Bacillus safensis and Bacillus haynesii, isolated from a saline-sodium environment: Resilience to multiple stresses and tolerance to nonsteroidal anti-inflammatory drugs, based on a genomic analysis and tolerance tests

Abstract

Nonsteroidal anti-inflammatory drugs (NSAIDs) are emerging contaminants whose bioremediation becomes challenging under multiple environmental stressors. This study evaluates the resilience and NSAID tolerance of Bacillus safensis HS4–2 and Bacillus haynesii TXO1–1SG1. The two strains were isolated from saline-sodium sediment. They were tested independently for tolerance to salinity (0 – 3.4µ M), pH (5–11), and the NSAIDs diclofenac, ibuprofen, and ketoprofen (10, 100, and 1000 µM). Genetic identification was performed using the 16S rRNA gene, followed by whole-genome sequencing with the Illumina and Nanopore platforms, and annotation through RAST. The Strain HS4–2 tolerated 0 – 2.5µ M NaCl, pH 5–11, 10 µM diclofenac, 1000 µM ibuprofen, and 100 µM ketoprofen. The strain TXO1–1SG1 tolerated 0 – 2.5 µM NaCl, pH 5–11, 10 µM diclofenac, 100 µM ibuprofen, and 1000 µM ketoprofen. Both genetic and genomic analyses confirm the species assignments (Bacillus safensis HS4–2, Bacillus haynesii TXO1–1SG1). Genome annotation revealed genes associated with stress response, and aromatic compound metabolism, suggesting their potential role in NSAID transformation, although no analytical validation of degradation was performed. All sequences are available in GenBank. The dataset provides a resource for comparative and functional genomic studies of extremotolerant Bacillus strains in environments with multiple stress conditions.

Specifications Table

Subject	Biology
Specific subject area	Resilience genes and biotechnological potential of Bacillus species
Type of data	Experimental data: Fig. 1, Fig. 2 (tolerance assays for NaCl, pH, and NSAIDs) Analytical data: Table 1, Fig. 3, and Fig. 4 (genome assembly, annotation, and functional gene analyses)
Data collection	Strains HS4–2 and TXO1–1SG1 were isolated from saline-sodium sediment from Lake Texcoco in Mexico. High-molecular-weight DNA was extracted from both strains using the MasterPure Complete DNA & RNA Purification Kit (LGC Biosearch Technologies, MV89010) according to the manufacturer's specifications. The extracted DNA was subsequently sent to Plasmidsaurus (CA, USA) for whole-genome sequencing using a hybrid Illumina-Nanopore approach. Species identification was performed using RapdTool v2.1.0, followed by genome annotation with the RAST server. Finally, genes associated with stress response and the metabolism of aromatic compounds were screened within the annotated genomes.
Data source location	Country: México City: Texcoco Location: Texcoco’s Lake (19°30′N 98°59′O)
Data accessibility	Repository name: National Center for Biotechnology Information GenBankData identification number:HS4–2: •16S rRNA sequence: PX401006 •Genome sequence: JBLQUJ000000000.1 TXO1–1SG1 •16S rRNA sequence: PX401007 •Genome sequence: NZ_JBLGEH000000000.1 Direct URL to data:16S rRNA sequences: https://www.ncbi.nlm.nih.gov/nuccore/?term=PX401006:PX401007[accn] Genome sequences:https://www.ncbi.nlm.nih.gov/nuccore/JBLQUJ000000000.1/ https://www.ncbi.nlm.nih.gov/nuccore/NZ_JBLGEH000000000.1
Related research article	None

1. Value of the Data

• •Bacillus safensis HS4–2 and Bacillus haynesii TXO1–1SG1 have a remarkable resilience to multiple stress factors. This stability in the face of environmental stress factors positions these bacteria as strong candidates for biotechnological applications, instilling confidence in their potential.
• •The Bacillus species show promise for tolerate diclofenac, ibuprofen, and ketoprofen, which are common environmental contaminants. Furthermore, according to their genomic information, their potential is likely not limited to these compounds but could also tolerate various contaminants, whether emerging or persistent such as polycyclic aromatic hydrocarbons (PAH).
• •The data reported in this study can serve as background information for further investigations that delve deeper into genomic details to harness the biotechnological potential of Bacillus safensis HS4–2 and Bacillus haynesii TXO1–1SG1.

2. Background

Nonsteroidal anti-inflammatory drugs (NSAIDs) are widely used in human and veterinary medicine. Their presence in the environment is concerning because they are designed to exert biological effects at low doses, posing a potential risk to non-target organisms exposed to them [1]. In Mexico, although NSAIDs such as diclofenac, ibuprofen, and ketoprofen are available over the counter, there is no specific legislation regulating their use or environmental control. Several reports document their presence in water bodies, soil, and living organisms; internationally, they are classified as priority pollutants in the European Union [2].

Given this situation, microorganisms have emerged as sustainable tools for eliminating pharmaceutical contaminants. Species such as Bacillus mojavensis and B. paralicheniformis have demonstrated diclofenac-degrading activity [3,4], while B. siamensis, B. thuringiensis, and B. freudenreichii have been reported as ibuprofen degraders [[5], [6], [7]]. Likewise, B. thuringiensis and B. paralicheniformis show the capacity to degrade naproxen and ketoprofen, respectively [4,8].

This study evaluates the tolerance of Bacillus strains against diclofenac, ibuprofen, and ketoprofen through in vitro assays and genomic analysis. The findings highlight the strains' resilience, evidenced by their growth under high salinity, pH variation, and the presence of pharmaceutical contaminants. These results reinforce their potential application in bioremediation processes in extreme environments.

3. Data Description

3.1. Morphological description of isolated strains HS4–2 and TXO1–1SG1

The strains isolated from saline-sodium sediment were macroscopically described as colonies 2–4 mm in diameter, flat and oval, beige in color, and dry. They differ in that strain HS4–2 has serrated edges and is friable, whereas strain TXO1–1SG1 has defined edges and a creamy consistency. Microscopically, both strains are Gram-positive bacilli.

3.2. Resilience to multiple stresses

The two strains were characterized based on their growth across different NaCl concentrations, pH levels, and various concentrations of diclofenac, ibuprofen, and ketoprofen. Strain HS4–2 had an adequate development in a NaCl range of 0 – 2.5µ M, with optimal growth at 0µ M, showing the beginning of the stationary phase after 96 h of incubation; while its growth at different pH levels indicates that it tolerates both acidic and alkaline environments, as it grew in the range of 5–11, with optimal growth at pH 6, 7, and 8, and the stationary phase observed after 96 h of incubation.

The strain was moderately tolerant to 10 µM diclofenac in the medium. In contrast, at 100 µM and 1000 µM, growth was minimal compared to the biotic control. Tolerance to ibuprofen was higher, as it developed at concentrations of 10 µM, 100 µM, and 1000 µM of the contaminant. Observing that cell death began at 96 h at the highest concentration, while at the other concentrations, this phase began at 120 h. Growth in the presence of ketoprofen was moderate at 10 µM and 100 µM, whereas at 1000 µM, growth was minimal compared to the biotic control. Fig. 1 illustrates the development of strain HS4–2 under various stress factors, highlighting its resilience.

Fig. 1.

Growth of Bacillus safensis HS4–2 under different concentrations of a) NaCl, b) different pH conditions, c) in the presence of diclofenac, d) in the presence of ibuprofen, and e) in the presence of ketoprofen.

The growth range of strain TXO1–1SG1 in the presence of NaCl was 0 – 2.5 µM, with optimal growth at 1.7 µM, where the stationary phase begins after 168 h of incubation, and the cell death phase is observed after 240 h of incubation. Likewise, the strain grew over a pH range of 5–11, with optimal growth at 6. Again, the stationary phase began at 168 h, and cell death at 240 h.

The TXO1–1SG strain grows best at a 10 µM concentration of diclofenac, starting cell death after 120 h of incubation. Growth at 100 µM and 1000 µM diclofenac was minimal compared to the biotic control. With ibuprofen, optimal growth occurs at 10 µM after 96 h, aligning with biotic control. At 100 µM, growth is moderate; at 1000 µM, it is minimal. In the presence of ketoprofen at 10 µM, 100 µM, and 1000 µM, growth is mild relative to biotic control. Fig. 2 illustrates the resilience of the TXO1–1SG1 strain.

Fig. 2.

Growth of Bacillus haynesii TXO1–1SG1 at different concentrations of a) NaCl, b) different pH conditions, c) in the presence of diclofenac, d) in the presence of ibuprofen, and e) in the presence of ketoprofen.

Based on observed resilience, strains HS4–2 and TXO1–1SG1 are moderately halotolerant, exhibiting broad tolerance to both acidic and alkaline pH levels, as well as adaptability to pharmaceutical contaminants such as diclofenac, ibuprofen, and ketoprofen.

3.3. Genetic identification based on the 16S rRNA gene

The sequences were deposited and compared with the databases. The fragment size of strain HS4–2 was 1406 base pairs, identified as Bacillus safensis FO-36b in BLAST, with 100 % coverage and a similarity percentage of 99.72 %; while in EzBiocloud, the similarity percentage was 99.72 % with Bacillus safensis subsp safensis FO-36b, with 95.4 % coverage. The sequence is deposited in the National Center for Biotechnology Information (NCBI) database with the access code PX401006.

For TXO1–1SG1, a fragment of 1412 base pairs were obtained; in BLAST, it had a similarity percentage of 99.72 % with Bacillus haynesii NRRL B-41,327 with 100 % coverage, while in EzBiocloud, the coverage was 95.9 % and a similarity percentage of 99.72 % with the previously mentioned species. The sequence is deposited in the National Center for Biotechnology Information (NCBI) database with the access code PX401007.

3.4. Species assignment and genomic annotation

The HS4–2 strain was identified as Bacillus safensis, with an Average Nucleotide Identity (ANI) of 98.5 %, a CheckM completeness of 99.12 %, and a genomic distance of 0.015. Three contigs were obtained, totaling 3697,781 base pairs (bp) and with a GC content of 41.6 %. 3849 genes were annotated, of which 3658 are coding, 24 are rRNA, 20 are ncRNA, and 81 are tRNA. Fig. 3 shows the annotated genome of strain HS4–2, which reveals six genes encoding enzymes involved in aromatic compound metabolism. The genome is deposited in the National Center for Biotechnology Information (NCBI) database with the access code JBLQUJ000000000.1 (The NCBI Prokaryotic Genome Annotation Pipeline (PGAP) annotations can be accessed at: Bacillus safensis: https://www.ncbi.nlm.nih.gov/datasets/gene/GCF_048159725.1/).

Fig. 3.

Bacillus safensis HS4–2, a) position in the genome of osmotic stress response genes, b) position in the genome of genes participating in encoding enzymes involved in the metabolism of aromatic compounds, and c) distribution of subsystems and enzymes implicated in osmotic stress response and in the metabolism of aromatic compounds.

Fig. 4 displays the genome of strain TXO1–1SG1, classified as Bacillus haynesii, with an ANI of 99.5 %, CheckM completeness of 98.78 %, and a genomic distance of 0.005. Three contigs were assembled; the genome is 4232,822 bp long with a GC content of 46 %. A total of 4346 genes were annotated, including 4132 coding genes, 24 rRNA, 43 ncRNA, and 85 tRNA genes. Eleven genes encoding enzymes for metabolizing aromatic compounds were identified. The NCBI accession number for the Bacillus haynesii TXO1–1SG1 genome data is NZ_JBLGEH000000000.1 (NCBI PGAP can be accessed at Bacillus haynesii: https://www.ncbi.nlm.nih.gov/datasets/gene/GCF_049068305.1/).

Fig. 4.

Bacillus haynesii TXO1–1SG1, a) position in the genome of osmotic stress response genes, b) position in the genome ofgenes participating in encoding enzymes involved in the metabolism of aromatic compounds, and c) distribution of subsystems and enzymes implicated in osmotic stress response and in the metabolism of aromatic compounds.

Table 1 shows the genes involved in the metabolism of aromatic compounds found in the genomes of Bacillus safensis HS4–2, also shows the genes implicated in osmotic stress. On the other hand, Table 2 shows Bacillus haynesii TXO1–1SG1 genes for osmotic stress and genes involved in the metabolism of aromatic compounds. Both strains harbor genes for the metabolism of aromatic compounds like yisk, aroD and aroQ, as well as genes that participate in osmoregulation like opuAC, opuAB, opuD and betB.

Table 1.

Genes involved in the metabolism of aromatic compounds and osmotic stress were found in the genome of Bacillus safensis HS4–2.

Genes involved in the metabolism of aromatic compounds
Genome location	Locus tag	Name	Symbol
NZ_JBLQUJ010000002.1:286,235–286,630	ACLKGD_RS014	VOC family protein
NZ_JBLQUJ010000002.1:551,122–551,895	ACLKGD_RS02620	type I 3-dehydroquinate dehydratase	aroD
NZ_JBLQUJ010000002.1:2378,748–2379,191	ACLKGD_RS12020	type II 3-dehydroquinate dehydratase	aroQ
NZ_JBLQUJ010000002.1:981,934–982,836	ACLKGD_RS04890	shikimate dehydrogenase	aroE_1
NZ_JBLQUJ010000002.1:3499,403–3500,308	ACLKGD_RS17820	fumarylacetoacetate hydrolase family protein	yisK

Genes involved in osmotic stress
Genome location	Locus tag	Name	Symbol
NZ_JBLQUJ010000002.1:576,832–577,713	ACLKGD_RS02	glycine betaine ABC transporter substrate-binding protein	opuAC
NZ_JBLQUJ010000002.1:577,713–578,558	ACLKGD_RS027	glycine/proline betaine ABC transporter permease subunit	opuAB
NZ_JBLQUJ010000002.1:578,569–579,834	ACLKGD_RS02750	glycine/proline betaine ABC transporter ATP-binding protein	opuAA
NZ_JBLQUJ010000002.1:1544,053–1545,201	ACLKGD_RS076	betaine/proline/choline family ABC transporter ATP-binding	opuCA_2
NZ_JBLQUJ010000002.1:1545,215–1545,868	ACLKGD_RS07	ABC transporter permease	opuCB
NZ_JBLQUJ010000002.1:1545,882–1546,799	ACLKGD_RS07655	osmoprotectant ABC transporter substrate-binding protein	opuCC
NZ_JBLQUJ010000002.1:1546,821–1547,510	ACLKGD_RS07660	ABC transporter permease	opuCD
NZ_JBLQUJ010000002.1:1825,907–1827,376	ACLKGD_RS09155	betaine-aldehyde dehydrogenase	betB
NZ_JBLQUJ010000002.1:1827,404–1828,612	ACLKGD_RS0916	iron-containing alcohol dehydrogenase	gbsB
NZ_JBLQUJ010000002.1:1910,106–1911,638	ACLKGD_RS09690	glycine betaine transporter OpuD	opuD
NZ_JBLQUJ010000002.1:3623,482–3624,297	ACLKGD_RS184	Glycerol uptake facilitator protein	glpF

Table 2.

Genes involved in the metabolism of aromatic compounds and osmotic stress were found in the genome of Bacillus haynesii TXO1–1SG1.

Genes involved in the metabolism of aromatic compounds
Genome location	Locus tag	Name	Symbol
NZ_JBLGEH010000001.1:37,299–38,130	ACLKGG_RS00235	Nitrilotriacetate monooxygenase
NZ_JBLGEH010000003.1:381,545–382,309	ACLKGG_RS2037	type I 3-dehydroquinate dehydratase	aroD
NZ_JBLGEH010000001.1:689,931–690,836	ACLKGG_RS03580	fumarylacetoacetate hydrolase family protein	yisK
NZ_JBLGEH010000002.1:694,391–694,828	ACLKGG_RS0708	type II 3-dehydroquinate dehydratase	aroQ
NZ_JBLGEH010000002.1:1956,036–1957,241	ACLKGG_RS13670	4-hydroxybenzoate 3-monooxygenase	pobA
NZ_JBLGEH010000002.1:1957,257–1958,633	ACLKGG_RS13675	MFS transporter	pcaK
NZ_JBLGEH010000002.1:1958,672–1959,523	ACLKGG_RS13680	extradiol ring-cleavage dioxygenase	hpcB
NZ_JBLGEH010000002.1:2768,205–2768,774	ACLKGG_RS17670	non-oxidative hydroxyarylic acid decarboxylases subunit B	bsdB
NZ_JBLGEH010000002.1:2768,788–2770,209	ACLKGG_RS17675	non-oxidative hydroxyarylic acid decarboxylases subunit C	bsdC
NZ_JBLGEH010000002.1:2770,229–2770,456	ACLKGG_RS17680	non-oxidative hydroxyarylic acid decarboxylases subunit D	bsdD

Genes involved in osmotic stress
Genome location	Locus tag	Name	Symbol
NZ_JBLGEH010000002.1:700,407–701,291	ACLKGG_RS07120	glycine betaine ABC transporter substrate-binding protein	opuAC
NZ_JBLGEH010000002.1:701,291–702,145	ACLKGG_RS07125	glycine/proline betaine ABC transporter permease subunit OpuAB	OpuAB
NZ_JBLGEH010000002.1:702,145–703,401	ACLKGG_RS07130	glycine/proline betaine ABC transporter ATP-binding protein OpuAA	opuAA
NZ_JBLGEH010000002.1:1224,565–1226,100	ACLKGG_RS09855	glycine betaine transporter OpuD	opuD
NZ_JBLGEH010000002.1:1317,667–1318,875	ACLKGG_RS10410	iron-containing alcohol dehydrogenase	gbsB
NZ_JBLGEH010000002.1:1318,893–1320,365	ACLKGG_RS10415	betaine-aldehyde dehydrogenase	betB
NZ_JBLGEH010000002.1:1620,702–1621,379	ACLKGG_RS120	ABC transporter permease	opuCD
NZ_JBLGEH010000002.1:1621,395–1622,312	ACLKGG_RS120	osmoprotectant ABC transporter substrate-binding lipoprotein OpuCC	opuCC
NZ_JBLGEH010000002.1:1622,326–1622,979	ACLKGG_RS12030	ABC transporter permease	opuCB
NZ_JBLGEH010000002.1:1623,001–1624,140	ACLKGG_RS12035	betaine/proline/choline family ABC transporter ATP-binding protein	opuCA

The datasets generated in this study, including phenotypic stress tolerance profiles and annotated draft genomes, provide a resource for comparative analyses of extremotolerant Bacillus species. These data may be useful for studies focused on microbial adaptation to saline and variable pH environments, as well as for genome-based screening of metabolic pathways associated with aromatic compound transformation.

4. Experimental Design, Materials, and Methods

4.1. Isolation and morphological characterization of strains

Strains HS4–2 and TXO1–1SG1 were isolated from saline-sodium sediment from Lake Texcoco in Mexico. The culture medium used was a halophilic medium (MH) for moderate halophiles (10 g/L yeast extract, 5 g/L proteose peptone, 1 g/L dextrose, and 18 g/L bacteriological agar; supplemented with 5 % NaCl solution, sufficient for 1 L).

Macroscopic morphology was assessed based on size, color, shape, edges, elevation, texture, appearance, and malleability. Gram staining was also conducted on the strains to identify microscopic morphology.

4.2. Tolerance to multiple stress factors

4.2.1. NaCl and pH tolerance

Lake Texcoco is characterized as a naturally saline and alkaline environment, with reports of elevated salt concentrations and pH values frequently exceeding 9 in several zones. Based on this environmental background, the tolerance assays were designed to encompass an ecologically relevant interval while also extending beyond it to evaluate the full physiological tolerance of the strains. The range and concentration of NaCl for optimal growth of each strain were determined by inoculating them in MH broth at varying NaCl concentrations (0µ M, 0.5µ M, 0.9µ M, 1.7µ M, 2.5µ M y 3.4µ M) at a pH of 7. Aliquots of 1 mL were taken every 24 h, and the absorbance was measured at 600 nm in a spectrophotometer (EPOCH, Biotek), this wavelength indicates cellular growth [9]. The optimal range and pH for the strain's growth were determined by inoculating the strain into MH broth and adjusting the NaCl concentration to achieve optimal growth. The pH was adjusted to 5, 6, 7, 8, 9, 10, and 11, and absorbance was measured daily at 600 nm in a spectrophotometer (EPOCH, Biotek). The tests were conducted in triplicate, and the means and standard deviations were used to construct the graphs.

4.3. Tolerance to nonsteroidal anti-inflammatory drugs

Flasks containing MH broth with diclofenac (Sigma-Aldrich®, D6899–25 G), ibuprofen (Sigma-Aldrich®, I4883–5 G), or ketoprofen (PROFENID®, DMXA004) at three concentrations (10 µM, 100 µM, and 1000 µM, methanol reactive degree was used as a solvent) were prepared. Published studies have reported degradation analyses of these drugs at a concentration of 100µ M [[10], [11], [12]]; for this reason, concentrations of 10µ M (lower than 100µ M) were also tested to determine whether the bacteria could adapt to the medium containing the contaminant, and concentrations of 1000µ M (higher than 100 µM) were tested because, although this is a high concentration, it is believed that the medium contains additional sources of carbon and growth factors that could facilitate tolerance.

A bacterial inoculum of strains HS4–2 or TXO1–1SG1 at 6 × 10⁸ cells/mL was added to these flasks. Biotic controls containing only the culture medium and bacterial inoculum were also prepared to compare the strain's normal growth with its growth in the presence of the contaminant

Both samples and controls were prepared in triplicate and incubated at 37 °C for 120 h (5 days). Aliquots of 200 µL were taken every 24 h and measured in the spectrophotometer (EPOCH, Biotek) at 600 nm to assess bacterial growth [9]; culture medium without NSAIDs or bacterial inoculum was used as a blank for background-corrections of the lectures; the means and standard deviations were used to construct the graphs. The measurements taken indicate only the growth of the strains in the presence of the contaminants; no additional markers were measured.

4.4. DNA extraction

The Promega Wizard Genomic DNA Purification Kit was used to extract DNA according to the manufacturer's instructions. DNA was visualized by agarose gel electrophoresis at 1 %. DNA extraction products were used to verify the presence of DNA and assess its quality.

4.5. Amplification of the 16S rRNA gene

The amplification of the 16S rRNA gene was performed using polymerase chain reaction (PCR) and universal primers 27F: 5′-AGA GTT TGA TCM TGG CTC AG-3′ and 1492R: 5′-TAC GGT TAC CTT GTT ACG ACT T-3′

Commercial Taq DNA polymerase (Meridian Bioscience, Bio-21,105) was used to perform the reaction. The thermal cycle was set as follows: a pre-denaturation cycle at 94 °C for 5 min, denaturation at 94 °C for 60 s, annealing at 59 °C for 30 s, and elongation at 72 °C for 60 s, repeated 30 times, followed by a post-elongation cycle at 72 °C for 10 min. The amplified products are visualized on a 1 % agarose gel and revealed with ethidium bromide for 7 s. Macrogen Korea's sequencing service was selected to process the amplified products.

4.6. Genetic identification of strains based on the 16S rRNA gene

The sequences obtained were reviewed and corrected with the Chromas Pro program [10]. Consensus sequences were assembled using BioEdit version 7.0.9 [11].

The consensus sequences were compared with sequences deposited in the GenBank databases using the BLAST [12] program from the National Center for Biotechnology Information (NCBI) and the EzBiocloud [13] server to determine the percentage of similarity.

4.7. DNA extraction for genome sequencing

Strains HS4–2 and TXO1–1SG1 were inoculated into MH broth from a pure colony and incubated at 37 °C for 72 h until sufficient biomass was obtained for DNA isolation. High-molecular-weight DNA was extracted from the strains using the MasterPure Complete DNA & RNA Purification Kit (LGC Biosearch Technologies, MV89010) according to the manufacturer's specifications. These two strains were sent to Plasmidsaurus (CA, USA) for sequencing [14].

4.8. Taxonomic assignment and genomic annotation

Genome sequencing was performed using a hybrid approach that combined Illumina and Nanopore platforms. The 5 % of lowest-quality reads were removed using Filtlong v0.2.1 [15] with default parameters. Subsequently, pre-assembly was performed with Miniasm v0.3 [16], after which the coverage was adjusted to a minimum of 100x. With the filtered sequences, final assembly and polishing were performed using Flye v2.9.1, with specific parameters optimized for high-quality ONT reads. Genome annotation was performed with Bakta v1.6.1 [17]. Contig analysis was performed with Bandage v0.8.1 [18]. Genome integrity and contamination were assessed with CheckM v1.2.2 [19]. Finally, species identification was performed with RapdTool v2.1.0. An additional annotation was added to the RAST server [20] to identify genes involved in stress response regulation and aromatic compound metabolism, given that the molecular structure of NSAIDs includes this type of ring. GCView [20] was used to generate a circular genome map.

Limitations

This study is based on phenotypic growth assays and genomic annotation. The potential for NSAID degradation is inferred from the presence of genes associated with aromatic compound metabolism; however, no analytical validation was performed. Therefore, the results should be interpreted as evidence of tolerance and a metabolic potential suggested by genomic annotation, rather than confirmed biodegradation. Additionally, the experiments were conducted under controlled laboratory conditions, which may not fully reflect the environmental conditions of the original sampling site.

Ethics Statement

The authors have read and follow the ethical requirements for publication in Data in Brief and confirm that the current work does not involve human subjects, animal experiments, or any data collected from social media platforms.

CRediT authorship contribution statement

Lorna Catalina Can-Ubando: Methodology, Investigation, Formal analysis, Writing – original draft, Writing – review & editing. Keila Isaac-Olivé: Methodology, Supervision, Writing – review & editing. Ayixon Sánchez-Reyes: Data curation, Writing – review & editing. Gauddy Lizeth Manzanares-Leal: Writing – review & editing. Ninfa Ramírez-Durán: Conceptualization, Project administration, Resources, Supervision, Writing – review & editing.

Appendix. Supplementary materials

Figure 5. Growth of Bacillus safensis HS4–2 in petri plates at different salt concentrations a)0µ M, b)0.5µ M, c)0.9µ M, d)1.7µ M, e)2.5µ M and f)3.4µM

Figure 6. Growth of Bacillus safensis HS4–2 in petri plates at different pH conditions a) pH=5, b) pH=6, c) pH=7, d) pH=8, e) pH=9, f) pH=10 and g) pH=11

Figure 7. Growth of Bacillus haynesii TXO1–1SG1 in petri plates at different salt concentrations a)0µ M, b)0.5µ M, c)0.9µ M, d)1.7µ M, e)2.5µ M and f)3.4µM

Figure 8. Growth of Bacillus haynesii TXO1–1SG1 in petri plates at different pH conditions a) pH=5, b) pH=6, c) pH=7, d) pH=8, e) pH=9, f) pH=10 and g) pH=11

Figure 9. Agarose gel electrophoresis (1 %) stained with ethidium bromide showing amplification of the 16S rRNA gene. Lane 1: 1 kb DNA Ladder molecular weight marker (Thermo Scientific); Lane 2: negative control; Lane 3: positive control; Lane 4: Bacillus safensis HS4–2; Lane 5: Bacillus haynesii TXO1–1SG1

Figure 10. Agarose gel electrophoresis (1 %) stained with ethidium bromide showing amplification of DNA. Lane 1: 1 kb DNA Ladder molecular weight marker (Thermo Scientific); Lane 2: positive control; Lane 3: Bacillus safensis HS4–2; Lane 4: Bacillus haynesii TXO1–1SG1; Lane 5: negative control

Data Availability

• National Center for Biotechnology InformationBacillus haynesii strain TXO1-1SG1, whole genome shotgun sequencing project (Original data).

• National Center for Biotechnology InformationBacillus safensis strain HS4-2, whole genome shotgun sequencing project (Original data).