Quantitative trait loci for cell wall composition traits measured using near-infrared spectroscopy in the model C4 perennial grass Panicum hallii

Elbaiomy R, Chen L, Wang Z, Jiao J, Zhu J, Zhou W, Chen B, Soaud SA, Abbas M, Lin N, El-Sappah AH. Evolution analysis of FRIZZY PANICLE (FZP) orthologs explored the mutations in DNA coding sequences in the grass family (Poaceae)

FRIZZY PANICLE (FZP), an essential gene that controls spikelet differentiation and development in the grass family (Poaceae), prevents the formation of axillary bud meristems and is closely associated with crop yields. It is unclear whether the FZP gene or its orthologs were selected during the evolutionary process of grass species, which possess diverse spike morphologies. In the present study, we adopted bioinformatics methods for the evolutionary analysis of FZP orthologs in species of the grass family. Thirty-five orthologs with protein sequences identical to that of the FZP gene were identified from 29 grass species. Analysis of conserved domains revealed that the AP2/ERF domains were highly conserved with almost no amino acid mutations. However, species of the tribe Triticeae, genus Oryza, and C4 plants exhibited more significant amino acid mutations in the acidic C-terminus region. Results of the phylogenetic analysis showed that the 29 grass species could be classified into three groups, namely, Triticeae, Oryza, and C4 plants. Within the Triticeae group, the FZP genes originating from the same genome were classified into the same sub-group. When selection pressure analysis was performed, significant positive selection sites were detected in species of the Triticeae and Oryza groups. Our results show that the FZP gene was selected during the grass family's evolutionary process, and functional divergence may have already occurred among the various species. Therefore, researchers investigating the FZP gene's functions should take note of the possible presence of various roles in other grass species.

Integrated NIRS and QTL assays reveal minor mannose and galactose as contrast lignocellulose factors for biomass enzymatic saccharification in rice

BACKGROUND

Identifying lignocellulose recalcitrant factors and exploring their genetic properties are essential for enhanced biomass enzymatic saccharification in bioenergy crops. Despite genetic modification of major wall polymers has been implemented for reduced recalcitrance in engineered crops, it could most cause a penalty of plant growth and biomass yield. Alternatively, it is increasingly considered to improve minor wall components, but an applicable approach is required for efficient assay of large population of biomass samples. Hence, this study collected total of 100 rice straw samples and characterized all minor wall monosaccharides and biomass enzymatic saccharification by integrating NIRS modeling and QTL profiling.

RESULTS

By performing classic chemical analyses and establishing optimal NIRS equations, this study examined four minor wall monosaccharides and major wall polymers (acid-soluble lignin/ASL, acid-insoluble lignin/AIL, three lignin monomers, crystalline cellulose), which led to largely varied hexoses yields achieved from enzymatic hydrolyses after two alkali pretreatments were conducted with large population of rice straws. Correlation analyses indicated that mannose and galactose can play a contrast role for biomass enzymatic saccharification at P < 0.0 l level (n = 100). Meanwhile, we found that the QTLs controlling mannose, galactose, lignin-related traits, and biomass saccharification were co-located. By combining NIRS assay with QTLs maps, this study further interpreted that the mannose-rich hemicellulose may assist AIL disassociation for enhanced biomass enzymatic saccharification, whereas the galactose-rich polysaccharides should be effectively extracted with ASL from the alkali pretreatment for condensed AIL association with cellulose microfibrils.

CONCLUSIONS

By integrating NIRS assay with QTL profiling for large population of rice straw samples, this study has identified that the mannose content of wall polysaccharides could positively affect biomass enzymatic saccharification, while the galactose had a significantly negative impact. It has also sorted out that two minor monosaccharides could distinctively associate with lignin deposition for wall network construction. Hence, this study demonstrates an applicable approach for fast assessments of minor lignocellulose recalcitrant factors and biomass enzymatic saccharification in rice, providing a potential strategy for bioenergy crop breeding and biomass processing.

Precise high-throughput online near-infrared spectroscopy assay to determine key cell wall features associated with sugarcane bagasse digestibility

BACKGROUND

Sugarcane is one of the most crucial energy crops that produces high yields of sugar and lignocellulose. The cellulose crystallinity index (CrI) and lignin are the two kinds of key cell wall features that account for lignocellulose saccharification. Therefore, high-throughput screening of sugarcane germplasm with excellent cell wall features is considered a promising strategy to enhance bagasse digestibility. Recently, there has been research to explore near-infrared spectroscopy (NIRS) assays for the characterization of the corresponding wall features. However, due to the technical barriers of the offline strategy, it is difficult to apply for high-throughput real-time analyses. This study was therefore initiated to develop a high-throughput online NIRS assay to rapidly detect cellulose crystallinity, lignin content, and their related proportions in sugarcane, aiming to provide an efficient and feasible method for sugarcane cell wall feature evaluation.

RESULTS

A total of 838 different sugarcane genotypes were collected at different growth stages during 2018 and 2019. A continuous variation distribution of the near-infrared spectrum was observed among these collections. Due to the very large diversity of CrI and lignin contents detected in the collected sugarcane samples, seven high-quality calibration models were developed through online NIRS calibration. All of the generated equations displayed coefficient of determination (R2) values greater than 0.8 and high ratio performance deviation (RPD) values of over 2.0 in calibration, internal cross-validation, and external validation. Remarkably, the equations for CrI and total lignin content exhibited RPD values as high as 2.56 and 2.55, respectively, indicating their excellent prediction capacity. An offline NIRS assay was also performed. Comparable calibration was observed between the offline and online NIRS analyses, suggesting that both strategies would be applicable to estimate cell wall characteristics. Nevertheless, as online NIRS assays offer tremendous advantages for large-scale real-time screening applications, it could be implied that they are a better option for high-throughput cell wall feature prediction.

CONCLUSIONS

This study, as an initial attempt, explored an online NIRS assay for the high-throughput assessment of key cell wall features in terms of CrI, lignin content, and their proportion in sugarcane. Consistent and precise calibration results were obtained with NIRS modeling, insinuating this strategy as a reliable approach for the large-scale screening of promising sugarcane germplasm for cell wall structure improvement and beyond.

Rapid detection of cellulose and hemicellulose contents of corn stover based on near-infrared spectroscopy combined with chemometrics

The feasibility of near-infrared spectroscopy (NIRS) combined with chemometrics for the rapid detection of the cellulose and hemicellulose contents in corn stover is discussed. Competitive adaptive reweighted sampling (CARS) and genetic simulated annealing algorithm (GSA) were combined (CARS-GSA) to select the characteristic wavelengths of cellulose and hemicellulose and to reduce the dimensionality and multicollinearity of the NIRS data. The whole spectra contained 1845 wavelength variables. After CARS-GSA optimization, the number of characteristic wavelengths of cellulose (hemicellulose) was reduced to 152 (260), accounting for 8.24% (14.09%) of all wavelengths. The coefficients of determination of the regression models for predicting the cellulose and hemicellulose contents were 0.968 and 0.996, the root mean square errors of prediction (RMSEPs) were 0.683 and 0.648, and the residual predictive deviations (RPDs) were 5.213 and 16.499, respectively. The RMSEP of the cellulose and hemicellulose regression models was 0.152 and 0.190 lower for CARS-GSA than for the full-spectrum, and the RPD was increased by 0.949 and 3.47, respectively. The results showed that the CARS-GSA model substantially reduced the number of characteristic wavelengths and significantly improved the predictive ability of the regression model.