Plant ecological genomics at the limits of life in the Atacama Desert.

The Atacama Desert in Chile-hyperarid and with high-ultraviolet irradiance levels-is one of the harshest environments on Earth. Yet, dozens of species grow there, including Atacama-endemic plants. Herein, we establish the Talabre-Lejía transect (TLT) in the Atacama as an unparalleled natural laboratory to study plant adaptation to extreme environmental conditions. We characterized climate, soil, plant, and soil-microbe diversity at 22 sites (every 100 m of altitude) along the TLT over a 10-y period. We quantified drought, nutrient deficiencies, large diurnal temperature oscillations, and pH gradients that define three distinct vegetational belts along the altitudinal cline. We deep-sequenced transcriptomes of 32 dominant plant species spanning the major plant clades, and assessed soil microbes by metabarcoding sequencing. The top-expressed genes in the 32 Atacama species are enriched in stress responses, metabolism, and energy production. Moreover, their root-associated soils are enriched in growth-promoting bacteria, including nitrogen fixers. To identify genes associated with plant adaptation to harsh environments, we compared 32 Atacama species with the 32 closest sequenced species, comprising 70 taxa and 1,686,950 proteins. To perform phylogenomic reconstruction, we concatenated 15,972 ortholog groups into a supermatrix of 8,599,764 amino acids. Using two codon-based methods, we identified 265 candidate positively selected genes (PSGs) in the Atacama plants, 64% of which are located in Pfam domains, supporting their functional relevance. For 59/184 PSGs with an Arabidopsis ortholog, we uncovered functional evidence linking them to plant resilience. As some Atacama plants are closely related to staple crops, these candidate PSGs are a "genetic goldmine" to engineer crop resilience to face climate change.

Multiscale climate change impacts on plant diversity in the Atacama Desert.

Comprehending ecological dynamics requires not only knowledge of modern communities but also detailed reconstructions of ecosystem history. Ancient DNA (aDNA) metabarcoding allows biodiversity responses to major climatic change to be explored at different spatial and temporal scales. We extracted aDNA preserved in fossil rodent middens to reconstruct late Quaternary vegetation dynamics in the hyperarid Atacama Desert. By comparing our paleo-informed millennial record with contemporary observations of interannual variations in diversity, we show local plant communities behave differentially at different timescales. In the interannual (years to decades) time frame, only annual herbaceous expand and contract their distributional ranges (emerging from persistent seed banks) in response to precipitation, whereas perennials distribution appears to be extraordinarily resilient. In contrast, at longer timescales (thousands of years) many perennial species were displaced up to 1,000 m downslope during pluvial events. Given ongoing and future natural and anthropogenically induced climate change, our results not only provide baselines for vegetation in the Atacama Desert, but also help to inform how these and other high mountain plant communities may respond to fluctuations of climate in the future.

Whole Genome Sequence, Variant Discovery and Annotation in Mapuche-Huilliche Native South Americans.

Whole human genome sequencing initiatives help us understand population history and the basis of genetic diseases. Current data mostly focuses on Old World populations, and the information of the genomic structure of Native Americans, especially those from the Southern Cone is scant. Here we present annotation and variant discovery from high-quality complete genome sequences of a cohort of 11 Mapuche-Huilliche individuals (HUI) from Southern Chile. We found approximately 3.1 × 106 single nucleotide variants (SNVs) per individual and identified 403,383 (6.9%) of novel SNVs events. Analyses of large-scale genomic events detected 680 copy number variants (CNVs) and 4,514 structural variants (SVs), including 398 and 1,910 novel events, respectively. Global ancestry composition of HUI genomes revealed that the cohort represents a sample from a marginally admixed population from the Southern Cone, whose main genetic component derives from Native American ancestors. Additionally, we found that HUI genomes contain variants in genes associated with 5 of the 6 leading causes of noncommunicable diseases in Chile, which may have an impact on the risk of prevalent diseases in Chilean and Amerindian populations. Our data represents a useful resource that can contribute to population-based studies and for the design of early diagnostics or prevention tools for Native and admixed Latin American populations.

Step-by-Step Construction of Gene Co-expression Networks from High-Throughput Arabidopsis RNA Sequencing Data.

The rapid increase in the availability of transcriptomics data generated by RNA sequencing represents both a challenge and an opportunity for biologists without bioinformatics training. The challenge is handling, integrating, and interpreting these data sets. The opportunity is to use this information to generate testable hypothesis to understand molecular mechanisms controlling gene expression and biological processes (Fig. 1). A successful strategy to generate tractable hypotheses from transcriptomics data has been to build undirected network graphs based on patterns of gene co-expression. Many examples of new hypothesis derived from network analyses can be found in the literature, spanning different organisms including plants and specific fields such as root developmental biology.In order to make the process of constructing a gene co-expression network more accessible to biologists, here we provide step-by-step instructions using published RNA-seq experimental data obtained from a public database. Similar strategies have been used in previous studies to advance root developmental biology. This guide includes basic instructions for the operation of widely used open source platforms such as Bio-Linux, R, and Cytoscape. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be easily adapted to work with RNA-seq data from any organism.