Ganoderma spore lipid ameliorates docetaxel, cisplatin, and 5-fluorouracil chemotherapy-induced damage to bone marrow mesenchymal stem cells and hematopoiesis.

BACKGROUND: A triplet chemotherapy regimen of docetaxel, cisplatin, and 5-fluorouracil (TPF) is used to treat head and neck squamous cell carcinoma; however, it is toxic to bone marrow mesenchymal stem cells (BMSCs). We previously demonstrated that Ganoderma spore lipid (GSL) protect BMSCs against cyclophosphamide toxicity. In this study, we investigated the protective effects of GSL against TPF-induced BMSCs and hematopoietic damage. METHODS: BMSCs and C57BL/6 mice were divided into control, TPF, co-treatment (simultaneously treated with GSL and TPF for 2 days), and pre-treatment (treated with GSL for 7 days before 2 days of TPF treatment) groups. In vitro, morphology, phenotype, proliferation, senescence, apoptosis, reactive oxygen species (ROS), and differentiation of BMSCs were evaluated. In vivo, peripheral platelets (PLTs) and white blood cells (WBCs) from mouse venous blood were quantified. Bone marrow cells were isolated for hematopoietic colony-forming examination. RESULTS: In vitro, GSL significantly alleviated TPF-induced damage to BMSCs compared with the TPF group, recovering their morphology, phenotype, proliferation, and differentiation capacity (p < 0.05). Annexin V/PI and senescence-associated ß-galactosidase staining showed that GSL inhibited apoptosis and delayed senescence in TPF-treated BMSCs (p < 0.05). GSL downregulated the expression of caspase-3 and reduced ROS formation (p < 0.05). In vivo, GSL restored the number of peripheral PLTs and WBCs and protected the colony-forming capacity of bone marrow cells (p < 0.05). CONCLUSIONS: GSL efficiently protected BMSCs from damage caused by TPF and recovered hematopoiesis.

Sophoridine Counteracts Obesity via Src-Mediated Inhibition of VEGFR Expression and PI3K/AKT Phosphorylation.

Sophoridine (SRP) is a natural quinolizidine alkaloid found in many traditional Chinese herbs, though its effect on adipose tissue is unclear. We improved serum lipid levels by administering SRP by gavage in high-fat diet (HFD)-fed C57BL/6 mice. After 11 weeks, SRP supplementation significantly reduced body weight gain and improved glucose homeostasis, while reducing subcutaneous fat and liver weight. SRP also inhibited cell proliferation and differentiation of 3T3-L1 cells. Proteomics analysis revealed that SRP inhibits adipocyte differentiation by interacting with Src, thereby suppressing vascular endothelial growth factor receptor 2 (VEGFR2) expression and PI3K/AKT phosphorylation. This study provides an empirical basis for the treatment of obesity with small molecules.

Dietary grape pomace extract supplementation improved meat quality, antioxidant capacity, and immune performance in finishing pigs.

In pig production, reducing production costs and improving immunity are important. Grape pomace, a good agricultural by-product, has been thrown away as food waste for a long time. Recently, we found that it could be used as a new source of pig feed. We investigated the effect of grape pomace on inflammation, gut barrier function, meat quality, and growth performance in finishing pigs. Our results indicated that treatment samples showed a significant decrease in water loss, IL-1ß, DAO, ROS, and MDA content (p < 0.05). IgA, IgG, IgM, CAT, T-AOC, SOD, and IFN-γ significantly increased compared with those in control samples (p < 0.05). Meanwhile, the relative mRNA expression of the tight junction protein occludin showed a significant difference (p < 0.05). Analysis of metagenomic sequencing indicated that grape pomace significantly decreased the relative abundance of Treponema and Streptococcus (p < 0.05). In summary, our results demonstrated that grape pomace could improve meat quality, alleviate inflammation, and decrease oxidative stress.

Combined Treatment with JFKD and Gefitinib Overcomes Drug Resistance in Non-Small Cell Lung Cancer.

BACKGROUND: Gefitinib is an important drug used to treat Non-Small Cell Lung Cancer (NSCLC) with EGFR activating mutations, but drug resistance restricts its clinical application. In this present study, combined Jin Fu Kang Decoction (JFKD) and gefitinib showed specific cytotoxicity to gefitinib-resistant cancer cells (PC-9/gef). OBJECTIVE: This study aimed to decipher the molecular mechanism of the JFKD on drug resistance when used together with Gefitinib and to find the contributing bio-active substance(s) in JFKD based on the putative mechanism. METHODS: To investigate the combined effect of gefitinib and JFKD, in vitro experiments were conducted on the established gefitinib-resistant PC-9 subclone, while in vivo experiments were conducted on the BALB/c nude mice with PC-9/gef xenografts. Western blot was used to evaluate the protein expression, and Ultra-Performance Liquid Chromatography (UPLC) coupled with quadrupole time-offlight Mass Spectrometry (MS) was used to detect the bio-active compounds of JFKD. RESULTS: The expression of the PTEN-relevant protein p-EGFR, p-Akt in vitro was inhibited more when combined JKFD and gefitinib were used, whereas the activities of PDCD4 and PTEN were increased; remarkably, in vivo experiments showed enhanced tumor growth inhibition when treated with this combination. Due to this combination, the effect on the gefitinib-resistant cell line, one of the JFKD-induced anti-cancer mechanisms, was found. To link the putative mechanism and the anticancer compounds in JFKD, 14 saponins and flavonoids were detected. CONCLUSION: The results suggested that a promising TCM-participated therapy can be established by the putative mechanism of the combined treatment in resistant NSCLC and screening the contributing bio-active substance(s) in JFKD is meaningful on new TCM formula discovery.

Expression of RAD51 and Its Clinical Impact in Oral Squamous Cell Carcinoma.

PURPOSE: To examine the expression of RAD51 in oral squamous cell carcinoma (OSCC) and analyze its connection with pathological grade, clinical stage, and lymphatic metastasis potential. METHODS: For this study, 74 OSCC samples, 15 normal mucosa tissues, and 11 normal skin tissue samples were collected. RAD51 expression was investigated using immunohistochemistry. A follow-up visit was used to assess the prognosis of each patient. We compared RAD51 expression in oral mucosa epithelial cells (OMECs), keratinocytes, and tongue squamous cell carcinoma cells (TSCCs) by Western blot analysis. RESULTS: RAD51 expression was higher in tumor cells than in normal mucosal tissues. In addition, RAD51 expression was associated with higher tumor differentiation (P < 0.05). Also, RAD51 expression was higher (P < 0.05). Also, RAD51 expression was higher (P < 0.05). Also, RAD51 expression was higher (. CONCLUSION: A strong positive correlation was found between RAD51 expression and the degree of malignancy in OSCC patients, suggesting that RAD51 could be an excellent prognostic indicator for OSCC patients.

Leptin Receptor Mediates Bmal1 Regulation of Estrogen Synthesis in Granulosa Cells.

Chronobiology affects female fertility in mammals. Lepr is required for leptin regulation of female reproduction. The presence of E-box elements in the Lepr promoter that are recognized and bound by clock genes to initiate gene transcription suggested that circadian systems might regulate fertility through Lepr. However, it is unclear whether Bmal1, a key oscillator controlling other clock genes, is involved in leptin regulation in hormone synthesis through Lepr. In this study, serum estradiol (E2) concentration and the expressions of Bmal1, Lepr, Cyp19a1, and Cyp11a1 genes were found to display well-synchronized circadian rhythms. Knockdown of Bmal1 significantly reduced expression levels of Lepr, Fshr, and Cyp19a1 genes; protein production of Bmal1, Lepr, and Cyp19a1; and the E2 concentration in granulosa cells. Knockdown of Lepr reduced the expression levels of Cyp19a1 and Cyp11a1 genes and Cyp19a1 protein, and also reduced E2 concentration. Addition of leptin affected the expression of Cyp19a1, Cyp11a1, and Fshr genes. Bmal1 deficiency counteracted leptin-stimulated upregulation of the genes encoding E2 synthesis in granulosa cells. These results demonstrated that Bmal1 participates in the process by which leptin acts on Lepr to regulate E2 synthesis.

The antiresoptive effects of recombinant Lingzhi-8 protein against retinoic acid-induced osteopenia.

The antiresorptive agents still are the mainstay of osteoporosis treatment. This study aimed to investigate the efficacy of recombinant Lingzhi-8 (rLZ-8) on osteoclast in vitro and bone resorption in vivo. The rLZ-8 protein was derived from Ganoderma lucidum transformation and produced by a genetic system. Receptor activator of nuclear factor kappa-Β ligand induced RAW 264.7 cells to differentiate into osteoclastic cells in vitro. Cells were exposed to different doses of rLZ-8 for 7 days to measure differences of osteoclastic differentiation, apoptosis rate and gene expression. rLZ-8 was labeled with Alexa Fluor 568 to observe its intracellular distribution under super-resolution light microscopy. In addition, retinoic acid was administered to female rats for 14 days to develop osteopenia changes. Different doses of rLZ-8 were simultaneously administered to rats treated with retinoic acid to observe changes of bone mineral density, biochemical parameters and organ weight ratio. Results indicated that rLZ-8 regulated receptor activator of nuclear factor kappa-Β (RANK) - tumor necrosis factor receptor-associated factor 6 (TRAF6) - c-Jun N-terminal kinase (JNK) signaling pathway, by which rLZ-8 inhibited osteoclastic differentiation and promoted osteoclastic apoptosis. Through 3D-structured illumination microscopy, it was observed that rLZ-8 entered RAW264.7 cells and accumulated gradually into the cytoplasm but little into nucleus. Administration with rLZ-8 reversed loss of bone mass and improved ALP activity in osteoporotic rats. Low-to high-dose rLZ-8 treatments displayed little toxic effects on rat organs and did not seem to impact their overall health. All data suggested that rLZ-8 has possible action against osteoporosis.

High-content screening of Aspergillus niger with both increased production and high secretion rate of glucose oxidase.

OBJECTIVES: To develop a rapid, dual-parameter, plate-based screening process to improve production and secretion rate of glucose oxidase simultaneously in Aspergillus niger. RESULTS: A morphology engineering based on CaCO3 was implemented, where the yield of GOD by A. niger was increased by up to 50%. Analysis of extracellular GOD activity was achieved in 96-well plates. There was a close negative correlation between the total GOD activity and its residual glucose of the fermentation broth. Based on this, a rapid, plate-based, qualitative analysis method of the total GOD activity was developed. Compared with the conventional analysis method using o-dianisidine, a correlation coefficient of -0.92 by statistical analysis was obtained. CONCLUSION: Using this dual-parameter screening method, we acquired a strain with GOD activity of 3126 U l-1, which was 146% higher than the original strain. Its secretion rate of GOD was 83, 32% higher than the original strain.

A systematic analysis of FDA-approved anticancer drugs.

BACKGROUND: The discovery of novel anticancer drugs is critical for the pharmaceutical research and development, and patient treatment. Repurposing existing drugs that may have unanticipated effects as potential candidates is one way to meet this important goal. Systematic investigation of efficient anticancer drugs could provide valuable insights into trends in the discovery of anticancer drugs, which may contribute to the systematic discovery of new anticancer drugs. RESULTS: In this study, we collected and analyzed 150 anticancer drugs approved by the US Food and Drug Administration (FDA). Based on drug mechanism of action, these agents are divided into two groups: 61 cytotoxic-based drugs and 89 target-based drugs. We found that in the recent years, the proportion of targeted agents tended to be increasing, and the targeted drugs tended to be delivered as signal drugs. For 89 target-based drugs, we collected 102 effect-mediating drug targets in the human genome and found that most targets located on the plasma membrane and most of them belonged to the enzyme, especially tyrosine kinase. From above 150 drugs, we built a drug-cancer network, which contained 183 nodes (150 drugs and 33 cancer types) and 248 drug-cancer associations. The network indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs. From 89 targeted drugs, we built a cancer-drug-target network, which contained 214 nodes (23 cancer types, 89 drugs, and 102 targets) and 313 edges (118 drug-cancer associations and 195 drug-target associations). Starting from the network, we discovered 133 novel drug-cancer associations among 52 drugs and 16 cancer types by applying the common target-based approach. Most novel drug-cancer associations (116, 87%) are supported by at least one clinical trial study. CONCLUSIONS: In this study, we provided a comprehensive data source, including anticancer drugs and their targets and performed a detailed analysis in term of historical tendency and networks. Its application to identify novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development.

Investigating MicroRNA and transcription factor co-regulatory networks in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is one of the most common malignancies worldwide with poor prognosis. Studies have showed that abnormal microRNA (miRNA) expression can affect CRC pathogenesis and development through targeting critical genes in cellular system. However, it is unclear about which miRNAs play central roles in CRC's pathogenesis and how they interact with transcription factors (TFs) to regulate the cancer-related genes. RESULTS: To address this issue, we systematically explored the major regulation motifs, namely feed-forward loops (FFLs), that consist of miRNAs, TFs and CRC-related genes through the construction of a miRNA-TF regulatory network in CRC. First, we compiled CRC-related miRNAs, CRC-related genes, and human TFs from multiple data sources. Second, we identified 13,123 3-node FFLs including 25 miRNA-FFLs, 13,005 TF-FFLs and 93 composite-FFLs, and merged the 3-node FFLs to construct a CRC-related regulatory network. The network consists of three types of regulatory subnetworks (SNWs): miRNA-SNW, TF-SNW, and composite-SNW. To enhance the accuracy of the network, the results were filtered by using The Cancer Genome Atlas (TCGA) expression data in CRC, whereby we generated a core regulatory network consisting of 58 significant FFLs. We then applied a hub identification strategy to the significant FFLs and found 5 significant components, including two miRNAs (hsa-miR-25 and hsa-miR-31), two genes (ADAMTSL3 and AXIN1) and one TF (BRCA1). The follow up prognosis analysis indicated all of the 5 significant components having good prediction of overall survival of CRC patients. CONCLUSIONS: In summary, we generated a CRC-specific miRNA-TF regulatory network, which is helpful to understand the complex CRC regulatory mechanisms and guide clinical treatment. The discovered 5 regulators might have critical roles in CRC pathogenesis and warrant future investigation.

Disrupted cooperation between transcription factors across diverse cancer types.

BACKGROUND: Transcription Factors (TFs), essential for many cellular processes, generally work coordinately to induce transcriptional change in response to internal and external signals. Disrupted cooperation between TFs, leading to dysregulation of target genes, contributes to the pathogenesis of many diseases, including cancer. Although the aberrant activation of individual TFs and the functional effects have been widely studied, the perturbation of TF cooperativity in cancer has rarely been explored. RESULTS: We used TF co-expression as proxy as cooperativity and performed a large-scale study on disrupted TF cooperation across seven cancer types. While the connectivity of downstream effectors, like metabolic genes and TF targets, were more or similarly disrupted than/with non-TFs, the cooperativity of TFs (upstream regulators) were consistently less disturbed in all studied cancer types. Highly coordinated TFs in normal, however, generally lost that cooperation in cancer. Although different types of cancer shared very few TF pairs with highly disrupted cooperation, the cooperativity of interferon regulatory factors (IRF) was highly disrupted in six cancer types. Specifically, the cooperativity of IRF8 was highly perturbed in lung cancer, which was further validated by two independent lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) datasets. More interestingly, the cooperativity of IRF8 was markedly associated with tumor progression and even contributed to the patient survival independent of tumor stage. CONCLUSIONS: Our findings underscore the far more important role of TF cooperativity in tumorigenesis than previously appreciated. Disrupted cooperation of TFs provides potential clinical utility as prognostic markers for predicting the patient survival.

Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations.

An accurate classification of human cancer, including its primary site, is important for better understanding of cancer and effective therapeutic strategies development. The available big data of somatic mutations provides us a great opportunity to investigate cancer classification using machine learning. Here, we explored the patterns of 1,760,846 somatic mutations identified from 230,255 cancer patients along with gene function information using support vector machine. Specifically, we performed a multiclass classification experiment over the 17 tumor sites using the gene symbol, somatic mutation, chromosome, and gene functional pathway as predictors for 6,751 subjects. The performance of the baseline using only gene features is 0.57 in accuracy. It was improved to 0.62 when adding the information of mutation and chromosome. Among the predictable primary tumor sites, the prediction of five primary sites (large intestine, liver, skin, pancreas, and lung) could achieve the performance with more than 0.70 in F-measure. The model of the large intestine ranked the first with 0.87 in F-measure. The results demonstrate that the somatic mutation information is useful for prediction of primary tumor sites with machine learning modeling. To our knowledge, this study is the first investigation of the primary sites classification using machine learning and somatic mutation data.

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action.

A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.

Oncogenes and tumor suppressor genes: comparative genomics and network perspectives.

BACKGROUND: Defective tumor suppressor genes (TSGs) and hyperactive oncogenes (OCGs) heavily contribute to cell proliferation and apoptosis during cancer development through genetic variations such as somatic mutations and deletions. Moreover, they usually do not perform their cellular functions individually but rather execute jointly. Therefore, a comprehensive comparison of their mutation patterns and network properties may provide a deeper understanding of their roles in the cancer development and provide some clues for identification of novel targets. RESULTS: In this study, we performed a comprehensive survey of TSGs and OCGs from the perspectives of somatic mutations and network properties. For comparative purposes, we choose five gene sets: TSGs, OCGs, cancer drug target genes, essential genes, and other genes. Based on the data from Pan-Cancer project, we found that TSGs had the highest mutation frequency in most tumor types and the OCGs second. The essential genes had the lowest mutation frequency in all tumor types. For the network properties in the human protein-protein interaction (PPI) network, we found that, relative to target proteins, essential proteins, and other proteins, the TSG proteins and OCG proteins both tended to have higher degrees, higher betweenness, lower clustering coefficients, and shorter shortest-path distances. Moreover, the TSG proteins and OCG proteins tended to have direct interactions with cancer drug target proteins. To further explore their relationship, we generated a TSG-OCG network and found that TSGs and OCGs connected strongly with each other. The integration of the mutation frequency with the TSG-OCG network offered a network view of TSGs, OCGs, and their interactions, which may provide new insights into how the TSGs and OCGs jointly contribute to the cancer development. CONCLUSIONS: Our study first discovered that the OCGs and TSGs had different mutation patterns, but had similar and stronger protein-protein characteristics relative to the essential proteins or control proteins in the whole human interactome. We also found that the TSGs and OCGs had the most direct interactions with cancer drug targets. The results will be helpful for cancer drug target identification, and ultimately, understanding the etiology of cancer and treatment at the network level.

Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature.

Ambiguous gene names in the biomedical literature are a barrier to accurate information extraction. To overcome this hurdle, we generated Ontology Fingerprints for selected genes that are relevant for personalized cancer therapy. These Ontology Fingerprints were used to evaluate the association between genes and biomedical literature to disambiguate gene names. We obtained 93.6% precision for the test gene set and 80.4% for the area under a receiver-operating characteristics curve for gene and article association. The core algorithm was implemented using a graphics processing unit-based MapReduce framework to handle big data and to improve performance. We conclude that Ontology Fingerprints can help disambiguate gene names mentioned in text and analyse the association between genes and articles. Database URL: http://www.ontologyfingerprint.org

A comparative study of disease genes and drug targets in the human protein interactome.

BACKGROUND: Disease genes cause or contribute genetically to the development of the most complex diseases. Drugs are the major approaches to treat the complex disease through interacting with their targets. Thus, drug targets are critical for treatment efficacy. However, the interrelationship between the disease genes and drug targets is not clear. RESULTS: In this study, we comprehensively compared the network properties of disease genes and drug targets for five major disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease). We first collected disease genes from genome-wide association studies (GWAS) for five disease categories and collected their corresponding drugs based on drugs' Anatomical Therapeutic Chemical (ATC) classification. Then, we obtained the drug targets for these five different disease categories. We found that, though the intersections between disease genes and drug targets were small, disease genes were significantly enriched in targets compared to their enrichment in human protein-coding genes. We further compared network properties of the proteins encoded by disease genes and drug targets in human protein-protein interaction networks (interactome). The results showed that the drug targets tended to have higher degree, higher betweenness, and lower clustering coefficient in cancer Furthermore, we observed a clear fraction increase of disease proteins or drug targets in the near neighborhood compared with the randomized genes. CONCLUSIONS: The study presents the first comprehensive comparison of the disease genes and drug targets in the context of interactome. The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing.

Colorectal cancer drug target prediction using ontology-based inference and network analysis.

Identification of novel drug targets is a critical step in drug development. Many recent studies have produced multiple types of data, which provides an opportunity to mine the relationships among them to predict drug targets. In this study, we present a novel integrative approach that combines ontology reasoning with network-assisted gene ranking to predict new drug targets. We utilized colorectal cancer (CRC) as a proof-of-concept use case to illustrate the approach. Starting from FDA-approved CRC drugs and the relationships among disease, drug, gene, pathway, and SNP in an ontology representing PharmGKB data, we inferred 113 potential CRC drug targets. We further prioritized these genes based on their relationships with CRC disease genes in the context of human protein-protein interaction networks. Thus, among the 113 potential drug targets, 15 were selected as the promising drug targets, including some genes that are supported by previous studies. Among them, EGFR, TOP1 and VEGFA are known targets of FDA-approved drugs. Additionally, CCND1 (cyclin D1), and PTGS2 (prostaglandin-endoperoxide synthase 2) have reported to be relevant to CRC or as potential drug targets based on the literature search. These results indicate that our approach is promising for drug target prediction for CRC treatment, which might be useful for other cancer therapeutics.