The Potential Mechanism of Bufadienolide-Like Chemicals on Breast Cancer via Bioinformatics Analysis

Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM

With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug-disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net.

Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies

Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.

The mmu_circRNA_37492/hsa_circ_0012138 function as potential ceRNA to attenuate obstructive renal fibrosis

Circular RNAs (circRNAs) are involved in the pathogenesis of certain renal diseases, however, the function and mechanism of them in renal fibrosis remains largely unknown. In the present study, RNA expression data in unilateral ureteral obstruction (UUO) kidneys was obtained from our previous circRNA Microarray and public Gene Expression Omnibus datasets to construct a ceRNA network. The effects of target circRNA as long as the homologous human circRNA on renal fibrosis was examined in vitro and in vivo. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was further performed among genes regulated by the human circRNA. We found that circRNA_37492, showing well connection degree in the ceRNA network, was abundant expression and high sequence conservation. We observed that the expression of circRNA_37492 was induced by the TGF-β1 or UUO in BUMPT cells and C57BL/6 mice, respectively. In vitro, cytoplasmic circRNA_37492 inhibited type I, III collagen and fibronectin deposition by sponging miR-7682-3p and then upregulated its downstream target Fgb. In vivo, overexpression of circRNA_37492 attenuated fibrotic lesions in the kidneys of UUO mice via targeting miR-7682-3p/Fgb axis. Furthermore, hsa_circ_0012138, homologous with circRNA_37492, may potentially target miR-651-5p/FGB axis in human renal fibrosis. Not only that, GO and KEGG enrichment revealed that hsa_circ_0012138-regulated genes were previously demonstrated to related to the fibrosis. In conclusion, we for the first time demonstrated that circRNA_37492 attenuated renal fibrosis via targeting miR-7682-3p/Fgb axis, and the homologous hsa_circRNA_0012138 was speculated as a possible ceRNA to regulate multiple gene expressions and involve in human renal fibrosis, suggesting that circRNA_37492/hsa_circ_0012138 may serve as potent therapy target for obstructive renal fibrosis disease.

Quantitative proteomic characterization of human sperm cryopreservation: using data-independent acquisition mass spectrometry

Background

Human sperm cryopreservation is a simple and effective approach for male fertility preservation.

Methods

To identify potential proteomic changes in this process, data-independent acquisition (DIA), a technology with high quantitative accuracy and highly reproducible proteomics, was used to quantitatively characterize the proteomics of human sperm cryopreservation.

Results

A total of 174 significantly differential proteins were identified between fresh and cryoperservated sperm: 98 proteins decreased and 76 proteins increased in the cryopreservation group. Bioinformatic analysis revealed that metabolic pathways play an important role in cryopreservation, including: propanoate metabolism, glyoxylate and dicarboxylate metabolism, glycolysis/gluconeogenesis, and pyruvate metabolism. Four different proteins involved in glycolysis were identified by Western blotting: GPI, LDHB, ADH5, and PGAM1.

Conclusions

Our work will provide valuable information for future investigations and pathological studies involving sperm cryopreservation.