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.

Copresentation of natural HIV-1 agonist and antagonist ligands fails to induce the T cell receptor signaling cascade

It is not known how human immunodeficiency virus type 1 (HIV-1)-derived antagonist peptides interfere with intracellular activation of cytotoxic T lymphocytes (CTL). We identified Gag epitope variants in HIV-1-infected patients that act as antagonists of CTL responses to unmutated epitopes. We then investigated the effect that presentation of each variant has on the early events of T cell receptor (TCR) signal transduction. We found that altered peptide ligands (APL) failed to induce phosphorylation of pp36, a crucial adaptor protein involved in TCR signal transduction. We further investigated the effect that simultaneous presentation of APL and native antigen at low, physiological, peptide concentrations (1 nM) has on TCR signal transduction, and we found that the presence of APL can completely inhibit induction of the protein tyrosine phosphorylation events of the TCR signal transduction cascade.

Social stress results in altered glucocorticoid regulation and shorter survival in simian acquired immune deficiency syndrome

From early in the AIDS epidemic, psychosocial stressors have been proposed as contributors to the variation in disease course. To test this hypothesis, rhesus macaques were assigned to stable or unstable social conditions and were inoculated with the simian immunodeficiency virus. Animals in the unstable condition displayed more agonism and less affiliation, shorter survival, and lower basal concentrations of plasma cortisol compared with stable animals. Early after inoculation, but before the emergence of group differences in cortisol levels, animals receiving social threats had higher concentrations of simian immunodeficiency virus RNA in plasma, and those engaging in affiliation had lower concentrations. The results indicate that social factors can have a significant impact on the course of immunodeficiency disease. Socially induced changes in pituitary–adrenal hormones may be one mechanism mediating this relationship.

Molecular cloning, sequence, expression, and processing of the interleukin 16 precursor

Interleukin 16 (IL-16) has been shown to function as chemoattractant factor, as a modulator of T-cell activation, and as an inhibitor of immunodeficiency virus replication. The recent identification of inconsistencies in published IL-16 cDNA nucleotide sequences led to the proposal that IL-16 is synthesized in the form of a large precursor protein (pro-IL-16). To identify the true transcriptional start of the IL-16 mRNA rapid amplification of cDNA ends methods were applied. The complete pro-IL-16 cDNA was subsequently molecularly cloned, sequenced, and expressed in COS-7 cells. We report here that pro-IL-16 is most likely synthesized as a 67-kDa protein and is encoded from a major 2.6-kb transcript. Recombinant pro-IL-16 polypeptides are specifically cleaved in lysates of CD8(+) cells, suggesting that the naturally secreted bioactive form of IL-16 is smaller than the originally published 130 amino acids fragment. Moreover, in contrast to other interleukins such as IL-15, IL-16 mRNA expression is almost exclusively limited to lymphatic tissues underlining the potential of IL-16 as an immune regulatory molecule.

Cluster of Differentiation Antigen 4 (CD4) Endocytosis and Adaptor Complex Binding Require Activation of the CD4 Endocytosis Signal by Serine Phosphorylation

Cluster of differentiation antigen 4 (CD4), the T lymphocyte antigen receptor component and human immunodeficiency virus coreceptor, is down-modulated when cells are activated by antigen or phorbol esters. During down-modulation CD4 dissociates from p56lck, undergoes endocytosis through clathrin-coated pits, and is then sorted in early endosomes to late endocytic organelles where it is degraded. Previous studies have suggested that phosphorylation and a dileucine sequence are required for down-modulation. Using transfected HeLa cells, in which CD4 endocytosis can be studied in the absence of p56lck, we show that the dileucine sequence in the cytoplasmic domain is essential for clathrin-mediated CD4 endocytosis. However, this sequence is only functional as an endocytosis signal when neighboring serine residues are phosphorylated. Phosphoserine is required for rapid endocytosis because CD4 molecules in which the cytoplasmic domain serine residues are substituted with glutamic acid residues are not internalized efficiently. Using surface plasmon resonance, we show that CD4 peptides containing the dileucine sequence bind weakly to clathrin adaptor protein complexes 2 and 1. The affinity of this interaction is increased 350- to 700-fold when the peptides also contain phosphoserine residues.

Somatic Cell Mutants Resistant to Retrovirus Replication: Intracellular Blocks during the Early Stages of Infection

To identify cellular functions involved in the early phase of the retroviral life cycle, somatic cell mutants were isolated after selection for resistance to infection. Rat2 fibroblasts were treated with chemical mutagens, and individual virus-resistant clones were recovered after selection for resistance to infection. Two clones were characterized in detail. Both mutant lines were resistant to infection by both ecotropic and amphotropic murine viruses, as well as by human immunodeficiency virus type 1 pseudotypes. One clone showed a strong block to reverse transcription of the retroviral RNA, including formation of the earliest DNA products. The second clone showed normal levels of viral DNA synthesis but did not allow formation of the circular DNAs normally found in the nucleus. Cell fractionation showed that the viral preintegration complex was present in a form that could not be extracted under conditions that readily extracted the complex from wild-type cells. The results suggest that the DNA was trapped in a nonproductive state and excluded from the nucleus of the infected cell. The properties of these two mutant lines suggest that host gene products play important roles both before and after reverse transcription.

CD8+ T-cell-derived soluble factor(s), but not β-chemokines RANTES, MIP-1α, and MIP-1β, suppress HIV-1 replication in monocyte/macrophages

It has been demonstrated that CD8+ T cells produce a soluble factor(s) that suppresses human immunodeficiency virus (HIV) replication in CD4+ T cells. The role of soluble factors in the suppression of HIV replication in monocyte/macrophages (M/M) has not been fully delineated. To investigate whether a CD8+ T-cell-derived soluble factor(s) can also suppress HIV infection in the M/M system, primary macrophages were infected with the macrophage tropic HIV-1 strain Ba-L. CD8+ T-cell-depleted peripheral blood mononuclear cells were also infected with HIV-1 IIIB or Ba-L. HIV expression from the chronically infected macrophage cell line U1 was also determined in the presence of CD8+ T-cell supernatants or β-chemokines. We demonstrate that: (i) CD8+ T-cell supernatants did, but β-chemokines did not, suppress HIV replication in the M/M system; (ii) antibodies to regulated on activation normal T-cell expressed and Secreted (RANTES), macrophage inflammatory protein 1α (MIP-1α) and MIP-1β did not, whereas antibodies to interleukin 10, interleukin 13, interferon α, or interferon γ modestly reduced anti-HIV activity of the CD8+ T-cell supernatants; and (iii) the CD8+ T-cell supernatants did, but β-chemokines did not, suppress HIV-1 IIIB replication in peripheral blood mononuclear cells as well as HIV expression in U1 cells. These results suggest that HIV-suppressor activity of CD8+ T cells is a multifactorial phenomenon, and that RANTES, MIP-1α, and MIP-1β do not account for the entire scope of CD8+ T-cell-derived HIV-suppressor factors.

Comparative mutational analysis of cis-acting RNA signals for translational frameshifting in HIV-1 and HTLV-2

Human immunodeficiency virus type 1 (HIV-1) and human T cell leukemia virus type II (HTLV-2) use a similar mechanism for –1 translational frameshifting to overcome the termination codon in viral RNA at the end of the gag gene. Previous studies have identified two important RNA signals for frameshifting, the slippery sequence and a downstream stem–loop structure. However, there have been somewhat conflicting reports concerning the individual contributions of these sequences. In this study we have performed a comprehensive mutational analysis of the cis-acting RNA sequences involved in HIV-1 gag–pol and HTLV-2 gag–pro frameshifting. Using an in vitro translation system we determined frameshifting efficiencies for shuffled HIV-1/HTLV-2 RNA elements in a background of HIV-1 or HTLV-2 sequences. We show that the ability of the slippery sequence and stem–loop to promote ribosomal frameshifting is influenced by the flanking upstream sequence and the nucleotides in the spacer element. A wide range of frameshift efficiency rates was observed for both viruses when shuffling single sequence elements. The results for HIV-1/HTLV-2 chimeric constructs represent strong evidence supporting the notion that the viral wild-type sequences are not designed for maximal frameshifting activity but are optimized to a level suited to efficient viral replication.

Negative Factor from SIV Binds to the Catalytic Subunit of the V-ATPase to Internalize CD4 and to Increase Viral Infectivity

The accessory protein negative factor (Nef) from human immunodeficiency virus (HIV) and simian immunodeficiency virus (SIV) is required for optimal viral infectivity and the progression to acquired immunodeficiency syndrome (AIDS). Nef interacts with the endocytic machinery, resulting in the down-regulation of cluster of differentiation antigen 4 (CD4) and major histocompatibility complex class I (MHCI) molecules on the surface of infected cells. Mutations in the C-terminal flexible loop of Nef result in a lower rate of internalization by this viral protein. However, no loop-dependent binding of Nef to adaptor protein-2 (AP-2), which is the adaptor protein complex that is required for the internalization of proteins from the plasma membrane, could be demonstrated. In this study we investigated the relevance of different motifs in Nef from SIVmac239 for its internalization, CD4 down-regulation, binding to components of the trafficking machinery, and viral infectivity. Our data suggest that the binding of Nef to the catalytic subunit H of the vacuolar membrane ATPase (V-ATPase) facilitates its internalization. This binding depends on the integrity of the whole flexible loop. Subsequent studies on Nef mutant viruses revealed that the flexible loop is essential for optimal viral infectivity. Therefore, our data demonstrate how Nef contacts the endocytic machinery in the absence of its direct binding to AP-2 and suggest an important role for subunit H of the V-ATPase in viral infectivity.

A vaccine for HIV type 1: The antibody perspective

Antibodies that bind well to the envelope spikes of immunodeficiency viruses such as HIV type 1 (HIV-1) and simian immunodeficiency virus (SIV) can offer protection or benefit if present at appropriate concentrations before viral exposure. The challenge in antibody-based HIV-1 vaccine design is to elicit such antibodies to the viruses involved in transmission in humans (primary viruses). At least two major obstacles exist. The first is that very little of the envelope spike surface of primary viruses appears accessible for antibody binding (low antigenicity), probably because of oligomerization of the constituent proteins and a high degree of glycosylation of one of the proteins. The second is that the mature oligomer constituting the spikes appears to stimulate only weak antibody responses (low immunogenicity). Viral variation is another possible obstacle that appears to present fewer problems than anticipated. Vaccine design should focus on presentation of an intact mature oligomer, increasing the immunogenicity of the oligomer and learning from the antibodies available that potently neutralize primary viruses.

Hydrogen peroxide-mediated neuronal cell death induced by an endogenous neurotoxin, 3-hydroxykynurenine.

3-Hydroxykynurenine (3-HK) is a tryptophan metabolite whose level in the brain is markedly elevated under several pathological conditions, including Huntington disease and human immunodeficiency virus infection. Here we demonstrate that micromolar concentrations (1-100 microM) of 3-HK cause cell death in primary neuronal cultures prepared from rat striatum. The neurotoxicity of 3-HK was blocked by catalase and desferrioxamine but not by superoxide dismutase, indicating that the generation of hydrogen peroxide and hydroxyl radical is involved in the toxicity. Measurement of peroxide levels revealed that 3-HK caused intracellular accumulation of peroxide, which was largely attenuated by application of catalase. The peroxide accumulation and cell death caused by 1-10 microM 3-HK were also blocked by pretreatment with allopurinol or oxypurinol, suggesting that endogenous xanthine oxidase activity is involved in exacerbation of 3-HK neurotoxicity. Furthermore, NADPH diaphorase-containing neurons were spared from toxicity of these concentrations of 3-HK, a finding reminiscent of the pathological characteristics of several neurodegenerative disorders such as Huntington disease. These results suggest that 3-HK at pathologically relevant concentrations renders neuronal cells subject to oxidative stress leading to cell death, and therefore that this endogenous compound should be regarded as an important factor in pathogenesis of neurodegenerative disorders.

Accurate reconstruction of a known HIV-1 transmission history by phylogenetic tree analysis.

Phylogenetic analyses are increasingly used in attempts to clarify transmission patterns of human immunodeficiency virus type 1 (HIV-1), but there is a continuing discussion about their validity because convergent evolution and transmission of minor HIV variants may obscure epidemiological patterns. Here we have studied a unique HIV-1 transmission cluster consisting of nine infected individuals, for whom the time and direction of each virus transmission was exactly known. Most of the transmissions occurred between 1981 and 1983, and a total of 13 blood samples were obtained approximately 2-12 years later. The p17 gag and env V3 regions of the HIV-1 genome were directly sequenced from uncultured lymphocytes. A true phylogenetic tree was constructed based on the knowledge about when the transmissions had occurred and when the samples were obtained. This complex, known HIV-1 transmission history was compared with reconstructed molecular trees, which were calculated from the DNA sequences by several commonly used phylogenetic inference methods [Fitch-Margoliash, neighbor-joining, minimum-evolution, maximum-likelihood, maximum-parsimony, unweighted pair group method using arithmetic averages (UPGMA), and a Fitch-Margoliash method assuming a molecular clock (KITSCH)]. A majority of the reconstructed trees were good estimates of the true phylogeny; 12 of 13 taxa were correctly positioned in the most accurate trees. The choice of gene fragment was found to be more important than the choice of phylogenetic method and substitution model. However, methods that are sensitive to unequal rates of change performed more poorly (such as UPGMA and KITSCH, which assume a constant molecular clock). The rapidly evolving V3 fragment gave better reconstructions than p17, but a combined data set of both p17 and V3 performed best. The accuracy of the phylogenetic methods justifies their use in HIV-1 research and argues against convergent evolution and selective transmission of certain virus variants.

Long-term expression of erythropoietin in the systemic circulation of mice after intramuscular injection of a plasmid DNA vector.

Erythropoietin (Epo)-responsive anemia is a common and debilitating complication of chronic renal failure and human immunodeficiency virus infection. Current therapy for this condition involves repeated intravenous or subcutaneous injections of recombinant Epo. In this report, we describe the development of a novel muscle-based gene transfer approach that produces long-term expression of physiologically significant levels of Epo in the systemic circulation of mice. We have constructed a plasmid expression vector, pVRmEpo, that contains the murine Epo cDNA under the transcriptional control of the cytomegalovirus immediate early (CMV-IE) promoter, the CMV-IE 5' untranslated region, and intron A. A single intramuscular (i.m.) injection of as little as 10 micrograms of this plasmid into immunocompetent adult mice produced physiologically significant elevations in serum Epo levels and increased hematocrits from preinjection levels of 48 +/- 0.4% to levels of 64 +/- 3.3% 45 days after injection. Hematocrits in these animals remained elevated at greater than 60% for at least 90 days after a single i.m. injection of 10 micrograms of pVRmEpo. We observed a dose-response relationship between the amount of plasmid DNA injected and subsequent elevations in hematocrits. Mice injected once with 300 micrograms of pVRmEpo displayed 5-fold increased serum Epo levels and elevated hematocrits of 79 +/- 3.3% at 45 days after injection. The i.m. injected plasmid DNA remained localized to the site of injection as assayed by the PCR. We conclude that i.m. injection of plasmid DNA represents a viable nonviral gene transfer method for the treatment of acquired and inherited serum protein deficiencies.

Initiation of (-) strand DNA synthesis from tRNA(3Lys) on lentiviral RNAs: implications of specific HIV-1 RNA-tRNA(3Lys) interactions inhibiting primer utilization by retroviral reverse transcriptases.

Initiation of minus (-) strand DNA synthesis was examined on templates containing R, U5, and primer-binding site regions of the human immunodeficiency virus type 1 (HIV-1), feline immunodeficiency virus (FIV), and equine infectious anemia virus (EIAV) genomic RNA. DNA synthesis was initiated from (i) an oligoribonucleotide complementary to the primer-binding sites, (ii) synthetic tRNA(3Lys), and (iii) natural tRNA(3Lys), by the reverse transcriptases of HIV-1, FIV, EIAV, simian immunodeficiency virus, HIV type 2 (HIV-2), Moloney murine leukemia virus, and avian myeloblastosis virus. All enzymes used an oligonucleotide on wild-type HIV-1 RNA, whereas only a limited number initiated (-) strand DNA synthesis from either tRNA(3Lys). In contrast, all enzymes supported efficient tRNA(3Lys)-primed (-) strand DNA synthesis on the genomes of FIV and EIAV. This may be in part attributable to the observation that the U5-inverted repeat stem-loop of the EIAV and FIV genomes lacks an A-rich loop shown with HIV-1 to interact with the U-rich tRNA anticodon loop. Deletion of this loop in HIV-1 RNA, or disrupting a critical loop-loop complex by tRNA(3Lys) extended by 9 nt, restored synthesis of HIV-1 (-) strand DNA from primer tRNA(3Lys) by all enzymes. Thus, divergent evolution of lentiviruses may have resulted in different mechanisms to use the same host tRNA for initiation of reverse transcription.

Apoptosis mediated by HIV protease is preceded by cleavage of Bcl-2.

Expression of the human immunodeficiency virus type 1 (HIV) protease in cultured cells leads to apoptosis, preceded by cleavage of bcl-2, a key negative regulator of cell death. In contrast, a high level of bcl-2 protects cells in vitro and in vivo from the viral protease and prevents cell death following HIV infection of human lymphocytes, while reducing the yields of viral structural proteins, infectivity, and tumor necrosis factor alpha. We present a model for HIV replication in which the viral protease depletes the infected cells of bcl-2, leading to oxidative stress-dependent activation of NF kappa B, a cellular factor required for HIV transcription, and ultimately to cell death. Purified bcl-2 is cleaved by HIV protease between phenylalanine 112 and alanine 113. The results suggest a new option for HIV gene therapy; bcl-2 muteins that have noncleavable alterations surrounding the HIV protease cleavage site.