The Pharus latifolius Genome Bridges the Gap of Early Grass Evolution

The grass family (Poaceae) includes all commercial cereal crops and is a major contributor to biomass in various terrestrial ecosystems. The ancestry of all grass genomes includes a shared whole-genome duplication (WGD), named rho (ρ) WGD, but the evolutionary significance of ρ-WGD remains elusive. We sequenced the genome of Pharus latifolius, a grass species (producing a true spikelet) in the subfamily Pharoideae, a sister lineage to the core Poaceae including the PACMAD and BOP clades. Our results indicate that the P. latifolius genome has evolved slowly relative to cereal grass genomes, as reflected by moderate rates of molecular evolution, limited chromosome rearrangements and a low rate of gene loss for duplicated genes. We show that the ρ-WGD event occurred ∼98.2 million years ago (Ma) in a common ancestor of the Pharoideae and the PACMAD and BOP grasses. This was followed by contrasting patterns of diploidization in the Pharus and core Poaceae lineages. The presence of two FRIZZY PANICLE (FZP)-like genes in P. latifolius, and duplicated MADS-box genes, support the hypothesis that the ρ-WGD may have played a role in the origin and functional diversification of the spikelet, an adaptation in grasses related directly to cereal yields. The P. latifolius genome sheds light on the origin and early evolution of grasses underpinning the biology and breeding of cereals.

Reproductive phasiRNA loci and DICER-LIKE5, but not microRNA loci, diversified in monocotyledonous plants

In monocots other than maize and rice, the repertoire and diversity of microRNAs (miRNAs) and the populations of phased, secondary, small interfering RNAs (phasiRNAs) are poorly characterized. To remedy this, we sequenced small RNAs from vegetative and dissected inflorescence tissue in 28 phylogenetically diverse monocots and from several early-diverging angiosperm lineages, as well as publicly available data from 10 additional monocot species. We annotated miRNAs, siRNAs and phasiRNAs across the monocot phylogeny, identifying miRNAs apparently lost or gained in the grasses relative to other monocot families, as well as a number of tRNA fragments misannotated as miRNAs. Using our miRNA database cleaned of these misannotations, we identified conservation at the 8th, 9th, 19th and 3’ end positions that we hypothesize are signatures of selection for processing, targeting, or Argonaute sorting. We show that 21-nt reproductive phasiRNAs are far more numerous in grass genomes than other monocots. Based on sequenced monocot genomes and transcriptomes, DICER-LIKE5 (DCL5), important to 24-nt phasiRNA biogenesis, likely originated via gene duplication before the diversification of the grasses. This curated database of phylogenetically diverse monocot miRNAs, siRNAs, and phasiRNAs represents a large collection of data that should facilitate continued exploration of small RNA diversification in flowering plants.

GPRED-GC: a Gene PREDiction model accounting for 5 ′- 3′ GC gradient

Background Gene is a key step in genome annotation. Ab initio gene prediction enables gene annotation of new genomes regardless of availability of homologous sequences. There exist a number of ab initio gene prediction tools and they have been widely used for gene annotation for various species. However, existing tools are not optimized for identifying genes with highly variable GC content. In addition, some genes in grass genomes exhibit a sharp 5 ′- 3′ decreasing GC content gradient, which is not carefully modeled by available gene prediction tools. Thus, there is still room to improve the sensitivity and accuracy for predicting genes with GC gradients. Results In this work, we designed and implemented a new hidden Markov model (HMM)-based ab initio gene prediction tool, which is optimized for finding genes with highly variable GC contents, such as the genes with negative GC gradients in grass genomes. We tested the tool on three datasets from Arabidopsis thaliana and Oryza sativa. The results showed that our tool can identify genes missed by existing tools due to the highly variable GC contents. Conclusions GPRED-GC can effectively predict genes with highly variable GC contents without manual intervention. It provides a useful complementary tool to existing ones such as Augustus for more sensitive gene discovery. The source code is freely available at https://sourceforge.net/projects/gpred-gc/.

Reconstructed protein sequence evolution consistent with the evolution of C4 photosynthesis via a C2 ancestor in the Paniceae

The grass tribe Paniceae includes a monophyletic subclade of species, the MPC clade, which specialize in each of the three primary C4 sub-pathways NADP-ME, NAD-ME and PCK. The evolutionary history of C4 photosynthesis in this subclade remains ambiguous. Leveraging newly sequenced grass genomes and syntenic orthology data, we estimated rates of protein sequence evolution on ancestral branches for both core enzymes shared across different C4 sub-pathways and enzymes specific to C4 sub-pathways. While core enzymes show elevated rates of protein sequence evolution in ancestral branches consistent with a transition from C3 to C4 photosynthesis in the ancestor for this clade, no subtype specific enzymes showed similar patterns. At least one protein involved in photorespiration also showed elevated rates of protein sequence evolution in the ancestral branch. The set of core C4 enzymes examined here combined with the photorespiratory pathway are necessary for the C2 photosynthetic cycle, a previously proposed intermediate between C3 and C4 photosynthesis. The patterns reported here are consistent with, but not conclusive proof that, C4 photosynthesis in the MPC clade of the Paniceae evolved via a C2 intermediate.

A modified GC-specific MAKER gene annotation method reveals improved and novel gene predictions of high and low GC content in Oryza sativa

Accurate structural annotation depends on well-trained gene prediction programs. Training data for gene prediction programs are often chosen randomly from a subset of high-quality genes that ideally represent the variation found within a genome. One aspect of gene variation is GC content, which differs across species and is bimodal in grass genomes. We find that gene prediction programs trained on genes with random GC content do not completely predict all grass genes with extreme GC content. We present a new GC-specific MAKER annotation protocol to predict new and improved gene models and assess the biological significance of this method in Oryza sativa.