CART-a chemical annotation retrieval toolkit

Transcripts of repetitive DNA elements signal to block phagocytosis of hematopoietic stem cells

Macrophages maintain hematopoietic stem cell (HSC) quality by assessing cell surface Calreticulin (Calr), an "eat-me" signal induced by reactive oxygen species (ROS). Using zebrafish genetics, we identified Beta-2-microglobulin (B2m) as a crucial "don't eat-me" signal on blood stem cells. A chemical screen revealed inducers of surface Calr that promoted HSC proliferation without triggering ROS or macrophage clearance. Whole-genome CRISPR-Cas9 screening showed that Toll-like receptor 3 (Tlr3) signaling regulated b2m expression. Targeting b2m or tlr3 reduced the HSC clonality. Elevated B2m levels correlated with high expression of repetitive element (RE) transcripts. Overall, our data suggest that RE-associated double-stranded RNA could interact with TLR3 to stimulate surface expression of B2m on hematopoietic stem and progenitor cells. These findings suggest that the balance of Calr and B2m regulates macrophage-HSC interactions and defines hematopoietic clonality.

Drug repurposing platform for deciphering the druggable SARS-CoV-2 interactome

The coronavirus disease 2019 (COVID-19) pandemic has heavily challenged the global healthcare system. Despite the vaccination programs, the new virus variants are circulating. Further research is required for understanding of the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and for discovery of therapeutic agents against the virus. Here, we took advantage of drug repurposing to identify if existing drugs could inhibit SARS-CoV-2 infection. We established an open high throughput platform for in vitro screening of drugs against SARS-CoV-2 infection. We screened ∼1000 drugs for their ability to inhibit SARS-CoV-2-induced cell death in the African green monkey kidney cell line (Vero-E6), analyzed how the hit compounds affect the viral N (nucleocapsid) protein expression in human cell lines using high-content microscopic imaging and analysis, determined the hit drug targets in silico, and assessed their ability to cause phospholipidosis, which can interfere with the viral replication. Duvelisib was found by in silico interaction assay as a potential drug targeting virus-host protein interactions. The predicted interaction between PARP1 and S protein, affected by Duvelisib, was further validated by immunoprecipitation. Our results represent a rapidly applicable platform for drug repurposing and evaluation of the new emerging viruses' responses to the drugs. Further in silico studies help us to discover the druggable host pathways involved in the infectious cycle of SARS-CoV-2.

DenovoProfiling: A webserver for de novo generated molecule library profiling

Various deep learning-based architectures for molecular generation have been proposed for de novo drug design. The flourish of the de novo molecular generation methods and applications has created a great demand for the visualization and functional profiling for the de novo generated molecules. An increasing number of publicly available chemogenomic databases sets good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization module for chemical structure visualization and identify the reported structures, (2) chemical space module for chemical space exploration using similarity maps, principal components analysis (PCA), drug-like properties distribution, and scaffold-based clustering, (3) ADMET prediction module for predicting the ADMET properties of the de novo molecules, (4) molecular alignment module for three dimensional molecular shape analysis, (5) drugs mapping module for identifying structural similar drugs, and (6) target & pathway module for identifying the reported targets and corresponding functional pathways. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their de novo library and could guide the further selection of candidates for chemical synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

A Review of Bioinformatics Tools to Understand Acetaminophen-Alcohol Interaction

Background: Drug-ethanol interaction can result in hepatotoxicity. The liver is capable of metabolizing both acetaminophen and ethanol; however, severe acute or moderate chronic simultaneous exposure can cause cell and tissue damage. Therapeutic doses can become harmful if gene activity is altered via competition for metabolic pathways. Simultaneous intake of ethanol and acetaminophen results in overactive CYP2E1 and depletion of glutathione, leaving NAPQI to build up in the liver. NAPQI is a hepatotoxic substance typically neutralized by glutathione. Methods: Bioinformatics tools including PharmGKB, Chemical Annotation Retrieval Toolkit, Transcriptome Analysis Console 4.0 (TAC), wikipathways, STRING, and Ingenuity Pathway Analysis (IPA) were used to explore interactive metabolic pathways of ethanol-acetaminophen exposure as a proof of concept for assessing drug-drug or drug-alcohol interactions. Results: As the ethanol-acetaminophen comparison indicates, bioinformatics tools may be used to understand interactive pathways following exposure to ethanol and acetaminophen, with potential extrapolation to other drug-drug/drug-ethanol interactions. Conclusions: Direct interactive effects were not able to be confirmed through this bioinformatics study due to the lack of existing ethanol-acetaminophen simultaneous exposure data. This work suggests that a battery of software applications should be used to assess interactive effects.

Extensive impact of non-antibiotic drugs on human gut bacteria

A few commonly used non-antibiotic drugs have recently been associated with changes in gut microbiome composition, but the extent of this phenomenon is unknown. Here, we screened more than 1,000 marketed drugs against 40 representative gut bacterial strains, and found that 24% of the drugs with human targets, including members of all therapeutic classes, inhibited the growth of at least one strain in vitro. Particular classes, such as the chemically diverse antipsychotics, were overrepresented in this group. The effects of human-targeted drugs on gut bacteria are reflected on their antibiotic-like side effects in humans and are concordant with existing human cohort studies. Susceptibility to antibiotics and human-targeted drugs correlates across bacterial species, suggesting common resistance mechanisms, which we verified for some drugs. The potential risk of non-antibiotics promoting antibiotic resistance warrants further exploration. Our results provide a resource for future research on drug-microbiome interactions, opening new paths for side effect control and drug repurposing, and broadening our view of antibiotic resistance.