Alberto Santos-Zavaleta

Microarray data can be used to identify co-expressed genes that may play a role in the same biological process. An enormous amount of gene expression data is currently available online in repositories for several organisms. Therefore, the analysis and interpretation of this information could help us organize, make sense and notice the knowledge that it contains which represents a major challenge in the postgenomic era. Here, we grouped genes of Escherichia coli K-12 via expression data to infer meaningful transcriptional regulatory information, namely functionally relevant clusters, which were validated with curated transcriptional regulatory information in RegulonDB. Our method is based on the assumption that co-expressed genes reflect functional units provided by their genetic structure, i.e. the arrangement of the genes, their regulation, and their participation in defined biological processes. We showed that co-expressed genes are involved in the same metabolic pathways and type of regulation (through transcription factors, σ-factors, allosteric regulation or microRNA regulation) and are helpful for identifying novel transcriptional regulatory interactions.

The ability to express the same meaning in different ways is a well-known property of natural language. This amazing property is the source of major difficulties in natural language processing. Given the constant increase in published literature, its curation and information extraction would strongly benefit from efficient automatic processes, for which corpora of sentences evaluated by experts are a valuable resource. Given our interest in applying such approaches to the benefit of curation of the biomedical literature, specifically that about gene regulation in microbial organisms, we decided to build a corpus with graded textual similarity evaluated by curators and that was designed specifically oriented to our purposes. Based on the predefined statistical power of future analyses, we defined features of the design, including sampling, selection criteria, balance, and size, among others. A non-fully crossed study design was applied. Each pair of sentences was evaluated by 3 annotators from a total of 7; the scale used in the semantic similarity assessment task within the Semantic Evaluation workshop (SEMEVAL) was adapted to our goals in four successive iterative sessions with clear improvements in the agreed guidelines and interrater reliability results. Alternatives for such a corpus evaluation have been widely discussed. To the best of our knowledge, this is the first similarity corpus—a dataset of pairs of sentences for which human experts rate the semantic similarity of each pair—in this domain of knowledge. We have initiated its incorporation in our research towards high-throughput curation strategies based on natural language processing.

Crl, identified for curli production, is a small transcription factor that stimulates the association of the σS factor (RpoS) with the RNA polymerase core through direct and specific interactions, increasing the transcription rate of genes during the transition from exponential to stationary phase at low temperatures, using indole as an effector molecule. The lack of a comprehensive collection of information on the Crl regulon makes it difficult to identify a dominant function of Crl and to generate any hypotheses concerning its taxonomical distribution in archaeal and bacterial organisms. In this work, based on a systematic literature review, we identified the first comprehensive dataset of 86 genes under the control of Crl in the bacterium Escherichia coli K-12; those genes correspond to 40% of the σS regulon in this bacterium. Based on an analysis of orthologs in 18 archaeal and 69 bacterial taxonomical divisions and using E. coli K-12 as a framework, we suggest three main events that resulted in this regulon’s actual form: (i) in a first step, rpoS, a gene widely distributed in bacteria and archaea cellular domains, was recruited to regulate genes involved in ancient metabolic processes, such as those associated with glycolysis and the tricarboxylic acid cycle; (ii) in a second step, the regulon recruited those genes involved in metabolic processes, which are mainly taxonomically constrained to Proteobacteria, with some secondary losses, such as those genes involved in responses to stress or starvation and cell adhesion, among others; and (iii) in a posterior step, Crl might have been recruited in Enterobacteriaceae; because its taxonomical pattern constrained to this bacterial order, however further analysis are necessary. Therefore, we suggest that the regulon Crl is highly flexible for phenotypic adaptation, probably as consequence of the diverse growth environments associated with all organisms in which members of this regulatory network are present.

EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli . EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli . The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed via EcoCyc.org. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

RegulonDB, first published 20 years ago, is a comprehensive electronic resource about regulation of transcription initiation of Escherichia coli K-12 with decades of knowledge from classic molecular biology experiments, and recently also from high-throughput genomic methodologies. We curated the literature to keep RegulonDB up to date, and initiated curation of ChIP and gSELEX experiments. We estimate that current knowledge describes between 10% and 30% of the expected total number of transcription factor- gene regulatory interactions in E. coli. RegulonDB provides datasets for interactions for which there is no evidence that they affect expression, as well as expression datasets. We developed a proof of concept pipeline to merge binding and expression evidence to identify regulatory interactions. These datasets can be visualized in the RegulonDB JBrowse. We developed the Microbial Conditions Ontology with a controlled vocabulary for the minimal properties to reproduce an experiment, which contributes to integrate data from high throughput and classic literature. At a higher level of integration, we report Genetic Sensory-Response Units for 200 transcription factors, including their regulation at the metabolic level, and include summaries for 70 of them. Finally, we summarize our research with Natural language processing strategies to enhance our biocuration work.

Crl, identified for curli production, is a small transcription factor that stimulates the association of the σ ^S factor (RpoS) with the RNA polymerase core through direct and specific interactions, increasing the transcription rate of genes during the transition from exponential to stationary phase at low temperatures, and it uses indole as an effector molecule. The lack of a comprehensive collection of information on the Crl regulon makes it difficult to identify a dominant function of Crl and to generate any hypotheses concerning its taxonomical distribution in archaeal and bacterial organisms. In this work, based on a systematic literature review, we identified the first comprehensive dataset of 86 genes under the control of Crl in the bacterium Escherichia coli K-12; those genes correspond to 40% of the σ ^S regulon in this bacterium. Based on an analysis of orthologs in 18 archaeal and 69 bacterial taxonomical divisions and using E . coli K-12 as a framework, we suggest three main events that resulted in this regulon’s actual form: (i) in a first step, rpoS , a gene widely distributed in bacteria and archaea cellular domains, was recruited to regulate genes involved in ancient metabolic processes, such as those associated with glycolysis and the tricarboxylic acid cycle; (ii) in a second step, the regulon recruited those genes involved in metabolic processes, which are mainly taxonomically constrained to Proteobacteria , with some secondary losses, such as those genes involved in responses to stress or starvation and cell adhesion, among others; and (iii) in a posterior step, Crl was recruited as a consequence of its emergence in Enterobacteriaceae . Therefore, we suggest that the regulon Crl is highly flexible for phenotypic adaptation, probably as consequence of the diverse growth environments associated with all organisms in which members of this regulatory network are present.

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. 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. 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.

Our understanding of the regulation of gene expression has been strongly benefited by the availability of high throughput technologies that enable questioning the whole genome for the binding of specific transcription factors and expression profiles. In the case of genome models, such as Escherichia coli K-12, this knowledge needs to be integrated with the legacy of accumulated genetics and molecular biology pre-genomic knowledge in order to attain deeper levels in the understanding of their biology. In spite of the several repositories and curated databases, there is no effort, nor electronic site yet, to comprehensively integrate the available knowledge from all these different sources around the regulation of gene expression of E. coli K-12. In this paper, we describe a first effort to expand 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 data set collections from 25 ChIP and 18 gSELEX publications, respectively, in addition to around 60 expression profiles 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 sites, and third, an expanded computational model supporting this knowledge. Altogether, this constitutes the basis for adequately gathering and enabling the comparisons and integration strongly needed to manage and access such wealth of knowledge. This version of RegulonBD 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 well be emulated for gathering knowledge on other microbial organisms.

EcoCyc (EcoCyc.org) is a freely accessible, comprehensive database that collects and summarizes experimental data for Escherichia coli K-12, the best-studied bacterial model organism. New experimental discoveries about gene products, their function and regulation, new metabolic pathways, enzymes and cofactors are regularly added to EcoCyc. New SmartTable tools allow users to browse collections of related EcoCyc content. SmartTables can also serve as repositories for user- or curator-generated lists. EcoCyc now supports running and modifying E. coli metabolic models directly on the EcoCyc website.

RegulonDB (http://regulondb.ccg.unam.mx) is one of the most useful and important resources on bacterial gene regulation,as it integrates the scattered scientific knowledge of the best-characterized organism, Escherichia coli K-12, in a database that organizes large amounts of data. Its electronic format enables researchers to compare their results with the legacy of previous knowledge and supports bioinformatics tools and model building. Here, we summarize our progress with RegulonDB since our last Nucleic Acids Research publication describing RegulonDB, in 2013. In addition to maintaining curation up-to-date, we report a collection of 232 interactions with small RNAs affecting 192 genes, and the complete repertoire of 189 Elementary Genetic Sensory-Response units (GENSOR units), integrating the signal, regulatory interactions, and metabolic pathways they govern. These additions represent major progress to a higher level of understanding of regulated processes. We have updated the computationally predicted transcription factors, which total 304 (184 with experimental evidence and 120 from computational predictions); we updated our position-weight matrices and have included tools for clustering them in evolutionary families. We describe our semiautomatic strategy to accelerate curation, including datasets from high-throughput experiments, a novel coexpression distance to search for ‘neighborhood’ genes to known operons and regulons, and computational developments.