Variable cellular decision-making behavior in a constant synthetic network topology.

BACKGROUND: Modules of interacting components arranged in specific network topologies have evolved to perform a diverse array of cellular functions. For a network with a constant topological structure, its function within a cell may still be tuned by changing the number of instances of a particular component (e.g., gene copy number) or by modulating the intrinsic biochemical properties of a component (e.g., binding strength or catalytic efficiency). How such perturbations affect cellular response dynamics remains poorly understood. Here, we explored these effects in a common decision-making motif, cross-antagonism with autoregulation, by synthetically constructing this network in yeast. RESULTS: We employed the engineering design strategy of reuse to build this topology with a single protein building block, TetR, creating necessary components through TetR mutations and fusion partners. We then studied the impact of several topology-preserving perturbations - strength of cross-antagonism, number of operator sites in a promoter, and gene dosage - on decision-making behavior. We found that reducing TetR repression strength, which hinders cross-antagonism, resulted in a loss of mutually exclusive cell responses. Unexpectedly, increasing the number of operator sites also impeded decision-making exclusivity, which may be a consequence of the averaging effect that arises when multiple transcriptional activators and repressors are accommodated at a given locus. Stochastic simulations of this topology revealed that, even for networks with high TetR repression strength and a low number of operator sites, increasing gene dosage can reduce exclusivity in response dynamics. We further demonstrated this result experimentally by quantifying gene copy numbers in selected yeast clones with differing phenotypic responses. CONCLUSIONS: Our study illustrates how parameters that do not change the topological structure of a decision-making network can nonetheless exert significant influence on its response dynamics. These findings should further inform the study of native motifs, including the effects of topology-preserving mutations, and the robust engineering of synthetic networks.

Robust hematopoietic progenitor cell commitment in the presence of a conflicting cue.

Hematopoietic lineage commitment is regulated by cytokines and master transcription factors, but it remains unclear how a progenitor cell chooses a lineage in the face of conflicting cues. Through transcript counting in megakaryocyte-erythroid progenitors undergoing erythropoiesis, we show that the expression levels of the pro-erythropoiesis transcription factor EKLF (also known as KLF1) and receptor EpoR are inversely correlated with their pro-megakaryopoiesis counterparts, FLI-1 and TpoR (also known as MPL). Notably, as progenitors commit to the erythrocyte lineage, EpoR is upregulated and TpoR is strongly downregulated, thus boosting the potency of the pro-erythropoiesis cue erythropoietin and effectively eliminating the activity of the pro-megakaryopoiesis cue thrombopoietin. Based on these findings, we propose a new model for exclusive decision making that explicitly incorporates signals from extrinsic cues, and we experimentally confirm a model prediction of temporal changes in transcript noise levels in committing progenitors. Our study suggests that lineage-specific receptor levels can modulate potencies of cues to achieve robust commitment decisions.

Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease.

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease.

Computationally guided design of robust gene circuits.

The inability to rationally design and construct circuits that robustly enable complex behaviors is perhaps the most fundamental challenge in synthetic biology. While systems modeling can aid this process and help reduce the space of design strategies, the unavailability and dynamic variability of kinetic parameters limits the utility of such models. Here, we present a general approach that employs an exhaustive enumeration of network architectures to suggest topologies that robustly enable a desired behavior.

Mortality among recipients of the Merck V710 Staphylococcus aureus vaccine after postoperative S. aureus infections: an analysis of possible contributing host factors.

In a blinded randomized trial, preoperative receipt of the Merck V710 Staphylococcus aureus vaccine was associated with a higher mortality rate than placebo in patients who later developed postoperative S. aureus infections. Of the tested patients, all 12 V710 recipients (but only 1 of 13 placebo recipients) with undetectable serum IL2 levels prior to vaccination and surgery died after postoperative S. aureus infection. The coincidence of 3 factors (low prevaccination IL-2 levels, receipt of V710, and postoperative S. aureus infection) appeared to substantially increase mortality in our study population after major cardiothoracic surgery. Furthermore, 9 of the 10 V710 recipients with undetectable preoperative IL17a levels and postoperative S. aureus infections died. Although the current study is hypothesis-generating and the exact pathophysiology remains speculative, these findings raise concern that immune predispositions may adversely impact the safety and efficacy of staphylococcal vaccines actively under development. The potential benefits of an effective vaccine against S. aureus justify continued but cautious pursuit of this elusive goal.

Evidence for context-dependent complementarity of non-Shine-Dalgarno ribosome binding sites to Escherichia coli rRNA.

While the ribosome has evolved to function in complex intracellular environments, these contexts do not easily allow for the study of its inherent capabilities. We have used a synthetic, well-defined Escherichia coli (E. coli)-based translation system in conjunction with ribosome display, a powerful in vitro selection method, to identify ribosome binding sites (RBSs) that can promote the efficient translation of messenger RNAs (mRNAs) with a leader length representative of natural E. coli mRNAs. In previous work, we used a longer leader sequence and unexpectedly recovered highly efficient cytosine-rich sequences with complementarity to the 16S ribosomal RNA (rRNA) and similarity to eukaryotic RBSs. In the current study, Shine-Dalgarno (SD) sequences were prevalent, but non-SD sequences were also heavily enriched and were dominated by novel guanine- and uracil-rich motifs that showed statistically significant complementarity to the 16S rRNA. Additionally, only SD motifs exhibited position-dependent decreases in sequence entropy, indicating that non-SD motifs likely operate by increasing the local concentration of ribosomes in the vicinity of the start codon, rather than by a position-dependent mechanism. These results further support the putative generality of mRNA-rRNA complementarity in facilitating mRNA translation but also suggest that context (e.g., leader length and composition) dictates the specific subset of possible RBSs that are used for efficient translation of a given transcript.

Broad-specificity mRNA-rRNA complementarity in efficient protein translation.

Studies of synthetic, well-defined biomolecular systems can elucidate inherent capabilities that may be difficult to uncover in a native biological context. Here, we used a minimal, reconstituted translation system from Escherichia coli to identify efficient ribosome binding sites (RBSs) in an unbiased, high-throughput manner. We applied ribosome display, a powerful in vitro selection method, to enrich only those mRNA sequences which could direct rapid protein translation. In addition to canonical Shine-Dalgarno (SD) motifs, we unexpectedly recovered highly efficient cytosine-rich (C-rich) sequences that exhibit unmistakable complementarity to the 16S rRNA of the small subunit of the ribosome, indicating that broad-specificity base-pairing may be an inherent, general mechanism for efficient translation. Furthermore, given the conservation of ribosomal structure and function across species, the broader relevance of C-rich RBS sequences identified through our in vitro evolution approach is supported by multiple, diverse examples in nature, including C-rich RBSs in several bacteriophage and plants, a poly-C consensus before the start codon in a lower eukaryote, and Kozak-like sequences in vertebrates.

Robust network topologies for generating switch-like cellular responses.

Signaling networks that convert graded stimuli into binary, all-or-none cellular responses are critical in processes ranging from cell-cycle control to lineage commitment. To exhaustively enumerate topologies that exhibit this switch-like behavior, we simulated all possible two- and three-component networks on random parameter sets, and assessed the resulting response profiles for both steepness (ultrasensitivity) and extent of memory (bistability). Simulations were used to study purely enzymatic networks, purely transcriptional networks, and hybrid enzymatic/transcriptional networks, and the topologies in each class were rank ordered by parametric robustness (i.e., the percentage of applied parameter sets exhibiting ultrasensitivity or bistability). Results reveal that the distribution of network robustness is highly skewed, with the most robust topologies clustering into a small number of motifs. Hybrid networks are the most robust in generating ultrasensitivity (up to 28%) and bistability (up to 18%); strikingly, a purely transcriptional framework is the most fragile in generating either ultrasensitive (up to 3%) or bistable (up to 1%) responses. The disparity in robustness among the network classes is due in part to zero-order ultrasensitivity, an enzyme-specific phenomenon, which repeatedly emerges as a particularly robust mechanism for generating nonlinearity and can act as a building block for switch-like responses. We also highlight experimentally studied examples of topologies enabling switching behavior, in both native and synthetic systems, that rank highly in our simulations. This unbiased approach for identifying topologies capable of a given response may be useful in discovering new natural motifs and in designing robust synthetic gene networks.

A comprehensive survey of clonal diversity measures in Barrett's esophagus as biomarkers of progression to esophageal adenocarcinoma.

Neoplastic progression is an evolutionary process driven by the generation of clonal diversity and natural selection on that diversity within a neoplasm. We hypothesized that clonal diversity is associated with risk of progression to cancer. We obtained molecular data from a cohort of 239 participants with Barrett's esophagus, including microsatellite shifts and loss of heterozygosity, DNA content tetraploidy and aneuploidy, methylation, and sequence mutations. Using these data, we tested all major diversity measurement methods, including genetic divergence and entropy-based measures, to determine which measures are correlated with risk of progression to esophageal adenocarcinoma. We also tested whether the use of different sets of loci and alterations to define clones (e.g., selectively advantageous versus evolutionarily neutral) improved the predictive value of the diversity indices. All diversity measures were strong and highly significant predictors of progression (Cox proportional hazards model, P < 0.001). The type of alterations evaluated had little effect on the predictive value of most of the diversity measures. In summary, diversity measures are robust predictors of progression to cancer in this cohort.

Accurate, precise modeling of cell proliferation kinetics from time-lapse imaging and automated image analysis of agar yeast culture arrays.

BACKGROUND: Genome-wide mutant strain collections have increased demand for high throughput cellular phenotyping (HTCP). For example, investigators use HTCP to investigate interactions between gene deletion mutations and additional chemical or genetic perturbations by assessing differences in cell proliferation among the collection of 5000 S. cerevisiae gene deletion strains. Such studies have thus far been predominantly qualitative, using agar cell arrays to subjectively score growth differences. Quantitative systems level analysis of gene interactions would be enabled by more precise HTCP methods, such as kinetic analysis of cell proliferation in liquid culture by optical density. However, requirements for processing liquid cultures make them relatively cumbersome and low throughput compared to agar. To improve HTCP performance and advance capabilities for quantifying interactions, YeastXtract software was developed for automated analysis of cell array images. RESULTS: YeastXtract software was developed for kinetic growth curve analysis of spotted agar cultures. The accuracy and precision for image analysis of agar culture arrays was comparable to OD measurements of liquid cultures. Using YeastXtract, image intensity vs. biomass of spot cultures was linearly correlated over two orders of magnitude. Thus cell proliferation could be measured over about seven generations, including four to five generations of relatively constant exponential phase growth. Spot area normalization reduced the variation in measurements of total growth efficiency. A growth model, based on the logistic function, increased precision and accuracy of maximum specific rate measurements, compared to empirical methods. The logistic function model was also more robust against data sparseness, meaning that less data was required to obtain accurate, precise, quantitative growth phenotypes. CONCLUSION: Microbial cultures spotted onto agar media are widely used for genotype-phenotype analysis, however quantitative HTCP methods capable of measuring kinetic growth rates have not been available previously. YeastXtract provides objective, automated, quantitative, image analysis of agar cell culture arrays. Fitting the resulting data to a logistic equation-based growth model yields robust, accurate growth rate information. These methods allow the incorporation of imaging and automated image analysis of cell arrays, grown on solid agar media, into HTCP-driven experimental approaches, such as global, quantitative analysis of gene interaction networks.