Abstract
Despite the benefits of petroleum hydrocarbon as essential raw energy sources in many industries, they cause major global environmental pollution. Petroleum hydrocarbons pollutants are highly toxic and recalcitrant, making them dangerous and persistent over long periods in an ecosystem. However, oil contaminated soil is enriched with microorganisms that can utilize petroleum products and hydrocarbons for their growth, nutrition, and metabolic activities. This study aimed to isolate and characterize hydrocarbons‐degrading bacteria capable of degrading hydrocarbons in soil samples obtained from oil‐polluted garage sites in Kericho County, Kenya. One hundred and ten (110) bacterial isolates were isolated after enrichment, with 67 of the isolates (60.9%) having visible petroleum diesel‐degrading capability. The bacteria were characterized based on phenotypic characteristics and 16S rRNA gene sequence analyses. Forty‐nine of the isolates were Gram negative rods, and majority (56) of the isolates reacted positively for catalase and negatively for oxidase (38), methyl red (59), and Voges Proskauer (65); 50.9% of the isolates tested positive for citrate utilization. More than half of the isolated bacteria (69.7%) demonstrated strong evidence of diesel degradation. Bacteria with moderate diesel degradation demonstration accounted for 18.2% of the isolates, while isolates with substantial diesel residues contributed 12.1%. Following 16S rRNA gene sequence analysis, the bacterial strains were identified as belonging to the genera Acinetobacter (8), Pseudarthrobacter (4), Corynebacterium (2), Gordonia (2), Athrobacter (2), Microbacterium (2), Acidivorax (1), Pseudoxanthomonas (1), Priestia (1), Cellulosimicrobium (1), Cupriavidus (1), Paenarthrobacter (1), Exiguobacterium (1), Shewanella (1), Stutzerimonas (1), and Pseudomonas (1). This study has demonstrated that garage soils with petroleum hydrocarbon contamination in Kericho County harbor a rich and diverse indigenous population of microbes with the ability to biodegrade diesel. The findings suggest potential application of these bacterial strains to facilitate the biodegradation of petroleum hydrocarbons.
Keywords: bacterial diversity, bioremediation, hydrocarbon, hydrocarbon biodegradation, petroleum contaminated soils
1. Introduction
Many economic activities depend on fossil fuels to meet their energy demands, leading to the growth of the petrochemical industry [1, 2]. Fossil fuels (crude oil) are naturally formed hydrocarbons derived from the remains of dead plants and animals in the Earth’s crust [3]. Crude oil is a dark, viscous, and flammable liquid mixture containing (83%–87%) hydrocarbons, along with varying amounts of hydrogen (10%–14%), oxygen (0.05%–1.5%), sulfur (0.005%–6.0%), nitrogen (0.1%–0.2%), and metals such as nickel, iron, and copper [2]. Despite the availability of alternative energy sources, fossil fuels remain the most affordable option, which contributes to the continued growth of the petrochemical industry [4]. The production of crude oil, its transportation, chemical processing, and distribution are considered the main sources of anthropogenic hydrocarbons pollution [5]. Crude oil are toxic residual pollutant substances that negatively impact the environment [6]. This is especially true in developing countries where environmental regulations on toxic waste disposal are not adhered to, especially by small enterprises [7]. This is particularly evident in garages and motor vehicle repair sites, where soils become major recipients of waste petroleum products spilled during maintenance activities. However, concerns about environmental pollution due to crude oil remain prevalent, as research indicate that accumulating it can severely damage living organisms, leading to death or mutations in plants and animals [1].
Petroleum hydrocarbons are among the major and most commonly occurring environmental pollutants [8]. Hydrocarbons can be classified based on their chemical structure, namely aliphatic hydrocarbons or saturated hydrocarbons, aromatic hydrocarbons (monocyclic aromatic and polycyclic aromatic hydrocarbons [PAHs]), and heteroatomic compounds (saturated and aromatic ones), including resins and asphaltenes [9]. Petroleum diesel is primarily composed of hydrocarbons with carbon chain lengths ranging from C11 to C25, principally consisting of normal alkanes. Branched alkanes and PAHs are also among key components of petroleum hydrocarbons [1].
Hydrocarbons interact with both the biotic and abiotic components of the ecosystem in various ways that can be either natural or anthropogenic. Because of their complex characteristics, the lightest and most volatile hydrocarbons fractions are released into the atmosphere, the amphipathic and hydrophilic fractions dissolve in water, and the lipophilic bind to soil/sediment particles and organic matter [3]. PAHs can potentially affect the nervous, immune, and excretory systems and cause tumors and mutations. The toxicity of hydrocarbons affects humans, plants, animals, and microorganisms, compromising ecosystem biodiversity and functioning [10]. In addition, environmental contamination by hydrocarbons and their derivatives has been associated with the extinction of many plant and animal species [3]. Arising from these challenges, there is a growing global interest to research on environmental pollution associated with hydrocarbons to identify microorganisms that thrive in contaminated sites [11]. Such microbes can be used in managing petroleum hydrocarbons contamination through biodegradation and bioremediation [12, 13].
Hydrocarbons in the environment are biodegraded primarily by bacteria, yeast, and fungi. Bacteria are the most active agents in petroleum degradation that work as primary degraders of spilled oil. Multiple studies have demonstrated that bacterial communities exposed to hydrocarbons quickly transition to species capable of degrading and utilizing hydrocarbons compounds as carbon sources [3]. This shift is also essential for alleviating the physiological stress induced by the presence of petroleum hydrocarbon [2, 14]. Hydrocarbons‐degrading bacteria have evolved adaptive mechanisms such as the ability to emulsify and metabolize the hydrocarbons using specialized enzymes like oxygenases [14]. These traits can be readily transferred to other bacteria within the same environment, facilitated by plasmids and evolutionary forces that favor specific traits. This enables bacteria to develop the ability to break down persistent waste, such as petroleum hydrocarbons [15]. Because of this ability, the use of bacteria for environmental biodegradation has become the preferred approach.
Over time, various crude oil products have been discharged into the environment, highlighting the need to identify hydrocarbons‐degrading microorganisms that can aid in cleaning up contaminated sites in Kenya. This study focused on characterizing bacteria from soils polluted with petroleum hydrocarbons across three sub‐counties in Kericho County, Kenya.
2. Materials and Methods
2.1. Description of the Study Area
Soil samples were obtained from motor vehicle garages within the three most populous sub‐Counties of Kericho County (latitude 0° 22 ′ 1.2 ″ [0.367°] South, longitude 35° 18 ′ 10.63 ″ [35.3°] E, altitude 2094 m and annual rainfall 1735 mm), Kenya. The County has a population of 901,777 [16] occupying an area of 2111 km2. The County is divided into six subcounties (Figure 1; Table 1).
Figure 1.
Map showing the distribution of sampling points in three (Ainamoi, Belgut and Bureti) selected subcounties of Kericho County, Kenya. The country, county, and subcounty boundaries were obtained from Global Administrative Areas [17] Version 4.1, accessed June 23, 2023. Geographical coordinates of the sampling points were obtained using a handheld Global Positioning System (GPS) with Position Dilution of Precision (PDOP) of less than 25–100 M during the fieldwork period between October 2020 and July 2021.
Table 1.
Coordinates of the sample collection garage.
| S/N | Sample site | Latitude | Longitude |
|---|---|---|---|
| 1 | Taplotin Ronda spares | 0.38633 | 35.15148 |
| 2 | Taplotin Nyota spares | 0.38593 | 35.15159 |
| 3 | Taplotin Elshaddai spares | 0.38696 | 35.15084 |
| 4 | Sosiot booster spares | 0.36275 | 35.17284 |
| 5 | Sosiot power saw | 0.36314 | 35.17124 |
| 6 | Sosiot Weston spares | 0.36296 | 35.17119 |
| 7 | Sosiot power saw market | 0.36336 | 35.17149 |
| 8 | Sosiot Joykem spares | 0.36825 | 35.16701 |
| 9 | Chepnyogaa junction | 0.43363 | 35.13521 |
| 10 | Kabianga centre 1 | 0.44558 | 35.14496 |
| 11 | Premier puncture workshop | 0.46372 | 35.18034 |
| 12 | Kapsoit Tripoli | 0.31972 | 35.21821 |
| 13 | Kapsoit Ramogi | 0.31873 | 35.21777 |
| 14 | Kapsoit Josee | 0.31958 | 35.21835 |
| 15 | Kapsoit Dan | 0.31942 | 35.21845 |
| 16 | Kapsoit tractor garage | 0.32014 | 35.21791 |
| 17 | Litein Onyancha | 0.57957 | 35.19093 |
| 18 | Kapkatet precious | 0.62677 | 35.19672 |
| 19 | Litein bush mouth | 0.58595 | 35.19059 |
| 20 | Kapkatet opp. Police station | 0.62758 | 35.19605 |
| 21 | Litein transformer | 0.58585 | 35.19029 |
| 22 | Litein mabwai | 0.58497 | 35. 18985 |
| 23 | Litein Josam | 0.58447 | 35.18987 |
| 24 | Litein mama Neno tractor | 0.58581 | 35.19068 |
| 25 | Litein Davis Tvs service | 0.58111 | 35.18918 |
| 26 | Litein boxer spare services | 0.58242 | 35.19119 |
| 27 | Litein power saw | 0.57996 | 35 18981 |
| 28 | Litein Jakarabok tractor | 0.58331 | 35.18864 |
| 29 | Litein Malel general | 0.58335 | 35.18927 |
| 30 | Kapkatet Nick’s services | 0.62211 | 35.19616 |
| 31 | Litein modern spares | 0.58288 | 35.18959 |
| 32 | Litein Vinny Tvs service | 0.58197 | 35.18868 |
| 33 | Litein sebuleni parking | 0.58306 | 35.18932 |
| 34 | Kericho town four bob | 0.36827 | 35.28053 |
| 35 | Town Bovaline KCC | 0.3672 | 35.28177 |
| 36 | Town Guru Nanak | 0.36968 | 35.28045 |
| 37 | Town Mutua | 0.36797 | 35.28125 |
| 38 | Town Auma KCC | 0.36722 | 35.28137 |
| 39 | Town KCC mosque | 0.36726 | 35.28188 |
| 40 | Town Kericho industrial enterprices | 0.36592 | 35.283 |
| 41 | Town Lomet | 0.36712 | 35.28189 |
| 42 | Town Amani | 0.36771 | 35.2807 |
| 43 | Town Kiptebes | 0.36796 | 35.28044 |
| 44 | Town nyota garage lower | 0.36546 | 35.28146 |
| 45 | Brook Halane energy | 0.33467 | 35.32479 |
| 46 | Brook Mutai Spares kapsaos | 0.33438 | 35.3251 |
| 47 | Town Jua Kali Magret | 0.36752 | 35.28085 |
| 48 | Town Jadeja garage | 0.36795 | 35.2805 |
| 49 | Town Jumbo garage | 0.37263 | 35.28154 |
| 50 | Town next to stage garage | 0.3642 | 35.28451 |
| 51 | Town KCC kwa Ken garage | 0.36416 | 35.28306 |
| 52 | Town Kiprono Tegecha | 0.37044 | 35.28144 |
| 53 | Town jua kali | 0.36747 | 35.28158 |
| 54 | Town fountain garage | 0.36721 | 35.27919 |
| 55 | Brook tea view garage | 0.33357 | 35.32578 |
| 56 | Town Sinendet | 0.37204 | 35.2831 |
| 57 | Town nyota upper | 0.36812 | 35.28149 |
| 58 | Town Tengecha Mutua | 0.36969 | 35.28109 |
| 59 | Total Kipsigis town | 0.36803 | 35.29497 |
| 60 | Brook next to Brook inn | 0.33487 | 35.32596 |
| 61 | Town blue triangle | 0.36579 | 35.28294 |
| 62 | Town Kaplong garage | 0.36641 | 35.28238 |
| 63 | Town new highway garage | 0.36939 | 35.28125 |
2.2. Collection of Soil Samples
The study was undertaken between October 2020 and July 2021. A total of 63 motor vehicle garages in operation for more than 5 years in Ainamoi, Belgut and Bureti subcounties, Kericho County were selected and numbered. Oil contamination of the soil was assessed by visual examination in the field as described by Kostka et al. [18], where 0 = clean or no visual contamination, 1 = low, and 2 = high levels of contamination. In each garage, three soil samples were collected from a depth of 10 cm using a soil auger and homogenized, then a composite representative sample of 50 g were collected into clean brown 1 kg sugar paper. The soil samples were coded based on the collection order and sites, stored in a cool box and immediately transported to the Kenya Agricultural and Livestock Research Organization‐Tea Research Institute (KALRO‐TRI) plant molecular laboratory. Thereafter, the soil samples were sieved using a 2‐mm sieve to remove stones and plant debris, and stored at 4°C.
2.3. Preparation of Culture Media
The diesel‐degrading bacteria were isolated on Bushnell Hass (BH) broth (Himedia, India). The medium was supplemented with 1.25% v/v of a 0.22‐μm pore size filter (Sigmaaldrich, United States), sterilized petro‐diesel. Subculturing was done on soya casein digest agar medium (SCDA) (Himedia, India) to obtain pure colony cultures. Nutrient broth (NB) (Himedia, India) was used to grow cultures for DNA extraction.
2.4. Isolation of Petroleum Diesel‐Degrading Bacteria
The soil sample was pulverized with a spatula on a large clean polythene paper. Ten grams of soil was suspended in 100 mL of sterile physiological saline (0.85% NaCl, Lab prepared) and stirred for 30 min at approximately 200 ×g with a Teflon‐coated magnetic bar (Sigma‐Aldrich, United States) to suspend the bacteria [19]. The soil suspension was serially diluted by adding 1 mL of the soil suspension to a test tube with 9 mL physiological saline (Lab prepared), vortexed (Stuttgart, Germany) at approximately 150 ×g for 1 min, then a 1‐mL aliquot was rapidly transferred to a new test tube with 9 mL saline. Subsequently, other dilutions were serially made from 10−1 to 10−5. Of the 10−5 soil dilution, 10 mL was introduced into the 40 mL BH media with 1.25% v/v diesel in loosely capped flat bottom flasks and incubated for 7 days in an incubator shaker (SNS INNOVA 42 Labnet International, United States) at 30°C and 100 ×g [18]. Non‐inoculated flasks containing BH broth with 1.25% v/v diesel served as controls. After 7 days of incubation, 500 μL of the BH enrichment media bacteria culture was inoculated on SCDA (Himedia, India and incubated for 48 h at 30°C. The cultures were re‐coded and pure colonies obtained by serial plating on SCDA for 24 h until a pure culture was obtained [20].
2.5. Morphological and Biochemical Characterization of Isolates
The bacterial isolates were characterized for cell shape, size, pigmentation, elevation, margin, form, and mucoid consistency using both dissecting and compound microscopy [19, 20]. Gram staining was performed to determine the shape and size of the isolate cells, with observations made using oil immersion microscopy. The results were further confirmed through the 3% (w/v) potassium hydroxide (KOH) string test. Biochemical tests on diesel‐degrading bacteria included citrate utilization, catalase, oxidase activity, and carbohydrate use [20].
2.6. Molecular Identification of the Diesel‐Degrading Bacteria
2.6.1. Genomic DNA Extraction
Genomic DNA was extracted from overnight cell cultures grown on Nutrient broth (NB) at 30°C as described by [21]. The cell pellets were suspended in 200 μL of solution 1 [50 mM Tris (pH 8.5; Sigma‐Aldrich, United States), 50 mM EDTA (pH 8.0; Sigma‐Aldrich, United States), and 25% sucrose solution (Fluka United States)], 5 μL of lysozyme (20 mg/mL; Glentham, United Kingdom), and 5 μL of RNase A (20 mg/mL; Sigma‐Aldrich, United States), mixed gently and incubated at 37°C for 1 h. Thereafter, 600 μL of solution 2 [10 mM Tris (pH 8.7), 5 mM EDTA (pH 8.0), and 1% sodium dodecyl sulfate (Avantor, United States)], and 10 μL proteinase K (20 mg/mL; Glentham, United Kingdom) were added, mixed gently, and the mixture incubated at 55°C for 30 min. Phenol‐chloroform (Sigma‐Aldrich, United States) extraction was performed, followed by chloroform alcohol (Lab prepared) washing. The DNA was precipitated with ice‐cold absolute ethanol (Sigma‐Aldrich, United States) and sodium chloride (Sigma‐Aldrich, United States), left overnight at −20°C, and centrifuged. The pellet was washed with 70% ethanol, air‐dried, and re‐suspended in TE buffer (Lab prepared).
2.6.2. Amplification of Bacterial 16S rRNA Gene
The isolated bacteria were identified using the 16S rRNA gene sequencing, and gene amplification was performed using the bacterial primer set, 27‐F (5′‐TAGAGTTTGATCCTGGCTCAG‐3′) and 1392‐R (5′‐GACGGGCGGTGTGTACA‐3′) [22]. Polymerase chain reaction (PCR) reactions were performed in 25 μL volume using 12.5 μL one Taq Quick‐Load 2× Master Mix with a standard PCR buffer (New England Biolabs, United Kingdom), 10 mΜ primer, 50 ng template DNA, and the reaction mixtures were topped up to 25 μL with nuclease‐free water (ElgaBiotech, Germany). The negative control reaction contained all the PCR components except the DNA template. Amplifications were performed in a Veriti thermocycler (Bio‐Rad, Singapore), the reaction mixtures were subjected to the following thermal cycling profiles: 1 cycle of initial denaturation at 94°C for 5 min, followed by 32 cycles of denaturation at 94°C for 1 min, primer annealing at 49°C for 1 min, extension at 72°C for 2 min, and a final extension at 72°C for 10 min [23].
The amplified PCR product (7 μL) was resolved on a 1% agarose gel (Sigma‐Aldrich, United States) stained with ethidium bromide (Fisher Scientific, United States) in 1× TBE buffer, electrophoresis (Clever Scientific, United Kingdom) carried out at 70 V for 60 min and the PCR product was visualized using a Gel‐Doc XR+ Imaging System (Bio‐Rad, Germany). The molecular weight of the PCR product was estimated using a Gene Ruler 1 kb Plus DNA marker (Thermo Fisher Scientific, United States), a blank control was run alongside the ladder and samples for quality check. The PCR product was purified using Zymoclean Gel DNA Recovery Kit (Zymo Research, United States) according to the manufacturer’s instructions. The purified amplicon was Sanger sequenced at Inqaba Biotechnical Industries, Pretoria, South Africa.
2.6.3. Phylogenetic Analysis
The 16S rRNA gene sequence of the bacterial isolates were preprocessed using ChromasPro 2.1.8 software package (http://technelysium.com.au/wp/, accessed March 19, 2024). Annotation of high‐quality transcripts was performed by searching against available nucleotide sequences deposited in the National Center for Biotechnology Information (NCBI) database (http://www.ncbi.nlm.nih.gov/accessed March 19, 2024) using Basic Local Alignment Search Tool (BLAST) e‐value threshold of zero. The 16S rRNA gene sequence with high similarity to those determined in the study was retrieved, added to the database, and aligned with MEGA (Molecular Evolutionary Genetic Analysis) 7.0 software package [24]. The trees’ topologies were evaluated using the bootstrap resampling method [25] based on 1000 replicates. The sequence of Trichoderma (HQ630962.1) was used as an out group.
2.7. Data Analysis
Numerical data were presented as percentiles, while the data on cultural, morphological, and biochemical characteristics were presented as tables and graphs using Microsoft Excel 2021. Correlation was computed using R (version 4.5.1, accessed July 13, 2025). The same data was scored as binary data for presence (1) or absence (0) and used to generate distance matrices based on Jaccard’s similarity coefficient. These similarity matrices were analyzed using cluster analysis via the unweighted pair group method with arithmetic mean (UPGMA), and a dendrogram was constructed using FigTree software (Version 1.4.2; accessed on April 30, 2024). The similarity coefficient data from the 67 bacterial isolates were then used to reduce the sample size based on relatedness before molecular characterization. 16S rRNA gene sequences of the bacterial isolates were viewed and edited using Chromas Pro 5 software package. They were aligned using CLUSTAL W 1.6 to provide a full sequence of about 1500 nucleotide bases, and compared with sequences in the public databases with the BLAST search program on the National Center for Biotechnology Information (NCBI) website to find closely related bacterial 16S rRNA gene sequence, parameters such as percentage similarities and E‐values were used. The 16S rRNA gene sequence of the isolates and those of the closely related bacteria were then aligned and processed to produce phylogenetic trees using MEGA 5 software package.
3. Results
3.1. Soil Bacteria Communities in Garage Sites
A total of 110 bacterial isolates were obtained from the 63 composite motor vehicle garage soil samples from the three subcounties in Kericho County. Of these, 75 (68%), 15 (11.8%), and 20 (16.4%) were from Ainamoi, Belgut, and Bureti subcounties, respectively. Mixed bacterial communities were obtained from 24 (72.7%), 5 (15.2%), and 4 (12.1%) soil samples collected from Ainamoi, Belgut, and Bureti, respectively (Table 2).
Table 2.
Number of bacteria isolates from selected garage sites in three sub‐counties.
| Subcounty | Sampling sites | Number of isolates |
|---|---|---|
| Ainamoi | 35 | 75 |
| Belgut | 11 | 15 |
| Bureti | 17 | 20 |
| Total | 63 | 110 |
The 110 bacterial isolates exhibited three distinct diesel degradation capabilities (Table 3). Most of the isolates (67, representing 61%), degraded the added diesel with no traces of diesel droplets, while the remaining isolates had either low (23, representing 21%) or large (20, representing 18%) traces of diesel droplets, the negative controls run along the experiment did not show any microbial growth (Table 3).
Table 3.
Diesel degradation properties by bacteria isolated from soil sampled from garages in Kericho County.
| Biodegradation capability | Number of isolates | Bacteria associationsa | Group/diesel residue |
|---|---|---|---|
| No traces of diesel | 67 | 23 | 1/Low |
| Low traces of diesel | 23 | 6 | 2/Moderate |
| Large traces of diesel | 20 | 4 | 3/High |
| Negative control (media add treatment only) | 0 | 0 | |
| Total | 110 | 33 |
aNumber of samples, which yielded more than one pure bacteria isolate after enrichment culture.
Pearson correlation analysis was conducted to assess the relationship between diesel residue levels and the number of bacterial isolates recovered per the sampling sites. At 5% level of significance, analysis showed that there was a weak negative relationship between diesel residue levels and the number of bacterial isolates recovered per the sampling sites [Calculated Pearsons correlation coefficient ρ = −0.344, with p value = 0.004].
3.2. Morpho‐Cultural Characteristics of Bacteria
Most colonies grew within 48 h of incubation at 30°C. The isolates had different cultural characteristics on SCDA medium (Figure 2). The colonies presented different colors: white, whitish, pink, cream, orange, brownish, luminous green, and cream white. They were either nonmucoid, sticky on media, or mucoid. The margins were entire, filamentous or irregular with either round, filamentous, or irregular colonies. The colony margins appeared to be raised, umbonate, with spreading edges or growing into the media. The morphology of the bacterial cells was either cocci or rods (Table 4).
Figure 2.
Cultures of different diesel‐degrading bacteria isolated from soils sampled.
Table 4.
Cultural, morphological, and biochemical characteristics of the 67 diesel‐degrading bacteria.
| Isolate code | Colony color | Colony size | Whole colony | Colony consistency | Colony margin | Colony elevation | Cell shape | CIT | CAT | Gram status | MR | VP | OXI |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| z1 | Orange | Small | Round | Nonmucoid | Entire | Convex | Rods | + | + | + | + | — | — |
| z2 | Cream white | Medium | Round | Nonmucoid | Entire | Raised | Rods | + | + | — | — | — | — |
| z3 | White | Small | Filamentous | Nonmucoid | Filamentous | Umbonate | Cocci | — | — | — | — | — | — |
| z4 | Brownish | Small | Round | Nonmucoid | Entire | Convex | Cocci | — | — | — | — | — | — |
| z5 | White | Small | Filamentous | Nonmucoid | Filamentous | Umbonate | Cocci | — | + | + | + | + | — |
| z6 | Whitish | Small | Round | Nonmucoid | Entire | Convex | Cocci | — | + | — | — | — | — |
| z7 | White | Small | Round | Nonmucoid | Entire | Convex | Cocci | + | + | + | + | — | — |
| z8 | Brownish | Large | Round | Mucoid | Entire | Convex | Cocci | + | + | — | — | — | — |
| z9 | Orange | Small | Round | Nonmucoid | Entire | Convex | Rods | — | + | — | — | — | — |
| z10 | Orange | Large | Round | Mucoid | Entire | Convex | Rods | + | + | + | + | — | — |
| z11 | Orange | Small | Round | Nonmucoid | Entire | Convex | Cocci | + | — | — | — | — | — |
| z12 | Brownish | Small | Round | Nonmucoid | Entire | Convex | Cocci | + | — | — | — | — | — |
| z13 | Luminous green | Medium | Round | Nonmucoid | Entire | Convex | Cocci | — | + | — | — | + | + |
| z14 | Luminous green | Medium | Round | Nonmucoid | Entire | Convex | Cocci | — | + | — | — | + | — |
| z15 | Cream white | Large | Round | Nonmucoid | Entire | Convex | Rods | + | + | + | + | — | — |
| z16 | Luminous green | Small | Round | Mucoid | Entire | Convex | Cocci | — | — | — | — | + | — |
| z17 | Orange | Small | Round | Nonmucoid | Entire | Convex | Rods | — | + | — | — | — | — |
| z18 | Cream white | Medium | Round | Nonmucoid | Entire | Convex | Cocci | + | + | — | — | — | — |
| z19 | Luminous green | Small | Round | Mucoid | Entire | Convex | Cocci | — | + | — | — | — | — |
| z20 | Orange | Medium | Round | Mucoid | Entire | Convex | Cocci | + | + | — | — | — | — |
| z21 | Orange | Large | Round | Nonmucoid | Entire | Convex | Cocci | + | + | + | + | — | — |
| z22 | Orange | Medium | Round | Mucoid | Entire | Convex | Cocci | — | + | + | + | + | — |
| z23 | White | Large | Round | Mucoid | Entire | Umbonate | Rods | + | + | + | + | — | — |
| z24 | Orange | Large | Round | Mucoid | Entire | Convex | Rods | + | + | + | + | — | — |
| z25 | White | Large | Round | Mucoid | Entire | Umbonate | Cocci | + | + | — | — | — | — |
| z26 | White | Large | Round | Mucoid | Entire | Convex | Cocci | + | + | — | — | — | — |
| z27 | White | Small | Round | Nonmucoid | Entire | Convex | Cocci | — | + | + | + | — | — |
| z28 | White | Small | Round | Mucoid | Entire | Convex | Cocci | + | + | — | — | — | — |
| z29 | Orange | Small | Round | Nonmucoid | Entire | Convex | Rods | + | + | — | — | — | — |
| z30 | Brownish | Large | Round | Mucoid | Entire | Umbonate | Cocci | + | + | — | — | + | — |
| z31 | Cream | Small | Round | Mucoid | Entire | Convex | Rods | + | + | — | — | — | — |
| z32 | Brownish | Medium | Round | Mucoid | Entire | Convex | Cocci | — | + | — | — | — | — |
| z33 | Orange | Large | Round | Nonmucoid | Entire | Umbonate | Rods | + | + | — | — | — | — |
| z34 | Orange | Medium | Round | Nonmucoid | Entire | Convex | Rods | — | — | — | — | — | — |
| z35 | Luminous green | Small | Round | Nonmucoid | Entire | Convex | Cocci | — | + | + | + | — | — |
| z36 | Orange | Small | Round | Nonmucoid | Entire | Convex | Rods | — | + | — | — | — | — |
| z37 | Orange | Medium | Round | Mucoid | Entire | Convex | Rods | — | + | — | — | — | — |
| z38 | White | Large | Round | Nonmucoid | Entire | Convex | Rods | + | + | + | + | — | — |
| z39 | Orange | Medium | Round | Nonmucoid | Entire | Convex | Cocci | + | + | + | + | — | — |
| z40 | Luminous green | Small | Round | Mucoid | Entire | Convex | Rods | — | — | — | — | + | — |
| z41 | White | Large | Round | Mucoid | Entire | Convex | Cocci | + | + | + | + | — | — |
| z42 | Brownish | Medium | Round | Nonmucoid | Entire | Convex | Cocci | — | + | — | — | — | — |
| z43 | Brownish | Small | Round | Mucoid | Entire | Convex | Cocci | — | + | — | — | — | — |
| z44 | White | Medium | Round | Nonmucoid | Entire | Convex | Cocci | — | + | + | + | — | — |
| z45 | Brownish | Medium | Round | Nonmucoid | Entire | Convex | Cocci | + | + | — | — | — | — |
| z46 | White | Medium | Round | Mucoid | Entire | Umbonate | Rods | + | + | + | + | — | — |
| z47 | White | Medium | Round | Nonmucoid | Entire | Convex | Cocci | — | + | + | + | — | — |
| z48 | Orange | Medium | Round | Nonmucoid | Entire | Convex | Rods | + | + | — | — | — | — |
| z49 | Yellow | Large | Round | Nonmucoid | Entire | Umbonate | Rods | — | + | + | + | + | + |
| z50 | White | Small | Round | Nonmucoid | Entire | Convex | Rods | — | + | + | + | — | — |
| z51 | Orange | Large | Round | Nonmucoid | Entire | Umbonate | Rods | — | — | — | — | — | — |
| z52 | Luminous green | Large | Filamentous | Nonmucoid | Irregular | Umbonate | Rods | + | — | — | — | — | — |
| z53 | Orange | Large | Round | Nonmucoid | Entire | Umbonate | Rods | — | — | — | — | — | — |
| z54 | Orange | Large | Irregular | Nonmucoid | Irregular | Umbonate | Rods | + | + | — | — | — | — |
| z55 | Orange | Large | Filamentous | Mucoid | Filamentous | Umbonate | Rods | + | — | — | — | — | — |
| z56 | Orange | Medium | Filamentous | Nonmucoid | Filamentous | Umbonate | Rods | + | + | — | — | — | — |
| z57 | White | Medium | Round | Nonmucoid | Entire | Convex | Rods | — | + | — | — | — | — |
| z58 | Orange | Large | Round | Mucoid | Entire | Convex | Rods | — | + | — | — | — | — |
| z59 | Brownish | Small | Round | Nonmucoid | Entire | Convex | Rods | + | + | — | — | — | — |
| z60 | Pink | Small | Filamentous | Nonmucoid | Entire | Flat | Rods | — | + | + | + | — | — |
| z61 | Orange | Small | Round | Nonmucoid | Entire | Convex | Rods | — | + | — | — | — | — |
| z62 | Brownish | Medium | Round | Nonmucoid | Entire | Umbonate | Cocci | + | + | — | — | — | — |
| z63 | Orange | Large | Round | Nonmucoid | Entire | Umbonate | Rods | + | — | — | — | — | — |
| z64 | Cream | Large | Round | Mucoid | Entire | Convex | Rods | — | + | — | — | — | — |
| z65 | Orange | Large | Round | Mucoid | Entire | Umbonate | Rods | + | — | — | — | — | — |
| z66 | Cream | Medium | Round | Nonmucoid | Entire | Convex | Rods | + | + | — | — | — | — |
| z67 | Cream | Medium | Round | Nonmucoid | Entire | Convex | Rods | + | + | — | — | — | — |
Note: NB: − Negative, + Positive, A, B, C, D, E – data codes representing different isolates from a single soil sample. Based on the cultural, morphological and biochemical characteristics, the probable bacteria genera that were isolated are Acinetobacter, Arthobacter, Pseudomonads, and Corynebacteria.
Abbreviations: CAT, catalase, CIT, citrate, MR, methyl red, OXI, oxidase, VP, Voges–Proskauer.
3.3. Morphological Characteristics of Degrading Bacteria
Morphological characteristics of the bacterial isolates were categorized based on color, consistency, elevation, shape, margin, and the whole colony. Eight different colony colors were observed, isolate consistency was mainly nonmucoid (65.7%) and mucoid (34.3%). More than half of the isolates had convex elevation (71.6%) compared with umbonate (17.9%), and others (10.5%). In addition, slightly over half of the isolated bacteria shapes were rods (53.7%), with slightly less than half exhibiting a cocci shapes (46.3%). A significant number of the isolates (91.0%) had entire margins as compared with filamentous (6.0%), and irregular margins (3.0%). Based on colony characteristics, almost all of the isolates (89.6%) had round margins.
3.4. Biochemical Characteristics of Diesel‐Degrading Bacteria
The diesel‐degrading bacterial isolates had diverse biochemical characteristics, over half (60%) of high diesel residue isolates demonstrated the capability to utilize citrate as its sole carbon and energy source, unlike group one (49.3%), and group two (47.8%). The capacity to produce the catalase enzyme was significant among the isolates in low diesel residue group (83.6%) followed by the high diesel residue group (80%). There was significant Gram negative isolates in the moderate diesel degrading group (82.6%) and in the low diesel residue group (70.1%), as compared with the high residue group (55%). A significant number of bacterial groups exhibited unstable acid production during glucose fermentation, as demonstrated by the Methyl Red (MR) test, which showed similar results to the Voges–Proskauer (VP). In the VP test, over half of the isolates across all three degradation groups tested negative for the production of acetylmethyl carbinol from glucose fermentation. There was variation in the outcome of oxidase test among the groups, with almost equal positive (43.3%) and negative (56.7%) observations in low residue group, in contrast, moderate residue group (60.9%) of isolates showed positive reactions and (80%) of high residue group isolates tested negative.
3.5. Clustering of Bacterial Isolates
The cultural, morphological, and biochemical diversity, and relationships among the bacteria isolates were determined by Jaccard’s similarity coefficient using the UPGMA method. The dendrogram divided the bacteria into seven major clusters (Figure 3). The similarity coefficient among the 67 isolates ranged from 0.177 to 0.977 with an average of 0.577. The seven clusters comprised 1 to 26 bacterial isolates with majority placed in cluster six (26 isolates) and cluster seven (22 isolates). Cluster 6 had multiple isolates that were highly similar, while cluster seven had two highly similar isolates according to the cultural, morphological, and biochemical characteristics.
Figure 3.
Dendrogram based on UPGMA showing the relationships among 67 bacteria isolates—isolated from garage soils—based on their cultural, morphological, and biochemical properties on Jaccard’s similarity index. The seven major bacterial clusters are presented by different colors, wherein isolates with similar colors are presumed close relatives.
3.6. Molecular Identification of Diesel‐Degrading Bacteria
The partial 6S rRNA gene sequence of diesel‐degrading bacterial isolates was compared with nucleotide sequence in the GenBank to determine the degree of similarity between them, and closely related strains. BLASTn results revealed percentage identities between diesel‐degrading bacterial isolates with closely related bacteria in the GenBank (Table 5). The accession numbers listed in Table 5 represent reference 16S rRNA gene sequences from GenBank that were used for comparative analysis with the sequences obtained in this study Table 5.
Table 5.
Closest GenBank matches for the 16S rRNA gene sequences of the 29 diesel‐degrading bacterial isolates based on BLASTn analysis.
| S. no | Genus | Species | Query coverages (%) | E value | Percentage identity (%) | 16S rRNA gene NCBI accession no. |
|---|---|---|---|---|---|---|
| z2_27F_D09 | Corynebacterium | Variabile | 100 | 0 | 99.89 | MT573863.1 |
| z5_27F_E09 | Pseudoxanthomonas spp. | 100 | 0 | 99.65 | LC133669.1 | |
| z6_27F_F09 | Gordonia | Alkanivorans | 100 | 0 | 99.76 | MT549097.1 |
| z7_27F_G09 | Arthrobacter spp. | 100 | 0 | 99.89 | MT373551.1 | |
| Pseudarthrobacter | Siccitolerans | 100 | 0 | 99.89 | MN006554.1 | |
| z8_27F_H09 | Acinetobacter | Junii | 100 | 0 | 100.00 | MT613873.1 |
| z10_27F_A10 | Paenarthrobacter | Ureafaciens | 100 | 0 | 99.64 | MT409549.1 |
| z11_27F_B10 | Acidovorax spp. | 100 | 0 | 99.36 | MT255158.1 | |
| z14_27F_D10 | Cellulosimicrobium | Cellulans | 100 | 0 | 99.39 | MT533986.1 |
| z15_27F_E10 | Pseudarthrobacter | Phenanthrenivorans | 100 | 0 | 100.00 | OR964107.1 |
| z17_27F_F10 | Gordonia | Amicalis | 100 | 0 | 99.51 | MT533952.1 |
| z20_27F_G10 | Microbacterium | Album | 100 | 0 | 98.61 | OP847077.1 |
| z23_27F_H10 | Pseudarthrobacter | Chlorophenolicus | 100 | 0 | 99.70 | KU647201.1 |
| z25_27F_A11 | Acinetobacter | Junii | 99 | 0 | 99.89 | MT613873.1 |
| z26_27F_B11 | Acinetobacter | Junii | 100 | 0 | 99.69 | MT613873.1 |
| z27_27F_C11 | Acinetobacter | Junii | 100 | 0 | 99.78 | MT613873.1 |
| z30_27F_D11 | Priestia | Megaterium | 100 | 0 | 99.55 | MK318797.1 |
| z38_27F_E11 | Cupriavidus | Alkaliphilus | 100 | 0 | 99.58 | MN810330.1 |
| z39_27F_F11 | Microbacterium | Saccharophilum | 100 | 0 | 99.37 | MN314492.1 |
| z43_27F_G11 | Acinetobacter spp. | 100 | 0 | 99.09 | GU566343.1 | |
| z44_27F_H11 | Arthrobacter spp. | 100 | 0 | 99.38 | MW033810.1 | |
| z45_27F_A12 | Pseudarthrobacter | Oxydans | 100 | 0 | 99.54 | MN826535.1 |
| z47_27F_B12 | Exiguobacterium spp. | 100 | 0 | 99.89 | MT355759.1 | |
| z49_27F_C12 | Shewanella | Putrefaciens | 100 | 0 | 99.33 | KU163441.1 |
| z53_27F_D12 | Stutzerimonas | Frequens | 100 | 0 | 97.85 | OR742106.1 |
| z55_27F_E12 | Corynebacterium | Variabile | 100 | 0 | 99.89 | MT573863.1 |
| z56_27F_F12 | Acinetobacter | Junii | 100 | 0 | 98.56 | MT613873.1 |
| z60_27F_G12 | Acinetobacter | Junii | 100 | 0 | 99.47 | MT613873.1 |
| z61_27F_H12 | Acinetobacter | Junii | 100 | 0 | 99.90 | MT613873.1 |
| z62_27F_A01 | Pseudomonas spp. | 100 | 0 | 99.46 | MT256239.1 |
Phylogenetic analysis of the 16S rRNA gene sequence identified the diesels degrading bacteria isolated from the petroleum contaminated soils in Kericho County to belong to the genera Acinetobacter, Arthrobacter, Pseudoxanthomonas, Corynebacterium, Gordonia, Pseudarthrobacter, Paenarthrobacteria, Acidovorax, Cellulosimicrobacter, Microbacterium, Priestia, Cupriavidus, Microbacterium, Exiguobacterium, Shewanella, Stutzerimonas, and Pseudomonas.
The phylogenetic tree (Figure 4) illustrates the evolutionary connections among diesel‐degrading bacterial isolates, marked with red diamonds and designated as Z isolates, alongside closely related reference strains. These relationships are inferred through an analysis of 16S rRNA gene sequence. The tree reveals two main clades with nine distinct sub‐clades, highlighting genetic similarities and divergences among the isolates and reference species.
Figure 4.
A phylogenetic tree based on 16S rRNA gene sequence showing the relationship among the diesel degrading bacterial isolates and representatives of other related taxa. The tree is drawn to scale with branch lengths measured in the number of substitutions per site. The scale bar indicates 0.05 substitution per nucleotide position. The red diamonds represent the diesel degrading bacteria isolates. The number beside the node is the bootstrap value. In brackets are the GenBank accession numbers as accessed on March 16, 2024. The gene sequence of Trichoderma (HQ630962.1) was used as an out‐group.
The first sub‐clade consisted of sequences from seven isolates, which showed a high degree of sequence similarity with Arthrobacter spp. (MT373551.1, and MW033810.1), Pseudarthrobacter spp. (MN006554.1, OR964107.1, KU647201.1, and MN826535.1), respectively, forming a closely clustered sub‐clade. They were assigned to the genera Pseudarthrobacter, Arthrobacter, Paenarthrobacter, and Priestia. The second sub‐clade consisted of a sequence from three isolates, with Cellulosimicrobium spp. (MT533986.1), Microbacterium (OP847077.1, and MN314492.1). Z14, Z20, and Z39 were assigned to the genera Cellulosimicrobium, and Microbacterium.
The third sub‐clade consists of two isolates, Z2 and Z55, both of which showed a sequence similarity of 99.89% with Corynebacterium variable (MT573863.1). Z2 and Z55 were assigned to the genus Corynebacterium. The fourth sub‐clade comprises two isolates, Z6 and Z17, with a high similarity to Gordonia (MT549097.1, and MT5333952.1) both were assigned to the genus Gordonia. The fifth sub‐clade had one isolate Z47, with a similarity of 99.89% with Exiguobacterium spp. (MT35579.1), assigned to the genus Exiguobacterium. The sixth sub‐clade includes one isolate Z5, with a similarity to Pseudoxanthomonas spp. (LC133669.1), it was assigned to the genus Pseudoxanthomonas.
The seventh sub‐clade contains three isolates Z11, Z38, and Z49, with a similarity to Acidovorax spp. (MT255158.1), Cupriavidus alkaliphilus (MN810330.1), and Shewanella putrefacience (KU163441.1). A bootstrap value of 37% was between Z11, Z38, and Z49, indicating a weak support for the relationship between these genera. A bootstrap value of 87% between Z11 and Z38 represents a high level of confidence in their close phylogenetic relationship based on 16S rRNA gene sequence analysis. Z11, Z38, and Z49 were assigned to the genera Acidovorax, Cupriavidus, and Shewanella, respectively.
The eighth sub‐clade contains two isolates Z53 and Z62, with a similarity to Stutzerimonas spp. (OR742106.1) and Pseudomonas spp. (MN256239.1) based on 16S rRNA gene sequence analysis Z53 could be assigned to the genus Stutzerimonas, while Z62 to the genus Pseudomonas.
The ninth sub‐clade consisted of sequence from eight isolates Z8, Z25, Z26, Z27, Z43, Z56, Z60, and Z61, which showed a high degree of sequence similarity of between 98.56% and 100% with Acinetobacter (MT613873.1, and GU566343.1), forming a closely clustered sub‐clade. They were therefore assigned to the genus Acinetobacter.
4. Discussion
The present study found that the prolonged exposure to petroleum hydrocarbons in garage soils appeared to promote microbial diversity. In previous studies, the scarcity of microorganisms capable of degrading hydrocarbons has been highlighted as a key factor limiting the biodegradability of oil contaminants [2]. A total of 110 bacterial isolates were obtained from a microbial community across 63 soil sampling sites. The high number of isolates may be attributed to the fact that the soil samples were collected from garages that had been operational for over 5 years. This observation is further supported by findings from four garages where surface re‐carpeting with freshly excavated murram that may have disrupted microbial communities yielded nothing during enrichment, despite continued pollution from auto repair activities. Indicating that biodegradability of petroleum oil contaminants is significantly restricted by the limited availability of colonizing hydrocarbon‐degrading microorganisms in that environment.
The grouping of isolates into high, moderate, and low residue categories was based on the amount of diesel remaining after 7 days of incubation, with low residue indicating high degradation efficiency and high residue representing low degradation capacity. The study found that 67 out of 110 bacterial isolates (60.9%) exhibited a strong ability to degrade diesel. These isolates also showed a higher number of bacterial community associations, totaling 23 (69.7%) [1], stated that the ability of a soil’s microbial community to degrade hydrocarbons depends on both their abundance and catabolic activity. Similarly, [26] reported that, mixed microbial populations with broad enzymatic capacities are essential for breaking down complex hydrocarbon mixtures such as crude oil in soil, freshwater, and marine environments as they expand the range of degradable substrates and facilitate commensalism and co‐metabolism. Consistent with the findings of the current study, Pearson correlation analysis indicated a weak negative association, rho = −0.344, with p value = 0.0058, between diesel residue concentrations and the number of culturable bacterial isolates. This relationship highlights the ecological significance of hydrocarbon‐degrading bacterial communities and supports their critical role in the natural attenuation and biodegradation of diesel and other petroleum‐based contaminants in affected environments. Moreover, the rich bacterial diversity in garage soils capable of thriving in petroleum‐contaminated environments, may be attributed to the availability of diverse nutrient sources within this ecological niche [27, 28].
Morphological and biochemical profiling of bacterial isolates was performed to assess functional traits associated with diesel hydrocarbon degradation. Dissimilarities were observed in the morphological and biochemical characteristics of the isolated bacteria, the Gram‐stain distribution across degradation groups showed that most isolates were Gram‐negative. Gram‐negative bacteria, especially genera like Pseudomonas and Acinetobacter, are widely recognized for their hydrocarbon‐degrading ability due to biosurfactant production, outer membrane adaptations, and efficient substrate uptake systems [29], aligning with the findings of [30]. A similar trend was noted among group two microorganisms, which were classified as having moderate biodegradation capabilities. These findings, however, contrast with those of [31], who observed a higher prevalence of Gram‐positive bacteria than Gram‐negative ones. This discrepancy may stem from the experimental conditions in their study where contaminated soil was bioaugmented with vermicompost, biostimulants, and, in some cases, an additional nutrient consortium, which likely influenced the abundance of Gram‐positive bacteria. A similar pattern was observed in group three isolates, which left noticeable diesel residues in the enrichment culture. This group also showed a higher proportion of Gram‐positive bacteria compared with the first two groups.
A substantial proportion of highly efficient degraders (low residue group) exhibited catalase and oxidase positivity. Catalase decomposes hydrogen peroxide into water and oxygen, mitigating oxidative stress induced by reactive oxygen species during petroleum hydrocarbon oxidation [9, 32]. Oxidase activity reflects the presence of cytochrome oxidase, an enzyme essential in electron transport during aerobic respiration, which is the predominant mechanism for petroleum hydrocarbon degradation in many bacteria [28]. The MR and VP tests showed low positivity across all categories, these tests assess mixed acid fermentative pathways. However, slightly higher MR positivity among low‐efficiency degraders may indicate accumulation of organic acids due to incomplete degradation or alternative metabolic stress responses by the bacterial isolates.
Citrate utilization was also common among moderate‐ and high‐efficiency diesel‐degrading isolates, suggesting enhanced metabolic versatility. Citrate‐positive bacteria can exploit a broader range of organic acids and metabolic intermediates, many of which are by‐products of diesel degradation. As a central intermediate of the tricarboxylic acid (TCA) cycle, citrate is endogenously produced by most aerobic bacteria; however, the capacity to utilize exogenous citrate depends on the presence of a functional citrate‐permease system, which facilitates its transport into the cell, followed by enzymatic conversion to pyruvate and CO2 [20, 32]. Though these biochemical tests were primarily employed to assess the physiological and morphological relatedness of the isolates in this study, their outcomes also provide valuable insights into functional traits linked to hydrocarbon degradation. This metabolic adaptability likely supports bacterial survival and functional activity in petroleum hydrocarbon‐contaminated environments, where carbon sources are chemically complex and often limited. Although this study provides preliminary evidence linking citrate utilization to diesel degradation potential, further targeted investigations are warranted to expound the pathways and regulatory controls governing citrate assimilation in petroleum hydrocarbon degrading bacteria. Notably, this study reinforces these trends, the clustering of positive biochemical traits including catalase activity, oxidase production, and citrate utilization within the low‐residue (high‐efficiency) group highlights their potential as key metabolic indicators of diesel degradation capacity. In contrast, the greater absence of these traits among high‐residue (low‐efficiency) isolates is consistent with their diminished biodegradation performance, though these tests were mainly used to for testing physiological and morphological relatedness of the isolates in this study.
Molecular analysis revealed a diverse community of biodegrading bacteria, including the genera Corynebacterium, Pseudoxanthomonas, Gordonia, Pseudarthrobacter, Acinetobacter, Arthrobacter, Acidivorax, Cellulosimicrobium, Microbacterium, Cupriavidus, Exiguobacterium, Shewanella, Priestia, Paenarthrobacter, Stutzerimonas, and Pseudomonas. These findings are consistent with previous studies which reported microbiomes dominated by bacterial species of the same genera [15, 28, 31].
In the current study, Acinetobacter was the most predominant microorganism isolated, aligning with the findings of [1, 28, 30]. Genera such as Arthrobacter, Pseudomonas, Corynebacterium, Gordonia, and Microbacterium were also isolated more than once. Other genera, including Acidivorax, Cytobacillus, Cellulosimicrobium, Cupriavidus, Exiguobacterium, Shewanella, and Pseudoxanthomonas, were represented by a single isolate each. All bacteria identified in this study are soil associated microorganisms and have been previously documented for their biodegradation capabilities [1, 26, 28, 31, 33]. They are known to degrade various compounds, including petroleum hydrocarbons and diesel [28, 33, 34].
Exiguobacteria is a Gram‐positive, rod‐shaped bacterium with activity on catalase and oxidase. Exiguobacteria is alkalophilic, halophilic, thermophilic, psychrophilic, and tolerant to heavy metals which enhances its biodegradation potential as well as thrive in extreme conditions. This genus has been shown to play a significant role in the biodegradation of various pollutants, including petroleum hydrocarbons [12, 13, 35]. Similarly, Cytobacillus firmus has been associated with the degradation of several environmental pollutants. It is a facultative anaerobe and thermophile, capable of surviving in diverse environments [28]. However, its specific role in petroleum hydrocarbon biodegradation is unknown. Notably, Microbacterium found in petroleum‐contaminated environments has been shown to express alkane hydroxylases, a key enzyme required for bacterial degradation of petroleum hydrocarbons [36].
Pseudoxanthomonas species have been widely recognized for their involvement in breaking down various environmental contaminants [37]. Additionally, Hao et al. and Révész et al. [37, 38] found Acidovorax to be the predominant genus. Hao et al. and Yang et al. [37, 39] highlighted Microbacterium as an important microorganism in petroleum biodegradation. While Arthrobacter [28, 39] has been identified as a key contributor to the degradation of environmental pollutants. According to Li et al. [28], the major genera involved in petroleum hydrocarbons biodegradation are Pseudoxanthomonas, Arthrobacter, Microbacterium, Acinetobacter, and Pseudomonas.
Different bacterial genera exhibit varying capacities for petroleum hydrocarbons degradation. Many members of Acinetobacter, possess genes encoding hydrocarbon‐degrading enzymes, such as n‐alkane dioxygenase and n‐alkane hydroxylase. These primarily act on C10–C30 petroleum products under aerobic conditions [1, 26, 28, 30]. Together with Pseudomonas [26, 28, 30] have biodegradation capabilities. Acinetobacter and Pseudomonas are most abundant in the environment particularly the soil ecosystem [26]. Additionally, the two genera are known to produce bio‐surfactants that enhance the breakdown of petroleum hydrocarbons, leading to increased bacterial petroleum hydrocarbon uptake and improved biodegradation efficiency in the polluted soils [26, 30, 40]. However, Wang et al. [26] suggested that the effects of surfactants may involve complex modifications at the cellular and omics levels; they noted that while surfactants can enhance degradation, they may also have negative effects on microbial communities, necessitating further research to fully understand their impact on petroleum‐degrading bacteria.
4.1. Conclusion
A diverse and complex prokaryotic community resides in motor vehicle garage soils contaminated with petroleum hydrocarbons in Kericho County. The biochemical properties of some isolates, like oxidase, catalase, and citrate reactions, which most biodegrading microorganisms tested positive for could contribute to their ability to biodegrade petroleum hydrocarbons. This study has demonstrated that microorganisms with petroleum hydrocarbons‐degrading potential exist in garage soils, with members of genera Acinetobacter, Arthrobacter, Pseudomonas, Corynebacterium, Gordonia, and Microbacterium are dominant.
The findings from this study can be used by environmentalists and institutions in selecting and improving the isolated bacteria for commercial exploitation in environmental cleaning, in the production of bacteria and in developing recombinant cells with enhanced capabilities in environmental petroleum hydrocarbon clean‐up.
Funding
This study was supported by University of Kabianga, UOK/DIR/RLE/RG/022VOL.4/133.
Ethics Statement
The authors have nothing to report.
Consent
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
The authors acknowledge the Kenya Agricultural and Livestock Research Organization‐Tea Research Institute (KALRO‐TRI) for allowing us to use their research facility. Dr. T. Maritim KALRO‐TRI, R. Korir KALRO‐TRI, P. Kamau KALRO‐TRI, C. Kawira KALRO‐TRI, and Dr. F. Kiprotich University of Kabianga are particularly acknowledged for their technical support.
Yegon, Zeddy , Wagacha, John M. , Nyaboga, Evans , Chalo, Richard , Wafula, Eliud , Screening and Identification of Diesel Biodegrading Bacteria Isolated From Petroleum Hydrocarbon Contaminated Garage Soils of Kericho County, Kenya, International Journal of Microbiology, 2026, 8823953, 16 pages, 2026. 10.1155/ijm/8823953
Academic Editor: Diriba Muleta
Contributor Information
Zeddy Yegon, Email: zyegon@kabianga.ac.ke.
Diriba Muleta, Email: dmuleta@gmail.com.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
- 1. Su Q., Yu J., Fang K., Dong P., Li Z., Zhang W., Liu M., Xiang L., and Cai J., Microbial Removal of Petroleum Hydrocarbons From Contaminated Soil Under Arsenic Stress, Toxics. (2023) 11, no. 2, 10.3390/toxics11020143, 36851017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Xu X., Liu W., Tian S., Wang W., Qi Q., Jiang P., Gao X., Li F., Li H., and Yu H., Petroleum Hydrocarbon-Degrading Bacteria for the Remediation of Oil Pollution Under Aerobic Conditions: A Perspective Analysis, Frontiers in Microbiology. (2018) 9, 10.3389/fmicb.2018.02885. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Pandolfo E., Barra Caracciolo A., and Rolando L., Recent Advances in Bacterial Degradation of Hydrocarbons, Water. (2023) 15, no. 2, 10.3390/w15020375. [DOI] [Google Scholar]
- 4. Barreto R. A., Fossil Fuels, Alternative Energy and Economic Growth, Economic Modelling. (2018) 75, 196–220, 10.1016/j.econmod.2018.06.019, 2-s2.0-85049644774. [DOI] [Google Scholar]
- 5. Aboud E. M., Burghal A., and Laftah A. H., Genetic Identification of Hydrocarbons Degrading Bacteria Isolated From Oily Sludge and Petroleum-Contaminated Soil in Basrah City, Iraq, Biodiversitas Journal of Biological Diversity. (2021) 22, no. 4, 10.13057/biodiv/d220441. [DOI] [Google Scholar]
- 6. Manisalidis I., Stavropoulou E., Stavropoulos A., and Bezirtzoglou E., Environmental and Health Impacts of Air Pollution: A Review, Frontiers in Public Health. (2020) 8, 10.3389/fpubh.2020.00014, 32154200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Chisholm J. M., Zamani R., Negm A. M., Said N., Abdel Daiem M. M., Dibaj M., and Akrami M., Sustainable Waste Management of Medical Waste in African Developing Countries: A Narrative Review, Waste Management and Research: The Journal for a Sustainable Circular Economy. (2021) 39, no. 9, 1149–1163, 10.1177/0734242X211029175. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Ławniczak Ł., Woźniak-Karczewska M., Loibner A. P., Heipieper H. J., and Chrzanowski Ł., Microbial Degradation of Hydrocarbons—Basic Principles for Bioremediation: A Review, Molecules. (2020) 25, no. 4, 10.3390/molecules25040856. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Türkay F. Ş. H., Durmuş M., and Yakupoğlu T., Exploring Catalase Activity as a Biological Indicator in Degraded Soils, Anadolu Journal of Agricultural Sciences. (2024) 39, no. 2, 401–417, 10.7161/omuanajas.1426932. [DOI] [Google Scholar]
- 10. Flandroy L., Poutahidis T., Berg G., Clarke G., Dao M. C., Decaestecker E., Furman E., Haahtela T., Massart S., Plovier H., Sanz Y., and Rook G., The Impact of Human Activities and Lifestyles on the Interlinked Microbiota and Health of Humans and of Ecosystems, Science of the Total Environment. (2018) 627, 1018–1038, 10.1016/j.scitotenv.2018.01.288, 2-s2.0-85041473860. [DOI] [PubMed] [Google Scholar]
- 11. Rafeeq H., Afsheen N., Rafique S., Arshad A., Intisar M., Hussain A., Bilal M., and Iqbal H. M. N., Genetically Engineered Microorganisms for Environmental Remediation, Chemosphere. (2023) 310, 136751, 10.1016/j.chemosphere.2022.136751. [DOI] [PubMed] [Google Scholar]
- 12. Zhang X., Bao D., Li M., Tang Q., Wu M., Zhou H., Liu L., and Qu Y., Bioremediation of Petroleum Hydrocarbons by Alkali–Salt-Tolerant Microbial Consortia and Their Community Profiles, Journal of Chemical Technology and Biotechnology. (2021) 96, no. 3, 809–817, 10.1002/jctb.6594. [DOI] [Google Scholar]
- 13. Radhakrishnan A., Balaganesh P., Vasudevan M., Natarajan N., Chauhan A., Arora J., Ranjan A., Rajput V. D., Sushkova S., Minkina T., Basniwal R. K., Kapardar R., and Srivastav R., Bioremediation of Hydrocarbon Pollutants: Recent Promising Sustainable Approaches, Scope, and Challenges, Sustainability. (2023) 15, no. 7, 10.3390/su15075847. [DOI] [Google Scholar]
- 14. Ruiz O. N., Radwan O., and Striebich R. C., GC–MS Hydrocarbon Degradation Profile Data of Pseudomonas Frederiksbergensis SI8, a Bacterium Capable of Degrading Aromatics at Low Temperatures, Data in Brief. (2021) 35, 106864, 10.1016/j.dib.2021.106864, 33665259. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Mohapatra B. and Phale P. S., Microbial Degradation of Naphthalene and Substituted Naphthalenes: Metabolic Diversity and Genomic Insight for Bioremediation, Frontiers in Bioengineering and Biotechnology. (2021) 9, 602445, 10.3389/fbioe.2021.602445, 33791281. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Kenya National Bureau of Statistics, 2019 Kenya Population and Housing Census (Analytical Report on Household and Family Dynamics, p. 101), 2022, https://www.knbs.or.ke/wp-content/uploads/2024/05/KPHC-Census-Analytical-Report-on-Household-and-Family-Dynamics-Vol.XI_.pdf.
- 17. GADM, GADM Database of Global Administrative Areas, Version 4.1, 2023, https://gadm.org/index.html.
- 18. Kostka J. E., Prakash O., Overholt W. A., Green S. J., Freyer G., Canion A., Delgardio J., Norton N., Hazen T. C., and Huettel M., Hydrocarbon-Degrading Bacteria and the Bacterial Community Response in Gulf of Mexico Beach Sands Impacted by the Deepwater Horizon Oil Spill, Applied and Environmental Microbiology. (2011) 77, no. 22, 7962–7974, 10.1128/AEM.05402-11, 2-s2.0-83055184882. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Wafula E. N., Kuja J. O., Wekesa T. B., and Wanjala P. M., Isolation and Identification of Autochthonous Lactic Acid Bacteria From Commonly Consumed African Indigenous Leafy Vegetables in Kenya, Bacteria. (2023) 2, no. 1, 1–20, 10.3390/bacteria2010001. [DOI] [Google Scholar]
- 20. Cappuccino J. G. and Sherman N., Microbiology: A Laboratory Manual, 2005, Pearson. [Google Scholar]
- 21. Sambrook J., Fritsch E. F., and Maniatis T., Molecular Cloning: A Laboratory Manual, 1989, Cold Spring Harbor Laboratory. [Google Scholar]
- 22. Wafula E. N., Kinyua J., Kariuki D., Muigai A., and Mwirichia R., Molecular and Culture-Based Analyses of Soil Bacteria in Ngere Tea Catchment Area of Murang’a County, Kenya, Science Journal Publication. (2017) 2014, no. 105, 10.7237/sjbt/105. [DOI] [Google Scholar]
- 23. Roux K. H., Optimization and Troubleshooting in PCR, Genome Research. (1995) 4, no. 5, S185–S194. [DOI] [PubMed] [Google Scholar]
- 24. Tamura K., Dudley J., Nei M., and Kumar S., MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) Software Version 4.0, Molecular Biology and Evolution. (2007) 24, no. 8, 1596–1599, 10.1093/molbev/msm092, 2-s2.0-34547781750, 17488738. [DOI] [PubMed] [Google Scholar]
- 25. Felsenstein J., Confidence Limits on Phylogenies: An Approach Using the Bootstrap, Evolution. (1985) 39, no. 4, 783–791, 10.1111/j.1558-5646.1985.tb00420.x. [DOI] [PubMed] [Google Scholar]
- 26. Wang M., Ding M., and Yuan Y., Bioengineering for the Microbial Degradation of Petroleum Hydrocarbon Contaminants, Bioengineering. (2023) 10, no. 3, 10.3390/bioengineering10030347. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Logeshwaran P., Subashchandrabose S. R., Krishnan K., Sivaram A. K., Annamalai P., Naidu R., and Megharaj M., Polycyclic Aromatic Hydrocarbons Biodegradation by Fenamiphos Degrading Microbacterium Esteraromaticum MM1, Environmental Technology and Innovation. (2022) 27, 102465, 10.1016/j.eti.2022.102465. [DOI] [Google Scholar]
- 28. Li C., Cui C., Zhang J., Shen J., He B., Long Y., and Ye J., Biodegradation of Petroleum Hydrocarbons Based Pollutants in Contaminated Soil by Exogenous Effective Microorganisms and Indigenous Microbiome, Ecotoxicology and Environmental Safety. (2023) 253, 114673, 10.1016/j.ecoenv.2023.114673. [DOI] [PubMed] [Google Scholar]
- 29. Liu R., Zhao S., Zhang B., Li G., Fu X., Yan P., and Shao Z., Biodegradation of Polystyrene (PS) by Marine Bacteria in Mangrove Ecosystem, Journal of Hazardous Materials. (2023) 442, 130056, 10.1016/j.jhazmat.2022.130056. [DOI] [PubMed] [Google Scholar]
- 30. Mwaura A., Mbatia B., Muge E., and Okanya P., Screening and Characterization of Hydrocarbonoclastic Bacteria Isolated From Oil-Contaminated Soils From Auto Garages, International Journal of Microbiology and Biotechnology. (2018) 3, no. 1, 10.11648/j.ijmb.20180301.13. [DOI] [Google Scholar]
- 31. Curiel-Alegre S., Velasco-Arroyo B., Rumbo C., Khan A. H. A., Tamayo-Ramos J. A., Rad C., Gallego J. L. R., and Barros R., Evaluation of Biostimulation, Bioaugmentation, and Organic Amendments Application on the Bioremediation of Recalcitrant Hydrocarbons of Soil, Chemosphere. (2022) 307, 135638, 10.1016/j.chemosphere.2022.135638. [DOI] [PubMed] [Google Scholar]
- 32. Takio N., Yadav M., and Yadav H. S., Catalase-Mediated Remediation of Environmental Pollutants and Potential Application: A Review, Biocatalysis and Biotransformation. (2021) 39, no. 6, 389–407, 10.1080/10242422.2021.1932838. [DOI] [Google Scholar]
- 33. Li Z., Rosenzweig R., Chen F., Qin J., Li T., Han J., Istvan P., Diaz-Reck D., Gelman F., Arye G., and Ronen Z., Bioremediation of Petroleum-Contaminated Soils With Biosurfactant-Producing Degraders Isolated From the Native Desert Soils, Microorganisms. (2022) 10, no. 11, 10.3390/microorganisms10112267. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Das S., Das N., Choure K., and Pandey P., Biodegradation of Asphaltene by Lipopeptide-Biosurfactant Producing Hydrocarbonoclastic, Crude Oil Degrading Bacillus spp, Bioresource Technology. (2023) 382, 129198, 10.1016/j.biortech.2023.129198. [DOI] [PubMed] [Google Scholar]
- 35. Amaresan N., Senthil Kumar M., Annapurna K., Kumar K., and Sankaranarayanan A., Beneficial Microbes in Agro-Ecology: Bacteria and Fungi, 2020, Academic Press. [Google Scholar]
- 36. Yue W., Yin C.-F., Sun L., Zhang J., Xu Y., and Zhou N.-Y., Biodegradation of Bisphenol—A Polycarbonate Plastic by Pseudoxanthomonas Sp. Strain NyZ600, Journal of Hazardous Materials. (2021) 416, 125775, 10.1016/j.jhazmat.2021.125775, 33838511. [DOI] [PubMed] [Google Scholar]
- 37. Hao P., Wu S., Zhang X., Gou C., Wang Y., Wang L., Zhu Y., Basang W., and Gao Y., Characterization and Degradation Pathways of Microbacterium Resistens MZT7, A Novel 17β-Estradiol-Degrading Bacterium, International Journal of Environmental Research and Public Health. (2022) 19, no. 17, 11097, 10.3390/ijerph191711097, 36078812. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Révész F., Figueroa-Gonzalez P. A., Probst A. J., Kriszt B., Banerjee S., Szoboszlay S., Maróti G., and Táncsics A., Microaerobic Conditions Caused the Overwhelming Dominance of Acinetobacter Spp. and the Marginalization of Rhodococcus spp. In Diesel Fuel/Crude Oil Mixture-Amended Enrichment Cultures, Archives of Microbiology. (2020) 202, no. 2, 329–342, 10.1007/s00203-019-01749-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Yang X.-G., Wen P.-P., Yang Y.-F., Jia P.-P., Li W.-G., and Pei D.-S., Plastic Biodegradation by In Vitro Environmental Microorganisms and In Vivo Gut Microorganisms of Insects, Frontiers in Microbiology. (2022) 13, 1001750, 10.3389/fmicb.2022.1001750, 36687617. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Xia M., Fu D., Chakraborty R., Singh R. P., and Terry N., Enhanced Crude Oil Depletion by Constructed Bacterial Consortium Comprising Bioemulsifier Producer and Petroleum Hydrocarbon Degraders, Bioresource Technology. (2019) 282, 456–463, 10.1016/j.biortech.2019.01.131, 2-s2.0-85062893126. [DOI] [PubMed] [Google Scholar]
Associated Data
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Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.