Use of 16S rRNA gene sequences to identify cyanobacteria that can grow in far-red light

Although most cyanobacteria use visible light (VL; & lambda; = 400-700 nm) for photosynthesis, some have evolved strategies to use far-red light (FRL; & lambda; = 700-800 nm). These cyanobacteria are defined as far-red light-utilizing cyanobacteria (FRLCyano), including two groups: (1) chlorophyll d-producing Acaryochloris spp. and (2) polyphyletic cyanobacteria that produce chlorophylls d and f in response to FRL. Numerous ecological studies examine pigments, such as chlorophylls d and f, to investigate the presence of FRLCyano in the environment. This method is not ideal because it can only detect FRLCyano that have made chlorophylls d or f. Here we develop a new method, far-red cyanobacteria identification (FRCI), to identify FRLCyano based on 16S rRNA gene sequences. From public databases and published articles, 62 16S rRNA gene sequences of FRLCyano were extracted. Comparing with related lineages, we determined that 97% sequence identity is the optimal cut-off for distinguishing FRLCyano from other cyanobacteria. To test the method experimentally, we collected samples from 17 sites in Taipei, Taiwan, and conducted VL and FRL enrichments. Our results demonstrate that FRCI can detect FRLCyano during FRL enrichments more sensitively than pigment analysis. FRCI can also resolve the composition of FRLCyano at the genus level, which pigment analysis cannot do. In addition, we applied FRCI to published datasets and discovered putative FRLCyano in diverse environments, including soils, hot springs and deserts. Overall, our results indicate that FRCI is a sensitive and high-resolution method using 16S rRNA gene sequences to identify FRLCyano.

Automated summary

This article introduces a new method, far-red cyanobacteria identification (FRCI), based on 16S rRNA gene sequences to identify far-red light-utilizing cyanobacteria (FRLCyano). Experimental results demonstrate that FRCI can detect FRLCyano more sensitively than pigment analysis during far-red light enrichments and can also resolve their composition at the genus level. Additionally, the method is applied to published datasets and reveals potential FRLCyano in diverse environments.