Sungeun Lee

Ella T Sieradzki

Alexa M Nicolas

Robin L Walker

Mary K Firestone

Christina Hazard

Graeme W Nicol

Methane-derived carbon flow through host-virus trophic networks in soil

The concentration of atmospheric methane continues to increase with microbial communities controlling soil-atmosphere fluxes. While there is substantial knowledge of the diversity and function of organisms regulating methane production and consumption, the frequency and impact of interactions with viruses on their activity in soil is unknown. Metagenomic sequencing of soil microbial communities has enabled identification of linkages between viruses and hosts. However, determining host-virus linkages through sequencing does not determine whether a virus or a host are active.

In this study, we identified active individual interactions in situ by following the transfer of assimilated carbon from active hosts to viruses. Using DNA stable-isotope probing combined with metagenomic analyses, we characterized methane-fueled microbial networks in acidic and neutral pH soils, specifically primary and secondary utilisers of carbon, together with the recent transfer of methane-derived carbon to viruses. Sixtythree percent of viral contigs from replicated soil incubations contained genes associated with known methanotrophic bacteria. Genomic sequences from 13 Cenriched viruses were present in clustered regularly interspaced short palindromic repeats (CRISPR) arrays of multiple, closely-related Methylocystis populations,

Reads from individual metagenomes were assembled before taxonomic assignment of individual contigs. Reproducibly distinct communities were enriched in the two soils (Supplementary Fig. 2), with six bacterial families representing annotated contigs >5 kbp to which >1% of reads were mapped and all including known C1utilising taxa (Beijerinckiaceae, Bradyrhizobiaceae, Hyphomicrobiaceae, Methylococcaceae, Methylocystaceae and Methylophilaceae). We resolved twentythree medium and high-quality [START_REF] Bowers | Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea[END_REF] Rudaea, the latter two, to our knowledge, not having been previously associated with methylotrophy but possessed predicted methanol and formate dehydrogenases (Supplementary Table 2). Two MAGs represented strains of Bdellovibrio and Myxococcus, known predatory bacteria, indicating that growing methylotrophic populations were preyed upon [START_REF] Pérez | Bacterial predation: 75 years and counting![END_REF].

Lytic virus populations linked to C1-hosts were analysed using metagenome viral contigs (mVCs), predicted using established tools. Using contigs >10 kbp [START_REF] Roux | Minimum information about an uncultivated virus genome (MIUViG)[END_REF] VirSorter (Roux et al., 2015) predicted 270 metagenome viral contigs (mVCs) with a further 4 'likely' mVCs predicted uniquely by DeepVirFinder (Ren et al., 2020) (see Supplementary Text), together representing 227 viral operational taxonomic units (vOTUs) [START_REF] Paez-Espino | Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data[END_REF]. Analysis of the normalised read mapping for mVCs demonstrated that, as with the bacterial communities, active 13 Cenriched viral populations were reproducibly distinct between acidic and neutral pH soils (Supplementary Fig. 3).

mVCs were linked to host bacteria using three different approaches: identifying incorporation of viral DNA into spacers of bacterial CRISPR arrays, similarity of homologous genes possessed by both host and virus contigs, and k-mer similarity between potential host and virus contigs, the last approach being considered only partially successful (see Supplementary Text). CRISPR arrays were identified in 3 of preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

The copyright holder for this this version posted February 13, 2021. ; https://doi.org /10.1101/2020.12.16.423115 doi: bioRxiv preprint 5 23 MAGs, each associated with the genus Methylocystis or Methylosinus of the Methylocystaceae (Fig. 1). In the acidic soil, complete CRISPR arrays of growing methanotrophs were associated with two Methylocystis MAGs (MAG identifiers 5 and 6) sharing 79.2% average nucleotide identity (ANI) and likely representing different species [START_REF] Jain | High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries[END_REF]. A further six CRISPR arrays were identified in unbinned bacterial contigs all possessing the same direct repeat (DR) sequence. These eight arrays varied in size ranging from 9 to 114 DRs and contained a total of 432 spacers, and were in the same size range of Methylocystaceae CRISPR arrays from sequenced genomes (Supplementary Table 3). Comparison of spacer incorporation between arrays revealed that these multiple closely-related populations had different histories of viral interaction and subsequent spacer incorporation. Genome sequences from 13 C-enriched viral populations were represented by seven mVCs and matched 29.5% of spacers. In addition, 7.9% of spacers possessed a one nucleotide mismatch, all of which represented a synonymous substitution, indicating that variation was the result of mutations in viral genomes increasing their ability to evade CRISPR-CAS defense systems or genetic variation in closely related viral populations. Only three pairs of spacers were identical, with each pair member located on a different array. Variation in virus host range was also observed, with 3 and 2 mVCs linked to only one or both Methylocystis MAGs, respectively. Surprisingly, a large number of spacers in individual Methylocystis CRISPR arrays were linked to the same virus, with up to 31 being homologous to protospacer sequences in one mVC. To provide support that these multiple spacers were derived from Methylocystis-associated viruses, mVCs were examined for host-specific conserved protospacer-adjacent motif (PAM) sequences [START_REF] Mojica | Short motif sequences determine the targets of the prokaryotic CRISPR defence system[END_REF].

Consistent with the identification of genuine protospacers, 138 of linked 146 spacers (i.e. all possessing ≤ 1 mismatch) had the conserved PAM sequence 'TTC' (targetcentric orientation) [START_REF] Leenay | communicating, and engineering the CRISPR PAM[END_REF]. The variation in spacer position between the arrays revealed temporal differences in virus infections. For example, the most recently integrated spacer in 3 of 8 different Methylocystis CRISPRs was derived from a 13 C-enriched virus represented by mVC_12213_cat2, suggesting the possibility of incorporation occurring during the incubation of the experiment.

Further analysis of all CRISPR arrays (i.e. including those in unbinned contigs) linked to 13 C-enriched viruses revealed that the majority of viruses were associated with methanotrophic populations (Fig. 2a). In total, 11 different variants were identified (i.e. each having a unique DR sequence) with 9 linked to Methylocystaceae or Methylococcaceae populations. DR sequences generally possessed high sequence similarity to those in CRISPR arrays from cultivated strain genomes of the same family, although only CRISPR array 6 had a DR sequence that was identical (Supplementary Table 3). Individual DR variants were restricted to either pH 4.5 or 7.5 soil. Using 100% sequence identity in searches between CRISPR spacer and mVC protospacer sequences, 19 VirSorter-predicted mVCs were linked to all CRISPR array variants. In addition, analysis of shorter mVCs ranging 5-10 kbp identified two additional linked mVCs (mVC_08964_cat.3 (9.8 kbp) and mVC_28139_DVF (5.1 kbp)). One third of CRISPR linked-mVCs were categorized at the lowest level of confidence (i.e. category-3 by VirSorter (Roux et al., 2015) or 'possible' by DeepVirFinder (Ren et al., 2020), suggesting that retaining only higher confidence contigs may exclude a substantial proportion of bona fide methanotroph virus-derived contigs in uncharacterised environments such as soil.

Analysis of tetranucleotide frequencies (TETRA) [START_REF] Wang | Genomic sequence of 'Candidatus Liberibacter solanacearum' haplotype C and its comparison with haplotype A and B genomes[END_REF] clustered the 21 mVCs into three groups that were associated with the Methylocystaceae, Methylococcoceae and an unknown group (Fig. 2b). The majority of viruses infected members of the Methylocystaceae family, with those infecting populations of the Methylocystis and Methylosinus genera restricted to acidic and neutral pH soils, respectively. TETRA correlation coefficients of all Methylocystaceae-linked viruses were in the same range both within and between either genus, suggesting co-evolution with their host rather than genetic drift and divergence was the primary mechanism for defining specific associations with Methylocystis or Methylosinus strains.

Identification of homologous genes in mVCs that were shared with prokaryotic genomes were always consistent with host-virus linkages established using spacer sequences from MAG CRISPR arrays. Specifically, BLASTp searches of genes present in the 9 mVCs linked to Methylocystaceae MAGs via CRISPR spacer sequences all contained 'best hits' (identity >30%, e-value <10 -5 , bit score >50 and query cover >70%) to a minimum of 5 homologues also found in Methylocystaceae genomes. This was therefore used as a criterion for establishing host-virus linkages.

Sixty-three percent of mVCs contained a homologue that was linked to genomes of known C1-utilising bacteria, with 35% linked specifically to populations from the Methylocystaceae, Methylococcaceae or Hyphomicrobiaceae (Fig. 3a). While analysis of bacterial homologues in mVCs identified the taxonomic family of the assumed dominant host, they also indicated that individual viruses may infect hosts of other families of the same taxonomic order, including those at other trophic levels.

Specifically, within the Rhizobiales, mVCs linked to Methylocystaceae also contained homologues shared with Bradyrhizobiaceae, Methylobacteriaceae and Rhizobiaceae (Fig. 3b), indicating that viruses of methanotrophs may also infect non-methanotrophic methylotrophs that are active at the same time.

CH4-derived C was also transferred to viruses of secondary or tertiary utilisers.

One group of mVCs were linked to methylotrophic Hyphomicrobiaceae and a second to a phylogenetically diverse range of nitrogen-fixing Rhizobia i.e. Bradyrhizobiaceae, Phyllobacteriaceae and Rhizobiaceae. These lineages contain known methylotrophs, methanol dehydrogenases have been identified in a range of rhizobial species [START_REF] Huang | Rare earth element alcohol dehydrogenases widely occur among globally distributed, numerically abundant and environmentally important microbes[END_REF] and these mVCs also contained homologues found in the genomes of nodulating Methylobacterium strains (Green and Ardley, 2018). Viruses of predatory Bdellovibrio and Myxococcales bacteria were predicted, consistent with the recovery of corresponding bacterial MAGs in 13 C-enriched DNA. One mVC (20210-cat_2) was linked to the genus Bdellovibrio (sharing 11 of 67 mVC genes) and three category-3 mVCs (i.e. possible viruses) was linked to Myxococcales populations, containing gene homologous to four families within the order. The high isotopic labelling of both heterotrophic predators (with no identifiable C1-utilising capability) and their viruses indicate that the predators were feeding primarily on C1-utilisers, as carbon incorporated by feeding on unlabelled bacteria would dilute the enrichment in predators. As such, it also indicates that predatory bacteria may have preference for preying upon growing populations rather than the non-C1-utilising majority.

Gene-sharing network analysis of mVCs with viruses in the NCBI RefSeq database and other metagenome studies were analysed using vConTACT 2.0 (Jang et al., 2019). Any linkages with RefSeq viruses typically had low scores (i.e. sharing a low number of homologues) and were linked to viruses of hosts that were inconsistent with our homologue-based predictions (Supplementary Table 4). No linkages were observed with recently reported giant viruses of methanotrophs in freshwater lakes (Chen et al., 2020). However, in a recent study of 197 metagenomes from Swedish peatland soil, [START_REF] Emerson | Host-linked soil viral ecology along a permafrost thaw gradient[END_REF] identified 13 viruses linked to methanotrophs.

Intriguingly, 8 of these were linked in our viral gene-sharing network, with both studies predicting Methylocystaceae hosts using different methods of host annotation (Supplementary Text, Supplementary Fig. 5) and revealing the distribution of specific Methylocystaceae-associated viral groups present in different geographical areas and soil types. Analysis of gene-sharing networks of mVCs from this study indicated that there were two distinct Methylocystaceae-linked viral clusters which also varied in their distribution in both soils. Specifically, one cluster was associated with low pH only whereas the second cluster contained viruses found in both pH 4.5 and 7.5 soils, including those linked by CRISPR spacer sequences. Individual networks of Methylococcaceae-and rhizobia-associated mVCs were also identified, typically associated with one of the two soils of contrasting pH. Taxonomically-linked mVCs with ³5 homologues were consistently placed in networks with other mVCs containing 1-4 homologues from the same methylotrophic families, confirming host linkage to a larger number of mVCs. mVCs contained 8,174 genes, with 49.6% (4,054) annotated representing 606 unique functions. Of these, genes encoding viral proteins accounted for 9.8% (397 genes) and included major capsid proteins, tail proteins, integrases, portal proteins and terminases. Bacterial proteins used for viral replication accounted for 5.1% (206 genes). A number of metagenomic studies have demonstrated that viruses can possess genes encoding sub-unit C of ammonia or particulate methane monooxygenases as auxiliary metabolic genes (AMGs) (Chen et al., 2020;[START_REF] Ahlgren | Discovery of several novel, widespread, and ecologically distinct marine Thaumarchaeota viruses that encode amoC nitrification genes[END_REF] which are also typically found as isolated genes in genomes in addition to being present in clusters or operons encoding A and B sub-units [START_REF] Nicol | Ammonia-oxidising Crenarchaeota: important players in the nitrogen cycle?[END_REF]. In this study, one low confidence mVC (7.3 kbp, category-3) was identified as containing an isolated pmoC gene that was phylogenetically related to growing Methylocystis populations but was distinct from pmoC sequences found in viruses associated with freshwater Methylocystis populations (Chen et al., 2020) (Supplementary Fig. 6).

In summary, these results demonstrate that by following carbon flow, viruses and hosts associated with a critical biogeochemical process can be identified at the scale of individual populations, and currently active interactions at different trophic levels examined within the highly complex soil environment. Type I and II methanotrophs interact with evolutionarily distinct groups of viruses and the composition of CRISPR arrays of Methylocystaceae reveal that they have a continual dynamic interaction with individual viruses. Analysis of shared homologues in individual viral genomes show that they may interact with host populations at different trophic levels within a methane-fuelled network.

Methods

Soil microcosms

Triplicate soil samples were collected in February 2018 at 1 m intervals from the upper 10 cm of pH 4.5 and 7.5 soil sub-plots of a pH gradient maintained since 1961 and under an 8-year crop rotation (SRUC, Craibstone Estate, Aberdeen, Scotland; UK grid reference NJ872104) [START_REF] Kemp | Leaching of genetically modified Pseudomonas fluorescens through organic soils: influence of temperature, soil pH, and roots[END_REF]. The crop at the time of sampling was potatoes. Soil (podzol, sandy-loam texture) was sieved (2 mm mesh size) and microcosms established in triplicate for each soil pH and isotope in 144 ml serum bottles containing 14.30 g soil (10 g dry weight equivalent) with a 30% volumetric water content, equivalent to ~60% water-filled pore space. Bottles were capped and established with a 10% (v/v) 12 C-CH4 or 13 C-CH4 (Sigma-Aldrich) headspace (99% atom enriched), re-opening every 10 days to maintain aerobic conditions before sealing and re-establishing CH4 headspace concentrations. Microcosms were incubated at 25 o C and destructively sampled after 30 days with soil archived immediately at -20 o C.

DNA-SIP

Genomic DNA was extracted from 0.5 g soil samples using a CTAB buffer phenol:chloroform: isoamyl alcohol bead-beating protocol and subjected to isopycnic centrifugation in CsCl gradients, recovery and purification as previously described (Nicol and Prosser, 2011). Briefly, 6 ug of genomic DNA was added to 8 ml CsCl-Tris EDTA solution (refractive index (RI) of 1.4010; buoyant density of 1.71 g ml -1 ) in polyallomer tubes before sealing and ultracentrifugation at 152,000 × g (50,000 rpm) in a MLN80 rotor (Beckman-Coulter) for 72 h at 25°C. CsCl gradients were fractionated into 350 ul aliquots using an in-house semi-automated fraction recovery system before determining RI and recovering DNA. The relative abundance of bacterial 16S rRNA and methanotrophic pmoA genes in genomic DNA distributed across the CsCl gradients was determined by qPCR in a Corbett Rotor-Gene 6000 thermocycler (Qiagen) using primer sets P1(341f)/P2(534r) [START_REF] Muyzer | Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA[END_REF] and A189F/A682R [START_REF] Holmes | Evidence that particulate methane monooxygenase and ammonia monooxygenase may be evolutionarily related[END_REF] respectively. Twenty-five μl reactions contained 12.5 μl 2X QuantiFast SYBR Green Mix (Qiagen), 1 μM of each primer, 100 ng of T4 gene protein 32 (Thermo Fisher), 2 μl of standard (10 8 -10 2 copies of an amplicon-derived standard) or 1/10 diluted DNA. Thermocycling conditions consisted of an initial denaturation step of 15 min at 95°C for both assays followed by 30 cycles of 15 s at 94°C, 30 s at 60°C, 30 s at 72°C for the 16S rRNA gene assay or 60 s at 94°C, 60 s at 56°C, 60 s at 72°C for the pmoA gene assay, followed by melt-curve analysis. All assays had an efficiency between 93-97% with an r 2 value >0.99. Genomic DNA from four fractions with a buoyant density >1.732 g ml -1 were then pooled for each 12 C-and 13 C-CH4-incubated replicate for 16S rRNA gene amplicon sequence and metagenomic analysis.

Metagenome sequencing, assembly & annotation

Library preparation and sequencing was performed at the Joint genome Institute (JGI), Berkeley, USA. Libraries were produced from fragmented DNA using KAPA Biosystems Library Preparation Kits (Roche) and quantified using KAPA Biosystems NGS library qPCR kits. Indexed samples were sequenced (2 x 150 bp) on the Illumina NovaSeq platform with NovaSeq XP v1 reagent kits and a S4 flowcell. Raw reads were processed with JGI's RQCFilter2 pipeline that utilised BBTools v38.51 [START_REF] Bushnell | BBTools software package[END_REF]. Reads containing adapter sequences were trimmed and those with ≥3 N bases or ≤51 bp or ≤33% of full-read length were removed along with PhiX sequences using BBDuk, and reads mapped to human, cat, dog or mouse references at 95% identity were removed using BBMap. De novo contig assembly of the 100 -196 million qualitycontrolled reads per metagenome was performed using MetaSPAdes v3.13.0 (Nurk et al., 2016). The 1 -2 million contigs per metagenome were then concatenated together, and contigs larger than 5 kbp were dereplicated at 99% average nucleotide identity (ANI) using PSI-CD-HIT v4. 6.1 (Fu et al., 2012) and binned using MetaWRAP v1.2.1 [START_REF] Uritskiy | MetaWRAP-a flexible pipeline for genomeresolved metagenomic data analysis[END_REF] (Supplementary Table 5). Bin completion and contamination was determined by CheckM v1.0.12 [START_REF] Parks | CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes[END_REF]. Taxonomic annotation of contigs was performed using Kaiju [START_REF] Menzel | Fast and sensitive taxonomic classification for metagenomics with Kaiju[END_REF] with the NCBI RefSeq database (Release 94; 25 June 2019) (O' Leary et al., 2016) and MAGs using GTDB-Tk v0.3.2 [START_REF] Chaumeil | Tk: a toolkit to classify genomes with the Genome Taxonomy Database[END_REF] with the Genome Taxonomy Database (release 89, 21 June 2019) [START_REF] Parks | A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life[END_REF]. Protein sequence annotation was performed using InterProScan 5 (e-value <10 -5 ) [START_REF] Jones | InterProScan 5: genome-scale protein function classification[END_REF]. Pairwise ANI comparison of MAGs was calculated using FastANI (Jain et al., 2019).

16S rRNA genes were amplified using primers 515F/806R [START_REF] Walters | Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys[END_REF] followed by library preparation and sequencing on an Illumina MiSeq sequencer as previously described (Finn et al., 2020). Reads with a quality score <20 and length < 100 bp were discarded using FASTX-Toolkit v0.0.13 (http://hannonlab.cshl.edu/fastx_toolkit/). High-quality reads were merged using PANDAseq v2.11 [START_REF] Masella | PANDAseq: paired-end assembler for illumina sequences[END_REF], and denoising and chimera removal performed with UNOISE3 (Edgar, 2016). Amplicon sequence variants (ASVs) were annotated using the RDP classifier v2.11 [START_REF] Wang | Naïve bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy[END_REF]. Non-metric multidimensional scaling of Bray-Curtis dissimilarity derived from the relative abundance of ASVs was performed with the metaMDS function in the vegan package (Oksanen et al., 2019) in R v3.6.0.

Virus prediction

Metagenomic viral contigs (mVCs) were predicted from 9,190 contigs >10 kbp using

VirSorter (Roux et al., 2015), retaining non-prophage category-1, -2 or -3 mVCs, representing "most confident", "likely" and "possible". DeepVirFinder (Ren et al., 2020) was also used to predict mVCs from contigs >10 kbp, with those with a p-value <0.05 and a score ≥0.9 or ≥0.7, representing "confident" and "possible", respectively (Supplementary Table 6). The relative abundance of each mVC in the six metagenomes was determined using the MetaWRAP-Quant_bins module [START_REF] Uritskiy | MetaWRAP-a flexible pipeline for genomeresolved metagenomic data analysis[END_REF] and a heatmap produced using the heatmaply package in R v3.6.0.

Virus-host linkage

CRISPR arrays within MAGs and unbinned contigs were identified using the CRISPR Recognition Tool v1.2 [START_REF] Bland | CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats[END_REF] (Supplementary Table 7). DR and spacer sequences were extracted before performing 100% identity searches against positive and negative strands to identify MAGs or contigs with direct repeats and the viral origin of spacers using Seqkit commands [START_REF] Shen | SeqKit: A cross-platform and ultrafast toolkit for FASTA/Q file manipulation[END_REF]. After identification of matched spacer sequences in mVCs, 10 nucleotides before and after the spacer sequence were extracted to identify associated host-specific PAM sequences. Conserved and variant PAM sequences were manually identified. Correlation coefficients of pairwise comparison of the tetra-nucleotide frequencies (TETRA) between unique CRISPRassociated mVCs were calculated using Python package pyani v0. 2.10 (Pritchard et al., 2016). To identify homologous genes shared between CRISPR-linked viruses and hosts, gene prediction was performed using Prodigal v2.6.3 [START_REF] Hyatt | Prodigal: prokaryotic gene recognition and translation initiation site identification[END_REF] with the -p meta option followed by protein alignment with Blastp (identity >30%, e-value < 10 -5 , bit score >50 and query cover >70%) and protein sequence annotation using InterProScan 5 (e-value <10 -5 ). Gene homology between all mVCs and prokaryotes in the NCBI nr database was determined using Diamond Blastp (e-value <10 -5 ) [START_REF] Buchfink | Fast and sensitive protein alignment using DIAMOND[END_REF]. Virus-host prediction using k-mer frequencies was performed with WIsH v1.0 (p-values <0.05) [START_REF] Galiez | WisH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs[END_REF]. Networks based on shared gene content was constructed using vConTACT 2.0 (Jang et al., 2019) [START_REF] Chistoserdova | The expanding world of methylotrophic metabolism[END_REF][START_REF] Trubl | Coming-of-age characterization of soil viruses: a user's guide to virus isolation, detection within metagenomes, and viromics[END_REF] or unbinned contigs [START_REF] Mclaren | Consistent and correctable bias in metagenomic sequencing experiments[END_REF][START_REF] Chistoserdova | The expanding world of methylotrophic metabolism[END_REF][START_REF] Roux | VirSorter: mining viral signal from microbial genomic data[END_REF][START_REF] Ren | Identifying viruses from metagenomic data by deep learning[END_REF][START_REF] Galiez | WisH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs[END_REF][START_REF] Jang | Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks[END_REF][START_REF] Emerson | Host-linked soil viral ecology along a permafrost thaw gradient[END_REF], or inferred from shared homologues between linked mVCs and bacterial genomes [START_REF] Bartram | Exploring links between pH and bacterial community 822 composition in soils from the Craibstone Experimental Farm[END_REF]11). All mVCs were >10 kbp except mVC_08964_cat.3 (9.8 kbp) and mVC_28139_DVF (5.1 kbp). These two mVCs were also the only two predicted using DeepVirFinder, with calculated probabilities describing 'likely' and 'probable' viruses, respectively. b) TETRA correlation coefficients between 21 CRISPR-linked mVCs.

Colour-coded hexagon symbols denote linkage to MAG-associated CRISPR arrays as per Fig. 1 indicate that substantially more soil virus genomes are required for training datasets using an alignment-free approach.

Evaluation of k-mer analysis for identifying host-virus linkages

The matching of protospacers in mVCs to spacers in CRISPR arrays with 100% identity was also used to validate criteria for linkages via a 'best hit' homologue approach, specifically the sharing of a minimum of five homologues to the same taxonomic family. These two approaches were then compared to linkages predicted using a k-mer based analysis with the tool WIsH 7 , which involves the comparison and prediction of linkage of previously defined host-or virus-derived contigs on the basis of k-mer frequency analysis. Seven of the 21 mVCs predicted to hosts via CRISPR spacer analysis were linked to a host using WIsH and a probability score ≤0.05 (Supplementary Table 9), all of which were consistent at the family level. Of the 103 mVCs linked in vConTACT 2.0 analysis with an assigned host, 35 had a host predicted using WIsH, of which only 23 (66%) and 8 (23%) was the same as the homologuebased prediction at the order and family levels, respectively. These analyses therefore indicated that while the k-mer based approach using WIsH was partially successful in identifying correct linkages at the family level, it was not robust for identifying linkages with a high level of taxonomic resolution or confidence.

Gene-sharing networks of viral metagenomic contigs

Predicted hosts of mVCs from this study (using CRISPR and homologue-based approaches) were generally inconsistent with the known hosts of RefSeq viruses that were linked through vConTACT 2.0 gene-sharing network analysis 8 . However, mVCs from this study placed into individual clusters all had the same predicted host (Supplementary Figure 4). Initial networks used mVCs with a conservative host prediction only (i.e. a minimum of ≥5 homologues linked to one taxonomic family).

With the exception of one cluster of 3 mVCs, which were predicted to be linked to Methylococcaceae and non-methane oxidising methylotrophic Hyphomicrobiaceae, all individual networks were restricted to one family for methanotrophs or one cluster of exclusively rhizobia-linked mVCs. Further analyses included all mVCs >10 kbp in this study (i.e. including those with <5 host associated homologues) and the same networks were identified i.e. mVCs linked within the same cluster all had homologues (i.e. with <5 or ≥5) linking them to the same host family.

For Methylocystaceae mVCs, two separate but linked clusters were identified.

The first contained CRISPR-linked Methylocystaceae-associated mVCs, which were recovered from both pH 4.5 and 7.5 soil (although individual mVCs were restricted to one soil pH). A second cluster was dominated by mVCs from pH 4.5 soil only, indicating that most active Methylocystaceae viruses belonged to one of two distinct lineages. There was a clear difference in the prediction of category-2 ('likely') and category-3 ('possible') mVCs associated with these two clusters. While category-3

viruses are often excluded prior to analysis of soil viromes 9 , CRISPR analysis demonstrated that one-third of linked mVCs were of the lowest category of confidence.

It must be recognised that a proportion of the predicted category-3 mVCs in this study will not be derived from viruses, and clusters composed exclusively of category-3 mVCs without further validation (e.g. without CRISPR spacer linkages) must be interpreted with caution. However, all major clusters identified through gene-sharing network analysis contained a mixture of category-2 and -3 mVCs indicating that they represented groupings of genuine virus-derived genomes.

An intriguing finding was the linkage of host family-specific viruses from two different geographical regions (Scotland and Sweden) and contrasting soil types (agricultural loamy-sand and permafrost peatland soils), from this study and that of Emerson et al. 9 , respectively. In the latter study, 13 of 1,907 mVCs were predicted to have a methanotroph host, of which 9 were linked specifically to the Methyocystaceae.

Intriguingly, 7 of these were also linked to a predicted Methyocystaceae mVC in our study, with one additional mVC predicted to have a Methylocapsa host (i.e. belonging to the Beijerinckiaceae which is another methylotrophic family of the Rhizobiales) but also contained one predicted gene with a 'best hit' match to a Methylocystis genome homologue. As soil pH is recognised as one of the dominant factors driving microbial community structures in soil 10 , it is interesting to note that linked mVCs from both studies were also from acidic soils only.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Linkage of active 13 C-enriched viruses to Methylocystaceae populations in soil by comparison of spacer sequences in CRISPR arrays. a) Distribution of spacers from 562 8 mVCs in Methylocystis CRISPR arrays (MAGs 5, 6 and six unbinned contigs). b) CRISPR array of Methylosinus MAG 11 containing spacers linked to two mVCs. 564 CRISPR array names describe the individual soil microcosm that the contig was recovered from. DRs for complete arrays are numbered (in grey), with the spacer after 566 DR 1 being the most recently incorporated. Two partial arrays are denoted with an *. Spacers with 100% identity or 1 mismatch to sequences in mVCs are represented by 568 colour-coded squares and circles, respectively, with stripes representing sequences found in two different mVCs. 570

Fig. 3 .

 3 Fig. 3. Linkage of 13 C-enriched viruses to methanotrophic, methylotrophic and predator bacterial host populations through identification of shared homologous genes. a) Association of viruses with different bacterial families and functional groups inferred from the presence of ≥5 shared homologous genes in category-1, -2 and -3 VirSorter-predicted mVCs. b) Proportion of homologues in methanotroph, non-methanotrophic methylotroph or predator viruses linked to individual bacterial families. Each chart summarises those mVCs that all contain ≥5 homologues to one family (number of mVCs given in parentheses) but with other taxonomic linkages also given. 'Other' describes the proportion found in families each represented by less than <5% of homologues or those not annotated to the family level.

2 .

 2 Taxonomic affiliation of metagenome reads and 16S rRNA gene amplified sequence variants (ASVs) derived from high buoyant density DNA from triplicate pH 4.5 and pH 7.5 soil microcosms after incubation with 12 C-or 13 C-CH4. a) Relative abundance of metagenome sequences mapped to contigs of families that recruited ≥1% reads in either soil. Reads were mapped to annotated contigs ≥5 kbp from 13 C-incubated microcosms only. b) and c) Relative abundance of 16S rRNA gene ASVs derived from the six dominant families in 12 C-or 13 C-CH4 incubations of pH 4.5 and 7.5 soil, respectively. d) Non-metric multidimensional scaling of Bray-Curtis dissimilarities derived from the relative abundance of annotated 16S rRNA gene ASVs. Due to the close overlap of replicates (small symbols), samples were resolved for visualisation using a jitter function (large symbols). Significant differences between samples are indicated with * (p <0.05, two-sample Student's t-test or Welsch's t-test when variances were not homogenous).

3 . 4 . 5 .

 345 Heat-map displaying the relative abundance of 270 mVCs >10 kbp in length from 13 C-enriched viral DNA derived from triplicate pH 4.5 and pH 7.5 soil microcosms. The values of normalised relative abundance are presented as reads per kbp after ln transformation. Gene sharing network analysis of mVCs (one representative per vOTU) from 13 C-enriched viruses in pH 4.5 and 7.5 soils. a) Taxonomic affiliation of hosts predicted by homologue analysis, with mVCs containing ≥5 or <5 linked homologues highlighted. Eight mVCs from peatland soils linked to the Methylocystaceae (Emerson et al., 2018) are also shown. b) Distribution of mVCs in pH 4.5 and 7.5 soil determined by the mean ratio of normalised relative abundance from triplicate samples. Seven mVCs from peatland were only found in soils with pH ≤4.7. c) VirSorter category prediction and linkage to CRISPR arrays via spacer sequence analysis. Maximum likelihood phylogenetic tree of derived amino acid sequences of PmoC and PxmC found in three type I and seven type II methanotroph MAGs and one potential viral-derived contig. MAG-derived sequences are described by MAG number, genus and contig identifier (see Supplementary Table

8

 8

  ). Sequences in reference methanotroph genomes and freshwater-derived viruses (Chen et al., 2020) were included with NCBI accession numbers given in parenthesis. Circles at nodes describe percentage bootstrap support from 100 replicates and the scale bar represents 0.02 changes per amino acid position.

  with the NCBI RefSeq database (Release 94; 25 June 2019).

	2021 programme. The authors would like to thank Dr. Joanne Emerson for valuable Chen, L.-X. et al. Large freshwater phages with the potential to augment aerobic
	discussion. methane oxidation. Nat. Microbiol. In press (2020).
	Chistoserdova, L., Kalyuzhnaya, M.G. & Lidstrom, M.E. The expanding world of
	methylotrophic metabolism. Annu. Rev. Microbiol. 63, 477-499 (2009).
	Curry, C.L. Modeling the soil consumption of atmospheric methane at the global scale,
	Global Biogeochem. Cycles 21, GB4012 (2007).
	Edgar, R.C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon
	sequencing. Preprint at https://doi.org/10.1101/081257 (2016).
	Emerson, J.B. et al. Host-linked soil viral ecology along a permafrost thaw gradient.
	Nat. Microbiol. 3, 870-880 (2018).
	Bootstrap support was calculated from 100 replicates.
	3113-3114 (2017).
	Gómez, P. & Buckling. Bacteria-phage antagonistic coevolution in soil. Science 332,
	106-109 (2011).
	Green, P.N and Ardley, J.K. Review of the genus Methylobacterium and closely
	related organisms: a proposal that some Methylobacterium species be reclassified into
	a new genus, Methylorubrum gen. nov. Int. J. Syst. Evol. Microbiol. 68, 2727-2748
	(2018).

Phylogenetic analysis of PmoC and PxmC protein sequences

Maximum likelihood analysis of inferred protein sequences of membrane-bound monooxygenase C sub-units from methanotroph MAGs and reference sequences (Supplementary Table

8

) was performed on unambiguously aligned sequences using PhyML 3.0 (Guindon et al., 2010) with automatic model selection (LG substitution, gamma distribution (0.06) and proportion of invariable sites (0.087) estimated). Emerson, J.B. Soil viruses: A new hope. mSystems 4, e00120-19 (2019). Finn, D.R., Lee, S., Lazén, M.B., Nicol, G.W. & Hazard, C. Cropping systems that improve richness convey greater resistance and resilience to soil fungal, relative to prokaryote, communities. Preprint at https://doi.org/10.1101/2020.03.15.992560 (2020). Frossard, A., Hammes, F., & Gessner, M.O. Flow cytometric assessment of bacterial abundance in soils, sediments and sludge. Front. Microbiol. 7, 903 (2016). Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the nextgeneration sequencing data. Bioinformatics 28, 3150-3152 (2012). Galiez, C, Siebert, M., Enault, F., Vincent, J. & Söding, J. WisH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics 33, Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the Performance of PhyML 3.0. Syst. Biol. 59, 307-21 (2010).

  .

	a)				b)	TETRA
					10-19
	mVC_00108_cat.2 mVC_12494_cat.3 mVC_01376_cat.3 mVC_13605_cat.3 mVC_08964_cat.3	pH 4.5			m V C _ 0 0 1 0 8 _ c a t . 2 m V C _ 0 0 2 3 4 _ c a t . 2	20-29 30-39 40-49 50-59 60-69 70-79
	mVC_00234_cat.2				80-89
	mVC_07548_cat.2				90-99
	mVC_08211_cat.3				100
	mVC_12213_cat.2
	mVC_12249_cat.2
	mVC_07580_cat.2
	mVC_20191_cat.2			pH 7.5
	mVC_29706_cat.2
	mVC_20158_cat.2
	mVC_20187_cat.2
	mVC_20457_cat.2
	mVC_29139_DVF
	mVC_21051_cat.3
	mVC_20126_cat.2
	mVC_16898_cat.2
	mVC_20180_cat.2
		1 2 3 4 5 6	7 8	9	10 11
		CRISPR array
		(unique direct repeat variant)

Acknowledgments

The sequencing data were generated under JGI Community Science Program proposal 503702 awarded to GWN and CH. The work conducted by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This work was funded by an AXA Research Chair awarded to GWN and a France-Berkeley Fund grant (2018-2019) awarded to GWN and MKF.

The pH gradient experiment is funded through the Scottish Government RESAS 2016-

Data availability

Metagenome sequence reads are deposited under NCBI BioProject accession numbers PRJNA621430 -PRJNA621435. Metagenome draft assemblies are accessible through the JGI Genome Portal (DOI: 10.25585/1487501). Amplicon sequence data is deposited in the NCBI Sequence Read Archive with BioProject accession number PRJNA676099.

Author contributions

The research program was conceived by and funded from grants awarded to GWN, CH and MF. SL, CH and GWN designed

Supplementary text

Comparison of 16S rRNA gene amplicon and metagenomic libraries of high buoyant density DNA from 12 C and 13 C-CH4 incubations

The relative abundance of annotated contigs belonging to different families in replicate metagenomic libraries was reproducible and distinct between acidic and neutral pH soils (Supplementary Figure 2a). Twenty of the 23 medium and high-quality MAGs recovered in 13 C-derived metagenomic libraries were from methanotrophic or nonmethanotrophic methylotrophic populations and therefore consistent with targeting a methane-fuelled community using stable isotope probing (Supplementary Table 2).

Nevertheless, comparison with equivalent 12 C incubations was performed as per standard practice with DNA-SIP experiments 1 . Genomic DNA was recovered and purified from high buoyant density fractions (>1.732 g ml -1 ) from triplicate microcosms of both 12 C-and 13 C-CH4 incubations. While recovered DNA from 12 C incubations was considered too low for metagenome sequencing, with DNA concentrations below the limit of detection in some fractions, PCR amplification enabled characterisation of 16S rRNA gene-based community structures and comparison to the equivalent fractions from 13 C incubations.

Six taxonomic families were represented by contigs to which a minimum of ≥1% of reads were mapped from metagenome analysis of 13 C-enriched DNA in at least one soil. These families represented 39.8% and 83.7% of 16S rRNA gene ASVs in pH 4.5 and 7.5 samples, respectively, compared to 6.9 and 18.3% in the equivalent DNA fractions from 12 C-incubations (Supplementary Fig. 2). Annotated metagenome and 16S rRNA amplicon libraries generated from the same 13 C-enriched DNA contained representatives of the same C1-utilising groups, although substantial differences were observed in relative abundance. For example, while the Methylococcaceae was the dominant family in pH 7.5 13 C-enriched DNA using both approaches, it represented 5.3% (±0.3% s.e.) and 61.1% (±3.2% s.e.) of annotated contigs and 16S rRNA gene ASVs, respectively. In 13 C-enriched pH 4.5 DNA, the dominant family in the metagenomic libraries was the Methylocystaceae, representing 13.9% (±1.6% s.e.) of annotated contigs but only 0.02% (±0.006% s.e.) in the amplicon libraries, where the non-methanotrophic methylotrophic Hyphomicrobiaceae was the most abundant at 22.0% (±5.7% s.e.). Observed differences were therefore likely due to a combination of variation in genome size and 16S rRNA gene copy number between strains in preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.


C Guerra
email: caguerra2@uc.cl

A Ringuedé

M I Azocar

M Walter

C Galarce

F Bedioui

M Cassir

M Sancy
email: mamiesancy@gmail.com

Corrosion Analysis of AISI 430 Stainless Steel in the presence of Escherichia coli and Staphylococcus aureus

Keywords: Escherichia coli, Staphylococcus aureus, Stainless Steel, AISI 430, Corrosion

AISI 430 stainless steel is an attractive material to be used in the healthcare industry, particularly as a sensor due to its low cost, corrosion resistance, as well as being Ni-free. AISI 430 was evaluated in an artificial sweat solution with the presence of Escherichia coli, and Staphylococcus aureus. Surface microbial analyses did not reveal colonization of bacteria on metallic surfaces, even when bacteria adhesion was investigated in a Müeller-Hinton solution. However, by electrochemical techniques, the AISI 430 surfaces demonstrated clear signs of corrosion mainly in a sterile medium after two weeks of exposure.

Introduction

AISI 430 is ferritic stainless steel, which has been utilized in both the food [START_REF] Partington | Stainless Steel in the Food and Beverage Industry[END_REF] and medical industry [START_REF] Newson | Stainless Steels -Applications, Grades and Human Exposure[END_REF] since it is relatively cheaper when compared to austenitic alloys as AISI 304 [START_REF] Calmet | Corrosion Behavior of Biocompatible Stainless Steels in Physiological Medium for Non-invasive Diagnosis of Small Fiber Neuropathies Applications[END_REF], as well as having an appropriated corrosion resistance and high sensitivity to chlorides [START_REF] Calmet | Corrosion Behavior of Biocompatible Stainless Steels in Physiological Medium for Non-invasive Diagnosis of Small Fiber Neuropathies Applications[END_REF].

Moreover, it is also essentially Ni-free and does not produce allergies [START_REF] Calmet | Electrochemical Behavior of Stainless Steels for Sudomotor Dysfunction Applications[END_REF], making it a valuable material for use in contact with the skin. Therefore, its features are appropriately used on door handles, handrails, medical devices [START_REF] Newson | Stainless Steels -Applications, Grades and Human Exposure[END_REF][START_REF] Heubner | Stainless Steel When Health Comes First[END_REF][START_REF] Davis | Handbook of Materials for Medical Devices[END_REF], and in particular, as a sensor for early diagnosis for type 2 diabetes by electrochemical skin conductance (ESC). This is a simple electrophysiological method used in the functional evaluation of the sweat gland that probes sudomotor/small fiber dysfunction [START_REF] Calmet | Electrochemical Behavior of Stainless Steels for Sudomotor Dysfunction Applications[END_REF]. In this case, small fiber nerves lacking myelinated axons produce a durable shift in the ionic balance and pH value of sweat conducts [START_REF] Calmet | Corrosion Behavior of Biocompatible Stainless Steels in Physiological Medium for Non-invasive Diagnosis of Small Fiber Neuropathies Applications[END_REF][START_REF] Voortman | Small fiber neuropathy: A disabling and underrecognized syndrome[END_REF][START_REF] Calmet | Small fiber neuropathy diagnosis by a non-invasive electrochemical method: Mimicking the in-vivo responses by optimization of electrolytic cell parameters[END_REF]. Indeed, it has been reported that the pH value of the sweat can vary from 4.5 to 7.0 [9 11] due to the damage in the glands [START_REF] Voortman | Small fiber neuropathy: A disabling and underrecognized syndrome[END_REF], sweat-rate [START_REF] Coyle | Textile sensors to measure sweat pH and sweat-rate during exercise[END_REF], and glucose concentration [START_REF] Bruen | Glucose sensing for diabetes monitoring: Recent developments[END_REF].

In previous investigations, stainless steels have been analyzed as new biocompatible sensors [START_REF] Calmet | Corrosion Behavior of Biocompatible Stainless Steels in Physiological Medium for Non-invasive Diagnosis of Small Fiber Neuropathies Applications[END_REF][START_REF] Calmet | Electrochemical Behavior of Stainless Steels for Sudomotor Dysfunction Applications[END_REF][START_REF] Ayoub | Electrochemical Characterization of Nickel Electrodes in Phosphate and Carbonate Electrolytes in View of Assessing a Medical Diagnostic Device for the Detection of Early Diabetes[END_REF]. However, the behavior of stainless steels, such AISI 430 surface, has never been adequately determined neither under sweat conditions nor with bacterial adhesion. So far what is known is that Escherichia coli is frequently present in human hands and feet [START_REF] Noor | Understanding Diabetic Foot Infection and its Management[END_REF], while Staphylococcus aureus is typically transmitted during hospital stays, since they are able to adhere to surfaces due to cell adhesion molecules being closely located around the outer part of the bacterial cell wall; they may even adhere to several types of surfaces, that include medical equipment [START_REF] Kiedrowski | New approaches for treating staphylococcal biofilm infections[END_REF]. The latter is substantially important due to the damage that bacterial colonization can cause, for instance, biocorrosion (MIC) [START_REF] Medilanski | Influence of the surface topography of stainless steel on bacterial adhesion[END_REF][START_REF] Javed | The effect of metal microstructure on the initial attachment of Escherichia coli to 1010 carbon steel[END_REF].

The main goal of this investigation is to analyze the influence of Escherichia coli and

Staphylococcus aureus bacteria on the corrosion behavior of AISI 430 surface after exposure to acidic and neutral artificial sweat solutions through the use of surface analysis and electrochemical techniques, specifically the bacterial growth on the surface and its effect on the corrosion rate of the steel.

Experimental

Sample and solutions

The material studied was a stainless steel AISI 430 (Good Fellow) plate with the following chemical composition (in wt.%): 0.12 max. % C, 1 max. % Mn, 0.004 max. P, 0.030 max. % S, 1 max. % Si, 16-18% Cr, 0.50 max. % Ni, balance Fe. The AISI 430 samples were used as received, which have been polished according to ASTM A480 N°8 to achieve mirror surface, in order to minimize the surface imperfection effects on the electrochemical response. Before exposure, the samples were cleaned using bi-distilled water, and dried under airflow.

In this work, the artificial sweat solution (ASS) was prepared under ISO-3160-2 protocol, which contains (in g L -1 ): 20 sodium chloride, 17.5 ammonium chloride, 4.76 acetic acid, 15 2 hydroxy-propanoic acid. The solution was made using distilled water, with the pH being set at 4.7 and 6.5 by using 0.1M sodium hydroxide solution. Notice that the acid solution corresponds to the case when the peripheral neuropathy is early developed in patients with diabetes [START_REF] Bedioui | Electrochemical Behavior of Electrode Materials (Nickel and Stainless Steels) for Sudomotor Dysfunction Applications: A Review[END_REF], producing a durable shift in the ionic balance and pH value of sweat conducts [3,7 11]. In this condition, glands [START_REF] Voortman | Small fiber neuropathy: A disabling and underrecognized syndrome[END_REF], sweat-rate [START_REF] Coyle | Textile sensors to measure sweat pH and sweat-rate during exercise[END_REF], and glucose concentration [START_REF] Bruen | Glucose sensing for diabetes monitoring: Recent developments[END_REF] can be strongly damaged, therefore, analyzing the metal surfaces that can be in contact with the sweat is very interesting from a corrosion point of view. Additionally, the ASS solution was inoculated with an active inoculum of Escherichia coli BW25113 and Staphylococcus aureus BAA977, separately, which are frequently found on the human skin.

Microbiological characterization

E. coli and S. aureus were grown in a nutritive Müeller-Hinton medium containing (mg•L - 1 ): 2 beef extract, 17.5 casein acid hydrolysate, 1.5 starch, 17.0 agar, along with 1000 mL of distilled water at 37 °C overnight. Bacterial growth was controlled by direct Petri dish counting and 96-well plate growth curves under shaking conditions in a Multiskan FC (Thermo) equipment. Bacterial growth was measured at 600 nm. Tests for bacteria adhesion were performed by dipping a sample of 1 cm 2 of AISI 430 into a stock of E. coli and S. aureus (10 6 cells per milliliter) in different solutions, using Müeller-Hinton (MH) broth as a positive control and an artificial sweat solution (ASS) without a nutritional agent, with 0.5 wt.% glucose (G), 1.0 wt.% glucose, 0.5 wt.% lactose (L), 1.0 wt.% lactose, respectively, at pH 4.7. Additionally, the artificial sweat solution was set at pH: 7.0, 6.5, 6.0, and 5.5.

The metallic samples were incubated at 37 °C during 24 hours in the presence of each type of bacteria, under convective conditions. A 0.01 M sodium cacodylate/0.15 M NaCl buffer with a 7.0 pH was used to rinse the samples twice, with them later being fixed with 1% glutaraldehyde at 23± 1 °C for 2 hours. Samples were then dehydrated by using ascending percentages of ethanol (30%, 50%, 70%, 80%, 90%, and 100%), and later coated with a thin film of Au, all done before being observed by FE-SEM [START_REF] Tamayo | Porous Nanogold/Polyurethane Scaffolds with Improved Antibiofilm, Mechanical, and Thermal Properties and with Reduced Effects on Cell Viability: A Suitable Material for Soft Tissue Applications[END_REF].

Surface analysis

The surface of AISI 430 samples was cleaned with bi-distilled water and alcohol before to be characterized by field-emission scanning electron microscopy (FE-SEM) model QUANTA 250 FEG that was equipped with an energy dispersion spectrometer. This was carried out prior to and after 14 days of exposure. Additionally, the chemical compositions of samples were analyzed in depth, before and after exposure to sterile and inoculated artificial sweat solution, by using a Glow-Discharge Optical Emission Spectroscopy (GD-OES) model Spectruma Analytik GmbH GDA 750 HR, with a 2.5 mm diameter anode that operates in DC excitation mode. The analysis was carried out in an Ar atmosphere (5.0 quality) with an average discharge pressure of 5 x 10 -2 hPa, by using the following excitation parameters: 1000 V, 12 mA and 0.1 m•min -1 sputtering rate [START_REF]GDA 750 HR -Spectruma Website[END_REF] 10 kHz to 10 mHz was scanned, with seven points per decade, and impedance data were collected using a 10 mV peak-to-peak sinusoidal voltage at E = OCP. The EIS data was then fitted using SIMAD (LISE UPR 15 CNRS, France) after performing Kramer-Krönig tests, in order to determine the frequency range to analyze [START_REF] You | Application of the Kramers Kronig Relations to Multi-Sine Electrochemical Impedance Measurements[END_REF][START_REF] Liao | Physical Properties Obtained from Measurement Model Analysis of Impedance Measurements[END_REF]. Linear sweep voltammetry (LSV)

was also performed at E = OCP ± 10 mV vs. SCE by using a sweep rate of 0.05 mV•s -1 . OCP, EIS, and LSV were collected after 1, 3, 7, and 14 days of exposure. All samples were electrochemically measured in duplicate.

Results and discussion

Microbial growth in simulated sweat solution

A microbial growth analysis of E. coli and S. aureus on AISI 430 was carried out in both Müeller-Hinton medium (MH) and artificial sweat solution (ASS). Figure 1 shows a dramatic increase in the optical density of S. aureus and E. coli in the Müller-Hinton medium when exposed for short times, followed by a plateau close to 0.35 and 0.4 after a 10 h exposure, respectively, and a rapid rise after 18 h, reaching a maximum growth near 0.55 after 22 h.

However, bacteria did not grow in inoculated ASS, even when glucose and lactose were added as nutritional agents (see Figure S1 in supplementary material). Indeed, the optical density only increased slightly after 13 h of exposure in an ASS with a neutral pH value. It should be mentioned that bacterial adhesion and colonization correspond to the earlier stages in a biofilm formation, and even though those processes do not take place, the metabiological activity may provoke biocorrosion [24,25][26].

Surface analysis

Figure 2 shows confocal images of the AISI 430 surface after 24 hours of exposure to E. coli and S. aureus in different growth conditions. For both bacterial strains, no adhesion or biofilm formation was observed on the AISI 430 surface FE-SEM images after 24 hours of exposure to various growth solutions for E. coli. Even in the presence of a MH nutrient, both microorganisms were rarely detected, as also seen in Figures S2 andS3. The high chromium content in the AISI 430 steel alloy, and its high toxicity, could explain the extremely low colonization of the microorganism on these types of surfaces [START_REF] Paine | Mechanisms of chromium toxicity, carcinogenicity and allergenicity: Review of the literature from 1985 to 2000[END_REF]. The low presence of bacteria on the metal surface can indicate that the effect of microbially influenced corrosion (MIC) could be ruled out with this type of alloy, which will be later discussed. 8 FE-SEM images of AISI 430 prior and after 1 day of exposure to ASS sterilized and inoculated at pH 4.7, are presented in Figure 3, revealing some polished defects (aligned lines) and imperfection before exposure (see Figure 3(a)), and some damage related to exposure mainly in inoculated media (see Figure 3(b-d)), which can be associated with the release of the metabolic products, although only some bacteria could be observed due to the cleaning surface treatment carried out after exposure. It has been reported that the adhesion and colonization of E. coli on metal surfaces produce changes in the interface due to its metabolic products. For instance, Baeza [START_REF] Baeza | New evidence on the role of catalase in Escherichia coli-mediated biocorrosion[END_REF] reported how the catalase activity of E. coli modifies the oxygen uptake after the colonization on stainless steel. The enzymatic action of catalase promotes an increase in the cathodic current and, consequently, an increase in the overall corrosion process. It has also been found that the S. aureus colonization can produce several metabolic products, where the main one is polysaccharide intercellular adhesin (PIA) [START_REF] Lister | Staphylococcus aureus biofilms: Recent developments in biofilm dispersal[END_REF]. This type of compound acts as an active barrier for the metal [START_REF] Beech | Biocorrosion: Towards understanding interactions between biofilms and metals[END_REF]. However, after the biofilm establishment, S. aureus produces exoenzymes and surfactants, such as protease that corresponds to a type of hydrolase [START_REF] Vázquez-Sánchez | Biofilm Formation of Staphylococcus aureus[END_REF], and the hydrolase can promote the oxygen concentration at the metal interface, influencing the local pH, and therefore, a modification of the corrosion rate. On the other hand, FE-SEM analysis also revealed that the worst surface damage occurred with the destruction of passive oxide film after 14 days exposed in sterilized medium at pH 4.7, as shown in Figure 4. Similar surface damage has been reported previously by Sekine et al. [START_REF] Sekine | The effect of fluoride ions on the corrosion of steel materials in H2SO4 and CH3COOH solutions[END_REF] and Pistorius [33], who studied the same stainless steel but exposed to an acid solution with pH=2 and 2.5, respectively. Figure 5 shows the composition profile of AISI 430 determined by GD-OES prior to and after exposure to a sterilized as well as inoculated media at pH 4.7 for 14 days. The composition profile reveals a depletion of the alloying elements until 200 s of abrasion, which increases until 400 s in the inoculated media with E. coli and S. aureus, and a rise of oxygen content toward the surface. Figure 5(a) insert shows that the oxide layer response was observed until 2.5 s before exposure, decreasing until 1.5 s after 14 days of exposure to the sterilized solution; therefore, the oxide could be present, but with greater roughness and possibly a different chemical composition that can also influence its protective properties.

The inserts in figures c,d for inoculated media show a rising of alloying elements after 15 s, which were five times greater than in the sterilized solution. This can be associated with an oxide thickness increase, as previously reported [START_REF] Shimizu | Impact of RF-GD-OES in practical surface analysis[END_REF][START_REF] Olsson | Passive films on stainless steels -Chemistry, structure and growth[END_REF]. Furthermore, the oxide film is composed of a bi-layer, an outer film with a Fe-enrichment at the surface, formed by FeOOH and Fe3O4, as well as an inner layer with a Cr-enrichment that can be formed by Cr2O3 and Cr(OH)3, as was previously described by Luo et al. [START_REF] Luo | Electrochemical and passivation behavior investigation of ferritic stainless steel in alkaline environment[END_REF] and Wonneberger et al. [START_REF] Wonneberger | Oxidation of stainless steel 316L Oxide grains with pronounced inhomogeneous composition[END_REF].

Electrochemical behavior

It is worth mentioning first that the validity of the impedance diagrams was evaluated using Kramers-Kronig relations that showed residuals of real and imaginary parts of the impedance increased mainly in higher and lower frequency domains [38]. Therefore, the frequency range of the impedance data analyzed generally varied between 10 4 to 10 -2 Hz, except for inoculated media with S. aureus at pH 4.7. Figures 6 &7 show the impedance response for AISI 430 after being exposed to both the sterilized and inoculated ASS with S. aureus and E. coli at pH 4.7 and pH 6.5, respectively. For all the evaluated solutions, a capacitive behavior was observed, which can be associated with the oxide film formed on the surface, but also with the charge transfer resistance ( ) related to the cathodic reaction that can be in parallel with the double layer capacitance, therefore, two-time constant could be superposed in the frequency domain studied, as was previously described by Euch et al. [START_REF] El Euch | Temperature dependence of the electrochemical behavior of the 690 Ni-base Electrochim[END_REF][START_REF] Zhang | Investigation of the passive behavior of a Ni Cr binary alloy using successive electrochemical impedance measurements[END_REF]. It should be noted that the characteristic frequency ( ) is shifted during exposure to higher frequencies in sterilized solution and inoculated medium with S. aureus. This suggests that the corrosion process took place [START_REF] Meeusen | The effect of time evolution and timing of the electrochemical data recording of corrosion inhibitor protection of hot-dip galvanized steel[END_REF]. However, was slightly shifted to lower frequencies in inoculated medium with E. coli. Additionally, Figures 6 and7 (b,d,f) show the Bode plots that were corrected by the electrolyte resistance ( ), as described by Orazem et al. [START_REF] Orazem | Enhanced Graphical Representation of Electrochemical Impedance Data[END_REF], revealing a plateau possibly due to a constant phase element behavior, however, the phase was slightly influenced mainly at high frequency (10 2 -10 4 Hz). This can be associated with the non-uniform primary current distribution in the electrode periphery, as was described by Gharbi et al. [START_REF] Gharbi | From frequency dispersion to ohmic impedance: A new insight on the high-frequency impedance analysis of electrochemical systems[END_REF]. But also, this can be also attributed to the difficulty in estimating the electrolyte resistance in some solutions. On the other hand, after being immersed in a sterilized solution with pH 4.7 during 14 days, the sample showed corrosion signs, whose constant-time was solely associated with the charge transfer resistance in parallel to the double layer capacitance.

Figures 6 (b) also show in the low-frequency range, the impedance modulus ( ) showed a drastic drop in the sterilized medium after exposure, from 10 5 cm 2 to 10 2 cm 2 , suggesting the development of the corrosion process with a break on the passive layer and an increase of surface roughness. While in the inoculated medium, the decreased in the presence of S. aureus but significantly less than in the sterilized solution, indicating that its metabolic products could act as a barrier to metal release [START_REF] Beech | Biocorrosion: Towards understanding interactions between biofilms and metals[END_REF]. Conversely, in the presence of E. coli after 14 days, the drop of was stronger than in the presence of the S. aureus 1 in both pHs. It could be due to the fact that E. coli develops microorganism induced corrosion 2 as demonstrated by Baeza et al. and Nan et al. [START_REF] Baeza | New evidence on the role of catalase in Escherichia coli-mediated biocorrosion[END_REF][START_REF] Nan | Microbiological in fl uenced corrosion resistance characteristics of a 304L-Cu stainless steel against Escherichia coli[END_REF]. Table 1 shows the variation of the 3 for all evaluated systems. At pH 4.7, the phase angle (-) decreased significantly from 4 82º to 45º in the sterile medium, corroborating that the dissolution of the oxide took place. In 5 the inoculated media, -decreased during exposure, yet the fall being less evident. A similar 6 trend exhibited the impedance response of AISI 430 after exposure at pH 6.5, but less 7 significant, as shown in Figure 7.

6

The variation of the imaginary part ( ) versus frequency suggests that the experimental 7 impedance data can be related to a constant phase element (CPE) behavior, as shown in 8

Figure S4 in the supplementary material [38], revealing a linear domain at MF and HF, with 9 a slope ( ) lower than -1 [38], as can be seen in Table S1 in the supplementary material. The 10 CPE behavior can be associated with the oxide film formed on the metal surface, but also 11 can be related to the charge transfer resistance due to the cathodic reaction at MF domain. In 12 order to better understand this behavior. Figure 8 shows the normalized resistivity ( ) as a function of the position ( or [START_REF] Liao | Physical Properties Obtained from Measurement Model Analysis of Impedance Measurements[END_REF][START_REF] Tribollet | EIS technique in passivity studies: Determination of the dielectric properties of passive films[END_REF] for all systems evaluated, where the resistivity in the oxide/solution and metal/oxide interfaces are represented as and , respectively, while the oxide thickness is . It should be noted that showed a quick drop after 1 day for all media and after 14 days in Ph 4.7, but revealed a more homogeneous oxide layer mainly after 14 days in sterilized solution. The impedance response related to an oxide film has been described by [START_REF] Tribollet | EIS technique in passivity studies: Determination of the dielectric properties of passive films[END_REF][START_REF] Hirschorn | Constant-Phase-Element Behavior Caused by Resistivity Distributions in Films[END_REF]:

Under the assumption of a uniform permittivity, the impedance of the oxide can be obtained as follow:

(2) To better understand the impedance response, CPE parameters related to film properties were estimated using the power-law model (PLM) conforming to:

(

where corresponds to a CPE parameter, which can also be estimated from the slope of the Log-Log vs. frequency plots (see Figure S5), as was previously described. The vacuum permittivity and the dielectric constant of the oxide layer are represented as , which is equal to 8.8542 x10 14 F cm -1 , and . Additionally, is a numerical coefficient close to 1 when is close to 1, given as: (4) where is estimated from the following relation:

, and if the . It should be mentioned that the and were determined in the frequency range where the CPE behavior was observed, taking the lower and upper limits, respectively, which varied between 60-10,000 Hz. Moreover, corresponds to a CPE parameter, estimated as:

(5) The capacitance of the oxide film was estimated as follow:

(6) where is the capacitance of the oxide film [START_REF] Liao | Physical Properties Obtained from Measurement Model Analysis of Impedance Measurements[END_REF][START_REF] Tribollet | EIS technique in passivity studies: Determination of the dielectric properties of passive films[END_REF]. Table 1 summarizes the PLM parameters obtained for all evaluated systems. In the sterile solution, was influenced by the pH, the lowest being at pH 4.7 rather than at pH 6.5, suggesting a higher metal ion concentration at the electrolyte/oxide interface, with the more acidic pH possibly being due to a corrosion process. The was modified in the presence of E. coli and S. aureus media, being close to cm 2 . As was reported above, Figure S4 revealed two slopes identified with different values for . Therefore, two coefficients were determined, which can be associated with the double layer capacitance with the oxide layer. In both cases, the coefficients increased slightly at pH 4.7, and any trend was observed at pH 6.5. It should be noted that the coefficient at HF range rised in inoculated medium with E. coli between 7 and 14 days of exposure, which can be associated with a modification of the passive film properties [START_REF] Zanna | Escherichia coli siderophoreinduced modification of passive films on stainless steel[END_REF], that was named as in this work. Wan et al. [START_REF] Rafizah | Comparison of biocorrosion behavior of stainless steel 316 L and mild steel[END_REF] investigated SS 316L

exposed to E. coli, showing that it had negatively influenced the corrosion process, which was observed after 7 days of exposure. This was attributed to the metabolic products generated and exo-polymeric substances (EPC), which could modify the metal/electrolyte chemical interface. On the other hand, did not show a clear trend in the acidic and neutral pHs, at MF and HF domains, nor with the presence of bacteria. Nonetheless, it should be mentioned that at MF than at HF range, which can reveal the frequency distribution over the oxide surface possibly due to its roughness and heterogeneity. From PLM [38] the thickness of the oxide was estimated, revealing a decrease in the sterile solution and in the inoculated media, which was more significant in sterilized solution at pH 4.7, as shown in Table 1. Those values are in accordance with the drop of the |Z|LF.

Complex capacitance was estimated, as was previously described by Benoit et al. [49 51] and Barrès et al. [49 51], who used the following equations:

where , symbolize the real and imaginary parts of the impedance, |Z| being the impedance modulus that must be corrected by the [START_REF] Tran | New insights into the cathodic dissolution of aluminium using electrochemical methods[END_REF]. Figure 9 shows the infinitive complex capacitance (Ccomplex) of AISI 430 after exposure to ASS sterile and inoculated conditions at pH 4.7 and 6.5, which were obtained by using the extrapolation of Cole-Cole plots at HF range, as was previously described [START_REF] Orazem | Electrochemical Impedance Spectroscopy[END_REF] (not shown here). In the sterile solution, the Ccomplex was strongly modified overtime at pH 4.7 and pH 6.5. In inoculated media, the complex capacitances were not influenced by S. aureus, but the complex capacitances were affected by E. coli, in particular, after 7 days of exposure. From the Ccomplex, the oxide thickness was also estimated, as was previously described by Tran [START_REF] Hirschorn | Constant-Phase-Element Behavior Caused by Resistivity Distributions in Films[END_REF], Benoit [START_REF] Zanna | Escherichia coli siderophoreinduced modification of passive films on stainless steel[END_REF], and Barrès [48]:

Table 1. Impedance parameters for AISI 430 exposed to ASS estimated graphically. The oxide thickness was estimated by using a PLM with an average of

t day Media •cm 2 CPEdl Cdl x10 -6 •cm -2 x10 3 •cm 2 CPEPLM x10 -5 F•cm -2 •s ( x10 -5 F•cm
In this case, and are also the vacuum permittivity and the dielectric constant for the oxide film [4,49 51] = 15.6 value [START_REF] Simões | Study of Passive Films Formed on AISI 304 Stainless Steel by Impedance Measurements and Photoelectrochemistry[END_REF] since, as previously reported for AISI 304, the value should be close for AISI 430 [ 49 51]. Figures 9 (b,d) shows that the oxide thickness decreased overtime in the sterilized at pH 4.7, while in the neutral pH, it increased slightly from 3 to 8 nm. Notice that the error bars were obtained from measurements made in duplicate samples to show the reproducibility. The thickness was not influenced in inoculated media at pH 4.7, but surprisingly, it was affected in neutral pH. Therefore, it seems that the bacteria did not accelerate the corrosion process, even in an acidic medium. It should be mentioned that the values obtained through Ccomplex were slightly higher than the values obtained by PLM (see Table 1), being similar to that reported previously for stainless steel [START_REF] Shimizu | Impact of RF-GD-OES in practical surface analysis[END_REF][START_REF] Olsson | Passive films on stainless steels -Chemistry, structure and growth[END_REF]. This can be associated with the capacitive contribution of the complex ohmic impedance, but also due to the estimation, since a low error in its value can strongly influence the infinitive complex capacitances and the oxide thickness. For instance, an error around 5% in the can mean an error around 20% of the capacitances estimation, as was previously described by Liao et al. [START_REF] Liao | Physical Properties Obtained from Measurement Model Analysis of Impedance Measurements[END_REF]. Furthermore, the corrosion potential ( ), anodic current ( ), and polarization resistance ( ) of AISI 430 were estimated from the LSV (see Figure S6 in the supplementary material) according to ASTM G102. Table 2 shows that in the sterilized acidic medium, was shifted towards more negative values, increased, and decreased drastically. This is in agreement with what was reported by Hamidah et al. [START_REF] Hamidah | Corrosion study of AISI 304 on KOH, NaOH, and NaCl solution as an electrode on electrolysis process[END_REF], who studied the corrosion behavior of AISI 304 stainless steel at different NaCl concentrations by using gravimetric measurements. However, those parameters were not significantly influenced in the neutral sterilized solution. Therefore, if the AISI 430 is exposed to a corrosive solution, as can be considered in the ASS at pH 4.7, it is expected that a corrosion process of this material could take place after 7 days of continuous exposure. In the inoculated media with a neutral pH, and did not show a clear trend, while increased in the presence S. aureus, but decreased significantly in the presence of E. coli. In the inoculated media with acidic pH, the electrochemical parameters did not show a trend with exposure.

Conclusion

Figure 1 :

 1 Figure 1: Growth kinetic curves of (a) S. aureus and (b) E. coli in ( )MH medium at pH 7.4 and ASS solutions at pH ( ) 7.0, ( ) 6.5, ( ) 6.0, ( ) 5.5, ( ) 4.7.

Figure 2 :

 2 Figure 2: Confocal images of AISI 430 after 24 h exposure to (a,b) MH at pH 7.4 and (c-f) ASS media inoculated with S. aureus and E. coli at pH (c,d) 4.7 and (e,f) 6.5.

Figure 3 .

 3 Figure 3. FE-SEM images of AISI 430 (a) prior to and after 14 days of exposure to ASS (b) sterilized and inoculated with (c) S. aureus and (d) E. coli at pH 4.7.

Figure 4 .

 4 Figure 4. FE-SEM images of AISI 430 after 14 days of exposure to ASS sterilized at pH 4.7. (a) 1000x and (b) 5000x

Figure 5 .

 5 Figure 5. Composition profile by GD-OES of AISI 430 (a) prior to and after 14 days of exposure to ASS (b) sterilized and inoculated with (c) S. aureus and (d) E. coli to at pH 4.7.

8

 8

Figure 6 . 2 3Figure 7 .

 627 Figure 6. Nyquist diagrams and corrected Bode plots of AISI 430 after ( ) 1 and ( ) 14 days of

  Figure 8 ,b) 1 and (c,d) 14 days of exposure to ASS ( S. aureus and ( ) E. coli (a,c) pH 4.7 and (b,d) pH 6.5.

Figure 9 .

 9 Figure 9. (a,c) Infinitive complex capacitance estimated from high-frequency limit of the real part Cole-Cole plots and (b,d) oxide thickness of AISI 430 after exposure to ASS in ( ) sterilized and inoculated media with ( ) S. aureus and ( ) E. coli as a function of time. (a,b) pH 4.7 and (c,d) pH 6.5.

Table 2 .

 2 LSV parameters of AISI 430 after exposure to ASS at pH 4.7 and 6.5

			Sterilized at pH 4.7			Sterilized at pH 6.5
	t (day)	(mV vs. SCE)	×10 -4 at E=Eocp+10mV (mA•cm -2 )	•cm 2 )	(mV vs. SCE)	×10 -4 at E=Eocp+10mV (mA•cm -2 )	•cm 2 )
	1	-403.8	0.1	9090.9	-465.4	1.3	8842.1
	3	-535.3	0.5	2105.2	-527.7	0.6	13927.6
	7	-642.3	4.6	246.9	-406.9	6.6	1287.0
	14	-654.8	120.9	11.1	-455.3	5.3	1234.5
			S. aureus at pH 4.7			S. aureus at pH 6.5
	1	-285.8	3.6	1023.2	-356.0	0.4	2083.3
	3	-316.2	5.0	64102.5	-530.0	0.6	13210.0
	7	-317.8	2.3	2127.7	-355.3	0.7	11363.6
	14	-137.6	3.6	270.3	-334.9	0.8	10183.3
			E. coli at pH 4.7			E. coli at pH 6.5
	1	-324.4	1.1	12820.5	-104.7	0.2	33222.6
	3	-641.1	2.6	2923	-22.7	0.04	27777.8
	7	-655.3	7.4	970.8	-442.2	6.7	1574.8
	14	-316.3	1.5	5714.3	-273.3	4.8	1789.5

Acknowledgments

The authors would like to thank: France-Chile ECOS (Grants C14E03 and C17E07), Fondecyt (Grant 1160604), Conicyt PFCHA/Doctorado Becas Chile/2019 (Grant 21190220), Fondequip (EQM 160091 and 150101), VRIDEI Usach (021942PC-PAP & 051742PC_DAS), and DICYT PS:539 for financial support.

The behavior of AISI 430 after being exposed to mimic electrolytic sweat solutions used in Sudoscan TM technology, was analyzed by electrochemical measurements and surface analysis. AISI 430 can be considered as a suitable material that can be employed in massive spaces.

Kinetics analyses reveal that Escherichia coli and Staphylococcus aureus did not grow in the inoculated artificial sweat solution, as was observed in Müeller-Hinton medium, even with a differentiating pH between 4.7 and 6.5. Additionally, scanning electron microscopy analysis did not show colonization of such bacteria over the metallic surface. Electrochemical measurements show that the corrosion effect caused by S. aureus can be discarded, however, E. coli should be considered.

Therefore, AISI 430 has excellent performance as a sensor, even in an aggressive medium, i.e. for people with acidic sweat. Nevertheless, the electrochemical impedance response for this material reveals a short lifespan. Indeed, the corrosion rate increases dramatically as a function of exposure time, particularly after 7 days.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Declarations of interest: none

Author statement

Manuscript title:

the presence of Escherichia coli and Staphylococcus aureus.

Authors: C. Guerra, A. Ringuedé, M. I. Azocar, M. Walter, C. Galarce, F. Bedioui, M. Cassir and M. Sancy

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