Development of symbiogenetic approaches for studying variation and heredity of superspecies systems

Based on the knowledge on the structural and functional organization, ecological potential, and evolution of symbiotic complexes, we suggest to formulate the subject, aims, and methodology of symbiogenetics as a science studying the genetic control of interspecies interactions. It is based on the view on the superspecies system of variation and heredity (symbiogenome) controlling the development of novel properties lacking in the unitary organisms and radically extending their adaptive potentials. Investigation of symbiogenomes represents the first step toward genetic analysis of microbiomes and metagenomes, which are superspecies hereditary systems responsible for developing the multicomponent complexes of biocenotic type, such as rumen microflora, endophytic and rhizospheric communities, soil microbial consortia. The approaches of symbiogenetics can be used for developing biotechnologies of integration of plants or animals with beneficial microbes ensuring host nutrition and development as well as resistance to biotic and abiotic stresses.     

Development of symbiogenetic approaches for studying variation and heredity of superspecies systems

Based on the knowledge on the structural and functional organization, ecological potential, and evolution of symbiotic complexes, we suggest to formulate the subject, aims, and methodology of symbiogenetics as a science studying the genetic control of interspecies interactions. It is based on the view on the superspecies system of variation and heredity (symbiogenome) controlling the development of novel properties lacking in the unitary organisms and radically extending their adaptive potentials. Investigation of symbiogenomes represents the first step toward genetic analysis of microbiomes and metagenomes, which are superspecies hereditary systems responsible for developing the multicomponent complexes of biocenotic type, such as rumen microflora, endophytic and rhizospheric communities, soil microbial consortia. The approaches of symbiogenetics can be used for developing biotechnologies of integration of plants or animals with beneficial microbes ensuring host nutrition and development as well as resistance to biotic and abiotic stresses.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes

Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. Identifying susceptibility genes for these diseases using genetic and genomic technologies is accelerating, and the expectation over the next several years is that a number of genes will be identified for common diseases. However, the identification of single genes for disease has limited utility, given that diseases do not originate in complex systems from single gene changes. Further, the identification of single genes for disease may not lead directly to genes that can be targeted for therapeutic intervention. Therefore, uncovering single genes for disease in isolation of the broader network of molecular interactions in which they operate will generally limit the overall utility of such discoveries. Several integrative approaches have been developed and applied to reconstructing networks. Here we review several of these approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease. Networks reconstructed from these data provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.     

Systematic discovery of drug interaction mechanisms

Drug combinations are increasingly important in disease treatments, for combating drug resistance, and for elucidating fundamental relationships in cell physiology. When drugs are combined, their individual effects on cells may be amplified or weakened. Such drug interactions are crucial for treatment efficacy, but their underlying mechanisms remain largely unknown. To uncover the causes of drug interactions, we developed a systematic approach based on precise quantification of the individual and joint effects of antibiotics on growth of genome‐wide Escherichia coli gene deletion strains. We found that drug interactions between antibiotics representing the main modes of action are highly robust to genetic perturbation. This robustness is encapsulated in a general principle of bacterial growth, which enables the quantitative prediction of mutant growth rates under drug combinations. Rare violations of this principle exposed recurring cellular functions controlling drug interactions. In particular, we found that polysaccharide and ATP synthesis control multiple drug interactions with previously unexplained mechanisms, and small molecule adjuvants targeting these functions synthetically reshape drug interactions in predictable ways. These results provide a new conceptual framework for the design of multidrug combinations and suggest that there are universal mechanisms at the heart of most drug interactions.     

Gene-environment interactions in human diseases

Studies of gene-environment interactions aim to describe how genetic and environmental factors jointly influence the risk of developing a human disease. Gene-environment interactions can be described by using several models, which take into account the various ways in which genetic effects can be modified by environmental exposures, the number of levels of these exposures and the model on which the genetic effects are based. Choice of study design, sample size and genotyping technology influence the analysis and interpretation of observed gene-environment interactions. Current systems for reporting epidemiological studies make it difficult to assess whether the observed interactions are reproducible, so suggestions are made for improvements in this area.     

Genetic Causation in Complex Regulatory Systems: An Integrative Dynamic Perspective

The logic of genetic discovery has changed little over time, but the focus of biology is shifting from simple genotype–phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the traditional reductionist view of individual genes as privileged difference-making causes of phenotypes is re-examined. The scope and nature of genetic effects in complex regulatory systems, in which dynamics are driven by regulatory feedback and hierarchical interactions across levels of organization are considered. This review argues that it is appropriate to treat genes as specific actual difference-makers for the molecular regulation of gene expression. However, they are often neither stable, proportional, nor specific as causes of the overall dynamic behavior of regulatory networks. Dynamical models, properly formulated and validated, provide the tools to probe cause-and-effect relationships in complex biological systems, allowing to go beyond the limitations of genetic reductionism to gain an integrative understanding of the causal processes underlying complex phenotypes.     

Reciprocal sign epistasis is a necessary condition for multi-peaked fitness landscapes

Having multiple peaks within fitness landscapes critically affects the course of evolution, but whether their presence imposes specific requirements at the level of genetic interactions remains unestablished. Here we show that to exhibit multiple fitness peaks, a biological system must contain reciprocal sign epistatic interactions, which are defined as genetic changes that are separately unfavorable but jointly advantageous. Using Morse theory, we argue that it is impossible to formulate a sufficient condition for multiple peaks in terms of local genetic interactions. These findings indicate that systems incapable of reciprocal sign epistasis will always possess a single fitness peak. However, reciprocal sign epistasis should be pervasive in nature as it is a logical consequence of specificity in molecular interactions. The results thus predict that specific molecular interactions may yield multiple fitness peaks, which can be tested experimentally.     

Quantitative Analyses of Interbreeding in Populations of vannus-Morphotype Euplotes, with Special Attention to the Nominal Species E. vannus and E. crassus

ABSTRACT. Classical genetic techniques were applied to clonal cultures of the Euplotes vannus-crassus-minuta sibling species complex in an effort to provide some resolution to the species problem among these hypotrichs. Complex mating interactions were observed among clonal stock cultures derived from samples collected from sympatric and allopatric populations in a wide geographic survey. These results suggested that the classical model for the mating type inheritance and determination in these Euplotes is necessary but not sufficient to describe the mating mating interactions among populations of these ciliates. Successful conjugation between the nominal species E. crassus and E. vannus was observed routinely, and crosses between these two nominal species did not differ significantly from those among the other clonal stock cultures with respect to mating intensity and exconjugant survival. Data from backcrosses suggests that E. vannus and E. crassus can and do exchange genes. Based upon these data, we conclude that E. vannus and E. crassus comprise a single, highly polymorphic species with countless small populations, among which incomplete genetic exchange takes place.     

Predicting quantitative genetic interactions by means of sequential matrix approximation

Despite the emerging experimental techniques for perturbing multiple genes and measuring their quantitative phenotypic effects, genetic interactions have remained extremely difficult to predict on a large scale. Using a recent high-resolution screen of genetic interactions in yeast as a case study, we investigated whether the extraction of pertinent information encoded in the quantitative phenotypic measurements could be improved by computational means. By taking advantage of the observation that most gene pairs in the genetic interaction screens have no significant interactions with each other, we developed a sequential approximation procedure which ranks the mutation pairs in order of evidence for a genetic interaction. The sequential approximations can efficiently remove background variation in the double-mutation screens and give increasingly accurate estimates of the single-mutant fitness measurements. Interestingly, these estimates not only provide predictions for genetic interactions which are consistent with those obtained using the measured fitness, but they can even significantly improve the accuracy with which one can distinguish functionally-related gene pairs from the non-interacting pairs. The computational approach, in general, enables an efficient exploration and classification of genetic interactions in other studies and systems as well.     

Systematic interpretation of genetic interactions using protein networks

Genetic interaction analysis,in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional linkages between genes. In the yeast Saccharomyces cerevisiae, ongoing screens have generated >4,800 such genetic interaction data. We demonstrate that by combining these data with information on protein-protein, prote in-DNA or metabolic networks, it is possible to uncover physical mechanisms behind many of the observed genetic effects. Using a probabilistic model, we found that 1,922 genetic interactions are significantly associated with either between- or within-pathway explanations encoded in the physical networks, covering approximately 40% of known genetic interactions. These models predict new functions for 343 proteins and suggest that between-pathway explanations are better than within-pathway explanations at interpreting genetic interactions identified in systematic screens. This study provides a road map for how genetic and physical interactions can be integrated to reveal pathway organization and function.     

On the lack of specificity of proteins and its consequences for a theory of biological organization

It is now widely recognized that gene expression and cellular processes include a probabilistic component. However, this does not essentially modify the theory of genetic programming. This stochastic aspect, which is called noise, is usually conceived as a margin of fluctuation in the way the genetic program functions and the latter remains understood as a specific mechanism guided by genetic information. In contrast, recent data show that proteins do not possess a high level of specificity. They can interact with numerous molecular partners. As a consequence molecular interactions are not simply “noisy”. Because they are subject to large combinatorial interaction possibilities, they are also intrinsically stochastic and must be sorted out by the cell structure. This contradicts the genetic programming theory which is based on the idea that protein interactions are directed by their stereospecificity and genetic information. Taking into account the lack of protein specificity leads to a new theory. Natural selection acts not only in evolution but also in ontogenesis by sorting stochastic molecular interactions. In this frame, the making up of an organism, instead of being a simple bottom-top process in which information flows from genes to phenotypes, is both a bottom-top and top-bottom process. Genes provide proteins, but their stochastic interactions are sorted by selective constraints arising from the cell and multi-cellular structures, which are themselves subject to the action of natural selection.     

Next-generation sequencing and genome evolution in allopolyploids

?Premise of the study: Hybridization and polyploidization (allopolyploidy) are ubiquitous in the evolution of plants, but tracing the origins and subsequent evolution of the constituent genomes of allopolyploids has been challenging. Genome doubling greatly complicates genetic analyses, and this has long hindered investigation in that most allopolyploid species are "nonmodel" organisms. However, recent advances in sequencing and genomics technologies now provide unprecedented opportunities to analyze numerous genetic markers in multiple individuals in any organism. ?Methods: Here we review the application of next-generation sequencing technologies to the study of three aspects of allopolyploid genome evolution: duplicated gene loss and expression in two recently formed Tragopogon allopolyploids, intergenomic interactions and chromosomal evolution in Tragopogon miscellus, and repetitive DNA evolution in Nicotiana allopolyploids. ?Key results: For the first time, we can explore on a genomic scale the evolutionary processes that are ongoing in natural allopolyploids and not be restricted to well-studied crops and genetic models. ?Conclusions: These approaches can be easily and inexpensively applied to many other plant species-making any evolutionarily provocative system a new "model" system.     

GENETIC REGULATORY NETWORK MOTIFS CONSTRAIN ADAPTATION THROUGH CURVATURE IN THE LANDSCAPE OF MUTATIONAL (CO)VARIANCE

Systems biology is accumulating a wealth of understanding about the structure of genetic regulatory networks, leading to a more complete picture of the complex genotype–phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network‐based view of genetic variation. Here we model a set of two‐node, two‐phenotype genetic network motifs, covering a full range of regulatory interactions. We find that network interactions result in different patterns of mutational (co)variance at the phenotypic level (the M ‐matrix), not only across network motifs but also across phenotypic space within single motifs. This effect is due almost entirely to mutational input of additive genetic (co)variance. Variation in M has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space–time under general relativity, and similar mathematical tools may apply in each case. We explored the consequences of curvature in mutational variation by simulating adaptation under divergent selection with gene flow. Both standing genetic variation (the G ‐matrix) and rate of adaptation are constrained by M , so that G and adaptive trajectories are curved across phenotypic space. Under weak selection the phenotypic mean at migration‐selection balance also depends on M .     

Understanding diet-gene interactions: lessons from studying nutrigenomics and cardiovascular disease

Socioeconomic development has resulted in an epidemiologic transition which has involved an increase in mortality and morbidity from chronic non-communicable diseases. Cardiovascular disease is one such disease. The rapidity with which this transition has occurred suggests that genetic factors are unlikely to be responsible. However, studies in twins suggest significant heritability for cardiovascular disease and its associated risk factors. We present data showing diet-gene interactions involving polymorphisms at the PPARA and PLIN loci. These data support the hypothesis that chronic diseases such as cardiovascular disease are a consequence of a complex interplay of genetic and environmental factors, of which diet plays an important role. They suggest that the effects of diet on chronic disease may be masked by heterogeneity of effect related to genetic variability between individuals and that consideration of diet-gene interactions may contribute to our understanding of the pathogenesis of cardiovascular disease. The identification of diet-gene interactions offers us an opportunity to develop dietary interventions that will obviate the effects of genetic factors on the risk of disease. In this way, we may be able to develop personalized dietary recommendations that optimize the outcome for the individual concerned. Nevertheless, while existing data points to the value of these studies, significant challenges need to be met to ensure that our conclusions are scientifically valid.     

Plant reproductive systems and evolution during biological invasion

Recent biological invasions provide opportunities to investigate microevolution during contemporary timescales. The tempo and scope of local adaptation will be determined by the intensity of natural selection and the amounts and kinds of genetic variation within populations. In flowering plants, genetic diversity is strongly affected by interactions between reproductive systems and stochastic forces associated with immigration history and range expansion. Here, we explore the significance of reproductive system diversity for contemporary evolution during plant invasion. We focus in particular on how reproductive modes influence the genetic consequences of long-distance colonization and determine the likelihood of adaptive responses during invasion. In many clonal invaders, strong founder effects and restrictions on sexual reproduction limit opportunities for local adaptation. In contrast, adaptive changes to life-history traits should be a general expectation in both outbreeding and inbreeding species. We provide evidence that evolutionary modifications to reproductive systems promote the colonizing ability of invading populations and that reproductive timing is an important target of selection during range expansion. Knowledge of the likelihood and speed at which local adaptation evolves in invasive plants will be particularly important for management practices when evolutionary changes enhance ecological opportunities and invasive spread.     

A test of linkage for complex discrete and continuous traits in nuclear families

This article presents a score test for genetic linkage in nuclear families which applies to any trait having a distribution belonging to the exponential family, which includes binary and normal distributions, and distributions which are skewed or have nonnormal kurtosis. The specific distribution need not be specified and the method applies to sibships of arbitrary size. Tests of complex genetic effects are given, including unspecified mode of inheritance or additive, dominant, overdominant, and recessive modes of inheritance, covariates, multiple-locus models, including gene-gene interactions, and gene-environment interactions. The relation of our method to the Haseman-Elston methods is studied theoretically and by simulation.     

Synthetic lethality of cohesins with PARPs and replication fork mediators

Synthetic lethality has been proposed as a way to leverage the genetic differences found in tumor cells to affect their selective killing. Cohesins, which tether sister chromatids together until anaphase onset, are mutated in a variety of tumor types. The elucidation of synthetic lethal interactions with cohesin mutants therefore identifies potential therapeutic targets. We used a cross-species approach to identify robust negative genetic interactions with cohesin mutants. Utilizing essential and non-essential mutant synthetic genetic arrays in Saccharomyces cerevisiae, we screened genome-wide for genetic interactions with hypomorphic mutations in cohesin genes. A somatic cell proliferation assay in Caenorhabditis elegans demonstrated that the majority of interactions were conserved. Analysis of the interactions found that cohesin mutants require the function of genes that mediate replication fork progression. Conservation of these interactions between replication fork mediators and cohesin in both yeast and C. elegans prompted us to test whether other replication fork mediators not found in the yeast were required for viability in cohesin mutants. PARP1 has roles in the DNA damage response but also in the restart of stalled replication forks. We found that a hypomorphic allele of the C. elegans SMC1 orthologue, him-1(e879), genetically interacted with mutations in the orthologues of PAR metabolism genes resulting in a reduced brood size and somatic cell defects. We then demonstrated that this interaction is conserved in human cells by showing that PARP inhibitors reduce the viability of cultured human cells depleted for cohesin components. This work demonstrates that large-scale genetic interaction screening in yeast can identify clinically relevant genetic interactions and suggests that PARP inhibitors, which are currently undergoing clinical trials as a treatment of homologous recombination-deficient cancers, may be effective in treating cancers that harbor cohesin mutations.     

Applications of congenic strains in the mouse

The sequencing of the human and the mouse genomes has shown that the chromosomes of these two species contain approximately 30,000 genes. The biological systems that can be studied in an individual or in a tissue result from complex interactions within this multitude of genes. Before describing these interactions, it is necessary to understand the function of each gene. In the mouse, congenic strains are developed to introduce a chromosomal segment in a given inbred genetic background. One can then compare the biological effects of different alleles at the same locus in the same genetic background or the effect of a given allele in different genetic backgrounds. One can also introduce into different congenic strains with the same genetic background genes which control a complex genetic trait, then combine these genes by appropriate crosses to study their interactions. Although the chromosomal segment transferred into a congenic strain usually contains up to several hundreds of genes, molecular markers can be used to reduce this number as well as the number of crosses required for the development of congenic strains.     

Etiology in psychiatry: embracing the reality of poly‐gene‐environmental causation of mental illness

Intriguing findings on genetic and environmental causation suggest a need to reframe the etiology of mental disorders. Molecular genetics shows that thousands of common and rare genetic variants contribute to mental illness. Epidemiological studies have identified dozens of environmental exposures that are associated with psychopathology. The effect of environment is likely conditional on genetic factors, resulting in gene‐environment interactions. The impact of environmental factors also depends on previous exposures, resulting in environment‐environment interactions. Most known genetic and environmental factors are shared across multiple mental disorders. Schizophrenia, bipolar disorder and major depressive disorder, in particular, are closely causally linked. Synthesis of findings from twin studies, molecular genetics and epidemiological research suggests that joint consideration of multiple genetic and environmental factors has much greater explanatory power than separate studies of genetic or environmental causation. Multi‐factorial gene‐environment interactions are likely to be a generic mechanism involved in the majority of cases of mental illness, which is only partially tapped by existing gene‐environment studies. Future research may cut across psychiatric disorders and address poly‐causation by considering multiple genetic and environmental measures across the life course with a specific focus on the first two decades of life. Integrative analyses of poly‐causation including gene‐environment and environment‐environment interactions can realize the potential for discovering causal types and mechanisms that are likely to generate new preventive and therapeutic tools.