Niche partitioning of marine group I Crenarchaeota in the euphotic and upper mesopelagic zones of the East China Sea

Abstract

Marine group I Crenarchaeota (MGI) represents a ubiquitous and numerically predominant microbial population in marine environments. An understanding of the spatial dynamics of MGI and its controlling mechanisms is essential for an understanding of the role of MGI in energy and element cycling in the ocean. In the present study, we investigated the diversity and abundance of MGI in the East China Sea (ECS) by analysis of crenarchaeal 16S rRNA gene, the ammonia monooxygenase gene amoA, and the biotin carboxylase gene accA. Quantitative PCR analyses revealed that these genes were higher in abundance in the mesopelagic than in the euphotic zone. In addition, the crenarchaeal amoA gene was positively correlated with the copy number of the MGI 16S rRNA gene, suggesting that most of the MGI in the ECS are nitrifiers. Furthermore, the ratios of crenarchaeal accA to amoA or to MGI 16S rRNA genes increased from the euphotic to the mesopelagic zone, suggesting that the role of MGI in carbon cycling may change from the epipelagic to the mesopelagic zones. Denaturing gradient gel electrophoretic profiling of the 16S rRNA genes revealed depth partitioning in MGI community structures. Clone libraries of the crenarchaeal amoA and accA genes showed both "shallow" and "deep" groups, and their relative abundances varied in the water column. Ecotype simulation analysis revealed that MGI in the upper ocean could diverge into special ecotypes associated with depth to adapt to the light gradient across the water column. Overall, our results showed niche partitioning of the MGI population and suggested a shift in their ecological functions between the euphotic and mesopelagic zones of the ECS.

Fig. 1.

Map of the ECS showing the approximate bottom topography and hydrographic structure and the location of the sampling stations. CDW, Changjiang diluted water; TWC, Taiwan Warm Current; KBCNT, Kuroshio Branch Current north of Taiwan; KBCWK, Kuroshio Branch Current west of Kyushu; TSWC, Tsushima Strait Warm Current; YSWC, Yellow Sea Warm Current; Kuroshio, Kuroshio Current. The map was created by use of Planiglobe, with information on currents taken from references and .

Fig. 2.

Depth profiles of abundances of archaeal and MGI 16S rRNA genes and crenarchaeal amoA and accA genes measured by using qPCR at stations 608 and 712 in the ECS. Bars denote 1 standard error of triplicate qPCR determinations and are not visible when they are less than the width of the data point.

Fig. 3.

Ratios of crenarchaeal accA to MGI 16S rRNA genes or crenarchaeal amoA genes.

Fig. 4.

Clustering of the T-RFLP profiles of bacterial 16S rRNA genes (a) and clustering of the DGGE profiles of crenarchaeal 16S rRNA genes (b) in the ECS. Clustering analyses were performed based on the Sorensen algorithm, and the scale bar indicates the Sorensen distance.

Fig. 5.

Depth distribution of phylogenetic community structures of crenarchaeal amoA and accA genes recovered from the ECS. The relative abundance of each phylotype named in Fig. S2 in the supplemental material was calculated and is represented in a column diagram.

Fig. 6.

Ecotype simulation of crenarchaeal amoA (a) and accA (b) gene sequences recovered from the ECS and shift in relative abundances of different predicted ecotypes for amoA (c) and accA (d) across the water column of the ECS. Sampling stations and sampling depths are coded by colored squares. Predicted ecotypes are coded by colored circles at the phylogenetic nodes or the colored squares shown in panels c and d. Two more-detailed images for panels a and b are shown in Fig. S3 in the supplemental material.

Fig. 7.

Clustering of the crenarchaeal amoA and accA clone libraries based on the weighted UniFrac algorithm (a and b) or based on the Bray-Curtis algorithm of ecotype abundance (c and d). The scale bar indicates the UniFrac distance (a and b) or the Bray-Curtis distance (c and d).