pH and thiosulfate dependent microbial sulfur oxidation strategies across diverse environments

Abstract

Sulfur oxidizing bacteria (SOB) play a key role in sulfur cycling in mine tailings impoundment (TI) waters, where sulfur concentrations are typically high. However, our understanding of SOB sulfur cycling via potential S oxidation pathways (sox, rdsr, and S₄I) in these globally ubiquitous contexts, remains limited. Here, we identified TI water column SOB community composition, metagenomics derived metabolic repertoires, physicochemistry, and aqueous sulfur concentration and speciation in four Canadian base metal mine, circumneutral-alkaline TIs over four years (2016 - 2019). Identification and examination of genomes from nine SOB genera occurring in these TI waters revealed two pH partitioned, metabolically distinct groups, which differentially influenced acid generation and sulfur speciation. Complete sox (csox) dominant SOB (e.g., Halothiobacillus spp., Thiomonas spp.) drove acidity generation and S₂O₃ ^2- consumption via the csox pathway at lower pH (pH ~5 to ~6.5). At circumneutral pH conditions (pH ~6.5 to ~8.5), the presence of non-csox dominant SOB (hosting the incomplete sox, rdsr, and/or other S oxidation reactions; e.g. Thiobacillus spp., Sulfuriferula spp.) were associated with higher [S₂O₃ ^2-] and limited acidity generation. The S₄I pathway part 1 (tsdA; S₂O₃ ^2- to S₄O₆ ^2-), was not constrained by pH, while S4I pathway part 2 (S₄O₆ ^2- disproportionation via tetH) was limited to Thiobacillus spp. and thus circumneutral pH values. Comparative analysis of low, natural (e.g., hydrothermal vents and sulfur hot springs) and high (e.g., Zn, Cu, Pb/Zn, and Ni tailings) sulfur systems literature data with these TI results, reveals a distinct TI SOB mining microbiome, characterized by elevated abundances of csox dominant SOB, likely sustained by continuous replenishment of sulfur species through tailings or mining impacted water additions. Our results indicate that under the primarily oxic conditions in these systems, S₂O₃ ^2- availability plays a key role in determining the dominant sulfur oxidation pathways and associated geochemical and physicochemical outcomes, highlighting the potential for biological management of mining impacted waters via pH and [S₂O₃ ^2-] manipulation.

Keywords: pH; sox genes; sulfur oxidizing bacteria (SOB); tailings impoundments; thiosulfate.

Figure 1

Metabolic potentials and pathways for sulfur oxidizing bacteria genera from the four TIs investigated here. Figure was adapted from Whaley-Martin et al. (2023) and based on Watanabe et al. (2019). Genomes were reconstructed from TI samples collected from the four target mines between 2016 and 2018. Due to low abundance of some organisms, variable quantities of genomes were used including 3 Thiomonas, 31 Halothiobacillus, 3 Thiovirga, 31 Thiobacillus, 30 Sediminibacterium, 4 Sulfuricurvum, and 14 Sulfuriferula.

Figure 2

16S rRNA relative abundance (%) of top nine sulfur oxidizing bacteria genera from 2016 – 2019, non-SOB genera and unknown genera for each of the four mines mapped to their sample locations across Canada. Individual samples are shown as bar graphs and mine average abundances are shown as pie graphs. “Non-SOB genera” are all other identified sequences and “unknown genera” are identified sequences not matched to genera in the Silva Database v138.1. Asterisks (*) denote samples with metagenome data included in this study.

Figure 3

Box and whisker plots and statistical analysis (ANOVA and post-hoc tukey pairwise statistical comparison) of cross mine pH, total S (mmol/L), S₂O₃^2- (mmol/L), and S_React (mmol/L). Box limits represent the first and third quartile of each dataset, with a black line indicating the median value and an “x” denoting the mean.

Figure 4

Genus level identification of (A–C) csox dominant SOB relative abundances, (Ai, Bi, Ci) non-csox dominant SOB relative abundances and (Aii, Bii, Cii) average 16S rRNA SOB communities identified in (A, Ai, Aii) tailings impoundment water samples from this study, (B, Bi, Bii) other mining and anthropogenic sample data from the literature and (C, Ci, Cii) environmental sample data obtained from the literature. “Non-SOB genera” are all other identified sequences in the samples and “unknown genera” are identified sequences not matched to genera in the Silva Database v138.1 (Data included in Bi, Bii, Ci, and Cii was collected from the following papers and can be identified using the superscript number: ¹Kadnikov et al., 2019, ^2-5Miettinen et al., 2021, ^6-7Auld et al., 2017, ^8-9Lopes et al., 2020, ^10-12Chen et al., 2013, ¹³Haosagul et al., 2020, ¹⁴Patwardhan et al., 2018, ¹⁵Arce-Rodríguez et al., 2019, ¹⁶Vavourakis et al., 2019, ^17-18Meier et al., 2017, ^19-20Reigstad et al., 2011). Black outlines indicate non-water samples (e.g., soil, rock, tailings, biofilm, etc.).

Figure 5

Conceptual model of pH bound metabolic activity with the associated responsible genera and geochemical pathway and outcomes in metal mining TI waters.