NCS-FO: Developing engineering solutions to investigate microbiome-to-neuron communication

这个项目的目标是创造一种工程解决方案来测量和预测肠道-微生物组-大脑轴上的分子通讯。这个平台有可能促进对肠道微生物群与神经系统交流的基本理解。该项目将量化参与这种串扰的关键分子的释放模式，并确定它们对神经激活和行为的影响。肠道-微生物组-大脑轴，包括一个巨大的神经网络，支配肠道并向大脑传播信号，是行为和认知的主要影响因素。神经递质血清素是这一途径中的关键分子；肠道上皮细胞感知腔内状况并释放血清素刺激附近的神经元。肠道微生物组已被证明可以调节这种血清素的释放，这一过程也与胃和神经疾病的共同发生有关。这项工作的技术基础包括设计和构建一种设备，使研究人员能够组装肠道-微生物组-神经元组织界面的基本组成部分。该装置由传感器组成，以获取目前无法获取的信息——收集被调查细胞和组织的长度和时间尺度上的分子信息。从该平台提取的数据将使微生物、肠道和神经信号模式的时间相关性和预测成为可能。这项工作为来自不同学科的研究人员和利益相关者提供了机会，包括电气和计算机工程、生物工程、分子生物学、神经科学和数据科学，以开发一种面向系统的方法。此外，该项目还通过诸如工程研究女性奖学金和第一年创新与研究经验（FIRE）等项目，促进历史上在工程领域代表性不足的女性和本科生的参与。多学科工程方法对于建立一个能够直接监测肠道-神经元轴化学转导模式的体外发现平台至关重要。在任务1中，电化学传感器将直接在多孔细胞培养基质上制造，从而可以直接接触体外模型肠上皮的细胞和分子机制。细胞层的阻抗监测将检测随时间的物理变化（例如，屏障完整性）。电位监测将检测肠道细胞由于细菌代谢物刺激而释放的实时血清素。在TASK 2中，将通过将这种细胞释放的5 -羟色胺暴露于分离的体外小龙虾神经索中，研究肠道5 -羟色胺信号的神经效应。神经行为激活模式将记录在后肠蠕动运动和感觉神经元，双向连接中枢和肠神经系统。机器学习方法将确定量化离散血清素释放和神经元激活模式的关键变量。在TASK 3中，肠道上皮粘膜层将被特定肠道微生物定植，以评估细菌对屏障完整性、血清素释放模式和由此产生的神经肌肉激活的影响。通过机器学习进行分类将量化不同微生物组合对时间依赖性血清素释放谱和下游效应的整体和协同效应。这项工作有许多新颖的方面。首先，这是一个实现广泛集成细胞界面传感器的新平台，可直接访问实时细胞和分子数据。其次，利用这项技术来研究肠道和神经系统之间的相互作用，可以前所未有地深入了解广阔且相对难以接近的肠-脑转导途径。第三，机器学习分析可以识别血清素能通信的有意义的模式，并预测肠道细菌对神经行为的预期影响。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 goal of this project is to create an engineering solution to measure and predict the molecular communication across the gut-microbiome-brain axis. This platform has the potential to facilitate the fundamental understanding of gut microbiome communication with the nervous system. The project will quantify release patterns of key molecules involved in this cross-talk and identify their influence on neural activation and behavior. The gut-microbiome-brain axis, comprising a vast network of nerves innervating the gut and propagating signals to the brain, is a major influencer of behavior and cognition. The neurotransmitter serotonin is a key molecule in this pathway; gut epithelial cells sense luminal conditions and release serotonin to stimulate nearby neurons. The gut microbiome has been shown to mediate this serotonin release, a process that is also linked to the co-occurrence of gastric and neural disorders. The technical underpinnings of this work involve designing and constructing a device that enables researchers to assemble the essential components of the gut-microbiome-neuron tissue interface. The device is fabricated with sensors to obtain information that is currently inaccessible - collecting molecular information at the length and time scales of the cells and tissues under investigation. The data extracted from this platform will enable temporal correlation and prediction of microbial, gut, and neural signaling patterns. This work provides opportunities to bring together researchers and stakeholders from various disciplines including electrical and computer engineering, bioengineering, molecular biology, neuroscience, and data science to develop a system-oriented approach. Further, this project promotes the participation of women, historically underrepresented in engineering, and undergraduates through programs such as Women in Engineering Research Fellowship and First-Year Innovation and Research Experience (FIRE). Multidisciplinary engineering methods are essential to building an in vitro discovery platform capable of directly monitoring chemical transduction patterns along the gut-neuron axis. In TASK 1, electrochemical sensors will be directly fabricated on a porous cell culture substrate, allowing direct access to cellular and molecular mechanisms of an in vitro model gut epithelium. Impedance monitoring of the cell layer will detect physical changes over time (e.g., barrier integrity). Potentiometric monitoring will detect real-time serotonin released from gut cells due to bacterial metabolite stimulation. In TASK 2, the neural effect of gut serotonin signaling will be studied by exposing this cell-released serotonin to an isolated ex vivo crayfish nerve cord with connected and innervated hindgut. Neurobehavioral activation patterns will be recorded during hindgut peristalsis in motor and sensory neurons that bidirectionally connect the central and enteric nervous systems. Machine learning approaches will identify key variables to quantify discrete serotonin release and neuronal activation patterns. In TASK 3, the mucosal layer of the gut epithelium will be colonized with specific gut microbes to assess bacterial influence on barrier integrity, serotonin release patterns, and resulting neuromuscular activation. Classification via machine learning will quantify the wholistic and synergistic effects of different microbial combinations on time-dependent serotonin release profiles and downstream effects. There are multiple novel aspects of this work. First, this is a new platform implementing extensive integrated cell-interfacial sensors for direct access to real-time cell and molecular data. Second, the use of this technology to investigate the interplay between gut and nervous system can give unprecedented insight into the vast and relatively inaccessible gut-brain transduction pathways. Third, machine learning analysis can identify meaningful patterns of serotonergic communication and predict the expected impact of gut bacteria on neural behavior.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.