Archive 20/10/9

Mutations in S-proteins of the new coronavirus (SARS-CoV-2): 25 mutations, including A930V, D614G, A706S, and A879S

From the RNA sequence of the new coronavirus (SARS-CoV-2) registered from India in NCBI-Virus-database as of June 6, 2020, a research result has been reported on what mutations occurred compared to the original strain that began to spread in Wuhan, China.

There are 25 mutations, and they are divided into four clusters (1-100, 148-255, 570-680, 820-930).

Mutations in T572I, A879S, A892V, and A930V have caused significant changes in the secondary structure of proteins, T572I causes a structural change to coilded→helix and the other three to helix→beta sheet. Mutations in the A930V, D614G, A706S, and A879S seem to make a relatively large difference in protein structure. Of particular interest is that the mutation of D614G occurs in 88% of RNA sequences, which is a mainstream among new coronaviruses that are spreading in India. For the D614G, it is reported that it promotes the open conformation of RBD, and as a result, the infectivity of the new coronavirus is increased.

R408I, E471Q is said to be a mutation that is seen only in India.

Diagnosis of serum SARS-CoV-2 nucleocapsid protein (N-protein) is highly effective in early diagnosis of new coronavirus (COVID-19)

It is well known that the diagnosis for the new coronavirus is twofold; RT-PCR to check for the infection and antibody testing to check for the infection history.
Antibody testing can not be used for early diagnosis of SARS-CoV-2, because it take more than a week before the antibody rises up. However, by focusing on serum nucleocapsid protein of SARS-CoV-2, it seems that the infection of the new coronavirus can be detected even early before the antibody (IgG) rises up.

The ROC curve showed that AUC was 0.9756 (95% CI), sensitivity was 92%, and specificity was 96.8%. The cut-off value of the capsid protein was 1.85 pg/mL.

Wouldn’t it be a great achievement?

Fusion protein of lectins derived from Ricinus communis and Phytolacca americana expected as a novel therapeutic agent for the new coronavirus (COVID-19)

As a treatment for the new coronavirus (COVID-19), the usefulness of the fusion protein (RTAM-PAP1) between a mutant of the lysine A chain extracted from Ricinus communis (RTAM) and a lectin extracted from the leaves of Phytolacca americana (PAP1) is reported from the viewpoint of affinity evaluation between various proteins of SARS-CoV-2 and RTAM-PAP1 and also from toxicity testing using mice.

The binding affinity between a patient-derived antibody B38 of SARS-CoV-2 (as a control), ACE2, RTAM-PAP1 and various proteins of SARS-CoV-2 were evaluated using three-dimensional molecular structure analysis software named CoDockPP, HASSOCK2.2, and ZDOCK. The binding of RTAM-PAP1 was stronger than ACE2, and showed the same binding affinity as B38 comprehensively.
In addition, the toxicity tests using mice showed no side effects even at 1 mg/kg of dose.

This paper shows the usefulness of the fusion protein of lectins derived from Ricinus communis and Phytolacca americana as a novel drug for the new coronavirus.

Ricinus communis



Phytolacca americana

How to reduce cross-reactivity to other coronaviruses in the detection of new coronaviruses (SARS-CoV-2) using RT-PCR

RT-PCR is used as you know to test for suspected infection of the new coronavirus (SARS-CoV-2). Sensitivity and specificity are very important in testing, but there are some doubts as to how high the generally implemented RT-PCR could detect SARS-CoV-2 without cross-reactivity to other coronaviruses including influenza viruses. Therefore, the following groups have reported a novel methodology for improving the specificity of SARS-CoV-2 detection without sacrificing sensitivity.

As the methodology, they adopted the followings, (1) in the domain of structural protein of SRAS-CoV-2, design dual-target PCR primers targeting on coding region of the accessory and envelope proteins (ORF3ab-E primers) and that of the capsid protein (N primers), and (2) use a peptide nucleic acid (PAN) designed to target on the N region as a blocker of PCR reaction.

Peptide nucleic acids are artificial compounds in which the deoxyribose phosphate backbone is replaced by a pseudo-peptide polymer to which the nucleobases are linked, and the binding affinity for target DNA and RNA is remarkably increased by nearly 1000 times, and therefore, it does not act as a primer but act as an inhibitor of PCR.

As a result, cross-reactivity to other coronaviruses and influenza viruses disappeared completely, and the detection rate of SARS-CoV-2 was 100% for ORF3ab-E and 82.6% for PNA-N.

About infection inhibitors of the new coronavirus (SARS-CoV-2) as its therapeutic drugs: Are inhibitors on furin and transmembrane protease serine 2 etc. effective?

The S protein of the new coronavirus (SARS-CoV-2) is divided into S1 sites that bind to the host receptors and S2 sites for membrane fusion. The boundary site of this S1 site and S2 site has a furin cleavage site, and there is a target domain of transmembrane protease serine 2 (TMPRSS2) in the S2 site. The following research has been reported that the use of inhibitors on those proteases may be able to reduce the infection of the new coronavirus.

decanoyl-RVKR-chlorometylketone (CMK) for a furin inhibitor, camostat for a TMPRSS2 inhibitor, as well as naphthofluorescein which inhibits RNA replication, were studied. VeroE6 cells are used for the experiments.





Obtained efficacy and toxicity were as follows; the 50% inhibitory concentration (IC50) was 0.057 μM for CMK, 9.025 μM for naphthofluorescein, and 0.025 μM for camostat. The 50% cytotoxic concentration (CC50) was 318.2 μM for CMK, 57.44 μM for naphthofluorescein, and 2,000 μM for camostat. The resulting selection index is 5,567 for CMK, 6.36 for naphthofluorescein, and 81,004 for camostat.

Note that there is a difference among these inhibitors, CMK and camostat prevent the initial infection of the virus, and naphthofluorescein prevents the replication of the virus. We look forward to further consideration as a lead compound for the development of therapeutic drugs in the future.

To suppress infection with the new coronavirus (SARS-CoV-2), pineapple intake will be GOOD.

The Univ. of Nebraska Medical Center group has reported a research finding that bromelain extracted from pineapples (enzyme classified as a cysteine protease in proteolytic enzymes) is effective in suppressing infection with the new coronavirus (SARS-Co-2).






Bromelain targets angiotensin-converting enzyme 2 (ACE2), type II membrane-penetrating serine protease (TMPRSS2) and SARS-CoV-2 S-protein, and thereby suppresses SAS-CoV-2 infection. Because bromelain is well absorbed through digestive organs and maintains its biochemical activity in the body, it is said that ingesting pineapples rich in bromelain will suppress infection with the new coronavirus (SARS-CoV-2).

The new coronavirus (COVID-19) has a higher rate of severity in men than in women, but the rate of severity seems to skyrocket, especially in baldness.

In the new coronavirus (COVID-19), there are some reports that men are more likely to become more severe than women due to the effects of male hormones. The following studies have reported that men with baldness have a very high rate of severity among them.

The following NHS-scale is used as the indicator of baldness.





It can be noted that the rate of severity get very high in NHS-scale from 3 to 7.


Biomarker research for pancreatic duct adenocarcinoma using its organoids

A group of Harvard Medical Schools has reported potential biomarkers for pancreatic duct adenocarcinoma using pancreatic duct adenocarcinoma derived organoids.

(1) From the viewpoint of glycans: High mannose and Lewis X epitope structures increase in pancreatic duct adenocarcinoma.

(2) From the viewpoint of extracellular vesicles (EV) proteins: ANXA11 (anexine A11: calcium-dependent phospholipid binding protein mobilized in the budding region of the transport endoplasmic reticulum COPII of the endoplasmic reticulum) is increased.

Let’s look forward to further research progress.

The D614G mutation in the S protein of the new coronavirus (SARS-CoV-2) causes increased infectability due to changes in Conformation of RBD (Receptor Binding Domain)

In the new coronavirus (SARS-CoV-2), viruses with D614G mutation in S proteins are now the mainstream of infection. A group of University of Massachusetts Medical School et al. has reported their findings on the causes of why D614G mutation is to be more infectious than the past D614.


Using SPR, the experimental results of the interaction between D614G and ACE2 indicate that it is not due to increased affinity of D614G mutation to ACE2 (rather, slightly affinity is lowered), Structural analysis of S proteins by cryo-EM shows that the presence rates of Close and Open status in RBD (Receptor Binding Domain) have changed, and that D614G mutation is more likely to have Open structures than D614. Therefore, it has been concluded that the increased infectivity of D614G is due to the more exposed RBD of the S protein.

Diagnosis of chest X-ray imaging by Deep Learning greatly improves the diagnostic accuracy of the new coronavirus (COVID-19)

Chest X-rays and CT are used as a diagnosis of the new coronavirus (COVID-19). The Univ. of Oklahoma group has succeeded in improved diagnostic accuracy by using Deep Learning techniques to determine whether or not the breast X-ray images are from COVID-19-derived pneumonia.

Deep Learning uses a six-layer Convolutional Neural Network (CNN), with a chest X-ray image resized into 224 x 224 x 3, and a Convolution of 3 x 3. The x3 in the input image indicates that it is three colored (R, G, and B). Since the X-ray image is black and white gray, the three colors of R, G, and B are pre-processed for the image as shown in the figure below. In the figure below, (Ip) is an image with the diaphragm removed, (Ieq) is an image processing method that adjusts the contrast using the intensity histogram of the image, and (Ib) is an image obtained by bilateral filtering on (Ieq). R, G, and B images are simulated by using these three (Ip), (Ib), and (Ieq) images.






The results of Deep Learning are,
with the simple model using the X-ray image as it is, 88% of the accuracy was obtained, and the accuracy was improved to 94.5% by adding the pre-processing of the above mentioned image editing method.

Wouldn’t it be time for diagnosis using Deep Learning to be used more and more in the medical field?

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