NSF Convergence Accelerator Track L: Engineered microbial sensors for assessing water quality

该项目寻求合成生物学、生物电子学和机器学习方法的融合，以提供用于评估水质的使用点传感器，对公共卫生和环境保护具有广泛影响。这项活动的主要成果将是新的低成本传感系统，用于评估在整个水循环中应用的水中的化学物质，包括在世界上最大的都市区域之一纽约市监测废水处理、饮用水监测、工业用水和雨水排放。这项工作通过纽约市环境保护部以及大学和工业合作伙伴的参与，吸引了公共部门的参与。这些聚合研究活动的结果将是一个框架，用于快速开发基于微生物的生物传感器，以检测各种分析物，同时与互补的金属氧化物半导体读出装置相连接。传感器将根据环境保护署（EPA）建立的完善的质量指标进行评估。在之前重要工作的基础上，该项目将采用酵母（Saccharomyces cerevisiae）作为工程传感微生物，这是真核重组蛋白表达的强大基础。该项目将主要关注将g蛋白偶联受体（gpcr）作为酵母中的识别蛋白，利用人工智能（AI）中最新的大型语言模型，在现有的gpcr上进行训练，以设计新的识别蛋白。高通量DNA合成和筛选方法将用于快速评估候选蛋白质。该项目将开发这些传感器的“模拟”和“数字”读数，同时采用其他带有反馈的遗传控制系统，以提高在噪声和混杂因素存在时的传感稳健性。对于输出，该项目将使用光学吸收（色素）和酵母在感应时表达的氧化还原活性肽。这两种方法都允许酵母很容易地与低成本的互补金属氧化物半导体（CMOS）读出设备相连接，这将是这项工作的另一个融合方面。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The project seeks the convergence of synthetic biology, bioelectronics, and machine learning approaches to provide point-of-use sensors for assessing water quality with broad implications for public health and environmental protection. The primary results of this activity will be new low-cost sensing systems for assessing chemicals in water as applied throughout the water cycle, including waste-water treatment monitoring, drinking-water monitoring, industrial water use, and storm-water discharges in one of the largest metropolitan regions in the world, New York City. This work engages the public sector through the participation of the New York City Department of Environmental Protection as well as university and industry partners. The result of these convergence research activities will be a framework for quickly developing microbial-based biosensors to detect broad classes of analytes, while interfacing to complementary metal-oxide-semiconductor read-out devices. Sensors will be evaluated against well-established quality metrics established by the Environmental Protection Agency (EPA). Building on significant prior work, this project will employ yeast (Saccharomyces cerevisiae) as the engineered sensing microbe, a powerful chassis for the expression of eukaryotic recombinant proteins. The project will focus primarily on engineering G-protein-coupled receptors (GPCRs) as recognition proteins in yeast, using the latest advanced in large language models in artificial intelligence (AI) trained on existing GPCRs to engineer new recognition proteins. High-through DNA synthesis and screening approaches will be employed to rapidly assess candidate proteins. The project will develop both “analog” and “digital” readout from these sensors, while employing other genetic control systems with feedback to improve sensing robustness in the presence of noise and confounders. For output, the project will use optical absorption (pigments) and redox-active peptides expressed by the yeast upon sensing. Both of these approaches allow the yeast to be easily interfaced with low-cost complementary metal-oxide-semiconductor (CMOS) read-out devices, which will be another convergent aspect of this effort.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.