Salinity poses a significant environmental challenge, limiting plant growth and development. To cultivate salt-tolerant plants, it is crucial to understand the physiological, biochemical, and molecular responses and adaptations to salt stress, as well as to explore natural genetic resources linked to salt tolerance. In this review, we provide a detailed overview of the mechanisms behind morphological and physiological responses triggered by salt stress, including salt damage to plants, the disturbance of cell osmotic potentials and ion homeostasis, lipid peroxidation, and the suppression of photosynthesis and growth. We also describe the physiological mechanisms that confer salt tolerance in plants, such as osmotic adjustments, reactive oxygen species (ROS) scavenging, photosynthetic responses, phytohormone regulation, and ion regulation. Additionally, we summarize the salt-stress sensing and signaling pathways, gene regulatory networks, as well as salt-tolerance mechanisms in plants. The key pathways involved in salt-stress signal perception and transduction, including Ca²⁺-dependent protein kinase (CDPK) cascades, the salt overly sensitive (SOS) pathway, and the abscisic acid (ABA) pathway, are discussed, along with relevant salt-stress-responsive genes and transcription factors. In the end, the important issues and challenges related to salt tolerance for future research are addressed. Overall, this review aims to provide essential insights for the future cultivation and breeding of crops and fruits.

Chestnut (Castanea spp., Fagaceae family) is an economically and ecologically valuable species. The main goals of chestnut production vary among species and countries and depend on the ecological characteristics of orchards, agronomic management, and the architecture of chestnut trees. Here, we review recent research on chestnut trees, including the effects of fungal diseases (Cryphonectria parasitica and Phytophthora cinnamomi) and insect pests (Dryocosmus kuriphilus Yasumatsu), molecular markers for breeding, ecological effects, endophytic fungi, and extracts with human health benefits. We also review research on chestnut in the food science field, technological improvements, the soil and fertilizer used for chestnut production, and the postharvest biology of chestnut. We noted differences in the factors affecting chestnut production among regions, including China, the Americas, and Europe, especially in the causal agents of disease and pests. For example, there is a major difference in the resistance of chestnut to C. parasitica in Asian, European, and American countries. Our review provides new insights into the integrated disease and pest management of chestnut trees in China. We hope that this review will foster collaboration among regions and help to clarify differences in the direction of breeding efforts among countries.

Chinese chestnut (Castanea mollissima Bl.) is one of the earliest domesticated and cultivated fruit trees, and it is widely distributed in China. Because of the high quality of its nuts and its high resistance to abiotic and biotic stresses, Chinese chestnut could be used to improve edible chestnut varieties worldwide. However, the unclear domestication history and highly complex genetic background of Chinese chestnut have prevented the efficiency of breeding efforts. To explore the genetic diversity and structure of Chinese chestnut populations and generate new insights that could aid chestnut breeding, heterozygosity statistics, molecular variance analysis, ADMIXTURE analysis, principal component analysis, and phylogenetic analysis were conducted to analyze single nucleotide polymorphism data from 185 Chinese chestnut landraces from five geographical regions in China via genotyping by sequencing. Results showed that the genetic diversity level of the five populations from different regions was relatively high, with an observed heterozygosity of 0.2796–0.3427. The genetic diversity level of the population in the mid-western regions was the highest, while the population north of the Yellow River was the lowest. Molecular variance analysis showed that the variation among different populations was only 2.07%, while the intra-group variation reached 97.93%. The Chinese chestnut samples could be divided into two groups: a northern and southern population, separated by the Yellow River; however, some samples from the southern population were genetically closer to samples from the northern population. We speculate that this might be related to the migration of humans during the Han dynasty due to the frequent wars that took place during this period, which might have led to the introduction of chestnut to southern regions. Some samples from Shandong Province and Beijing City were outliers that did not cluster with their respective groups, and this might be caused by the special geographical, political, and economic significance of these two regions. The findings of our study showed the complex genetic relationships among Chinese chestnut landraces and the high genetic diversity of these resources.

Soluble sugars are critical determinants of fruit quality and play a significant role in human nutrition. Chestnuts, rich in soluble sugars, derive their sweetness from them. However, their content varies with cultivar, location, and environmental conditions. Traditional methods for determining soluble sugar content in chestnuts are time-consuming, laborious, and destructive. Therefore, there is a pressing need for rapid, non-destructive, and straightforward methods for determining soluble sugars in chestnuts to expedite genetic selection. This study aimed to develop a hyperspectral imaging-based prediction model for soluble sugar content in Chinese chestnuts. Firstly, abnormal samples were eliminated using ensemble partial least squares for outlier detection. We then compared the average original and block scale (BS) spectra, with the latter demonstrating significant differences. The BS pretreatment exhibited two small absorption peaks in the 403.7 ∼ 429.1 nm band and 454.7 ∼ 500 nm band, less fluctuation in the spectral curves from 503.2 to 687.2 nm, and a substantial increase in spectral absorption between 690.6 and 927.8 nm. Subsequently, we developed a partial least squares (PLS) model using BS pretreatment and regularized elimination (rep) variable selection, which showed better accuracy in predicting chestnut soluble sugar content than other variable selection methods. The model fitting accuracy after the spectra treatment was marginally better than that of the original spectra, with a calibration set correlation coefficient (R2) of 0.59 and root mean square error (RMSE) of 1.02, and a validation set R2 of 0.66 and RMSE of 0.94. The wavelengths at 464.3, 503.2, 539.3, 579, and 711.3 nm were identified as critical for developing the soluble sugar content prediction model. The study demonstrated the potential of Near-Infrared Spectroscopy (NIS) as a rapid and non-destructive method for predicting chestnut soluble sugar content, which could be beneficial for quality control and sorting in the food industry.

Chinese chestnut is an economically and ecologically valuable tree species that is extensively cultivated in China. Leaf traits play a vital role in the photosynthetic capacity, chestnut yield, and quality, making them important breeding objectives. However, there has been limited research on constructing high-density linkage maps of Chinese chestnut and conducting quantitative trait loci (QTL) analyses for these leaf traits. This knowledge gap has hindered the progress of selection in Chinese chestnut breeding. In this study, we selected a well-established interspecific F₁ population, consisting of Castanea mollissima ‘Kuili’ × C. henryi ‘YLZ1’, to construct comprehensive genetic maps for chestnut. Through the use of a genotyping-by-sequencing (GBS) technique, we successfully created a high-density linkage map based on single-nucleotide polymorphisms (SNPs) from the F₁ cross. The results showed that 4578 SNP markers were identified in the genetic linkage map, and the total length was 1812.46 cM, which was distributed throughout 12 linkage groups (LGs) with an average marker distance of 0.4 cM. Furthermore, we identified a total of 71 QTLs associated with nine chestnut leaf traits: chlorophyll b content (chlb), stomatal conductance (Gs), leaf area (LA), leaf dry weight (LDW), leaf fresh weight (LFW), leaf length (LL), leaf width (LW), petiole length (PL), and specific leaf weight (SLW). These QTLs were identified based on phenotypic data collected from 2017 to 2018. Notably, among the 71 QTLs, 29 major QTLs were found to control leaf area (LA), leaf dry weight (LDW), and leaf width (LW). The high-density genetic mapping and QTL identification related to leaf traits in this study will greatly facilitate marker-assisted selection (MAS) in chestnut breeding programs.