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Genetic Determinants for Enzymatic Digestion of Lignocellulosic Biomass Are Independent of Those for Lignin Abundance in a Maize Recombinant Inbred Population - PubMed

Genetic Determinants for Enzymatic Digestion of Lignocellulosic Biomass Are Independent of Those for Lignin Abundance in a Maize Recombinant Inbred Population

Bryan W Penning et al. Plant Physiol. 2014 Aug.

Abstract

Biotechnological approaches to reduce or modify lignin in biomass crops are predicated on the assumption that it is the principal determinant of the recalcitrance of biomass to enzymatic digestion for biofuels production. We defined quantitative trait loci (QTL) in the Intermated B73 × Mo17 recombinant inbred maize (Zea mays) population using pyrolysis molecular-beam mass spectrometry to establish stem lignin content and an enzymatic hydrolysis assay to measure glucose and xylose yield. Among five multiyear QTL for lignin abundance, two for 4-vinylphenol abundance, and four for glucose and/or xylose yield, not a single QTL for aromatic abundance and sugar yield was shared. A genome-wide association study for lignin abundance and sugar yield of the 282-member maize association panel provided candidate genes in the 11 QTL of the B73 and Mo17 parents but showed that many other alleles impacting these traits exist among this broader pool of maize genetic diversity. B73 and Mo17 genotypes exhibited large differences in gene expression in developing stem tissues independent of allelic variation. Combining these complementary genetic approaches provides a narrowed list of candidate genes. A cluster of SCARECROW-LIKE9 and SCARECROW-LIKE14 transcription factor genes provides exceptionally strong candidate genes emerging from the genome-wide association study. In addition to these and genes associated with cell wall metabolism, candidates include several other transcription factors associated with vascularization and fiber formation and components of cellular signaling pathways. These results provide new insights and strategies beyond the modification of lignin to enhance yields of biofuels from genetically modified biomass.

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Figures

Figure 1.
Figure 1.

Example of PyMBMS spectra for two IBM RILs and their digital subtraction spectrum. A, PyMBMS spectra from the high-lignin IBM063 (top) and the low-lignin IBM205 (bottom). B, Digital subtraction of IBM205 from IBM063. Assigned fragments are as follows: Xyl, m/z 114; 4-vinylphenol, m/z 120; hexose, m/z 126; G lignin, m/z 124, 137, 138, and 151; S lignin, m/z 154, 167, 168, and 194 (Penning et al., 2014). IBM063 has many lignin-related fragment ions in higher abundance than does IBM205.

Figure 2.
Figure 2.

Frequency distributions for the relative abundance of G and S lignin and the relative yield of Glc and Xyl from saccharification. A, Abundance of S lignin (sum of m/z 154, 167, 168, and 194). B, Glc yield from saccharification. C, Abundance of G lignin (sum of m/z 124, 137, 138, and 151). D, Xyl yield from saccharification.

Figure 3.
Figure 3.

Comparison of lignin and cell wall polysaccharide abundance with saccharification yield in the IBM population. A and B, S (A) and G (B) lignins calculated from PyMBMS m/z ion abundances are compared with relative Glc and Xyl release in standardized saccharification yield assays conducted in 2 years. S lignin is the sum of the relative abundance of mass fragments m/z 154, 167, 168, and 194; G lignin was calculated from PyMBMS ion abundances m/z 124, 137, 138, and 151. C and D, Cellulose content (mg mg−1 cell wall; C) and cell wall acid-soluble Glc and Xyl (mg mg−1 cell wall; D) are compared with Glc and Xyl release in standardized saccharification yield assays. Year, sugar, and Pearson’s correlation coefficients are shown. All values are mean-normalized relative abundances.

Figure 4.
Figure 4.

Lignin abundance, 4-vinylphenol abundance, and saccharification yield QTL. QTL for 4-vinylphenol, S and G lignin, and saccharification yield are shown on the 10 maize chromosomes. The 95% experiment-wise confidence cutoff is shown as a thin gray bar. QTL with multiple year overlap for each trait are numbered with color indicating type (see key). QTL1, QTL2, and QTL3 are Glc release, and QTL7 is Xyl release. QTL4 and QTL6′ are 4-vinylphenol abundance. QTL5 is S lignin, QTL6 and QTL10 are S-G lignin ratio, QTL8 is G lignin, and QTL9 is both S and G lignin. Bar = 100 cM.

Figure 5.
Figure 5.

Marker and gene positions as identified by GWAS and differential expression, respectively, in QTL3 and QTL8. Physical map locations in Mb (A and B) were identified using flanking markers, and a threshold value of 0.001 is indicated by red lines. Differential expression fold change (C and D) is shown in logarithmic scale, with higher B73 fold change upward and higher Mo17 fold change downward. GWAS predictions for QTL3 for Glc yield (A) are paired with expression fold differences between B73 and Mo17 parents across the QTL interval (C). GWAS predictions for QTL8 for G and S lignin (B) are paired with expression fold differences between B73 and Mo17 parents across the QTL interval (D). Markers flanking the QTL in cM were used to match the closest markers with physical map positions (

www.maizegdb.org

). Expression analysis (RNA sequencing) was performed on complementary DNA populations derived from developing internodes 4 and 5 of greenhouse-grown B73 and Mo17 parents 63 d after planting during peak secondary wall formation. Color codes for symbols in A and C, all Glc yield related, are as follows: orange, gene of unknown function; green, SCARECROW9-LIKE transcription factor; red, BR-SIGNALING KINASE1; and blue, β-XYLOSIDASE. Dark red symbols (S lignin) and light red symbols (G lignin) both indicate the same candidate gene of unknown function in B and D.

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