Using Selective Enzymes to Measure Noncanonical DNA Building Blocks: dUTP, 5-Methyl-dCTP, and 5-Hydroxymethyl-dCTP

Cells maintain a fine-tuned balance of deoxyribonucleoside 5'-triphosphates (dNTPs), a crucial factor in preserving genomic integrity. Any alterations in the nucleotide pool's composition or chemical modifications to nucleotides before their incorporation into DNA can lead to increased mutation frequency and DNA damage. In addition to the chemical modification of canonical dNTPs, the cellular de novo dNTP metabolism pathways also produce noncanonical dNTPs. To keep their levels low and prevent them from incorporating into the DNA, these noncanonical dNTPs are removed from the dNTP pool by sanitizing enzymes. In this study, we introduce innovative protocols for the high-throughput fluorescence-based quantification of dUTP, 5-methyl-dCTP, and 5-hydroxymethyl-dCTP. To distinguish between noncanonical dNTPs and their canonical counterparts, specific enzymes capable of hydrolyzing either the canonical or noncanonical dNTP analogs are employed. This approach provides a more precise understanding of the composition and noncanonical constituents of dNTP pools, facilitating a deeper comprehension of DNA metabolism and repair. It is also crucial for accurately interpreting mutational patterns generated through the next-generation sequencing of biological samples.

Effect of Copper-Modification of g-C3N4 on the Visible-Light-Driven Photocatalytic Oxidation of Nitrophenols

Graphitic carbon nitride (g-C3N4) has proved to be a promising heterogeneous photocatalyst in the visible range. It can be used, among others, for the oxidative conversion of environmentally harmful nitrophenols occurring in wastewater. However, its photocatalytic activity needs to be enhanced, which can be achieved by modification with various dopants. In our work, copper-modified g-C3N4 was prepared by ultrasonic impregnation of the pristine g-C3N4 synthesized from thiourea. The morphology, microstructure, and optical properties of the photocatalysts were characterized by XRD, FT-IR, DRS, SEM, XPS, and TEM. DRS analysis indicated a slight change in both the CB and the VB energies of Cu/g-C3N4 compared to those of g-C3N4. The efficiency of the photocatalysts prepared was tested by the degradation of nitrophenols. Copper modification caused a sevenfold increase in the rate of 4-nitrophenol degradation in the presence of H2O2 at pH = 3. This dramatic enhancement can be attributed to the synergistic effect of copper and H2O2 in this photocatalytic system. A minor Fenton reaction role was also detected. The reusability of the Cu/g-C3N4 catalyst was demonstrated through five cycles. Copper-modified g-C3N4 with H2O2 proved to be applicable for efficient visible-light-driven photocatalytic oxidative degradation of nitrophenols.

Cardiopulmonary examinations of athletes returning to high-intensity sport activity following SARS-CoV-2 infection

After SARS-CoV-2 infection, strict recommendations for return-to-sport were published. However, data are insufficient about the long-term effects on athletic performance. After suffering SARS-CoV-2 infection, and returning to maximal-intensity trainings, control examinations were performed with vita-maxima cardiopulmonary exercise testing (CPET). From various sports, 165 asymptomatic elite athletes (male: 122, age: 20y (IQR: 17-24y), training:16 h/w (IQR: 12-20 h/w), follow-up:93.5 days (IQR: 66.8-130.0 days) were examined. During CPET examinations, athletes achieved 94.7 ± 4.3% of maximal heart rate, 50.9 ± 6.0 mL/kg/min maximal oxygen uptake (V̇O2max), and 143.7 ± 30.4L/min maximal ventilation. Exercise induced arrhythmias (n = 7), significant horizontal/descending ST-depression (n = 3), ischemic heart disease (n = 1), hypertension (n = 7), slightly elevated pulmonary pressure (n = 2), and training-related hs-Troponin-T increase (n = 1) were revealed. Self-controlled CPET comparisons were performed in 62 athletes: due to intensive re-building training, exercise time, V̇O2max and ventilation increased compared to pre-COVID-19 results. However, exercise capacity decreased in 6 athletes. Further 18 athletes with ongoing minor long post-COVID symptoms, pathological ECG (ischemic ST-T changes, and arrhythmias) or laboratory findings (hsTroponin-T elevation) were controlled. Previous SARS-CoV-2-related myocarditis (n = 1), ischaemic heart disease (n = 1), anomalous coronary artery origin (n = 1), significant ventricular (n = 2) or atrial (n = 1) arrhythmias were diagnosed. Three months after SARS-CoV-2 infection, most of the athletes had satisfactory fitness levels. Some cases with SARS-CoV-2 related or not related pathologies requiring further examinations, treatment, or follow-up were revealed.

Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity

Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced gene expression signatures is a promising bioinformatic tool to repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature-based drug repurposing is that drugs with inverse similarity to a disease signature can reverse disease phenotype and thus be effective against it. However, in the case of viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so that the relationship between effective drug and disease signature can be more ambiguous. To address this question, we analysed gene expression data from in vitro SARS-CoV-2 infected cell lines, and gene expression signatures of drugs showing anti-SARS-CoV-2 activity. Our extensive functional genomic analysis showed that both infection and treatment with in vitro effective drugs leads to activation of antiviral pathways like NFkB and JAK-STAT. Based on the similarity-and not inverse similarity-between drug and infection-induced gene expression signatures, we were able to predict the in vitro antiviral activity of drugs. We also identified SREBF1/2, key regulators of lipid metabolising enzymes, as the most activated transcription factors by several in vitro effective antiviral drugs. Using a fluorescently labeled cholesterol sensor, we showed that these drugs decrease the cholesterol levels of plasma-membrane. Supplementing drug-treated cells with cholesterol reversed the in vitro antiviral effect, suggesting the depleting plasma-membrane cholesterol plays a key role in virus inhibitory mechanism. Our results can help to more effectively repurpose approved drugs against SARS-CoV-2, and also highlights key mechanisms behind their antiviral effect.

Machine learning models for classification tasks related to drug safety

In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts.