Grass cell wall properties influence food, feed, and biofuel feedstock usage efficiency. The glucuronoarabinoxylan of grass cell walls is esterified with the phenylpropanoid-derived hydroxycinnamic acids ferulic acid (FA) and para-coumaric acid (p-CA). Feruloyl esters undergo oxidative coupling with neighboring phenylpropanoids on glucuronoarabinoxylan and lignin. Examination of rice (Oryza sativa) mutants in a grass-expanded and -diverged clade of BAHD acyl-coenzyme A-utilizing transferases identified four mutants with altered cell wall FA or p-CA contents. Here, we report on the effects of overexpressing one of these genes, OsAt10 (LOC_Os06g39390), in rice. An activation-tagged line, OsAT10-D1, shows a 60% reduction in matrix polysaccharide-bound FA and an approximately 300% increase in p-CA in young leaf tissue but no discernible phenotypic alterations in vegetative development, lignin content, or lignin composition. Two additional independent OsAt10 overexpression lines show similar changes in FA and p-CA content. Cell wall fractionation and liquid chromatography-mass spectrometry experiments isolate the cell wall alterations in the mutant to ester conjugates of a five-carbon sugar with p-CA and FA. These results suggest that OsAT10 is a p-coumaroyl coenzyme A transferase involved in glucuronoarabinoxylan modification. Biomass from OsAT10-D1 exhibits a 20% to 40% increase in saccharification yield depending on the assay. Thus, OsAt10 is an attractive target for improving grass cell wall quality for fuel and animal feed.

Allopolyploidy is an important process in plant speciation, yet newly formed allopolyploid species typically suffer from extreme genetic bottlenecks. One escape from this impasse might be homoeologous meiotic pairing, during which homoeologous exchanges (HEs) generate phenotypically variable progeny. However, the immediate genome-wide patterns and resulting phenotypic diversity generated by HEs remain largely unknown. Here, we analyzed the genome composition of 202 phenotyped euploid segmental allopolyploid individuals from the fourth selfed generation following chromosomal doubling of reciprocal F1 hybrids of crosses between rice subspecies, using whole-genome sequencing. We describe rampant occurrence of HEs that, by overcoming incompatibility or conferring superiority of hetero-cytonuclear interactions, generate extensive and individualized genomic mosaicism across the analyzed tetraploids. We show that the resulting homoeolog copy number alteration in tetraploids affects known-function genes and their complex genetic interactions, in the process creating extraordinary phenotypic diversity at the population level following a single initial hybridization. Our results illuminate the immediate genomic landscapes possible in a tetraploid genomic environment, and underscore HE as an important mechanism that fuels rapid phenotypic diversification accompanying the initial stages of allopolyploid evolution.

Thermal conversion of biomass is a rapid, low-cost way to produce a dense liquid product, known as bio-oil, that can be refined to transportation fuels. However, utilization of bio-oil is challenging due to its chemical complexity, acidity, and instability—all results of the intricate nature of biomass. A clear understanding of how biomass properties impact yield and composition of thermal products will provide guidance to optimize both biomass and conditions for thermal conversion. To aid elucidation of these associations, we first describe biomass polymers, including phenolics, polysaccharides, acetyl groups, and inorganic ions, and the chemical interactions among them. We then discuss evidence for three roles (i.e., models) for biomass components in formation of liquid pyrolysis products: (1) as direct sources, (2) as catalysts, and (3) as indirect factors whereby chemical interactions among components and/or cell wall structural features impact thermal conversion products. We highlight associations that might be utilized to optimize biomass content prior to pyrolysis, though a more detailed characterization is required to understand indirect effects. In combination with high-throughput biomass characterization techniques this knowledge will enable identification of biomass particularly suited for biofuel production and can also guide genetic engineering of bioenergy crops to improve biomass features.

Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine mapping process to find the causal gene controlling the trait. With the availability of a rich assortment of genomic and functional genomic data, it is possible to develop a computational method to facilitate faster identification of causal genes. We developed QTG-Finder, a machine learning based algorithm to prioritize causal genes by ranking genes within a quantitative trait locus (QTL). Two predictive models were trained separately based on known causal genes in Arabidopsis and rice. An independent validation analysis showed that the models could recall about 64% of Arabidopsis and 79% of rice causal genes when the top 20% ranked genes were considered. The top 20% ranked genes can range from 10 to 100 genes, depending on the size of a QTL. The models can prioritize different types of traits though at different efficiency. We also identified several important features of causal genes including paralog copy number, being a transporter, being a transcription factor, and containing SNPs that cause premature stop codon. This work lays the foundation for systematically understanding characteristics of causal genes and establishes a pipeline to predict causal genes based on public data.

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.