Articular cartilage has poor capability for repair following trauma or degenerative pathology due to avascular property, low cell density and migratory ability. Discovery of novel therapeutic approaches for articular cartilage repair remains a significant clinical need. Hypoxia is a hallmark for cartilage development and pathology. Hypoxia inducible factor-1alpha (HIF-1α) has been identified as a key mediator for chondrocytes to response to fluctuations of oxygen availability during cartilage development or repair. This suggests that HIF-1α may serve as a target for modulating chondrocyte functions. In this study, using phenotypic cellular screen assays, we identify that Icariin, an active flavonoid component from Herba Epimedii, activates HIF-1α expression in chondrocytes. We performed systemic in vitro and in vivo analysis to determine the roles of Icariin in regulation of chondrogenesis. Our results show that Icariin significantly increases hypoxia responsive element luciferase reporter activity, which is accompanied by increased accumulation and nuclear translocation of HIF-1α in murine chondrocytes. The phenotype is associated with inhibiting PHD activity through interaction between Icariin and iron ions. The upregulation of HIF-1α mRNA levels in chondrocytes persists during chondrogenic differentiation for 7 and 14 days. Icariin (10−6 M) increases the proliferation of chondrocytes or chondroprogenitors examined by MTT, BrdU incorporation or colony formation assays. Icariin enhances chondrogenic marker expression in a micromass culture including Sox9, collagen type 2 (Col2α1) and aggrecan as determined by real-time PCR and promotes extracellular matrix (ECM) synthesis indicated by Alcian blue staining. ELISA assays show dramatically increased production of aggrecan and hydroxyproline in Icariin-treated cultures at day 14 of chondrogenic differentiation as compared with the controls. Meanwhile, the expression of chondrocyte catabolic marker genes including Mmp2, Mmp9, Mmp13, Adamts4 and Adamts5 was downregulated following Icariin treatment for 14 days. In a differentiation assay using bone marrow mesenchymal stem cells (MSCs) carrying HIF-1α floxed allele, the promotive effect of Icariin on chondrogenic differentiation is largely decreased following Cre recombinase-mediated deletion of HIF-1α in MSCs as indicated by Alcian blue staining for proteoglycan synthesis. In an alginate hydrogel 3D culture system, Icariin increases Safranin O positive (SO+) cartilage area. This phenotype is accompanied by upregulation of HIF-1α, increased proliferating cell nuclear antigen positive (PCNA+) cell numbers, SOX9+ chondrogenic cell numbers, and Col2 expression in the newly formed cartilage. Coincide with the micromass culture, Icariin treatment upregulates mRNA levels of Sox9, Col2α1, aggrecan and Col10α1 in the 3D cultures. We then generated alginate hydrogel 3D complexes incorporated with Icariin. The 3D complexes were transplanted in a mouse osteochondral defect model. ICRS II histological scoring at 6 and 12 weeks pos...

Development of new medications is a lengthy and costly process, and drug repositioning might help to shorten the development cycle. We present a machine learning (ML) workflow to drug discovery or repositioning by predicting indication for a particular disease based on drug expression profiles, with a focus on applications in psychiatry. Drugs that are not originally indicated for the disease but with high predicted probabilities serve as candidates for repurposing. This approach is widely applicable to any chemicals or drugs with expression profiles measured, even if drug targets are unknown. It is also highly flexible as virtually any supervised learning algorithms can be used. We employed the ML approach to identify repositioning opportunities for schizophrenia as well as depression and anxiety disorders. We applied various state-of-the-art ML approaches, including deep neural networks (DNN), support vector machines (SVM), elastic net regression, random forest and gradient boosted trees. The predictive performance of the five approaches in cross-validation did not differ substantially, with SVM slightly outperforming the others. However, other methods also reveal literature-supported repositioning candidates of different mechanisms of actions. As a further validation, we showed that the repositioning hits are enriched for psychiatric medications considered in clinical trials. We also examined the correlation between predicted probabilities of treatment potential and the number of related research articles, and found significant correlations for all methods, especially DNN. Finally, we propose that ML may provide a new avenue to exploring drug mechanisms via examining the variable importance of gene features.

IntroductionLung adenocarcinoma (LAC) accounts for more than a half of non-small cell lung cancer with high morbidity and mortality. Progression of treatment has not accelerated the improvement of its prognosis. Hence, it is an urgent need to develop novel biomarkers for its early diagnosis and treatment.Materials and methodsIn this study, we proposed to identify LAC survival-related genes through comprehensive analysis of large-scale gene expression profiles. LAC gene expression data sets were obtained from The Cancer Genome Atlas (TCGA). Identification of differentially expressed genes (DEGs) in LAC compared with adjacent normal lung tissues was first performed followed by univariate Cox regression analysis to obtain genes that are significantly associated with LAC survival (SurGenes). Then, we conducted sure independence screening (SIS) for SurGenes to identify more reliable genes and the prognostic signature for LAC survival prediction. Another two lung cancer data sets from TCGA and Gene Expression Omnibus (GEO) were used for the validation of prognostic signature.ResultsA total of 20 genes were obtained, which were significantly associated with the overall survival (OS) of LAC patients. The prognostic signature, a weighted linear combination of the 20 genes, could successfully separate LAC samples with high OS from those with low OS and had robust predictive performance for survival (training set: p-value <2.2×10−16; testing set: p-value =2.04×10−5, area under the curve (AUC) =0.615). Combined with GEO data set, we obtained four genes, that is, FUT4, SLC25A42, IGFBP1, and KLHDC8B that are found in both the prognostic signature and DEGs of LAC in GEO data set.DiscussionThe prognostic signature combined with multi-gene expression profiles provides a moderate OS prediction for LAC and should be helpful for appropriate treatment method selection.