David Alberto Velázquez-Ramírez

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.

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.