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Nutrients Changed the Assembly Processes of Profuse and Rare Microbial Communities in Coals

Yuanyuan Zhang

Yuanyuan Zhang

School of Safety Science and Engineering, Anhui University of Science and TechnologyHuainan, China
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Zhang, Yuanyuan
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Sheng Xue

Sheng Xue

  • School of Safety Science and Engineering, Anhui University of Science and TechnologyHuainan, China
  • Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining, Anhui University of Science and TechnologyHuainan, China
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Xue, Sheng
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Xiaohua Chang

Xiaohua Chang

Jinneng Holding Shanxi Science and Technology Research Institute Co. LTD.Taiyuan, China
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Chang, Xiaohua
, 
Yang Li

Yang Li

  • State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and TechnologyHuainan, China
  • Institute of Energy, Hefei Comprehensive National Science CenterHefei, China
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Li, Yang
 y 
Xuelian Yue

Xuelian Yue

Jinneng Holding Shanxi Science and Technology Research Institute Co. LTD.Taiyuan, China
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Yue, Xuelian
  
24 sept 2022
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Categoría del artículo: Original Paper
Publicado en línea: 24 sept 2022
Páginas: 359 - 370
Recibido: 18 abr 2022
Aceptado: 09 jul 2022
DOI: https://doi.org/10.33073/pjm-2022-032
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© 2022 Yuanyuan Zhang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

a) Chao1 index and b) Shannon index of the abundant and rare genera in coals. The Wilcoxon test was used to compare the differences in microbial diversities.
a) Chao1 index and b) Shannon index of the abundant and rare genera in coals. The Wilcoxon test was used to compare the differences in microbial diversities.

Fig. 2

a) Ordering of the abundant community compositions at the genus level by nonmetric multidimensional scaling (NMDS) based on the Bray-Curtis dissimilarity index; b) ordering of the rare community compositions at the genus level by NMDS based on the Bray-Curtis distance index; c) difference for Bray-Curtis dissimilarities among nutrient and ck groups. The Wilcoxon test was used to compare the differences in Bray-Curtis dissimilarities.
a) Ordering of the abundant community compositions at the genus level by nonmetric multidimensional scaling (NMDS) based on the Bray-Curtis dissimilarity index; b) ordering of the rare community compositions at the genus level by NMDS based on the Bray-Curtis distance index; c) difference for Bray-Curtis dissimilarities among nutrient and ck groups. The Wilcoxon test was used to compare the differences in Bray-Curtis dissimilarities.

Fig. 3

Manhattan plot of the changes in a) abundant and b) rare genera in coals under nutrient stimulation. Detailed information is shown in Tables SII and SIII.
Manhattan plot of the changes in a) abundant and b) rare genera in coals under nutrient stimulation. Detailed information is shown in Tables SII and SIII.

Fig. 4

Heatmap for a) the profuse and b) rare known genera (detected in more than 30% of samples).
Heatmap for a) the profuse and b) rare known genera (detected in more than 30% of samples).

Fig. 5

Redundancy analysis (RDA) for coal characteristics (including TC, TN, TO, TH, Vdaf, Ad, and Mad) on the coal microbial compositions. a) the effect of coal characteristics on profuse microbial compositions in the ck group; b) the effect of coal characteristics on rare microbial compositions in the ck group; c) the effect of coal characteristics on profuse microbial compositions in the nutrient group; d) the effect of coal characteristics on rare microbial compositions in the nutrient group; e) the effect of nutrients on profuse microbial compositions; f) the effect of nutrients on rare microbial compositions.
Redundancy analysis (RDA) for coal characteristics (including TC, TN, TO, TH, Vdaf, Ad, and Mad) on the coal microbial compositions. a) the effect of coal characteristics on profuse microbial compositions in the ck group; b) the effect of coal characteristics on rare microbial compositions in the ck group; c) the effect of coal characteristics on profuse microbial compositions in the nutrient group; d) the effect of coal characteristics on rare microbial compositions in the nutrient group; e) the effect of nutrients on profuse microbial compositions; f) the effect of nutrients on rare microbial compositions.

Fig. 6

Deterministic and stochastic processes in community assembly. a) βNTI index in abundant and rare groups; b) the relative influences of deterministic and stochastic assembly processes in shaping abundant and rare groups. Detailed information is shown in Tables SIV–SVII.
Deterministic and stochastic processes in community assembly. a) βNTI index in abundant and rare groups; b) the relative influences of deterministic and stochastic assembly processes in shaping abundant and rare groups. Detailed information is shown in Tables SIV–SVII.

Fig. 7

Co-occurrence networks of abundant and rare groups in coals based on the correlation analysis. Detailed information is shown in Tables SVIII–SXI. Pro – Proteobacteria, Act – Actinobacteria, Bact – Bacteroidetes, Fir – Firmicutes, Ver – Verrucomicrobia, Pla – Planctomycetes, Spi – Spirochaetes, Acido – Acidobacteria, Eury – Euryarchaeota, Cren – Crenarchaeota, Gem – Gemmatimonadetes, Chl – Chloroflexi, and Syn – Synergistetes.
Co-occurrence networks of abundant and rare groups in coals based on the correlation analysis. Detailed information is shown in Tables SVIII–SXI. Pro – Proteobacteria, Act – Actinobacteria, Bact – Bacteroidetes, Fir – Firmicutes, Ver – Verrucomicrobia, Pla – Planctomycetes, Spi – Spirochaetes, Acido – Acidobacteria, Eury – Euryarchaeota, Cren – Crenarchaeota, Gem – Gemmatimonadetes, Chl – Chloroflexi, and Syn – Synergistetes.

Detailed sample information on 59 microbial communities in coals for meta-analysis_

ID/NCBI accession number Sites Primer sequences Coal rank Treatment References
SRR9312778-SRR9312784 Erlian Basin 515F: GTGCCAGCMGCCGCGG907R: CCGTCAATTCMTTTRAGTTT Lignite ck (no treat (Wang et al. 2019a)
SRR6998887 Moghla 515F: GTGCCAGCMGCCGCGGTAA806R: GGACTACHVGGGTWTCTAAT Bituminous (Sharma et al. 2019)
SRR1695964 Queensland 926F: AAACTYAAAKGAATTGACGG1392R: ACGGGCGGTGTGTRC Bituminous (Raudsepp et al. 2016)
SRR1695967
SRR1695969
SRR1695971
SRR5342611 Powder River Basin 341F: CCTACGGGNBGCASCAG805R: GACTACNVGGGTATCTAATCC Bituminous (Davis et al. 2018)
SRR8373697-SRR8373705 Anhui 338F: ACTCCTACGGGAGGCAGCAG806R: GGACTACHVGGGTWTCTAAT Bituminous (Liu et al. 2019)
SRR8373722-SRR8373724
SRR8373719-SRR8373721 Guizhou Anthracite
SRR8373696 Shanxi Bituminous
SRR8373718
SRR8373734-SRR8373745
SRR8373746 Anthracite
SRR8373747
SRR7271165 Huaibei Coalfield 515F: GTGCCAGCMGCCGCGG907R: CCGTCAATTCMTTTRAGTTT Bituminous nutrients (Wang et al. 2019b)
SRR11128868 Konin Basin 341F: CCTACGGGNGGCWGCAG785R: GACTACHVGGGTATCTAATCC Lignite (Bucha et al. 2020)
SRR5826886 New South Wales Bituminous (in 't Zandt et al. 2018)
SRR5826888
SRR5826889
SRR11241403 Upper Coal Basin Silesian Bituminous (Pytlak et al. 2020)
SRR5397976 Konin Basin 314F: CCTACGGGNGGCWGCAG805R: GACTACHCGGGTATCTAATCC Bituminous (Detman et al. 2018)
SRR7422168 Jiaozuo Anthracite (Su et al. 2018)
SRR7422169 Neimeng Bituminous
SRR7422170 Suzhou Bituminous
SRR7422171 Jingcheng Anthracite
SRR7422172 Hebi Bituminous
SRR7422173 Shaqu Bituminous
SRR7422174 Liyazhuang Bituminous
SRR7422175 Yima Bituminous
SRR7422176 Pingdingshan Bituminous
SRR7422177 Shoushan Bituminous
SRR5342597 Powder River Basin 341F: CCTACGGGNBGCASCAG805R: GACTACNVGGGTATCTAATCC Bituminous (Davis et al. 2018)
SRR5342605 Bituminous
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