PWN: enhanced random walk on a warped network for disease target prioritization.

BACKGROUND: Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks. RESULTS: We developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge. CONCLUSIONS: We showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN.

Natural-killer cell cytotoxicity as a diagnostic and prognostic marker for adult patients with secondary hemophagocytic lymphohistiocytosis: a prospective phase II observational study.

BACKGROUND: Hemophagocytic lymphohistiocytosis (HLH) can be life-threatening if not detected and treated appropriately. The diagnosis of HLH can be confusing due to other similar febrile diseases that present with cytopenia. Natural-killer cell (NK)-cytotoxicity is an important diagnostic parameter for primary HLH; however, its role in secondary HLH in adults has not been well-elucidated. METHODS: We prospectively enrolled 123 adult patients with febrile conditions accompanied by cytopenia or marrow hemophagocytosis. A diagnosis of HLH was based on HLH-2004 criteria and treated based on HLH-94 protocol. NK-cytotoxicity was calculated at the time of diagnosis by K562-cell direct lysis using flow-cytometry. RESULTS: HLH (n = 60) was determined to be caused by Epstein-Barr virus (EBV) (n = 11), infection other than EBV (n = 16), malignancies (n = 19), and unknown (n = 14). Febrile diseases other than HLH (n = 63) were diagnosed as autoimmune disease (n = 22), malignancies (n = 21), infection (n = 12), non-malignant hematological diseases (n = 6), and unknown (n = 2). A lower NK-cytotoxicity level was observed at diagnosis in patients with HLH, compared with other causes of febrile disease (12.1% versus 26.2%, p < 0.001). However, NK-cytotoxicity had a borderline effect on diagnosis of HLH, with an area under receiver operation characteristic curve of 0.689. It also showed no significant role for the prediction of survival outcome. Multivariate analysis revealed that malignant disease and high ferritin level were related with poor survival outcome. In non-malignant disease subgroups, old age, EBV-association, and low NK-cytotoxicity were related with poor survival. CONCLUSIONS: Febrile disease with cytopenia was associated with decreased NK-cytotoxicity, especially in adults with HLH; however, its diagnostic role for adult HLH is still arguable. The diagnostic criteria for adult HLH should be further discussed. TRIAL REGISTRATION: Clinical Research Information Service [Internet]; Osong (Chungcheongbuk-do), Korea, Centers for Disease Control and Prevention, Ministry of Health and Welfare (Republic of Korea); https://cris.nih.go.kr/cris/index.jsp; Feb, 16th 2016; KCT0001886 (KC15TISE0936).

Diagnostic performance of artificial intelligence model for pneumonia from chest radiography.

OBJECTIVE: The chest X-ray (CXR) is the most readily available and common imaging modality for the assessment of pneumonia. However, detecting pneumonia from chest radiography is a challenging task, even for experienced radiologists. An artificial intelligence (AI) model might help to diagnose pneumonia from CXR more quickly and accurately. We aim to develop an AI model for pneumonia from CXR images and to evaluate diagnostic performance with external dataset. METHODS: To train the pneumonia model, a total of 157,016 CXR images from the National Institutes of Health (NIH) and the Korean National Tuberculosis Association (KNTA) were used (normal vs. pneumonia = 120,722 vs.36,294). An ensemble model of two neural networks with DenseNet classifies each CXR image into pneumonia or not. To test the accuracy of the models, a separate external dataset of pneumonia CXR images (n = 212) from a tertiary university hospital (Gachon University Gil Medical Center GUGMC, Incheon, South Korea) was used; the diagnosis of pneumonia was based on both the chest CT findings and clinical information, and the performance evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, we tested the change of the AI probability score for pneumonia using the follow-up CXR images (7 days after the diagnosis of pneumonia, n = 100). RESULTS: When the probability scores of the models that have a threshold of 0.5 for pneumonia, two models (models 1 and 4) having different pre-processing parameters on the histogram equalization distribution showed best AUC performances of 0.973 and 0.960, respectively. As expected, the ensemble model of these two models performed better than each of the classification models with 0.983 AUC. Furthermore, the AI probability score change for pneumonia showed a significant difference between improved cases and aggravated cases (Δ = -0.06 ± 0.14 vs. 0.06 ± 0.09, for 85 improved cases and 15 aggravated cases, respectively, P = 0.001) for CXR taken as a 7-day follow-up. CONCLUSIONS: The ensemble model combined two different classification models for pneumonia that performed at 0.983 AUC for an external test dataset from a completely different data source. Furthermore, AI probability scores showed significant changes between cases of different clinical prognosis, which suggest the possibility of increased efficiency and performance of the CXR reading at the diagnosis and follow-up evaluation for pneumonia.

The mechanistic insight of a specific interaction between 15d-Prostaglandin-J2 and eIF4A suggests an evolutionary conserved role across species.

15-deoxy-delta 12,14-prostaglandin J2 (15d-PGJ2) is an anti-inflammatory/anti-neoplastic prostaglandin that functions through covalent binding to cysteine residues of various target proteins. We previously showed that 15d-PGJ2 mediated anti-inflammatory responses are dependent on the translational inhibition through its interaction with eIF4A (Kim et al., 2007). Binding of 15d-PGJ2 to eIF4A specifically blocks the interaction between eIF4G and eIF4A, which leads to the formation of stress granules (SGs), which then cluster mRNAs with inhibited translation. Here, we show that the binding between 15d-PGJ2 and eIF4A specifically blocks the interaction between the MIF4G domain of eIF4G and eIF4A. To reveal the mechanism of this interaction, we used computational simulation-based docking studies and identified that the carboxyl tail of 15d-PGJ2 could stabilize the binding of 15d-PGJ2 to eIF4A through arginine 295 of eIF4A, which is the first suggestion that the 15d-PGJ2 tail plays a physiological role. Interestingly, the putative 15d-PGJ2 binding site on eiF4A is conserved across many species, suggesting a biological role. Our data propose that studying 15d-PGJ2 and its targets may uncover new therapeutic approaches in anti-inflammatory drug discovery.

Development of a Rapid Automated Fluorescent Lateral Flow Immunoassay to Detect Hepatitis B Surface Antigen (HBsAg), Antibody to HBsAg, and Antibody to Hepatitis C.

BACKGROUND: Accurate, rapid, and cost-effective screening tests for hepatitis B virus (HBV) and hepatitis C virus (HCV) infection may be useful in laboratories that cannot afford automated chemiluminescent immunoassays (CLIAs). We evaluated the diagnostic performance of a novel rapid automated fluorescent lateral flow immunoassay (LFIA). METHODS: A fluorescent LFIA using a small bench-top fluorescence reader, Automated Fluorescent Immunoassay System (AFIAS; Boditech Med Inc., Chuncheon, Korea), was developed for qualitative detection of hepatitis B surface antigen (HBsAg), antibody to HBsAg (anti-HBs), and antibody to HCV (anti-HCV) within 20 minutes. We compared the diagnostic performance of AFIAS with that of automated CLIAs-Elecsys (Roche Diagnostics GmbH, Penzberg, Germany) and ARCHITECT (Abbott Laboratories, Abbott Park, IL, USA)-using 20 seroconversion panels and 3,500 clinical serum samples. RESULTS: Evaluation with the seroconversion panels demonstrated that AFIAS had adequate sensitivity for HBsAg and anti-HCV detection. From the clinical samples, AFIAS sensitivity and specificity were 99.8% and 99.3% for the HBsAg test, 100.0% and 100.0% for the anti-HBs test, and 98.8% and 99.1% for the anti-HCV test, respectively. Its agreement rates with the Elecsys HBsAg, anti-HBs, and anti-HCV detection assays were 99.4%, 100.0%, and 99.0%, respectively. AFIAS detected all samples with HBsAg genotypes A-F and H and anti-HCV genotypes 1, 1a, 1b, 2a, 2b, 4, and 6. Cross-reactivity with other infections was not observed. CONCLUSIONS: The AFIAS HBsAg, anti-HBs, and anti-HCV tests demonstrated diagnostic performance equivalent to current automated CLIAs. AFIAS could be used for a large-scale HBV or HCV screening in low-resource laboratories or low-to middle-income areas.

Systematic identification of an integrative network module during senescence from time-series gene expression.

BACKGROUND: Cellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have indicated that senescence is a multi-step evolving process related to important complex biological processes. Most studies analyzed only the genes and their functions representing each senescence phase without considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process. RESULTS: We suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result, it was confirmed that the difference of average perturbation scores of common networks at both two time points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator. CONCLUSIONS: Heretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches.

Pyrilamine inhibits nicotine-induced catecholamine secretion.

Function of nicotine, which induces activation of all parts of the body including our brain, has been receiving much attention for a long period of time and also been actively studied by researchers for its pharmacological actions in the central nervous system. The modulation of nicotine concentration and the inhibition of nicotine binding on target receptors in the brain are the key factors for smoking addiction therapy. In previous studies showed that influx of nicotine at the blood-brain barrier was through the pyrilamine-sensitive organic cation transporters. But the direct interacting mechanism of pyrilamine on the nicotine binding target receptors has not yet been clarified. The aim of the present study is to investigate the direct binding mechanisms of a pyrilamine on the nicotinic acetylcholine receptors (nAChRs). We found that pyrilamine shares the same ligand binding pocket of nicotine (NCT) on nAChRs but interacts with more amino acid residues than NCT does. The extended part of pyrilamine interacts with additional residues in the ligand binding pocket of nAChRs which are located nearby the entrance of the binding pocket. The catecholamine (CA) secretion induced by nAChR agonist (NCT') was significantly inhibited by the pyrilamine pretreatment. Real time carbon-fiber amperometry confirmed the inhibition of the NCT'-induced exocytosis by pyrilamine in a single cell level. We also found that pyrilamine inhibited the NCT'-induced [Ca(2+)]i. In contrast, pyrilamine did not affect the increase in calcium induced by high K(+). Overall, these data suggest that pyrilamine directly docks into the ligand binding site of nAChRs and specifically inhibits the nAChR-mediated effects thereby causing inhibition of CA secretion. Therefore, pyrilamine may play an important role to explore new treatments to aid smoking cessation.

Proteomic analysis of microvesicles derived from human mesenchymal stem cells.

Mesenchymal stem cells (MSCs) have emerged as a promising means for treating degenerative or incurable diseases. Recent studies have shown that microvesicles (MVs) from MSCs (MSC-MVs) contribute to recovery of damaged tissues in animal disease models. Here, we profiled the MSC-MV proteome to investigate their therapeutic effects. LC-MS/MS analysis of MSC-MVs identified 730 MV proteins. The MSC-MV proteome included five positive and two variable known markers of MSCs, but no negative marker, as well as 43 surface receptors and signaling molecules controlling self-renewal and differentiation of MSCs. Functional enrichment analysis showed that cellular processes represented by the MSC-MV proteins include cell proliferation, adhesion, migration, and morphogenesis. Integration of MSC's self-renewal and differentiation-related genes and the proteome of MSC-conditioned media (MSC-CM) with the MSC-MV proteome revealed potential MV protein candidates that can be associated with the therapeutic effects of MSC-MVs: (1) surface receptors (PDGFRB, EGFR, and PLAUR); (2) signaling molecules (RRAS/NRAS, MAPK1, GNA13/GNG12, CDC42, and VAV2); (3) cell adhesion (FN1, EZR, IQGAP1, CD47, integrins, and LGALS1/LGALS3); and (4) MSC-associated antigens (CD9, CD63, CD81, CD109, CD151, CD248, and CD276). Therefore, the MSC-MV proteome provides a comprehensive basis for understanding the potential of MSC-MVs to affect tissue repair and regeneration.

TOFSIMS-P: a web-based platform for analysis of large-scale TOF-SIMS data.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) has been a useful tool to profile secondary ions from the near surface region of specimens with its high molecular specificity and submicrometer spatial resolution. However, the TOF-SIMS analysis of even a moderately large size of samples has been hampered due to the lack of tools for automatically analyzing the huge amount of TOF-SIMS data. Here, we present a computational platform to automatically identify and align peaks, find discriminatory ions, build a classifier, and construct networks describing differential metabolic pathways. To demonstrate the utility of the platform, we analyzed 43 data sets generated from seven gastric cancer and eight normal tissues using TOF-SIMS. A total of 87 138 ions were detected from the 43 data sets by TOF-SIMS. We selected and then aligned 1286 ions. Among them, we found the 66 ions discriminating gastric cancer tissues from normal ones. Using these 66 ions, we then built a partial least square-discriminant analysis (PLS-DA) model resulting in a misclassification error rate of 0.024. Finally, network analysis of the 66 ions showed disregulation of amino acid metabolism in the gastric cancer tissues. The results show that the proposed framework was effective in analyzing TOF-SIMS data from a moderately large size of samples, resulting in discrimination of gastric cancer tissues from normal tissues and identification of biomarker candidates associated with the amino acid metabolism.

Altered branching patterns of Purkinje cells in mouse model for cortical development disorder.

Disrupted cortical cytoarchitecture in cerebellum is a typical pathology in reeler. Particularly interesting are structural problems at the cellular level: dendritic morphology has important functional implication in signal processing. Here we describe a combinatorial imaging method of synchrotron X-ray microtomography with Golgi staining, which can deliver 3-dimensional(3-D) micro-architectures of Purkinje cell(PC) dendrites, and give access to quantitative information in 3-D geometry. In reeler, we visualized in 3-D geometry the shape alterations of planar PC dendrites (i.e., abnormal 3-D arborization). Despite these alterations, the 3-D quantitative analysis of the branching patterns showed no significant changes of the 77 ± 8° branch angle, whereas the branch segment length strongly increased with large fluctuations, comparing to control. The 3-D fractal dimension of the PCs decreased from 1.723 to 1.254, indicating a significant reduction of dendritic complexity. This study provides insights into etiologies and further potential treatment options for lissencephaly and various neurodevelopmental disorders.

Transcriptional regulatory networks associated with self-renewal and differentiation of neural stem cells.

Neural stem cells (NSCs) are self-renewing, multipotent cells that can generate neurons, astrocytes, and oligodendrocytes of the nervous system. NSCs have been extensively studied because they can be used to treat impaired cells and tissues or improve regenerative power of degenerating cells in neurodegenerative diseases or spinal cord injuries. For successful clinical applications of NSCs, it is essential to understand the mechanisms underlying self-renewal and differentiation of NSCs, which involve complex interplays among key factors including transcription factors, epigenetic control, microRNAs, and signaling pathways. Despite numerous studies on such factors, a holistic view of their interplays during neural development still remains elusive. In this review, we present recently identified potential regulatory factors and their targets by genomics and proteomics technologies and then integrate them into regulatory networks that describe their complex interplays to achieve self-renewal and differentiation of NSCs.