RESUMEN
We have taken the first steps towards a complete reconstruction of the Mycobacterium tuberculosis regulatory network based on ChIP-Seq and combined this reconstruction with system-wide profiling of messenger RNAs, proteins, metabolites and lipids during hypoxia and re-aeration. Adaptations to hypoxia are thought to have a prominent role in M. tuberculosis pathogenesis. Using ChIP-Seq combined with expression data from the induction of the same factors, we have reconstructed a draft regulatory network based on 50 transcription factors. This network model revealed a direct interconnection between the hypoxic response, lipid catabolism, lipid anabolism and the production of cell wall lipids. As a validation of this model, in response to oxygen availability we observe substantial alterations in lipid content and changes in gene expression and metabolites in corresponding metabolic pathways. The regulatory network reveals transcription factors underlying these changes, allows us to computationally predict expression changes, and indicates that Rv0081 is a regulatory hub.
Asunto(s)
Redes Reguladoras de Genes , Hipoxia/genética , Redes y Vías Metabólicas/genética , Mycobacterium tuberculosis/genética , Mycobacterium tuberculosis/metabolismo , Adaptación Fisiológica , Proteínas Bacterianas/genética , Proteínas Bacterianas/metabolismo , Sitios de Unión , Inmunoprecipitación de Cromatina , Perfilación de la Expresión Génica , Redes Reguladoras de Genes/genética , Genómica , Hipoxia/metabolismo , Metabolismo de los Lípidos/genética , Modelos Biológicos , Mycobacterium tuberculosis/efectos de los fármacos , Mycobacterium tuberculosis/fisiología , Oxígeno/farmacología , Proteolisis , ARN Mensajero/genética , ARN Mensajero/metabolismo , Reproducibilidad de los Resultados , Análisis de Secuencia de ADN , Factores de Transcripción/genética , Factores de Transcripción/metabolismo , Tuberculosis/metabolismo , Tuberculosis/microbiologíaRESUMEN
BACKGROUND: Enteric Escherichia coli survives the highly acidic environment of the stomach through multiple acid resistance (AR) mechanisms. The most effective system, AR2, decarboxylates externally-derived glutamate to remove cytoplasmic protons and excrete GABA. The first described system, AR1, does not require an external amino acid. Its mechanism has not been determined. The regulation of the multiple AR systems and their coordination with broader cellular metabolism has not been fully explored. RESULTS: We utilized a combination of ChIP-Seq and gene expression analysis to experimentally map the regulatory interactions of four TFs: nac, ntrC, ompR, and csiR. Our data identified all previously in vivo confirmed direct interactions and revealed several others previously inferred from gene expression data. Our data demonstrate that nac and csiR directly modulate AR, and leads to a regulatory network model in which all four TFs participate in coordinating acid resistance, glutamate metabolism, and nitrogen metabolism. This model predicts a novel mechanism for AR1 by which the decarboxylation enzymes of AR2 are used with internally derived glutamate. This hypothesis makes several testable predictions that we confirmed experimentally. CONCLUSIONS: Our data suggest that the regulatory network underlying AR is complex and deeply interconnected with the regulation of GABA and glutamate metabolism, nitrogen metabolism. These connections underlie and experimentally validated model of AR1 in which the decarboxylation enzymes of AR2 are used with internally derived glutamate.