A minimalistic model predicts that low expression of condition-dependent genes under a permissive growth environment, compared with a restrictive environment, implies larger fraction of all other proteins out of the proteome.

<p>With this, the ratio of bio-synthesis genes to the rest of the proteome is higher in permissive environments, resulting in faster growth.</p

A Minimalistic Resource Allocation Model to Explain Ubiquitous Increase in Protein Expression with Growth Rate

<div><p>Most proteins show changes in level across growth conditions. Many of these changes seem to be coordinated with the specific growth rate rather than the growth environment or the protein function. Although cellular growth rates, gene expression levels and gene regulation have been at the center of biological research for decades, there are only a few models giving a base line prediction of the dependence of the proteome fraction occupied by a gene with the specific growth rate. We present a simple model that predicts a widely coordinated increase in the fraction of many proteins out of the proteome, proportionally with the growth rate. The model reveals how passive redistribution of resources, due to active regulation of only a few proteins, can have proteome wide effects that are quantitatively predictable. Our model provides a potential explanation for why and how such a coordinated response of a large fraction of the proteome to the specific growth rate arises under different environmental conditions. The simplicity of our model can also be useful by serving as a baseline null hypothesis in the search for active regulation. We exemplify the usage of the model by analyzing the relationship between growth rate and proteome composition for the model microorganism <i>E.coli</i> as reflected in recent proteomics data sets spanning various growth conditions. We find that the fraction out of the proteome of a large number of proteins, and from different cellular processes, increases proportionally with the growth rate. Notably, ribosomal proteins, which have been previously reported to increase in fraction with growth rate, are only a small part of this group of proteins. We suggest that, although the fractions of many proteins change with the growth rate, such changes may be partially driven by a global effect, not necessarily requiring specific cellular control mechanisms.</p></div

Histogram of the slopes of regression lines for the highly correlated with growth proteins (473 and 305 proteins in the left and right panels respectively).

<p>Ribosomal proteins are stacked in green on top of the non ribosomal proteins, marked in blue. Proteins fractions were normalized to account for differences in slopes resulting from differing average fractions (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#sec018" target="_blank">Methods</a>). The expected distribution of slopes given the individual deviations of every protein from a linear regression line, assuming all proteins are coordinated, is plotted in gray. Dashed vertical lines at 0.5 and 2 represent the range at which the slopes of of the proteins lie. Left panel—data from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#pone.0153344.ref029" target="_blank">29</a>], right panel—data from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#pone.0153344.ref013" target="_blank">13</a>]. High correlation proteins share similar normalized slopes, implying they are coordinated, maintaining their relative ratios across conditions (see text for further details). Ribosomal proteins, shown in green, scale with growth rate in a manner similar to the rest of the high correlation proteins (see text and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#pone.0153344.s016" target="_blank">S7 Fig</a>).</p

Fraction of the proteome occupied by proteins that are strongly positively correlated with growth rate.

<p>The accumulated sum of the proteins that are strongly positively correlated with growth rate (defined as having a correlation above 0.5), as a fraction out of the proteome, with linear regression lines is shown. These proteins form a large fraction (≥ 50%) out of the proteome at higher growth rates. The accumulated fraction of the strongly correlated proteins doubles as the growth rate changes by about 5-fold. Assuming constant degradation rates, the trend lines correspond to protein half life times of ≈ 1.7 hours. Randomized data sets result in much fewer strongly positively correlated with growth rate proteins, implying a much smaller accumulated fraction (hollow circles).</p

A strong positive Pearson correlation between the fraction out of the proteome and the growth rate is observed for a large number of proteins in two data sets.

<p>(A-B) Shown are histograms displaying the correlations of all proteins to growth rate in the data from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#pone.0153344.ref029" target="_blank">29</a>] (A) and [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#pone.0153344.ref013" target="_blank">13</a>] (B). Functional protein groups are denoted by different colors. Thresholds defining high correlation are marked in dashed lines and further discussed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#pone.0153344.s007" target="_blank">S4 Text</a>. (C) Shuffling the amounts of every protein across conditions for the data set from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153344#pone.0153344.ref013" target="_blank">13</a>] reveals the bias towards positive correlation with growth rate is non-trivial.</p

Illustration of the master strain and library construction procedure.

<p>A master strain was constructed such that it will contain two main constructs in the HIS deletion locus: a constant control construct with mCherry driven by the TEF2 promoter and terminated by a constant ADH1 terminator; and a test construct with a YFP gene driven by Gal1/10 promoter. Following master strain construction, a library of PCR products containing the downstream intergenic regions of 85 tested genes was amplified from the genome by PCR and extended to also contain the URA3 promoter and start codon. This library of DNA sequences was then integrated into the master strain such that only integrations in the exact genomic location would result in an intact selection marker.</p

Higher A/T content upstream of the polyadenylation site is associated with higher YFP expression.

<p>(<b>A</b>) The correlation between A/T content and YFP levels in different window sizes and different locations with respect to the main polyadenylation site. Each point in the matrix represents a different window size (y-axis) centered on a different location (x-axis) with respect to the polyadenylation site. Colors represent the Pearson correlation coefficient (side bar). (<b>B</b>) Same as A using genome wide sequence and mRNA levels <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002934#pcbi.1002934-Nagalakshmi1" target="_blank">[6]</a> data. (<b>C</b>) Shown is the average G/C content of three sets of genes grouped by their mRNA expression levels (0.2 percentile of the lowest and highest expressing genes and the intermediate group contains all the rest) as a function of the distance from mapped transcription end sites, in windows of 20 bp centered around each point.</p

YFP expression is correlated with noise strength.

<p>(<b>A</b>) For several different galactose concentrations (represented by different colors), shown is the YFP expression of each 3′ end library strain (x-axis) versus its noise (y-axis, expression variance divided by mean expression squared). Each point represents the noise computed from single cell flow cytometry measurements of the corresponding 3′ end strain. (<b>B</b>) Same as panel (A) only with noise strength (expression variance divided by mean expression) on the y-axis.</p

The effect of 3′ end sequences on expression is large and is correlated with endogenous mRNA levels.

<p>(<b>A</b>) Dynamic range of YFP levels of library strains at different galactose induction levels. YFP production per cell per second was measured and calculated in different Galactose concentrations resulting in different promoter activation levels for all library strains at every galactose concentration. Shown are YFP measurements of the 3′ end library strains. Note that the ratio between the highest and lowest strain at the highest induction level (0.1% galactose) shows a fold difference of more than 10-fold. (<b>B</b>) Comparison of the span of expression values between promoter and 3′ end strains for the same group of genes. A box plot is added to show the difference in IQR between the groups. (<b>C</b>) Comparison of YFP levels in the 3′ end library (y-axis) with endogenous mRNA levels measured by RNA-seq (x-axis). The Pearson correlation is given (inset). (<b>D</b>) Same as (C) but for a different strain library in which promoters of the same respective genes are fused to a YFP reporter.</p

Effect of the 3′ end sequences on YFP accumulation in batch measurements.

<p>(<b>A</b>) YFP measurements of clones with three different 3′ end sequences. Shown are YFP measurements of three different strains, each with a unique 3′ end sequence. Lines of the same color represent measurements of different clones from the same type of 3′ end sequence, demonstrating that the effect of the different constructs on expression is above the variability of our experimental system. The lowest expressing strain (red) contains the COX17 3′ end and serves as a positive control for our experimental system. (<b>B,C,D</b>) Plate fluorometer measurements over time. Following inoculation of the cells in a fresh media containing 2% galactose, optical density (OD), mCherry and YFP are measured over time (B,C and D respectively). Note that as expected, OD and mCherry measurements remain highly similar between different library strains, while YFP expression varies considerably.</p