Clustering analysis of SAGE data using a Poisson approach

Serial analysis of gene expression (SAGE) data have been poorly exploited by clustering analysis owing to the lack of appropriate statistical methods that consider their specific properties. We modeled SAGE data by Poisson statistics and developed two Poisson-based distances. Their application to simulated and experimental mouse retina data show that the Poisson-based distances are more appropriate and reliable for analyzing SAGE data compared to other commonly used distances or similarity measures such as Pearson correlation or Euclidean distance.     

How phenological tracking shapes species and communities in non-stationary environments

Climate change alters the environments of all species. Predicting species responses requires understanding how species track environmental change, and how such tracking shapes communities. Growing empirical evidence suggests that how species track phenologically – how an organism shifts the timing of major biological events in response to the environment – is linked to species performance and community structure. Such research tantalizingly suggests a potential framework to predict the winners and losers of climate change, and the future communities we can expect. But developing this framework requires far greater efforts to ground empirical studies of phenological tracking in relevant ecological theory. Here we review the concept of phenological tracking in empirical studies and through the lens of coexistence theory to show why a community-level perspective is critical to accurate predictions with climate change. While much current theory for tracking ignores the importance of a multi-species context, basic community assembly theory predicts that competition will drive variation in tracking and trade-offs with other traits. We highlight how existing community assembly theory can help understand tracking in stationary and non-stationary systems. But major advances in predicting the species- and community-level consequences of climate change will require advances in theoretical and empirical studies. We outline a path forward built on greater efforts to integrate priority effects into modern coexistence theory, improved empirical estimates of multivariate environmental change, and clearly defined estimates of phenological tracking and its underlying environmental cues.     

Experimental evolution of evolutionary potential in fluctuating environments

Variation is the raw material for evolution. Evolutionary potential is determined by the amount of genetic variation, but evolution can also alter the visibility of genetic variation to natural selection. Fluctuating environments are suggested to maintain genetic variation but they can also affect environmental variance, and thus, the visibility of genetic variation to natural selection. However, experimental studies testing these ideas are relatively scarce. In order to determine differences in evolutionary potential we quantified variance attributable to population, genotype and environment for populations of the bacterium Serratia marcescens. These populations had been experimentally evolved in constant and two fluctuating environments. We found that strains that evolved in fluctuating environments exhibited larger environmental variation suggesting that adaptation to fluctuations has decreased the visibility of genetic variation to selection.     

Partial offloading with stable equilibrium in fog-cloud environments using replicator dynamics of evolutionary game theory

Cluster Computing - Today, the use of fog computing is increasing due to the development of delay-sensitive applications in areas such as e-health, agriculture, and smart city management. In such...     

Clustering of water bodies in unpolluted and polluted environments based on Escherichia coli phylogroup abundance using a simple interaction database

Different types of water bodies, including lakes, streams, and coastal marine waters, are often susceptible to fecal contamination from a range of point and nonpoint sources, and have been evaluated using fecal indicator microorganisms. The most commonly used fecal indicator is Escherichia coli, but traditional cultivation methods do not allow discrimination of the source of pollution. The use of triplex PCR offers an approach that is fast and inexpensive, and here enabled the identification of phylogroups. The phylogenetic distribution of E. coli subgroups isolated from water samples revealed higher frequencies of subgroups A₁ and B2₃ in rivers impacted by human pollution sources, while subgroups D₁ and D₂ were associated with pristine sites, and subgroup B1 with domesticated animal sources, suggesting their use as a first screening for pollution source identification. A simple classification is also proposed based on phylogenetic subgroup distribution using the w-clique metric, enabling differentiation of polluted and unpolluted sites.     

A microbial evolutionary approach for a sustainable future

With the continued population increase, more sustainable use of water, land, air and chemicals is imperative. Microorganisms will need to be called upon to aid in many sustainability efforts. Prokaryotes are the fastest-evolving cellular life, and most manipulatable via synthetic biology. Moreover, their natural diversity in processing organic and inorganic chemicals, and their survivability in extreme niches, make them prime agents to enlist for solving many of society's pressing problems.     

Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees

MOTIVATION: Gene expression data clustering provides a powerful tool for studying functional relationships of genes in a biological process. Identifying correlated expression patterns of genes represents the basic challenge in this clustering problem. RESULTS: This paper describes a new framework for representing a set of multi-dimensional gene expression data as a Minimum Spanning Tree (MST), a concept from the graph theory. A key property of this representation is that each cluster of the expression data corresponds to one subtree of the MST, which rigorously converts a multi-dimensional clustering problem to a tree partitioning problem. We have demonstrated that though the inter-data relationship is greatly simplified in the MST representation, no essential information is lost for the purpose of clustering. Two key advantages in representing a set of multi-dimensional data as an MST are: (1) the simple structure of a tree facilitates efficient implementations of rigorous clustering algorithms, which otherwise are highly computationally challenging; and (2) as an MST-based clustering does not depend on detailed geometric shape of a cluster, it can overcome many of the problems faced by classical clustering algorithms. Based on the MST representation, we have developed a number of rigorous and efficient clustering algorithms, including two with guaranteed global optimality. We have implemented these algorithms as a computer software EXpression data Clustering Analysis and VisualizATiOn Resource (EXCAVATOR). To demonstrate its effectiveness, we have tested it on three data sets, i.e. expression data from yeast Saccharomyces cerevisiae, expression data in response of human fibroblasts to serum, and Arabidopsis expression data in response to chitin elicitation. The test results are highly encouraging. AVAILABILITY: EXCAVATOR is available on request from the authors.     

Biodiversity inhibits species' evolutionary responses to changing environments

Despite growing interplay between ecological and evolutionary studies, the question of how biodiversity influences evolutionary dynamics within species remains understudied. Here, using a classical model of phenotypic evolution in species occupying a patchy environment, but introducing global change affecting patch conditions, we show that biodiversity can inhibit species' evolution during global change. The presence of several species increases the chance that one or more species are pre-adapted to new conditions, which restricts the ecological opportunity for evolutionary responses in all the species. Consequently, environmental change tends to select for changes in species abundances rather than for changing phenotypes within each species. The buffering effects of species diversity that we describe might be one important but neglected explanation for widely observed niche conservatism in natural systems. Furthermore, the results show that attempts to understand biotic responses to environmental change need to consider both ecological and evolutionary processes in a realistically diverse setting.     

Comparative studies of evolutionary responses to light environments in Arabidopsis

In this article, we compare the reaction norms to foliage shade (changes in light quality, spatially fine-grained environmental variation) and photoperiod (day length, spatially coarse-grained environmental variation) in several haplotypes of Arabidopsis thaliana from Scandinavia. We found that both across-environment means and phenotypic plasticities evolved continuously and very rapidly within this group. Both character means and trait plasticities were highly integrated, in part as predicted by the adaptive plasticity hypothesis for response to foliage shade (the so-called shade-avoidance syndrome). We found that a significant but small fraction of the variance in across-treatment trait means and plasticities in response to one environmental factor is explained by variation of the same traits in response to the other factor. Genetic relatedness based on chloroplast DNA sequence variation among haplotypes was not associated with variation in across-treatment character means or their plasticities, suggesting that evolution of these characters has occurred on a local geographic scale via reticulation (outcrossing) among maternal lines rather than by the differential survival of selfing lineages.     

Constraints on microbial metabolism drive evolutionary diversification in homogeneous environments

Understanding the evolution of microbial diversity is an important and current problem in evolutionary ecology. In this paper, we investigated the role of two established biochemical trade-offs in microbial diversification using a model that connects ecological and evolutionary processes with fundamental aspects of biochemistry. The trade-offs that we investigated are as follows:(1) a trade-off between the rate and affinity of substrate transport; and (2) a trade-off between the rate and yield of ATP production. Our model shows that these biochemical trade-offs can drive evolutionary diversification under the simplest possible ecological conditions: a homogeneous environment containing a single limiting resource. We argue that the results of a number of microbial selection experiments are consistent with the predictions of our model.     

Population and evolutionary dynamics in spatially structured seasonally varying environments

Increasingly imperative objectives in ecology are to understand and forecast population dynamic and evolutionary responses to seasonal environmental variation and change. Such population and evolutionary dynamics result from immediate and lagged responses of all key life‐history traits, and resulting demographic rates that affect population growth rate, to seasonal environmental conditions and population density. However, existing population dynamic and eco‐evolutionary theory and models have not yet fully encompassed within‐individual and among‐individual variation, covariation, structure and heterogeneity, and ongoing evolution, in a critical life‐history trait that allows individuals to respond to seasonal environmental conditions: seasonal migration. Meanwhile, empirical studies aided by new animal‐tracking technologies are increasingly demonstrating substantial within‐population variation in the occurrence and form of migration versus year‐round residence, generating diverse forms of ‘partial migration’ spanning diverse species, habitats and spatial scales. Such partially migratory systems form a continuum between the extreme scenarios of full migration and full year‐round residence, and are commonplace in nature. Here, we first review basic scenarios of partial migration and associated models designed to identify conditions that facilitate the maintenance of migratory polymorphism. We highlight that such models have been fundamental to the development of partial migration theory, but are spatially and demographically simplistic compared to the rich bodies of population dynamic theory and models that consider spatially structured populations with dispersal but no migration, or consider populations experiencing strong seasonality and full obligate migration. Second, to provide an overarching conceptual framework for spatio‐temporal population dynamics, we define a ‘partially migratory meta‐population’ system as a spatially structured set of locations that can be occupied by different sets of resident and migrant individuals in different seasons, and where locations that can support reproduction can also be linked by dispersal. We outline key forms of within‐individual and among‐individual variation and structure in migration that could arise within such systems and interact with variation in individual survival, reproduction and dispersal to create complex population dynamics and evolutionary responses across locations, seasons, years and generations. Third, we review approaches by which population dynamic and eco‐evolutionary models could be developed to test hypotheses regarding the dynamics and persistence of partially migratory meta‐populations given diverse forms of seasonal environmental variation and change, and to forecast system‐specific dynamics. To demonstrate one such approach, we use an evolutionary individual‐based model to illustrate that multiple forms of partial migration can readily co‐exist in a simple spatially structured landscape. Finally, we summarise recent empirical studies that demonstrate key components of demographic structure in partial migration, and demonstrate diverse associations with reproduction and survival. We thereby identify key theoretical and empirical knowledge gaps that remain, and consider multiple complementary approaches by which these gaps can be filled in order to elucidate population dynamic and eco‐evolutionary responses to spatio‐temporal seasonal environmental variation and change.     

SMpred: a support vector machine approach to identify structural motifs in protein structure without using evolutionary information

Knowledge of three dimensional structure is essential to understand the function of a protein. Although the overall fold is made from the whole details of its sequence, a small group of residues, often called as structural motifs, play a crucial role in determining the protein fold and its stability. Identification of such structural motifs requires sufficient number of sequence and structural homologs to define conservation and evolutionary information. Unfortunately, there are many structures in the protein structure databases have no homologous structures or sequences. In this work, we report an SVM method, SMpred, to identify structural motifs from single protein structure without using sequence and structural homologs. SMpred method was trained and tested using 132 proteins domains containing 581 motifs. SMpred method achieved 78.79% accuracy with 79.06% sensitivity and 78.53% specificity. The performance of SMpred was evaluated with MegaMotifBase using 188 proteins containing 1161 motifs. Out of 1161 motifs, SMpred correctly identified 1503 structural motifs reported in MegaMotifBase. Further, we showed that SMpred is useful approach for the length deviant superfamilies and single member superfamilies. This result suggests the usefulness of our approach for facilitating the identification of structural motifs in protein structure in the absence of sequence and structural homologs. The dataset and executable for the SMpred algorithm is available at http://www3.ntu.edu.sg/home/EPNSugan/index_files/SMpred.htm.     

Clustering of protein domains for functional and evolutionary studies

Background  

The number of protein family members defined by DNA sequencing is usually much larger than those characterised experimentally. This paper describes a method to divide protein families into subtypes purely on sequence criteria. Comparison with experimental data allows an independent test of the quality of the clustering.     

Toll-like receptors--taking an evolutionary approach

The Toll receptor was initially identified in Drosophila melanogaster for its role in embryonic development. Subsequently, D. melanogaster Toll and mammalian Toll-like receptors (TLRs) have been recognized as key regulators of immune responses. After ten years of intense research on TLRs and the recent accumulation of genomic and functional data in diverse organisms, we review the distribution and functions of TLRs in the animal kingdom. We provide an evolutionary perspective on TLRs, which sheds light on their origin at the dawn of animal evolution and suggests that different TLRs might have been co-opted independently during animal evolution to mediate analogous immune functions.     

Distinct evolutionary strategies of human leucocyte antigen loci in pathogen-rich environments

Human leucocyte antigen (HLA) loci have a complex evolution where both stochastic (e.g. genetic drift) and deterministic (natural selection) forces are involved. Owing to their extraordinary level of polymorphism, HLA genes are useful markers for reconstructing human settlement history. However, HLA variation often deviates significantly from neutral expectations towards an excess of genetic diversity. Because HLA molecules play a crucial role in immunity, this observation is generally explained by pathogen-driven-balancing selection (PDBS). In this study, we investigate the PDBS model by analysing HLA allelic diversity on a large database of 535 populations in relation to pathogen richness. Our results confirm that geographical distances are excellent predictors of HLA genetic differentiation worldwide. We also find a significant positive correlation between genetic diversity and pathogen richness at two HLA class I loci (HLA-A and -B), as predicted by PDBS, and a significant negative correlation at one HLA class II locus (HLA-DQB1). Although these effects are weak, as shown by a loss of significance when populations submitted to rapid genetic drift are removed from the analysis, the inverse relationship between genetic diversity and pathogen richness at different loci indicates that HLA genes have adopted distinct evolutionary strategies to provide immune protection in pathogen-rich environments.     

Microbial diversity of hypersaline environments: a metagenomic approach

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Cultivating inclusive instructional and research environments in ecology and evolutionary science

As we strive to lift up a diversity of voices in science, it is important for ecologists, evolutionary scientists, and educators to foster inclusive environments in their research and teaching. Academics in science often lack exposure to research on best practices in diversity, equity, and inclusion and may not know where to start to make scientific environments more welcoming and inclusive. We propose that by approaching research and teaching with empathy, flexibility, and a growth mind‐set, scientists can be more supportive and inclusive of their colleagues and students. This paper provides guidance, explores strategies, and directs scientists to resources to better cultivate an inclusive environment in three common settings: the classroom, the research laboratory, and the field. As ecologists and evolutionary scientists, we have an opportunity to adapt our teaching and research practices in order to foster an inclusive educational ecosystem for students and colleagues alike.