An official website of the United States government
Abstract Lignin is a universal waste product of the agricultural industry and is currently seen as a potential feedstock for more sustainable manufacturing. While it is the second most abundant biopolymer in the world, most of it is currently burned as it is a very recalcitrant material. Many recent studies, however, have demonstrated the viability of biocatalysis to improve the value of this feedstock and convert it into more useful chemicals, such as polyhydroxybutyrate, and clean fuels like hydrogen and n-butanol.Rhodopseudomonas palustrisis a gram-negative bacterium which demonstrates a plethora of desirable metabolic capabilities, including aromatic catabolism useful for lignin degradation. This study uses a multi-omics approach, including the first usage of CRISPRi inR. palustris, to investigate the lignin consumption mechanisms ofR. palustris, the essentiality of redox homeostasis to lignin consumption, elucidate a potential lignin catabolic superpathway, and enable more economically viable sustainable lignin valorization processes.
more »
« less
Complete Genome Sequence of Serratia quinivorans Strain 124R, a Facultative Anaerobe Isolated on Organosolv Lignin as a Sole Carbon Source
ABSTRACT The complete genome sequence of the gammaproteobacterial isolate Serratia quinivorans 124R consists of 5 Mb over 2 scaffolds and a G+C content of 52.85%. Genes relating to aromatic metabolism reflect its isolation on organosolv lignin as a sole carbon source under anoxic conditions as well as the potential for lignin biorefinery applications.
more »
« less
- Award ID(s):
- 1832210
- PAR ID:
- 10128076
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Microbiology Resource Announcements
- Volume:
- 8
- Issue:
- 18
- ISSN:
- 2576-098X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Lignin-derivable bisphenols are potential alternatives to bisphenol A (BPA), a suspected endocrine disruptor; however, a greater understanding of structure–activity relationships (SARs) associated with such lignin-derivable building blocks is necessary to move replacement efforts forward. This study focuses on the prediction of bisphenol estrogenic activity (EA) to inform the design of potentially safer BPA alternatives. To achieve this goal, the binding affinities to estrogen receptor alpha (ERα) of lignin-derivable bisphenols were calculated via molecular docking simulations and correlated to median effective concentration (EC 50 ) values using an empirical correlation curve created from known EC 50 values and binding affinities of commercial (bis)phenols. Based on the correlation curve, lignin-derivable bisphenols with binding affinities weaker than ∼−6.0 kcal mol −1 were expected to exhibit no EA, and further analysis suggested that having two methoxy groups on an aromatic ring of the bio-derivable bisphenol was largely responsible for the reduction in binding to ERα. Such dimethoxy aromatics are readily sourced from the depolymerization of hardwood biomass. Additionally, bulkier substituents on the bridging carbon of lignin-bisphenols, like diethyl or dimethoxy, were shown to weaken binding to ERα. And, as the bio-derivable aromatics maintain major structural similarities to BPA, the resultant polymeric materials should possess comparable/equivalent thermal ( e.g. , glass transition temperatures, thermal decomposition temperatures) and mechanical ( e.g. , tensile strength, modulus) properties to those of polymers derived from BPA. Hence, the SARs established in this work can facilitate the development of sustainable polymers that maintain the performance of existing BPA-based materials while simultaneously reducing estrogenic potential.more » « less
-
Abstract Integrating multidisciplinary research in plant genetic engineering and renewable deep eutectic solvents (DESs) can facilitate a sustainable and economic biorefinery. Herein, we leveraged a plant genetic engineering approach to specifically incorporate C6C1monomers into the lignin structure. By expressing the bacterialubiCgene in sorghum,p‐hydroxybenzoic acid (PB)‐rich lignin was incorporated into the plant cell wall while this monomer was completely absent in the lignin of the wild‐type (WT) biomass. A DES was synthesized with choline chloride (ChCl) and PB and applied to the pretreatment of the PB‐rich mutant biomass for a sustainable biorefinery. The release of fermentable sugars was significantly enhanced (∼190 % increase) compared to untreated biomass by the DES pretreatment. In particular, the glucose released from the pretreated mutant biomass was up to 12 % higher than that from the pretreated WT biomass. Lignin was effectively removed from the biomass with the preservation of more than half of the β‐Ο‐4 linkages without condensed aromatic structures. Hydrogenolysis of the fractionated lignin was conducted to demonstrate the potential of phenolic compound production. In addition, a simple hydrothermal treatment could selectively extract PB from the same engineered lignin, showing a possible circular biorefinery. These results suggest that the combination of PB‐based DES and engineered PB‐rich biomass is a promising strategy to achieve a sustainable closed‐loop biorefinery.more » « less
-
Abstract Rhodopseudomonas palustris, a versatile bacterium with diverse biotechnological applications, can effectively breakdown lignin, a complex and abundant polymer in plant biomass. This study investigates the metabolic response ofR. palustriswhen catabolizing various lignin breakdown products (LBPs), including the monolignolsp-coumaryl alcohol, coniferyl alcohol, sinapyl alcohol,p-coumarate, sodium ferulate, and kraft lignin. Transcriptomics and proteomics data were generated for those specific LBP breakdown conditions and used as features to train machine learning models, with growth rates as the target. Three models—Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machine (SV)—were compared, with ANN achieving the highest predictive accuracy for both transcriptomics (94%) and proteomics (96%) datasets. Permutation feature importance analysis of the ANN models identified the top twenty genes and proteins influencing growth rates. Combining results from both transcriptomics and proteomics, eight key transport proteins were found to significantly influence the growth ofR. palustrison LBPs. Re-training the ANN using only these eight transport proteins achieved predictive accuracies of 86% and 76% for proteomics and transcriptomics, respectively. This work highlights the potential of ANN-based models to predict growth-associated genes and proteins, shedding light on the metabolic behavior ofR. palustrisin lignin degradation under aerobic and anaerobic conditions. ImportanceThis study is significant as it addresses the biotechnological potential ofRhodopseudomonas palustrisin lignin degradation, a key challenge in converting plant biomass into commercially important products. By training machine learning models with transcriptomics and proteomics data, particularly Artificial Neural Networks (ANN), the work achieves high predictive accuracy for growth rates on various lignin breakdown products (LBPs). Identifying top genes and proteins influencing growth, especially eight key transport proteins, offers insights into the metabolic niche ofR. palustris. The ability to predict growth rates using just these few proteins highlights the efficiency of ANN models in distilling complex biological systems into manageable predictive frameworks. This approach not only enhances our understanding of lignin derivative catabolism but also paves the way for optimizingR. palustrisfor sustainable bioprocessing applications, such as bioplastic production, under varying environmental conditions.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1128/MRA.00409-19
