Impact of Oil on the Bacterial Community of the Sierozems of the ‘Daulet Asia’ Landfill in Southern Kazakhstan

Abstract

In the Republic of Kazakhstan (one of the top 10 oil-producing countries in the world), the remediation of oil pollution found in unproductive soils under the conditions of a sharply continental arid climate is a highly relevant problem. The aims of this work are to study the biodegradation capacity of the gray soil of the ‘Daulet Asia’ landfill, assess the degradative potential of indigenous oil-degrading strains and changes in the composition of the soil microbial community. Analytical chemistry methods, distillation and chromatographic mass spectrometry were used for oil analysis; gravimetry and IR spectroscopy were used to evaluate oil degradation. Standard microbiological techniques were employed to isolate and cultivate microorganisms and metagenomic sequencing was carried out using Oxford Nanopore technology. Raw data processing and subsequent analysis were performed using modern software packages. Three isolated strains of interest were identified based on the analysis of 16S rRNA gene fragment sequences. The studied soil has low biodegradation capacity (oil loss was 6.2% on day 60), possibly due to the low abundance and weak activity of indigenous hydrocarbon-oxidizing microorganisms. The taxonomic composition of the microbiome in the studied soil suggests some potential for oil degradation. Assessment of the effectiveness of oil degradation by the indigenous microbiome indicates that this potential can be realized only marginally in situ. Isolated oil-degrading strains were identified as belonging to the Rhodococcus and Kocuria genera. Effective oil removal from the studied soil requires the introduction of active microorganisms (e.g., as part of biopreparations). Considering the characteristics of the hot arid climate, for bioremediation of contaminated sierozems of Southern Kazakhstan, it is advisable to use halotolerant oil-degrading microorganisms with a wide temperature range that are capable of degrading hydrocarbons under moisture deficiency.

1. Introduction

Currently, environmental pollution by oil and petroleum products poses a global threat, both in scale and toxicity [1]. Kazakhstan is one of the top 10 oil-producing countries in the world, so the problem of cleaning up oil-polluted soils is particularly pressing, and is compounded by the Republic of Kazakhstan’s sharply continental, arid climate.

Oil losses during production, transportation, processing, and storage reach 60–70 million tons annually worldwide, accounting for approximately 2% of total global production [2]. The problem of oil spill cleanup in regions with hot, arid climates has several unique characteristics: high temperatures reduce oil viscosity, accelerating oil’s diffusion into the soil; intense evaporation of light oil fractions leads to air pollution with toxic products; and the remaining non-volatile components with high molecular weights form films that are difficult to degrade. Furthermore, drilling fluids released into the soil during oil production contribute to salinization at well sites [2]. This leads to a decrease in the number of soil microorganisms and reductions in the diversity of soil microbial communities.

The largest oil fields in Kazakhstan are Tengiz, Karashyganak, Uzen, Zhanazhol, and Kumkol. Some 0.6 million hectares of oil-contaminated land have been identified in the Caspian region of Kazakhstan alone. Available data indicate that between 1.0% and 18.2% of oil and petroleum products are lost during production, preparation for refining, and transportation [1]. A distinctive feature of oil pollution in the Aral Sea region of Kazakhstan is that these territories are located primarily in a sharply continental arid climate with wide temperature fluctuations.

South of the 48th parallel, where the true desert begins, characterized by rainless summers and cold winters, brown and gray-brown soils (southern semi-desert zone and northern desert zone) form under very sparse vegetation, occupying 44% of the country’s land area (117.3 million hectares). Due to declining groundwater levels, these soils are almost always saline. Primitive sandy soils called arenosols (carbonate soils with up to 0.5% humus, and virtually saline-free) are common in the semi-desert and desert regions of southern and western Kazakhstan.

Physical, chemical, and thermal methods for removing oil spills are not always effective and can cause additional environmental harm. Bioremediation offers enormous potential and competitive advantages, primarily due to its environmental safety and low cost [3].

Previously, to assess the biodegradation potential, microbiological and biochemical activity in the soil had been studied in microfield experiments at the Akshabulak oil field under oil amendment at different doses (3, 5 and 7%) [4,5]. It was shown that oil at these concentrations, in combination with the application of fertilizers, stimulates microbiological processes of soil restoration (increases the activity level of soil urease and dehydrogenase).

In dry deserts, such as those found in Southern Kazakhstan, there is a near-constant water shortage in sandy soils. For fungi to fully realize their metabolic potential, they require constant moisture and fertile soil. Therefore, our study focused on the bacterial community.

The aims of this work are to investigate changes in the abundance and taxonomic diversity of the microbial community during oil amendment of the gray soil of the ‘Daulet Asia’ landfill and to evaluate the degradative potential of indigenous oil-degrading strains.

2. Materials and Methods

2.1. Site for Collecting Soil for Subsequent Microfield Experiment

The object of the study was the native, uncontaminated soil (about 500 kg) collected near the ‘Daulet Asia’ test site (46°04′51″ N 65°32′31″ E, Kyzylorda, Kazakhstan) that was then delivered to Kyzylorda University to perform a field test (Section 2.3).

Soil density, water content, mechanical impurities, pH values and kinematic viscosity were analyzed using standardized methods [6,7,8,9,10], respectively.

All soil experiments, including the study of oil degradation processes by the indigenous microbial community and the isolation of cultivable microorganisms from samples, were conducted between August and November 2024. Afternoon daytime temperatures (14:00–16:00) ranged from 41 °C in August to 0 °C in November. The average temperature values were 31 °C at daytime and 23 °C at night time in August, and in November, they were 5 °C at daytime and −1°C at night time.

2.2. Properties of the Oil Used in the Study

The oil was obtained from the Kumkol field (Kazakhstan) located in the Kyzylorda Region, approximately 200 km from the city of Kyzylorda (46°04′51″ N 65°32′31″ E).

The fractional composition was determined by distillation according to [11] using an ARN LAB-02 petroleum product distillation device (Garant, Moscow, Russia). The composition of crude oil purified from asphaltenes and resins was determined using chromatograph mass spectrometry using an Agilent 7890A/5975C (Santa Clara, CA, USA-Shenzhen, China) instrument. The pour point was determined according to ISO 3016:2019 [12].

2.3. Model Experiment in Soil

A model experiment to study oil degradation by the indigenous soil microbiome began on 1 August 2024 on a territory of Korkyt-Ata Kyzylorda University. Two fenced, polyethylene-lined plots measuring 1 × 1 m and with a 20 cm soil layer were set up. Crude oil was added to one plot at a rate of 50 mL/kg of soil (4.1% w/w). The oil was carefully distributed over the soil by loosening to a depth of approximately 20 cm.

To study changes in the total microbial population and the abundance of oil-oxidizing microorganisms, samples were collected every two weeks (Table S1). To study the microbial community’s response to contamination and the efficiency of oil degradation by the indigenous microbiome, soil samples were collected on days 0, 30, and 60 of the experiment. All soil samples were immediately placed in refrigerated (5–7 °C) bags and transported for further analysis.

The amount of oil in the soil was determined gravimetrically according to [13]. Soil samples were extracted with chloroform and separated from polar compounds by column chromatography after replacing the solvent with hexane.

2.4. Sample Collection and Processing

Soil samples were collected on days 0, 30, and 60 of the experiment. Each sample (1 kg) was collected using the envelope method, with sampling depths ranging from 0 to 20 cm, corresponding to the arable soil horizon. Five soil samples, collected no more than 1 m apart, were combined and thoroughly mixed to obtain a single averaged mixed sample. The samples were delivered to the laboratory in sealed bags with constant refrigeration. Large particles (plant debris and other anthropogenic or natural inclusions) were removed before analysis.

2.5. Metagenomic Sequencing of the Soil Microbiome

DNA extraction from the soil was performed no later than 7 days after sampling. During this time, the soil was stored at 0–4 °C.

2.5.1. Sample Preparation and Sequencing

Total DNA was isolated using the FastDNA Spin Kit For Soil (MP Biomedicals, UK) according to the manufacturer’s instructions. The concentration of the resulting preparations was 21–27 ng/µL.

The isolated DNA (1–5 ng) was amplified using primers 27F (5′-AGAGTTTTGATYMTGGCTCAG-3′) and 1492R (5′-GGTTACCTTGTTAYGACTT-3′) (Eurogen, Moscow, Russia) and the Tersus Plus PCR kit (Eurogen, Moscow, Russia) in a total volume of 25 μL. The reaction mode is presented in

Table 1. PCR mode for obtaining 16S rRNA amplicons.

Stage	Cycle Quantity	Temperature, °C	Duration, min
initial denaturation	1	95	2
product synthesis	27	95	1
		60	1
		72	3
final elongation	1	72	2
cooling	1	4	10

.

The quality of the amplicons was checked by electrophoresis in a 1.5% agarose gel. The final amplicons were purified using KAPA HyperPure Beads (Roche, Basel, Switzerland) according to the manufacturer’s protocol.

Libraries were prepared according to the manufacturer’s protocol (Ligation sequencing amplicons) with modifications. Amplicons were processed using the NEBNext^® UltraTM II End Repair/dA-Tailing Module (NEB, Ipswich, MA, USA). Barcodes (Native Barcoding Kit 96 (SQK-NBD114.96)) were ligated using Blunt/TA Ligase Master Mil (NEB, Ipswich, MA, USA). Libraries with barcodes were purified using KAPA Pure Beads (Roche, Basel, Switzerland). Library concentrations were measured using the Quant-iT dsDNA Assay Kit, High Sensitivity (Thermo Fisher Scientific, Waltham, MA, USA), and samples were mixed equimolarly. The final adapter (Adapter Mix II Expansion (Oxford Nanopore Technologies, Oxford, UK) was ligated with the pooled library using the NEBNext Quick Ligation Module (NEB, Ipswich, MA, USA). The prepared DNA library (12 μL) was mixed with 37.5 μL Sequencing Buffer and, for data collection, 25.5 μL Loading Beads were loaded into an R10.4.1 flow cell (FLO-MIN106; Oxford Nanopore Technologies) and sequenced using MinIONTM Mk1B software version 22.12.7 (Oxford Nanopore Technologies).

2.5.2. Bioinformatic Analysis

After sequencing, raw demultiplexed paired-end sequences were obtained. The quality of these reads was checked using FastQC ver. 11.0.13 [14]. Adapters and chimeras were removed using Porechop ver. 0.2.4 [15]. To improve the specificity of the analysis, reads between 1000 and 2000 bp in length were filtered, as they are more informative from a taxonomic point of view since they include both conserved and hypervariable regions of the 16S rRNA gene. In addition, filtering was also performed based on a minimum average read quality score of 9 (phred ≥ Q9) in accordance with the recommendations of Nygaard et al. [16]. This step was performed using Fastp ver. 0.23.4 [17].

Taxonomic classification of sequences was performed using Kraken2 ver. 2.1.3 software [18] and the SILVA database version 138 [19] with a confidence level of 0.1. This tool uses a minimization method to select k-mers (all subsequences of reads of length k) in a deterministic manner. It also masks low-complexity sequences using dustmasker.

Microbial community analysis was performed with R ver. 4.3.3 using the phyloseq [20], ggplot2 [21], tidyverse [22], breakaway [23], microbiome [24], VEGAN [25] packages, and the Pavian web application [26]. Sequence diversity for each native microbiome sample (alpha diversity) was calculated using the Shannon, Simpson (species diversity), and Chao1 (richness) indices. The analysis of dissimilarity between samples (beta diversity) was performed using principal coordinate analysis (PCoA) based on Bray–Curtis distance metrics.

2.6. Analysis of Cultivable Bacteria Diversity in Soil Samples and Characterization of Individual Strains

2.6.1. Culture Media and Conditions

Complete LB medium [27] and Evans minimal salt medium [28] were used as culture media. Solid media were obtained by adding agar (‘Pronadisa’, Madrid, Spain) at a concentration of 20 g/L. Cultures were grown at 28 °C, and an Infors HT Multitron rotary shaker (Infors AG, Bottmingen, Switzerland) was used for the incubation of samples with liquid medium.

2.6.2. Determination of the Number of Cultivable Microorganisms

A 10 g sample of soil was placed in 100 mL of phosphate buffer and stirred (2 h, 180 rpm, 28 °C). Total numbers of cultivable microorganisms were determined by direct plating of serial dilutions from the resulting suspension onto Petri dishes with LB agar medium (Section 3.5), and hydrocarbon-utilizing microorganisms were determined on Evans agar medium in diesel fuel vapor. To do this after plating diluted suspensions a 5 × 5 cm square piece of filter paper was placed onto the inner side of each Petri dish lid, and 150 μL of diesel fuel was added onto the filter paper. Than inverted Petri dishes were incubated in a dark room at 28 °C. In the first case, the cultivation duration was 5 days; in the second, it was 7 days.

2.6.3. Isolation of Oil-Degrading Strains from Soil Samples

Sample 60 (collected on day 60 of the experiment) was used to prepare enrichment cultures. The inoculum was prepared as described in Section 2.6.8. Cultivation was performed in flasks containing 100 mL of Evans medium and (1) 2% oil (w/w) or (2) 2% diesel fuel (v/v). The enrichment cultures were incubated for 7 days at 180 rpm and 28 °C.

After cultivation, a 100 µL aliquot of the enrichment culture was plated on LB agar medium. The morphotypes of the resulting microorganisms were determined visually (taking into account colony size, color, shape, and other characteristics).

2.6.4. Determination of the Spectrum of Substrates Utilized by Degrading Microorganisms

The experiment was conducted with strains of various morphotypes selected based on the results of Section 2.6.2. Cultivation was performed in liquid Evans medium supplemented with the test compounds. The experiment was performed in triplicate for each strain–substrate combination. Naphthalene was added as a powder at a concentration of 250 mg/L; phenol, toluene, benzoate, and salicylate were added at a concentration of 7.5 mL/L; hexadecane, dodecane, and diesel fuel were added at a concentration of 20 mL/L. Cultivation was carried out for seven days at 28 °C with continuous stirring at 180 rpm. Growth was confirmed using a CINTRA 6 spectrophotometer (GBC Scientific Equipment, Melbourne, Australia) (OD₆₀₀ < 0.05—no growth, OD₆₀₀ ≥ 0.5—growth detected). The same procedure was performed for Section 2.6.5, Section 2.6.6 and Section 2.6.7.

2.6.5. Determination of the Temperature Range of the Studied Strains

The temperature range of the studied strains was determined during growth on LB agar medium at temperatures of 6–45 °C. The strains’ ability to grow on oil and fuel oil as the sole source of carbon and energy in liquid Evans mineral salt medium was determined at temperatures of 30, 37, and 45 °C.

2.6.6. Determination of Halotolerance of the Studied Strains When Grown on Diesel Fuel

The strains were cultured in test tubes in liquid Evans medium containing 3, 5, 7, and 10% NaCl and 2% (v/v) diesel fuel. Cultivation was carried out for two days at 28 °C. Microorganism growth was also assessed visually by the degree of turbidity of the culture fluid.

2.6.7. Determination of the pH Range of the Studied Strains During Growth on Diesel Fuel

The strains were cultured in test tubes in liquid Evans medium, the pH of which was adjusted to 3, 4, 5, 6, 7, 8, 9, and 10 by adding concentrated HCl or NaOH. Diesel fuel (2% v/v) was used as the carbon and energy source. Cultivation was carried out for two days at 28 °C. Microbial growth was assessed visually by the degree of turbidity of the culture fluid.

For strains that demonstrated growth on various hydrocarbons, the procedure described in Section 2.6.8 was carried out.

2.6.8. Determination of Degradation Efficiency

The inoculum for all the studied strains was prepared using the following method: two to three bacterial colonies were resuspended in 10 mL of Evans medium containing sodium succinate (10 g/L) as a carbon source. Cultivation was carried out for 48 h at a temperature of 28 °C under continuous stirring (180 rpm). The resulting culture was centrifuged (10,000 rpm, 10 min), and the pellet was washed with sterile PBS buffer and resuspended until an optical density corresponding to 1 × 10⁷ CFU/mL (according to the McFarland standard) was achieved. Then 1 mL of the resulting suspension was added to flasks containing 100 mL of sterile Evans medium and 50 g/L of oil. To assess the abiotic loss of oil, a control system containing 100 mL of sterile Evans medium and 50 g/L of oil without microorganisms was used. All model systems were presented in quadruplicate. Cultivation was carried out for 10 days at a temperature of 28 °C and continuous shaking (180 rpm).

To assess the residual oil content in the liquid medium, IR spectroscopy was used in accordance with [29]. The method is based on measuring the absorption of IR radiation by C–H bonds of hydrocarbons in the region of 2930 cm⁻¹ (asymmetric vibrations of –CH₃ and –CH₂– groups) and allows for the determination of aliphatic, cyclic and aromatic hydrocarbons (but not volatile hydrocarbons such as benzene and toluene). Oil was extracted from the liquid medium by adding carbon tetrachloride (tetrachloromethane for extraction from aqueous media, chemically pure grade B, ‘Promreactive’, Krasnodar, Russia) (1:10). The resulting solution was vigorously stirred for 5 min and the fraction containing oil hydrocarbons was separated. To remove water, the resulting extract was filtered through anhydrous Na₂SO₄. Data was measured using a KN-2 concentration meter (‘Sibecopribor’, Novosibirsk, Russia). If hydrocarbon concentrations exceeded the instrument’s measurement limits, the extract was diluted using carbon tetrachloride.

The concentration of oil in the aqueous solution samples was calculated using the following formula:

$${\displaystyle \frac{P_k}{i}}={\displaystyle \frac{C\times V_I\times \eta }{v}}[\mathrm{mg}/\mathrm{d}{\mathrm{m}}^3],$$

(1)

where

${\displaystyle \frac{P_k}{i}}$ is the concentration of oil (oil products) in the eluate, found from the instrument readings or calibration curve, mg/dm³;

V_I is the eluate volume, dm³;

v is the volume of water sample analyzed, dm³;

η is the degree of dilution of the eluate—if dilution was not carried out, η = 1;

The value of C corresponds to the mass concentration of oil in the eluate, determined on the basis of the readings of the KN-2 device and recalculated according to the calibration curve constructed using standard solutions of oil hydrocarbons, expressed in mg/dm³.

The degree of oil destruction (D) in the studied systems was determined taking into account the data obtained for the control system (P_K), according to the following formula:

$$D,\%={\displaystyle \frac{P_k-P_i}{P_k}},$$

(2)

where

P_i is the oil concentration in the test system containing microorganisms, determined using IR spectroscopy.

For the strains that were selected as the most promising based on the results of the procedures in Section 2.6.4 and Section 2.6.8, the procedure described in Section 2.6.9 was completed.

2.6.9. Identification of Strains

The strains were identified based on the results of 16S rRNA gene sequence analysis. Strain DNA was isolated from fresh biomass grown in liquid LB medium (20 h of growth) using the Blood & Tissue Kit (QIAGEN, Hilden, Germany). The reaction was performed in a volume of 25 μL, and the composition of the mixture was as follows (final concentrations are indicated): buffer—1X, primers—0.25 μM, MgCl₂—2.5 mM, dNTP—0.5 mM, Taq—1U per reaction. The composition was brought to the final volume with deionized water. Information on the primers and reaction mode is presented in

Table 2. Primers and modes used for amplification of 16S rRNA gene fragments.

Primer	Sequence	Annealing Temperature, °C	Amplicon Size, bp	Ref.
63F	CAG GCC TAA CAC ATG CAA GTC	55	1300	[30]
1387R	GGG CGG WGT GTA CAA GGC

.

Amplicons were purified using the Zymoclean Gel DNA Recovery Kit (# D4001) (Zymo Research, Tustin, CA, USA). Amplicon sequencing was performed at NPO Evrogen (Moscow, Russia). Chromas, ver. 2.6.6 [31] and MEGA11 [32] were used to interpret the results.

3. Results and Discussion

3.1. Physicochemical Analysis of Soil at the Daulet Asia Test Site

The properties of the used soil are summarized in

Table 3. Main soil properties at the study site.

Trait		Value
Soil type		Medium loam (sierozem)
Water extract pH		7.5–8.3
Anion content, %	Cl−	0.06–0.14
	SO42−	0.18–0.52
	CO32−	0.03–0.06
NPK content, mg/kg soil	P2O5	2.6–33.6
	Total nitrogen	7–21
	K2O	50–60
Cation content, %	Na+	0.11–0.12
	Ca2+	0.05–0.07
	Mg2+	0.003–0.021
Humus content, %		0.102

.

In terms of composition, the soil of the Daulet Asia test site is a medium loam (sierozem), typical for Southern Kazakhstan. In terms of the degree of salinity, the soil is only slightly saline type.

Soil moisture during the observation period (2 August–1 November 2024) was 1.2–1.4%. The pH of the aqueous extract ranged from 7.5 to 8.3, and the air temperature fluctuated from 41 °C in August to 0 °C in November 2024.

The soil under investigation was characterized by low moisture content, depleted organic matter, and limited availability of key nutrients (N, P, and K).

3.2. Physicochemical Analysis of Crude Oil (Fractions and Individual Hydrocarbons)

The properties and composition of the oil used in the work are given in

Table 4. Properties of crude oil from the Kumkol field.

Trait		Value
Density (t = 20 °C), g/cm3		0.821
Water content, % w/w		0.46
Mechanical impurities, % w/w		0.02
Water extract pH		6
Freezing point, °C		−4.8
Viscosity characteristics (t = 20 °C)	Kinematic viscosity, mm2/s	18.065
	Dynamic viscosity, Pa·s	14.831·10−3

and

Table 5. Fractional composition of oil from the Kumkol field.

Proportion (% w/w) of Various Fractions in Oil
Alkanes	Cycloalkanes	Alkenes	Arenes	PAHs
70.580	17.402	2.817	2.040	7.161

.

The initial boiling point of the crude oil was 53 °C. The total yield of fractions boiling below 350 °C was 54%. The largest volume of distilled volume was accounted for by fractions in the 140–160 °C and 320–340 °C ranges, which correspond to the gasoline and diesel fractions. The crude oil, purified from asphaltenes and resins, contained 143 different compounds; the crude oil composition by fraction is shown in Table 5.

According to the data obtained, the oil can be considered light, with a high content of low-molecular-weight hydrocarbons. Its viscosity characteristics define it as a medium-viscosity oil, which is acceptable for standard pumping and refining conditions.

3.3. Estimation of Oil Loss in Field Conditions

Immediately after introducing oil into the test plot (4.1% w/w), gravimetric measurements revealed only 3.26% w/w. Apparently, the difference can be explained by irreversible sorption of oil hydrocarbons onto clay particles. Reliably distinguishing between abiotic losses and microbial degradation in situ proved unfeasible.

Soil samples were collected every two weeks to determine the total number of cultivable microorganisms, the number of oil-degrading microorganisms, and oil loss. Samples for metagenomic analysis were collected on days 0, 30, and 60. As shown in

Table 6. Dynamics of oil content in soil (w/w) in experimental plots during the model soil experiment 2 August 2024–1 November 2024 (12 weeks).

No	Sampling Date	Oil-Free Control	Oil Content, %
1	2 August 2024 (initial point)	0.00	3.26
2	15 August 2024 (two weeks)	0.00	3.12
3	30 August 2024 (four weeks)	0.00	3.04
4	16 September 2024 (six weeks)	0.00	2.99
5	30 September 2024 (eight weeks)	0.00	2.98
6	15 October 2024 (ten weeks)	0.00	2.69
7	1 November 2024 (twelve weeks)	0.00	2.02

, on day 60, the total oil loss was 9.14% of the initial amount. The low percentage of oil loss may be related to the low numbers and weak degradative activity of native soil hydrocarbon-oxidizing microorganisms (see Section 3.5, Table S1).

3.4. Analysis of the Microbial Response of an Indigenous Soil Community to Soil Contamination with Oil

Information on the results of the sequencing of soil samples collected on days 0, 30 and 60 of the experiment is presented in

Table 7. Total number of reads before and after filtering and taxonomic classification.

No	Before Filtering		After Filtering		Total Classified Sequences	Total Unclassified Sequences
	Total Sequences	Total Bases, Mbp	Total Sequences	Total Bases, Mbp
sample 0	12,413	17.1	10,576	15.6	10,235	341
sample 30	8,236	11.2	6,861	10.0	6,604	257
sample 60	2,080	2.8	1,759	2.6	1,697	62

.

A total of 6 phyla, 7 classes, 50 families, and 127 genera of bacteria were identified across all samples. At the phylum level, microbial communities were represented by the following phyla: Pseudomonadota, Bacteroidota, Bacillota, Actinomycetota, Deinococcota, and Acidobacteriota. Only one phylum, Pseudomonadota, was dominant (Table S2), with the percentage of representatives varying from 91 to 93%, and was represented by two classes, Gammaproteobacteria and Alphaproteobacteria.

Rarefaction curves constructed using the VEGAN package reached saturation at approximately 1000 reads for all three samples, indicating sufficient sequencing depth.

Analysis of alpha diversity metrics (

Table 8. Alpha diversity of analyzed samples.

No	Chao1	Shannon	Simpson
sample 0	80.1	1.97	0.68
sample 30	77.1	1.91	0.66
sample 60	55.9	2.15	0.71

, Figure S1) for the sparse dataset revealed no significant variation between the different microbiomes, and statistical comparison based on these indices also revealed no significant differences (Kruskal–Wallis, p-value = 0.37). Sample 60 showed the greatest species evenness and diversity, despite a marked decrease in overall abundance compared to the other samples. In contrast, Samples 0 and 30 maintained similar levels of species richness, but displayed less evenness in their community structure.

Analysis of the similarity degree of the indigenous soil community to oil contamination, based on the Bray–Curtis dissimilarity matrix and the principal coordinate analysis (PCoA) ordination method, revealed a distinct pattern of microbial community distribution (Figure 1). It was found that microbiomes from all soil samples formed separate clusters without grouping between them (p-value: 0.067). The first two principal coordinates, PCoA1 and PCoA2, accounted for 51% and 49% of the variance in the Bray–Curtis matrix, respectively.

The core microbiome of the communities (minimum prevalence 95%) consists of 45 bacterial genera (Figure 2A) out of the 127 detected. The genus Stenotrophomonas exhibits high abundance (on average 55% of the total microbial diversity) at low detection thresholds, while Lysobacter (0.04%), Klebsiella (0.06%), Sphaerotilus (0.03%), Verticiella (0.05%), Candidatus Kinetoplastibacterium (0.04%), Bartonella (0.05%), and Micrococcus (0.03%) exhibit low abundances even at minimum detection thresholds. Other dominant genera belonging to the core microbiome include the following (Figure 2A,B): Achromobacter, Ochrobactrum, Pedobacter, Methylococcus, Brevundimonas, Pseudomonas, Rhizobium, Alcaligenes, Sphingobacterium, Escherichia/Shigella, Serratia, and Rhodococcus.

A Venn diagram (Figure 3) was used to compare the similarities and differences between communities in different samples. Since they were more likely to reflect community function, only highly abundant sequences (prevalence = 0.75, detection = 0.001, i.e., detected in more than 90% of samples) were used for calculations. In this case, 22 common taxa were found: Stenotrophomonas, Alcaligenaceae, Achromobacter, Alcaligenes, Bordetella, Comamonadaceae, Curvibacter, Variovorax, Diaphorobacter, Serratia, Escherichia/Shigella, Pseudomonas, Methylococcus, Rhizobiaceae, Ochrobactrum, Rhizobium, Mesorhizobium, Brevundimonas, Pedobacter, Sphingobacterium, Asinibacterium, and Mycoplasma. In each sample, unique sequences belonging to various taxa were identified, so in Sample 0, Sample 30 and Sample 60 the numbers of unique sequences were 8 (Leucobacter, Streptococcus, Halomonas, Yersinia, Yersiniaceae, Enterobacterales, Delftia, and the GKS98 freshwater group), 2 (Cutibacterium, Sanguibacter) and 18 (Paenibacillus, Lactococcus, Enterococcus, Pseudochrobactrum, Meiothermus, Rhodococcus, Rothia, Hydrotalea, Massilia, Ralstonia, Burkholderia, Aquabacterium, Acidovorax, Pusillimonas, Paralcaligenes, Castellaniella, Castellaniella, and Thermomonas), respectively.

A literature analysis revealed that all 13 dominant genera had previously demonstrated the ability to degrade oil components (Table S2), and representatives of these genera had been isolated from oil-contaminated environments.

To summarize the above, it can be concluded that the taxonomic composition of the microbiome in the studied soil suggests some potential for oil degradation. However, the results of assessing the oil degradation efficiency of the indigenous microbiome (see Section 3.3) indicate that this potential is being realized only to a limited extent.

3.5. Determination of the Total Number of Cultivable Microorganisms and the Number of Hydrocarbon-Oxidizing Microorganisms During a Model Soil Experiment

In both the uncontaminated and oil-contaminated soils studied, the total number of cultivable microorganisms was approximately 2–4 × 10⁶ CFU/g of soil over 60 days (Table S1). The number of oil-degrading microorganisms during the first 30 days was 2–4 × 10² CFU/g of soil (approximately 0.01% of the total number of cultivable microorganisms). However, by the 60th day, a slight increase in the number of oil-degrading microorganisms to 4–5 × 10³ CFU/g of soil (approximately 0.1% of the total number of cultivable microorganisms) was observed.

It is known that the abundance of soil microorganisms depends on abiotic environmental factors such as soil moisture, nutrient content (N, P, and K), organic matter content, pH, and temperature. Under the experimental conditions, the soil microbiome was exposed to clearly unfavorable conditions: extremely low soil moisture (no more than 1.2%), a low NPK content, and a low organic matter content.

The absence of an obvious process causing the loss of oil hydrocarbons in the soil may be associated with the low number and low activity of native soil hydrocarbon-oxidizing microorganisms, caused by unfavorable physicochemical environmental factors.

3.6. Isolation and Characterization of Cultivable Microorganisms

Twenty-one different microbial colony morphotypes were isolated from soil samples using enrichment culture on Evans medium with diesel fuel or oil. Based on substrate specificity analysis (Table S3), most of them are not hydrocarbon degraders.

Enrichment cultivation yielded three strains—K1, K18(2), and MF2—capable of growth on various hydrocarbon substrates as the sole source of carbon and energy. Strains K1, K18(2), and MF2 were selected for further analysis (

Table 9. Ability of strains K1, K18(2) and MF2 to utilize hydrocarbon compounds.

Strain	Substrate for Isolation	Tested Compounds
		Nah	Hde	Ddc	Sal	Bzt	Tol	Phe	Dsf
K1	crude oil	+	+	−	+	+	+	+	+
K18(2)	crude oil	+	+	+	−	−	+	+	+
MF2	crude oil	−	+	+	−	−	−	+	+

Nah—naphthalene, Hde—hexadecane, Ddc—dodecane, Sal—salycilate, Bzt—benzoate, Tol—toluene, Phe—phenol, Dsf—diesel fuel; (−) OD₆₀₀ < 0.05, (+) OD₆₀₀ ≥ 0.5.

), where they were assessed for their growth ability at various temperatures and their ability to degrade diesel fuel at various pH values and sodium chloride contents in the growth medium (

Table 10. Ability of strains to degrade diesel fuel under different environmental conditions.

Strain	Growth Substrate	Conditions
		Temperature Range, °C *	pH Range	NaCl, %
K1	diesel fuel	6–45	4–10	Up to 10
K18(2)	diesel fuel	20–45	5–10	Up to 3
MF2	diesel fuel	20–37	6–10	Up to 3

* growth on LB medium.

).

All the studied strains (K1, K18(2) and MF2) are capable of growth at 37 °C on oil and fuel oil as the sole source of carbon and energy in the liquid Evans mineral medium. All three strains utilize different hydrocarbons and their derivatives, possess mesotrophy and halotolerance traits. Strain K1 has a wider temperature, NaCl and pH growth range. Evidently, such traits allow the strains survive under the severe conditions of deserts in Southern Kazakhstan.

The studied strains were identified based on analysis of 16S rRNA gene fragment sequences (

Table 11. Results of identification of strains K1, K18 and MF2 (performed using BLAST 2.17.0 [33]).

Strain	Species	Query Cover, %	Percent Identity, %
K1	Kocuria sp./Kocuria rosea	99	96.13
K18(2)	Rhodococcus corynebacterioides	95	95.73
MF2	Rhodococcus corynebacterioides	98	93.60

).

The genus ‘Rhodococcoides’, as a taxon is the result of an attempt to subdivide the genus Rhodococcus, thereby making it less heterogeneous. According to the LPSN—List of Prokaryotic Names with Standing in Nomenclature [34], the name ‘Rhodococcoides’ is currently a synonym (https://lpsn.dsmz.de/species/rhodococcoides-kroppenstedtii, accessed on 15 October 2025), so we will adhere to established terminology and classify strains MF2 and K18(2) as Rhodococcus sp. We designate strain K1 as Kocuria sp.

In addition to the ability to utilize individual compounds, these strains also demonstrated the ability to utilize oil (Figure 4).

Thus, the isolated native oil-degrading strains of the Rhodococcus and Kocuria genus have significant degradative potential and can be used for reintroduction into oil-contaminated soils to clean them.

The remediation strategy for soils contaminated with oil and petroleum products depends on the metabolic potential of microorganisms, the diversity of the microbiome, soil properties, and climatic conditions. Microorganisms from various taxonomic groups are capable of utilizing petroleum hydrocarbons. In many cases, the oil degradation process can be activated by the addition of mineral and/or organic additives (biostimulation), as well as by the introduction of specialized microbial cultures capable of degrading various petroleum hydrocarbons (bioaugmentation). Stimulating indigenous microflora appears to be a more advantageous approach, as it activates a large number of different microbial groups and species. This approach is most feasible in areas where crude oil and petroleum products are regularly released into the environment and where indigenous oil-degrading strains are present in sufficient numbers, such as at sites of natural crude oil leaks from oil fields or at oil production sites. In this case, indigenous microorganisms possess genes for the catabolism of various hydrocarbons, obtained through the exchange of genetic determinants.

In soils chronically contaminated with petroleum hydrocarbons, rhodococci are often the dominant group [35]. Possessing unique biological properties and characterized by extensive catabolic capabilities and unique enzymatic systems, coupled with the ability to survive in unfavorable environmental conditions, rhodococci can degrade pollutants with a wide variety of chemical structures [36,37,38].

The intensity of biological processes in soil ecosystems is determined by a combination of abiotic and biotic factors. It has been shown that the biodegradation of petroleum hydrocarbons depends significantly on salinity, temperature, and acidity, which slows down the mineralization process [39]. Chronic soil pollution is not necessarily accompanied by a decrease in the abundance and diversity of microbial communities, since microorganisms are highly adaptable, ensuring resistance by activating defense mechanisms. However, this significantly alters their functional characteristics, such as biodegradation potential [40,41,42]. Furthermore, some taxa, such as Stenotrophomonas, exhibit reduced efficiency of bioemulsification or surfactant synthesis, which limits their participation in self-purification processes [43]. Thus, the presence of genetic potential or even the dominance of degrading microorganisms in a community does not always guarantee high bioremediation rates. For effective biodegradation, both the composition of microbial communities and environmental conditions are important.

Since the degradative potential of the microbiome in the studied soil at the Davlet Asia site is extremely low, the introduction of active oil-oxidizing microorganisms is not only justified but absolutely necessary. Therefore, work on the development of biopreparations for environmental cleanup of oil pollution and technologies for their application will remain relevant. Given the characteristics of the hot, arid climate, it is advisable to use halotolerant oil-degrading microorganisms able to perform bioremediation of oil-contaminated areas over a wide temperature rang and capable of degrading hydrocarbons under conditions of moisture deficiency [44,45,46].