The enzymes in COVID-19: A review

Prediction of influential proteins and enzymes of certain diseases using a directed unimodular hypergraph

Protein-protein interaction (PPI) analysis based on mathematical modeling is an efficient means of identifying hub proteins, corresponding enzymes and many underlying structures. In this paper, a method for the analysis of PPI is introduced and used to analyze protein interactions of diseases such as Parkinson's, COVID-19 and diabetes melitus. A directed hypergraph is used to represent PPI interactions. A novel directed hypergraph depth-first search algorithm is introduced to find the longest paths. The minor hypergraph reduces the dimension of the directed hypergraph, representing the longest paths and results in the unimodular hypergraph. The property of unimodular hypergraph clusters influential proteins and enzymes that are related thereby providing potential avenues for disease treatment.

ACE2, ACE, DPPIV, PREP and CAT L enzymatic activities in COVID-19: imbalance of ACE2/ACE ratio and potential RAAS dysregulation in severe cases

OBJECTIVE AND DESIGN

Circulating enzymatic activity and RAAS regulation in severe cases of COVID-19 remains unclear, therefore we measured the serum activity of several proteases as potential targets to control the SARS-CoV-2 infection.

MATERIAL OR SUBJECTS

152 patients with COVID-19-like symptoms were grouped according to the severity of symptoms (COVID-19 negative, mild, moderate and severe).

METHODS

Serum samples of COVID-19 patients and controls were subjected to biochemical analysis and enzymatic assays of ACE2, ACE, DPPIV, PREP and CAT L. One-way ANOVA and multivariate logistic regression analysis were used. Statistical significance was accepted at p < 0.05.

RESULTS

We detected a positive correlation among comorbidities, higher C-reactive protein (CRP) and D-dimer levels with disease severity. Enzymatic assays revealed an increase in serum ACE2 and CAT L activities in severe COVID-19 patients, while ACE, DPPIV and PREP activities were significantly reduced. Notably, analysis of ACE2/ACE activity ratio suggests a possible imbalance of ANG II/ANG(1-7) ratio, in a positive association with the disease severity.

CONCLUSION

Our findings reveal a correlation between proteases activity and the severity of COVID-19. These enzymes together contribute to the activation of pro-inflammatory pathways, trigger a systemic activation of inflammatory mediators, leading to a RAAS dysregulation and generating a significant damage in several organs, contributing to poor outcomes of severe cases.

Enzymatic approaches against SARS-CoV-2 infection with an emphasis on the telomere-associated enzymes

The pandemic phase of coronavirus disease 2019 (COVID-19) appears to be over in most countries. However, the unexpected behaviour and unstable nature of coronaviruses, including temporary hiatuses, re-emergence, emergence of new variants, and changing outbreak epicentres during the COVID-19 pandemic, have been frequently reported. The mentioned trend shows the fact that in addition to vaccine development, different strategies should be considered to deal effectively with this disease, in long term. In this regard, the role of enzymes in regulating immune responses to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has recently attracted much attention. Moreover, several reports confirm the association of short telomeres with sever COVID-19 symptoms. This review highlights the role of several enzymes involved in telomere length (TL) regulation and explains their relevance to SARS-CoV-2 infection. Apparently, inhibition of telomere shortening (TS) through inhibition and/or activation of these enzymes could be a potential target in the treatment of COVID-19, which may also lead to a reduction in disease severity.

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19. The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

Human Cell Organelles in SARS-CoV-2 Infection: An Up-to-Date Overview

Since the end of 2019, the whole world has been struggling with the life-threatening pandemic amongst all age groups and geographic areas caused by Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2). The Coronavirus Disease 2019 (COVID-19) pandemic, which has led to more than 468 million cases and over 6 million deaths reported worldwide (as of 20 March 2022), is one of the greatest threats to human health in history. Meanwhile, the lack of specific and irresistible treatment modalities provoked concentrated efforts in scientists around the world. Various mechanisms of cell entry and cellular dysfunction were initially proclaimed. Especially, mitochondria and cell membrane are crucial for the course of infection. The SARS-CoV-2 invasion depends on angiotensin converting enzyme 2 (ACE2), transmembrane serine protease 2 (TMPRSS2), and cluster of differentiation 147 (CD147), expressed on host cells. Moreover, in this narrative review, we aim to discuss other cell organelles targeted by SARS-CoV-2. Lastly, we briefly summarize the studies on various drugs.