Lucas B. Carey

Deciphering the rules by which 5′-UTR sequences affect protein expression in yeast

Significance This study quantifies how protein levels are determined by the underlying 5′-UTR sequence of an mRNA. We accurately measured protein abundance in 2,041 5′-UTR sequence variants, differing only in positions −10 to −1. We show that a few nucleotide substitutions can significantly alter protein expression. We also developed a predictive model that explains two-thirds of the expression variation. We provide convincing evidence that key regulatory elements, including AUG sequence context, mRNA secondary structure, and out-of-frame upstream AUGs conjointly modulate protein levels. Our study can aid in synthetic biology applications, by suggesting sequence manipulations for fine-tuning protein expression in a predictable manner. The 5′-untranslated region (5′-UTR) of mRNAs contains elements that affect expression, yet the rules by which these regions exert their effect are poorly understood. Here, we studied the impact of 5′-UTR sequences on protein levels in yeast, by constructing a large-scale library of mutants that differ only in the 10 bp preceding the translational start site of a fluorescent reporter. Using a high-throughput sequencing strategy, we obtained highly accurate measurements of protein abundance for over 2,000 unique sequence variants. The resulting pool spanned an approximately sevenfold range of protein levels, demonstrating the powerful consequences of sequence manipulations of even 1-10 nucleotides immediately upstream of the start codon. We devised computational models that predicted over 70% of the measured expression variability in held-out sequence variants. Notably, a combined model of the most prominent features successfully explained protein abundance in an additional, independently constructed library, whose nucleotide composition differed greatly from the library used to parameterize the model. Our analysis reveals the dominant contribution of the start codon context at positions −3 to −1, mRNA secondary structure, and out-of-frame upstream AUGs (uAUGs) to phenotypic diversity, thereby advancing our understanding of how protein levels are modulated by 5′-UTR sequences, and paving the way toward predictably tuning protein expression through manipulations of 5′-UTRs.

Promoter sequence determines the relationship between expression level and noise.

A single transcription factor can activate or repress expression by three different mechanisms: one that increases cell-to-cell variability in target gene expression (noise) and two that decrease noise.

Publication metrics and success on the academic job market

The number of applicants vastly outnumbers the available academic faculty positions. What makes a successful academic job market candidate is the subject of much current discussion [1-4]. Yet, so far there has been no quantitative analysis of who becomes a principal investigator (PI). We here use a machine-learning approach to predict who becomes a PI, based on data from over 25,000 scientists in PubMed. We show that success in academia is predictable. It depends on the number of publications, the impact factor (IF) of the journals in which those papers are published, and the number of papers that receive more citations than average for the journal in which they were published (citations/IF). However, both the scientists gender and the rank of their university are also of importance, suggesting that non-publication features play a statistically significant role in the academic hiring process. Our model (www.pipredictor.com) allows anyone to calculate their likelihood of becoming a PI.

Recruitment of Cln3 Cyclin to Promoters Controls Cell Cycle Entry via Histone Deacetylase and Other Targets

In yeast, titration of an increasing number of molecules of the G1 cyclin Cln3 by a fixed number of DNA-bound molecules of the transcription factor SBF might underlie the dependence of cell cycle entry on cell size.

Measurements of the impact of 3' end sequences on gene expression reveal wide range and sequence dependent effects.

A full understanding of gene regulation requires an understanding of the contributions that the various regulatory regions have on gene expression. Although it is well established that sequences downstream of the main promoter can affect expression, our understanding of the scale of this effect and how it is encoded in the DNA is limited. Here, to measure the effect of native S. cerevisiae 3′ end sequences on expression, we constructed a library of 85 fluorescent reporter strains that differ only in their 3′ end region. Notably, despite being driven by the same strong promoter, our library spans a continuous twelve-fold range of expression values. These measurements correlate with endogenous mRNA levels, suggesting that the 3′ end contributes to constitutive differences in mRNA levels. We used deep sequencing to map the 3′UTR ends of our strains and show that determination of polyadenylation sites is intrinsic to the local 3′ end sequence. Polyadenylation mapping was followed by sequence analysis, we found that increased A/T content upstream of the main polyadenylation site correlates with higher expression, both in the library and genome-wide, suggesting that native genes differ by the encoded efficiency of 3′ end processing. Finally, we use single cells fluorescence measurements, in different promoter activation levels, to show that 3′ end sequences modulate protein expression dynamics differently than promoters, by predominantly affecting the size of protein production bursts as opposed to the frequency at which these bursts occur. Altogether, our results lead to a more complete understanding of gene regulation by demonstrating that 3′ end regions have a unique and sequence dependent effect on gene expression.

Nonlinear Fitness Consequences of Variation in Expression Level of a Eukaryotic Gene

Levels of gene expression show considerable variation in eukaryotes, but no fine-scale maps have been made of the fitness consequences of such variation in controlled genetic backgrounds and environments. To address this, we assayed fitness at many levels of up- and down-regulated expression of a single essential gene, LCB2, involved in sphingolipid synthesis in budding yeast Saccharomyces cerevisiae. Reduced LCB2 expression rapidly decreases cellular fitness, yet increased expression has little effect. The wild-type expression level is therefore perched on the edge of a nonlinear fitness cliff. LCB2 is upregulated when cells are exposed to osmotic stress; consistent with this, the entire fitness curve is shifted upward to higher expression under osmotic stress, illustrating the selective force behind gene regulation. Expression levels of LCB2 are lower in wild yeast strains than in the experimental lab strain, suggesting that higher levels in the lab strain may be idiosyncratic. Reports indicate that the effect sizes of alleles contributing to variation in complex phenotypes differ among environments and genetic backgrounds; our results suggest that such differences may be explained as simple shifts in the position of nonlinear fitness curves.

RNA polymerase errors cause splicing defects and can be regulated by differential expression of RNA polymerase subunits

Errors during transcription may play an important role in determining cellular phenotypes: the RNA polymerase error rate is >4 orders of magnitude higher than that of DNA polymerase and errors are amplified >1000-fold due to translation. However, current methods to measure RNA polymerase fidelity are low-throughout, technically challenging, and organism specific. Here I show that changes in RNA polymerase fidelity can be measured using standard RNA sequencing protocols. I find that RNA polymerase is error-prone, and these errors can result in splicing defects. Furthermore, I find that differential expression of RNA polymerase subunits causes changes in RNA polymerase fidelity, and that coding sequences may have evolved to minimize the effect of these errors. These results suggest that errors caused by RNA polymerase may be a major source of stochastic variability at the level of single cells. DOI: http://dx.doi.org/10.7554/eLife.09945.001

Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damage

Isogenic cells show a large degree of variability in growth rate, even when cultured in the same environment. Such cell-to-cell variability in growth can alter sensitivity to antibiotics, chemotherapy and environmental stress. To characterize transcriptional differences associated with this variability, we have developed a method—FitFlow—that enables the sorting of subpopulations by growth rate. The slow-growing subpopulation shows a transcriptional stress response, but, more surprisingly, these cells have reduced RNA polymerase fidelity and exhibit a DNA damage response. As DNA damage is often caused by oxidative stress, we test the addition of an antioxidant, and find that it reduces the size of the slow-growing population. More generally, we find a significantly altered transcriptome in the slow-growing subpopulation that only partially resembles that of cells growing slowly due to environmental and culture conditions. Slow-growing cells upregulate transposons and express more chromosomal, viral and plasmid-borne transcripts, and thus explore a larger genotypic—and so phenotypic — space.

Effect of specific amino acid substitutions in the putative fusion peptide of structural glycoprotein E2 on Classical Swine Fever Virus replication

E2, along with E(rns) and E1, is an envelope glycoprotein of Classical Swine Fever Virus (CSFV). E2 is involved in several virus functions: cell attachment, host range susceptibility and virulence in natural hosts. Here we evaluate the role of a specific E2 region, (818)CPIGWTGVIEC(828), containing a putative fusion peptide (FP) sequence. Reverse genetics utilizing a full-length infectious clone of the highly virulent CSFV strain Brescia (BICv) was used to evaluate how individual amino acid substitutions within this region of E2 may affect replication of BICv. A synthetic peptide representing the complete E2 FP amino acid sequence adopted a β-type extended conformation in membrane mimetics, penetrated into model membranes, and perturbed lipid bilayer integrity in vitro. Similar peptides harboring amino acid substitutions adopted comparable conformations but exhibited different membrane activities. Therefore, a preliminary characterization of the putative FP (818)CPIGWTGVIEC(828) indicates a membrane fusion activity and a critical role in virus replication.

Coevolution trumps pleiotropy: carbon assimilation traits are independent of metabolic network structure in budding yeast.

Phenotypic traits may be gained and lost together because of pleiotropy, the involvement of common genes and networks, or because of simultaneous selection for multiple traits across environments (multiple-trait coevolution). However, the extent to which network pleiotropy versus environmental coevolution shapes shared responses has not been addressed. To test these alternatives, we took advantage of the fact that the genus Saccharomyces has variation in habitat usage and diversity in the carbon sources that a given strain can metabolize. We examined patterns of gain and loss in carbon utilization traits across 488 strains of Saccharomyces to investigate whether the structure of metabolic pathways or selection pressure from common environments may have caused carbon utilization traits to be gained and lost together. While most carbon sources were gained and lost independently of each other, we found four clusters that exhibit non-random patterns of gain and loss across strains. Contrary to the network pleiotropy hypothesis, we did not find that these patterns are explained by the structure of metabolic pathways or shared enzymes. Consistent with the hypothesis that common environments shape suites of phenotypes, we found that the environment a strain was isolated from partially predicts the carbon sources it can assimilate.