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<p><i>Leptolyngbya</i> JSC-1 is a thermophilic and siderophilic cyanobacterium inhabiting iron-rich hot springs. Response surface method (RSM) is being reported for the first time for optimizing growth conditions of this thermophilic and siderophilic cyanobacterium. Using response surface quadratic model of Box-Behnken design, optimal culture conditions (A: temperature, 45&#xa0;°C; B: Fe concentration, 42 µM; and C: light intensity, 2000&#xa0;lx which is equivalent to 27 µmol photons m⁻² s⁻¹ intensity of cool white fluorescent lamp) were determined. The significant model terms were found to be B, AB, A<sup>2</sup>, B<sup>2</sup>, and C<sup>2</sup>. The model <i>R</i><sup>2</sup> value (coefficient of determination) was 0.939, suggesting that the fitted model could explain 93.9% of the total variation. Both the predicted response (OD<sub>730</sub> = 2.133) and experimental response (OD<sub>730</sub> = 2.1) were in proximity, suggested the appropriateness of the model and RSM. Moreover, an unusual inverse proportion was observed between the Fe concentration and ROS generation with the least ROS generation in JSC-1 grown with 42 µM Fe concentration. Hence, RSM allows evaluating the effects of multiple factors and their interactions on one or more response variables and is recommended to be used for multifactorial optimization studies.</p>

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Fe-driven ROS mitigation in Leptolyngbya JSC-1: optimizing growth using response surface method

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Sikandar Khan,
  • Maryam Akhoundian,
  • Pengcheng Fu

摘要

Leptolyngbya JSC-1 is a thermophilic and siderophilic cyanobacterium inhabiting iron-rich hot springs. Response surface method (RSM) is being reported for the first time for optimizing growth conditions of this thermophilic and siderophilic cyanobacterium. Using response surface quadratic model of Box-Behnken design, optimal culture conditions (A: temperature, 45 °C; B: Fe concentration, 42 µM; and C: light intensity, 2000 lx which is equivalent to 27 µmol photons m⁻² s⁻¹ intensity of cool white fluorescent lamp) were determined. The significant model terms were found to be B, AB, A2, B2, and C2. The model R2 value (coefficient of determination) was 0.939, suggesting that the fitted model could explain 93.9% of the total variation. Both the predicted response (OD730 = 2.133) and experimental response (OD730 = 2.1) were in proximity, suggested the appropriateness of the model and RSM. Moreover, an unusual inverse proportion was observed between the Fe concentration and ROS generation with the least ROS generation in JSC-1 grown with 42 µM Fe concentration. Hence, RSM allows evaluating the effects of multiple factors and their interactions on one or more response variables and is recommended to be used for multifactorial optimization studies.