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Yu-Meng Wang

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Open access Sep 2026

Growth-Phase-Dependent Shift in GABA Biosynthetic Pathways Under Temperature Stress in Isochrysis zhanjiangensis

Temperature stress is a major constraint on the productivity of microalgae used in aquaculture. γ-Aminobutyric acid (GABA) is well-established as a key player in the stress tolerance of higher plants, yet its role in microalgae remains largely unexplored. Here, we examined the effects of low (15 °C), optimal (25 °C), and high (35 °C) temperatures on the GABA shunt in Isochrysis zhanjiangensis during the initial and mid-exponential growth phases. The results demonstrated that temperature stress significantly inhibited cell growth and photosynthetic efficiency (assessed by Fv/Fm and Fv’/Fm’), with soluble protein decreasing and soluble sugar accumulating. During the initial exponential phase, both low and high temperature stress triggered marked GABA accumulation, accompanied by coordinated increases in glutamate decarboxylase (GAD) and diamine oxidase (DAO) activities. Interestingly, the transcript levels of IzGAD and IzDAO decreased under these conditions, suggesting that GABA accumulation at this stage is predominantly governed by post-translational activation rather than transcriptional upregulation. Upon entry into the mid-exponential phase, a distinct phase-dependent shift in GABA biosynthetic regulation emerged. Under low temperature stress, GAD activity and IzGAD expression were both suppressed, whereas DAO activity and IzDAO transcripts increased significantly, indicating the transition to DAO-mediated GABA production as the dominant route. Under high temperature stress, both GAD and DAO activities increased, yet their corresponding gene transcription remained repressed, revealing a persistent asynchrony between enzyme activities and gene expression across both phases. Meanwhile, the expression of catabolic genes (IzGABA-T, IzSSADH1, and IzSSADH2) was consistently downregulated, further facilitating the net accumulation of GABA. Promoter analysis revealed multiple stress- and hormone-responsive cis-elements in these genes, implying a complex regulatory network. Collectively, our findings uncover a growth-phase-dependent reconfiguration of GABA biosynthetic pathways in I. zhanjiangensis under temperature stress. These insights provide a mechanistic basis for strain-specific temperature management in aquaculture applications.

Jian-Sen Luo, Lin Zhang, Ji-Chang Han et al. · Microorganisms · 0 citations

Open access Sep 2026

Identifying critical nodes in complex networks via semiclustering weighted leverage centrality

Identifying key nodes in complex networks is of great significance for optimizing information dissemination, containing epidemics, and analyzing network robustness. The use of clustering coefficients to identify key nodes is a current research focus; however, existing studies have not yet applied this approach to leverage centrality (LC). Furthermore, in the identification of nodes based on LC, negative-leverage nodes are simply regarded as unimportant followers, overlooking the bridging role that nodes with low clustering coefficients may play. This paper proposes a new node centrality—semi-clustering weighted LC (SWLC)—for identifying critical nodes, from the perspective of synergizing node’s own attributes with its neighborhood structure. This method uses the clustering coefficient to apply differential weighting to negative-leverage nodes; it comprehensively evaluates a node’s importance by summing the transformed leverage values of the node itself and all nodes within its two-hop neighborhood through a two-hop aggregation strategy. The rationality analysis and ablation experiments conducted on the proposed method demonstrate that SWLC achieves stable and excellent performance, and that the two innovative modules need to work together to attain the desired effectiveness. To verify the effectiveness and applicability of the proposed method, this paper compares SWLC with nine other centrality methods using the susceptible–infected–recovered model on nine real-world networks of varying sizes and domains. Experimental results demonstrate that SWLC achieves favorable performance in terms of discriminability, overall node ranking accuracy, and critical node ranking accuracy, and is capable of effectively identifying critical nodes in complex networks.

Rong-Rong Yin, M. Shen, En-You Zhu et al. · Physica Scripta · 0 citations