Tikariha Hitesh, Purohit Hemant J
Environmental Biotechnology and Genomics Division, CSIR-National Environmental Engineering Research Institute (NEERI), Nehru Marg, Nagpur, 440 020 India.
Indian J Microbiol. 2019 Sep;59(3):387-390. doi: 10.1007/s12088-019-00813-1. Epub 2019 Jun 13.
With the omics tool, the challenges in understanding the microbial community functions are becoming more intriguing. It is the environment created scenario, which demands alignment of the different members of the community for the desired output leading to common condition for their survival. The resultant community pathways provide a broad umbrella of metabolic options giving the desired plasticity, which plays decision making role in the adaptation process. The initial step in community characterization must involve the discovery of key and core member of the community and monitoring the fluctuations in functional abundance over the space and time. The concept of entropy and metabolic fluxes must reflect the inner metabolic machinery of the taxon selection and route of functional operation in a community. The segregation of member based on their functional role and hierarchical level in the community must be an essential step to be followed by interaction mapping and measurement of metabolic fluxes to derive the flow of metabolites within the community. This conceptual framework and integrated omics tools with supported statistical modeling algorithm can help in bringing out finer details in the process of community functional adaptation in any given scenario.
借助组学工具，理解微生物群落功能所面临的挑战正变得愈发引人入胜。这是由环境创造的情景，它要求群落的不同成员协同一致以实现期望的输出，从而为它们的生存创造共同条件。由此产生的群落途径提供了广泛的代谢选择，赋予了所需的可塑性，这在适应过程中发挥着决策作用。群落特征描述的第一步必须包括发现群落的关键和核心成员，并监测功能丰度在空间和时间上的波动。熵和代谢通量的概念必须反映分类群选择的内在代谢机制以及群落中功能运作的途径。根据成员在群落中的功能作用和层次水平进行分类，这必然是后续进行相互作用图谱绘制和代谢通量测量以推导群落内代谢物流动的一个重要步骤。这个概念框架以及集成的组学工具与支持的统计建模算法，有助于在任何给定情景下揭示群落功能适应过程中的更细微细节。
