TY - JOUR
T1 - A quantitative method for proteome reallocation using minimal regulatory interventions
AU - Lastiri-Pancardo, Gustavo
AU - Mercado-Hernández, Jonathan S.
AU - Kim, Juhyun
AU - Jiménez, José I.
AU - Utrilla, José
N1 - Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Engineering resource allocation in biological systems is an ongoing challenge. Organisms allocate resources for ensuring survival, reducing the productivity of synthetic biology functions. Here we present a new approach for engineering the resource allocation of Escherichia coli by rationally modifying its transcriptional regulatory network. Our method (ReProMin) identifies the minimal set of genetic interventions that maximizes the savings in cell resources. To this end, we categorized transcription factors according to the essentiality of its targets and we used proteomic data to rank them. We designed the combinatorial removal of transcription factors that maximize the release of resources. Our resulting strain containing only three mutations, theoretically releasing 0.5% of its proteome, had higher proteome budget, increased production of an engineered metabolic pathway and showed that the regulatory interventions are highly specific. This approach shows that combining proteomic and regulatory data is an effective way of optimizing strains using conventional molecular methods. [Figure not available: see fulltext.].
AB - Engineering resource allocation in biological systems is an ongoing challenge. Organisms allocate resources for ensuring survival, reducing the productivity of synthetic biology functions. Here we present a new approach for engineering the resource allocation of Escherichia coli by rationally modifying its transcriptional regulatory network. Our method (ReProMin) identifies the minimal set of genetic interventions that maximizes the savings in cell resources. To this end, we categorized transcription factors according to the essentiality of its targets and we used proteomic data to rank them. We designed the combinatorial removal of transcription factors that maximize the release of resources. Our resulting strain containing only three mutations, theoretically releasing 0.5% of its proteome, had higher proteome budget, increased production of an engineered metabolic pathway and showed that the regulatory interventions are highly specific. This approach shows that combining proteomic and regulatory data is an effective way of optimizing strains using conventional molecular methods. [Figure not available: see fulltext.].
UR - http://www.scopus.com/inward/record.url?scp=85087842798&partnerID=8YFLogxK
U2 - 10.1038/s41589-020-0593-y
DO - 10.1038/s41589-020-0593-y
M3 - Article
C2 - 32661378
AN - SCOPUS:85087842798
SN - 1552-4450
VL - 16
SP - 1026
EP - 1033
JO - Nature Chemical Biology
JF - Nature Chemical Biology
IS - 9
ER -