Impacts of Projected Climate Warming and Wetting on Soil Microbial Communities in Alpine Grassland Ecosystems of the Tibetan Plateau

Tibetan Plateau grasslands might increase sequestration of microbial necromass carbon under future warming

Microbial necromass carbon (MNC) can reflect soil carbon (C) sequestration capacity. However, changes in the reserves of MNC in response to warming in alpine grasslands across the Tibetan Plateau are currently unclear. Based on large-scale sampling and published observations, we divided eco-clusters based on dominant phylotypes, calculated their relative abundance, and found that their averaged importance to MNC was higher than most other environmental variables. With a deep learning model based on stacked autoencoder, we proved that using eco-cluster relative abundance as the input variable of the model can accurately predict the overall distribution of MNC under current and warming conditions. It implied that warming could lead to an overall increase in the MNC in grassland topsoil across the Tibetan Plateau, with an average increase of 7.49 mg/g, a 68.3% increase. Collectively, this study concludes that alpine grassland has the tendency to increase soil C sequestration capacity on the Tibetan Plateau under future warming.

Response of Carbon-Fixing Bacteria to Patchy Degradation of the Alpine Meadow in the Source Zone of the Yellow River, West China

This study aims to enlighten our understanding of the distribution of soil carbon-fixing bacteria (cbbL-harboring bacteria) and their community diversity in differently degraded patches at three altitudes. MiSeq high-throughput sequencing technology was used to analyze the soil carbon-fixing bacteria community diversity of degraded patches and healthy meadow at three altitudes. Redundancy analysis (RDA) and structural equation model (SEM) were used to analyze the correlation and influence path between environmental factors and carbon-fixing bacteria. The results showed that degradation reduced the relative abundance of Proteobacteria from 99.67% to 95.57%. Sulfurifustis, Cupriavidus, and Alkalispirillum were the dominant genera at the three altitudes. Hydrogenophaga and Ectothiorhodospira changed significantly with altitude. RDA results confirmed that available phosphorus (AP) was strongly and positively correlated with Proteobacteria. AP and total nitrogen (TN) were strongly and positively correlated with Hydrogenophaga. Grass coverage and sedge aboveground biomass were strongly and positively correlated with Sulfurifustis and Ectothiorhodospira, respectively. Elevation adversely affected the relative abundance of dominant carbon-fixing bacteria and diversity index by reducing the coverage of grass and soil volumetric moisture content (SVMC) indirectly, and also had a direct positive impact on the Chao1 index (path coefficient = 0.800). Therefore, increasing the content of nitrogen, phosphorus and SVMC and vegetation coverage, especially sedge and grass, will be conducive to the recovery of the diversity of soil carbon-fixing bacteria and improve the soil autotrophic microbial carbon sequestration potential in degraded meadows, especially in high-altitude areas.

Effects of Land Use Type Transformation on the Structure and Diversity of Soil Bacterial Communities

Soil microbiota are significantly influenced by their microenvironments. Therefore, to understand the impacts of various land use patterns on the diversity and composition of soil bacterial communities, this study focused on three typical land use types-NF (natural forest), AF (artificial forests), and FL (farmland)-in the Heilongjiang Central Station Black-billed Capercaillie National Nature Reserve, located in the southwestern part of Heihe City, Heilongjiang Province, China. Using high-throughput sequencing of the 16S rRNA gene, we examined the soil bacterial community structures in these different land use types and explored their correlation with soil environmental factors. The following were our main observations: (1) Significant variations in soil chemical properties among different land use patterns were observed. In artificial forests, total nitrogen (TN), alkali hydrolyzed nitrogen (AN), total phosphorus (TP), and available phosphorus (AP) were higher compared to farmland and significantly higher than those in natural forests. Furthermore, the organic carbon content (SOC) in natural forests was higher than in artificial forests and significantly higher than in farmland. (2) Comparative analysis using the Shannon and Simpson indices revealed that bacterial community diversity was higher in artificial forests than in natural forests, which was significantly higher than in farmland. (3) The effect of different land use types on soil bacterial community structure was not significant. The three land types were dominated by Proteobacteria, Acidobacteria, and Actinobacteria. Proteobacteria exhibited a higher relative abundance in farmland and artificial forests compared to natural forests, whereas Actinobacteria exhibited the lowest relative abundance in natural forests. (4) Redundancy analysis (RDA) revealed that SOC, TN, AN, and AP were key environmental factors influencing the microbial communities of soil. Collectively, our findings demonstrated that land use practices can significantly alter soil nutrient levels, thereby influencing the structure of bacterial communities.

Composition and Diversity of Soil Microbial Community Associated With Land Use Types in the Agro-Pastoral Area in the Upper Yellow River Basin

The microorganisms of soil are sensitive to their living microenvironment, and their community structure and function will change with the environmental conditions. In the agro-pastoral area on the Qinghai-Tibet Plateau, revealing the diversity of the soil microbial communities and its response to different soil physicochemical properties and environmental factors are important for ecosystem management. The microbial (bacteria and archaea) community composition and diversity under different land use types (cultivated land, grazing grassland and planted forest) were analyzed by 16S rRNA (V4 region) method in a typical agro-pastoral region in the upper Yellow River basin. Also, the soil nutrients were studied and correlated with the microbial community. The results showed that the soil nutrient contents in grassland were low, but the available nutrients were relatively high. There was a great spatial variability under different distances to the river. The microbial community diversity was lower in the grassland than the cultivated land and forest land closer to the river. For all land uses, the dominant phyla of soil microorganisms included Proteobacteria, Actinobacteria, and Bacteroidetes, while the abundance of Clostridia was significantly higher than that of the other groups, indicating that Clostridia dominated the Firmicutes and affected soil microbial community composition. The linear discriminant analysis (LDA) effect size (LefSe) analysis showed different biomarkers were more abundant in grassland than other land use types, suggesting that the structure and diversity of soil microorganisms in grassland were significantly different compared with cultivated land and forest land. The distance-based redundancy analysis (db-RDA) results showed that the total phosphorus (TP) and calcium (Ca) were the key environmental factors affecting the diversity and abundance of the soil microbial community in cultivated land and forestland, respectively. However, the microbial diversity in grassland was more related to spatial distance of the river. These results provided a theoretical basis for the changes in the composition, structure, and function of soil microbial communities in agro-pastoral areas.

Possible effects of temperature on bacterial communities in the rhizosphere of rice under different climatic regions

Global warming is an indisputable fact. However, the effect of warming on the rhizosphere bacterial community of crops is not well understood. Therefore, we carried out pot experiments with three rice (Oryza sativa L.) varieties in black soil across three climatic regions of northeast China to simulate temperature change, and analyzed the response of the rhizosphere bacterial community to different temperatures. Results showed that climate had stronger effects on rhizosphere bacterial communities than rice variety. The rhizosphere bacterial diversity differed significantly among the three climatic regions and positively correlated with the mean daily average temperature (MAveT), mean daily maximum temperature (MMaxT), and mean daily minimum temperature (MMinT), and negatively correlated with the daily temperature range (DTR). Principal co-ordinate analysis revealed that bulk soil bacterial communities maintained a high similarity across the three climatic regions, while rhizosphere bacterial communities notably varied. This change was significantly correlated with MAveT, MMaxT, MMinT, and DTR. Compared with bulk soil, Proteobacteria and Bacteroidetes were enriched in the rhizosphere, while Actinobacteria was depleted. Moreover, these changes were strengthened by increasing the temperature and decreasing DTR. Additionally, correlation analysis revealed that changes in rhizosphere bacterial communities were closely related to the formation of rice yields. Our study revealed that the increasing temperature indirectly reshapes the rhizosphere bacterial community that may promote rice production in areas with lower temperatures.

Composition and diversity of soil microbial communities in the alpine wetland and alpine forest ecosystems on the Tibetan Plateau

While the composition and diversity of soil microbial communities play a central and essential role in biogeochemical cycling of nutrients, they are known to be shaped by the physical and chemical properties of soils and various environmental factors. This study investigated the composition and diversity of microbial communities in 48 samples of seasonally frozen soils collected from 16 sites in an alpine wetland region (Lhasa River basin) and an alpine forest region (Nyang River basin) on the Tibetan Plateau using high-throughput sequencing that targeted the V3-V4 region of 16S rRNA gene. The dominant soil microbial phyla included Proteobacteria, Acidobacteria, and Actinobacteria in the alpine wetland and alpine forest ecosystems, and no significant difference was observed for their microbial composition. Linear discriminant analysis Effect Size (LEfSe) analysis showed that significant enrichment of Hymenobacteraceae and Cytophagales (belonging to Bacteroidetes) existed in the alpine wetland soils, while the alpine forest soils were enriched with Alphaproteobacteria (belonging to Proteobacteria), suggesting that these species could be potential biomarkers for alpine wetland and alpine forest ecosystems. Results of redundancy analysis (RDA) suggest that the microbial community diversity and abundance in the seasonally frozen soils on the Tibetan Plateau were mainly related to the total potassium in the alpine wetland ecosystem, and available potassium and soil moisture in the alpine forest ecosystem, respectively. In addition, function prediction analysis by Tax4Fun revealed the existence of potential functional pathways involved in human diseases in all soil samples. These results provide insights on the structure and function of soil microbial communities in the alpine wetland and alpine forest ecosystems on the Tibetan Plateau, while the potential risk to human health from the pathogenic microbes in the seasonally frozen soils deserves attention.

Responses of soil extracellular enzyme activities and microbial community properties to interaction between nitrogen addition and increased precipitation in a semi-arid grassland ecosystem

Both atmospheric nitrogen (N) deposition and precipitation can strongly impact below-ground biogeochemical processes. Soil extracellular enzymes activities (EEAs) and microorganisms are considered as the key agents in ecosystem nutrient cycling. However, how the interaction between increasing N deposition and precipitation may affect soil EEAs and microbes remain poorly understood. In a 5-year field experiment in a meadow steppe in northern China, we tested the effects of N addition (N0, 0; N1, 5; N2, 10 g N m^-2 yr^-1) and increased precipitation (W0, ambient precipitation; W1, increase of 15% ambient precipitation; W2, increase of 30% ambient precipitation) on soil EEAs, microbial and chemical properties. Results showed that their interaction significantly affected all hydrolase activities, except for β-1,4-xylosidase (βX). Furthermore, increased precipitation and N addition interactively affected bacterial gene copies (P ≤ 0.05), and increased precipitation comparatively had a stronger effects. The results on the combination of N addition and increased precipitation showed that increased precipitation alleviated the positive effects of N addition on soil EEAs. This implies that the effects of either treatment alone on grassland biogeochemical processes may be alleviated by their simultaneous occurrence. Our results suggested that soil EEAs were mainly controlled by the content of N and phosphorus (P), and the ratio of C: N and C: P. Therefore, soil element content and stoichiometry could better explain the responses of EEAs to global changes. Moreover, soil microbial communities were mainly controlled by soil P content. Overall, our study highlights that the interaction between N deposition and precipitation may play a vital role in predicting the responses of soil enzyme activities to global changes in grassland ecosystems.

miR-129-5p Alleviates Neuropathic Pain Through Regulating HMGB1 Expression in CCI Rat Models

Recently, microRNAs are reported to be participated in the development of pain and persistence of neuropathic and inflammatory pain in animal models. Here, we characterized the functional role of miR-129-5p in pain processing in chronic constriction injury (CCI) rat models. Bilateral CCI operation was used to generate neuropathic pain rat model. Paw withdrawal threshold (PWT) and paw withdrawal latency (PWL) were used to assess pain-related behaviors. Gene expression was evaluated using qRT-PCR, luciferase assay, western blotting, and enzyme-linked immunosorbent assay. Compared with the control rats, expression level of miR-129-5p was downregulated significantly over time in CCI rats post operation. Interestingly, downregulation of miR-129-5p in CCI rats was correlated with increased proinflammatory cytokine expression and pain-related behaviors. Furthermore, we found that miR-129-5p alleviated neuropathic pain through downregulating high mobility group protein B1 (HMGB1) expression in CCI rats as overexpression of miR-129-5p suppressed expression of both HMGB1 and proinflammatory cytokine and alleviated pain sensation in CCI rats. In summary, our results show that alteration in miR-129-5p expression contributes to pain processing in our CCI pain rat model, suggesting miR-129-5p could be a causal factor in neuropathic pain and serve as a promising potential biomarker and therapeutic target for neuropathic pain.

The aridity index governs the variation of vegetation characteristics in alpine grassland, Northern Tibet Plateau

The vegetation dynamic (e.g., community productivity) is an important index used to evaluate the ecosystem function of grassland ecosystem. However, the critical factors that affect vegetation biomass are disputed continuously, and most of the debates focus on mean annual precipitation (MAP) or temperature (MAT). This article integrated these two factors, used the aridity index (AI) to describe the dynamics of MAP and MAT, and tested the hypothesis that vegetation traits are influenced primarily by the AI. We sampled 275 plots at 55 sites (five plots at each site, including alpine steppe and meadow) across an alpine grassland of the northern Tibet Plateau, used correlation analysis and redundancy analysis (RDA) to explore which key factors determine the biomass dynamic, and explained the mechanism by which they affect the vegetation biomass in different vegetation types via structural equation modelling (SEM). The results supported our hypothesis, in all of the environmental factors collected, the AI made the greatest contribution to biomass variations in RDA , and the correlation between the AI and biomass was the largest (R = 0.85, p < 0.05). The final SEM also validated our hypothesis that the AI explained 79.3% and 84.4% of the biomass variations in the alpine steppe and the meadow, respectively. Furthermore, we found that the soils with higher carbon to nitrogen ratio and soil total nitrogen had larger biomass, whereas soil organic carbon had a negative effect on biomass in alpine steppe; however, opposite effects of soil factors on biomass were observed in an alpine meadow. The findings demonstrated that the AI was the most critical factor affecting biomass in the alpine grasslands, and different reaction mechanisms of biomass response to the AI existed in the alpine steppe and alpine meadow.