Long-term trends of HIV type 1 drug resistance prevalence among antiretroviral treatment-experienced patients in Switzerland

BACKGROUND:Accurate quantification of the prevalence of human immunodeficiency virus type 1 (HIV-1) drug resistance in patients who are receiving antiretroviral therapy (ART) is difficult, and results from previous studies vary. We attempted to assess the prevalence and dynamics of resistance in a highly representative patient cohort from Switzerland. METHODS:On the basis of genotypic resistance test results and clinical data, we grouped patients according to their risk of harboring resistant viruses. Estimates of resistance prevalence were calculated on the basis of either the proportion of individuals with a virologic failure or confirmed drug resistance (lower estimate) or the frequency-weighted average of risk group-specific probabilities for the presence of drug resistance mutations (upper estimate). RESULTS:Lower and upper estimates of drug resistance prevalence in 8064 ART-exposed patients were 50% and 57% in 1999 and 37% and 45% in 2007, respectively. This decrease was driven by 2 mechanisms: loss to follow-up or death of high-risk patients exposed to mono- or dual-nucleoside reverse-transcriptase inhibitor therapy (lower estimates range from 72% to 75%) and continued enrollment of low-risk patients who were taking combination ART containing boosted protease inhibitors or nonnucleoside reverse-transcriptase inhibitors as first-line therapy (lower estimates range from 7% to 12%). A subset of 4184 participants (52%) had >or= 1 study visit per year during 2002-2007. In this subset, lower and upper estimates increased from 45% to 49% and from 52% to 55%, respectively. Yearly increases in prevalence were becoming smaller in later years. CONCLUSIONS:Contrary to earlier predictions, in situations of free access to drugs, close monitoring, and rapid introduction of new potent therapies, the emergence of drug-resistant viruses can be minimized at the population level. Moreover, this study demonstrates the necessity of interpreting time trends in the context of evolving cohort populations.

Treatment-naive individuals are the major source of transmitted HIV-1 drug resistance in men who have sex with men in the Swiss HIV Cohort Study

BACKGROUND Human immunodeficiency virus type 1 (HIV-1) transmitted drug resistance (TDR) can compromise antiretroviral therapy (ART) and thus represents an important public health concern. Typically, sources of TDR remain unknown, but they can be characterized with molecular epidemiologic approaches. We used the highly representative Swiss HIV Cohort Study (SHCS) and linked drug resistance database (SHCS-DRDB) to analyze sources of TDR. METHODS ART-naive men who have sex with men with infection date estimates between 1996 and 2009 were chosen for surveillance of TDR in HIV-1 subtype B (N = 1674), as the SHCS-DRDB contains pre-ART genotypic resistance tests for >69% of this surveillance population. A phylogeny was inferred using pol sequences from surveillance patients and all subtype B sequences from the SHCS-DRDB (6934 additional patients). Potential sources of TDR were identified based on phylogenetic clustering, shared resistance mutations, genetic distance, and estimated infection dates. RESULTS One hundred forty of 1674 (8.4%) surveillance patients carried virus with TDR; 86 of 140 (61.4%) were assigned to clusters. Potential sources of TDR were found for 50 of 86 (58.1%) of these patients. ART-naive patients constitute 56 of 66 (84.8%) potential sources and were significantly overrepresented among sources (odds ratio, 6.43 [95% confidence interval, 3.22-12.82]; P < .001). Particularly large transmission clusters were observed for the L90M mutation, and the spread of L90M continued even after the near cessation of antiretroviral use selecting for that mutation. Three clusters showed evidence of reversion of K103N or T215Y/F. CONCLUSIONS Many individuals harboring viral TDR belonged to transmission clusters with other Swiss patients, indicating substantial domestic transmission of TDR in Switzerland. Most TDR in clusters could be linked to sources, indicating good surveillance of TDR in the SHCS-DRDB. Most TDR sources were ART naive. This, and the presence of long TDR transmission chains, suggests that resistance mutations are frequently transmitted among untreated individuals, highlighting the importance of early diagnosis and treatment.

Origin of Minority Drug-Resistant HIV-1 Variants in Primary HIV-1 Infection

Background. Drug-resistant human immunodeficiency virus type 1 (HIV-1) minority variants (MVs) are present in some antiretroviral therapy (ART)–naive patients. They may result from de novo mutagenesis or transmission. To date, the latter has not been proven. Methods. MVs were quantified by allele-specific polymerase chain reaction in 204 acute or recent seroconverters from the Zurich Primary HIV Infection study and 382 ART-naive, chronically infected patients. Phylogenetic analyses identified transmission clusters. Results. Three lines of evidence were observed in support of transmission of MVs. First, potential transmitters were identified for 12 of 16 acute or recent seroconverters harboring M184V MVs. These variants were also detected in plasma and/or peripheral blood mononuclear cells at the estimated time of transmission in 3 of 4 potential transmitters who experienced virological failure accompanied by the selection of the M184V mutation before transmission. Second, prevalence between MVs harboring the frequent mutation M184V and the particularly uncommon integrase mutation N155H differed highly significantly in acute or recent seroconverters (8.2% vs 0.5%; P < .001). Third, the prevalence of less-fit M184V MVs is significantly higher in acutely or recently than in chronically HIV-1–infected patients (8.2% vs 2.5%; P = .004). Conclusions. Drug-resistant HIV-1 MVs can be transmitted. To what extent the origin—transmission vs sporadic appearance—of these variants determines their impact on ART needs to be further explored.

Dengue risk factor distribution in Harris County, Texas

As an important emerging arboviral disease in Texas and throughout the world, dengue fever has the potential to make a re-emergence in the Harris County/Houston metropolitan area. Harris County has seen dengue epidemics in the past. The area has a competent vector, Aedes aegypti, capable of transmission of the virus should it be introduced. It is important to examine areas of highest risk for dengue emergence and transmission in Harris County so that surveillance and educational programs can be properly implemented. This study uses mapping software to visually represent risk factor information with areas of known Ae. aegypti populations. This study focused on known demographic risk factors such as race/ethnicity, place of birth, gender as well as socioeconomic status represented by educational attainment and income. This study found that there are several areas, particularly in central Harris County that are at particular risk for dengue transmission. The findings support the hypothesis that in areas of lower socioeconomic status there were increased populations of foreign born populations, Hispanic populations, and identified locations of a competent vector present. These findings suggest that more specific surveillance of Ae. aegypti, testing of the mosquitoes for dengue virus, and active surveillance for human cases should be implemented in these areas. ^

Evidence of secondary dengue infection and potential for association with clinical depression in a subset of a randomly selected cohort in South Texas

Globally, dengue is an emerging disease resulting in an estimated 50 million new cases and 22, 000 deaths each year. Anecdotally, depression has been reported as a possible sequelae of dengue virus infection. To test the association, we performed a cross-sectional analysis in a selected sub-set of participants from the Cameron County Hispanic Cohort (CCHC) in South Texas. All study subjects in the analysis had Center for Epidemiological Studies Depression scale (CES-D) scores and were tested for dengue antibodies using stored plasma. We found that 5.0% of participants tested either positive or equivocal for anti-dengue IgG antibodies using the capture antibody test, which detects acute secondary infections. Logistic regression identified that evidence of acute secondary dengue infection was not associated with depression (Odds Ratio [OR] = 0.97, 95%Confidence Interval [CI] 0.47-1.98); however, both being female (OR = 1.53, 95%CI 1.09-2.15) and obese body mass index (BMI > 30) (OR = 1.84, 95%CI 1.19-2.84) were associated with depression. ^

Suspected outbreak of dengue fever in Houston: Clinical characteristics and determination of autochthonous transmission between 2003 and 2005

Houston, Texas maintains the appropriate climate and mosquito populations to support the circulation of dengue viruses. The city is susceptible to the introduction and subsequent local transmission of dengue virus with its proximity to dengue-endemic Mexico and the high degree of international travel routed through its airports. In 2008, a study at the University of Texas School of Public Health identified 58 suspected dengue fever cases that presented at hospitals and clinics in the Houston area. Serum or CSF samples of the 58 samples tested positive or equivocal for the presence of anti-dengue IgM antibodies (Rodriguez, 2008). Here, we present the results of an investigation aimed to describe the clinical characteristics of the 58 suspected dengue fever cases and to determine if local transmission had occurred. Data from medical record abstractions and personal telephone interviews were used to describe clinical characteristics and travel history of the suspected cases. Our analysis classified six probable dengue fever cases based on the case definition from the World Health Organization. Three of the probable cases for which we were able to obtain travel history had not recently traveled to an endemic area prior to onset of symptoms suggesting the illnesses were locally acquired in Houston. Further analysis led us to hypothesize that additional cases of dengue fever are present in our study population. Fifty-one percent of the study population was diagnosed with meningitis and/or encephalitis. Sixty percent of the individuals who received a lumbar puncture had abnormal CSF. Together these findings indicate viral infection with neurological involvement, which has been reported to occur with dengue fever. Among the individuals who received liver enzyme analysis, 54% had evidence of abnormal liver enzyme levels, a clinical sign commonly observed with dengue. Our results indicate that a suspected outbreak of dengue fever with autochthonous transmission occurred in the Houston area between 2003 and 2005. ^

Dengue transmission risk maps of the continental United States

Dengue fever is a strictly human and non-human primate disease characterized by a high fever, thrombocytopenia, retro-orbital pain, and severe joint and muscle pain. Over 40% of the world population is at risk. Recent re-emergence of dengue outbreaks in Texas and Florida following the re-introduction of competent Aedes mosquito vectors in the United States have raised growing concerns about the potential for increased occurrences of dengue fever outbreaks throughout the southern United States. Current deficiencies in vector control, active surveillance and awareness among medical practitioners may contribute to a delay in recognizing and controlling a dengue virus outbreak. Previous studies have shown links between low-income census tracts, high population density, and dengue fever within the United States. Areas of low-income and high population density that correlate with the distribution of Aedes mosquitoes result in higher potential for outbreaks. In this retrospective ecologic study, nine maps were generated to model U.S. census tracts’ potential to sustain dengue virus transmission if the virus was introduced into the area. Variables in the model included presence of a competent vector in the county and census tract percent poverty and population density. Thirty states, 1,188 counties, and 34,705 census tracts were included in the analysis. Among counties with Aedes mosquito infestation, the census tracts were ranked high, medium, and low risk potential for sustained transmission of the virus. High risk census tracts were identified as areas having the vector, ≥20% poverty, and ≥500 persons per square mile. Census tracts with either ≥20% poverty or ≥500 persons per square mile and have the vector present are considered moderate risk. Census tracts that have the vector present but have <20% poverty and <500 persons per square mile are considered low risk. Furthermore, counties were characterized as moderate risk if 50% or more of the census tracts in that county were rated high or moderate risk, and high risk if 25% or greater were rated high risk. Extreme risk counties, which were primarily concentrated in Texas and Mississippi, were considered having 50% or greater of the census tracts ranked as high risk. Mapping of geographic areas with potential to sustain dengue virus transmission will support surveillance efforts and assist medical personnel in recognizing potential cases. ^

Characterizing Overall Response to Two-shot Vaccination for BVDV Type 2 in Angus Weanling Calves

Vaccination is a management strategy utilized to help reduce prevalence of bovine respiratory disease in feedlots. However, not all animals respond similarly to vaccinations. It is believed that an animal’s genetics control part of the ability to respond to a vaccination protocol. In order to evaluate the genetic control of a new trait such as response to vaccination, it is important to understand the non-genetic factors that affect an animal’s response to vaccination. The objective of this study was to characterize the non-genetic factors affecting overall response to a two-shot vaccination for bovine viral diarrhea virus type 2 (BVDV2) in Angus weanling calves.

Dengue

Fil: Attorri, Silvia. Universidad Nacional de Cuyo. Facultad de Ciencias Médicas

Phytoplankton composition and biomass during leg 7 of Galathea 3 cruise across the southern Indian Ocean

Phytoplankton composition and biomass was investigated across the southern Indian Ocean. Phytoplankton composition was determined from pigment analysis with subsequent calculations of group contributions to total chlorophyll a (Chl a) using CHEMTAX and, in addition, by examination in the microscope. The different plankton communities detected reflected the different water masses along a transect from Cape Town, South Africa, to Broome, Australia. The first station was influenced by the Agulhas Current with a very deep mixed surface layer. Based on pigment analysis this station was dominated by haptophytes, pelagophytes, cyanobacteria, and prasinophytes. Sub-Antarctic waters of the Southern Ocean were encountered at the next station, where new nutrients were intruded to the surface layer and the total Chl a concentration reached high concentrations of 1.7 µg Chl a/L with increased proportions of diatoms and dinoflagellates. The third station was also influenced by Southern Ocean waters, but located in a transition area on the boundary to subtropical water. Prochlorophytes appeared in the samples and Chl a was low, i.e., 0.3 µg/L in the surface with prevalence of haptophytes, pelagophytes, and cyanobacteria. The next two stations were located in the subtropical gyre with little mixing and general oligotrophic conditions where prochlorophytes, haptophytes and pelagophytes dominated. The last two stations were located in tropical waters influenced by down-welling of the Leeuwin Current and particularly prochlorophytes dominated at these two stations, but also pelagophytes, haptophytes and cyanobacteria were abundant. Haptophytes Type 6 (sensu Zapata et al., 2004), most likely Emiliania huxleyi, and pelagophytes were the dominating eucaryotes in the southern Indian Ocean. Prochlorophytes dominated in the subtrophic and oligotrophic eastern Indian Ocean where Chl a was low, i.e., 0.043-0.086 µg total Chl a/L in the surface, and up to 0.4 µg Chl a/L at deep Chl a maximum. From the pigment analyses it was found that the dinoflagellates of unknown trophy enumerated in the microscope at the oligotrophic stations were possibly heterotrophic or mixotrophic. Presence of zeaxanthin containing heterotrophic bacteria may have increased the abundance of cyanobacteria determined by CHEMTAX.

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.

Inhibition of luciferase expression in transgenic Aedes aegypti mosquitoes by Sindbis virus expression of antisense luciferase RNA

A rapid and reproducible method of inhibiting the expression of specific genes in mosquitoes should further our understanding of gene function and may lead to the identification of mosquito genes that determine vector competence or are involved in pathogen transmission. We hypothesized that the virus expression system based on the mosquito-borne Alphavirus, Sindbis (Togaviridae), may efficiently transcribe effector RNAs that inhibit expression of a targeted mosquito gene. To test this hypothesis, germ-line-transformed Aedes aegypti that express luciferase (LUC) from the mosquito Apyrase promoter were intrathoracically inoculated with a double subgenomic Sindbis (dsSIN) virus TE/3′2J/anti-luc (Anti-luc) that transcribes RNA complementary to the 5′ end of the LUC mRNA. LUC activity was monitored in mosquitoes infected with either Anti-luc or control dsSIN viruses expressing unrelated antisense RNAs. Mosquitoes infected with Anti-luc virus exhibited 90% reduction in LUC compared with uninfected and control dsSIN-infected mosquitoes at 5 and 9 days postinoculation. We demonstrate that a gene expressed from the mosquito genome can be inhibited by using an antisense strategy. The dsSIN antisense RNA expression system is an important tool for studying gene function in vivo.

Proliferation of adult T cell leukemia/lymphoma cells is associated with the constitutive activation of JAK/STAT proteins

Human T cell leukemia/lymphotropic virus type I (HTLV-I) induces adult T cell leukemia/lymphoma (ATLL). The mechanism of HTLV-I oncogenesis in T cells remains partly elusive. In vitro, HTLV-I induces ligand-independent transformation of human CD4+ T cells, an event that correlates with acquisition of constitutive phosphorylation of Janus kinases (JAK) and signal transducers and activators of transcription (STAT) proteins. However, it is unclear whether the in vitro model of HTLV-I transformation has relevance to viral leukemogenesis in vivo. Here we tested the status of JAK/STAT phosphorylation and DNA-binding activity of STAT proteins in cell extracts of uncultured leukemic cells from 12 patients with ATLL by either DNA-binding assays, using DNA oligonucleotides specific for STAT-1 and STAT-3, STAT-5 and STAT-6 or, more directly, by immunoprecipitation and immunoblotting with anti-phosphotyrosine antibody for JAK and STAT proteins. Leukemic cells from 8 of 12 patients studied displayed constitutive DNA-binding activity of one or more STAT proteins, and the constitutive activation of the JAK/STAT pathway was found to persist over time in the 2 patients followed longitudinally. Furthermore, an association between JAK3 and STAT-1, STAT-3, and STAT-5 activation and cell-cycle progression was demonstrated by both propidium iodide staining and bromodeoxyuridine incorporation in cells of four patients tested. These results imply that JAK/STAT activation is associated with replication of leukemic cells and that therapeutic approaches aimed at JAK/STAT inhibition may be considered to halt neoplastic growth.

Herpes simplex virus DNA packaging sequences adopt novel structures that are specifically recognized by a component of the cleavage and packaging machinery

The product of the herpes simplex virus type 1 UL28 gene is essential for cleavage of concatemeric viral DNA into genome-length units and packaging of this DNA into viral procapsids. To address the role of UL28 in this process, purified UL28 protein was assayed for the ability to recognize conserved herpesvirus DNA packaging sequences. We report that DNA fragments containing the pac1 DNA packaging motif can be induced by heat treatment to adopt novel DNA conformations that migrate faster than the corresponding duplex in nondenaturing gels. Surprisingly, these novel DNA structures are high-affinity substrates for UL28 protein binding, whereas double-stranded DNA of identical sequence composition is not recognized by UL28 protein. We demonstrate that only one strand of the pac1 motif is responsible for the formation of novel DNA structures that are bound tightly and specifically by UL28 protein. To determine the relevance of the observed UL28 protein–pac1 interaction to the cleavage and packaging process, we have analyzed the binding affinity of UL28 protein for pac1 mutants previously shown to be deficient in cleavage and packaging in vivo. Each of the pac1 mutants exhibited a decrease in DNA binding by UL28 protein that correlated directly with the reported reduction in cleavage and packaging efficiency, thereby supporting a role for the UL28 protein–pac1 interaction in vivo. These data therefore suggest that the formation of novel DNA structures by the pac1 motif confers added specificity on recognition of DNA packaging sequences by the UL28-encoded component of the herpesvirus cleavage and packaging machinery.

Oncolytic herpes simplex virus vector with enhanced MHC class I presentation and tumor cell killing

Oncolytic herpes simplex virus type 1 (HSV-1) vectors are promising therapeutic agents for cancer. Their efficacy depends on the extent of both intratumoral viral replication and induction of a host antitumor immune response. To enhance these properties while employing ample safeguards, two conditionally replicating HSV-1 vectors, termed G47Δ and R47Δ, have been constructed by deleting the α47 gene and the promoter region of US11 from γ34.5-deficient HSV-1 vectors, G207 and R3616, respectively. Because the α47 gene product is responsible for inhibiting the transporter associated with antigen presentation (TAP), its absence led to increased MHC class I expression in infected human cells. Moreover, some G47Δ-infected human melanoma cells exhibited enhanced stimulation of matched antitumor T cell activity. The deletion also places the late US11 gene under control of the immediate-early α47 promoter, which suppresses the reduced growth properties of γ34.5-deficient mutants. G47Δ and R47Δ showed enhanced viral growth in a variety of cell lines, leading to higher virus yields and enhanced cytopathic effect in tumor cells. G47Δ was significantly more efficacious in vivo than its parent G207 at inhibiting tumor growth in both immune-competent and immune-deficient animal models. Yet, when inoculated into the brains of HSV-1-sensitive A/J mice at 2 × 106 plaque forming units, G47Δ was as safe as G207. These results suggest that G47Δ may have enhanced antitumor activity in humans.