Gene expression levels are influenced by multiple coexisting molecular mechanisms. Some of these interactions, such as those of transcription factors and promoters have been studied extensively. However, predicting phenotypes of gene regulatory networks remains a major challenge. Here, we use a well-defined synthetic gene regulatory network to study in Escherichia coli how network phenotypes depend on local genetic context, i.e. the genetic neighborhood of a transcription factor and its relative position. We show that one gene regulatory network with fixed topology can display not only quantitatively but also qualitatively different phenotypes, depending solely on the local genetic context of its components. Transcriptional read-through is the main molecular mechanism that places one transcriptional unit within two separate regulons without the need for complex regulatory sequences. We propose that relative order of individual transcriptional units, with its potential for combinatorial complexity, plays an important role in shaping phenotypes of gene regulatory networks.

Gene expression levels are influenced by multiple coexisting molecular mechanisms. Some of these interactions, such as those of transcription factors and promoters have been studied extensively. However, predicting phenotypes of gene regulatory networks remains a major challenge. Here, we use a well-defined synthetic gene regulatory network to study how network phenotypes depend on local genetic context, i.e. the genetic neighborhood of a transcription factor and its relative position. We show that one gene regulatory network with fixed topology can display not only quantitatively but also qualitatively different phenotypes, depending solely on the local genetic context of its components. Our results demonstrate that changes in local genetic context can place a single transcriptional unit within two separate regulons without the need for complex regulatory sequences. We propose that relative order of individual transcriptional units, with its potential for combinatorial complexity, plays an important role in shaping phenotypes of gene regulatory networks.

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