Prokaryotic expression and characterization of the heterodimeric construction of ZnT8 and its application for autoantibodies detection in diabetes mellitus

Background: In the present work we described the recombinant production and characterization of heterodimeric construction ZnT8-Arg-Trp325 fused to thioredoxin using a high-performance expression system such as Escherichia coli. In addition, we apply this novel recombinant antigen in a non-radiometric method, with high sensitivity, low operational complexity and lower costs. Results: ZnT8 was expressed in E. coli as a fusion protein with thioredoxin (TrxZnT8). After 3 h for induction, recombinant protein was obtained from the intracellular soluble fraction and from inclusion bodies and purified by affinity chromatography. The expression and purification steps, analyzed by SDS-PAGE and western blot, revealed a band compatible with TrxZnT8 expected theoretical molecular weight (≈ 36.8 kDa). The immunochemical ability of TrxZnT8 to compete with [35S]ZnT8 (synthesized with rabbit reticulocyte lysate system) was assessed qualitatively by incubating ZnT8A positive patient sera in the presence of 0.2-0.3 μM TrxZnT8. Results were expressed as standard deviation scores (SDs). All sera became virtually negative under antigen excess (19.26-1.29 for TrxZnT8). Also, radiometric quantitative competition assays with ZnT8A positive patient sera were performed by adding TrxZnT8 (37.0 pM-2.2 μM), using [35S]ZnT8. All dose-response curves showed similar protein concentration that caused 50% inhibition (14.9-0.15 nM for TrxZnT8). On the other hand, preincubated bridge ELISA for ZnT8A detection was developed. This assay showed 51.7% of sensitivity and 97.1% of specificity. Conclusions: It was possible to obtain with high-yield purified heterodimeric construction of ZnT8 in E. coli and it was applied in cost-effective immunoassay for ZnT8A detection.Fil: Faccinetti, Natalia Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaFil: Guerra, Luciano Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaFil: Sabljic, Adriana Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaFil: Bombicino, Silvina Sonia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaFil: Rovitto, Bruno David. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaFil: Iacono, Ruben Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaFil: Poskus, Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaFil: Trabucchi, Aldana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaFil: Valdez, Silvina Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; Argentin

The Goldilocks Approach: A Review of Employing Design of Experiments in Prokaryotic Recombinant Protein Production

The production of high yields of soluble recombinant protein is one of the main objectives of protein biotechnology. Several factors, such as expression system, vector, host, media composition and induction conditions can influence recombinant protein yield. Identifying the most important factors for optimum protein expression may involve significant investment of time and considerable cost. To address this problem, statistical models such as Design of Experiments (DoE) have been used to optimise recombinant protein production. This review examines the application of DoE in the production of recombinant proteins in prokaryotic expression systems with specific emphasis on media composition and culture conditions. The review examines the most commonly used DoE screening and optimisation designs. It provides examples of DoE applied to optimisation of media and culture conditions

Codon usage comparison of novel genes in clinical isolates of Haemophilus influenzae

A similarity statistic for codon usage was developed and used to compare novel gene sequences found in clinical isolates of Haemophilus influenzae with a reference set of 80 prokaryotic, eukaryotic and viral genomes. These analyses were performed to obtain an indication as to whether individual genes were Haemophilus-like in nature, or if they probably had more recently entered the H.influenzae gene pool via horizontal gene transfer from other species. The average and SD values were calculated for the similarity statistics from a study of the set of all genes in the H.influenzae Rd reference genome that encoded proteins of 100 amino acids or longer. Approximately 80% of Rd genes gave a statistic indicating that they were most like other Rd genes. Genes displaying codon usage statistics >1 SD above this range were either considered part of the highly expressed group of H.influenzae genes, or were considered of foreign origin. An alternative determinant for identifying genes of foreign origin was when the similarity statistics produced a value that was much closer to a non-H.influenzae reference organism than to any of the Haemophilus species contained in the reference set. Approximately 65% of the novel sequences identified in the H.influenzae clinical isolates displayed codon usages most similar to Haemophilus sp. The remaining novel sequences produced similarity statistics closer to one of the other reference genomes thereby suggesting that these sequences may have entered the H.influenzae gene pool more recently via horizontal transfer

Use of artificial genomes in assessing methods for atypical gene detection

Parametric methods for identifying laterally transferred genes exploit the directional mutational biases unique to each genome. Yet the development of new, more robust methods - as well as the evaluation and proper implementation of existing methods - relies on an arbitrary assessment of performance using real genomes, where the evolutionary histories of genes are not known. We have used the framework of a generalized hidden Markov model to create artificial genomes modeled after genuine genomes. To model a genome, "core" genes - those displaying patterns of mutational biases shared among large numbers of genes - are identified by a novel gene clustering approach based on the Akaike information criterion. Gene models derived from multiple "core" gene clusters are used to generate an artificial genome that models the properties of a genuine genome. Chimeric artificial genomes - representing those having experienced lateral gene transfer - were created by combining genes from multiple artificial genomes, and the performance of the parametric methods for identifying "atypical" genes was assessed directly. We found that a hidden Markov model that included multiple gene models, each trained on sets of genes representing the range of genotypic variability within a genome, could produce artificial genomes that mimicked the properties of genuine genomes. Moreover, different methods for detecting foreign genes performed differently - i.e., they had different sets of strengths and weaknesses - when identifying atypical genes within chimeric artificial genomes. © 2005 Azad and Lawrence

Markov Chain Ontology Analysis (MCOA)

<p>Abstract</p> <p>Background</p> <p>Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data.</p> <p>Results</p> <p>In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods.</p> <p>Conclusion</p> <p>A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches.</p

Relative Codon Adaptation: A Generic Codon Bias Index for Prediction of Gene Expression

The development of codon bias indices (CBIs) remains an active field of research due to their myriad applications in computational biology. Recently, the relative codon usage bias (RCBS) was introduced as a novel CBI able to estimate codon bias without using a reference set. The results of this new index when applied to Escherichia coli and Saccharomyces cerevisiae led the authors of the original publications to conclude that natural selection favours higher expression and enhanced codon usage optimization in short genes. Here, we show that this conclusion was flawed and based on the systematic oversight of an intrinsic bias for short sequences in the RCBS index and of biases in the small data sets used for validation in E. coli. Furthermore, we reveal that how the RCBS can be corrected to produce useful results and how its underlying principle, which we here term relative codon adaptation (RCA), can be made into a powerful reference-set-based index that directly takes into account the genomic base composition. Finally, we show that RCA outperforms the codon adaptation index (CAI) as a predictor of gene expression when operating on the CAI reference set and that this improvement is significantly larger when analysing genomes with high mutational bias

Optimization of a bioassay to evaluate Escherichia coli stress responses

Tese de mestrado em Microbiologia Aplicada, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2017Na última década, a espectroscopia por transformada de Fourier (FTIR) tem vindo a ser aplicada às mais diversificadas áreas, sendo uma técnica cada vez mais utilizada em microbiologia, por permitir obter resultados num curto espaço de tempo e por ter um baixo custo por análise. No entanto, a reprodutibilidade desta técnica é influenciada por um grande número de fatores, entre os quais, o estado fisiológico das células, o método de manipulação da amostra e as condições de crescimento. Para tentar solucionar este problema, o presente trabalho pretende otimizar um bioensaio, baseado em espectroscopia de infravermelho, para avaliar a resposta metabólica dos microrganismos quando expostos a condições de stress, utilizando a bactéria Escherichia coli como modelo, uma vez que as suas vias metabólicas são muito semelhantes com as de outros microrganismos. O bioensaio foi otimizado em termos da concentração de nutriente do meio de cultura, (tendo sido testadas as concentrações 1.25, 2.5 e 5x), fase de crescimento da bactéria E. coli (onde foram utilizadas células na fase exponencial, no início da fase estacionária e na fase estacionária) e o tempo de exposição das células aos agentes de stress (1, 8 e 24 h). Os agentes de stress utilizados foram: álcool, lixívia, cloreto de sódio, ácido clorídrico e hidróxido de sódio. Estes agentes foram escolhidos por teres diferentes mecanismos de ação nas células dos microrganismos. É de extrema importância o desenvolvimento e otimização de bioensaios que permitam avaliar a resposta metabólica das células dos microrganismos quando expostos a condições adversas, para aumentar o conhecimento sobre o metabolismo microbiano e, por exemplo, para o desenvolvimento de novos antibióticos. Após a otimização do bioensaio, foi decidido testar o ensaio expondo as células de E. coli a seis antibióticos diferentes, cujos mecanismos de ação são conhecidos. Desta maneira, é possível observar se a otimização do bioensaio cumpre o objetivo de diferenciar amostras expostas a diferentes substâncias. Observou-se que as condições que permitem obter uma maior reprodutibilidade e sensibilidade do bioensaio são diferentes para cada tipo de agente de stress, mas regra geral, expor as células aos agentes de stress durante 24 horas, usar o meio 1.25x concentrado e as células na fase de crescimento estacionária permite distinguir os vários mecanismos de ação no metabolismo das células de E. coli. A metodologia descrita permitiu identificar o impacto de cada parâmetro na resolução metabólica da impressão digital biomolécula adquirida pela espectroscopia FTIR e, portanto, o bioensaio otimizado maximiza a capacidade metabólica de impressão digital do método, nomeadamente ao caracterizar diferentes respostas metabólico ao stress.In the last decade, Fourier Transform Infrared Spectroscopy (FTIR) has been applied in several areas, being an increasingly used technique in microbiology, especially for whole cell metabolomics fingerprint. However, the reproducibility of this technique is influenced by a large number of factors such as the physiological state of cells, sample manipulation and growth conditions. In order to solve this problem, the present work intends to optimize a bioassay for evaluating the response of microorganisms to stress conditions, using Escherichia coli as a model, as its metabolic pathways share pronounced similarities to mainstream pathways across other microorganisms. The bioassay was optimized in terms of nutrient medium concentration, E.coli growth phase and exposed time to the stress agents. The stress agents used were: ethanol, bleach, sodium chloride, hydrochloric acid and sodium hydroxide. These agents were chosen for having different mechanisms of action in microorganism’s cells. A bioassay which allows to evaluate the metabolic response of the microorganism cell when exposed to stress conditions is important to increase knowledge about microbial metabolism and for example for the development of new drugs. After the bioassay optimization, it was decided to test the assay exposing the E. coli cells to six antibiotics, whose mechanism of action is known. This allows to observe if the optimization of the bioassay meets the objective of differentiating samples exposed to different substances. It was observed that the conditions that allow higher reproducibility and sensitivity of the bioassay are different for each type of stress agent, but as a general rule exposing the cells to stress agents for 24 hrs, use the nutrient medium 1.25x concentrate and the cells in the stationary growth phase were the global optimal conditions to distinguish the various mechanisms of action on the metabolism of E. coli cells. The approach described allowed to identify the impact of each parameter on the metabolic resolution of the biomolecular fingerprint acquired by FTIR spectroscopy, and therefore, the adapted bioassay maximizes the metabolic fingerprinting ability of the method, namely to characterize different metabolic stress responses