Characteristics of Bacterial Community and Function in Paddy Soil Profile around Antimony Mine and Its Response to Antimony and Arsenic Contamination

Abstract

Research of bacterial communities and metabolism potential of paddy soils contaminated by antimony (Sb) and arsenic (As) are vital to acquire understanding for their bioremediation. Here, the relative abundance of Sb and As metabolism genes, the diversity and composition of the bacterial community, and the influences of geochemical properties and the bacterial community and metabolism potential have been researched by Tax4Fun2 prediction and high-throughput sequencing. LEfSe (linear discriminant analysis effect size) analysis shown different taxa were enriched in dissimilar soil layers. RDA (Redundancy analysis) and relative importance analysis indicated the main properties including total sulfur (TS), total organic carbon (TOC), pH, and the bioavailable fractions of Sb and As affects the bacterial community, which Sbrec, Astot, and Asrec had greater impact on the bacterial taxonomic community. For example, Asrec, Astot, and Sbrec had a positive correlation with Chloroflexi and Rokubacteria, but negatively correlated with Proteobacteria and Actinobacteria. Obtaining metabolic function genes by using the tax prediction method. RDA, relative importance analysis, and co-occurrence network analysis showed the geochemical properties and bacterial community affected Sb and As related bacterial functions. The partial least squares path model (PLS-PM) analysis indicated Sb and As contamination fractions had negative effects on ecological function, bacterial community structure had positive influences on ecological function, and the direct effects of geochemical properties on ecological function was greater than community structure. The direct impact of As contamination fractions on bacterial community structure was greater than Sb, while the direct impact of Sb contamination fractions on bacterial function was more remarkable than As. Obviously, this study provides a scientific basis for the potential of biochemical remediation of Sb and As contamination in paddy soils profile.

Soil bacterial community composition in rice–fish integrated farming systems with different planting years

“…The V3-V4 hypervariable regions of the bacterial 16S rRNA gene were amplified with primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) by a thermocycler PCR system (GeneAmp 9700, ABI, USA) 21 . The PCRs were conducted as follows: 3 min of denaturation at 95 °C, 27 cycles of 30 s at 95 °C, 30 s for annealing at 55 °C, and 45 s for elongation at 72 °C, and a final extension at 72 °C for 10 min 22 . The resulting PCR products were extracted from a 2% agarose gel and further purified using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA).…”

Fate and Transformation of 6:2 Fluorotelomer Sulfonic Acid Affected by Plant, Nutrient, Bioaugmentation, and Soil Microbiome Interactions

“…The 20 taxa with the highest abundance were shown, while the other taxa were the sum and represented as “other”. The red star indicates known 6:2 FTSA degraders, , while the black triangle indicates known 6:2 FTOH degraders …”

“…Within a microbial community, diverse and multiple species can perform similar catabolic functions that affect the microbial community structure over time or in response to stimuli. To evaluate the functional capacity of the soil microbiome in sandy soil and rhizosphere sandy soil that received different treatments, Tax4Fun2 was used to predict functional features based on the 16S rRNA gene sequencing data. − Our previous study reported desulfonation and defluorination enzymes involved in 6:2 FTSA biodegradation by RHA1 . So, the analysis was focused on these known desulfonation and defluorination enzymes.…”

Evaluating the Heavy Metal Risk in Spinacia oleracea L. and Its Surrounding Soil with Varied Biochar Levels: A Pot Experiment

“…RDA describes the proportions of the variation of a dependent variable in the total variations of the dependent variables that are caused by the linear relationships between the dependent variables and typical variables. The RDA analysis of the relationships of the selected indices between soil and S. oleracea was performed using the ape package, vegan package, psych package, and reshape2 package in the R language (3.6.0) [25].…”