Evaluating Bacterial Population Changes and Ecological Dynamics in Oil-Impacted Soils Using 16S rRNA Amplicon Sequencing

Evaluating Bacterial Population Changes and Ecological Dynamics in Oil-Impacted Soils Using 16S rRNA Amplicon Sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating Bacterial Population Changes and Ecological Dynamics in Oil-Impacted Soils Using 16S rRNA Amplicon Sequencing Teddie Onkabetse Rahube, Ramganesh Selvarajan, Batendi Nduna, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3722259/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Improper disposal of used motor oil is a prevalent issue in developing countries, leading to a notable contribution to environmental pollution. This study was conducted using the 16S rRNA targeted metagenomic approach, to assess the changes in bacterial population diversity and abundance at an oil contamination experimental site in Botswana. To demonstrate the impact of used motor oil is on the soil ecosystem, soil samples collected at different depths before and after treatment with used motor oil were subjected to total community DNA extraction and Illumina sequencing. The taxonomic bacterial composition data revealed statistically significant differences among the treatments and controls. A notable shift from Gram-negative to Gram-positive bacterial populations was observed following treatment with used motor oil. Prevotella and Aerococcus were among the few genera within the enriched Gram-positive bacteria that could be directly linked to biodegradation of the polycyclic aromatic hydrocarbons associated with oil contamination. Agricultural and biotechnologically important, plant-associated bacterial genera; Methylobacterium-methylorumbrum, Bradyrhizobium , and, Phyllobacterium significantly declined in relative abundance, thus demonstrating the negative impact of oil contamination. The results from this study, improves our understanding of the roles of indigenous soil bacteria, and can help in guiding future strategies for the sustainable management of contaminated soils globally in countries with similar climatic and ecological conditions. Used Motor Oil Soil Contamination 16S rRNA amplicon sequencing Metagenomics Bacterial Diversity Ecosystem Health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction “Soils are vibrant and diverse natural entities situated at the juncture between Earth, air, water, and life. They serve as indispensable providers of ecosystem services that are vital for humanity's survival " (Needelman, 2013 ). Soil maintains ecological sustenance, acting as host and habitat to wide range of organisms including plants, animals, and microbes (De Deyn and Kooistra 2021 ). The typical composition of soil includes five primary components - organic matter, minerals, gases, liquids, and microorganisms. These components interact in harmony to support various functions, thereby having a direct or indirect impact on human, animal, and ecosystem health. The pollution and contamination of soil typically involve the introduction of man-made, foreign chemicals which subsequently alter soil properties. Such alterations may lead to undesirable ecological consequences. International organizations widely acknowledge soil pollution as a significant menace to soil health, causing land degradation and a decline in both terrestrial and aquatic biodiversity (FAO and UNEP, 2021 ). Activities resulting in soil pollution are largely anthropogenic, such as improper waste disposal or runoff of residues. These residues can include fertilizers, pesticides, antibiotics, heavy metals, oil, and petroleum, primarily originating from various sectors such as agriculture, pharmaceuticals, mining, textiles, and the automobile industry (Akhtar, et al. 2021; Garbuio, Howard, and dos Santos 2012; Demie 2015 ). Used Motor Oil (UMO) represents one of the numerous improperly disposed chemicals, leading to environmental pollution, particularly in developing countries. UMO carries a load of polycyclic aromatic hydrocarbons (PAHs) due to the incomplete combustion of fuel, which classifies it as a hazardous waste with potential harm to humans, animals, and the environment (Abdel-Shafy and Mansour 2016; Akintunde, Olugbenga, and Olufemi 2015). In Botswana, a considerable number of unlicensed auto mechanical repair shops are in residential areas. Regrettably, no research has been conducted to discern the extent and implications of UMO pollution in the country. This scientific data deficit leads to the unavailability of evidence-based guidance for environmental protection policies. Consequently, due to this knowledge gap, Botswana faces a heightened risk of damaging its natural biodiversity. Such a situation could have global health ramifications associated with ecological issues such as climate change. Microorganisms, including bacteria, fungi, archaea, and protozoa, are vital constituents of soil composition, playing a major role in biogeochemical cycling and the biodegradation of xenobiotic compounds (Shahid, et al. 2020). Among these, bacteria are the most abundant microorganisms in soil, with an estimated count of up to ten billion cells per gram of soil (Raynaud and Nunan 2014 ). However, despite these remarkable numbers, microbiologists estimate that less than two percent can be studied using traditional culture methods (Wade 2002 ). With the advent of polymerase chain reaction (PCR) for the detection and quantification of bacterial genes, molecular DNA-based approaches have since improved. Current research tends to leverage next-generation sequencing (NGS) techniques like amplicon sequencing, metagenomics for profiling bacterial communities and assessing their abundance, diversity, and functions across many uncultured samples. Metagenomics NGS is a technique involving the use of high throughput sequencing platforms like Illumina, PacBio, and Oxford nanopore to obtain large quantities of unbiased data from a complex mixture of microbial communities in an uncultured sample. Metagenomics approaches such as Shotgun and amplicon (marker gene) based metagenomics approaches have contributed significantly to the field of microbial ecology and have been applied in various ecosystems including contaminated water, soil, and air (Pérez-Cobas, Gomez-Valero, and Buchrieser 2020 ; Sonthiphand, et al. 2019; James, et al. 2021 ). 16S rRNA gene-based metagenomics has also been successfully employed globally and more recently in developing countries such as Botswana, Africa, for profiling community structure and predicting the functional potential of microbes in extreme environments (Ahmed, Rakan, and Ibrahim 2023; Mhete, et al. 2020). Linked to the current study, an investigation was previously carried out to monitor hydrocarbon contamination, through the application of electrical resistivity imaging (ERI), at an oil contamination experimental site at Botswana International Science of Technology (BIUST), Botswana (Nthaba et al., 2019 ). While the previous study focused on demonstrating the utility of the time-related 3D ERI methods to monitor progressive changes in the soil composition in the aftermath of an oil spill, fundamental questions remained on the link between UMO contamination with adaptation and biodegradation capabilities by indigenous microorganisms. This knowledge gap is particularly significant because understanding how indigenous microorganisms respond to and interact with hydrocarbon contamination is essential for devising effective strategies for its remediation. This study aims to demonstrate the effectiveness of using the 16S rRNA gene metagenomics approach for profiling the bacterial community in the soil in both pre-and post-treatment experiments with UMO. This study assesses the dynamics of bacterial abundance, species richness, and biodiversity to ultimately illuminate the impact of UMO on the soil ecosystem. In addition, we offer insights on the implications of the loss or enrichment of certain bacterial taxa, from phylum to genus levels, and their associated roles in soil ecosystem health or bioremediation functions. 2. Materials and Methods Site description and experimental design The climate in Botswana is semi-arid, almost throughout the country the weather is hot and dry with unpredictable rains during the summer months (November-April). This study was carried out on the campus of BIUST, located in Palapye, eastern Botswana, Africa. The BIUST campus, is a newly established university site covering 2500 hectares of land and has no documented history of anthropogenic influences. The experimental site is located at latitude 22.59460 S and longitude 27.12330 E as previously described by Nthaba et al., ( 2019 ) (Fig. S1 ). The site is characterized by clays and clay-dominated soils displaying minimal grain size variation to a maximum excavated depth, which tends to delay the percolation of pollutants in the subsurface. A pit of 2 m by 4 m was excavated to loosen the soil and prepare an impervious base. A plastic membrane was used to seal the base and the lower walls of the pit to confine the motor oil migration within the walls of the pit and to a maximum depth of 2 m. The pit was then refilled with the same excavated material to about 0.3 m beneath the ground level and UMO of about 30 L was subsequently spilled evenly on the 0.3 m depth surface before entirely covering the engine oil with the remaining excavated material to the ground level. Contaminants are usually disposed directly in the soil posing a high risk to the environment. Impermeability of the pit base and the lower walls is paramount for avoiding the unplanned leakage of hydrocarbon contaminants to the subsurface with the potential to pollute other ecosystems including groundwater. Dissolution of a contaminant such as UMO into the groundwater greatly affects groundwater quality (Seferou et al., 2012). Dissolution is one of the fundamental mass transfer processes that occur when oil is spilled on water (Bobra, 1992). Therefore, we loosened the soil and irrigated the site regularly to simulate an actual case study. Soil sample collection for microbiological analysis Before the pit was excavated for the experiment, pristine soil samples were collected. Standard augers were used to collect the soil samples, which were then placed in sterile zip-lock bags. These samples were transported to the laboratory in a cooler box with an ice pack and subsequently stored at -20°C until DNA extraction was conducted. The experiment incorporated three control samples (from 2019) and four treatment samples from both 2017 and 2019 as outlined in detail in Table S1 . DNA extraction and 16S rRNA gene sequencing Total DNA was extracted in triplicate from a homogeneous soil sample (1 gram), using the ZR Microbe DNA Extraction Kit from Zymo Research USA, following the manufacturer's guidelines. The extracted DNA was then quantified and assessed for purity using a NanoDrop spectrophotometer (Lasec, Jenway Genova Nano) at an absorbance of 260 nm. After measuring, all DNA samples were stored at -20 ⁰C for further analysis. The DNA samples were then processed for 16S rRNA gene amplicon sequencing on the Illumina MiSeq system, following a bacterial metagenomics workflow as described by Klindworth et al., (2013). Briefly, the genomic DNA samples were amplified through PCR using a universal primer pair, 341F, and 785R, which target the V3 and V4 region of the 16S rRNA gene. The resulting amplicons were purified using gel electrophoresis, and Illumina-specific adapter sequences were ligated to each amplicon. After library quantification and individual indexing of the samples, a further purification step was undertaken. The amplicons were sequenced using a 600-cycle MiSeq v3 kit (Illumina Inc). Each sample generated 20 Mb of data in the form of 2 x 300 bp paired-end reads. Bioinformatics and statistical analyses Before the bioinformatic analysis, the quality of the raw sequences obtained from the sequencing process was assessed using FastQC to ensure data integrity. Subsequently, the raw sequences were processed using the QIIME2 pipeline (Quantitative Insights into Microbial Ecology, v1.8.0 qiime.org) (Bolyen et al., 2019). This included adaptor and primer sequence removal and quality filtering, trimming, denoising, and merging using the DADA2 algorithm (Callahan et al., 2016) to infer amplicon sequence variants (ASVs), which represent biologically relevant variants. Taxonomic classification of the ASVs was accomplished by using the bacterial sequences database - Silva v138 . A taxa filter was implemented to remove sequences originating from other eukaryotic organisms, chloroplasts, and mitochondria. Various metrics were calculated to assess the diversity and richness of the bacterial communities, including the richness estimator (Chao1), diversity indices (Shannon and Simpson), and Goods coverage. The resulting data were utilized to generate bar plots and heatmaps representing taxonomic composition at different levels using Origin Pro 22 software. To identify significant differences in the bacterial community between the control and treatment groups, the Mann-Whitney U test was applied. The sequence data are available at the NCBI SRA under the BioProject Accession PRJNA781210. 3. Results After the completion of the filtering, denoising, and removal of chimeric sequences, the final sequence counts exhibited a range from 33,374 (T3B) to 119,809 (T1A). The percentage of sequences that remained non-chimeric, relative to the original input, varied among the samples, spanning from 64.54% (T4A) to 80.57% (C2). In terms of non-chimeric sequence percentages, the control samples, specifically C1, C2, and C4, displayed values of 68.98%, 80.57%, and 78.3%, respectively. In contrast, the treatment groups exhibited the following percentages: 73.58% (T1A), 65.05% (T1B), 66.62% (T2A), 68.37% (T2B), 67.82% (T3A), 69.72% (T3B), 64.54% (T4A), and 66.44% (T4B). The alpha diversity of the samples, characterized by multiple indices, displayed significant changes across different soil depths and between the control and treatment groups. At a depth of 10 cm, Control 1 (C1) demonstrated the highest diversity with 1264 operational taxonomic units (OTUs), a Chao1 index of 1278, a Shannon index of 9.39, a Simpson index of 0.997, and an evenness index of 0.911. In contrast, Treatment 1 samples (T1A, T1B) exhibited lower diversities after the treatment process. At 48 cm, Control 2 (C2) displayed lower diversity indices compared to Treatment 2 samples (T2A, T2B), with T2B registering the highest values. For the samples collected at 65 cm, both T3A and T3B exhibited similar levels of diversity. Lastly, at a depth of 80 cm, Control 4 (C4) had the lowest diversity among all the samples, while Treatment 4 samples (T4A, T4B) demonstrated higher diversity levels in comparison to their respective control samples (Fig. 1 ). The analysis of the bacterial composition in different samples revealed statistically significant differences among the treatments and controls ( p < 0.05 ). Control 1 (C1) exhibited a significantly higher abundance of Actinobacteria compared to other samples, making up 54.3% of the total bacterial composition. Conversely, Control 2 (C2) and Control 4 (C4) were significantly different with a dominance of Proteobacteria , constituting 71.5% and 80.6% of their respective bacterial populations (p < 0.05 ). Treatment 1A (T1A) also showed a significant presence of Proteobacteria , though at a relatively lower prevalence of 44.6% (p < 0.05). Furthermore, the Firmicutes phylum demonstrated the predominant bacterial group in Treatment samples, including 1B (T1B), 2A (T2A), 2B (T2B), 3A (T3A), 3B (T3B), 4A (T4A), and 4B (T4B), representing 70.8–78.7% of the bacterial communities under the treatment conditions ( p < 0.05 ). In terms of other phyla, significant variations were observed, with Chloroflexi ranging from 0.07–12.11%, Planctomycetota from 0–11.33%, Acidobacteriota from 0.29–5.10%, Gemmatimonadota from 0.02–9.57%, Verrucomicrobiota from 0–1.31%, and Myxococcota from 0–0.65% (p < 0.05). The variations in Firmicutes from 13.34–76.97% were also statistically significant, as were the fluctuations in Bacteroidota from 0.27–8.75% ( p < 0.05 ). Additionally, the collective group of Minor Phyla represented significant proportions ranging from 0.74–2.17% of the microbial populations across the samples (Fig. 2 ). The bacterial class distribution also demonstrated significant differences across the control and treatment samples (Fig. 3 ). For instance, C1 showed a significantly higher proportion of Acidimicrobiia (7.18%), Actinobacteria (12.85%), and Actinobacteriota (1.55%) ( p < 0.05 ). A shift in dominance was observed in C2, with Alphaproteobacteria taking the lead (40.20%), followed by Bacilli (14.72%), and a reduced presence of Actinobacteria and Acidimicrobiia at 3.44% and 0.53% respectively. Furthermore, there is clear dominance of Alphaproteobacteria (44.99%) and Gammaproteobacteria (35.60%) in the C3 sample ( p < 0.05 ). Sample T1A exhibited a high prevalence of Alphaproteobacteria (25.19%), accompanied by a notable presence of Bacilli (7.73%) and Gammaproteobacteria (19.37%). Similarly, the prevalence of Clostridia (51.59%) and Bacilli (24.40%) in T1B, and the dominance of Clostridia (51.87% and 47.41%) and Bacilli (24.69% and 23.38%) in T2A and T2B, respectively, were all significant ( p < 0.05 ). The samples T3A and T3B maintained the trend of Clostridia and Bacilli dominance, constituting 49.80% and 51.87%, as well as 24.77% and 25.21% of the respective communities. Finally, T4A and T4B continued with this pattern, harbouring a majority of Clostridia (52.07% and 52.84%) and Bacilli (24.84% and 25.80%). Detailed examination of the bacterial genus distribution (Fig. 4 ) across different sample groups revealed the Methylorubrum genus to be ubiquitous, with substantial variations in its relative abundance. While it achieved peak prevalence in the Treatment 2A group at 24.47%, it displayed relatively lower concentrations in the other groups, varying from 2.15–2.44%. This noticeable variation suggests the potential selectivity of Treatment 2A in promoting the proliferation of Methylorubrum , a trait absent in the other treatments and control groups. " Escherichia-Shigella " and Bradyrhizobium genera were also present in significant proportions across the different treatments, again with evident variation. The Comamonadaceae genus showed an interesting pattern, with a significant rise in its relative abundance across all treatment groups, surpassing 2%. Its maximum abundance, however, was found in the Control 4 group at 7.91%. This might imply that the conditions in the Control 4 group are especially favourable to the proliferation of this bacterial genus. Meanwhile, Bacillus and Paenibacillus genera showed relatively limited occurrence, suggesting that these genera were less influenced by the applied treatments. In Treatment 3, notable abundance was observed for the Lactobacillus and Clostridium_sensu_stricto_1 genus. The Lactobacillus genus exhibited an evident increase in Treatment 3A and 3B, peaking at 3.91% and 3.42% respectively. On the other hand, Clostridium_sensu_stricto_1 displayed a significantly improved occurrence, achieving 28.58% in Treatment 3A and 25.18% in Treatment 3B. This strongly high representation in these specific treatment groups hints towards a selective stimulatory effect of Treatment 3A and 3B on Clostridium_sensu_stricto_1 . Romboutsia also showed similar shifts, with high prevalence observed solely in Treatment 3A and 3B groups. The Mann-Whitney U test was used to identify significant differences in the bacterial community between the control and treatment groups. Notably, a significant change was observed between Control 1 and Treatment 1, while other control-treatment pairs did not show any significant differences. In terms of bacterial composition, Treatment 3 exhibited differentiation from Treatments 1 and 2 but not from Treatment 4 (Fig. 5 ). The most significant changes in bacterial populations from Control 1 to Treatment 1 were observed in several genera. The genus 67 − 14 ( Thermoleophilia class) and an uncultured genus ( Acidimicrobiia class) exhibited a significant reduction of approximately 84.09% and 72.86%, respectively. In contrast, Methylobacterium-Methylorubrum exhibited a significant increase of about 1987.62%, indicating a highly favourable response to the treatment conditions. The genus Escherichia-Shigella displayed a notable increase of approximately 1244.86%. In Treatment 2, Methylobacterium-Methylorubrum exhibited a marked decrease of 864.58%, and Escherichia-Shigella demonstrated a significant decline of 680.91%. Bradyrhizobium and Ralstonia encountered considerable reductions of 909.66% and 1081.32%, respectively, while Bacillus showed a significant reduction of 3590.48%. In contrast, Rubrobacter displayed an increase of 25.51%. Comparison of Treatment 4 with Control 4 revealed considerable variations, with Methylobacterium-Methylorubrum decreasing by approximately 642.85%, and Escherichia-Shigella experiencing a significant drop of 591.46%. Bradyrhizobium exhibited a loss of nearly 694.04%, and Ralstonia suffered a remarkable drop of 890.70%. Conversely, Lactobacillus demonstrated a notable increase of 58.11%, and Bacillus displayed a significant rise of 692.61%. Clostridium_sensu_stricto_1 ( Clostridia class) displayed the most significant increase, with a shift of approximately 27.07%, followed by Romboutsia (Clostridia class) with an increase of about 15.67%. Turicibacter ( Bacilli class) exhibited an increase of 10.69%, while the genus Muribaculaceae ( Bacteroidota phylum) showed a smaller but notable increase of 4.74%. Additionally, the well-known genus Lactobacillus from the Bacilli class showed an increase of 3.75%. Interestingly, Methylobacterium-Methylorubrum , despite decreasing in other treatments, exhibited a modest increase of 2.25% in Treatment 3. These findings highlight the diverse microbial responses and shifts to different treatments, potentially driven by selective pressures in response to oil exposure. The co-network analysis of the top 30 bacterial genera across various control and experimental conditions provides some valuable insight into the complex interactions within the microbial community (Fig. 6 ). For instance, the genus Vicinamibacteraceae demonstrates a notable ability to maintain positive relationships across multiple conditions, with significant positive correlation indicating strong associations with other genera. While g_67 presents a contrasting dynamic, with a prevalence of negative correlation, especially notable in experimental conditions. Genera like Methylobacterium and Escherichia show a network of positive correlations, that are particularly strong in experimental conditions implying that these genera may thrive or respond similarly under the conditions tested in the experiments. Romboutsia and Turicibacter have a combination of positive and negative correlations across both control and experimental conditions. Notably, Uncultured, Burkholderia , and Providencia are characterized by several significant positive correlations that are higher in experimental conditions compared to control, suggesting that the experimental manipulations may favour their association with other genera. The absence of significant negative correlations for genera such as Rubrobacter and Alcaligenes , and for several uncultured genera, across both control and experimental conditions, is indicative of either a non-competitive stance or a broad ecological niche that allows for coexistence without direct antagonism. while others exhibit a balance of positive and negative correlations across control and experimental conditions. This pattern suggests that experimental manipulations can either promote cooperation or competition among genera, highlighting the dynamic nature of microbial interactions in response to environmental changes. 4. Discussion Soil bacterial diversity is known to be influenced by changes in the environmental physico-chemical factors such as temperature, pH, water content, nutrients, texture, and vegetation types. ((Mhete, et al. 2020; Aguado-Norese, et al. 2023). In this study, we observed high diversity and variation both within and across different treatment groups and depths, highlighting the complexity of the soil samples. At 0–10 cm, which is characteristic of topsoil, there is high variation compared to other depths. Considering T1A and T1B, topsoil collected in 2017 and 2019 respectively, it is not surprising considering the direct exposure of topsoil to environmental factors, especially temperature. The influence of broader factors such as climate change and human activities cannot be overlooked. Rising temperatures and increased frequency of extreme weather events can alter soil conditions, affecting microbial life (Jansson and Hofmockel 2020 ). In this study, a total of 10 major phyla, and 22 major classes were observed in the topsoil of the control samples. Considering only the sub-surface (due to expected variation in the topsoil), there is a significant shift in bacterial community composition between control and treatment in the corresponding soil depth. The phylum Proteobacteria which is most predominant in the control samples when compared to treatment samples is replaced by the phylum Firmicutes which appears predominant in the treatment samples. The phylum Proteobacteria , known for its diverse metabolic capabilities and predominance in various environments, is commonly affected by soil disturbances, including anthropogenic activities. In contrast, Firmicutes , with many members known for their resilience and ability to degrade hydrocarbons, often increase in abundance in contaminated sites. This pattern has been observed in other studies, which reported an increase in Firmicutes in oil-contaminated soils, reflecting their potential role in hydrocarbon degradation (Shahi, et al. 2016; Liu, et al. 2020; Devi, et al. 2021). The Proteobacteria is a phylum characterized mainly by Gram-negative bacteria while Firmicutes are characterised mainly by Gram-positive bacteria. The decrease in Gram-negative bacteria and the enrichment of Gram-positive bacteria in contaminated soils reveals that hydrocarbon contamination can selectively inhibit certain microbial groups while favouring others, particularly those capable of hydrocarbon degradation. Further, the shift in microbial communities due to contamination not only reflects the resilience and adaptability of soil bacteria but also has implications for soil health. As Sharma et al., ( 2011 ) pointed out, changes in microbial community composition can affect soil fertility, structure, and its capacity to support plant life. Enrichment of bacterial genera: Implications to soil ecosystem function and health The bacterial genera Muribaculaceae, Prevotella, Aerococcus, Romboutsia, clostridium_sensu_stricto, Dubosiella, Faecalibaculum , and Turicibacter , all categorically linked to the major phylum Firmicutes were enriched following exposure to UMO in this experiment. This enrichment, while indicative of a potential adaptive mechanism to hydrocarbon pollutants, warrants a deeper investigation into the specific metabolic pathways activated under these conditions. Only the genus Prevotella and Aerococcus could be directly associated with positive ecosystem services such as biodegradation of xenobiotic and recalcitrant pollutants. Prevotella’s primary involvement in crude oil degradation was first reported by Mukjang, et al. (2022), and Aerococcus has also been known to be present in hydrocarbon-contaminated environments (Biswas, et al. 2022; Dey, Das, and Kazy 2018 ) suggest a targeted approach in bioremediation strategies, yet the efficiency and specificity of these bacteria in different hydrocarbon types remain unclear. Interestingly, many bacterial genera of animal origin, associated with human and animal gut microbiota were found to have increased in relative abundance. These include Turicibacter , which has been reported from metagenomic and meta-transcriptome studies to be predominant, especially in the fermentation of compost, and known to harbour various hydrolytic enzymes associated with the degradation of plant lignocellulose (Huang, et al. 2022). The genus Romboutsia , belong to Peptostreptococcaceae family within the Firmicutes , these anaerobic bacteria species are adapted to environments that are rich in nutrients but primarily known to play a major role in the degradation of carbohydrates (Jacoline, et al. 2019). Based on the co-occurrence network analysis, Romboutsia and Turicibacter reveal complex interactions that could reflect a flexible adaptation strategy to a variety of environmental states or perhaps indicate that their abundance is sensitive to the specific conditions of each experiment. Similarly, the role of Dubosiella, Faecalibaculum, and Muribaculaceae in degrading complex carbohydrates (Zuo, et al. 2023; Li, et al. 2020) points to their functional diversity, but the extent to which these functions are leveraged in hydrocarbon-contaminated soils is not fully understood. Clostridium_sensu_stricto , is considered a true genus of Clostridium (Li, et al. 2023), and often associated with the gut microbiota, has also been found to play a role in Microcystis biomass decomposing especially in aquatic ecosystems (Zhao, et al. 2017). Genus Clostridium produces endospores that help in adaptations and survival in harsh environmental conditions (Dréan, et al. 2015). A substantial proportion of uncultured bacteria also appear in higher abundance in the treatments relative to controls, these unidentified and mysterious representatives warrant further investigation, these uncultured populations may have both positive and negative impact on soil ecosystem function and health. Our study further emphasizes and demonstrates the power of utilizing 16S rRNA gene-based metagenomics approach towards discovery of novel bacteria, albeit the challenges that may still exists in identification due to information deficiencies in sequence archives or databases. Nonetheless, more recent next generation sequencing studies remain hopeful of the potential role that many uncultured bacteria may have in relation to ecosystem services such as the biodegradation of environmental pollutants (Bodor, et al. 2020). The loss of bacterial genera and its impact on soil ecological functions The family Vicinamibacteraceae , the first described within Acidobacteria, constitutes a globally widespread group of Gram-negative, non-spore-forming, aerobic, chemo-organoheterotrophic bacteria inhabiting soil environments (Huber and Overmann, 2018). As observed in the co-network analysis, Vicinamibacteraceae may play a key role in the microbial ecosystem across varied environmental states, as revealed in both control and experimental settings. Vicinamibacteraceae are known to be functionally versatile and play many important roles including nutrient mobilization during decomposition, and thus maintaining soil ecosystem health (Chiba, et al. 2021). Again, disregarding the topsoil, there is some light of evidence suggesting the loss of certain bacterial genera within the phylum Proteobacteria following soil treatment with UMO. Notable bacterial genera representatives of the phylum Proteobacteria ; Methylobacterium-Methylorumbrum, Escherichia-Shigella, Bradyrhizobium, Ralstonia, Phyllobacterium significantly declined in relative abundance in the soil sub-surface and below. Methylobacterium and Escherichia show a network of positive correlations, that are particularly strong in experimental conditions implying that these genera may thrive or respond similarly under the conditions tested in the experiments. Genus Methylobacterium-methylorumbrum comprises closely related facultatively methylotrophic bacterial species that some literature reported their use in bioaugmentation and efficiency in biodegradation of polyaromatic hydrocarbons in contaminated environments (Dhar, et al. 2022; Giri, et al. 2021 ). Certain species of Methylobacterium are also known to be of agricultural importance, plant-associated bacteria, and model organisms in microbiology (Leducq, et al. 2022) playing a vital role as biostimulators by producing phytohormones and provide important nutrients to plants as they fix nitrogen and solubilize phosphorus and iron, to promote plant growth (Zhang, et al. 2021). The biotechnological role of other methylorumbrum species in environmental bioremediation has also been recently documented in the literature (Rojas-Gätjens, et al. 2022; Quynh Le, et al. 2022 ). Similarly, Bradyrhizobium , symbiotic bacteria encodes multiple functions that are critical to plant growth including nitrogen fixation and nodulation (Wongdee, et al. 2023). Phyllobacterium is another well researched plant probiotic that has not been associated with causing diseases in humans and described as a good candidate for use as a biofertilizer, supplying phosphorus for plants, especially during dry seasons (Breitkreuz, et al. 2020). In contrast, genera like Escherichia-Shigella and Ralstonia are often associated with negative impacts on human and plant health, suggesting a complex interplay of beneficial and harmful microbial elements within the same ecosystem. This duality emphasizes the need for a better understanding of microbial community compositions, especially in the milieu of environmental changes and ecosystem health. 5. Conclusion This study has shed light on the profound impact that UMO contamination has on soil ecosystem health, particularly through its alteration of bacterial diversity and abundance. The presence of oil pollution in the soil leads to significant ecosystem alterations, marked by notable changes in bacterial community structure. This includes both the depletion and proliferation of specific bacterial genera, thereby influencing the ecological functions of the soil. In addition, the study successfully identified key bacterial genera and their potential roles, providing valuable insights that could inform future strategies for the effective management of soil contamination in similar regions worldwide. Further, the use of the 16S rRNA amplicon sequencing approach was found to be effective in providing high resolution of the OTUs down to the genus level. Consequently, this study encourages for the expanded use of the 16S rRNA gene metagenomics approach in long-term environmental pollution studies, coupled with physicochemical methods to fully understand the roles and relationships among the physical, chemical parameters, and microbial populations in a contaminated environmental setting. Declarations Acknowledgments: Authors would like to thank the Centre for High Performance Computing (CHPC) facility, Pretoria, South Africa for providing computational support for sequence data analysis, Mr Boniface Kgosidintsi and Mr Phenyo Tlale for tireless efforts in assisting with this research work. Funding: This work was supported by the Botswana International University of Science and Technology. Author Contributions: Loago Molwalefhe, Elisha Shemang, and Teddie Onkabetse Rahube contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by Bokani Nthaba, Batendi Nduna, and Ramganesh Selvarajan . The manuscript draft was prepared by Teddie Onkabetse Rahube and Ramganesh Selvarajan. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Ethical Approval: Not applicable. Consent to Participate: Not applicable. Consent to Publish: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. References Abdel-Shafy, Hussein I., and Mona S. M. Mansour. 2016. "A Review on Polycyclic Aromatic Hydrocarbons: Source, Environmental Impact, Effect on Human Health and Remediation." Egyptian Journal of Petroleum 25, no. 1 (2016/03/01/): 107-123. http://dx.doi.org/https://doi.org/10.1016/j.ejpe.2015.03.011. Aguado-Norese, Constanza, et al. 2023. "Topsoil and Subsoil Bacterial Community Assemblies across Different Drainage Conditions in a Mountain Environment." 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"Trypoxylus Dichotomus Gut Bacteria Provides an Effective System for Bamboo Lignocellulose Degradation." Microbiol Spectr 10, no. 5 (Oct 26): e0214722. http://dx.doi.org/10.1128/spectrum.02147-22. Jacoline, Gerritsen, et al. 2019. "A Comparative and Functional Genomics Analysis of the Genus Romboutsia Provides Insight into Adaptation to an Intestinal Lifestyle." bioRxiv : 845511. http://dx.doi.org/10.1101/845511. James, Grace Lymie, et al. 2021. "Metagenomic Datasets of Air Samples Collected During Episodes of Severe Smoke-Haze in Malaysia." Data in Brief 36 (2021/06/01/): 107124. http://dx.doi.org/https://doi.org/10.1016/j.dib.2021.107124. Jansson, Janet K., and Kirsten S. Hofmockel. 2020. "Soil Microbiomes and Climate Change." Nature Reviews Microbiology 18, no. 1 (2020/01/01): 35-46. http://dx.doi.org/10.1038/s41579-019-0265-7. Leducq, J. B., et al. 2022. "Comprehensive Phylogenomics of Methylobacterium Reveals Four Evolutionary Distinct Groups and Underappreciated Phyllosphere Diversity." Genome Biol Evol 14, no. 8 (Aug 3). http://dx.doi.org/10.1093/gbe/evac123. Li, Cong-Jian, et al. 2023. "Comparative Genomic Analysis and Proposal of Clostridium Yunnanense Sp. Nov., Clostridium Rhizosphaerae Sp. Nov., and Clostridium Paridis Sp. Nov., Three Novel Clostridium Sensu Stricto Endophytes with Diverse Capabilities of Acetic Acid and Ethanol Production." Anaerobe 79 (2023/02/01/): 102686. http://dx.doi.org/https://doi.org/10.1016/j.anaerobe.2022.102686. Li, Li-Li, et al. 2020. "Inulin with Different Degrees of Polymerization Protects against Diet-Induced Endotoxemia and Inflammation in Association with Gut Microbiota Regulation in Mice." Scientific Reports 10, no. 1 (2020/01/22): 978. http://dx.doi.org/10.1038/s41598-020-58048-w. Liu, H., et al. 2020. "Distribution Characteristics of Bacterial Communities and Hydrocarbon Degradation Dynamics During the Remediation of Petroleum-Contaminated Soil by Enhancing Moisture Content." Microb Ecol 80, no. 1 (Jul): 202-211. http://dx.doi.org/10.1007/s00248-019-01476-7. Mhete, Modise, et al. 2020. "Soil Properties Influence Bacterial Abundance and Diversity under Different Land-Use Regimes in Semi-Arid Environments." Scientific African 7 (2020/03/01/): e00246. http://dx.doi.org/https://doi.org/10.1016/j.sciaf.2019.e00246. Mukjang, N., et al. 2022. "Bacterial Communities Associated with Crude Oil Bioremediation through Composting Approaches with Indigenous Bacterial Isolate." Life (Basel) 12, no. 11 (Oct 27). http://dx.doi.org/10.3390/life12111712. Needelman, B. A. 2013. "What Are Soils? "Nature Education Knowledge 4(3):2 https://www.nature.com/scitable/knowledge/library/what-are-soils-67647639/ Nthaba, B., Shemang, E., Gareutlwane, O., & Molwalefhe, L. 2019." Application of electrical resistivity imaging (ERI) to investigate an oil contaminated experimental site". Environmental Engineering and Management Journal , 18(12), 2623-2634. https://doi.org/10.30638/eemj.2019.247 Pérez-Cobas, A. E., L. Gomez-Valero, and C. Buchrieser. 2020. "Metagenomic Approaches in Microbial Ecology: An Update on Whole-Genome and Marker Gene Sequencing Analyses." Microb Genom 6, no. 8 (Aug). http://dx.doi.org/10.1099/mgen.0.000409. Quynh Le, Hoa Thi, et al. 2022. "Development of Methylorubrum Extorquens Am1 as a Promising Platform Strain for Enhanced Violacein Production from Co-Utilization of Methanol and Acetate." Metabolic Engineering 72 (2022/07/01/): 150-160. http://dx.doi.org/https://doi.org/10.1016/j.ymben.2022.03.008. Raynaud, X., and N. Nunan. 2014. "Spatial Ecology of Bacteria at the Microscale in Soil." PLoS One 9, no. 1: e87217. http://dx.doi.org/10.1371/journal.pone.0087217. Rojas-Gätjens, D., et al. 2022. "Methylotrophs and Hydrocarbon-Degrading Bacteria Are Key Players in the Microbial Community of an Abandoned Century-Old Oil Exploration Well." Microb Ecol 83, no. 1 (Jan): 83-99. http://dx.doi.org/10.1007/s00248-021-01748-1. Shahi, Aiyoub, et al. 2016. "Reconstruction of Bacterial Community Structure and Variation for Enhanced Petroleum Hydrocarbons Degradation through Biostimulation of Oil Contaminated Soil." Chemical Engineering Journal 306 (2016/12/15/): 60-66. http://dx.doi.org/https://doi.org/10.1016/j.cej.2016.07.016. Shahid, Munazzam J., et al. 2020. "Role of Microorganisms in the Remediation of Wastewater in Floating Treatment Wetlands: A Review." Sustainability 12, no. 14. http://dx.doi.org/10.3390/su12145559. Sharma, Sushil K., et al. 2011. "Microbial Community Structure and Diversity as Indicators for Evaluating Soil Quality." In Biodiversity, Biofuels, Agroforestry and Conservation Agriculture , edited by Eric Lichtfouse, 317-358. Dordrecht: Springer Netherlands. Sonthiphand, Prinpida, et al. 2019. "Metagenomic Insights into Microbial Diversity in a Groundwater Basin Impacted by a Variety of Anthropogenic Activities." Environmental Science and Pollution Research 26, no. 26 (2019/09/01): 26765-26781. http://dx.doi.org/10.1007/s11356-019-05905-5. Wade, William. 2002. "Unculturable Bacteria--the Uncharacterized Organisms That Cause Oral Infections." Journal of the Royal Society of Medicine 95, no. 2: 81-83. http://dx.doi.org/10.1258/jrsm.95.2.81. Wongdee, Jenjira, et al. 2023. "Role of Two Rpon in Bradyrhizobium Sp. Strain Doa9 in Symbiosis and Free-Living Growth." Frontiers in Microbiology 14. Zhang, Cong, et al. 2021. "Potentials, Utilization, and Bioengineering of Plant Growth-Promoting Methylobacterium for Sustainable Agriculture." Sustainability 13, 7. http://dx.doi.org/10.3390/su13073941. Zhao, D., et al. 2017. "Variation of Bacterial Communities in Water and Sediments During the Decomposition of Microcystis Biomass." PLoS One 12, no. 4: e0176397. http://dx.doi.org/10.1371/journal.pone.0176397. Zuo, Wei-Fang, et al. 2023. "Gut Microbiota: A Magical Multifunctional Target Regulated by Medicine Food Homology Species." Journal of Advanced Research 52 (2023/10/01/): 151-170. http://dx.doi.org/https://doi.org/10.1016/j.jare.2023.05.011. Supplementary Files SUPPLEMENTARYFILE.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3722259","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266298727,"identity":"40b06fc0-3e92-43b0-ae3a-c291af825e57","order_by":0,"name":"Teddie Onkabetse Rahube","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFACHgZmIClnwAwTkCBOi4ExWMsBUrQkbmAgVot5+9lj0gUVf9K3s/MYf/7AYCfPIN1jgFeLzJm8NOkZZwxydzbzmEkcYEg2bJA5g1+LBEOOmTRvm0HuhsM8ZkCHMScwSORuwK+F/w1Qyz+DdIPDPMYfDjDUE6FFAmRLg0ECUIsB0GGHidHyxth6xjFjw53NbGUSZwyOG7bJnP9AwGE5hrcLauTkzfkPb/5QUVEtzy/dloBXCxoAhhUbKepHwSgYBaNgFGAHALg7O2/xHSFtAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-5342-7276","institution":"Botswana International University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Teddie","middleName":"Onkabetse","lastName":"Rahube","suffix":""},{"id":266298728,"identity":"2f8d6be2-2ccc-498c-b648-d4455e7bed5c","order_by":1,"name":"Ramganesh Selvarajan","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ramganesh","middleName":"","lastName":"Selvarajan","suffix":""},{"id":266298729,"identity":"ece28099-e8aa-48c1-a0ae-e6f6fa7346d8","order_by":2,"name":"Batendi Nduna","email":"","orcid":"","institution":"Botswana International University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Batendi","middleName":"","lastName":"Nduna","suffix":""},{"id":266298730,"identity":"c1bb0ef8-0d8f-4293-af86-061aff14dfd7","order_by":3,"name":"Bokani Nthaba","email":"","orcid":"","institution":"Botswana International University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Bokani","middleName":"","lastName":"Nthaba","suffix":""},{"id":266298731,"identity":"9e6855ee-d2de-48d2-8605-008a6654cb9a","order_by":4,"name":"Loago Molwalefhe","email":"","orcid":"","institution":"Botswana International University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Loago","middleName":"","lastName":"Molwalefhe","suffix":""},{"id":266298732,"identity":"ddbda006-fef7-4d70-9335-b58939410bd1","order_by":5,"name":"Elisha Shemang","email":"","orcid":"","institution":"Botswana International University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Elisha","middleName":"","lastName":"Shemang","suffix":""}],"badges":[],"createdAt":"2023-12-07 20:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3722259/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3722259/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49504186,"identity":"2b84cf03-6dcf-4cf2-80a4-d66cc6f2a00f","added_by":"auto","created_at":"2024-01-12 02:39:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":301117,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlpha diversity index of bacterial communities across different soil depths (T1; 0-10 cm, T2; 42-48 cm, T3; 61-67 cm, T4; 70-80 cm) and between control (C1, C2, C4) and treatment groups (T1A, T1B, T2A, T2B, T3A, T3B, T4A, T4B).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3722259/v1/17b85fb3b2e0affbca92145f.png"},{"id":49504185,"identity":"c8dcae59-b9f8-4fcf-b9e6-03762695dfae","added_by":"auto","created_at":"2024-01-12 02:39:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":276845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClustered stacked column chart showing phylum level distribution of bacterial composition in control (C1, C2, and C4) and treatment soil (T1, T2, T3, T4).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3722259/v1/1555ba6108b8b99df884fd1e.png"},{"id":49504183,"identity":"fed4d0de-8809-447c-b183-63ffc434969b","added_by":"auto","created_at":"2024-01-12 02:39:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":384658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClustered stacked column chart showing the class-level distribution of bacterial composition in control (C1, C2, and C4) and treatment soil (T1, T2,T3, T4).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3722259/v1/3cc8250b597135984f76a308.png"},{"id":49504187,"identity":"296f1d63-9fa6-4059-a723-45f6fe365de2","added_by":"auto","created_at":"2024-01-12 02:39:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":913530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeat map revealing abundance of bacteria genus in control (C1, C2, C4) and treatment soil (T1, T2, T3, T4). Nine bacterial phyla are represented using (A) clustered stacked column chart and (B) heat map.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3722259/v1/f159b8f8f9865a5f0648b711.png"},{"id":49504184,"identity":"5ad30f13-4d84-45f1-9ef1-b7f5961c14b1","added_by":"auto","created_at":"2024-01-12 02:39:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":182187,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends and abundance of the bacterial genus within the major phyla Firmicutes (F) and Proteobacteria (P) across controls (C1, C2, C4) and treatments (T1, T2, T3, T4)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3722259/v1/6b4f5a91a8362ed4187b6c9d.png"},{"id":49504188,"identity":"f380392c-8dbe-4bad-88bd-4ed40c92a9eb","added_by":"auto","created_at":"2024-01-12 02:39:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2047066,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-occurrence network analysis at the bacterial genus level\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3722259/v1/5a6f4e56a51091215c8b7142.png"},{"id":50961217,"identity":"cb60cb90-62d6-4137-b47d-358c04e6cdfe","added_by":"auto","created_at":"2024-02-11 04:35:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3418458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3722259/v1/445472ea-6e21-4705-980b-9fd37364608e.pdf"},{"id":49504190,"identity":"d88c263f-f44e-4ced-b727-a10a173fa2d0","added_by":"auto","created_at":"2024-01-12 02:39:54","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1757337,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYFILE.docx","url":"https://assets-eu.researchsquare.com/files/rs-3722259/v1/05e16296109e31add314a969.docx"}],"financialInterests":"","formattedTitle":"Evaluating Bacterial Population Changes and Ecological Dynamics in Oil-Impacted Soils Using 16S rRNA Amplicon Sequencing","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003e\u0026ldquo;Soils are vibrant and diverse natural entities situated at the juncture between Earth, air, water, and life. They serve as indispensable providers of ecosystem services that are vital for humanity's survival\u003c/em\u003e\" (Needelman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Soil maintains ecological sustenance, acting as host and habitat to wide range of organisms including plants, animals, and microbes (De Deyn and Kooistra \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The typical composition of soil includes five primary components - organic matter, minerals, gases, liquids, and microorganisms. These components interact in harmony to support various functions, thereby having a direct or indirect impact on human, animal, and ecosystem health. The pollution and contamination of soil typically involve the introduction of man-made, foreign chemicals which subsequently alter soil properties. Such alterations may lead to undesirable ecological consequences. International organizations widely acknowledge soil pollution as a significant menace to soil health, causing land degradation and a decline in both terrestrial and aquatic biodiversity (FAO and UNEP, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Activities resulting in soil pollution are largely anthropogenic, such as improper waste disposal or runoff of residues. These residues can include fertilizers, pesticides, antibiotics, heavy metals, oil, and petroleum, primarily originating from various sectors such as agriculture, pharmaceuticals, mining, textiles, and the automobile industry (Akhtar, et al. 2021; Garbuio, Howard, and dos Santos 2012; Demie \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsed Motor Oil (UMO) represents one of the numerous improperly disposed chemicals, leading to environmental pollution, particularly in developing countries. UMO carries a load of polycyclic aromatic hydrocarbons (PAHs) due to the incomplete combustion of fuel, which classifies it as a hazardous waste with potential harm to humans, animals, and the environment (Abdel-Shafy and Mansour 2016; Akintunde, Olugbenga, and Olufemi 2015). In Botswana, a considerable number of unlicensed auto mechanical repair shops are in residential areas. Regrettably, no research has been conducted to discern the extent and implications of UMO pollution in the country. This scientific data deficit leads to the unavailability of evidence-based guidance for environmental protection policies. Consequently, due to this knowledge gap, Botswana faces a heightened risk of damaging its natural biodiversity. Such a situation could have global health ramifications associated with ecological issues such as climate change.\u003c/p\u003e \u003cp\u003eMicroorganisms, including bacteria, fungi, archaea, and protozoa, are vital constituents of soil composition, playing a major role in biogeochemical cycling and the biodegradation of xenobiotic compounds (Shahid, et al. 2020). Among these, bacteria are the most abundant microorganisms in soil, with an estimated count of up to ten billion cells per gram of soil (Raynaud and Nunan \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, despite these remarkable numbers, microbiologists estimate that less than two percent can be studied using traditional culture methods (Wade \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). With the advent of polymerase chain reaction (PCR) for the detection and quantification of bacterial genes, molecular DNA-based approaches have since improved. Current research tends to leverage next-generation sequencing (NGS) techniques like amplicon sequencing, metagenomics for profiling bacterial communities and assessing their abundance, diversity, and functions across many uncultured samples. Metagenomics NGS is a technique involving the use of high throughput sequencing platforms like Illumina, PacBio, and Oxford nanopore to obtain large quantities of unbiased data from a complex mixture of microbial communities in an uncultured sample. Metagenomics approaches such as Shotgun and amplicon (marker gene) based metagenomics approaches have contributed significantly to the field of microbial ecology and have been applied in various ecosystems including contaminated water, soil, and air (P\u0026eacute;rez-Cobas, Gomez-Valero, and Buchrieser \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sonthiphand, et al. 2019; James, et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 16S rRNA gene-based metagenomics has also been successfully employed globally and more recently in developing countries such as Botswana, Africa, for profiling community structure and predicting the functional potential of microbes in extreme environments (Ahmed, Rakan, and Ibrahim 2023; Mhete, et al. 2020).\u003c/p\u003e \u003cp\u003eLinked to the current study, an investigation was previously carried out to monitor hydrocarbon contamination, through the application of electrical resistivity imaging (ERI), at an oil contamination experimental site at Botswana International Science of Technology (BIUST), Botswana (Nthaba et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While the previous study focused on demonstrating the utility of the time-related 3D ERI methods to monitor progressive changes in the soil composition in the aftermath of an oil spill, fundamental questions remained on the link between UMO contamination with adaptation and biodegradation capabilities by indigenous microorganisms. This knowledge gap is particularly significant because understanding how indigenous microorganisms respond to and interact with hydrocarbon contamination is essential for devising effective strategies for its remediation. This study aims to demonstrate the effectiveness of using the 16S rRNA gene metagenomics approach for profiling the bacterial community in the soil in both pre-and post-treatment experiments with UMO. This study assesses the dynamics of bacterial abundance, species richness, and biodiversity to ultimately illuminate the impact of UMO on the soil ecosystem. In addition, we offer insights on the implications of the loss or enrichment of certain bacterial taxa, from phylum to genus levels, and their associated roles in soil ecosystem health or bioremediation functions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e \u003cb\u003eSite description and experimental design\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe climate in Botswana is semi-arid, almost throughout the country the weather is hot and dry with unpredictable rains during the summer months (November-April). This study was carried out on the campus of BIUST, located in Palapye, eastern Botswana, Africa. The BIUST campus, is a newly established university site covering 2500 hectares of land and has no documented history of anthropogenic influences. The experimental site is located at latitude 22.59460 S and longitude 27.12330 E as previously described by Nthaba et al., (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The site is characterized by clays and clay-dominated soils displaying minimal grain size variation to a maximum excavated depth, which tends to delay the percolation of pollutants in the subsurface. A pit of 2 m by 4 m was excavated to loosen the soil and prepare an impervious base. A plastic membrane was used to seal the base and the lower walls of the pit to confine the motor oil migration within the walls of the pit and to a maximum depth of 2 m. The pit was then refilled with the same excavated material to about 0.3 m beneath the ground level and UMO of about 30 L was subsequently spilled evenly on the 0.3 m depth surface before entirely covering the engine oil with the remaining excavated material to the ground level.\u003c/p\u003e \u003cp\u003eContaminants are usually disposed directly in the soil posing a high risk to the environment. Impermeability of the pit base and the lower walls is paramount for avoiding the unplanned leakage of hydrocarbon contaminants to the subsurface with the potential to pollute other ecosystems including groundwater. Dissolution of a contaminant such as UMO into the groundwater greatly affects groundwater quality (Seferou et al., 2012). Dissolution is one of the fundamental mass transfer processes that occur when oil is spilled on water (Bobra, 1992). Therefore, we loosened the soil and irrigated the site regularly to simulate an actual case study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSoil sample collection for microbiological analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBefore the pit was excavated for the experiment, pristine soil samples were collected. Standard augers were used to collect the soil samples, which were then placed in sterile zip-lock bags. These samples were transported to the laboratory in a cooler box with an ice pack and subsequently stored at -20\u0026deg;C until DNA extraction was conducted. The experiment incorporated three control samples (from 2019) and four treatment samples from both 2017 and 2019 as outlined in detail in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDNA extraction and 16S rRNA gene sequencing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTotal DNA was extracted in triplicate from a homogeneous soil sample (1 gram), using the ZR Microbe DNA Extraction Kit from Zymo Research USA, following the manufacturer's guidelines. The extracted DNA was then quantified and assessed for purity using a NanoDrop spectrophotometer (Lasec, Jenway Genova Nano) at an absorbance of 260 nm. After measuring, all DNA samples were stored at -20 ⁰C for further analysis. The DNA samples were then processed for 16S rRNA gene amplicon sequencing on the Illumina MiSeq system, following a bacterial metagenomics workflow as described by Klindworth et al., (2013). Briefly, the genomic DNA samples were amplified through PCR using a universal primer pair, 341F, and 785R, which target the V3 and V4 region of the 16S rRNA gene. The resulting amplicons were purified using gel electrophoresis, and Illumina-specific adapter sequences were ligated to each amplicon. After library quantification and individual indexing of the samples, a further purification step was undertaken. The amplicons were sequenced using a 600-cycle MiSeq v3 kit (Illumina Inc). Each sample generated 20 Mb of data in the form of 2 x 300 bp paired-end reads.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBioinformatics and statistical analyses\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBefore the bioinformatic analysis, the quality of the raw sequences obtained from the sequencing process was assessed using FastQC to ensure data integrity. Subsequently, the raw sequences were processed using the QIIME2 pipeline (Quantitative Insights into Microbial Ecology, v1.8.0 qiime.org) (Bolyen et al., 2019). This included adaptor and primer sequence removal and quality filtering, trimming, denoising, and merging using the DADA2 algorithm (Callahan et al., 2016) to infer amplicon sequence variants (ASVs), which represent biologically relevant variants. Taxonomic classification of the ASVs was accomplished by using the bacterial sequences database - Silva \u003cem\u003ev138\u003c/em\u003e. A taxa filter was implemented to remove sequences originating from other eukaryotic organisms, chloroplasts, and mitochondria. Various metrics were calculated to assess the diversity and richness of the bacterial communities, including the richness estimator (Chao1), diversity indices (Shannon and Simpson), and Goods coverage. The resulting data were utilized to generate bar plots and heatmaps representing taxonomic composition at different levels using Origin Pro 22 software. To identify significant differences in the bacterial community between the control and treatment groups, the Mann-Whitney U test was applied. The sequence data are available at the NCBI SRA under the BioProject Accession PRJNA781210.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eAfter the completion of the filtering, denoising, and removal of chimeric sequences, the final sequence counts exhibited a range from 33,374 (T3B) to 119,809 (T1A). The percentage of sequences that remained non-chimeric, relative to the original input, varied among the samples, spanning from 64.54% (T4A) to 80.57% (C2). In terms of non-chimeric sequence percentages, the control samples, specifically C1, C2, and C4, displayed values of 68.98%, 80.57%, and 78.3%, respectively. In contrast, the treatment groups exhibited the following percentages: 73.58% (T1A), 65.05% (T1B), 66.62% (T2A), 68.37% (T2B), 67.82% (T3A), 69.72% (T3B), 64.54% (T4A), and 66.44% (T4B). The alpha diversity of the samples, characterized by multiple indices, displayed significant changes across different soil depths and between the control and treatment groups. At a depth of 10 cm, Control 1 (C1) demonstrated the highest diversity with 1264 operational taxonomic units (OTUs), a Chao1 index of 1278, a Shannon index of 9.39, a Simpson index of 0.997, and an evenness index of 0.911. In contrast, Treatment 1 samples (T1A, T1B) exhibited lower diversities after the treatment process. At 48 cm, Control 2 (C2) displayed lower diversity indices compared to Treatment 2 samples (T2A, T2B), with T2B registering the highest values. For the samples collected at 65 cm, both T3A and T3B exhibited similar levels of diversity. Lastly, at a depth of 80 cm, Control 4 (C4) had the lowest diversity among all the samples, while Treatment 4 samples (T4A, T4B) demonstrated higher diversity levels in comparison to their respective control samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of the bacterial composition in different samples revealed statistically significant differences among the treatments and controls (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Control 1 (C1) exhibited a significantly higher abundance of \u003cem\u003eActinobacteria\u003c/em\u003e compared to other samples, making up 54.3% of the total bacterial composition. Conversely, Control 2 (C2) and Control 4 (C4) were significantly different with a dominance of \u003cem\u003eProteobacteria\u003c/em\u003e, constituting 71.5% and 80.6% of their respective bacterial populations \u003cem\u003e(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Treatment 1A (T1A) also showed a significant presence of \u003cem\u003eProteobacteria\u003c/em\u003e, though at a relatively lower prevalence of 44.6% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, the \u003cem\u003eFirmicutes\u003c/em\u003e phylum demonstrated the predominant bacterial group in Treatment samples, including 1B (T1B), 2A (T2A), 2B (T2B), 3A (T3A), 3B (T3B), 4A (T4A), and 4B (T4B), representing 70.8\u0026ndash;78.7% of the bacterial communities under the treatment conditions (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). In terms of other phyla, significant variations were observed, with \u003cem\u003eChloroflexi\u003c/em\u003e ranging from 0.07\u0026ndash;12.11%, \u003cem\u003ePlanctomycetota\u003c/em\u003e from 0\u0026ndash;11.33%, \u003cem\u003eAcidobacteriota\u003c/em\u003e from 0.29\u0026ndash;5.10%, \u003cem\u003eGemmatimonadota\u003c/em\u003e from 0.02\u0026ndash;9.57%, \u003cem\u003eVerrucomicrobiota\u003c/em\u003e from 0\u0026ndash;1.31%, and \u003cem\u003eMyxococcota\u003c/em\u003e from 0\u0026ndash;0.65% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The variations in \u003cem\u003eFirmicutes\u003c/em\u003e from 13.34\u0026ndash;76.97% were also statistically significant, as were the fluctuations in \u003cem\u003eBacteroidota\u003c/em\u003e from 0.27\u0026ndash;8.75% (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Additionally, the collective group of Minor Phyla represented significant proportions ranging from 0.74\u0026ndash;2.17% of the microbial populations across the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe bacterial class distribution also demonstrated significant differences across the control and treatment samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For instance, C1 showed a significantly higher proportion of \u003cem\u003eAcidimicrobiia\u003c/em\u003e (7.18%), \u003cem\u003eActinobacteria\u003c/em\u003e (12.85%), and \u003cem\u003eActinobacteriota\u003c/em\u003e (1.55%) (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). A shift in dominance was observed in C2, with \u003cem\u003eAlphaproteobacteria\u003c/em\u003e taking the lead (40.20%), followed by \u003cem\u003eBacilli\u003c/em\u003e (14.72%), and a reduced presence of \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003eAcidimicrobiia\u003c/em\u003e at 3.44% and 0.53% respectively. Furthermore, there is clear dominance of \u003cem\u003eAlphaproteobacteria\u003c/em\u003e (44.99%) and \u003cem\u003eGammaproteobacteria\u003c/em\u003e (35.60%) in the C3 sample (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Sample T1A exhibited a high prevalence of \u003cem\u003eAlphaproteobacteria\u003c/em\u003e (25.19%), accompanied by a notable presence of \u003cem\u003eBacilli\u003c/em\u003e (7.73%) and \u003cem\u003eGammaproteobacteria\u003c/em\u003e (19.37%). Similarly, the prevalence of \u003cem\u003eClostridia\u003c/em\u003e (51.59%) and \u003cem\u003eBacilli\u003c/em\u003e (24.40%) in T1B, and the dominance of \u003cem\u003eClostridia\u003c/em\u003e (51.87% and 47.41%) and \u003cem\u003eBacilli\u003c/em\u003e (24.69% and 23.38%) in T2A and T2B, respectively, were all significant (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). The samples T3A and T3B maintained the trend of \u003cem\u003eClostridia\u003c/em\u003e and \u003cem\u003eBacilli\u003c/em\u003e dominance, constituting 49.80% and 51.87%, as well as 24.77% and 25.21% of the respective communities. Finally, T4A and T4B continued with this pattern, harbouring a majority of \u003cem\u003eClostridia\u003c/em\u003e (52.07% and 52.84%) and \u003cem\u003eBacilli\u003c/em\u003e (24.84% and 25.80%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDetailed examination of the bacterial genus distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) across different sample groups revealed the \u003cem\u003eMethylorubrum\u003c/em\u003e genus to be ubiquitous, with substantial variations in its relative abundance. While it achieved peak prevalence in the Treatment 2A group at 24.47%, it displayed relatively lower concentrations in the other groups, varying from 2.15\u0026ndash;2.44%. This noticeable variation suggests the potential selectivity of Treatment 2A in promoting the proliferation of \u003cem\u003eMethylorubrum\u003c/em\u003e, a trait absent in the other treatments and control groups. \"\u003cem\u003eEscherichia-Shigella\u003c/em\u003e\" and \u003cem\u003eBradyrhizobium\u003c/em\u003e genera were also present in significant proportions across the different treatments, again with evident variation. The \u003cem\u003eComamonadaceae\u003c/em\u003e genus showed an interesting pattern, with a significant rise in its relative abundance across all treatment groups, surpassing 2%. Its maximum abundance, however, was found in the Control 4 group at 7.91%. This might imply that the conditions in the Control 4 group are especially favourable to the proliferation of this bacterial genus. Meanwhile, \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003ePaenibacillus\u003c/em\u003e genera showed relatively limited occurrence, suggesting that these genera were less influenced by the applied treatments. In Treatment 3, notable abundance was observed for the \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e genus. The \u003cem\u003eLactobacillus\u003c/em\u003e genus exhibited an evident increase in Treatment 3A and 3B, peaking at 3.91% and 3.42% respectively. On the other hand, \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e displayed a significantly improved occurrence, achieving 28.58% in Treatment 3A and 25.18% in Treatment 3B. This strongly high representation in these specific treatment groups hints towards a selective stimulatory effect of Treatment 3A and 3B on \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e. \u003cem\u003eRomboutsia\u003c/em\u003e also showed similar shifts, with high prevalence observed solely in Treatment 3A and 3B groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Mann-Whitney U test was used to identify significant differences in the bacterial community between the control and treatment groups. Notably, a significant change was observed between Control 1 and Treatment 1, while other control-treatment pairs did not show any significant differences. In terms of bacterial composition, Treatment 3 exhibited differentiation from Treatments 1 and 2 but not from Treatment 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The most significant changes in bacterial populations from Control 1 to Treatment 1 were observed in several genera. The genus \u003cem\u003e67\u0026thinsp;\u0026minus;\u0026thinsp;14\u003c/em\u003e (\u003cem\u003eThermoleophilia\u003c/em\u003e class) and an uncultured genus (\u003cem\u003eAcidimicrobiia\u003c/em\u003e class) exhibited a significant reduction of approximately 84.09% and 72.86%, respectively. In contrast, \u003cem\u003eMethylobacterium-Methylorubrum\u003c/em\u003e exhibited a significant increase of about 1987.62%, indicating a highly favourable response to the treatment conditions. The genus \u003cem\u003eEscherichia-Shigella\u003c/em\u003e displayed a notable increase of approximately 1244.86%. In Treatment 2, \u003cem\u003eMethylobacterium-Methylorubrum\u003c/em\u003e exhibited a marked decrease of 864.58%, and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e demonstrated a significant decline of 680.91%. \u003cem\u003eBradyrhizobium\u003c/em\u003e and \u003cem\u003eRalstonia\u003c/em\u003e encountered considerable reductions of 909.66% and 1081.32%, respectively, while \u003cem\u003eBacillus\u003c/em\u003e showed a significant reduction of 3590.48%. In contrast, \u003cem\u003eRubrobacter\u003c/em\u003e displayed an increase of 25.51%. Comparison of Treatment 4 with Control 4 revealed considerable variations, with \u003cem\u003eMethylobacterium-Methylorubrum\u003c/em\u003e decreasing by approximately 642.85%, and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e experiencing a significant drop of 591.46%. \u003cem\u003eBradyrhizobium\u003c/em\u003e exhibited a loss of nearly 694.04%, and \u003cem\u003eRalstonia\u003c/em\u003e suffered a remarkable drop of 890.70%. Conversely, \u003cem\u003eLactobacillus\u003c/em\u003e demonstrated a notable increase of 58.11%, and \u003cem\u003eBacillus\u003c/em\u003e displayed a significant rise of 692.61%. \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e (\u003cem\u003eClostridia\u003c/em\u003e class) displayed the most significant increase, with a shift of approximately 27.07%, followed by \u003cem\u003eRomboutsia\u003c/em\u003e (Clostridia class) with an increase of about 15.67%. \u003cem\u003eTuricibacter\u003c/em\u003e (\u003cem\u003eBacilli\u003c/em\u003e class) exhibited an increase of 10.69%, while the genus \u003cem\u003eMuribaculaceae\u003c/em\u003e (\u003cem\u003eBacteroidota\u003c/em\u003e phylum) showed a smaller but notable increase of 4.74%. Additionally, the well-known genus \u003cem\u003eLactobacillus\u003c/em\u003e from the \u003cem\u003eBacilli\u003c/em\u003e class showed an increase of 3.75%. Interestingly, \u003cem\u003eMethylobacterium-Methylorubrum\u003c/em\u003e, despite decreasing in other treatments, exhibited a modest increase of 2.25% in Treatment 3. These findings highlight the diverse microbial responses and shifts to different treatments, potentially driven by selective pressures in response to oil exposure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe co-network analysis of the top 30 bacterial genera across various control and experimental conditions provides some valuable insight into the complex interactions within the microbial community (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For instance, the genus \u003cem\u003eVicinamibacteraceae\u003c/em\u003e demonstrates a notable ability to maintain positive relationships across multiple conditions, with significant positive correlation indicating strong associations with other genera. While g_67 presents a contrasting dynamic, with a prevalence of negative correlation, especially notable in experimental conditions. Genera like \u003cem\u003eMethylobacterium\u003c/em\u003e and \u003cem\u003eEscherichia\u003c/em\u003e show a network of positive correlations, that are particularly strong in experimental conditions implying that these genera may thrive or respond similarly under the conditions tested in the experiments. \u003cem\u003eRomboutsia\u003c/em\u003e and \u003cem\u003eTuricibacter\u003c/em\u003e have a combination of positive and negative correlations across both control and experimental conditions. Notably, \u003cem\u003eUncultured, Burkholderia\u003c/em\u003e, and \u003cem\u003eProvidencia\u003c/em\u003e are characterized by several significant positive correlations that are higher in experimental conditions compared to control, suggesting that the experimental manipulations may favour their association with other genera. The absence of significant negative correlations for genera such as \u003cem\u003eRubrobacter\u003c/em\u003e and \u003cem\u003eAlcaligenes\u003c/em\u003e, and for several uncultured genera, across both control and experimental conditions, is indicative of either a non-competitive stance or a broad ecological niche that allows for coexistence without direct antagonism. while others exhibit a balance of positive and negative correlations across control and experimental conditions. This pattern suggests that experimental manipulations can either promote cooperation or competition among genera, highlighting the dynamic nature of microbial interactions in response to environmental changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSoil bacterial diversity is known to be influenced by changes in the environmental physico-chemical factors such as temperature, pH, water content, nutrients, texture, and vegetation types. ((Mhete, et al. 2020; Aguado-Norese, et al. 2023). In this study, we observed high diversity and variation both within and across different treatment groups and depths, highlighting the complexity of the soil samples. At 0\u0026ndash;10 cm, which is characteristic of topsoil, there is high variation compared to other depths. Considering T1A and T1B, topsoil collected in 2017 and 2019 respectively, it is not surprising considering the direct exposure of topsoil to environmental factors, especially temperature. The influence of broader factors such as climate change and human activities cannot be overlooked. Rising temperatures and increased frequency of extreme weather events can alter soil conditions, affecting microbial life (Jansson and Hofmockel \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, a total of 10 major phyla, and 22 major classes were observed in the topsoil of the control samples. Considering only the sub-surface (due to expected variation in the topsoil), there is a significant shift in bacterial community composition between control and treatment in the corresponding soil depth. The phylum \u003cem\u003eProteobacteria\u003c/em\u003e which is most predominant in the control samples when compared to treatment samples is replaced by the phylum \u003cem\u003eFirmicutes\u003c/em\u003e which appears predominant in the treatment samples. The phylum \u003cem\u003eProteobacteria\u003c/em\u003e, known for its diverse metabolic capabilities and predominance in various environments, is commonly affected by soil disturbances, including anthropogenic activities. In contrast, \u003cem\u003eFirmicutes\u003c/em\u003e, with many members known for their resilience and ability to degrade hydrocarbons, often increase in abundance in contaminated sites. This pattern has been observed in other studies, which reported an increase in \u003cem\u003eFirmicutes\u003c/em\u003e in oil-contaminated soils, reflecting their potential role in hydrocarbon degradation (Shahi, et al. 2016; Liu, et al. 2020; Devi, et al. 2021). The \u003cem\u003eProteobacteria\u003c/em\u003e is a phylum characterized mainly by Gram-negative bacteria while \u003cem\u003eFirmicutes\u003c/em\u003e are characterised mainly by Gram-positive bacteria. The decrease in Gram-negative bacteria and the enrichment of Gram-positive bacteria in contaminated soils reveals that hydrocarbon contamination can selectively inhibit certain microbial groups while favouring others, particularly those capable of hydrocarbon degradation. Further, the shift in microbial communities due to contamination not only reflects the resilience and adaptability of soil bacteria but also has implications for soil health. As Sharma et al., (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) pointed out, changes in microbial community composition can affect soil fertility, structure, and its capacity to support plant life.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEnrichment of bacterial genera: Implications to soil ecosystem function and health\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe bacterial genera \u003cem\u003eMuribaculaceae, Prevotella, Aerococcus, Romboutsia, clostridium_sensu_stricto, Dubosiella, Faecalibaculum\u003c/em\u003e, and \u003cem\u003eTuricibacter\u003c/em\u003e, all categorically linked to the major phylum \u003cem\u003eFirmicutes\u003c/em\u003e were enriched following exposure to UMO in this experiment. This enrichment, while indicative of a potential adaptive mechanism to hydrocarbon pollutants, warrants a deeper investigation into the specific metabolic pathways activated under these conditions. Only the genus \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eAerococcus\u003c/em\u003e could be directly associated with positive ecosystem services such as biodegradation of xenobiotic and recalcitrant pollutants. \u003cem\u003ePrevotella\u0026rsquo;s\u003c/em\u003e primary involvement in crude oil degradation was first reported by Mukjang, et al. (2022), and \u003cem\u003eAerococcus\u003c/em\u003e has also been known to be present in hydrocarbon-contaminated environments (Biswas, et al. 2022; Dey, Das, and Kazy \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggest a targeted approach in bioremediation strategies, yet the efficiency and specificity of these bacteria in different hydrocarbon types remain unclear. Interestingly, many bacterial genera of animal origin, associated with human and animal gut microbiota were found to have increased in relative abundance. These include \u003cem\u003eTuricibacter\u003c/em\u003e, which has been reported from metagenomic and meta-transcriptome studies to be predominant, especially in the fermentation of compost, and known to harbour various hydrolytic enzymes associated with the degradation of plant lignocellulose (Huang, et al. 2022). The genus \u003cem\u003eRomboutsia\u003c/em\u003e, belong to \u003cem\u003ePeptostreptococcaceae\u003c/em\u003e family within the \u003cem\u003eFirmicutes\u003c/em\u003e, these anaerobic bacteria species are adapted to environments that are rich in nutrients but primarily known to play a major role in the degradation of carbohydrates (Jacoline, et al. 2019). Based on the co-occurrence network analysis, \u003cem\u003eRomboutsia\u003c/em\u003e and \u003cem\u003eTuricibacter\u003c/em\u003e reveal complex interactions that could reflect a flexible adaptation strategy to a variety of environmental states or perhaps indicate that their abundance is sensitive to the specific conditions of each experiment. Similarly, the role of \u003cem\u003eDubosiella, Faecalibaculum, and Muribaculaceae\u003c/em\u003e in degrading complex carbohydrates (Zuo, et al. 2023; Li, et al. 2020) points to their functional diversity, but the extent to which these functions are leveraged in hydrocarbon-contaminated soils is not fully understood.\u003c/p\u003e \u003cp\u003e \u003cem\u003eClostridium_sensu_stricto\u003c/em\u003e, is considered a true genus of \u003cem\u003eClostridium\u003c/em\u003e (Li, et al. 2023), and often associated with the gut microbiota, has also been found to play a role in Microcystis biomass decomposing especially in aquatic ecosystems (Zhao, et al. 2017). Genus \u003cem\u003eClostridium\u003c/em\u003e produces endospores that help in adaptations and survival in harsh environmental conditions (Dr\u0026eacute;an, et al. 2015). A substantial proportion of uncultured bacteria also appear in higher abundance in the treatments relative to controls, these unidentified and mysterious representatives warrant further investigation, these uncultured populations may have both positive and negative impact on soil ecosystem function and health. Our study further emphasizes and demonstrates the power of utilizing 16S rRNA gene-based metagenomics approach towards discovery of novel bacteria, \u003cem\u003ealbeit\u003c/em\u003e the challenges that may still exists in identification due to information deficiencies in sequence archives or databases. Nonetheless, more recent next generation sequencing studies remain hopeful of the potential role that many uncultured bacteria may have in relation to ecosystem services such as the biodegradation of environmental pollutants (Bodor, et al. 2020).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe loss of bacterial genera and its impact on soil ecological functions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe family \u003cem\u003eVicinamibacteraceae\u003c/em\u003e, the first described within Acidobacteria, constitutes a globally widespread group of Gram-negative, non-spore-forming, aerobic, chemo-organoheterotrophic bacteria inhabiting soil environments (Huber and Overmann, 2018). As observed in the co-network analysis, \u003cem\u003eVicinamibacteraceae\u003c/em\u003e may play a key role in the microbial ecosystem across varied environmental states, as revealed in both control and experimental settings. \u003cem\u003eVicinamibacteraceae\u003c/em\u003e are known to be functionally versatile and play many important roles including nutrient mobilization during decomposition, and thus maintaining soil ecosystem health (Chiba, et al. 2021). Again, disregarding the topsoil, there is some light of evidence suggesting the loss of certain bacterial genera within the phylum \u003cem\u003eProteobacteria\u003c/em\u003e following soil treatment with UMO. Notable bacterial genera representatives of the phylum \u003cem\u003eProteobacteria\u003c/em\u003e; \u003cem\u003eMethylobacterium-Methylorumbrum, Escherichia-Shigella, Bradyrhizobium, Ralstonia, Phyllobacterium\u003c/em\u003e significantly declined in relative abundance in the soil sub-surface and below.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMethylobacterium\u003c/em\u003e and \u003cem\u003eEscherichia\u003c/em\u003e show a network of positive correlations, that are particularly strong in experimental conditions implying that these genera may thrive or respond similarly under the conditions tested in the experiments. Genus \u003cem\u003eMethylobacterium-methylorumbrum\u003c/em\u003e comprises closely related facultatively methylotrophic bacterial species that some literature reported their use in bioaugmentation and efficiency in biodegradation of polyaromatic hydrocarbons in contaminated environments (Dhar, et al. 2022; Giri, et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Certain species of \u003cem\u003eMethylobacterium\u003c/em\u003e are also known to be of agricultural importance, plant-associated bacteria, and model organisms in microbiology (Leducq, et al. 2022) playing a vital role as biostimulators by producing phytohormones and provide important nutrients to plants as they fix nitrogen and solubilize phosphorus and iron, to promote plant growth (Zhang, et al. 2021). The biotechnological role of other \u003cem\u003emethylorumbrum\u003c/em\u003e species in environmental bioremediation has also been recently documented in the literature (Rojas-G\u0026auml;tjens, et al. 2022; Quynh Le, et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, \u003cem\u003eBradyrhizobium\u003c/em\u003e, symbiotic bacteria encodes multiple functions that are critical to plant growth including nitrogen fixation and nodulation (Wongdee, et al. 2023). \u003cem\u003ePhyllobacterium\u003c/em\u003e is another well researched plant probiotic that has not been associated with causing diseases in humans and described as a good candidate for use as a biofertilizer, supplying phosphorus for plants, especially during dry seasons (Breitkreuz, et al. 2020). In contrast, genera like \u003cem\u003eEscherichia-Shigella\u003c/em\u003e and \u003cem\u003eRalstonia\u003c/em\u003e are often associated with negative impacts on human and plant health, suggesting a complex interplay of beneficial and harmful microbial elements within the same ecosystem. This duality emphasizes the need for a better understanding of microbial community compositions, especially in the milieu of environmental changes and ecosystem health.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study has shed light on the profound impact that UMO contamination has on soil ecosystem health, particularly through its alteration of bacterial diversity and abundance. The presence of oil pollution in the soil leads to significant ecosystem alterations, marked by notable changes in bacterial community structure. This includes both the depletion and proliferation of specific bacterial genera, thereby influencing the ecological functions of the soil. In addition, the study successfully identified key bacterial genera and their potential roles, providing valuable insights that could inform future strategies for the effective management of soil contamination in similar regions worldwide. Further, the use of the 16S rRNA amplicon sequencing approach was found to be effective in providing high resolution of the OTUs down to the genus level. Consequently, this study encourages for the expanded use of the 16S rRNA gene metagenomics approach in long-term environmental pollution studies, coupled with physicochemical methods to fully understand the roles and relationships among the physical, chemical parameters, and microbial populations in a contaminated environmental setting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eAuthors would like to thank the Centre for High Performance Computing (CHPC) facility, Pretoria, South Africa for providing computational support for sequence data analysis, Mr Boniface Kgosidintsi and Mr Phenyo Tlale for tireless efforts in assisting with this research work.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: \u0026nbsp;\u003c/strong\u003eThis\u0026nbsp;work was supported by the Botswana International University of Science and Technology.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eLoago Molwalefhe,\u0026nbsp;Elisha Shemang,\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand Teddie Onkabetse Rahube\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003cem\u003econtributed to the study\u0026apos;s conception and design. Material preparation, data collection, and analysis were performed by Bokani Nthaba, Batendi Nduna, and\u0026nbsp;\u003c/em\u003eRamganesh Selvarajan\u003cem\u003e. The manuscript draft was prepared by\u0026nbsp;\u003c/em\u003eTeddie Onkabetse Rahube and Ramganesh Selvarajan.\u003cem\u003e\u0026nbsp;All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eNot applicable. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdel-Shafy, Hussein I., and Mona S. M. 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This study was conducted using the 16S rRNA targeted metagenomic approach, to assess the changes in bacterial population diversity and abundance at an oil contamination experimental site in Botswana. To demonstrate the impact of used motor oil is on the soil ecosystem, soil samples collected at different depths before and after treatment with used motor oil were subjected to total community DNA extraction and Illumina sequencing. The taxonomic bacterial composition data revealed statistically significant differences among the treatments and controls. A notable shift from Gram-negative to Gram-positive bacterial populations was observed following treatment with used motor oil. \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eAerococcus\u003c/em\u003e were among the few genera within the enriched Gram-positive bacteria that could be directly linked to biodegradation of the polycyclic aromatic hydrocarbons associated with oil contamination. Agricultural and biotechnologically important, plant-associated bacterial genera; \u003cem\u003eMethylobacterium-methylorumbrum, Bradyrhizobium\u003c/em\u003e, and, \u003cem\u003ePhyllobacterium\u003c/em\u003e significantly declined in relative abundance, thus demonstrating the negative impact of oil contamination. The results from this study, improves our understanding of the roles of indigenous soil bacteria, and can help in guiding future strategies for the sustainable management of contaminated soils globally in countries with similar climatic and ecological conditions.\u003c/p\u003e","manuscriptTitle":"Evaluating Bacterial Population Changes and Ecological Dynamics in Oil-Impacted Soils Using 16S rRNA Amplicon Sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-12 02:39:49","doi":"10.21203/rs.3.rs-3722259/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fa94a09-12a3-4baf-8aa0-172d2b979ea2","owner":[],"postedDate":"January 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-10T08:01:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-12 02:39:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3722259","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3722259","identity":"rs-3722259","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}