Department of Chemistry, University of Tennessee, 552 Buehler Hall, Knoxville, TN 37996-1600, USA.
Department of Chemistry, University of Tennessee, 552 Buehler Hall, Knoxville, TN 37996-1600, USA.
Anal Chim Acta. 2017 Feb 15;954:1-13. doi: 10.1016/j.aca.2016.11.058. Epub 2016 Dec 3.
In marine ecosystems, microalgae are an important component as they transform large quantities of inorganic compounds into biomass and thereby impact environmental chemistry. Of particular relevance is phytoplankton's sequestration of atmospheric CO, a greenhouse gas, and nitrate, one cause of harmful algae blooms. On the other hand, microalgae sensitively respond to changes in their chemical environment, which initiates an adaptation of their chemical composition. Analytical methodologies were developed in this study that utilize microalgae's adaptation as a novel approach for in-situ environmental monitoring. Longterm applications of these novel methods are investigations of environmental impacts on phytoplankton's sequestration performance and their nutritional value to higher organisms feeding on them. In order to analyze the chemical composition of live microalgae cells (Nannochloropsis oculata), FTIR-ATR spectroscopy has been employed. From time series of IR spectra, the formation of bio-sediment can be monitored and it has been shown that the nutrient availability has a small but observable impact. Since this bio-sediment formation is governed by several biological parameters of the cells such as growth rate, size, buoyancy, number of cells, etc., this enables studies of chemical environment's impact on biomass formation and the cells' physical parameters. Moreover, the spectroscopic signature of these microalgae has been determined from cultures grown under 25 different CO and NO mixtures (200 ppm-600 ppm CO, 0.35 mM-0.75 mM NO). A novel, nonlinear modeling methodology coined 'Predictor Surfaces' is being presented by means of which the nonlinear responses of the cells to their chemical environment could reliably be described. This approach has been utilized to measure the CO concentration in the atmosphere over the phytoplankton culture as well as the nitrate concentration dissolved in their growing environment. The achieved precision of concentration predictions were a few percent of the measurement range. Moreover, the Predictor Surface itself allows for a chemical interpretation of the cells' response to a shift in their chemical environment. This will open new approaches to study the link between concentration levels in an ecosystem and the biological consequences for this ecosystem.
在海洋生态系统中，微藻作为将大量无机化合物转化为生物量的重要组成部分，从而影响环境化学。特别值得关注的是浮游植物对大气 CO 的固定作用，CO 是一种温室气体，而硝酸盐是有害藻类大量繁殖的一个原因。另一方面，微藻对其化学环境的变化敏感，这会引发其化学成分的适应性变化。本研究开发了分析方法，利用微藻的适应性作为原位环境监测的新方法。这些新方法的长期应用是研究环境对浮游植物固定性能及其对以其为食的高等生物营养价值的影响。为了分析活微藻细胞（Nannochloropsis oculata）的化学组成，采用了傅里叶变换衰减全反射（FTIR-ATR）光谱法。从时间序列的红外光谱中，可以监测生物沉积物的形成，并表明营养物质的可用性虽然较小，但可以观察到影响。由于这种生物沉积物的形成受到细胞的几个生物学参数的控制，如生长速率、大小、浮力、细胞数量等，因此可以研究化学环境对生物量形成和细胞物理参数的影响。此外，还从在 25 种不同的 CO 和 NO 混合物（200 ppm-600 ppm CO，0.35 mM-0.75 mM NO）下培养的微藻中确定了其光谱特征。提出了一种新的、称为“预测曲面”的非线性建模方法，通过该方法可以可靠地描述细胞对其化学环境的非线性响应。该方法用于测量浮游植物培养物上方大气中的 CO 浓度以及其生长环境中溶解的硝酸盐浓度。浓度预测的精度达到了测量范围的百分之几。此外，预测曲面本身允许对细胞对化学环境变化的反应进行化学解释。这将为研究生态系统中浓度水平与对该生态系统的生物后果之间的联系开辟新途径。
