The perennial native switchgrass adapts better than other plant species do to marginal soils with low plant-available nutrients, including those with low phosphorus (P) content. Switchgrass roots and their associated microorganisms can alter the pools of available P throughout the whole soil profile making predictions of P availability in situ challenging. Plant P homeostasis makes monitoring of P limitation via measurements of plant P content alone difficult to interpret. To address these challenges, we developed a machine-learning model trained with high accuracy using the leaf tissue chemical profile, rather than P content. By applying this learned model in field trials across two sites with contrasting extractable soil P, we observed that actual plant available P in soil was more similar than expected, suggesting that adaptations occurred to alleviate the apparent P constraint. These adaptations come at a metabolic cost to the plant that have consequences for feedstock chemical components and quality. We observed that other biochemical signatures of P limitation, such as decreased cellulose-to-lignin ratios, were apparent, indicating re-allocation of carbon resources may have contributed to increased P acquisition. Plant P allocation strategies also differed across sites, and these differences were correlated with the subsequent year's biomass yields.

To identify switchgrass homologs of rice genes, known/predicted to control biomass and stress response-related traits, we screened 96,000 clones from two switchgrass bacterial artificial chromosome (BAC) libraries. Full-length sequencing of 311 BAC clones revealed sequence for ∼3.2 % (51.7 Mb) of the switchgrass genome, coding for 3948 genes. A comparison with Arabidopsis and five grass genomes revealed that switchgrass genes share the highest number of homologs with rice (95.5 %) followed by foxtail millet (91.7 %) and Sorghum (91.5 %). One hundred eighteen of the annotated genes are unique to switchgrass. Gene annotation and ontology analysis revealed 695 genes belonging to gene families targeted in the screening. These include 350 kinase, 203 glycosyltransferase (GT), 109 glycoside hydrolase (GH), and 33 ethylene responsive transcription factor (ERF) family genes. Rice homologs of 65 genes, identified here, have demonstrated roles in bioenergy-relevant traits. These include 14 GT2 family genes involved in the synthesis of cellulose and hemicelluloses. Comparative expression analysis in six switchgrass organs revealed a conserved expression pattern for three cellulose synthase (CesA1, CesA2, and CesA9) and five cellulose-synthase-like genes (CslA2, CslA11, CslC1, CslD4, and CslE6). CslF genes that encode mixed linkage glucans are expressed in wider range of tissues in switchgrass compared with rice.

Background

Switchgrass (Panicum virgatum L.) is an important bioenergy crop widely used for lignocellulosic research. While extensive transcriptomic analyses have been conducted on this species using short read-based sequencing techniques, very little has been reliably derived regarding alternatively spliced (AS) transcripts.

Results

We present an analysis of transcriptomes of six switchgrass tissue types pooled together, sequenced using Pacific Biosciences (PacBio) single-molecular long-read technology. Our analysis identified 105,419 unique transcripts covering 43,570 known genes and 8795 previously unknown genes. 45,168 are novel transcripts of known genes. A total of 60,096 AS transcripts are identified, 45,628 being novel. We have also predicted 1549 transcripts of genes involved in cell wall construction and remodeling, 639 being novel transcripts of known cell wall genes. Most of the predicted transcripts are validated against Illumina-based short reads. Specifically, 96% of the splice junction sites in all the unique transcripts are validated by at least five Illumina reads. Comparisons between genes derived from our identified transcripts and the current genome annotation revealed that among the gene set predicted by both analyses, 16,640 have different exon-intron structures.

Conclusions

Overall, substantial amount of new information is derived from the PacBio RNA data regarding both the transcriptome and the genome of switchgrass.

Nitrogen (N) is an essential but generally limiting nutrient for biological systems. Development of the Haber-Bosch industrial process for ammonia synthesis helped to relieve N limitation of agricultural production, fueling the Green Revolution and reducing hunger. However, the massive use of industrial N fertilizer has doubled the N moving through the global N cycle with dramatic environmental consequences that threaten planetary health. Thus, there is an urgent need to reduce losses of reactive N from agriculture, while ensuring sufficient N inputs for food security. Here we review current knowledge related to N use efficiency (NUE) in agriculture and identify research opportunities in the areas of agronomy, plant breeding, biological N fixation (BNF), soil N cycling, and modeling to achieve responsible, sustainable use of N in agriculture. Amongst these opportunities, improved agricultural practices that synchronize crop N demand with soil N availability are low-hanging fruit. Crop breeding that targets root and shoot physiological processes will likely increase N uptake and utilization of soil N, while breeding for BNF effectiveness in legumes will enhance overall system NUE. Likewise, engineering of novel N-fixing symbioses in non-legumes could reduce the need for chemical fertilizers in agroecosystems but is a much longer-term goal. The use of simulation modeling to conceptualize the complex, interwoven processes that affect agroecosystem NUE, along with multi-objective optimization, will also accelerate NUE gains.

Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

Long-term climate change and periodic environmental extremes threaten food and fuel security¹ and global crop productivity^2-4. Although molecular and adaptive breeding strategies can buffer the effects of climatic stress and improve crop resilience⁵, these approaches require sufficient knowledge of the genes that underlie productivity and adaptation⁶-knowledge that has been limited to a small number of well-studied model systems. Here we present the assembly and annotation of the large and complex genome of the polyploid bioenergy crop switchgrass (Panicum virgatum). Analysis of biomass and survival among 732 resequenced genotypes, which were grown across 10 common gardens that span 1,800 km of latitude, jointly revealed extensive genomic evidence of climate adaptation. Climate-gene-biomass associations were abundant but varied considerably among deeply diverged gene pools. Furthermore, we found that gene flow accelerated climate adaptation during the postglacial colonization of northern habitats through introgression of alleles from a pre-adapted northern gene pool. The polyploid nature of switchgrass also enhanced adaptive potential through the fractionation of gene function, as there was an increased level of heritable genetic diversity on the nondominant subgenome. In addition to investigating patterns of climate adaptation, the genome resources and gene-trait associations developed here provide breeders with the necessary tools to increase switchgrass yield for the sustainable production of bioenergy.