George Wang

Global metabolic impacts of recent climate warming

Documented shifts in geographical ranges, seasonal phenology, community interactions, genetics and extinctions have been attributed to recent global warming. Many such biotic shifts have been detected at mid- to high latitudes in the Northern Hemisphere—a latitudinal pattern that is expected because warming is fastest in these regions. In contrast, shifts in tropical regions are expected to be less marked because warming is less pronounced there. However, biotic impacts of warming are mediated through physiology, and metabolic rate, which is a fundamental measure of physiological activity and ecological impact, increases exponentially rather than linearly with temperature in ectotherms. Therefore, tropical ectotherms (with warm baseline temperatures) should experience larger absolute shifts in metabolic rate than the magnitude of tropical temperature change itself would suggest, but the impact of climate warming on metabolic rate has never been quantified on a global scale. Here we show that estimated changes in terrestrial metabolic rates in the tropics are large, are equivalent in magnitude to those in the north temperate-zone regions, and are in fact far greater than those in the Arctic, even though tropical temperature change has been relatively small. Because of temperature’s nonlinear effects on metabolism, tropical organisms, which constitute much of Earth’s biodiversity, should be profoundly affected by recent and projected climate warming.

1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana

Summary Arabidopsis thaliana serves as a model organism for the study of fundamental physiological, cellular, and molecular processes. It has also greatly advanced our understanding of intraspecific genome variation. We present a detailed map of variation in 1,135 high-quality re-sequenced natural inbred lines representing the native Eurasian and North African range and recently colonized North America. We identify relict populations that continue to inhabit ancestral habitats, primarily in the Iberian Peninsula. They have mixed with a lineage that has spread to northern latitudes from an unknown glacial refugium and is now found in a much broader spectrum of habitats. Insights into the history of the species and the fine-scale distribution of genetic diversity provide the basis for full exploitation of A. thaliana natural variation through integration of genomes and epigenomes with molecular and non-molecular phenotypes.

Century-scale Methylome Stability in a Recently Diverged Arabidopsis thaliana Lineage

There has been much excitement about the possibility that exposure to specific environments can induce an ecological memory in the form of whole-sale, genome-wide epigenetic changes that are maintained over many generations. In the model plant Arabidopsis thaliana, numerous heritable DNA methylation differences have been identified in greenhouse-grown isogenic lines, but it remains unknown how natural, highly variable environments affect the rate and spectrum of such changes. Here we present detailed methylome analyses in a geographically dispersed A. thaliana population that constitutes a collection of near-isogenic lines, diverged for at least a century from a common ancestor. Methylome variation largely reflected genetic distance, and was in many aspects similar to that of lines raised in uniform conditions. Thus, even when plants are grown in varying and diverse natural sites, genome-wide epigenetic variation accumulates mostly in a clock-like manner, and epigenetic divergence thus parallels the pattern of genome-wide DNA sequence divergence.

Quantifiable predictive features define epitope-specific T cell receptor repertoires

T cells are defined by a heterodimeric surface receptor, the T cell receptor (TCR), that mediates recognition of pathogen-associated epitopes through interactions with peptide and major histocompatibility complexes (pMHCs). TCRs are generated by genomic rearrangement of the germline TCR locus, a process termed V(D)J recombination, that has the potential to generate marked diversity of TCRs (estimated to range from 1015 (ref. 1) to as high as 1061 (ref. 2) possible receptors). Despite this potential diversity, TCRs from T cells that recognize the same pMHC epitope often share conserved sequence features, suggesting that it may be possible to predictively model epitope specificity. Here we report the in-depth characterization of ten epitope-specific TCR repertoires of CD8+ T cells from mice and humans, representing over 4,600 in-frame single-cell-derived TCRαβ sequence pairs from 110 subjects. We developed analytical tools to characterize these epitope-specific repertoires: a distance measure on the space of TCRs that permits clustering and visualization, a robust repertoire diversity metric that accommodates the low number of paired public receptors observed when compared to single-chain analyses, and a distance-based classifier that can assign previously unobserved TCRs to characterized repertoires with robust sensitivity and specificity. Our analyses demonstrate that each epitope-specific repertoire contains a clustered group of receptors that share core sequence similarities, together with a dispersed set of diverse ‘outlier’ sequences. By identifying shared motifs in core sequences, we were able to highlight key conserved residues driving essential elements of TCR recognition. These analyses provide insights into the generalizable, underlying features of epitope-specific repertoires and adaptive immune recognition.

Rapid divergence and high diversity of miRNAs and miRNA targets in the Camelineae

MicroRNAs (miRNAs) are short RNAs involved in gene regulation through translational inhibition and transcript cleavage. After processing from imperfect fold-back structures, miRNAs are incorporated into RNA-induced silencing complexes (RISCs) before targeting transcripts with varying degrees of complementarity. Some miRNAs are evolutionarily deep-rooted, and sequence complementarity with their targets is maintained through purifying selection. Both Arabidopsis and Capsella belong to the tribe Camelineae in the Brassicaceae, with Capsella rubella serving as an outgroup to the genus Arabidopsis. The genome sequence of C. rubella has recently been released, which allows characterization of its miRNA complement in comparison with Arabidopsis thaliana and Arabidopsis lyrata. Through next-generation sequencing, we identify high-confidence miRNA candidates specific to the C. rubella lineage. Only a few lineage-specific miRNAs have been studied for evolutionary constraints, and there have been no systematic studies of miRNA target diversity within or divergence between closely related plant species. Therefore we contrast sequence variation in miRNAs and their targets within A. thaliana, and between A. thaliana, A. lyrata and C. rubella. We document a surprising amount of small-scale variation in miRNA-target pairs, where many miRNAs are predicted to have species-specific targets in addition to ones that are shared between species. Our results emphasize that the transitive nature of many miRNA-target pairs can be observed even on a relatively short evolutionary time-scale, with non-random occurrences of differences in miRNAs and their complements in the miRNA precursors, the miRNA* sequences.

Adaptive diversification of growth allometry in the plant Arabidopsis thaliana

Significance Are there biological constants unifying phenotypic diversity across scales? Metabolic scaling theory (MST) predicts mathematical regularity and constancy in the allometric scaling of growth rate with body size across species. Here we show that adaptation to climate in Arabidopsis thaliana is associated with local strains that substantially deviate from the values predicted by MST. This deviation can be linked to increased stress tolerance at the expense of seed production, and it occurs through selection on genes that are involved in the abiotic stress response and are geographically correlated with climatic conditions. This highlights the evolutionary role of allometric diversification and helps establish the physiological bases of plant adaptation to contrasting environments. Seed plants vary tremendously in size and morphology; however, variation and covariation in plant traits may be governed, at least in part, by universal biophysical laws and biological constants. Metabolic scaling theory (MST) posits that whole-organismal metabolism and growth rate are under stabilizing selection that minimizes the scaling of hydrodynamic resistance and maximizes the scaling of resource uptake. This constrains variation in physiological traits and in the rate of biomass accumulation, so that they can be expressed as mathematical functions of plant size with near-constant allometric scaling exponents across species. However, the observed variation in scaling exponents calls into question the evolutionary drivers and the universality of allometric equations. We have measured growth scaling and fitness traits of 451 Arabidopsis thaliana accessions with sequenced genomes. Variation among accessions around the scaling exponent predicted by MST was correlated with relative growth rate, seed production, and stress resistance. Genomic analyses indicate that growth allometry is affected by many genes associated with local climate and abiotic stress response. The gene with the strongest effect, PUB4, has molecular signatures of balancing selection, suggesting that intraspecific variation in growth scaling is maintained by opposing selection on the trade-off between seed production and abiotic stress resistance. Our findings suggest that variation in allometry contributes to local adaptation to contrasting environments. Our results help reconcile past debates on the origin of allometric scaling in biology and begin to link adaptive variation in allometric scaling to specific genes.

Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana

BackgroundThe model species Arabidopsis thaliana has extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits. However, the cost of equipment and software required for high-throughput phenotyping is often a bottleneck for large-scale studies, such as mutant screening or quantitative genetics analyses. Simple tools are needed for the measurement of fitness-related traits, like relative growth rate and fruit production, without investment in expensive infrastructures. Here, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera.ResultsWe developed two models to predict plant dry mass and fruit number from the parameters extracted with the analysis of rosette and inflorescence images. Predictive models were trained by sacrificing growing individuals for dry mass estimation, and manually measuring a fraction of individuals for fruit number at maturity. Using a cross-validation approach, we showed that quantitative parameters extracted from image analysis predicts more 90% of both plant dry mass and fruit number. When used on 451 natural accessions, the method allowed modeling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H2 < 0.93), as well as estimated fruit number (H2 = 0.68). In addition, we validated the method for estimating fruit number with rev5, a mutant with increased flower abortion.ConclusionsThe method we propose here is an application of automated computerization of plant images with ImageJ, and subsequent statistical modeling in R. It allows plant biologists to measure growth dynamics and fruit number in hundreds of individuals with simple computing steps that can be repeated and adjusted to a wide range of laboratory conditions. It is thus a flexible toolkit for the measurement of fitness-related traits in large populations of a model species.

A rainfall-manipulation experiment with 517 Arabidopsis thaliana accessions

The gold standard for studying natural selection and adaptation in the wild is to quantify lifetime fitness of individuals from natural populations that have been grown together in a common garden, or that have been reciprocally transplanted. By combining fitness values with species traits and genome sequences, one can infer selection coefficients at the genetic level. Here we present a rainfall-manipulation experiment with 517 whole-genome sequenced natural accessions of the plant Arabidopsis thaliana spanning the global distribution of the species. The experiments were conducted in two field stations in contrasting climates, in the Mediterranean and in Central Europe, where we built rainout shelters and simulated high and low rainfall. Using custom image analysis we quantified fitness- and phenology-related traits for 23,154 pots, which contained about 14,500 plants growing independently, and over 310,000 plants growing in small populations (max. 30 plants). This large field experiment dataset, which associates fitness and ecologically-relevant traits with genomes, will provide an important resource to test eco-evolutionary genetic theories and to understand the potential evolutionary impacts of future climates on an important plant model species.

Image-based methods for phenotyping growth dynamics and fitness in large plant populations

Background With the development of next-generation sequencing technologies, high-throughput phenotyping has become the new bottleneck of quantitative genetics analyses. The model species Arabidopsis thaliana offers extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits, such as growth dynamics and reproductive allocation. However, reproducible and cost-effective methods need to be developed for the measurement of growth and especially fitness related traits in large populations. Here we describe image-based methods that can be adapted to a wide range of laboratory conditions, and enable the reliable estimation of biomass accumulation and fruit production in thousands of A. thaliana individuals. Results We propose a semi-invasive approach, where part of a population is used to predict plant biomass from image analysis. The other part of the population is daily imaged during three weeks, then harvested at the end of the life cycle where rosette and inflorescence are separately imaged. We developed ImageJ macros and R codes for image segmentation, 2D skeletonization and subsequent statistical analysis. First, ontogenetic growth is modelled from estimated and measured dry mass for all individuals with non-linear regressions, from which the dynamics of absolute growth rate (GR) and relative growth rate (RGR) are calculated. Second, analysis of the 2D inflorescence skeleton allows the estimation of fruit production, an important component of plant fitness. Our method was evaluated across 451 natural accessions of A. thaliana. Cross-validation revealed that our image-based method allows predicting approximately 90% of biomass variation and 70% of fruit production. Furthermore, estimated traits - like measured traits - showed high heritabilities and inter-experiment reproducibility. Conclusions We propose a flexible toolkit for the measurement of growth and fitness related traits in large plant populations. It is based on simple imaging, making the method reproducible at low cost in different facilities. However, as manual imaging of large plant populations can quickly become a limiting factor, we also describe an automated high-throughput imaging coupled with micro-computers that enables large phenotypic screening for genome-wide association studies and stress experiments.