Cysteine-Mediated Extracellular Electron Transfer of Lysinibacillus varians GY32

Microbial extracellular electron transfer (EET) is essential in many natural and engineering processes. Compared with the versatile EET pathways of Gram-negative bacteria, the EET of Gram-positive bacteria has been studied much less and is mainly limited to the flavin-mediated pathway. Here, we investigate the EET pathway of a Gram-positive filamentous bacterium Lysinibacillus varians GY32. Strain GY32 has a wide electron donor spectrum (including lactate, acetate, formate, and some amino acids) in electrode respiration. Transcriptomic, proteomic, and electrochemical analyses show that the electrode respiration of GY32 mainly depends on electron mediators, and c-type cytochromes may be involved in its respiration. Fluorescent sensor and electrochemical analyses demonstrate that strain GY32 can secrete cysteine and flavins. Cysteine added shortly after inoculation into microbial fuel cells accelerated EET, showing cysteine is a new endogenous electron mediator of Gram-positive bacteria, which provides novel information to understand the EET networks in natural environments. IMPORTANCE Extracellular electron transport (EET) is a key driving force in biogeochemical element cycles and microbial chemical-electrical-optical energy conversion on the Earth. Gram-positive bacteria are ubiquitous and even dominant in EET-enriched environments. However, attention and knowledge of their EET pathways are largely lacking. Gram-positive bacterium Lysinibacillus varians GY32 has extremely long cells (>1 mm) and conductive nanowires, promising a unique and enormous role in the microenvironments where it lives. Its capability to secrete cysteine renders it not only an EET pathway to respire and survive, but also an electrochemical strategy to connect and shape the ambient microbial community at a millimeter scale. Moreover, its incapability of using flavins as an electron mediator suggests that the common electron mediator is species-dependent. Therefore, our results are important to understanding the EET networks in natural and engineering processes.