Thermophilic bacteria have recently received particular attention for their ability to produce thermostable enzymes (i.e., proteases, lipases, amylases, cellulases, xylanases, and DNA polymerases), which can also be active under extreme conditions (Sharma et al. 2019; Oztas Gulmus and Gormez 2020). Therefore, these enzymes can be used in high-temperature industrial fermenters, which accelerates the biochemical reactions, reduces contamination rates, and facilitates product recovery (Kambourova 2018; Wohlgemuth et al. 2018; Jin et al. 2019). In addition, thermophilic bacteria have several other biotechnological applications, including the production of bioactive compounds (e.g., exopolysaccharides, antibiotics, and biosurfactants) (Nicolaus et al. 2010; Mahajan and Balachandran 2017; Schultz et al. 2022). Moreover, they can be used for the removal of heavy metals by bioremediation as well as for the biodegradation of plastic polymers and hydrocarbons (Ilyas et al. 2014; Rajkumari et al. 2019; Ru et al. 2020).
Thermophilic microorganisms grow in various marine and terrestrial ecosystems such as hydrothermal vents, deep waters, compost, and hot springs; most of them come from the latter habitat (Oztas Gulmus and Gormez 2020). Several studies have investigated the microbial diversity in different hot springs in Algeria, the USA, Iceland, New Zealand, India, Japan, China, Turkey, and Russia by culture-dependent or independent methods (Verma et al. 2018). Hence, they provide insights into the composition of microbial communities colonizing these habitats, which leads to the discovery of new taxa and the understanding of the functional role of microbes in such ecosystems (Li and Ma 2019). These studies suggest that the dominant phyla and taxa depend on temperature, water geochemistry, and pH (Cihan et al. 2012). Algeria has more than 240 hot springs across the country. However, research on the isolation and characterization of microorganisms from these sources remains very limited. Therefore, it would be interesting to investigate these new ecological niches and study their diversity (Saibi 2009; Benammar et al. 2020). Benammar et al. (2020) examined the biodiversity of eight hot springs in Algeria using cultivation methods. They discovered that Firmicutes was the most abundant phylum (95%); however, strains belonging to Deinococcus-Thermus (5%), and Actinobacteria (2%) were also present. It was also reported that
The
Recently, it was found that some microbes can degrade plastic polymers through the action of particular enzymes (Yoshida et al. 2016; Danso et al. 2019; Wei et al. 2020). Thus, many hydrolases from various bacteria and fungi have been identified and investigated for their ability to degrade aliphatic and aromatic polyesters, including polyethylene terephthalate (PET). Microorganisms producing PET hydrolases (PETases) are attracting much interest, especially for their potential application in treating PET waste, one of the most commonly produced plastics in the world (Carr et al. 2020; Kawai 2021).
Thus, the aim of this study was to isolate bacteria from two hot springs located in eastern Algeria and analyze their hydrolytic enzyme and antifungal activities. Among the seventy-two isolated bacteria, the HGR5 strain belonging to
Six water samples were collected aseptically from two hydrothermal springs in Hammam Ouled Tebben and Hammam Guergour (GPS: 35°47΄40.32˝N, 5°07΄30.18˝E; 36°19΄17.19˝N, 5°03΄19.36˝E respectively) (Wilaya of Sétif) for the isolation of thermotolerant bacteria. Water temperature was measured on-site with an electronic thermometer (Electronic temperature controller; Ahlborn Mess- und Regelungstechnik GmbH, Germany), and pH measurements (pH 211 Microprocessor pH Meter; HANNA Instruments Deutschland GmbH, Germany) were performed in the laboratory.
Dilutions from 10–1 to 10–7 were prepared from each sample with physiological water and used for bacterial isolation on three different media: GYM, R2A, and King’s B, by streaking 100 μl of each dilution on the Petri dishes. The plates were incubated at 30°C for 96 hours. The colony-forming units (CFU) per plate were measured for all dilutions in the three different media. The bacterial colonies were selected based on their macroscopic characteristics (i.e., color, shape), and successive streaks on TSA medium obtained pure cultures. Finally, pure colonies were suspended in a TSB medium containing 20% glycerol and stored at –20°C.
The isolates (n = 72) were cultivated at 30°C on TSA medium for 48 h. The bacterial colonies were harvested and suspended in saline solution, and the concentration was adjusted to 108 CFU/ml, corresponding to an optical density (OD600) of 0.1.
All isolates were screened in triplicate for lipase, protease, amylase, chitinase, cellulase, and xylanase activity on agar media containing specific substrates for each enzyme. 2 μl of bacterial suspension was spread over the surface of the agar. The dishes were incubated at 30°C for 48 hours. The enzymatic index was measured for all these activities.
Lipolytic activity was screened using Tween 80 as substrate. The degradation was performed in media containing 1.0% peptone, 0.5% NaCl, 0.01% CaCl2·H2O, 1.5% agar, and 1.0% substrate (Tween 80) (Ramnath et al. 2017). After incubation, precipitations around colonies show Tween 80 degradation (Ramnath et al. 2017).
The detection of proteolytic activity was screened on media supplemented with 3% casein, a zone of hydrolysis around the colonies indicates caseinolytic activity (Zhang et al. 2015).
Starch degradation was measured on agar supplemented with 1% starch. After two days of incubation, the starch degradation was detected by flooding the dishes with Lugol iodine solution; clear zones around colonies indicate positive activity (Benammar et al. 2020). Colloidal chitin agar was used to screen chitinolytic activity, according to Gasmi et al. (2019). Positive isolates were identified based on the presence of hydrolysis zones. Whereas assays for cellulase and xylanase were carried out on agar mediums enriched with CMC and xylan, respectively (Liu et al. 2011; Liang et al. 2014). The plates were flooded in 1% Congo red solution for 15 minutes after 48 hours of incubation, then washed with 1 M NaCl solution to visualize hydrolysis clearance zones (Shanthi and Roymon 2018).
Nineteen of the isolates were examined through a confrontation test on agar for activity against the three phytopathogenic fungus Dc – control fungus diameter (average of 3 replicates), Dt – treated fungus diameter (average of 3 replicates).
The extraction of the genomic DNA was performed using a Wizard® Genomic DNA Purification Kit (Promega, USA) following the manufacturer’s instructions. The two primers, 27F (5’AGAGTTTGATCCTGGCTCAG3’) and 1492R (5’GGTTACCTTGTTACGACTT-3’) were used for the amplification of the 16S rRNA genes using HOT FIREPol® DNA polymerase (Solis BioDyne, Estonia). The PCR amplification mixture was composed of a 1 × concentration of reaction buffer, 2 mM magnesium MgCl2, 200 μM of each dNTP, 0.3 μM of both the forward and reverse primers, 1 unit (U) of HOT FIREPol® DNA polymerase enzyme, and 2 μl of isolated template DNA. The total reaction volume was adjusted to 20 μl using nuclease-free water. Cycling conditions were: initial activation at 95°C for 10 min, followed by 35 cycles of denaturation at 95°C for 30 sec, primers annealing at 58°C for 30 sec, and extension at 72°C for 1 min, then the final extension at 72°C for 10 min. Sequencing was performed by Eurofins Genomics (France), and based on the fluorescence-adapted Sanger method. Fragments were separated by capillary electrophoresis on an Applied Biosystems™ 3730xl Genetic Analyzer (Thermo Fisher Scientific, Inc., USA), and the resulting gene sequences were analyzed using the 16S Based-ID tool of EzBioCloud (
The genomic DNA of
Default parameters were used for all bioinformatic tools unless otherwise noted. TrimGalore v0.6.2 (Krueger 2015) was used for read trimming, and read quality was checked with FastQC v0.11.9 (Andrews 2010). Raw paired-end reads were
The whole-genome shotgun project of
Nucleotide sequences for the four housekeeping genes 16S rDNA,
Functional sequence annotation to establish clusters of orthologous groups (COG) categories was achieved using EggNOG-mapper (Cantalapiedra et al. 2021), where gene sequences were assigned gene ontology (GO) terms. Then, the distribution analysis of GO terms was performed and displayed using TB tools (Chen et al. 2020).
The biosynthetic gene clusters (BGCs) involved in the synthesis of secondary metabolites (SMs) were identified using the online tools antiSMASH v7.0.0 (Blin et al. 2023) and BAGEL4 (van Heel et al. 2018). Also, the ClusterBlast and KnownClusterBlast modules were employed for the comparative analysis of the obtained gene clusters as described by (Tsalgatidou et al. 2022). The detection of CRISPR-Cas systems was carried out online by the CRISPRCasFinder (Couvin et al. 2018).
Annotation of genes involved in antifungal activities was achieved using the carbohydrate-active enzymes (CAZymes) (
The biodegradation of coconut oil, tributyrin, and polycaprolactone diol (PLCD; Mn ~ 2,000) by
To investigate the thermotolerance of strain HGR5, tubes containing 5 ml of TSB broth were inoculated with 50 μl of the bacterial suspension and then incubated with shaking at 160 rpm at 30, 35, 40, 45, 50, 55 and 60°C respectively. After 48 h, the growth was recorded by measuring the optical density at 600 nm (Baltaci et al. 2020). For halotolerance, 50 μl of the bacterial suspension was inoculated into 5 ml of TSB broth with NaCl concentrations of 0; 2,5; 5; 7,5; 10; 12,5; 15%. The tubes were then incubated at 35°C (growth optimal temperature) under shaking at 160 rpm for 96 hours. Every 24 h, a volume of 300 μl was collected, and the OD600 was measured using a microplate reader (VEG 500;
ESSE 3, Italy). Finally, the optimal pH was also determined by inoculating 50 μl of bacterial suspension into 5 ml of TSB broth with a pH range adjusted from 5 to 11 (Baltaci et al. 2020). The optical density of the bacteria was recorded at 24, 48, 72, and 96 h using a microplate reader (Wu et al. 2021). All experiments described above were performed with three repetitions.
The water temperature and pH of Hammam Ouled Tebben and Hammam Guergour were at 52°C; 44.5°C and 6; 6.9, respectively.
Three media (i.e., R2A, King B, and GYM) were employed for bacterial isolation, and enumeration was performed by membrane filtration of 100 ml of water from each source. The highest densities were observed in the R2A medium, around 39 CFU/100 ml for Hammam Ouled Tebben and 49 CFU/100 ml for Hammam Guergour. However, the other two media also showed significant densities, with the GYM medium exhibiting 33 CFU/100 ml for the two samples. For the King’s B medium, an average density of 35 CFU/100 ml and 30 CFU/100 ml was obtained for Hammam Ouled Tebben and Hammam Guergour, respectively. Thus, 72 isolates were obtained, 35 from Hammam Ouled Tebben and 37 from Hammam Guergour.
Screening of enzymatic activities revealed that 68% (n = 49/72) of the isolates produced protease, whereas 66,6% (n = 48/72) exhibited lipolytic activity against Tween 80. On the other hand, for glycosyl hydrolases, 62.5%, 73.6%, 50%, and 5.55% of the isolates were able to degrade starch, chitin, cellulose, and xylan, respectively (Table SI). The enzymatic profiles of bacteria and actinomycetes isolated from Algerian hot springs have been described in several studies (Gomri et al. 2018; Benammar et al. 2020; Medjemadj et al. 2020; Bouacem et al. 2022). These bacteria displayed a variety of typical extracellular enzymatic activities, suggesting their potential applications in various biotechnological applications.
Nineteen of the isolates with the best enzymatic profiles (Fig. 1, highlighted in bold in Table SI) were selected for the antifungal activity against
Numerous studies have highlighted the promising antifungal activity of
On the other hand, the B. halotolerans KKD1 strain showed a potent antagonistic effect against F. graminearum, with an average size of the zone of inhibition above 11 mm. Additionally, the lipopeptide within the extracts of this strain presented a suppressive effect on this phytopathogen (Wu et al. 2021). The mining of its genome revealed that this antifungal activity resulted from the production of several bioactive molecules. The production of fengycin/plipastatin, surfactin, bacillibactin, and polyketides was mostly responsible for the activity attained. Therefore, the expression of surfactin and fengycin lipopeptides, which have been recognized as potent antifungals of KKD1, was confirmed by HPLC and MALDI-TOF-MS (Wu et al. 2021). In addition, the
The two
The generated16S DNA genes sequences (i.e., MN420494.2, MN420495.1, MN420495.1 GenBank accession numbers for HGR5, HGG15, and HGK11 strains, respectively) were submitted to the EzBioCloud database through the 16S-Based ID tool. Unfortunately, the obtained sequences did not cover the whole 16S rDNA gene sequence (i.e., completeness of about 80%), making partial taxonomic identification with a validation at least of the genus. Thus, we obtained a 98.01% and 97.46% similarity sequence with
Whole genome sequencing of
Several phylogenetic and phylogenomic analyses were conducted to validate the taxonomic position of the biotechnologically interesting isolate HGR5. First, the 16S rDNA-based phylogenetic tree showed that HGR5 is more closely related to the
The
A total of 3,821 (92.74 %) genes were identified and assigned clusters of orthologous groups (COG) category using eggNOG-mapper (Fig. 4). The majority of annotated genes were found associated with “Translation, ribosomal structure and biogenesis”, indicating the importance of protein synthesis in this microorganism. Other prominent categories include “Transcription”, “Replication, recombination and repair”, and “Signal transduction mechanisms” categories, highlighting key processes involved in genetic information management and cellular communication. Additionally, numerous genes were found involved in “Cell wall/membrane/ envelope biogenesis”, “Energy production and conversion”, “Carbohydrate transport and metabolism”, and “Amino acid transport and metabolism”, suggesting a versatile metabolic capability of HGR5. Interestingly, a substantial number of genes were classified as having “General function prediction only” implying that their specific functions are not yet well-defined. Furthermore, a significant proportion of genes fell under the category of “Function unknown,” highlighting the need for further investigation to uncover their roles and potential functions.
Identifying novel genes encoding for secondary metabolites through genome mining has recently emerged as an alternative to more traditional approaches. The clusters encoding these molecules in microorganisms generally have the same composition, enabling the development of tools to search and identify BGCs of secondary metabolites in genomes and metagenomes (Weber et al. 2015). Therefore, an approach based on the antiSMASH and ClusterBlast algorithms platforms was used to identify these clusters within the genome of the HGR5 strain. Thus, fourteen BGCs encoding for secondary metabolites were found. They are distributed over 10 biosynthetic regions, covering a significant proportion (13.5 %) of its genome (Table I, Fig. S4). These regions include clusters encoding for known metabolites but also those that code for substances that are not listed in published databases. A similar proportion was found in the genome of
BGCs encoding secondary metabolites discovered by the AntiSMASH server in the
Cluster | Predicted size (bp) | BGC type | Compound | MIBiG accession | Similarity | Closest strain |
---|---|---|---|---|---|---|
1 | 41,419 | other | bacilycin | BGC0001184 | 100% | |
2 | 21,613 | sactipeptide | subtilosin A | BGC0000602 | 100% | |
3 | 47,140 | NPRS | bacillibactin | BGC0000309 | 100% | |
4 | 106,121 | NRPS, transAT-PKS | bacillaene | BGC0001089 | 100% | |
5 | 79,855 | NRPS | fengycin | BGC0001095 | 80% | |
NRPS, transAT-PKS | mycosubtilin | BGC0001103 | 100% | |||
6 | 41,098 | T3PKS | - | - | - | - |
7 | 21,899 | terpene | - | - | - | - |
8 | 65,396 | NRPS | surfactin | BGC0000433 | 86% | |
9 | 20,807 | terpene | - | - | - | - |
10 | 51,225 | PKS | myxovirescin A1 | BGC0001025 | 13% | |
macrolactin H | BGC0000181 | 40% | ||||
11 | 32,261 | PKS | macrobrevin | BGC0001470 | 26% | |
phormidolide | BGC0001350 | 21% | ||||
12 | 13,175 | NRPS | fengycin | BGC0001095 | 20% | |
plipastatin | BGC0000407 | 38% | ||||
13 | 10,326 | NRPS | plipastatin | BGC0000407 | 23% | |
fengycin | BGC0001095 | 20% | ||||
14 | 7,314 | PKS | bryostatin | BGC0000174 | 100% |
The first four BGCs successively code for bacilycin, subtilosin A, bacillibactin, and bacillaene. These regions show 100% similarity to known BGCs in the Minimum Information about a Biosynthetic Gene Cluster (MIBiG) database (Terlouw et al. 2023). Furthermore, ClusterBlast analysis revealed that the genetic organization of these regions was 100% similar to other
The fifth BGC contains two biosynthetic gene clusters encoding fengycin and mojavencin, respectively, like those of
Moreover, the HGR5 strain also has two NRPS-like BGCs, which, according to the KnownClusterBlast analysis, contain two clusters that have partial similarities to the clusters encoding plipastatin and fengycin from
AntiSMASH analysis revealed that HGR5 contains three BGCs encoding polyketide synthases, with relatively low similarity to known BGCs in the MIBiG database (Table I). ClusterBlast analysis of these regions also showed low similarity to gene clusters present in bacteria. The nucleotide sequences of these three BGCs were used as Blastn queries, and the results showed that they shared 99% similarity and 100% query coverage with regions found in
This analysis revealed the presence of 155 CAZymes within the HGR5 genome sequence, encoding enzymes that target several components of the fungal cell wall i.e., chitin, chitosan, starch, cellulose, xylan, fructan, and pectin. Six classes were identified, including carbohydrate esterases (CE, n = 14), glycoside hydrolases (GH, n = 55), and polysaccharide lyases (PL, n = 6) that are known as plant cell wall degrading enzymes. The three other classes with indirect roles in degrading carbohydrates are auxiliary activity (AA, n = 3), carbohydrate-binding module (CBM, n = 38), and glycosyl-transferases (GT, n = 38). 28.38% of these CAZyme proteins harbor amino-terminal signal peptides in their sequences, indicating their secreted enzymes status via protein export through the cytoplasmic membrane.
Solid-state activity assays have recently been developed to screen microbes that rapidly degrade specific polymers. Clear or colored zones that form around bacterial colonies indicate the production of catalytically active enzymes on the polymer incorporated in the medium. Polycaprolactone (PCL) and PET nanoparticles have been used several methods to identify bacteria with polyesterase activity (Jarrett et al. 1984; Nishida and Tokiwa 1993; Wei et al. 2014). Nevertheless, the production of these nanoparticles is based on the use of toxic or dangerous solvents. Hence, a new protocol was developed using polycaprolactone diol, a water-emulsifiable polyester (Molitor et al. 2020).
The microbial degradation of PCL, which is used as a model substrate for the hydrolysis of polyesters, indicates possible action against the most complex polyesters, including polyethylene terephthalate PET (Danso et al. 2018). Since polyesterases are lipolytic enzymes, they can be screened via substrates such as tributyrin and coconut oil, which are non-specific esterase assays (Molitor et al. 2020). These assays showed that HGR5 was able to degrade tributyrin and coconut oil. In addition, polyesterase activity screening showed that this strain was capable of degrading polycaprolactone diol by secreting one or several lipolytic enzymes (Fig. 5). Carr et al. (2022) examined 51 bacterial strains for their activity against PCLD; only five strains showed positive activity. Otherwise, some studies have reported lipolytic enzyme production from strains of this species. Hence, a 38 kDa lipase from
According to the results, HGR5 can grow in the pH range of 5 to 11, tolerate temperatures up to 55°C, and survive salinities up to 12.5%. It exhibits an optimum temperature of 35°C and an optimum pH of 7. It grows best in a media devoid of salts. Its growth is affected by high temperatures, extreme pH, and high NaCl concentrations (Fig. 6). Our findings are consistent with those of (Wu et al. 2021), who reported that
Meanwhile, three strains of the genus Bacillus, isolated from a hot spring in India, could grow at temperatures of 40, 50, and 60°C, indicating that they can withstand unfavorable conditions (Verma et al. 2018). In contrast, bacteria isolated from hot springs in Algeria could grow at 55°C, with only 5.11 percent of these isolates could grow at 80°C. These isolates exhibited optimal growth at neutral pH and could survive a concentration of 15% NaCl (Benammar et al. 2020). In addition, in a different collection of bacteria isolated from two hydrothermal springs in Algeria, the isolates demonstrated optimal development at temperatures between 50 and 60°C, at a pH between 6.5 and 7.5, and they tolerated up to 10% salt (Gomri et al. 2018).
Based on these results, the hot springs of Hammam Ouled Tebben and Hammam Guergour in Algeria harbor diverse bacterial communities with interesting biotechnological applications, highlighting the importance of exploring the microbial diversity of such ecosystems and their potential for biotechnological applications. Their harsh conditions (i.e., water temperature and pH) may select specific bacteria and genotypes with such interesting properties.