Functional divergence in the Arabidopsis beta-1,3-glucanase gene family inferred by phylogenetic reconstruction of expression states

Plant beta-1,3-glucanases (beta-1,3-Gs) (E.C. 3.2.1.39) comprise large, highly complex gene families involved in pathogen defense as well as a wide range of normal developmental processes. In spite of previous phylogenetic analyses that classify beta-1,3-Gs by sequence relatedness, the functional evolution of beta-1,3-Gs remains unclear. Here, expression and phylogenetic analyses have been integrated in order to investigate patterns of functional divergence in the Arabidopsis beta-1,3-G gene family. Fifty beta-1,3-G genes were grouped into expression classes through clustering of microarray data, and functions were inferred based on knowledge of coexpressed genes and existing literature. The resulting expression classes were mapped as discrete states onto a phylogenetic tree and parsimony reconstruction of ancestral expression states was performed, providing a model of expression divergence. Results showed a highly nonrandom distribution of developmental expression states in the phylogeny (P = 0.0002) indicating a significant degree of coupling between sequence and developmental expression divergence. A weaker, yet significant level of coupling was found using stress response data, but not using hormone-response or pathogen-response data. According to the model of developmental expression divergence, the ancestral function was most likely involved in cell division and/or cell wall remodeling. The associated expression state is widely distributed in the phylogeny, is retained by over 25% of gene family members, and is consistent with the known functions of beta-1,3-Gs in distantly related species and gene families. Consistent with previous hypotheses, pathogenesis-related (PR) beta-1,3-Gs appear to have evolved from ancestral developmentally regulated beta-1,3-Gs, acquiring PR function through a number of evolutionary events: divergence from the ancestral expression state, acquisition of pathogen/stress-responsive expression patterns, and loss of the C-terminal region including the glycosylphosphatidylinisotol (GPI)-anchoring site thus allowing for extracellular secretion.     

Variation in evolutionary processes at different codon positions

Evolutionary studies commonly model single nucleotide substitutions and assume that they occur as independent draws from a unique probability distribution across the sequence studied. This assumption is violated for protein-coding sequences, and we consider modeling approaches where codon positions (CPs) are treated as separate categories of sites because within each category the assumption is more reasonable. Such "codon-position" models have been shown to explain the evolution of codon data better than homogenous models in previous studies. This paper examines the ways in which codon-position models outperform homogeneous models and characterizes the differences in estimates of model parameters across CPs. Using the PANDIT database of multiple species DNA sequence alignments, we quantify the differences in the evolutionary processes at the 3 CPs in a systematic and comprehensive manner, characterizing previously undescribed features of protein evolution. We relate our findings to the functional constraints imposed by the genetic code, protein function, and the types of mutation that cause synonymous and nonsynonymous codon changes. The results increase our understanding of selective constraints and could be incorporated into phylogenetic analyses or gene-finding techniques in the future. The methods used are extended to an overlapping reading frame data set, and we discover that overlapping reading frames do not necessarily cause more stringent evolutionary constraints.     

Probabilistic models of eukaryotic evolution: time for integration

In spite of substantial work and recent progress, a global and fully resolved picture of the macroevolutionary history of eukaryotes is still under construction. This concerns not only the phylogenetic relations among major groups, but also the general characteristics of the underlying macroevolutionary processes, including the patterns of gene family evolution associated with endosymbioses, as well as their impact on the sequence evolutionary process. All these questions raise formidable methodological challenges, calling for a more powerful statistical paradigm. In this direction, model-based probabilistic approaches have played an increasingly important role. In particular, improved models of sequence evolution accounting for heterogeneities across sites and across lineages have led to significant, although insufficient, improvement in phylogenetic accuracy. More recently, one main trend has been to move away from simple parametric models and stepwise approaches, towards integrative models explicitly considering the intricate interplay between multiple levels of macroevolutionary processes. Such integrative models are in their infancy, and their application to the phylogeny of eukaryotes still requires substantial improvement of the underlying models, as well as additional computational developments.     

Genomic analysis of the terpenoid synthase (<Emphasis Type="Italic">AtTPS</Emphasis>) gene family of<Emphasis Type="Italic"> Arabidopsis thaliana</Emphasis>

A family of 40 terpenoid synthase genes ( AtTPS) was discovered by genome sequence analysis in Arabidopsis thaliana. This is the largest and most diverse group of TPS genes currently known for any species. AtTPS genes cluster into five phylogenetic subfamilies of the plant TPS superfamily. Surprisingly, thirty AtTPS closely resemble, in all aspects of gene architecture, sequence relatedness and phylogenetic placement, the genes for plant monoterpene synthases, sesquiterpene synthases or diterpene synthases of secondary metabolism. Rapid evolution of these AtTPS resulted from repeated gene duplication and sequence divergence with minor changes in gene architecture. In contrast, only two AtTPS genes have known functions in basic (primary) metabolism, namely gibberellin biosynthesis. This striking difference in rates of gene diversification in primary and secondary metabolism is relevant for an understanding of the evolution of terpenoid natural product diversity. Eight AtTPS genes are interrupted and are likely to be inactive pseudogenes. The localization of AtTPS genes on all five chromosomes reflects the dynamics of the Arabidopsis genome; however, several AtTPS genes are clustered and organized in tandem repeats. Furthermore, some AtTPS genes are localized with prenyltransferase genes ( AtGGPPS, geranylgeranyl diphosphate synthase) in contiguous genomic clusters encoding consecutive steps in terpenoid biosynthesis. The clustered organization may have implications for TPS gene evolution and the evolution of pathway segments for the synthesis of terpenoid natural products. Phylogenetic analyses highlight events in the divergence of the TPS paralogs and suggest orthologous genes and a model for the evolution of the TPS gene family.     

Comparative methods for the analysis of gene-expression evolution: an example using yeast functional genomic data

Understanding the evolution of gene function is a primary challenge of modern evolutionary biology. Despite an expanding database from genomic and developmental studies, we are lacking quantitative methods for analyzing the evolution of some important measures of gene function, such as gene-expression patterns. Here, we introduce phylogenetic comparative methods to compare different models of gene-expression evolution in a maximum-likelihood framework. We find that expression of duplicated genes has evolved according to a nonphylogenetic model, where closely related genes are no more likely than more distantly related genes to share common expression patterns. These results are consistent with previous studies that found rapid evolution of gene expression during the history of yeast. The comparative methods presented here are general enough to test a wide range of evolutionary hypotheses using genomic-scale data from any organism.     

Borrowing information across genes and experiments for improved error variance estimation in microarray data analysis

Statistical inference for microarray experiments usually involves the estimation of error variance for each gene. Because the sample size available for each gene is often low, the usual unbiased estimator of the error variance can be unreliable. Shrinkage methods, including empirical Bayes approaches that borrow information across genes to produce more stable estimates, have been developed in recent years. Because the same microarray platform is often used for at least several experiments to study similar biological systems, there is an opportunity to improve variance estimation further by borrowing information not only across genes but also across experiments. We propose a lognormal model for error variances that involves random gene effects and random experiment effects. Based on the model, we develop an empirical Bayes estimator of the error variance for each combination of gene and experiment and call this estimator BAGE because information is Borrowed Across Genes and Experiments. A permutation strategy is used to make inference about the differential expression status of each gene. Simulation studies with data generated from different probability models and real microarray data show that our method outperforms existing approaches.     

Phylogenetic analysis based on genome-scale metabolic pathway reaction content

Phylogenetic classifications based on single genes such as rRNA genes do not provide a complete and accurate picture of evolution because they do not account for evolutionary leaps caused by gene transfer, duplication, deletion and functional replacement. Here, we present a whole-genome-scale phylogeny based on metabolic pathway reaction content. From the genome sequences of 42 microorganisms, we deduced the metabolic pathway reactions and used the relatedness of these contents to construct a phylogenetic tree that represents the similarity of metabolic profiles (relatedness) as well as the extent of metabolic pathway similarity (evolutionary distance). This method accounts for horizontal gene transfer and specific gene loss by comparison of whole metabolic subpathways, and allows evaluation of evolutionary relatedness and changes in metabolic pathways. Thus, a tree based on metabolic pathway content represents both the evolutionary time scale (changes in genetic content) and the evolutionary process (changes in metabolism).     

Phylogenetic Patterns of Codon Evolution in the ACTIN-DEPOLYMERIZING FACTOR/COFILIN (ADF/CFL) Gene Family

The actin-depolymerizing factor/cofilin (ADF/CFL) gene family encodes a diverse group of relatively small proteins. Once known strictly as modulators of actin filament dynamics, recent research has demonstrated that these proteins are involved in a variety of cellular processes, from signal transduction to the cytonuclear trafficking of actin. In both plant and animal lineages, expression patterns of paralogs in the ADF/CFL gene family vary among tissue types and developmental stages. In this study we use computational approaches to investigate the evolutionary forces responsible for the diversification of the ADF/CFL gene family. Estimating the rate of non-synonymous to synonymous mutations (dN/dS) across phylogenetic lineages revealed that the majority of ADF/CFL codon positions were under strong purifying selection, with rare episodic events of accelerated protein evolution. In both plants and animals these instances of accelerated evolution were ADF/CFL subclass specific, and all of the sites under selection were located in regions of the protein that could serve in new functional roles. We suggest these sites may have been important in the functional diversification of ADF/CFL proteins.     

A mathematical programming approach for gene selection and tissue classification

MOTIVATION: Extracting useful information from expression levels of thousands of genes generated with microarray technology needs a variety of analytical techniques. Mathematical programming approaches for classification analysis outperform parametric methods when the data depart from assumptions underlying these methods. Therefore, a mathematical programming approach is developed for gene selection and tissue classification using gene expression profiles. RESULTS: A new mixed integer programming model is formulated for this purpose. The mixed integer programming model simultaneously selects genes and constructs a classification model to classify two groups of tissue samples as accurately as possible. Very encouraging results were obtained with two data sets from the literature as examples. These results show that the mathematical programming approach can rival or outperform traditional classification methods.     

Identification of DNA methylation changes associated with human gastric cancer

Background

Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes.

Methods

Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach.

Results

We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population.

Conclusions

Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.     

The phylogenetic structure of primate communities: variation within and across continents

Aim Our goals are: (1) to examine the relative degree of phylogenetic overdispersion or clustering of species in communities relative to the entire species pool, (2) to test for across‐continent differences in community phylogenetic structure, and (3) to examine the relationship between species richness and community phylogenetic structure. Location Africa, Madagascar, Asia, and the Neotropics. Methods We collected species composition and phylogenetic data for over 100 primate communities. For each community, we calculated two measures of phylogenetic structure: (1) the net relatedness index (NRI), which provides a measure of the mean pairwise phylogenetic distance among all species in the community; and (2) the nearest taxon index (NTI), which measures the relative phylogenetic distance among the closest related species in a community. Both measures are relative to the phylogeny of the species in the entire species pool. The phylocom package uses a randomization procedure to test whether the NRI and NTI values are higher or lower than expected by chance alone. In addition, we used a Kruskal–Wallis test to examine differences in NRI and NTI across continents, and linear regressions to examine the relationship between species richness and NRI/NTI. Results We found that the majority of individual primate communities in Africa, Asia and the Neotropics consist of member species that are neither more nor less closely related than expected by chance alone. Yet 37% of Malagasy communities contain species that are more distantly related to each other compared with random species assemblages. Also, we found that the average degree of relatedness among species in communities differed significantly across continents, with African and Malagasy communities consisting of more distantly related taxa compared with communities in Asia and the Neotropics. Finally, we found a significant negative relationship between species richness and phylogenetic distance among species in African, Asian and Malagasy communities. The average relatedness among species in communities decreased as community size increased. Main conclusions The majority of individual primate communities exhibit a phylogenetic structure no different from random. Yet there are across‐continent differences in the phylogenetic structure of primate communities that probably result from the unique ecological and evolutionary characteristics exhibited by the endemic species found on each continent. In particular, the recent extinctions of numerous primates on Madagascar are likely responsible for the low levels of evolutionary relatedness among species in Malagasy communities.     

Weak phylogenetic signal in physiological traits of methane‐oxidizing bacteria

The presence of phylogenetic signal is assumed to be ubiquitous. However, for microorganisms, this may not be true given that they display high physiological flexibility and have fast regeneration. This may result in fundamentally different patterns of resemblance, that is, in variable strength of phylogenetic signal. However, in microbiological inferences, trait similarities and therewith microbial interactions with its environment are mostly assumed to follow evolutionary relatedness. Here, we tested whether indeed a straightforward relationship between relatedness and physiological traits exists for aerobic methane‐oxidizing bacteria (MOB). We generated a comprehensive data set that included 30 MOB strains with quantitative physiological trait information. Phylogenetic trees were built from the 16S rRNA gene, a common phylogenetic marker, and the pmoA gene which encodes a subunit of the key enzyme involved in the first step of methane oxidation. We used a Blomberg's K from comparative biology to quantify the strength of phylogenetic signal of physiological traits. Phylogenetic signal was strongest for physiological traits associated with optimal growth pH and temperature indicating that adaptations to habitat are very strongly conserved in MOB. However, those physiological traits that are associated with kinetics of methane oxidation had only weak phylogenetic signals and were more pronounced with the pmoA than with the 16S rRNA gene phylogeny. In conclusion, our results give evidence that approaches based solely on taxonomical information will not yield further advancement on microbial eco‐evolutionary interactions with its environment. This is a novel insight on the connection between function and phylogeny within microbes and adds new understanding on the evolution of physiological traits across microbes, plants and animals.     

Discovering local patterns of co - evolution: computational aspects and biological examples

Background

Co-evolution is the process in which two (or more) sets of orthologs exhibit a similar or correlative pattern of evolution. Co-evolution is a powerful way to learn about the functional interdependencies between sets of genes and cellular functions and to predict physical interactions. More generally, it can be used for answering fundamental questions about the evolution of biological systems. Orthologs that exhibit a strong signal of co-evolution in a certain part of the evolutionary tree may show a mild signal of co-evolution in other branches of the tree. The major reasons for this phenomenon are noise in the biological input, genes that gain or lose functions, and the fact that some measures of co-evolution relate to rare events such as positive selection. Previous publications in the field dealt with the problem of finding sets of genes that co-evolved along an entire underlying phylogenetic tree, without considering the fact that often co-evolution is local.

Results

In this work, we describe a new set of biological problems that are related to finding patterns of local co-evolution. We discuss their computational complexity and design algorithms for solving them. These algorithms outperform other bi-clustering methods as they are designed specifically for solving the set of problems mentioned above. We use our approach to trace the co-evolution of fungal, eukaryotic, and mammalian genes at high resolution across the different parts of the corresponding phylogenetic trees. Specifically, we discover regions in the fungi tree that are enriched with positive evolution. We show that metabolic genes exhibit a remarkable level of co-evolution and different patterns of co-evolution in various biological datasets. In addition, we find that protein complexes that are related to gene expression exhibit non-homogenous levels of co-evolution across different parts of the fungi evolutionary line. In the case of mammalian evolution, signaling pathways that are related to neurotransmission exhibit a relatively higher level of co-evolution along the primate subtree.

Conclusions

We show that finding local patterns of co-evolution is a computationally challenging task and we offer novel algorithms that allow us to solve this problem, thus opening a new approach for analyzing the evolution of biological systems.     

Functional divergence of a syntenic invertase gene family in tomato,potato, and Arabidopsis

Comparative analysis of complex developmental pathways depends on our ability to resolve the function of members of gene families across taxonomic groups. LIN5, which belongs to a small gene family of apoplastic invertases in tomato (Lycopersicon esculentum), is a quantitative trait locus that modifies fruit sugar composition. We have compared the genomic organization and expression of this gene family in the two distantly related species: tomato and Arabidopsis. Invertase family members reside on segmental duplications in the near-colinear genomes of tomato and potato (Solanum tuberosum). These chromosomal segments are syntenically duplicated in the model plant Arabidopsis. On the basis of phylogenetic analysis of genes in the microsyntenic region, we conclude that these segmental duplications arose independently after the separation of the tomato/potato clade from Arabidopsis. Rapid regulatory divergence is characteristic of the invertase family. Interestingly, although the processes of gene duplication and specialization of expression occurred separately in the two species, synteny-based orthologs from both clades acquired similar organ-specific expression. This similar expression pattern of the genes is evidence of comparable evolutionary constraints (parallel evolution) rather than of functional orthology. The observation that functional orthology cannot be identified through analysis of expression similarity highlights the caution that needs to be exercised in extrapolating developmental networks from a model organism.     

A phylogenetic analysis of the evolution of Austronesian sibling terminologies

Social structure in human societies is underpinned by the variable expression of ideas about relatedness between different types of kin. We express these ideas through language in our kin terminology: to delineate who is kin and who is not, and to attach meanings to the types of kin labels associated with different individuals. Cross-culturally, there is a regular and restricted range of patterned variation in kin terminologies, and to date, our understanding of this diversity has been hampered by inadequate techniques for dealing with the hierarchical relatedness of languages (Galton’s Problem). Here I use maximum-likelihood and Bayesian phylogenetic comparative methods to begin to tease apart the processes underlying the evolution of kin terminologies in the Austronesian language family, focusing on terms for siblings. I infer (1) the probable ancestral states and (2) evolutionary models of change for the semantic distinctions of relative age (older/younger sibling) and relative sex (same-sex/opposite-sex). Analyses show that early Austronesian languages contained the relative-age, but not the relative-sex distinction; the latter was reconstructed firmly only for the ancestor of Eastern Malayo-Polynesian languages. Both distinctions were best characterized by evolutionary models where the gains and losses of the semantic distinctions were equally likely. A multi-state model of change examined how the relative-sex distinction could be elaborated and found that some transitions in kin terms were not possible: jumps from absence to heavily elaborated were very unlikely, as was piece-wise dismantling of elaborate distinctions. Cultural ideas about what types of kin distinctions are important can be embedded in the semantics of language; using a phylogenetic evolutionary framework we can understand how those distinctions in meaning change through time.