DetR DB: A Database of Ionizing Radiation Resistance Determinants

Adaptive laboratory evolution of Micrococcus luteus and identification of genes associated with radioresistance through genome-wide association study

Micrococcus luteus (V017) is a Gram-positive bacterium that was isolated from a sterilization area exposed to 60Co radiation. In this study, we performed an adaptive laboratory evolution experiment with M. luteus, exposing it to 24 continuous cycles of gamma irradiation at four different doses (1.5 kGy, 3.5 kGy, 5.5 kGy, and 7.5 kGy). This led to the creation of four evolved populations with different levels of radioresistance, which were positively correlated with the radiation dose applied. The survival rate of the evolved population that underwent adaptive treatment at the highest dose (7.5 kGy) was 0.69% after exposure to 5.5 kGy, which is about five orders of magnitude higher than that of the original strain V017. Furthermore, 76 evolved strains were selected from these populations, and their genomes were re-sequenced, uncovering a total of 3072 mutations. A genome-wide association study identified 56 single nucleotide polymorphisms (SNPs) significantly associated with radioresistance, linked to 62 candidate genes. Ultimately, 9 genes were selected for functional validation. Inactivating 6 of these genes, including H0H31_RS03855 (SMC family ATPase, SbcC), H0H31_RS04250 (ribonuclease HII), H0H31_RS04570 (endonuclease VIII), H0H31_RS07595 (bifunctional 3'-5' exonuclease/DNA polymerase I), H0H31_RS00170 (serine/threonine phosphatase PPP), and H0H31_RS05860 (CBS-domain-containing protein), significantly increased sensitivity to gamma radiation, underscoring their importance in radioresistance.

TolRad, a model for predicting radiation tolerance using Pfam annotations, identifies novel radiosensitive bacterial species from reference genomes and MAGs

The trait of ionizing radiation (IR) tolerance is variable between bacterium, with species succumbing to acute doses as low as 60 Gy and extremophiles able to survive doses exceeding 10,000 Gy. While survival screens have identified multiple highly radioresistant bacteria, such systemic searches have not been conducted for IR-sensitive bacteria. The taxonomy-level diversity of IR sensitivity is poorly understood, as are genetic elements that influence IR sensitivity. Using the protein domain (Pfam) frequencies from 61 bacterial species with experimentally determined D10 values (the dose at which only 10% of the population survives), we trained TolRad, a random forest binary classifier, to distinguish between radiosensitive (D10 < 200 Gy) and radiation-tolerant (D10 > 200 Gy) bacteria. On untrained species, TolRad had an accuracy of 0.900. We applied TolRad to 152 UniProt-hosted bacterial proteomes associated with the human microbiome, including 37 strains from the ATCC Human Microbiome Collection, and classified 34 species as radiosensitive. Whereas IR-sensitive species (D10 < 200 Gy) in the training data set had been confined to the phylum Proteobacterium, this initial TolRad screen identified radiosensitive bacteria in two additional phyla. We experimentally validated the predicted radiosensitivity of a Bacteroidota species from the human microbiome. To demonstrate that TolRad can be applied to metagenome-assembled genomes (MAGs), we tested the accuracy of TolRad on Egg-NOG assembled proteomes (0.965) and partial proteomes. Finally, three collections of MAGs were screened using TolRad, identifying further phyla with radiosensitive species and suggesting that environmental conditions influence the abundance of radiosensitive bacteria.

IMPORTANCE

Bacterial species have vast genetic diversity, allowing for life in extreme environments and the conduction of complex chemistry. The ability to harness the full potential of bacterial diversity is hampered by the lack of high-throughput experimental or bioinformatic methods for characterizing bacterial traits. Here, we present a computational model that uses de novo-generated genome annotations to classify a bacterium as tolerant of ionizing radiation (IR) or as radiosensitive. This model allows for rapid screening of bacterial communities for low-tolerance species that are of interest for both mechanistic studies into bacterial sensitivity to IR and biomarkers of IR exposure.

Archaeal lipids

The major archaeal membrane glycerolipids are distinguished from those of bacteria and eukaryotes by the contrasting stereochemistry of their glycerol backbones, and by the use of ether-linked isoprenoid-based alkyl chains rather than ester-linked fatty acyl chains for their hydrophobic moieties. These fascinating compounds play important roles in the extremophile lifestyles of many species, but are also present in the growing numbers of recently discovered mesophilic archaea. The past decade has witnessed significant advances in our understanding of archaea in general and their lipids in particular. Much of the new information has come from the ability to screen large microbial populations via environmental metagenomics, which has revolutionised our understanding of the extent of archaeal biodiversity that is coupled with a strict conservation of their membrane lipid compositions. Significant additional progress has come from new culturing and analytical techniques that are gradually enabling archaeal physiology and biochemistry to be studied in real time. These studies are beginning to shed light on the much-discussed and still-controversial process of eukaryogenesis, which probably involved both bacterial and archaeal progenitors. Puzzlingly, although eukaryotes retain many attributes of their putative archaeal ancestors, their lipid compositions only reflect their bacterial progenitors. Finally, elucidation of archaeal lipids and their metabolic pathways have revealed potentially interesting applications that have opened up new frontiers for biotechnological exploitation of these organisms. This review is concerned with the analysis, structure, function, evolution and biotechnology of archaeal lipids and their associated metabolic pathways.

Impact of ionizing radiation on the environmental microbiomes of Chornobyl wetlands

Radioactive contamination has the potential to cause damage to DNA and other biomolecules. Anthropogenic sources of radioactive contamination include accidents in nuclear power plants, such as the one in Chornobyl in 1986 which caused long-term radioactive pollution. Studies on animals within radioactive zones have provided us with a greater understanding of how wildlife can persevere despite chronic radiation exposure. However, we still know very little about the effects of radiation on the microbial communities in the environment. We examined the impact of ionizing radiation and other environmental factors on the diversity and composition of environmental microbiomes in the wetlands of Chornobyl. We combined detailed field sampling along a gradient of radiation together with 16 S rRNA high-throughput metabarcoding. While radiation did not affect the alpha diversity of the microbiomes in sediment, soil, or water, it had a significant effect on the beta diversity in all environment types, indicating that the microbial composition was affected by ionizing radiation. Specifically, we detected several microbial taxa that were more abundant in areas with high radiation levels within the Chornobyl Exclusion Zone, including bacteria and archaea known to be radioresistant. Our results reveal the existence of rich and diverse microbiomes in Chornobyl wetlands, with multiple taxonomic groups that are able to thrive despite the radioactive contamination. These results, together with additional field and laboratory-based approaches examining how microbes cope with ionizing radiation will help to forecast the functionality and re-naturalization dynamics of radiocontaminated environments.

Metagenomic Investigation of a Low Diversity, High Salinity Offshore Oil Reservoir

Oil reservoirs can represent extreme environments for microbial life due to low water availability, high salinity, high pressure and naturally occurring radionuclides. This study investigated the microbiome of saline formation water samples from a Gulf of Mexico oil reservoir. Metagenomic analysis and associated anaerobic enrichment cultures enabled investigations into metabolic potential for microbial activity and persistence in this environment given its high salinity (4.5%) and low nutrient availability. Preliminary 16S rRNA gene amplicon sequencing revealed very low microbial diversity. Accordingly, deep shotgun sequencing resulted in nine metagenome-assembled genomes (MAGs), including members of novel lineages QPJE01 (genus level) within the Halanaerobiaceae, and BM520 (family level) within the Bacteroidales. Genomes of the nine organisms included respiratory pathways such as nitrate reduction (in Arhodomonas, Flexistipes, Geotoga and Marinobacter MAGs) and thiosulfate reduction (in Arhodomonas, Flexistipes and Geotoga MAGs). Genomic evidence for adaptation to high salinity, withstanding radioactivity, and metal acquisition was also observed in different MAGs, possibly explaining their occurrence in this extreme habitat. Other metabolic features included the potential for quorum sensing and biofilm formation, and genes for forming endospores in some cases. Understanding the microbiomes of deep biosphere environments sheds light on the capabilities of uncultivated subsurface microorganisms and their potential roles in subsurface settings, including during oil recovery operations.