Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing.

Sci Rep. 2016;6:32745

Authors: Udrescu L, Sbârcea L, Topîrceanu A, Iovanovici A, Kurunczi L, Bogdan P, Udrescu M

Abstract
Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

PMID: 27599720 [PubMed - in process]

New derivatives of the antimalarial drug Pyrimethamine in the control of melanoma tumor growth: an in vitro and in vivo study.

J Exp Clin Cancer Res. 2016;35(1):137

Authors: Tommasino C, Gambardella L, Buoncervello M, Griffin RJ, Golding BT, Alberton M, Macchia D, Spada M, Cerbelli B, d'Amati G, Malorni W, Gabriele L, Giammarioli AM

Abstract
BACKGROUND: The antimalarial drug Pyrimethamine has been suggested to exert an antitumor activity by inducing apoptotic cell death in cancer cells, including metastatic melanoma cells. However, the dose of Pyrimethamine to be considered as an anticancer agent appears to be significantly higher than the maximum dose used as an antiprotozoal drug.
METHODS: Hence, a series of Pyrimethamine analogs has been synthesized and screened for their apoptosis induction in two cultured metastatic melanoma cell lines. One of these analogs, the Methylbenzoprim, was further analyzed to evaluate cell-cycle and the mechanisms of cell death. The effects of Methylbenzoprim were also analyzed in a severe combined immunodeficiency (SCID)-mouse xenotransplantation model.
RESULTS: Low dose of Methylbenzoprim was capable of inducing cytotoxic activity and a potent growth-inhibitory effect by arresting cell cycle in S-phase in melanoma cells. Methylbenzoprim was also detected as powerful antineoplastic agents in SCID-mouse although used at very low dose and as a single agent.
CONCLUSIONS: Our screening approach led to the identification of a "low cost" newly synthesized drug (methylbenzoprim), which is able to act as an antineoplastic agent in vitro and in vivo, inhibiting melanoma tumor growth at very low concentrations.

PMID: 27599543 [PubMed - in process]

The health care and life sciences community profile for dataset descriptions.

PeerJ. 2016;4:e2331

Authors: Dumontier M, Gray AJ, Marshall MS, Alexiev V, Ansell P, Bader G, Baran J, Bolleman JT, Callahan A, Cruz-Toledo J, Gaudet P, Gombocz EA, Gonzalez-Beltran AN, Groth P, Haendel M, Ito M, Jupp S, Juty N, Katayama T, Kobayashi N, Krishnaswami K, Laibe C, Le Novère N, Lin S, Malone J, Miller M, Mungall CJ, Rietveld L, Wimalaratne SM, Yamaguchi A

Abstract
Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.

PMID: 27602295 [PubMed]

Advantages of Array-Based Technologies for Pre-Emptive Pharmacogenomics Testing.

Microarrays (Basel). 2016;5(2)

Authors: Shahandeh A, Johnstone DM, Atkins JR, Sontag JM, Heidari M, Daneshi N, Freeman-Acquah E, Milward EA

Abstract
As recognised by the National Institutes of Health (NIH) Precision Medicine Initiative (PMI), microarray technology currently provides a rapid, inexpensive means of identifying large numbers of known genomic variants or gene transcripts in experimental and clinical settings. However new generation sequencing techniques are now being introduced in many clinical genetic contexts, particularly where novel mutations are involved. While these methods can be valuable for screening a restricted set of genes for known or novel mutations, implementation of whole genome sequencing in clinical practice continues to present challenges. Even very accurate high-throughput methods with small error rates can generate large numbers of false negative or false positive errors due to the high numbers of simultaneous readings. Additional validation is likely to be required for safe use of any such methods in clinical settings. Custom-designed arrays can offer advantages for screening for common, known mutations and, in this context, may currently be better suited for accredited, quality-controlled clinical genetic screening services, as illustrated by their successful application in several large-scale pre-emptive pharmacogenomics programs now underway. Excessive, inappropriate use of next-generation sequencing may waste scarce research funds and other resources. Microarrays presently remain the technology of choice in applications that require fast, cost-effective genome-wide screening of variants of known importance, particularly for large sample sizes. This commentary considers some of the applications where microarrays continue to offer advantages over next-generation sequencing technologies.

PMID: 27600079 [PubMed]

Hereditary pancreatitis of 3 Chinese children: Case report and literature review.

Medicine (Baltimore). 2016 Sep;95(36):e4604

Authors: Dai LN, Chen YW, Yan WH, Lu LN, Tao YJ, Cai W

Abstract
BACKGROUND: Hereditary pancreatitis (HP) is quite rare and is distinguished by incomplete penetrance presentation as early-onset relapsing pancreatitis, usually beginning in childhood. HP is now known to be commonly relevant to mutations in the PRSS1 (gene-encoding cationic trypsinogen), SPINK1 (serine protease inhibitor, Kazal type 1), CFTR (cystic fibrosis), carboxypeptidase A1 (CPA1), and chymotrypsin C (CTRC) genes as reported in some Caucasian studies. HP has a variable spectrum of severity and may develop complications.
METHODS & RESULTS: We describe the clinical course of 3 preschool children, hospitalized with postprandial abdominal pain, whose laboratory tests showed high serum amylase. Similar episodes of abdominal pain led to readmission, and the patients recovered quickly after using symptomatic therapy. The condition of the first boy, who developed a pancreatic tail pseudocyst and splenic infarction, was especially complicated. The boy underwent 2 endoscopic retrograde cholangiopancreatographies and stenting, along with a surgical procedure that completely relieved his symptoms for 3 months. The 3 patients and their parents were given genetic testing. All of the patients carried 1 or more gene mutations inherited from their mothers, fathers, or both parents; however, none of the parents were affected.
CONCLUSION: For children with repeated pancreatitis, clinicians should consider HP in the differential diagnosis. It is reliable to perform gene sequencing on suspicious patients and their parents. Multidisciplinary and comprehensive treatment should be recommended to manage HP and its complications. Cholangiopancreatography and stenting is a relatively minimally invasive approach when compared with surgery and can be tried as an early intervention. Surgical procedures should be reserved for patients with complications.

PMID: 27603351 [PubMed - as supplied by publisher]

Particle-Tracking Microrheology Using Micro-Optical Coherence Tomography.

Biophys J. 2016 Sep 6;111(5):1053-1063

Authors: Chu KK, Mojahed D, Fernandez CM, Li Y, Liu L, Wilsterman EJ, Diephuis B, Birket SE, Bowers H, Martin Solomon G, Schuster BS, Hanes J, Rowe SM, Tearney GJ

Abstract
Clinical manifestations of cystic fibrosis (CF) result from an increase in the viscosity of the mucus secreted by epithelial cells that line the airways. Particle-tracking microrheology (PTM) is a widely accepted means of determining the viscoelastic properties of CF mucus, providing an improved understanding of this disease as well as an avenue to assess the efficacies of pharmacologic therapies aimed at decreasing mucus viscosity. Among its advantages, PTM allows the measurement of small volumes, which was recently utilized for an in situ study of CF mucus formed by airway cell cultures. Typically, particle tracks are obtained from fluorescence microscopy video images, although this limits one's ability to distinguish particles by depth in a heterogeneous environment. Here, by performing PTM with high-resolution micro-optical coherence tomography (μOCT), we were able to characterize the viscoelastic properties of mucus, which enables simultaneous measurement of rheology with mucociliary transport parameters that we previously determined using μOCT. We obtained an accurate characterization of dextran solutions and observed a statistically significant difference in the viscosities of mucus secreted by normal and CF human airway cell cultures. We further characterized the effects of noise and imaging parameters on the sensitivity of μOCT-PTM by performing theoretical and numerical analyses, which show that our system can accurately quantify viscosities over the range that is characteristic of CF mucus. As a sensitive rheometry technique that requires very small fluid quantities, μOCT-PTM could also be generally applied to interrogate the viscosity of biological media such as blood or the vitreous humor of the eye in situ.

PMID: 27602733 [PubMed - as supplied by publisher]

Metabolomics of pulmonary exacerbations reveals the personalized nature of cystic fibrosis disease.

PeerJ. 2016;4:e2174

Authors: Quinn RA, Lim YW, Mak TD, Whiteson K, Furlan M, Conrad D, Rohwer F, Dorrestein P

Abstract
Background. Cystic fibrosis (CF) is a genetic disease that results in chronic infections of the lungs. CF patients experience intermittent pulmonary exacerbations (CFPE) that are associated with poor clinical outcomes. CFPE involves an increase in disease symptoms requiring more aggressive therapy. Methods. Longitudinal sputum samples were collected from 11 patients (n = 44 samples) to assess the effect of exacerbations on the sputum metabolome using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The data was analyzed with MS/MS molecular networking and multivariate statistics. Results. The individual patient source had a larger influence on the metabolome of sputum than the clinical state (exacerbation, treatment, post-treatment, or stable). Of the 4,369 metabolites detected, 12% were unique to CFPE samples; however, the only known metabolites significantly elevated at exacerbation across the dataset were platelet activating factor (PAF) and a related monacylglycerophosphocholine lipid. Due to the personalized nature of the sputum metabolome, a single patient was followed for 4.2 years (capturing four separate exacerbation events) as a case study for the detection of personalized biomarkers with metabolomics. PAF and related lipids were significantly elevated during CFPEs of this patient and ceramide was elevated during CFPE treatment. Correlating the abundance of bacterial 16S rRNA gene amplicons to metabolomics data from the same samples during a CFPE demonstrated that antibiotics were positively correlated to Stenotrophomonas and Pseudomonas, while ceramides and other lipids were correlated with Streptococcus, Rothia, and anaerobes. Conclusions. This study identified PAF and other inflammatory lipids as potential biomarkers of CFPE, but overall, the metabolome of CF sputum was patient specific, supporting a personalized approach to molecular detection of CFPE onset.

PMID: 27602256 [PubMed]

Are allergic fungal rhinosinusitis and allergic bronchopulmonary aspergillosis lifelong conditions?

Med Mycol. 2016 Sep 6;

Authors: Agarwal R, Bansal S, Chakrabarti A

Abstract
Aspergillus fumigatus can cause several allergic disorders including Aspergillus-sensitized asthma, allergic bronchopulmonary aspergillosis (ABPA), and allergic fungal rhinosinusitis (AFRS). ABPA is an immunological pulmonary disorder caused by allergic reactions mounted against antigens of A. fumigatus colonizing the airways of patients with asthma (and cystic fibrosis). Allergic bronchopulmonary mycosis is an allergic fungal airway disease caused by thermotolerant fungi other than A. fumigatus On the other hand, AFRS is a type of chronic rhinosinusitis that is also a result of hypersensitivity reactions to the presence of fungi that become resident in the sinuses. The pathogenesis of ABPA and AFRS share several common features, and in fact, AFRS can be considered as the upper airway counterpart of ABPA. Despite sharing similar immunopathogenetic features, the simultaneous occurrence of the two disorders is uncommon. Due to the lacuna in understanding of the causative mechanisms, and deficiencies in the diagnosis and treatment, these disorders unfortunately are lifelong illnesses. This review provides an overview of the pathogenesis, diagnosis, and long-term outcomes of both these disorders.

PMID: 27601608 [PubMed - as supplied by publisher]

Enteral tube feeding for individuals with cystic fibrosis: Cystic Fibrosis Foundation evidence-informed guidelines.

J Cyst Fibros. 2016 Sep 3;

Authors: Schwarzenberg SJ, Hempstead SE, McDonald CM, Powers SW, Wooldridge J, Blair S, Freedman S, Harrington E, Murphy PJ, Palmer L, Schrader AE, Shiel K, Sullivan J, Wallentine M, Marshall BC, Leonard AR

Abstract
Nutrition is integral to the care of individuals with cystic fibrosis (CF). Better nutritional status is associated with improved pulmonary function. In some individuals with CF, enteral tube feeding can be useful in achieving optimal nutritional status. Current nutrition guidelines do not include detailed recommendations for enteral tube feeding. The Cystic Fibrosis Foundation convened an expert panel to develop enteral tube feeding recommendations based on a systematic review of the evidence and expert opinion. These guidelines address when to consider enteral tube feeding, assessment of confounding causes of poor nutrition in CF, preparation of the patient for placement of the enteral feeding tube, management of the tube after placement and education about enteral feeding. These recommendations are intended to guide the CF care team, individuals with CF, and their families through the enteral tube feeding process.

PMID: 27599607 [PubMed - as supplied by publisher]

Identification of candidate anti-cancer molecular mechanisms of compound kushen injection using functional genomics.

Oncotarget. 2016 Sep 1;

Authors: Qu Z, Cui J, Harata-Lee Y, Aung TN, Feng Q, Raison JM, Kortschak RD, Adelson DL

Abstract
Compound Kushen Injection (CKI) has been clinically used in China for over 15 years to treat various types of solid tumours. However, because such Traditional Chinese Medicine (TCM) preparations are complex mixtures of plant secondary metabolites, it is essential to explore their underlying molecular mechanisms in a systematic fashion. We have used the MCF-7 human breast cancer cell line as an initial in vitro model to identify CKI induced changes in gene expression. Cells were treated with CKI for 24 and 48 hours at two concentrations (1 and 2 mg/mL total alkaloids), and the effect of CKI on cell proliferation and apoptosis were measured using XTT and Annexin V/Propidium Iodide staining assays respectively. Transcriptome data of cells treated with CKI or 5-Fluorouracil (5-FU) for 24 and 48 hours were subsequently acquired using high-throughput Illumina RNA-seq technology. In this report we show that CKI inhibited MCF-7 cell proliferation and induced apoptosis in a dose-dependent fashion. We integrated and applied a series of transcriptome analysis methods, including gene differential expression analysis, pathway over-representation analysis, de novo identification of long non-coding RNAs (lncRNA) as well as co-expression network reconstruction, to identify candidate anti-cancer molecular mechanisms of CKI. Multiple pathways were perturbed and the cell cycle was identified as the potential primary target pathway of CKI in MCF-7 cells. CKI may also induce apoptosis in MCF-7 cells via a p53 independent mechanism. In addition, we identified novel lncRNAs and showed that many of them might be expressed as a response to CKI treatment.

PMID: 27602759 [PubMed - as supplied by publisher]

Time-scale dynamics of proteome and transcriptome of the white-rot fungus Phlebia radiata: growth on spruce wood and decay effect on lignocellulose.

Biotechnol Biofuels. 2016;9(1):192

Authors: Kuuskeri J, Häkkinen M, Laine P, Smolander OP, Tamene F, Miettinen S, Nousiainen P, Kemell M, Auvinen P, Lundell T

Abstract
BACKGROUND: The white-rot Agaricomycetes species Phlebia radiata is an efficient wood-decaying fungus degrading all wood components, including cellulose, hemicellulose, and lignin. We cultivated P. radiata in solid state cultures on spruce wood, and extended the experiment to 6 weeks to gain more knowledge on the time-scale dynamics of protein expression upon growth and wood decay. Total proteome and transcriptome of P. radiata were analyzed by peptide LC-MS/MS and RNA sequencing at specific time points to study the enzymatic machinery on the fungus' natural growth substrate.
RESULTS: According to proteomics analyses, several CAZy oxidoreductase class-II peroxidases with glyoxal and alcohol oxidases were the most abundant proteins produced on wood together with enzymes important for cellulose utilization, such as GH7 and GH6 cellobiohydrolases. Transcriptome additionally displayed expression of multiple AA9 lytic polysaccharide monooxygenases indicative of oxidative cleavage of wood carbohydrate polymers. Large differences were observed for individual protein quantities at specific time points, with a tendency of enhanced production of specific peroxidases on the first 2 weeks of growth on wood. Among the 10 class-II peroxidases, new MnP1-long, characterized MnP2-long and LiP3 were produced in high protein abundances, while LiP2 and LiP1 were upregulated at highest level as transcripts on wood together with the oxidases and one acetyl xylan esterase, implying their necessity as primary enzymes to function against coniferous wood lignin to gain carbohydrate accessibility and fungal growth. Majority of the CAZy encoding transcripts upregulated on spruce wood represented activities against plant cell wall and were identified in the proteome, comprising main activities of white-rot decay.
CONCLUSIONS: Our data indicate significant changes in carbohydrate-active enzyme expression during the six-week surveillance of P. radiata growing on wood. Response to wood substrate is seen already during the first weeks. The immediate oxidative enzyme action on lignin and wood cell walls is supported by detected lignin substructure sidechain cleavages, release of phenolic units, and visual changes in xylem cell wall ultrastructure. This study contributes to increasing knowledge on fungal genetics and lignocellulose bioconversion pathways, allowing us to head for systems biology, development of biofuel production, and industrial applications on plant biomass utilizing wood-decay fungi.

PMID: 27602055 [PubMed]

In silico bone mechanobiology: modeling a multifaceted biological system.

Wiley Interdiscip Rev Syst Biol Med. 2016 Sep 7;

Authors: Giorgi M, Verbruggen SW, Lacroix D

Abstract
Mechanobiology, the study of the influence of mechanical loads on biological processes through signaling to cells, is fundamental to the inherent ability of bone tissue to adapt its structure in response to mechanical stimulation. The immense contribution of computational modeling to the nascent field of bone mechanobiology is indisputable, having aided in the interpretation of experimental findings and identified new avenues of inquiry. Indeed, advances in computational modeling have spurred the development of this field, shedding new light on problems ranging from the mechanical response to loading by individual cells to tissue differentiation during events such as fracture healing. To date, in silico bone mechanobiology has generally taken a reductive approach in attempting to answer discrete biological research questions, with research in the field broadly separated into two streams: (1) mechanoregulation algorithms for predicting mechanobiological changes to bone tissue and (2) models investigating cell mechanobiology. Future models will likely take advantage of advances in computational power and techniques, allowing multiscale and multiphysics modeling to tie the many separate but related biological responses to loading together as part of a larger systems biology approach to shed further light on bone mechanobiology. Finally, although the ever-increasing complexity of computational mechanobiology models will inevitably move the field toward patient-specific models in the clinic, the determination of the context in which they can be used safely for clinical purpose will still require an extensive combination of computational and experimental techniques applied to in vitro and in vivo applications. For further resources related to this article, please visit the WIREs website.

PMID: 27600060 [PubMed - as supplied by publisher]

Network dynamics: quantitative analysis of complex behavior in metabolism, organelles, and cells, from experiments to models and back.

Wiley Interdiscip Rev Syst Biol Med. 2016 Sep 7;

Authors: Kurz FT, Kembro JM, Flesia AG, Armoundas AA, Cortassa S, Aon MA, Lloyd D

Abstract
Advancing from two core traits of biological systems: multilevel network organization and nonlinearity, we review a host of novel and readily available techniques to explore and analyze their complex dynamic behavior within the framework of experimental-computational synergy. In the context of concrete biological examples, analytical methods such as wavelet, power spectra, and metabolomics-fluxomics analyses, are presented, discussed, and their strengths and limitations highlighted. Further shown is how time series from stationary and nonstationary biological variables and signals, such as membrane potential, high-throughput metabolomics, O2 and CO2 levels, bird locomotion, at the molecular, (sub)cellular, tissue, and whole organ and animal levels, can reveal important information on the properties of the underlying biological networks. Systems biology-inspired computational methods start to pave the way for addressing the integrated functional dynamics of metabolic, organelle and organ networks. As our capacity to unravel the control and regulatory properties of these networks and their dynamics under normal or pathological conditions broadens, so is our ability to address endogenous rhythms and clocks to improve health-span in human aging, and to manage complex metabolic disorders, neurodegeneration, and cancer. For further resources related to this article, please visit the WIREs website.

PMID: 27599643 [PubMed - as supplied by publisher]

Automatic classification of communication logs into implementation stages via text analysis.

Implement Sci. 2016;11(1):119

Authors: Wang D, Ogihara M, Gallo C, Villamar JA, Smith JD, Vermeer W, Cruden G, Benbow N, Brown CH

Abstract
BACKGROUND: To improve the quality, quantity, and speed of implementation, careful monitoring of the implementation process is required. However, some health organizations have such limited capacity to collect, organize, and synthesize information relevant to its decision to implement an evidence-based program, the preparation steps necessary for successful program adoption, the fidelity of program delivery, and the sustainment of this program over time. When a large health system implements an evidence-based program across multiple sites, a trained intermediary or broker may provide such monitoring and feedback, but this task is labor intensive and not easily scaled up for large numbers of sites. We present a novel approach to producing an automated system of monitoring implementation stage entrances and exits based on a computational analysis of communication log notes generated by implementation brokers. Potentially discriminating keywords are identified using the definitions of the stages and experts' coding of a portion of the log notes. A machine learning algorithm produces a decision rule to classify remaining, unclassified log notes.
RESULTS: We applied this procedure to log notes in the implementation trial of multidimensional treatment foster care in the California 40-county implementation trial (CAL-40) project, using the stages of implementation completion (SIC) measure. We found that a semi-supervised non-negative matrix factorization method accurately identified most stage transitions. Another computational model was built for determining the start and the end of each stage.
CONCLUSIONS: This automated system demonstrated feasibility in this proof of concept challenge. We provide suggestions on how such a system can be used to improve the speed, quality, quantity, and sustainment of implementation. The innovative methods presented here are not intended to replace the expertise and judgement of an expert rater already in place. Rather, these can be used when human monitoring and feedback is too expensive to use or maintain. These methods rely on digitized text that already exists or can be collected with minimal to no intrusiveness and can signal when additional attention or remediation is required during implementation. Thus, resources can be allocated according to need rather than universally applied, or worse, not applied at all due to their cost.

PMID: 27600612 [PubMed - in process]

11 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

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"Cystic Fibrosis"

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13 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

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PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

IL-33 Signaling Protects from Murine Oxazolone Colitis by Supporting Intestinal Epithelial Function.

Inflamm Bowel Dis. 2015 Dec;21(12):2737-46

Authors: Waddell A, Vallance JE, Moore PD, Hummel AT, Wu D, Shanmukhappa SK, Fei L, Washington MK, Minar P, Coburn LA, Nakae S, Wilson KT, Denson LA, Hogan SP, Rosen MJ

Abstract
BACKGROUND: IL-33, a member of the IL-1 cytokine family that signals through ST2, is upregulated in ulcerative colitis (UC); however, the role of IL-33 in colitis remains unclear. IL-33 augments type 2 immune responses, which have been implicated in UC pathogenesis. We sought to determine the role of IL-33 signaling in oxazolone (OXA) colitis, a type 2 cytokine-mediated murine model of UC.
METHODS: Colon mucosal IL-33 expression was compared between pediatric and adult UC and non-IBD patients using immunohistochemistry and real-time PCR. OXA colitis was induced in WT, IL-33, and ST2 mice, and histopathology, cytokine levels, and goblet cells were assessed. Transepithelial resistance was measured across IL-33-treated T84 cell monolayers.
RESULTS: Colon mucosal IL-33 was increased in pediatric patients with active UC and in OXA colitis. IL-33 and ST2 OXA mice exhibited increased disease severity compared with WT OXA mice. OXA induced a mixed mucosal cytokine response, but few differences were observed between OXA WT and IL-33 or ST2 mice. Goblet cells were significantly decreased in IL-33 and ST2 OXA compared with WT OXA mice. IL-33 augmented transepithelial resistance in T84 cells, and this effect was blocked by the ERK1/2 inhibitor PD98,059.
CONCLUSIONS: OXA colitis is exacerbated in IL-33 and ST2 mice. Increased mucosal IL-33 in human UC and murine colitis may be a homeostatic response to limit inflammation, potentially through effects on epithelial barrier function. Further investigation of IL-33 protective mechanisms would inform the development of novel therapeutic approaches.

PMID: 26313694 [PubMed - indexed for MEDLINE]

Exploiting Semantic Web Technologies to Develop OWL-Based Clinical Practice Guideline Execution Engines.

IEEE J Biomed Health Inform. 2016 Jan;20(1):388-98

Authors: Jafarpour B, Abidi SR, Abidi SS

Abstract
Computerizing paper-based CPG and then executing them can provide evidence-informed decision support to physicians at the point of care. Semantic web technologies especially web ontology language (OWL) ontologies have been profusely used to represent computerized CPG. Using semantic web reasoning capabilities to execute OWL-based computerized CPG unties them from a specific custom-built CPG execution engine and increases their shareability as any OWL reasoner and triple store can be utilized for CPG execution. However, existing semantic web reasoning-based CPG execution engines suffer from lack of ability to execute CPG with high levels of expressivity, high cognitive load of computerization of paper-based CPG and updating their computerized versions. In order to address these limitations, we have developed three CPG execution engines based on OWL 1 DL, OWL 2 DL and OWL 2 DL + semantic web rule language (SWRL). OWL 1 DL serves as the base execution engine capable of executing a wide range of CPG constructs, however for executing highly complex CPG the OWL 2 DL and OWL 2 DL + SWRL offer additional executional capabilities. We evaluated the technical performance and medical correctness of our execution engines using a range of CPG. Technical evaluations show the efficiency of our CPG execution engines in terms of CPU time and validity of the generated recommendation in comparison to existing CPG execution engines. Medical evaluations by domain experts show the validity of the CPG-mediated therapy plans in terms of relevance, safety, and ordering for a wide range of patient scenarios.

PMID: 25532198 [PubMed - indexed for MEDLINE]

Identifying Multi-Dimensional Co-Clusters in Tensors Based on Hyperplane Detection in Singular Vector Spaces.

PLoS One. 2016;11(9):e0162293

Authors: Zhao H, Wang DD, Chen L, Liu X, Yan H

Abstract
Co-clustering, often called biclustering for two-dimensional data, has found many applications, such as gene expression data analysis and text mining. Nowadays, a variety of multi-dimensional arrays (tensors) frequently occur in data analysis tasks, and co-clustering techniques play a key role in dealing with such datasets. Co-clusters represent coherent patterns and exhibit important properties along all the modes. Development of robust co-clustering techniques is important for the detection and analysis of these patterns. In this paper, a co-clustering method based on hyperplane detection in singular vector spaces (HDSVS) is proposed. Specifically in this method, higher-order singular value decomposition (HOSVD) transforms a tensor into a core part and a singular vector matrix along each mode, whose row vectors can be clustered by a linear grouping algorithm (LGA). Meanwhile, hyperplanar patterns are extracted and successfully supported the identification of multi-dimensional co-clusters. To validate HDSVS, a number of synthetic and biological tensors were adopted. The synthetic tensors attested a favorable performance of this algorithm on noisy or overlapped data. Experiments with gene expression data and lineage data of embryonic cells further verified the reliability of HDSVS to practical problems. Moreover, the detected co-clusters are well consistent with important genetic pathways and gene ontology annotations. Finally, a series of comparisons between HDSVS and state-of-the-art methods on synthetic tensors and a yeast gene expression tensor were implemented, verifying the robust and stable performance of our method.

PMID: 27598575 [PubMed - as supplied by publisher]