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16. Additional file 3 of Mutational Bias and Natural Selection Driving the Synonymous Codon Usage of Single-Exon Genes in Rice (Oryza sativa L.)

Author

Hu, Huan, Dong, Boran, Fan, Xiaoji, Wang, Meixia, Wang, Tingzhang, and Liu, Qingpo

Abstract

Additional file 3. Figure 1. Internal correspondence analysis of single-exon genes with different gene expression level in rice. The total codon usage variability is decomposed into the synonymous codon usage variability (a, d, g), amino acid usage variability (b, e, h), and variability of within (a, b, c) and between different gene expression groups (d, e, f). The performance of ICA yields nine elementary analyses (a-i). In each peculiar analysis, the contribution to the total codon usage variability is indicated, where only the first 10 eigenvalues are represented for comparison. Figure 2. Internal correspondence analysis of single-exon genes with different gene function in rice. The total codon usage variability is decomposed into the synonymous codon usage variability (a, d, g), amino acid usage variability (b, e, h), and variability of within (a, b, c) and between different gene function groups (d, e, f). The performance of ICA yields nine elementary analyses (a-i). In each peculiar analysis, the contribution to the total codon usage variability is indicated, where only the first 10 eigenvalues are represented for comparison. Figure 3. Internal correspondence analysis of single-exon genes and multiple-exon genes in rice. In this analysis, the 581 paralogous gene pairs of single-exon genes and multiple-exon genes are excluded from the dataset. The total codon usage variability is decomposed into the synonymous codon usage (within-AA) variability (a, d, g), amino acid usage (between-AA) variability (b, e, h), and variability of within (a, b, c) and between gene types (d, e, f). In each peculiar analysis, the contribution to the total codon usage variation is indicated, where only the first 10 eigenvalues are represented for comparison.

Published
2023
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20. HPV‐related cervical diseases: Alteration of vaginal microbiotas and promising potential for diagnosis.

Author

Xu, Xiaoxian, Rao, Huiting, Fan, Xiaoji, Pang, Xiangwei, Wang, Yichen, Zhao, Lingqin, Xing, Jie, Lv, Xiaojuan, Wang, Tingzhang, Tao, Jin, Zhang, Xiaojing, Qian, Jianhua, and Lou, Hanmei

Subjects
BACTERIAL vaginitis, VAGINAL diseases, RECEIVER operating characteristic curves, RANDOM forest algorithms, RANDOM sets
Abstract

Vaginal microbiota is closely associated with women's health, however, the correlation between HPV‐related cervical disease (HRCD) and vaginal microbiota is still obscure. In this study, patients with HRCD (n = 98) and healthy controls (n = 58) in Hangzhou were recruited, and their vaginal microbiota were collected and analyzed. The composition of the vaginal microbial community was explored, and a disease classification model was developed by random forest algorithm. The results suggested that the diversity of vaginal microbiota was significantly higher in HRCDs than that in healthy controls (p < 0.05). Firmicutes is the dominant phylum in vaginal microbiota, and Lactobacillus was identified as the most altered genus between two groups (p < 0.01). Kyoto Encyclopedia of Genes and Genomes analysis suggested the difference in vaginal microbial community functions between two groups. Furthermore, we identified 10 biomarkers as the optimal marker sets for the random forest model and found a higher probability of disease values in HRCD group in discovery cohort (p < 0.0001), with an area under the receiver operating characteristic curve reaching 89.7% (95% confidence interval: 78.3%–100%). We further validated the model in both validation and independent diagnosis cohorts, confirming its accuracy in the prediction of HRCD. In conclusion, this study revealed the community composition of vaginal microbiota in HRCDs and successfully constructed a diagnostic model for HRCD. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Rhizosphere microorganisms can influence the timing of plant flowering

Author

National Natural Science Foundation of China, Chinese Academy of Sciences, European Research Council, Lu, Tao, Ke, Mingjing, Lavoie, Michel, Jin, Yujian, Fan, Xiaoji, Zhang, Zhenyan, Fu, Zhengwei, Sun, Liwei, Gillings, Michael, Peñuelas, Josep, Qian, Haifeng, Zhu, Yong-Guan, National Natural Science Foundation of China, Chinese Academy of Sciences, European Research Council, Lu, Tao, Ke, Mingjing, Lavoie, Michel, Jin, Yujian, Fan, Xiaoji, Zhang, Zhenyan, Fu, Zhengwei, Sun, Liwei, Gillings, Michael, Peñuelas, Josep, Qian, Haifeng, and Zhu, Yong-Guan

Abstract

[Background]: Plant phenology has crucial biological, physical, and chemical effects on the biosphere. Phenological drivers have largely been studied, but the role of plant microbiota, particularly rhizosphere microbiota, has not been considered., [Results]: We discovered that rhizosphere microbial communities could modulate the timing of flowering of Arabidopsis thaliana. Rhizosphere microorganisms that increased and prolonged N bioavailability by nitrification delayed flowering by converting tryptophan to the phytohormone indole acetic acid (IAA), thus downregulating genes that trigger flowering, and stimulating further plant growth. The addition of IAA to hydroponic cultures confirmed this metabolic network., [Conclusions]: We document a novel metabolic network in which soil microbiota influenced plant flowering time, thus shedding light on the key role of soil microbiota on plant functioning. This opens up multiple opportunities for application, from helping to mitigate some of the effects of climate change and environmental stress on plants (e.g. abnormal temperature variation, drought, salinity) to manipulating plant characteristics using microbial inocula to increase crop potential.

Published
2018

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