Juenger, T. | Search bioRxiv

bioRxiv2018

Loci, genes, and gene networks associated with life history variation in a model ecological organism, Daphnia pulex (complex)

BackgroundIdentifying the molecular basis of heritable variation provides insight into the underlying mechanisms generating phenotypic variation and the evolutionary history of organismal traits. Life history trait variation is of central importance to ecological and evolutionary dynamics, and contemporary genomic tools permit studies of the basis of this variation in non-genetic model organisms. We used high density genotyping, RNA-Seq gene expression assays, and detailed phenotyping of fourteen ecologically important life history traits in a wild-caught panel of 32 Daphnia pulex clones to explore the molecular basis of trait variation in a model ecological species.\n\nResultsWe found extensive phenotypic and a range of heritable genetic variation (~0 < H2 < 0.44) in the panel, and accordingly identify 75-261 genes--organized in 3-6 coexpression modules--associated with genetic variation in each trait. The trait-related coexpression modules possess well-supported promoter motifs, and in conjunction with marker variation at trans- loci, suggest a relatively small number of important expression regulators. We further identify a candidate genetic network with SNPs in eight known transcriptional regulators, and dozens of differentially expressed genes, associated with life history variation. The gene-trait associations include numerous un-annotated genes, but also support several a priori hypotheses, including an ecdysone-induced protein and several Gene Ontology pathways.\n\nConclusionThe genetic and gene expression architecture of Daphnia life history traits is complex, and our results provide numerous candidate loci, genes, and coexpression modules to be tested as the molecular mechanisms that underlie Daphnia eco-evolutionary dynamics.

genomics↗

bioRxiv2016

Artificial Microbiome-Selection to Engineer Microbiomes That Confer Salt-Tolerance to Plants

We develop a method to artificially select for rhizosphere microbiomes that confer salt-tolerance to the model grass Brachypodium distachyon. We differentially propagate microbiomes within the background of a non-evolving, highly-inbred plant population, and therefore only microbiomes evolve in our experiment, but not the plants. To optimize methods, we conceptualize artificial microbiome-selection as a special case of indirect selection: We do not measure microbiome properties directly, but we use host performance (e.g., biomass; seed set) as an indicator to infer association with rhizosphere microbiomes that confer salt-tolerance to a plant. We previously called this indirect-selection scheme host-mediated indirect selection on microbiomes (Mueller & Sachs 2015). Our methods aim to maximize evolutionary changes due to differential microbiome-propagation, while minimizing some (but not all) ecological processes affecting microbiome composition. Specifically, our methods aim to maximize microbiome perpetuation between selection-cycles and maximize response to artificial microbiome-selection by (a) controlling microbiome assembly when inoculating seeds at the beginning of each selection cycle; (b) using low-carbon soil to enhance host-control mediated by carbon secretions of plants during initial microbiome assembly and subsequent microbiome persistence; (c) fractionating microbiomes before transfer between plants to perpetuate and select only on bacterial and viral (but not fungal) microbiome components; and (d) ramping of salt-stress between selection-cycles to minimize the chance of over-stressing plants. Our selection protocol generates microbiomes that enhance plant fitness after only 1-3 rounds of artificial selection on rhizosphere microbiomes. Relative to fallow-soil control treatments, artificially-selected microbiomes increase plant fitness by 75% under sodium-sulfate stress, and by 38% under aluminum-sulfate stress. Relative to null control treatments, artificially-selected microbiomes increase plant fitness by 13% under sodium-sulfate stress, and by 12% under aluminum-sulfate stress. When testing microbiomes after nine rounds of differential microbiome propagation, the effect of bacterial microbiomes selected to confer tolerance to sodium-sulfate stress appears specific (these microbiomes do not confer tolerance to aluminum-sulfate stress), but the effect of microbiomes selected to confer tolerance to aluminum-sulfate stress appears non-specific (selected microbiomes ameliorate both sodium- and aluminum-sulfate stresses). Complementary metagenomic analyses of the artificially selected microbiomes will help elucidate metabolic properties of microbiomes that confer specific versus non-specific salt-tolerance to plants.

plant biology↗