Ray Ming, Robert Paull, Qingyi Yu and colleagues report the genome sequences of two cultivated pineapple varieties and one wild pineapple relative. Their analysis supports the use of the pineapple as a reference genome for monocot comparative genomics and provides insight into the evolution of crassulacean acid metabolism photosynthesis. Pineapple (Ananas comosus (L.) Merr.) is the most economically valuable crop possessing crassulacean acid metabolism (CAM), a photosynthetic carbon assimilation pathway with high water-use efficiency, and the second most important tropical fruit. We sequenced the genomes of pineapple varieties F153 and MD2 and a wild pineapple relative, Ananas bracteatus accession CB5. The pineapple genome has one fewer ancient whole-genome duplication event than sequenced grass genomes and a conserved karyotype with seven chromosomes from before the ρ duplication event. The pineapple lineage has transitioned from C3 photosynthesis to CAM, with CAM-related genes exhibiting a diel expression pattern in photosynthetic tissues. CAM pathway genes were enriched with cis-regulatory elements associated with the regulation of circadian clock genes, providing the first cis-regulatory link between CAM and circadian clock regulation. Pineapple CAM photosynthesis evolved by the reconfiguration of pathways in C3 plants, through the regulatory neofunctionalization of preexisting genes and not through the acquisition of neofunctionalized genes via whole-genome or tandem gene duplication.

The ordering and orientation of genomic scaffolds to reconstruct chromosomes is an essential step during de novo genome assembly. Because this process utilizes various mapping techniques that each provides an independent line of evidence, a combination of multiple maps can improve the accuracy of the resulting chromosomal assemblies. We present ALLMAPS, a method capable of computing a scaffold ordering that maximizes colinearity across a collection of maps. ALLMAPS is robust against common mapping errors, and generates sequences that are maximally concordant with the input maps. ALLMAPS is a useful tool in building high-quality genome assemblies. ALLMAPS is available at: https://github.com/tanghaibao/jcvi/wiki/ALLMAPS.

Hyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS–NIR–SWIR, 400–2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS–NIR–SWIR as a high-throughput tool to measure six leaf properties of maize plants including chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K). This assessment was performed using the lines of the maize diversity panel. Data were collected from plants grown in greenhouse condition, as well as in the field under two nitrogen application regimes. Leaf-level hyperspectral data were collected with a VIS–NIR–SWIR spectroradiometer at tasseling. Two multivariate modeling approaches, partial least squares regression (PLSR) and support vector regression (SVR), were employed to estimate the leaf properties from hyperspectral data. Several common vegetation indices (VIs: GNDVI, RENDVI, and NDWI), which were calculated from hyperspectral data, were also assessed to estimate these leaf properties. Some VIs were able to estimate CHL and N (R2 > 0.68), but failed to estimate the other four leaf properties. Models developed with PLSR and SVR exhibited comparable performance to each other, and provided improved accuracy relative to VI models. CHL were estimated most successfully, with R2 (coefficient of determination) > 0.94 and ratio of performance to deviation (RPD) > 4.0. N was also predicted satisfactorily (R2 > 0.85 and RPD > 2.6). LWC, SLA and K were predicted moderately well, with R2 ranging from 0.54 to 0.70 and RPD from 1.5 to 1.8. The lowest prediction accuracy was for P, with R2 < 0.5 and RPD < 1.4. This study showed that VIS–NIR–SWIR reflectance spectroscopy is a promising tool for low-cost, nondestructive, and high-throughput analysis of a number of leaf physiological and biochemical properties. Full-spectrum based modeling approaches (PLSR and SVR) led to more accurate prediction models compared to VI-based methods. We called for the construction of a leaf VIS–NIR–SWIR spectral library that would greatly benefit the plant phenotyping community for the research of plant leaf traits.

Summary: The complexity of polyploid Saccharum genomes hindered progress of genome research and crop improvement in sugarcane. To understand their genome structure, transcriptomes of 59 F₁ individuals derived from S. officinarumLA Purple and S. robustum Molokai 5829 (2n = 80, x = 10 for both) were sequenced, yielding 11 157 and 8998 SNPs and 83 and 105 linkage groups, respectively. Most markers in each linkage group aligned to single sorghum chromosome. However, 71 interchromosomal rearrangements were detected between sorghum and S. officinarum or S. robustum, and 24 (33.8%) of them were shared between S. officinarum and S. robustum, indicating their occurrence before the speciation event that separated these two species. More than 2000 gene pairs from S. spontaneum, S. officinarum and S. robustum were analysed to estimate their divergence time. Saccharum officinarum and S. robustum diverged about 385 thousand years ago, and the whole‐genome duplication events occurred after the speciation event because of shared interchromosomal rearrangements. The ancestor of these two species diverged from S. spontaneum about 769 thousand years ago, and the reduction in basic chromosome number from 10 to 8 in S. spontaneum occurred after the speciation event but before the two rounds of whole‐genome duplication. Our results proved that S. officinarum is a legitimate species in its own right and not a selection from S. robustum during the domestication process in the past 10 000 years. Our findings rejected a long‐standing hypothesis and clarified the timing of speciation and whole‐genome duplication events in Saccharum.

Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize (Zea mays) and sorghum (Sorghum bicolor), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from >3,300 maize roots and >1,470 sorghum roots. Single nucleotide polymorphism (SNP)-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via expression read depth GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are coregulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves transe–quantitative trait locus for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa.