Anti-Sigma Factors in E. coli: Common Regulatory Mechanisms Controlling Sigma Factors Availability

Abstract

In bacteria, transcriptional regulation is a key step in cellular gene expression. All bacteria contain a core RNA polymerase that is catalytically competent but requires an additional σ factor for specific promoter recognition and correct transcriptional initiation. The RNAP core is not able to selectively bind to a given σ factor. In contrast, different σ factors have different affinities for the RNAP core. As a consequence, the concentration of alternate σ factors requires strict regulation in order to properly control the delicate interplay among them, which favors the competence for the RNAP core. This control is archived by different σ/anti-σ controlling mechanisms that shape complex regulatory networks and cascades, and enable the response to sudden environmental cues, whose global understanding is a current challenge for systems biology. Although there have been a number of excellent studies on each of these σ/anti-σ post-transcriptional regulatory systems, no comprehensive comparison of these mechanisms in a single model organism has been conducted. Here, we survey all these systems in E. coli dissecting and analyzing their inner workings and highlightin their differences. Then, following an integral approach, we identify their commonalities and outline some of the principles exploited by the cell to effectively and globally reprogram the transcriptional machinery. These principles provide guidelines for developing biological synthetic circuits enabling an efficient and robust response to sudden stimuli.

Cis-Antisense Transcription Gives Rise to Tunable Genetic Switch Behavior: A Mathematical Modeling Approach

“…Tunable regulation of gene expression is essential to overcome multitude of adverse environmental conditions encountered by biological systems [ 1 , 2 ]. Non-coding genomic regions and closely spaced promoters observed in genome-wide studies have revealed the role of transcription in controlling gene expression [ 3 – 7 ].…”

Explainable Transformer Models for Functional Genomics in Prokaryotes

“…SEnd-seq combines results from both the exponential and log growth phase, SMRT-Cappabe-seq has been executed during the late-log growth phase and Cappable-seq during the mid-log growth phase. Consequently, the detection of TSSs activated by specialized σ-factors, such as σ 38 for stress responses, σ 32 for heat shock response and σ 28 associated with flagellar genes [32], is bound to be limited. Due to limitations to the existing methods, it is not possible to define which of the seven well-defined sigma factors in E. coli are associated with each TSS.…”

“…Through evaluation of the reconstruction loss for autoencoders with varying sizes of the bottleneck layer, which restricts the amount of information available to compact the sequence, nucleotide positions indicative of the presence of a TSS can be ranked by importance. Figure 2d gives the mean reconstruction error off all the annotated TSS for different sizes of the bottleneck layer (4,8,12,16,24,32), alongside the positional frequencies of the input samples on which the model was trained. In contrast to a position weight matrix, returning no discernable motif, the autoencoder is able to map higher-order interactions between the nucleotides.…”

Perfect and Near-Perfect Adaptation in Cell Signaling

“…The question then is whether antithetical integral feedback motifs are present in natural signaling systems. One nice example, pointed out by Briat et al (Briat et al, 2016), comes from prokaryotic transcription, where a positive transcription factor (s 70 ) promotes the transcription and synthesis of an anti-sigma factor (Rsd), a transcriptional repressor that functions by stoichiometrically binding to and inhibiting s 70 (Jishage and Ishihama, 1999;Treviñ o-Quintanilla et al, 2013). An analogous process can be found in EGFR signaling.…”