As global soil salinization continues to intensify, there is a need to enhance salt tolerance in crops. Understanding the molecular mechanisms of tomato (Solanum lycopersicum) roots’ adaptation to salt stress is of great significance to enhance its salt tolerance and promote its planting in saline soils. A combined analysis of the metabolome and transcriptome of S. lycopersicum roots under different periods of salt stress according to changes in phenotypic and root physiological indices revealed that different accumulated metabolites and differentially expressed genes (DEGs) associated with phenylpropanoid biosynthesis were significantly altered. The levels of phenylpropanoids increased and showed a dynamic trend with the duration of salt stress. Ferulic acid (FA) and spermidine (Spd) levels were substantially up-regulated at the initial and mid-late stages of salt stress, respectively, and were significantly correlated with the expression of the corresponding synthetic genes. The results of canonical correlation analysis screening of highly correlated DEGs and construction of regulatory relationship networks with transcription factors (TFs) for FA and Spd, respectively, showed that the obtained target genes were regulated by most of the TFs, and TFs such as MYB, Dof, BPC, GRAS, and AP2/ERF might contribute to the regulation of FA and Spd content levels. Ultimately, FA and Spd attenuated the harm caused by salt stress in S. lycopersicum, and they may be key regulators of its salt tolerance. These findings uncover the dynamics and possible molecular mechanisms of phenylpropanoids during different salt stress periods, providing a basis for future studies and crop improvement.

Background: Stellera chamaejasmeLinn, an important poisonous plant of the China grassland, is toxic to humans and livestock. The rapid expansion ofS. chamaejasmehas greatly damaged the grassland ecology and, consequently, seriously endangered the development of animal husbandry. To draft efficient prevention and control measures, it has become more urgent to carry out research on its adaptive and expansion mechanisms in different unfavorable habitats at the genetic level. Quantitative real-time polymerase chain reaction (qRT-PCR) is a widely used technique for studying gene expression at the transcript level; however, qRT-PCR requires reference genes (RGs) as endogenous controls for data normalization and only through appropriate RG selection and qRT-PCR can we guarantee the reliability and robustness of expression studies and RNA-seq data analysis. Unfortunately, little research on the selection of RGs for gene expression data normalization inS. chamaejasmehas been reported.Method: In this study, 10 candidate RGs namely,18S,60S,CYP,GAPCP1,GAPDH2,EF1B,MDH,SAND,TUA1, andTUA6, were singled out from the transcriptome database ofS. chamaejasme, and their expression stability under three abiotic stresses (drought, cold, and salt) and three hormone treatments (abscisic acid, ABA; gibberellin, GA; ethephon, ETH) were estimated with the programs geNorm, NormFinder, and BestKeeper.Result: Our results showed thatGAPCP1andEF1Bwere the best combination for the three abiotic stresses, whereasTUA6andSAND,TUA1andCYP,GAPDH2and60Swere the best choices for ABA, GA, and ETH treatment, respectively. Moreover,GAPCP1and60Swere assessed to be the best combination for all samples, and18Swas the least stable RG for use as an internal control in all of the experimental subsets. The expression patterns of two target genes (P5CS2andGI) further verified that the RGs that we selected were suitable for gene expression normalization.Discussion: This work is the first attempt to comprehensively estimate the stability of RGs inS. chamaejasme. Our results provide suitable RGs for high-precision normalization in qRT-PCR analysis, thereby making it more convenient to analyze gene expression under these experimental conditions.

Background: Late embryogenesis abundant (LEA) proteins are widely distributed in higher plants and play crucial roles in regulating plant growth and development processes and resisting abiotic stress. Cultivated tomato (Solanum lycopersicum) is an important vegetable crop worldwide; however, its growth, development, yield, and quality are currently severely constrained by abiotic stressors. In contrast, wild tomato species are more tolerant to abiotic stress and can grow normally in extreme environments. The main objective of this study was to identify, characterize, and perform gene expression analysis of LEA protein families from cultivated and wild tomato species to mine candidate genes and determine their potential role in abiotic stress tolerance in tomatoes. Results: Total 60, 69, 65, and 60 LEA genes were identified in S. lycopersicum, Solanum pimpinellifolium, Solanum pennellii, and Solanum lycopersicoides, respectively. Characterization results showed that these genes could be divided into eight clusters, with the LEA_2 cluster having the most members. Most LEA genes had few introns and were non-randomly distributed on chromosomes; the promoter regions contained numerous cis-acting regulatory elements related to abiotic stress tolerance and phytohormone responses. Evolutionary analysis showed that LEA genes were highly conserved and that the segmental duplication event played an important role in evolution of the LEA gene family. Transcription and expression pattern analyses revealed different regulatory patterns of LEA genes between cultivated and wild tomato species under normal conditions. Certain S. lycopersicum LEA (SlLEA) genes showed similar expression patterns and played specific roles under different abiotic stress and phytohormone treatments. Gene ontology and protein interaction analyses showed that most LEA genes acted in response to abiotic stimuli and water deficit. Five SlLEA proteins were found to interact with 11 S. lycopersicum WRKY proteins involved in development or resistance to stress. Virus-induced gene silencing of SlLEA6 affected the antioxidant and reactive oxygen species defense systems, increased the degree of cellular damage, and reduced drought resistance in S. lycopersicum. Conclusion: These findings provide comprehensive information on LEA proteins in cultivated and wild tomato species and their possible functions under different abiotic and phytohormone stresses. The study systematically broadens our current understanding of LEA proteins and candidate genes and provides a theoretical basis for future functional studies aimed at improving stress resistance in tomato.

Medical image classification, a pivotal task for diagnostic accuracy, poses unique challenges due to the intricate and variable nature of medical images compared to their natural counterparts. While Convolutional Neural Networks (CNNs) and Transformers are prevalent in this domain, each architecture has its drawbacks. CNNs, despite their strength in local feature extraction, fall short in capturing global context, whereas Transformers excel at global information but can overlook fine-grained details. The integration of CNNs and Transformers in a hybrid model aims to bridge this gap by enabling simultaneous local and global feature extraction. However, this approach remains constrained in its capacity to model long-range dependencies, thereby hindering the efficient extraction of distant features. To address these issues, we introduce the MambaConvT model, which employs a state-space approach. It begins by locally processing input features through multi-core convolution, enhancing the extraction of deep, discriminative local details. Next, depth-separable convolution with a 2D selective scanning module (SS2D) is employed to maintain a global receptive field and establish long-distance connections, capturing the fine-grained features. The model then combines hybrid features for comprehensive feature extraction, followed by global feature modeling to emphasize on global detail information and optimize feature representation. This paper conducts thorough performance experiments on different algorithms across four publicly available datasets and two private datasets. The results demonstrate that MambaConvT outperforms the latest classification algorithms in terms of accuracy, precision, recall, F1 score, and AUC value ratings, achieving superior performance in the precise classification of medical images.