Abundant fungi dominate the complexity of microbial networks in soil of contaminated site: High-precision community analysis by full-length sequencing.

During the past decade, the characterization of microbial community in soil of contaminated sites was primarily done by high-throughput short-read amplicon sequencing. However, due to the similarity of 16S rRNA and ITS genes amplicon sequences, the short-read approach often limits the microbial composition analysis at the species level. Here, we simultaneously performed full-length and short-read amplicon sequencing to clarify the community composition and ecological status of different microbial taxa in contaminated soil from a high-resolution perspective. We found that (1) full-length 16S rRNA gene sequencing gave better resolution for bacterial identification at all levels, while there were no significant differences between the two sequencing platforms for fungal identification in some samples. (2) Abundant taxa were vital for microbial co-occurrences network constructed by both full-length and short-read sequencing data, and abundant fungal species such as Mortierella alpine, Fusarium solani, Mrakia frigida, and Chaetomium homopilatum served as the keystone species. (3) Heavy metal correlated with the microbial community significantly, and bacterial community and its abundant taxa were assembled by deterministic process, while the other taxa were dominated by stochastic process. These findings contribute to the understanding of the ecological mechanisms and microbial interactions in site soil ecosystems and demonstrate that full-length sequencing has the potential to provide more details of microbial community.

High-precision early warning system for rice cadmium accumulation risk assessment.

Rapid global industrialization has resulted in widespread cadmium contamination in agricultural soils and products. A considerable proportion of rice consumers are exposed to Cd levels above the provisional safe intake limit, raising widespread environmental concerns on risk management. Therefore, a generalized approach is urgently needed to enable correct evaluation and early warning of cadmium contaminants in rice products. Combining big data and computer science together, this study developed a system named "SMART Cd Early Warning", which integrated 4 modules including genotype-to-phenotype (G2P) modelling, high-throughput sequencing, G2P prediction and rice Cd contamination risk assessment, for rice cadmium accumulation early warning. This system can rapidly assess the risk of rice cadmium accumulation by genotyping leaves at seeding stage. The parameters including statistical methods, population size, training population-testing population ratio, SNP density were assessed to ensure G2P model exhibited superior performance in terms of prediction precision (up to 0.76 ± 0.003) and computing efficiency (within 2 h). In field trials of cadmium-contaminated farmlands in Wenling and Fuyang city, Zhejiang Province, "SMART Cd Early Warning" exhibited superior capability for identification risk rice varieties, suggesting a potential of "SMART Cd Early-Warning system" in OsGCd risk assessment and early warning in the age of smart.