Occurrence of chromosome- or plasmid-mediated aerobactin iron transport systems and hemolysin production among clonal groups of human invasive strains of Escherichia coli K1

The incidence of the aerobactin system and the genetic location of aerobactin genes were investigated in Escherichia coli K1 neonatal isolates belonging to different clonal groups. A functional aerobactin system was found in all members of the O7 MP3, O1 MP5, O1 MP9, and O18 MP9 clonal groups examined and also in K1 strains having O6, O16, and O75 lipopolysaccharide types, which are less frequently associated with neonatal infections. In contrast, the aerobactin system was not detected in strains from the O18 MP6 clone. The combined results of plasmid and colony hybridization experiments showed that the aerobactin genes were located on the chromosome in the majority (75%) of the aerobactin-producing K1 isolates, the genetic location of the aerobactin genes was closely correlated with the outer membrane protein profile rather than the O lipopolysaccharide type, the K1 strains harboring a chromosome-mediated aerobactin system did not possess colicin V genes, and five of six K1 isolates possessing a plasmid-borne aerobactin system contained colicin V genes which were located on the same plasmids carrying the aerobactin genes. The comparison of hemolysin production with possession of the aerobactin system in virulent clones of E. coli K1 strains showed that all of the aerobactin-producing strains from the O18 MP9 and O7 MP3 clonal groups did not synthesize hemolysin, whereas 11 of 12 aerobactin-nonproducing O18 MP6 isolates were hemolytic. Of the K1 strains examined, 92.5% possessed either the aerobactin system or the ability to produce hemolysin or both.

A Bayesian analysis of the chromosome architecture of human disorders by integrating reductionist data

In this paper, we present a Bayesian approach to estimate a chromosome and a disorder network from the Online Mendelian Inheritance in Man (OMIM) database. In contrast to other approaches, we obtain statistic rather than deterministic networks enabling a parametric control in the uncertainty of the underlying disorder-disease gene associations contained in the OMIM, on which the networks are based. From a structural investigation of the chromosome network, we identify three chromosome subgroups that reflect architectural differences in chromosome-disorder associations that are predictively exploitable for a functional analysis of diseases.