BioTech

Nonylphenol (NP) bioremediation is constrained by the scarcity of efficient and non-pathogenic degrading strains. To clarify the role of acetyl-CoA C-acetyltransferase (AtoB) in NP degradation, we generated an atoB-overexpressed strain (LY-OE) from the environmentally tolerant Bacillus cereus LY and compared its degradation rate with the wild type using HPLC. Untargeted lipidomics was conducted to characterize metabolic responses under NP stress, and key differential lipid metabolites (DELMs) were further validated by ELISA. Additionally, AtoB concentration and ATP content were quantified using commercial assay kits in Bacillus cereus. LY-OE showed a markedly higher NP degradation rate (96%) than LY (85%). Lipidomic analysis identified 34 significant DELMs (VIP > 1, p < 0.05), including elevated cardiolipin (CL) and phosphatidylglycerol (PG), and reduced phosphatidylcholine (PC) and triglycerides (TG). ELISA confirmed these changes (p < 0.01 or p < 0.001), consistent with lipidomic findings. LY-OE showed significantly higher AtoB concentration during the logarithmic growth phase and exhibited higher ATP content during NP degradation. These findings suggest that atoB overexpression enhances NP degradation by both boosting energy supply and remodeling lipid metabolism. This work identifies atoB as a key factor for NP biodegradation and provides a promising strategy for developing high-performance bioremediation strains.

Mass spectrometry imaging (MSI) visualizes the spatial distribution of biomolecules in tissues, whereas ion mobility–mass spectrometry (IM-MS) separates ions through the collision cross-section (CCS) with an inert gas, providing the structural characteristics of isomers. Recent advances have established an integrated workflow, ion mobility–mass spectrometry imaging (IM-MSI), that couples IM with MSI, uniting molecular discrimination with spatial mapping. This synergy has been widely applied in oncology and neuropsychiatric disorders, offering unprecedented insights into biomarker discovery and disease mechanisms. Here, we summarize the principles and classifications of IM-MSI, review their combined biomedical applications, and discuss data processing workflows and commonly used tools.

The genus Kalanchoe (Crassulaceae) comprises approximately 125 species of succulents distributed across Madagascar, Africa, Arabia, Australia, Southeast Asia, and tropical America. Traditionally regarded as “miracle plants”, Kalanchoe species are employed for treating inflammatory, infectious, metabolic, and cardiovascular conditions; this is associated with their abundant content of polyphenols, including phenolic acids and flavonoids such as quercetin, kaempferol, luteolin, rutin, and patuletin. However, robust clinical evidence remains limited. This review integrates pharmacological and bioinformatic perspectives by analyzing more than 70 studies published since 2000 on 15 species, including Bryophyllum. As an in silico complement, the genome of Kalanchoe fedtschenkoi was used to predict genes (AUGUSTUS), perform homology searches against Arabidopsis thaliana, and model three key enzymes: CHS, CYP90, and VEP1. The AlphaFold2/ColabFold models showed conserved catalytic motifs, and molecular docking with representative ligands supported the plausibility of biosynthetic pathways for flavonoids, brassinosteroids, and bufadienolides. The available evidence highlights chemopreventive, antibacterial, anti-inflammatory, antiviral, antioxidant, and cytotoxic activities, primarily associated with flavonoids and bufadienolides. Significant gaps remain, such as the lack of gene–metabolite correlations and the absence of standardized clinical trials. Overall, Kalanchoe represents a promising model that requires multi-omics approaches to enhance its phytopharmaceutical potential.