SlIAA23-SlARF6 module controls arbuscular mycorrhizal symbiosis by regulating strigolactone biosynthesis in tomato.

Auxins are a class of phytohormones with roles involved in the establishment and maintenance of the arbuscular mycorrhizal symbiosis (AMS). Auxin response factors (ARFs) and Auxin/Indole-acetic acids (AUX/IAAs), as two transcription factors of the auxin signaling pathway, coregulate the transcription of auxin response genes. However, the interrelation and regulatory mechanism of ARFs and AUX/IAAs in regulating AMS are still unclear. In this study, we found that the content of auxin in tomato roots increased sharply and revealed the importance of the auxin signaling pathway in the early stage of AMS. Notably, SlARF6 was found to play a negative role in AMF colonization. Silencing SlARF6 significantly increased the expression of AM-marker genes, as well as AMF-induced phosphorus uptake. SlIAA23 could interact with SlARF6 in vivo and in vitro, and promoted the AMS and phosphorus uptake. Interestingly, SlARF6 and SlIAA23 played a contrary role in strigolactone (SL) synthesis and accumulation in AMF-colonized roots of tomato plants. SlARF6 could directly bind to the AuxRE motif of the SlCCD8 promoter and inhibited its transcription, however, this effect was attenuated by SlIAA23 through interaction with SlARF6. Our results suggest that SlIAA23-SlARF6 coregulated tomato-AMS via an SL-dependent pathway, thus affecting phosphorus uptake in tomato plants.

Endophytic fungus Falciphora oryzae enhances salt tolerance by modulating ion homeostasis and antioxidant defense systems in pepper.

Endophytic fungi play an important role in the induction of plant tolerance to abiotic and biotic stresses. However, the role of endophytic fungi in the response of horticultural plants to plant stress remains largely unknown. Here, we addressed the role of the endophytic fungus Falciphora oryzae in enhancing salt tolerance in pepper (Capsicum annuum L.) by inoculation with the endophyte in the rhizosphere. F. oryzae could indeed colonize the roots of pepper and significantly improved the tolerance of pepper to salt stress. This resulted in increased plant growth and photosynthetic performance compared with control plants, which was accompanied by increases in indole acetic acid and abscisic acid biosynthesis and signaling. Furthermore, inoculation with F. oryzae significantly upregulated a subset of transcripts involved in Na+ homeostasis (NHX3, NHX6, NHX8, HKT2-1, and SOS1) and the high-affinity K+ transporter protein-related gene HAK1 in the leaves to maintain Na+ /K+ homeostasis. Moreover, the activity of antioxidant enzymes (catalase, peroxidase, glutathione, and ascorbate peroxidase), the content of glutathione, the transcript level of genes related to antioxidants (catalase, ascorbate peroxidase, glutathione reductase, peroxidase, glutamate-cysteine ligase, and glutamine synthetase) in the leaves were significantly upregulated after inoculation with F. oryzae, which led to decreased levels of lipid peroxidation (malondialdehyde) and reactive oxygen species. These results indicate that inoculation with F. oryzae can enhance the salt tolerance of pepper by promoting ion homeostasis and upregulating antioxidant defense systems.

Influence of vegetation on heavy metal Cr release process from bottom sediment under unidirectional flows and regular waves.

As human activities become more intensive, a substantial number of heavy metals are discharged into estuarine or wetland environments. Due to the poor degradability, heavy metals are prone to adsorption and deposition on suspended particles in bottom sediments. Subsequently, under the influence of disturbances, there is a potential for their re-release, causing secondary pollution. To investigate the release process of the heavy metal Cr from sediment, laboratory experiments were conducted under both unidirectional flow and regular wave conditions. At the initial stage, the temporal trends of particulate (CrP) and dissolved (CrD) Chromium concentrations were both characterized by initial increments followed by stabilization and continuous escalation. Vertically, the stable concentrations of CrP and CrD increased with the presence of vegetation and the enhancement of hydrodynamics. The Elovich equation, pseudo-second-order kinetic equation, Double constant equation (Freundlich model), and parabolic diffusion equation were employed to predict the release process of CrD from bottom sediment. The Elovich equation proved most suitable for describing the release process of CrD, with an R2 exceeding 0.9. In order to assess the influence of vegetation on the Cr release process, the Stem-Reynolds were introduced to modify the Elovich equation. The final maximum error was 12 % (excluding the initial stage), which was much lower than that using the original Elovich equation (maximum error of 32 %). The study findings provide practical support for estuarine and wetland managers to formulate effective heavy metal management measures, which contribute to the conservation and sustainable management of aquatic ecosystems.

Potential risk assessment and occurrence characteristic of heavy metals based on artificial neural network model along the Yangtze River Estuary, China.

Pollution from heavy metals in estuaries poses potential risks to the aquatic environment and public health. The complexity of the estuarine water environment limits the accurate understanding of its pollution prediction. Field observations were conducted at seven sampling sites along the Yangtze River Estuary (YRE) during summer, autumn, and winter 2021 to analyze the concentrations of seven heavy metals (As, Cd, Cr, Pb, Cu, Ni, Zn) in water and surface sediments. The order of heavy metal concentrations in water samples from highest to lowest was Zn > As > Cu > Ni > Cr > Pb > Cd, while that in surface sediments samples was Zn > Cr > As > Ni > Pb > Cu > Cd. Human health risk assessment of the heavy metals in water samples indicated a chronic and carcinogenic risk associated with As. The risks of heavy metals in surface sediments were evaluated using the geo-accumulation index (Igeo) and potential ecological risk index (RI). Among the seven heavy metals, As and Cd were highly polluted, with Cd being the main contributor to potential ecological risks. Principal component analysis (PCA) was employed to identify the sources of the different heavy metals, revealing that As originated primarily from anthropogenic emissions, while Cd was primarily from atmospheric deposition. To further analyze the influence of water quality indicators on heavy metal pollution, an artificial neural network (ANN) model was utilized. A modified model was proposed, incorporating biochemical parameters to predict the level of heavy metal pollution, achieving an accuracy of 95.1%. This accuracy was 22.5% higher than that of the traditional model and particularly effective in predicting the maximum 20% of values. Results in this paper highlight the pollution of As and Cd along the YRE, and the proposed model provides valuable information for estimating heavy metal pollution in estuarine water environments, facilitating pollution prevention efforts.

Predictions of heavy metal concentrations by physiochemical water quality parameters in coastal areas of Yangtze river estuary.

Due to the degradation-resistant and strong toxicity, heavy metals pose a serious threat to the safety of water environment and aquatic ecology. Rapid acquisition and prediction of heavy metal concentrations are of paramount importance for water resource management and environmental preservation. In this study, heavy metal concentrations (Cr, Ni, Cu, Pb, Zn, Cd) and physicochemical parameters of water quality including Temperature (Temp), pH, Oxygen redox potential (ORP), Dissolved oxygen (DO), Electrical conductivity (EC), Electrical resistivity (RES), Total dissolved solids (TDS), Salinity (SAL), Cyanobacteria (BGA-PE), and turbidity (NTU) were measured at seven stations in the Yangtze river estuary. Principal Component Analysis (PCA) and Spearman correlation analysis were employed to analyze the main factors and sources of heavy metals. Results of PCA revealed that the main sources of Cr, Ni, Zn, and Cd were steel industry wastewater, domestic and industrial sewage, whereas shipping and vessel emissions were typically considered sources of Pb and Cu. Spearman correlation analysis identified Temp, pH, ORP, EC, RES, TDS, and SAL as the key physicochemical parameters of water quality, exhibiting the strongest correlation with heavy metal concentrations in sediment and water samples. Based on these results, multiple linear regression as well as non-linear models (SVM and RF) were constructed for predicting heavy metal concentrations. The results showed that the results of the nonlinear model were more suitable for predicting the concentrations of most heavy metals than the linear model, with average R values of the SVM test set and RF test set being 0.83 and 0.90. The RF model showed better applicability for simulating the concentration of heavy metals along the Yangtze river estuary. It was demonstrated that non-linear research methods provided efficient and accurate predictions of heavy metal concentrations in a simple and rapid manner, thereby offering decision-making support for watershed managers.

Development of global soil erosion research at the watershed scale: a bibliometric analysis of the past decade.

At the watershed scale, soil erosion is a cascading system that includes detachment-transport-deposition processes while sediment yield is the net balance of detachment and deposition at the watershed outlet. Due to the temporal-spatial variations of rainfall and landscapes, the relationships between soil erosion and sediment yield are complex and non-linear. Soil erosion processes and sediment yield at the watershed scale have attracted widespread attention; however, few systematic studies have been performed. In this study, a bibliometric analysis and visualization are used to understand the global research status of soil erosion and sediment yield at the watershed scale and provide a reference for researchers to establish future research directions. The USA and China were the most active contributors and had the most publications and active institutions, while Jean Poesen, D.E. Walling, and Xingmin Mu were the top three lead authors in this field. A keyword evolution analysis showed that determining the relationship between soil erosion and the watershed landscape and identifying the sediment source and off-site environmental and ecological effects caused by soil erosion have attracted considerable research attention. Additionally, significant progress has been made in the study of "connectivity," and future research should integrate connectivity and soil erosion models to explain the soil erosion, sediment transport, and deposition processes at the watershed scale.

Complete plastome sequence of magnolia fordiana var. hainanensis (dandy) Noot. (Magnoliaceae), an endemic and ornamental tree in South China.

Magnolia fordiana var. hainanensis (Dandy) Noot. (Magnoliaceae) is an endemic and ornamental tree distributed in Hainan, China. In this study, we report and characterize the complete plastid genome sequence of M. fordiana var. hainanensis in order to provide genomic resources helpful for promoting its conservation and garden utilization. The complete plastome is 160,157 bp in length and contains the typical quadripartite structure of angiosperm, including two Inverted Repeat (IRs) regions of 26,573 bp, a Large Single-Copy (LSC) region of 88,255 bp and a Small Single-Copy (SSC) region of 18,756 bp. The plastome contains 114 genes, consisting of 80 unique protein-coding genes, 30 unique tRNA gene and 4 unique rRNA genes. The overall A/T content in the plastome of M. fordiana var. hainanensis is 60.70%. The complete plastome sequence of M. fordiana var. hainanensis will provide a useful resource for the conservation and garden utilization of this species as well as for the phylogenetic studies of Magnoliaceae.