Engineering topics
Publications and source records attributed to Wang, Peipei.
The results establish the adsorption capacity, kinetics, interaction strength of pyridine/indole with sorbents and regeneration, highlighting silica gel's potential as an efficient and cost-effective adsorbent for biofuel nitrogen compounds removal.
The formation of complex traits is the consequence of genotype and activities at multiple molecular levels. However, connecting genotypes and these activities to complex traits remains challenging. Here, we investigate whether integrating genomic, transcriptomic, and methylomic data can improve prediction for six Arabidopsis traits. We find that transcriptome- and methylome-based models have performances comparable to those of genome-based models. However, models built for flowering time using different omics data identify different benchmark genes. Nine additional genes identified as important for flowering time from our models are experimentally validated as regulating flowering. Gene contributions to flowering time prediction are accession-dependent and distinct genes contribute to trait prediction in different genotypes. Models integrating multi-omics data perform best and reveal known and additional gene interactions, extending knowledge about existing regulatory networks underlying flowering time determination. These results demonstrate the feasibility of revealing molecular mechanisms underlying complex traits through multi-omics data integration.
This report describes our effort in developing a novel zeolite encapsulated metal nanoparticle catalyst for the conversion of syngas to light olefins. We have adopted a ligand assisted synthesis protocol in which first metal-amine was prepared using metal salts and suitable amine ligands. The stable metal-amine complex thus prepared was then used as the seed for the growth of zeolite in alkaline solution. We have optimized the synthesis condition as well as different amine ligand to identify the condition to obtain stable metal-amine complex. Upon synthesis of zeolite, metal particles are encapsulated inside the zeolite in a confined space. We have synthesized both Fe and Co catalyst inside silicalite and chabazite zeolite. Catalytic activity of these catalysts were evaluated for syngas conversion reaction and compared with a baseline catalyst. The activity trend of zeolite encapsulated catalyst show very different activity trend compared to the baseline catalyst. While a distribution of products with different carbon number was obtained with baseline catalyst, zeolite encapsulated catalyst show ethylene as the major product. While ~73% selectivity to C 2 -C 6 products were obtained with cahabazite encapsulated Fe catalyst, only 53% selectivity was obtained for the corresponding products with silica-alumina supported Fe catalyst. Not only higher selectivity, Fe-chabazite also produces only C 2 products (ethylene/ethane) while a distribution of products was obtained with Fe-SiAl catalyst. The significantly different reactivity associated with the chabazite encapsulated Fe catalyst was attributed to the confinement effect. We hypothesized that due to the small size of the pore, only ethylene was obtained as the product.
The unique band structure in topological materials frequently results in unusual magneto-transport phenomena, one of which is in-plane longitudinal negative magnetoresistance (NMR) with the magnetic field aligned parallel to the electrical current direction. This NMR is widely considered as a hallmark of chiral anomaly in topological materials. Here we report the observation of in-plane NMR in the topological material ZrTe 5 when the in-plane magnetic field is both parallel and perpendicular to the current direction, revealing an unusual case of quantum transport beyond the chiral anomaly. We find that a general theoretical model, which considers the combined effect of Berry curvature and orbital moment, can quantitatively explain this in-plane NMR. In conclusion, our results provide new insights into the understanding of in-plane NMR in topological materials.
Hydrothermal liquefaction of wet wastes (e.g., food waste and sewage sludge) offers a promising avenue for producing biocrude, but the resulting product is often contaminated with metals and water, impacting the biocrude upgrading steps to produce final fuel (e.g., jet and diesel). Here this study investigates the use of aqueous solutions of formic acid for desalting biocrudes and improving their quality. The results show that this approach significantly reduces both metal and water content, enabling mild hydrotreatment with reduced hydrogen consumption to further enhance the biocrude's quality. A preliminary technoeconomic analysis based on these experimental results shows that even without optimization, this washing process can yield biocrude with a minimum selling price of $1.87/GGE. Overall, our study demonstrates the potential for using dilute formic acid aqueous solutions to improve the quality of biocrudes, which can lead to more efficient and cost-effective conversion into biofuels.
Switchgrass low-land ecotypes have significantly higher biomass but lower cold tolerance compared to up-land ecotypes. Understanding the molecular mechanisms underlying cold response, including the ones at transcriptional level, can contribute to improving tolerance of high-yield switchgrass under chilling and freezing environmental conditions. Here, by analyzing an existing switchgrass transcriptome dataset, the temporal cis- regulatory basis of switchgrass transcriptional response to cold is dissected computationally. We found that the number of cold-responsive genes and enriched Gene Ontology terms increased as duration of cold treatment increased from 30 min to 24 hours, suggesting an amplified response/cascading effect in cold-responsive gene expression. To identify genomic sequences likely important for regulating cold response, machine learning models predictive of cold response were established using k -mer sequences enriched in the genic and flanking regions of cold-responsive genes but not non-responsive genes. These k -mers, referred to as putative cis -regulatory elements (pCREs) are likely regulatory sequences of cold response in switchgrass. There are in total 655 pCREs where 54 are important in all cold treatment time points. Consistent with this, eight of 35 known cold-responsive CREs were similar to top-ranked pCREs in the models and only these eight were important for predicting temporal cold response. More importantly, most of the top-ranked pCREs were novel sequences in cold regulation. Our findings suggest additional sequence elements important for cold-responsive regulation previously not known that warrant further studies.
Abstract Developing tunable and stable peroxidase mimetics with high catalytic efficiency provides a promising opportunity to improve and expand enzymatic catalysis in lignin depolymerization. A class of peptoid-based peroxidase mimetics with tunable catalytic activity and high stability is developed by constructing peptoids and hemins into self-assembled crystalline nanomaterials. By varying peptoid side chain chemistry to tailor the microenvironment of active sites, these self-assembled peptoid/hemin nanomaterials (Pep/hemin) exhibit highly modulable catalytic activities toward two lignin model substrates 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) and 3,3’,5,5’-tetramethylbenzidine. Among them, a Pep/hemin complex containing the pyridyl side chain showed the best catalytic efficiency ( V max / K m = 5.81 × 10 −3 s −1 ). These Pep/hemin catalysts are highly stable; kinetics studies suggest that they follow a peroxidase-like mechanism. Moreover, they exhibit a high efficacy on depolymerization of a biorefinery lignin. Because Pep/hemin catalysts are highly robust and tunable, we expect that they offer tremendous opportunities for lignin valorization to high value products.
Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r 2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r 2 = 0.9996) and highly accurate fruit counts (r 2 = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.
We report hydrodynamic fluidity in condensed matter physics has been experimentally demonstrated only in a limited number of compounds due to the stringent conditions that must be met. Herein, we performed thermal and electrical transport experiments in three-dimensional topological semimetal ZrTe 5 . By measuring the thermal properties in a wide temperature range, two representative experimental evidences of the hydrodynamics are observed in temperature window between the ballistic and diffusive regimes: a faster evolution of the thermal conductivity than in the ballistic regime and the non-monotonic temperature-dependent effective quasiparticle mean-free-path. In addition, magneto-thermal conductivity results indicate that charged quasiparticles, as well as phonons, may also play an important role in this hydrodynamic-like flow in ZrTe 5 .
Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. In this work, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.
Plants respond to wounding stress by changing gene expression patterns and inducing the production of hormones including jasmonic acid. This wounding transcriptional response activates specialized metabolism pathways such as the glucosinolate pathways in Arabidopsis thaliana. While the regulatory factors and sequences controlling a subset of wound-response genes are known, it remains unclear how wound response is regulated globally. Here, we how these responses are regulated by incorporating putative cis-regulatory elements, known transcription factor binding sites, in vitro DNA affinity purification sequencing, and DNase I hypersensitive sites to predict genes with different wound-response patterns using machine learning. We observed that regulatory sites and regions of open chromatin differed between genes upregulated at early and late wounding time-points as well as between genes induced by jasmonic acid and those not induced. Expanding on what we currently know, we identified cis-elements that improved model predictions of expression clusters over known binding sites. Using a combination of genome editing, in vitro DNA-binding assays, and transient expression assays using native and mutated cis-regulatory elements, we experimentally validated four of the predicted elements, three of which were not previously known to function in wound-response regulation. Our study provides a global model predictive of wound response and identifies new regulatory sequences important for wounding without requiring prior knowledge of the transcriptional regulators.
ZrTe 5 is a layered material that exhibits intricate topological effects. Intensive theoretically and experimental efforts have been devoted to try to understand the physics in this materials. In this paper the temperature dependent magneto-transport properties of ZrTe 5 thin flakes are investigated. A characteristic temperature T* is observed in the temperature dependence of three different types of magnetoresistance simultaneously, which are the saturated Hall anomaly, the chiral anomaly and the longitudinal magnetoresistance. Furthermore, the value of T* decreases monotonically from 200K to 160K with increasing thickness of the ZrTe 5 thin flakes from 42nm to 89nm. Temperature induced topological phase transitions are attributed to the cause of such anomaly in the three types of magnetoresistance at T*. Overall, our findings provide a multi-parameter indicator for the emergence of topological phase transition in ZrTe 5 and could be extended to the study of other topological materials. The temperature dependence of the three types of magnetoresistance also shed light on the role of anomalous Hall Effect in the transport properties of ZrTe 5 .
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Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.
Abstract Background Availability of plant genome sequences has led to significant advances. However, with few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues that could significantly impact any downstream analysis of plant genomes. In tomato for example, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. Results To understand what the causes may be for such uneven coverage, we first established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have higher simple sequence repeat and tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available tomato long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially misassembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a predictive model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Conclusions Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to plant genome misassembly when using short reads and the generality of these causes and factors should be tested further in other species.
Plants produce phylogenetically and spatially restricted, as well as structurally diverse specialized metabolites via multistep metabolic pathways. Hallmarks of specialized metabolic evolution include enzymatic promiscuity and recruitment of primary metabolic enzymes and examples of genomic clustering of pathway genes. Solanaceae glandular trichomes produce defensive acylsugars, with sidechains that vary in length across the family. We describe a tomato gene cluster on chromosome 7 involved in medium chain acylsugar accumulation due to trichome specific acyl-CoA synthetase and enoyl-CoA hydratase genes. This cluster co-localizes with a tomato steroidal alkaloid gene cluster and is syntenic to a chromosome 12 region containing another acylsugar pathway gene. We reconstructed the evolutionary events leading to this gene cluster and found that its phylogenetic distribution correlates with medium chain acylsugar accumulation across the Solanaceae. This work reveals insights into the dynamics behind gene cluster evolution and cell-type specific metabolite diversity.
Plants balance their competing requirements for growth and stress tolerance via a sophisticated regulatory circuitry that controls responses to the external environments. We have identified a plant-specific gene, COST1 (constitutively stressed 1), that is required for normal plant growth but negatively regulates drought resistance by influencing the autophagy pathway. An Arabidopsis thaliana cost1 mutant has decreased growth and increased drought tolerance, together with constitutive autophagy and increased expression of drought-response genes, while overexpression of COST1 confers drought hypersensitivity and reduced autophagy. The COST1 protein is degraded upon plant dehydration, and this degradation is reduced upon treatment with inhibitors of the 26S proteasome or autophagy pathways. The drought resistance of a cost1 mutant is dependent on an active autophagy pathway, but independent of other known drought signaling pathways, indicating that COST1 acts through regulation of autophagy. In addition, COST1 colocalizes to autophagosomes with the autophagosome marker ATG8e and the autophagy adaptor NBR1, and affects the level of ATG8e protein through physical interaction with ATG8e, indicating a pivotal role in direct regulation of autophagy. We propose a model in which COST1 represses autophagy under optimal conditions, thus allowing plant growth. Under drought, COST1 is degraded, enabling activation of autophagy and suppression of growth to enhance drought tolerance. Our research places COST1 as an important regulator controlling the balance between growth and stress responses via the direct regulation of autophagy.