Waller Diane, Putnam Joel, Steiner J Nolan, Fisher Brant, Burcham Grant N, Oliver John, Smith Stephen B, Erickson Richard, Remek Anne, Bodoeker Nancy
United States Geological Survey, Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La Crosse, WI 54603, USA.
Conagen, Inc., 15 Deangelo Dr, Bedford, MA 01730, USA.
Conserv Physiol. 2023 Jun 10;11(1):coad040. doi: 10.1093/conphys/coad040. eCollection 2023.
Freshwater mussels (order Unionida) play a key role in freshwater systems as ecosystem engineers and indicators of aquatic ecosystem health. The fauna is globally imperilled due to a diversity of suspected factors; however, causes for many population declines and mortality events remain unconfirmed due partly to limited health assessment tools. Mussel-monitoring activities often rely on population-level measurements, such as abundance and age structure, which reflect delayed responses to environmental conditions. Measures of organismal health would enable preemptive detection of declining condition before population-level effects manifest. Metabolomic analysis can identify shifts in biochemical pathways in response to stressors and changing environmental conditions; however, interpretation of the results requires information on inherent variability of metabolite concentrations in mussel populations. We targeted metabolites in the haemolymph of two common mussels, and , from three Indiana streams (USA) using ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectroscopy. The influence of species, stream and sex on metabolite variability was examined with distance-based redundancy analysis. Metabolite variability was most influenced by species, followed by site and sex. Inter- and intraspecies metabolite variability among sexes was less distinct than differences among locations. We further categorized metabolites by occurrence and variability in mussel populations. Metabolites with high occurrence (Categories 1 and 2) included those indicative of energy status (catabolism versus anabolism; arginine, proline, carnitine, nicotinic acid, pantothenic acid), oxidative stress (proline, glutamine, glutamate) and protein metabolism (thymidine, cytidine, inosine). Metabolites with lower occurrence (Category 3) are constituents of assorted metabolic pathways and can be important biomarkers with additional temporal sampling to characterize their variability. These data provide a reference for future temporal (before/after) monitoring and for studies of stressor-metabolite linkages in freshwater mussels.
淡水贻贝（蚌目）作为生态系统工程师和水生生态系统健康指标，在淡水系统中发挥着关键作用。由于多种疑似因素，全球范围内该动物群受到威胁；然而，许多种群数量下降和死亡事件的原因仍未得到证实，部分原因是健康评估工具有限。贻贝监测活动通常依赖于种群水平的测量，如丰度和年龄结构，这些反映了对环境条件的延迟反应。生物体健康指标能够在种群水平效应显现之前预先检测到状况下降。代谢组学分析可以识别生物化学途径因应激源和环境条件变化而发生的变化；然而，结果的解释需要关于贻贝种群中代谢物浓度固有变异性的信息。我们使用超高效液相色谱结合四极杆飞行时间质谱法，对来自美国印第安纳州三条溪流中的两种常见贻贝（ 和 ）的血淋巴中的代谢物进行了靶向分析。通过基于距离的冗余分析，研究了物种、溪流和性别对代谢物变异性的影响。代谢物变异性受物种影响最大，其次是地点和性别。不同性别间和种内代谢物变异性不如不同地点间的差异明显。我们还根据贻贝种群中的出现情况和变异性对代谢物进行了分类。出现频率高的代谢物（第1类和第2类）包括那些指示能量状态（分解代谢与合成代谢；精氨酸、脯氨酸、肉碱、烟酸、泛酸）、氧化应激（脯氨酸、谷氨酰胺、谷氨酸）和蛋白质代谢（胸苷、胞苷、肌苷）的代谢物。出现频率较低的代谢物（第3类）是各种代谢途径的组成部分，通过额外的时间采样来表征其变异性，它们可能是重要的生物标志物。这些数据为未来的时间（前后）监测以及淡水贻贝应激源 - 代谢物联系的研究提供了参考。
