A Novel L-Asparaginase from Enterobacter sp. Strain M55 from Maras Salterns in Peru

L-Asparaginase (ASNase) is used in medicine for neoplasms treatment and in food industry for mitigation of acrylamide in high-temperature processed food. In medicine, commercial ASNases have exhibited side effects and l-glutaminase (GLNase) activity affecting the clinical treatment. The aim of this work was to study a novel ASNase from Enterobacter sp. M55 isolated from Maras Salters in Peru, which was purified and biochemically characterised. This ASNase exhibited a Km of 5.71 mM and a Vmax of 0.16 µmol mL–1 min–1, as well as an optimum temperature and pH of 37 °C and 6, respectively. Moreover, a good activity (80 %) was observed at physiological pH. Likewise, the enzyme increased its activity by around 50% in presence of urea, glutathione, and glucose. Whilst in presence of serum compounds, it kept more that 60 % of activity. In addition, this ASNase showed low GLNase activity

EcoCyc: A comprehensive view of Escherichia coli biology

EcoCyc (http://EcoCyc.org) provides a comprehensive encyclopedia of Escherichia coli biology. EcoCyc integrates information about the genome, genes and gene products; the metabolic network; and the regulatory network of E. coli. Recent EcoCyc developments include a new initiative to represent and curate all types of E. coli regulatory processes such as attenuation and regulation by small RNAs. EcoCyc has started to curate Gene Ontology (GO) terms for E. coli and has made a dataset of E. coli GO terms available through the GO Web site. The curation and visualization of electron transfer processes has been significantly improved. Other software and Web site enhancements include the addition of tracks to the EcoCyc genome browser, in particular a type of track designed for the display of ChIP-chip datasets, and the development of a comparative genome browser. A new Genome Omics Viewer enables users to paint omics datasets onto the full E. coli genome for analysis. A new advanced query page guides users in interactively constructing complex database queries against EcoCyc. A Macintosh version of EcoCyc is now available. A series of Webinars is available to instruct users in the use of EcoCyc

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

EcoCyc: a comprehensive database of Escherichia coli biology

EcoCyc (http://EcoCyc.org) is a comprehensive model organism database for Escherichia coli K-12 MG1655. From the scientific literature, EcoCyc captures the functions of individual E. coli gene products; their regulation at the transcriptional, post-transcriptional and protein level; and their organization into operons, complexes and pathways. EcoCyc users can search and browse the information in multiple ways. Recent improvements to the EcoCyc Web interface include combined gene/protein pages and a Regulation Summary Diagram displaying a graphical overview of all known regulatory inputs to gene expression and protein activity. The graphical representation of signal transduction pathways has been updated, and the cellular and regulatory overviews were enhanced with new functionality. A specialized undergraduate teaching resource using EcoCyc is being developed

Towards the reconstruction of integrated genome-scale models of metabolism and gene expression

The reconstruction of integrated genome-scale models of metabolism and gene expression has been a challenge for a while now. In fact, various methods that allow integrating reconstructions of Transcriptional Regulatory Networks, gene expression data or both into Genome-Scale Metabolic Models have been proposed. Several of these methods are surveyed in this article, which allowed identifying their strengths and weaknesses concerning the reconstruction of integrated models for multiple prokaryotic organisms. Additionally, the main resources of regulatory information were also surveyed, as the existence of novel sources of regulatory information and gene expression data may contribute for the improvement of methodologies referred herein.This study was supported by the Portuguese Foundation for Science andTechnology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit andBioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European RegionalDevelopment Fund under the scope of Norte2020-Programa Operacional Regional do Norte. Fernando Cruz holds a doctoral fellowship (SFRH/BD/139198/2018) funded by the FCT. The authors thank project SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway (814408) funded by the European Commission.info:eu-repo/semantics/publishedVersio

A unified resource for transcriptional regulation in Escherichia coli K-12 incorporating high-throughput-generated binding data into RegulonDB version 10.0

Abstract Background Our understanding of the regulation of gene expression has benefited from the availability of high-throughput technologies that interrogate the whole genome for the binding of specific transcription factors and gene expression profiles. In the case of widely used model organisms, such as Escherichia coli K-12, the new knowledge gained from these approaches needs to be integrated with the legacy of accumulated knowledge from genetic and molecular biology experiments conducted in the pre-genomic era in order to attain the deepest level of understanding possible based on the available data. Results In this paper, we describe an expansion of RegulonDB, the database containing the rich legacy of decades of classic molecular biology experiments supporting what we know about gene regulation and operon organization in E. coli K-12, to include the genome-wide dataset collections from 32 ChIP and 19 gSELEX publications, in addition to around 60 genome-wide expression profiles relevant to the functional significance of these datasets and used in their curation. Three essential features for the integration of this information coming from different methodological approaches are: first, a controlled vocabulary within an ontology for precisely defining growth conditions; second, the criteria to separate elements with enough evidence to consider them involved in gene regulation from isolated transcription factor binding sites without such support; and third, an expanded computational model supporting this knowledge. Altogether, this constitutes the basis for adequately gathering and enabling the comparisons and integration needed to manage and access such wealth of knowledge. Conclusions This version 10.0 of RegulonDB is a first step toward what should become the unifying access point for current and future knowledge on gene regulation in E. coli K-12. Furthermore, this model platform and associated methodologies and criteria can be emulated for gathering knowledge on other microbial organisms

The incidence of stroke in Indigenous populations of countries with a Very High Human Development Index: a systematic review protocol

Background and Aims: Despite known Indigenous health and socioeconomic disadvantage in countries with a Very High Human Development Index, data on the incidence of stroke in these populations are sparse. With oversight from an Indigenous Advisory Board, we will undertake a systematic review of the incidence of stroke in Indigenous populations of developed countries or regions, with comparisons between Indigenous and non-Indigenous populations of the same region, though not between different Indigenous populations. Methods: Using PubMed, OVID-EMBASE, and Global Health databases, we will examine population-based incidence studies of stroke in Indigenous adult populations of developed countries published 1990-current, without language restriction. Non-peer-reviewed sources, studies including <10 Indigenous People, or with insufficient data to determine incidence, will be excluded. Two reviewers will independently validate the search strategies, screen titles and abstracts, and record reasons for rejection. Relevant articles will undergo full-text screening, with standard data extracted for all studies included. Quality assessment will include Sudlow and Warlow's criteria for population-based stroke incidence studies, the Newcastle-Ottawa Scale for risk of bias, and the CONSIDER checklist for Indigenous research. Results: Primary outcomes include crude, age-specific and/or age-standardized incidence of stroke. Secondary outcomes include overall stroke rates, incidence rate ratio and case-fatality. Results will be synthesized in figures and tables, describing data sources, populations, methodology, and findings. Within-population meta-analysis will be performed if, and where, methodologically sound and comparable studies allow this. Conclusion: We will undertake the first systematic review assessing disparities in stroke incidence in Indigenous populations of developed countries. Data outputs will be disseminated to relevant Indigenous stakeholders to inform public health and policy research.Anna H. Balabanski, Angela Dos Santos, John A. Woods, Amanda G. Thrift, Timothy J. Kleinig, Astrid Suchy-Dicey, Susanna Ragnhild Siri, Bernadette Boden-Albala, Rita Krishnamurthi, Valery L. Feigin, Dedra Buchwald, Annemarei Ranta, Christina S. Mienna, Carol Zavaleta, Leonid Churilov, Luke Burchill, Deborah Zion, W.T. Longstreth Jr., David L. Tirschwell, Sonia Anand, Mark W. Parsons, Alex Brown, Donald K. Warne, Matire Harwood, and Judith M. Katzenellenboge

Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≥1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≤6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)