Drug repositioning for enzyme modulator based on human metabolite-likeness

Myofibrillogenesis Regulator-1 Regulates the Ubiquitin Lysosomal Pathway of Notch3 Intracellular Domain Through E3 Ubiquitin-Protein Ligase Itchy Homolog in the Metastasis of Non-Small Cell Lung Cancer

Myofibrillogenesis regulator-1 (MR-1) is a multifunctional protein involved in the development of various human tumors. The study is the first to report the promoting effect of MR-1 on the development and metastasis of non-small cell lung cancer (NSCLC). MR-1 is upregulated in NSCLC and positively associated with poor prognosis. The overexpression of MR-1 promotes the metastasis of NSCLC cells by stabilizing the expression of Notch3-ICD (NICD3) in the cytoplasm through enrichment analysis, in vitro and in vivo experimental researches. And Notch3 signaling can upregulate many genes related to metastasis. The stabilizing effect of MR-1 on NICD3 is achieved through the mono-ubiquitin lysosomal pathway and the specific E3 ubiquitin ligase is Itchy homolog (ITCH). There is a certain interaction between MR-1 and NICD3. Elevated MR-1 can affect the level of ITCH phosphorylation, reduce its E3 enzyme activity, and thus lead to reduce the ubiquitination and degradation of NICD3. Interference with the interaction between MR-1 and NICD3 can increase the degradation of NICD3 and impair the metastatic ability of NSCLC cells, which is a previously overlooked treatment option in NSCLC. In summary, interference with the interaction between MR-1 and NICD3 in the progression of lung cancer may be a promising therapeutic target.

Inhibition of O-acetylserine (thiol) lyase as a promising new mechanism of action for herbicides

Enzymes of the sulfur assimilation pathway of plants have been identified as potential targets for herbicide development, given their crucial role in synthesizing amino acids, coenzymes, and various sulfated compounds. In this pathway, O-acetylserine (thiol) lyase (OAS-TL; EC 2.5.1.47) catalyzes the synthesis of L-cysteine through the incorporation of sulfate into O-acetylserine (OAS). This study used an in silico approach to select seven inhibitors for OAS-TL. The in silico experiments revealed that S-benzyl-L-cysteine (SBC) had a better docking score (-7.0 kcal mol-1) than the substrate OAS (-6.6 kcal mol-1), indicating its suitable interaction with the active site of the enzyme. In vitro experiments showed that SBC is a non-competitive inhibitor of OAS-TL from Arabidopsis thaliana expressed heterologously in Escherichia coli, with a Kic of 4.29 mM and a Kiu of 5.12 mM. When added to the nutrient solution, SBC inhibited the growth of maize and morning glory weed plants due to the reduction of L-cysteine synthesis. Remarkably, morning glory was more sensitive than maize. As proof of its mechanism of action, L-cysteine supplementation to the nutrient solution mitigated the inhibitory effect of SBC on the growth of morning glory. Taken together, our data suggest that reduced L-cysteine synthesis is the primary cause of growth inhibition in maize and morning glory plants exposed to SBC. Furthermore, our findings indicate that inhibiting OAS-TL could potentially be a novel approach for herbicidal action.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

The novel ER stress inducer Sec C triggers apoptosis by sulfating ER cysteine residues and degrading YAP via ER stress in pancreatic cancer cells

Pancreatic adenocarcinoma (PAAD) is one of the most lethal malignancies. Although gemcitabine (GEM) is a standard treatment for PAAD, resistance limits its application and therapy. Secoemestrin C (Sec C) is a natural compound from the endophytic fungus Emericella, and its anticancer activity has not been investigated since it was isolated. Our research is the first to indicate that Sec C is a broad-spectrum anticancer agent and could exhibit potently similar anticancer activity both in GEM-resistant and GEM-sensitive PAAD cells. Interestingly, Sec C exerted a rapid growth-inhibiting effect (80% death at 6 h), which might be beneficial for patients who need rapid tumor shrinkage before surgery. Liquid chromatography/mass spectrometry and N-acetyl-l-cysteine (NAC) reverse assays show that Sec C sulfates cysteines to disrupt disulfide-bonds formation in endoplasmic reticulum (ER) proteins to cause protein misfolding, leading to ER stress and disorder of lipid biosynthesis. Microarray data and subsequent assays show that ER stress-mediated ER-associated degradation (ERAD) ubiquitinates and downregulates YAP to enhance ER stress via destruction complex (YAP-Axin-GSK-βTrCP), which also elucidates a unique degrading style for YAP. Potent anticancer activity in GEM-resistant cells and low toxicity make Sec C a promising anti-PAAD candidate.

CircRNA_0043691 sponges miR-873-3p to promote metastasis of gastric cancer

Circular RNAs (circRNAs) are a class of novel RNAs, and aberrant expression of circRNAs has been implicated in human diseases, including gastric cancer (GC). This study aimed to identify the mechanism of circRNA_0043691 in regulating the progression of GC. GSE141977 was downloaded from Gene Expression Omibus ( http://www.ncbi.nlm.nih.gov/geo/ ). Differentially expressed circRNAs were obtained by R software. The expression levels of circRNA_0043691 in GC tissue and normal tissue were identified by quantitative real-time polymerase chain reaction (qRT-PCR). Knockdown of circRNA_0043691 was then constructed and verified by qRT-PCR. Cell viability, migration, and invasion capacity were determined by Cell Counting Kit-8 assay, Transwell migration, and invasion, respectively. Next, knockdown of miR-873-3p was constructed and co-cultured with circRNA_0043691 knockdown to identify whether knockdown of miR-873-3p could attenuate the circRNA_0043691 knockdown on GC cells proliferation, migration, and invasion. The relationship between miR-873-3p and circRNA_0043691 or GART was predicted by bioinformatics tools and verified by dual-luciferase reporter. A total of 211 circRNAs were significantly differentially expressed, including 143 remarkably downregulated circRNAs and 68 significantly upregulated circRNAs. CircRNA_0043691 was upregulated in GC tissue. Knockdown of circRNA_0043691 decreased cell viability, migration, and invasion in GC cells. CircRNA_0043691 has potential putative binding sites with miR-873-3p. Moreover, CircRNA_0043691 positively regulated GART expression by sponging miR-873-3p. Furthermore, knockdown of miR-591 could partially attenuate the si-circRNA_0043691 on the GART expression. GART was upregulated in GC tissue and knockdown of GART could inhibit GC cells proliferation and invasion. Knockdown of circRNA_0043691 delayed the progression of GC via modulating the miR-873-3p-GART axis.

Antimalarial drugs and their metabolites are potent Zika virus inhibitors

Studies aimed at repurposing existing drugs revealed that some antimalarial compounds possess anti-Zika virus (anti-ZIKV) activity. Here, we further tested 14 additional antimalarial drugs and their metabolites or analogs for anti-ZIKV activity using a phenotypic screening approach. We identified four compounds with varying anti-ZIKV activity, including a metabolite of amodiaquine termed desethylamodiaquine (DAQ) and N-desethylchloroquine (DECQ), a metabolite of chloroquine, which both exhibited low micromolar effective concentrations against three different ZIKV strains. Two other compounds termed dihydroartemisinin (DHA) and quinidine (QD) exhibited only partial inhibition of ZIKV replication. Characterization of the inhibitory mechanisms of DAQ and DECQ showed that both drugs target the entry step as well as postentry events of the viral replication cycle. These hits represent attractive starting points for future optimization of new anti-ZIKV drug candidates derived from antimalarial drugs and their analogs.

Classification of Widely and Rarely Expressed Genes with Recurrent Neural Network

A tissue-specific gene expression shapes the formation of tissues, while gene expression changes reflect the immune response of the human body to environmental stimulations or pressure, particularly in disease conditions, such as cancers. A few genes are commonly expressed across tissues or various cancers, while others are not. To investigate the functional differences between widely and rarely expressed genes, we defined the genes that were expressed in 32 normal tissues/cancers (i.e., called widely expressed genes; FPKM >1 in all samples) and those that were not detected (i.e., called rarely expressed genes; FPKM <1 in all samples) based on the large gene expression data set provided by Uhlen et al. Each gene was encoded using the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment scores. Minimum redundancy maximum relevance (mRMR) was used to measure and rank these features on the mRMR feature list. Thereafter, we applied the incremental feature selection method with a supervised classifier recurrent neural network (RNN) to select the discriminate features for classifying widely expressed genes from rarely expressed genes and construct an optimum RNN classifier. The Youden's indexes generated by the optimum RNN classifier and evaluated using a 10-fold cross validation were 0.739 for normal tissues and 0.639 for cancers. Furthermore, the underlying mechanisms of the key discriminate GO and KEGG features were analyzed. Results can facilitate the identification of the expression landscape of genes and elucidation of how gene expression shapes tissues and the microenvironment of cancers.

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Some genes are widely expressed across tissues or various cancers.

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A number of genes are rarely expressed across tissues or various cancers.

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The functional differences between widely and rarely expressed genes were studied.

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Several GO terms and KEGG pathways were extracted and analyzed.

Repurposing approach identifies new treatment options for invasive fungal disease

Drug repositioning is the process of discovery, validation and marketing of previously approved drugs for new indications. Our aim was drug repositioning, using ligand-based and structure-based computational methods, of compounds that are similar to two hit compounds previously selected by our group that show promising antifungal activity. Through the ligand-based method, 100 compounds from each of three databases (MDDR, DrugBank and TargetMol) were selected by the Tanimoto coefficient, as similar to LMM5 or LMM11. These compounds were analyzed by the scaffold trees, and up to 10 compounds from each database were selected. The structure-based method (molecular docking) using thioredoxin reductase as the target drug was performed as a complementary approach, resulting in six compounds that were tested in an in vitro assay. All compounds, particularly raltegravir, showed antifungal activity against the genus Paracoccidioides. Raltegravir, an antiviral drug, showed promising antifungal activity against the experimental murine paracoccidioidomycosis, with significant reduction of the fungal burden and decreased alterations in the lung structure of mice treated with 1 mg/kg of raltegravir. In conclusion, the combination of two in silico methods for drug repositioning was able to select an antiviral drug with promising antifungal activity for treatment of paracoccidioidomycosis.

Identification and analysis of the cleavage site in a signal peptide using SMOTE, dagging, and feature selection methods

Several machine learning algorithms were adopted to investigate cleavage sites in a signal peptide. An optimal dagging based classifier was constructed and 870 important features were deemed to be important for this classifier.