Advanced glycation end products regulate extracellular matrix protein and protease expression by human glomerular mesangial cells

Advanced glycation end products (AGEs) may play a role in the pathogenesis of diabetic nephropathy, by modulating extracellular matrix turnover. AGEs are known to activate specific membrane receptors, including the receptor for AGE (RAGE). In the present study, we analyzed the various receptors for AGEs expressed by human mesangial cells and we studied the effects of glycated albumin and of carboxymethyl lysine on matrix protein and remodelling enzyme synthesis. Membrane RAGE expression was confirmed by FACS analysis. Microarray methods, RT-PCR, and Northern blot analysis were used to detect and confirm specific gene induction. Zymographic analysis and ELISA were used to measure the induction of tPA and PAI-1. We show herein that cultured human mesangial cells express AGE receptor type 1, type 2 and type 3 and RAGE. AGEs (200 microg/ml) induced at least a 2-fold increase in mRNA for 10 genes involved in ECM remodelling, including tPA, PAI-1 and TIMP-3. The increase in tPA synthesis was confirmed by fibrin zymography. The stimulation of PAI-1 synthesis was confirmed by ELISA. AGEs increased PAI-1 mRNA through a signalling pathway involving reactive oxygen species, the MAP kinases ERK-1/ERK-2 and the nuclear transcription factor NF-kappaB, but not AP-1. Carboxymethyl lysine (CML, 5 microM), which is a RAGE ligand, also stimulated PAI-1 synthesis by mesangial cells. In addition, a blocking anti-RAGE antibody partially inhibited the AGE-stimulated gene expression and decreased the PAI-1 accumulation induced by AGEs and by CML. Inhibition of AGE receptors or neutralization of the protease inhibitors TIMP-3 and PAI-1 could represent an important new therapeutic strategy for diabetic nephropathy.

Cancer risk and use of protease inhibitor or nonnucleoside reverse transcriptase inhibitor-based combination antiretroviral therapy: the d: a: d study.

BACKGROUND The association between combination antiretroviral therapy (cART) and cancer risk, especially regimens containing protease inhibitors (PIs) or nonnucleoside reverse transcriptase inhibitors (NNRTIs), is unclear. METHODS Participants were followed from the latest of D:A:D study entry or January 1, 2004, until the earliest of a first cancer diagnosis, February 1, 2012, death, or 6 months after the last visit. Multivariable Poisson regression models assessed associations between cumulative (per year) use of either any cART or PI/NNRTI, and the incidence of any cancer, non-AIDS-defining cancers (NADC), AIDS-defining cancers (ADC), and the most frequently occurring ADC (Kaposi sarcoma, non-Hodgkin lymphoma) and NADC (lung, invasive anal, head/neck cancers, and Hodgkin lymphoma). RESULTS A total of 41,762 persons contributed 241,556 person-years (PY). A total of 1832 cancers were diagnosed [incidence rate: 0.76/100 PY (95% confidence interval: 0.72 to 0.79)], 718 ADC [0.30/100 PY (0.28-0.32)], and 1114 NADC [0.46/100 PY (0.43-0.49)]. Longer exposure to cART was associated with a lower ADC risk [adjusted rate ratio: 0.88/year (0.85-0.92)] but a higher NADC risk [1.02/year (1.00-1.03)]. Both PI and NNRTI use were associated with a lower ADC risk [PI: 0.96/year (0.92-1.00); NNRTI: 0.86/year (0.81-0.91)]. PI use was associated with a higher NADC risk [1.03/year (1.01-1.05)]. Although this was largely driven by an association with anal cancer [1.08/year (1.04-1.13)], the association remained after excluding anal cancers from the end point [1.02/year (1.01-1.04)]. No association was seen between NNRTI use and NADC [1.00/year (0.98-1.02)]. CONCLUSIONS Cumulative use of PIs may be associated with a higher risk of anal cancer and possibly other NADC. Further investigation of biological mechanisms is warranted.

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.

Cytokine suppression of protease activation in wild-type p53-dependent and p53-independent apoptosis

M1 myeloid leukemic cells overexpressing wild-type p53 undergo apoptosis. This apoptosis can be suppressed by some cytokines, protease inhibitors, and antioxidants. We now show that induction of apoptosis by overexpressing wild-type p53 is associated with activation of interleukin-1β-converting enzyme (ICE)-like proteases, resulting in cleavage of poly(ADP- ribose) polymerase and the proenzyme of the ICE-like protease Nedd-2. Activation of these proteases and apoptosis were suppressed by the cytokine interleukin 6 or by a combination of the cytokine interferon γ and the antioxidant butylated hydroxyanisole, and activation of poly(ADP-ribose) polymerase and apoptosis were suppressed by some protease inhibitors. In a clone of M1 cells that did not express p53, vincristine or doxorubicin induced protease activation and apoptosis that were not suppressed by protease inhibitors, but were suppressed by interleukin 6. In another myeloid leukemia (7-M12) doxorubicin also induced protease activation and apoptosis that were not suppressed by protease inhibitors, but were suppressed by granulocyte–macrophage colony-stimulating factor. The results indicate that (i) overexpression of wild-type p53 by itself or treatment with cytotoxic compounds in wild-type p53-expressing or p53-nonexpressing myeloid leukemic cells is associated with activation of ICE-like proteases; (ii) cytokines exert apoptosis-suppressing functions upstream of protease activation; (iii) the cytotoxic compounds induce additional pathways in apoptosis; and (iv) cytokines can also suppress these other components of the apoptotic machinery.

Potentiation of the Oxidative Burst and Isoflavonoid Phytoalexin Accumulation by Serine Protease Inhibitors1

Treatment of soybean (Glycine max L. cv Williams 82) cell-suspension cultures with Pseudomonas syringae pv glycinea (Psg) harboring an avirulence gene (avrA) or with yeast elicitor resulted in an oxidative burst characterized by the accumulation of H2O2. This burst, and the resultant induction of glutathione S-transferase transcripts, occurred more rapidly and was more prolonged if cells were simultaneously treated with serine protease inhibitors such as phenylmethylsulfonyl fluoride (PMSF) or diisopropylfluorophosphate. PMSF and diisopropylfluorophosphate potentiate a large oxidative burst in cells exposed to Psg harboring the avrC avirulence gene, which is not recognized by the soybean cultivar used in this study. The potentiated burst was inhibited by diphenylene iodonium, an inhibitor of NADPH oxidase, and by the protein kinase inhibitor K252a. PMSF treatment of elicited cells or cells exposed to Psg:avrA caused a large increase in the accumulation of the isoflavonoid phytoalexin glyceollin; however, this was not associated with increased levels of transcripts encoding key phytoalexin biosynthetic enzymes. Glyceollin accumulation was inhibited by diphenylene iodonium; however, the oxidative burst in cells treated with Psg:avrC and PMSF was not followed by phytoalexin accumulation. We conclude that active oxygen species from the oxidative burst are necessary but not sufficient for inducing isoflavonoid phytoalexin accumulation in soybean cells.

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.

Antiretrovirals as antimalarial agents

Recent studies have indicated that antiretroviral protease inhibitors may affect outcome in malarial disease. We have investigated the antimalarial activities of 6 commonly used antiretroviral agents. Our data indicate that, in addition to the previously published effects on cytoadherence and phagocytosis, the human immunodeficiency virus (HIV)-1 protease inhibitors saquinavir, ritonavir, and indinavir directly inhibit the growth of Plasmodium falciparum in vitro at clinically relevant concentrations. These findings are particularly important in light of both the high rate of malaria and HIV-1 coinfection in sub-Saharan Africa and the effort to employ highly active antiretroviral therapy in these regions.

Site-directed mutagenesis and kinetic studies of the West Nile virus NS3 protease identify key enzyme-substrate interactions

The flavivirus West Nile virus (WNV) has spread rapidly throughout the world in recent years causing fever, meningitis, encephalitis, and fatalities. Because the viral protease NS2B/NS3 is essential for replication, it is attracting attention as a potential therapeutic target, although there are currently no antiviral inhibitors for any flavivirus. This paper focuses on elucidating interactions between a hexapeptide substrate (Ae-KPGLKR-p-nitroanilide) and residues at S1 and S2 in the active site of WNV protease by comparing the catalytic activities of selected mutant recombinant proteases in vitro. Homology modeling enabled the predictions of key mutations in VWNV NS3 protease at S1 (V115A/F, D129A/ E/N, S135A, Y150A/F, S160A, and S163A) and S2 (N152A) that might influence substrate recognition and catalytic efficiency. Key conclusions are that the substrate P1 Arg strongly interacts with S1 residues Asp-129, Tyr-150, and Ser-163 and, to a lesser extent, Ser-160, and P2 Lys makes an essential interaction with Asn-152 at S2. The inferred substrate-enzyme interactions provide a basis for rational protease inhibitor design and optimization. High sequence conservation within flavivirus proteases means that this study may also be relevant to design of protease inhibitors for other flavivirus proteases.

Functional expression of a novel Kunitz type protease inhibitor from the human blood fluke Schistosoma mansoni

BACKGROUND: Schistosomes are able to survive for prolonged periods in the blood system, despite continuous contact with coagulatory factors and mediators of the host immune system. Protease inhibitors likely play a critical role in host immune modulation thereby promoting parasite survival in this extremely hostile environment. Even though Kunitz type serine protease inhibitors have been shown to play important physiological functions in a range of organisms these proteins are less well characterised in parasitic helminths.

METHODS: We have cloned one gene sequence from S. mansoni, Smp_147730 (SmKI-1) which is coded for single domain Kunitz type protease inhibitor, E. coli-expressed and purified. Immunolocalisation and western blotting was carried out using affinity purified polyclonal anti-SmKI-1 murine antibodies to determine SmKI-1 expression in the parasite. Protease inhibitor assays and coagulation assays were performed to evaluate the functional roles of SmKI-1.

RESULTS: SmKI-1 is localised in the tegument of adult worms and the sub-shell region of eggs. Furthermore, this Kunitz protein is secreted into the host in the ES products of the adult worm. Recombinant SmKI-1 inhibited mammalian trypsin, chymotrypsin, neutrophil elastase, FXa and plasma kallikrein with IC50 values of 35 nM, 61 nM, 56 nM, 142 nM and 112 nM, respectively. However, no inhibition was detected for pancreatic elastase or cathepsin G. SmKI-1 (4 μM) delayed blood clot formation, reflected in an approximately three fold increase in activated partial thromboplastin time and prothrombin time.

CONCLUSIONS: We have functionally characterised the first Kunitz type protease inhibitor (SmKI-1) from S. mansoni and show that it has anti-inflammatory and anti-coagulant properties. SmKI-1 is one of a number of putative Kunitz proteins in schistosomes that have presumably evolved as an adaptation to protect these parasites from the defence mechanisms of their mammalian hosts. As such they may represent novel vaccine candidates and/or drug targets for schistosomiasis control.

Nuclear import of HIV-1 integrase is inhibited in vitro by styrylquinoline derivatives

Nuclear import of HIV-1 preintegration complexes (PICs) allows the virus to infect nondividing cells. Integrase (IN), the PIC-associated viral enzyme responsible for the integration of the viral genome into the host cell DNA, displays karyophilic properties and has been proposed to participate to the nuclear import of the PIC. Styrylquinolines (SQs) have been shown to block viral replication at nontoxic concentrations and to inhibit IN 3'-processing activity in vitro by competing with the DNA substrate binding. However, several lines of evidence suggested that SQs could have a postentry, preintegrative antiviral effect in infected cells. To gain new insights on the mechanism of their antiviral activity, SQs were assayed for their ability to affect nuclear import of HIV-1 IN and compared with the effect of a specific strand transfer inhibitor. Using an in vitro transport assay, we have previously shown that IN import is a saturable mechanism, thus showing that a limiting cellular factor is involved in this process. We now demonstrate that SQs specifically and efficiently inhibit in vitro nuclear import of IN without affecting other import pathways, whereas a specific strand transfer inhibitor does not affect IN import. These data suggest that SQs not only inhibit IN-DNA interaction but would also inhibit the interaction between IN and the cellular factor required for its nuclear import.

Associação entre o proteassoma e o inibidor de protease BTCI : caracterização fisico-química e efeitos moleculares em células de câncer de mama (MCF-7)

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Biológicas, Departamento de Biologia Celular, Pós-Graduação em Biologia Molecular, 2010.

Enzymatic Kinetic Resolution of tert-Butyl 2-(1-Hydroxyethyl)phenylcarbamate, A Key Intermediate to Chiral Organoselenanes and Organotelluranes

The enzymatic kinetic resolution of tert-butyl 2-(1-hydroxyethyl) phenylcarbamate via lipase-catalyzed transesterification reaction was studied. We investigated several reaction conditions and the carbamate was resolved by Candida antarctica lipase B (CAL-B), leading to the optically pure (R)- and (S)-enantiomers. The enzymatic process showed excellent enantioselectivity (E > 200). (R)- and (S)-tert-butyl 2-(1-hydroxyethyl) phenylcarbamate were easily transformed into the corresponding (R)and (S)-1-(2-aminophenyl)ethanols.

Molecular characterization of a miraculin-like gene differentially expressed during coffee development and coffee leaf miner infestation

The characterization of a coffee gene encoding a protein similar to miraculin-like proteins, which are members of the plant Kunitz serine trypsin inhibitor (STI) family of proteinase inhibitors (PIs), is described. PIs are important proteins in plant defence against insects and in the regulation of proteolysis during plant development. This gene has high identity with the Richadella dulcifica taste-modifying protein miraculin and with the tomato protein LeMir; and was named as CoMir (Coffea miraculin). Structural protein modelling indicated that CoMir had structural similarities with the Kunitz STI proteins, but suggested specific folding structures. CoMir was up-regulated after coffee leaf miner (Leucoptera coffella) oviposition in resistant plants of a progeny derived from crosses between C. racemosa (resistant) and C. arabica (susceptible). Interestingly, this gene was down-regulated during coffee leaf miner herbivory in susceptible plants. CoMir expression was up-regulated after abscisic acid application and wounding stress and was prominent during the early stages of flower and fruit development. In situ hybridization revealed that CoMir transcripts accumulated in the anther tissues that display programmed cell death (tapetum, endothecium and stomium) and in the metaxylem vessels of the petals, stigma and leaves. In addition, the recombinant protein CoMir shows inhibitory activity against trypsin. According to the present results CoMir may act in proteolytic regulation during coffee development and in the defence against L. coffeella. The similarity of CoMir with other Kunitz STI proteins and the role of CoMir in plant development and plant stress are discussed.

Adenanthera pavonina TRYPSIN INHIBITOR RETARD GROWTH OF Anagasta kuehniella (LEPIDOPTERA: PYRALIDAE)

Anagasta kuehniella is a polyphagous pest that feeds on a wide variety of stored products. The possible roles suggested for seed proteinase inhibitors include the function as a part of the plant defensive system against pest via inhibition of their proteolytic enzymes. In this study, a trypsin inhibitor (ApTI) was purified from Adenanthera pavonina seed and was tested for insect growth regulatory effect. The chronic ingestion of ApTI did result in a significant reduction in larval survival and weight. Larval and pupal developmental time of larvae fed on ApTI diet at 1% was significantly longer; the larval period was extended by 5 days and pupal period was 10 days longer, therefore delaying by up to 20 days and resulting in a prolonged period of development from larva to adult. As a result, the ApTI diet emergence rate was only 28% while the emergence rate of control larvae was 80%. The percentage of surviving adults (%S) decreased to 62%. The fourth instar larvae reared on a diet containing 1% ApTI showed a decrease in tryptic activity of gut and that no novel proteolytic form resistant to ApTI was induced. In addition, the tryptic activity in ApTI -fed larvae was sensitive to ApTI. These results suggest that ApTI have a potential antimetabolic effect when ingested by A. kuehniella. (C) 2010 Wiley Periodicals, Inc.

Regulatory effects of an inhibitor from Plathymenia foliolosa seeds on the larval development of Anagasta kuehniella (Lepidoptera)

The Mediterranean flour moth, Anagasta kuehniella, is one of the most important insect pests of grains, reported worldwide, feeding on stored grains and products of rice, rye, corn and wheat. Plants synthesize a variety of molecules, including trypsin inhibitors, to defend themselves against attack by insects. In this study, a trypsin inhibitor (PFTI) was purified from Plathymenia foliolosa (Benth.) seeds and was tested for insect growth regulatory effect. The survival and mass of A. kuehniella larvae feeding on control seeds were about 82.7% and 5 ring, respectively, whereas survival on seeds containing 0.7% PFTI was about 56%, while a 66.1% reduction in the average mass of the larvae was observed. The results from dietary utilization experiments with A. kuehniella larvae showed a reduction in efficiency of conversion of ingested food and digested food, and an increase in approximate digestibility and metabolic cost. The level of trypsin was significantly decreased in larval midgut and increased in the feces of larvae reared on a diet containing 0.7% PFTI. Results indicate that PFTI possesses a toxic effect against A. kuehniella larvae. (C) 2008 Elsevier Inc. All rights reserved.