Gut microbiota of wild fish as reporters of compromised aquatic environments sleuthed through machine learning.
Gut microbiota of wild fish as reporters of compromised aquatic environments sleuthed through machine learning.
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通过机器学习来侦查野生鱼类的肠道微生物群，作为受损水生环境的报告者。
DOI：
10.1152/physiolgenomics.00002.2022
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发表时间：
2022
影响因子：
4.6
通讯作者：
Joe,Bina
中科院分区：
文献类型：
--
作者：
TurnerJr,JohnW;Cheng,Xi;Saferin,Nilanjana;Yeo,Ji-Youn;Yang,Tao;Joe,Bina
人类活动对水生环境的负面影响正在增加。尽管野生鱼类在水生生态中发挥着关键作用，并构成了全球主要的食物来源，但人们对这些影响对它们的生理后果知之甚少。在这里，我们通过野生鱼类和它们的肠道微生物群之间的相互关系的透镜来解决这个问题，假设鱼类微生物群是水生环境的报告者。研究了两个地理上独立的硬骨鱼野生鱼种（俄亥俄州的伊利湖和美属维尔京群岛的加勒比海）。在每个地理位置，从存在或不存在已知水生危害的区域的鱼中收集新鲜粪便样品。通过微生物16 S-rRNA基因测序评估了肠道微生物群，这是两种鱼类的第一份完整报告。尽管地理，气候，水类型，鱼类，栖息地，饮食和肠道微生物组成的显着差异，由于水生妥协，两种鱼类共享的微生物群的变化模式几乎相同。接下来，这些数据经过机器学习（ML），以检查使用鱼肠道微生物群作为人类水生影响的生态标志物的可靠性。独立于地理位置，ML预测水生妥协具有显着的准确性（> 90%）。总的来说，这项研究代表了第一次与多物种压力相关的比较，并通过ML展示了人工智能作为生物监测和检测受损水生条件的工具的潜力。
Human-generated negative impacts on aquatic environments are rising. Despite wild fish playing a key role in aquatic ecologies and comprising a major global food source, physiological consequences of these impacts on them are poorly understood. Here we address the issue through the lens of interrelationship between wild fish and their gut microbiota, hypothesizing that fish microbiota are reporters of the aquatic environs. Two geographically separate teleost wild-fish species were studied (Lake Erie, Ohio, and Caribbean Sea, US Virgin Islands). At each geolocation, fresh fecal samples were collected from fish in areas of presence or absence of known aquatic compromise. Gut microbiota was assessed via microbial 16S-rRNA gene sequencing and represents the first complete report for both fish species. Despite marked differences in geography, climate, water type, fish species, habitat, diet, and gut microbial compositions, the pattern of shifts in microbiota shared by both fish species was nearly identical due to aquatic compromise. Next, these data were subjected to machine learning (ML) to examine reliability of using the fish-gut microbiota as an ecomarker for anthropogenic aquatic impacts. Independent of geolocation, ML predicted aquatic compromise with remarkable accuracy (> 90%). Overall, this study represents the first multispecies stress-related comparison of its kind and demonstrates the potential of artificial intelligence via ML as a tool for biomonitoring and detecting compromised aquatic conditions.