Adverse effects of the 5-alpha-reductase inhibitor finasteride on Daphnia magna: Endocrine system and lipid metabolism disruption.

Finasteride, a steroid 5-alpha reductase inhibitor, is commonly used for the treatment of benign prostatic hyperplasia and hair loss. However, despite continued use, its environmental implications have not been thoroughly investigated. Thus, we investigated the acute and chronic adverse impacts of finasteride on Daphnia magna, a crucial planktonic crustacean in freshwater ecosystems selected as bioindicator organism for understanding the ecotoxicological effects. Chronic exposure (for 23 days) to finasteride negatively affected development and reproduction, leading to reduced fecundity, delayed first brood, reduced growth, and reduced neonate size. Additionally, acute exposure (< 24 h) caused decreased expression levels of genes crucial for reproduction and development, especially EcR-A/B (ecdysone receptors), Jhe (juvenile hormone esterase), and Vtg2 (vitellogenin), with oxidative stress-related genes. Untargeted lipidomics/metabolomic analyses revealed lipidomic alteration, including 19 upregulated and 4 downregulated enriched lipid ontology categories, and confirmed downregulation of metabolites. Pathway analysis implicated significant effects on metabolic pathways, including the pentose phosphate pathway, histidine metabolism, beta-alanine metabolism, as well as alanine, aspartate, and glutamate metabolism. This comprehensive study unravels the intricate molecular and metabolic responses of D. magna to finasteride exposure, underscoring the multifaceted impacts of this anti-androgenic compound on a keystone species of freshwater ecosystems. The findings emphasize the importance of understanding the environmental repercussions of widely used pharmaceuticals to protect biodiversity in aquatic ecosystems.

MicroRNA and Messenger RNA Expression Profiles in Canine Mammary Gland Tumor.

Canine mammary gland tumor (CMT) is the most frequently diagnosed neoplasm in intact female dogs. As prognosis depends on the malignancy of tumors and metastasis levels, early and accurate diagnosis are crucial for prolongation of life expectancy. The genetic similarity of dogs with humans in addition to environmental and physiological similarities make them ideal models for the study of cancer. In this study, we analyzed differentially expressed microRNAs followed by RNA-Seq to investigate the alterations in mRNA levels based on the malignancy (benign, malignant) and the biopsy locations (tumors, surrounding normal tissues). We identified multiple breast cancer-related genes regardless of malignancy. We found cfa-miR-503 to be the only miRNA that showed altered expression in response to malignancy in CMTs. Although further validation is needed, cfa-miR-503 could be used as a potential diagnostic biomarker as well as a potential RNA-based anti-tumor drug in malignant CMTs.

PISTON: Predicting drug indications and side effects using topic modeling and natural language processing.

The process of discovering novel drugs to treat diseases requires a long time and high cost. It is important to understand side effects of drugs as well as their therapeutic effects, because these can seriously damage the patients due to unexpected actions of the derived candidate drugs. In order to overcome these limitations, computational methods for predicting the therapeutic effects and side effects have been proposed. In particular, text mining is a widely used technique in the field of systems biology, because it can discover hidden relationships between drugs, genes and diseases from a large amount of literature data. Compared with in vivo/in vitro experiments, text mining derives meaningful results with less time and cost. In this study, we propose an algorithm for predicting novel drug-phenotype associations and drug-side effect associations using topic modeling and natural language processing (NLP). We extract sentences in which drugs and genes co-occur from the abstracts of the literature and identify words that describe the relationship between them using NLP. Considering the characteristics of the identified words, we determine if the drug has an up-regulation effect or a down-regulation effect on the gene. Based on genes that affect drugs and their regulatory relationships, we group the frequently occurring genes and regulatory relationships into topics, and build a drug-topic probability matrix by calculating the score that the drug will have a topic using topic modeling. Using the matrix, a classifier is constructed for predicting the novel indications and side effects of drugs considering the characteristics of known drug-phenotype associations or drug-side effect associations. The proposed method predicts both indications and side effects with a single algorithm, and it can exclude drugs with serious side effects or side effects that patients do not want to experience from among the candidate drugs provided for the treatment of the phenotype. Furthermore, lists of novel candidate drugs for phenotypes and side effects can be continuously updated with our algorithm every time a document is added. More than a thousand documents are produced per day, and it is possible for our algorithm to efficiently derive candidate drugs because it requires less cost than the existing drug repositioning methods. The resource of PISTON is available at databio.gachon.ac.kr/tools/PISTON.

Drug voyager: a computational platform for exploring unintended drug action.

BACKGROUND: The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. Advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking. RESULTS: We suggest a novel platform to construct a drug-specific pathway in which a molecular-level mechanism of action is formulated based on pharmacologic, pharmacogenomic, transcriptomic, and phenotypic data related to drug response ( http://databio.gachon.ac.kr/tools/ ). In this platform, an adoption of three conceptual levels imitating drug perturbation allows these pathways to be realistically rendered in comparison to those of other models. Furthermore, we propose a new method that exploits functional features of the drug-specific pathways to predict new indications as well as adverse reactions. For therapeutic uses, our predictions significantly overlapped with clinical trials and an up-to-date drug-disease association database. Also, our method outperforms existing methods with regard to classification of active compounds for cancers. For adverse reactions, our predictions were significantly enriched in an independent database derived from the Food and Drug Administration (FDA) Adverse Event Reporting System and meaningfully cover an Adverse Reaction Database provided by Health Canada. Lastly, we discuss several predictions for both therapeutic indications and side-effects through the published literature. CONCLUSIONS: Our study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening.

Three-Dimensional Bio-Printed Autologous Omentum Patch Ameliorates Unilateral Ureteral Obstruction-Induced Renal Fibrosis.

Recent advances in the field of tissue engineering and regenerative medicine have contributed to the repair of damaged tissues and organs. Renal dysfunctions such as chronic kidney disease (CKD) are considered intractable owing to its cellular heterogeneity. In addition, the absence of definitive treatment options other than dialysis or kidney transplantation in advanced CKD. In this study, we investigated therapeutic effects of a three-dimensional (3D) bio-printed omentum patch as treatment source. Because omentum contains a lot of biological sources for immune regulation and tissue regeneration, it has been used in clinic for >100 years. By using autologous tissue as a bio-ink, the patch could minimize the immune response. The mechanically micronized omentum without any additives became small enough to print, but the original components could be preserved. Then, the 3D printed omentum patch was transplanted under renal subcapsular layer in unilateral ureteral obstruction (UUO) rat model. After 14 days of patch transplantation, the kidneys were analyzed through bulk RNA sequencing and histopathological staining. From the results, decreased tubular injury was observed in the omentum patch group. In addition, the omentum patch significantly altered biological process of gene ontology such as fibrosis-related gene and growth factors. RNA sequencing confirmed the antifibrotic effect by inhibiting fibrosis-inducing mechanisms within PI3K-AKT and JAK-STAT pathways. In conclusion, the omentum patch showed the effect of antitubular injury and antifibrosis on UUO kidneys. In particular, the omentum patch is expected to protect the organ from further degeneration and loss of function by inhibiting the progression of fibrosis. The omentum patch can be a novel therapeutic option for renal dysfunction. Impact statement Many studies and clinical trials are being conducted to develop new treatments for kidney disease. However, there are no newly developed renal replacement therapies. In this study, we developed a new treatment that can ameliorate renal interstitial fibrosis using three-dimensional (3D) bio-printed autologous omentum patch. The 3D printer enables precise patch printing, and the bio-ink made of autologous tissue minimizes the immune response after transplantation. The whole kidneys were analyzed by RNA sequencing and histopathological staining 14 days after transplantation. From the results, the omentum patch had the effect of relieving tubular injury in the injured state. Also, the omentum patch significantly altered biological process of gene ontology. In particular, genes related to fibrosis were observed to be downregulated by the omentum patch. RNA sequencing confirmed that the antifibrotic effect was owing to inducing mechanisms of PI3K-AKT and JAK-STAT pathways. The findings reported in this study represent a significant advancement in the application of 3D bio-printer to damaged organ treatments, especially fibrosis-related diseases.

Literature-based prediction of novel drug indications considering relationships between entities.

There have been many attempts to identify and develop new uses for existing drugs, which is known as drug repositioning. Among these efforts, text mining is an effective means of discovering novel knowledge from a large amount of literature data. We identify a gene regulation by a drug and a phenotype based on the biomedical literature. Drugs or phenotypes can activate or inhibit gene regulation. We calculate the therapeutic possibility that a drug acts on a phenotype by means of these two types of regulation. We assume that a drug treats a phenotype if the genes regulated by the phenotype are inversely correlated with the genes regulated by the drug. Based on this hypothesis, we identify drug-phenotype associations with therapeutic possibility. To validate the drug-phenotype associations predicted by our method, we make an enrichment comparison with known drug-phenotype associations. We also identify candidate drugs for drug repositioning from novel associations and thus reveal that our method is a novel approach to drug repositioning.

Identifying the common genetic networks of ADR (adverse drug reaction) clusters and developing an ADR classification model.

Adverse drug reactions (ADRs) are one of the major concerns threatening public health and have resulted in failures in drug development. Thus, predicting ADRs and discovering the mechanisms underlying ADRs have become important tasks in pharmacovigilance. Identification of potential ADRs by computational approaches in the early stages would be advantageous in drug development. Here we propose a computational method that elucidates the action mechanisms of ADRs and predicts potential ADRs by utilizing ADR genes, drug features, and protein-protein interaction (PPI) networks. If some ADRs share similar features, there is a high possibility that they may appear together in a drug and share analogous mechanisms. Proceeding from this assumption, we clustered ADRs according to interactions of ADR genes in the PPI networks and the frequency of co-occurrence of ADRs in drugs. ADR clusters were verified based on a side effect database and literature data regarding whether ADRs have relevance to other ADRs in the same cluster. Gene networks shared by ADRs in each cluster were constructed by cumulating the shortest paths between drug target genes and ADR genes in the PPI network. We developed a classification model to predict potential ADRs using these gene networks shared by ADRs and calculated cross-validation AUC (area under the curve) values for each ADR cluster. In addition, in order to demonstrate correlations between gene networks shared by ADRs and ADRs in a cluster, we applied the Wilcoxon rank sum statistical test to the literature data and results of a Google query search. We attained statistically meaningful p-values (<0.05) for every ADR cluster. The results suggest that our approach provides insights into discovering the action mechanisms of ADRs and is a novel attempt to predict ADRs in a biological aspect.