Mitochondrial Effects in the Liver of C57BL/6 Mice by Low Dose, High Energy, High Charge Irradiation.

Galactic cosmic rays are primarily composed of protons (85%), helium (14%), and high charge/high energy ions (HZEs) such as 56Fe, 28Si, and 16O. HZE exposure is a major risk factor for astronauts during deep-space travel due to the possibility of HZE-induced cancer. A systems biology integrated omics approach encompassing transcriptomics, proteomics, lipidomics, and functional biochemical assays was used to identify microenvironmental changes induced by HZE exposure. C57BL/6 mice were placed into six treatment groups and received the following irradiation treatments: 600 MeV/n 56Fe (0.2 Gy), 1 GeV/n 16O (0.2 Gy), 350 MeV/n 28Si (0.2 Gy), 137Cs (1.0 Gy) gamma rays, 137Cs (3.0 Gy) gamma rays, and sham irradiation. Left liver lobes were collected at 30, 60, 120, 270, and 360 days post-irradiation. Analysis of transcriptomic and proteomic data utilizing ingenuity pathway analysis identified multiple pathways involved in mitochondrial function that were altered after HZE irradiation. Lipids also exhibited changes that were linked to mitochondrial function. Molecular assays for mitochondrial Complex I activity showed significant decreases in activity after HZE exposure. HZE-induced mitochondrial dysfunction suggests an increased risk for deep space travel. Microenvironmental and pathway analysis as performed in this research identified possible targets for countermeasures to mitigate risk.

Associations between lipids in selected brain regions, plasma miRNA, and behavioral and cognitive measures following 28Si ion irradiation.

The space radiation environment consists of multiple species of charged particles, including 28Si ions, that may impact brain function during and following missions. To develop biomarkers of the space radiation response, BALB/c and C3H female and male mice and their F2 hybrid progeny were irradiated with 28Si ions (350 MeV/n, 0.2 Gy) and tested for behavioral and cognitive performance 1, 6, and 12 months following irradiation. The plasma of the mice was collected for analysis of miRNA levels. Select pertinent brain regions were dissected for lipidomic analyses and analyses of levels of select biomarkers shown to be sensitive to effects of space radiation in previous studies. There were associations between lipids in select brain regions, plasma miRNA, and cognitive measures and behavioral following 28Si ion irradiation. Different but overlapping sets of miRNAs in plasma were found to be associated with cognitive measures and behavioral in sham and irradiated mice at the three time points. The radiation condition revealed pathways involved in neurodegenerative conditions and cancers. Levels of the dendritic marker MAP2 in the cortex were higher in irradiated than sham-irradiated mice at middle age, which might be part of a compensatory response. Relationships were also revealed with CD68 in miRNAs in an anatomical distinct fashion, suggesting that distinct miRNAs modulate neuroinflammation in different brain regions. The associations between lipids in selected brain regions, plasma miRNA, and behavioral and cognitive measures following 28Si ion irradiation could be used for the development of biomarker of the space radiation response.

Effects of Low Dose Space Radiation Exposures on the Splenic Metabolome.

Future space missions will include a return to the Moon and long duration deep space roundtrip missions to Mars. Leaving the protection that Low Earth Orbit provides will unavoidably expose astronauts to higher cumulative doses of space radiation, in addition to other stressors, e.g., microgravity. Immune regulation is known to be impacted by both radiation and spaceflight and it remains to be seen whether prolonged effects that will be encountered in deep space can have an adverse impact on health. In this study, we investigated the effects in the overall metabolism of three different low dose radiation exposures (γ-rays, 16O, and 56Fe) in spleens from male C57BL/6 mice at 1, 2, and 4 months after exposure. Forty metabolites were identified with significant enrichment in purine metabolism, tricarboxylic acid cycle, fatty acids, acylcarnitines, and amino acids. Early perturbations were more prominent in the γ irradiated samples, while later responses shifted towards more prominent responses in groups with high energy particle irradiations. Regression analysis showed a positive correlation of the abundance of identified fatty acids with time and a negative association with γ-rays, while the degradation pathway of purines was positively associated with time. Taken together, there is a strong suggestion of mitochondrial implication and the possibility of long-term effects on DNA repair and nucleotide pools following radiation exposure.

Comparative RNA-Seq transcriptome analyses reveal dynamic time-dependent effects of 56Fe, 16O, and 28Si irradiation on the induction of murine hepatocellular carcinoma.

BACKGROUND: One of the health risks posed to astronauts during deep space flights is exposure to high charge, high-energy (HZE) ions (Z > 13), which can lead to the induction of hepatocellular carcinoma (HCC). However, little is known on the molecular mechanisms of HZE irradiation-induced HCC. RESULTS: We performed comparative RNA-Seq transcriptomic analyses to assess the carcinogenic effects of 600 MeV/n 56Fe (0.2 Gy), 1 GeV/n 16O (0.2 Gy), and 350 MeV/n 28Si (0.2 Gy) ions in a mouse model for irradiation-induced HCC. C3H/HeNCrl mice were subjected to total body irradiation to simulate space environment HZE-irradiation, and liver tissues were extracted at five different time points post-irradiation to investigate the time-dependent carcinogenic response at the transcriptomic level. Our data demonstrated a clear difference in the biological effects of these HZE ions, particularly immunological, such as Acute Phase Response Signaling, B Cell Receptor Signaling, IL-8 Signaling, and ROS Production in Macrophages. Also seen in this study were novel unannotated transcripts that were significantly affected by HZE. To investigate the biological functions of these novel transcripts, we used a machine learning technique known as self-organizing maps (SOMs) to characterize the transcriptome expression profiles of 60 samples (45 HZE-irradiated, 15 non-irradiated control) from liver tissues. A handful of localized modules in the maps emerged as groups of co-regulated and co-expressed transcripts. The functional context of these modules was discovered using overrepresentation analysis. We found that these spots typically contained enriched populations of transcripts related to specific immunological molecular processes (e.g., Acute Phase Response Signaling, B Cell Receptor Signaling, IL-3 Signaling), and RNA Transcription/Expression. CONCLUSIONS: A large number of transcripts were found differentially expressed post-HZE irradiation. These results provide valuable information for uncovering the differences in molecular mechanisms underlying HZE specific induced HCC carcinogenesis. Additionally, a handful of novel differentially expressed unannotated transcripts were discovered for each HZE ion. Taken together, these findings may provide a better understanding of biological mechanisms underlying risks for HCC after HZE irradiation and may also have important implications for the discovery of potential countermeasures against and identification of biomarkers for HZE-induced HCC.

Efficient identification of multiple pathways: RNA-Seq analysis of livers from 56Fe ion irradiated mice.

BACKGROUND: mRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual modules (clusters) are neither too small to make any general inferences, nor too large to be biologically interpretable. Clustering thresholds for identification of modules are not systematically determined and depend on user-settable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features. RESULTS: In this study, we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline employs WGCNA, a software widely used to perform different aspects of gene co-expression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose 56Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results, along with experimental validation, show that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCNA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCNA, and identified a module that was biologically validated by a mitochondrial complex I assay. CONCLUSIONS: We present a pipeline that can reduce the problem of parameter selection that occurs with the existing algorithm in WGCNA, for applicable RNA-Seq datasets. This may assist in the future discovery of novel mRNA interactions, and elucidation of their potential downstream molecular effects.

Non-targeted lipidomics of CSF and frontal cortex grey and white matter in control, mild cognitive impairment, and Alzheimer's disease subjects.

OBJECTIVE: We undertook a non-targeted lipidomics analysis of post-mortem cerebrospinal fluid (CSF), frontal cortex grey matter, and subjacent white matter to define potential biomarkers that distinguish cognitively intact subjects from those with incipient or established dementia. Our objective was to increase our understanding of the role of brain lipids in pathophysiology of aging and age-related cognitive impairment. METHODS: Levels of 650 individual lipids, across 26 lipid subclasses, were measured utilising a high-resolution mass spectrometric analysis platform. RESULTS: Monoacylglycerols (MAG), diacylglycerols (DAG), and the very-long-chain fatty acid 26:0 were elevated in the grey matter of the mild cognitive impairment (MCI) and old dementia (OD) cohorts. Ethanolamine plasmalogens (PlsEtn) were decreased in the grey matter of the young dementia (YD) and OD cohorts while and phosphatidylethanolamines (PtdEth) were lower in the MCI, YD and OD cohorts. In the white matter, decrements in sulphatide levels were detected in the YD group, DAG levels were elevated in the MCI group, and MAG levels were increased in the YD and OD groups. CONCLUSION: The parallel changes in grey matter MAGs and DAGs in the MCI and OD groups suggest that these two cohorts may have a similar underlying pathophysiology; consistent with this, MCI subjects were more similar in age to OD than to YD subjects. While PlsEtn and phosphatidylethanolamine were decreased in the YD and OD groups they were unaltered in the MCI group indicating that alterations in plasmalogen synthesis are unlikely to represent an initiating event in the transition from MCI to dementia.