SummaryHexaploid wheat (Triticum aestivum, genomes AABBDD) originated by hybridization of tetraploid Triticum turgidum (genomes AABB) with Aegilops tauschii (genomes DD). Genetic relationships between A. tauschii and the wheat D genome are of central importance for the understanding of wheat origin and subsequent evolution.Genetic relationships among 477 A. tauschii and wheat accessions were studied with the A. tauschii 10K Infinium single nucleotide polymorphism (SNP) array.Aegilops tauschii consists of two lineages (designated 1 and 2) having little genetic contact. Each lineage consists of two closely related sublineages. A population within lineage 2 in the southwestern and southern Caspian appears to be the main source of the wheat D genome. Lineage 1 contributed as little as 0.8% of the wheat D genome. Triticum aestivum is subdivided into the western and Far Eastern populations. The Far Eastern population conserved the genetic make-up of the nascent T. aestivum more than the western population. In wheat, diversity is high in chromosomes 1D and 2D and it correlates in all wheat D-genome and A. tauschii chromosomes with recombination rates.Gene flow from A. tauschii was an important source of wheat genetic diversity and shaped its distribution along the D-genome chromosomes.
The advent of high-throughput biology has catalyzed a remarkable improvement in our ability to identify new genes. A large fraction of newly discovered genes have an unknown functional role, particularly when they are specific to a particular lineage or organism. These genes, currently labeled ''hypothetical,'' might support important biological cell functions and could potentially serve as targets for medical, diagnostic, or pharmacogenomic studies. An important challenge to the scientific community is to associate these newly predicted genes with a biological function that can be validated by experimental screens. In the absence of sequence or structural homology to known genes, we must rely on advanced biotechnological methods, such as DNA chips and protein-protein interaction screens as well as computational techniques to assign putative functions to these genes. In this article, we propose an effective methodology for combining biological evidence obtained in several high-throughput experimental screens and integrating this evidence in a way that provides consistent functional assignments to hypothetical genes. We use the visualization method of propagation diagrams to illustrate the flow of functional evidence that supports the functional assignments produced by the algorithm. Our results contain a number of predictions and furnish strong evidence that integration of functional information is indeed a promising direction for improving the accuracy and robustness of functional genomics.
Gametes are highly specialised cells that can give rise to the next generation through their ability to generate a totipotent zygote. In mouse, germ cells are first specified in the developing embryo as primordial germ cells (PGCs) starting around embryonic day (E) 6.251 (Fig. 1a). Following subsequent migration into the developing gonad, PGCs undergo a wave of extensive epigenetic reprogramming at E10.5/E11.52–11, including genome-wide loss of 5-methylcytosine (5mC)2–5,7–11 (Fig. 1a). The underlying molecular mechanisms of this process have remained enigmatic leading to our inability to recapitulate this step of germline development in vitro12–14. Using an integrative approach, we show that this complex reprogramming process involves the coordinated interplay between promoter sequence characteristics, DNA (de)methylation, Polycomb (PRC1) complex and both DNA demethylation-dependent and -independent functions of Tet1 to enable the activation of a critical set of germline reprogramming responsive (GRR) genes involved in gamete generation and meiosis. Our results also unexpectedly reveal a role for Tet1 in safeguarding but not driving DNA demethylation in gonadal PGCs. Collectively, our work uncovers a fundamental biological role for gonadal germline reprogramming and identifies the epigenetic principles of the PGC-to-gonocyte transition that will be instructive towards recapitulating complete gametogenesis in vitro.
T4 RNA ligases are commonly used to attach adapters to RNAs, but large differences in ligation efficiency make detection and quantitation problematic. We developed a ligation selection strategy using random RNAs in combination with high-throughput sequencing to gain insight into the differences in efficiency of ligating pre-adenylated DNA adapters to RNA 3′-ends. After analyzing biases in RNA sequence, secondary structure and RNA-adapter cofold structure, we conclude that T4 RNA ligases do not show significant primary sequence preference in RNA substrates, but are biased against structural features within RNAs and adapters. Specifically, RNAs with less than three unstructured nucleotides at the 3′-end and RNAs that are predicted to cofold with an adapter in unfavorable structures are likely to be poorly ligated. The effect of RNA-adapter cofold structures on ligation is supported by experiments where the ligation efficiency of specific miRNAs was changed by designing adapters to alter cofold structure. In addition, we show that using adapters with randomized regions results in higher ligation efficiency and reduced ligation bias. We propose that using randomized adapters may improve RNA representation in experiments that include a 3′-adapter ligation step.
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