Xiaojing Yang’s research while affiliated with Yangzhou University and other places

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Publications (3)


Comparative Genomic and Transcriptomic Analysis of Phenol Degradation and Tolerance in Acinetobacter lwoffii through Adaptive Evolution
  • Article
  • Full-text available

November 2023

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37 Reads

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3 Citations

Nan Xu

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Xiaojing Yang

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Qiyuan Yang

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Minliang Guo

Microorganism-based methods have been widely applied for the treatment of phenol-polluted environments. The previously isolated Acinetobacter lwoffii NL1 strain could completely degrade 0.5 g/L phenol within 12 h, but not higher concentrations of phenol. In this study, we developed an evolutionary strain NL115, through adaptive laboratory evolution, which possessed improved degradation ability and was able to degrade 1.5 g/L phenol within 12 h. Compared with that of the starting strain NL1, the concentration of degradable phenol by the developed strain increased three-fold; its phenol tolerance was also enhanced. Furthermore, comparative genomics showed that sense mutations mainly occurred in genes encoding alkyl hydroperoxide reductase, phenol hydroxylase, 30S ribosomal protein, and mercury resistance operon. Comparative transcriptomics between A. lwoffii NL115 and NL1 revealed the enrichment of direct degradation, stress resistance, and vital activity processes among the metabolic responses of A. lwoffii adapted to phenol stress. Among these, all the upregulated genes (log2fold-change > 5) encoded peroxidases. A phenotypic comparison of A. lwoffii NL1 and NL115 found that the adapted strain NL115 exhibited strengthened antioxidant capacity. Furthermore, the increased enzymatic activities of phenol hydroxylase and alkyl hydroperoxide reductase in A. lwoffii NL115 validated their response to phenol. Overall, this study provides insight into the mechanism of efficient phenol degradation through adaptive microbial evolution and can help to drive improvements in phenol bioremediation.

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Genes, reactions, and gene‐associated reactions for each metabolic subsystem of the model iNX1344. Total genes and total reactions denote the number of genes or reactions in each metabolic subsystem. Gene‐associated reactions denote the number of reactions by annotating their encoding genes. The metabolic subsystems were based on KEGG pathway maps
Distribution of essential genes of Agrobacterium tumefaciens in nine metabolic subsystems. The pie chart represents the metabolic subsystem distribution of 287 metabolic reactions associated with 195 essential genes for growth on synthetic minimal medium. The table shows the gene names and functions of six invalidated essential genes on synthetic minimal medium
Comparison of central metabolic flux distributions between the model predictions and the published data. Values in black are the flux from the literature; values in red are the flux from the model iNX1344. “−” represents no associated value in the literature. Estimated fluxes in the iNX1344 and the literature are expressed in mmol·g ⁻¹ dry weight·hr⁻¹
Correlations of the reaction flux and fitness effect in the model iNX1344 between bulk soil, the rhizosphere, and the nodule. Four scatter plots showing the flux correlations or the fitness correlations under the nodule/rhizobium and rhizobium/soil conditions are shown. Among these, flux correlations of the reactions were converted to a base‐10 logarithm of the absolute value, excluding the zero‐flux reactions. Blue dotted lines are the trend lines
Significantly changed metabolites and pathways of Agrobacterium tumefaciens in plant tumours. Red circles and green circles represent the up‐regulated and down‐regulated reporter metabolites. Red backgrounds and green backgrounds represent the up‐regulated and down‐regulated reporter pathways. Metabolic pathways are shown in dotted rounded rectangles. A small black arrow represents one reaction, and a large grey arrow represents more than three steps connecting two metabolites or between one metabolite and one pathway. GLC, l‐glucose; PYR, pyruvate, AKG, α‐ketoglutaric acid; ORN, l‐ornithine; ARG, l‐arginine; SAM, S‐adenosyl‐l‐methionine; VAL, valine; LEU, leucine; ILE, isoleucine; [e], extracellular compartment
Reconstruction and analysis of a genome‐scale metabolic model for Agrobacterium tumefaciens

January 2021

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90 Reads

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5 Citations

Nan Xu

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Qiyuan Yang

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Xiaojing Yang

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[...]

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Minliang Guo

The plant pathogen Agrobacterium tumefaciens causes crown gall disease and is a widely used tool for generating transgenic plants owing to its virulence. The pathogenic process involves a shift from an independent to a living form within a host plant. However, comprehensive analyses of metabolites, genes, and reactions contributing to this complex process are lacking. To gain new insights about the pathogenicity from the viewpoints of physiology and cellular metabolism, a genome-scale metabolic model (GSMM) was reconstructed for A. tumefaciens. The model, referred to as iNX1344, contained 1,344 genes, 1,441 reactions, and 1,106 metabolites. It was validated by analyses of in silico cell growth on 39 unique carbon or nitrogen sources and the flux distribution of carbon metabolism. A. tumefaciens metabolic characteristics under three ecological niches were modelled. A high capacity to access and metabolize nutrients is more important for rhizosphere colonization than in the soil, and substantial metabolic changes were detected during the shift from the rhizosphere to tumour environments. Furthermore, by integrating transcriptome data for tumour conditions, significant alterations in central metabolic pathways and secondary metabolite metabolism were identified. Overall, the GSMM and constraint-based analysis could decode the physiological and metabolic features of A. tumefaciens as well as interspecific interactions with hosts, thereby improving our understanding of host adaptation and infection mechanisms.


Fig. 1. Chemotactic pathway in A. tumefaciens C58.
Fig. 2. Swim plate colonies of A. tumefaciens C58 strains with mutations of the ternary signalling complex.
Fig. 3. Organization of the chemotaxis operons in A. tumefaciens strains and a phylogenetic tree reconstructed based on published 16S rRNA sequences of A. tumefaciens.
Fig. 4. Proportions of the six types of LBD in A. tumefaciens.
In silico analysis of the chemotactic system of Agrobacterium tumefaciens

October 2020

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102 Reads

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7 Citations

Microbial Genomics

Agrobacterium tumefaciens is an efficient tool for creating transgenic host plants. The first step in the genetic transformation process involves A. tumefaciens chemotaxis, which is crucial to the survival of A. tumefaciens in changeable, harsh and even contaminated soil environments. However, a systematic study of its chemotactic signalling pathway is still lacking. In this study, the distribution and classification of chemotactic genes in the model A. tumefaciens C58 and 21 other strains were annotated. Local blast was used for comparative genomics, and hmmer was used for predicting protein domains. Chemotactic phenotypes for knockout mutants of ternary signalling complexes in A. tumefaciens C58 were evaluated using a swim agar plate. A major cluster, in which chemotaxis genes were consistently organized as MCP (methyl-accepting chemotaxis protein), CheS, CheY1, CheA, CheR, CheB, CheY2 and CheD, was found in A. tumefaciens, but two coupling CheW proteins were located outside the 'che' cluster. In the ternary signalling complexes, the absence of MCP atu0514 significantly impaired A. tumefaciens chemotaxis, and the absence of CheA (atu0517) or the deletion of both CheWs abolished chemotaxis. A total of 465 MCPs were found in the 22 strains, and the cytoplasmic domains of these MCPs were composed of 38 heptad repeats. A high homology was observed between the chemotactic systems of the 22 A. tumefaciens strains with individual differences in the gene and receptor protein distributions, possibly related to their ecological niches. This preliminary study demonstrates the chemotactic system of A. tumefaciens, and provides some reference for A. tumefaciens sensing and chemotaxis to exogenous signals.

Citations (3)


... For example, A. lwoffii DNS32 can degrade atrazine (100 mg/L) [10], and its degradation capacity has been optimized in a microbial consortium [11]. Adaptive laboratory evolution 2 of 19 produced A. lwoffii NL115, capable of degrading 1500 mg/L phenol [12]. A. lwoffii MG04 can degrade pyrethroids [13]. ...

Reference:

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Acinetobacter lwoffii
Comparative Genomic and Transcriptomic Analysis of Phenol Degradation and Tolerance in Acinetobacter lwoffii through Adaptive Evolution

... For example, 'omics' combined with robust physiological/morphological/symptom training data sets can be used for predicting different aspects of plant-pathogen interactions including gene regulatory networks, pathogen effector proteins, pathogen adaptive strategies and genes involved in plantpathogen interactions under different climate change scenarios. Genome-scale network reconstructions can model intracellular metabolism to predict virulence and pathogen-host interactions under a range of environmental and physiological conditions [162][163][164] . Such models are now being used to provide detailed insights into the interactions between the invading pathogen and the host-associated microbiome to predict disease incidence and interventions 165 . ...

Reconstruction and analysis of a genome‐scale metabolic model for Agrobacterium tumefaciens

... The abundance of receptors is a characteristic shared with other proteobacteria living in a complex environment such as soil [37]. For example, Agrobacterium tumefaciens C58 was found to present 22 MCP receptors [38]. ...

In silico analysis of the chemotactic system of Agrobacterium tumefaciens

Microbial Genomics