TY - JOUR
T1 - Controlled Measurement and Comparative Analysis of Cellular Components in E. coli Reveals Broad Regulatory Changes in Response to Glucose Starvation
AU - Houser, John R.
AU - Barnhart, Craig
AU - Boutz, Daniel R.
AU - Carroll, Sean M.
AU - Dasgupta, Aurko
AU - Michener, Joshua K.
AU - Needham, Brittany D.
AU - Papoulas, Ophelia
AU - Sridhara, Viswanadham
AU - Sydykova, Dariya K.
AU - Marx, Christopher J.
AU - Trent, M. Stephen
AU - Barrick, Jeffrey E.
AU - Marcotte, Edward M.
AU - Wilke, Claus O.
N1 - Publisher Copyright:
© 2015 Houser et al.
PY - 2015/8/1
Y1 - 2015/8/1
N2 - How do bacteria regulate their cellular physiology in response to starvation? Here, we present a detailed characterization of Escherichia coli growth and starvation over a time-course lasting two weeks. We have measured multiple cellular components, including RNA and proteins at deep genomic coverage, as well as lipid modifications and flux through central metabolism. Our study focuses on the physiological response of E. coli in stationary phase as a result of being starved for glucose, not on the genetic adaptation of E. coli to utilize alternative nutrients. In our analysis, we have taken advantage of the temporal correlations within and among RNA and protein abundances to identify systematic trends in gene regulation. Specifically, we have developed a general computational strategy for classifying expression-profile time courses into distinct categories in an unbiased manner. We have also developed, from dynamic models of gene expression, a framework to characterize protein degradation patterns based on the observed temporal relationships between mRNA and protein abundances. By comparing and contrasting our transcriptomic and proteomic data, we have identified several broad physiological trends in the E. coli starvation response. Strikingly, mRNAs are widely down-regulated in response to glucose starvation, presumably as a strategy for reducing new protein synthesis. By contrast, protein abundances display more varied responses. The abundances of many proteins involved in energy-intensive processes mirror the corresponding mRNA profiles while proteins involved in nutrient metabolism remain abundant even though their corresponding mRNAs are down-regulated.
AB - How do bacteria regulate their cellular physiology in response to starvation? Here, we present a detailed characterization of Escherichia coli growth and starvation over a time-course lasting two weeks. We have measured multiple cellular components, including RNA and proteins at deep genomic coverage, as well as lipid modifications and flux through central metabolism. Our study focuses on the physiological response of E. coli in stationary phase as a result of being starved for glucose, not on the genetic adaptation of E. coli to utilize alternative nutrients. In our analysis, we have taken advantage of the temporal correlations within and among RNA and protein abundances to identify systematic trends in gene regulation. Specifically, we have developed a general computational strategy for classifying expression-profile time courses into distinct categories in an unbiased manner. We have also developed, from dynamic models of gene expression, a framework to characterize protein degradation patterns based on the observed temporal relationships between mRNA and protein abundances. By comparing and contrasting our transcriptomic and proteomic data, we have identified several broad physiological trends in the E. coli starvation response. Strikingly, mRNAs are widely down-regulated in response to glucose starvation, presumably as a strategy for reducing new protein synthesis. By contrast, protein abundances display more varied responses. The abundances of many proteins involved in energy-intensive processes mirror the corresponding mRNA profiles while proteins involved in nutrient metabolism remain abundant even though their corresponding mRNAs are down-regulated.
UR - http://www.scopus.com/inward/record.url?scp=84940780039&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1004400
DO - 10.1371/journal.pcbi.1004400
M3 - Article
C2 - 26275208
AN - SCOPUS:84940780039
SN - 1553-734X
VL - 11
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 8
M1 - e1004400
ER -