Effects of oil pollution on the growth and rhizosphere microbial community of Calamagrostis epigejos

Abstract

Bacteria, fungi, archaea, and viruses are reflective organisms that indicate soil health. Investigating the impact of crude oil pollution on the community structure and interactions among bacteria, fungi, archaea, and viruses in Calamagrostis epigejos soil can provide theoretical support for remediating crude oil pollution in Calamagrostis epigejos ecosystems. In this study, Calamagrostis epigejos was selected as the research subject and subjected to different levels of crude oil addition (0 kg/hm², 10 kg/hm², 40 kg/hm²). Metagenomic sequencing technology was employed to analyze the community structure and diversity of soil bacteria, fungi, archaea, and viruses. Additionally, molecular ecological network analysis was integrated to explore species interactions and ecosystem stability within these microbial communities. The functional profiles of soil microorganisms were elucidated based on data from the KEGG database. Results demonstrated a significant increase in petroleum hydrocarbon content, polyphenol oxidase activity, hydrogen peroxide enzyme activity, and acid phosphatase activity upon crude oil addition, while β-glucosidase content, fiber disaccharide hydrolase content, and tiller number decreased (P < 0.05). Proteobacteria and Actinobacteria were identified as dominant bacterial phyla; Ascomycota, Basidiomycota, and Mucoromycota were found to be dominant fungal phyla; Thaumarchaeota emerged as a dominant archaeal phylum; and Uroviricota represented a dominant viral phylum. The diversity of soil bacterial, fungal, archaeal, and viral communities increased with higher amounts of added crude oil. Ecological network analysis revealed a robust collaborative relationship among bacterial, fungal, archaeal, and viral community species in the control treatment (CK), while strong competitive relationships were observed among these species in the treatments with 10% (F10) and 40% (F40) crude oil concentrations. Structural equation modeling analysis indicated significant positive correlations between fungal community, viral community, enzyme activity, and plant growth; conversely, bacterial and archaeal communities showed significant negative correlations with plant growth (P < 0.05). Correlation analysis identified acid phosphatase as the primary environmental factor influencing soil microbial function. Acid phosphatase levels along with tiller number, aboveground biomass, and petroleum hydrocarbons significantly influenced the fungal community (P < 0.05), while underground biomass had a significant impact on the archaeal community (P < 0.05). Acid phosphatase levels along with cellulose-hydrolyzing enzymes, tiller number, and petroleum hydrocarbons exhibited significant effects on the viral community (P < 0.05). This study investigated variations in bacterial, fungal, archaeal, and viral communities under different crude oil concentrations as well as their driving factors, providing a theoretical foundation for evaluating Calamagrostis epigejos' potential to remediate crude oil pollution.

Keywords: Calamagrostis epigejos; Crude oil addition; Ecological network; Soil microorganisms.

Fig. 1

Physical and chemical properties of soil. Note: CK-0 g/kg, F10-10 g/kg, F40-40 g/kg. Different lowercase letters indicate significant treatment differences following LSD test adjustment for the addition of crude oil wastewater (P < 0.05). These values are presented as mean ± SE. The same applies below.

Fig. 2

Composition of soil microbial communities (%). Note: * indicates a significant difference between treatments (P < 0.05).

Fig. 3

PCA analysis of soil microbial communities.

Fig. 4

Index of soil microbial community diversity. Note: P < 0.05 indicates significant differences between treatments, while P > 0.05 indicates no significant differences between treatments. The same applies below.

Fig. 5

Co-occurrence network analysis of soil microbial communities. Note: the size of the nodes in the network diagram corresponds to their degree, which represents the number of connections they have. Positive connections are depicted by red lines, while negative connections are represented by green lines. Additionally, nodes are color-coded based on distinct categories.

Fig. 6

KEGG functional annotation of different treatment communities. Note: (a) KEGG primary pathway function heat map, (b) KEGG primary pathway function heat map, (c) KEGG primary pathway function and soil enzyme activity redundancy analysis, (d) KEGG secondary pathway function and soil enzyme activity redundancy analysis. PPO: polyphenol oxidase activity, CAT: catalase activity, ACP: acid phosphatase, NAG: N-acetylglucosaminidase, CBH: cellobiohydrolase, BG: β-glucosidase, Aam: Amino acid metabolism, Bio: Biosynthesis of other secondary metabolites, Cano: Cancer overview, Cans: Cancer specific types, Carm: Carbohydrate metabolism, Card: Cardiovascular disease, Celld: Cell growth and death, Cellm: Cell motility, Cellp: Cellular community – prokaryotes, Cirm: Circulatory system, Dever: Development and regeneration, Druga: Drug resistance: antimicrobial, Ends: Endocrine system, Enva: Environmental adaptation, Excs: Excretory system, Glym: Glycan biosynthesis and metabolism, Immd: Immune disease, Infeb: Infectious disease: bacterial, Infev: Infectious disease viral, Lipm: Lipid metabolism, Met: Membrane transport, Metaa: Metabolism of other amino acids, Mettp: Metabolism of terpenoids and polyketides, Ners: Nervous system, Neu: Neurodegenerative disease, Nuc: Nucleotide metabolism, Repr: Replication and repair, Sens: Sensory system, Sigt: Signal transduction, Sigi: Signaling molecules and interaction, Subd: Substance dependence, Tranc: Transport and catabolism, Xenm: Xenobiotics biodegradation and metabolism.

Fig. 7

Correlation analysis between soil physicochemical factors and microorganisms. (a) correlation heatmap analysis; (b) Mantel heatmap analysis; (c) structural equation model diagram; (d) archaea redundancy analysis; (e) virus redundancy analysis; (f) bacteria redundancy analysis; g-fungi redundancy analysis. In the structural equation model, solid red line arrows represent statistically significant positive paths, dashed green line arrows indicate negative paths, and the numbers on the arrows denote standardized path coefficients. Additionally, X2 = 127.74, df = 11, GFI = 0.39, RMSEA = 1.152 were obtained as evaluation metrics for model fit. PPO refers to polyphenol oxidase activity; CAT represents catalase activity; ACP denotes acid phosphatase activity; NAG stands for N-acetylglucosaminidase activity; CBH indicates cellulose hydrolysis enzyme activity; BG signifies β-glucosidase activity; TIN corresponds to tiller number of Calamagrostis epigejos; AB represents aboveground biomass; BB refers to belowground biomass; AK denotes available potassium in soil; AN represents alkaline nitrogen in soil; AP signifies available phosphorus in soil; PEH stands for petroleum hydrocarbon.