Polycyclic aromatic hydrocarbon (PAH) biodegradation capacity revealed by a genome-function relationship approach

BACKGROUND

Polycyclic aromatic hydrocarbon (PAH) contamination has been a worldwide environmental issue because of its impact on ecosystems and human health. Biodegradation plays an important role in PAH removal in natural environments. To date, many PAH-degrading strains and degradation genes have been reported. However, a comprehensive PAH-degrading gene database is still lacking, hindering a deep understanding of PAH degraders in the era of big data. Furthermore, the relationships between the PAH-catabolic genotype and phenotype remain unclear.

RESULTS

Here, we established a bacterial PAH-degrading gene database and explored PAH biodegradation capability via a genome-function relationship approach. The investigation of functional genes in the experimentally verified PAH degraders indicated that genes encoding hydratase-aldolase could serve as a biomarker for preliminarily identifying potential degraders. Additionally, a genome-centric interpretation of PAH-degrading genes was performed in the public genome database, demonstrating that they were ubiquitous in Proteobacteria and Actinobacteria. Meanwhile, the global phylogenetic distribution was generally consistent with the culture-based evidence. Notably, a few strains affiliated with the genera without any previously known PAH degraders (Hyphomonas, Hoeflea, Henriciella, Saccharomonospora, Sciscionella, Tepidiphilus, and Xenophilus) also bore a complete PAH-catabolic gene cluster, implying their potential of PAH biodegradation. Moreover, a random forest analysis was applied to predict the PAH-degrading trait in the complete genome database, revealing 28 newly predicted PAH degraders, of which nine strains encoded a complete PAH-catabolic pathway.

CONCLUSIONS

Our results established a comprehensive PAH-degrading gene database and a genome-function relationship approach, which revealed several potential novel PAH-degrader lineages. Importantly, this genome-centric and function-oriented approach can overcome the bottleneck of conventional cultivation-based biodegradation research and substantially expand our current knowledge on the potential degraders of environmental pollutants.

Patch formation driven by stochastic effects of interaction between viruses and defective interfering particles

Defective interfering particles (DIPs) are virus-like particles that occur naturally during virus infections. These particles are defective, lacking essential genetic materials for replication, but they can interact with the wild-type virus and potentially be used as therapeutic agents. However, the effect of DIPs on infection spread is still unclear due to complicated stochastic effects and nonlinear spatial dynamics. In this work, we develop a model with a new hybrid method to study the spatial-temporal dynamics of viruses and DIPs co-infections within hosts. We present two different scenarios of virus production and compare the results from deterministic and stochastic models to demonstrate how the stochastic effect is involved in the spatial dynamics of virus transmission. We compare the spread features of the virus in simulations and experiments, including the formation and the speed of virus spread and the emergence of stochastic patchy patterns of virus distribution. Our simulations simultaneously capture observed spatial spread features in the experimental data, including the spread rate of the virus and its patchiness. The results demonstrate that DIPs can slow down the growth of virus particles and make the spread of the virus more patchy.