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BackgroundThe prevalence of pulmonary tuberculosis (PTB) as an infectious disease continues to contribute significantly to global mortality. According to recent studies, the gut microbiota of PTB patients and healthy controls (HCs) show significant disparities. However, the causal relationship between them has yet to be elucidated.MethodsWe conducted a study using Mendelian Randomization (MR) to explore the potential causal link between gut microbiota and pulmonary tuberculosis (PTB). The summary statistics of the gut microbiota were acquired from the MiBioGen consortium, while data on PTB were sourced from pheweb.jp. A range of statistical methodologies were employed to evaluate causality, encompassing inverse variance weighting (IVW), MR-Egger, weighted median (WM), weighted model, and simple model. We utilized instrumental variables (IVs) that have a direct causal relationship with PTB to annotate SNPs, aiming to discover the genes harboring these genetic variants and uncover potential associations between host genes and the microbiome in patients with PTB.ResultsAmong the 196 bacterial traits in the gut microbiome, we have identified a total of three microbiomes that exhibit a significant association with PTB. The occurrence of Dorea (P = 0.0458, FDR-adjusted P = 0.0458) and Parasutterella (P = 0.0056, FDR-adjusted P = 0.0168) was linked to an elevated risk of PTB, while the presence of Lachnoclostridium (P = 0.0347, FDR-adjusted P = 0.0520) demonstrated a protective effect against PTB. Our reverse Two-Sample Mendelian Randomization (TSMR) analysis did not yield any evidence supporting the hypothesis of reverse causality from PTB to alterations in the intestinal flora.ConclusionWe have established a connection between the gut microbiota and PTB through gene prediction analysis, supporting the use of gut microecological therapy in managing PTB and paving the way for further understanding of how gut microbiota contributes to PTB’s development.

Praziquantel (PZQ), a traditional helminthicide drug, has been shown to exert an anti-inflammatory effect on splenomegaly induced by schistosomiasis via regulating macrophage polarization. Meanwhile, miR-21 has been demonstrated to control macrophage polarization. However, the role of miR-21 in the regulation of macrophage polarization by PZQ in schistosomiasis is still unclear. In the present study, we found that M1-type macrophages were the predominant splenic macrophages in chronic schistosomiasis and that NLRP3 inflammasome–related molecules were upregulated. PZQ inhibited NLRP3 inflammasome in M1 macrophages and reduced the expression of miR-21. Furthermore, using the methods of quantitative real-time PCR and transfection, the downregulation of NLRP3/IL-1β by PZQ in M1 macrophages were reversed by miR-21 overexpression. These results indicated that miR-21 was involved in the inhibiting effect of PZQ on activation of NLRP3 inflammasome. Moreover, miR-21 might target Smad7 to mediate the anti-inflammatory effect of PZQ in polarized macrophages. This study provides an in-depth mechanism of PZQ in the treatment of schistosomiasis.

Abstract Soil organic carbon (SOC) persistence is predominantly governed by mineral protection, consequently, soil mineral-associated (MAOC) and particulate organic carbon (POC) turnovers have different impacts on the vulnerability of SOC to climate change. Here, we generate the global MAOC and POC maps using 8341 observations and then infer the turnover times of MAOC and POC by a data-model integration approach. Global MAOC and POC storages are $${975}_{964}^{987}$$ 975 964 987 Pg C (mean with 5% and 95% quantiles) and $${330}_{323}^{337}$$ 330 323 337 Pg C, while global mean MAOC and POC turnover times are $${129}_{45}^{383}$$ 129 45 383 yr and $${23}_{5}^{82}$$ 23 5 82 yr in the top meter, respectively. Climate warming-induced acceleration of MAOC and POC decomposition is greater in subsoil than that in topsoil. Overall, the global atlas of MAOC and POC turnover, together with the global distributions of MAOC and POC stocks, provide a benchmark for Earth system models to diagnose SOC-climate change feedback.

Agriculture faces the dual challenge of sustainably increasing productivity to meet the food demand of a rapidly growing population and adapting to climate change. Despite significant efforts to develop more adaptive and productive crop cultivars and to improve water and nutrient management practices, the potential of crops to tackle this challenge by optimizing soil resource utilization remains underexplored. Here, we propose that optimizing root systems to promote the efficient acquisition of soil resources can increase yield, improve resilience to climate variability, and reduce environmental impacts. This optimization can be achieved through genetic manipulation at the crop species level and effective management of cropping systems at the field level (e.g., intercropping, rotation, and agroforestry). Advances in three-dimensional soil data collection, linking root traits to plant performance, and modelling of climate-soil–plant-management interactions are paving the way for soil-smart cropping. Effective communication and knowledge exchange with stakeholders beyond the scientific community are vital for accelerating the development and adoption of soil-smart practices for climate-smart and sustainable agricultural production.

Soil organic carbon (SOC) is crucial for soil health and quality, and its sequestration has been suggested as a natural solution to climate change. Accurate and cost-efficient determination of SOC and its functional fractions is essential for effective SOC management. Visible near-infrared spectroscopy (vis-NIR) has emerged as a cost-efficient approach. However, its ability to predict whole-profile SOC content and its fractions has rarely been assessed. Here, we measured SOC and its two functional fractions, particulate (POC) and mineral-associated organic carbon (MAOC), down to a depth of 200 ​cm in seven sequential layers across 183 dryland cropping fields in northwest, southwest, and south China. Then, vis-NIR spectra of the soil samples were collected to train a machine learning model (partial least squares regression) to predict SOC, POC, MAOC, and the ratio of MAOC to SOC (MAOC/SOC – an index of carbon vulnerability). We found that the accuracy of the model indicated by the determination coefficient of validation (Rval2) is 0.39, 0.30, 0.49, and 0.48 for SOC, POC, MAOC, and MAOC/SOC, respectively. Incorporating mean annual temperature improved model performance, and Rval2 was increased to 0.64, 0.31, 0.63, and 0.51 for the four carbon variables, respectively. Further incorporating SOC into the model increased Rval2 to 0.82, 0.64, and 0.59, respectively. These results suggest that combining vis-NIR spectroscopy with readily-available climate data and total SOC measurements enables fast and accurate estimation of whole-profile POC and MAOC across diverse environmental conditions, facilitating reliable prediction of whole-profile SOC dynamics over large spatial extents.

Abstract Climate and land management changes are altering carbon inputs to soil. The consequence of such input changes on long‐term soil organic carbon (SOC) balance depends on the transit behavior of carbon inputs. Using observational carbon input and radiocarbon data in global soil profiles, we reveal that on average nearly 25% of new entering carbon leave soil in 1 year irrespective of entering depth, and the remained fraction after 30 years is only ∼13%. Nevertheless, the majority of SOC is older than 30 years in all soil depths. Together, these results demonstrate low transfer efficiency of carbon inputs to aged SOC which is the meaningful carbon component for long‐term SOC sequestration. Additionally, we reveal that SOC aging and carbon input transiting are two distinct processes, which should be simultaneously, but mechanistical‐separately, considered to predict and manage SOC dynamics in response to carbon input changes under climate and land management changes.

Soil microbes drive soil organic carbon (SOC) mineralization. Because microbial groups differ in metabolic efficiency and respond differently to temperature variation, it is reasonable to expect a close association of SOC mineralization and its temperature sensitivity (Q10 which is defined as the factor of the change of soil carbon mineralization induced by 10 °C temperature increase) with microbial community diversity and composition. However, these relations have rarely been tested. Here, we conducted an incubation experiment to assess the temperature responses of microbial α diversity and the relative abundance of microbial r- and K-strategists in soils from a wide range of ecosystems across a climate gradient in the southeast Tibet. The results indicated that the instantaneous α diversity and the relative abundance of r- and K-strategists are significantly (P

The response of soil organic carbon to climate warming may be soil depth-dependent, but remains unquantified in situ. Here the authors show that warming induces more proportional soil carbon losses in topsoil than in subsoil, particularly from high-latitudinal carbon-rich soils.

Heat stress around flowering is harmful to maize growth and yield. Ear traits are closely related to yield; however, the effects of heat stress before and after flowering on ear development and yield traits remain unclear for different heat-tolerant cultivars. In this study, field experiments were conducted in 2020 and 2021, including (i) three sowing dates, (ii) three temperature regimes: control (CK), heated before silking (V9-R1, TBS) and heated after silking (R1-R1 + 15 d, TAS), and (iii) two hybrids (ZD958: heat-tolerant; DH605: heat-sensitive). The results showed that heating had negative effects on all surveyed ear and yield traits except for increased ear length under TBS. The negative effects were larger (i) for TAS than for TBS, (ii) for DH605 than for ZD958, and (iii) for kernel number per plant (KNP) than for kernel weight (KW). The decreased ear traits were a result of a decreased growth rate during rapid ear growth periods. Floret pollination failure and kernel abortion were the main reasons for the decrease in KNP, mainly depending on the daily maximum temperature during V15-R1 + 7 d. The strong linear relationships between ear and yield traits suggested that ear traits could be used as important indicators for breeding heat-resistant varieties in the future.

The sensitivity of soil organic carbon (SOC) in subsoil (below 0.3 m) to climate change is poorly constrained. Here, the authors map global subsoil (0.3–1 m soil layer) SOC turnover times and find that temperature and in general climate effects are secondary to effects due to soil properties at both local and global scales—this now needs to be regarded for diagnosing subsoil SOC dynamics.

The magnitudes of replenishment and priming, two important but opposing fluxes in soil organic carbon (SOC) dynamics, have not been compared. Here the authors show that the magnitude of replenishment is greater than that of priming, resulting in a net SOC accumulation after additional carbon input to soils.

Process-based agricultural system models are a major tool for assessing climate-agriculture-management interactions. However, their application across large scales is limited by computational cost, model uncertainty, and data availability, hindering policy-making for sustainable agricultural production at the scale meaningful for land management by farmers. Using the Agricultural Production System sIMulator (APSIM) as an example model, the APSIM model was run for 101 years from 1980 to 2080 in a typical cropping region (i.e., the Huang-Huai-Hai plain) of China. Then, machine learning (ML)-based models were trained to emulate the performance of the APSIM model and used to map crop production and soil carbon (which is a key indicator of soil health and quality) dynamics under a great number of nitrogen and water management scenarios. We found that ML-based emulators can accurately and quickly reproduce APSIM predictions of crop yield and soil carbon dynamics across the region under different spatial resolutions, and capture main processes driving APSIM predictions with much less input data. In addition, the emulators can be easily and quickly applied to identify optimal nitrogen management to achieve yield potential and sequester soil carbon across the region. The approach can be used for modelling other complex systems and amplifying the usage of agricultural system models for guiding agricultural management strategies and policy-making to address global environmental challenges from agriculture intensification.

Organic amendment (OA) additions may profoundly regulate the turnover behaviours of soil organic carbon (SOC). Explicit understanding of such role of OA is crucial for accurately assessing the potential of carbon sequestration in agricultural soils. To explore the effects of OA additions on the detailed SOC stabilization and destabilization processes, we collected SOC measurements from 29 trials with experimental duration ranging from 14 to 85 years across the globe. Using these datasets, we constrained a soil carbon model to analyse SOC turnover and built-up processes as impacted by OA additions. We found that OA generally decreases microbial carbon use efficiency (CUE) and the fraction of inert SOC that is resistant to decomposition (finert), but has divergent effects on the decay rate of humic SOC (khum). Across the sites, there was great variability in the effects of OA on CUE, khum, and finert, which can be largely explained by local soil and climate conditions and the quantity and quality of OA. Long-term simulations suggested that, without considering the effects of OA on CUE, khum, and finert, the effectiveness of OA additions for carbon sequestration could be largely overestimated. Our results suggest that the strong site-specific regulations of OA on SOC dynamics as demonstrated in this study must be properly considered and better constrained by observational data when assessing SOC sequestration in agricultural soils under the management of OA additions.

Global terrestrial vegetation is greening, particularly in mountain areas, providing strong feedbacks to a series of ecosystem processes. This greening has been primarily attributed to climate change. However, the spatial variability and magnitude of such greening do not synchronize with those of climate change in mountain areas. By integrating two data sets of satellite-derived normalized difference vegetation index (NDVI) values, which are indicators of vegetation greenness, in the period 1982–2015 across the Tibetan Plateau (TP), we test the hypothesis that climate-change-induced greening is regulated by terrain, baseline climate and soil properties. We find a widespread greening trend over 91% of the TP vegetated areas, with an average greening rate (i.e. increase in NDVI) of 0.011 per decade. The linear mixed-effects model suggests that climate change alone can explain only 26% of the variation in the observed greening. Additionally, 58% of the variability can be explained by the combination of the mountainous characteristics of terrain, baseline climate and soil properties, and 32% of this variability was explained by terrain. Path analysis identified the interconnections of climate change, terrain, baseline climate and soil in determining greening. Our results demonstrate the important role of mountainous effects in greening in response to climate change.