Abstract: The study of parental lifespan has emerged as an innovative tool to advance aging biology and our understanding of the genetic architecture of human longevity and aging-associated diseases. Here, we leveraged summary statistics of a genome-wide association study including over one million parental lifespans to identify genetically regulated genes from the Genotype-Tissue Expression project. Through a combination of multi-tissue transcriptome-wide association analyses and genetic colocalization, we identified novel genes that may be associated with parental lifespan. Mendelian randomization (MR) analyses also identified circulating proteins and metabolites causally associated with parental lifespan and chronic diseases offering new drug repositioning opportunities such as those targeting apolipoprotein-B-containing lipoproteins. Liver expression of HP, the gene encoding haptoglobin, and plasma haptoglobin levels were causally linked with parental lifespan. Phenome-wide MR analyses were used to map genetically regulated genes, proteins and metabolites with other human traits as well as the disease-related phenome in the FinnGen cohorts (n = 135,638). Altogether, this study identified new candidate genes, circulating proteins and metabolites that may influence human aging as well as potential therapeutic targets for chronic diseases that warrant further investigation.

Abstract: Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.

Abstract: Understanding the relationship between protein structure and function is a core-learning goal in biochemistry. Students often struggle to visualize proteins as three-dimensional objects that interact with other molecules to affect its biochemical consequences. We describe here a partial course-based undergraduate research experiences that has students exploring protein structure and function hands-on while authoring a molecular case study intended for others to use.

Abstract: Since the beginning of the COVID-19 pandemic, large in silico screening studies and numerous in vitro studies have assessed the antiviral activity of various drugs on SARS-CoV-2. In the context of health emergency, drug repurposing represents the most relevant strategy because of the reduced time for approval by international medicines agencies, the low cost of development and the well-known toxicity profile of such drugs. Herein, we aim to review drugs with in vitro antiviral activity against SARS-CoV-2, combined with molecular docking data and results from preliminary clinical studies. Finally, when considering all these previous findings, as well as the possibility of oral administration, 11 molecules consisting of nelfinavir, favipiravir, azithromycin, clofoctol, clofazimine, ivermectin, nitazoxanide, amodiaquine, heparin, chloroquine and hydroxychloroquine, show an interesting antiviral activity that could be exploited as possible drug candidates for COVID-19 treatment.

Abstract: BACKGROUND/AIM: Colon cancer is one of the most common cancer types and the second leading cause of death due to cancer. Many efforts have been performed towards the investigation of molecular alterations during colon cancer progression. However, the identification of stage-specific molecular markers remains a challenge. The aim of this study was to develop a novel computational methodology for the analysis of alterations in differential gene expression and pathway deregulation across colon cancer stages in order to reveal stage-specific biomarkers and reinforce drug repurposing investigation. MATERIALS AND METHODS: Transcriptomic datasets of colon cancer were used to identify (a) differentially expressed genes with monotonicity in their fold changes (MEGs) and (b) perturbed pathways with ascending monotonic enrichment (MEPs) related to the number of the participating differentially expressed genes (DEGs), across the four colon cancer stages. Through an in silico drug repurposing pipeline we identified drugs that regulate the expression of MEGs and also target the resulting MEPs. RESULTS: Our methodology highlighted 15 MEGs and 32 candidate repurposed drugs that affect their expression. We also found 51 MEPs divided into two groups according to their rate of DEG content alteration across colon cancer stages. Focusing on the target MEPs of the highlighted repurposed drugs, we found that one of them, the neuroactive ligand-receptor interaction, was targeted by the majority of the candidate drugs. Moreover, we observed that two of the drugs (PIK-75 and troglitazone) target the majority of the resulting MEPs. CONCLUSION: These findings highlight significant genes and pathways that can be used as stage-specific biomarkers and facilitate the discovery of new potential repurposed drugs for colon cancer. We expect that the computational methodology presented can be applied in a similar way to the analysis of any progressive disease.

Abstract: In recent decades, herbal medicines have played more and more important roles in the healthcare system in the world because of the good efficacy. However, with the increasing use of herbal medicines, the toxicity induced by herbal medicines has become a global issue. Therefore, it is needed to investigate the mechanism behind the efficacy and toxicity of herbal medicines. In this study, using Aconiti Lateralis Radix Praeparata (Fuzi) as an example, we adopted a systems pharmacology approach to investigate the mechanism of Fuzi in treating rheumatoid arthritis and in inducing cardiac toxicity and neurotoxicity. The results showed that Fuzi has 25 bioactive compounds that act holistically on 61 targets and 27 pathways to treat rheumatoid arthritis, and modulation of inflammation state is one of the main mechanisms of Fuzi. In addition, the toxicity of Fuzi is linked to 32 compounds that act on 187 targets and 4 pathways, and the targets and pathways can directly modulate the flow of Na(+), Ca(2+), and K(+). We also found out that non-toxic compounds such as myristic acid can act on targets of toxic compounds and therefore may influence the toxicity. The results not only reveal the efficacy and toxicity mechanism of Fuzi, but also add new concept for understanding the toxicity of herbal medicines, i.e., the compounds that are not directly toxic may influence the toxicity as well.

Abstract: BACKGROUND: Chronic chagasic cardiomyopathy (CCC) is the leading cause of heart failure in Latin America and often causes severe inflammation and fibrosis in the heart. Studies on myocardial function and its molecular mechanisms in patients with Chronic chagasic cardiomyopathy are very limited. In order to understand the development and progression of Chronic chagasic cardiomyopathy and find targets for its diagnosis and treatment, the field needs to better understand the exact molecular mechanisms involved in these processes. METHODS: The mRNA microarray datasets GSE84796 (human) and GSE24088 (mouse) were obtained from the Gene Expression Omnibus (GEO) database. Homologous genes between the two species were identified using the online database mining tool Biomart, followed by differential expression analysis, gene enrichment analysis and protein-protein interaction (PPI) network construction. Cytohubba plug-in of Cytoscape software was used to identify Hub gene, and miRNet was used to construct the corresponding miRNA-mRNA regulatory network. miRNA-related databases: miRDB, Targetscan and miRWalk were used to further evaluate miRNAs in the miRNA-mRNA network. Furthermore, Comparative Toxicogenomics Database (CTD) and L1000 Platform were used to identify hub gene-related drugs. RESULTS: A total of 86 homologous genes were significantly differentially expressed in the two datasets, including 73 genes with high expression and 13 genes with low expression. These differentially expressed genes were mainly enriched in the terms of innate immune response, signal transduction, protein binding, Natural killer cell mediated cytotoxicity, Tuberculosis, Chemokine signaling pathway, Chagas disease and PI3K-Akt signaling pathway. The top 10 hub genes LAPTM5, LCP1, HCLS1, CORO1A, CD48, TYROBP, RAC2, ARHGDIB, FERMT3 and NCF4 were identified from the PPI network. A total of 122 miRNAs were identified to target these hub genes and 30 of them regulated two or more hub genes at the same time. miRDB, Targetscan and miRWalk were further analyzed and screened out hsa-miR-34c-5p, hsa-miR-34a-5p and hsa-miR-16-5p as miRNAs regulating these hub genes. Finally, Progesterone, Flutamide, Nimesulide, Methotrexate and Temozolomide were identified to target these hub genes and might be targeted therapies for Chronic chagasic cardiomyopathy. CONCLUSIONS: In this study, the potential genes associated with Chronic chagasic cardiomyopathy are identified and a miRNA-mRNA regulatory network is constructed. This study explores the molecular mechanisms of Chronic chagasic cardiomyopathy and provides important clues for finding new therapeutic targets.

Abstract: The identification of drug-target interaction (DTI) plays a key role in drug discovery and development. Benefitting from large-scale drug databases and verified DTI relationships, a lot of machine-learning methods have been developed to predict DTIs. However, due to the difficulty in extracting useful information from molecules, the performance of these methods is limited by the representation of drugs and target proteins. This study proposes a new model called EmbedDTI to enhance the representation of both drugs and target proteins, and improve the performance of DTI prediction. For protein sequences, we leverage language modeling for pretraining the feature embeddings of amino acids and feed them to a convolutional neural network model for further representation learning. For drugs, we build two levels of graphs to represent compound structural information, namely the atom graph and substructure graph, and adopt graph convolutional network with an attention module to learn the embedding vectors for the graphs. We compare EmbedDTI with the existing DTI predictors on two benchmark datasets. The experimental results show that EmbedDTI outperforms the state-of-the-art models, and the attention module can identify the components crucial for DTIs in compounds.

Abstract: BACKGROUND: Experimental verification of a drug discovery process is expensive and time-consuming. Therefore, recently, the demand to more efficiently and effectively identify drug-target interactions (DTIs) has intensified. RESULTS: We treat the prediction of DTIs as a ranking problem and propose a neural network architecture, NeuRank, to address it. Also, we assume that similar drug compounds are likely to interact with similar target proteins. Thus, in our model, we add drug and target similarities, which are very effective at improving the prediction of DTIs. Then, we develop NeuRank from a point-wise to a pair-wise, and further to list-wise model. CONCLUSION: Finally, results from extensive experiments on five public data sets (DrugBank, Enzymes, Ion Channels, G-Protein-Coupled Receptors, and Nuclear Receptors) show that, in identifying DTIs, our models achieve better performance than other state-of-the-art methods.

Abstract: At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods-comparative genomics and network-based methods-for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.