Association between gut health and gut microbiota in a polluted environment

Animals host complex bacterial communities in their gastrointestinal tracts, with which they share a mutualistic interaction. The numerous effects these interactions grant to the host include regulation of the immune system, defense against pathogen invasion, digestion of otherwise undigestible foodstuffs, and impacts on host behaviour. Exposure to stressors, such as environmental pollution, parasites, and/or predators, can alter the composition of the gut microbiome, potentially affecting host-microbiome interactions that can be manifest in the host as, for example, metabolic dysfunction or inflammation. However, whether a change in gut microbiota in wild animals associates with a change in host condition is seldom examined. Thus, we quantified whether wild bank voles inhabiting a polluted environment, areas where there are environmental radionuclides, exhibited a change in gut microbiota (using 16S amplicon sequencing) and concomitant change in host health using a combined approach of transcriptomics, histological staining analyses of colon tissue, and quantification of short-chain fatty acids in faeces and blood. Concomitant with a change in gut microbiota in animals inhabiting contaminated areas, we found evidence of poor gut health in the host, such as hypotrophy of goblet cells and likely weakened mucus layer and related changes in Clca1 and Agr2 gene expression, but no visible inflammation in colon tissue. Through this case study we show that inhabiting a polluted environment can have wide reaching effects on the gut health of affected animals, and that gut health and other host health parameters should be examined together with gut microbiota in ecotoxicological studies.

The successes and pitfalls: Deep-learning effectiveness in a Chernobyl field camera trap application

Camera traps have become in situ sensors for collecting information on animal abundance and occupancy estimates. When deployed over a large landscape, camera traps have become ideal for measuring the health of ecosystems, particularly in unstable habitats where it can be dangerous or even impossible to observe using conventional methods. However, manual processing of imagery is extremely time and labor intensive. Because of the associated expense, many studies have started to employ machine-learning tools, such as convolutional neural networks (CNNs). One drawback for the majority of networks is that a large number of images (millions) are necessary to devise an effective identification or classification model. This study examines specific factors pertinent to camera trap placement in the field that may influence the accuracy metrics of a deep-learning model that has been trained with a small set of images. False negatives and false positives may occur due to a variety of environmental factors that make it difficult for even a human observer to classify, including local weather patterns and daylight. We transfer-trained a CNN to detect 16 different object classes (14 animal species, humans, and fires) across 9576 images taken from camera traps placed in the Chernobyl Exclusion Zone. After analyzing wind speed, cloud cover, temperature, image contrast, and precipitation, there was not a significant correlation between CNN success and ambient conditions. However, a possible positive relationship between temperature and CNN success was noted. Furthermore, we found that the model was more successful when images were taken during the day as well as when precipitation was not present. This study suggests that while qualitative site-specific factors may confuse quantitative classification algorithms such as CNNs, training with a dynamic training set can account for ambient conditions so that they do not have a significant impact on CNN success.

Radioactive contamination in feral dogs in the Chernobyl exclusion zone: Population body-burden survey and implications for human radiation exposure

This report describes a two-year effort to survey the internal 137Cs and external β-emitter contamination present in the feral dog population near the Chernobyl nuclear power plant (ChNPP) site, and to understand the potential for human radiation exposure from this contamination. This work was performed as an integral part of the radiation safety and control procedures of an animal welfare oriented trap-neuter-release (TNR) program. The measurement program focused on external contamination surveys using handheld β-sensitive probes, and internal contamination studies using a simple whole-body counter. Internal 137Cs burden was measured non-invasively during post-surgical observation and recovery. External β contamination surveys performed during intake showed that 21/288 animals had significant, removable external contamination, though not enough to pose a large hazard for incidental contact. Measurements with the whole-body counter indicated internal 137Cs body burdens ranging from undetectable (minimum detection level ∼100 Bq/kg in 2017, ∼30 Bq/kg in 2018) to approximately 30,000 Bq/kg. A total of 33 animals had 137Cs body-burdens above 1 kBq/kg, though none posed an external exposure hazard. The large variation in the 137Cs concentration in these animals is not well-understood, could be due to prey selection, access to human food scraps, or extended residence in highly contaminated areas. The small minority of animals with external contamination may pose a contamination risk allowing exposures in excess of regulatory standards.

Population dynamics and genome-wide selection scan for dogs in Chernobyl

BACKGROUND

Natural and anthropogenic disasters can have long-lasting impacts on the genetics and structure of impacted populations. The 1986 Chernobyl Nuclear Power Plant disaster led to extensive contamination of the local environment and the wildlife therein. Several ecological, environmental, and genetic studies reported various effects of this disaster on animal, insect, and plant species; however, little work has been done to investigate the genetics of the free-breeding dogs that occupy the Chernobyl Exclusion Zone (CEZ).

RESULTS

We define the population genetic structure of two groups of dogs that reside within the CEZ, one around the reactor site itself and another living within Chernobyl City. We found little evidence of gene flow and a significant degree of genetic differentiation between the two populations dogs, suggesting that these are two distinct populations despite occupying areas located just 16 km apart. With an F_ST-based outlier analysis, we then performed a genome-wide scan for evidence of directional selection within the dog populations. We found 391 outlier loci associated with genomic regions influenced by directional selection, from which we identified 52 candidate genes.

CONCLUSIONS

Our genome scan highlighted outlier loci within or near genomic regions under directional selection, possibly in response to the multi-generational exposure faced. In defining the population structure and identifying candidate genes for these dog populations, we take steps towards understanding how these types of prolonged exposures have impacted these populations.

The dogs of Chernobyl: Demographic insights into populations inhabiting the nuclear exclusion zone

The 1986 Chernobyl nuclear disaster initiated a series of catastrophic events resulting in long-term and widespread environmental contamination. We characterize the genetic structure of 302 dogs representing three free-roaming dog populations living within the power plant itself, as well as those 15 to 45 kilometers from the disaster site. Genome-wide profiles from Chernobyl, purebred and free-breeding dogs, worldwide reveal that the individuals from the power plant and Chernobyl City are genetically distinct, with the former displaying increased intrapopulation genetic similarity and differentiation. Analysis of shared ancestral genome segments highlights differences in the extent and timing of western breed introgression. Kinship analysis reveals 15 families, with the largest spanning all collection sites within the radioactive exclusion zone, reflecting migration of dogs between the power plant and Chernobyl City. This study presents the first characterization of a domestic species in Chernobyl, establishing their importance for genetic studies into the effects of exposure to long-term, low-dose ionizing radiation.

Spring 2020 Atmospheric Aerosol Contamination over Kyiv City

Extraordinarily high aerosol contamination was observed in the atmosphere over the city of Kyiv, Ukraine, during the March–April 2020 period. The source of contamination was the large grass and forest fires in the northern part of Ukraine and the Kyiv region. The level of PM2.5 load was investigated using newly established AirVisual sensor mini-networks in five areas of the city. The aerosol data from the Kyiv AERONET sun-photometer site were analyzed for that period. Aerosol optical depth, Ångström exponent, and the aerosol particles properties (particle size distribution, single-scattering albedo, and complex refractive index) were analyzed using AERONET sun-photometer observations. The smoke particles observed at Kyiv site during the fires in general correspond to aerosol with optical properties of biomass burning aerosol. The variability of the optical properties and chemical composition indicates that the aerosol particles in the smoke plumes over Kyiv city were produced by different burning materials and phases of vegetation fires at different times. The case of enormous PM2.5 aerosol contamination in the Kyiv city reveals the need to implement strong measures for forest fire control and prevention in the Kyiv region, especially in its northwest part, where radioactive contamination from the Chernobyl disaster is still significant.

Response of Arabidopsis thaliana and Mizuna Mustard Seeds to Simulated Space Radiation Exposures

One of the major concerns for long-term exploration missions beyond the Earth’s magnetosphere is consequences from exposures to solar particle event (SPE) protons and galactic cosmic rays (GCR). For long-term crewed Lunar and Mars explorations, the production of fresh food in space will provide both nutritional supplements and psychological benefits to the astronauts. However, the effects of space radiation on plants and plant propagules have not been sufficiently investigated and characterized. In this study, we evaluated the effect of two different compositions of charged particles-simulated GCR, and simulated SPE protons on dry and hydrated seeds of the model plant Arabidopsis thaliana and the crop plant Mizuna mustard [Brassica rapa var. japonica]. Exposures to charged particles, simulated GCRs (up to 80 cGy) or SPEs (up to 200 cGy), were performed either acutely or at a low dose rate using the NASA Space Radiation Laboratory (NSRL) facility at Brookhaven National Lab (BNL). Control and irradiated seeds were planted in a solid phytogel and grown in a controlled environment. Five to seven days after planting, morphological parameters were measured to evaluate radiation-induced damage in the seedlings. After exposure to single types of charged particles, as well as to simulated GCR, the hydrated Arabidopsis seeds showed dose- and quality-dependent responses, with heavier ions causing more severe defects. Seeds exposed to simulated GCR (dry seeds) and SPE (hydrated seeds) had significant, although much less damage than seeds exposed to heavier and higher linear energy transfer (LET) particles. In general, the extent of damage depends on the seed type.

Metabolomic Profiles of the Creeping Wood Sorrel Oxalis corniculata in Radioactively Contaminated Fields in Fukushima: Dose-Dependent Changes in Key Metabolites

The biological impacts of the Fukushima nuclear accident, in 2011, on wildlife have been studied in many organisms, including the pale grass blue butterfly and its host plant, the creeping wood sorrel Oxalis corniculata. Here, we performed an LC–MS-based metabolomic analysis on leaves of this plant collected in 2018 from radioactively contaminated and control localities in Fukushima, Miyagi, and Niigata prefectures, Japan. Using 7967 peaks detected by LC–MS analysis, clustering analyses showed that nine Fukushima samples and one Miyagi sample were clustered together, irrespective of radiation dose, while two Fukushima (Iitate) and two Niigata samples were not in this cluster. However, 93 peaks were significantly different (FDR < 0.05) among the three dose-dependent groups based on background, low, and high radiation dose rates. Among them, seven upregulated and 15 downregulated peaks had single annotations, and their peak intensity values were positively and negatively correlated with ground radiation dose rates, respectively. Upregulated peaks were annotated as kudinoside D (saponin), andrachcinidine (alkaloid), pyridoxal phosphate (stress-related activated vitamin B6), and four microbe-related bioactive compounds, including antibiotics. Additionally, two peaks were singularly annotated and significantly upregulated (K₁R₁H₁; peptide) or downregulated (DHAP(10:0); decanoyl dihydroxyacetone phosphate) most at the low dose rates. Therefore, this plant likely responded to radioactive pollution in Fukushima by upregulating and downregulating key metabolites. Furthermore, plant-associated endophytic microbes may also have responded to pollution, suggesting their contributions to the stress response of the plant.