RESUMEN
Sigma factors are RNA polymerase subunits engaged in promoter recognition and DNA strand separation during transcription initiation in bacteria. Primary sigma factors are responsible for the expression of housekeeping genes and are essential for survival. RpoD, the primary sigma factor of Escherichia coli, a γ-proteobacteria, recognizes consensus promoter sequences highly similar to those of some α-proteobacteria species. Despite this resemblance, RpoD is unable to sustain transcription from most of the α-proteobacterial promoters tested so far. In contrast, we have found that SigA, the primary sigma factor of Rhizobium etli, an α-proteobacteria, is able to transcribe E. coli promoters, although it exhibits only 48% identity (98% coverage) to RpoD. We have called this the transcriptional laxity phenomenon. Here, we show that SigA partially complements the thermo-sensitive deficiency of RpoD285 from E. coli strain UQ285 and that the SigA region σ4 is responsible for this phenotype. Sixteen out of 74 residues (21.6%) within region σ4 are variable between RpoD and SigA. Mutating these residues significantly improves SigA ability to complement E. coli UQ285. Only six of these residues fall into positions already known to interact with promoter DNA and to comprise a helix-turn-helix motif. The remaining variable positions are located on previously unexplored sites inside region σ4, specifically into the first two α-helices of the region. Neither of the variable positions confined to these helices seem to interact directly with promoter sequence; instead, we adduce that these residues participate allosterically by contributing to correct region folding and/or positioning of the HTH motif. We propose that transcriptional laxity is a mechanism for ensuring transcription in spite of naturally occurring mutations from endogenous promoters and/or horizontally transferred DNA sequences, allowing survival and fast environmental adaptation of α-proteobacteria.
RESUMEN
BACKGROUND: Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority. METHODOLOGY/PRINCIPAL FINDINGS: This work presents a constraint-base modeling of the most experimentally studied metabolic pathways supporting cancer cells: glycolysis, TCA cycle, pentose phosphate, glutaminolysis and oxidative phosphorylation. To evaluate its predictive capacities, a growth kinetics study for Hela cell lines was accomplished and qualitatively compared with in silico predictions. Furthermore, based on pure computational criteria, we concluded that a set of enzymes (such as lactate dehydrogenase and pyruvate dehydrogenase) perform a pivotal role in cancer cell growth, findings supported by an experimental counterpart. CONCLUSIONS/SIGNIFICANCE: Alterations on metabolic activity are crucial to initiate and sustain cancer phenotype. In this work, we analyzed the phenotype capacities emerged from a constructed metabolic network conformed by the most experimentally studied pathways sustaining cancer cell growth. Remarkably, in silico model was able to resemble the physiological conditions in cancer cells and successfully identified some enzymes currently studied by its therapeutic effect. Overall, we supplied evidence that constraint-based modeling constitutes a promising computational platform to: 1) integrate high throughput technology and establish a crosstalk between experimental validation and in silico prediction in cancer cell phenotype; 2) explore the fundamental metabolic mechanism that confers robustness in cancer; and 3) suggest new metabolic targets for anticancer treatments. All these issues being central to explore cancer cell metabolism from a systems biology perspective.