Near-infrared spectroscopy enables the genetic analysis of chemical properties in a large set of wood samples from Populus nigra (L.) natural populations

Gebreselassie, Mesfin Nigussie; Ader, Kévin; Boizot, Nathalie; Millier, Frédéric; Charpentier, Jean-Paul; Alves, Ana; Simões, Rita; Rodrigues, José Carlos; Bodineau, Guillaume; Fabbrini, Francesco; Sabatti, Maurizio; Bastien, Catherine; Segura, Vincent

doi:10.1016/j.indcrop.2017.05.013

Cited by 29 publications

(39 citation statements)

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“…For example, partial least squares (PLS) multivariate regression analysis can be used to predict, with good accuracy, the hydroxyl group content (e.g., phenolic, aliphatic, COOH), lignin purity, syringyl/guaiacyl (S/G) ratio and radical scavenging ability of different technical lignins from their FTIR spectra . The technique has also been applied extensively for the characterisation of whole biomass, for example, to predict wood and in planta lignin composition . The objective of this present study was to further extend the scope of this approach and to extract more detailed structural information from the FTIR spectra of technical lignins.…”

Section: Introductionmentioning

confidence: 99%

“…[30,31] The technique has also been applied extensively for the characterisation of whole biomass, for example, to predict wood andi nplanta lignin composition. [18,[32][33][34][35][36][37] The objective of this present study was to further extendt he scopeo ft his approach and to extract more detaileds tructural information from the FTIR spectrao ft echnical lignins.…”

Section: Introductionmentioning

confidence: 99%

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Linkage Abundance and Molecular Weight Characteristics of Technical Lignins by Attenuated Total Reflection‐FTIR Spectroscopy Combined with Multivariate Analysis

Lancefield

Constant

Peinder³

et al. 2019

ChemSusChem

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Lignin is an attractive material for the production of renewable chemicals, materials and energy. However, utilization is hampered by its highly complex and variable chemical structure, which requires an extensive suite of analytical instruments to characterize. Here, we demonstrate that straightforward attenuated total reflection (ATR)‐FTIR analysis combined with principle component analysis (PCA) and partial least squares (PLS) modelling can provide remarkable insight into the structure of technical lignins, giving quantitative results that are comparable to standard gel‐permeation chromatography (GPC) and 2D heteronuclear single quantum coherence (HSQC) NMR methods. First, a calibration set of 54 different technical (fractionated) lignin samples, covering kraft, soda and organosolv processes, were prepared and analyzed using traditional GPC and NMR methods, as well as by readily accessible ATR‐FTIR spectroscopy. PLS models correlating the ATR‐FTIR spectra of the broad set of lignins with GPC and NMR measurements were found to have excellent coefficients of determination ( R 2 Cal.>0.85) for molecular weight ( M n , M w ) and inter‐unit abundances (β‐ O ‐4, β‐5 and β‐β), with low relative errors (6.2–14 %) as estimated from cross‐validation results. PLS analysis of a second set of 28 samples containing exclusively (fractionated) kraft lignins showed further improved prediction ability, with relative errors of 3.8–13 %, and the resulting model could predict the structural characteristics of an independent validation set of lignins with good accuracy. The results highlight the potential utility of this methodology for streamlining and expediting the often complex and time consuming technical lignin characterization process.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Linkage Abundance and Molecular Weight Characteristics of Technical Lignins by Attenuated Total Reflection‐FTIR Spectroscopy Combined with Multivariate Analysis

Lancefield

Constant

Peinder³

et al. 2019

ChemSusChem

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“…We hypothesize that this is due to the fact that RNA extraction was performed on trees in Orléans. One exception to this pattern is the Lignin content, that we suspect to be due to a methodological difference between assessments, as previously discussed 24 . On average, genes that were specific to single traits represented 62% of selected genes, genes shared across sites for a given trait were 4%, genes shared by trait category (growth, phenology, physical, chemical) were 18%, and genes shared among all traits were 16%.…”

Section: Boruta Gene Expression Selectionmentioning

confidence: 59%

“…Wood collection and phenotypic data (Table S1) have been previously described 24 . Further details are provided in the methods section.…”

Section: Wood Samples Phenotypes and Transcriptomesmentioning

confidence: 99%

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To what extent gene connectivity within co-expression network matters for phenotype prediction?

Chateigner¹,

Lesage-Descauses²,

Rogier³

et al. 2019

Preprint

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Recent literature on the differential role of genes within networks distinguishes core from peripheral genes. If previous works have shown contrasting features between them, whether such categorization matters for phenotype prediction remains to be studied. We sequenced RNA in a Populus nigra collection and built co-expression networks to define core and peripheral genes. We found that cores were more differentiated between populations than peripherals while being less variable, suggesting that they have been constrained through potentially divergent selection. We also showed that while cores were overrepresented in a subset of genes deemed important for trait prediction, they did not systematically predict better than peripherals or even random genes. Our work is the first attempt to assess the importance of co-expression network connectivity in phenotype prediction. While highly connected core genes appear to be important, they do not bear enough information to systematically predict better quantitative traits than other gene sets.Gene-to-gene interaction is a pervasive although elu-2 sive phenomenon underlying phenotype expression. 3Genes operate within networks with more or less me-4 diated actions on the phenome. Systems biology ap-5 proaches are required to grasp the functional topol-6 ogy of these networks and ultimately gain insights 7 into how gene interactions interplay at different bio-8 logical levels to produce global phenotypes (Mackay 9 et al., 2009). New sources of information and their 10 subsequent use in the inference of gene networks are 11 populating the wide gap existing between phenotypes 12 and DNA sequences and, therefore, opening the door 13 to systems biology approaches for the development 14 of context-dependent phenotypic predictions. RNA 15 98 Montanucci et al., 2011). On the other hand, genes 99 being better predictors with an interactive model are 100supposed to be upstream in pathways. We expect pe-101 ripheral genes to behave interactively, with a lower 102 but relatively more variable expression level. With a 103 lower variation, we also expect core genes to be worse 104 predictors for traits than peripheral genes unless the 105 former also bear larger effects. 106 To answer the questions concerning the respective 107 roles of core and peripheral genes on phenotypic vari-108 ation, we have sequenced the RNA of 459 samples of 109 black poplar (Populus nigra), corresponding to 241 110 genotypes, from 11 populations representing the nat-111 ural distribution of the species across Western Eu-112 rope. We also have for each of these trees phenotypic 113 records for 17 traits, covering the growth, phenol-114 ogy, physical and chemical properties of wood. They 115 cover two different environments where the trees were 116 grown in common gardens, in central France and 117 northern Italy. With the transcriptomic data, we 118 built a co-expression network in order to define con-119 trasting gene sets according to their connectivity 120 within the network. We then asked whether these 121 c...

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Monitoring Molecular Weight Changes during Technical Lignin Depolymerization by Operando Attenuated Total Reflectance Infrared Spectroscopy and Chemometrics

Khalili

Peinder²,

Donkers

et al. 2021

ChemSusChem

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Technical lignins are increasingly available at industrial scale, offering opportunities for valorization, such as by (partial) depolymerization. Any downstream lignin application requires careful tailoring of structural properties, such as molecular weight or functional group density, properties that are difficult to control or predict given the structure variability and recalcitrance of technical lignins. Online insight into changes in molecular weight (Mw), to gauge the extent of lignin depolymerization and repolymerization, would be highly desired to improve such control, but cannot be readily provided by the standard ex‐situ techniques, such as size exclusion chromatography (SEC). Herein, operando attenuated total reflectance infrared (ATR‐IR) spectroscopy combined with chemometrics provided temporal changes in Mw during lignin depolymerization with high resolution. More specifically, ex‐situ SEC‐derived Mw and polydispersity data of kraft lignin subjected to aqueous phase reforming conditions could be well correlated with ATR‐IR spectra of the reaction mixture as a function of time. The developed method showed excellent regression results and relative error, comparable to the standard SEC method. The method developed has the potential to be translated to other lignin depolymerization processes.

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Near-infrared spectroscopy enables the genetic analysis of chemical properties in a large set of wood samples from Populus nigra (L.) natural populations

Cited by 29 publications

References 50 publications

Linkage Abundance and Molecular Weight Characteristics of Technical Lignins by Attenuated Total Reflection‐FTIR Spectroscopy Combined with Multivariate Analysis

Linkage Abundance and Molecular Weight Characteristics of Technical Lignins by Attenuated Total Reflection‐FTIR Spectroscopy Combined with Multivariate Analysis

To what extent gene connectivity within co-expression network matters for phenotype prediction?

Monitoring Molecular Weight Changes during Technical Lignin Depolymerization by Operando Attenuated Total Reflectance Infrared Spectroscopy and Chemometrics

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