The coronavirus disease (COVID-19) pandemic is a leading global health and economic challenge. What defines the disease’s progression is not entirely understood, but there are strong indications that oxidative stress and the defense against reactive oxygen species are crucial players. A big influx of immune cells to the site of infection is marked by the increase in reactive oxygen and nitrogen species. Our article aims to highlight the critical role of oxidative stress in the emergence and severity of COVID-19 and, more importantly, to shed light on the underlying molecular and genetic mechanisms. We have reviewed the available literature and clinical trials to extract the relevant genetic variants within the oxidative stress pathway associated with COVID-19 and the anti-oxidative therapies currently evaluated in the clinical trials for COVID-19 treatment, in particular clinical trials on glutathione and N-acetylcysteine.

Connective tissue growth factor (CTGF) is involved in the regulation of extracellular matrix (ECM) production. Elevated levels of CTGF can be found in plasma from patients with liver fibrosis and in experimental animal models of liver fibrosis, but the exact role of CTGF in, e.g., diet-induced human liver fibrosis is not entirely known. To address this question, we utilized a 3D human liver co-culture spheroid model composed of hepatocytes and non-parenchymal cells, in which fibrosis is induced by TGF-β1, CTGF or free fatty acids (FFA). Treatment of the spheroids with TGF-β1 or FFA increased COL1A1 deposition as well as the expression of TGF-β1 and CTGF. Recombinant CTGF, as well as angiotensin II, caused increased expression and/or production of CTGF, TGF-β1, COL1A1, LOX, and IL-6. In addition, silencing of CTGF reduced both TGF-β1- and FFA-induced COL1A1 deposition. Furthermore, we found that IL-6 induced CTGF, COL1A1 and TGF-β1 production, suggesting that IL-6 is a mediator in the pathway of CTGF-induced fibrosis. Taken together, our data indicate a specific role for CTGF and CTGF downstream signaling pathways for the development of liver inflammation and fibrosis in the human 3D liver spheroid model.

IntroductionDevelopment and worsening of most common neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis, have been associated with COVID-19 However, the mechanisms associated with neurological symptoms in COVID-19 patients and neurodegenerative sequelae are not clear. The interplay between gene expression and metabolite production in CNS is driven by miRNAs. These small non-coding molecules are dysregulated in most common neurodegenerative diseases and COVID-19.MethodsWe have performed a thorough literature screening and database mining to search for shared miRNA landscapes of SARS-CoV-2 infection and neurodegeneration. Differentially expressed miRNAs in COVID-19 patients were searched using PubMed, while differentially expressed miRNAs in patients with five most common neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, and multiple sclerosis) were searched using the Human microRNA Disease Database. Target genes of the overlapping miRNAs, identified with the miRTarBase, were used for the pathway enrichment analysis performed with Kyoto Encyclopedia of Genes and Genomes and Reactome.ResultsIn total, 98 common miRNAs were found. Additionally, two of them (hsa-miR-34a and hsa-miR-132) were highlighted as promising biomarkers of neurodegeneration, as they are dysregulated in all five most common neurodegenerative diseases and COVID-19. Additionally, hsa-miR-155 was upregulated in four COVID-19 studies and found to be dysregulated in neurodegeneration processes as well. Screening for miRNA targets identified 746 unique genes with strong evidence for interaction. Target enrichment analysis highlighted most significant KEGG and Reactome pathways being involved in signaling, cancer, transcription and infection. However, the more specific identified pathways confirmed neuroinflammation as being the most important shared feature.DiscussionOur pathway based approach has identified overlapping miRNAs in COVID-19 and neurodegenerative diseases that may have a valuable potential for neurodegeneration prediction in COVID-19 patients. Additionally, identified miRNAs can be further explored as potential drug targets or agents to modify signaling in shared pathways.Graphical AbstractShared miRNA molecules among the five investigated neurodegenerative diseases and COVID-19 were identified. The two overlapping miRNAs, hsa-miR-34a and has-miR-132, present potential biomarkers of neurodegenerative sequelae after COVID-19. Furthermore, 98 common miRNAs between all five neurodegenerative diseases together and COVID-19 were identified. A KEGG and Reactome pathway enrichment analyses was performed on the list of shared miRNA target genes and finally top 20 pathways were evaluated for their potential for identification of new drug targets. A common feature of identified overlapping miRNAs and pathways is neuroinflammation. AD, Alzheimer’s disease; ALS, amyotrophic lateral sclerosis; COVID-19, coronavirus disease 2019; HD, Huntington’s disease; KEGG, Kyoto Encyclopedia of Genes and Genomes; MS, multiple sclerosis; PD, Parkinson’s disease.

Cognitive impairment is a common non-motor symptom of Parkinson’s disease (PD), which often progresses to PD dementia. PD patients with and without dementia may differ in certain biochemical parameters, which could thus be used as biomarkers for PD dementia. The enzyme paraoxonase 1 (PON1) has previously been investigated as a potential biomarker in the context of other types of dementia. In a cohort of PD patients, we compared a group of 89 patients with cognitive impairment with a group of 118 patients with normal cognition. We determined the kinetic parameters Km and Vmax for PON1 for the reaction with dihydrocoumarin and the genotype of four single nucleotide polymorphisms in PON1. We found that no genotype or kinetic parameter correlated significantly with cognitive impairment in PD patients. However, we observed associations between PON1 rs662 and PON1 Km (p < 10−10), between PON1 rs662 and PON1 Vmax (p = 9.33 × 10−7), and between PON1 rs705379 and PON1 Vmax (p = 2.21 × 10−10). The present study is novel in three main aspects. (1) It is the first study to investigate associations between the PON1 genotype and enzyme kinetics in a large number of subjects. (2) It is the first study to report kinetic parameters of PON1 in a large number of subjects and to use time-concentration progress curves instead of initial velocities to determine Km and Vmax in a clinical context. (3) It is also the first study to calculate enzyme-kinetic parameters in a clinical context with a new algorithm for data point removal from progress curves, dubbed iFIT. Although our results suggest that in the context of PD, there is no clinically useful correlation between cognitive status on the one hand and PON1 genetic and enzyme-kinetic parameters on the other hand, this should not discourage future investigation into PON1’s potential associations with other types of dementia.