# Effect of subtherapeutic and therapeutic sulfamethazine concentrations on transcribed genes and translated proteins involved in Microbacterium sp. C448 resistance and degradation

Laurianne Paris, Marion Devers-Lamrani, Muriel Joly, Didier Viala, Marie de Antonio, Bruno Pereira, Nadine Rouard, Pascale Besse-Hoggan, Michel Hébraud, Edward Topp, et al.

► To cite this version:

Laurianne Paris, Marion Devers-Lamrani, Muriel Joly, Didier Viala, Marie de Antonio, et al. Effect of subtherapeutic and therapeutic sulfamethazine concentrations on transcribed genes and translated proteins involved in Microbacterium sp. C448 resistance and degradation. FEMS Microbiology Ecology, 2023, 99 (7), pp.fiad064. 10.1093/femsec/fiad064. hal-04131399

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1 Effect of subtherapeutic and therapeutic sulfamethazine concentrations on transcribed genes and

2 translated proteins involved in *Microbacterium* sp. C448 resistance and degradation

3

4 Laurianne Paris¹,²,*, Marion Devers-Lamrani³, Muriel Joly¹,², Didier Viala⁴, Marie De Antonio⁵, Bruno

5 Pereira⁵, Nadine Rouard³, Pascale Besse-Hoggan², Michel Hébraud⁴,⁶, Edward Topp⁷,⁸, Fabrice Martin-

6 Laurent³, Isabelle Batisson¹

7

8 1 Université Clermont Auvergne, CNRS, LMGE, F-63000 Clermont-Ferrand, France. 2 Université

9 Clermont Auvergne, CNRS, ICCF, F-63000 Clermont-Ferrand, France. 3 Institut Agro, INRAE, Université

10 de Bourgogne, Université de Bourgogne Franche-Comté, Agroécologie, F-21000 Dijon, France. 4 INRAE

11 Site de Theix, Plate-forme d'exploration du métabolisme, F-63122 Saint-Genès Champanelle, France.

12 5 Biostatistics Unit (DRCI), Clermont-Ferrand University Hospital, F-63000 Clermont-Ferrand, France. 6

13 Université Clermont Auvergne, INRAE, UMR MEDIS, F-63122 Saint-Genès Champanelle, France. 7

14 London Research and Development Centre, Agriculture and Agri-Food Canada, London, ON N5V 4T3,

15 Canada. 8 Department of Biology, University of Western Ontario, London, ON N6A 3K7.

16

17 *Corresponding author: Laboratoire Microorganismes: Génome et Environnement, 1 Impasse Amélie

18 Murat, TSA 60026, CS 60026, 63178 Aubière Cedex, France. E-mail address: laurianne.paris@uca.fr

19

20 Abstract

21 *Microbacterium* sp. C448, isolated from a soil regularly exposed to sulfamethazine (SMZ), can use

22 various sulphonamide antibiotics as the sole carbon source for growth. The basis for the regulation of

23 genes encoding the sulphonamide metabolism pathway, the dihydropteroate synthase sulphonamide

24 target (folP), and the sulphonamide resistance (sul1) genes, is unknown in this organism. In the present

25 study, the response of the transcriptome and proteome of *Microbacterium* sp. C448 following

26 exposure to subtherapeutic (33 µM) or therapeutic (832 µM) SMZ concentrations was evaluated.
Therapeutic concentration induced the highest *sad* expression and Sad production, consistent with the activity of SMZ degradation observed *in cellulo*. Following complete SMZ degradation, Sad production tended to return to the basal level observed prior to SMZ exposure. Transcriptomic and proteomic kinetics were concomitant for the resistance genes and proteins. The abundance of Sul1 protein, 100-fold more abundant than FolP protein, did not change in response to SMZ exposure. Moreover, non-targeted analyses highlighted the increase of a deaminase RidA and a putative sulphate exporter expression and production. These two novel factors involved in the 4-aminophenol metabolite degradation and the export of sulphate residues formed during SMZ degradation, respectively, provided new insights into *Microbacterium* sp. C448 SMZ detoxification process.

36

**Keywords:** Sulphonamide antibiotic, *sad* gene, dihydropteroate synthase, omic approaches, deaminase RidA, sulphate exporter

39

40

**Introduction**

In commercial food-animal production, antibiotics are used therapeutically, for prophylaxis and, in some jurisdictions, for growth promotion (van Boeckel *et al.* 2017). Following ingestion by farm animals, some antibiotics are excreted intact, and thus animal wastes can represent a major source of antibiotic entry into the environment (Feng *et al.* 2017; Deng *et al.* 2018; Mulla *et al.* 2021). Amendment of crop production systems with manures can lead to the contamination of soil, surface and ground-waters with antibiotics, representing a potential threat to human and environmental health (Spielmeyer *et al.* 2017; Spielmeyer 2018; Ben *et al.* 2019; National Academy of Pharmacy 2019; Duan *et al.* 2022).

In France, the sulphonamides are the second most widely used class of antibiotics used in food animal production, representing 19.8% or 73.6 tonnes sold in 2021. Although their use has decreased since the beginning of the 2000s (Anses 2022), their presence in the environment represents a potential risk for ecosystems because of the possible selection and dissemination of sulphonamide-
53 resistance genes and resistant bacteria. Sulphonamides inhibit the dihydropteroate synthase (DHPS, 54 EC 2.5.1.15; encoded by the *folP* gene), a key enzyme involved in the folate biosynthetic pathway in 55 bacteria and some unicellular eukaryotes, by competition with the substrate *p*-amino benzoic acid 56 (Capasso and Supuran 2014). Folate is a precursor for the biosynthesis of tetrahydrofolate, a key 57 coenzyme required for the synthesis of some nucleotides and amino acids. Sulphonamides are 58 bacteriostatic to many Gram-negative and Gram-positive bacteria (Connor 1998). The bacterial 59 resistance is mainly due to mutations in the *folP* gene or the acquisition of alternative genes (*sul1*, *sul2*, 60 and *sul3*) encoding DHPS proteins that do not interact with the sulphonamide antibiotics (Sköld 2000; 61 Perreten and Boerlin 2003; Martin-Laurent *et al.* 2014; Zhou *et al.* 2021).

62 In the environment, the removal of sulphonamides can result from chemical or biological 63 degradation (Chen and Xie 2018; Mulla *et al.* 2021; Hu *et al.* 2022) and is dependent on a variety of 64 factors such as the sulphonamide concentration, and key rate-controlling parameters such as 65 temperature and pH (Cao *et al.* 2019; Chen *et al.* 2019). Different biodegradation pathways have been 66 described for various sulphonamide-degrading bacteria isolated from activated sludge, wastewater or 67 contaminated soil (Chen and Xie 2018; Nunes *et al.* 2020; Perri, Kolvenbach and Corvini 2020). Notably, 68 some *Microbacterium* spp. strains isolated from manured soil, sediment or activated sludge, use 69 sulphonamides as the sole carbon source (Bouju *et al.* 2012; Tappe *et al.* 2013; Topp *et al.* 2013; Kim 70 *et al.* 2019b). Sulphonamide biodegradation by *Microbacterium* spp. requires a gene cluster encoding 71 three proteins; two flavin-dependent monooxygenases, SadA and SadB, possessing an acyl-CoA 72 dehydrogenase domain, and the flavin reductase, SadC (Ricken *et al.* 2017). The latter reduces the 73 flavin mononucleotide (FMN) cofactor required for the activity of the two monooxygenases. The role 74 of the Sad component system in sulphonamide degradation has only been demonstrated *in vitro* using 75 purified enzymes (Ricken *et al.* 2017; Kim *et al.* 2019b). Sulfamethazine (SMZ, also called 76 sulfadimidine), which can reach maximum concentration of 20-25 mg per kg manure or soil (Cycoń *et 77 al.* 2019), is transformed by SadA into 4-aminophenol (4AP), the dead-end product 2-amino-4,6- 78 dimethylpyrimidine (ADMP) in stoichiometric amounts and sulphur dioxide (Ricken *et al.* 2017) (Fig.
79 S1). The 4-aminophenol is then transformed by SadB to 1,2,4-trihydroxybenzene which is ultimately
80 mineralized. This degradation pathway is conserved in the Micrococcales order (Kim et al. 2019b).

81 In addition to carrying the *sad* gene cluster, *Microbacterium* sp. strain C448 carries the *sul1*
82 gene (Martin-Laurent *et al.* 2014). The relative significance and interplay of the sulphonamide
83 biodegradation pathway and the *sul1* resistance gene in the organism's ability to tolerate and then
84 metabolize the sulphonamides remains unclear.

85 In the present study, transcriptomic and proteomic approaches were used to evaluate the
86 expression levels of the *sad*, *folP* and *sul1* genes, and the abundance of their corresponding proteins.
87 Changes in response to exposure to low subtherapeutic-relevant concentrations and to higher
88 therapeutically-relevant concentrations of SMZ were evaluated. We hypothesized that at low
89 subtherapeutic concentration, *sul1* would not be required to permit biodegradation of the drug
90 whereas at high concentration it would. Time-series transcript- and protein-profiles were analysed and
91 compared to a non-exposed *Microbacterium* culture.

92

## 93 **Materials and Methods**

94 **Strain and medium**

95 *Microbacterium* sp. strain C448 was isolated from field soil on the Agriculture and Agri-Food Canada
96 research farm in London (Ontario) that was intentionally treated annually for several years with 10 mg
97 SMZ per kg soil. Descriptions of the field, methods for primary isolation, identification and
98 characterization of the isolate are available in Topp *et al.* (2013) and Martin-Laurent *et al.* (2014). The
99 bacterium was cultured in a defined medium (DM) containing glucose (0.5% w/v), amino acids (0.1 g/L
100 of Cys, Trp, Leu, Ile, Val and Met; 0.2 g/L of Arg, and 0.6 g/L of His, Glu), and salts, vitamins and trace
101 elements prepared as described in Amezaga *et al.* (1995). Sulfamethazine sodium salt was purchased
102 from Sigma Aldrich (Saint-Quentin-Fallavier, France; purity ≥ 98%). A stock solution (333 mM, 100 g/L)
103 was prepared in water, sterilized by filtration (0.2 µm) and stored at -20 °C until used.
104

104
105 Culture conditions and sample preparation

Transcriptomic and proteomic analyses were performed after cultivation of *Microbacterium* sp. C448
106
strain in DM medium at 28 °C with agitation (150 rpm). The growth was monitored by measuring
107
absorbance at 600 nm (OD₆₀₀). The bacterial cells were collected at the mid-exponential growth phase
108
by centrifugation (5 min, 5,000 x g) and washed twice in DM medium. For each experiment, forty
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Erlenmeyer flasks (50 and 500 mL) containing respectively 20 mL (transcriptomic) or 180 mL
110
(proteomic) of DM medium, were inoculated with the washed cells at an OD₆₀₀ of 0.4. The flasks were
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supplemented with 33 µM of SMZ sodium salt (corresponding to 10 mg/L, low concentration, LC), 832
112
µM of SMZ sodium salt (corresponding to 250 mg/L, high concentration, HC) or unsupplemented
113
(Control, no SMZ) before being incubated at 28 °C with agitation (150 rpm) in the dark. A set of four
114
flasks were sacrificed for each concentration immediately after inoculation (T0), at an early SMZ
115
degradation rate (0.6 h and 1 h post-inoculation (pi)), at an advanced SMZ degradation rate (5 h pi)
116
and after complete SMZ degradation (9 h and 24 h pi) for the transcriptomic and proteomic analyses,
117
respectively. For each sacrificed flask, OD₆₀₀ was measured, and 1 mL of culture was centrifuged (3
118
min, 10,000 x g) to quantify SMZ and ADMP in the supernatant by HPLC. The remaining culture was
119
used for the RNA or protein extractions.
120

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122 SMZ and ADMP quantification by HPLC

The concentrations of SMZ and its dead-end transformation product ADMP were determined by HPLC
123
on an Agilent 1100 apparatus (Agilent Technologies, Courtaboeuf, France) equipped with a reverse-
124
phase column (C18 Zorbax Eclipse Plus column, 75 mm × 4.6 mm, 3.5 µm) at 22 °C and a diode array
125
detector set at λ = 260 nm (SMZ, retention time = 9.3 min) and 298 nm (ADMP, retention time = 1.1
126
min). The mobile phase was composed of aqueous H3PO4 (0.01% v/v, pH = 2.9) (A) and acetonitrile (B)
127
at a flow rate of 1 mL/min. Gradient (linear): 0–5 min: 2% B; 5–8 min: 2–30% B; 8–10 min: 30–90% B;
128
10–11 min: 90–100% B; 11–13 min: 100–2% B. Injection volume: 5 µL. Each sample was analysed twice
129
(technical duplicates). SMZ (Sigma Aldrich, purity > 99%) and ADMP (Alfa Aesar, Thermo Fischer
130
Scientific, Walthman, MA, USA; purity 98%) were used as analytical standards. Concentrated solutions (100 µM and 1 mM, respectively) were prepared in distilled water and diluted to obtain known concentration solutions in order to have a six-point standard curve for each compound at each concentration range (0-100 µM and 0-1 mM, respectively).

135

Transcriptomic experiment

RNA extraction and sequencing

RNeasy® Protect Bacteria Mini Kit (QIAGEN, Germantown, MD, USA) was used for the RNA extraction, according to the manufacturer recommendations. Briefly, 2.5 mL of cell suspension were mixed with 5 mL of bacterial RNAprotect™ Reagent (QIAGEN) and incubated for 5 min at room temperature before centrifugation (5,000 g, 10 min). The cells were lysed using lysozyme, proteinase K digestion and mechanical disruption by mixing cells with 0.2 g of acid washed beads (212-300 µm size, Sigma Aldrich) in a FastPrep-24™ classic apparatus (twice 30 s at 6 m/s, MP Biomedicals, Ilkirch, France). RNA was purified on a column treated with DNAse. The quality and the quantity of extracted RNA were estimated using the RNA 6000 Nano LabChip® Kit and the 2100 Bioanalyzer following the manufacturer recommendations (Agilent Technologies). For each sample, more than 500 ng of total RNA were sent to GENEWIZ® (Azenta Life Science, Leipzig, Germany) which performed the rRNA depletion, the cDNA synthesis, and the adapter ligation. The obtained library was sequenced in an Illumina® NovaSeq™ 6000 apparatus (2 x 150 bp paired-end reads, Illumina, San Diego, CA, USA). More than 18 Mreads were generated per sample with a quality score ≥ 36.

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Bioinformatic analysis and data treatment

The reads were first quality-filtered and trimmed using the wrapper script trim galore (v0.6.4) using cutadapt (v2.6) to trim the reads (Babraham Bioinformatics) (Martin 2011). The reads were then aligned to the genome of *Microbacterium* sp. C448 using hisat2 (v2.2.1) (Kim *et al.* 2019a), and counted

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154

155
156 using featureCounts (v2.0.1) (Liao et al. 2014). The differences of normalized gene expressions
157 between control and SMZ-treated conditions were highlighted using DESeq2 (Love et al. 2014). The
158 RNA counting was standardised between samples using Moose2 (polynoMial nOrmalization Of RNA-
159 SEq data), allowing to normalize FPKM (Fragments per kilo-base per million) or RPKM (reads per
160 kilobase per million) values from multiple samples to correct for non-linear artifacts, introduced by the
161 library construction and/or sequencing process (Annergren and Larsson 2016).

162
163 *Data availability*
164 The sequencing data have been submitted to the Sequence Read Archive (SRA, NIH, NCBI), with the
165 project number PRJNA860753.

166
167 **Proteomic experiment**

168 Protein extraction
169 The proteins were extracted according to the cell fractionation method described by Esbelin et al.
170 (2018) and Santos and Hébraud (2021) with some modifications. For each flask, 180 mL of bacterial
171 culture was centrifuged (5 min, 4,800 x g) and the pellet was washed twice in 30 mL of Phosphate
172 Buffered Saline (pH 7.4). Washed pellet was suspended in 4 mL of Tris-EDTA (20 mM Tris; 5 mM, pH
173 7). Bacterial cells were broken by three passages through a French-press cell disrupter (One Shot Cell
174 Disruptor, Constant Systems Ltd., Daventury, United Kingdom) set at a pressure of 2.6 kbar. After
175 centrifugation (13,000 x g, 15 min, 4 °C), the supernatants, mainly containing the soluble proteins
176 (cytosol) but also some membrane pieces and their integrated proteins, were recovered and conserved
177 at -20 °C in 50 mL Protein LoBind® Tubes (Eppendorf, Hamburg, Germany). The pellets, containing the
178 proteins of the cell envelope, were washed twice with 5 mL of Tris 40 mM (pH 8.5), suspended in 200
179 to 600 µL of Tris 25 mM (pH 6.8), depending to the pellet viscosity, and conserved at -20 °C in 1.5 ml
180 Protein LoBind® Tubes (Eppendorf). The quantification of the proteins was performed in triplicate
following the protocol of the Pierce™ Coomassie (Bradford) Protein Assay Kit (Thermo Fischer Scientific, Waltham, MA, USA). The absorbance was read at 596 nm using a microplate reader (Thermo Multiskan™ FC, Thermo Fischer Scientific). Short electrophoresis was performed on 12% SDS-polyacrylamide electrophoresis gels to concentrate 10 µg of proteins per sample in the first millimetres of the resolution gel. The concentrated protein band was manually excised with a sterile scalpel blade and washed, reduced, alkylated and digested by trypsin treatment before the nano-LC-MS/MS analysis as detailed in Esbelin *et al.* (2018) and Santos and Hébraud (2021), with some details. The reduction of disulphide bonds was achieved in 10 mM dithiothreitol prepared in 50 mM ammonium bicarbonate buffer and the incubation was carried out for 30 min at 56 °C. The alkylation of proteins was carried out with 55 mM iodoacetamide prepared in 50 mM ammonium bicarbonate buffer for 30 min in darkness. Finally, bands were dehydrated with 100% acetonitrile for 10 min and the liquid was discarded. The proteins were hydrolysed in 600 ng of trypsin in a 50 mM ammonium bicarbonate buffer for 5 h at 37 °C, ensuring that bands were always in liquid by addition of buffer. Peptides were extracted for 15 min in ultrasound bath with 40 µL of acetonitrile/trifluoroacetic acid (TFA) (99.9/0.1 v/v). The supernatants were dry concentrated with a SpeedVac® concentrator (Thermo Savant SPD 1010, Thermo Fischer Scientific) for 2 h. The volume was adjusted to 40 µL with a solution of Water/Acetonitrile/TFA (95/5/0.05 v/v/v). After 10 min of ultrasonic bath (VWR® USC, Ultrasonic cleaner USC 600TH, Avantor®, Radnor, PA, USA), the entire supernatant was transferred to a glass HPLC vial prior to LC MS/MS analysis.

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201

Quantification of proteins by nano-LC-MS/MS and bioinformatic analyses

Peptide mixtures were randomised before being analysed by nano-LC-MS/MS using the RSLC nano Ultimate™ 3000 (Thermo Fischer Scientific) coupled to the Q Exactive HF-X Hybrid Quadrupole-Orbitrap mass spectrometer (MS) (Thermo Fischer Scientific) with a nano-electrospray ion source. Initially, 1 µL of hydrolysate was preconcentrated and desalted at a flow rate of 30 µL/min on a C18 pre-column 5 cm length x 100 µm (Acclaim™ PepMap™ 100 C18, 5 µm, 100 Å nanoViper, Thermo Fisher
Scientific) equilibrated with TFA 0.05% in water. In a second step, the concentration column was switched online with a nanoflow analytical C18 column (Acclaim™ PepMap™ 100 - 75 µm inner diameter × 25 cm length; C18 - 3 µm – 100 Å, Thermo Fisher Scientific) equilibrated with a 95% solvent A (99.9% H₂O, 0.1% formic acid) flow at 300 nL/min. The peptides were then separated according to their hydrophobicity with a 55 min gradient of solvent B (99.9% acetonitrile, 0.1% formic acid) from 5 to 32%. For MS analysis, eluted peptides were electrosprayed in positive-ion mode at 1.6 kV through a nano-electrospray ion source heated to 250 °C. The mass spectrometer operated in data dependent mode: the parent ion was selected in the orbitrap cell (FTMS) at a resolution of 120,000 and each MS analysis was followed by 18 MS/MS with analysis of the MS/MS fragments at a resolution of 15,000.

For raw data processing, MS/MS ion search was carried out with Mascot v2.5.1 (http://www.matrixscience.com, Matrix Science) against the UniProt reference database of *Microbacterium* sp. C448 (i.e. ref_microb_spc448 20190711-3165 sequences) with the following parameters during the request: precursor mass tolerance of 10 ppm and fragment mass tolerance of 0.02 Da, a maximum of two missed cleavage sites of trypsin, carbamidomethylation, oxidation of Methionine and deamidation Asparagine and Glutamine set as variable modifications. Protein identification was validated when at least two peptides from one protein showed statistically significant identity above Mascot scores with a False Discovery Rate of 1%. Ion scores was -10 log(P), where *P* was the probability that the observed match was a random event. The Mascot score was respectively 14 with an adjusted *p*-value of 0.05 for supernatants and 34 with an adjusted *p*-value of 0.05 for pellets. The Progenesis® QI for proteomics v4.2 software (Nonlinear Dynamics©, Waters™, Milford, MA, USA) was used for the label-free protein quantitation analysis, with the same identification parameters as described above. All unique validated peptides of an identified protein were included, and the total cumulative abundance was calculated by summing the abundances of all peptides allocated to the respective protein. With the Progenesis® QI software, the proteomic LC-MS/MS data were statistically analysed using the “between subject design” and *p*-values were calculated by a repeated measures analysis of variance using the normalized abundances across all
233

runs. Obtained data were expressed in arbitrary unit (AU) corresponding to the areas under the peaks
obtained by nano LC-MS/MS normalized by the number of cells.

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The SMZ degradation genes and proteins *sadA/SadA* (ORF 2030/UniProt number W0Z5L8), *sadB/SadB* (ORF 2028/W0Z833) and *sadC/SadC* (ORF 2026/W0Z7H5), and the SMZ target genes and proteins: *folP/FolP* (ORF 1952/W0Z6Y9) and *sul1/Sul1* (ORF 2696/W0Z673) were specifically studied.

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## Statistical analyses

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The statistical analyses were performed using R software (The R Foundation). For the transcriptomic data, the package DESeq2 was used. It is based on the use of negative binomial generalized linear models on un-normalized counts. It allows to determine for each open reading frame (ORF) and at each sampling time whether its level of expression is different between the control and LC or HC condition. Since transcriptional regulation is a short-term response and mRNAs are labile, ORF presenting an expression level different by at least by a factor of two in the presence of SMZ in the medium, and a Benjamini-Hochberg adjusted *p*-value (Benjamini and Hochberg 1995) inferior to 0.001, was considered as having a significant differential gene expression level compared to the SMZ-free control (Love *et al.* 2014).

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For the proteomic data, the assumptions of normality were assessed using several tests (Shapiro-Wilk, Kolmogorov-Smirnov, Jarque-Bera and D'Agostino tests). Box-Cox transformation was applied to achieve normality when appropriate and an ANOVA test followed by Tukey post-hoc were applied with a α risk of 0.05. For the analysis of resistance markers, which turned out to be non-parametric even with the transformation, a Kruskal-Wallis test followed by Dunn's test were applied. The fold-change was calculated at the three sampling time, comparing LC and HC SMZ concentrations versus control.

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For proteomic data, the significant comparisons with a minimum fold-change of ± 1.5 were considered as relevant for the *a priori* analyses on Sad and DHPS proteins, and of 2.0 concerning the analyses on resistance markers without *a priori* consideration.
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260

## Results and Discussion

261

### Kinetics of SMZ degradation by *Microbacterium* sp. strain C448

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The degradation of SMZ by *Microbacterium* sp. C488 started immediately following the addition of 33 µM SMZ, and followed a typical single first-order kinetics (Fig. 1A and 1B). The degradation rate was very rapid with similar rate constants in the transcriptomic ($k = 0.96 \ h^{-1} \ r^2 = 0.9753$) and proteomic ($k = 1.1 \ h^{-1} \ r^2 = 1$) experiments. The SMZ was completely degraded within 5 h of incubation. Under the HC condition (Fig. 1C and 1D), the degradation kinetics were rather different between both experiments. The transcriptomic experiments showed a disappearance of 600 µM (72%) of the initial SMZ concentration at 5 h pi, and complete degradation at 9 h pi (Fig. 1C). For the proteomic experiment, almost 20% of SMZ (146 µM) was dissipated in 5 h pi and complete SMZ degradation observed at 24 h pi (Fig. 1D). As measured in the proteomic experiments, the ADMP end-transformation product was stoichiometrically produced upon the SMZ degradation, reaching 34 ± 2 (Fig. 1B) and 778 ± 23 µM (Fig. 1D) at 24 h pi for low and high SMZ concentrations, respectively. As expected, the strong ability of the *Microbacterium* sp. C448 strain to degrade SMZ is in accordance with literature, other *Microbacterium* strains also having the capacity to degrade sulphonamides (Tappe *et al.* 2013; Birkigt *et al.* 2015; Hirth *et al.* 2016; Ricken *et al.* 2017; Martin-Laurent *et al.* 2019).
<figure><img src="image_1.png" /><figcaption>**Figure. 1.** The degradation of SMZ by *Microbacterium* sp. C448. The SMZ degradation (squares) and the end-product ADMP production (▲) were quantified by HPLC in the *Microbacterium* sp. C448 cultures treated with the low (LC, 33 µM, orange) or the high (HC, 832 µM, green) concentrations of SMZ, in the transcriptomic (A, C) and in the proteomic experiments (B, D). The values are mean ± SD (n = 4).</figcaption></figure>

Sad responses to the SMZ biodegradation in *Microbacterium* sp. C448

In the absence of SMZ (control), *sadABC* transcripts (Fig. 2A, 2C, and 2E) and the three Sad proteins, found in the cytosolic fraction, were expressed or produced at baseline levels at the beginning of the experiment (T0) (3.24 ± 0.06 AU of proteins in mean, Fig. 2B, 2D and 2F).

In the LC condition, as compared to the control, *sadb* (ORF 2028) and SadB (UniProt number W0Z833) showed no significant difference in abundance throughout the incubation (Fig. 2C and D). In contrast, the *sada* (ORF 2030) and *sadC* (ORF 2026) transcripts increased significantly at 0.6 h pi by a factor of 1.8 and 1.9-fold, respectively (Fig. 2A and 2E, Table S1). Likewise, the abundance of SadA (W0Z5L8) and SadC (W0Z7H5) protein increased by 1.5-fold at 1 h pi with SMZ (p = 0.011 and 0.002, respectively)
292 (Fig. 2B and 2F, Table S2). From 5 h pi, the abundances of the *sada* and *sadC* transcripts were the same
293 as the control. However, the abundances of SadA and SadC proteins were still increased at 5 h pi by
294 1.8 and 1.5-fold, respectively (*p* = 0.001 and 0.010). At 24 h pi, the abundance of the SadA protein
295 remained increased by 1.5-fold (*p* = 0.009) while SadC proteins returned to the basal state (Fig. 2B and
296 2F). This slight difference observed between the transcriptomic and proteomic responses might be
297 explained by the fact that the bacterial transcriptome responded more rapidly than the proteome to
298 such an environmental change (Bathke *et al.* 2019).

299 In the HC condition, *sadb* transcript abundance was not changed (Fig. 2C), but the SadB protein
300 abundance increased by 1.6-fold (*p* = 0.044) at 5 h pi (Fig. 2D). The abundances of *sada* and *sadC*
301 transcripts increased by 1.5 and 2.2-fold, respectively, at 0.6 h pi and by 3.1 and 2.6-fold, respectively,
302 at 5 h pi (Fig. 2A and 2E, Table S1). At 9 h pi, these transcripts did not differ from the control. SadA
303 protein abundance increased by 2.5-fold at 5h pi (*p* < 0.001) and last until the end of experiment (2.6-
304 fold at 24 h pi, *p* < 0.001) (Fig. 2B). SadC protein abundance was increased all along the experiment by
305 in mean 1.8-fold (*p* < 0.001) (Fig. 2F and Table S2). Therefore, while the *sada* and *sadC* transcript
306 production was transient, decreasing when the concentration of SMZ decreased in the medium, the
307 SadA and SadC proteins persisted.

308 Overall, SadA and SadC proteins were found to be produced *in cellulo* by *Microbacterium* sp. C448
309 concomitantly to SMZ degradation and then continue to accumulate even after complete dissipation
310 of the antibiotic. Moreover, their production depended on the initial exposure concentration to SMZ,
311 suggesting their possible involvement in SMZ biodegradation, as observed before with purified
312 enzymes (Kim *et al.* 2019b).

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<figure><img src="image_2.png" /><figcaption>Figure. 2. Effect of the SMZ concentration on the abundance of *sad* transcripts and Sad proteins. The abundances of *sadA* (A), *sadB* (C) and *sadC* (E) transcripts and of the corresponding proteins (B, D and F) were reported, in the absence of SMZ (CTRL, □) and after exposure of *Microbacterium* sp. C448 to a low (LC, □) and a high (HC, □) concentration of SMZ at the different sampling times. Stars and asterisks represent the significant differences of transcripts (★: *p* < 0.001) and protein abundances (*: *p* ≤ 0.05, **: *p* ≤ 0.01 and ***: *p* ≤ 0.001, see Table S1 for details) compared with the control for the same considered time.</figcaption></figure>

DHPS gene expression and protein production

Numerous studies report the widespread occurrence of *sul1* and *sul2* genes coding for DHPS variants insensitive to SMZ in antibiotic contaminated environments as substitute of SMZ sensitive DHPS coded
by *folP* (e.g. Byrne-Bailey *et al.* 2009; Duan *et al.* 2019; Sardar *et al.* 2021). *Microbacterium* sp. C448
harbouring the *folP* and only *sul1* genes (Martin-Laurent *et al.* 2014) like other sulphonamide-
degrading Micrococcales (Kim *et al.* 2019b), their regulations were studied in presence of SMZ. The
*folP* transcript (ORF 1952) abundance was at least 7-fold lower than that of *sul1* (ORF 2696) for each
condition tested here (Fig. 3A and 3B). This trend was even more marked for the abundance of FolP
proteins (W0Z6Y9) detected in the cytosolic fraction which was 100-fold lower than that of Sul1
(W0Z673) all along the experiment (Fig. 3C and 3D). During the first five hours of incubation, the
abundances of *folP* and *sul1* transcripts were not affected by SMZ neither at LC nor HC. However, at
9h pi, the abundance of *folP* transcripts increased by 4.1- and 4.5-fold while the *sul1* transcripts
decreased by 2.1 and 3.4-fold in LC and HC conditions, respectively (Table S1). Nevertheless, given the
fact that these modifications in the abundances of *folP* and of *sul1* transcripts were observed only at a
time point where no more SMZ remained in the medium, one can hypothesise that these modifications
were not directly related to SMZ resistance or the metabolite ADMP which was previously shown not
to affect the *sul1* gene (Wu *et al.* 2022). The abundance of the FolP and Sul1 proteins was not affected
by SMZ either at LC nor at HC, whatever the sampling time, suggesting that the constitutive expression
of *sul1* allows a basal production enough to resist sulphonamide antibiotics (Fig. 3C and 3D, Table S2).
<figure><img src="image_3.png" /><figcaption>**Figure 3.** Effect of the SMZ concentration on the abundance of *foIP* and *sul1* transcripts and FoIP and *Sul1* proteins. The abundance of *foIP* (A) and *sul1* (B) transcripts were determined and the abundances of their corresponding proteins FoIP (C) and Sul1 (D) were quantified in the presence of a low (LC, □) or a high (HC, □) SMZ concentration and compared with control (CTRL, □) for each sampling time (See Table S1). Stars represent the significant differences of transcript abundances compared with the control for the same considered time (*p* < 0.001).</figcaption></figure>

Search for other genes/proteins potentially involved in the SMZ resistance

To further investigate the response of *Microbacterium* sp. C448 to SMZ exposure, other potential markers of resistance were explored such as those involved in stress response (Dorrian *et al.* 2011; Kim *et al.* 2017). Those showing a significant response were mainly overexpressed during the SMZ degradation phase and include chaperones, translation regulation, and DNA replication and repair factors (Table S3). Only one gene/protein (ORF 2019/W0Z991) was jointly evidenced by transcriptomic and proteomic data in the cytosolic fraction both in the LC and HC conditions (Table 1), and could have a preponderant role in the SMZ degradation process. The abundance of the RidA (Reactive
intermediate deaminase A) protein family coding gene transcript was increased by 2.3-fold in LC condition at 0.6 h pi and by 2.7- and 3.0-fold at 0.6 and 5 h pi, respectively, in HC condition. It was accompanied by a 2.3-fold increase of the RidA protein abundance at 5 h pi (p = 0.013). From 5 h pi, the abundances of the RidA coding gene transcript and its protein were not significantly different from the control. The RidA protein is a putative translation initiation inhibitor of the Rid family (formerly known as YjgF/YER057c/UK114). It leads to an enamine/imine deamination on intermediary metabolites produced during amino acid catabolism and converts them into keto acids, avoiding their accumulation and a metabolic perturbation of the cells (Flynn and Downs 2013; Ernst et al. 2014; Irons et al. 2020). Moreover, the Rid enzyme family was also described as having the capacity to degrade aromatic compounds, such as 2-aminophenol (Irons et al. 2020). The RidA protein family was also suggested to be involved in deamination of 4-aminophenol (4AP) (Rios-Miguel et al. 2022), an intermediate in the metabolism of sulphonamides by *Microbacterium* sp. C448. The hypothetical degradation of 4AP by RidA could also explain the low expression/production of SadB in our experiment, due to removal of its substrate (Fig. 2 and S1).

Except the stress response, the membrane-based efflux pumps are well known to be another resistance mechanism of bacteria to various antibiotics (Lubelski et al. 2007; Handzlik et al. 2013; Kumar and Patial 2016; Munita and Arias 2016; Schindler and Kaatz 2016; Greene et al. 2018; Reygaert 2018). Thus, although the sulphonamides are considered to passively pass through the cellular membrane, and that an uptake through unspecific transporters is not entirely excluded (Zarfl et al. 2007), the possible involvement of efflux pumps in the resistance of *Microbacterium* sp. C448 to SMZ exposure is unknown. Recently, the increase in the expression/production of ABC transporters was reported in *Escherichia coli* over-expressing *sul1* and *sul2*, suggesting the involvement of transporters in the bacterial resistance to sulphonamides (Zhou et al. 2021). In the present study, the exposure of *Microbacterium* sp. C448 to SMZ led to the modulation of 17 transporters in the cell envelope fraction, mainly related to the ABC transporter family. Under LC conditions, there were differences in the abundance of only a few transcripts and proteins (Table 1). At 9 h pi the abundance of one transporter
transcript was decreased (ORF 712: X 2.9) and another one was increased (ORF 327: X 2.3), but the abundances of the corresponding two proteins were not modified. However, the abundance of other transporter proteins was increased by 2.3-fold at 1 h pi (W0ZBZ8, *p* = 0.018) or decreased by 6.9-fold at 1 h pi (W0Z6W7, *p* = 0.032) and by 4.1-fold at 5 h pi (W0ZB95, *p* = 0.041). Exposure to antibiotics can cause up- or down-regulation of efflux pumps (Hemmerlin *et al.* 2014).

Under HC conditions, much more changes in gene expressions were recorded. At 0.6 h pi, the abundances of five transporter transcripts involved in sulphur, sugar or acetate metabolism were decreased by 2.0 to 2.9-fold while the abundance of one transporter transcript was increased by 2.4-fold (ORF 2808). From 5 h pi, the abundance of these transcripts was not affected by SMZ exposure except that of ORF 3047 which remained decreased by 2.6-fold. In addition, the abundance of three other ABC transporter transcripts probably belonging to glycine betaine transport systems were increased by 2.0 or 2.1-fold only at 5 h pi. Finally, the abundance of ORF 327 transcript was decreased by 2.3-fold at 5 h pi and 2.6-fold overexpressed at 9 h pi. The abundance of one putative ABC transporter protein (W0Z5S8) was decreased by 2.2-fold (*p* = 0.013) at 5 h pi and that of two others W0Z4F3 and W0ZDD8 were increased by 2.2 (*p* = 0.024) and 2.5-fold (*p* = 0.032) at 24 h pi, respectively (Table 1).

The combination of transcriptomic and proteomic analysis showed that the high abundance of one transcript (ORF 721) in particular was congruent with that of its related protein (W0Z8D9) in both LC and HC conditions (Table 1). It corresponds to a putative sulphate exporter family. Indeed, the abundance of this transcript was increased in LC conditions by 3-fold at 0.6 h pi and in HC conditions by 3.8-, 13.4- and 74.8-fold at 0.6, 5 and 9 h pi, respectively. Concomitantly, the abundance of the corresponding protein was increased in response to SMZ exposure in HC condition (by 5.4- and 75.8-fold at 5 and 24 h pi, respectively (*p* < 0.001)) (Table 1). This observation suggests that this efflux pump could be involved in the export of the sulphate residues, resulting from SMZ transformation, such as sulphurous acid H₂SO₃, or sulphuric acid H₂SO₄ (Macris and Markakis 1974; Kim *et al.* 2019b; Yu *et al.* 2020). We hypothesize that the up-regulation and -production of a sulphate efflux pump in
411 *Microbacterium* sp. C448 is associated with the removal of sulphur compounds produced by SMZ metabolism that otherwise would reach toxic intracellular concentrations.

413 **Table 1. Genes and proteins potentially involved in Microbacterium sp. C448 resistance to SMZ.** The
414 nano-LC-MS/MS identified proteins harbouring a significant difference and with a minimum ± 2.0-fold
415 change between treated and control conditions are shown. A “/” indicates no differential expression.
416 Not detected indicates the protein abundance was below the detection threshold of the nano-LC-
417 MS/MS; LC and HC: low and high concentrations in SMZ; CTRL: control.
<table><thead><tr><th rowspan="2">Category</th><th rowspan="2">Type</th><th rowspan="2">Domain</th><th rowspan="2">ORF</th><th rowspan="2">Gene name</th><th rowspan="2">UniProt code</th><th rowspan="2">Characteristics</th><th colspan="6">LC</th><th colspan="2">HC</th></tr><tr><th>Transcriptomic</th><th>Proteomic</th><th>Transcriptomic</th><th>Proteomic</th><th>Protec</th><th>Protec</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>0.6 h pi</th><th>1 h pi</th><th>5 h pi</th><th>24 h pi</th><th>0.6 h pi</th><th>5 h pi</th><th>9 h pi</th><th>1 h pi</th><th>5 h pi</th></tr></thead><tbody><tr><td rowspan="2">Translation</td><td>RidA family protein</td><td></td><td>2019</td><td>-</td><td>W0Z991</td><td>Putative translation initiation inhibitor Y1gf family</td><td>+2.3</td><td>/</td><td>/</td><td>/</td><td>+2.7</td><td>+3.0</td><td>/</td><td>/</td><td>+2.3</td></tr><tr><td>Putative sulphate exporter</td><td>Uncharacterized</td><td>721</td><td>-</td><td>W0Z8D9</td><td>Putative membrane protein YelH</td><td>+3.0</td><td>/</td><td>/</td><td>/</td><td>+3.8</td><td>+13.8</td><td>+74.8</td><td>/</td><td>+5.4</td></tr><tr><td></td><td></td><td></td><td>712</td><td>-</td><td>W0Z5S8</td><td>Putative protein</td><td>/</td><td>/</td><td>-2.9</td><td>/</td><td>/</td><td>/</td><td>/</td><td>-2.6</td><td>/</td></tr><tr><td></td><td></td><td></td><td>2176</td><td>-</td><td>W0ZB95</td><td>NMT1/THI5 like domain protein</td><td>/</td><td>/</td><td>/</td><td>-4.1</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td></td><td></td><td></td><td>1014</td><td>-</td><td>W0Z911</td><td>Nitrate/sulfonate/bicarbonate transporter</td><td>/</td><td>/</td><td>/</td><td>Not detected</td><td>-2.2</td><td>/</td><td>/</td><td>Not det</td><td>Not det</td></tr><tr><td></td><td>Uncharacterized</td><td></td><td>1016</td><td>-</td><td>W0ZA46</td><td>Alkanesulfonate transport system permease protein</td><td>/</td><td>/</td><td>/</td><td>Not detected</td><td>-2.3</td><td>/</td><td>/</td><td>Not det</td><td>Not det</td></tr><tr><td></td><td></td><td></td><td>2228</td><td>opuAC</td><td>W0ZAEG</td><td>Proline/glycine betaine transport systems</td><td>/</td><td>/</td><td>/</td><td>Not detected</td><td>/</td><td>+2.1</td><td>/</td><td>Not det</td><td>Not det</td></tr><tr><td></td><td></td><td></td><td>2230</td><td>proV</td><td>W0ZBF3</td><td>Glycine betaine transporter</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>+2.0</td><td>/</td><td>/</td><td>/</td></tr><tr><td></td><td></td><td></td><td>2229</td><td>opuAB</td><td>W0ZC16</td><td>Glycine betaine transport system permease protein OpuAB</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>+2.0</td><td>/</td><td>/</td><td>/</td></tr><tr><td rowspan="4">ABC</td><td></td><td></td><td>585</td><td>-</td><td>W0Z4F3</td><td>Transport permease protein</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td></td><td></td><td>244</td><td>ssuC</td><td>W0ZDD8</td><td>Alkanesulfonate transporter subunit membrane component</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td></td><td></td><td>NBD</td><td>265</td><td>livF</td><td>W0ZBZ8</td><td>Leucine/isoleucine/valine transporter</td><td>/</td><td>/</td><td>/</td><td>+2.3</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td></td><td></td><td></td><td>327</td><td>drrA</td><td>W0ZCT4</td><td>Daunorubicin/doxorubicin resistance</td><td>/</td><td>+2.3</td><td>/</td><td>/</td><td>/</td><td>-2.3</td><td>+2.6</td><td>/</td></tr><tr><td rowspan="2">BMP</td><td></td><td></td><td>533</td><td>-</td><td>W0Z6W7</td><td>Uncharacterized protein</td><td>/</td><td>/</td><td>/</td><td>-6.9</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td></td><td></td><td>2159</td><td>-</td><td>W0ZEBS</td><td>Cluster maltose/g3p/polyamine/iron - extracellular solute-binding protein</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>-2.1</td><td>/</td><td>/</td><td>/</td></tr><tr><td>Monosaccharide</td><td></td><td></td><td>SBP</td><td>2446</td><td>W0Z4P9</td><td>Cluster ribose/xylose/arabinose/galactose</td><td>/</td><td>/</td><td>/</td><td>Not detected</td><td>-2.9</td><td>/</td><td>/</td><td>Not det</td><td>Not det</td></tr><tr><td rowspan="2">APC</td><td></td><td></td><td>3047</td><td>actP</td><td>W0ZC79</td><td>Cation/acetate symporter</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>-2.0</td><td>-2.6</td><td>/</td><td>/</td></tr><tr><td></td><td></td><td>2808</td><td>-</td><td>W0ZCN7</td><td>Permease of MSF-type transporter</td><td>/</td><td>/</td><td>/</td><td>Not detected</td><td>+2.4</td><td>/</td><td>/</td><td>Not det</td><td>Not det</td></tr><tr><td>MSF</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
419 **Conclusions**

420 In agreement with *in vitro* findings, the congruence of our transcriptomic and proteomic results reveals
421 for the first time the involvement, *in cellulo*, of Sad pathway in the SMZ degradation in *Microbacterium*
422 sp. C448. Contrary to our initial hypothesis whereby *sul1* would be required to permit biodegradation
423 of the drug at therapeutic concentration, we found that the basal expression of *sul1* conferred a
424 natural resistance to *Microbacterium* sp. C448, enough to cope with SMZ. Furthermore, this study has
425 revealed for the first time proteins that may be involved in the metabolism of the key sulphonamide
426 intermediate 4-aminophenol, and the export of inorganic sulphur species that may be toxic if they
427 accumulate in the cell. These conclusions need to be verified and confirmed experimentally. This study
428 contributes to a better understanding of the response of the SMZ-degrading *Microbacterium* sp. C448
429 strain to sulphonamide exposure and provided new insights into SMZ detoxification process.
430

430

431 **Acknowledgments**

432 This work was supported by the Agence Nationale de la Recherche (ANTIBIOTOX project; grant number
433 ANR-17-CE34-0003).

434

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571
572

573 Supplemental data

574 **Fig. S1. Proposed SMZ degradation pathway catalysed by Sad proteins in Microbacterium sp. C448.**
575 SadA and SadB: monooxygenases; SadC: flavine reductase (according to Ricken *et al.* 2017 and Kim *et
576 al.* 2019b).

577
578

579
<figure><img src="image_4.png" /></figure>

581

582 Table S1. Modification of the abundance of *sadABC*, *folP* and *sul1* transcripts obtained from DeSeq2 analysis. Any statistically significant difference of transcript abundance is expressed as fold-change as compared to control (n = 4, *p* < 0.001). CTRL: control; LC: low SMZ concentration; HC: high SMZ concentration; +: increase; -: decrease; NS: not significant.
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<table><thead><tr><th>Time (h)</th><th>Comparison</th><th>sadA ORF 2030</th><th>sadB ORF 2028</th><th>sadC ORF 2026</th><th>folP ORF 1952</th><th>sul1 ORF 2696</th></tr></thead><tbody><tr><td rowspan="2">0.6</td><td>LC vs CTRL</td><td>+1.8</td><td>NS</td><td>+1.9</td><td>NS</td><td>NS</td></tr><tr><td>HC vs CTRL</td><td>+1.5</td><td>NS</td><td>+2.2</td><td>NS</td><td>NS</td></tr><tr><td rowspan="2">5</td><td>LC vs CTRL</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>HC vs CTRL</td><td>+3.1</td><td>NS</td><td>+2.6</td><td>NS</td><td>NS</td></tr><tr><td rowspan="2">9</td><td>LC vs CTRL</td><td>NS</td><td>NS</td><td>NS</td><td>+4.1</td><td>-2.1</td></tr><tr><td>HC vs CTRL</td><td>NS</td><td>NS</td><td>NS</td><td>+4.5</td><td>-3.4</td></tr></tbody></table>
<table><caption>Table S2. Adjusted *p*-values obtained after the Dunn tests was applied on the abundance of Sad and DHPS proteins after exposure of *Microbacterium* sp. C448 to SMZ. CTRL: control; LC: low concentration; HC: high concentration; +: increase; -: decrease; =: no difference; NS: not significant.

Bold characters: significant data.</caption><thead><tr><th rowspan="2">Time (h)</th><th rowspan="2">Comparison</th><th colspan="2">Sad A W0Z5L8</th><th colspan="2">Sad B W0Z833</th><th colspan="2">Sad C W0Z7H5</th><th colspan="2">DHPS-folP W0Z6Y9</th><th colspan="2">DHPS-sul1 W0Z673</th></tr><tr><th>Fold-change</th><th>p value</th><th>Fold-change</th><th>p value</th><th>Fold-change</th><th>p value</th><th>Fold-change</th><th>p value</th><th>Fold-change</th><th>p value</th></tr></thead><tbody><tr><td rowspan="2">1</td><td>LC vs CTRL</td><td>+1.5</td><td>0.011</td><td>+1.3</td><td>NS</td><td>+1.5</td><td>0.002</td><td>+1.3</td><td>NS</td><td>=1.0</td><td>NS</td></tr><tr><td>HC vs CTRL</td><td>+1.2</td><td>NS</td><td>+1.2</td><td>NS</td><td>+1.5</td><td>0.000</td><td>-1.1</td><td>NS</td><td>+1.1</td><td>NS</td></tr><tr><td rowspan="2">5</td><td>LC vs CTRL</td><td>+1.8</td><td>0.001</td><td>+1.4</td><td>NS</td><td>+1.5</td><td>0.010</td><td>+1.1</td><td>NS</td><td>+1.1</td><td>NS</td></tr><tr><td>HC vs CTRL</td><td>+2.5</td><td>0.000</td><td>+1.6</td><td>0.044</td><td>+2.0</td><td>0.000</td><td>=1.0</td><td>NS</td><td>=1.0</td><td>NS</td></tr><tr><td rowspan="2">24</td><td>LC vs CTRL</td><td>+1.5</td><td>0.009</td><td>+1.1</td><td>NS</td><td>+1.1</td><td>NS</td><td>-1.1</td><td>NS</td><td>=1.0</td><td>NS</td></tr><tr><td>HC vs CTRL</td><td>+2.6</td><td>0.000</td><td>+1.4</td><td>NS</td><td>+1.9</td><td>0.000</td><td>+1.2</td><td>NS</td><td>+1.1</td><td>NS</td></tr></tbody></table>
605 Table S3. List of the stress markers modulated in *Microbacterium* sp. C448 exposed to SMZ. Any statistically significant modification of transcript abundance

606 is expressed as fold change as compared to control (p < 0.001). The adjusted p-values of proteins are indicated in parentheses.

<table><thead><tr><th rowspan="3">Category</th><th rowspan="3">Type</th><th rowspan="3">ORF</th><th rowspan="3">Gene name</th><th rowspan="3">UniProt code</th><th rowspan="3">Characteristics</th><th colspan="10">LC</th><th colspan="3">HC</th></tr><tr><th colspan="3">Transcriptomic</th><th colspan="3">Proteomic</th><th colspan="3">Transcriptomic</th><th colspan="3">Proteomic</th></tr><tr><th>0 h pi</th><th>5 h pi</th><th>9 h pi</th><th>1 h pi</th><th>5 h pi</th><th>24 h pi</th><th>0.6 h pi</th><th>5 h pi</th><th>9 h pi</th><th>1 h pi</th><th>5 h pi</th><th>24 h pi</th></tr></thead><tbody><tr><td rowspan="3">Translation</td><td>RidA family protein</td><td>2019</td><td>-</td><td>W0Z991</td><td>Putative translation inhibitor YjgF family</td><td>+ 2.3</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>+ 2.7</td><td>+ 3.0</td><td>/</td><td>/</td><td>+ 2.3 (p = 0.013)</td><td>/</td></tr><tr><td>Initiation factor IF1</td><td>189</td><td>infA</td><td>W0Z9Z0</td><td>-</td><td>/</td><td>/</td><td>- 3.6</td><td>/</td><td>/</td><td>/</td><td>/</td><td>+ 2.2</td><td>- 6.4</td><td>/</td><td>/</td><td>/</td></tr><tr><td>30S ribosomal protein S1</td><td>1506</td><td>rpsA</td><td>W0ZEG5</td><td>Cell division-ribosomal stress proteins cluster</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>+ 2.1</td><td>- 2.5</td><td>/</td><td>/</td><td>/</td></tr><tr><td></td><td>Ribonuclease P protein component</td><td>2283</td><td>rnpA</td><td>W0ZBK8</td><td>Cell division subsystem</td><td>/</td><td>/</td><td>- 3.4</td><td colspan="3">Not detected</td><td>/</td><td>+ 2.1</td><td>- 5.3</td><td colspan="3">Not detected</td></tr><tr><td rowspan="6">Chaperone</td><td>Foldase YidC</td><td>2285</td><td>yidC</td><td>W0ZD69</td><td>Membrane protein insertase</td><td>/</td><td>/</td><td>/</td><td>+ 2.0 (p = 0.005)</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td rowspan="3">Heat shock</td><td>2616</td><td>-</td><td>W0ZER6</td><td>18 kDa antigen 2, belongs to the HSP20 family</td><td>/</td><td>/</td><td>/</td><td colspan="3">Not detected</td><td>+ 2.2</td><td>+ 2.3</td><td>/</td><td colspan="3">Not detected</td></tr><tr><td>251</td><td>groS</td><td>W0ZAX2</td><td>10 kDa family GroES</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>+ 2.5</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>2931</td><td>-</td><td>W0Z6T0</td><td>60 kDa family GroEL</td><td>/</td><td>/</td><td>/</td><td colspan="3">Not detected</td><td>+ 2.4</td><td colspan="3">Not detected</td></tr><tr><td>Cold shock</td><td>2938</td><td>-</td><td>W0Z7K5</td><td>Cold shock domain-containing protein</td><td>/</td><td>/</td><td>/</td><td>+ 2.0 (p = 0.032)</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>Trigger factor</td><td>1439</td><td>tig</td><td>W0Z731</td><td>EC:5.2.1.8</td><td>/</td><td>/</td><td>/</td><td>- 2.6 (p = 0.024)</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>DNA replication &amp; repair</td><td>Primosomal protein</td><td>1745</td><td>-</td><td>W0ZB93</td><td>TPR-repeat-containing protein</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>+ 2.5</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td></td><td>Single-stranded DNA-binding protein</td><td>2306</td><td>ssb</td><td>W0ZC46</td><td>RecFOR pathway</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>+ 2.4</td><td>/</td><td>/</td><td>/</td><td>/</td></tr></tbody></table>

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