Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning

Despite the fact that coronavirus disease 2019 (COVID-19),
caused
by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has
been disrupting human life and health worldwide since the outbreak
in late 2019, the impact of exogenous substance exposure on the viral
infection remains unclear. It is well-known that, during viral infection,
organism receptors play a significant role in mediating the entry
of viruses to enter host cells. A major receptor of SARS-CoV-2 is
the angiotensin-converting enzyme 2 (ACE2). This study proposes a
deep learning model based on the graph convolutional network (GCN)
that enables, for the first time, the prediction of exogenous substances
that affect the transcriptional expression of the ACE2 gene. It outperforms
other machine learning models, achieving an area under receiver operating
characteristic curve (AUROC) of 0.712 and 0.703 on the validation
and internal test set, respectively. In addition, quantitative polymerase
chain reaction (qPCR) experiments provided additional
supporting evidence for indoor air pollutants identified by the GCN
model. More broadly, the proposed methodology can be applied to predict
the effect of environmental chemicals on the gene transcription of
other virus receptors as well. In contrast to typical deep learning
models that are of black box nature, we further highlight the interpretability
of the proposed GCN model and how it facilitates deeper understanding
of gene change at the structural level.