Kundu Pritam, Ghosh Amit
School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.
P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.
Biotechnol Bioeng. 2025 Apr;122(4):1010-1024. doi: 10.1002/bit.28918. Epub 2025 Jan 5.
Microbial communities have shown promising potential in degrading complex biopolymers, producing value-added products through collaborative metabolic functionality. Hence, developing synthetic microbial consortia has become a predominant technique for various biotechnological applications. However, diverse microbial entities in a consortium can engage in distinct biochemical interactions that pose challenges in developing mutualistic communities. Therefore, a systems-level understanding of the inter-microbial metabolic interactions, growth compatibility, and metabolic synergisms is essential for developing effective synthetic consortia. This study demonstrated a genome-scale community modeling approach to assess the inter-microbial interaction pattern and screen metabolically compatible bacterial pairs for designing the lignocellulolytic coculture system. Here, we have investigated the pairwise growth and biochemical synergisms among six termite gut bacterial isolates by implementing flux-based parameters, i.e., pairwise growth support index (PGSI) and metabolic assistance (PMA). Assessment of the PGSI and PMA helps screen nine beneficial bacterial pairs that were validated by designing a coculture experiment with lignocellulosic substrates. For the cocultured bacterial pairs, the experimentally measured enzymatic synergisms (DES) showed good coherence with model-derived biochemical compatibility (PMA), which explains the fidelity of the in silico predictions. The highest degree of enzymatic synergisms has been observed in C. denverensis P3 and Brevibacterium sp P5 coculture, where the total cellulase activity has been increased by 53%. Hence, the flux-based assessment of inter-microbial interactions and metabolic compatibility helps select the best bacterial coculture system with enhanced lignocellulolytic functionality. The flux-based parameters (PGSI and PMA) in the proposed community modeling strategy will help optimize the composition of microbial consortia for developing synthetic microcosms for bioremediation, bioengineering, and biomedical applications.
微生物群落已显示出在降解复杂生物聚合物方面的巨大潜力，通过协作代谢功能生产增值产品。因此，开发合成微生物群落已成为各种生物技术应用的主要技术。然而，群落中不同的微生物实体可能会进行不同的生化相互作用，这给互利共生群落的开发带来了挑战。因此，从系统层面理解微生物间的代谢相互作用、生长兼容性和代谢协同作用对于开发有效的合成群落至关重要。本研究展示了一种基因组规模的群落建模方法，用于评估微生物间的相互作用模式，并筛选代谢兼容的细菌对，以设计木质纤维素共培养系统。在此，我们通过实施基于通量的参数，即成对生长支持指数（PGSI）和代谢辅助（PMA），研究了六种白蚁肠道细菌分离株之间的成对生长和生化协同作用。对PGSI和PMA的评估有助于筛选出九对有益细菌对，通过设计使用木质纤维素底物的共培养实验对其进行了验证。对于共培养的细菌对，实验测量的酶促协同作用（DES）与模型推导的生化兼容性（PMA）显示出良好的一致性，这解释了计算机模拟预测的准确性。在丹佛梭菌P3和短杆菌属P5共培养中观察到最高程度的酶促协同作用，其中总纤维素酶活性提高了53%。因此，基于通量的微生物间相互作用和代谢兼容性评估有助于选择具有增强木质纤维素分解功能的最佳细菌共培养系统。所提出的群落建模策略中的基于通量的参数（PGSI和PMA）将有助于优化微生物群落的组成，以开发用于生物修复、生物工程和生物医学应用的合成微观世界。
