Rethinking 'secondary' metabolism: physiological roles for phenazine antibiotics

Microorganisms exist in the environment as multicellular communities that face the challenge of surviving under nutrient-limited conditions. Chemical communication is an essential part of the way in which these populations coordinate their behavior, and there has been an explosion of understanding in recent years regarding how this is accomplished. Much less, however, is understood about the way these communities sustain their metabolism. Bacteria of the genus Pseudomonas are ubiquitous, and are distinguished by their production of colorful secondary metabolites called phenazines. In this article, we suggest that phenazines, which are produced under conditions of high cell density and nutrient limitation, may be important for the persistence of pseudomonads in the environment

A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies

One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data.One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data