Differential Expression and PAH Degradation: What Burkholderia vietnamiensis G4 Can Tell Us?
Guilherme Pinto CauduroAna Lusia LealTiago Falcón LopesMarcela MarmittVictor Hugo ValiatiPublished in: International journal of microbiology (2020)
Petroleum is the major energy matrix in the world whose refining generates chemical byproducts that may damage the environment. Among such waste, polycyclic aromatic hydrocarbons (PAH) are considered persistent pollutants. Sixteen of these are considered priority for remediation, and among them is benzo(a)pyrene. Amid remediation techniques, bioremediation stands out. The genus Burkholderia is amongst the microorganisms known for being capable of degrading persistent compounds; its strains are used as models to study such ability. High-throughput sequencing allows researchers to reach a wider knowledge about biodegradation by bacteria. Using transcripts and mRNA analysis, the genomic regions involved in this aptitude can be detected. To unravel these processes, we used the model B. vietnamiensis strain G4 in two experimental groups: one was exposed to benzo(a)pyrene and the other one (control) was not. Six transcriptomes were generated from each group aiming to compare gene expression and infer which genes are involved in degradation pathways. One hundred fifty-six genes were differentially expressed in the benzo(a)pyrene exposed group, from which 33% are involved in catalytic activity. Among these, the most significant genomic regions were phenylacetic acid degradation protein paaN, involved in the degradation of organic compounds to obtain energy; oxidoreductase FAD-binding subunit, related to the regulation of electrons within groups of dioxygenase enzymes with potential to cleave benzene rings; and dehydrogenase, described as accountable for phenol degradation. These data provide the basis for understanding the bioremediation of benzo(a)pyrene and the possible applications of this strain in polluted environments.
Keyphrases
- polycyclic aromatic hydrocarbons
- gene expression
- heavy metals
- healthcare
- dna methylation
- high throughput sequencing
- genome wide
- oxidative stress
- escherichia coli
- copy number
- risk assessment
- big data
- electronic health record
- protein protein
- machine learning
- genome wide identification
- amino acid
- data analysis
- dna binding
- human health