Whole-genome duplication (WGD), or polyploidy, followed by gene loss and diploidization has long been recognized as an important evolutionary force in animals, fungi and other organisms, especially plants. The success of angiosperms has been attributed, in part, to innovations associated with gene or whole-genome duplications, but evidence for proposed ancient genome duplications pre-dating the divergence of monocots and eudicots remains equivocal in analyses of conserved gene order. Here we use comprehensive phylogenomic analyses of sequenced plant genomes and more than 12.6 million new expressed-sequence-tag sequences from phylogenetically pivotal lineages to elucidate two groups of ancient gene duplications-one in the common ancestor of extant seed plants and the other in the common ancestor of extant angiosperms. Gene duplication events were intensely concentrated around 319 and 192 million years ago, implicating two WGDs in ancestral lineages shortly before the diversification of extant seed plants and extant angiosperms, respectively. Significantly, these ancestral WGDs resulted in the diversification of regulatory genes important to seed and flower development, suggesting that they were involved in major innovations that ultimately contributed to the rise and eventual dominance of seed plants and angiosperms.

/pdf/ancestral-polyploidy-in-seed-plants-and-angiosperms-nby5urfvpu.pdf

Ancestral polyploidy in seed plants and angiosperms

Vascular plants appeared ~410 million years ago, then diverged into several lineages of which only two survive: the euphyllophytes (ferns and seed plants) and the lycophytes. We report here the genome sequence of the lycophyte Selaginella moellendorffii (Selaginella), the first nonseed vascular plant genome reported. By comparing gene content in evolutionarily diverse taxa, we found that the transition from a gametophyte- to a sporophyte-dominated life cycle required far fewer new genes than the transition from a nonseed vascular to a flowering plant, whereas secondary metabolic genes expanded extensively and in parallel in the lycophyte and angiosperm lineages. Selaginella differs in posttranscriptional gene regulation, including small RNA regulation of repetitive elements, an absence of the trans-acting small interfering RNA pathway, and extensive RNA editing of organellar genes.

https://science.sciencemag.org/content/sci/332/6032/960.full.pdf

The Selaginella genome identifies genetic changes associated with the evolution of vascular plants.

Genomic comparisons provide evidence for ancient genome-wide duplications in a diverse array of animals and plants. We developed a birth–death model to identify evidence for genome duplication in EST data, and applied a mixture model to estimate the age distribution of paralogous pairs identified in EST sets for species representing the basal-most extant flowering plant lineages. We found evidence for episodes of ancient genome-wide duplications in the basal angiosperm lineages including Nuphar advena (yellow water lily: Nymphaeaceae) and the magnoliids Persea americana (avocado: Lauraceae), Liriodendron tulipifera (tulip poplar: Magnoliaceae), and Saruma henryi (Aristolochiaceae). In addition, we detected independent genome duplications in the basal eudicot Eschscholzia californica (California poppy: Papaveraceae) and the basal monocot Acorus americanus (Acoraceae), both of which were distinct from duplications documented for ancestral grass (Poaceae) and core eudicot lineages. Among gymnosperms, we found equivocal evidence for ancient polyploidy in Welwitschia mirabilis (Gnetales) and no evidence for polyploidy in pine, although gymnosperms generally have much larger genomes than the angiosperms investigated. Cross-species sequence divergence estimates suggest that synonymous substitution rates in the basal angiosperms are less than half those previously reported for core eudicots and members of Poaceae. These lower substitution rates permit inference of older duplication events. We hypothesize that evidence of an ancient duplication observed in the Nuphar data may represent a genome duplication in the common ancestor of all or most extant angiosperms, except Amborella.

/pdf/widespread-genome-duplications-throughout-the-history-of-2o2h7rwoo1.pdf

Widespread genome duplications throughout the history of flowering plants

The mutualistic symbiosis involving Glomeromycota, a distinctive phylum of early diverging Fungi, is widely hypothesized to have promoted the evolution of land plants during the middle Paleozoic. These arbuscular mycorrhizal fungi (AMF) perform vital functions in the phosphorus cycle that are fundamental to sustainable crop plant productivity. The unusual biological features of AMF have long fascinated evolutionary biologists. The coenocytic hyphae host a community of hundreds of nuclei and reproduce clonally through large multinucleated spores. It has been suggested that the AMF maintain a stable assemblage of several different genomes during the life cycle, but this genomic organization has been questioned. Here we introduce the 153-Mb haploid genome of Rhizophagus irregularis and its repertoire of 28,232 genes. The observed low level of genome polymorphism (0.43 SNP per kb) is not consistent with the occurrence of multiple, highly diverged genomes. The expansion of mating-related genes suggests the existence of cryptic sex-related processes. A comparison of gene categories confirms that R. irregularis is close to the Mucoromycotina. The AMF obligate biotrophy is not explained by genome erosion or any related loss of metabolic complexity in central metabolism, but is marked by a lack of genes encoding plant cell wall-degrading enzymes and of genes involved in toxin and thiamine synthesis. A battery of mycorrhiza-induced secreted proteins is expressed in symbiotic tissues. The present comprehensive repertoire of R. irregularis genes provides a basis for future research on symbiosis-related mechanisms in Glomeromycota.

/pdf/genome-of-an-arbuscular-mycorrhizal-fungus-provides-insight-3cw1ku1wgw.pdf

Genome of an arbuscular mycorrhizal fungus provides insight into the oldest plant symbiosis

Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis

Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive.

Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.

Availability: An R-program for implementing the approach is freely available at http://www.maths.uq.edu.au/~gjm/

Contact: gjm@maths.uq.edu.au

Supplementary information: http://www.maths.uq.edu.au/~gjm/bioinf061supp_data.pdf

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

We consider the fitting of normal or t-component mixture models to multivariate data, using maximum likelikhood via the EM algorithm. This approach requires the initial specification of an initial estimate of the vector of unknown parameters, or equivalently of an initial classification of the data with respect to the components of the mixture model under fit. We describe an algorithm called EMMIX that automatically undertakes this fitting: including the provision of suitable initial values if not supplied by the user. The EMMIX algorithm has several options, including the option to carry out a resampling-based test for the number of components in the mixture model.

The EMMIX Algorithm for the Fitting of Normal and t-Components

Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes.

Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation)and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

Availability: A Fortran program blue called EMMIX-WIRE (EM-based MIXture analysis WIth Random Effects) is available on request from the corresponding author.

Contact: gjm@maths.uq.edu.au

Supplementary information:http://www.maths.uq.edu.au/~gjm/bioinf0602_supp.pdf. Colour versions of Figures 1 and 2 are available as Supplementary material on Bioinformatics online.

/pdf/a-mixture-model-with-random-effects-components-for-1119o2ug5p.pdf

Geoff McLachlan

Papers

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

The EMMIX Algorithm for the Fitting of Normal and t-Components

Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Characteristic traffic load effects from a mixture of loading events on short to medium span bridges