Formation of morphogen gradients

Morphogens are signaling molecules that play a key role in animal development. They spread from a restricted source into an adjacent target tissue forming a concentration gradient. The fate of cells in the target tissue is determined by the local concentration of such morphogens. Morphogen transport through the tissue has been studied in experiments which lead to the suggestion of several transport mechanisms. While diffusion in the extracellular space contributes to transport, recent experiments on the morphogen Decapentaplegic (Dpp) in the fruit fly Drosophila provide evidence for the importance of a cellular transport mechanism that was termed "planar transcytosis". In this mechanism, morphogens are transported through cells by repeated rounds of internalization and externalization. Starting from a microscopic theoretical description of these processes, we derive systems of nonlinear transport equations which describe the interplay of transcytosis and passive diffusion. We compare the results of numerical calculations based on this theoretical description of morphogen transport to recent experimental data on the morphogen Dpp in the Drosophila wing disk. Agreement with the experimental data is only achieved if the parameters entering the theoretical description are chosen such that transcytosis contributes strongly to transport. Analyzing the derived transport equations, we find that transcytosis leads to an increased robustness of the created gradients with respect to morphogen over-expression. Indications for this kind of robustness have been found in experiments. Furthermore, we theoretically investigate morphogen gradient formation in disordered systems. Here, an important question is how the position of concentration thresholds can be defined with high precision in the noisy environment present in typical developing tissues. Among other things, we find that the dimensionality of the system in which the gradient is formed plays an important role for the precision. Comparing gradients formed by transcytosis to those formed by extracellular diffusion, we find substantial differences that may result in a higher precision of gradients formed by transcytosis. Finally, we suggest several experiments to test the theoretical predictions of this work

Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution

Combining antibiotics is a promising strategy for increasing treatment efficacy and for controlling resistance evolution. When drugs are combined, their effects on cells may be amplified or weakened, that is the drugs may show synergistic or antagonistic interactions. Recent work revealed the underlying mechanisms of such drug interactions by elucidating the drugs'; joint effects on cell physiology. Moreover, new treatment strategies that use drug combinations to exploit evolutionary tradeoffs were shown to affect the rate of resistance evolution in predictable ways. High throughput studies have further identified drug candidates based on their interactions with established antibiotics and general principles that enable the prediction of drug interactions were suggested. Overall, the conceptual and technical foundation for the rational design of potent drug combinations is rapidly developing

Toward a quantitative understanding of antibiotic resistance evolution

The rising prevalence of antibiotic resistant bacteria is an increasingly serious public health challenge. To address this problem, recent work ranging from clinical studies to theoretical modeling has provided valuable insights into the mechanisms of resistance, its emergence and spread, and ways to counteract it. A deeper understanding of the underlying dynamics of resistance evolution will require a combination of experimental and theoretical expertise from different disciplines and new technology for studying evolution in the laboratory. Here, we review recent advances in the quantitative understanding of the mechanisms and evolution of antibiotic resistance. We focus on key theoretical concepts and new technology that enables well-controlled experiments. We further highlight key challenges that can be met in the near future to ultimately develop effective strategies for combating resistance

Systematic discovery of drug interaction mechanisms

Abstract Drug combinations are increasingly important in disease treatments, for combating drug resistance, and for elucidating fundamental relationships in cell physiology. When drugs are combined, their individual effects on cells may be amplified or weakened. Such drug interactions are crucial for treatment efficacy, but their underlying mechanisms remain largely unknown. To uncover the causes of drug interactions, we developed a systematic approach based on precise quantification of the individual and joint effects of antibiotics on growth of genome-wide Escherichia coli gene deletion strains. We found that drug interactions between antibiotics representing the main modes of action are highly robust to genetic perturbation. This robustness is encapsulated in a general principle of bacterial growth, which enables the quantitative prediction of mutant growth rates under drug combinations. Rare violations of this principle exposed recurring cellular functions controlling drug interactions. In particular, we found that polysaccharide and ATP synthesis control multiple drug interactions with previously unexplained mechanisms, and small molecule adjuvants targeting these functions synthetically reshape drug interactions in predictable ways. These results provide a new conceptual framework for the design of multidrug combinations and suggest that there are universal mechanisms at the heart of most drug interactions. Synopsis A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways. Drug interactions between antibiotics are highly robust to genetic perturbations. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions. Diverse drug interactions are controlled by recurring cellular functions, including LPS synthesis and ATP synthesis. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways

Precision of the Dpp gradient

Morphogen concentration gradients provide positional information by activating target genes in a concentration-dependent manner. Recent reports show that the gradient of the syncytial morphogen Bicoid seems to provide precise positional information to determine target gene domains. For secreted morphogenetic ligands, the precision of the gradients, the signal transduction and the reliability of target gene expression domains have not been studied. Here we investigate these issues for the TGF-β-type morphogen Dpp. We first studied theoretically how cell-to-cell variability in the source, the target tissue, or both, contribute to the variations of the gradient. Fluctuations in the source and target generate a local maximum of precision at a finite distance to the source. We then determined experimentally in the wing epithelium: (1) the precision of the Dpp concentration gradient; (2) the precision of the Dpp signaling activity profile; and (3) the precision of activation of the Dpp target gene spalt. As captured by our theoretical description, the Dpp gradient provides positional information with a maximal precision a few cells away from the source. This maximal precision corresponds to a positional uncertainly of about a single cell diameter. The precision of the Dpp gradient accounts for the precision of the spalt expression range, implying that Dpp can act as a morphogen to coarsely determine the expression pattern of target genes

Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli

Understanding interactions between antibiotics used in combination is an important theme in microbiology. Using the interactions between the antifolate drug trimethoprim and the ribosome-targeting antibiotic erythromycin in Escherichia coli as a model, we applied a transcriptomic approach for dissecting interactions between two antibiotics with different modes of action. When trimethoprim and erythromycin were combined, the transcriptional response of genes from the sulfate reduction pathway deviated from the dominant effect of trimethoprim on the transcriptome. We successfully altered the drug interaction from additivity to suppression by increasing the sulfate level in the growth environment and identified sulfate reduction as an important metabolic determinant that shapes the interaction between the two drugs. Our work highlights the potential of using prioritization of gene expression patterns as a tool for identifying key metabolic determinants that shape drug-drug interactions. We further demonstrated that the sigma factor-binding protein gene crl shapes the interactions between the two antibiotics, which provides a rare example of how naturally occurring variations between strains of the same bacterial species can sometimes generate very different drug interactions

Kinetics of Morphogen Gradient Formation

In the developing fly wing, secreted morphogens such as Decapentaplegic (Dpp) and Wingless (Wg) form gradients of concentration providing positional information. Dpp forms a longer-range gradient than Wg. To understand how the range is controlled, we measured the four key kinetic parameters governing morphogen spreading: the production rate, the effective diffusion coefficient, the degradation rate, and the immobile fraction. The four parameters had different values for Dpp versus Wg. In addition, Dynamin-dependent endocytosis was required for spreading of Dpp, but not Wg. Thus, the cellular mechanisms of Dpp and Wingless spreading are different: Dpp spreading requires endocytic, intracellular trafficking

Quantifying the determinants of evolutionary dynamics leading to drug resistance

The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution