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1. Prioritized polycystic kidney disease drug targets and repurposing candidates from pre-cystic and cystic mouse Pkd2 model gene expression reversion

   https://doi.org/10.1186/s10020-023-00664-z  

   Wilk, Elizabeth J. ; Howton, Timothy C. ; Fisher, Jennifer L. ; Oza, Vishal H. ; Brownlee, Ryan T. ; McPherson, Kasi C. ; Cleary, Hannah L. ; Yoder, Bradley K. ; George, James F. ; Mrug, Michal ; et al ( May 2023 , Molecular Medicine)

   Autosomal dominant polycystic kidney disease (ADPKD) is one of the most prevalent monogenic human diseases. It is mostly caused by pathogenic variants inPKD1orPKD2genes that encode interacting transmembrane proteins polycystin-1 (PC1) and polycystin-2 (PC2). Among many pathogenic processes described in ADPKD, those associated with cAMP signaling, inflammation, and metabolic reprogramming appear to regulate the disease manifestations. Tolvaptan, a vasopressin receptor-2 antagonist that regulates cAMP pathway, is the only FDA-approved ADPKD therapeutic. Tolvaptan reduces renal cyst growth and kidney function loss, but it is not tolerated by many patients and is associated with idiosyncratic liver toxicity. Therefore, additional therapeutic options for ADPKD treatment are needed.

   As drug repurposing of FDA-approved drug candidates can significantly decrease the time and cost associated with traditional drug discovery, we used the computational approach signature reversion to detect inversely related drug response gene expression signatures from the Library of Integrated Network-Based Cellular Signatures (LINCS) database and identified compounds predicted to reverse disease-associated transcriptomic signatures in three publicly availablePkd2kidney transcriptomic data sets of mouse ADPKD models. We focused on a pre-cystic model for signature reversion, as it was less impacted by confounding secondary disease mechanisms in ADPKD, and then compared the resulting candidates’ target differential expression in the two cystic mouse models. We further prioritized these drug candidates based on their known mechanism of action, FDA status, targets, and by functional enrichment analysis.

   With this in-silico approach, we prioritized 29 unique drug targets differentially expressed inPkd2ADPKD cystic models and 16 prioritized drug repurposing candidates that target them, including bromocriptine and mirtazapine, which can be further tested in-vitro and in-vivo.

   Collectively, these results indicate drug targets and repurposing candidates that may effectively treat pre-cystic as well as cystic ADPKD.

    

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2. Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing

   https://doi.org/10.1038/s41467-021-21056-z  

   Belyaeva, Anastasiya ; Cammarata, Louis ; Radhakrishnan, Adityanarayanan ; Squires, Chandler ; Yang, Karren Dai ; Shivashankar, G. V. ; Uhler, Caroline ( February 2021 , Nature Communications)

   Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs.

    

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3. KGML-xDTD: a knowledge graph–based machine learning framework for drug treatment prediction and mechanism description

   https://doi.org/10.1093/gigascience/giad057  

   Ma, Chunyu ; Zhou, Zhihan ; Liu, Han ; Koslicki, David ( August 2023 , GigaScience)

   Computational drug repurposing is a cost- and time-efficient approach that aims to identify new therapeutic targets or diseases (indications) of existing drugs/compounds. It is especially critical for emerging and/or orphan diseases due to its cheaper investment and shorter research cycle compared with traditional wet-lab drug discovery approaches. However, the underlying mechanisms of action (MOAs) between repurposed drugs and their target diseases remain largely unknown, which is still a main obstacle for computational drug repurposing methods to be widely adopted in clinical settings.

   In this work, we propose KGML-xDTD: a Knowledge Graph–based Machine Learning framework for explainably predicting Drugs Treating Diseases. It is a 2-module framework that not only predicts the treatment probabilities between drugs/compounds and diseases but also biologically explains them via knowledge graph (KG) path-based, testable MOAs. We leverage knowledge-and-publication–based information to extract biologically meaningful “demonstration paths” as the intermediate guidance in the Graph-based Reinforcement Learning (GRL) path-finding process. Comprehensive experiments and case study analyses show that the proposed framework can achieve state-of-the-art performance in both predictions of drug repurposing and recapitulation of human-curated drug MOA paths.

   KGML-xDTD is the first model framework that can offer KG path explanations for drug repurposing predictions by leveraging the combination of prediction outcomes and existing biological knowledge and publications. We believe it can effectively reduce “black-box” concerns and increase prediction confidence for drug repurposing based on predicted path-based explanations and further accelerate the process of drug discovery for emerging diseases.

    

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4. End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins

   https://doi.org/10.1371/journal.pcbi.1010851  

   Cai, Tian ; Xie, Li ; Zhang, Shuo ; Chen, Muge ; He, Di ; Badkul, Amitesh ; Liu, Yang ; Namballa, Hari Krishna ; Dorogan, Michael ; Harding, Wayne W. ; et al ( January 2023 , PLOS Computational Biology)

   Skolnick, Jeffrey (Ed.)

   Systematically discovering protein-ligand interactions across the entire human and pathogen genomes is critical in chemical genomics, protein function prediction, drug discovery, and many other areas. However, more than 90% of gene families remain “dark”—i.e., their small-molecule ligands are undiscovered due to experimental limitations or human/historical biases. Existing computational approaches typically fail when the dark protein differs from those with known ligands. To address this challenge, we have developed a deep learning framework, called PortalCG, which consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to encode the evolutionary links between ligand-binding sites across gene families; (ii) an end-to-end pretraining-fine-tuning strategy to reduce the impact of inaccuracy of predicted structures on function predictions by recognizing the sequence-structure-function paradigm; (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family; and (iv) a stress model selection step, using different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for target identifications and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the rational design from medicinal chemists. Our results also suggest that a differentiable sequence-structure-function deep learning framework, where protein structural information serves as an intermediate layer, could be superior to conventional methodology where predicted protein structures were used for the compound screening. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of dopamine receptors for the treatment of opioid use disorder (OUD), and illuminating the understudied human genome for target diseases that do not yet have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring understudied regions of protein functional space. 

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5. Transcriptomic analysis provides insight into the mechanism of IKKβ-mediated suppression of HPV18E6-induced cellular abnormalities

   https://doi.org/10.1093/g3journal/jkad020  

   Collins, Quincy P ; Grunsted, Michael J ; Arcila, Dahiana ; Xiong, Yi ; Padash Barmchi, Mojgan ( February 2023 , G3 Genes|Genomes|Genetics)

   Arbeitman, M (Ed.)

   Abstract High-risk human papillomaviruses (HPVs) 16 and 18 are responsible for more than 70% of cervical cancers and majority of other HPV-associated cancers world-wide. Current treatments for these cancers have limited efficacy, which in turn has resulted in disease recurrence and poor survival rates in advanced disease stages. Hence, there is a significant need for development of novel molecularly-targeted therapeutics. This can only be achieved through improved understanding of disease mechanism. Recently, we developed a Drosophila model of HPV18E6 plus human E3 ubiquitin ligase (hUBE3A) and demonstrated that the E6-induced cellular abnormalities are conserved between humans and flies. Subsequently, we demonstrated that reduced level and activity of IKKβ, a regulator of NF-κB, suppresses the cellular abnormalities induced by E6 oncoprotein and that the interaction of IKKβ and E6 is conserved in human cells. In this study, we performed transcriptomic analysis to identify differentially expressed genes that play a role in IKKβ-mediated suppression of E6-induced defects. Transcriptome analysis identified 215 genes whose expression was altered due to reduced levels of IKKβ. Of these 215 genes, 151 genes showed annotations. These analyses were followed by functional genetic interaction screen using RNAi, overexpression, and mutant fly strains for identified genes. The screen identified several genes including genes involved in Hippo and Toll pathways as well as junctional complexes whose downregulation or upregulation resulted in alterations of E6-induced defects. Subsequently, RT-PCR analysis was performed for validation of altered gene expression level for a few representative genes. Our results indicate an involvement for Hippo and Toll pathways in IKKβ-mediated suppression of E6 + hUBE3A-induced cellular abnormalities. Therefore, this study enhances our understanding of the mechanisms underlying HPV-induced cancer and can potentially lead to identification of novel drug targets for cancers associated with HPV. 

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