Genome sequence of a novel deep-sea vent epsilonproteobacterial phage provides new insight into the co-evolution of Epsilonproteobacteria and their phages

Epsilonproteobacteria are among the predominant primary producers in deep-sea hydrothermal vent ecosystems. However, phages infecting deep-sea vent Epsilonproteobacteria have never been isolated and characterized. Here, we successfully isolated a novel temperate phage, NrS-1, that infected a deep-sea vent chemolithoautotrophic isolate of Epsilonproteobacteria, Nitratiruptor sp. SB155-2, and its entire genome sequence was obtained and analyzed. The NrS-1 genome is linear, circularly permuted, and terminally redundant. The NrS-1 genome is 37,159 bp in length and contains 51 coding sequences. Five major structural proteins including major capsid protein and tape measure protein were identified by SDS-PAGE and mass spectrometry analysis. NrS-1 belongs to the family Siphoviridae, but its sequence and genomic organization are distinct from those of any other previously known Siphoviridae phages. Homologues of genes encoded in the NrS-1 genome were widely distributed among the genomes of diverse Epsilonproteobacteria. The distribution patterns had little relation to the evolutionary traits and ecological and physiological differentiation of the host epsilonproteobacterial species. The widespread occurrence of phage genes in diverse Epsilonproteobacteria supports early co-evolution between temperate phages and Epsilonproteobacteria prior to the divergence of their habitats and physiological adaptation.     

Simple methods for the estimation and sensitivity analysis of principal strata effects using marginal structural models: Application to a bone fracture prevention trial

In randomized clinical trials, it is often of interest to estimate the effect of treatment on quality of life (QOL), in addition to those on the event itself. When an event occurs in some patients prior to QOL score assessment, investigators may compare QOL scores between patient subgroups defined by the event after randomization. However, owing to postrandomization selection bias, this analysis can mislead investigators about treatment efficacy and result in paradoxical findings. The recent Japanese Osteoporosis Intervention Trial (JOINT‐02), which compared the benefits of a combination therapy for fracture prevention with those of a monotherapy, exemplifies the case in point; the average QOL score was higher in the combination therapy arm for the unfractured subgroup but was lower for the fractured subgroup. To address this issue, principal strata effects (PSEs), which are treatment effects estimated within subgroups of individuals stratified by potential intermediate variable, have been discussed in the literature. In this paper, we describe a simple procedure for estimating the PSEs using marginal structural models. This procedure utilizes SAS code for the estimation. In addition, we present a simple sensitivity analysis method for examining the resulting estimates. The analyses of JOINT‐02 data using these methods revealed that QOL scores were higher in the combination therapy arm than in the monotherapy arm for both subgroups.