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Common wheat (Triticum aestivum) is a hexaploid crop comprising three diploid sub-genomes labeled A, B, and D. The objective of this study is to investigate whether there is a discernible influence pattern from the D sub-genome with epistasis in genomic models for wheat diseases. Four genomic statistical models were employed; two models considered the linear genomic relationship of the lines. The first model (G) utilized all molecular markers, while the second model (ABD) utilized three matrices representing the A, B, and D sub-genomes. The remaining two models incorporated epistasis, one (GI) using all markers and the other (ABDI) considering markers in sub-genomes A, B, and D, including inter- and intra-sub-genome interactions. The data utilized pertained to three diseases: tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB), for synthetic hexaploid wheat (SHW) lines. The results (variance components) indicate that epistasis makes a substantial contribution to explaining genomic variation, accounting for approximately 50% in SNB and SB and only 29% for TS. In this contribution of epistasis, the influence of intra- and inter-sub-genome interactions of the D sub-genome is crucial, being close to 50% in TS and higher in SNB (60%) and SB (60%). This increase in explaining genomic variation is reflected in an enhancement of predictive ability from the G model (additive) to the ABDI model (additive and epistasis) by 9%, 5%, and 1% for SNB, SB, and TS, respectively. These results, in line with other studies, underscore the significance of the D sub-genome in disease traits and suggest a potential application to be explored in the future regarding the selection of parental crosses based on sub-genomes.
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Ascomicetos , Triticum , Triticum/genética , Epistasia Genética , Fenótipo , Ascomicetos/genéticaRESUMO
Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.
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Interação Gene-Ambiente , Herança Multifatorial , Animais , Genoma de Planta , Genótipo , Modelos Genéticos , Fenótipo , Reprodutibilidade dos Testes , Seleção GenéticaRESUMO
The rapid development of molecular markers and sequencing technologies has made it possible to use genomic prediction (GP) and selection (GS) in animal and plant breeding. However, when the number of observations (n) is large (thousands or millions), computational difficulties when handling these large genomic kernel relationship matrices (inverting and decomposing) increase exponentially. This problem increases when genomic × environment interaction and multi-trait kernels are included in the model. In this research we propose selecting a small number of lines m(m < n) for constructing an approximate kernel of lower rank than the original and thus exponentially decreasing the required computing time. First, we describe the full genomic method for single environment (FGSE) with a covariance matrix (kernel) including all n lines. Second, we select m lines and approximate the original kernel for the single environment model (APSE). Similarly, but including main effects and G × E, we explain a full genomic method with genotype × environment model (FGGE), and including m lines, we approximated the kernel method with G × E (APGE). We applied the proposed method to two different wheat data sets of different sizes (n) using the standard linear kernel Genomic Best Linear Unbiased Predictor (GBLUP) and also using eigen value decomposition. In both data sets, we compared the prediction performance and computing time for FGSE versus APSE; we also compared FGGE versus APGE. Results showed a competitive prediction performance of the approximated methods with a significant reduction in computing time. Genomic prediction accuracy depends on the decay of the eigenvalues (amount of variance information loss) of the original kernel as well as on the size of the selected lines m.
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When including genotype × environment interactions (G × E) in genomic prediction models, Hadamard or Kronecker products have been used to model the covariance structure of interactions. The relation between these two types of modeling has not been made clear in genomic prediction literature. Here, we demonstrate that a certain model based on a Hadamard formulation and another using the Kronecker product lead to exactly the same statistical model. Moreover, we illustrate how a multiplication of entries of covariance matrices is related to modeling locus × environmental-variable interactions explicitly. Finally, we use a wheat and a maize data set to illustrate that the environmental covariance E can be specified easily, also if no information on environmental variables - such as temperature or precipitation - is available. Given that lines have been tested in different environments, the corresponding environmental covariance can simply be estimated from the training set as phenotypic covariance between environments. To achieve a high level of increase in predictive ability, the environmental covariance has to be defined appropriately and records on the performance of the lines of the test set under different environmental conditions have to be included in the training set.
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Interação Gene-Ambiente , Modelos Genéticos , Genômica , Genótipo , Triticum/genéticaRESUMO
OBJECTIVE: Describe the methodological design of the National Health and Nutrition Survey 2018-19 (Ensanut 2018-19). MATERIALS AND METHODS: Ensanut 2018-19 is a probabilistic household survey. The following design elements are described: survey scope, sampling procedure, measurement procedure, inference procedure, and logistics organization. RESULTS: 44 069 full housing interviews and 82 490 full interviews of individuals were obtained. The home response rate was 87%. The response rate for individuals was 98%. CONCLUSIONS: The probabilistic design of the survey allows to create valid statistical inferences on parameters of public health interest at the national level and for all 32 states. In addition, some of the results are comparable with Ensanut 2012 in order to identify potential changes in the health and nutrition status of the Mexican population, so that health policies can be adjusted if necessary.
OBJETIVO: Describir el diseño metodológico de la Encuesta Nacional de Salud y Nutrición 2018-19 (Ensanut 2018-19). MATERIAL Y MÉTODOS: La Ensanut 2018-19 es una encuesta probabilística de hogares. Se describen los siguientes elementos del diseño: alcance de la encuesta, procedimiento de muestreo, procedimiento de medición, procedimiento de inferencia y organización logística. RESULTADOS: Se obtuvieron 44 069 entrevistas de viviendas completas y 82 490 entrevistas completas de individuos. La tasa de respuesta de hogar fue 87%. La tasa de respuesta de individuos fue de 98%. CONCLUSIONES: El diseño probabilístico de la encuesta permite hacer inferencias estadísticas válidas sobre parámetros de interés para la salud pública a nivel nacional y para las 32 entidades federativas. Además, algunos de sus resultados son comparables con los de la Ensanut 2012 para poder identificar potenciales cambios en los estados de salud y nutrición de la población mexicana, para que en caso de ser necesario s adecuen las políticas de salud.
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Inquéritos Nutricionais/métodos , Adulto , Idoso , Feminino , Humanos , Masculino , México , Pessoa de Meia-Idade , Fatores de Tempo , Adulto JovemRESUMO
Resumen: Objetivo Describir el diseño metodológico de la Encuesta Nacional de Salud y Nutrición 2018-19 (Ensanut 2018-19). Material y métodos La Ensanut 2018-19 es una encuesta probabilística de hogares. Se describen los siguientes elementos del diseño: alcance de la encuesta, procedimiento de muestreo, procedimiento de medición, procedimiento de inferencia y organización logística. Resultados Se obtuvieron 44 069 entrevistas de viviendas completas y 82 490 entrevistas completas de individuos. La tasa de respuesta de hogar fue 87%. La tasa de respuesta de individuos fue de 98%. Conclusiones El diseño probabilístico de la encuesta permite hacer inferencias estadísticas válidas sobre parámetros de interés para la salud pública a nivel nacional y para las 32 entidades federativas. Además, algunos de sus resultados son comparables con los de la Ensanut 2012 para poder identificar potenciales cambios en los estados de salud y nutrición de la población mexicana, para que en caso de ser necesario se adecuen las políticas de salud.
Abstract: Objective Describe the methodological design of the National Health and Nutrition Survey 2018-19 (Ensanut 2018-19). Materials and methods Ensanut 2018-19 is a probabilistic household survey. The following design elements are described: survey scope, sampling procedure, measurement procedure, inference procedure, and logistics organization. Results 44 069 full housing interviews and 82 490 full interviews of individuals were obtained. The home response rate was 87%. The response rate for individuals was 98%. Conclusions The probabilistic design of the survey allows to create valid statistical inferences on parameters of public health interest at the national level and for all 32 states. In addition, some of the results are comparable with Ensanut 2012 in order to identify potential changes in the health and nutrition status of the Mexican population, so that health policies can be adjusted if necessary.
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Humanos , Masculino , Feminino , Adulto , Idoso , Adulto Jovem , Inquéritos Nutricionais/métodos , Fatores de Tempo , MéxicoRESUMO
Deep learning (DL) is a promising method for genomic-enabled prediction. However, the implementation of DL is difficult because many hyperparameters (number of hidden layers, number of neurons, learning rate, number of epochs, batch size, etc.) need to be tuned. For this reason, deep kernel methods, which only require defining the number of layers, may be an attractive alternative. Deep kernel methods emulate DL models with a large number of neurons, but are defined by relatively easily computed covariance matrices. In this research, we compared the genome-based prediction of DL to a deep kernel (arc-cosine kernel, AK), to the commonly used non-additive Gaussian kernel (GK), as well as to the conventional additive genomic best linear unbiased predictor (GBLUP/GB). We used two real wheat data sets for benchmarking these methods. On average, AK and GK outperformed DL and GB. The gain in terms of prediction performance of AK and GK over DL and GB was not large, but AK and GK have the advantage that only one parameter, the number of layers (AK) or the bandwidth parameter (GK), has to be tuned in each method. Furthermore, although AK and GK had similar performance, deep kernel AK is easier to implement than GK, since the parameter "number of layers" is more easily determined than the bandwidth parameter of GK. Comparing AK and DL for the data set of year 2015-2016, the difference in performance of the two methods was bigger, with AK predicting much better than DL. On this data, the optimization of the hyperparameters for DL was difficult and the finally used parameters may have been suboptimal. Our results suggest that AK is a good alternative to DL with the advantage that practically no tuning process is required.
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One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were employed to improve selection by using markers and account for GE interaction simultaneously. Some of these models use special genetic covariance matrices. In addition, the scale of multi-environment trials is getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genomic GE models. Here we propose two functions: one to prepare the genomic kernels accounting for the genomic GE and another to perform genomic prediction using a Bayesian linear mixed model. A specific treatment is given for sparse covariance matrices, in particular, to block diagonal matrices that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option for creating genomic GE kernels and making genomic predictions.
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Interação Gene-Ambiente , Genótipo , Modelos Genéticos , Teorema de Bayes , Valor Preditivo dos TestesRESUMO
Pirfenidone is a non-steroidal antifibrotic compound that has been proposed in clinical protocols and experimental studies as a pharmacological treatment for fibroproliferative diseases. The objective of this study was to determine the genotoxicity or cytotoxicity of three doses of pirfenidone using the micronuclei test in peripheral blood erythrocytes of rodent models. Pirfenidone was administered orally to Balb-C mice for 3 days, and also was administered topically to hairless Sprague Dawley rats during the final stage of gestation. Mice were sampled every 24 h over the course of 6 days; pregnant rats were sampled every 24 h during the last 6 days of gestation, and pups were sampled at birth. Blood smears were analyzed and the frequencies of micronucleated erythrocytes (MNEs), micronucleated polychromatic erythrocytes (MNPCEs), and the proportion of polychromatic erythrocytes (PCEs), were recorded in samples from mice, pregnant rats and rat neonates. Increases in MN frequencies (p<0.03) were noted only in the positive control groups. No genotoxic effects or decreased PCE values were observed neither in newborn rats transplacentally exposed to pirfenidone, or in two adult rodent models when pirfenidone was administered orally or topically.
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Anti-Inflamatórios não Esteroides/toxicidade , Eritrócitos/efeitos dos fármacos , Testes para Micronúcleos , Mutagênicos/toxicidade , Piridonas/toxicidade , Animais , Eritrócitos/ultraestrutura , Feminino , Masculino , Camundongos , Camundongos Endogâmicos BALB C , Gravidez , Ratos , Ratos Sprague-DawleyRESUMO
Polarized T helper type 2 (Th2) response is linked with fibrosis. Here, we evaluated the effect of the anti-fibrotic agent pirfenidone on Th type 1 (Th1) and Th2 responses. For in vivo testing; Wistar rats were made cirrhotic by intraperitoneal administration of thioacetamide. Once hepatic damage was established, pirfenidone was administered intragastrically on a daily basis during three weeks. Gene expression of Th marks was evaluated by RT-PCR and Western blot assays from liver homogenates. Pirfenidone therapy induced down-regulation of Th2 transcripts and proteins (GATA3 and IL-4), without affecting significantly Th1 genes expression (T-bet and IFN-γ). We found that the activated form of p38 MAPK (identified by Western blot) was reduced by pirfenidone treatment, which is consistent with the anti-Th2 activity observed. Pirfenidone reduced GATA3 nuclear localization without modifying its DNA binding activity (evaluated by electrophoretic mobility shift assay). For in vitro testing; human naive CD4+ T cells were cultured in either Th1 or Th2 polarizing conditions in the presence of pirfenidone and flow cytometric analysis of intracellular synthesis of IFN-γ and IL-4 was conducted. Pirfenidone impaired development of Th2 subpopulation. In conclusion, pirfenidone is capable of impairing Th2 differentiation and limits Th2 profibrogenic response. The mechanism involves p38 inhibition and regulation of GATA3 expression and translocation.
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Anti-Inflamatórios não Esteroides/farmacologia , Diferenciação Celular/efeitos dos fármacos , Cirrose Hepática Experimental/tratamento farmacológico , Cirrose Hepática Experimental/metabolismo , Piridonas/farmacologia , Células Th1/citologia , Células Th2/citologia , Animais , Anti-Inflamatórios não Esteroides/uso terapêutico , Diferenciação Celular/genética , Células Cultivadas , Colágeno Tipo I/metabolismo , DNA/metabolismo , Regulação para Baixo/efeitos dos fármacos , Avaliação Pré-Clínica de Medicamentos/métodos , Fator de Transcrição GATA3/metabolismo , Expressão Gênica , Interferon gama , Interleucina-4/metabolismo , Fígado/metabolismo , Fígado/patologia , Cirrose Hepática Experimental/patologia , Masculino , Piridonas/uso terapêutico , Ratos , Ratos Wistar , Proteínas com Domínio T/metabolismo , Células Th1/efeitos dos fármacos , Células Th1/metabolismo , Células Th2/efeitos dos fármacos , Células Th2/metabolismo , Proteínas Quinases p38 Ativadas por Mitógeno/metabolismoRESUMO
Gene therapy represents a promising approach in the treatment of several diseases. Currently, the ideal vector has yet to be designed; though, adenoviral vectors (Ad-v) have provided the most utilized tool for gene transfer due principally to their simple production, among other specific characteristics. Ad-v viability represents a critical variable that may be affected by storage or shipping conditions and therefore it is advisable to be assessed previously to protocol performance. The present work is unique in this matter, as the complete detailed process to obtain Ad-v of preclinical grade is explained. Amplification in permissive HEK-293 cells, purification in CsCl gradients in a period of 10 h, spectrophotometric titration of viral particles (VP) and titration of infectious units (IU), yielding batches of AdßGal, AdGFP, AdHuPA and AdMMP8, of approximately 10¹³-10¹4 VP and 10¹²-10¹³ IU were carried out. In vivo functionality of therapeutic AdHuPA and AdMMP8 was evidenced in rats presenting CCl4-induced fibrosis, as more than 60% of fibrosis was eliminated in livers after systemic delivery through iliac vein in comparison with irrelevant AdßGal. Time required to accomplish the whole Ad-v production steps, including IU titration was 20 to 30 days. We conclude that production of Ad-v following standard operating procedures assuring vector functionality and the possibility to effectively evaluate experimental gene therapy results, leaving aside the use of high-cost commercial kits or sophisticated instrumentation, can be performed in a conventional laboratory of cell culture.
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Adenoviridae/genética , Vetores Genéticos/isolamento & purificação , Animais , Linhagem Celular , Centrifugação com Gradiente de Concentração , Césio , Cloretos , Técnicas de Transferência de Genes , Terapia Genética , Masculino , Ratos , EspectrofotometriaRESUMO
BACKGROUND: Plasmin role in transforming growth factor-beta (TGF-beta)-responsive gene regulation remains to be elucidated. Also, plasmin action on co-repressor Ski-related novel protein N (SnoN) and differential activation of matrix metalloproteinases (MMPs) are unknown. Thus, the role of plasmin on profibrogenic molecule expression, SnoN transcriptional kinetics and gelatinase activation was investigated. METHODS: Hepatic stellate cells (HSC) were transduced with adenovirus-mediated human urokinase plasminogen activator (Ad-huPA) (4 x 10(9) viral particles/ml). Overexpression of urokinase plasminogen activator and therefore of plasmin, was blocked by tranexamic acid (TA) in transduced HSC. Gene expression was monitored by reverse transcriptase polymerase chain reaction. HSC-free supernatants were used to evaluate MMP-2 and MMP-9 by zymography. SnoN, TGF-beta and tissue inhibitor of metalloproteinase (TIMP)-1 were analysed by Western blot. Plasmin and SnoN expression kinetics were evaluated in bile duct-ligated (BDL) rats. RESULTS: Plasmin overexpression in Ad-huPA-transduced HSC significantly decreased gene expression of profibrogenic molecules [alpha1(I)collagen 66%, TIMP-1 59%, alpha-smooth muscle actin 90% and TGF-beta 55%]. Interestingly, both SnoN gene and protein expression increased prominently. Plasmin inhibition by TA upregulated the profibrogenic genes, which respond to TGF-beta-intracellular signalling. In contrast, SnoN mRNA and protein dropped importantly. Plasmin-activated MMP-9 and MMP-2 in HSC supernatants. Taken together, these findings indicate that MMP-9 activation is totally plasmin dependent. SnoN levels significantly decreased in cholestatic-BDL rats (82%) as compared with control animals. Interestingly, hepatic plasmin levels dropped 46% in BDL rats as compared with control. CONCLUSION: Plasmin plays a key role in regulating TGF-beta-responding genes. In particular, regulation of TGF-beta-co-repressor (SnoN) is greatly affected, which suggests SnoN as a cardinal player in cholestasis-induced fibrogenesis.
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Fibrinolisina/fisiologia , Regulação da Expressão Gênica , Células Estreladas do Fígado/metabolismo , Proteínas do Tecido Nervoso/genética , Fatores de Transcrição/genética , Fator de Crescimento Transformador beta/genética , Animais , Linhagem Celular , Colágeno Tipo I/metabolismo , Células Estreladas do Fígado/efeitos dos fármacos , Células Estreladas do Fígado/patologia , Humanos , Cirrose Hepática Experimental/genética , Cirrose Hepática Experimental/metabolismo , Cirrose Hepática Experimental/patologia , Proteínas do Tecido Nervoso/metabolismo , Ratos , Inibidor Tecidual de Metaloproteinase-1/genética , Inibidor Tecidual de Metaloproteinase-1/metabolismo , Inibidor Tecidual de Metaloproteinase-2/genética , Ácido Tranexâmico/farmacologia , Fatores de Transcrição/metabolismo , Transdução Genética , Fator de Crescimento Transformador beta/metabolismo , Ativador de Plasminogênio Tipo Uroquinase/genética , Ativador de Plasminogênio Tipo Uroquinase/metabolismoRESUMO
BACKGROUND: Dominant-negative transforming growth factor beta type II receptor (TbetaRIIDeltacyt) is a protein that blocks transforming growth factor (TGF-beta) signaling. Because the consequences of blocking TGF-beta have not been completely elucidated in liver fibrosis, we analysed the effects of adenoviral delivery of TbetaRIIDeltacyt on profibrogenic genes and matrix metalloproteinase (MMP) proteins, as well as on TGF-beta signal repressor SKI-like oncogene (SnoN), in cultured hepatic stellate cells (HSCs) and in a rat model of liver fibrosis. METHODS: To induce liver fibrosis, rats were treated with thioacetamide for 7 weeks and administrated once with Ad-TbetaRIIDeltacyt or Ad-betagal through the iliac vein. Fibrosis was measured by morphometric analysis. We evaluated SnoN by western blot, immunocytochemistry and immunohistochemistry; MMP activity was determined by zymography and profibrogenic gene expression by the real-time reverse transcriptase-polymerase chain reaction in cultured HSCs and liver tissue. RESULTS: Profibrogenic gene expression of collagen alpha1 (I), TGF-beta1, platelet-derived growth factor-B, plasminogen activator inhibitor (PAI)-1, tissue inhibitor of matrix metalloproteinase-1 and MMP-2 was down-regulated; whereas MMP-3 was over-expressed in response to Ad-TbetaRIIDeltacyt in HSCs. Moreover, zymography assays corroborated MMP-2 and MMP-3 changes in activity. Surprisingly, anti-TGF-beta molecular intervention increased nuclear SnoN in HSCs. In vivo, Ad-TbetaRIIDeltacyt reduced liver fibrosis, increased nuclear SnoN in sinusoidal cells, and also produced significant suppression in collagen alpha1 (I), TGF-beta1, PAI-1, MMP-2 and over-expression in MMP-3 in thioacetamide-intoxicated animals. CONCLUSIONS: The results obtained in the present study suggest that the molecular mechanism for the blocking effects of Ad-TbetaRIIDeltacyt in TGF-beta signaling acts via up-regulation of the transcriptional repressor SnoN, which antagonizes TGF-beta signaling (TGF-beta/Smad-pathway inhibitor). Consequently, profibrogenic genes are down-regulated.
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Adenoviridae/genética , Técnicas de Transferência de Genes , Células Estreladas do Fígado/metabolismo , Peptídeos e Proteínas de Sinalização Intracelular/genética , Cirrose Hepática/prevenção & controle , Metaloproteinase 2 da Matriz/metabolismo , Proteínas Oncogênicas/genética , Proteínas Serina-Treonina Quinases/genética , Proteínas Proto-Oncogênicas/genética , Receptores de Fatores de Crescimento Transformadores beta/genética , Proteínas Repressoras/genética , Adenoviridae/metabolismo , Animais , Peso Corporal , Células Cultivadas , Células Estreladas do Fígado/citologia , Peptídeos e Proteínas de Sinalização Intracelular/metabolismo , Cirrose Hepática/induzido quimicamente , Cirrose Hepática/patologia , Metaloproteinase 2 da Matriz/genética , Metaloproteinase 3 da Matriz/genética , Metaloproteinase 3 da Matriz/metabolismo , Proteínas Oncogênicas/metabolismo , Proteínas Serina-Treonina Quinases/metabolismo , Proteínas Proto-Oncogênicas/metabolismo , Ratos , Receptor do Fator de Crescimento Transformador beta Tipo II , Receptores de Fatores de Crescimento Transformadores beta/metabolismo , Proteínas Repressoras/metabolismo , Transdução de Sinais/fisiologia , Tioacetamida/farmacologia , Transdução Genética , Transgenes , Regulação para CimaRESUMO
BACKGROUND: The authors' previous data support the notion that adenoviral-driven urokinase plasminogen activator (u-PA) expression results in reversion of experimental liver cirrhosis. The specific aim of the present study was to decipher the mechanisms involved in the regulation by endogenous/gene-delivered u-PA of matrix metalloproteinases (MMP) and related proteins engaged in degradation of excessive hepatic connective tissue. METHODS: Tissue slices from cirrhotic rat livers were incubated with u-PA-rich supernatants from 24-h-cultured hepatic stellate cells (HSC). Matrix metalloproteinase-2, -9 and tissue inhibitor of metalloproteinases-1 (TIMP-1) were detected by western blot and biologic activity. The HSC that discontinued u-PA production were transfected with the adenovector Adu-PA and serum-free supernatants evaluated for proteolytic activity by MMP-3, MMP-2 and MMP-9. Collagen I, transforming growth factor-beta1 (TGF-beta1), plasminogen activator inhibitor-1 (PAI-1) and TIMP-1 mRNA levels were also evaluated. RESULTS AND CONCLUSION: Endogenous u-PA from cultured HSC significantly induced the active forms of MMP-2 (68 kDa) and MMP-9 (78 kDa) in cirrhotic tissue slices. The TIMP-1 molecular forms demonstrated that u-PA pushed the presence of 'free' TIMP-1 (not complexed with MMP; 71%) in cirrhotic tissue. When non-producing u-PA-HSC were transfected with adenoviral vector coding for the functional human protein u-PA (Adhu-PA), an overactivation of MMP-3, MMP-2 and MMP-9 (800%, 48% and 100%, respectively) was found as compared with HSC transfected with control adenovirus encoding green fluorescent protein (Ad-GFP). Finally, gene expression of collagen I, TGF-beta1, PAI-1 and TIMP-1 were downregulated by Adhu-PA action as well.