Soil microorganisms are crucial contributors to the function of permafrost ecosystems, as well as the regulation of biogeochemical cycles. However, little is known about the distribution patterns and drivers of high‐latitude permafrost microbial communities subject to climate change and human activities. In this study, the vertical distribution patterns of soil bacterial communities in the Greater Khingan Mountain permafrost region were systematically analyzed via Illumina Miseq high‐throughput sequencing. Bacterial diversity in the active layer was significantly higher than in the permafrost layer. Principal coordinate analysis (PCoA) indicated that the bacterial community structure in the active layer and the permafrost layer was completely separated. Permutational multivariate analysis of variance (PERMANOVA) detected statistically significant differentiation across the different depths. The relative abundance of the dominant phyla Chloroflexi (17.92%–52.79%) and Actinobacteria (6.34%–34.52%) was significantly higher in the permafrost layer than in the active layer, whereas that of Acidobacteria (4.98%–38.82%) exhibited the opposite trend, and the abundance of Proteobacteria (2.49%–22.51%) generally decreased with depth. More importantly, the abundance of bacteria linked to human infectious diseases was significantly higher in the permafrost layer according to Tax4Fun prediction analysis. Redundancy analysis (RDA) showed that ammonium nitrogen (NH4+‐N), total organic carbon (TOC), and total phosphorus (TP) were major factors affecting the bacterial community composition. Collectively, our findings provide insights into the soil bacterial vertical distribution patterns and major environmental drivers in high‐latitude permafrost regions, which is key to grasping the response of cold region ecosystem processes to global climate changes.

Climate warming is accelerating permafrost degradation. Soil microorganisms play key roles in the maintenance of high-latitude permafrost regions and forest ecosystems’ functioning and regulation of biogeochemical cycles. In this study, we used Illumina MiSeq high-throughput sequencing to investigate soil bacterial community composition at a primeval Larix gmelinii forest and a secondary Betula platyphylla forest in a permafrost region of the Greater Xing’an Mountains. The Shannon diversity index tended to decrease and then increase with increasing soil depth, which was significantly higher in the L. gmelinii forest than in the B. platyphylla forest at 40–60 cm. Proteobacteria (19.86–29.68%), Acidobacteria (13.59–31.44%), Chloroflexi (11.04–27.19%), Actinobacteria (7.05–25.57%), Gemmatimonadetes (1.76–9.18%), and Verrucomicrobia (2.03–7.00%) were the predominant phyla of the bacterial community in two forest types. The relative abundance of Proteobacteria showed a decreasing trend in the B. platyphylla forest and an increasing trend in the L. gmelinii forest, whereas that of Chloroflexi increased and then decreased in the B. platyphylla forest and decreased in the L. gmelinii forest with increasing soil depth. The relative abundance of Acidobacteria was significantly higher in the B. platyphylla forest than in the L. gmelinii forest at 0–20 cm depth, whereas that of Actinobacteria was significantly higher in the L. gmelinii forest than in the B. platyphylla forest at 0–20 cm and 40–60 cm depth. Principal coordinate analysis (PCoA) and two-way analysis of variance (ANOVA) indicated that microbial community composition was more significantly influenced by forest type than soil depth. Redundancy analysis (RDA) showed that microbial community structure was strongly affected by soil physicochemical properties such as nitrate nitrogen (NO3−-N), pH, and total organic carbon (TOC). These results offer insights into the potential relationship between soil microbial community and forest conversion in high latitude permafrost ecosystems.

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Soil microorganisms are crucial contributors to the function of permafrost ecosystems, as well as the regulation of biogeochemical cycles. However, little is known about the distribution patterns and drivers of high-latitude permafrost microbial communities subject to climate change and human activities. In this study, the vertical distribution patterns of soil bacterial communities in the Greater Khingan Mountain permafrost region were systematically analyzed via Illumina Miseq high-throughput sequencing. Bacterial alpha diversity varied significantly at different soil depths, and the bacterial diversity and richness in the active layer were significantly higher than in the permafrost layer. The relative abundance of the dominant phyla Chloroflexi (17.92%–52.79%) and Actinobacteria (6.34%–34.52%) was significantly higher in the permafrost layer than in the active layer, whereas that of Acidobacteria (4.98%–38.82%) exhibited the opposite trend, and the abundance of Proteobacteria (2.49%–22.51%) generally decreased with depth. More importantly, the abundance of microorganisms linked to human infectious diseases was significantly higher in the permafrost layer according to Tax4Fun prediction analysis. Redundancy analysis (RDA) showed that NH4+-N, TOC and TP were major factors affecting the bacterial community composition. Collectively, our findings provide insights into the soil bacteria at different depths in high-latitude permafrost regions, as well as their vertical distribution patterns and major environmental drivers, which is key to grasping the response of cold region ecosystem processes to global climate changes.

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The effect of the different training samples is different for the classifier when pattern recognition system is established. The training samples were selected randomly in the past protein disulfide bond prediction methods, therefore the prediction accuracy of protein contact was reduced. In order to improve the influence of training samples, a prediction method of protein disulfide bond on the basis of pattern selection and Radical Basis Function neural network has been brought forward in this paper. The attributes related with protein disulfide bond are extracted and coded in the method and pattern selection is used to select training samples from coded samples in order to improve the precision of protein disulfide bond prediction. 200 proteins with disulfide bond structure from the PDB database are encoded according to the encoding approach and are taken as models of training samples. Then samples are taken on the pattern selection based on the nearest neighbor algorithm and corresponding prediction models are set by using RBF neural network. The simulation experiment result indicates that this method of pattern selection can improve the prediction accuracy of protein disulfide bond.

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