Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Posttranscriptional modification of retroviral primers is required for late stages of DNA replication

During reverse transcription of retroviral RNA, synthesis of (−) strand DNA is primed by a cellular tRNA that anneals to an 18-nt primer binding site within the 5′ long terminal repeat. For (+) strand synthesis using a (−) strand DNA template linked to the tRNA primer, only the first 18 nt of tRNA are replicated to regenerate the primer binding site, creating the (+) strand strong stop DNA intermediate and providing a 3′ terminus capable of strand transfer and further elongation. On model HIV templates that approximate the (−) strand linked to natural modified or synthetic unmodified tRNA3Lys, we find that a (+) strand strong stop intermediate of the proper length is generated only on templates containing the natural, modified tRNA3Lys, suggesting that a posttranscriptional modification provides the termination signal. In the presence of a recipient template, synthesis after strand transfer occurs only from intermediates generated from templates containing modified tRNA3Lys. Reverse transcriptase from Moloney murine leukemia virus and avian myoblastosis virus shows the same requirement for a modified tRNA3Lys template. Because all retroviral tRNA primers contain the same 1-methyl-A58 modification, our results suggest that 1-methyl-A58 is generally required for termination of replication 18 nt into the tRNA sequence, generating the (+) strand intermediate, strand transfer, and subsequent synthesis of the entire (+) strand. The possibility that the host methyl transferase responsible for methylating A58 may provide a target for HIV chemotherapy is discussed.

Crystal structure of the RNA-dependent RNA polymerase of hepatitis C virus

We report the crystal structure of the RNA-dependent RNA polymerase of hepatitis C virus, a major human pathogen, to 2.8-Å resolution. This enzyme is a key target for developing specific antiviral therapy. The structure of the catalytic domain contains 531 residues folded in the characteristic fingers, palm, and thumb subdomains. The fingers subdomain contains a region, the “fingertips,” that shares the same fold with reverse transcriptases. Superposition to the available structures of the latter shows that residues from the palm and fingertips are structurally equivalent. In addition, it shows that the hepatitis C virus polymerase was crystallized in a closed fingers conformation, similar to HIV-1 reverse transcriptase in ternary complex with DNA and dTTP [Huang H., Chopra, R., Verdine, G. L. & Harrison, S. C. (1998) Science 282, 1669–1675]. This superposition reveals the majority of the amino acid residues of the hepatitis C virus enzyme that are likely to be implicated in binding to the replicating RNA molecule and to the incoming NTP. It also suggests a rearrangement of the thumb domain as well as a possible concerted movement of thumb and fingertips during translocation of the RNA template-primer in successive polymerization rounds.

kappa opioid receptors in human microglia downregulate human immunodeficiency virus 1 expression.

Microglial cells, the resident macrophages of the brain, play an important role in the neuropathogenesis of human immunodeficiency virus type 1 (HIV-1), and recent studies suggest that opioid peptides regulate the function of macrophages from somatic tissues. We report herein the presence of kappa opioid receptors (KORs) in human fetal microglia and inhibition of HIV-1 expression in acutely infected microglial cell cultures treated with KOR ligands. Using reverse transcriptase-polymerase chain reaction and sequencing analyses, we found that mRNA for the KOR was constitutively expressed in microglia and determined that the nucleotide sequence of the open reading frame was identical to that of the human brain KOR gene. The expression of KOR in microglial cells was confirmed by membrane binding of [3H]U69,593, a kappa-selective ligand, and by indirect immunofluorescence. Treatment of microglial cell cultures with U50,488 or U69,593 resulted in a dose-dependent inhibition of expression of the monocytotropic HIV-1 SF162 strain. This antiviral effect of the kappa ligands was blocked by the specific KOR antagonist, nor-binaltrophimine. These findings suggest that kappa opioid agonists have immunomodulatory activity in the brain, and that these compounds could have potential in the treatment of HIV-1-associated encephalopathy.

Viral dynamics in vivo: limitations on estimates of intracellular delay and virus decay.

Anti-viral drug treatment of human immunodeficiency virus type I (HIV-1) and hepatitis B virus (HBV) infections causes rapid reduction in plasma virus load. Viral decline occurs in several phases and provides information on important kinetic constants of virus replication in vivo and pharmacodynamical properties. We develop a mathematical model that takes into account the intracellular phase of the viral life-cycle, defined as the time between infection of a cell and production of new virus particles. We derive analytic solutions for the dynamics following treatment with reverse transcriptase inhibitors, protease inhibitors, or a combination of both. For HIV-1, our results show that the phase of rapid decay in plasma virus (days 2-7) allows precise estimates for the turnover rate of productively infected cells. The initial quasi-stationary phase (days 0-1) and the transition phase (days 1-2) are explained by the combined effects of pharmacological and intracellular delays, the clearance of free virus particles, and the decay of infected cells. Reliable estimates of the first three quantities are not possible from data on virus load only; such estimates require additional measurements. In contrast with HIV-1, for HBV our model predicts that frequent early sampling of plasma virus will lead to reliable estimates of the free virus half-life and the pharmacological properties of the administered drug. On the other hand, for HBV the half-life of infected cells cannot be estimated from plasma virus decay.

Viral dynamics in hepatitis B virus infection.

Treatment of chronic hepatitis B virus (HBV) infections with the reverse transcriptase inhibitor lamivudine leads to a rapid decline in plasma viremia and provides estimates for crucial kinetic constants of HBV replication. We find that in persistently infected patients, HBV particles are cleared from the plasma with a half-life of approximately 1.0 day, which implies a 50% daily turnover of the free virus population. Total viral release into the periphery is approximately 10(11) virus particles per day. Although we have no direct measurement of the infected cell mass, we can estimate the turnover rate of these cells in two ways: (i) by comparing the rate of viral production before and after therapy or (ii) from the decline of hepatitis B antigen during treatment. These two independent methods give equivalent results: we find a wide distribution of half-lives for virus-producing cells, ranging from 10 to 100 days in different patients, which may reflect differences in rates of lysis of infected cells by immune responses. Our analysis provides a quantitative understanding of HBV replication dynamics in vivo and has implications for the optimal timing of drug treatment and immunotherapy in chronic HBV infection. This study also represents a comparison for recent findings on the dynamics of human immunodeficiency virus (HIV) infection. The total daily production of plasma virus is, on average, higher in chronic HBV carriers than in HIV-infected patients, but the half-life of virus-producing cells is much shorter in HIV. Most strikingly, there is no indication of drug resistance in HBV-infected patients treated for up to 24 weeks.

Inhibition of human immunodeficiency virus type 1 transcription and replication by DNA sequence-selective plant lignans.

A plant lignan, 3'-O-methyl nordihydroguaiaretic acid (3'-O-methyl NDGA, denoted Malachi 4:5-6 or Mal.4; molecular weigth 316), was isolated from Larrea tridentata and found to be able to inhibit human immunodeficiency virus (HIV) Tat-regulated transactivation in vivo, induce protection of lymphoblastoid CEM-SS cells from HIV (strain IIIB) killing, and suppress the replication of five HIV-1 strains (WM, MN, VS, JR-CSF, and IIIB) in mitogen-stimulated peripheral blood mononuclear cells, all in a dose-dependent manner. Mal.4 inhibits both basal transcription and Tat-regulated transactivation in vitro. The target of Mal.4 has been localized to nucleotides -87 to -40 of the HIV long terminal repeat. Mal.4 directly and specifically interferes with the binding of Sp1 to Sp1 sites in the HIV long terminal repeat. By inhibiting proviral expression, Mal.4 may be able to interrupt the life cycles of both wild-type and reverse transcriptase or protease mutant viruses in HIV-infected patients.

Amino-terminal alteration of the HLA-A*0201-restricted human immunodeficiency virus pol peptide increases complex stability and in vitro immunogenicity.

Initial studies suggested that major histocompatibility complex class I-restricted viral epitopes could be predicted by the presence of particular residues termed anchors. However, recent studies showed that nonanchor positions of the epitopes are also significant for class I binding and recognition by cytotoxic T lymphocytes (CTLs). We investigated if changing nonanchor amino acids could increase class I affinity, complex stability, and T-cell recognition of a natural viral epitope. This concept was tested by using the HLA-A 0201-restricted human immunodeficiency virus type 1 epitope from reverse transcriptase (pol). Position 1 (P1) amino acid substitutions were emphasized because P1 alterations may not alter the T-cell receptor interaction. The peptide with the P1 substitution of tyrosine for isoleucine (I1Y) showed a binding affinity for HLA-A 0201 similar to that of the wild-type pol peptide in a cell lysate assembly assay. Surprisingly, I1Y significantly increased the HLA-A 0201-peptide complex stability at the cell surface. I1Y sensitized HLA-A 0201-expressing target cells for wild-type pol-specific CTL lysis as well as wild-type pol. Peripheral blood lymphocytes from three HLA-A2 HIV-seropositive individuals were stimulated in vitro with I1Y and wild-type pol. I1Y stimulated a higher wild-type pol-specific CTL response than wild-type pol in all three donors. Thus, I1Y may be an "improved" epitope for use as a CTL-based human immunodeficiency virus vaccine component. The design of improved epitopes has important ramifications for prophylaxis and therapeutic vaccine development.

The inhibitory effect of pentobarbitone on reverse transcription-PCR

Pentobarbitone sodium (Sodium 5-ethyl-5[1-methylbutyl]-pentobarbitone) is a short-acting barbiturate that is commonly used to euthanase animals. As part of our studies into the molecular genetics of copper toxicosis in Bedlington terrier dogs, reverse-transcription (RT)-PCR was noted to always fail on RNA samples collected from livers of dogs sacrificed by pentobarbitone injection. When samples were collected without pentobarbitone, however, RTPCR was always successful. We suspected the possible inhibition by pentobarbitone sodium of either reverse transcriptase or Taq polymerase. In vitro studies showed that pentobarbitone interference of PCR occurred at >4 mug/mul. To identify if pentobarbitone produced competitive inhibition, each components (Taq polymerase, MgCl2, dNTP, etc.) of the PCR was individually altered. However, inhibition still persisted, suggesting that multiple PCR components may be affected. Also it was shown that pentobarbitone interference was not dependent on the PCR product size. Simple dilution of pentobarbitone contaminated DNA solutions, and the addition of bovine serum albumin (BSA) to the PCR mix overcame pentobarbitone interference. In vivo, PCR by pentobarbitone was found to be compounded by high DNA concentration and pentobarbitone contamination. In addition, both high DNA concentration and pentobarbitone contamination could be overcome through dilution and the addition of BSA. Further work is required to quantify pentobarbitone concentration in the liver-extracted DNA and RNA samples before this inhibition effect on PCR can be fully elucidated. (C) 2004 Elsevier B.V. All rights reserved.

Will diverse Tat interactions lead to novel antiretroviral drug targets?

More than fifteen years following the description of Tat as a critical HIV gene expression regulatory protein, additional roles for Tat in HIV replication have been described, including reverse transcription. Tat achieves function through direct interaction with viral proteins, including reverse transcriptase, and numerous cellular proteins including cyclin T1, RNA polymerase 11, protein kinase R (PKR), p300/CBP, and P/CAF. Despite our advanced knowledge of how Tat operates, this has not yet resulted in the discovery of effective agents capable of targeting various Tat functions. Nevertheless, Tat remains an attractive, virus-specific molecule and detailed understanding of specific protein interaction holds promise for future drug discovery.

Magnetic nanoformulation of azidothymidine 5’-triphosphate for targeted delivery across the blood–brain barrier

Despite significant advances in highly active antiretroviral therapy (HAART), the prevalence of neuroAIDS remains high. This is mainly attributed to inability of antiretroviral therapy (ART) to cross the blood–brain barrier (BBB), thus resulting in insufficient drug concentration within the brain. Therefore, development of an active drug targeting system is an attractive strategy to increase the efficacy and delivery of ART to the brain. We report herein development of magnetic azidothymidine 5′-triphosphate (AZTTP) liposomal nanoformulation and its ability to transmigrate across an in vitro BBB model by application of an external magnetic field. We hypothesize that this magnetically guided nanoformulation can transverse the BBB by direct transport or via monocyte-mediated transport. Magnetic AZTTP liposomes were prepared using a mixture of phosphatidyl choline and cholesterol. The average size of prepared liposomes was about 150 nm with maximum drug and magnetite loading efficiency of 54.5% and 45.3%, respectively. Further, magnetic AZTTP liposomes were checked for transmigration across an in vitro BBB model using direct or monocyte-mediated transport by application of an external magnetic field. The results show that apparent permeability of magnetic AZTTP liposomes was 3-fold higher than free AZTTP. Also, the magnetic AZTTP liposomes were efficiently taken up by monocytes and these magnetic monocytes showed enhanced transendothelial migration compared to normal/non-magnetic monocytes in presence of an external magnetic field. Thus, we anticipate that the developed magnetic nanoformulation can be used for targeting active nucleotide analog reverse transcriptase inhibitors to the brain by application of an external magnetic force and thereby eliminate the brain HIV reservoir and help to treat neuroAIDS.

The comparative utility of fluorescence in situ hybridization and reverse transcription-polymerase chain reaction in the diagnosis of alveolar rhabdomyosarcoma.

Fluorescence in situ hybridization (FISH) for FOXO1 gene rearrangement and reverse transcription-polymerase chain reaction (PCR) for PAX3/7-FOXO1 fusion transcripts have become routine ancillary tools for the diagnosis of alveolar rhabdomyosarcomas (ARMS). Here we summarize our experience of these adjunct diagnostic modalities at a tertiary center, presenting the largest comparative series of FISH and PCR for suspected or possible ARMS to date. All suspected or possible ARMS tested by FISH or PCR for FOXO1 rearrangement or PAX3/7-FOXO1 fusion transcripts over a 7-year period were included. FISH and PCR results were correlated with clinical and histologic findings. One hundred samples from 95 patients had FISH and/or PCR performed. FISH had higher rates of technical success (96.8 %) compared with PCR (88 %). Where both tests were utilized successfully, there was high concordance rate between them (94.9 %). In 24 histologic ARMS tested for FISH or PCR, 83.3 % were translocation-positive (all for PAX3-FOXO1 by PCR) and included 3 histologic solid variants. In 76 cases where ARMS was excluded, there were 3 potential false-positive cases with FISH but none with PCR. PCR had similar sensitivity (85.7 %) and better specificity (100 %) in aiding the diagnosis of ARMS, compared with FISH (85 and 95.8 %, respectively). All solid variant ARMS harbored FOXO1 gene rearrangements and PAX3-FOXO1 ARMS were detected to the exclusion of PAX7-FOXO1. In comparative analysis, both FISH and PCR are useful in aiding the diagnosis of ARMS and excluding its sarcomatous mimics. FISH is more reliable technically but has less specificity than PCR. In cases where ARMS is in the differential diagnosis, it is optimal to perform both PCR and FISH: both have similar sensitivities for detecting ARMS, but FISH may confirm or exclude cases that are technically unsuccessful with PCR, while PCR can detect specific fusion transcripts that may be useful prognostically.

Angiomatoid fibrous histiocytoma: comparison of fluorescence in situ hybridization and reverse transcription polymerase chain reaction as adjunct diagnostic modalities

Angiomatoid fibrous histiocytoma (AFH) is a rare soft tissue neoplasm of intermediate biologic potential and uncertain differentiation, most often arising in the extremities of children and young adults. Although it has characteristic histologic features of a lymphoid cuff surrounding nodules of ovoid cells with blood-filled cystic cavities, diagnosis is often difficult due to its morphologic heterogeneity and lack of specific immunoprofile. Angiomatoid fibrous histiocytoma is associated with recurrent chromosomal translocations, leading to characteristic EWSR1-CREB1, EWSR1-ATF1, and, rarely, FUS-ATF1 gene fusions; fluorescence in situ hybridization (FISH), detecting EWSR1 or FUS rearrangements, and reverse transcription-polymerase chain reaction (RT-PCR) for EWSR1-CREB1 and EWSR1-ATF1 fusion transcripts have become routine ancillary tools. We present a large comparative series of FISH and RT-PCR for AFH. Seventeen neoplasms (from 16 patients) histologically diagnosed as AFH were assessed for EWSR1 rearrangements or EWSR1-CREB1 and EWSR1-ATF1 fusion transcripts. All 17 were positive for either FISH or RT-PCR or both. Of 16, 14 (87.5%) had detectable EWSR1-CREB1 or EWSR1-ATF1 fusion transcripts by RT-PCR, whereas 13 (76.5%) of 17 had positive EWSR1 rearrangement with FISH. All 13 of 13 non-AFH control neoplasms failed to show EWSR1-CREB1 or EWSR1-ATF1 fusion transcripts, whereas EWSR1 rearrangement was present in 2 of these 13 cases (which were histopathologically myoepithelial neoplasms). This study shows that EWSR1-CREB1 or EWSR1-ATF1 fusions predominate in AFH (supporting previous reports that FUS rearrangement is rare in AFH) and that RT-PCR has a comparable detection rate to FISH for AFH. Importantly, cases of AFH can be missed if RT-PCR is not performed in conjunction with FISH, and RT-PCR has the added advantage of specificity, which is crucial, as EWSR1 rearrangements are present in a variety of neoplasms in the histologic differential diagnosis of AFH, that differ in behavior and treatment.

Identification of protein targets of nevirapine reactive metabolites using click chemistry and mass spectrometry-based differential proteomics

Abstract : Adverse drug reactions (ADRs) are undesirable effects caused after administration of a single dose or prolonged administration of drug or result from the combination of two or more drugs. Idiosyncratic drug reaction (IDR) is an adverse reaction that does not occur in most patients treated with a drug and does not involve the therapeutic effect of the drug. IDRs are unpredictable and often life-threatening. Idiosyncratic reaction is dependent on drug chemical characteristics or individual immunological response. IDRs are a major problem for drug development because they are usually not detected during clinical trials. In this study we focused on IDRs of Nevirapine (NVP), which is a non-nucleoside reverse transcriptase inhibitor used for the treatment of Human Immunodeficiency Virus (HIV) infections. The use of NVP is limited by a relatively high incidence of skin rash. NVP also causes a rash in female Brown Norway (BN) rats, which we use as animal model for this study. Our hypothesis is that idiosyncratic skin reactions associated with NVP treatment are due to post-translational modifications of proteins (e.g., glutathionylation) detectable by MS. The main objective of this study was to identify the proteins that are targeted by a reactive metabolite of Nevirapine in the skin. The specific objectives derived from the general objective were as follow: 1) To implement the click chemistry approach to detect proteins modified by a reactive NVP-Alkyne (NVP-ALK) metabolite. The purpose of using NVP-ALK was to couple it with Biotin using cycloaddition Click Chemistry reaction. 2) To detect protein modification using Western blotting and Mass Spectrometry techniques, which is important to understand the mechanism of NVP induced toxicity. 3) To identify the proteins using MASCOT search engine for protein identification, by comparing obtained spectrum from Mass Spectrometry with theoretical spectrum to find a matching peptide sequence. 4) To test if the drug or drug metabolites can cause harmful effects, as the induction of oxidative stress in cells (via protein glutathionylation). Oxidative stress causes cell damage that mediates signals, which likely induces the immune response. The results showed that Nevirapine is metabolized to a reactive metabolite, which causes protein modification. The extracted protein from the treated BN rats matched 10% of keratin, which implies that keratin was the protein targeted by the NVP-ALK.

Intermittent Fasting Induces Hypothalamic Modifications Resulting In Low Feeding Efficiency, Low Body Mass And Overeating.

Intermittent fasting (IF) is an often-used intervention to decrease body mass. In male Sprague-Dawley rats, 24 hour cycles of IF result in light caloric restriction, reduced body mass gain, and significant decreases in the efficiency of energy conversion. Here, we study the metabolic effects of IF in order to uncover mechanisms involved in this lower energy conversion efficiency. After 3 weeks, IF animals displayed overeating during fed periods and lower body mass, accompanied by alterations in energy-related tissue mass. The lower efficiency of energy use was not due to uncoupling of muscle mitochondria. Enhanced lipid oxidation was observed during fasting days, whereas fed days were accompanied by higher metabolic rates. Furthermore, an increased expression of orexigenic neurotransmitters AGRP and NPY in the hypothalamus of IF animals was found, even on feeding days, which could explain the overeating pattern. Together, these effects provide a mechanistic explanation for the lower efficiency of energy conversion observed. Overall, we find that IF promotes changes in hypothalamic function that explain differences in body mass and caloric intake.