TL;DR: One of the genes identified using this unbiased, whole-genome approach is the well-known circadian oscillator frequency, suggesting that the 2.4°–10.6° difference in latitude between the populations may be another important environmental parameter.
Abstract: Elucidating the connection between genotype, phenotype, and adaptation in wild populations is fundamental to the study of evolutionary biology, yet it remains an elusive goal, particularly for microscopic taxa, which comprise the majority of life. Even for microbes that can be reliably found in the wild, defining the boundaries of their populations and discovering ecologically relevant phenotypes has proved extremely difficult. Here, we have circumvented these issues in the microbial eukaryote Neurospora crassa by using a “reverse-ecology” population genomic approach that is free of a priori assumptions about candidate adaptive alleles. We performed Illumina whole-transcriptome sequencing of 48 individuals to identify single nucleotide polymorphisms. From these data, we discovered two cryptic and recently diverged populations, one in the tropical Caribbean basin and the other endemic to subtropical Louisiana. We conducted high-resolution scans for chromosomal regions of extreme divergence between these populations and found two such genomic “islands.” Through growth-rate assays, we found that the subtropical Louisiana population has a higher fitness at low temperature (10 °C) and that several of the genes within these distinct regions have functions related to the response to cold temperature. These results suggest the divergence islands may be the result of local adaptation to the 9 °C difference in average yearly minimum temperature between these two populations. Remarkably, another of the genes identified using this unbiased, whole-genome approach is the well-known circadian oscillator frequency, suggesting that the 2.4°–10.6° difference in latitude between the populations may be another important environmental parameter.
TL;DR: In this paper, the authors propose that microbial diversity must be viewed in light of gene flow and selection, which define units of genetic similarity, and of phenotype and ecological function, respectively.
TL;DR: This study identified ecological roles of key taxa in the human GI microbiota and compared the time series analysis results with those obtained through a reverse ecology approach, providing further evidence in favour of the limiting similarity hypothesis first put forth by Darwin.
Abstract: Determining ecological roles of community members and the impact of specific taxa on overall biodiversity in the gastrointestinal (GI) microbiota is of fundamental importance. A step towards a systems-level understanding of the GI microbiota is characterization of biotic interactions. Community time series analysis, an approach based on statistical analysis of changing population abundances within a single system over time, is needed in order to say with confidence that one population is affecting the dynamics of another. Here, we characterize biotic interaction structures and define ecological roles of major bacterial groups in four healthy individuals by analysing high-resolution, long-term (>180 days) GI bacterial community time series. Actinobacteria fit the description of a keystone taxon since they are relatively rare, but have a high degree of ecological connectedness, and are positively correlated with diversity both within and between individuals. Bacteriodetes were found to be a foundation taxon in that they are numerically dominant and interact extensively, in particular through positive interactions, with other taxa. Although community structure, diversity and biotic interaction patterns were specific to each individual, we observed a strong tendency towards more intense competition within than between phyla. This is in agreement with Darwin’s limiting similarity hypothesis as well as a published biotic interaction model of the GI microbiota based on reverse ecology. Finally, we link temporal enterotype switching to a reciprocal positive interaction between two key genera. In this study, we identified ecological roles of key taxa in the human GI microbiota and compared our time series analysis results with those obtained through a reverse ecology approach, providing further evidence in favour of the limiting similarity hypothesis first put forth by Darwin. Larger longitudinal studies are warranted in order to evaluate the generality of basic ecological concepts as applied to the GI microbiota, but our results provide a starting point for achieving a more profound understanding of the GI microbiota as an ecological system.
TL;DR: This book discusses how evolutionary systems biology will help understand adaptive landscapes and distributions of mutational effects and building synthetic systems to learn Nature's design principles.
Abstract: Preface.- Evolutionary Systems Biology: Historical and Philosophical Persepctives on an Emerging Synthesis.- Metabolic Networks and their Evolution.- Organization Principles and their Evolution.- Organization principles in genetic interaction networks.- Evolution of regulatory networks: Nematode vulva induction as an example of developmental systems drift.- Life's attractors understanding developmental systems through reverse engineering and in silico evolution.- Evolutionary characteristics of bacterial two-component systems.- Comparative interaction networks - bridging genotype to phenotype.- Evolution in silico: from network structure to bifurcation theory.- On the search for design principles in biological systems.- Towards a theory of multilevel evolution long term information integration shapes the mutational landscape and enhances evolvability.- Evolutionary principles underlying structure and response dynamics of cellular networks.- Phenotypic plasticity and robustness: Evolutionary stability theory, gene expression dynamics model, and laboratory experiments.- Genetic redundancies and their evolutionary maintenance.- Evolution of resource and energy management in biologically realistic gene regulatory network models.- Reverse ecology: From systems to environments and back.- Bacteria-virus coevolution.- The genotype-phenotype maps of systems biology and quantitative genetics: distinct and complementary.- How evolutionary systems biology will help understand adaptive landscapes and distributions of mutational effects.- Building synthetic systems to learn Nature's design principles.- The robustness continuum.- Index.
TL;DR: The concept of adaptive divergence can be used in a 'reverse ecology' approach to identify lineages that are in the process of speciation or genes involved in initial adaptive divergence.