Abstract: We previously reported the bioaccumulation of contaminants of emerging concern (CECs), including pharmaceuticals and personal care products (PPCPs) and perfluorinated compounds, in field-collected juvenile Chinook salmon from urban estuaries of Puget Sound, WA (Meador et al., 2016). Although the toxicological impacts of CECs on salmon are poorly understood, several of the detected contaminants disrupt mitochondrial function in other species. Here, we sought to determine whether environmental exposures to CECs are associated with hepatic mitochondrial dysfunction in juvenile Chinook. Fish were exposed in the laboratory to a dietary mixture of 16 analytes representative of the predominant CECs detected in our field study. Liver mitochondrial content was reduced in fish exposed to CECs, which occurred concomitantly with a 24-32% reduction in expression of peroxisome proliferator-activated receptor (PPAR) Y coactivator-1a (pgc-1alpha), a positive transcriptional regulator of mitochondrial biogenesis. The laboratory exposures also caused a 40-70% elevation of state 4 respiration per unit mitochondria, which drove a 29-38% reduction of efficiency of oxidative phosphorylation relative to controls. The mixture-induced elevation of respiration was associated with increased oxidative injury as evidenced by increased mitochondrial protein carbonyls, elevated expression of glutathione (GSH) peroxidase 4 (gpx4), a mitochondrial-associated GSH peroxidase that protects against lipid peroxidation, and reduction of mitochondrial GSH. Juvenile Chinook sampled in a WWTP effluent-impacted estuary with demonstrated releases of CECs showed similar trends toward reduced liver mitochondrial content and elevated respiratory activity per mitochondria (including state 3 and uncoupled respiration). However, respiratory control ratios were greater in fish from the contaminated site relative to fish from a minimally-polluted reference site, which may have been due to differences in the timing of exposure to CECs under laboratory and field conditions. Our results indicate that exposure to CECs can affect both mitochondrial quality and content, and support the analysis of mitochondrial function as an indicator of the sublethal effects of CECs in wild fish.

Abstract: OBJECTIVE: To investigate the synergistic effects of Chuanxiong-Chishao herb-pair (CCHP) on promoting angiogenesis in silico and in vivo. METHODS: The mechanisms of action of an herb-pair, Chuanxiong-Chishao, were investigated using the network pharmacological and pharmacodynamic strategies involving computational drug target prediction and network analysis, and experimental validation. A set of network pharmacology methods were created to study the herbs in the context of targets and diseases networks, including prediction of target profiles and pharmacological actions of main active compounds in Chuanxiong and Chishao. Furthermore, the therapeutic effects and putative molecular mechanisms of Chuanxiong-Chishao actions were experimentally validated in a chemical-induced vascular insuffificiency model of transgenic zebrafifish in vivo. The mRNA expression of the predicted targets were further analyzed by real-time polymerase chain reaction (RT-PCR). RESULTS: The computational prediction results found that the compounds in Chuanxiong have antithrombotic, antihypertensive, antiarrhythmic, and antiatherosclerotic activities, which were closely related to protecting against hypoxic-ischemic encephalopathy, ischemic stroke, myocardial infarction and heart failure. In addition, compounds in Chishao were found to participate in anti-inflflammatory effect and analgesics. Particularly, estrogen receptor alpha (ESRalpha) and hypoxia-inducible factor 1-alpha (HIF-1alpha) were the most important potential protein targets in the predicted results. In vivo experimental validation showed that post-treatment of tetramethylpyrazine hydrochloride (TMP*HCl) and paeoniflorin (PF) promoted the regeneration of new blood vessels in zebrafifish involving up-regulating ESRalpha mRNA expression. Co-treatment of TMP*HCl and PF could enhance the vessel sprouting in chemical-induced vascular insuffificiency zebrafifish at the optimal compatibility proportion of PF 10 mumol/L with TMP*HCl 1 mumol/L. CONCLUSIONS: The network pharmacological strategies combining drug target prediction and network analysis identified some putative targets of CCHP. Moreover, the transgenic zebrafifish experiments demonstrated that the Chuanxiong-Chishao combination synergistically promoted angiogenic activity, probably involving ESRalpha signaling pathway.

Abstract: Next-generation sequencing enables simultaneous analysis of hundreds of human genomes associated with a particular phenotype, for example, a disease. These genomes naturally contain a lot of sequence variation that ranges from single-nucleotide variants (SNVs) to large-scale structural rearrangements. In order to establish a functional connection between genotype and disease-associated phenotypes, one needs to distinguish disease drivers from neutral passenger variants. Functional annotation based on experimental assays is feasible only for a limited number of candidate mutations. Thus alternative computational tools are needed. A possible approach to annotating mutations functionally is to consider their spatial location relative to functionally relevant sites in three-dimensional (3D) structures of the harboring proteins. This is impeded by the lack of available protein 3D structures. Complementing experimentally resolved structures with reliable computational models is an attractive alternative. We developed a structure-based approach to characterizing comprehensive sets of non-synonymous single-nucleotide variants (nsSNVs): associated with cancer, non-cancer diseases and putatively functionally neutral. We searched experimentally resolved protein 3D structures for potential homology-modeling templates for proteins harboring corresponding mutations. We found such templates for all proteins with disease-associated nsSNVs, and 51 and 66% of proteins carrying common polymorphisms and annotated benign variants. Many mutations caused by nsSNVs can be found in protein-protein, protein-nucleic acid or protein-ligand complexes. Correction for the number of available templates per protein reveals that protein-protein interaction interfaces are not enriched in either cancer nsSNVs, or nsSNVs associated with non-cancer diseases. Whereas cancer-associated mutations are enriched in DNA-binding proteins, they are rarely located directly in DNA-interacting interfaces. In contrast, mutations associated with non-cancer diseases are in general rare in DNA-binding proteins, but enriched in DNA-interacting interfaces in these proteins. All disease-associated nsSNVs are overrepresented in ligand-binding pockets, and nsSNVs associated with non-cancer diseases are additionally enriched in protein core, where they probably affect overall protein stability.

Abstract: The U.S. Food and Drug Administration (FDA) approves new drugs every year. Drug targets are some of the most important interactive molecules for drugs, as they have a significant impact on the therapeutic effects of drugs. In this work, we thoroughly analyzed the data of small molecule drugs approved by the U.S. FDA between 2000 and 2015. Specifically, we focused on seven classes of new molecular entity (NME) classified by the anatomic therapeutic chemical (ATC) classification system. They were NMEs and their corresponding targets for the cardiovascular system, respiratory system, nerve system, general anti-infective systemic, genito-urinary system and sex hormones, alimentary tract and metabolisms, and antineoplastic and immunomodulating agents. To study the drug-target interaction on the systems level, we employed network topological analysis and multipartite network projections. As a result, the drug-target relations of different kinds of drugs were comprehensively characterized and global pictures of drug-target, drug-drug, and target-target interactions were visualized and analyzed from the perspective of network models.

Abstract: BACKGROUND: Rare diseases represent a challenge for physicians because patients are rarely seen, and they can manifest with symptoms similar to those of common diseases. In this work, genetic confirmation of diagnosis is derived from DNA sequencing. We present a tutorial for the molecular analysis of a rare disease using Fabry disease as an example. METHODS: An exonic sequence derived from a hypothetical male patient was matched against human reference data using a genome browser. The missense mutation was identified by running BlastX, and information on the affected protein was retrieved from the database UniProt. The pathogenic nature of the mutation was assessed with PolyPhen-2. Disease-specific databases were used to assess whether the missense mutation led to a severe phenotype, and whether pharmacological therapy was an option. RESULTS: An inexpensive bioinformatics approach is presented to get the reader acquainted with the diagnosis of Fabry disease. The reader is introduced to the field of pharmacological chaperones, a therapeutic approach that can be applied only to certain Fabry genotypes. CONCLUSION: The principle underlying the analysis of exome sequencing can be explained in simple terms using web applications and databases which facilitate diagnosis and therapeutic choices.

Abstract: The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

Abstract: Bitter taste is an innately aversive taste modality that is considered to protect animals from consuming toxic compounds. Yet, bitterness is not always noxious and some bitter compounds have beneficial effects on health. Hundreds of bitter compounds were reported (and are accessible via the BitterDB http://bitterdb.agri.huji.ac.il/dbbitter.php ), but numerous additional bitter molecules are still unknown. The dramatic chemical diversity of bitterants makes bitterness prediction a difficult task. Here we present a machine learning classifier, BitterPredict, which predicts whether a compound is bitter or not, based on its chemical structure. BitterDB was used as the positive set, and non-bitter molecules were gathered from literature to create the negative set. Adaptive Boosting (AdaBoost), based on decision trees machine-learning algorithm was applied to molecules that were represented using physicochemical and ADME/Tox descriptors. BitterPredict correctly classifies over 80% of the compounds in the hold-out test set, and 70-90% of the compounds in three independent external sets and in sensory test validation, providing a quick and reliable tool for classifying large sets of compounds into bitter and non-bitter groups. BitterPredict suggests that about 40% of random molecules, and a large portion (66%) of clinical and experimental drugs, and of natural products (77%) are bitter.

Abstract: BACKGROUND: There are a huge variety of data sources relevant to chemical, biological and pharmacological research, but these data sources are highly siloed and cannot be queried together in a straightforward way. Semantic technologies offer the ability to create links and mappings across datasets and manage them as a single, linked network so that searching can be carried out across datasets, independently of the source. We have developed an application called PIBAS FedSPARQL that uses semantic technologies to allow researchers to carry out such searching across a vast array of data sources. RESULTS: PIBAS FedSPARQL is a web-based query builder and result set visualizer of bioinformatics data. As an advanced feature, our system can detect similar data items identified by different Uniform Resource Identifiers (URIs), using a text-mining algorithm based on the processing of named entities to be used in Vector Space Model and Cosine Similarity Measures. According to our knowledge, PIBAS FedSPARQL was unique among the systems that we found in that it allows detecting of similar data items. As a query builder, our system allows researchers to intuitively construct and run Federated SPARQL queries across multiple data sources, including global initiatives, such as Bio2RDF, Chem2Bio2RDF, EMBL-EBI, and one local initiative called CPCTAS, as well as additional user-specified data source. From the input topic, subtopic, template and keyword, a corresponding initial Federated SPARQL query is created and executed. Based on the data obtained, end users have the ability to choose the most appropriate data sources in their area of interest and exploit their Resource Description Framework (RDF) structure, which allows users to select certain properties of data to enhance query results. CONCLUSIONS: The developed system is flexible and allows intuitive creation and execution of queries for an extensive range of bioinformatics topics. Also, the novel "similar data items detection" algorithm can be particularly useful for suggesting new data sources and cost optimization for new experiments. PIBAS FedSPARQL can be expanded with new topics, subtopics and templates on demand, rendering information retrieval more robust.

Abstract: BACKGROUND: While a large number of well-known knowledge bases (KBs) in life science have been published as Linked Open Data, there are few KBs in Chinese. However, KBs in Chinese are necessary when we want to automatically process and analyze electronic medical records (EMRs) in Chinese. Of all, the symptom KB in Chinese is the most seriously in need, since symptoms are the starting point of clinical diagnosis. RESULTS: We publish a public KB of symptoms in Chinese, including symptoms, departments, diseases, medicines, and examinations as well as relations between symptoms and the above related entities. To the best of our knowledge, there is no such KB focusing on symptoms in Chinese, and the KB is an important supplement to existing medical resources. Our KB is constructed by fusing data automatically extracted from eight mainstream healthcare websites, three Chinese encyclopedia sites, and symptoms extracted from a larger number of EMRs as supplements. METHODS: Firstly, we design data schema manually by reference to the Unified Medical Language System (UMLS). Secondly, we extract entities from eight mainstream healthcare websites, which are fed as seeds to train a multi-class classifier and classify entities from encyclopedia sites and train a Conditional Random Field (CRF) model to extract symptoms from EMRs. Thirdly, we fuse data to solve the large-scale duplication between different data sources according to entity type alignment, entity mapping, and attribute mapping. Finally, we link our KB to UMLS to investigate similarities and differences between symptoms in Chinese and English. CONCLUSIONS: As a result, the KB has more than 26,000 distinct symptoms in Chinese including 3968 symptoms in traditional Chinese medicine and 1029 synonym pairs for symptoms. The KB also includes concepts such as diseases and medicines as well as relations between symptoms and the above related entities. We also link our KB to the Unified Medical Language System and analyze the differences between symptoms in the two KBs. We released the KB as Linked Open Data and a demo at https://datahub.io/dataset/symptoms-in-chinese .

Abstract: We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.