Inducible gene expression systems and plant biotechnology

Plant biotechnology relies heavily on the genetic manipulation of crops. Almost invariantly, the gene of interest is expressed in a constitutive fashion, although this may not be strictly necessary for several applications. Currently, there are several regulatable expression systems for the temporal, spatial and quantitative control of transgene activity. These molecular switches are based on components derived from different organisms, which range from viruses to higher eukaryotes. Many inducible systems have been designed for fundamental and applied research and since their initial development, they have become increasingly popular in plant molecular biology.This review covers a broad number of inducible expression systems examining their properties and relevance for plant biotechnology in its various guises, from molecular breeding to pharmaceutical and industrial applications. For each system, we examine some advantages and limitations, also in relation to the strategy on which they rely. Besides being necessary to control useful genes that may negatively affect crop yield and quality, we discuss that inducible systems can be also used to increase public acceptance of GMOs, reducing some of the most common concerns. Finally, we suggest some directions and future developments for their further diffusion in agriculture and biotechnology.     

Evolution of plant breeding systems

Breeding systems are important, and often neglected, aspects of the natural biology of organisms, affecting homozygosity and thus many aspects of their biology, including levels and patterns of genetic diversity and genome evolution. Among the different plant mating systems, it is useful to distinguish two types of systems: 'sex systems', hermaphroditic versus male/female and other situations; and the 'mating systems' of hermaphroditic populations, inbreeding, outcrossing or intermediate. Evolutionary changes in breeding systems occur between closely related species, and some changes occur more often than others. Understanding why such changes occur requires combined genetical and ecological approaches. I review the ideas of some of the most important theoretical models, showing how these are based on individual selection using genetic principles to ask whether alleles affecting plants' outcrossing rates or sex morphs will spread in populations. After discussing how the conclusions are affected by some of the many relevant ecological factors, I relate these theoretical ideas to empirical data from some of the many recent breeding system studies in plant populations.     

Challenges in analysis and interpretation of microsatellite data for population genetic studies

Advancing technologies have facilitated the ever‐widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (F_ST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity‐based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration‐specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context‐dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non‐human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high‐throughput technologies in population genetics.     

Connections between species diversity and genetic diversity

Species diversity and genetic diversity remain the nearly exclusive domains of community ecology and population genetics, respectively, despite repeated recognition in the literature over the past 30 years of close parallels between these two levels of diversity. Species diversity within communities and genetic diversity within populations are hypothesized to co‐vary in space or time because of locality characteristics that influence the two levels of diversity via parallel processes, or because of direct effects of one level of diversity on the other via several different mechanisms. Here, we draw on a wide range of studies in ecology and evolution to examine the theoretical underpinnings of these hypotheses, review relevant empirical literature, and outline an agenda for future research. The plausibility of species diversity–genetic diversity relationships is supported by a variety of theoretical and empirical studies, and several recent studies provide direct, though preliminary support. Focusing on potential connections between species diversity and genetic diversity complements other approaches to synthesis at the ecology–evolution interface, and should contribute to conceptual unification of biodiversity research at the levels of genes and species.     

Combinatorial biosynthesis--potential and problems

Because of their ecological functions, natural products have been optimized in evolution for interaction with biological systems and receptors. However, they have not necessarily been optimized for other desirable drug properties and thus can often be improved by structural modification. Using examples from the literature, this paper reviews the opportunities for increasing structural diversity among natural products by combinatorial biosynthesis, i.e., the genetic manipulation of biosynthetic pathways. It distinguishes between combinatorial biosynthesis in a narrower sense to generate libraries of modified structures, and metabolic engineering for the targeted formation of specific structural analogs. Some of the problems and limitations encountered with these approaches are also discussed. Work from the author's laboratory on ansamycin antibiotics is presented which illustrates some of the opportunities and limitations.     

Human behavioural genetics of cognitive abilities and disabilities

Although neither the genome nor the environment can be manipulated in research on human behaviour, some of the new tools of molecular genetics can be brought to bear on human behavioural disorders (e.g. cognitive disabilities) and quantitative traits (e.g. cognitive abilities). The inability to manipulate the human genome experimentally has had the positive effect of focusing attention on naturally occuring genetic variation responsible for behavioural differences among individuals in all their complex multifactorial splendour. Genes in such complex multiple-gene systems are called quantitative trait loci (QTLs), which merge the two worlds of genetic research, quantitative genetics and molecular genetics. Although most genetic research on complex human behaviour has focused on severe mental disorders, cognitive abilities and disabilities may be even more immediately relevant to neuroscience. For example, verbal ability and spatial ability are two of the most heritable cognitive abilities, and reading disability is the first behavioural disability for which replicated QTL linkage has been found. The purpose of this essay is to provide an overview of the genetics of cognitive abilities and disabilities as an example of the impending merger of quantitative genetics and molecular genetics in QTL analysis of complex traits.     

Gene networks regulating secondary metabolism in actinomycetes: Pleiotropic regulators

Current advances in the research and practical application of pleiotropic regulatory genes for antibiotic production in actinomycetes are reviewed. The basic regulatory mechanisms discovered in these bacteria are outlined. The examples described in the review show the importance of the manipulation of regulatory systems that affect the synthesis of antibiotics for the metabolic engineering of actinomycetes. Also, the study of these genes is the basis for the development of genetic engineering approaches to the induction of the “cryptic” part of the actinomycetes secondary metabolome, the capacity of which for the production of biologically active compounds is much larger than the diversity of antibiotics underpinned by traditional microbiological screening. Besides practical problems, the study of regulatory genes for antibiotic biosynthesis will provide insights into the process of evolution of complex regulatory systems that coordinate the expression of gene operons, clusters, and regulons, involved in the control of the secondary metabolism and morphogenesis of actinomycetes.     

Genetics-squared: combining host and pathogen genetics in the analysis of innate immunity and bacterial virulence

The interaction of bacterial pathogens with their hosts’ innate immune systems can be extremely complex and is often difficult to disentangle experimentally. Using mouse models of bacterial infections, several laboratories have successfully applied genetic approaches to identify novel host genes required for innate immune defense. In addition, a variety of creative bacterial genetic schemes have been developed to identify key bacterial genes involved in triggering or evading host immunity. In cases where both the host and pathogen are amenable to genetic manipulation, a combination of host and pathogen genetic approaches can be used. Focusing on bacterial infections of mice, this review summarizes the benefits and limitations of applying genetic analysis to the study of host–pathogen interactions. In particular, we consider how prokaryotic and eukaryotic genetic strategies can be combined, or “squared,” to yield new insights in host–pathogen biology.     

蚜虫种群遗传多样性的影响因素及分子基础

蚜虫是一个复杂的类群 ,不同种群之间常常表现遗传多样性 ,特别是同种蚜虫的不同种群 ,这种多样性与环境因素 (寄主植物、地理气候条件等 )的影响密切相关 ,而且蚜虫种群多样性无论在细胞学水平 ,还是分子生物学水平均表现明显的遗传分化。该文在分析了蚜虫种群遗传多样性影响因素的基础上 ,从蚜虫核型变化、核DNA和线粒体DNA遗传分化和多样性方面总结了导致蚜虫种群遗传多样性的内在分子基础 ,并讨论了研究蚜虫种群遗传多样性的重要意义和前景     

Genetic constraints on wing pattern variation in Lycaeides butterflies: A case study on mapping complex,multifaceted traits in structured populations

Patterns of phenotypic variation within and among species can be shaped and constrained by trait genetic architecture. This is particularly true for complex traits, such as butterfly wing patterns, that consist of multiple elements. Understanding the genetics of complex trait variation across species boundaries is difficult, as it necessitates mapping in structured populations and can involve many loci with small or variable phenotypic effects. Here, we investigate the genetic architecture of complex wing pattern variation in Lycaeides butterflies as a case study of mapping multivariate traits in wild populations that include multiple nominal species or groups. We identify conserved modules of integrated wing pattern elements within populations and species. We show that trait covariances within modules have a genetic basis and thus represent genetic constraints that can channel evolution. Consistent with this, we find evidence that evolutionary changes in wing patterns among populations and species occur in the directions of genetic covariances within these groups. Thus, we show that genetic constraints affect patterns of biological diversity (wing pattern) in Lycaeides, and we provide an analytical template for similar work in other systems.     

Analytical aspects of plant metabolite profiling platforms: current standings and future aims

Over the past years, metabolic profiling has been established as a comprehensive systems biology tool. Mass spectrometry or NMR spectroscopy-based technology platforms combined with unsupervised or supervised multivariate statistical methodologies allow a deep insight into the complex metabolite patterns of plant-derived samples. Within this review, we provide a thorough introduction to the analytical hard- and software requirements of metabolic profiling platforms. Methodological limitations are addressed, and the metabolic profiling workflow is exemplified by summarizing recent applications ranging from model systems to more applied topics.     

The design and analysis of microarray experiments: applications in parasitology

Microarray experiments can generate enormous amounts of data, but large datasets are usually inherently complex, and the relevant information they contain can be difficult to extract. For the practicing biologist, we provide an overview of what we believe to be the most important issues that need to be addressed when dealing with microarray data. In a microarray experiment we are simply trying to identify which genes are the most "interesting" in terms of our experimental question, and these will usually be those that are either overexpressed or underexpressed (upregulated or downregulated) under the experimental conditions. Analysis of the data to find these genes involves first preprocessing of the raw data for quality control, including filtering of the data (e.g., detection of outlying values) followed by standardization of the data (i.e., making the data uniformly comparable throughout the dataset). This is followed by the formal quantitative analysis of the data, which will involve either statistical hypothesis testing or multivariate pattern recognition. Statistical hypothesis testing is the usual approach to "class comparison," where several experimental groups are being directly compared. The best approach to this problem is to use analysis of variance, although issues related to multiple hypothesis testing and probability estimation still need to be evaluated. Pattern recognition can involve "class prediction," for which a range of supervised multivariate techniques are available, or "class discovery," for which an even broader range of unsupervised multivariate techniques have been developed. Each technique has its own limitations, which need to be kept in mind when making a choice from among them. To put these ideas in context, we provide a detailed examination of two specific examples of the analysis of microarray data, both from parasitology, covering many of the most important points raised.     

Properties of Equilibria in Multi-Locus Genetic Systems

The classical mathematical theory of population genetics considered, for simplicity, almost exclusively one-locus systems. In the last two decades much work has been done on two-locus and, less frequently, multi-locus systems. This research has usually involved investigating properties of systems with given, and usually rather special, fitness parameters. Real genetic fitness systems are undoubtedly multi-locus and seldom will possess simplifying characteristics. One aim of this paper is to study generalized systems where no special assumptions are made about fitness structure, the number of alleles at each locus, the number of loci involved or the recombination structure between loci. A second aim is to consider marginal properties (often one-locus properties) of complex systems: the fact that many observations involve data from only on locus makes this second aim relevant.     

The diversity effect of inductive reasoning under segment manipulation of complex cognition

Forming a general reasonable conclusion from a limited number of examples is one of the most mar-velous psychological activities of human beings. This kind of activities is inductive reasoning which is a key issue rising from the cross fields of psychology, phi-losophy, cognitive science, and so on. It is a thinking process of generalizing from concrete cases. The di-versity effect of inductive reasoning refers to the phe-nomenon that the inductive argument of more diverse premise can obtain s…     

Interpreting a genetic case-control finding: What can be said, what cannot be said and implications in Indian populations

Identification of genetic variants responsible for complex disorders using association mapping is an active area of research. There are two broad classes of association methodologies: population-based case-control studies and family-based transmission analyses. While case-control analyses are more popular and in general, more powerful than family-based analyses, they suffer from some inherent limitations. Thus, it is of importance, to understand the implications of an association finding obtained from a case-control study design. This article discusses the relative advantages and disadvantages of the two classes of association analyses, particularly in the context of genetic diversity in Indian populations.     

Genetic studies in rat models: insights into cardiovascular disease

PURPOSE OF REVIEW: Limited to 2003-2004 publications, this review focuses on 'big picture' concepts learned from rat genetic studies of cardiovascular disease. RECENT DEVELOPMENTS: Analysis reveals insights into pathogenic paradigms, as well as experimental perspectives into rat-based systems of analyses of complex cardiovascular disease. Key concepts are forwarded. Multiple susceptibility genes underlie several quantitative trait loci for blood pressure suggesting a 'quantitative trait loci cluster' concept; hypertension end-organ disease quantitative trait loci are distinct from blood pressure quantitative trait loci indicating differential susceptibility paradigms for hypertension and each complication (stroke, renal disease, cardiac hypertrophy); distinct blood pressure quantitative trait loci are found in males and females indicating gender-specific susceptibility; and genetic subtypes comprise polygenic hypertension in rat models suggesting a genetic basis for clinical heterogeneity of human essential hypertension. Gender specific genetic susceptibility plays a key role in coronary artery disease susceptibility; multiple distinct quantitative trait loci underlie hyperlipidemia and type-2 diabetes, indicating multiple susceptibilities in risk factors for cardiovascular disease. Studies in transgenic inbred rat-strain models demonstrate value for serial, complex, cardiovascular pathophysiological analyses within a genetic context. SUMMARY: Cognizant of the limitations of animal model studies, observations from rat genetic studies provide insight into respective modeled human cardiovascular diseases and risk factor susceptibility, as well as systematically dissect the multifaceted complexities apparent in human complex cardiovascular disease. Given the recapitulation of many features of human cardiovascular disease, the value of rat model-based genetic studies for complex cardiovascular disease is unequivocal, thus mandating the expansion of resources for maximization of rat-based genetic studies.     

Design of experiments (DOE)—history,concepts, and relevance to <Emphasis Type="Italic">in vitro</Emphasis> culture

Design of experiments (DOE) is a large and well-developed field for understanding and improving the performance of complex systems. Because in vitro culture systems are complex and easily manipulated in controlled conditions, they are particularly well-suited for the application of DOE principles and techniques. Successful use of in vitro technologies in horticultural, plant breeding, or genetic applications typically involves improving some aspect of a system’s growth response—organogenesis, somatic embryogenesis, metabolite biosynthesis, or responses required for crop improvement such as ploidy manipulation, embryo rescue, creation and manipulation of chimeras, somaclonal variation, and mutant isolation. How and why DOE is the appropriate research approach for developing and understanding in vitro systems research is explained. The presentation is a narrative of the historical context and the geometric basis of DOE to explain the underlying concepts. Examples illustrate the use of DOE in in vitro plant culture research.     

Systems microscopy: An emerging strategy for the life sciences

Dynamic cellular processes occurring in time and space are fundamental to all physiology and disease. To understand complex and dynamic cellular processes therefore demands the capacity to record and integrate quantitative multiparametric data from the four spatiotemporal dimensions within which living cells self-organize, and to subsequently use these data for the mathematical modeling of cellular systems. To this end, a raft of complementary developments in automated fluorescence microscopy, cell microarray platforms, quantitative image analysis and data mining, combined with multivariate statistics and computational modeling, now coalesce to produce a new research strategy, “systems microscopy”, which facilitates systems biology analyses of living cells. Systems microscopy provides the crucial capacities to simultaneously extract and interrogate multiparametric quantitative data at resolution levels ranging from the molecular to the cellular, thereby elucidating a more comprehensive and richly integrated understanding of complex and dynamic cellular systems. The unique capacities of systems microscopy suggest that it will become a vital cornerstone of systems biology, and here we describe the current status and future prospects of this emerging field, as well as outlining some of the key challenges that remain to be overcome.     

Optimizing sampling design and sequencing strategy for the genomic analysis of quantitative traits in natural populations

Mapping the genes underlying ecologically relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterization of a species’ genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling and which sequencing strategy to implement. Here we determine, through simulation, the sampling scheme that maximizes the power to map the genetic basis of a complex trait in an outbreeding species across an idealized landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that quantitative trait locus detection power and prediction accuracy are higher when more populations over the landscape are sampled and this is more cost-effectively done with pool sequencing than with individual sequencing. Additionally, we recommend sampling populations from areas of high genetic diversity. As progress in sequencing enables the integration of trait-based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.