Unraveling Antimicrobial Resistance Genes and Phenotype Patterns among Enterococcus faecalis Isolated from Retail Chicken Products in Japan

Multidrug-resistant enterococci are considered crucial drivers for the dissemination of antimicrobial resistance determinants within and beyond a genus. These organisms may pass numerous resistance determinants to other harmful pathogens, whose multiple resistances would cause adverse consequences. Therefore, an understanding of the coexistence epidemiology of resistance genes is critical, but such information remains limited. In this study, our first objective was to determine the prevalence of principal resistance phenotypes and genes among Enterococcus faecalis isolated from retail chicken domestic products collected throughout Japan. Subsequent analysis of these data by using an additive Bayesian network (ABN) model revealed the co-appearance patterns of resistance genes and identified the associations between resistance genes and phenotypes. The common phenotypes observed among E. faecalis isolated from the domestic products were the resistances to oxytetracycline (58.4%), dihydrostreptomycin (50.4%), and erythromycin (37.2%), and the gene tet(L) was detected in 46.0% of the isolates. The ABN model identified statistically significant associations between tet(L) and erm(B), tet(L) and ant(6)-Ia, ant(6)-Ia and aph(3’)-IIIa, and aph(3’)-IIIa and erm(B), which indicated that a multiple-resistance profile of tetracycline, erythromycin, streptomycin, and kanamycin is systematic rather than random. Conversely, the presence of tet(O) was only negatively associated with that of erm(B) and tet(M), which suggested that in the presence of tet(O), the aforementioned multiple resistance is unlikely to be observed. Such heterogeneity in linkages among genes that confer the same phenotypic resistance highlights the importance of incorporating genetic information when investigating the risk factors for the spread of resistance. The epidemiological factors that underlie the persistence of systematic multiple-resistance patterns warrant further investigations with appropriate adjustments for ecological and bacteriological factors.     

Site-specific mutagenesis of human interleukin-6 and its biological activity

Amino acid substitutions of human interleukin-6 (IL-6) were performed. Single substitution Met162----Ala and double substitutions Leu159.166----Val resulted in a significant decrease of IL-6 activity in the production of immunoglobulin (Ig) from B-cells. Single substitution Leu166----Val or Leu159----Val gave a slight or no significant decrease in the Ig-induction activity, respectively. The receptor-binding activity of each IL-6 mutant was also examined. It was observed that the decrease of the receptor-binding activity was generally in parallel with that of the Ig-induction activity. We therefore suggest that hydrophobic side-chains existing in Met162, Leu159, and Leu166 are significantly involved in the receptor-binding of IL-6.     

Immunolocalization of β-1-4-galactan and its relationship with lignin distribution in developing compression wood of Cryptomeria japonica

Compression wood (CW) contains higher quantities of β-1-4-galactan than does normal wood (NW). However, the physiological roles and ultrastructural distribution of β-1-4-galactan during CW formation are still not well understood. The present work investigated deposition of β-1-4-galactan in differentiating tracheids of Cryptomeria japonica during CW formation using an immunological probe (LM5) combined with immunomicroscopy. Our immunolabeling studies clearly showed that differences in the distribution of β-1-4-galactan between NW (and opposite wood, OW) and CW are initiated during the formation of the S₁ layer. At this stage, CW was strongly labeled in the S₁ layer, whereas no label was observed in the S₁ layer of NW and OW. Immunogold labeling showed that β-1-4-galactan in the S₁ layer of CW tracheids significantly decreased during the formation of the S₂ layer. Most β-1-4-galactan labeling was present in the outer S₂ region in mature CW tracheids, and was absent in the inner S₂ layer that contained helical cavities in the cell wall. In addition, delignified CW tracheids showed significantly more labeling of β-1-4-galactan in the secondary cell wall, suggesting that lignin is likely to mask β-1-4-galactan epitopes. The study clearly showed that β-1-4-galactan in CW was mainly deposited in the outer portion of the secondary cell wall, indicating that its distribution may be spatially consistent with lignin distribution in CW tracheids of Cryptomeria japonica.     

Detection of G protein-activator regions in M4 subtype muscarinic, cholinergic, and alpha 2-adrenergic receptors based upon characteristics in primary structure.

The 14-residue region Arg2410-Lys2423 of the human insulin-like growth factor II receptor possesses the ability to stimulate Gi, the activity being dependent on two structural characteristics: (i) at least two basic residues at the N-terminal side and (ii) the C-terminal motif, B-B-X-B or B-B-X-X-B (where B is a basic residue and X is a non-basic residue). The regions satisfying (i) and (ii) with 10 less than or equal to residue length less than or equal to 26 were located in all of the third inner loops and some of the other intracellular domains of the Gi-coupled M4 sub-type muscarinic cholinergic receptor (M4AChR) and the alpha 2-adrenergic receptor (alpha 2AR). Both the second inner loop 130-147 and the C-terminal portion of the third inner loop 382-400 (MIII) of human M4AChR had the ability to stimulate G proteins with the order Gi approximately Go greater than Gs, but only MIII could activate Gi/Go at nanomolar concentrations. In contrast, the N-terminal portion of the third inner loop 218-228 of human alpha 2AR-C10 activated Gi, Go, and Gs at micromolar concentrations with equal potency, whereas the further C-terminal portion of the third inner loop 301-313 of this receptor lacked the ability to activate any G protein. Among these active regions, only MIII indicated Mg(2+)-dependent Gi-stimulating function. Therefore, the search for the regions satisfying (i) and (ii) was useful to localize the G protein-activating activity of Gi-coupled receptors in limited regions, which were not always in the C-terminal portions of the third intracellular loops and activated G proteins in various modes of actions.     

Role of virus inhibitory factor or interferon in the antitumoral effect of whole-body hyperthermia

The interferon inducing effect of hyperthermia was studied in normal and tumor-bearing mice. Circulating interferon was temporarily detected one day after subcutaneous transplantation of 1.6 X 10(6) Ehrlich's ascites tumor cells. Hyperthermia of 43.5 degrees C for 5 min did not induce the interferon formation in mice with or without subcutaneous tumor of the cells. These findings showed that the induction of interferon formation was not main cause of the hyperthermia-induced tumor inhibition.     

Differential effect of ik3-1/cables on p53- and p73-induced cell death.

ik3-1/Cables is associated with cdk3 in self-replicating cells. In postmitotic neurons, it may serve as an adaptor molecule, functionally connecting c-abl and cdk5, and supporting neurite growth. Here we report that ik3-1 binds to p53 and p73 in vivo. Ectopically expressed ik3-1 potentiates p53-induced cell death but not p73-induced cell death in U2OS cells. On the contrary, coexpression of ik3-1-DeltaC, an ik3-1 deletion mutant lacking the C-terminal 139 [corrected] amino acids (corresponding to the cyclin box-homologous region), inhibits p73-induced cell death but not p53-induced cell death. ik3-1-DeltaC-mediated inhibition of p73-induced cell death are partially attenuated by overexpression of ik3-1. These data indicate that ik3-1 is not only a regulator for p53-induced cell death but also an essential regulator for p73-induced cell death, and ik3-1-DeltaC competes with ik3-1 only in p73-induced cell death. Furthermore, functional domains of p53 responsible for its interaction with ik3-1 are partially different from those of p73. In conclusion, we found that ik3-1, a putative component of cell cycle regulation, is functionally connected with p53 and p73, but in distinct fashions.     

Sampling Strategies in Antimicrobial Resistance Monitoring: Evaluating How Precision and Sensitivity Vary with the Number of Animals Sampled per Farm

Because antimicrobial resistance in food-producing animals is a major public health concern, many countries have implemented antimicrobial monitoring systems at a national level. When designing a sampling scheme for antimicrobial resistance monitoring, it is necessary to consider both cost effectiveness and statistical plausibility. In this study, we examined how sampling scheme precision and sensitivity can vary with the number of animals sampled from each farm, while keeping the overall sample size constant to avoid additional sampling costs. Five sampling strategies were investigated. These employed 1, 2, 3, 4 or 6 animal samples per farm, with a total of 12 animals sampled in each strategy. A total of 1,500 Escherichia coli isolates from 300 fattening pigs on 30 farms were tested for resistance against 12 antimicrobials. The performance of each sampling strategy was evaluated by bootstrap resampling from the observational data. In the bootstrapping procedure, farms, animals, and isolates were selected randomly with replacement, and a total of 10,000 replications were conducted. For each antimicrobial, we observed that the standard deviation and 2.5–97.5 percentile interval of resistance prevalence were smallest in the sampling strategy that employed 1 animal per farm. The proportion of bootstrap samples that included at least 1 isolate with resistance was also evaluated as an indicator of the sensitivity of the sampling strategy to previously unidentified antimicrobial resistance. The proportion was greatest with 1 sample per farm and decreased with larger samples per farm. We concluded that when the total number of samples is pre-specified, the most precise and sensitive sampling strategy involves collecting 1 sample per farm.