The unpredictability of microbial growth and subsequent localized corrosion of steel can cause significant cost for the oil and gas industry, due to production downtime, repair, and replacement. Despite a long tradition of academic research and industrial experience, microbial corrosion is not yet fully understood and thus not effectively controlled. In particular, biomarkers suitable for diagnosing microbial corrosion which abstain from the detection of the classic signatures of sulfate-reducing bacteria are urgently required. In this study, a natural microbial community was enriched anaerobically with carbon steel coupons and in the presence of a variety of physical and chemical conditions. With the characterization of the microbiome and of its functional properties inferred through predictive metagenomics, a series of proteins were identified as biomarkers in the water phase that could be correlated directly to corrosion. This study provides an opportunity for the further development of a protein-based biomarker approach for effective and reliable microbial corrosion detection and monitoring in the field.

中文翻译：

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通过预测宏基因组学鉴定的蛋白质作为检测微生物影响腐蚀的潜在生物标志物


由于生产停机、维修和更换，微生物生长的不可预测性以及随后的钢材局部腐蚀可能会给石油和天然气行业带来巨大的成本。尽管有着悠久的学术研究传统和工业经验，但微生物腐蚀尚未被完全理解，因此无法得到有效控制。特别是，迫切需要适合诊断微生物腐蚀的生物标志物，而无需检测硫酸盐还原菌的经典特征。在这项研究中，天然微生物群落在各种物理和化学条件下用碳钢试样进行厌氧富集。通过预测宏基因组学推断微生物组及其功能特性的特征，一系列蛋白质被鉴定为水相中可与腐蚀直接相关的生物标志物。这项研究为进一步开发基于蛋白质的生物标志物方法提供了机会，以在现场进行有效且可靠的微生物腐蚀检测和监测。