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

yes. Taguchi, a professor at Chu University, looks at a COVID-19 infection model that uses the blood of human patients who have been infected with the COVID-19 virus

The year 2020 onwards saw the spread of the COVID-19 pandemic to almost all countries. Although vaccines currently appear to be effective in reducing the death rate of COVID-19, the virus continues to alter the likelihood of another lockdown that continues to rise. In order to avoid these situations, we definitely need effective drugs that have not yet been developed and an effective model of COVID-19 infection.

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

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

Using human cell lines to understand COVID-19

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

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

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

covid-19 infection model

Practical experience of genetic data sets

The research team downloaded two publicly available data sets, and applied our method that they called PCAUFE to them.

They found that up to 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 only a small part of them.

To confirm whether this sounds too small, a number of genes have the potential to distinguish COVID-19 patients from healthy controls, the research group built three machine learning models to classify two groups, patients and healthy controls, using only the 123 identified genes; Three models were tested using the second general data set independent of the first data set.

In order to verify the efficiency of classification performance, the research group used the American University in Cairo, which takes 1.00 for the ideal performance and 0.5 for random selection. Three models trained by 123 genes can achieve an AUC of more than 0.9, which means an excellent performance. Although the same procedure is repeated with two sets of data exchanged, that is, the model is trained with the second data set and tested with the first data set, it can achieve similar performance. This means that the results are powerful. Thus, despite the very small number of genes selected, they can successfully distinguish COVID-19 patients from healthy controls.

In addition, to confirm the superiority of PCAUFE, the research group also used other recent methods to select for genes that are differentially expressed between COVID-19 patients and healthy controls. Although the performance of classification using genes selected by the latest methods is comparable with PCAUFE when only the same number of higher-order genes as those identified by PCAUFE are used. While the number of probes selected with the latest methods reaches several thousand to eighteen thousand. Thus, modern methods have less ability to restrict the number of genes used for classification.

enrichment of 123 genes

Next, the research group investigated what kind of functions are enriched in the 123 selected genes. They then discovered that the expression of several immune-related genes present in these 123 genes is downregulated in the blood of COVID-19 patients. In addition, several biological pathways and transcription factors enriched in these genes have been previously reported to suppress COVID-19 patients.

These studies indicate that not only can PCAUFE identify genes whose expression can distinguish between COVID-19 patients and a healthy control (i.e. biomarkers), but it can also identify a limited number of potentially pathogenic genes.

The discovery that patients’ blood samples can be used to investigate COVID-19 disease and establish a COVID-19 infection model is remarkable.

First, if it is not lung tissue but blood can be an effective tissue to be examined, it will be much easier to collect. Collecting a massive number of lung samples from COVID-19 is hopeless, but collecting blood samples is possible. Since blood samples can be used for diagnosis, it is easy to monitor disease progression, enabling us to know when it is appropriate to treat with drugs if indicated.

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

references

[1] yes. Taguchi, how does COVID-19 compete with a computer? Open Access Government, No. 33, Jan (2022) pp. 210-211.

[2] yes. Taguchi, could mice be an effective Covid-19 model animal? Open Access Government, No. 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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