A systems-level study reveals host-targeted repurposable drugs against SARS-CoV-2 infection.

Understanding the mechanism of SARS-CoV-2 infection and identifying potential therapeutics are global imperatives. Using a quantitative systems pharmacology approach, we identified a set of repurposable and investigational drugs as potential therapeutics against COVID-19. These were deduced from the gene expression signature of SARS-CoV-2-infected A549 cells screened against Connectivity Map and prioritized by network proximity analysis with respect to disease modules in the viral-host interactome. We also identified immuno-modulating compounds aiming at suppressing hyperinflammatory responses in severe COVID-19 patients, based on the transcriptome of ACE2-overexpressing A549 cells. Experiments with Vero-E6 cells infected by SARS-CoV-2, as well as independent syncytia formation assays for probing ACE2/SARS-CoV-2 spike protein-mediated cell fusion using HEK293T and Calu-3 cells, showed that several predicted compounds had inhibitory activities. Among them, salmeterol, rottlerin, and mTOR inhibitors exhibited antiviral activities in Vero-E6 cells; imipramine, linsitinib, hexylresorcinol, ezetimibe, and brompheniramine impaired viral entry. These novel findings provide new paths for broadening the repertoire of compounds pursued as therapeutics against COVID-19.

Predicting Protein-Protein Interactions Using Symmetric Logistic Matrix Factorization.

Accurate assessment of protein-protein interactions (PPIs) is critical to deciphering disease mechanisms and developing novel drugs, and with rapidly growing PPI data, the need for more efficient predictive methods is emerging. We propose here a symmetric logistic matrix factorization (symLMF)-based approach to predict PPIs, especially useful for large PPI networks. Benchmarked against two widely used datasets (Saccharomyces cerevisiae and Homo sapiens benchmarks) and their extended versions, the symLMF-based method proves to outperform most of the state-of-the-art data-driven methods applied to human PPIs, and it shows a performance comparable to those of deep learning methods despite its conceptual and technical simplicity and efficiency. Tests performed on humans, yeast, and tissue (brain and liver)- and disease (neurodegenerative and metabolic disorders)-specific datasets further demonstrate the high capability to capture the hidden interactions. Notably, many "de novo predictions" made by symLMF are verified to exist in PPI databases other than those used for training/testing the method, indicating that the method could be of broad utility as a simple, yet efficient and accurate, tool applicable to PPI datasets.

Mechanisms of Action of Autophagy Modulators Dissected by Quantitative Systems Pharmacology Analysis.

Autophagy plays an essential role in cell survival/death and functioning. Modulation of autophagy has been recognized as a promising therapeutic strategy against diseases/disorders associated with uncontrolled growth or accumulation of biomolecular aggregates, organelles, or cells including those caused by cancer, aging, neurodegeneration, and liver diseases such as α1-antitrypsin deficiency. Numerous pharmacological agents that enhance or suppress autophagy have been discovered. However, their molecular mechanisms of action are far from clear. Here, we collected a set of 225 autophagy modulators and carried out a comprehensive quantitative systems pharmacology (QSP) analysis of their targets using both existing databases and predictions made by our machine learning algorithm. Autophagy modulators include several highly promiscuous drugs (e.g., artenimol and olanzapine acting as activators, fostamatinib as an inhibitor, or melatonin as a dual-modulator) as well as selected drugs that uniquely target specific proteins (~30% of modulators). They are mediated by three layers of regulation: (i) pathways involving core autophagy-related (ATG) proteins such as mTOR, AKT, and AMPK; (ii) upstream signaling events that regulate the activity of ATG pathways such as calcium-, cAMP-, and MAPK-signaling pathways; and (iii) transcription factors regulating the expression of ATG proteins such as TFEB, TFE3, HIF-1, FoxO, and NF-κB. Our results suggest that PKA serves as a linker, bridging various signal transduction events and autophagy. These new insights contribute to a better assessment of the mechanism of action of autophagy modulators as well as their side effects, development of novel polypharmacological strategies, and identification of drug repurposing opportunities.

Dynamic gut microbiota changes in patients with advanced malignancies experiencing secondary resistance to immune checkpoint inhibitors and immune-related adverse events.

Background: Immune checkpoint inhibitors (ICIs) have been a breakthrough in cancer immunotherapy, but secondary resistance (SR) and immune-related adverse events (irAEs) are significant clinical dilemmas. Although the gut microbiota is associated with ICI efficacy and irAEs, the knowledge of longitudinal gut microbiota dynamics during SR and irAE development is still quite limited. Methods: This was a prospective observational cohort study of cancer patients initially receiving anti-programmed cell death-1 (PD-1) treatment between May 2020 and October 2022. Clinical information was collected to evaluate therapy response and AEs. Patients were divided into a secondary resistance (SR) group, a non-secondary resistance (NSR) group, and an irAE group. Fecal samples were longitudinally obtained from baseline across multiple timepoints and analyzed with 16S rRNA sequencing. Results: Thirty-five patients were enrolled, and 29 were evaluable. After a median follow-up of 13.3 months, NSR patients had a favorable progression-free survival (PFS) compared with SR (457.9 IQR 241.0-674.0 days vs. 141.2 IQR 116.9-165.4 days, P=0.003) and irAE patients (457.9 IQR 241.0-674.0 days vs. 269.9, IQR 103.2-436.5 days, P=0.053). There were no significant differences in the microbiota between groups at baseline. Several previously reported beneficial microbiomes for ICI efficacy including Lachnospiraceae, Ruminococcaceae, Agathobacter, and Faecalibacterium showed decreasing trends as secondary resistance developed, yet not achieved significance (P>0.05). Significant changes in butyrate-producing bacteria were also presented in the SR cohort (P=0.043) with a decreasing trend upon secondary resistance occurrence (P=0.078). While the abundance of IgA-coated bacteria was stable in the SR cohort, there was a temporary decrease upon ICI treatment initiation and reestablishment after continuation of ICI treatment in the NSR cohort (primary ICI response: 0.06, IQR 0.04-0.10; durable ICI response: 0.11, IQR 0.07-0.14; P=0.042). Bacteroides contributed most to the difference between baseline and irAE occurrence, which decreased after irAE occurrence (Baseline: 0.10 IQR 0.07-0.36; irAE occurrence: 0.08 IQR 0.06-0.12) and was restored upon irAE remission to a comparable level as baseline (irAE remission: 0.10 IQR 0.09-0.18). Conclusions: The development of SR and irAEs is related to the longitudinal dynamics of the intestinal microbiota. The investigation into the preventative and protective effects of enteric microbe manipulation strategies is further required.

Artificial intelligence in pancreatic cancer.

Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.