Computer-aided design and implementation of efficient biosynthetic pathways to produce high added-value products derived from tyrosine in Escherichia coli.

Developing efficient bioprocesses requires selecting the best biosynthetic pathways, which can be challenging and time-consuming due to the vast amount of data available in databases and literature. The extension of the shikimate pathway for the biosynthesis of commercially attractive molecules often involves promiscuous enzymes or lacks well-established routes. To address these challenges, we developed a computational workflow integrating enumeration/retrosynthesis algorithms, a toolbox for pathway analysis, enzyme selection tools, and a gene discovery pipeline, supported by manual curation and literature review. Our focus has been on implementing biosynthetic pathways for tyrosine-derived compounds, specifically L-3,4-dihydroxyphenylalanine (L-DOPA) and dopamine, with significant applications in health and nutrition. We selected one pathway to produce L-DOPA and two different pathways for dopamine-one already described in the literature and a novel pathway. Our goal was either to identify the most suitable gene candidates for expression in Escherichia coli for the known pathways or to discover innovative pathways. Although not all implemented pathways resulted in the accumulation of target compounds, in our shake-flask experiments we achieved a maximum L-DOPA titer of 0.71 g/L and dopamine titers of 0.29 and 0.21 g/L for known and novel pathways, respectively. In the case of L-DOPA, we utilized, for the first time, a mutant version of tyrosinase from Ralstonia solanacearum. Production of dopamine via the known biosynthesis route was accomplished by coupling the L-DOPA pathway with the expression of DOPA decarboxylase from Pseudomonas putida, resulting in a unique biosynthetic pathway never reported in literature before. In the context of the novel pathway, dopamine was produced using tyramine as the intermediate compound. To achieve this, tyrosine was initially converted into tyramine by expressing TDC from Levilactobacillus brevis, which, in turn, was converted into dopamine through the action of the enzyme encoded by ppoMP from Mucuna pruriens. This marks the first time that an alternative biosynthetic pathway for dopamine has been validated in microbes. These findings underscore the effectiveness of our computational workflow in facilitating pathway enumeration and selection, offering the potential to uncover novel biosynthetic routes, thus paving the way for other target compounds of biotechnological interest.

Genetic variants as ovarian cancer first-line treatment hallmarks: A systematic review and meta-analysis.

BACKGROUND: The potential predictive value of genetic polymorphisms in ovarian cancer first-line treatment is inconsistently reported. We aimed to review ovarian cancer pharmacogenetic studies to update and summarize the available data and to provide directions for further research. METHODS: A systematic review followed by a meta-analysis was conducted on cohort studies assessing the involvement of genetic polymorphisms in ovarian cancer first-line treatment response retrieved through a MEDLINE database search by November 2016. Studies were pooled and summary estimates and 95% confidence intervals (CI) were calculated using random or fixed-effects models as appropriate. RESULTS: One hundred and forty-two studies gathering 106871 patients were included. Combined data suggested that GSTM1-null genotype patients have a lower risk of death compared to GSTM1-wt carriers, specifically in advanced stages (hazard ratio (HR), 0.68; 95% CI, 0.48-0.97) and when submitted to platinum-based chemotherapy (aHR, 0.61; 95% CI, 0.39-0.94). ERCC1 rs11615 and rs3212886 might have also a significant impact in treatment outcome (aHR, 0.67; 95% CI, 0.51-0.89; aHR, 1.28; 95% CI, 1.01-1.63, respectively). Moreover, ERCC2 rs13181 and rs1799793 showed a distinct ethnic behavior (Asians: aHR, 1.41; 95% CI, 0.80-2.49; aHR, 1.07; 95% CI, 0.62-1.86; Caucasians: aHR, 0.10; 95% CI, 0.01-0.96; aHR, 0.18; 95% CI, 0.05-0.68, respectively). CONCLUSION(S): The definition of integrative predictive models should encompass genetic information, especially regarding GSTM1 homozygous deletion. Justifying additional pharmacogenetic investigation are variants in ERCC1 and ERCC2, which highlight the DNA Repair ability to ovarian cancer prognosis. Further knowledge could aid to understand platinum-treatment failure and to tailor chemotherapy strategies.

Metabolic Profiling and Classification of Propolis Samples from Southern Brazil: An NMR-Based Platform Coupled with Machine Learning.

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching ∼90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

CBFA: phenotype prediction integrating metabolic models with constraints derived from experimental data.

BACKGROUND: Flux analysis methods lie at the core of Metabolic Engineering (ME), providing methods for phenotype simulation that allow the determination of flux distributions under different conditions. Although many constraint-based modeling software tools have been developed and published, none provides a free user-friendly application that makes available the full portfolio of flux analysis methods. RESULTS: This work presents Constraint-based Flux Analysis (CBFA), an open-source software application for flux analysis in metabolic models that implements several methods for phenotype prediction, allowing users to define constraints associated with measured fluxes and/or flux ratios, together with environmental conditions (e.g. media) and reaction/gene knockouts. CBFA identifies the set of applicable methods based on the constraints defined from user inputs, encompassing algebraic and constraint-based simulation methods. The integration of CBFA within the OptFlux framework for ME enables the utilization of different model formats and standards and the integration with complementary methods for phenotype simulation and visualization of results. CONCLUSIONS: A general-purpose and flexible application is proposed that is independent of the origin of the constraints defined for a given simulation. The aim is to provide a simple to use software tool focused on the application of several flux prediction methods.

Semantic annotation of biological concepts interplaying microbial cellular responses.

BACKGROUND: Automated extraction systems have become a time saving necessity in Systems Biology. Considerable human effort is needed to model, analyse and simulate biological networks. Thus, one of the challenges posed to Biomedical Text Mining tools is that of learning to recognise a wide variety of biological concepts with different functional roles to assist in these processes. RESULTS: Here, we present a novel corpus concerning the integrated cellular responses to nutrient starvation in the model-organism Escherichia coli. Our corpus is a unique resource in that it annotates biomedical concepts that play a functional role in expression, regulation and metabolism. Namely, it includes annotations for genetic information carriers (genes and DNA, RNA molecules), proteins (transcription factors, enzymes and transporters), small metabolites, physiological states and laboratory techniques. The corpus consists of 130 full-text papers with a total of 59043 annotations for 3649 different biomedical concepts; the two dominant classes are genes (highest number of unique concepts) and compounds (most frequently annotated concepts), whereas other important cellular concepts such as proteins account for no more than 10% of the annotated concepts. CONCLUSIONS: To the best of our knowledge, a corpus that details such a wide range of biological concepts has never been presented to the text mining community. The inter-annotator agreement statistics provide evidence of the importance of a consolidated background when dealing with such complex descriptions, the ambiguities naturally arising from the terminology and their impact for modelling purposes.Availability is granted for the full-text corpora of 130 freely accessible documents, the annotation scheme and the annotation guidelines. Also, we include a corpus of 340 abstracts.

@Note: a workbench for biomedical text mining.

Biomedical Text Mining (BioTM) is providing valuable approaches to the automated curation of scientific literature. However, most efforts have addressed the benchmarking of new algorithms rather than user operational needs. Bridging the gap between BioTM researchers and biologists' needs is crucial to solve real-world problems and promote further research. We present @Note, a platform for BioTM that aims at the effective translation of the advances between three distinct classes of users: biologists, text miners and software developers. Its main functional contributions are the ability to process abstracts and full-texts; an information retrieval module enabling PubMed search and journal crawling; a pre-processing module with PDF-to-text conversion, tokenisation and stopword removal; a semantic annotation schema; a lexicon-based annotator; a user-friendly annotation view that allows to correct annotations and a Text Mining Module supporting dataset preparation and algorithm evaluation. @Note improves the interoperability, modularity and flexibility when integrating in-home and open-source third-party components. Its component-based architecture allows the rapid development of new applications, emphasizing the principles of transparency and simplicity of use. Although it is still on-going, it has already allowed the development of applications that are currently being used.