A toxin-antitoxin system confers stability to the IncP-7 plasmid pCAR1.

Toxin-antitoxin (TA) systems were initially discovered as plasmid addiction systems. Previously, our studies implied that the high stability of the IncP-7 plasmid pCAR1 in different Pseudomonas spp. hosts was due to the presence of a TA system on the plasmid. Bioinformatics approaches suggested that ORF174 and ORF175 could constitute a type II TA system, a member of the RES-Xre family, and that these two open reading frames (ORFs) constitute a single operon. As expected, the ORF175 product is a toxin, which decreases the viability of the host, P. resinovorans, while the ORF174 product functions as an antitoxin that counteracts the effect of ORF175 on cell growth. Based on these findings, we renamed ORF174 and ORF175 as prcA (antitoxin gene) and prcT (toxin gene), respectively. The prcA and prcT genes were cloned into the unstable plasmid vector pSEVA644. The recombinant vector was stably maintained in P. resinovorans and Escherichia coli cells under nonselective conditions following 6 days of daily subculturing. The empty vector (without the prcA and prcT genes) could not be maintained, which suggested that the PrcA/T system can be used as a tool to improve the stability of otherwise unstable plasmids in P. resinovorans and E. coli strains.

Development and evaluation of an automated quantification tool for amyloid PET images.

BACKGROUND: Quantitative evaluation of amyloid positron emission tomography (PET) with standardized uptake value ratio (SUVR) plays a key role in clinical studies of Alzheimer's disease (AD). We have proposed a PET-only (MR-free) amyloid quantification method, although some commercial software packages are required. The aim of this study was to develop an automated quantification tool for amyloid PET without using commercial software. METHODS: The quantification tool was created by combining four components: (1) anatomical standardization to positive and negative templates using NEUROSTAT stereo.exe; (2) similarity calculation between standardized images and respective templates based on normalized cross-correlation (selection of the image for SUVR measurement); (3) voxel value normalization by the mean value of reference regions (making an SUVR-scaled image); and (4) SUVR calculation based on pre-defined regions of interest (ROIs). We examined 166 subjects who underwent a [11C] Pittsburgh compound-B PET scan through the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) study. SUVRs in five ROIs (frontal lobe, temporal lobe, parietal lobe, occipital lobe, and posterior cingulate cortex and precuneus) were calculated with the cerebellar cortex as the reference region. The SUVRs obtained by our tool were compared with manual step-by-step processing and the conventional PMOD-based method (PMOD Technologies, Switzerland). RESULTS: Compared with manual step-by-step processing, our developed automated quantification tool reduced processing time by 85%. The SUVRs obtained by the developed quantification tool were consistent with those obtained by manual processing. Compared with the conventional PMOD-based method, the developed quantification tool provided 1.5% lower SUVR values, on average. We determined that this bias is likely due to the difference in anatomical standardization methods. CONCLUSIONS: We developed an automated quantification tool for amyloid PET images. Using this tool, SUVR values can be quickly measured without individual MRI and without commercial software. This quantification tool may be useful for clinical studies of AD.