covid-19 infection model

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

Yeah. Taguchi, teacher at Chuo University, turns to a model of COVID-19 infection that uses blood from human patients who have been infected with COVID-19

As of 2020, the COVID-19 pandemic has started to converge in almost all countries. Although vaccines currently appear to be effective in lowering the COVID-19 death rate, the virus continues to mutate, the likelihood of another lockdown being introduced continues to increase. In order to avoid these situations, we definitely need effective drugs that are not yet developed and an effective COVID-19 infection model.

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

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

Using human cell lines to understand COVID-19

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

This study has both advantages and disadvantages over studies described in previous manuscripts. [1,2]. Since this is the direct measurement from human patients, the measurement is more direct than those that used cell lines.

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

covid-19 infection model

Hands-on trial of genetic datasets

The research team downloaded two sets of publicly available datasets and applied our method to them, which they named PCAUFE.

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

To confirm if it seems too small a number of genes have the ability to differentiate COVID-19 patients from healthy controls, the research group built three machine learning models to classify two groups, the patients and the healthy control, using only the 123 selected genes; three models were tested using the second public dataset independently of the first dataset.

In order to validate the efficiency of classification performance, the research group used AUC, which takes 1.00 for perfect performance and 0.5 for random selection. Three models driven by 123 genes could achieve AUC greater than 0.9, signifying excellent performance. Although the same procedure is repeated with the exchange of two sets of data, i.e. the model is trained with the second set of data and is tested with the first set of data, it can achieve performance similar. This means that the results are robust. Thus, despite the very small number of selected genes, they can successfully discriminate COVID-19 patients from healthy controls.

In addition to this, to confirm the superiority of PCAUFE, the research group also used other state-of-the-art methods to select genes that are differentially expressed between COVID-19 patients and healthy controls. Although the classification performance using genes selected by state-of-the-art methods is comparable to that of PCAUFE, when only the same number of top-ranked genes as those selected by PCAUFE are used. Whereas the number of probes selected by the methods of the state of the art is from several thousand to eighteen thousand. Thus, state-of-the-art methods have a lower ability to restrict the number of genes used for classification.

Enrich 123 genes

Next, the research group studied the type of functions enriched in the 123 selected genes. Next, they found that the expression of many immune system-related genes included in these 123 genes is down-regulated in Blood from COVID-19 patients. In addition to this, many biological pathways and transcription factors enriched in these genes are previously reported to be suppressed in COVID-19 patients.

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

The finding that patient blood samples can be used for COVID-19 disease investigation creating a model of COVID-19 infection is remarkable.

First, if it is not lung tissue but blood that can be an effective tissue to study, it is much easier to sample. Collecting massive numbers of COVID-19 lung samples is hopeless, but collecting blood samples is doable. Since blood samples can be used for diagnosis, it is easy to monitor disease progression, allowing us to find out when to treat with medication if identified.

Unfortunately, the research team has not yet started to identify possible drug candidate compounds using the 123 identified genes, this will be realized soon and they will be able to obtain promising drug candidate compounds.

References

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

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

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

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