Microbe-plant partnership in phytoremediation involves a synergistic interaction that leads to degradation of contaminants. The identification and characterization of these microorganisms is fundamental in environmental management. This study is aimed at investigating the influence of
Crude oil contamination and pollution are inevitable in oil producing regions [
The combined action of plants and microbes (the rhizosphere-mediated bioremediation) [
Various microbes have been reported by different scholars to colonize the rhizosphere or endosphere of plants involved in the degradation of petroleum hydrocarbons [
The aim of this study was to characterize hydrocarbon degrading microorganisms from crude oil contaminated soil remediated with
Soil samples were collected from the root zones of
Isolation of the bacterial and fungal isolates from the soil samples followed same protocols described in Njoku et al [
The DNA was subjected to a cocktail mix consisting of 10 μL of 5 x GoTaq colourless reaction, 3 μL of MgCl2, 1 μL of 10 mM of dNTPs mix, 1 μL of 10 pmol each. Internal Transcribed Spacer (ITS) gene was used to characterize fungi and 16S rRNA was used to characterize bacteria. The purified fragment was checked on 2% agarose gel ran on a voltage of 120 V for one hour [
- ITS4 TCCTCCGCTTATTGACATGS
- ITS5 GGAACTAAAAGTCGTAACAAGG
- 16SF: GTGCCAGCAGCCGCGCTAA
- 16SR: AGACCCGGGAACGTATTCAC
The amplified fragments were sequenced using a genetic analyser 3130 x1 sequencer from Applied Biosystems, Waltham, Massachusetts, USA, using manufacturer’s manual and the kit used was Big Dye terminator v3.1 cycle sequencing kit. All genetic analyses were carried out using Bio-Edit software version 7.0 as was described by Hall [
Phylogenic relationship analysis was performed on the isolates’ sequences using Molecular Evolutionary Genetic Analysis version 6 [
One-way analysis of variance (ANOVA) at P<0.05 was used to test the significance of experiment followed by LSD test at P<0.05 confidence interval. All data were processed using GraphPad (Version 9.0) [
The microbial colony counts from soil treated with different concentrations of crude oil are shown in
Crude oil contamination caused a reductive effect on the fungal counts
Plant activities are influenced by many aspects of the soil environment, including nutrient availability and the abiotic factors. It has been reported by Arslan et al. [
This study shows that although
The quantity and quality of DNA from the isolates are shown in
The molecularly identified isolates are presented in
The greater abundance of
Aside from changes in the abundance of organisms, the abiotic environment can influence the nucleotide composition [
The phylogenic relationship of the nucleotide sequences of the organisms compared with each other is shown in
The reason for the presence of the two clusters in the remediated soil could be due to combination of selective factors, proximity and functional capacity of these microbes [
The annotated microorganism’s genomes using FGENESB and GENESCAN are shown in
The predicted potential genes and proteins using FGENESB are shown in
Protein coding genes from the bacterial and fungal strains annotated above
The gene statistics suggested that there is intraspecies variation of genetic elements in the microbes. The genome data supported and extended various laboratory observations in the plant growth promotion attributes. Different genes that are involved in environmental, cellular, biosynthesis, degradation, enhancers and genetic information are identified. dnaA gene was found to be responsible for cellular processing (replication) in
Interestingly, except
The bacteria and fungi were isolated from oil-contaminated soil in this study have potential for implementation in oilfield bioremediation. Numerous genes associated with hydrocarbon degradation were identified. The gene sets available in the genome indicate that the organisms metabolize hydrocarbons using tricarboxylic acid cycle pathways intermediates.
Interactions between plants, bacteria and fungi occur within the rhizosphere; however, the link between these groups is altered in soils that have been disturbed by high concentrations of hydrocarbon contaminants. The study shows that fungal communities were more sensitive to hydrocarbons relative to bacteria by low enumeration of fungal counts compared to hydrocarbon utilizing bacteria. The enumeration of large hydrocarbon utilizing bacteria could be because the microorganisms use crude oil as a sole source of carbon and energy, hence suggesting their role in hydrocarbon remediation. A significant homology of the genes involved in hydrocarbon degradation was observed through the comparative analysis of the gene sequence. Dehydrogenase enzymes that function in removal of toxic substances were predicted in these sequences, suggesting a possible role for bioremediation. Actually, overlapping functional characteristics like biodegradation and virulence were identified in some sequence data. This can be exploited for the remediation of crude oil polluted sites as these microbes have diverse metabolic potential that could be exploited for additional environmental and biotechnological applications. In this study it is suggested that microbial structure and composition can be modelled and enhanced if appropriate plants are in partnership with bacteria in a stressed condition like crude oil.
Microbial colony count from rhizosphere soil on day 1 (initial count) and day 120 (final count) of the study.
Predicted infections related diseases in the bacteria and fungi strains.
The gel electrophoresis of purified amplified PCR products with band sizes run on the gel for bacteria (a) and fungi (b). M=DNA Ladder; a=
The authors declare that there is no competing interest to influence the work reported in this paper.
KLN: Conceptualization, Supervision, Editing ; EOU: Investigation, Data Analyses, Writing; TOJ: Investigation, visulization, OZA: Original Draft Preparation; POI: Reviewing, Editing
The microbial colony count from the rhizosphere soil on day 1 (initial count) and on day 120 (final count) of the study; a =total heterotrophic bacteria, b = total hydrocarbon utilizing bacteria, total fungi; IC =initial count, FG = final count+
The quality and quantity of the DNA from the different isolates: a= K. aerogenes, strain 77, b = K. aerogenes strain UISO178, c= S. enterica strain ABUH 7, d= K. aerogenes strain M242, e= E. sp. NCCP-607, f= G. geotrichum CBS774.7, g= A. niger YMCHA 73, h= T. virens A701
Phylogenetic relationship among all microbial isolates observed in the study.
Summary of bacterial and fungal sequence characteristics
Organism type | Sample ID | Organism identified by BLAST | Identity (%) | Sequence length (bp) | %Guanine +Cytosine |
---|---|---|---|---|---|
a | 97 | 865.00 | 54.22 | ||
b | 98 | 847.00 | 55.14 | ||
c | 98 | 860.00 | 55.17 | ||
d | 94 | 925.00 | 54.05 | ||
e | 99 | 850.00 | 54.71 | ||
|
|||||
f | 93 | 550.00 | 42.54 | ||
g | 90 | 524.00 | 57.63 | ||
h | 99 | 635.00 | 54.19 |
Data are sequence information obtained from BLAST output.
The protein-coding genes annotated with GENESCAN and FGENESB.
Organism | Compared group | Number of predicted protein coding genes | Number of transcription units | Number of operons | Protein sequence with FGENESB | Protein sequence with GENESCAN |
---|---|---|---|---|---|---|
5 | 5 | 0 | 5 | 1 | ||
5 | 2 | 1 | 5 | 1 | ||
6 | 2 | 1 | 4 | 1 | ||
4 | 3 | 1 | 5 | 1 | ||
5 | 2 | 1 | 5 | 1 | ||
1 | 1 | 0 | 1 | 1 | ||
3 | 2 | 1 | 3 | 0 | ||
2 | 2 | 0 | 2 | 1 |
All data were predicted genes using FGENESB and GENESCAN from the gene sequence of the organisms.
Predicted potential genes and protein in microbial gene sequence using FGENESB.
Organism | No of predicted genes | Corresponding protein sequences |
---|---|---|
5 | Llptlshlsvslcpggrlrhryssrslrisplhlefypplqdsslpvsnavprlspgilrseldrpahvsaaastese | |
5 | VRGCAAAALTCVIASPDVYSRAHLGTAFETGRLESCRGG | |
6 | LLPTLSHLSVSLCPGGRLRHRYSSRSLRISPLHLEFYPPLQDSSLPVSNAVPRLSP | |
4 | LLPTLSHLSVSLCPGGRLRHRYSSRSLRISPLHLEFYPPLQDSSLPVSNAVPRLSP | |
5 | VQQPRYVRGLSPRTCTPRAQPGNCIRNWQARVL | |
1 | LFVRSTFTNNKKSLMILPQVHLRKPCYVFTSCPEGPDLAKSKAPRGYKKIWGP | |
3 | VQCGLWLVTSAGAGHPTEHVTKPHTLEDRTRCRRCLSGPSPRRGGRRPNTQ | |
2 | VDAPRSRCECANYCAGEAAARPPLYFGAGPVKGRSPTPTPRRGSRVEMTLGQ |
All data were predicted genes using FGENESB from the genes sequences of the organisms.
Predicted potential peptides in microbial genomes using GENESCAN.
Organism | Proteins | Corresponding protein sequences |
---|---|---|
Erythrose-4-phosphate dehydrogenase | XGRLNALAPEATPQGHNLQVDIVYGVDYQDLYAFHRYTWNSTPLYKTLACQFRM | |
Erythrose-4-phosphate dehydrogenase | XGRLNALAPEATPQGHNLQVDIVYGVDYQDLYAFHRYTWNSTPLYKTLACQFRM | |
Erythrose-4-phosphate dehydrogenase | MRRDLEEYRWRRRPPGQRLTLSGPAGNSKETASDKLEEGGDDVKSSWPLRVGLHT | |
Erythrose-4-phosphate dehydrogenase | YKEKRPRESKRDLIX | |
Erythrose-4-phosphate dehydrogenase | XGRLNALAPEATPQGHNLQVDIVYGVDYQDLYAFHRYTWNSTPLYKTLACQFRM | |
Erythrose-4-phosphate dehydrogenase | XGRLNALAPEATPQGHNLQVDIVYGVDYQDLYAFHRYTWNSTPLYKTLACQFRM | |
New protein | KANAIPREKQRSNKYTLGDTPKCNVRSKTDDSLLQFTRNIAFRCVLHRYENQEIHC | |
Erythrose-4-phosphate dehydrogenase | MARLRVRMLGVF |
All information were obtained using GENESCAN from the genes sequences of the organisms.
Protein coding genes and genetic elements of the bacteria and fungi strains.
S/N | Organism | Genes | Enzymes/proteins | Pathways | Functional annotation |
---|---|---|---|---|---|
LVE, AALB, VEM, ECO, ECJ, EBW, ECE, ECF, dnaA | Nucleoid associated proteins | Two-component system, Cell cycle - Caulobacter | dnaA, Environmental, cellular and genetic Information Processing | ||
pbpC, LCQ, ECO, ECJ, ECD, EBW, ECOK, ECE, ECS, ECF, ETW | Transferases, pbpC Glycosyl transferases, Hexosyl transferases, peptidoglycan glycosyl transferase | Peptidoglycan Biosynthesis. | mrcA, Metabolism, biosynthesis and degradation, pbpC. | ||
pbpC, LCQ, ECO, ECJ, ECD, EBW, ECOK, ECE, ECS, ECF, ETW | Transferases, pbpC Glycosyltransferases, Hexosyltransferases, peptidoglycan glycosyltransferase | Peptidoglycan Biosynthesis. | PbpC, Metabolism, biosynthesis and degradation, pbpC. | ||
PYG, glgP, HAS, PTR, PPS, GGO, PON, NLE, MCC, MCF, CSAB, RRO, EDEM1 | Transferases, Glycosyltransferases, Hexosyltransferases, PYG, glgP; glycogen phosphorylase | Protein processing in endoplasmic reticulum, Starch and sucrose metabolism, Metabolic pathways, Biosynthesis of secondary metabolites, Biofilm formation, Necroptosis, Insulin signaling pathway, Glucagon signaling pathway, Insulin resistance | EDEM1, PYG, glgP, Degradation enhancer, cellular and genetic Information Processing. | ||
mrcA, LCQ, PXY, TVA (TVAG), ECO, ECJ, ECD (ECDH10B), EBW, ECOK, ECE, ECS. | Transferases, Glycosyltransferases, Hexosyltransferases, peptidoglycan glycosyltransferase, mrcA; penicillin-binding protein 1A, Hydrolases, peptidases, Serine-type carboxypeptidases. | Peptidoglycan biosynthesis, Metabolic pathways, beta-Lactam resistance | mrcA, pbpC, Metabolism, Biosynthesis, Degradation and Drug resistance, ATP-dependent DNA helicase Rep. | ||
COL5AS, HSA, PTR, PPS, GGO, PON, NLE, MCC, MCF, CSAB, RRO. | Glycosaminoglycan binding proteins, Haparin, collagen | Protein digestion and absorption | signaling and cellular processes | ||
LAMA3_5, HAS, PTR, PPS, GGO, PON, NLE, MCC, MCF, CSAB, RRO. | Laminin | PI3K-Akt signaling, Focal adhesion, ECM-receptor interaction, Toxoplasmosis, Amoebiasis, Human papillomavirus infection, Pathways in cancer, Small cell lung cancer | LAMA3_5, Environmental Information Processing, Signal transduction | ||
pnp, PNPT1, HAS, PTR, PPS, GGO, PON, NLE, MCC, MCF, CSAB, RRO | Transferases, Nucleotidyltransferases, pnp, PNPT1; polyribonucleotide nucleotidyltransferase | RNA degradation | pnp, PNPT1, Transferring phosphorus-containing groups, Genetic Information Processing, Folding, sorting and degradation |
All data were predicted genes using FGENESB from the genes sequences of the organisms.