The sequence of the mouse genome is a key informational tool for understanding the contents of the human genome and a key experimental tool for biomedical research. Here, we report the results of an international collaboration to produce a high-quality draft sequence of the mouse genome. We also present an initial comparative analysis of the mouse and human genomes, describing some of the insights that can be gleaned from the two sequences. We discuss topics including the analysis of the evolutionary forces shaping the size, structure and sequence of the genomes; the conservation of large-scale synteny across most of the genomes; the much lower extent of sequence orthology covering less than half of the genomes; the proportions of the genomes under selection; the number of protein-coding genes; the expansion of gene families related to reproduction and immunity; the evolution of proteins; and the identification of intraspecies polymorphism.

Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data.

Methanobacterium thermoautotrophicum ⌬H, isolated in 1971 from sewage sludge in Urbana, Ill. (72), is a lithoautotrophic, thermophilic archaeon that grows at temperatures ranging from 40 to 70°C and optimally at 65°C. M. thermoautotrophicum conserves energy by using H 2 to reduce CO 2 to CH 4 and synthesizes all of its cellular components from these same gaseous substrates plus N 2 or NH 4 ϩ and inorganic salts, but despite this impressive biosynthetic capacity, M. thermoautotrophicum ⌬H and related strains have very small genomes (ϳ1.7 Ϯ 0.2 Mb [57,58]). M. thermoautotrophicum ⌬H, Marburg, and Winter are the foci of many methanogenesis, archaeal physiology, and molecular biology investigations, and M. thermoautotrophicum ⌬H was chosen as a representative of this group for genome sequencing. These thermophilic methanogens have mesophilic and hyperthermophilic relatives, Methanobacterium formicicum and Methanothermus fervidus, respectively, so that comparisons can be made of homologous

Summary Aminoglycoside antibiotics directly target the ribosome, yet the mechanisms by which they induce cell death are not fully understood. Recently, oxidative stress has been implicated as one of the mechanisms whereby bactericidal antibiotics kill bacteria. Here we work out, using systems-level approaches and phenotypic analyses, the pathway whereby aminoglycosides ultimately trigger hydroxyl radical formation. We show, by disabling systems which facilitate membrane protein traffic, that mistranslation and misfolding of membrane proteins are central to aminoglycoside-induced oxidative stress and cell death. We also demonstrate that signaling through the envelope stress response two-component system is a key player in this process, and highlight an associated role for the redox-responsive two-component system. Additionally, we show that these two-component systems play a general role in bactericidal antibiotic-mediated oxidative stress and cell death, expanding our understanding of the common mechanism of killing induced by bactericidal antibiotics.

No abstract