Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies ß-catenin/CBP inhibitor PRI-724 as a potent antiviral.

Expanding antiviral treatment options against SARS-CoV-2 remains crucial as the virus evolves under selection pressure which already led to the emergence of several drug resistant strains. Broad spectrum host-directed antivirals (HDA) are promising therapeutic options, however the robust identification of relevant host factors by CRISPR/Cas9 or RNA interference screens remains challenging due to low consistency in the resulting hits. To address this issue, we employed machine learning, based on experimental data from several knockout screens and a drug screen. We trained classifiers using genes essential for virus life cycle obtained from the knockout screens. The machines based their predictions on features describing cellular localization, protein domains, annotated gene sets from Gene Ontology, gene and protein sequences, and experimental data from proteomics, phospho-proteomics, protein interaction and transcriptomic profiles of SARS-CoV-2 infected cells. The models reached a remarkable performance suggesting patterns of intrinsic data consistency. The predicted HDF were enriched in sets of genes particularly encoding development, morphogenesis, and neural processes. Focusing on development and morphogenesis-associated gene sets, we found ß-catenin to be central and selected PRI-724, a canonical ß-catenin/CBP disruptor, as a potential HDA. PRI-724 limited infection with SARS-CoV-2 variants, SARS-CoV-1, MERS-CoV and IAV in different cell line models. We detected a concentration-dependent reduction in cytopathic effects, viral RNA replication, and infectious virus production in SARS-CoV-2 and SARS-CoV-1-infected cells. Independent of virus infection, PRI-724 treatment caused cell cycle deregulation which substantiates its potential as a broad spectrum antiviral. Our proposed machine learning concept supports focusing and accelerating the discovery of host dependency factors and identification of potential host-directed antivirals.

Optimized metrics for orthogonal combinatorial CRISPR screens.

CRISPR-based gene perturbation enables unbiased investigations of single and combinatorial genotype-to-phenotype associations. In light of efforts to map combinatorial gene dependencies at scale, choosing an efficient and robust CRISPR-associated (Cas) nuclease is of utmost importance. Even though SpCas9 and AsCas12a are widely used for single, combinatorial, and orthogonal screenings, side-by-side comparisons remain sparse. Here, we systematically compared combinatorial SpCas9, AsCas12a, and CHyMErA in hTERT-immortalized retinal pigment epithelial cells and extracted performance-critical parameters for combinatorial and orthogonal CRISPR screens. Our analyses identified SpCas9 to be superior to enhanced and optimized AsCas12a, with CHyMErA being largely inactive in the tested conditions. Since AsCas12a contains RNA processing activity, we used arrayed dual-gRNAs to improve AsCas12a and CHyMErA applications. While this negatively influenced the effect size range of combinatorial AsCas12a applications, it enhanced the performance of CHyMErA. This improved performance, however, was limited to AsCas12a dual-gRNAs, as SpCas9 gRNAs remained largely inactive. To avoid the use of hybrid gRNAs for orthogonal applications, we engineered the multiplex SpCas9-enAsCas12a approach (multiSPAS) that avoids RNA processing for efficient orthogonal gene editing.

The close interaction between hypoxia-related proteins and metastasis in pancarcinomas.

Many primary-tumor subregions exhibit low levels of molecular oxygen and restricted access to nutrients due to poor vascularization in the tissue, phenomenon known as hypoxia. Hypoxic tumors are able to regulate the expression of certain genes and signaling molecules in the microenvironment that shift it towards a more aggressive phenotype. The transcriptional landscape of the tumor favors malignant transformation of neighboring cells and their migration to distant sites. Herein, we focused on identifying key proteins that participate in the signaling crossroads between hypoxic environment and metastasis progression that remain poorly defined. To shed light on these mechanisms, we performed an integrated multi-omics analysis encompassing genomic/transcriptomic alterations of hypoxia-related genes and Buffa hypoxia scores across 17 pancarcinomas taken from the PanCancer Atlas project from The Cancer Genome Atlas consortium, protein-protein interactome network, shortest paths from hypoxia-related proteins to metastatic and angiogenic phenotypes, and drugs involved in current clinical trials to treat the metastatic disease. As results, we identified 30 hypoxia-related proteins highly involved in metastasis and angiogenesis. This set of proteins, validated with the MSK-MET Project, could represent key targets for developing therapies. The upregulation of mRNA was the most prevalent alteration in all cancer types. The highest frequencies of genomic/transcriptomic alterations and hypoxia score belonged to tumor stage 4 and positive metastatic status in all pancarcinomas. The most significantly associated signaling pathways were HIF-1, PI3K-Akt, thyroid hormone, ErbB, FoxO, mTOR, insulin, MAPK, Ras, AMPK, and VEGF. The interactome network revealed high-confidence interactions among hypoxic and metastatic proteins. The analysis of shortest paths revealed several ways to spread metastasis and angiogenesis from hypoxic proteins. Lastly, we identified 23 drugs enrolled in clinical trials focused on metastatic disease treatment. Six of them were involved in advanced-stage clinical trials: aflibercept, bevacizumab, cetuximab, erlotinib, ipatasertib, and panitumumab.

Pulmonary Inflammatory Response in Lethal COVID-19 Reveals Potential Therapeutic Targets and Drugs in Phases III/IV Clinical Trials.

Background: It is imperative to identify drugs that allow treating symptoms of severe COVID-19. Respiratory failure is the main cause of death in severe COVID-19 patients, and the host inflammatory response at the lungs remains poorly understood. Methods: Therefore, we retrieved data from post-mortem lungs from COVID-19 patients and performed in-depth in silico analyses of single-nucleus RNA sequencing data, inflammatory protein interactome network, and shortest pathways to physiological phenotypes to reveal potential therapeutic targets and drugs in advanced-stage COVID-19 clinical trials. Results: Herein, we analyzed transcriptomics data of 719 inflammatory response genes across 19 cell types (116,313 nuclei) from lung autopsies. The functional enrichment analysis of the 233 significantly expressed genes showed that the most relevant biological annotations were inflammatory response, innate immune response, cytokine production, interferon production, macrophage activation, blood coagulation, NLRP3 inflammasome complex, and the TLR, JAK-STAT, NF-κB, TNF, oncostatin M signaling pathways. Subsequently, we identified 34 essential inflammatory proteins with both high-confidence protein interactions and shortest pathways to inflammation, cell death, glycolysis, and angiogenesis. Conclusion: We propose three small molecules (baricitinib, eritoran, and montelukast) that can be considered for treating severe COVID-19 symptoms after being thoroughly evaluated in COVID-19 clinical trials.

Genotoxic and Carcinogenic Potential of Compounds Associated with Electronic Cigarettes: A Systematic Review.

BACKGROUND: Many studies, comparing the health associated risks of electronic cigarettes with conventional cigarettes focus mainly on the common chemical compounds found between them. AIM: Review chemical compounds found exclusively in electronic cigarettes and describe their toxic effects, focusing on electronic-cigarette-only and dual electronic-cigarette and conventional cigarette users. DATA SOURCES: Literature search was carried out using PubMed. STUDY ELIGIBILITY CRITERIA: Articles related exclusively to conventional and electronic cigarettes' chemical composition. Articles which reported to be financed from tobacco or electronic cigarettes industries, not reporting source of funding, not related to the chemical composition of electronic and conventional cigarettes and not relevant to tobacco research were excluded. METHODS AND RESULTS: Chemical compounds reported in the selected studies were tabulated using the Chemical Abstracts Service registry number for chemical substances information. A total of 50 chemical compounds were exclusively reported to be present in electronic cigarettes. Crucial health risks identified were: eye, skin, and respiratory tract irritation, with almost 50% of incidence, an increment of 10% in cytotoxic effects, when compared to compounds in common with conventional cigarettes and around 11% of compounds with unknown effects to human health. LIMITATIONS: Articles reporting conflicts of interest. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Despite being considered as less harmful for human health, compounds found in electronic cigarettes are still a matter of research and their effects on health are yet unknown. The use of these devices is not recommended for first time users and it is considered hazardous for dual users.