Optimization of newly isolated Bacillus cereus α-amylase production using orange peels and crab shells and application in wastewater treatment

Abstract

A newly isolated amylolytic strain was identified as Bacillus cereus spH1 based on 16S and 16-23S gene sequencing (Accession numbers OP811441.1 and OP819558, respectively), optimization strategies, using one variable at time (OVAT) and Plackett–Burman design, were employed to improve the alpha-amylase (α-amylase) production. Condition inferred revealed that the optimal physical parameters for maximum enzyme production were 30 °C, pH 7.5, and 12 h of incubation, using tryptone, malt extract, orange (Citrus sinensis) peels, crab (Portunus segnis) shells, calcium, and sodium chloride (NaCl) as culture medium. The full factorial design (FFD) model was observed to possess a predicted R² and adjusted R² values of 0.9788 and 0.9862, respectively, and it can effectively predict the response variables (p = 0). Following such efforts, α-amylase activity was increased 141.6-folds, ranging from 0.06 to 8.5 U/mL. The ideal temperature and pH for the crude enzyme activity were 65 °C and 7.5, respectively. The enzyme exhibited significant stability, with residual activity over 90% at 55 °C. The maltose was the only product generated during the starch hydrolysis. Moreover, the Bacillus cereus spH1 strain and its α-amylase were used in the treatment of effluents from the pasta industry. Germination index percentages of 143% and 139% were achieved when using the treated effluent with α-amylase and the strain, respectively. This work proposes the valorization of agro-industrial residues to improve enzyme production and to develop a green and sustainable approach that holds great promise for environmental and economic challenges.

Introduction

Enzymes are the backbone of bioprocessing sectors due to their ability to operate under gentle conditions, exhibit high specificity, and deliver outstanding productivity (Londoño-Hernandez et al. 2020). Given their sustainability and eco-friendly nature, scientists search for novel enzymes to supplant chemical processes with bioprocessing (Sarmiento et al. 2015). Therefore, it is not surprising to witness a boom in the global enzyme market, worth $10.69 billion in 2020, with annual growth of 6.5% from 2021 to 2028 (Industrial enzymes market size, share, growth opportunities 2023).

The main enzymes employed within the industries are α-amylases, lipases, and proteases (Esparza et al. 2020). Furthermore, α-amylases constituted 65% of the total global enzyme market in 2017 (Ali et al. 2017). They are starch hydrolyzing enzymes, randomly breaking the internal α-1–4 glycosidic linkage in polysaccharides while maintaining α- anomeric conformation in the generated products (Amin et al. 2021). In fact, the α-amylases have a large spectrum of biotechnological applications, including food, starch processing, paper, detergent, textile, pharmacy industries, and environmental applications (Tiwari et al. 2015). Although they can be extracted from plants, humans, animals, and microorganisms, undoubtedly microbial α-amylase is preferred over other types of α-amylases thanks to its biochemical variety, stability, higher production rate, and easy accessibility of a broad range of microbial strains. Moreover, microbial α-amylases can be easily subject to genetic modifications, making them highly suitable for specific applications (Yassin et al. 2021).

Within microbial α-amylases producers, Bacillus genus stands out as the predominant group because of its rapid growth, short fermentation cycles, and capacity to release enzymes into the extracellular environment (Farias et al. 2021). The commercial production of α-amylase by Bacillus spp typically occurs through submerged fermentation media hinges on the easier control of the environmental factors (temperature, pH, and aeration) (Deljou and Arezi 2016).

Indeed, the production of amylolytic enzymes using syntenic media is costly. However, according to the current environmental legislation, any waste can be deemed as raw material if there is a method for its valorization (Embaby et al. 2014). Relying on this, agro-industrial biomass has gained recognition as a valuable source material for producing a wide range of industrially important enzymes (Awasthi et al. 2021). One step taken was the incorporation of readily available agricultural residues as substrates to reduce amylase production cost, including moong husk (Bhatt et al. 2020), potato peels (Mojumdar and Deka 2019), and grape fruit peels (Iram et al. 2021).

On one hand, citrus fruits are one of the most produced on earth, with 130 million tons each year and contain mostly 57% oranges, 24% mandarins, 11% lemons, and 9% grapefruit and others (Uygut and Tanyildizi 2016). In Tunisia, for example, 135,000 tons of oranges are produced, as reported by the statistical database of the Food and Agriculture Organization of the United Nations (FAOSTAT-July, 2021) (Food and Agricultural Organization Statistics 2021). This production generates roughly 50–60% of residues from the initial mass of orange with a substantial portion of organic material (95% of the total solids), high water content (80–90%), and low pH level (3–4). As such, these agro wastes could potentially cause serious environmental issues (Ousaadi et al. 2021). These residues contain high amounts of carbohydrates (30–70%), providing a natural carbon and substrate storage tool for extracellular α-amylase production with an economical cost (Uygut and Tanyildizi 2016).

On the other hand, as per the 2016 report from the Food and Agriculture Organization of the United Nations (FAO, 2016), global seafood production in 2014 amounted to 167.2 million tons, with 87.5% being utilized for direct human consumption (Ousaadi et al. 2021), with approximately 50–80% w/w of non-edible wastes (viscera, head, skin, scales, bones, etc.) squandered (Suresh et al. 2017). In Tunisia, crustaceans are the second described taxon of non-native species within the Mediterranean Sea (24%), with the blue crab, Portunus segnis (Forskål, 1775) being the most consumed by Tunisian consumers (1450 tons January–May 2018) (Bejaoui et al. 2017; Oueslati et al. 2019). Therefore, these residues can also aggravate the pollution problem. Hence, it is crucial to consider the potential of degrading recalcitrant biomass by microorganisms (Li et al. 2017). Interestingly, these wastes can serve as micronutrients for enzyme production as they comprise chitin, minerals, and amino acids (Suresh et al. 2017; Li et al. 2017).

Along with the fact the agricultural sector is the primary consumer of water in Tunisia (Chouchane et al. 2015), we opted to save the country’s acute water scarcity, by treating effluent from the pasta industry via the bioremediation method. The choice of the pasta industry is explained by the fact that this kind of agro-industry generates wastewater with notable concentrations of organic compounds such as starch, a major environmental issue (Tiwari et al. 2015). Bioremediation is one of the most promising ways of dealing with toxic waste cleansing. It is an environmental technique that breaks down or detoxifies toxins and turns them into harmless by-products using microorganisms and/or their metabolites, such as enzymes (Okino-Delgado et al. 2019). Thanks to this biological effluent treatment, the sustainability of water resources will be ensured.

More recently, researchers have focused on optimizing enzyme production using statistical methods to achieve higher economic yields for biotechnology applications (Lakshmi et al. 2020; Ojha et al. 2020). In this study, the one variable at a time approach (OVAT) was used to determine the range for the selected independent variables based on the literature (Deljou and Arezi 2016). Whereas the Plackett–Burman design (PB design) was handled for screening the influential factors in a few experimental runs, followed by a full factorial design (FFD) to study the effect of factors and their interactions simultaneously. This feature improves the design’s efficiency in dealing with many variables (Keskin Gündoğdu et al. 2014).

This research aims to isolate a new bacterium from Tunisian extreme biotopes and optimize its amylase production on an eco-friendly fermentation medium using orange peels and blue crab wastes. Moreover, the capacity of our strain and its α-amylase activity was used for treating pasta industry effluent to ensure the long-term viability and sustainability of Tunisian water resources.

Material and methods

Sample collection and isolation

The sample was collected from a hot water source at Hamma Lake-Gabes governorate, Tunisia. 1 g of the sample was suspended in 9 mL of sterile distilled water. Serial dilutions (10–2-10–8) were then performed by transferring 1 mL of each tube to a new 9 mL sterile distilled water tube. Two hundred and fifty µL from each dilution were coated on a starch agar plate containing 1% soluble starch, 0.5% yeast extract, 1% peptone, and 2% agar at pH 7.0. After that, the plates were incubated at 37 °C for 24 h. Colonies exhibiting halo-forming zones in the presence of iodine solution (85% distilled water, 10% Potassium iodide, and 5% Iodine crystals) demonstrated their capacity to produce α-amylase (Abd-Elhalim et al. 2023).

Molecular identification of the bacterial isolate

The total genomic DNA was extracted according to the manufacturer’s guidelines (Thermo Scientific kit). The 16S rDNA PCR reaction was carried out in 20 µl reaction and amplified via polymerase chain reaction containing 50 ng of the DNA, 20 µmol of each primer Fwd1 (5′AGACTTTGATCCTGGCTCAG3′) and Rev1 (3′AAGGAGGTGATCCAAGCC5′), 0.2 mM dNTPs and 1U Taq polymerase (Dreamtaq) in 1 × PCR buffer. Meanwhile, the ITS region (16S-23S rDNA) was performed with the same reaction composition using the primers Fwd2.

(5′GGCTTGGATCACCTCCTT3′) and Rev2 (3′ACTTAGATGTTTCAGTTC5′). The amplified PCR products were purified with a PCR purification kit (Pure Link quick gel extraction PCR purification Cambo Kit) and ligated into the pGEM-T easy cloning vector according to the manufacturer’s guidelines (pGEM-T clone kit, Promega). The recombinant vectors were transformed into E.coli DH5α competent cells (Bouaziz et al. 2011), and verified by PCR and by restriction analysis.

The 16S and 16S-23S rDNA were sequenced and sequences were subjected to the BLAST tool (http://www.ncbi.nlm.nih.gov/blast) to retrieve homologous sequences from the database. Multiple alignments were obtained using the CLUSTALW tool (https://www.genome.jp/toolsbin/clustalw), and the phylogenetic tree was constructed using the neighbor-joining method in MEGA11.

Nucleotide sequence access number

The nucleotide sequences of the amplified 16S and 16S-23S rDNA were deposited in the GenBank database under Access Numbers OP811441.1 and OP819558, respectively.

Growth pattern in submerged fermentation

One bacterial colony was precultured in 250 mL culture flasks with 50 mL of Luria–Bertani broth (10 g/L peptone, 5 g/L sodium chloride, 5 g/L yeast extract, pH 7.0) for 15 h at 37 °C and 150 rpm. The overnight preculture was inoculated in 250 mL culture flasks with the same culture medium composition added with starch 10 g/L at initial OD at 600 nm of 0.2 (23.10⁵ CFU/mL) and pH 7.5. After incubation for 24 h, the culture was centrifuged for 20 min at 4 °C and 8000 rpm to remove cells. The obtained supernatant was used for α-amylase activity measurement.

Alpha-amylase activity assay

α-Amylase activity was assessed using the 3,5-dinitrosalicylic acid (DNS) method as described previously (Salem et al. 2020). The reaction mixture, 0.5 mL of 1% soluble starch (prepared in 0.1 M phosphate buffer) and 0.5 mL of crude enzyme, was incubated at 65 °C and pH 7.5 for 15 min. After incubation, 1.5 mL of DNS was added to determine the amount of the released reducing sugars. One unit of α-amylase activity was defined as the quantity of enzyme that extricated one µmol of glucose equivalent per minute under the given conditions. The enzyme assays were handled in triplicates, and the α-amylase activity was measured as follows:

$$\text{Alpha-amylase activity}\left(\text{U}/\text{mL}/\text{min}\right)=\frac{K\times OD}{180}\times {10}^3\times {10}^{-6}\times \frac{1}{t}\times \frac{1}{\mathit{Ve}}$$ 1

In which OD = optical density, t = incubation time of the mixture reaction, Ve = volume of crude enzymatic solution, 180 = molecular weight of glucose, and K = conversion coefficient of the DNS (K = 1.8).

Effect of temperature and pH on α-amylase activity and stability

To determine the optimal temperature of the α-amylase activity, the enzymatic activity was measured at pH 7.5 and different temperatures extending from 40 to 80 °C. Regarding thermostability, the α-amylase was pre-incubated at temperatures ranging from 40 to 75 °C for 1 h. Residual activity was then measured at optimal conditions described previously. The activity of the non-treated enzyme was considered 100%.

The optimal pH for α-amylase activity was measured at different pH ranging from 4 to 10.5. For the pH stability, the α-amylase was incubated for 1 h at different pH (4–10.5) using various buffer solutions: 0.1 M acetate buffer (pH 4–6), 0.1 M phosphate buffer (6–8), 0.1 M Tris/HCl (8–9) and 0.1 M glycine/NaOH buffer (9–10.5). The residual activity was then measured under standard conditions (see Alpha-Amylase activity assay section). The non-treated enzyme activity was taken as 100% (Salem et al. 2021).

High-performance liquid chromatography analysis HPLC

To analyze the final hydrolysis products, the enzyme was incubated with 1% (w/v) starch in a 0.1 M phosphate buffer pH 7.5 at 37 °C for three different durations (3 h, 6 h, and 24 h). Later on, all the samples were boiled at 100 °C for 5 min to stop the reaction. Samples were collected for HPLC analysis using Aminex HPX-87–71 column. The hydrolysis products were eluted with water at a 0.5 mL/min flow and detected using a refractive index detector (Waters RI 401). Glucose (5 mg/mL), maltose (5 mg/mL), and starch (1%) were used as standard solutions (Sigma-Aldrich).

Wastes’ collection, preparation, and chemical composition

Citrus sinensis orange residues were gathered at a vegetable and fruit market in Sfax, Tunisia. These residues were washed with water for three times, sliced, left to air dry for 7–8 days until fully dehydrated, and subsequently ground into a powder. These residues possess a rich composition containing 16.9% soluble sugars, 3.75% starch, fiber (9.21% cellulose, 10.5% hemicelluloses, 0.84% lignin, and 42.5% pectin), 3.5% ashes, 1.95% of fats, 6.5% of proteins, and 4.35% of other compounds. This composition may provide a natural carbon and substrate storage tool for extracellular α-amylase production with an economical cost (Uygut and Tanyildizi 2016).

Portunus segnis shells are collected locally, washed with water twice, dried for 10–12 days until thoroughly desiccated, and subsequently ground with a grinder until a fine and homogeneous powder was obtained.

Experimental design for media optimization of Bacillus cereus α-amylase production

The OVAT was used to study the effect of pH and temperature and to select the best nitrogen sources, agricultural residues, and marine wastes on Bacillus cereus α-amylase production.

Different factors affecting bacterial growth and amylase production were tested. In this context, culture media were prepared at various pH (6, 6.5, 7, 7.5, and 8) and temperatures (25, 30, 37, and, 40 °C); and in the presence of 1% of different simple or complex nitrogen sources (Tryptone, malt extract, beef extract, yeast extract, urea, casein peptone, soya peptone), agro residues (1%) and/or 0.5% marine wastes (shells waste of shrimps and crabs). All experiments were conducted using 250 mL Erlenmeyer flasks containing 50 mL of culture medium including (g/L) (10 peptone, 5 yeast extract, 5 NaCl, and 10 soluble starch) inoculated with the 15-h-old preculture as described previously. For the optimal pH and temperature, samples for α-amylase activity were taken every 3 h for 24 h. For carbon source selection, cultures were conducted at 30 °C, pH 7.5 for 12 h.

The optimal outcomes achieved through the OVAT approach serve as input for applying the Plackett–Burman design. A two-factorial plan Plackett–Burman design was chosen to screen the significant factors required for high enzyme production. Each row represents a trial, and each column represents independent variables. In the current study, a total of eight independent variables: tryptone (X₁), malt extract (X₂), orange peels (X₃), NaCl (X₄), blue crab shells (X₅), calcium (X₆), shaking speeds (X₇), and inoculum size (X₈) were screened at two extensive intervals + 1 (high level) and -1 (low level) (Table 1). The fermentation medium was formulated with the experimental design shown in Table 2. Fifteen trials and 12 runs, including 3 center points (level 0), were conducted to gage the linear and curvature effects of the variables (Table 2). For mathematical modeling, the first-order polynomial model was expressed as:

$$y1={\beta }_0+\sum _{t=1}^k\left({\beta }_t{x}_t\right)$$ 2

where y was the predicted response (amylase activity), β₀: model intercept, β_t: linear coefficient, and x_t: independent variable level.

Table 1.

The runs matrix of Plackett–Burman design

Variable	Unit	Symbol code	Levels
			−1	0	1
Tryptone	g/L	X1	2.5	3.75	5
Malt Extract	g/L	X2	1	1.75	2.5
Orange peels	g/L	X3	5	7.5	10
NaCl	g/L	X4	0	1.25	2.5
Crab shells	g/L	X5	0	1.25	2.5
Calcium	g/L	X6	0	0.5	1
Shaking speeds	rpm	X7	150	200	250
Inoculum size	OD	X8	0.2	0.4	0.6

Table 2.

Plackett–Burman experimental design with the observed α-amylase activity

Run	X1	X2	X3	X4	X5	X6	X7	X8	Response AA* (U/mL)
									Trial	Duplicate	Triplicate
1	1	− 1	1	− 1	− 1	− 1	1	1	5.8	5.770	5.820
2	1	1	− 1	1	− 1	− 1	− 1	1	2.340	2.360	2.370
3	− 1	1	1	− 1	1	− 1	− 1	− 1	4.510	4.430	4.480
4	1	− 1	1	1	− 1	1	− 1	− 1	2.462	2.379	2.420
5	1	1	− 1	1	1	− 1	1	− 1	2.319	2.314	2.317
6	1	1	1	− 1	1	1	− 1	1	7.750	7.700	7.679
7	− 1	1	1	1	− 1	1	1	− 1	2.988	3.100	2.950
8	− 1	− 1	1	1	1	− 1	1	1	4.682	4.570	4.680
9	− 1	− 1	− 1	1	1	1	− 1	1	3.250	3.190	3.230
10	1	− 1	− 1	− 1	1	1	1	− 1	2.610	2.534	2.565
11	− 1	1	− 1	− 1	− 1	1	1	1	2.893	2.956	3.000
12	− 1	− 1	− 1	− 1	− 1	− 1	− 1	− 1	2.943	2.882	2.950
13	0	0	0	0	0	0	0	0		3.600
14	0	0	0	0	0	0	0	0		3.660
15	0	0	0	0	0	0	0	0		3.650

^*AA: amylase activity

Then, 2ⁿ factorial design was used as it requires fewer experiments for outsize factors. Thus, materials and time significantly decrease (Phanphet and Bangphan 2021). To realize the best overall optimization of the amylase production, 4 variables (screened by the PBD), crab shells (A), orange peels (B), NaCl (C), and inoculum size (D), were varied at 2 levels, as given in (Table 3) to investigate their effects on α-amylase production. In our study, the 2⁴ FFD led to 16 experiments (Table 4). The FFD empowered the variation of the following mathematical model:

$$y_2=a_0+a_{1}A+a_{2}B+a_{3}C+a_{4}D+a_{5}AB+a_{6}AC+a_{7}AD+a_{8}BC+a_{9}BD+a_{10}CD+a_{11}ABC+a_{12}ABD+a_{13}ACD+a_{14}BCD+a_{15}ABCD$$ 3

y₂: predicted response / a₁, a₂, a₃ and a_4: linear coefficient / a₅, a₆, a₇, a₈, a_9, a₁₀: 2-way interactions / a₁₁, a₁₂, a₁₃, a₁₄: 3-way interactions and a₁₅: 4-wayinteractions.

Table 3.

Investigated factors used in full factorial design and their levels

Factor	Crab shells	Orange peels	Inoculum size	Shaking speeds
Code	A	B	C	D
Unit	g/L	g/L	OD	rpm
Upper level ( +)	2.5	10	0.6	250
Lower level (−)	0	5	0.2	150

Table 4.

Design matrix and the results of 2⁴ full factorial design

Run	Factors				Activity response (U/ml)			Average	Standard deviation
	Crab shells	Orange peels	NaCl	Inoculum size	Trial	Duplicate	Triplicate
1	− 1	− 1	− 1	− 1	3.686	3.250	3.400	3.440	0.22
2	1	− 1	− 1	− 1	2.460	2.410	2.027	2.290	0.23
3	− 1	1	− 1	− 1	3.656	3.426	3.380	3.480	0.14
4	1	1	− 1	− 1	8.500	8.650	8.540	8.560	0.07
5	− 1	− 1	1	− 1	2.770	2.512	2.736	2.670	0.14
6	1	− 1	1	− 1	5.700	5.200	5.800	5.560	0.32
7	− 1	1	1	− 1	5.678	5.615	5.586	5.620	0.04
8	1	1	1	− 1	3.664	3.377	3.652	3.560	0.16
9	− 1	− 1	− 1	1	2.934	3.000	3.100	3.010	0.08
10	1	− 1	− 1	1	3.710	3.705	3.710	3.700	0.00
11	− 1	1	− 1	1	4.053	4.080	3.800	3.970	0.15
12	1	1	− 1	1	4.066	4.078	4.243	4.120	0.09
13	− 1	− 1	1	1	3.870	3.757	3.940	3.850	0.09
14	1	− 1	1	1	3.432	3.000	3.370	3.260	0.23
15	− 1	1	1	1	3.356	3.160	3.100	3.200	0.13
16	1	1	1	1	2.174	2.600	2.258	2.340	0.22

Statistical analysis

The experimental designs, the data analysis, the different statistical tests (p < 0.05), the figure representations, and the optimization were carried out using Minitab statistical package software version 16 (Minitab Inc., State College, USA) (Kini et al. 2019). The statistical analysis outcomes data was performed by analysis of variance (ANOVA) at a 95% significance level. All experiments were performed in triplicate and the results are presented as means ± SE.

Application of Bacillus cereus α-amylase in wastewater treatment

The strain and the produced α-amylase were evaluated for their potential to degrade starch present in an industrial effluent which is the water used for cleaning the dough preparation machines. The effluent was collected from the Tunisian pasta industry (RANDA Industry).

Two conditions were used for the wastewater treatment. The first one is incubating 0.5 mL of the 15-h-old preculture of the strain in 10 mL of the effluent for 12 h at 30 °C. The second condition is incubating 5 mL of the enzymatic extract (40 units) with 5 mL of the wastewater at 65 °C for 15 min. Tap water was used as the positive control.

Seed germination bioassay was used to assess the level of phytotoxicity of the treated and non-treated water. The germination indexes (GI) were estimated for radish species (Raphanus raphanistrum subsp. sativus). These seeds were washed the first time with sterile water for 30 min under agitation, then with 4% bleach for 10 min, and a second wash with sterile water for 10 min to avoid any source of contamination. Ten seeds were placed on Petri dishes lined with filter paper submerged with 10 mL of each treated water. Then the Petri dishes were covered and kept at room temperature for one week. A germination index percentage was determined by counting the total germinating seeds and the average sum of seed root elongation in a sample and compared with the control.

The germination index (GI) was expressed using the following formula:

$$\text{GI}=\frac{\text{Number of germinated seeds in a sample}}{\text{number of germinated seeds in control}}\times \frac{\text{Average of root lengths in sample}}{\text{Average of root lengths in control}}\times 100$$ 4

A seed's root length of 5 mm or more was considered as germinated. A value of 0 was assigned to root lengths lower than 5 mm. The presented results are the average of the root length of all germinated seeds (Soares et al. 2013).

Results and discussion

Screening and molecular identification of the amylolytic isolate

Forty five bacterial strains were isolated from a natural hot water source at Hamma Lake-Gabes, Tunisia. The retained strain exhibited the highest halo-forming zones in the presence of iodine solution. The primers mentioned above were used to amplify the 16S and 16S-23S rDNA from the bacterial isolate, yielding amplicons of expected sizes of 1500 bp and 400 pb, respectively. After transformation into E. coli DH5α cells, the recombinant plasmid was subsequently extracted from one positive colony and sequenced. Sequences of 1548 pb and 354 pb corresponding to 16S and 16S-23S rDNA were obtained (Access Number OP811441.1 and OP819558, respectively). The rDNA sequences were compared to the previously reported Bacillus sp. sequences and found to have 100% identity with Bacillus cereus strains. The phylogeny study illustrated that the strain could be assigned as Bacillus cereus spH1 (Fig. 1).

Fig. 1.

Phylogenetic tree inferred from the 16S rRNA gene sequence analysis of the Bacillus cereus spH1 and the closely related species. The tree was created using the Neighbor-Joining method using the MEGA software version 11. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (500 replicates) are shown next to the branches. The evolutionary distances were computed using the Maximum Composite Likelihood method and are in the units of the number of base substitutions per site. This analysis involved five nucleotide sequences

Effect of temperature on activity and stability

The Effect of temperature on enzyme activity is shown in Fig. 2a. As it can be seen, the maximal α–amylase activity was measured at 65 °C. Elevated temperatures beyond the ideal range may cause changes in the active site's configuration, resulting in a decrease or cessation of enzyme activity (Sharif et al. 2023). At 70 °C, 75 °C, and 80 °C, 68.93%, 46.31%, and 44.0% of α-amylase activity was obtained, respectively. A previous work described the α-amylase from Geobacillus sp. D413, which presents an optimal activity at 65 °C (Caliskan Ozdemir et al. 2016). Noticeably, the optimal temperature of our α–amylase is higher than previous findings for α-amylase from Bacillus Licheniformis (Iram et al. 2021) or Bacillus cereus AS2 (Afrisham et al. 2016), which present an optimal activity at 40 °C or 50 °C, respectively. Regarding the thermostability (Fig. 2b), our results show that the stability of the enzyme gradually declined at 60 °C, 65 °C, and 70 °C, corresponding to 65.15%, 51.31%, and 39.82% residual activity, respectively. Subsequently, it sharply dropped at 75 °C, due certainly to the protein denaturation. The α-amylase from Bacillus licheniformis AT70 demonstrated good stability at 55 °C after 1 h of preincubation, which is similar to our outcomes (90.54%) (Afrisham et al. 2016). In comparison, certain α-amylases from Bacillus species, involving Bacillus lichenformis B4-423 and Bacillus subtilis W3SFR5 were stable in the temperature range of 20–70 °C and 60 °C respectively, after 1 h of heating (Wu et al. 2018; Niyomukiza et al. 2022).

Fig. 2.

Characterization of alpha-amylase. a The impact of temperature on amylase activity. The activity was measured using 0.5 ml of 1% substrate at temperatures 40–80 °C for 10 min. b The thermostability of the current α-amylase was measured after preincubation of the enzyme without substrate for 1 h at temperatures ranging from 40 to 75 °C. The unheated enzyme was the control, representing 100% of the activity. c Effect of pH using sodium acetate (pH 4–6), potassium phosphate (pH 6–8), Tris/HCl (8–9), and glycine/NaOH (9–10.5) buffers on the α-amylase activity. d pH stability in different buffers was obtained by determination of α-amylase activity under optimal conditions (pH 7.5, 65 °C, 10 min) after preincubation for 1 h at room temperature without substrate. The untreated enzyme was considered 100%. Three parallel replicates are averaged out for each value

Effect of pH on activity and stability

Alterations in pH can disrupt the ionic interactions upholding the tertiary structure of the protein, ultimately causing the loss of its functionality (Uygut and Tanyildizi 2016). Therefore, each enzyme has a specific pH for maximum activity. Figure 2c illustrates the pH activity profile within the range of 5.0 to 10.5. It was found that the α-amylase activity reaches its maximum at pH 7.5. Although, at pH extremes on either side, activity dropped substantially. Distinct outcomes were observed for Bacillus subtilis strain W3SFR5, Bacillus licheniformis B4-423, or Bacillus subtilis MTCC 121, which present a maximal α-amylase activity at pH 9, pH 5, or pH 7, respectively (Wu et al. 2018; Niyomukiza et al. 2022; Raul et al. 2014). The impact of pH on the stability of Bacillus cereus spH1 α-amylase was explored by incubating the enzyme at various pH levels, ranging from 6.0 to 9.0 for one hour. Subsequently, the remaining enzyme activities were determined using the assay reaction mixture previously described. The enzyme showed optimum stability at pH 7 and retained 87.05% of its initial activity at pH 7.5 (Fig. 2d). More than half of the activity was obtained at pH 6 and pH 6.5. In contrast, there is a drastic increase in residual activity for pH above 8. Whereas previous research indicates that the α-amylase of Bacillus pacificus and Bacillus atrophaeus NRC1 were stable in pH ranges from 6 to 11 and from 4.5 to 8, respectively (Alonazi et al. 2020; Abd-Elaziz et al. 2020).

High-performance liquid chromatography analysis (HPLC)

Α-amylase is an endo-hydrolase that acts randomly inside starch chains to produce a combination of malto-oligosaccharides, maltose, and glucose. Our enzyme was incubated with 1% (w/v) starch in a 0.1 M phosphate buffer pH 7.5 at 65 °C for 3 h (Fig. 3a), 6 h (Fig. 3b), or 24 h of incubation (Fig. 3c). The hydrolysis products were analyzed using HPLC, as indicated in the Material and Methods section. Our results show that after different incubation times, the only final hydrolysis product obtained was found to be maltose (Fig. 3d–f). The end products of starch hydrolysis by anoxybacillus rupiensis TS‐4 amylase were maltose and glucose (Kikani et al. 2019). Moreover, in another study, Shaukat (2021) reported that the end products of starch hydrolysis by α-amylases from different bacillus species were glucose, maltose, maltotriose, and maltotetraose (Shaukat 2022).

Fig. 3.

Hydrolysis products of the Bacillus cereus spH1 amylase using soluble starch after various incubation times. The standards used are 1% starch solution (a), glucose solution 5 mg/mL (b), and maltose solution 5 mg/mL (c). Three solutions containing (500 µl of starch 1% + 500 µl of enzymatic crude) were incubated at 65 °C for 3 time intervals: 3 h (d), 6 h (e), and 24 h (f)

Optimization of Bacillus cereus α-amylase production via OVAT, PBD, and FFD

In this study, Bacillus cereus spH1 was inoculated in production media at various pH (6 to 8), while the rest of the growth parameters were held constant. Findings indicated that Bacillus cereus spH1 exhibited more efficient α-amylase production and growth at pH 7.5. Likewise, the optimal enzyme production from Bacillus velezensis D1 was observed at pH 7.5 (Hu et al. 2022). Conversely, an alkaline pH led to a significant reduction in α-amylase secretion. This result contrasts with previous findings (Niyomukiza et al. 2022; Anupama and Jayaraman 2011).

During the process of the enzyme production process, it is critical to maintain two specific temperatures. The first is for promoting microbial growth, and the second is for achieving the highest level of enzyme production (Sundarram and Murthy 2014). To determine the optimal temperature for both strain growth and α-amylase production, the production medium (pH 7.5) was incubated at 25–40 °C. Throughout fermentation, Bacillus cereus spH1 grew and produced α-amylase at 30 °C, which aligns with previous findings (Duque et al. 2016). Enzyme production decreases as the temperature rises. This decline could be attributed to substrate moisture loss, which has a negative impact on microbial metabolic processes, ultimately leading to decreased growth and enzyme production (Sundarram and Murthy 2014). In the Bacillus family, such as Bacillus megaterium (Elyasi Far et al. 2020) and Bacillus cereus amy 3 (Saha and Mazumdar 2019), 35 °C and 37 °C were recorded as the best incubation temperature for α-amylase production. A positive correlation between growth and enzyme production was reported in previous studies (Farias et al. 2021; Sharif et al. 2023).

To determine the time course of α-amylase production by Bacillus cereus spH1 strain, samples from bacterial culture (pH 7.5, 30 °C) were withdrawn at 3-h intervals for a while of 24 h. The data in Fig. 4a revealed that α-amylase production gradually increases during the first 9 h of incubation. Maximum enzyme activity was reached after 12 h of incubation. After 24 h of incubation, the enzyme activity dropped by 59%. This reduction could be attributed to many factors, such as bacterial cell death, the formation of toxins, and the presence of inhibitors. Nevertheless, some Bacillus cereus strains, namely Bacillus cereus AS2 and Bacillus cereus amy3, reach a maximum yield of α-amylase production after 24 h and 48 h, respectively (Rehman et al. 2019; Saha and Mazumdar 2019). Our outcomes are advantageous since the brief incubation period can reduce price and energy, meeting the industry requirement.

Fig. 4.

Effect of fermentation conditions and medium composition on the production of Bacillus cereus spH1. a Production of α-amylase by Bacillus cereus spH1 in the starch medium. Dash with square amylase activity at 550 nm (gray), dash with triangle represents the bacterial biomass (black). b Effect of nitrogen source (g/L). The activity of the control is measured after the growth of the strain without any nitrogen source. c Effect of fruit peels (g/L). The activity of the control is measured after the growth of the strain using 1% of starch as a unique carbon source. d Effect of marine (shrimp and blue crab) shells (g/L); the activity of the control is measured after the growth of the strain without adding marine shells. Data is the mean ± SD of three experiments

The α-amylase production by B. cereus spH1 was examined also in the presence of various nitrogen sources; each added individually to the basal medium at a concentration of 1%. As shown in Fig. 4b, among the various sources tested, (tryptone + malt extract) causes maximum α-amylase production (2.95 U/mL) followed by tryptone + yeast extract (2.69 U/mL). Whereas, beef extract (1.3 U/mL), and soya peptone (1.057 U/mL) exhibited less enzyme production than other sources. It can be concluded that a combination of nitrogen sources is more effective in achieving the highest α-amylase activity. This observation aligns with other findings (Nusrat and Rahman 2007), which demonstrated that using a combination of nitrogen sources was the most suitable approach for enhancing extracellular α-amylase production by Bacillus isolates. Many studies affirmed that organic nitrogen sources promote the production of α-amylase (Ojha et al. 2020; Rehman et al. 2019). In contrast, inorganic origins such as sodium nitrate and ammonium nitrate enhance enzyme production for Bacillus cereus amy 3 and Bacillus lichenformis (Lakshmi et al. 2020; Saha and Mazumdar 2019).

Historically, the food system has been viewed as an environmental hazard. According to available data, the growing industrialization of the agricultural sector has resulted in increased waste, posing an environmental challenge (Tripathi et al. 2019). Amylase production by B. cereus spH1 was carried out in the presence of the most available fruit peels in the Tunisian market, [1.0% (w/v) each] as primary carbon and substrate sources in α-amylase production medium at pH = 7.5, 30 °C for 12 h. Figure 4c shows a remarkable difference in α-amylase production using different residues. This difference might be correlated to the structure of the substrate and its particular composition (Bhatia et al. 2020). Maximum activity (3.42 U/mL) was recorded with orange peels compared to the other peels. Our findings agree with some previous studies (Uygut and Tanyildizi 2016; Ousaadi et al. 2021). The highest activity observed may be attributed to the composition of orange peels, which contain essential elements conducive to bacterial growth and enzymatic production, as highlighted previously (Venugopal 2021). Therefore, in this study, the orange peels serve as promoting carbon and substrate sources.

Furthermore, this discovery contributes to reducing environmental pollution associated with producing orange juice. It also significantly lowers the cost of the α-amylase production medium. Several authors have introduced agro wastes, such as moong husk, mosambi peels, or pineapple and lotus stem, in the culture media to improve the α-amylase production from Bacillus velezensis KB2216 (Bhatt et al. 2020), Bacillus cereus amy3 (Saha and Mazumdar 2019) or Bacillus sp Q-164 (Lakshmi et al. 2020), respectively.

Nevertheless, the seafood industry generates large amounts of solid waste as a source of environmental hazards. Simultaneously, the various wastes produced during seafood processing are high in proteins, collagen, lipids, carotenoids, polysaccharides, and minerals (Rajagopalan and Krishnan 2008). Thus, to minimize the disposal of seafood by-products, we also attempt to valorize crab and shrimp shells owing to their richness in proteins, minerals, vitamins, and amino acids (Suresh et al. 2017), which may improve the α-amylase production by Bacillus cereus spH1. As shown in Fig. 4d, maximal α-amylase production (4.5 U/mL) was obtained using crab wastes compared to shrimp shells (3.91 U/mL) since it is very rich in vitamins and amino acids (aspartate, glutamate, serine, asparagine, glutamine, histidine, glycine, threonine, alanine, proline). Hence, there is no need to use synthetic amino acids in the production medium. According to (Suresh et al. 2017), 60–70% of the original crab mass (w/w) are wastes.

Using these residues in fermentation media represents an alternative to convert these shells into valuable products and lessen the environmental pollution caused by these waste materials. Many studies have focused on improving enzyme production caused by including amino acids in the culture medium. For example, tyrosine and thiamine were selected to stimulate α-amylase production by Bacillus cereus amy3 (Saha and Mazumdar 2019) and Bacillus KCC103 (Plackett and Burman 1946). It is worth noting that blue crab wastes can be used as a nitrogen source or as an additional nitrogen supplement, aiming to reduce the need for commercialized nitrogen sources whenever possible. The Plackett–Burman design is used to select the most influencing factors on the α-amylase production by B. cereus spH1 (Mouna Imen and Mahmoud 2015).

Too identify the most effective medium components for enzyme production, eight independent factors were investigated at a high level (+) and low level (−), as illustrated by Table 1. Twelve trials were carried out to assess the effects of these variables on α-amylase yield. Table 2 showed an improvement in α-amylase production from 2.19 to 7.7 U/mL. According to ANOVA analysis (Table 5), tryptone (X₁), malt extract (X₂), calcium (X₆), and shaking speeds (X₇) did not show a substantial impact on enzyme production. Whereas the production of α-amylase by Bacillus cereus spH1 was significantly affected by orange peels (X₃) (p = 0, F = 105.53) followed by NaCl (X₄) (p = 0, F = 54.9), crab shells (X₅) (p = 0, F = 23.05), and inoculum size (X₈) (p = 0, F = 61.76).

Table 5.

Statistical analysis of the model (ANOVA) from the Plackett–Burman design

Source	DF	Seq SS	Adj SS	MS	F	P	Effect	Coef
Main effects	8	81.995	81.995	10.2494	31.52	0		3.6998
X1	1	0.9425	0.9425	0.9425	2.9	0.099	0.3236	0.1618
X2	1	0.3842	0.3842	0.3842	1.18	0.286	0.2066	0.1033
X3	1	34.3142	34.3142	34.3142	105.53	0	1.9526	0.9763
X4	1	17.852	17.852	17.852	54.9	0	− 1.4084	− 0.7042
X5	1	7.4957	7.4957	7.4957	23.05	0	0.9126	0.4563
X6	1	0.0983	0.0983	0.0983	0.3	0.587	− 0.1045	− 0.0522
X7	1	0.8272	0.8272	0.8272	2.54	0.122	− 0.3032	− 0.1516
X8	1	0.011	0.011	20.0809	61.76	0	1.4937	0.7469
Residual error	29	9.4296	9.4296	0.3252

Based on the Pareto chart, factors were categorized according to their effects (Fig. 5a). It can be observed that orange peels were the most critical factor for α-amylase production by B. cereus spH1, which is the same important factor for Bacillus amyloliquefaciens (Uygut and Tanyildizi 2016). Nonetheless, the findings in this study contradict other findings describing that orange peels have negligible effects on enzymatic production (Plackett and Burman 1946).

Fig. 5.

Classification of factors according to their effects. a Pareto chart of the standardized effects of α-amylase activity of PBD, b the main effects plot for the significant factors

In addition, the size of the inoculum was discovered to be an essential factor for α-amylase production, which disagrees with previous findings, which found that the inoculum size did not affect the α-amylase production by Streptomyces sp. (Rivas et al. 2008).

Some salts, such as NaCl and CaCl_2, are required since they can boost α-amylase production by enhancing microorganism growth rates. The culture medium of the strain supplemented by 2.5 g L⁻¹ NaCl decreased α-amylase production. Simultaneously, adding calcium to the culture medium did not affect the α-amylase yield. Therefore, the Na and Ca present in orange peels are sufficient, and this strain was able to satisfy its salt requirements to produce the amylase. This hypothesis could be valid based on other published work (Ahmed et al. 2017), which explained that orange peels that contain Ca (5.457 mg kg⁻¹) and Na (506 mg kg⁻¹) are recommended for use in fermentation media.

Although, the addition of tryptone and malt extract shows no effect on enzyme production. Subsequently, the nitrogen requirement for enhancing this α-amylase was provided by the nature of orange peels and/or the blue crab shells. As well as the rich composition of the crab shells explains the significance of this waste in improving α-amylase synthesis by our strain. These outcomes are promising in terms of the reduction of production costs.

Further, one of the most critical factors in aerobic fermentation is aeration. Rotary speed agitation intensity influences the mixing and O₂ transfer rate in the medium, which affects microbial growth and product synthesis (Deshmukh and Naik 2013). In the recent work, the shaking speeds did not affect α-amylase production. In contrast, the optimal shaking speeds of α-amylase synthesis from B. amyloliquefaciens, Bacillus sp, and Bacillus subtilis were 160, 170, and 180, respectively (Nusrat and Rahman 2007).

As previously reported, a model with an R² value nearer to 1.0 would better explain the variability of experimental values and predict the response (Rehman et al. 2019). Thus, the high value of determination coefficient R² of our model was found to be 0.8969, implying that the predicted values (R² pred = 0.8649) agreed with the experimental data (R² adj = 0.8166).

Using the experimental data (Table 4), the first-order polynomial model that fitted the response was written as:

$$\text{Y}=3.6998+0.{\text{1618 X}}_1+0.10{\text{33 X}}_2+0.{\text{9763 X}}_3-0.70{\text{42 X}}_4+0.{\text{4563 X}}_5--0.0{\text{522 X}}_6-0.{\text{1516 X}}_7+0.{\text{7469 X}}_8$$

Above all, the α-amylase production by Bacillus cereus spH1 was influenced by four factors worth further optimization. Indeed, from Fig. 5b, the three red center points prove the linearity effects of the selected variables.

To intensify the enzyme yield, the optimization of α-amylase production was pursued using a full factorial design. A (2⁴) FFD was investigated to determine the optimal level of the screened variables through Plackett–Burman design (orange peels, crab shells, NaCl, and inoculum size). This design was also created to clarify the influence of each variable and its interactions against α-amylase yield. To our knowledge, ours is the first report about α-amylase production by a Bacillus strain using full factorial design. The insignificant factors were kept at a low level. Sixteen sets were handled during the experimental study in a non-random order. As shown in Table 3, the highest enzyme activity was recorded in trial 4, with an average of 8.56 U/mL. According to the F and p values derived from ANOVA (Table 6), all the main factors significantly influence α-amylase activity (p < 0.05). Furthermore, the dyadic and trinary interactions of B*C, B*D, A*C, A*D, C*D, A*B*C, B*C*D, and A*B*C had substantial effects on enzyme activity. Even slight variations can impact the production rate when these terms are considered. Whereas the interacted terms of A*B and A*C*D did not affect the response.

Table 6.

Analysis of variance (ANOVA) of 2⁴ full factorial design

Source	DF	Seq SS	Adj SS	Adj MS	F	P
Main effects	4	25.034	25.034	6.2586	193.31	0
Crab shells (A)	1	3.364	3.3636	3.3636	103.89	0
Orange peels (B)	1	9.574	9.5743	9.5743	295.72	0
NaCl (C)	1	1.120	1.1201	1.1201	34.60	0
Inoculum size (D)	1	10.976	10.9763	10.9763	339.03	0
2-way interactions	6	35.636	35.6357	5.9393	183.45	0
A * B	1	0.026	0.0260	0.0260	0.80	0.377
A * C	1	5.612	5.6119	5.6119	173.34	0
A * D	1	5.544	5.5444	5.5444	171.25	0
B * C	1	13.200	13.2001	13.2001	407.71	0
B * D	1	10.602	10.6018	10.6018	327.46	0
C * D	1	0.652	0.6516	0.6516	20.13	0
3-way interactions	4	24.038	24.0382	6.0095	185.62	0
A * B * C	1	21.994	21.9937	21.9937	679.32	0
A * B * D	1	0.758	0.7576	0.7576	23.40	0
A * C * D	1	0.143	0.1434	0.1434	4.34	0.043
B * C * D	1	1.143	1.1435	1.1435	35.32	0
4way interactions	1	24.266	24.2657	24.2657	749.50	0
A * B * C * D	1	24.266	24.2657	24.2654	749.50	0

Those outcomes are in line with Pareto analysis, and the values eclipsing beyond the reference line (student's test T = 2.04) are considered significant (data not shown). A greater T value magnitude and a smaller p-value signify the importance of the associated variable. The interactions A*B*C*D and A*B*C illustrated a higher impact with F-values 749.5 and 679.32, respectively.

The main effects plot for each independent variable of α-amylase activity show that orange peels and inoculum size were marked by steeper slopes, proving the dominant effects compared to crab wastes and NaCl (Fig. 6a). Graphically, two parallel lines of factors indicate no interaction, such as orange peels vs. inoculum size (p = 0.260 > 0.05). Although, significant interactions were observed between A*C, A*D, B*C, B*D, and C*D (Fig. 6b) the correlation between α-amylase activity and the selected factors (A, B, C, and D) can be described by the following polynomial model:

$$\begin{array}{cc}\hfill Y=& 3.9156+0.2647A+0.4466B+0.1528C-0.4782D-0.0233AB\hfill \\ \hfill -0.3419AC-0.3399AD-0.5244BC-0.47BD-0.1165CD\\ \hfill & -0.6769ABC-0.1256ABD+0.0547ACD+0.1543BCD.\hfill \\ \hfill \end{array}$$

Fig. 6.

The main effects plot for each independent variable of α-amylase activity a Main effects plot for α-amylase activity of full factorial design, b interactions plot for α-amylase activity

The sign of the coefficient indicates how the related variable influences the response. If the coefficient is positive, the response is increased (synergistic Effect) and vice versa (Chouchane et al. 2015).

This model presented a high R-value of 0.9906, explaining 99.06% of the response variability. The adjusted R-value was 0.9788, which suggests an excellent correlation between the predicted and experimental values, leading to a significant model. However, the R adjusted agrees with the predicted R (98.62%) closer to 100%.

The response optimizer of the α-amylase activity is presented in Fig. 7. It leads to the following optimum level of parameters with the desirability of crab shell waste (+ 1), orange peels (+ 1), inoculum size (− 1), and NaCl (− 1). Under this ideal condition, the α-amylase activity was found to be Y = 8.5633 U/mL.

Fig. 7.

Response optimizer of α-amylase activity. The vertical red lines on the graph represent the current factor settings. The horizontal blue lines represent the response for the current factor level. AA amylase activity

It can be seen that the α-amylase production raised by 143-fold (from 0.06 to 8.56 U/mL) for Bacillus spp when applying the optimized condition. This improvement proved that industrial waste, regardless of animal or plant origin, could cover the needs of a microorganism for microbial growth and enzyme induction, reducing the cost of enzyme production.

The predicted results were validated by conducting three sets of additional experiments using the recommended solution by numerical modeling. We successively obtained the following results: 8.4 U/mL, 8.54 U/ml, and 8.24 U/mL with an average of 8.49 U/mL ± 0.13. The close alignment between the predicted and experimental outcomes confirmed the model’s practical applicability and the presence of the optimal point.

Application of Bacillus cereus spH1 strain and its α-amylase in wastewater treatment

Still, to minimize pollution problems, our bacteria and the produced α-amylase have been used to treat a starch-rich effluent from the Tunisian pasta industry. The goal is to turn it into a safe effluent for irrigation, the highest water-consuming sector in Tunisia (Chouchane et al. 2015; Crini and Lichtfouse 2018). Physicochemical treatments are limited by enormous sludge production, heavy investment, generation of toxic wastes, and high energy conception. Hence, bioremediation provides an alternative to circumvent the issues associated with physicochemical methods, given their biologic processes, cost-effectiveness, and public acceptance (Okino-Delgado et al. 2019).

The highest germination index percentage was achieved when the effluent was treated with α-amylase (143% ± 1.76%) (Fig. 8a), followed by treatment with the strain (139% ± 1.83%) (Fig. 8b). These two values are very close and prove the effectiveness of this treatment by comparing them with the percentage of germination using tap water (100%) (Fig. 8c) and pasta industry effluent (85% ± 2.1%) (Fig. 8d). Our results show that the current strain and its α-amylase have good potential in bioremediation. It is well known that bioremediation with microorganisms represents an efficient, low-cost green process, as well as a potential method for isolating and exploiting compounds with biotechnological interest (Okino-Delgado et al. 2019).

Fig. 8.

Radish (Raphanus raphanistrum subsp. sativus) seed germination experiments. Germination was performed using a effluent treated with 41.15 units of the amylase, b effluent treated by Bacillus cereus spH1 strain, c tap water as positive control, d non-treated effluent as negative control

Furthermore, the use of enzymes may be more beneficial because it can lend greater process control and remediation of complex compounds involving recalcitrant ones, which are difficult to remove by metabolic or physicochemical processes (Venugopal 2021). These results encourage carrying out irrigation tests of different species to confirm their effectiveness.

Conclusion

This work focused on optimizing α-amylase production from newly isolated and identified Bacillus cereus spH1. According to the OVAT method, the best conditions for maximum growth and enzymatic yield are 30 °C, pH 7.5, and 12 h of incubation. Furthermore, the medium's components were optimized, and orange peels and crab shells were discovered to improve production significantly. The Plackett–Burman design was then used to perform the full factorial design to investigate the interactions between the significant factors. The α-amylase production was increased to 8.5 U/mL using 10 g orange peels, 2.5 crab shells, 2.5 g tryptone, 1 g malt extract (per 1 litter) with shaking speed 150 rpm, and inoculum size of OD 0.2. Statistical design is an efficient method to maximize α-amylase production at the lowest possible cost using agro-industrial biomass as raw material for fermentation and enzyme production. The isolated strain and its α-amylase have shown promising results in wastewater treatment. The Increased agricultural reuse of treated effluent helps to achieve goals such as improving sustainable agriculture, conserving scarce water resources, and preserving environmental quality.

Author Contributions

BBHH: Data curation, Formal analysis, Investigation, Methodology, Writing—Original Draft. SBM and AF: Investigation, Methodology, Writing — Review & Editing. AS and AH-S: Conceptualization, Writing — Review & Editing, Supervision, Project administration.

Data Availability

All the relevant data have been provided in the manuscript. Links to the submitted sequences: Bacillus cereus strain H1 16S ribosomal RNA gene, partial sequence: https://www.ncbi.nlm.nih.gov/nucleotide/OP811441.1?report=genbank&log$=nuclalign&blast_rank=1&RID=TZEBW479016; Bacillus cereus 16S-23S ribosomal RNA intergenic spacer and 23S ribosomal RNA gene, partial sequence: https://www.ncbi.nlm.nih.gov/nucleotide/OP819558.1?report=genbank&log$=nuclalign&blast_rank=1&RID=TZEPY6CJ013.

Declarations

Conflict of interest

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

Ethical approval

Not applicable.

Consent to Publish

All authors have consent to publish the paper.

Consent to participate

All authors have consent to participate in the study.

Statement of informed consent

The research does not involve human participants and animal experiments.