Subtype-MGTP: a cancer subtype identification framework based on multi-omics translation.

MOTIVATION: The identification of cancer subtypes plays a crucial role in cancer research and treatment. With the rapid development of high-throughput sequencing technologies, there has been an exponential accumulation of cancer multi-omics data. Integrating multi-omics data has emerged as a cost-effective and efficient strategy for cancer subtyping. While current methods primarily rely on genomics data, protein expression data offers a closer representation of phenotype. Therefore, integrating protein expression data holds promise for enhancing subtyping accuracy. However, the scarcity of protein expression data compared to genomics data presents a challenge in its direct incorporation into existing methods. Moreover, striking a balance between omics-specific learning and cross-omics learning remains a prevalent challenge in current multi-omics integration methods. RESULTS: We introduce Subtype-MGTP, a novel cancer subtyping framework based on the translation of Multiple Genomics To Proteomics. Subtype-MGTP comprises two modules: a translation module, which leverages available protein data to translate multi-type genomics data into predicted protein expression data, and an improved deep subspace clustering module, which integrates contrastive learning to cluster the predicted protein data, yielding refined subtyping results. Extensive experiments conducted on benchmark datasets demonstrate that Subtype-MGTP outperforms nine state-of-the-art cancer subtyping methods. The interpretability of clustering results is further supported by the clinical and survival analysis. Subtype-MGTP also exhibits strong robustness against varying rates of missing protein data and demonstrates distinct advantages in integrating multi-omics data with imbalanced multi-omics data. AVAILABILITY AND IMPLEMENTATION: The code and results are available at https://github.com/kybinn/Subtype-MGTP.

LPI-IBWA: Predicting lncRNA-protein interactions based on an improved Bi-Random walk algorithm.

Many studies have shown that long-chain noncoding RNAs (lncRNAs) are involved in a variety of biological processes such as post-transcriptional gene regulation, splicing, and translation by combining with corresponding proteins. Predicting lncRNA-protein interactions is an effective approach to infer the functions of lncRNAs. The paper proposes a new computational model named LPI-IBWA. At first, LPI-IBWA uses similarity kernel fusion (SKF) to integrate various types of biological information to construct lncRNA and protein similarity networks. Then, a bounded matrix completion model and a weighted k-nearest known neighbors algorithm are utilized to update the initial sparse lncRNA-protein interaction matrix. Based on the updated lncRNA-protein interaction matrix, the lncRNA similarity network and the protein similarity network are integrated into a heterogeneous network. Finally, an improved Bi-Random walk algorithm is used to predict novel latent lncRNA-protein interactions. 5-fold cross-validation experiments on a benchmark dataset showed that the AUC and AUPR of LPI-IBWA reach 0.920 and 0.736, respectively, which are higher than those of other state-of-the-art methods. Furthermore, the experimental results of case studies on a novel dataset also illustrated that LPI-IBWA could efficiently predict potential lncRNA-protein interactions.

Graph regularized non-negative matrix factorization with [Formula: see text] norm regularization terms for drug-target interactions prediction.

BACKGROUND: Identifying drug-target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are costly and time consuming. Effective computational methods to predict DTIs are useful to speed up the process of drug discovery. A variety of non-negativity matrix factorization based methods are proposed to predict DTIs, but most of them overlooked the sparsity of feature matrices and the convergence of adopted matrix factorization algorithms, therefore their performances can be further improved. RESULTS: In order to predict DTIs more accurately, we propose a novel method iPALM-DLMF. iPALM-DLMF models DTIs prediction as a problem of non-negative matrix factorization with graph dual regularization terms and [Formula: see text] norm regularization terms. The graph dual regularization terms are used to integrate the information from the drug similarity matrix and the target similarity matrix, and [Formula: see text] norm regularization terms are used to ensure the sparsity of the feature matrices obtained by non-negative matrix factorization. To solve the model, iPALM-DLMF adopts non-negative double singular value decomposition to initialize the nonnegative matrix factorization, and an inertial Proximal Alternating Linearized Minimization iterating process, which has been proved to converge to a KKT point, to obtain the final result of the matrix factorization. Extensive experimental results show that iPALM-DLMF has better performance than other state-of-the-art methods. In case studies, in 50 highest-scoring proteins targeted by the drug gabapentin predicted by iPALM-DLMF, 46 have been validated, and in 50 highest-scoring drugs targeting prostaglandin-endoperoxide synthase 2 predicted by iPALM-DLMF, 47 have been validated.

LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier.

Numerous scientific evidences have revealed that long non-coding RNAs (lncRNAs) are involved in the progression of human complex diseases and biological life activities. Therefore, identifying novel and potential disease-related lncRNAs is helpful to diagnosis, prognosis and therapy of many human complex diseases. Since traditional laboratory experiments are cost and time-consuming, a great quantity of computer algorithms have been proposed for predicting the relationships between lncRNAs and diseases. However, there are still much room for the improvement. In this paper, we introduce an accurate framework named LDAEXC to infer LncRNA-Disease Associations with deep autoencoder and XGBoost Classifier. LDAEXC utilizes different similarity views of lncRNAs and human diseases to construct features for each data sources. Then, the reduced features are obtained by feeding the constructed feature vectors into a deep autoencoder, and at last an XGBoost classifier is leveraged to calculate the latent lncRNA-disease-associated scores using reduced features. The fivefold cross-validation experiments on four datasets showed that LDAEXC reached AUC scores of 0.9676 ± 0.0043, 0.9449 ± 0.022, 0.9375 ± 0.0331 and 0.9556 ± 0.0134, respectively, significantly higher than other advanced similar computer methods. Extensive experiment results and case studies of two complex diseases (colon and breast cancers) further indicated the practicability and excellent prediction performance of LDAEXC in inferring unknown lncRNA-disease associations. TLDAEXC utilizes disease semantic similarity, lncRNA expression similarity, and Gaussian interaction profile kernel similarity of lncRNAs and diseases for feature construction. The constructed features are fed to a deep autoencoder to extract reduced features, and an XGBoost classifier is used to predict the lncRNA-disease associations based on the reduced features. The fivefold and tenfold cross-validation experiments on a benchmark dataset showed that LDAEXC could achieve AUC scores of 0.9676 and 0.9682, respectively, significantly higher than other state-of-the-art similar methods.

Drug response prediction using graph representation learning and Laplacian feature selection.

BACKGROUND: Knowing the responses of a patient to drugs is essential to make personalized medicine practical. Since the current clinical drug response experiments are time-consuming and expensive, utilizing human genomic information and drug molecular characteristics to predict drug responses is of urgent importance. Although a variety of computational drug response prediction methods have been proposed, their effectiveness is still not satisfying. RESULTS: In this study, we propose a method called LGRDRP (Learning Graph Representation for Drug Response Prediction) to predict cell line-drug responses. At first, LGRDRP constructs a heterogeneous network integrating multiple kinds of information: cell line miRNA expression profiles, drug chemical structure similarity, gene-gene interaction, cell line-gene interaction and known cell line-drug responses. Then, for each cell line, learning graph representation and Laplacian feature selection are combined to obtain network topology features related to the cell line. The learning graph representation method learns network topology structure features, and the Laplacian feature selection method further selects out some most important ones from them. Finally, LGRDRP trains an SVM model to predict drug responses based on the selected features of the known cell line-drug responses. Our five-fold cross-validation results show that LGRDRP is significantly superior to the art-of-the-state methods in the measures of the average area under the receiver operating characteristics curve, the average area under the precision-recall curve and the recall rate of top-k predicted sensitive cell lines. CONCLUSIONS: Our results demonstrated that the usage of multiple types of information about cell lines and drugs, the learning graph representation method, and the Laplacian feature selection is useful to the improvement of performance in predicting drug responses. We believe that such an approach would be easily extended to similar problems such as miRNA-disease relationship inference.

Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug-target interactions prediction.

BACKGROUND: Identifying drug-target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are expensive and time consuming. Effective computational methods to predict DTIs are useful to narrow the searching scope of potential drugs and speed up the process of drug discovery. There are a variety of non-negativity matrix factorization based methods to predict DTIs, but the convergence of the algorithms used in the matrix factorization are often overlooked and the results can be further improved. RESULTS: In order to predict DTIs more accurately and quickly, we propose an alternating direction algorithm to solve graph regularized non-negative matrix factorization with prior knowledge consistency constraint (ADA-GRMFC). Based on known DTIs, drug chemical structures and target sequences, ADA-GRMFC at first constructs a DTI matrix, a drug similarity matrix and a target similarity matrix. Then DTI prediction is modeled as the non-negative factorization of the DTI matrix with graph dual regularization terms and a prior knowledge consistency constraint. The graph dual regularization terms are used to integrate the information from the drug similarity matrix and the target similarity matrix, and the prior knowledge consistency constraint is used to ensure the matrix decomposition result should be consistent with the prior knowledge of known DTIs. Finally, an alternating direction algorithm is used to solve the matrix factorization. Furthermore, we prove that the algorithm can converge to a stationary point. Extensive experimental results of 10-fold cross-validation show that ADA-GRMFC has better performance than other state-of-the-art methods. In the case study, ADA-GRMFC is also used to predict the targets interacting with the drug olanzapine, and all of the 10 highest-scoring targets have been accurately predicted. In predicting drug interactions with target estrogen receptors alpha, 17 of the 20 highest-scoring drugs have been validated.

Predict potential miRNA-disease associations based on bounded nuclear norm regularization.

Increasing evidences show that the abnormal microRNA (miRNA) expression is related to a variety of complex human diseases. However, the current biological experiments to determine miRNA-disease associations are time consuming and expensive. Therefore, computational models to predict potential miRNA-disease associations are in urgent need. Though many miRNA-disease association prediction methods have been proposed, there is still a room to improve the prediction accuracy. In this paper, we propose a matrix completion model with bounded nuclear norm regularization to predict potential miRNA-disease associations, which is called BNNRMDA. BNNRMDA at first constructs a heterogeneous miRNA-disease network integrating the information of miRNA self-similarity, disease self-similarity, and the known miRNA-disease associations, which is represented by an adjacent matrix. Then, it models the miRNA-disease prediction as a relaxed matrix completion with error tolerance, value boundary and nuclear norm minimization. Finally it implements the alternating direction method to solve the matrix completion problem. BNNRMDA makes full use of available information of miRNAs and diseases, and can deals with the data containing noise. Compared with four state-of-the-art methods, the experimental results show BNNRMDA achieved the best performance in five-fold cross-validation and leave-one-out cross-validation. The case studies on two complex human diseases showed that 47 of the top 50 prediction results of BNNRMDA have been verified in the latest HMDD database.

DNRLCNN: A CNN Framework for Identifying MiRNA-Disease Associations Using Latent Feature Matrix Extraction with Positive Samples.

Emerging evidence indicates that miRNAs have strong relationships with many human diseases. Investigating the associations will contribute to elucidating the activities of miRNAs and pathogenesis mechanisms, and providing new opportunities for disease diagnosis and drug discovery. Therefore, it is of significance to identify potential associations between miRNAs and diseases. The existing databases about the miRNA-disease associations (MDAs) only provide the known MDAs, which can be regarded as positive samples. However, the unknown MDAs are not sufficient to regard as reliable negative samples. To deal with this uncertainty, we proposed a convolutional neural network (CNN) framework, named DNRLCNN, based on a latent feature matrix extracted by only positive samples to predict MDAs. First, by only considering the positive samples into the calculation process, we captured the latent feature matrix for complex interactions between miRNAs and diseases in low-dimensional space. Then, we constructed a feature vector for each miRNA and disease pair based on the feature representation. Finally, we adopted a modified CNN for the feature vector to predict MDAs. As a result, our model achieves better performance than other state-of-the-art methods which based CNN in fivefold cross-validation on both miRNA-disease association prediction task (average AUC of 0.9030) and miRNA-phenotype association prediction task (average AUC of 0. 9442). In addition, we carried out case studies on two human diseases, and all the top-50 predicted miRNAs for lung neoplasms are confirmed by HMDD v3.2 and dbDEMC 2.0 databases, 98% of the top-50 predicted miRNAs for heart failure are confirmed. The experiment results show that our model has the capability of inferring potential disease-related miRNAs.

Predicting gene expression from histone modifications with self-attention based neural networks and transfer learning.

It is well known that histone modifications play an important part in various chromatin-dependent processes such as DNA replication, repair, and transcription. Using computational models to predict gene expression based on histone modifications has been intensively studied. However, the accuracy of the proposed models still has room for improvement, especially in cross-cell lines gene expression prediction. In the work, we proposed a new model TransferChrome to predict gene expression from histone modifications based on deep learning. The model uses a densely connected convolutional network to capture the features of histone modifications data and uses self-attention layers to aggregate global features of the data. For cross-cell lines gene expression prediction, TransferChrome adopts transfer learning to improve prediction accuracy. We trained and tested our model on 56 different cell lines from the REMC database. The experimental results show that our model achieved an average Area Under the Curve (AUC) score of 84.79%. Compared to three state-of-the-art models, TransferChrome improves the prediction performance on most cell lines. The experiments of cross-cell lines gene expression prediction show that TransferChrome performs best and is an efficient model for predicting cross-cell lines gene expression.

A novel essential protein identification method based on PPI networks and gene expression data.

BACKGROUND: Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins. RESULTS: In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression. CONCLUSIONS: We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms.

BACKGROUND: Long non-coding RNAs (lncRNAs) regulate diverse biological processes via interactions with proteins. Since the experimental methods to identify these interactions are expensive and time-consuming, many computational methods have been proposed. Although these computational methods have achieved promising prediction performance, they neglect the fact that a gene may encode multiple protein isoforms and different isoforms of the same gene may interact differently with the same lncRNA. RESULTS: In this study, we propose a novel method, DeepLPI, for predicting the interactions between lncRNAs and protein isoforms. Our method uses sequence and structure data to extract intrinsic features and expression data to extract topological features. To combine these different data, we adopt a hybrid framework by integrating a multimodal deep learning neural network and a conditional random field. To overcome the lack of known interactions between lncRNAs and protein isoforms, we apply a multiple instance learning (MIL) approach. In our experiment concerning the human lncRNA-protein interactions in the NPInter v3.0 database, DeepLPI improved the prediction performance by 4.7% in term of AUC and 5.9% in term of AUPRC over the state-of-the-art methods. Our further correlation analyses between interactive lncRNAs and protein isoforms also illustrated that their co-expression information helped predict the interactions. Finally, we give some examples where DeepLPI was able to outperform the other methods in predicting mouse lncRNA-protein interactions and novel human lncRNA-protein interactions. CONCLUSION: Our results demonstrated that the use of isoforms and MIL contributed significantly to the improvement of performance in predicting lncRNA and protein interactions. We believe that such an approach would find more applications in predicting other functional roles of RNAs and proteins.

Proteoform characterization based on top-down mass spectrometry.

Proteins are dominant executors of living processes. Compared to genetic variations, changes in the molecular structure and state of a protein (i.e. proteoforms) are more directly related to pathological changes in diseases. Characterizing proteoforms involves identifying and locating primary structure alterations (PSAs) in proteoforms, which is of practical importance for the advancement of the medical profession. With the development of mass spectrometry (MS) technology, the characterization of proteoforms based on top-down MS technology has become possible. This type of method is relatively new and faces many challenges. Since the proteoform identification is the most important process in characterizing proteoforms, we comprehensively review the existing proteoform identification methods in this study. Before identifying proteoforms, the spectra need to be preprocessed, and protein sequence databases can be filtered to speed up the identification. Therefore, we also summarize some popular deconvolution algorithms, various filtering algorithms for improving the proteoform identification performance and various scoring methods for localizing proteoforms. Moreover, commonly used methods were evaluated and compared in this review. We believe our review could help researchers better understand the current state of the development in this field and design new efficient algorithms for the proteoform characterization.

Quantifying functional impact of non-coding variants with multi-task Bayesian neural network.

MOTIVATION: Advances in high-throughput genotyping and sequencing technologies during recent years have revealed essential roles of non-coding regions in gene regulation. Genome-wide association studies (GWAS) suggested that a large proportion of risk variants are located in non-coding regions and remain unexplained by current expression quantitative trait loci catalogs. Interpreting the causal effects of these genetic modifications is crucial but difficult owing to our limited knowledge of how regulatory elements function. Although several computational methods have been designed to prioritize regulatory variants that substantially impact human phenotypes, few of them achieve consistently high performance even when large-scale multi-omic data are integrated. RESULTS: We propose a novel multi-task framework based on Bayesian deep neural networks, MtBNN, to quantify the deleterious impact of single nucleotide polymorphisms in non-coding genomic regions. With the high-efficiency provided by the multi-task Bayesian framework to integrate information from different sources, MtBNN is capable of extracting features from genomic sequences of large-scale chromatin-profiling data, such as chromatin accessibility and transcript factor binding affinities, and calculating the distribution of the probability that a non-coding variant disrupts regulatory activities. A series of comprehensive experiments show that MtBNN quantifies the functional impact of cis-regulatory variations with high accuracy, including expression quantitative trait locus, DNase I sensitivity quantitative trait locus and functional genetic variants located within ATAC-peaks that affect the accessibility of the corresponding peak and achieves significantly better performance than the existing methods. Moreover, MtBNN has applications in the discovery of potentially causal disease-associated single-nucleotide polymorphisms (SNPs), thus helping fine-map the GWAS SNPs. AVAILABILITY AND IMPLEMENTATION: Code can be downloaded from https://github.com/Zoesgithub/MtBNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Novel Approach Based on Bipartite Network Recommendation and KATZ Model to Predict Potential Micro-Disease Associations.

Accumulating evidence indicates that the microbes colonizing human bodies have crucial effects on human health and the discovery of disease-related microbes will promote the discovery of biomarkers and drugs for the prevention, diagnosis, treatment, and prognosis of diseases. However clinical experiments of disease-microbe associations are time-consuming, laborious and expensive, and there are few methods for predicting potential microbe-disease association. Therefore, developing effective computational models utilizing the accumulated public data of clinically validated microbe-disease associations to identify novel disease-microbe associations is of practical importance. We propose a novel method based on the KATZ model and Bipartite Network Recommendation Algorithm (KATZBNRA) to discover potential associations between microbes and diseases. We calculate the Gaussian interaction profile kernel similarity of diseases and microbes based on validated disease-microbe associations. Then, we construct a bipartite graph and execute a bipartite network recommendation algorithm. Finally, we integrate the disease similarity, microbe similarity and bipartite network recommendation score to obtain the final score, which is used to infer whether there are some novel disease-microbe interactions. To evaluate the predictive power of KATZBNRA, we tested it with the walk length 2 using global leave-one-out cross validation (LOOV), two-fold and five-fold cross validations, with AUCs of 0.9098, 0.8463 and 0.8969, respectively. The test results also show that KATZBNRA is more accurate than two recent similar methods KATZHMDA and BNPMDA.

XGBFEMF: An XGBoost-Based Framework for Essential Protein Prediction.

Essential proteins as a vital part of maintaining the cells' life play an important role in the study of biology and drug design. With the generation of large amounts of biological data related to essential proteins, an increasing number of computational methods have been proposed. Different from the methods which adopt a single machine learning method or an ensemble machine learning method, this paper proposes a predicting framework named by XGBFEMF for identifying essential proteins, which includes a SUB-EXPAND-SHRINK method for constructing the composite features with original features and obtaining the better subset of features for essential protein prediction, and also includes a model fusion method for getting a more effective prediction model. We carry out experiments on Yeast data to assess the performance of the XGBFEMF with ROC analysis, accuracy analysis, and top analysis. Meanwhile, we set up experiments on E. coli data for the validation of performance. The test results show that the XGBFEMF framework can effectively improve many essential indicators. In addition, we analyze each step in the XGBFEMF framework; our results show that both each step of the SUB-EXPAND-SHRINK method as well as the step of multi-model fusion can improve prediction performance.

Research progress in machine learning methods for gene-gene interaction detection.

Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.

Species-related exposure of phase II metabolite gemfibrozil 1-O-ß-glucuronide between human and mice: A net induction of mouse P450 activity was revealed.

Gemfibrozil is a fibrate drug used widely for dyslipidemia associated with atherosclerosis. Clinically, both gemfibrozil and its phase II metabolite gemfibrozil 1-O-ß-glucuronide (gem-glu) are involved in drug-drug interaction (DDI). But the DDI risk caused by gem-glu between human and mice has not been compared. In this study, six volunteers were recruited and took a therapeutic dose of gemfibrozil for 3 days for examination of the gemfibrozil and gem-glu level in human. Male mice were fed a gemfibrozil diet (0.75%) for 7 days, following which a cocktail-based inhibitory DDI experiment was performed. Plasma samples and liver tissues from mice were collected for determination of gemfibrozil, gem-glu concentration and cytochrome p450 enzyme (P450) induction analysis. In human, the molar ratio of gem-glu/gemfibrozil was 15% and 10% at the trough concentration and the concentration at 1.5 h after the 6th dose. In contrast, this molar ratio at steady state in mice was 91%, demonstrating a 6- to 9-fold difference compared with that in human. Interestingly, a net induction of P450 activity and in vivo inductive DDI potential in mice was revealed. The P450 activity was not inhibited although the gem-glu concentration was high. These data suggested species difference of relative gem-glu exposure between human and mice, as well as a net inductive DDI potential of gemfibrozil in mouse model.

Inhibition of JNK signalling mediates PPARα-dependent protection against intrahepatic cholestasis by fenofibrate.

BACKGROUND AND PURPOSE: Fenofibrate, a PPARα agonist, is the most widely prescribed drug for treating hyperlipidaemia. Although fibrate drugs are reported to be beneficial for cholestasis, their underlying mechanism has not been determined. EXPERIMENTAL APPROACH: Wild-type mice and Pparα-null mice were pretreated orally with fenofibrate for 3 days, following which α-naphthylisothiocyanate (ANIT) was administered to induce cholestasis. The PPARα agonist WY14643 and JNK inhibitor SP600125 were used to determine the role of PPARα and the JNK pathway, respectively, in cholestatic liver injury. The same fenofibrate regimen was applied to investigate its beneficial effects on sclerosing cholangitis in a DDC-induced cholestatic model. KEY RESULTS: Fenofibrate, 25 mg·kg-1 twice a day, totally attenuated ANIT-induced cholestasis and liver injury as indicated by biochemical and histological analyses. This protection occurred in wild-type, but not in Pparα-null, mice. Alterations in bile acid synthesis and transport were found to be an adaptive response rather than a direct effect of fenofibrate. WY14643 attenuated ANIT-induced cholestasis and liver injury coincident with inhibition of JNK signalling. Although SP600125 did not affect cholestasis, it inhibited liver injury in the ANIT model when the dose of fenofibrate used was ineffective. Fenofibrate was also revealed to have a beneficial effect in the sclerosing cholangitis model. CONCLUSIONS AND IMPLICATIONS: These data suggest that the protective effects of fenofibrate against cholestasis-induced hepatic injury are dependent on PPARα and fenofibrate dose, and are mediated through inhibition of JNK signalling. This mechanism of fenofibrate protection against intrahepatic cholestasis may offer additional therapeutic opportunities for cholestatic liver diseases.

Dual action of peroxisome proliferator-activated receptor alpha in perfluorodecanoic acid-induced hepatotoxicity.

Perfluorodecanoic acid (PFDA) is widely used in production of many daily necessities based on their surface properties and stability. It was assigned as a Persistent Organic Pollutant in 2009 and became a public concern partly because of its potential for activation of the peroxisome proliferator-activated receptor alpha (PPARα). In this study, wild-type and Ppara-null mice were administered PFDA (80 mg/kg). Blood and liver tissues were collected and subjected to systemic toxicological and mechanistic analysis. UPLC-ESI-QTOFMS-based metabolomics was used to explore the contributing components of the serum metabolome that led to variation between wild-type and Pparα-null mice. Bile acid homeostasis was disrupted, and slight hepatocyte injury in wild-type mice accompanied by adaptive regulation of bile acid synthesis and transport was observed. The serum metabolome in wild-type clustered differently from that in Pparα-null, featured by sharp increases in bile acid components. Differential toxicokinetic tendency was supported by regulation of UDP-glucuronosyltransferases dependent on PPARα, but it did not contribute to the hepatotoxic responses. Increase in Il-10 and activation of the JNK pathway indicated inflammation was induced by disruption of bile acid homeostasis in wild-type mice. Inhibition of p-p65 dependent on PPARα activation by PFDA stopped the inflammatory cascade, as indicated by negative response of Il-6, Tnf-α, and STAT3 signaling. These data suggest disruptive and protective role of PPARα in hepatic responses induced by PFDA.

Oral administration of nano-titanium dioxide particle disrupts hepatic metabolic functions in a mouse model.

TiO2 nano-particle (TiO2 NP) is widely used in industrial, household necessities, as well as medicinal products. However, the effect of TiO2 NP on liver metabolic function has not been reported. In this study, after mice were orally administered TiO2 NP (21nm) for 14days, the serum and liver tissues were assayed by biochemical analysis, real time quantitative polymerase chain reaction, western blot and transmission electron microscopy. The serum bilirubin was increased in a dose dependent manner. Deposition of TiO2 NP in hepatocytes and the abnormality of microstructures was observed. Expression of metabolic genes involved in the endogenous and exogenous metabolism was modified, supporting the toxic phenotype. Collectively, oral administration of TiO2 NP (21nm) led to deposition of particles in hepatocytes, mitochondrial edema, and the disturbance of liver metabolism function. These data suggested oral administration disrupts liver metabolic functions, which was more sensitive than regular approaches to detect material hepatotoxicity. This study provided useful information for risk analysis and regulation of TiO2 NPs by administration agencies.