Construction of dose prediction model and identification of sensitive genes for space radiation based on single-sample networks under spaceflight conditions

Abstract

Purpose

To identify sensitive genes for space radiation, we integrated the transcriptomic samples of spaceflight mice from GeneLab and predicted the radiation doses absorbed by individuals in space.

Methods and materials

A single-sample network (SSN) for each individual sample was constructed. Then, using machine learning and genetic algorithms, we built the regression models to predict the absorbed dose equivalent based on the topological structure of SSNs. Moreover, we analyzed the SSNs from each tissue and compared the similarities and differences among them.

Results

Our model exhibited excellent performance with the following metrics: $R^2=0.980$, $\mathit{\text{MSE}}=6.74e-04$, and the Pearson correlation coefficient of 0.990 (p value <.0001) between predicted and actual values. We identified 20 key genes, the majority of which had been proven to be associated with radiation. However, we uniquely established them as space radiation sensitive genes for the first time. Through further analysis of the SSNs, we discovered that the different tissues exhibited distinct mechanisms in response to space stressors.

Conclusions

The topology structures of SSNs effectively predicted radiation doses under spaceflight conditions, and the SSNs revealed the gene regulatory patterns within the organisms under space stressors.

Keywords:

• Space stressors
• radiation dose prediction
• radiation sensitive genes
• single-sample network
• genetic algorithm
• GeneLab

Author contributions

Yan Zhang: conceptualization, formal analysis, implementation of experiment, data curation and methodology, software, visualization, and writing original draft. Xiaohui Du: data curation and methodology, software, validation, and visualization. Lei Zhao: investigation, methodology, software, supervision, review, and editing. Yeqing Sun: project design and administration, funding acquisition, supervision, review, and editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The raw RNA-Seq data used in this study have been deposited in the GeneLab (https://genelab.nasa.gov/).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 32071244), the Space Science Experiment Project in Chinese Space Station (Grant No. SCP03-01-02), and the China Postdoctoral Science Foundation (Grant No. 2020M670720).

Notes on contributors

Yan Zhang

Yan Zhang is a PhD student in the Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University. His research interest includes space radiation biology and bioinformatics.

Xiaohui Du

Xiaohui Du is a PhD student in the Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University. Her research interest is space radiation biology.

Lei Zhao

Lei Zhao is a professor in the Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University. His research focuses on the adverse effects of space radiation and low-dose radiation on model organisms.

Yeqing Sun

Yeqing Sun is a professor in the Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University. Her research interest is space radiation biology.