# The Bacterial Antimonite Oxidase AnoA: Unexpected Diversity and Environmental Widespread Occurrence

Claire da Costa, Thierry Berthe, Titouan Dehaies, Sophie Ayrault, Yannick Colin

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Claire da Costa, Thierry Berthe, Titouan Dehaies, Sophie Ayrault, Yannick Colin. The Bacterial Antimonite Oxidase AnoA: Unexpected Diversity and Environmental Widespread Occurrence. Environmental Microbiology, 2025, 27 (3), pp.e70069. 10.1111/1462-2920.70069. hal-04986528

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# The Bacterial Antimonite Oxidase AnoA: Unexpected Diversity and Environmental Widespread Occurrence

Claire Da Costa, UNICAEN,

Thierry Berthe, UNICAEN, UMR METIS

Titouan Dehaies, UNICAEN

Sophie Ayrault, LSCE/IPSL

Yannick Colin, UNICAEN, UMR METIS

Published in: *Environmental Microbiology*, vol. 27, Issue 3, 2025. https://doi.org/10.1111/1462-2920.70069

## ABSTRACT

The growing contamination of urban areas by antimony (Sb) has sparked interest in microbial processes that modulate Sb speciation in ecosystems. The bacterial antimonite oxidase AnoA is the only oxidase known so far whose gene expression is specifically induced by Sb(III), but its annotation in public databases is currently lacking. Here, the computational search for AnoA orthologs predicted an unexpected phylogenetic distribution across the *Pseudomonadota* and *Actinomycetota*. Putative orthologs were identified in both known Sb(III)-oxidisers (e.g., *Shinella*, *Hydrogenophaga*, *Bosea*, *Cupriavidus* and *Pseudomonas*) and taxa not previously linked to the Sb cycle (e.g., *Bradyrhizobium*, *Mesorhizobium*, *Methylobacterium* and *Paraburkholderia*). The *anoA* gene is single-copy in most Proteobacterial genomes, but is often detected in multiple copies in the *Actinomycetota*. Furthermore, sequence evolutionary distances suggest that it is mainly inherited vertically, with horizontal transfer events, in particular towards the *Gammaproteobacteria*. Using the constructed database, new PCR primers were designed and outperformed existing strain-specific primers in amplifying the *anoA* gene from samples with varying Sb levels and microbial profiles. Sequencing and quantification of PCR amplicons revealed a diverse range of sequences in sediments and natural biofilms, indicating that the oxidase is more environmentally diverse and widespread than previously thought and may play a significant role in Sb(III) detoxification.
# 1 Introduction

Antimony (Sb) is a toxic metalloid belonging to group 15 of the periodic table (atomic number 51). Whereas Sb is less studied, it is frequently found as a co-contaminant of arsenic in the environment, with which it shares some geochemical and toxicological properties (Arai 2010; Herath et al. 2017; Li et al. 2016; Wilson et al. 2010). Sb is increasingly used in various anthropogenic activities (e.g., flame retardants, alloys and batteries, brake pad lubricants, ammunition, pharmaceuticals and pesticides) (Ahmad et al. 2014; Chan and Stachowiak 2004; Dupont et al. 2016; Haldar et al. 2011; Henckens et al. 2016; Lee

et al. 2013; Multani et al. 2016; Okkenhaug et al. 2016; Turner and Filella 2017). Emissions from road traffic, waste incineration and mining activities have led to significant contamination of environmental, biological and geochemical compartments (Li, Zheng, et al. 2018; Wang et al. 2011). As a result, increased Sb contamination was recently observed in waters (Le Pape et al. 2012; Mohiuddin et al. 2010), sediments (Mohiuddin et al. 2010; Rzetala, 2011), soils (Thestorf and Makki 2021) and atmosphere (Ayrault et al. 2010; Jiang et al. 2021) of urban areas. Sb speciation modulates the fate of the metalloid in the environment and its toxic effect on the biota. In particular, the myriad of processes favouring changes between the two main forms of Sb—antimonite [Sb(III)] and antimonate [Sb(V)]—are critical, as Sb(V) is believed to be more mobile and less toxic than Sb(III) (Wilson et al. 2010; Filella et al. 2002; Krachler et al. 2001).

Sb speciation occurring in natural receiving ecosystems is, to some extent, achieved via directly or indirectly microbially mediated pathways. In particular, metabolic end-products of some functional microbial groups (e.g., dissimilatory sulfate reducers and dissimilatory nitrate reducers) were reported to be involved in indirect Sb(III)-oxidation or Sb(V)-reduction processes (Li, Zhang, et al. 2019; Loni et al. 2020; Nguyen et al. 2017; Terry et al. 2015; Wang et al. 2013; Xiao et al. 2023; Zhang et al. 2016; Zhu et al. 2018). Reactive oxygen species (e.g., $H_2O_2$ and $O_2^-$), produced during cellular oxidative stresses, were also demonstrated to contribute indirectly to Sb(III)-oxidation (Gu et al. 2019; Li et al. 2017; Wang et al. 2016; Wang, Ye, et al. 2022). On the other hand, enzymatically mediated Sb oxidation/reduction pathways were reported among phylogenetically distinct bacteria affiliated to the *Pseudomonadota*, *Actinomycetota*, *Firmicutes* and *Bacteroidota* (Li et al. 2016; Deng et al. 2021; Liu et al. 2022; Zhang, O'Loughlin, et al. 2022). Specifically, the respiratory arsenate reductase *ArrAB* and antimonate reductase *AnrA* were identified to perform Sb(V)-reduction (Abin and Hollibaugh 2019; Wang et al. 2020), while the periplasmic arsenite oxidase *AioAB* and cytoplasmic antimonite oxidase *AnoA* carry out Sb(III)-oxidation (Li et al. 2015; Wang et al. 2015). More recent studies further suggested that three flavoproteins, namely *ArsO*, *ArsV* and *ArsH*, are involved in the oxidation of Sb(III) (Xiao et al. 2023; Gu et al. 2019, 2020; Tang et al. 2023; Zhang, Chen, et al. 2022), although the role of *ArsH* has yet to be confirmed. While the bacterial reduction of Sb(V) is usually associated with energy conservation (Wang et al. 2020; Wang, Ye, et al. 2018; Abin and Hollibaugh 2014; Kulp et al. 2014; Yamamura et al. 2021), Sb(III) oxidation has, on the contrary, been predicted in most cases as a detoxification process (Terry et al. 2015; Li et al. 2013; Lu et al. 2018; Nguyen and Lee 2015; Sun et al. 2020). Among the aforementioned bacterial Sb-oxidation pathways, the antimonite oxidase *AnoA* is the only oxidase described so far whose gene expression is specifically induced by Sb(III) and was shown to oxidise Sb(III) preferentially and more rapidly than As(III) (Li et al. 2017, 2015). *AnoA* belongs to the short-chain dehydrogenase/reductase (SDR) superfamily. It uses NADP+ as a co-factor (Li et al. 2015) and its transcription is under the control of the phosphate regulator *phoB2* belonging to the *phoB/phoR* two-component system (Li, Qiao, et al. 2019). The *anoA* gene has been reported in only a few members of the *Alphaproteobacteria* (e.g., *Agrobacterium*, *Rhizobium* and *Sinorhizobium/Ensifer*) and *Betaproteobacteria* (e.g., *Comamonas* and *Ralstonia*) (Li et al. 2016, 2015; Li, Zhang, et al. 2019; Yu et al. 2024). To date, two primer pairs were designed from the sequences of *Agrobacterium tumefaciens* strain GW4 (amplicon size of 119 bp) (Li et al. 2015) or *Sinorhizobium sp. strain GW3* (amplicon size of 295 bp) (Li, Zhang, et al. 2019), two strains in which *AnoA* function has been demonstrated. However, these strain-specific primers were formerly reported to fail in amplifying the *anoA* gene when applied to Sb-contaminated environmental samples (Zhang, Kolton, et al. 2021; Zhang, Li, et al. 2021) and are thus unlikely to cover the full gene diversity.

Since its discovery, the occurrence and diversity of the antimonite oxidase *AnoA* have not been systematically studied. This protein is therefore only poorly annotated in current public databases. These shortcomings, also experienced for other microbial genes involved in arsenic or sulphur metabolisms, have recently been improved by the construction of comprehensive databases from public orthology databases or by functionally annotating orthologs when necessary (González et al. 2019; Mirza et al. 2017; Song et al. 2022). The aim of this study was therefore to screen for putative *AnoA* orthologous sequences to (i) investigate their diversity, taxonomic distribution,
and transmission mode over bacterial lineages, (ii) identify potential new Sb(III)-oxidisers and (iii) develop new PCR primers covering a broader diversity spectrum for assessing *anoA* gene diversity and abundance in environmental matrices.

## 2 Experimental Procedures

### 2.1 Search for AnoA Putative Orthologs

Computational identification of putative AnoA orthologs was achieved by conducting BLASTp searches against the Genbank RefSeq protein database restricted to the Bacteria domain (downloaded in February 2023). For this purpose, three reference sequences, recovered from *Agrobacterium tumefaciens* GW4 (KDR88348.1), *Sinorhizobium* sp. GW3 (WP_060605207.1) (Li, Zhang, et al. 2019; Li et al. 2015) and *Comamonas testosteroni* JL40 (KGH30493) (Li et al. 2016, 2013), were used as queries (20,000 hits maximum per request). As reported by Li et al. (2016), proteins KDR88348 and WP_060605207 share moderate sequence similarity (68%) and are more distantly related to KGH30493 (33% and 36%, respectively). To target putative orthologs and eliminate false positives, BLAST hits were filtered according to their size (±0.2 × query length) and full-length amino-acid sequences were recovered. Key structural motifs of the intermediate SDR class for metal (YxxxK) and co-factor ([GA]xxGxx[GA]) binding sites (Kavanagh et al. 2008) were searched, and only sequences containing the conserved ‘YNVSK’ motif, shared by the three reference sequences, were retained. Quality-filtered sequences were subsequently taxonomically classified using the ‘taxizedb’ R package (Chamberlain and Arendsee 2020). Finally, sequence redundancy was reduced by clustering single sequences using the CD-HIT program (v.4.8.1) (Fu et al. 2012). By default, a threshold of 80% similarity was used for clustering, and the longest sequences were selected as the representative cluster sequences.

### 2.2 Phylogenetic Reconstruction

Maximum likelihood consensus phylogenetic trees were constructed for predicted orthologs with RAxML (v.8.2.12) (Stamatakis 2014). Briefly, representative cluster sequences were aligned with MUSCLE, using the default parameters (Edgar 2004). Poorly aligned positions were removed with TrimAl with the -gappyout option (Capella-Gutiérrez et al. 2009). The most appropriate best-fit evolutionary model (i.e., PROTGAMMAILG) was predicted with Prottest (v.3.4.2) (Darriba et al. 2011). In order to estimate the robustness of the phylogenetic inference, bootstrapping analyses were conducted with the extended majority rule consensus tree criterion, which automatically determines the sufficient bootstrap replicates for getting stable support values. The ‘ggtree’ R package (v.3.8.2) (Yu et al. 2017) was used to visualise and edit phylogenetic trees.

### 2.3 Bacterial Genomes Carrying Genes Encoding Predicted AnoA Orthologs

To complete the phylogenetic analysis, a total of 335 bacterial genomes carrying genes encoding some of the predicted AnoA orthologs were recovered to search for horizontal gene transfer (HGT) events and determine the gene copy number per genome (Table S1). Encoded orthologs were evenly distributed throughout the database and assigned to 293 different clusters previously defined (Figure S1). Assuming that the evolutionary distance between orthologous genes is approximately proportional to the distance between their species, discrepancies between the pairwise distances between putative *anoA* gene sequences and their corresponding 16S rRNA gene sequences were sought as markers of horizontal transfers (i.e., implicit phylogenetic approach) (Ravenhall et al. 2015). A linear regression was plotted with all pairwise comparisons, and bacterial genomes for which the mean distance of *anoA* gene sequence pairwise comparisons differed by 5% or more from the one predicted using the corresponding 16S rRNA gene sequences were considered to have acquired *anoA* through horizontal transfers.

### 2.4 Design of *anoA* Gene-Targeted PCR Primers

The high sequence dissimilarities between predicted AnoA orthologs precluded the design of universal primers. Primers specific to the *Alphaproteobacteria*-related orthologs were therefore developed as this class predominates the AnoA database. For this purpose, 3643 unique nucleic sequences encoding the 1681 *Alphaproteobacteria*-related putative orthologs were down-loaded. A multiple alignment of nucleic sequences was then performed by ‘reverse translation’ of the aligned proteins with the MUSCLE algorithm (Edgar 2004), using default parameters.

Consensus sequences from conserved regions were selected to design candidate degenerate primers using the HYDEN
soft- ware (Linhart and Shamir 2007). A total of 28 candidate primer pairs were screened through end point PCR assays, using the ge- nomic DNA of *Agrobacterium radiobacter* DSM 30147 as a tem- plate. All PCR amplifications were performed in a total volume of 20 µL containing 10 µM of each primer, 10 mM of each dNTP, 0.5 U of GoTaq G2 DNA polymerase and 1x of GoTaq buffer.

## 2.5 In Silico *anoA* Amplification

The primer pair anoA-H-F2B (5'-CAGGGCATCACCACVCC-3')/ anoA-H-R2A (5'-GTRWARACGAARCCGGGDAT-3'), amplifying a 148-bp *anoA* gene fragment (Table 1), was selected based on end point PCR assays and further assessed by performing in silico PCR amplification on the 3643 nucleic acid sequences from the database. For this purpose, a maximum of two mis- matches was tolerated between each primer and the target se- quence, with no mismatches in the first three bases at the 3' end of the primers. The ratio between the number of predicted am- plified sequences and the total sequences was estimated for each predicted ortholog and for each cluster in the ML- phylogenetic tree. Predicted amplification coverage was mapped on the ML- phylogenetic tree to determine if specific taxonomic groups might be potentially underestimated. In parallel, the taxonomic resolution of the AnoA sequence fragment corresponding to the nucleotide region targeted by the primer pair anoA-H-F2B/ anoA-H-R2A was investigated. Specifically, pairwise sequence comparisons were performed to ensure that trimmed AnoA se- quences remained specific to their respective cluster and to eval- uate the number of mismatches between trimmed sequences within the same cluster vs. sequences from different clusters.

## 2.6 *anoA* Gene Amplification From Environmental Samples

Independent sediments and biofilm samples, recovered from urban areas of the Seine and Artois-Picardie River basins (France), were processed to evaluate the *anoA-H-F2B/anoA-H-R2A* primer pair and to investigate the occurrence of the *anoA* gene in environmental microbial communities. Samples were collected in the Seine (i.e., Mar.AS20, Mar.AS160, Biof.Bougival, Biof.Triel and Biof.Poses), Orge (i.e., Orge.Amont) and Deule Rivers (i.e., Met. Surface) and in highway stormwater retention ponds at the Marcoussis (i.e., S.Inoculum, S.Wanox.1.T7 and S.Wox.1.T28) and Brethencourt sites (i.e., Breth.d, Breth.e and Breth.s) (Baratelli et al. 2023; Philippe et al. 2023; Da Costa et al. 2023) (Table S2). Inductively coupled plasma-mass spectrometry (ICP-MS) was used to assess Sb concentrations and revealed contrasting levels of contamination within the samples (1.7–93 mgkg⁻¹) (Baratelli et al. 2023; Philippe et al. 2023; Da Costa et al. 2023). Moreover, X-Ray Absorption Spectroscopy Near Edge Structure (XANES) measurements performed on sediments from the Brethencourt stormwater retention pond suggested that Sb speciation and its molecular environment also vary greatly (Philippe et al. 2023) (Table S2). Total DNA was extracted using the DNeasy Power Soil Pro extraction kit (Qiagen) following the manufacturer's instructions. Total microbial communities were characterised using Miseq Illumina paired-end sequencing with the primers 341F/785R targeting the V3–V4 hypervariable region of the 16S rRNA gene (Klindworth et al. 2013). A detailed description of the methodology used to process 16S rRNA gene libraries is de- scribed elsewhere (Colin et al. 2023).

For the *anoA* gene, end-point PCRs were first achieved with the *anoA-H-F2B/anoA-H-R2A* primer pair using the following PCR program: initial denaturation at 94°C for 5 min, followed by 30 cy- cles at 94°C for 45 s and 54°C for 45 s, without an elongation step. The amplicons generated were then compared with those yielded by the two strain-specific primer pairs developed earlier based on the sequences of *Agrobacterium tumefaciens* GW4 (Li et al. 2015) or *Sinorhizobium* sp. GW3 (Li, Zhang, et al. 2019) (Table 1). In parallel, amplification specificity and taxonomic coverage of the *anoA-H-F2B/anoA-H-R2A* primer pair were assessed using Miseq Illumina paired-end sequencing of all environmental am- plicons (MrDNA Molecular Research LP; Shallowater; TX, USA). Using the 'DADA2' R package (v.1.29.0) (Callahan et al. 2016), raw reads were trimmed, and error rates were determined. Reads were subsequently dereplicated, and chimeras were removed be- fore downstream analysis. Amplicon sequence variants (ASVs) were inferred and taxonomically assigned by mapping reads to the AnoA database using the BLASTx alignment program. Reads with query coverage ≥ 99% and sequence similarity ≥ 80% were considered as *anoA* sequences and affiliated to the genus of the corresponding cluster. Before alpha and beta-diversity analyses, gene libraries were rarefied to the same sequencing depth by ran- domly resampling. Alpha-diversity indices (Richness, Shannon and Pielou indexes) were estimated using the “estimate_richness” function from the ‘phyloseq’ R package. Moreover, Bray-Curtis dissimilarity metrics were calculated based on read abundance, and ASV profiles were compared using a principal coordinate analysis (PCoA). The relative abundance of the main bacterial gen- era (≥ 1% of total reads in at least one sample) was studied across the samples.
## 2.7 Quantitative PCR Assays

The anoA-H-F2B/anoA-H-R2A primer pair was also used to quantify the anoA gene in the environmental samples using the LightCycler 96 system (Roche, Basel, Switzerland) and the qPCR FastStart Essential DNA Green Master kit (Roche). Standard calibration curves were prepared by cloning a fragment of the anoA gene from *A. radiobacter* DSMZ 30147 into the pGEMT vector (Promega) according to the manufacturer's instructions. Subsequent transformation of competent DH5-alpha strains of *Escherichia coli* allowed plasmid DNA extraction using the NucleoSpin plasmid extraction kit (Macherey-Nagel), followed by its quantification using a Nanodrop 2000c spectrophotometer. PCR reactions were performed in a 20 µL volume containing 1X SYBR green master mix, 10 mg/mL BSA, 0.5 µM of each primer, and 5 µL of pure and diluted DNA samples. The qPCR program consisted of an initial denaturation at 94°C for 5 min, followed by 35 cycles at 94°C for 45 s and 54°C for 45 s, without an elongation step. The anoA gene copy number was estimated for each sample and normalised with the microbial abundance. To that end, the 16S rRNA gene copy number was quantified using the 63F/BU16S4 primer set as described elsewhere (Plassart et al. 2008).

# 3 Results

## 3.1 AnoA Orthologs Database

BLASTp searches against the Genbank RefSeq protein data-base identified 3387 hits as putative AnoA orthologs (database provided as Supporting Information S1). Most of the sequences affiliated with the *Alphaproteobacteria*, *Betaproteobacteria*, *Actinomycetota* and *Gammaproteobacteria* (49%, 24%, 17% and 8%, respectively), followed by the *Myxococcota*, *Bacteroidota* and *Thermodesulfobacteriota* (< 1%). Protein lengths ranged from 250 to 319 residues and differed according to the taxonomic groups (e.g., 284 amino acids ±5.7 in *Alphaproteobacteria*; 299 aa ±6.1 in *Betaproteobacteria*; 284 aa ±8.4 in *Actinomycetota* and 277 aa ±6.7 in *Gammaproteobacteria*) (Figure S2). Over 83% of the predicted orthologs were recovered from 53 bacterial genera, many of which were members of the *Hyphomicrobiales* (e.g., *Rhizobium*, *Bradyrhizobium*, *Mesorhizobium*, *Methylobacterium* and *Agrobacterium*), *Burkholderiales* (e.g., *Cupriavidus*, *Acidovorax*, *Paraburkholderia*, *Massilia*, *Ralstonia* and *Comamonas*) and *Mycobacteriales* (e.g., *Mycobacteroides*, *Mycolicibacterium*, *Mycobacterium* and *Nocardia*) (Table S3). Redundancy in the database was reduced by grouping putative orthologous sequences sharing ≥ 80% similarity into 562 clusters. Phylogenetic reconstruction of the cluster representative sequences showed that the *Alphaproteobacteria*, *Betaproteobacteria* and *Actinomycetota* formed phylogenetically distinct groups, while orthologs related to the *Gammaproteobacteria* were scattered in the phylogeny (Figure 1). Clusters in which the taxonomic assignment of sequences diverged were fairly rare (i.e., none at the phylum level, 5 at the class level, 9 at the order level, 20 at the family level and 88 at the genus level). If so, a consensus affiliation was defined and shown on the tree if more than 50% of the sequences in a given cluster shared the same affiliation.

## 3.2 Bacterial Genomes Analysis

Most of the Proteobacterial genomes investigated harbour a single anoA gene copy (> 93%), while a few were reported to carry up to two copies (Table S1). In contrast, half of the *Actinomycetota* genomes contain more than one and up to four gene copies. Overall, *anoA* gene sequences found within the same genome were relatively dissimilar (< 80% identity) but belonged to clusters with the same taxonomic affiliation at the class level. Moreover, evolutionary distances between *anoA* and 16S rRNA gene sequences were found to be congruent (Figure 2), although some divergences do occur. Notably, pairwise comparisons revealed high-scoring sequence matches in distantly related bacterial strains and vice versa, suggesting cross-lineage horizontal transfers of the *anoA* gene. Detailed analysis revealed that 20 of the 28 Gammaproteobacterial genomes tested display discrepancies between *anoA* gene sequences and their corresponding 16S rRNA gene sequences (Figure S3A). In particular, members of the genera *Marinobacter* and *Acinetobacter* displayed high sequence similarities with orthologous *anoA* sequences from the *Actinomycetota*, while other genera (e.g., *Klebsiella* and *Pseudomonas*) were more closely related to the *Alphaproteobacteria* and *Betaproteobacteria* (Figure 1). To a much lesser extent, discrepancies were also observed in other taxonomic groups, with potential gene transfers between members of the *Alphaproteobacteria* (e.g., *Bradyrhizobium*, *Ruegeria*, *Phyllobacterium* and *Shinella*), *Betaproteobacteria* (e.g., *Undibacterium*, *Rhodoferax*, *Paraburkholderia* and *Caballeronia*) and *Actinobacteria* (*Aquihabitans*) (Figure S3B-D).
## 3.3 PCR Primers Design

Degenerate PCR primers were designed to amplify *anoA* gene sequences related to the *Alphaproteobacteria* in the da- tabase (Table S4). Using genomic DNA of *A. radiobacter* DSM 30147 as a positive control, the primer pair *anoA-H- F2B* (5'-CAGGGCATCACCACVCC-3') and *anoA-H-R2A* (5'-GTRWAGACGAARCCGGGRAT-3') proved the most effective in generating a single, intense amplification band of the expected size (ca. 148 bp) (Table S5). The amplified gene fragment is located in the region 457–605 bp on the *anoA* gene sequence of *A. tumefa- ciens* GW4. Mapping the coverage of in silico PCR amplification on the consensus ML-phylogenetic tree showed that the *anoA-H- F2B/anoA-H-R2A* primer pair spans a large part of the diversity (Figure 3). In silico primer coverage, rates exceeded 80% for most of the main bacterial genera identified in this work, including *Rhizobium*, *Bradyrhizobium*, *Methylobacterium*, *Agrobacterium*, *Ensifer*, *Shinella*, *Aureimonas* and *Phyllobacterium* (Table 2). High primer coverage was also observed for less represented bacterial genera, such as *Rhodopseudomonas*, *Bosea*, *Microvirga*, *Thioclava*, *Sinorhizobium* and *Aurantimonas*. Finally, sequences from a few genera were predicted to be only partially ampli- fied (e.g., *Mesorhizobium* [65.0% of sequences], *Ruegeria* [8.8%], *Sphingomonas* [29.1%] and *Caulobacter* [5.6%]), or even completely missed (e.g., *Leisingera*) (Table 2).

With the aim of investigating the taxonomic diversity of *anoA* gene amplicons, the 1681 *Alphaproteobacteria*-related ortho- logs in the database were trimmed to retain only residues corresponding to the nucleotide region amplified by the *anoA- H-F2B/anoA-H-R2A* primer pair. Sequence trimming results in 843 unique sequences. Pairwise comparison of trimmed amino acid sequences revealed that sequences from the same cluster exhibited an average of 3 mismatches, while those from distinct clusters showed an average of 11 mismatches (Figure S4). Although working with short sequences ineluctably affects tax- onomic resolution compared to the full-length sequences, most of the trimmed amino acid sequences could be reliably assigned to only 1 of the 562 sequence clusters defined earlier. Only 25 trimmed orthologous sequences were found to match more than one cluster, even though taxonomic affiliation at the genus level remained unchanged in most cases (Table S6).

## 3.4 Characterisation of Environmental Samples

The composition of microbial communities thriving in envi- ronmental samples was investigated using Miseq Illumina sequencing. Filtering of the 16S rRNA gene libraries yielded 790,712 high-quality sequences (Table S7) and re- vealed that samples harboured distinct microbial commu- nities at both the phylum (Figure S5, Table S8) and genus levels (Table S9). Sediment samples were mainly domi- nated by the *Gammaproteobacteria*, *Alphaproteobacteria*, *Bacteroidota*, *Firmicutes*, *Chloroflexi* and *Acidobacteriota*, while a lower diversity was observed for biofilms, domi- nated by the *Gammaproteobacteria*, *Alphaproteobacteria* and *Cyanobacteria*. Notably, the relative abundance of the *Alphaproteobacteria* class was highly variable, ranging from 0.5% to 22% of the total number of reads.

## 3.5 Amplification and Sequencing of *anoA* Gene From Environmental Samples

The *anoA-H-F2B/anoA-H-R2A* primer pair successfully am- plified the *anoA* gene from all sediment and biofilm samples tested in this study, without the need for prior enrichment (Figure S6A). In comparison, strain-specific primers published by Li et al. (2015) yielded an amplicon of the expected size (ca. 119 bp) for only one sediment sample (i.e., Breth.e) and *A. radio- bacter* DSM 30147 (positive control) (Figure S6B). The second pair of strain- specific primers (Li, Zhang, et al. 2019) yielded a band of the expected size (ca. 295 bp) for a few samples (i.e., Biof.Triel, Breth.d, Breth.s and S.Inoculum) but failed to amplify *anoA* in *A. radiobacter* DSM 30147 (Figure S6C).

Sequencing of the *anoA* gene with the *anoA-H-F2B/anoA-H-R2A* primer pair led to 530,451 high-quality sequences (Table S10). Following BLASTx searches against the database, high primer specificity was reported for sediment samples, in which 95.3%–99.8% of high-quality reads were identified as *anoA* sequences

(Table S11). Lower primer specificity was observed for biofilm samples, in which *anoA* sequences were estimated to account for 51.8%–92.2% of reads (Table S11). Most of the non-*anoA* reads amplified in biofilms (> 91%) were found to encode ami- notransferases and mainly affiliated with *Candidatus Nitrotoga* sp. or members of the *Mycobacteriaceae* (i.e., *Mycobacterium* and *Mycolicibacterium*) (data not shown). Altogether, 491,272 trans- lated *anoA* reads (1928 ASVs) sharing 80% or more sequence similarity with one of the predicted AnoA orthologs were tax-
onomically assigned to the corresponding genus. Mapping the abundance of these reads on the consensus ML-
phylogenetic tree indicated that the phylogenetic diversity assessed by the anoA-H-F2B/anoA-H-R2A primer pair
overlapped closely with the previously estimated in silico primer coverage (Figure 3).

Estimating alpha-diversity indices revealed marked differences between *anoA*-carrying microbial communities (Figure S7A). In particular, biofilms and sediments collected in rivers had low richness and diversity values and were characterised by a few dominant ASVs (e.g., Biof.Triel, Mar.AS20 and Mar.AS160). In contrast, sediments sampled in highway stormwater retention ponds tended to have a higher number of ASVs, which were more evenly distributed in terms of relative abundance (e.g., S.Inoculum, S.WAnox.1.T7 and Breth.d). Accordingly, patterns of beta-diversity revealed distinct *anoA* gene sequences depending on their origin. Namely, sediments from highway stormwater ponds separated from river samples along the first component of the PCoA explaining 28.9% of the total variability (Figure S7B). The *anoA* reads are distributed across 24 main bacterial genera. *Agrobacterium*, *Bradyrhizobium* and *Rhizobium* were the most frequently identified genera, accounting for 41.6%–98.7% of reads per sample (Figure 4, Table S12). The genera *Ciceribacter*, *Bosea*, *Microvirga* and *Polyangium* sometime reached substantial relative abundances, but were poorly represented in most of the samples. Again, genus composition profiles varied according to the sample origin, with highway stormwater retention ponds showing higher proportions of *Bradyrhizobium*, *Ciceribacter*, *Ensifer* and *Polyangium* and several river samples being dominated by *Agrobacterium* (Figure 4, Table S12).

**3.6 Quantitative PCR Assays**

The anoA-H-F2B/anoA-H-R2A primer pair was used to assess the abundance of the *anoA* gene in sediments. Biofilm samples were not quantified due to a lack of primer specificity, as revealed by sequencing analysis (Table S11), leading to a substantial overestimation of gene abundance in these samples. No adjustment factor for the copy number of the *anoA* gene was applied, since only a few *Alphaproteobacteria*-related genomes analysed here were found to contain multiple *anoA* gene copies (< 5%) (Table S1). The *anoA* gene abundance varied from 1.7·10⁸ to 2.2·10⁹ gene copies per gram of wet sediment (Figure S8A, Table S13) and the estimated *anoA/16S* ratios indicated that members of the *Alphaproteobacteria* possessing the *anoA* gene account for 5%–30% of total microbial communities (Figure S8B, Table S13).

**4 Discussion**

Research into Sb(III)-oxidation processes over the last decade has brought to light the bacterial antimonite oxidase AnoA, for which gene expression was shown to be specifically induced by Sb(III) (Li et al. 2016, 2015). Despite this finding, AnoA is still poorly annotated in public databases and has so far only been reported in five bacterial genera within the *Pseudomonadota* (i.e., *Agrobacterium*, *Rhizobium*, *Sinorhizobium*, *Comamonas* and *Ralstonia*) (Li et al. 2016, 2015; Li, Zhang, et al. 2019; Yu et al. 2024). This lack of knowledge on the diversity and taxonomic distribution of AnoA may have led research to under-estimate its contribution to the Sb cycle and its prevalence in environmental systems. To tackle this shortcoming, the present work aimed to computationally predict new AnoA orthologs throughout the bacterial domain and resulted in a database of 3387 putative orthologs. All sequences were manually curated and confirmed as possessing both the co-factor and metal-binding sites, typical of the intermediate SDR class (Kavanagh et al. 2008; Kallberg et al. 2002; Persson and Kallberg 2013).

Predicted AnoA orthologs affiliated with 272 distinct bacterial genera that primarily belong to the *Pseudomonadota* and *Actinomycetota* illustrate a much broader phylogenetic distribution than previously thought. Part of these newly identified genera have, to our knowledge, never been described as containing Sb(III)-oxidisers (e.g., *Bradyrhizobium*, *Mesorhizobium*, *Methylobacterium*, *Phyllobacterium*, *Paraburkholderia*, *Burkholderia*, *Caballeronia* and *Massilia*), although some of them have already been detected in Sb-contaminated environments (Duan et al. 2021; Li et al. 2021; Sun et al. 2017). Moreover, other genera identified in this work include members already known as Sb(III)-oxidisers (e.g., *Shinella*, *Hydrogenophaga*, *Bosea*, *Cupriavidus* and *Microvirga*), some of which also possess the *aioA* gene (Nguyen et al. 2017, 2021; Terry et al. 2015; Lu et al. 2018; Nguyen and Lee 2015; Zhang, Kolton, et al. 2021; Li, Yu, et al. 2018). These microorganisms would therefore be likely to catalyse Sb(III)-oxidation using different enzymatic pathways, as previously evidenced with *Sinorhizobium* sp. GW3, for which the preferred enzymatic pathway depends on whether the strain is grown under oxic or anoxic conditions (Li, Zhang, et al. 2019). Of note, AnoA is not systematically found in all members of the above-mentioned genera. This applies to *Bosea* and *Roseomonas*, for which the strain *Bosea* sp. AS-1 and *Roseomonas rhizosphaerae* YW11 lack the *anoA* gene in their genomes and therefore oxidise Sb(III) probably via *AioA* (Lu et al. 2018; Sun et al. 2020).
Though ortholog diversity extends well beyond the few bacte- rial genera already known, it is likely that AnoA diversity
re- mains somewhat underestimated due to the limited number of queries used to construct the database. The three
reference sequences used here have certainly introduced biases, favour- ing the identification of orthologs within the
*Hyphomicrobiales* and *Burkholderiales* (65.3% of total sequences), at the expense of evolutionary distant taxa. Namely,
the AnoA of *Comamonas testosteroni* JI40 exhibits low sequence similarity (< 38%) with the ones from
*Agrobacterium tumefaciens* GW4 and *Sinorhizobium* sp. GW3 and would thus never have been iden- tified if only these two
*Alphaproteobacteria* sequences had been used as queries. Moreover, since AnoA belongs to the SDR su- perfamily
(Kavanagh et al. 2008; Kallberg et al. 2002; Persson and Kallberg 2013), only sequences featuring the 'YNVSK'
Sb-binding motif, shared by the three reference proteins, were considered to minimise over-annotation. This quality filter is
probably too restrictive, since a number of residue substitutions were observed at the active site in BLAST results. Further
in- vestigations would therefore be necessary to determine whether substitutions involving amino acids of the same family
(e.g., YNISK, YNVAK, YNASK and YNVTK) alter enzyme-substrate specificity or instead modulate enzyme activity
and/or affinity for Sb (Meiering et al. 1992; Tanaka et al. 2001).

The *anoA* gene was usually found as a single copy in bacterial genomes, with the exception of *Actinomycetota*.
Moreover, pair- wise comparisons of their evolutionary distances with respect to the corresponding species supported
the hypothesis that the *anoA* gene was mainly inherited vertically. Accordingly, *anoA* gene sequences recovered in
*Agrobacterium*, *Sinorhizobium* and *Rhizobium* seem not to be localised on a plasmid or genomic is- land (Li et al.
2015). However, some phylogenetic divergences between the 16S rRNA and putative *anoA* sequences were ob- served
in all main taxonomic groups, suggesting the occur- rence of HGT events between distantly related taxa. In line with
these observations, previous studies have reported horizontal transfers between members of the *Pseudomonadota* for
genes implicated in the As and Sb cycles (i.e., *arsB*, *acr3* and *aioA*), although these events do not seem to constitute
the main trans- mission mechanism either (Cai et al. 2009; Fahy et al. 2015; Quéméneur et al. 2008). Here, major
evolutionary inconsisten- cies were observed for several *anoA* gene sequences recovered from members of the
*Gammaproteobacteria*, sharing unexpect- edly high similarities with orthologs associated with either the
*Alphaproteobacteria*, *Betaproteobacteria* or *Actinomycetota*.

Additional HGT may have occurred between more evolution- arily related taxa, but these events remain more
challenging to trace. The AnoA database constructed in the present study resulted in a large number of orthologous
sequences, paving the way for the development of PCR primers. The high sequence dis- similarities observed between the
predicted AnoA orthologs hampered the design of universal primers. A new primer pair (anoA-H-F2B/anoA-H-R2A) was
therefore developed here to target *Alphaproteobacteria*-related *anoA* gene sequences, as this class predominates the
database and contains the largest num- ber of genera with Sb-oxidising strains (Deng et al. 2021; Zhang, O'Loughlin, et
al. 2022). The newly developed primers showed good amplification coverage for most of the *Alphaproteobacteria* genera
referenced in the database. These primers amplify a frag- ment whose size (ca. 148 bp) is suitable for gene
quantification and which contains sufficient genetic information for taxonomic composition analyses. Highly specific PCR
assays were achieved in all sediments tested (> 98% high-quality filtered reads), al- though samples differed in terms of Sb
content (from 5 to 93 Sb mg kg⁻¹) and microbial assemblages. In contrast, the same task could not be fulfilled using the two
previously published strain- specific primer pairs (Li, Zhang, et al. 2019; Li et al. 2015). Accordingly, the primers
developed by Li et al. (2015) failed in amplifying the *anoA* gene in soils heavily contaminated with Sb (ca. 700 mg
kg⁻¹), leading the authors to conclude that AnoA was absent and thus of limited importance in Sb(III)-oxidation processes
compared to AioA (Zhang, Kolton, et al. 2021; Zhang, Li, et al. 2021). However, this statement could be misleading, as
some of the predominant taxa in these soil metagenomes corre- spond to those identified in the AnoA database (e.g.,
*Rhizobium* and *Bradyrhizobium*) and could therefore harbour *anoA* se- quences covered by the newly developed primer
pair.

PCR amplifications performed with the newly developed primer pair estimated that *anoA* accounts for 5%–30% of the total mi- crobial communities, demonstrating its ubiquity and suggest- ing that *anoA*-carrying microorganisms may substantially influence Sb cycling in the environment. It should be noted that the *anoA* gene abundance is probably still underestimated, as 16S rRNA gene sequencing showed that *Actinomycetota* and *Gammaproteobacteria*, not targeted by the primer pair anoA- H-F2B/anoA-H-R2A, were abundant in the samples. Moreover, high-throughput sequencing of amplicons revealed that high- way stormwater retention ponds harboured more diverse and evenly distributed *anoA* gene sequences than sediments and biofilms collected from rivers. These systems are chronically ex- posed to road traffic contaminants (Chang et al. 2021; Gasperi et al. 2022; Hjortenkrans et al. 2006) and recognised as Sb ac- cumulators (Philippe et al. 2023; Clozel et al. 2006; Hares and Ward 1999). Nevertheless, changes observed
in *anoA* gene sequences could not be reliably attributed to the Sb contamination levels as some of these samples exhibited concentrations similar to those measured in the river samples. The bioavailability, speciation, and molecular environment of Sb are expected to vary significantly between samples, as already reported in contaminated sites (Filella et al. 2002; Sun et al. 2016; Wang, Zhang, et al. 2018; Wang, Wang, et al. 2022; Xiao, Krumins, Tang, et al. 2016; Xiao, Krumins, Dong, et al. 2016; Xu, Sun, Han, et al. 2020; Xu, Sun, Lin, et al. 2020; Andreae and Froelich

# 5 Conclusion

The aim of this work was to gain a comprehensive understanding of the bacterial antimonite oxidase, AnoA, which has remained poorly characterised since its discovery a decade ago. Our findings revealed that *anoA*-carrying microorganisms are more diverse and ecologically widespread than previously thought and there- fore encourage further use of the *anoA* gene as a functional marker specific to Sb(III)-oxidation. Additional experiments on some of the bacterial taxa identified here are needed to confirm that pre- dicted AnoA sequences are actually involved in Sb(III) oxidation. The newly developed PCR primers led to the detection of the *anoA* gene in all the environmental samples tested here, ruling out the misconception that AnoA plays a minor role in the Sb biogeochem- ical cycling in natural systems. These primers will prove helpful in surveys wishing to assess the diversity and abundance of bacte- rial Sb(III)-oxidisers in complex environmental matrices. Further studies will be required to address the contribution of AnoA with respect to other Sb(III)-oxidation microbial pathways (e.g., AioAB, ArsV and ArsO) and could provide new prospects for Sb immobil- isation in the context of the bioremediation of contaminated sites. and are accessible under project accession numbers PRJEB54566, PRJEB54928, PRJEB82612, PRJEB82611 and PRJEB82613. Supporting Information S1 files are now accessible via the following DOI link: https://doi.org/10.5281/zenodo.14855017.
<table><caption>TABLE 1 Primer pairs used in this study to amplify the *anoA* gene.</caption><thead><tr><th>Primers names</th><th>Forward and reverse primers</th><th>Amplicon size</th><th>PCR cycling parameters</th><th>Reference</th></tr></thead><tbody><tr><td>anoA-F</td><td>5'-TCATGGTCGAAAGCATCGG-3'</td><td rowspan="2">119 pb</td><td rowspan="2">3 min 94°C; 35 x (30 s94°C; 30 s 57°C; 30 s72°C); 7 min 72°C</td><td rowspan="2">Li et al. (2015)</td></tr><tr><td>anoA-R</td><td>5'-GCGGTTCTGGACGATGTCATA-3'</td></tr><tr><td>anoA-F</td><td>5'-CGAGCGGGAGGTTCATCAG-3'</td><td rowspan="2">295 pb</td><td rowspan="2">3 min 94°C; 35 x (30 s94°C; 30 s 61°C; 30 s72°C); 7 min 72°C</td><td rowspan="2">Li, Zhang, et al. (2019)</td></tr><tr><td>anoA-R</td><td>5'-GTGCTCCAACGCCTCGGTA-3'</td></tr><tr><td>anoA-H-F2B</td><td>5'-CAGGGCATCACCACVCC-3'</td><td rowspan="2">148 pb</td><td rowspan="2">3 min 94°C; 30 x (45 s 94°C; 45 s 54°C); 5 min 72°C</td><td rowspan="2">This study</td></tr><tr><td>anoA-H-R2A</td><td>5'-GTRWARACGAARCCGGGDAT-3'</td></tr></tbody></table>

*Note*: Forward and reverse primer sequences, amplicon size, and cycling programs are provided. The two strain-specific primer pairs from Li et al. (2015) and Li, Zhang, et al. (2019) were designed using the *anoA* gene sequences of *Agrobacterium tumefaciens* GW4 and *Sinorhizobium* sp. GW3, respectively.
<figure><img src="image_2.png" /><figcaption>**FIGURE 1** | Maximum likelihood consensus phylogeny of the predicted AnoA orthologs. Only representative cluster sequences (n = 562) were used for phylogenetic reconstruction. Colour of the tip labels denotes the taxonomic affiliation of sequences at the phylum level (*Pseudomonadota* is detailed at the class level). The cluster ID corresponding to each representative sequence is provided in brackets in the tip labels. The inner and outer heatmaps indicate the consensus taxonomic affiliations of the clusters at the phylum/class and genus levels, respectively. Only the most frequently detected bacterial genera (≥ 10 sequences in the database) were shown on the tree. Diamonds at nodes are bootstrap values > 50% (600 replicates). Stars refer to the reference AnoA sequences used to build the database. A total of five non-AnoA bacterial SDR sequences (i.e., WP_000697823.1, BAP40335.1, CAH39728.1, WP_003856856.1 and Rv0148) were used as outgroups.</figcaption></figure>
<figure><img src="image_3.png" /></figure>

**FIGURE 2** | Detection of evolutionary discordances between sequences encoding the predicted AnoA orthologs and 16S rRNA. Pairwise sequence comparisons were carried out with nucleic sequences encoding predicted AnoA orthologs and 16S rRNA recovered from 335 bacterial genomes (Table S1). The observed *anoA* pairwise similarities were compared with values predicted from the regression equation (grey line; y = 1.38x - 62.52). Pairwise comparisons involving bacterial genomes for which the median of observed pairwise comparisons differs by 5% or more from predicted values are coloured in red (35% of comparisons) and detailed in Figure S3.
<figure><img src="image_4.png" /><figcaption>**FIGURE 3** | Phylogenetic coverage of the anoA-H-F2B/anoA-H-R2A primer pair. Primers coverage was assessed by (i) In silico PCR amplification carried out against the 3643 nucleic sequences encoding the predicted *Alphaproteobacteria*-related AnoA orthologs (blue heatmap) and (ii) Illumina Miseq sequencing analysis of sediment and biofilm samples (green heatmap). For in silico PCR amplification, the blue heatmap indicates the ratio between the number of amplified sequences and the total number of nucleic sequences in each cluster. For Illumina Miseq sequencing analysis, the phylogenetic diversity of reads was estimated using BLASTx searches against the constructed AnoA database. The number of *anoA* reads that could be taxonomically assigned were log₁₀-transformed, and their abundance was shown in the green heatmap for each cluster. The inner brown heat map indicates the distribution of the 3643 nucleic sequences for each cluster in the phylogenetic tree. Colour of the tip labels refers to the taxonomic affiliation of representative cluster sequences at the phylum or class level. AnoA orthologs that belong to clades dominated by the *Betaproteobacteria* and *Actinomycetota* were not targeted by the primers and were thus collapsed in the tree for reasons of legibility (black triangles). Stars refer to the reference AnoA sequences used to build the database.</figcaption></figure>
<table><caption>TABLE 2 | In silico amplification coverage of the main bacterial genera using the anoA-H-F2B/anoA-H-R2A primer pair.</caption><thead><tr><th>Genus</th><th>Nb of AnoA orthologs</th><th>Nb of nucleic sequences</th><th>Primer pair matches</th><th>In silico primer pair coverage</th></tr></thead><tbody><tr><td><strong>Alphaproteobacteria</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Bradyrhizobium</td><td>351</td><td>707</td><td>650</td><td>91.9%</td></tr><tr><td>Mesorhizobium</td><td>273</td><td>668</td><td>434</td><td>65.0%</td></tr><tr><td>Rhizobium</td><td>152</td><td>259</td><td>233</td><td>90.0%</td></tr><tr><td>Methylobacterium</td><td>140</td><td>196</td><td>191</td><td>97.4%</td></tr><tr><td>Agrobacterium</td><td>89</td><td>348</td><td>347</td><td>99.7%</td></tr><tr><td>Ruegeria</td><td>46</td><td>68</td><td>6</td><td>8.8%</td></tr><tr><td>Sphingomonas</td><td>45</td><td>55</td><td>16</td><td>29.1%</td></tr><tr><td>Ensifer</td><td>33</td><td>71</td><td>71</td><td>100.0%</td></tr><tr><td>Caulobacter</td><td>28</td><td>36</td><td>2</td><td>5.6%</td></tr><tr><td>Shinella</td><td>23</td><td>37</td><td>37</td><td>100.0%</td></tr><tr><td>Aureimonas</td><td>22</td><td>29</td><td>25</td><td>86.2%</td></tr><tr><td>Leisingera</td><td>22</td><td>25</td><td>0</td><td>0.0%</td></tr><tr><td>Phyllobacterium</td><td>22</td><td>27</td><td>22</td><td>81.5%</td></tr><tr><td>Rhodopseudomonas</td><td>18</td><td>26</td><td>25</td><td>96.2%</td></tr><tr><td>Bosea</td><td>16</td><td>18</td><td>15</td><td>83.3%</td></tr><tr><td>Microvirga</td><td>16</td><td>18</td><td>15</td><td>83.3%</td></tr><tr><td>Thioclava</td><td>14</td><td>20</td><td>18</td><td>90.0%</td></tr><tr><td>Sinorhizobium</td><td>13</td><td>34</td><td>34</td><td>100.0%</td></tr><tr><td>Aurantimonas</td><td>11</td><td>14</td><td>14</td><td>100.0%</td></tr><tr><td>Kaistia</td><td>11</td><td>13</td><td>9</td><td>69.2%</td></tr><tr><td>Mameliella</td><td>10</td><td>15</td><td>15</td><td>100.0%</td></tr><tr><td>Phenylobacterium</td><td>10</td><td>12</td><td>5</td><td>41.7%</td></tr><tr><td>Xanthobacter</td><td>10</td><td>11</td><td>11</td><td>100.0%</td></tr><tr><td><strong>Betaproteobacteria</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Paraburkholderia</td><td>28</td><td>39</td><td>7</td><td>17.9%</td></tr><tr><td>Caballeronia</td><td>15</td><td>18</td><td>12</td><td>66.7%</td></tr><tr><td><strong>Gammaproteobacteria</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Klebsiella</td><td>12</td><td>584</td><td>0</td><td>0.0%</td></tr><tr><td>Pseudomonas</td><td>12</td><td>13</td><td>2</td><td>15.4%</td></tr></tbody></table>

Note: Coverage values indicate the percentage of expected amplified nucleic acid sequences for the main genera within the *Alphaproteobacteria* (i.e., represented by ≥10 amino acid sequences in the database). Some genera belonging to the *Betaproteobacteria* and *Gammaproteobacteria* are also listed, as some of their sequences grouped with those of the *Alphaproteobacteria* in the ML phylogenetic tree (Figure 1) and could therefore be targeted by the newly developed primers.
<figure><img src="image_5.png" /><figcaption>**FIGURE 4** | Taxonomic affiliation of *anoA* gene sequences amplified from environmental samples. The relative abundance of the main bacterial genera (> 1% of total reads in at least one sample) was estimated for each sample using the anoA-H-F2B/anoA-H-R2A primer pair. Poorly represented genera were grouped in the category 'Others'.</figcaption></figure>
References

Abin, C. A., and J. T. Hollibaugh. 2014. "Dissimilatory Antimonate Reduction and Production of Antimony Trioxide Microcrystals by a Novel Microorganism." *Environmental Science & Technology* 48: 681–688.

Abin, C. A., and J. T. Hollibaugh. 2019. "Transcriptional Response of the Obligate Anaerobe *Desulfuribacillus stibiiarsenatis* MLFW-2 T to Growth on Antimonate and Other Terminal Electron Acceptors." *Environmental Microbiology* 21: 618–630.
https://doi.org/10.1111/1462-2920.14503.

Ahmad, M., S. S. Lee, J. E. Lim, et al. 2014. "Speciation and Phytoavailability of Lead and Antimony in a Small Arms Range Soil Amended With Mussel Shell, Cow Bone and Biochar: EXAFS Spectroscopy and Chemical Extractions." *Chemosphere* 95: 433–441.

Andreae, M. O., and P. N. Froelich Jr. 1984. "Arsenic, Antimony, and Germanium Biogeochemistry in the Baltic Sea." *Tellus B: Chemical and Physical Meteorology* 36: 101.

Arai, Y. 2010. "Arsenic and Antimony." In *Trace Elements in Soils*, edited by P. S. Hooda, 381–407. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781444319477.ch16.

Ayrault, S., A. Senhou, M. Moskura, and A. Gaudry. 2010. "Atmospheric Trace Element Concentrations in Total Suspended Particles Near Paris, France." *Atmospheric Environment* 44: 3700–3707.

Baratelli, F., F. Alliot, S. Ayrault, et al. 2023. "Caractérisation de l'état de Contamination de l'axe Seine." In *Dynamique des contaminants à l'échelle du bassin versant de la Seine, PIREN-Seine, Rapport de synthèse phase VIII*, edited by F. Dans Baratelli and T. Thiebault, vol. 9, 36–136. PIREN-Seine. https://univ-eiffel.hal.science/hal-04396894.

Cai, L., G. Liu, C. Rensing, and G. Wang. 2009. "Genes Involved in Arsenic Transformation and Resistance Associated With Different Levels of Arsenic-Contaminated Soils." *BMC Microbiology* 9: 4.

Callahan, B., D. Proctor, D. Relman, J. Fukuyama, and S. Holmes. 2016. "Reproducible Research Workflow in R for the Analysis of Personalized Human Microbiome Data." In *Biocomputing* 2016, 183–194. World Scientific. https://doi.org/10.1142/9789814749411_0018.

Capella-Gutiérrez, S., J. M. Silla-Martínez, and T. Gabaldón. 2009. "trimAl: A Tool for Automated Alignment Trimming in Large-Scale Phylogenetic Analyses." *Bioinformatics* 25: 1972–1973.

Chamberlain, S., and Z. Arendsee. 2020. "taxizedb: Tools for Working With "Taxonomic" Databases." R package version 0.2.2.pdf.

Chan, D., and G. W. Stachowiak. 2004. "Review of Automotive Brake Friction Materials." *Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering* 218: 953–966.

Chang, X., Y. Yu, and Y.-X. Li. 2021. "Response of Antimony Distribution in Street Dust to Urban Road Traffic Conditions." *Journal of Environmental Management* 296: 113219.

Chen, Y.-W., T.-L. Deng, M. Filella, and N. Belzile. 2003. "Distribution and Early Diagenesis of Antimony Species in Sediments and Porewaters of Freshwater Lakes." *Environmental Science & Technology* 37: 1163–1168.

Clozel, B., V. Ruban, C. Durand, and P. Conil. 2006. "Origin and Mobility of Heavy Metals in Contaminated Sediments From Retention and Infiltration Ponds." *Applied Geochemistry* 21: 1781–1798.

Colin, Y., C. Arcanjo, C. da Costa, et al. 2023. "Decoupled Responses of the Copepod *Eurytemora affinis* Transcriptome and Its Microbiota to Dissolved Copper Exposure." *Aquatic Toxicology* 259: 106546

Da Costa, C., Y. Colin, M. Debret, et al. 2023. "Shifts in Sediment Bacterial Communities Reflect Changes in Depositional Environments in a Fluviatile Context." *Science of the Total Environment* 885: 163890.

Darriba, D., G. L. Taboada, R. Doallo, and D. Posada. 2011. "ProtTest 3: Fast Selection of Best-Fit Models of Protein Evolution." *Bioinformatics* 27: 1164–1165.

Deng, R., Y. Chen, X. Deng, et al. 2021. "A Critical Review of Resistance and Oxidation Mechanisms of Sb-Oxidizing Bacteria for the Bioremediation of Sb(III) Pollution." *Frontiers in Microbiology* 12: 738596.

Duan, R., Y. Lin, J. Zhang, et al. 2021. "Changes in Diversity and Composition of Rhizosphere Bacterial Community During Natural Restoration Stages in Antimony Mine." *PeerJ* 9: e12302.

Dupont, D., S. Arnout, P. T. Jones, and K. Binnemans. 2016. "Antimony Recovery From End-of-Life Products and Industrial Process Residues: A Critical Review." *Journal of Sustainable Metallurgy* 2: 79–103.

Edgar, R. C. 2004. "MUSCLE: Multiple Sequence Alignment With High Accuracy and High Throughput." *Nucleic Acids Research* 32: 1792–1797.

Fahy, A., L. Giloteaux, P. Bertin, et al. 2015. "16S rRNA and As- Related Functional Diversity: Contrasting Fingerprints in Arsenic- Rich Sediments From an Acid Mine Drainage." *Microbial Ecology* 70: 154–167.

Filella, M., N. Belzile, and Y.-W. Chen. 2002. "Antimony in the Environment: A Review Focused on Natural Waters: I.
Occurrence.”

Kallberg, Y., U. Oppermann, H. Jörnvall, and B. Persson. 2002. "Short- Chain Dehydrogenase/Reductase (SDR) Relationships: A Large Family With Eight Clusters Common to Human, Animal, and Plant Genomes." *Protein Science* 11: 636-641.

Kavanagh, K. L., H. Jörnvall, B. Persson, and U. Oppermann. 2008. "Medium- and Short-Chain Dehydrogenase/Reductase Gene and Protein Families: The SDR Superfamily: Functional and Structural Diversity Within a Family of Metabolic and Regulatory Enzymes." *Cellular and Molecular Life Sciences* 65: 3895-3906.

Klindworth, A., E. Pruesse, T. Schweer, et al. 2013. "Evaluation of General 16S Ribosomal RNA Gene PCR Primers for Classical and Next-Generation Sequencing-Based Diversity Studies." *Nucleic Acids Research* 41: e1.

Krachler, M., H. Emons, and J. Zheng. 2001. "Speciation of Antimony for the 21st Century: Promises and Pitfalls." *Trends in Analytical Chemistry* 20: 12-90.

Kulp, T. R., L. G. Miller, F. Braiotta, et al. 2014. "Microbiological Reduction of Sb(V) in Anoxic Freshwater Sediments." *Environmental Science & Technology* 48: 218-226.

Le Pape, P., S. Ayrault, and C. Quantin. 2012. "Trace Element Behavior and Partition Versus Urbanization Gradient in an Urban River (Orge River, France)." *Journal of Hydrology* 472-473: 99-110.

Lee, W. K., T. H. Rhee, H. S. Kim, and H. Jang. 2013. "Effects of Antimony Trisulfide (Sb S) on Sliding Friction of Automotive Brake Earth-Science Reviews 57: 125-176.

Friction Materials." 2 3 *Metals and Materials International* 19: 1101-1107.

Fu, L., B. Niu, Z. Zhu, S. Wu, and W. Li. 2012. "CD-HIT: Accelerated for Clustering the Next-Generation Sequencing Data." *Bioinformatics* 28: 3150-3152.

Gasperi, J., J. le Roux, S. Deshayes, et al. 2022. "Micropollutants in Urban Runoff From Traffic Areas: Target and Non-Target Screening on Four Contrasted Sites." *Water* 14: 394.

González, J. M., L. Hernández, I. Manzano, and C. Pedrós-Alió. 2019. "Functional Annotation of Orthologs in Metagenomes: A Case Study of Genes for the Transformation of Oceanic Dimethylsulfoniopropionate." *ISME Journal* 13: 1183-1197.

Gu, J., G. Sunahara, R. Duran, et al. 2019. "Sb(III)-Resistance Mechanisms of a Novel Bacterium From Non-Ferrous Metal Tailings." *Ecotoxicology and Environmental Safety* 186: 109773.

Gu, J., J. Yao, R. Duran, and G. Sunahara. 2020. "Comprehensive Genomic and Proteomic Profiling Reveal *Acinetobacter johnsonii* JH7 Responses to Sb(III) Toxicity." *Science of the Total Environment* 748: 141174.

Haldar, A. K., P. Sen, and S. Roy. 2011. "Use of Antimony in the Treatment of Leishmaniasis: Current Status and Future Directions." *Molecular Biology International* 2011: 1-23.

Hares, R. J., and N. I. Ward. 1999. "Comparison of the Heavy Metal Content of Motorway Stormwater Following Discharge Into Wet Biofiltration and Dry Detention Ponds Along the London Orbital ZM25/ Motorway." *Science of the Total Environment* 235: 169-178.

Henckens, M. L. C. M., P. P. J. Driessen, and E. Worrell. 2016. "How Can We Adapt to Geological Scarcity of Antimony? Investigation of Antimony's Substitutability and of Other Measures to Achieve a Sustainable Use." *Resources, Conservation and Recycling* 108: 54-62.

Herath, I., M. Vithanage, and J. Bundschuh. 2017. "Antimony as a Global Dilemma: Geochemistry, Mobility, Fate and Transport." *Environmental Pollution* 223: 545-559.

Hjortenkrans, D., B. Bergbäck, and A. Häggerud. 2006. "New Metal Emission Patterns in Road Traffic Environments." *Environmental Monitoring and Assessment* 117: 85-98.

Jiang, J., Y. Wu, G. Sun, et al. 2021. "Characteristics, Accumulation, and Potential Health Risks of Antimony in Atmospheric Particulate Matter." *ACS Omega* 6: 9460-9470.

Li, J., Z. Qiao, M. Shi, Y. Zhang, and G. Wang. 2019. "Regulation of Antimonite Oxidation and Resistance by the Phosphate Regulator PhoB in *Agrobacterium tumefaciens* GW4." *Microbiological Research* 226: 10-18.

Li, J., Q. Wang, M. Li, et al. 2015. "Proteomics and Genetics for Identification of a Bacterial Antimonite Oxidase in *Agrobacterium tumefaciens*." *Environmental Science & Technology* 49: 5980-5989.

Li, J., Q. Wang, R. S. Oremland, T. R. Kulp, C. Rensing, and G. Wang. 2016. "Microbial Antimony Biogeochemistry: Enzymes, Regulation, and Related Metabolic Pathways." *Applied and Environmental Microbiology* 82: 5482-5495.

Li, J., Q. Wang, S. Zhang, D. Qin, and G. Wang. 2013. "Phylogenetic and Genome Analyses of Antimony-Oxidizing Bacteria Isolated From Antimony Mined Soil." *International Biodeterioration & Biodegradation* 76: 76-80.

Li, J., B. Yang, M. Shi, et al. 2017. "Abiotic and Biotic Factors Responsible for Antimonite Oxidation in *Agrobacterium tumefaciens* GW4." *Scientific Reports* 7: 43225.

Li, J., H. Yu, X. Wu, et al. 2018. "Novel Hyper Antimony-Oxidizing Bacteria Isolated From Contaminated Mine Soils in China."
*Geomicrobiology Journal* 35: 713–720.

Li, J., Y. Zhang, S. Zheng, F. Liu, and G. Wang. 2019. "Anaerobic Bacterial Immobilization and Removal of Toxic Sb(III) Coupled With Fe(II)/Sb(III) Oxidation and Denitrification." *Frontiers in Microbiology* 10: 360.

Li, J., B. H. Zheng, Y. He, et al. 2018. "Antimony Contamination, Consequences and Removal Techniques: A Review." *Ecotoxicology and Environmental Safety* 156: 125–134.

Li, Y., M. Zhang, R. Xu, et al. 2021. "Arsenic and Antimony Co- Contamination Influences on Soil Microbial Community Composition and Functions: Relevance to Arsenic Resistance and Carbon, Nitrogen, and Sulfur Cycling." *Environment International* 153: 106522.

Linhart, C., and R. Shamir. 2007. "Degenerate Primer Design: Theoretical Analysis and the HYDEN Program." *Methods in Molecular Biology* 402: 221–244.

Liu, H., W. Sun, and M. M. Häggblom. 2022. "Microbial Transformations of Antimony." In *Microbial Metabolism of Metals and Metalloids*, edited by C. J. Hurst, vol. 10, 223–254. Springer International Publishing.

Loni, P. C., M. Wu, W. Wang, et al. 2020. "Mechanism of Microbial Dissolution and Oxidation of Antimony in Stibnite Under Ambient Conditions." *Journal of Hazardous Materials* 385: 121561.

Lu, X., Y. Zhang, C. Liu, M. Wu, and H. Wang. 2018. "Characterization of the Antimonite- and Arsenite-Oxidizing Bacterium *Bosea* sp. AS-1 and Its Potential Application in Arsenic Removal." *Journal of Hazardous Materials* 359: 527–534.

Meiering, E. M., L. Serrano, and A. R. Fersht. 1992. "Effect of Active Site Residues in Barnase on Activity and Stability." *Journal of Molecular Biology* 225: 585–589.

Mirza, B. S., D. L. Sorensen, R. R. Dupont, and J. E. McLean. 2017. "New Arsenate Reductase Gene (arrA) PCR Primers for Diversity Assessment and Quantification in Environmental Samples." *Applied and Environmental Microbiology* 83: e02725-16.

Mohiuddin, K. M., H. M. Zakir, K. Otomo, S. Sharmin, and N. Shikazono. 2010. "Geochemical Distribution of Trace Metal Pollutants in Water and Sediments of Downstream of an Urban River." *International Journal of Environmental Science and Technology* 7: 17–28.

Multani, R. S., T. Feldmann, and G. P. Demopoulos. 2016. "Antimony in the Metallurgical Industry: A Review of Its Chemistry and Environmental Stabilization Options." *Hydrometallurgy* 164: 141–153.

Nguyen, V. K., W. Choi, J. Yu, and T. Lee. 2017. "Microbial Oxidation of Antimonite and Arsenite by Bacteria Isolated From Antimony- Contaminated Soils." *International Journal of Hydrogen Energy* 42: 27832–27842.

Nguyen, V. K., and J.-U. Lee. 2015. "Antimony-Oxidizing Bacteria Isolated From Antimony-Contaminated Sediment—A Phylogenetic Study." *Geomicrobiology Journal* 32: 50–58.

Nguyen, V. K., D. D. Nguyen, M.-G. Ha, and H. Y. Kang. 2021. "Potential of Versatile Bacteria Isolated From Activated Sludge for the Bioremediation of Arsenic and Antimony." *Journal of Water Process Engineering* 39: 101890.

Okkenhaug, G., K. A. Grasshorn Gebhardt, K. Amstaetter, et al. 2016. "Antimony (Sb) and Lead (Pb) in Contaminated Shooting Range Soils: Sb and Pb Mobility and Immobilization by Iron Based Sorbents, a Field Study." *Journal of Hazardous Materials* 307: 336–343.

Persson, B., and Y. Kallberg. 2013. "Classification and Nomenclature of the Superfamily of Short-Chain Dehydrogenases/Reductases (SDRs)." *Chemico-Biological Interactions* 202: 111–115.

Philippe, M., P. le Pape, E. Resongles, et al. 2023. "Fate of Antimony Contamination Generated by Road Traffic—A Focus on Sb Geochemistry and Speciation in Stormwater Ponds." *Chemosphere* 313: 137368.

Plassart, P., M. Akpa Vinceslas, C. Gangneux, A. Mercier, S. Barray, and K. Laval. 2008. "Molecular and Functional Responses of Soil Microbial Communities Under Grassland Restoration." *Agriculture, Ecosystems & Environment* 127: 286–293.

Quéméneur, M., A. Heinrich-Salmeron, D. Muller, et al. 2008. "Diversity Surveys and Evolutionary Relationships of aoxB Genes in Aerobic Arsenite-Oxidizing Bacteria." *Applied and Environmental Microbiology* 74: 4567–4573.

Ravenhall, M., N. Škunca, F. Lassalle, and C. Dessimoz. 2015. "Inferring Horizontal Gene Transfer." *PLoS Computational Biology* 11: e1004095.

Rzetala, M. A. 2011. "Arsenic and Antimony in Bottom Sediments of Anthropogenic Water Bodies in the Silesian Upland (Southern Poland)." https://doi.org/10.5593/SGEM2015/B11/S1.006.

Song, X., Y. Li, E. Stirling, et al. 2022. "AsgeneDB: A Curated Orthology Arsenic Metabolism Gene Database and Computational Tool for Metagenome Annotation." *NAR Genomics and Bioinformatics* 4: lqac080.

Stamatakis, A. 2014. "RAxML Version 8: A Tool for Phylogenetic Analysis and Post-Analysis of Large Phylogenies." *Bioinformatics* 30: 1312–1313.

Sun, L.-N., B. Guo, W.-G. Lyu, and X.-J. Tang. 2020. "Genomic and Physiological Characterization of an Antimony and Arsenite-Oxidizing Bacterium Roseomonas Rhizosphaerae." *Environmental Research* 191: 110136.
Sun, W., E. Xiao, Y. Dong, et al. 2016. "Profiling Microbial Community in a Watershed Heavily Contaminated by an Active Antimony (Sb) Mine in Southwest China." *Science of the Total Environment* 550: 297-308.

Sun, W., E. Xiao, T. Xiao, et al. 2017. "Response of Soil Microbial Communities to Elevated Antimony and Arsenic Contamination Indicates the Relationship Between the Innate Microbiota and Contaminant Fractions." *Environmental Science & Technology* 11: 9165-9175.

Tanaka, N., T. Nonaka, K. Nakamura, and A. Hara. 2001. "SDR Structure, Mechanism of Action, and Substrate Recognition." *Current Organic Chemistry* 5: 89-111.

Tang, S.-T., X. W. Song, J. Chen, et al. 2023. "Widespread Distribution of the arsO Gene Confers Bacterial Resistance to Environmental Antimony." *Environmental Science & Technology* 57: 14579-14588.

Terry, L. R., T. R. Kulp, H. Wiatrowski, L. G. Miller, and R. S. Oremland. 2015. "Microbiological Oxidation of Antimony(III) With Oxygen or Nitrate by Bacteria Isolated From Contaminated Mine Sediments." *Applied and Environmental Microbiology* 81: 8478-8488.

Thestorf, K., and M. Makki. 2021. "Pseudo-Total Antimony Content in Topsoils of the Berlin Metropolitan Area." *Journal of Soils and Sediments* 21: 2102-2117.

Turner, A., and M. Filella. 2017. "Field-Portable-XRF Reveals the Ubiquity of Antimony in Plastic Consumer Products." *Science of the Total Environment* 584-585: 982-989.

Wang, D., F. Zhu, Q. Wang, et al. 2016. "Disrupting ROS-Protection Mechanism Allows Hydrogen Peroxide to Accumulate and Oxidize Sb(III) to Sb(V) in *Pseudomonas stutzeri* TS44." *BMC Microbiology* 16: 279.

Wang, H., F. Chen, S. Mu, et al. 2013. "Removal of Antimony (Sb(V)) From Sb Mine Drainage: Biological Sulfate Reduction and Sulfide Oxidation-Precipitation." *Bioresource Technology* 146: 799-802.

Wang, L., L. Ye, and C. Jing. 2020. "Genetic Identification of Antimonate Respiratory Reductase in *Shewanella* sp. ANA-3." *Environmental Science & Technology* 54: 14107-14113.

Wang, L., L. Ye, Z. Yin, L. Zhang, and C. Jing. 2022. "Antimonite Oxidation by Microbial Extracellular Superoxide in *Pseudomonas* sp. SbB1." *Geochimica et Cosmochimica Acta* 316: 122-134.

Wang, L., L. Ye, Y. Yu, and C. Jing. 2018. "Antimony Redox Biotransformation in the Subsurface: Effect of Indigenous Sb(V) Respiring Microbiota." *Environmental Science & Technology* 52: 1200-1207.

Wang, N., S. Zhang, and M. He. 2018. "Bacterial Community Profile of Contaminated Soils in a Typical Antimony Mining Site." *Environmental Science and Pollution Research* 25: 141-152.

Wang, Q., T. P. Warelow, Y. S. Kang, et al. 2015. "Arsenite Oxidase Also Functions as an Antimonite Oxidase." *Applied and Environmental Microbiology* 81, no. 6: 1959-1965.

Wang, W., H. Wang, X. Cheng, et al. 2022. "Different Responses of Bacteria and Fungi to Environmental Variables and Corresponding Community Assembly in Sb-Contaminated Soil." *Environmental Pollution* 298: 118812.

Wang, X., M. He, J. Xi, and X. Lu. 2011. "Antimony Distribution and Mobility in Rivers Around the world's Largest Antimony Mine of Xikuangshan, Hunan Province, China." *Microchemical Journal* 97: 4-11.

Wilson, S. C., P. V. Lockwood, P. M. Ashley, and M. Tighe. 2010. "The Chemistry and Behaviour of Antimony in the Soil Environment With Comparisons to Arsenic: A Critical Review." *Environmental Pollution* 158: 1169-1181.

Xiao, E., V. Krumins, Y. Dong, et al. 2016. "Microbial Diversity and Community Structure in an Antimony-Rich Tailings Dump." *Applied Microbiology and Biotechnology* 100: 7751-7763.

Xiao, E., V. Krumins, S. Tang, et al. 2016. "Correlating Microbial Community Profiles With Geochemical Conditions in a Watershed Heavily Contaminated by an Antimony Tailing Pond." *Environmental Pollution* 215: 141-153.

Xiao, S., W. Wang, C. Amanze, R. Anaman, B. A. Fosua, and W. Zeng. 2023. "Antimony Oxidation and Whole Genome Sequencing of *Phytobacter* sp. X4 Isolated From Contaminated Soil Near a Flotation Site." *Journal of Hazardous Materials* 445: 130462.

Xu, R., X. Sun, F. Han, et al. 2020. "Impacts of Antimony and Arsenic Co-Contamination on the River Sedimentary Microbial Community in an Antimony-Contaminated River." *Science of the Total Environment* 11: 136451.

Xu, R., X. Sun, H. Lin, et al. 2020. "Microbial Adaptation in Vertical Soil Profiles Contaminated by an Antimony Smelting Plant." *FEMS Microbiology Ecology* 96: fiaa188.

Yamamura, S., C. Iida, Y. Kobayashi, M. Watanabe, and S. Amachi. 2021. "Production of Two Morphologically Different Antimony Trioxides by a Novel Antimonate-Reducing Bacterium, *Geobacter* sp. SVR." *Journal of Hazardous Materials* 411: 125100.

Yu, G., D. K. Smith, H. Zhu, Y. Guan, and T. T. Lam. 2017. "ggtree: An R Package for Visualization and Annotation of Phylogenetic Trees With Their Covariates and Other Associated Data." *Methods in Ecology and Evolution* 8: 28-36.

Yu, T., X. Chen, X.-C. Zeng, and Y. Wang. 2024. "Biological Oxidation of As(III) and Sb(III) by a Novel Bacterium With Sb(III) Oxidase Rather Than As(III) Oxidase Under Anaerobic and Aerobic Conditions." *Science of the Total Environment* 916: 169893.
Zhang, G., X. Ouyang, H. Li, Z. Fu, and J. Chen. 2016. "Bioremoval of Antimony From Contaminated Waters by a Mixed Batch Culture of Sulfate-Reducing Bacteria." *International Biodeterioration & Biodegradation* 115: 148-155.

Zhang, J., J. Chen, Y. F. Wu, et al. 2022. "Oxidation of Organoarsenicals and Antimonite by a Novel Flavin Monooxygenase Widely Present in Soil Bacteria." *Environmental Microbiology* 24: 752-761.

Zhang, M., M. Kolton, Z. Li, et al. 2021. "Bacteria Responsible for Antimonite Oxidation in Antimony-Contaminated Soil Revealed by DNA-SIP Coupled to Metagenomics." *FEMS Microbiology Ecology* 97: fiab057.

Zhang, M., Z. Li, M. M. Häggblom, et al. 2021. "Bacteria Responsible for Nitrate-Dependent Antimonite Oxidation in Antimony-Contaminated Paddy Soil Revealed by the Combination of DNA-SIP and Metagenomics." *Soil Biology and Biochemistry* 156: 108194.

Zhang, Y., E. J. O'Loughlin, and M. J. Kwon. 2022. "Antimony Redox Processes in the Environment: A Critical Review of Associated Oxidants and Reductants." *Journal of Hazardous Materials* 431: 128607.

Zhu, Y., M. Wu, N. Gao, et al. 2018. "Removal of Antimonate From Wastewater by Dissimilatory Bacterial Reduction: Role of the Coexisting Sulfate." *Journal of Hazardous Materials* 341: 36-45.