Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs' targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

MicroRNAs are a class of 18-22-nucleotide noncoding RNAs that posttranscriptionally regulate gene expression and have been shown to play an important role during myoblast differentiation. In this study, we found that the expression of miR-145a-5p was gradually increased during C2C12 myoblast differentiation, and miR-145a-5p inhibitors or mimics significantly suppressed or promoted the relative expression of specific myogenesis related marker genes. Moreover, overexpression or inhibition of miR-145a-5p enhanced or repressed the expression of some special genes involved in the endogenous Wnt signaling pathway during C2C12 myoblast differentiation, including Wnt5a, LRP5, Axin2, and β-catenin. These results indicated that miR-145a-5p might be considered as a new myogenic differentiation-associated microRNA that can promote C2C12 myoblast differentiation by enhancing genes related to myoblasts differentiation.

Myocardial fibrosis (MF) can cause heart remodeling and it is an independent risk factor for malignant arrhythmias, sudden cardiac death, and other malignant cardiovascular events. It is often characterized by myocardial interstitial collagen deposition and hyperproliferation of cardiac fibroblasts (CFs). The transforming growth factor-β1 (TGF-β1) is the most influential profibrogenic factor. Resveratrol (RSV) is an active polyphenol substance that inhibits myocardial fibrosis. The mechanism of RSV-mediated inhibition of the proliferation of CFs at the microRNA level is not fully understood. We used TGF-β1 to induce CFs proliferation to simulate the pathogenesis of myocardial fibrosis. Neonatal rat CFs were treated with TGF-β1 in the presence or absence of resveratrol. Cell proliferation was measured using the CCK-8 and EdU assay. Collagen secretion was measured using hydroxyproline kit. Further, qPCR analysis was performed to determine microRNA levels after TGF-β1 or resveratrol treatment. To identify the target gene for miR-17, miR-17 was overexpressed or silenced, and the mRNA and protein levels of Smad7 were assessed. The effects of miR-17 silencing or Smad7 overexpression on cell proliferation and collagen secretion were also examined. Resveratrol treatment significantly decreased the TGF-β1-induced CF proliferation and collagen secretion. Resveratrol also decreased the levels of miR-17, miR-34a, and miR-181a in TGF-β1-treated CFs. Overexpression of miR-17 decreased the Smad7 mRNA and protein levels while silencing miR-17 increased them. Additionally, silencing miR-17 or overexpressing Smad7 decreased the TGF-β1-induced CFs proliferation and collagen secretion. In conclusion, resveratrol inhibits TGF-β1-induced CFs proliferation and collagen secretion. This inhibitory effect of resveratrol is orchestrated by the downregulation of miR-17 and the regulation of Smad7.

Accumulating studies demonstrate that dihydromyricetin (DMY), a compound extracted from Chinese traditional herb, Ampelopsis grossedentata, attenuates atherosclerotic process by improvement of endothelial dysfunction. However, the underlying mechanism remains poorly understood. Thus, the aim of this study is to investigate the potential mechanism behind the attenuating effects of DMY on tumor necrosis factor alpha- (TNF-α-) induced endothelial dysfunction. In response to TNF-α, microRNA-21 (miR-21) expression was significantly increased in human umbilical vein endothelial cells (HUVECs), in line with impaired endothelial dysfunction as evidenced by decreased tube formation and migration, endothelial nitric oxide synthase (eNOS) (ser1177) phosphorylation, dimethylarginine dimethylaminohydrolases 1 (DDAH1) expression and metabolic activity, and nitric oxide (NO) concentration as well as increased asymmetric dimethylarginine (ADMA) levels. In contrast, DMY or blockade of miR-21 expression ameliorated endothelial dysfunction in HUVECs treated with TNF-α through downregulation of miR-21 expression, whereas these effects were abolished by overexpression of miR-21. In addition, using a nonspecific NOS inhibitor, L-NAME, also abrogated the attenuating effects of DMY on endothelial dysfunction. Taken together, these data demonstrated that miR-21-mediated DDAH1/ADMA/NO signal pathway plays an important role in TNF-α-induced endothelial dysfunction, and DMY attenuated endothelial dysfunction induced by TNF-α in a miR-21-dependent manner.

Coronaviruses are specific crown-shaped viruses that were first identified in the 1960s, and three typical examples of the most recent coronavirus disease outbreaks include severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19. Particularly, COVID-19 is currently causing a worldwide pandemic, threatening the health of human beings globally. The identification of viral pathogenic mechanisms is important for further developing effective drugs and targeted clinical treatment methods. The delayed revelation of viral infectious mechanisms is currently one of the technical obstacles in the prevention and treatment of infectious diseases. In this study, we proposed a random walk model to identify the potential pathological mechanisms of COVID-19 on a virus-human protein interaction network, and we effectively identified a group of proteins that have already been determined to be potentially important for COVID-19 infection and for similar SARS infections, which help further developing drugs and targeted therapeutic methods against COVID-19. Moreover, we constructed a standard computational workflow for predicting the pathological biomarkers and related pharmacological targets of infectious diseases.

Hepatocellular carcinoma (HCC) is the most common type among primary liver cancers (PLC). With its poor prognosis and survival rate, it is necessary for HCC patients to have a long-term follow-up. We believe that there are currently no relevant reports or literature about nomograms for predicting the cancer-specific mortality of HCC patients. Therefore, the primary goal of this study was to develop and evaluate nomograms to predict cancer-specific mortality and overall mortality. Data of 45,158 cases of HCC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) program database between 2004 and 2013, which were then utilized to develop the nomograms. Finally, the performance of the nomograms was evaluated by the concordance index (C-index) and the area under the time-dependent receiver operating characteristic (ROC) curve (td-AUC). The categories selected to develop a nomogram for predicting cancer-specific mortality included marriage, insurance, radiotherapy, surgery, distant metastasis, lymphatic metastasis, tumor size, grade, sex, and the American Joint Committee on Cancer (AJCC) stage; while the marriage, radiotherapy, surgery, AJCC stage, grade, race, sex, and age were selected to develop a nomogram for predicting overall mortality. The C-indices for predicted 1-, 3-, and 5-year cancer-specific mortality were 0.792, 0.776, and 0.774; the AUC values for 1-, 3-, and 5-year cancer-specific mortality were 0.830, 0.830, and 0.830. The C-indices for predicted 1-, 3-, and 5-year overall mortality were 0.770, 0.755, and 0.752; AUC values for predicted 1-, 3-, and 5-year overall mortality were 0.820, 0.820, and 0.830. The results showed that the nomograms possessed good agreement compared with the observed outcomes. It could provide clinicians with a personalized predicted risk of death information to evaluate the potential changes of the disease-specific condition so that clinicians can adjust therapy options when combined with the actual condition of the patient, which is beneficial to patients.

A number of studies have discovered various roles of PAK4 in human tumors, including osteosarcoma. However, the exact role of PAK4 in osteosarcoma and its mechanism have yet to be determined. Therefore, this study focused on interrogating the PAK4 effect on the proliferation and migration ability of osteosarcoma and its underlying mechanisms.

Most drugs have beneficial as well as adverse effects and exert their biological functions by adjusting and altering the functions of their target proteins. Thus, knowledge of drugs target proteins is essential for the improvement of therapeutic effects and mitigation of undesirable side effects. In the study, we proposed a novel prediction method based on drug/compound ontology information extracted from ChEBI to identify drugs target groups from which the kind of functions of a drug may be deduced. By collecting data in KEGG, a benchmark dataset consisting of 876 drugs, categorized into four target groups, was constructed. To evaluate the method more thoroughly, the benchmark dataset was divided into a training dataset and an independent test dataset. It is observed by jackknife test that the overall prediction accuracy on the training dataset was 83.12%, while it was 87.50% on the test dataset-the predictor exhibited an excellent generalization. The good performance of the method indicates that the ontology information of the drugs contains rich information about their target groups, and the study may become an inspiration to solve the problems of this sort and bridge the gap between ChEBI ontology and drugs target groups.

Identifying disease genes is one of the most important topics in biomedicine and may facilitate studies on the mechanisms underlying disease. Age-related macular degeneration (AMD) is a serious eye disease; it typically affects older adults and results in a loss of vision due to retina damage. In this study, we attempt to develop an effective method for distinguishing AMD-related genes. Gene ontology and KEGG enrichment analyses of known AMD-related genes were performed, and a classification system was established. In detail, each gene was encoded into a vector by extracting enrichment scores of the gene set, including it and its direct neighbors in STRING, and gene ontology terms or KEGG pathways. Then certain feature-selection methods, including minimum redundancy maximum relevance and incremental feature selection, were adopted to extract key features for the classification system. As a result, 720 GO terms and 11 KEGG pathways were deemed the most important factors for predicting AMD-related genes.

Human Schlafen 5 (SLFN5) is reported to inhibit or promote the proliferation of several specific types of cancer cells by our lab and other researchers. We are curious about its implications in lung adenocarcinoma (LUAC), a malignant tumor with a high incidence rate and high mortality.

One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). 119 RB genes were compiled from two previous RB related studies, while 5,500 non-RB genes were randomly selected from Ensemble genes. Ten datasets were constructed based on all these RB and non-RB genes. Each gene was encoded with a 13,126-dimensional vector including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 1061 GO terms and 8 KEGG pathways was obtained. Analysis showed that these features were closely related to RB. It is anticipated that the method can be applied to predict the other cancer related genes as well.

Inflammation response is an important reason for disc cell senescence during disc degeneration. Recently, melatonin is suggested to protect against disc degeneration. However, the effects of melatonin on annulus fibrosus (AF) cell senescence are not fully studied. The main purpose of this study was to investigate the effects of melatonin on AF cell senescence in an inflammatory environment and the underlying mechanism. Rat disc AF cells were cultured in a medium with tumor necrosis factor-α (TNF-α). Melatonin was added along with the medium to observe its protective effects. Compared with the control AF cells, TNF-α significantly declined cell proliferation potency and telomerase activity, elevated senescence-associated β-galactosidase (SA-β-Gal) activity, upregulated protein expression of senescence markers (p16 and p53), and increased reactive oxygen species (ROS) content and activity of the NF-κB pathway. However, when the TNF-α-treated AF cells were incubated with melatonin, ROS content and activity of the NF-κB pathway were decreased, and those parameters reflecting cell senescence indicated that AF cell senescence was also partly alleviated. Together, melatonin suppresses AF cell senescence through regulating the ROS/NF-κB pathway in an inflammatory environment. This study sheds a new light that melatonin may be promising to retard inflammation-caused disc degeneration.

COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.

Over 1,000 compounds, including ecteinascidin-743 and didemnin B, have been isolated from ascidians, with most having bioactive properties such as antimicrobial, antitumor, and enzyme-inhibiting activities. In recent years, direct and indirect evidence has shown that some bioactive compounds isolated from ascidians are not produced by ascidians themselves but by their symbiotic microorganisms. Isolated culturable bacteria associated with ascidians and investigating their potential bioactivity are an important approach for discovering novel compounds. In this study, a total of 269 bacteria were isolated from the ascidian Styela clava collected from the coast of Weihai in the north of the Yellow Sea, China. Phylogenetic relationships among 183 isolates were determined using their 16S rRNA gene sequences. Isolates were tested for antimicrobial activity against seven indicator strains, and an antiproliferative activity assay was performed to test for inhibition of human hepatocellular carcinoma Bel 7402 and human cervical carcinoma HeLa cell proliferation. Our results showed that the isolates belonged to 26 genera from 18 families in four phyla (Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes). Bacillus and Streptomyces were the most dominant genera; 146 strains had potent antimicrobial activities and inhibited at least one of the indicator strains. Crude extracts from 29 strains showed antiproliferative activity against Bel 7402 cells with IC50 values below 500 μg·mL-1, and 53 strains showed antiproliferative activity against HeLa cells, with IC50 values less than 500 μg·mL-1. Our results suggest that culturable bacteria associated with the ascidian Styela clava may be a promising source of novel bioactive compounds.

Isolation and identification of diverse porcine reproductive and respiratory syndrome viruses (PRRSVs) play a fundamental role in PRRSV research and disease management. However, PRRSV has a restricted cell tropism for infection. MARC-145 cells are routinely used for North American genotype PRRSV isolation and vaccine production. But MARC-145 cells have some limitations such as low virus yield. CD163 is a cellular receptor that mediates productive infection of PRRSV in various nonpermissive cell lines. In this study, we established a high and stable porcine CD163- (pCD163-) expressing MARC-145 cell line toward increasing its susceptibility to PRRSV infection. Indirect immunofluorescence assay (IFA) and Western blotting assays showed that pCD163 was expressed higher in pCD163-MARC cell line than MARC-145 cells. Furthermore, the ability of pCD163-MARC cell line to propagate PRRSV was significantly increased as compared with MARC-145 cells. Finally, we found that pCD163-MARC cell line had a higher isolation rate of clinical PRRSV samples and propagated live attenuated PRRS vaccine strains more efficiently than MARC-145 cells. This pCD163-MARC cell line will be a valuable tool for propagation and research of PRRSV.

Skeletal muscle is the most abundant and a highly plastic tissue of the mammals, especially when it comes to regenerate after trauma, but there is limited information about the mechanism of muscle repair and its regeneration. In the present study, we found that miR-204 is downregulated after skeletal muscle injury. In vitro experiments showed that over-expression of miR-204 by transfecting with miR-204 mimics suppressed C2C12 cell proliferation, migration, and blocked subsequent differentiation, whereas inhibition of miR-204 by transfecting with miR-204 inhibitor showed the converse effects. Furthermore, through the dual luciferase reporter system, we demonstrated that miR-204 can target the 3'UTR regions of Pax7, IGF1, and Mef2c and inhibit their expression. Taken together, our results suggest that Pax7, IGF1, and Mef2c are the target genes of miR-204 in the process of myoblasts proliferation, cell migration, and differentiation, respectively, and may contribute to mouse skeletal muscle regeneration. Our results may provide new ideas and references for the skeletal muscle study and may also provide therapeutic strategies of skeletal muscle injury.

Oncogene is a special type of genes, which can promote the tumor initiation. Good study on oncogenes is helpful for understanding the cause of cancers. Experimental techniques in early time are quite popular in detecting oncogenes. However, their defects become more and more evident in recent years, such as high cost and long time. The newly proposed computational methods provide an alternative way to study oncogenes, which can provide useful clues for further investigations on candidate genes. Considering the limitations of some previous computational methods, such as lack of learning procedures and terming genes as individual subjects, a novel computational method was proposed in this study. The method adopted the features derived from multiple protein networks, viewing proteins in a system level. A classic machine learning algorithm, random forest, was applied on these features to capture the essential characteristic of oncogenes, thereby building the prediction model. All genes except validated oncogenes were ranked with a measurement yielded by the prediction model. Top genes were quite different from potential oncogenes discovered by previous methods, and they can be confirmed to become novel oncogenes. It was indicated that the newly identified genes can be essential supplements for previous results.

Protein-protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used gene ontology (GO) terms and biological pathways to study an extended version of PPI (protein-protein functional associations) and subsequently identify some essential GO terms and pathways that can indicate the difference between two proteins with and without functional associations. The protein-protein functional associations validated by experiments were retrieved from STRING, a well-known database on collected associations between proteins from multiple sources, and they were termed as positive samples. The negative samples were constructed by randomly pairing two proteins. Each sample was represented by several features based on GO and KEGG pathway information of two proteins. Then, the mutual information was adopted to evaluate the importance of all features and some important ones could be accessed, from which a number of essential GO terms or KEGG pathways were identified. The final analysis of some important GO terms and one KEGG pathway can partly uncover the difference between proteins with and without functional associations.

Hyposmia is one of the most common and best-characterized conditions that is also one of the first nonmotor features of Parkinson's disease (PD). The association of hyposmia with PD is widely accepted; however the likelihood of developing PD is unclear. Our meta-analysis aimed to investigate the risk of PD in individuals with hyposmia.

Brain metastasis is a major cause of death in breast cancer patients. The greatest event for brain metastasis is the breaching of the blood-brain barrier (BBB) by cancer cells. The role of exosomes in cancer metastasis is clear, whereas the role of exosomes in the integrity of the BBB is unknown. Here, we established a highly brain metastatic breast cancer cell line by three cycles of in vivo selection. The effect of exosomes on the BBB was evaluated in vitro by tracking, transepithelial/transendothelial electrical resistance (TEER), and permeability assays. BBB-associated exosomal long noncoding RNA (lncRNA) was selected from the GEO dataset and verified by real-time PCR, TEER, permeability, and Transwell assays. The cells obtained by the in vivo selection showed higher brain metastatic capacity in vivo and higher migration and invasion in vitro compared to the parental cells. Exosomes from the highly brain metastatic cells were internalized by brain microvascular endothelial cells (BMECs), which reduced TEER and increased permeability of BBB. The exosomes derived from the highly metastatic cells promoted invasion of the breast cancer cells in the BBB model. lncRNA GS1-600G8.5 was highly expressed in the highly brain metastatic cells and their exosomes, as compared to the samples with reduced metastatic behavior. Silencing of GS1-600G8.5 significantly abrogated the BBB destructive effect of exosomes. GS1-600G8.5-deficient exosomes failed to promote the infiltration of cancer cells through the BBB. Furthermore, BMECs treated with GS1-600G8.5-deprived exosomes expressed higher tight junction proteins than those treated with the control exosomes. These data suggest the exosomes derived from highly brain metastatic breast cancer cells might destroy the BBB system and promote the passage of cancer cells across the BBB, by transferring lncRNA GS1-600G8.5.