Genetic determination of stomatal patterning in winter wheat (Triticum aestivum L.), bioRxiv, 2018-12-11

Leaf stomata are microscopic pores mediating plant-environment interactions. Their role in carbon uptake and transpiration make them prime candidates for improving water use efficiency (WUE). Stomatal density (SD), the number of stomata per unit area, has been shown to be negatively correlated with WUE. However, little is known about the genetic basis of SD in wheat (Triticum aestivum L.), and to what extant genetic variation exists in contemporary wheat germplasm. Here, we evaluated stomatal patterning over two growing seasons in a set of 333 wheat lines, representing the European winter wheat germplasm. Stomatal patterning was mainly determined by two underlying traits, the distance between files of stomata and the distance between stomata within a file. By haplotype association mapping, quantitative trait loci for SD were consistently detected in both seasons on wheat chromosomes (CHR) 2A, 3A and 7B. The single nucleotide polymorphism markers most significantly associated with SD coincided with the genes INDUCER OF CBF EXPRESSION 1 (ICE1) and STOMATAL CYTOKINESIS-DEFECTIVE 1 (SCD1) on CHR 3A, and genes involved in ethylene and auxin signaling on CHR 2A and 7B, respectively. Our study unlocks the phenotypic and genotypic variation for stomatal patterning traits in contemporary wheat germplasm. It provides gene targets for functional validation and practical tools to manipulate SD using marker-assisted selection for crop improvement.

biorxiv plant-biology 100-200-users 2018

Real-time capture of horizontal gene transfers from gut microbiota by engineered CRISPR-Cas acquisition, bioRxiv, 2018-12-11

AbstractHorizontal gene transfer (HGT) is central to the adaptation and evolution of bacteria. However, our knowledge about the flow of genetic material within complex microbiomes is lacking; most studies of HGT rely on bioinformatic analyses of genetic elements maintained on evolutionary timescales or experimental measurements of phenotypically trackable markers (e.g. antibiotic resistance). Consequently, our knowledge of the capacity and dynamics of HGT in complex communities is limited. Here, we utilize the CRISPR-Cas spacer acquisition process to detect HGT events from complex microbiota in real-time and at nucleotide resolution. In this system, a recording strain is exposed to a microbial sample, spacers are acquired from foreign transferred elements and permanently stored in genomic CRISPR arrays. Subsequently, sequencing and analysis of these spacers enables identification of the transferred elements. This approach allowed us to quantify transfer frequencies of individual mobile elements without the need for phenotypic markers or post-transfer replication. We show that HGT in human clinical fecal samples can be extensive and rapid, often involving multiple different plasmid types, with the IncX type being the most actively transferred. Importantly, the vast majority of transferred elements did not carry readily selectable phenotypic markers, highlighting the utility of our approach to reveal previously hidden real-time dynamics of mobile gene pools within complex microbiomes.

biorxiv microbiology 100-200-users 2018

Models of archaic admixture and recent history from two-locus statistics, bioRxiv, 2018-12-08

AbstractWe learn about population history and underlying evolutionary biology through patterns of genetic polymorphism. Many approaches to reconstruct evolutionary histories focus on a limited number of informative statistics describing distributions of allele frequencies or patterns of linkage disequilibrium. We show that many commonly used statistics are part of a broad family of two-locus moments whose expectation can be computed jointly and rapidly under a wide range of scenarios, including complex multi-population demographies with continuous migration and admixture events. A full inspection of these statistics reveals that widely used models of human history fail to predict simple patterns of linkage disequilibrium. To jointly capture the information contained in classical and novel statistics, we implemented a tractable likelihood-based inference framework for demographic history. Using this approach, we show that human evolutionary models that include archaic admixture in Africa, Asia, and Europe provide a much better description of patterns of genetic diversity across the human genome. We estimate that an unidentified, deeply diverged population admixed with modern humans within Africa both before and after the split of African and Eurasian populations, contributing 4 - 8% genetic ancestry to individuals in world-wide populations.Author SummaryThroughout human history, populations have expanded and contracted, split and merged, and ex-changed migrants. Because these events affected genetic diversity, we can learn about human history by comparing predictions from evolutionary models to genetic data. Here, we show how to rapidly compute such predictions for a wide range of diversity measures within and across populations under complex demographic scenarios. While widely used models of human history accurately predict common measures of diversity, we show that they strongly underestimate the co-occurence of low frequency mutations within human populations in Asia, Europe, and Africa. Models allowing for archaic admixture, the relatively recent mixing of human populations with deeply diverged human lineages, resolve this discrepancy. We use such models to infer demographic models that include both recent and ancient features of human history. We recover the well-characterized admixture of Neanderthals in Eurasian populations, as well as admixture from an as-yet unknown diverged human population within Africa, further suggesting that admixture with deeply diverged lineages occurred multiple times in human history. By simultaneously testing model predictions for a broad range of diversity statistics, we can assess the robustness of common evolutionary models, identify missing historical events, and build more informed models of human demography.

biorxiv genetics 100-200-users 2018

 

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