Using PCR-DGGE and Soil Respiration to Characterize Bacterial Changes in Heavy Metal Contaminated Soils

Introduction:Soil contamination to heavy metals such as lead and zinc in and around mines causes a change in structure, complexity, diversity and activity of soil microbial communities like bacteria. Materials and methods: In the present research, PCR-DGGE approach was used to investigate the effects of Pb and Zn-contamination in Bama mine near Isfahan city on bacterial diversity, structure and complexity. Basal Respiration (BR) and Substrate Induced Respiration (SIR) was also used to assess microbial activities. Nine samples from three locations (3 for each) with different levels of heavy metal contamination were taken (from low to high), then their DNA were directly extracted. Also a 468 base pair of their 16S rRNA genes were amplified using specific primers, and their fingerprints were obtained by denaturing gradient gel electrophoresis (DGGE). BR and SIR were measured, and metabolic quotient was calculated. Finally, soil microbial activity in polluted conditions was achieved. Results: Our findings illustrate that heavy metal contamination has negative effects on bacterial diversity. By increasing the bioavailability of Pb and Zn, the complexity and diversity of bacterial communities decreased and the frequency of resistant bacteria increased. By increasing Pb and Cd contamination, SIR reduced and this shows the reduction in microbial biomass. In these conditions, SIR and metabolic quotient was more sensitive than BR, so they are better ecological indicators in polluted soils. Discussion and conclusion: Although bacterial diversity showed reduction in polluted soils, diversity is still relatively high. Bacterial ability to adapt in heavy metal contamination, bacterial resistance and their important functional roles in such conditions are valuable in soil ecosystem suggesting further researches on them

Land use planning based on SWAT model in a mountainous watershed to reduce runoff and sediment load

Soil erosion is a major environmental threat to the sustainability and productive capacity of soils. This study aimed to identify optimal land use types for Zayandehrood watershed in central Iran for the first time which is large and mountainous to minimize runoff production and soil loss. Two different types of land use data for two scenarios were developed using Soil and Water Assessment Tool (SWAT) in combination with Sequential Uncertainty Fitting Program (SUFI-2) at the sub-basin level with uncertainty analysis to explicitly quantify hydrological components on a daily time step. In the first Scenario, the current land use map of the study area was used and the second Scenario was constructed using an optimal land use map obtained from a land evaluation study. Promotion of the land uses in the second scenario resulted in a noticeable reduction in discharge and sediment productions in the watershed. The simulated mean discharge values by the Scenarios 1 and 2 were about 14,658 and 13,290 m3/year, respectively. The mean annual sediment yield simulated by the Scenario 1 (about 122,220 ton/year) decreased to that of the Scenario 2 (94,440 ton/year). This study provides a strong basis for reducing runoff and sediment yields in central Iran; however, its general analytical framework could be applied to other parts of the world that are facing similar challenges.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Toxic trace element resistance genes and systems identified using the shotgun metagenomics approach in an Iranian mine soil

This study aimed to identify the microbial communities, resistance genes, and resistance systems in an Iranian mine soil polluted with toxic trace elements (TTE). The polluted soil samples were collected from a mining area and compared against non-polluted (control) collected soils from the vicinity of the mine. The soil total DNA was extracted and sequenced, and bioinformatic analysis of the assembled metagenomes was conducted to identify soil microbial biodiversity, TTE resistance genes, and resistance systems. The results of the employed shotgun approach indicated that the relative abundance of Proteobacteria, Firmicutes, Bacteroidetes, and Deinococcus-Thermus was significantly higher in the TTE-polluted soils compared with those in the control soils, while the relative abundance of Actinobacteria and Acidobacteria was significantly lower in the polluted soils. The high concentration of TTE increased the ratio of archaea to bacteria and decreased the alpha diversity in the polluted soils compared with the control soils. Canonical correspondence analysis (CCA) demonstrated that heavy metal pollution was the major driving factor in shaping microbial communities compared with any other soil characteristics. In the identified heavy metal resistome (HV-resistome) of TTE-polluted soils, major functional pathways were carbohydrates metabolism, stress response, amino acid and derivative metabolism, clustering-based subsystems, iron acquisition and metabolism, cell wall synthesis and capsulation, and membrane transportation. Ten TTE resistance systems were identified in the HV-resistome of TTE-polluted soils, dominated by “P-type ATPases,” “cation diffusion facilitators,” and “heavy metal efflux-resistance nodulation cell division (HME-RND).” Most of the resistance genes (69%) involved in resistance systems are affiliated to cell wall, outer membrane, periplasm, and cytoplasmic membrane. The finding of this study provides insight into the microbial community in Iranian TTE-polluted soils and their resistance genes and systems