Coupled Structural and Kinetic Model of Lignin Fast Pyrolysis

Lignocellulosic biomass is a promising feedstock for renewable fuels and chemical intermediates; in particular, lignin attracts attention for its favorable chemical composition. One obstacle to lignin utilization and valorization is the unknown chemical mechanism that gives rise to the complex product distributions observed upon deconstruction. Among possible deconstruction chemistries, fast pyrolysis is promising due to its short residence time, thus enabling high-volume production. However, the chemistry is inherently complex, thereby hampering the creation of detailed kinetic models describing pathways to specific low molecular products. To this end, we created a detailed kinetic model of lignin decomposition via pyrolysis comprised of 4313 reactions and 1615 species based on pathways suggested by pyrolysis of model compounds in the literature. Using a rule-based reaction network generation approach, a pathways-level reaction network is proposed to predict the evolution of macromolecular species and the formation of various low molecular weight products identified from experimental studies. This reaction network is coupled to a structural model of wheat straw lignin via a kinetic Monte Carlo framework to simulate lignin fast pyrolysis. The mass yields of and speciation within four commonly observed fractions, viz., light gases, an aqueous phase containing water and small oxygenates, char, and a highly complex aromatic fraction, are compared to an experimental report of a putatively similar biomass source. Additional capabilities of the model include the time-resolved prediction of volatilization profiles and the evolution of the molecular weight distribution, which may assist in efforts to valorize lignin to a higher degree than that achieved by current approaches

Polygenic, enrichment and heritability analysis of three histone modification marks across cell types.

<p>We performed three different analyses to test for cell type specific effects on the genetic risk for MI/CAD. Analyses were conducted on SNPs residing in the three histone marks (H3K27ac, H3K4me3, H3K9ac) that were present in the different cell types. (A) Polygenic risk score analysis. We performed polygenic risk score association analysis on SNPs with MIGen discovery association <i>P</i><0.05. Negative logarithm of <i>P</i> values from association testing of the polygenic risk score performed in the WTCCC CAD was shown. Cell types were sorted based on the strength of polygenic association. Orange vertical line represents a significant level with 5% alpha error. (B) Enrichment of association. Enrichment analyses were performed by comparing the proportion of significant variants passing a specific association <i>P</i> threshold of a variant set with that of a baseline set. Different association <i>P</i> thresholds 5×10⁻⁷, 5×10⁻⁶, 5×10⁻⁵ from the CARDIoGRAM study were tested [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.ref024" target="_blank">24</a>]. The variant sets in this analysis were SNPs in the specified histone marks that were present in the indicated cell type. For the baseline set, we test SNPs in regions that are outside of these histone marks within 10 kilobases (kb) of the protein coding regions of the genome. To reduce the effects of linkage disequilibrium, these baseline SNPs were selected to be 5 kb away from the histone marks. In the plot, each triangular point represents the strongest enrichment result for each mark in each cell type across the three possible association <i>P</i> thresholds. (C) Heritability analysis. Heritability analysis was performed within histone marks in the MIGen study. Each point in the plot represents the variance in liability generated from a joint model involving two variance components using the Genome-wide Complex Trait Analysis software [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.ref022" target="_blank">22</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.ref023" target="_blank">23</a>]. The two variance components include 1) SNPs in the specified histone mark that was present in the indicated cell type and 2) all other SNPs outside of these regions. The variance in liability is an estimate from the ratio of genetic variance to phenotypic variance for the specified variance component (i.e. the specified variance component is all SNPs within the specified histone mark) whereas the <i>P</i> value is from the likelihood ratio test of a reduce model with the specified genetic variance component dropped from the full model, from the restricted maximum likelihood method in the Genome-wide Complex Trait Analysis software [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.ref022" target="_blank">22</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.ref023" target="_blank">23</a>]. MI, myocardial infarction; CAD, coronary artery disease; SNP, single nucleotide polymorphism.</p

Contributions of three genomic compartments to the polygenicity of MI/CAD.

<p>Polygenic risk score analysis was performed across three different genomic compartments. The top bar plot represents the strength of association for the polygenic risk score analysis whereas the bottom bar plot represents the number of SNPs within each of the compartments. The strongest polygenic association signals were within noncoding regions adjacent to protein-coding genes (“genic noncoding”). MI, myocardial infarction; CAD, coronary artery disease; SNP, single nucleotide polymorphism. Genic coding, variants that code amino acid sequence within ±10 kilobases of the 3′ or 5′ untranslated regions of a gene. Genic noncoding, variants that do not code amino acid sequence within ±10 kilobases of the 3′ or 5′ untranslated regions of a gene. Intergenic, variants that are beyond ±10 kilobases of the 3′ or 5′ untranslated regions of a gene.</p

Heritability of MI/CAD explained by three genomic compartment sets.

<p>Heritability estimates were inferred independently first in MIGen and WTCCC CAD from a single model involving three variance components (“genic coding”, “genic noncoding” and “intergenic”) using the GCTA software [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.ref022" target="_blank">22</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.ref023" target="_blank">23</a>]. Heritability estimates shown here are from a meta-analysis of the Variance and standard error (V-SE) from these models using as weights the inverse variance from these models.</p><p>¹Variance and V-SE are estimates from the ratio of genetic variance to phenotypic variance for the specified variance component whereas the <i>P</i> value (V-P) is from the likelihood ratio test of a reduce model with the specified genetic variance component dropped from the full model, from the restricted maximum likelihood method in the GCTA software [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.ref022" target="_blank">22</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.ref023" target="_blank">23</a>].</p><p>²Enrichment of variance was calculated as the % variance of total divided by % SNPs of total. MI, myocardial infarction; CAD, coronary artery disease; SNP, single nucleotide polymorphism.</p><p>³<i>P</i> value from difference in the observed variance minus the expected variance (variance of whole genome as sum multiplied by % SNPs of total). Genic coding, variants that code amino acid sequence within ±10 kilobases of the 3′ or 5′ untranslated regions of a gene. Genic noncoding, variants that do not code amino acid sequence within ±10 kilobases of the 3′ or 5′ untranslated regions of a gene. Intergenic, variants that are beyond ±10 kilobases of the 3′ or 5′ untranslated regions of a gene.</p><p>We calculated the SNP-heritability in three genomic compartment sets for MI/CAD in a meta-analysis of the MIGen and WTCCC CAD studies using the Genome-wide Complex Trait Analysis (GCTA) software. We observed increased enrichment in variance in both “genic coding” and “genic noncoding” regions.</p

Hierarchical clustering of 45 MI/CAD GWAS SNPs and specific cell types for a histone modification mark (H3K27ac).

<p>We mapped 45 MI/CAD GWAS SNPs, as well as SNPs in high linkage disequilibrium (<i>r</i>²≥0.8), to H3K27ac in different cell types. Hierarchical clustering was based on the presence or absence of a SNP residing in H3K27ac in different cell types and was performed using the heatmap function in R (R Project for Statistical Computing). We observed unique patterns between the different GWAS loci and cell types. For example, 12 of the 45 GWAS loci were expressed in more than 80% of the cell types, whereas 13 of the 45 GWAS loci were expressed in less than 20%. Red color indicates a lead SNP or tag SNPs (linkage disequilibrium value of <i>r</i>²≥0.8) residing in H3K27ac in different cell types (See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.s009" target="_blank">S9</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005622#pgen.1005622.s010" target="_blank">S10</a> Figs for H3K9ac and H3K4me3, respectively). MI, myocardial infarction; CAD, coronary artery disease; GWAS, genome-wide association study; SNP, single nucleotide polymorphism.</p

Additional file 1: Supplemental Information. of A null mutation in ANGPTL8 does not associate with either plasma glucose or type 2 diabetes in humans

Figure S1. In-silico predictions of the impact of premature termination codon p.Q121X suggests that the variant would trigger nonsensemediated decay. Table S1. ANGPTL8 p.Q121X and type 2 diabetes status by study. (PDF 145 kb