Detecting the QTL-allele system conferring flowering date in a nested association mapping population of soybean using a novel procedure

Key message

The RTM-GWAS was chosen among five procedures to identify DTF QTL-allele constitution in a soybean NAM population; 139 QTLs with 496 alleles accounting for 81.7% of phenotypic variance were detected.

Abstract

Flowering date (days to flowering, DTF) is an ecological trait in soybean, closely related to its ability to adapt to areas. A nested association mapping (NAM) population consisting of four RIL populations (LM, ZM, MT and MW with M8206 as their common parent) was established and tested for their DTF under five environments. Using restriction-site-associated DNA sequencing the population was genotyped with SNP markers. The restricted two-stage multi-locus (RTM) genome-wide association study (GWAS) (RTM-GWAS) with SNP linkage disequilibrium block (SNPLDB) as multi-allele genomic markers performed the best among the five mapping procedures with software publicly available. It identified the greatest number of quantitative trait loci (QTLs) (139) and alleles (496) on 20 chromosomes covering almost all of the QTLs detected by four other mapping procedures. The RTM-GWAS provided the detected QTLs with highest genetic contribution but without overflowing and missing heritability problems (81.7% genetic contribution vs. heritability of 97.6%), while SNPLDB markers matched the NAM population property of multiple alleles per locus. The 139 QTLs with 496 alleles were organized into a QTL-allele matrix, showing the corresponding DTF genetic architecture of the five parents and the NAM population. All lines and parents comprised both positive and negative alleles, implying a great potential of recombination for early and late DTF improvement. From the detected QTL-allele system, 126 candidate genes were annotated and χ ² tested as a DTF candidate gene system involving nine biological processes, indicating the trait a complex, involving several biological processes rather than only a handful of major genes.

     

An ultra-dense integrated linkage map for hexaploid chrysanthemum enables multi-allelic QTL analysis

Key message

We constructed the first integrated genetic linkage map in a polysomic hexaploid. This enabled us to estimate inheritance of parental haplotypes in the offspring and detect multi-allelic QTL.

Abstract

Construction and use of linkage maps are challenging in hexaploids with polysomic inheritance. Full map integration requires calculations of recombination frequency between markers with complex segregation types. In addition, detection of QTL in hexaploids requires information on all six alleles at one locus for each individual. We describe a method that we used to construct a fully integrated linkage map for chrysanthemum (Chrysanthemum × morifolium, 2n = 6x = 54). A bi-parental F1 population of 406 individuals was genotyped with an 183,000 SNP genotyping array. The resulting linkage map consisted of 30,312 segregating SNP markers of all possible marker dosage types, representing nine chromosomal linkage groups and 107 out of 108 expected homologues. Synteny with lettuce (Lactuca sativa) showed local colinearity. Overall, it was high enough to number the chrysanthemum chromosomal linkage groups according to those in lettuce. We used the integrated and phased linkage map to reconstruct inheritance of parental haplotypes in the F1 population. Estimated probabilities for the parental haplotypes were used for multi-allelic QTL analyses on four traits with different underlying genetic architectures. This resulted in the identification of major QTL that were affected by multiple alleles having a differential effect on the phenotype. The presented linkage map sets a standard for future genetic mapping analyses in chrysanthemum and closely related species. Moreover, the described methods are a major step forward for linkage mapping and QTL analysis in hexaploids.

     

The Immunogenetic Architecture of Autoimmune Disease

The development of most autoimmune diseases includes a strong heritable component. This genetic contribution to disease ranges from simple Mendelian inheritance of causative alleles to the complex interactions of multiple weak loci influencing risk. The genetic variants responsible for disease are being discovered through a range of strategies from linkage studies to genome-wide association studies. Despite the rapid advances in genetic analysis, substantial components of the heritable risk remain unexplained, either owing to the contribution of an as-yet unidentified, “hidden,” component of risk, or through the underappreciated effects of known risk loci. Surprisingly, despite the variation in genetic control, a great deal of conservation appears in the biological processes influenced by risk alleles, with several key immunological pathways being modified in autoimmune diseases covering a broad spectrum of clinical manifestations. The primary translational potential of this knowledge is in the rational design of new therapeutics to exploit the role of these key pathways in influencing disease. With significant further advances in understanding the genetic risk factors and their biological mechanisms, the possibility of genetically tailored (or “personalized”) therapy may be realized.Autoimmune diseases affect a significant proportion of the population, with >4% of the European population suffering from one or more of these disorders (Vyse and Todd 1996; Cooper et al. 2009; Eaton et al. 2010). Although all autoimmune diseases share similarities in the basic immunological mechanisms, in other aspects, such as clinical manifestation and age of onset, individual diseases vary widely. A few rare autoimmune diseases with Mendelian inheritance patterns within families occur including APS-1 (autoimmune polyendocrine syndrome type 1), IPEX (immunodysregulation, polyendocrinopathy, and enteropathy X-linked) syndrome, and ALPS (autoimmune lymphoproliferative syndrome). Most autoimmune diseases are, however, multifactorial in nature, with susceptibility controlled by multiple genetic and environmental factors.The genetic component of more common autoimmune diseases can be calculated in several different manners, including the sibling recurrence risk (λ_s) and the twin concordance rate. The sibling recurrence risk is the ratio of the lifetime risk in siblings of patients to the lifetime population risk, whereas the twin concordance rate measures the proportion of the siblings of affected twins that are also affected. Most common autoimmune diseases, such as multiple sclerosis (MS), type 1 diabetes (T1D), rheumatoid arthritis (RA), and inflammatory bowel disease (IBD) are characterized by a sibling recurrence risk between 6 and 20 (Vyse and Todd 1996), and concordance rates of 25%–50% in monozygotic twins and 2%–12% in dizygotic twins (Cooper et al. 1999). A substantial proportion of relatives may also have subclinical evidence of autoimmunity without developing clinically overt disease. For example, 19% of healthy siblings of MS patients show antibody production in the cerebrospinal fluid, compared to 4% of unrelated healthy controls (Haghighi et al. 2000), whereas 4% of healthy first-degree relatives display lesions that are indistinguishable from those seen in patients and are not seen in unrelated healthy controls (De Stefano et al. 2006). Furthermore, comorbidity with the development of several autoimmune diseases in the same patient and clustering of several autoimmune diseases within families above what is expected by chance appear common (Cooper et al. 2009; Zhernakova et al. 2009). Together these data show a strong genetic component to autoimmune disease development.     

Phenotypic effects of genetic variability in human clock genes on circadian and sleep parameters

Circadian rhythms and sleep are two separate but intimately related processes. Circadian rhythms are generated through the precisely controlled, cyclic expression of a number of genes designated clock genes. Genetic variability in these genes has been associated with a number of phenotypic differences in circadian as well as sleep parameters, both in mouse models and in humans. Diurnal preferences as determined by the selfreported Horne-Östberg (HÖ) questionnaire, has been associated with polymorphisms in the human genes CLOCK, PER1, PER2 and PER3. Circadian rhythm-related sleep disorders have also been associated with mutations and polymorphisms in clock genes, with the advanced type cosegrating in an autosomal dominant inheritance pattern with mutations in the genes PER2 and CSNK1D, and the delayed type associating without discernible Mendelian inheritance with polymorphisms in CLOCK and PER3. Several mouse models of clock gene null alleles have been demonstrated to have affected sleep homeostasis. Recent findings have shown that the variable number tandem polymorphism in PER3, previously linked to diurnal preference, has profound effects on sleep homeostasis and cognitive performance following sleep loss, confirming the close association between the processes of circadian rhythms and sleep at the genetic level.     

Characterization of the contribution of shared environmental and genetic factors to metabolic syndrome methylation heritability and familial correlations

Background

Transgenerational epigenetic inheritance has been posited as a possible contributor to the observed heritability of metabolic syndrome (MetS). Yet the extent to which estimates of epigenetic inheritance for DNA methylation sites are inflated by environmental and genetic covariance within families is still unclear. We applied current methods to quantify the environmental and genetic contributors to the observed heritability and familial correlations of four previously associated MetS methylation sites at three genes (CPT1A, SOCS3 and ABCG1) using real data made available through the GAW20.

Results

Our findings support the role of both shared environment and genetic variation in explaining the heritability of MetS and the four MetS cytosine-phosphate-guanine (CpG) sites, although the resulting heritability estimates were indistinguishable from one another. Familial correlations by type of relative pair generally followed our expectation based on relatedness, but in the case of sister and parent pairs we observed nonsignificant trends toward greater correlation than expected, as would be consistent with the role of shared environmental factors in the inflation of our estimated correlations.

Conclusions

Our work provides an interesting and flexible statistical framework for testing models of epigenetic inheritance in the context of human family studies. Future work should endeavor to replicate our findings and advance these methods to more robustly describe epigenetic inheritance patterns in human populations.

     

The Origins of Cellular Life

Understanding the origin of cellular life on Earth requires the discovery of plausible pathways for the transition from complex prebiotic chemistry to simple biology, defined as the emergence of chemical assemblies capable of Darwinian evolution. We have proposed that a simple primitive cell, or protocell, would consist of two key components: a protocell membrane that defines a spatially localized compartment, and an informational polymer that allows for the replication and inheritance of functional information. Recent studies of vesicles composed of fatty-acid membranes have shed considerable light on pathways for protocell growth and division, as well as means by which protocells could take up nutrients from their environment. Additional work with genetic polymers has provided insight into the potential for chemical genome replication and compatibility with membrane encapsulation. The integration of a dynamic fatty-acid compartment with robust, generalized genetic polymer replication would yield a laboratory model of a protocell with the potential for classical Darwinian biological evolution, and may help to evaluate potential pathways for the emergence of life on the early Earth. Here we discuss efforts to devise such an integrated protocell model.The emergence of the first cells on the early Earth was the culmination of a long history of prior chemical and geophysical processes. Although recognizing the many gaps in our knowledge of prebiotic chemistry and the early planetary setting in which life emerged, we will assume for the purpose of this review that the requisite chemical building blocks were available, in appropriate environmental settings. This assumption allows us to focus on the various spontaneous and catalyzed assembly processes that could have led to the formation of primitive membranes and early genetic polymers, their coassembly into membrane-encapsulated nucleic acids, and the chemical and physical processes that allowed for their replication. We will discuss recent progress toward the construction of laboratory models of a protocell (Fig. 1), evaluate the remaining steps that must be achieved before a complete protocell model can be constructed, and consider the prospects for the observation of spontaneous Darwinian evolution in laboratory protocells. Although such laboratory studies may not reflect the specific pathways that led to the origin of life on Earth, they are proving to be invaluable in uncovering surprising and unanticipated physical processes that help us to reconstruct plausible pathways and scenarios for the origin of life.Open in a separate windowFigure 1.A simple protocell model based on a replicating vesicle for compartmentalization, and a replicating genome to encode heritable information. A complex environment provides lipids, nucleotides capable of equilibrating across the membrane bilayer, and sources of energy (left), which leads to subsequent replication of the genetic material and growth of the protocell (middle), and finally protocellular division through physical and chemical processes (right). (Reproduced from Mansy et al. 2008 and reprinted with permission from Nature Publishing ©2008.)The term protocell has been used loosely to refer to primitive cells or to the first cells. Here we will use the term protocell to refer specifically to cell-like structures that are spatially delimited by a growing membrane boundary, and that contain replicating genetic information. A protocell differs from a true cell in that the evolution of genomically encoded advantageous functions has not yet occurred. With a genetic material such as RNA (or perhaps one of many other heteropolymers that could provide both heredity and function) and an appropriate environment, the continued replication of a population of protocells will lead inevitably to the spontaneous emergence of new coded functions by the classical mechanism of evolution through variation and natural selection. Once such genomically encoded and therefore heritable functions have evolved, we would consider the system to be a complete, living biological cell, albeit one much simpler than any modern cell (Szostak et al. 2001).     

The <Emphasis Type="Italic">Bactrocera dorsalis</Emphasis> species complex: comparative cytogenetic analysis in support of Sterile Insect Technique applications

Background

The Bactrocera dorsalis species complex currently harbors approximately 90 different members. The species complex has undergone many revisions in the past decades, and there is still an ongoing debate about the species limits. The availability of a variety of tools and approaches, such as molecular-genomic and cytogenetic analyses, are expected to shed light on the rather complicated issues of species complexes and incipient speciation. The clarification of genetic relationships among the different members of this complex is a prerequisite for the rational application of sterile insect technique (SIT) approaches for population control.

Results

Colonies established in the Insect Pest Control Laboratory (IPCL) (Seibersdorf, Vienna), representing five of the main economic important members of the Bactrocera dorsalis complex were cytologically characterized. The taxa under study were B. dorsalis s.s., B. philippinensis, B. papayae, B. invadens and B. carambolae. Mitotic and polytene chromosome analyses did not reveal any chromosomal characteristics that could be used to distinguish between the investigated members of the B. dorsalis complex. Therefore, their polytene chromosomes can be regarded as homosequential with the reference maps of B. dorsalis s.s.. In situ hybridization of six genes further supported the proposed homosequentiallity of the chromosomes of these specific members of the complex.

Conclusions

The present analysis supports that the polytene chromosomes of the five taxa under study are homosequential. Therefore, the use of the available polytene chromosome maps for B. dorsalis s.s. as reference maps for all these five biological entities is proposed. Present data provide important insight in the genetic relationships among the different members of the B. dorsalis complex, and, along with other studies in the field, can facilitate SIT applications targeting this complex. Moreover, the availability of 'universal' reference polytene chromosome maps for members of the complex, along with the documented application of in situ hybridization, can facilitate ongoing and future genome projects in this complex.

     

An AFLP-based genetic linkage map of Plasmodium chabaudi chabaudi

Background

Plasmodium chabaudi chabaudi can be considered as a rodent model of human malaria parasites in the genetic analysis of important characters such as drug resistance and immunity. Despite the availability of some genome sequence data, an extensive genetic linkage map is needed for mapping the genes involved in certain traits.

Methods

The inheritance of 672 Amplified Fragment Length Polymorphism (AFLP) markers from two parental clones (AS and AJ) of P. c. chabaudi was determined in 28 independent recombinant progeny clones. These, AFLP markers and 42 previously mapped Restriction Fragment Length Polymorphism (RFLP) markers (used as chromosomal anchors) were organized into linkage groups using Map Manager software.

Results

614 AFLP markers formed linkage groups assigned to 10 of 14 chromosomes, and 12 other linkage groups not assigned to known chromosomes. The genetic length of the genome was estimated to be about 1676 centiMorgans (cM). The mean map unit size was estimated to be 13.7 kb/cM. This was slightly less then previous estimates for the human malaria parasite, Plasmodium falciparum

Conclusion

The P. c. chabaudi genetic linkage map presented here is the most extensive and highly resolved so far available for this species. It can be used in conjunction with the genome databases of P. c chabaudi, P. falciparum and Plasmodium yoelii to identify genes underlying important phenotypes such as drug resistance and strain-specific immunity.     

Pathway-based analyses

Background

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation andpenalties for multiple testing.

Methods

The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge.

Results

Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data.

Conclusions

The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

     

Ontology-based instance data validation for high-quality curated biological pathways

Background

Modeling in systems biology is vital for understanding the complexity of biological systems across scales and predicting system-level behaviors. To obtain high-quality pathway databases, it is essential to improve the efficiency of model validation and model update based on appropriate feedback.

Results

We have developed a new method to guide creating novel high-quality biological pathways, using a rule-based validation. Rules are defined to correct models against biological semantics and improve models for dynamic simulation. In this work, we have defined 40 rules which constrain event-specific participants and the related features and adding missing processes based on biological events. This approach is applied to data in Cell System Ontology which is a comprehensive ontology that represents complex biological pathways with dynamics and visualization. The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models.

Conclusions

A new rule-based approach has been developed to facilitate model validation and model complementation. Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways. This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.

     

Anecdotal,Historical and Critical Commentaries on Genetics: The Impact of Whole Genome Sequencing on Model System Genetics: Get Ready for the Ride

Much of our understanding of how organisms develop and function is derived from the extraordinarily powerful, classic approach of screening for mutant organisms in which a specific biological process is disrupted. Reaping the fruits of such forward genetic screens in metazoan model systems like Drosophila, Caenorhabditis elegans, or zebrafish traditionally involves time-consuming positional cloning strategies that result in the identification of the mutant locus. Whole genome sequencing (WGS) has begun to provide an effective alternative to this approach through direct pinpointing of the molecular lesion in a mutated strain isolated from a genetic screen. Apart from significantly altering the pace and costs of genetic analysis, WGS also provides new perspectives on solving genetic problems that are difficult to tackle with conventional approaches, such as identifying the molecular basis of multigenic and complex traits.GENETIC model systems, from bacteria, yeast, plants, worms, flies, and fish to mice allow the dissection of the genetic basis of virtually any biological process by isolating mutants obtained through random mutagenesis, in which the biological process under investigation is defective. Such forward genetic analysis is unbiased and free of assumptions. The rigor and conceptual simplicity of forward genetic analysis is striking, some may say, beautiful; and the unpredictability of what one finds—be that an unexpected phenotype popping out of a screen or the eventual molecular nature of the gene (take the discovery of miRNAs as an example; Lee et al. 1993)—appeals to the adventurous. Even though mutant phenotypic analysis alone can reveal the logic of underlying biological processes (take Ed Lewis'' analysis of homeotic mutants as an example; Lewis 1978)—it is the identification of the molecular lesions in mutant animals that provides the key mechanistic and molecular details that propel our understanding of biological processes.The identification of the molecular lesion in mutant organisms depends on how the mutation was introduced. Classically, two types of mutagens have been used in most model systems: biological agents such as plasmids, viruses, or transposons whose insertions disrupt functional DNA elements (either coding or regulatory elements) or chemical mutagens, such as ethyl methane sulfonate (EMS) or N-ethyl N-nitroso urea (ENU), that introduce point mutations or deletions. Point mutation-inducing chemical mutagens are in many ways a superior mutagenic agent because their mutational frequency is high and because the spectrum of their effects on a given locus—producing hypomorphs, hypermorphs, amorphs, neomorphs, etc.—is hard to match by biological mutagens. Moreover, chemical mutagens do not display the positional bias of many biological agents. In addition, point mutations in a gene are often crucial in dissecting the functionally relevant domains of the gene product. In spite of the advantages of chemical mutagens, model system geneticists often prefer biological mutagens simply because the molecular lesions induced by those agents are characterized by the easily locatable DNA footprint that these agents generate. In contrast, the location of a point mutation (or deletion) has to be identified through conventional mapping strategies, which tend to be tedious and time consuming. Even in model systems in which positional cloning is quite fast and straightforward (e.g., Caenorhabditis elegans, which has a short generation time and a multitude of mapping tools available), it nevertheless is a significant effort that can occasionally present hurdles that are difficult to surmount (e.g., if the gene maps into a region with few genetic markers that allow for mapping). These difficulties explain why RNAi-based “genetic screens” have gained significant popularity in C. elegans; they circumvent mapping and reveal molecular identities of genes involved in a given process straight away (Kamath and Ahringer 2003). However, genes and cells show differential susceptibility to RNAi; off-target effects and lack of reproducibility can be a problem, and the range of effects that RNAi has on gene activity is generally more limited compared to chemically induced gene mutations.The recent application of next generation, deep sequencing technology (see Bentley 2006; Morozova and Marra 2008 for technology reviews) is beginning to significantly alter the landscape of genetic analysis as it allows the use of chemical mutagens without having to deal with its disadvantages. Deep sequencing technology incorporated into platforms such as Illumina''s Genome Analyzer or ABI''s SOLiD, allows one-shot sequencing of the entire model system''s genome, resulting in the detection of mutagen-induced sequence alterations compared to a nonmutagenized reference genome. Proof-of-concept studies have so far been conducted in bacteria, yeast, plant, worms, and flies, all published within the last year (Sarin et al. 2008; Smith et al. 2008; Srivatsan et al. 2008; Blumenstiel et al. 2009; Irvine et al. 2009; Rigola et al. 2009). Many more studies are under way; for example, since our first proof-of-principle study (Sarin et al. 2008), my laboratory has identified the molecular basis of >10 C. elegans strains defective in neuronal development and homeostasis (V. Bertrand, unpublished data; M. Doitsidou, unpublished data; E. Flowers, unpublished data; S. Sarin, unpublished data).The advantages of whole genome sequencing (WGS) are obvious. The process is extraordinarily fast with the sequencing taking only ∼5 days and the subsequent sequence data analysis only a few hours, particularly if the end user employs bioinformatic tools customized for mutant detection (Bigelow et al. 2009). The process is also remarkably cost effective. For example, a C. elegans genome can be sequenced with a required sequence coverage of ∼10 times for <$2,000 in reagent and machine operating costs. The capacity of deep sequencing machines—and hence the costs associated with sequencing a genome—apparently follow Moore''s law of doubling its capacity about every 2 years, like many technological innovations do (Pettersson et al. 2009). That is, the <$1,000 genome for C. elegans (∼100-Mb genome) and Drosophila (∼123-Mb genome) is just around the corner and other models will sooner or later follow suit. The cost effectiveness becomes particularly apparent if one compares the cost of WGS to the personnel and reagent costs associated with multiple-month to multiple-year mapping-based cloning efforts.WGS identifies sequence variants between a mutated genome and a premutagenesis reference genome. Chemical mutagens randomly introduce many mutations in the genome and, therefore, the phenotype-causing sequence variant needs to be identified as such out of a large pool of sequence variants. Sequence variants that have no impact on the phenotype can be outcrossed before sequencing or eliminated through some rough mapping of the mutation, which allows the experimenter to focus only on those variants contained in a specific sequence interval. Ensuing functional tests such as transformation rescue or phenocopy by RNAi and the availability of other alleles of the same locus are critical means to validate a phenotype-causing sequence variant (Sarin et al. 2008). The latter approach—the availability of multiple alleles of the same locus—is in many ways the most powerful one to sift through a number of candidate variants revealed by WGS. In this approach, candidate loci revealed by WGS are resequenced by conventional Sanger sequencing in allelic strains and only those that are indeed phenotype causing will show up mutated in all allelic variants of the locus (Sarin et al. 2008). The availability of multiple alleles of a locus is highly desirable for many aspects of genetic analysis anyway and therefore does not represent an additional and specific burden for undertaking a WGS project.The importance of WGS on model system genetics will be substantial and wide ranging. Speed and cost effectiveness means that the wastelands of genetic mapping can be trespassed fast enough to allow an experimenter to multitask a whole mutant collection in parallel, thereby closing in on the “holy grail” of genetic analysis—the as-complete-as-possible mutational saturation of a biological process and the resulting deciphering of complete genetic pathways and networks. What will become limiting steps are not any more the tediousness of mapping, but rather the effectiveness with which mutant collections can be built. Novel technologies that involve machine-based, semiautomated selection of mutant animals have been developed over the past few years to study a variety of distinct biological processes in several metazoan model systems, e.g., gfp-based morphology or cell fate screens in worms (Crane et al. 2009; Doitsidou et al. 2008) or behavioral screens in flies (Dankert et al. 2009) and are important steps in this direction. Such an “industrial revolution” of genetic screening (i.e., the mutant selection part, followed by WGS) moves us geneticists away from, not into the trenches of factory life and frees us up to do what we should like to enjoy most—thinking of designing interesting screens, seeing how genes interact, and interpreting it all.Another important impact of WGS is that it will allow tackling problems that were previously hard to deal with. For example, the tediousness of following subtle phenotypes, low penetrance phenotypes, or phenotypes that are cumbersome to score often hampers positional cloning approaches that rely on identifying rare recombinants in a large sibling pool. Moreover, many genetic traits such as behavioral genetic traits are very sensitive to genetic background and are therefore also often hard to map in the conventional way. WGS hones in on candidate genes straight away. Taking this notion a step further, WGS will also be able to get at the molecular basis of multigenic traits and quantitative trait loci, which again are hard to molecularly identify through conventional mapping strategies; a proof-of-principle study has made this point already in bacteria (Srivatsan et al. 2008). In principle, such multigenic traits may have been mutationally induced or could be present in natural variants of a species, which provides intriguing perspective for the population geneticist.Model organisms of biological interest that were previously relatively intractable for classic genetic mutant analysis due to the absence of genetic markers or other practical problems such as prohibitive generation times, may also now be movable into the arena of genetic model systems, through the WGS-mediated molecular analysis of mutagen-induced variants or through the study of natural variants.The sequencing of human cancer genomes has already begun to illustrate the impact of WGS on human genetics (Campbell et al. 2008; Ley et al. 2008). However, those human WGS studies illustrate why model systems will continue to be extremely important—their experimental accessibility allows us to address which of the many variants detected by WGS is indeed the phenotype-causing one.The message to model system geneticists is clear: Get access to a deep sequencer, buckle up, and get ready for the ride.     

The fundamental units,processes and patterns of evolution,and the Tree of Life conundrum

Background

The elucidation of the dominant role of horizontal gene transfer (HGT) in the evolution of prokaryotes led to a severe crisis of the Tree of Life (TOL) concept and intense debates on this subject.

Concept

Prompted by the crisis of the TOL, we attempt to define the primary units and the fundamental patterns and processes of evolution. We posit that replication of the genetic material is the singular fundamental biological process and that replication with an error rate below a certain threshold both enables and necessitates evolution by drift and selection. Starting from this proposition, we outline a general concept of evolution that consists of three major precepts.1. The primary agency of evolution consists of Fundamental Units of Evolution (FUEs), that is, units of genetic material that possess a substantial degree of evolutionary independence. The FUEs include both bona fide selfish elements such as viruses, viroids, transposons, and plasmids, which encode some of the information required for their own replication, and regular genes that possess quasi-independence owing to their distinct selective value that provides for their transfer between ensembles of FUEs (genomes) and preferential replication along with the rest of the recipient genome.2. The history of replication of a genetic element without recombination is isomorphously represented by a directed tree graph (an arborescence, in the graph theory language). Recombination within a FUE is common between very closely related sequences where homologous recombination is feasible but becomes negligible for longer evolutionary distances. In contrast, shuffling of FUEs occurs at all evolutionary distances. Thus, a tree is a natural representation of the evolution of an individual FUE on the macro scale, but not of an ensemble of FUEs such as a genome.3. The history of life is properly represented by the "forest" of evolutionary trees for individual FUEs (Forest of Life, or FOL). Search for trends and patterns in the FOL is a productive direction of study that leads to the delineation of ensembles of FUEs that evolve coherently for a certain time span owing to a shared history of vertical inheritance or horizontal gene transfer; these ensembles are commonly known as genomes, taxa, or clades, depending on the level of analysis. A small set of genes (the universal genetic core of life) might show a (mostly) coherent evolutionary trend that transcends the entire history of cellular life forms. However, it might not be useful to denote this trend "the tree of life", or organismal, or species tree because neither organisms nor species are fundamental units of life.

Conclusion

A logical analysis of the units and processes of biological evolution suggests that the natural fundamental unit of evolution is a FUE, that is, a genetic element with an independent evolutionary history. Evolution of a FUE on the macro scale is naturally represented by a tree. Only the full compendium of trees for individual FUEs (the FOL) is an adequate depiction of the evolution of life. Coherent evolution of FUEs over extended evolutionary intervals is a crucial aspect of the history of life but a "species" or "organismal" tree is not a fundamental concept.

Reviewers

This articles was reviewed by Valerian Dolja, W. Ford Doolittle, Nicholas Galtier, and William Martin

     

Mutational Analysis of Mdm1p Function in Nuclear and Mitochondrial Inheritance

Nuclear and mitochondrial transmission to daughter buds of Saccharomyces cerevisiae depends on Mdm1p, an intermediate filament-like protein localized to numerous punctate structures distributed throughout the yeast cell cytoplasm. These structures disappear and organelle inheritance is disrupted when mdm1 mutant cells are incubated at the restrictive temperature. To characterize further the function of Mdm1p, new mutant mdm1 alleles that confer temperature-sensitive growth and defects in organelle inheritance but produce stable Mdm1p structures were isolated. Microscopic analysis of the new mdm1 mutants revealed three phenotypic classes: Class I mutants showed defects in both mitochondrial and nuclear transmission; Class II alleles displayed defective mitochondrial inheritance but had no effect on nuclear movement; and Class III mutants showed aberrant nuclear inheritance but normal mitochondrial distribution. Class I and II mutants also exhibited altered mitochondrial morphology, possessing primarily small, round mitochondria instead of the extended tubular structures found in wild-type cells. Mutant mdm1 alleles affecting nuclear transmission were of two types: Class Ia and IIIa mutants were deficient for nuclear movement into daughter buds, while Class Ib and IIIb mutants displayed a complete transfer of all nuclear DNA into buds. The mutations defining all three allelic classes mapped to two distinct domains within the Mdm1p protein. Genetic crosses of yeast strains containing different mdm1 alleles revealed complex genetic interactions including intragenic suppression, synthetic phenotypes, and intragenic complementation. These results support a model of Mdm1p function in which a network comprised of multimeric assemblies of the protein mediates two distinct cellular processes.Cytoplasmic organelles are propagated by growth and division of preexisting organelles (Palade, 1983; Yaffe, 1991; Warren and Wickner, 1996), so an essential feature of cell proliferation is the inheritance of organelles by daughter cells. Organelle inheritance is thought to depend on functions of the cytoskeleton. Such a role for cytoskeletal components has been suggested by microscopic studies that revealed colocalization of organelles with microtubules (Heggeness et al., 1978; Ball and Singer, 1982; Couchman and Rees, 1982), intermediate filaments (David-Ferreira and David-Ferreira, 1980; Mose-Larsen et al., 1982; Chen, 1988), or actin microfilaments (Wang and Goldman, 1978; Kachar and Reese, 1988) in various types of cells. In addition, studies in vitro have indicated possible functions of microtubule-based motor proteins (Vale, 1987) or unconventional myosins (Adams and Pollard, 1986; Allan, 1995) in facilitating organelle movement. However, many details of the activity and roles of particular cytoskeletal components in mediating organelle movement and distribution in living cells remain obscure.Nuclear and mitochondrial inheritance in the yeast Saccharomyces cerevisiae depends on Mdm1p, an intermediate filament-like protein that defines a series of punctate structures distributed throughout the yeast cytoplasm (McConnell and Yaffe, 1992, 1993). The punctate Mdm1p structures disappear at 37°C in cells harboring the temperature-sensitive mdm1-1 mutation (McConnell and Yaffe, 1992), and this disappearance coincides with a failure to transmit mitochondria from the mother portion of the cell into the growing bud. Additionally, the mdm1-1 lesion causes a disorientation of the mitotic spindle such that nuclear division occurs entirely within the mother portion of the cell (McConnell et al., 1990). These defects indicate that the Mdm1p network has a central function in facilitating organelle inheritance; however, the mechanism of Mdm1p function is unknown (Berger and Yaffe, 1996).To explore Mdm1p function further, we have generated new mdm1 mutant alleles that cause defects in organelle inheritance but yield stable Mdm1p punctate structures even during incubation of cells at the nonpermissive temperature. These novel alleles have facilitated a genetic dissection of Mdm1p functions in nuclear and mitochondrial inheritance.     

Protein complex detection with semi-supervised learning in protein interaction networks

Background

Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes. The systematic analysis of PPI networks can enable a great understanding of cellular organization, processes and function. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. However, protein complexes are likely to overlap and the interaction data are very noisy. It is a great challenge to effectively analyze the massive data for biologically meaningful protein complex detection.

Results

Many people try to solve the problem by using the traditional unsupervised graph clustering methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. In this paper, we utilize the neural network with the “semi-supervised” mechanism to detect the protein complexes. By retraining the neural network model recursively, we could find the optimized parameters for the model, in such a way we can successfully detect the protein complexes. The comparison results show that our algorithm could identify protein complexes that are missed by other methods. We also have shown that our method achieve better precision and recall rates for the identified protein complexes than other existing methods. In addition, the framework we proposed is easy to be extended in the future.

Conclusions

Using a weighted network to represent the protein interaction network is more appropriate than using a traditional unweighted network. In addition, integrating biological features and topological features to represent protein complexes is more meaningful than using dense subgraphs. Last, the “semi-supervised” learning model is a promising model to detect protein complexes with more biological and topological features available.