-specific way. At high levels of biological organization, all traits are multigenic. Nearly all individuals of a species may be phenotypically alike for some trait, no matter how many genes influence it, because there may be no genetic variation for the trait, or else such variation may be hidden.
Example of Multigenic Influence. When peas dry they shrink. Excessive shrinkage, due to a shortage of starch, causes the seed coat to wrinkle.
Cancer, a group of genetic diseases, is development gone wrong in a clone of somatic cells – a tumor. If a tumor destroys adjacent tissue it is malignant. Tumor cells:
Accumulate mutations and become genetically unstable
Grow in an unregulated manner
Lose contact inhibition; i.e., growth is not inhibited by adjacent cells
Lose the potential to undergo apoptosis
May metastasize – migrate and establish subclones in other body locations
Characteristics of Cancer
The hallmark of tumors is uncontrolled cell proliferation. Cancer cells proliferate exponentially because they have gained the ability to self-stimulate cell cycling and have lost the ability to respond to extrinsic growth inhibitors. A tumor's potential to be lethal principally depends on its uncontrolled growth. The growth of normal cells is inhibited by contact with adjacent cells, whereas cancer cells have lost contact inhibition. The morphology of cancer cells changes and telomerase synthesis (not present in normal somatic cells) resumes. Cancer cells become immortal – they can go through an indefinite number of cell division cycles – and gain the ability to be cultured; a normal somatic clone can survive for a limited time, ~102 cell division cycles. Cancer cells often lose the ability to undergo apoptosis. Solid tumors may stimulate angiogenesis, the growth of blood vessels supplying the tumor. Many cancer cells metastasize – move into the blood and migrate to other locations in the body. Cancer cells may evolve the ability to evade the immune system.
The eukaryal genome is located in the cell's nucleus, packaged in structurally complex chromosomes. Through the cell cycle, chromosomes condense and decondense, undergoing dramatic changes in thickness and length. With few exceptions, eukaryal chromosomes consist of thread-like chromatin, composed of particles of histones wrapped by DNA, called nucleosomes. Nearly all eukaryal chromosomes are linear and possess a pair of telomeres. In most species, each chromosome has a single centromere, which functions in chromosome separation and movement.
Condensed versus Uncondensed Nuclear Chromosomes
In eukarya, chromosomes reside in the nucleus, a large spherical organelle bounded by a double-membraned envelope. In most cases chromosomes are visible by light microscopy only during mitosis, the brief nuclear division phase. Mitotic chromosomes are short and thick (d ~12 µm, “packed to travel”) and take up colorful stains used to detect their presence. They consist of twin copies called sister chromatids and are attached to spindle fibers (microtubules that move chromosomes). Genes are not transcribed into RNA during mitosis. Mitotic chromosomes are useful for the study of chromosome structure, even though they bear little resemblance to “chromosomes at work,” and even though mitosis takes up a small fraction of the life span of a typical cell (Figure 5.1).
Chromosomes perform most of their genetic functions during interphase, the cell's lengthy, nondividing period.
Because the genes of all organisms consist of DNA, and because a gene's structure largely determines its functional properties, analysis of any particular gene demands the isolation and structural analysis of DNA. To manipulate DNA experimentally, or even to determine its base sequence, chromosomal DNA must be reduced to small fragments, which are then sorted, identified, copied, and stored for future use.
This chapter explains some basic principles of cutting DNA enzymatically into fragments, separating pieces of DNA or RNA by size, making DNA copies of RNA, identifying one particular piece of DNA or RNA, copying DNA, and establishing large collections of DNA fragments for storage and retrieval.
Cutting and Fractionating DNA
Restriction Enzymes
It is easy to isolate chromosomal DNA. However, chromosomes tend to be long, and, even after random breakage, the DNA isolated from chromosomes exists as intractably long pieces. It is convenient to cut DNA into appropriate-sized fragments with bacterial restriction endonucleases (often simply called restriction enzymes), which cut only at particular sites.
Bacteria protect themselves against viral infection by cutting foreign DNA with restriction enzymes, so called because they restrict foreign DNA's ability to invade the cell. The cell distinguishes its own DNA from foreign DNA by methylating certain bases shortly after replication, using modification enzymes. At least one strand of the bacterium's own DNA is modified and therefore resistant to restriction, whereas foreign DNA is not modified and therefore susceptible to restriction.
The past decade has yielded new tools for pig geneticists and breeders thanks to the considerable developments resulting from efforts to map the pig genome. The pig genetic linkage map now has nearly 5000 loci including several hundred genes, microsatellites and amplified fragment length polymorphisms (AFLP) markers. Using tools that include somatic cell hybrid panels and radiation hybrid panels, the physical genetic map is also growing rapidly and has over 4000 genes and markers. Scientists using both exotic and commercial breeds for quantitative trait loci (QTL) scans and candidate gene analyses have identified a number of important chromosomal regions and individual genes associated with growth rate, leanness, feed intake, meat quality, litter size and disease resistance. Using marker-assisted selection (MAS) the commercial pig industry is actively incorporating these gene markers and traditional performance information to improve traits of economic importance in pig production. Researchers now have novel tools including pig gene arrays and advanced bioinformatics that are being exploited to find new candidate genes and to advance the understanding of gene function in the pig. Sequencing of the pig genome has been initiated and further sequencing is now being considered. Advances in pig genomics and directions for future research and the implications to both the pig industry and human health are reviewed.
Quadratic indices are a general approach for the joint management of genetic gain and inbreeding in artificial selection programmes. They provide the optimal contributions that selection candidates should have to obtain the maximum gain when the rate of inbreeding is constrained to a predefined value. This study shows that, when using quadratic indices, the selective advantage is a function of the Mendelian sampling terms. That is, at all times, contributions of selected candidates are allocated according to the best available information about their Mendelian sampling terms (i.e. about their superiority over their parental average) and not on their breeding values. By contrast, under standard truncation selection, both estimated breeding values and Mendelian sampling terms play a major role in determining contributions. A measure of the effectiveness of using genetic variation to achieve genetic gain is presented and benchmark values of 0·92 for quadratic optimisation and 0·5 for truncation selection are found for a rate of inbreeding of 0·01 and a heritability of 0·25.
The Hudson–Kreitman–Aguade (HKA) test is based on the prediction from the neutral theory that levels of polymorphism within a species and the divergence between two closely related species should be correlated. Population subdivision has been shown to alter both the amounts of polymorphism segregating within species and the rate of divergence between species, meaning that genomic regions with different population structures also differ in their divergence to polymorphism ratios. Population subdivision may hence hamper the utility of the HKA test for detecting deviations from the standard neutral model, especially for organelle genomes that often have different patterns of population structure compared with nuclear genes. In this paper, I show that population subdivision inflates the number of instances where the HKA test detects deviations from the neutral model. Using coalescent simulations I show that this bias is most apparent when population subdivision is strong and differs substantially between the loci included. However, if divergence time is large and population structure substantial even changes in the levels of polymorphism and divergence associated with differences in the effective population size between two loci is enough to substantially alter the number of significant outcomes of the HKA test. A dataset on cytoplasmic diversity in Silene vulgaris and S. latifolia (Ingvarsson & Taylor, 2002) is also reanalysed. The previous study had shown a marked excess of intraspecific polymorphism in both species. However, when effects of population subdivision were removed, ad hoc, levels of intraspecific polymorphism were no longer significantly different from neutral expectations, suggesting that population subdivision contributed to the observed excess of intraspecific polymorphism seen in both species of Silene.
This report presents a theoretical formulation for predicting heterozygosity of a putative marker locus linked to two quantitative trait loci (QTL) in a recurrent selection and backcross (RSB) scheme. Since the heterozygosity at any given marker locus maintained in such a breeding programme reflects its map location relative to QTL, the present study develops the theoretical analysis of the QTL mapping method that recently appeared in the literature. The formulae take into account selection, recombination and finite population size during the multiple-generation breeding scheme. The single-marker and two-QTL model was compared numerically with the model involving two linked marker loci and two QTL. Without recombination interference, the two models predict the same expected heterozygosity at the linked marker loci, indicating that the model is valid for predicting marker heterozygosity maintained at any loci in an RSB breeding scheme. The formulation is demonstrated numerically for several RSB schemes and its implications in developing a likelihood-based statistical framework for modeling the RSB experiments are discussed.
We present a maximum likelihood method for mapping quantitative trait loci that uses linkage disequilibrium information from single and multiple markers. We made paired comparisons between analyses using a single marker, two markers and six markers. We also compared the method to single marker regression analysis under several scenarios using simulated data. In general, our method outperformed regression (smaller mean square error and confidence intervals of location estimate) for quantitative trait loci with dominance effects. In addition, the method provides estimates of the frequency and additive and dominance effects of the quantitative trait locus.
Drosophila ananassae is a cosmopolitan species with a geographic range throughout most of the tropical and subtropical regions of the world. Previous studies of DNA sequence polymorphism in three genes has shown evidence of selection affecting broad expanses of the genome in regions with low rates of recombination in geographically local populations in and around India. The studies suggest that extensive physical and genetic maps based on molecular markers, and detailed studies of population structure may provide insight into the degree to which natural selection affects DNA sequence polymorphism across broad regions of chromosomes. We have isolated 85 dinucleotide repeat microsatellite sequences and developed assay conditions for genotyping using PCR. The dinucleotide repeats we isolated are shorter, on average, than those isolated in many other Drosophila species. Levels of genetic variation are high, comparable to Drosophila melanogaster. The levels of variation indicate the effective population size of an Indonesian population of D. ananassae is 58692 (infinite allele model) and 217284 (stepwise mutation model), similar to estimates of effective population size for D. melanogaster calculated using dinucleotide repeat microsatellites. The data also show that the Indonesian population is in a rapid expansion phase. Cross-species amplification of the microsatellites in 11 species from the Ananassae, Elegans, Eugracilis and Ficusphila subgroups indicates that the loci may be useful for studies of the sister species, D. pallidosa, but will have limited use for more distantly related species.
Repeated efforts to estimate the genomic deleterious mutation rate per generation (U) in Drosophila melanogaster have yielded inconsistent estimates ranging from 0·01 to nearly 1. We carried out a mutation-accumulation experiment with a cryopreserved control population in hopes of resolving some of the uncertainties raised by these estimates. Mutation accumulation (MA) was carried out by brother–sister mating of 150 sublines derived from two inbred lines. Fitness was measured under conditions chosen to mimic the ancestral laboratory environment of these genotypes. We monitored the insertions of a transposable element, copia, that proved to accumulate at the unusually high rate of 0·24 per genome per generation in one of our MA lines. Mutational variance in fitness increased at a rate consistent with previous studies, yielding a mutational coefficient of variation greater than 3%. The performance of the cryopreserved control relative to the MA lines was inconsistent, so estimates of mutation rate by the Bateman–Mukai method are suspect. Taken at face value, these data suggest a modest decline in fitness of about 0·3% per generation. The element number of copia was a significant predictor of fitness within generations; on average, insertions caused a 0·76% loss in fitness, although the confidence limits on this estimate are wide.
We studied spatial and temporal variation in 20–23 Aedes aegypti samples collected in Phnom Penh and its suburbs to estimate the population genetic structure using allozymes and the susceptibility to a dengue-2 virus. Based on seven allozyme systems, we detected low levels of genetic exchanges (i.e. high, significant FST values) between populations collected in the city centre, and different patterns of genetic structure for samples collected in the suburbs, depending on the type of environment and the date of collection. In the southern suburbs and the Chroy Chang Var Peninsula, differentiation became highly significant at the end of the dry season, whereas the opposite situation was observed for collections from the northern suburbs. Vector competence assessed by oral infections with a dengue-2 virus was lower for samples collected in the city centre than in the suburbs. A significant decrease of dengue susceptibility was observed in populations during the dry season. This study allows a model of Ae. aegypti population functioning in Phnom Penh to be suggested. Dynamics of dengue virus diffusion depend on the population genetic structure of the vector and its evolution over space and time.
Normal mixed models with different levels of heterogeneity in the residual variance are fitted to pig litter size data. Exploratory analysis and model assessment is based on examination of various posterior predictive distributions. Comparisons based on Bayes factors and related criteria favour models with a genetically structured residual variance heterogeneity. There is, moreover, strong evidence of a negative correlation between the additive genetic values affecting litter size and those affecting residual variance. The models are also compared according to the purposes for which they might be used, such as prediction of ‘future’ data, inference about response to selection and ranking candidates for selection. A brief discussion is given of some implications for selection of the genetically structured residual variance model.
The Triplo-lethal locus (Tpl) of Drosophila is both triplo-lethal and haploinsufficient, but the function of the locus is unknown. We have examined Tpl-aneuploid embryos and find that, in both trisomics and monosomics, the midgut shows extensive cell death and the tracheae are abnormal. Shortly thereafter, all tissues die. PCR-based genotyping of individual embryos and larvae show that this phenotype occurs in the trisomics after hatching and in the monosomics before hatching. Weak alleles of the interacting gene Su(Tpl) delay the death of Tpl trisomics, but they still show the same tracheal and midgut phenotypes before dying. Hyperoxia (45% oxygen) partially suppresses the phenotype of Tpl aneuploids, even though the use of a hypoxia reporter strain shows that dying Tpl aneuploids are not hypoxic. This is the first report of a phenotype associated with the Tpl locus and the first report of an environmental condition that suppresses the phenotype.