Isolation of colonization-defective Escherichia coli mutants reveals critical requirement for fatty acids in bacterial colony formation.

Most bacterial cells in nature exhibit extremely low colony-forming activity, despite showing various signs of viability, impeding the isolation and utilization of many bacterial resources. However, the general causes responsible for this state of low colony formation are largely unknown. Because liquid cultivation typically yields more bacterial cell cultures than traditional solid cultivation, we hypothesized that colony formation requires one or more specific gene functions that are dispensable or less important for growth in liquid media. To verify our hypothesis and reveal the genetic background limiting colony formation among bacteria in nature, we isolated Escherichia coli mutants that had decreased frequencies of colony formation but could grow in liquid medium from a temperature-sensitive mutant collection. Mutations were identified in fabB, which is essential for the synthesis of long unsaturated fatty acids. We then constructed a fabB deletion mutant in a wild-type background. Detailed behavioural analysis of the mutant revealed that under fatty acid-limited conditions, colony formation on solid media was more sensitively and seriously impaired than growth in liquid media. Furthermore, growth under partial inhibition of fatty acid synthesis with cerulenin or triclosan brought about similar phenotypes, not only in E. coli but also in Bacillus subtilis and Corynebacterium glutamicum. These results indicate that fatty acids have a critical importance in colony formation and that depletion of fatty acids in the environment partly accounts for the low frequency of bacterial colony formation.

Analysis of gene expression profiles of soft tissue sarcoma using a combination of knowledge-based filtering with integration of multiple statistics.

The diagnosis and treatment of soft tissue sarcomas (STS) have been difficult. Of the diverse histological subtypes, undifferentiated pleomorphic sarcoma (UPS) is particularly difficult to diagnose accurately, and its classification per se is still controversial. Recent advances in genomic technologies provide an excellent way to address such problems. However, it is often difficult, if not impossible, to identify definitive disease-associated genes using genome-wide analysis alone, primarily because of multiple testing problems. In the present study, we analyzed microarray data from 88 STS patients using a combination method that used knowledge-based filtering and a simulation based on the integration of multiple statistics to reduce multiple testing problems. We identified 25 genes, including hypoxia-related genes (e.g., MIF, SCD1, P4HA1, ENO1, and STAT1) and cell cycle- and DNA repair-related genes (e.g., TACC3, PRDX1, PRKDC, and H2AFY). These genes showed significant differential expression among histological subtypes, including UPS, and showed associations with overall survival. STAT1 showed a strong association with overall survival in UPS patients (logrank p = 1.84 × 10(-6) and adjusted p value 2.99 × 10(-3) after the permutation test). According to the literature, the 25 genes selected are useful not only as markers of differential diagnosis but also as prognostic/predictive markers and/or therapeutic targets for STS. Our combination method can identify genes that are potential prognostic/predictive factors and/or therapeutic targets in STS and possibly in other cancers. These disease-associated genes deserve further preclinical and clinical validation.

Macrophage migration inhibitory factor and stearoyl-CoA desaturase 1: potential prognostic markers for soft tissue sarcomas based on bioinformatics analyses.

The diagnosis and treatment of soft tissue sarcomas (STSs) has been particularly difficult, because STSs are a group of highly heterogeneous tumors in terms of histopathology, histological grade, and primary site. Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis, treatment selection, and investigation of therapeutic targets. We had previously developed a novel bioinformatics method for marker gene selection and applied this method to gene expression data from STS patients. This previous analysis revealed that the extracted gene combination of macrophage migration inhibitory factor (MIF) and stearoyl-CoA desaturase 1 (SCD1) is an effective diagnostic marker to discriminate between subtypes of STSs with highly different outcomes. In the present study, we hypothesize that the combination of MIF and SCD1 is also a prognostic marker for the overall outcome of STSs. To prove this hypothesis, we first analyzed microarray data from 88 STS patients and their outcomes. Our results show that the survival rates for MIF- and SCD1-positive groups were lower than those for negative groups, and the p values of the log-rank test are 0.0146 and 0.00606, respectively. In addition, survival rates are more significantly different (p = 0.000116) between groups that are double-positive and double-negative for MIF and SCD1. Furthermore, in vitro cell growth inhibition experiments by MIF and SCD1 inhibitors support the hypothesis. These results suggest that the gene set is useful as a prognostic marker associated with tumor progression.