Is human blood better than cell lines as a COVID-19 infection model?

Yes. Taguchi, a professor at Chuo University, looks at a COVID-19 infection model that uses blood from human patients infected with COVID-19

From 2020, the COVID-19 pandemic began to converge in almost all countries. While vaccines currently appear to be effective in lowering the COVID-19 death rate, the virus continues to mutate, the chance of another lockdown being introduced continues to increase. To avoid these situations, we definitely need effective drugs that have not yet been developed and an effective COVID-19 infection model†

In our previous articles published in the Open Access Government publication [1,2] we introduced our recent efforts to develop effective drugs for COVID-19 using computers.

However, our studies described in the previous articles can only use human and mouse cell lines. If we can directly use measurements in human patients infected with COVID-19, we may be able to get better results.

Using human cell lines to understand COVID-19

Recently, the research groups led by Assistant Prof Miyata, Ryukyu University and Prof Ikematsu, National Institute of Technology, Okinawa College, in collaboration with us, used our methods to analyze gene expression of blood collected from human COVID-19 patients [3]†

This study has both advantages and disadvantages compared to the studies described in the previous manuscripts [1,2]† Since it is the direct measurement of human patients, the measurement is more direct than those using cell lines.

However, since it is not taken from the lung, where the infection occurs, but from the blood, it is indirect in that sense. Thus, it is unclear whether the replacement of human lung cell lines with human blood can improve the outcome or not. The only way to find out is a practical test.

Field trial of gene data sets

The research team downloaded two sets of publicly available data sets and applied our method which they called PCAUFE.

They found that only 123 genes are differentially expressed between healthy controls and COVID-19 patients in the first data set. Since the total number of human genes is 20,000, 123 genes are very limited and a small part of them.

To confirm whether this seems too small, a number of genes have the ability to distinguish COVID-19 patients from healthy controls, the research group constructed three machine learning models to classify two groups, patients and healthy control, where only the selected 123 genes were used; three models were tested using the second public dataset, independent of the first dataset.

To validate the efficiency of classification performance, the research group used AUC, which costs 1.00 for perfect performance and 0.5 for random selection. Three models trained by 123 genes were able to achieve an AUC greater than 0.9, signifying excellent performance. Although the same procedure is repeated when exchanging two data sets, ie the model is trained with the second data set and tested with the first data set, it can achieve comparable performance. This means that the results are robust. So, despite the very small number of selected genes, they can successfully distinguish COVID-19 patients from healthy controls.

In addition, to confirm the superiority of PCAUFE, the research group also used other advanced methods to select genes that are differentially expressed between COVID-19 patients and healthy controls. Although the classification performance using genes selected by the prior art is comparable to PCAUFE when using only the highest number of genes selected by PCAUFE. While the number of probes selected by the prior art is as many as several thousand to eighteen thousand. Thus, the prior art methods have an inferior ability to limit the number of genes used for classification.

enrich 123 genes

Next, the research group examined what kind of functions are enriched in the selected 123 genes. They then found that the expression of many immune-related genes included in these 123 genes is downregulated in Blood from COVID-19 patients† In addition, many biological pathways and transcription factors enriched with these genes have been previously reported to be suppressed in COVID-19 patients.

These suggest that PCAUFE can not only identify genes whose expression can discriminate between COVID-19 patients and healthy control (i.e., biomarkers), but also identify a limited number of likely disease-causing genes.

The finding that blood samples from patients can be used for COVID-19 disease research to create a COVID-19 infection model is noteworthy.

First, if not lung tissue but blood can be an effective tissue to examine, it is much easier to collect. Collecting a huge number of lung samples from COVID-19 is hopeless, but collecting blood samples is feasible. Because blood samples can be used for diagnosis, it is easy to monitor disease progression, which allows us to find the timing to treat with drugs if it is identified.

Unfortunately, the research team has not yet started identifying potential drug candidates using identified 123 genes, it will be done soon and they can get promising drug candidate compounds.

References

[1] Yes. Taguchi, how to compete with COVID-19 with a computer? Open Access Government, Issue 33, Jan. (2022) pp. 210-211.

[2] Yes. Taguchi, can mice be an effective model animal for Covid-19? Open Access Government, Issue 34, Apr (2022) pp.112-113.

[3] Fujisawa, K., Shimo, M., Taguchi, YH. et al. PCA-based uncontrolled function extraction for gene expression analysis of COVID-19 patients. Sci Rep 11, 17351 (2021).

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