Metagenomics-assembled genomes reveal microbial metabolic adaptation to athalassohaline environment, the case Lake Barkol, China

Abstract

Salt-tolerant and halophilic microorganisms are critical drivers of ecosystem stability and biogeochemical cycling in athalassohaline environments. Lake Barkol, a high-altitude inland saline lake, provides a valuable natural setting for investigating microbial community dynamics and adaptation mechanisms under extreme salinity. In this study, we employed high-throughput metagenomic sequencing to characterize the taxonomic composition, metabolic potential, and ecological functions of microbial communities in both water and sediment samples from Lake Barkol. We reconstructed 309 metagenome-assembled genomes (MAGs), comprising 279 bacterial and 30 archaeal genomes. Notably, approximately 97% of the MAGs could not be classified at the species level, indicating substantial taxonomic novelty in this ecosystem. Dominant bacterial phyla included Pseudomonadota, Bacteroidota, Desulfobacterota, Planctomycetota, and Verrucomicrobiota, while archaeal communities were primarily composed of Halobacteriota, Thermoplasmatota, and Nanoarchaeota. Metabolic reconstruction revealed the presence of diverse carbon fixation pathways, including the Calvin-Benson-Bassham (CBB) cycle, the Arnon-Buchanan reductive tricarboxylic acid (rTCA) cycle, and the Wood-Ljungdahl pathway. Autotrophic sulfur-oxidizing bacteria, alongside members of Cyanobacteria and Desulfobacterota, were implicated in primary production and carbon assimilation. Nitrogen metabolism was predominantly mediated by Gammaproteobacteria, with evidence for both nitrogen fixation and denitrification processes. Sulfur cycling was largely driven by Desulfobacterota and Pseudomonadota, contributing to sulfate reduction and sulfur oxidation pathways. Microbial communities exhibited distinct osmoadaptation strategies. The "salt-in" strategy was characterized by ion transport systems such as Trk/Ktr potassium uptake and Na⁺/H⁺ antiporters, enabling active intracellular ion homeostasis. In contrast, the "salt-out" strategy involved the biosynthesis and uptake of compatible solutes including ectoine, trehalose, and glycine betaine. These strategies were differentially enriched between water and sediment habitats, suggesting spatially distinct adaptive responses to local salinity gradients and nutrient regimes. Additionally, genes encoding microbial rhodopsins were widely distributed, suggesting that rhodopsin-based phototrophy may contribute to supplemental energy acquisition under osmotic stress conditions. The integration of functional and taxonomic data highlights the metabolic versatility and ecological roles of microbial taxa in sustaining biogeochemical processes under hypersaline conditions. Overall, this study reveals extensive taxonomic novelty and functional plasticity among microbial communities in Lake Barkol and underscores the influence of salinity in structuring microbial assemblages and metabolic pathways in athalassohaline ecosystems.

Keywords: athalassohaline Lake Barkol; metabolic adaptation; metagenome-assembled genomes; microbial consortia; osmoadaptation strategies.

Figure 1

Location of sampling sites in Lake Barkol, Xinjiang Province, China. The maps show the geographic position of Lake Barkol within China and Xinjiang Province. The right panel presents the detailed distribution of sampling sites around the lake, where red squares indicate sediment sampling locations and blue squares indicate lake water sampling locations. Insets provide photographs highlighting the lake’s typical salt flat environment and surrounding landscape. The base map was adapted from Tianditu (https://www.tianditu.gov.cn/), the National Platform for Common Geospatial Information Services of China, hosted by the Ministry of Natural Resources of the People’s Republic of China.

Figure 2

Microbial diversity in Lake Barkol water and sediment. A. Phylogenetic relationships among the bacterial MAGs recovered from the sediment and water of Lake Barkol were inferred from a multiple sequence alignment of 120 bacterial marker proteins, as defined by the Genome Taxonomy Database classifier (GTDB-Tk). The colors of the branches represent different phyla.

Figure 3

Phylogenetic relationships among the 30 archaeal MAGs were inferred by constructing a maximum-likelihood tree using 122 archaeal marker genes identified by GTDB-Tk. Concatenated multiple sequence alignment was performed using the GTDB-Tk alignment module. Branch colors represent different phyla.

Figure 4

Taxonomic composition of microbial communities in water (W_1, W_4, W_8) and sediment (S_1, S_2, S_3) samples from Lake Barkol. (A) Relative abundance of the top 20 microbial phyla. Archaeal phyla are highlighted in red. Phyla outside the top 20 are grouped as “Other.” “Unmapped” represents sequences that could not be taxonomically assigned at the phylum level. (B) Relative abundance of the top 30 microbial genera. Archaeal genera are highlighted in red. Genera outside the top 30 are grouped as “Other”.

Figure 5

Functional profiles of the main microbial groups involved in main carbon cycling in Lake Barkol. The red arrow is the Calvin-Benson-Bassham (CBB) cycle, the yellow arrow is the rTCA cycle, the green arrow is Wood-Ljungdahl (WL) pathway, the light blue arrow is Aerobic CO oxidation pathway, and the blue arrow is methanogenesis pathway. The square is bacteria phyla, and the triangle is archaea phyla. Complete lists of metabolic genes or pathways can be found in Supplementary Table S4.

Figure 6

Phylum-level distribution and relative abundance of key nitrogen cycling genes (KEGG Orthology identifiers) in microbial communities from Lake Barkol. Dot size represents gene abundance, and color indicates functional category. Archaeal phyla are shown in red.

Figure 7

Phylum-level distribution and relative abundance of key sulfur cycling genes (KEGG Orthology identifiers) in microbial communities from Lake Barkol. Dot size represents gene abundance, and color indicates sulfur metabolic pathway. Archaeal phyla are shown in red.

Figure 8

Heatmap showing the distribution of salt adaptation-related KEGG orthologs (KOs) across microbial phyla in Lake Barkol. KO abundance values are normalized and color-coded, with yellow indicating high relative contribution. Genes are grouped by function, including ion transporters, compatible solute synthesis and transport, mechanosensitive channels, rhodopsins, and stress response systems. Archaeal phyla are highlighted in red.